Personalization vs. Privacy

Personalization vs. PrivacyOvercoming the Users’ Privacy Concerns in the Indonesian

Peer-to-peer Ridesharing Service

by. Fauzi Dwi Reza Aditya

Personalization vs. Privacy

Overcoming the Users’ Privacy Concerns in the Indonesian Peer-to-peer Ridesharing Service

Master thesis submitted to Delft University of Technology

in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

in Management of Technology

Faculty of Technology, Policy and Management

by

Fauzi Dwi Reza Aditya

Student number: 4403150

To be defended in public on August 24th 2016

Graduation committee:

Chairperson : Prof.dr.ir. M.F.W.H.A. Janssen, Section ICT

First Supervisor : drs. J. Ubacht, Section ICT

Second Supervisor : Dr. ir. M. Kroesen, Section TLO

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Acknowledgment

I am indebted my gratitude to all people who helped and supported me in conducting and writing thisthesis.

Firstly, the Indonesian Endowment Fund for Education (LPDP) for providing me study grant for myentire study and research grant particularly for conducting this thesis. Without this opportunity, it isnearly impossible for me to arrive here in Delft and finish this study.

Secondly, my supervisors. I would like to specially thank Drs. Jolien Ubacht as my first supervisor, forher patient in providing me continuous constructive feedbacks in the highest exceptional manner,both to my academic fundamental theories as well as my writing style. My second supervisor, Dr. Ir.Maarten Kroesen for giving his exquisite guidance in designing the quantitative research and statisticalinterpretations as well as providing his critical constructive feedbacks for my overall thesis. And theChairman, Prof.dr.ir. Marijn Janssen for his clear critical comments that elevate my thesis in a higherlevel. I do owe you all thanks for showing your confidence as well as giving me challenge that motivatesme to produce a better thesis. Special thanks I also owe to Dr. Eric Molin for his clear (email) guidanceon the stated choice design and the use of Ngene software, and Shofi Andari, S.Stat, M.Si. for givingher guidance for me to understand how to interpret statistical results better.

Thirdly, Catur Apriono, S.T., M.T., Ph.D, Novi Mardining, and Riezka Rianty for helping me gatheringfirsthand data from the respondents. Without your help, my three weeks’ data collection timeline willbe hard to finish.

Fourthly, my fellow students in university, Reni, Erwanda, Buse, Adiska + Bagas, Rosa, Fitri, Revi, Kevin,Lusi M, Lusi P + Ilman, Riska, Aarun, Deep, Bill, Esme, and any other students who cannot be mentionedhere, thank you for the lunch talks that keep me sane for the last 6 months. Without any one of you,my thesis life will be very boring and monotonous.

Fifthly, my Mom who always gives me support and ear to listen to my outpouring talks. Even thoughnot many comments are coming out from you, you always help me in a very mysterious way.

And finally, my dear wife, Guninta, for always supporting me in my ups-and-downs, and always bepatient waiting me home. Thanks for giving me opportunity to push myself forward, from my comfortzone to the edge of my capacity, so I become a better person now. I surely will be indebted to you forthe rest of my life.

Fauzi Dwi Reza Aditya

Delft

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Forewords

Curiosity can kill a cat.

It all started when I spent my last summer holiday back in Indonesia. I saw a lot of Ojek online drivers

(with various branding jackets) were roaming in every main street in Jakarta, with a very cheap riding

fee (at that moment the users only needed to pay IDR 10.000 ≈ €0.67 per ride to anywhere they

want!!). My first curiosity appeared: How can they sustain their profit?

Afterward, I wanted to try to use the service. I was told to register my address, my email address, and

my mobile phone. My second curiosity came to my mind; will my mobile number appear directly in

the driver’s app when he is picking up my order?

Then I started researching the possible privacy violations when using this service. I found a lot of pri-

vacy intrusion in place, especially to female users. When she had finished using the order, sometimes

the driver still contacted her just for chat or any unimportant conversation, which made her felt irri-

tated. From this point, I elevated my curiosities to be a more strategic thinking. I know that there is

personal data involvement in practice, but how to use them to help the company win the market while

minimize the privacy violation?

Therefore, I designed this project with one clear goal in mind: my research results not only have to be

scientific, but also usable and applicable in the real market. It was quite a lot of works, challenges, ups

and downs, stress, and sleepless nights. Even my supervisor told me to be careful with my level of

ambition. Thankfully, I was surrounded by people who are very supportive and care about me.

But satisfaction brings it back.

When my research is finally done, with the findings and the conclusion can be applicable right away, I

am very relieved. I am very happy with the work I have done.

Nevertheless, this thesis only makes tiny-yet-significant contribution to the big picture of personaliza-

tion and recommendation system that utilize personal data analytic as the main source of value adding

factor. It only focuses on the ethical consideration in respect to the use of personal data and neither

explains anything about the design of the recommendation system nor the technical part of the data

analytic. But yet, this small contribution will help the company that wants to play in this area to design

and market their value sensitive system and avoid rejection once the product is in the market. Be-

cause, it provides insight not only on the theoretical explanation of privacy decision making, but also

several practical matters related to the users’ privacy that can be applied directly in the business set-

ting.

I do hope you enjoy reading my thesis...!!

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List of Acronyms

CFIP Concerns for internet privacy

DSP Data service providers

GLU General level of Utility

GRL Government regulation and legislation

ICT Information and communication technology

IPPR Individual privacy protection responses

ISP Individual self-protection

ISR Industry self-regulation

IUIPC Internet users' individual privacy concerns

LBS Location-based services

PBA Privacy of behavior and action

PbD Privacy by Design

PDI Privacy of data and image

PLS Privacy of location and space

RSP Ridesharing service provider

SD Standard deviation

WTA Willingness to accept

WTD Willingness to disclose

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Executive Summary

The ubiquity of smartphone, internet, and positioning system, that build the so-called peer-to-peer

(P2P) ridesharing service, have transformed the transportation industry. With the digital platform as

its technology backbone, the P2P ridesharing service has created a tremendous disruptive potential

to the existing traditional taxi business (Lacy & Rutqvist, 2015). Furthermore, with the help of the

digital platform’s loosely coupled potential, the P2P ridesharing company has the opportunity to ex-

pand and develop further its service by inviting other adjacent actors and partners to collaborate in

its business ecosystem. Together, they generate a multisided ecosystem.

Additionally, the recent information and communication technology (ICT) development - especially in

the tracking technology - has made the personal data capture becomes easily conducted. Indeed, the

customers’ personal data will also be valuable for the P2P ridesharing company to innovate its services

and create additional values both for the customers as well as to the company itself. One example of

the innovations is by offering personalization to its customers. However, to offer personalization, the

company will need more sensitive personal information disclosure from its customers, which will ele-

vate their privacy concerns.

The privacy, which has become a concern since computers began to be used in public service (Dutta

et al., 2011), is known to be the main impeding factor of company to offer personalization to its cus-

tomers (Chellappa & Sin, 2005; Xu et al., 2011). Consequently, the P2P ridesharing service company

needs to find a way to innovate its services which take advantage of the customers’ personal data

while making them acceptable to its customers in respect to their privacy disclosure. Therefore, the

research objective of this study is: To make recommendations to the peer-to-peer ridesharing platform

provider in the effort of mitigating the privacy disclosure issue, by investigating the general model of

privacy mitigation strategy, segmenting the users according to their preferred mitigation strategy, and

matching the mitigation strategy to segmentations of users with respect to the view of privacy as a

right as well as an interest.

The aforementioned objective is achieved by answering the following research question:

RQ: In the view of privacy as both right and interest, what strategy fits to certain segments of

users in the effort of mitigating the users’ privacy concern in the context of Indonesian peer-to-

peer ridesharing service?

1.1 Domain of study: Indonesian P2P ridesharing service The concept of P2P ridesharing services is a derivative of the collaborative consumption concept im-

plemented in transportation sector. It emerges because of several positive claims, such as: (1) it can

grow entrepreneurial mindset of society by exploiting the previously idle assets as additional income

(Sundararajan, 2014); (2) it can also grow the consumption since the customers do not need to pur-

chase and own the asset to enjoy the benefit of an asset (Sundararajan, 2014); (3) it can also increase

the productivity of idle assets (Sundararajan, 2014); and (4) it can eliminate the moral hazard by using

social-media-type of review system (Thierer, Koopman, Hobson, et al., 2015). Yet, this concept also

has several negative claims, such as: violation of zoning law (Malhotra & Van Alstyne, 2014), promotes

invisible workers (Sundararajan, 2014), and creates a rebound effect in the society (Schor, 2014).

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In the Indonesian market, the most well-known P2P ridesharing service is in the form of P2P motorcy-

cle taxi service, or famously known as Ojek online. Likewise, the Indonesian P2P ridesharing companies

also utilize digital platform as their backbone. Furthermore, they have also expanded their business

ecosystem to the adjacent actors, which enable them to offer not only a basic ridesharing service, but

also other innovated services, such as: restaurant delivery service, online shopping, courier service,

and any other services that may use a motorcycle as the logistic means.

Furthermore, with the expanded business ecosystem, the P2P ridesharing companies have the oppor-

tunity to offer personalization services as additional values to its users. This personalization, however,

will require personal data disclosure from its users, not only the basic personal data (such as: name,

telephone number, pickup and drop-off location), but also more sensitive data (such as: behavioral

information, traveling pattern, and spending pattern). Consequently, as the ecosystem becomes com-

plex, the users’ privacy concerns will be elevated and need to be mitigated. Therefore, to mitigate

their privacy concerns, we need also to study the users’ rationale in disclosing personal information

before offering a focused privacy mitigation strategy to them.

1.2 Theoretical framework: The privacy and privacy decision making the-

ory Privacy is traditionally seen as one of fundamental rights (Assembly, 1948; Europe, 1950). However,

the recent technology developments have made the users start to see privacy as an interest that can

be traded for certain benefits (Clarke, 2013). Furthermore, privacy can be categorized into several

dimensions, whereas the fit categorization of privacy in respect to the recent technology develop-

ments follows the Finn et al. (2013) privacy categorization, which are: Privacy of the person, Privacy

of behavior and action, Privacy of communication, Privacy of data and image, Privacy of thought

and feeling, Privacy of location and space, Privacy of association (including group privacy).

In accordance to our context of study, the involved privacy categories in the P2P ridesharing and per-

sonalization service are: the privacy of location and space (PLS), which is associated by the pickup and

drop-off location disclosure when using the ridesharing service; the privacy of behavior and action

(PBA), which is related to the traveling and buying pattern of users recorded by the company to offer

personalization; and the privacy of data and image (PDI), which is operationalized by the acceptance

of receiving advertisement from other parties.

The users’ rationale in disclosing personal information is commonly evaluated by using the privacy

calculus theory. The privacy calculus theory bases the analysis on the cost-benefit calculation of users

before deciding to disclose their personal data. The cost part of the analysis is represented by the

privacy concerns that the users have, whereas the benefit part of the analysis is described by the

potential benefits that the user may get in return to the disclosed personal information, in which they

only disclose their privacy if the benefits they may get outweigh the privacy they need to disclose. We

use two constructs to measure the privacy concerns, i.e., the internet user individual privacy concern

(IUIPC) (Malhotra et al., 2004) and the concern for internet privacy (CFIP) (Smith et al., 1996). Alto-

gether, they fit perfectly to the mainstream privacy frameworks (such as: APEC (2005) and OECD

(2013c)).

However, letting the privacy calculus theory alone to study the privacy disclosure rationale is not suf-

ficient as it has several critics (mainly because this theory bases the analysis on the cost-benefit anal-

ysis approach that unable to include the uncertainty factors), such as: (1) the assumption of all users

are utilitarian actors who have perfect foresight, especially to the risks and harms of disclosing per-

sonal information, is ill-advised (Acquisti & Grossklags, 2005); (2) disability of the theory to evaluate a

complex product or service with intertwined privacy concerns and (immaterial) benefits (Rohunen et

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al., 2014); and (3) failure to incorporate the users’ psychological factors in making the calculation of

costs and benefits (Acquisti et al., 2009).

Based on those aforementioned critics to the privacy calculus theory, we develop our privacy decision

making conceptual framework by combining the utilitarian view of privacy as well as the right view of

privacy. To incorporate the right view of privacy, we develop our model by involving privacy right

assurance approaches in the users’ privacy disclosure rationale as complements to the privacy calculus

theory. The privacy assurance approaches are developed by exercising the control mechanism of pri-

vacy, in which the control mechanism can be divided into two parts, i.e., active approach and passive

approach (Xu et al., 2012). The active control approach can be done by performing individual self-

protection (Son & Kim, 2008), whereas the passive control is applied by using industry self-regulation

and government regulation and legislation (Xu et al., 2012).

1.3 Research design and Findings In order to answer the research question, we present a survey questionnaire that consists of two parts

of survey, i.e., the exploratory part and the experimental part, to 265 Indonesian respondents. We use

stratified sampling strategy to target the Jakarta population who are the current users and/or aware

of the P2P ridesharing service concept.

We conduct three analyses in this thesis. Firstly, the multiple regression analysis to investigate the

influential constructs from our previously developed theory in the effort of increasing personal infor-

mation disclosure. Our findings suggest that the users’ personal information disclosure is mainly

driven by the availability of tangible benefits offered by the company. This incentive has been the

most dominant variable in predicting the users’ personal information disclosure consistently across

our segregated cases based on their demographic properties. Additionally, the availability of govern-

ment regulation and legislation will also become a catalyst in the event of disclosing personal infor-

mation in the general population.

Secondly, to perform a focused effort to target specific market segment, we also conduct cluster anal-

yses in order to make classification of users according to their preferred privacy mitigation strategy.

The classifications will derive the mitigation strategy that need to be prepared by the company in

order to target a specific market segment. We use our previously developed privacy decision making

model construct, i.e., the privacy benefits (tangible and intangible benefit) and privacy assurance ap-

proaches (individual self-protection, industry self-regulation, and government regulation and legisla-

tion) as the variable properties to form a cluster.

Our findings suggest that the total solution seeker (group of people who want all type of privacy

mitigation strategy) has been the dominant cluster formed in population. Furthermore, the average

size of the privacy right assurance dominant strategy is formed bigger than the utilitarian dominant

strategy. This suggests that more people are willing to have their privacy “assured” only than to be

“bought” only. Additionally, our findings conclude that there is no weak utilitarian cluster and weak

privacy right seeker cluster, which suggests that no one wants only one type of strategy only is applied

to increase the willingness to disclose personal information. Moreover, the findings also conclude that

there is no indifferent group formed, which suggests that everyone in the population wants their pri-

vacy concerns are mitigated before disclosing his/her personal information.

Finally, to measure the buy-off (or willingness to accept – WTA) value of privacy as well as to rank the

importance level of all privacy types involved in our context of study, we develop an experimental

analysis by using conjoint analysis. We present a new hypothetical personalization service of using P2P

ridesharing service to our respondents to draw the contextual setting of the study. The personalization

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service is developed by using PLS (pickup and drop-off information disclosure), PBA (traveling and

buying pattern disclosure), and PDI (acceptance to receive additional advertisement from 3rd party

company) variables as the main attributes. Furthermore, as the aim of the analysis to measure the

buy-off value of privacy, a monetary variable is introduced in the analysis in the form of expected

monetary saving.

Our conjoint analysis findings suggest that the most expensive privacy type is the PBA, which valued

(on average) at IDR 133K (≈ €9.2) per month. The next valuable privacy type is PDI, which valued at

(on average) IDR 29K (≈ €2) per month. However, our study cannot derive the value of PLS as this

privacy type intertwined with the basic requirement of providing ridesharing service, i.e., the pickup

and drop-off location disclosure. Nevertheless, our study concludes that PLS can be bought by using

the usefulness of the (basic) service only without giving any other tangible benefits to the data sub-

jects.

Our thesis suggests that contradictory behavior of users is found, although their level of privacy con-

cerns is high, they do not mind to disclose their privacy to the data acquirer company, regardless the

availability of any protection measures performed by the company. Additionally, their negative privacy

experience also does not influence the willingness to disclose personal data. Nevertheless, our find-

ings suggest that respondents wish to have the privacy assurance as the default condition rather than

as means to increase their willingness to disclose personal data. Also, our findings suggest that per-

sonalization is found to be the least expected benefits from disclosing privacy, but yet still significantly

high.

In addition to the aforementioned findings, the study also suggests that the most significant predictor

to predict the successfulness of the personalization service is the expected monetary saving gener-

ated by the service. The importance of the monetary saving is found higher than any type of privacy

disclosure. Furthermore, our findings also suggest that not all users can be the target of personaliza-

tion service. The sum of utility value of personalization service is not calculated positively by the high-

salaried population, the elderly population, and the high self-efficacy population; whereas the most

ideal targets for the personalization service are people in the low-salaried population and the young

population who produce the highest utility value of personalization among the others.

1.4 Contributions, limitations, and future research recommendations This thesis contributes to several scientific and business recommendations to the privacy scholars and

business decision makers as follows:

Assuring privacy right is not an option, but a mandatory condition,

Privacy should be defined in multidimensional contexts rather than unidimensional,

Contextual setting highly influences the perception of privacy,

Not everyone can be the target for personalization service,

Data subjects value different forms of privacy differently,

Be aware of the risk of storing and using personal data as more users incline to perform indi-

vidual self-protection that can be propagated to their peers,

Since the privacy assurance approaches are wished as the default condition, the company

should prepare the implementation of advance privacy enhancing technology,

Urge the regulator to create an adequate privacy protection law.

However, this thesis is written bound to several limitations, which are: firstly, limitation related to the

target population selection, in which we limit our respondents to people who are familiar with the

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P2P ridesharing only. Secondly, the sampling method used is stratified sampling based on Jakarta pop-

ulation which may produce different results if implemented in other cities/countries. Finally, the lim-

itation related to the data collection method, in which we use a utilitarian type of incentive as the

invitation which may bias to the people who have a high utilitarian mindset.

The future research recommendations that can be performed by future scholars are related to the

improvement of this thesis by answering the aforementioned limitations. However, we also strongly

suggest the privacy scholars to develop the expansion of this study, such as: design science research

to further develop this thesis to be more applicable in market and conduct innovative study to quantify

other types of privacy.

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Table of Contents

Acknowledgment ..................................................................................................................................... i

Forewords ............................................................................................................................................... ii

List of Acronyms ..................................................................................................................................... iii

Executive Summary ................................................................................................................................. v

1.1 Domain of study: Indonesian P2P ridesharing service ............................................................ v

1.2 Theoretical framework: The privacy and privacy decision making theory ............................ vi

1.3 Research design and Findings ............................................................................................... vii

1.4 Contributions, limitations, and future research recommendations .................................... viii

Table of Contents ................................................................................................................................... xi

List of Figures ........................................................................................................................................ xv

List of Tables .........................................................................................................................................xvi

List of Textboxes ................................................................................................................................. xviii

1 Introduction .................................................................................................................................... 1

1.1 Problem Identification ............................................................................................................ 1

1.2 Research Objective ................................................................................................................. 4

1.3 Research Question .................................................................................................................. 4

1.5 Research Strategy ................................................................................................................... 6

1.6 Report Structure ................................................................................................................... 10

2 Domain definition: Indonesian peer-to-peer ridesharing service ................................................ 11

2.1 Collaborative consumption: definition and concept ............................................................ 12

2.1.1 Claims of collaborative consumption ............................................................................ 14

2.1.2 The role of ICT in collaborative consumption ............................................................... 14

2.2 Ridesharing ........................................................................................................................... 17

2.2.1 Motivation to participate in the ridesharing ................................................................ 18

2.3 The ridesharing ecosystem ................................................................................................... 19

2.4 Ridesharing ecosystem in Indonesia: Two cases of Ojek Online .......................................... 23

2.4.1 Gojek ............................................................................................................................. 24

2.4.2 GrabBike ........................................................................................................................ 28

2.5 Conclusion ............................................................................................................................. 31

2.6 Discussion .............................................................................................................................. 32

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3 Theoretical Framework: The privacy and Privacy decision making theory .................................. 35

3.1 What is Privacy? .................................................................................................................... 36

3.1.1 Privacy as a right ........................................................................................................... 37

3.1.2 Privacy as an interest .................................................................................................... 41

3.2 Categorization of privacy ...................................................................................................... 42

3.2.1 Privacy in peer-to-peer ridesharing service .................................................................. 43

3.3 Privacy decision making theory ............................................................................................ 43

3.3.1 The privacy calculus ...................................................................................................... 43

3.3.2 Beyond the privacy calculus .......................................................................................... 51

3.4 Hypotheses development ..................................................................................................... 54

3.5 Preparing the implementation of privacy mitigation strategies ........................................... 57

3.6 Personal data valuation ........................................................................................................ 59

3.6.1 Market perspective ....................................................................................................... 59

3.6.2 Individual perspective ................................................................................................... 60

3.7 Conclusion ............................................................................................................................. 61

4 Survey design ................................................................................................................................ 65

4.1 Sampling strategy and data collection method .................................................................... 66

4.2 Explorative and predictive assessment ................................................................................. 67

4.2.1 Survey questionnaire and instruments development .................................................. 68

4.2.2 Data cleansing and preparation .................................................................................... 74

4.3 Conjoint assessment of the privacy buy-off value ................................................................ 79

4.3.1 Contextual setting: personalization service in peer-to-peer ridesharing service ......... 80

4.3.2 Personal data disclosure definition and attribute selection ......................................... 81

4.3.3 Choice set design .......................................................................................................... 83

4.3.4 Questionnaire design and development ....................................................................... 84

4.3.5 Data processing and analysis ........................................................................................ 84

5 Survey Results, Data Analyses, and Discussion ............................................................................. 87

5.1 Descriptive Statistics ............................................................................................................. 88

5.1.1 Willingness to disclose (WTD) ....................................................................................... 88

5.1.2 Privacy concerns ........................................................................................................... 88

5.1.3 Privacy benefits ............................................................................................................. 89

5.1.4 Privacy assurance approaches ...................................................................................... 89

5.2 Multiple Regression Analysis ................................................................................................ 91

5.2.1 Generic multiple regression analysis ............................................................................ 91

5.2.2 Consistency test based on demographic variables ....................................................... 93

5.2.3 Conclusion ..................................................................................................................... 99

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5.3 Cluster Analysis ................................................................................................................... 101

5.3.1 Generic cluster analysis............................................................................................... 101

5.3.2 Cluster analysis to the segregated data based on demographic variables ................. 103

5.3.3 Conclusion ................................................................................................................... 105

5.4 Conjoint Analysis ................................................................................................................. 105

5.4.1 Generic estimated coefficient ..................................................................................... 106

5.4.2 Estimation to the segregation data based on demographic variables ....................... 109

5.4.3 Estimation to segregated data based on initial perception of WTD, privacy concerns,

and the privacy mitigation construct .......................................................................................... 111

5.4.4 Conclusion ................................................................................................................... 113

5.5 Discussion ............................................................................................................................ 115

6 Concluding chapter ..................................................................................................................... 121

6.1 Conclusion ........................................................................................................................... 121

6.2 Contributions ...................................................................................................................... 127

6.2.1 Academic contribution ................................................................................................ 127

6.2.2 Societal implication and managerial recommendation .............................................. 127

6.3 Limitations........................................................................................................................... 129

6.4 Future research recommendations .................................................................................... 130

7 Critical reflections ....................................................................................................................... 133

References .......................................................................................................................................... 137

Appendixes .......................................................................................................................................... 147

Appendix 2.1. Digital platform categorization ................................................................................ 147

Appendix 4.1. Survey Questionnaire .............................................................................................. 149

Appendix 4.2. Respondents’ demography ...................................................................................... 150

Appendix 4.3 Normality and homoscedasticity test ....................................................................... 152

Standardized Residual total case ................................................................................................ 152

Standardized Residual gender split ............................................................................................. 153

Standardized Residual salary split ............................................................................................... 154

Standardized Residual previous privacy experience split ........................................................... 155

Standardized Residual education split ........................................................................................ 156

Standardized Residual age split .................................................................................................. 157

Standardized Residual self-efficacy split ..................................................................................... 158

Appendix 4.4 Linearity test ............................................................................................................. 159

Appendix 4.5 Factor Analysis .......................................................................................................... 160

Willingness to disclose = FAC_WTD ............................................................................................ 160

Privacy concerns = FAC_IUIPC & FAC_CFIP ................................................................................. 163

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Tangible Benefits = FAC_TANG_BEN ........................................................................................... 166

Intangible Benefits = FAC_INTANG_BEN .................................................................................... 167

Individual self-protection = FAC_IPPR......................................................................................... 168

Industry self-regulation = FAC_ISR .............................................................................................. 174

Appendix 4.6. Correlation matrix .................................................................................................... 177

Appendix 4.7. A more detailed explanation about Conjoint Analysis ............................................ 178

Appendix 4.8. Choice sets configuration ........................................................................................ 179

Appendix 5.1 Multiple regression analysis of IUIPC and CFIP......................................................... 180

IUIPC ............................................................................................................................................ 180

CFIP ............................................................................................................................................. 182

Appendix 5.2 Cluster properties of data segregated based on demographic variables ................. 184

Appendix 5.3 Mean difference of the WTD vs. demographic Variables ......................................... 187

wtd vs. self-efficacy ..................................................................................................................... 187

wtd vs. previous experience ....................................................................................................... 187

wtd vs. education ........................................................................................................................ 187

wtd vs. salary ............................................................................................................................... 188

wtd vs. age .................................................................................................................................. 188

wtd vs. dsex ................................................................................................................................. 189

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List of Figures

Figure 1.1. Research approach................................................................................................................ 9

Figure 2.1. Ecosystem of ridesharing platform overview ..................................................................... 21

Figure 2.2. Ridesharing value network ................................................................................................. 22

Figure 2.3. Gojek application's main page ............................................................................................ 25

Figure 2.4. Actors' relationship with Gojek platform ............................................................................ 27

Figure 2.5. Gojek value network ........................................................................................................... 27

Figure 2.6. Grab application main page (GrabBike page) ..................................................................... 29

Figure 2.7. Grab ecosystem .................................................................................................................. 30

Figure 2.8. Grab value network ............................................................................................................ 30

Figure 3.1. Summary of privacy mitigation strategy ............................................................................. 54

Figure 3.2. Conceptual model of Privacy decision making ................................................................... 57

Figure 4.1. Respondents’ demography dispersion ............................................................................... 75

Figure 4.2. Ngene syntax ....................................................................................................................... 83

Figure 4.3. Biogeme syntax ................................................................................................................... 85

Figure 5.1. Final cluster properties ..................................................................................................... 102

Figure 5.2. Groups’ potential market share ........................................................................................ 103

Figure 5.3. Graphical presentation of the estimated coefficients ...................................................... 107

Figure 5.4. Graphical presentation of privacy buy-off value to the segregated data based on control

variables .............................................................................................................................................. 114

Figure 5.5. Graphical presentation of privacy buy-off value to the segregated data based on privacy

mitigation strategies ........................................................................................................................... 114

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List of Tables

Table 1.1. Research Strategy and its deliverable(s) ................................................................................ 8

Table 1.2. Chapters overview................................................................................................................ 10

Table 2.1. Ridesharing topology............................................................................................................ 18

Table 3.1. Summary of the privacy conception .................................................................................... 37

Table 3.2. Privacy concern mapping ..................................................................................................... 46

Table 3.3. Previous studies of privacy mitigation efforts ..................................................................... 49

Table 3.4. Privacy assurance approach (Xu et al., 2012)....................................................................... 53

Table 3.5. Cluster profiling .................................................................................................................... 59

Table 3.6. Table of hypotheses ............................................................................................................. 63

Table 4.1. Stratified Sampling of Jakarta Population ............................................................................ 66

Table 4.2. Privacy concerns construct definition .................................................................................. 68

Table 4.3. Privacy concerns operationalization .................................................................................... 69

Table 4.4. Final instruments used to measure privacy concerns. ......................................................... 70

Table 4.5. Privacy benefits operationalization ...................................................................................... 71

Table 4.6. Privacy assurance approaches instruments ......................................................................... 72

Table 4.7. Willingness to disclose personal data instruments .............................................................. 72

Table 4.8. Variables and coding summary ............................................................................................ 73

Table 4.9. Respondents’ comparison to the population ....................................................................... 75

Table 4.10. Factor analysis results and the corresponding variables ................................................... 77

Table 4.11. Number of cases in each segregation based on demographic variables ........................... 79

Table 4.12. Privacy type operationalization attributes ......................................................................... 82

Table 4.13. Attribute dummy coding .................................................................................................... 82

Table 4.14. Attribute effect coding ....................................................................................................... 82

Table 5.1. Willingness to disclose descriptive statistic ......................................................................... 88

Table 5.2. Privacy concerns descriptive statistic ................................................................................... 89

Table 5.3. Descriptive statistic of privacy benefits ............................................................................... 89

Table 5.4. Descriptive statistic of Individual self-protection ................................................................ 90

Table 5.5. Descriptive statistic of Industry self-regulation ................................................................... 90

Table 5.6. Descriptive statistic of Government regulation and legislation ........................................... 91

Table 5.7. Correlation test of variables ................................................................................................. 91

Table 5.8. Multiple regression analysis output ..................................................................................... 92

Table 5.9. IUIPC and CFIP multiple regression outputs ........................................................................ 93

Table 5.10. Multiple regression output based on gender segregation ................................................ 94

Table 5.11. Multiple regression output based on salary segregation .................................................. 94

Table 5.12. Multiple regression output based on privacy experience segregation .............................. 95

Table 5.13. Multiple regression output based on education level segregation ................................... 96

Table 5.14. Multiple regression output based on age level segregation .............................................. 96

Table 5.15. Multiple regression output based on self-efficacy level segregation ................................ 97

Table 5.16. Multiple regression analyses summary .............................................................................. 98

Table 5.17. Hypotheses testing summary ........................................................................................... 100

Table 5.18. Potential market size ........................................................................................................ 104

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Table 5.19. Estimated coefficients for generic model ........................................................................ 107

Table 5.20. Recoded coefficients and the calculation of the importance .......................................... 108

Table 5.21. Buy-off value of each privacy type ................................................................................... 109

Table 5.22. Coefficient estimation based on demographic variables segregations ........................... 110

Table 5.23. Buy-off value of privacy based on control variables segregations ................................... 110

Table 5.24. Coefficient estimations based on privacy constructs segregations ................................. 112

Table 5.25. Buy-off value of privacy based on privacy constructs segregations ................................ 112

Table 5.26. Research strategic summary ............................................................................................ 118

Table 6.27. Recommendations for company related to privacy concerns mitigation strategy.......... 126

Table Ap.2.7.1. Platform categorization based on control over users and providers (Walravens &

Ballon, 2009) ....................................................................................................................................... 147

Table Ap.2.7.2. Platform categorization based on asset distribution (Hill & Wellman, 2011) ........... 148

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List of Textboxes

Textbox 2.1. ZipCar ............................................................................................................................... 16

Textbox 2.2. Lyft .................................................................................................................................... 16

Textbox 2.3. Uber .................................................................................................................................. 17

Textbox 2.4. Ridesharing illustration .................................................................................................... 19

Textbox 2.5. Ojek online ....................................................................................................................... 23

Textbox 4.1. Ojek online personalization use-case ............................................................................... 81

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1 Introduction1

1.1 Problem Identification Nowadays, the concept of ridesharing services has been frequently discussed. Online community and

ubiquity of data connection make ridesharing concept becomes more prevailing in the recent years

(Botsman & Rogers, 2011). One of the valid reasons could be: a company that plays in this field can

have a disruptive potential to the existing business without owning a big number of asset, and relies

on the technology instead (Jenk, 2015; Laurell & Sandström, 2016; Riemer et al., 2015). Take a look at

Uber for example. Uber, the largest transportation network company, has been valued $62.5 billion

and makes this company regarded as the most valuable private startup which disrupts traditional taxi

service (Laurell & Sandström, 2016; Shah, 2015). Yet, this company does not have (even a single) trans-

portation vehicle to deliver its service to their customers. Instead of calling itself as a transportation

company, Uber claims itself as a technology company that helps users and drivers to meet and transact

(Uber.com, 2016b).

Information and communication technology (ICT) has a big role in these ridesharing services. ICT can

minimize (if not remove) the transaction costs (e.g., information and searching cost, middleman cost,

advertising cost, and barrier to entry) in the value exchange process (Botsman & Rogers, 2011). More-

over, ICT can also become the main source of innovation to offer additional values, both for the cus-

tomers and the ridesharing company. The popular examples of technology use in ridesharing service

are the use of digital platform, which acts as the mediator that connects the users and the drivers in

the peer-to-peer type of ridesharing, and the positioning technology to locate the users and the driv-

ers.

Moreover, the emergence of smartphones and its context aware apps has supported the concept to

become massively approachable (Böckmann, 2013; Nadler, 2014). Supported by the rise of mobile

payment2, which takes its part to increase the convenience when making a payment, they have further

developed and strengthened the ridesharing service concept. With the help of mobile payment, ubiq-

uity of smartphone, and positioning service, the digital platform can amplify the disruptive potential

of the ridesharing business model innovation (Lacy & Rutqvist, 2015).

Those phenomena motivate several business actors to build their own ridesharing platform

(Böckmann, 2013), not only in developed market, but also in emerging markets. Indonesian market

for example. With numerous potential customers, Indonesian market is a very attractive place to put

an investment (captured by the 18.1% growth of Q3-2015 Foreign Direct Investment in Indonesia)

(indonesia-investment, 2015; tradingeconomics, 2016). In this country, the most well-known imple-

mentation of ridesharing is in the form of peer-to-peer ridesharing. In this form, the mobile app is

1 A big part of this chapter 1 was developed during preparation for master thesis course as a master thesis proposal 2 The number of commerce transactions from mobile devices has increased rapidly. For example: in the US market, the CAGR revenue share of commerce transaction via mobile phone is reaching 11% (statistica.com, 2015b), whereas more than 27% of global online transactions are coming from mobile devices. The number of global mobile transactions is pro-jected to grow to 28% - 32% in the upcoming years (adyen.com, 2015; Statistica.com, 2015a).

Page | 2

used as the ridesharing marketplace and the company behind the app does not own any vehicle as its

main assets. As of 2016, 37 Peer-to-peer motorcycle ridesharing providers are established in the mar-

ket (fantasticblue, 2016), excluding the car-sharing services; which will indeed make the competition

becomes malignant. Therefore, the platform owners have to innovate creatively to defend and grow

their market share in order to sustain their profit.

To innovate, the platform owners can choose not to benefit directly from the primary value exchange

process. Instead, they can innovate their business model in the workaround by expanding the ecosys-

tem to the adjacent actors and capturing the users’ behavioral data as their assets and presenting

them as a value adding factor in the ecosystem. For example: by using this data, combined with the

product/service offering from the adjacent actors in the ecosystem, they can offer personalized push

recommendation to the users by analyzing the data (common correlational data cross-matched with

the user behavior data) and trace it back to the user. However, this value offering process is also pre-

carious, because failing to meet the accurate personalization requirement will have dire and risky con-

sequences; for example, it will make the users ignore the future communication from the company

and even unsubscribe from the service completely (Gigya, 2015). Hence, the personal information

collection and data processing becomes very critical in the business process.

Managerial and Societal Problem

When it comes to the personal information and personal data processing, the company needs to com-

ply to (at least the fundamental) personal data protection practice (such as: OECD (2013b) and APEC

(2005)); for example, it needs to completely explain what kind of personal data collection activities

are in place. This activity is critical to minimize any information asymmetry (especially between the

data subjects and the company) that might happen later in the process, which may lead to unsustain

business practice (Hughes et al., 2008). However, at the same time, this activity might also elevate the

users’ privacy concerns as the users now gain more understanding on what, what for, and to whom

the data will be used and transferred.

The high level of privacy concerns is known to become one of the impeding factors in implementing

the personalization (Chellappa & Sin, 2005). Moreover, as the personalization requires submissions of

more sensitive personal information, it would further amplify the users’ privacy concerns level3

(Bansal & Gefen, 2010). Also, because of this reason, many users chose to submit their personal infor-

mation anonymously to the provider (Gruteser & Grunwald, 2003), thus will create another difficulty

for the company to deliver the personalization service.

This complication creates a dilemma for the company. On the one hand, the company needs more

sensitive personal information disclosure from the users to offer personalization. On the other hand,

the users have the right to reject any information disclosure because of their substantial privacy con-

cerns. Therefore, the company needs to ensure the privacy protection practice by applying privacy by

design and/or implementing sufficient privacy enhancing technologies prior to personal data acquisi-

tion activity (Goldberg et al., 1997; Van Audenhove et al., 2011). This activity is critically important to

increase the data security as well as minimize any loss for the company caused by personal data leak-

age.

3 Research concludes that more than 90% of customers say that they are concerned about their privacy and how companies will use their data (Gigya, 2015). A survey by Orange concludes that 78% of respondents are experiencing a difficulty to trust the company when it comes to use of their personal data (Orange, 2014).

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Furthermore, as many laws forbid any personal data collection without the users consent (written

and/or electronically) (MCIT, 2015; Wulansari & Hakim, 2015), the company cannot “harvest” the us-

ers’ personal data directly without having the users’ consent. Therefore, “persuasion” to users to give

their consent also becomes critical in the process.

From the above mentioned challenges, the managerial and societal problem that might appear when

implementing this opportunity is the difficulty to minimize information asymmetry between company

and its customers while at the same time avoid rejections by the customers from disclosing their per-

sonal information. Hence, the platform owners have to find solutions on extending their services on

the basis of the personal data analytics while making them acceptable to the users in the aspect of

disclosure of more sensitive personal information.

Scientific Problem and Knowledge Gap

In the scientific setting, privacy is initially understood as a right since it is mentioned in the universal

declaration of human right (Assembly, 1948). However, the recent technological and economic devel-

opment have opened another view of privacy as an interest that can be traded in order to receive

additional benefits (Clarke, 2013). Therefore, it is important to ensure the users’ right of privacy as

well as provide adequate benefits in order to mitigate the users’ privacy concerns in the process of

personal data acquisition.

With regard to both aforementioned views, many previous studies have concluded that the organiza-

tion can mitigate the privacy concern by applying two methods, i.e., (1) offering an adequate privacy

policy to the customer regarding the use and handling of their personal information to ensure their

privacy right, and/or (2) offering both tangible and intangible benefits to the customer (Hann et al.,

2007). The effort of offering a privacy policy can be done properly by implementing a privacy frame-

work in the organization, which is available and structured in some levels (for example: Privacy by

Design (PbD) (Cavoukian & Jonas, 2012; Van Audenhove et al., 2011) or Privacy Framework such as

(OECD, 2013c) & (APEC, 2005)). On the contrary, offering both tangible and intangible benefits will be

strongly reliant to the context of where the personal data capture is taking place (Acquisti et al., 2009;

Acquisti, Taylor, et al., 2015; Roosendaal et al., 2014).

In respect to the context of our study in the peer-to-peer ridesharing, there is no “silver bullet” proven

to be the effective measure to mitigate the users’ privacy concerns. Even though studies about privacy

concern mitigation efforts are found in various applications; such as: e-commerce industry (Dinev &

Hart, 2006; Li et al., 2010), social network (Sun et al., 2015), multi-modal transportation (Moussa et

al., 2013), location based services (Ahmed & Ho, 2011; Xu et al., 2011; Xu et al., 2009), pay-as-you-go

travel insurance (Derikx, 2014), mobile apps (Keith et al., 2013; Liu et al., 2014; Morosan & DeFranco,

2015), and e-health services (Lee & Kwon, 2015); little we know whether we can combine those miti-

gation efforts to the context of peer-to-peer ridesharing transportation.

Furthermore, to mitigate the privacy concern by offering both privacy right assurance and privacy

benefits, the companies have to be careful in planning, as offering all right assurance and all benefits

to all customer en masse may require a lot of investment. Hence, a focused and targeted measurable

effort should be performed to address specific segment of market. Consequently, segmentations of

customers and valuation of their privacy may be needed before deciding the privacy concern mitiga-

tion strategy.

Moreover, with respect to the newly emerged privacy perception as an interest, the mitigation effort

is further complicated by the absence of knowledge about the users’ willingness to accept (WTA) value

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of their privacy in order to offer privacy benefits, specifically in the ridesharing service context. There-

fore, privacy valuation may be needed to measure the investment effort required to mitigate the us-

ers’ privacy concerns.

As a conclusion, to implement business model innovation to capture the users’ data as the valuable

asset to offer value in the ecosystem, the platform owners might experience difficulty due to the pri-

vacy concerns of the users. Excluding the preventive efforts that can be conducted by the company

(such as: implementing privacy enhancing technology and privacy by design), this thesis focuses on

persuasion effort to the users to disclose their personal information with respect to the view of privacy

as a right as well as an interest. Aligned with the abovementioned background, this research aims to

make recommendations to the peer-to-peer ridesharing platform provider in the effort of mitigating

the users’ privacy disclosure issue. By gaining the knowledge about these mitigation strategies, the

company can better unleash the potential use of personal data analytic for business model innovation

in an ethical and appropriate way without promoting any trade-offs which may lead to unsustainable

business practice.

In this thesis, we try to understand the rationales of the users in disclosing personal information by

studying the privacy decision making theory, including human cost-benefit analysis and privacy assur-

ance approach, in the process of disclosing personal information. By these rationales, we perform

hypotheses testing to understand the influential variables in personal information disclosure in the

context of peer-to-peer ridesharing services. Furthermore, we also classify the users related to its mit-

igation strategy by using a cluster analysis approach. And finally, we measure the monetary value of

relevant types of personal information to quantify the expected investment to implement the recom-

mendation.

1.2 Research Objective From the previous problem identification, the main objective of this research is:

To make recommendations to the peer-to-peer ridesharing platform provider in the effort of mit-

igating the privacy disclosure issue, by investigating the general model of privacy mitigation strat-

egy, segmenting the users according to their preferred mitigation strategy, and matching the mit-

igation strategy to segmentations of users with respect to the view of privacy as a right as well as

an interest.

This thesis will help the platform owner to decide the type of investment toward the mitigating effort

of privacy concern effectively by choosing the class of users that fits to the company’s strategy (e.g.,

the biggest market share or the niche market), especially in Indonesian market.

1.3 Research Question In accordance with the aforementioned research objectives, the main research question is formed as

follows:




To answer the main research question, we need to formulate sub-questions to structure the study.

Firstly, we need to understand the domain of the study; i.e., the peer-to-peer (P2P) ridesharing ser-

vice, especially the trends that exist in Indonesia. By understanding this domain, we can determine

the setting of the study and the complication of offering personalization that utilizes personal data

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analytics in P2P ridesharing service. While in the same time, we can also prepare an appropriate strat-

egy to collect the empirical data to test our hypotheses. Therefore, the first sub-question is formed as

follows.

SQ1. What is the peer-to-peer ridesharing service and how is the elaboration of the concept and

its application in the Indonesian market?

Secondly, we need to understand the theoretical framework in the study, i.e., the definition of privacy,

the elaboration and the classification of privacy, and the privacy decision making theory. Also, we need

to understand the constructs that explain this component and how to operationalize these constructs.

Moreover, we would like to understand what the company can do to prepare the implementation of

privacy mitigation strategy as well as the privacy valuation theory in order to measure the effort to

implement the strategy. Consequently, the next sub-question is formed as follows:

SQ2: What is privacy, what are the constructs that build up the privacy decision making theory,

and how can the company prepare the implementation of privacy mitigation strategy?

Thirdly, we want to perform hypotheses testing based on the users in Jakarta about their rationale in

disclosing their personal information, in the context of peer-to-peer ridesharing services. These hy-

potheses testing will help us to understand the general influential variables in peer-to-peer transpor-

tation users’ privacy decision making. Therefore, this following question is formed:

SQ3. To what extent are the privacy decision making constructs influencing the users’ willingness

to disclose their personal information?

Furthermore, we also want to make segmentation of users related to the mitigation effort based on

the privacy decision making constructs accordingly. The segmentation of users will help the platform

owner to do focused and targeted mitigation efforts to increase the privacy disclosure of the specific

customer segment. Therefore, the following sub-question is formed:

SQ 4: How can users be segmented based on the privacy concern mitigation efforts?

However, the segmentation alone will not be enough, because the users may have a uniform percep-

tion toward privacy mitigation efforts. Therefore, there might be a chance that all users want a similar

(or even a same) mitigation strategy. Consequently, as supplementary approach as well as to make

the mitigation efforts become measureable, we also want to understand users’ perceived value of

each personal information, relative to other types of personal information, and the expected tangible

(monetary) value of specific personal information. Furthermore, the personal information valuation

will also be useful to quantify the effort need to be performed by the company to acquire the users’

personal data. Consequently, the following sub-question is formed:

SQ 5: To what extent do the users rank the value of each personal information relative to the

other type of personal information and how much is the buy-off value for each of their personal

data?

As the businesslike outcome, the service provider can harness the information of the user classifica-

tion (from the SQ4), combined with the buy-off value (from the SQ5), to offer the money value benefits

to the specific customer segment that it wants to target as well as to measure the investment needed

to offer the benefit.

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1.5 Research Strategy To answer the abovementioned research questions, the research strategy must be formed. Firstly, a

desk research approach is conducted to answer the first sub-question. The knowledge of the ecosys-

tem of the peer-to-peer ridesharing platform is obtained by conducting literature reviews through

case studies about the development of a similar concept, such as: Uber and Lyft. Moreover, the con-

cept of the collaborative consumption and the role of technology to the development of its concept

are also studied to develop this knowledge. Additionally, we also explore the ridesharing business

infrastructure to grasp the general value network and its business ecosystem. Specific to the rideshar-

ing implementation in Indonesian market, in which (to best of our knowledge) there is no scientific

literature available, we develop our literature through studying the Indonesian ridesharing companies’

website as well as online forums that discuss about their business operation.

Secondly, we also perform desk research about the concept of the privacy calculus and its develop-

ment and expansion. We understand there are a lot of studies about privacy, therefore, we will limit

the study to the literature that was published from 2010 onwards to keep the literature still up-to-

date with the development of e-commerce, mobile apps, and location based services. Still, we also

follow a snowball method to avoid the risk of exclusion of important literature from our study. We use

online archives to browse the literature, such as: TU Delft Worldcat Library database and Google

scholar, and use these following keywords: “privacy calculus transportation”, “personalization Pri-

vacy”, “location Based Service Privacy”, “mobile application privacy”, “mobile transaction privacy”,

and “e-commerce privacy”. The outcome of this strategy is the theoretical framework that will be

tested in the next step. We summarize the mitigation efforts of e-commerce, mobile apps, and LBS

applications as a buildup of our model, which will be tested in the next sub-question.

The empirical part of the research (SQ3, SQ4, and SQ5) uses the population of Jakarta as the object of

study. However, we understand that it is almost impossible to access all the population as the object

of study, therefore, we perform a stratified sampling for this research to get the overall image of the

population as well as to increase the generalizability of the research. The population of Jakarta is strat-

ified based on demographic age-ranges.

To answer the third sub-question, we perform hypotheses testing using multiple regression analysis

to the constructs (from SQ2) to investigate the influential variables of the privacy calculus in the pro-

cess of disclosing more sensitive personal information, especially in the context of the Indonesian

market’s peer-to-peer ridesharing service. Both offline and online survey questionnaire are used to

gather the firsthand data from our respondents. The questionnaire is designed by combining the con-

structs of the privacy calculus from the previous research, in which we use likert 7 scale as the rating

mechanism.

Next, to answer the fourth sub-question, the cluster analysis is performed to classify the users based

on their preferred privacy mitigation strategy. The goal of this approach is to find out the most efficient

means to get the maximum amount of personal information data in order to focusing the company’s

effort to perform mitigation strategy. We use a non-hierarchical (K-means) cluster analysis by using

the variables from the privacy mitigation strategies (SQ3) as the cluster properties. We classify the

cluster based on the important variables that appear in this cluster. We expect to have 4-5 clusters to

be classified as the segments of users and analyze the mitigation strategy for each cluster accordingly.

From this clusterization, we also get the information of the segments’ composition to measure the

market share potential of each cluster, and the means to acquire the personal information data from

this segment.

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Finally, we carry out an experimental analysis by using a conjoint analysis to investigate how the users

value each type of personal data, relative to other personal data. In this experiment, we introduce the

concept of buy-off value (represented by monetary benefit) to our respondent to measure their will-

ingness-to-accept (WTA) of each privacy type. Presenting the monetary benefit as the buy-off value

serves two main purposes in this experiment, i.e., to provide single uniform measurement to value

privacy and to achieve the practicability of the research outcomes.

In carrying the conjoint analysis, we present a new hypothetical service package to our respondents

in the form of personalization service, that enable the ridesharing users to save their transportation

cost, in return to their privacy disclosure. A set of choices to the users which are varying the disclosure

of: traveling pattern, buying pattern, and receiving advertisement from other companies is presented

to the respondents. Furthermore, the four levels expected monthly transportation saving will also be

presented to measure the buy-off value of those privacy disclosures.

As a conclusion, Table 1.1 summarizes the research sub questions and their deliverables. Also, Figure

1.1 summarizes the research strategy and approach that will be applied in the study.

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Table 1.1. Research Strategy and its deliverable(s)

Question Research Strategy

Sources Method for collecting data Deliverable(s)

RQ In the view of privacy as both right and interest, what strategy fits to certain segments of users in the effort of mitigating the users’ privacy con-cern in the context of Indonesian peer-to-peer ridesharing service?

- - The conclusion of the following sub-questions.

The table of user classification and its mitigation strategy to in-crease the personal information disclosure. The table of buy-off value of each type of personal information.

SQ1 What is the peer-to-peer ridesharing service and how is the elaboration of the concept and its ap-plication in the Indonesian market ?

Desk re-search

Documents Literature review on the collabo-rative consumption, especially in the peer-to-peer ridesharing.

Domain Specification.

SQ2 What is privacy, what are the constructs that build up the privacy decision making theory, and how can the company prepare the implementa-tion of privacy mitigation strategy?

Desk re-search

Documents Literature review on the privacy calculus and its development.

Theoretical Framework and the constructs of personal infor-mation disclosure rationale. Hypotheses of the constructs and its relation to the personal infor-mation disclosure.

SQ3 To what extent are the privacy decision making constructs influencing the users’ willingness to disclose their personal information?

Survey Individuals Online questionnaire and hy-potheses testing based on the conceptual framework (from SQ 2). Multiple regression analysis is used to perform the analysis.

The variables and their influential magnitude related to the per-sonal information disclosure ra-tionale.

SQ4 How can users be segmented based on the pri-vacy concern mitigation efforts?

Survey Individuals Online questionnaire and cluster analysis.

Classification of users, based on the perceived influential variables to increase the personal infor-mation disclosure.

SQ 5 To what extent do the users rank the value of each personal information relative to the other type of personal information and how much is the buy-off value for each of their personal in-formation?

Survey Individuals Stated choice conjoint analysis. The data collection will be per-formed at the same time with the survey.

The ranking of privacy and its buy-off value.

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Figure 1.1. Research approach

Research Objective and Research Question

Research Framework and conceptual model

Determine number of cluster

Full conceptual framework with magnitude value

Monetary value of specific personal

informationResult

Hypothesis testing Classifying the users

Investigating to what extent the user values

specific personal information

Cluster analysis

Non Hierarchical K-Means analysis

Multiple Regression Conjoint AnalysisResearch Methodology

Preliminary research

SQ3 SQ5

Literature review on Privacy

mitigation effortSQ2

Domain Specification

Literature review on collaborative

consumptionSQ1

SQ4

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1.6 Report Structure The rest of the paper is organized as follows. This introductory chapter explains the background, ob-

jective, research questions, and the organization of the paper. Next, the domain specification of peer-

to-peer ridesharing services and its implementation in Indonesia will be analyzed in chapter 2. Chapter

3 will discuss the privacy decision making theory and the hypotheses development. Furthermore, the

survey design will be explained in the chapter 4 whereas the analysis of the survey results and the

discussion will be discussed in the chapter 5. Next, the conclusion will be elaborated in chapter 6. And

finally, the critical reflection to this research is provided in the last chapter.

Table 1.2 details the chapters that will be written in respect to the abovementioned research question

and sub-questions.

Table 1.2. Chapters overview

Chapter Research Question Product of the chapter

1. Introduction - Research objective, research ques-tions, research methodology, organ-ization of the paper

2. Domain: Peer-to-peer ridesharing service

SQ 1 Domain specification

3. Theoretical framework SQ 2 Definition of privacy, typology of pri-vacy, hypotheses

4. Survey design - Survey and stated choice design

5. Survey results, data anal-ysis, and discussion

SQ 3, SQ4, SQ 5 Hypotheses testing, cluster analysis, conjoint analysis

6. Discussion and conclu-sion

RQ Conclusion, discussion, implications, and recommendations

7. Critical reflection Reflection of the research result and the research process

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2 Domain definition: Indonesian

peer-to-peer ridesharing service

This chapter is written to specifically answer SQ1 (What is the peer-to-peer ridesharing service and

how is the elaboration of the concept and its application in the Indonesian market?) to help us

understand and get acquainted with the peer-to-peer ridesharing service as the domain, contextual

factors, as well as the scope of the study. Additionally, the knowledge on the how the system creates

value to its users and the involvement of personal data to offer value added service is also explored.

To structure the chapter, the following sub-division questions will be answered:

SQ1a. What is collaborative consumption, its drivers, and categories?

SQ1b. What is the role of information technology to the development of collaborative con-

sumption?

SQ1c. What is ridesharing service, its categorization, and participants’ motivations in joining

the system?

SQ1d. How can the general ecosystem of a ridesharing company be drawn and how is its re-

lation to the users’ personal data involvement?

SQ1e. How is the ridesharing implementation and ecosystem in Indonesia?

By means of TU Delft Worldcat database and Google scholar, we performed a literature reviews of

online journals to elaborate and assess the collaborative consumption and the ridesharing concepts.

An additional effort was performed to filter the literature material that discussed the business model

of a certain type of collaborative consumption and/or ridesharing practice to understand their general

business ecosystem.

Focusing on the ridesharing ecosystem in Indonesia, two cases of ridesharing in Indonesia were stud-

ied to write this chapter. As the ridesharing implementation in Indonesia was still quite new and (to

best of our knowledge) there was no formal literature published about the Indonesian ridesharing, we

browsed the website of two Indonesian ridesharing companies, i.e., Gojek and Grab, to look for liter-

ature about ridesharing implementation in Indonesia. Furthermore, the business operation of both

companies were investigated by using online forums that discussed specific matters related to the

Gojek and Grab business operation and business model in Indonesia.

The intended contribution of this chapter is twofold: to help us to understand the context of the re-

search, i.e., the collaborative consumption which will be detailed on the ridesharing concept in Indo-

nesia, and to introduce a discussion of the personal data involvement in the ridesharing business

model. To do that, firstly, we introduce the general concept of collaborative consumption and the

detailed concept of the ridesharing (paragraph 2.2). Secondly, we also present the elaboration of the

ridesharing ecosystem to grasp the complication of the value network and the involvement of the

users’ personal data (paragraph 2.3 and paragraph 2.4). Finally, we illustrate the ridesharing imple-

mentation in Indonesia by using two cases of Indonesian Ojek online companies (paragraph 2.5).

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2.1 Collaborative consumption: definition and concept The concept of collaborative consumption has been frequently discussed recently. It can be imple-

mented and presented in many configurations, such as: ridesharing in transportation field, peer-to-

peer lending in financial, secondhand good swap market in e-commerce (such as Marktplaats.nl), and

any other peer-to-peer form of collaboration.

Even though the collaborative consumption has become a buzzword, defining the concept in a solid

definition, however, is almost imposible (Schor, 2014). A lot of words and definitions are often used

by scholars and economists to explain the collaborative consumption concept; such as: “sharing econ-

omy”, “collaborative consumption”, “collaborative economy”, “peer-economy”, and “on-demand ser-

vices”.

Botsman and Rogers (2011) use both collaborative consumption and sharing economy in their book

that discusses about the general idea and main drivers of collaborative consumption. Botsman (2015)

defines collaborative consumption as “[a]n economic system of decentralized networks and market-

places that unlocks the value of underutilized assets by matching the needs and haves, in ways that

bypass traditional middlemen” (Botsman, 2015, p. na.). Furthermore, she also explains that the shar-

ing economy, which is defined as “An economic system based on sharing underutilized assets or ser-

vices, for free or for a fee, directly from individuals” (Botsman, 2015, p. na.), as one form of collabo-

rative consumption. Additionally, according to April Rinne, the former chief strategy officer of Collab-

orative Labs, the main difference of sharing economy and collaborative consumption is in the social

component. The community involvement, which has been a central part of the sharing economy, has

a minimal role in collaborative consumption (Lacy & Rutqvist, 2015).

Conversely, Belk (2014) argues that defining collaborative economy as sharing economy is problematic

as “sharing” does not and should not involve any compensation in the value exchange process. He

therefore defines collaborative consumption as “people coordinating the acquisition and distribution

of a resource for a fee or other compensation” (Belk, 2014, p. 1597). Even though this definition in-

cludes other compensation; which covers barter, trade, and swap, that also involve giving and receiv-

ing non-monetary compensation; it excludes the gift-giving, which involves transfer the ownership

permanently; and a pure sharing, which does not require any compensation (Belk, 2014).

In contrast, Thierer, Koopman, Hobson, et al. (2015) do not really make specific definition to the col-

laborative consumption concept. Instead, they explain the collaborative consumption in a general

characteristic, i.e.: “any marketplace that uses the internet to bring together distributed networks of

individuals to share or exchange otherwise underutilized assets” (Thierer, Koopman, Hobson, et al.,

2015, p. 5). Additionally, Schor and Fitzmaurice (2015) use the term “connected consumption”, which

includes digital and social dimensions of the concept in the explanation. They argue that the con-

nected consumption can be distinguished from the traditional economy by its ability to facilitate shar-

ing of high cultural capital consumers with strangers by using digital technology (Schor & Fitzmaurice,

2015). Both definitions focus on the technology role in the collaborative consumption.

Botsman and Rogers (2011) categorize the collaborative consumption in the three distinct systems,

which are: (1) Redistribution markets, i.e., the activities to redistribute unused or underutilized assets

for free, in exchange for money, or for points; (2) Collaborative lifestyle, which is the new way to

exchange activities of tangible and intangible assets, such as: skills, time, space, and money; and (3)

Product service system (PSS), which means to purchase the use/access to the benefit of a product

without acquiring the ownership. All of those three systems can be operable in Business to Business

(B2B), Business to Consumers (B2C), and Peer to Peer (P2P) market.

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On the contrary, Schor (2014) categorizes the collaborative consumption concept into four broad

categories, i.e.: (1) Recirculation of goods, which shares the same explanation with Botsman and

Rogers (2011) redistributon markets; (2) Intensive (durable) assets utilization, which focuses the

activities to facilitate the assets’ usage more intensively; (3) Exchange of services, in which the collab-

orating actor uses his/her skills as “tradable service” to seek for help from another people who have

other type of skills; and (4) Sharing of productive assets, which focuses on assets sharing for productive

use rather than consumptive use, such as “farming land” sharing.

All of the words, definitions, and concepts have the same commonalities. The first commonality is the

distributed power which motivates the individuals to create communities and networks with a shared

value and trust (Botsman, 2013). It dismisses the role of a centralized institution as intermediary sys-

tem in the value exchange activity. Furthermore, the experience of the consumer is also changing.

From the passive role of customers towards the active and connected collaborators, producers, crea-

tors, providers, and even financers (Botsman, 2013).

The second commonality is all of the terms have shared drivers, which are, (1) the consumerization

and the ubiquity of digital technology, which help to expand the concept in a wide (or even global)

scale; (2) the shift in the concept of value, that promotes access to use goods rather than ownership

of goods; (3) a new thinking of the way assets create wealth from economic lenses, that grows the

entrepreneurial mindset of people; and (4) environmental awareness, that makes people aware of the

harm of “over consuming” new assets (Botsman, 2013; Sundararajan, 2014).

And the last commonality is the innovative and efficient asset utilization as a result of technology

innovation (Botsman, 2013). Technology enables us to value the idle assets more than they were. It

helps us to see where the idle assets are; to connect to them; and to monitor, to control, and to put a

billing to the usage of the assets.

Additionally, those collaborative consumption concepts also share the same principles. There are four

principles of collaborative consumption. The first principle is the critical mass which measures the

potential momentum of the collaborative consumption system before it becomes self-sustaining

(Botsman & Rogers, 2011). The reason why the critical mass point becomes vital is twofold: firstly, it

is related to choice, the more (broader) choices available in the collaborative consumption system,

the more the system becomes attractive to the users as the system will provide enough products for

them to find the product they may like (Botsman & Rogers, 2011). Secondly, the critical mass will be

able to attract the frequent users, which will become the core users of the collaborative consumption

(Botsman & Rogers, 2011).

The second principles of collaborative consumption is there are idling capacities (Botsman & Rogers,

2011). The idling capacity in the collaborative consumption system can be applied not only limited to

physical products (such as: car, bike, drill, and room), but also to less tangible assets (such as: time,

skills, and commodity). Without these idle products, there will be no things to share between the

collaborators.

The third principle of collaborative consumption is there are beliefs in common (Botsman & Rogers,

2011). The collaborative consumption concept builds its concept on the shared common, i.e., give to

get. Which means the actors collaborate in the system by giving something (time, skill, money) in

return to product and/or service they get (room, access to car, access to bike). This common builds

the “collaborative” way of “consuming” products and services.

And finally, the fourth principal of collaborative consumption is the trust between actors and the

strangers (Botsman & Rogers, 2011). In most forms of collaborative consumption, actors often do not

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know each other. Thus, the willingness to trust strangers will be mandatory. Since they all share a

common belief, they can self-govern the shared resource by using common tools to monitor, measure,

and provide feedback for another.

2.1.1 Claims of collaborative consumption There are several positive claims to the concept of collaborative consumption. Firstly, it can stimulate

entrepreneurship of people by exploiting their unused assets as additional incomes (Sundararajan,

2014). For example, an office worker can also work as a ridesharing driver to get additional value over

his/her car after office hour, or while he/she drives to go home. Secondly, Sundararajan (2014) also

argues that the collaborative consumption can grow the consumption since the users do not have to

own the asset to enjoy the service. It incredibly lowers the cost to get access to the asset’s functionality

and benefits. Thirdly, it can increase the productivity of the assets that were previously idle

(Sundararajan, 2014). Lastly, it could also eliminate moral hazards by using the crowd sourced review

system that provides information of the providers’ quality, hence the collaborators are informed

about the qualification of the person they are transacting with (Thierer, Koopman, & Mitchell, 2015).

However, this concept also has several negative claims. Malhotra and Van Alstyne (2014) argue that

collaborative consumption promotes abusement of zoning laws, especially in the use of residential

housing as a rented hotel (such as through the AirBnB platform). Furthermore, since the quality of the

service is derived from a crowd sourced review system, some of the user reputation ratings may be

biased by ingenuine reviews (Malhotra & Van Alstyne, 2014). Additionally, the system also promotes

the rise of an invisible workforce, as they get income without paying any taxes as well as potentially

disrupting the existing (traditional) business (Cheng, 2014; Malhotra & Van Alstyne, 2014; Nadler,

2014; Sundararajan, 2014).

Even worse, the system could also create a rebound effect. Instead of promoting the green environ-

ment due to a more efficient use of assets, the collaborative consumption system may increase the

carbon footprint (Schor, 2014). For example: AirBnB users that incline to do more journeys because

AirBnB is helping them reduce their staying costs (Schor, 2014). Another source also empirically con-

cludes that there is a shift in the use of public transportation to a ridesharing service, because the

ridesharing service is able to offer more comfortable and cheaper transportation, especially in the

area where a convenient public transportation system is not available (Bert et al., 2016; Rayle et al.,

2014).

2.1.2 The role of ICT in collaborative consumption Information and communication technology (ICT) has a big role in the emerging concept of collabora-

tive consumption. ICT can minimize (if not remove) the transaction costs (e.g., information and search-

ing costs, middleman costs, and advertising costs) in the value exchange process (Botsman & Rogers,

2011). Lacy and Rutqvist (2015) argue that all successful collaborative consumption companies use

the recent ICT developments including social communities, positioning service, and mobile application

as the backbone of the service. By providing the means to perform negotiation and gather all infor-

mation required on a real time basis (including the other providers/competitors available), ICT has

helped collaborative consumption to create a more competitive market. Moreover, the ubiquity of

smartphones and its context aware apps have made the collaborative consumption becomes ap-

proachable on a large scale (Böckmann, 2013; Nadler, 2014).

Additionally, the ICT has enabled a potential business model innovation in collaborative consumption

concept. The most prominent use of ICT that amplifies the disruption potential of the collaborative

consumption in creating business model innovation is the use of digital platforms (Lacy & Rutqvist,

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2015). The digital platform is used by all today’s successful collaborative consumption as its market-

place that acts as the mediator in transactions. Together with the previous mentioned ICT develop-

ments, the digital platform has improved the speed, security, and the convenience of the transaction

(Lacy & Rutqvist, 2015).

Kenney and Zysman (2015) argue that the use of a platform in the collaborative consumption ecosys-

tem becomes pervasive because the platform is proven to be a winner-takes-all model which often

positions the platform owners as the monopolists that can maximize their welfare in the place where

the transaction occurs. Yet, there are numerous platform types with a very strong heterogeneity be-

tween them which create a difficulty to explain how (a specific) platform works (Walravens & Ballon,

2009). Noting this argumentation and with regard to the collaborative consumption context, we argue

that the important classification of a platform can be determined from (1) its ability to control the

users and the providers (control mechanism), in which we can measure the monopolistic potential of

a platform, and (2) its assets distribution mechanism, in which we can measure the scalability potential

of a platform. The detail explanation of these categorizations is explained in Appendix 2.1. Digital plat-

form categorization

Furthermore, the companies that provide the platform and mediate the transaction will pose the us-

ers’ personal information. For example, a ridesharing company will collect and store information

about who is using the service, how, when, and where it is being used when a user uses its service

(Gansky, 2010). They can use this information to better understand their customers that can be di-

vided into certain demographic properties. They could also perform data analytics that will be useful

for better marketing activities; for example, to predict the users’ behaviors, interests, and preferences;

and to create additional values for the customers, so they can create a positive sum condition

(Walravens & Ballon, 2009). Furthermore, they could also resell this information to other companies

that might be interested in the data.

Despite the potential use of collected data involved in the digital platform, the use of a digital platform

will bring common challenges of developing the platform to the attention of the collaborative con-

sumption owners. There are two challenges that have to be solved to ensure the platform is sustaining

its ecosystem, i.e.: the chicken and egg dilemma and the penguin problem (Tiwana, 2013).

The chicken and egg dilemma might occur because of the indirect network externalities in which the

increasing number of users on the one side will interact with users from the other side to take part in

the system (Nguyen, 2014). Due to these indirect network externalities, a platform has to reach a

critical mass – a condition at which the number of users on both sides are able to interact with each

other and create positive feedback loops to the system – before it can sustain the ecosystem and make

the platform viable (Nguyen, 2014). Furthermore, the penguin problem also often becomes an issue.

The penguin problem is an uncertainty condition when users with potentially strong network effects

are unsure to take part in the system because they are not confident whether others will take part in

the system as well (Tiwana, 2013). To overcome this problem, the platform owner may perform a

divide and conquer strategy (Nguyen, 2014), in which the platform owner must choose one side as

the cost leader who will be receiving subsidies, and the other side as the revenue generator. This is

one of the logical reasons why most platform owners are “burning cash” in their initial release in the

market (to provide subsidies to the cost leader side to reach the critical mass) (Huet, 2014; Panji,

2015b).

Additionally, the platform owner can utilize the digital platform to reach its critical mass. By harnessing

the loosely-coupled ability of digital platform competently (Tiwana, 2013), it will be able to create

tremendous value added to its service. The loosely coupled ability of digital platform will be able to

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help the platform owner to easily combine its basic service (for example ridesharing services that will

be explained in detail in the next paragraph) with various adjacent services (such as: restaurant, online

shopping, offline shopping, etc.). This service expansion will indeed create a positive feedback loop to

help the platform to reach its critical mass faster. A more elaborated explanation of the ridesharing,

its ecosystem, and the possible ecosystem expansion will be discussed in the following paragraphs.

Textbox 2.1. ZipCar

Zipcar is a form of car sharing service which operates in US, France, Canada, UK, Spain, and Austria.

The main service of Zipcar is car rental which includes the gas fee and insurance fee in the service

(Zipcar.com, 2016). Zipcar users have to pay a membership fee in addition to their reservation

charges. After the users join the membership, they will receive a Zipcard that can be used as the

access key to the Zipcar car.

When a user wants to use a Zipcar, he/she needs to log in into its application to reserve a car. Once

he/she confirms the reservation, he/she can use the Zipcard to open a Zipcar car door. After finish-

ing the trip, he/she needs to park the car in the selected parking location.

In addition to that, the users are responsible to keep the car clean and has (at least) ¼ tank of fuel

(Zipcar.com, 2016). If the users find the car is not clean, they can complain directly to the company.

However, if they find the gas level is low, they can directly buy the gas to fill in the tank by using the

prepared gas card in the car.

Textbox 2.2. Lyft

Lyft is a transportation network company which facilitates peer-to-peer ridesharing service. The

company has a mobile app that connects the passengers who need a ride to the drivers who have a

car. As of April 2016, Lyft operates in 200 US cities.

To use Lyft service, the passengers must download the Lyft app, sign up, and enter a valid phone

number as well as a valid form of payment (Lyft.com, 2016a). When they need a ride, they need to

open the app and order a ride service from the nearby drivers. Once the order is confirmed, the

driver’s name, rating, photos as well as the estimation of trip fee will appear in their app (Lyft.com,

2016b).

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2.2 Ridesharing Ridesharing service is one of the collaborative consumption forms. It translates the Product service

system type of collaborative consumption in the transportation sector. When using a ridesharing ser-

vice, the user pays for the access to the benefit of a product (i.e., to move from one place to another)

without acquiring the ownership of the product. Similar to the collaborative consumption drivers, the

ridesharing concept has been ubiquitous due to three main drivers. Firstly, the ICT development. The

ridesharing is prevalent because most people, especially in the metropolitan area, have and use

smartphones on a daily basis. There will be no issue for them to operate the application which uses

the phone’s GPS capability. Secondly, the value shift that allows people to value the access to use

assets more than the ownership of the assets, for example in the Uber case, they enjoy riding a car

rental that looks like their own private car without owning the ownership of the car and being both-

ered by performing a regular maintenance of the car. Finally, ridesharing provides a new thinking in

the way assets create wealth. People who have idle assets can now act like micro-entrepreneurs who

are able to seek for additional incomes from their underutilized assets. There are many successful

companies that base their services on the ridesharing concept, such as: Zipcar (Textbox 2.1. ZipCar),

Lyft (Textbox 2.2. Lyft), and Uber (Textbox 2.3. Uber). They have similar core concepts, yet still have

differences on a more detailed level. Moreover, as the involved actors are increasing, to define the

complete characterization of ridesharing is nearly impossible, as the services are evolving rapidly

(Rayle et al., 2014).

Ridesharing companies can be classified based on their organizational perspective and market orien-

tation perspective. In the organizational perspective, the ridesharing can be distinguished to business-

to-consumers (B2C) and peer-to-peer (P2P) form (Schor & Fitzmaurice, 2015). In the B2C form, the

ridesharing service providers own all the assets and distribute the assets centrally. In other words, the

B2C ridesharing commonly adopts the centralized rental mechanism (see Appendix 2.1. Digital plat-

form categorization). On the contrary, the ridesharing company in the P2P form acts only as the

mediator of transactions between the collaborators, i.e., the asset owners and the users. As the com-

pany does not control the assets, both assets rental and transfer mechanism follow a decentralization

mechanism (also see Appendix 2.1. Digital platform categorization.).

Textbox 2.3. Uber

Uber is a transportation network company which facilitates car ridesharing taxi services. It estab-

lished in San Francisco US in 2009. As of May 2016 this service is operational in 71 countries

(Uber.com, 2016a).

Uber develops, markets, and operates its service by using its Uber app. When a user wants to use

the service, he/she needs to log in to the app and submit his/her trip information through the app.

Likewise, the Uber drivers also need to log in to this app to receive an order. The user’s trip infor-

mation then will be routed to the nearby Uber drivers who use their own cars. In some cities, Uber

allows the users to pay by cash, whereas normally, the application only allows user to pay by credit

card (Uber.com, 2016c).

Uber introduces surge charge when there are too many demands at the same time. The surge oper-

ates as the multiplication of the normal tariff. The surge charge has 2 main effects, i.e., it makes the

users wait till the high-demand ends and it motivates the drivers to head to the high-demand area

(Uber.com, 2016d). Once the demand level returns to normal, the fare calculation will back to nor-

mal.

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Furthermore, the market orientation model can also be distinguished as notprofit and for-profit (Schor

& Fitzmaurice, 2015). In the nonprofit ridesharing service, the collaborating actors act based on

altruism motives to provide rides to and use rides from strangers. While in the for-profit form the

actors, specifically the asset owners, act on the basis of revenue (and profit) seeking. However, those

previous mentioned classifications do not forclose the possibility of other forms of operation and

market orientation models; for example: a freemium model, which is a combination of nonprofit and

for-profit form for ridesharing (assets from peer providers, free use the basic service, pay-per-use pre-

mium service). Table 2.1 provides examples of each type of form.

Table 2.1. Ridesharing topology

Organizational form

Peer-to-peer (P2P) Business-to-consumer (B2C)

Mar

ket

O

rien

tati

on

Nonprofit Nonprofit Peer-to-peer: Haxi4

Nonprofit business-to-consumer5

For-profit For-profit Peer-to-peer: Uber, Lyft

For-profit Business-to-consumer: Zipcar

2.2.1 Motivation to participate in the ridesharing In several studies, the motivation of actors to participate in ridesharing were explored. Schor and

Fitzmaurice (2015) argue there are several motivations to participate in the ridesharing. Firstly, the

economic motivation. Since the assets owners have the opportunity to get additional income over

their underutilized assets, they are willing to participate in the system (Schor & Fitzmaurice, 2015).

Furthermore, as most of the income generated in ridesharing activity is untaxed (Sundararajan, 2014),

the price of getting access to use the assets become cheaper, which will interact the users to partici-

pate in the system as well (Lacy & Rutqvist, 2015).

Secondly, the ecological impact and carbon footprint reduction as a result of less consumption of

newly produced goods also becomes the motivation of environmentally aware users (Schor &

Fitzmaurice, 2015). However, there is no substantial amount of carbon footprint reduction generated

since the system also produces a rebound effect (Schor, 2014). Thirdly, people participating in the

system are also interested in the social impact and the social networking potential of the system (Schor

& Fitzmaurice, 2015), even though the network ties generated in these transactions are very weak

(Dubois et al., 2014; Fenton, 2013). Fourthly, the euphoria of doing something efficiently and effec-

tively via internet and smartphone that creates “technophilia” also becomes one of the motivations

to use ridesharing services (Schor & Fitzmaurice, 2015). And finally, the ideology that criticizes the

traditional taxi market; e.g., lack of competition and unsatisfactory services, also becomes one of the

motives of using ridesharing especially for the early adopters (Schor & Fitzmaurice, 2015).

Additionally, Van de Glind (2013) suggests that the motives of actors participating in the ridesharing

can be divided into two type of motives, i.e., intrinsic motive and extrinsic motive. He discovers the

intrinsic motivations that include social networking, helping others, and a contribution to make a

4According to Wikipedia, Haxi is a nonprofit P2P ridesharing, however, the Haxi’s website claims that currently the com-

pany is working on facilitating payment via application (haxi.no, 2016). 5To the best of our knowledge, there is no company that operates with a B2C nonprofit form, unless in a form of corporate

social responsibility or for its employees.

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“greener” environment still become the main motives of people participating in the ridesharing (Van

de Glind, 2013). Furthermore, he also concludes that the financial gain and convenience appear to be

the extrinsic motives of people participating in the ridesharing system (Van de Glind, 2013).

In contrast, Bellotti et al. (2015) in their research conclude that the idealistic motivation to make the

world a better place, such as: increasing sustainability and creating sustainability, is only found in the

service provider companies’ motivation, whereas the users only look for services that are able to pro-

vide them additional value and convenience (Bellotti et al., 2015).

In summary, ridesharing is a form of collaborative consumption concept implemented in transporta-

tion field. The topology of ridesharing can be distinguished by both its market orientation and organ-

izational form. However, the motivations of actors participating in the today’s ridesharing have moved

away from the initial utopian motives of ridesharing. Both of the providers and the users of the service

have lost their altruism motives of “sharing” and seek for profit and convenience instead (Lacy &

Rutqvist, 2015). Even though the service providers (sometimes) have a “pure” optimistic motivation

to create a better world, the participating actors are only motivated by the hedonistic and utilitarian

potentials of the system. Therefore, the term ridesourcing is more well suited to the today’s concept

of ridesharing (Rayle et al., 2014). Nevertheless, to avoid confusion, we continue to use the term rides-

haring in the rest of the study.

2.3 The ridesharing ecosystem The use of a digital platform in ridesharing will empower the ridesharing concept with the ability to

adopt the loosely coupled mechanism. Noting this, the ride sharing service providers (RSPs) can ex-

pand the ecosystem by inviting other (adjacent) actors to participate and collaborate in the system.

The adjacent actors can be but not limited to the companies/users that utilize the platform directly

(such as: transportation company and the ride providers), but also other companies that can exploit

the ridesharing users as their target customers and/or exercise the other peer actors as the value

differentiator. Furthermore, as personal data are involved in practice, the RSPs can also invite indirect

actors (such as: a company that might be interested in the mined behavioral user information) to join

in the ecosystem. Together, they are collaborating to create a multisided ridesharing ecosystem.

Textbox 2.4. Ridesharing illustration

Anna is a frequent car sharing service user. When she signs up to the service, she has to disclose her

name, age, email address, and mobile phone. Furthermore, if she wants to get a more convenience

payment method, she also needs to register her credit card information. When she opens the rides-

haring app from her mobile phone, the app will include her phone’s GPS information when com-

municating with the ridesharing server. This information is required to locate and inform the near-

est ride providers to her. Likewise, the ride providers will need to disclose their location information

to get informed about when potential users are nearby.

Anna’s pick up and drop off location will be announced to the potential providers. The providers

then propose the trip fee for Anna’s trip. When Anna is accepting a certain proposal, the server will

see and save the fee and from whom the fee was proposed. Sometimes, when there are no potential

providers willing to give proposal, the system will announce a “surge” condition, in which the pro-

posed fee will be multiplied by a certain surge factor to engage the providers to participate in the

transaction.

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With the help of technology development, the personal data capture becomes easier which makes

the ridesharing ecosystem become more appealing. For illustration purpose, we present the rideshar-

ing use case in Textbox 2.4.

If Anna always uses the service consistently at the same time and from/to the same location, the RSP’s

server can now learn Anna’s traveling behavior. Furthermore, when Anna is accepting a certain surge

factor, the server can learn Anna’s spending behavior indicated by the willingness to pay for the surge

level (which level of income Anna can be categorized at). By using the aggregation of these two types

of information only (location and willingness to pay), the ridesharing company can learn certain de-

mographic behavior that will be valuable to understand its customer better and to be sold to other

actors (such as: marketing agency or insurance providers).

Now we can imagine the case that the RSPs involve adjacent actors, such as online stores and restau-

rants. The ridesharing company will be able to gather complex-but-worth-to-extract behavioral infor-

mation of its users. Not only the users’ spending and traveling behavior, but also extended to the

users’ shopping behavior and their honest interest. This information can be useful to provide addi-

tional services that can deliver additional value of the company, such as: personalization services that

are tailored and presented “natively” to the users6. Furthermore, the “mined” behavioral information

can be sold to other parties, such as the transportation planning authorities and market research com-

panies.

From the abovementioned Ridesharing illustration and elaboration of ridesharing service example

(such as: Textbox 2.1. ZipCar, Textbox 2.2. Lyft, and Textbox 2.3. Uber), we can draw the possible

ridesharing ecosystem. We divide the actors into two types of actor, i.e., direct actors and indirect

actors. The first direct actors are the peer users as the transportation service users. These actors,

which are illustrated by Anna in the previous illustration, will become both revenue generator and

data provider for the RSPs. The second direct actors are the peer providers. The peer providers are

the drivers who rent out their vehicle through the use of RSPs’ digital platform. Likewise, the peer

providers will also become the revenue generator (by the collected fee per transaction) and the data

provider (the willingness to accept surge information). The third direct actors are the adjacent collab-

orators. These actors often only act as the revenue generator for the RSPs by paying an adequate

transaction fee for each transaction conducted with the users.

In contrast to the direct actors who utilize the digital platform directly when making transactions, the

indirect actors are more interested in the secondary product of the digital platform. Focusing on the

behavioral analysis of the (both) users, we classify the indirect actors as the adjacent actors (who ex-

ploit the behavioral analysis results as the value generator that can be offered back to the users) and

the adjacent users (who exploit the behavioral analysis results for their own purpose). In the adjacent

actors, we identify three possible companies, i.e., (1) the insurance company which provides the pay-

as-you-go and tailored insurance in which the fee is calculated based on the behavioral information

collected from both drivers and users (Feeney, 2015), (2) the data collectors, the parties who collect

and decide the purpose of the processing the data, and (3) data processors who process the users’

behavioral data on behalf of the data controller.

On the other hand, we identify two actors that can be classified into the adjacent users. The first ad-

jacent user is the transportation planning authority which can use the users’ behavioral data to per-

form traffic engineering and simulations, whereas the second adjacent user is the market research

6 Native advertising is a method to present advertisement in which the ads placement and experience follow the natural form of the application at which it is placed (sharethrough.com, 2016).

Page | 21

companies which can use the users’ behavioral data to formulate a new product that can be targeted

to certain users.

Additionally, we also make a distinction in the type of value transfer in the ecosystem, i.e., directly

and indirectly. We define the direct value transfer as the transfer of main value involved in the system,

whereas the indirect value transfer is the transfer of by-product value in the system. The direct value

transfer consists of the transfer of tangible and intangible value, such as: fee for using the service,

transaction fee, and exposure of product offerings to the ridesharing users. On the contrary, the indi-

rect value transfer revolves around the use of users’ data and the behavioral analysis of these data.

To summarize, Figure 2.1 portrays the overview of the ridesharing ecosystem while Figure 2.2 portrays

the value network of this ecosystem.

Digital Platform Ridesharing service provider

Peer providers

Peer users

Adjacent collaborator

Adjacent actors

Data Collector

Data Processor

Adjacent Users

Transport planning authority

Market research company

Payment gateway

Restaurant

Online stores

Offline stores

Insurance Company

Figure 2.1. Ecosystem of ridesharing platform overview

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Figure 2.2. Ridesharing value network

Ridesharing service provider

Peer providers

Peer users

Data Collector

Data Processor

Transport planning authority

Market research company

Payment gateway Restaurant Online stores Offline stores

Insurance Company

Payment handling

fee fee fee

Service, Peer providers data

fee

fee

Revenue, Users data

fee

Fee, analytics service

fee

Users data, peer providers data



Transportation service

Behavior analysis

Behavioranalysis

Exposure,Service fee

Client,Exposure

Exposure,Logistic service

Exposure,Logistic service

Logistic service

Direct value transfer Indirect value transfer

Tailored Insurancepremium

Pay-as-you-go insurance

MarketingadvisoryPersonalization

Marketingadvisory

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From the abovementioned illustration, we understand that the users’ personal information can be the

value differentiator as well as value generator to the ridesharing service provider. However, the use

of personal information is closely related to the privacy matter as using personal data (i.e., data that

can be referred back to the users) without consent of the data subjects will violate the users’ privacy

rights. Therefore, the RSPs have to ensure the users are aware about the companies’ privacy policy

and getting adequate compensations in return to their privacy disclosure. The detailed discussion

about privacy matters will be thoroughly explored in the chapter 3.

2.4 Ridesharing ecosystem in Indonesia: Two cases of Ojek Online The success and the media exposure of mainstream ridesharing startups have invited young innova-

tors to create similar services in Indonesia. There are various ridesharing service providers in Indone-

sia, from the carsharing service (such as: UberX and GrabCar) to the motorcycle sharing service, or

Ojek online (see Textbox 2.5). In the motorcycle sharing service only, 37 peer-to-peer motorcycle

ridesharing (Ojek online) providers are established in the Indonesian market (as of January 2016)

(fantasticblue, 2016). Furthermore, as of May 2016, Uber just released similar motorcycle sharing ser-

vice in Indonesia named UberMotor (Safitri, 2016). Noting that the number of competitors will make

the market become saturated, the Indonesian RSPs have to creatively innovate their business model

and offerings to their customers, especially in the use of personal data analytics to enhance the com-

panies’ value proposition. Because of this reason, we will focus on the Indonesian motorcycle rides-

haring service in this study.

To understand the P2P ridesharing ecosystem in Indonesia, especially in the case of Ojek online, we

will introduce two examples of Ojek online providers with the biggest market share in Indonesia, i.e.,

Textbox 2.5. Ojek online

As the traffic congestion in Jakarta has become a daily occurrence, motorcycle taxi (Ojek) has be-

come a solution competing with traditional taxi services (car based). By using motorcycle, the

Ojek users can reach their destination in a shorter time compared to their car based counterpart.

In contrast to the traditional taxi drivers who use the taxi company’s cars as the vehicle to pro-

vide the service, Ojek drivers use their own motorcycle to provide the ride service. Ojek drivers

usually base and park their motorcycle in the selected location (usually in the corner of an alley

or next to a bus stop). They will wait in this base until the Ojek user hails them to use the service.

As there is no specific information when the user wants to use their service, most of the drivers’

time is often wasted inefficiently for waiting for orders.

With development and ubiquity of smartphone, Ojek online has emerged as a form or P2P rides-

haring service. The drivers only need to install a driver app to receive an order from the cus-

tomer and locate the pickup and drop off point. Likewise, the users also need to install the user

app to “hail” the Ojek online. By using this app, the both the drivers and users have agreed to

the tariff policy set by the app company. Also, the Ojek drivers are now threatening the taxi busi-

ness as their previously informal work are now being formalized with the name of P2P rideshar-

ing brand.

By using this mobile app, the Ojek drivers do not need to base their operation in a certain place

anymore and they can wait for the order in any place they want. Yet, for the drivers, there is no

working time commitment held with the company.

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Gojek (60% market share) and GrabBike (35% Market share) (Bohang, 2016; Hermawan, 2015). Fur-

thermore, we also briefly elaborate the companies’ privacy policy to understand how the Indonesian

ridesharing companies deal with the users’ personal data. This knowledge will help us to understand

what kind of P2P ridesharing service the Indonesian customers are facing. Based on this knowledge,

we can then design our contextual framework better (especially the conjoint analysis study, see para-

graph 4.3.1).

2.4.1 Gojek Gojek is a social enterprise startup founded by Nadiem Makarim, Brian Cu, and Michaelangelo Moran

in February 2011 as the winner of the Global Entrepreneurship Program Indonesia (Jakarta, 2015b;

Silaban, 2015). Their main vision building this startup is to professionalize the Ojek drivers, who were

recognized as lazy workers (due to the fact that most of their time is spent on waiting for the custom-

ers – see Textbox 2.5), by using smartphones to optimize/improve the drivers’ productivity

(Koesmawardhani, 2015). After the company was founded in 2011, the activities were slowing down

because the founders were joining Rocket Internet to build Zalora (a fashion e-commerce platform) in

Indonesia. In February 2015, only Nadiem Makarim was back to Gojek and re-launched the Gojek

brand with support from angel investors (such as: Arthur Benjamin) and venture capitalists (such as:

NSI Ventures) (Christie, 2011; Silaban, 2015).

Initially, the ordering process was using a telephone call to the call center (Silaban, 2015). However,

the company realized that this way of working would not be able to keep up with the market devel-

opment of Ojek users, hence, the company adopted the digital platform business model and launched

a smartphone application that facilitated the users to perform an ordering process directly from their

hand (Silaban, 2015). Even though the company is famously known as ridesharing providers, Gojek

claims itself not as a transportation company but a technology company instead, which provides in-

termediary marketplaces to facilitate negotiation between providers (Ojek drivers) and customers

(Ojek users) (Gojek, 2016b).

Even though Gojek uses a peer-to-peer ridesharing platform model, the company wants to control the

quality over the motorcycle drivers. Adhering to the above mentioned ridesharing platform categori-

zation, Gojek is adopting a for-profit peer-to-peer type ridesharing platform. The Gojek platform acts

as the enabler of the service facilitating an efficient ordering and assets transfer process.

Gojek controls the Ojek drivers by providing a clear tariff and revenue sharing policy with the drivers.

The tariff policy of Gojek consists of: the minimum tariff, per kilometers tariff, per minute tariff (for

waiting), and surge tariff (in the busy hour) (gojakgojek.com, 2016b). When the users use the service,

the company will get 20% of the fee whereas the other 80% goes to the Ojek drivers (Satria, 2015).

Furthermore, the driver will receive another IDR 100.000 if he/she manages to get 10 or more orders

in one day (Hutabarat, 2015). On the other hand, Gojek also tries to control the users by expanding

the ecosystem and inviting other actors to collaborate in its platform. Gojek started to offer delivery

services from Gojek partners that advertise their products/services through the Gojek platform

(Gojek, 2015a).

As both users and partners have increased, heterogeneous service offerings can be offered to the

customers. The more customers are accessing the Gojek app, the more the app interacts the (poten-

tial) opposite users to collaborate in the platform. Thus, they create a positive feedback loop which

can help the company to reach its critical mass.

Anyone with a motorcycle can be a Gojek driver. To be Gojek drivers, a driver needs to submit an

application to get the driver app download access (Gojek, 2015b). However, before the driver can ride

Page | 25

as a Gojek driver, the company needs to ensure that this candidate is criminal-free and has legitimate

documents to proof the vehicle ownership. After all the documentations are complete, the driver then

is allowed to use the Gojek partner app.

Not only recruiting Ojek drivers, Gojek also expands its offering by inviting other actors, such as (but

not limited to): restaurants and stores to list their products offering on the Gojek platform. Addition-

ally, Gojek also makes a strategic partnership with major e-commerce services in Indonesia, namely

Lazada.co.id (one stop solution online shopping commerce) (Maulana, 2016), that uses the Gojek cou-

rier service to provide the same day delivery service to its customers. Moreover, apart from partners

listed in the platform application, the company also has a strategic partnership with CIMB Niaga Bank,

specifically Rekening Ponsel CIMB (CIMB mobile payment), to offer mobile payment services (Gojek

Credit) to both the users (to pay the Gojek service) and the drivers (to receive the payment from the

company) (sweetcheerysh.com, 2015).

Both Gojek’s customers and drivers are provided with insurance (Panji, 2015a). It has been reported

in its website that Allianz Indonesia is the one that provides the insurance (Allianz, 2015). However,

the insurance is covering passengers only when they are using the service (pay as you go insurance).

The insurance will cover both medical and death compensation.

Gojek offerings

The Gojek’s users only need to download the Gojek app from Google play or Apple AppStore to use

the service. They will be asked to provide their name, an email address, and a valid mobile phone

number. After receiving the activation code via a short message service, the users can order the ser-

vice directly via this mobile app. Figure 2.3 shows the main page of Gojek application.

Figure 2.3. Gojek application's main page

The main page is showing all Gojek offerings, i.e.:

1. Go-Send: to order a courier service,

2. Go-Ride: to order a ride service,

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3. Go-Food: to order food from listed restaurants,

4. Go-Mart: to order goods from listed stores,

5. Go-Busway: to monitor the schedule of Jakarta’s Bus Rapid Transit (Transjakarta) and to order

a ride to the nearest bus stop,

6. Go-Tix: to order various tickets from movie tickets to concert tickets,

7. Go-Box: to order a van to move bulky goods,

8. Go-Clean: to order a house cleaning service and ask the Ojek driver to pick up the house

cleaner,

9. Go-Glam: to order a beautician and ask the Ojek driver to pick up the beautician, and

10. Go-Massage: to order a massage therapist and ask the Ojek driver to pick up the massager.

Gojek ecosystem

Based on the Gojek’s service offering and its business model publications, we can list the actors in-

volved in Gojek ecosystem. The first actor is the users as the main users who use the Ojek service. The

second actor is the Ojek drivers, who will provide the basic ride sharing service to the users. Addition-

ally, as Gojek now offers a moving box rental service, the van owners and drivers are now involved in

the ecosystem as the peer providers. Furthermore, based on Gojek service offering, we also include

the beauticians, movie theater companies, ticketing offices, house cleaners (maid), massage thera-

pists, restaurants, and offline stores in the ecosystem as the Gojek partners.

The other actors involved in the ecosystem are the Transjakarta, a Jakarta bus rapid transit service

which advertises its bus schedule in the Gojek app, which also facilitates the users to order a ride to

Transjakarta bus stops, and the insurance provider which provides pay-as-you-go insurance to both

drivers and passengers. Even though there is no clear evidence, we might suggest that data processor

and 3rd party companies that utilize the users’ data are involved in the Gojek ecosystem.

In general, the relationship of actors with the Gojek platform can be divided into two types of rela-

tionship. The first type of relationship is the direct relationship, in which the actors will use the plat-

form directly when doing and processing transactions. The actors in the direct relationship are: the

peer providers (motorcycle owners and drivers and van owners and drivers), the partners (beauticians,

home cleaners, massage therapists, movie theaters, bank as the Gojek credit’s processor, restaurants,

ticketing offices, and stores – both offline and online), and the peer users. The second type of rela-

tionship is an indirect relationship, in which the transaction occurs only when transfer of personal data

related information is performed. Therefore, the actors in the indirect relationship are: the data pro-

cessor, insurance company, and the third party companies as the data recipients. Figure 2.4 and Figure

2.5 portray the relationship of actors and their value network within the Gojek ecosystem.

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GOJEK PLATFORM(also the data collector)

Peer providers

Peer users

Partners

Adjacent actors

Data Processor

Adjacent Users

3rd Party company

Payment gatewayGojek Credit

Restaurant

Online store: Lazada.co.id

Offline stores

Insurance Company

House cleaner

Massage therapist

TransjakartaMovie theaterConcert, sport, and art ticketing office

BeauticianMotorcycle owner and drivers

Van owner and drivers

Direct interaction Indirect interaction

Figure 2.4. Actors' relationship with Gojek platform

GOJEKPeer

providers

Peer users

Data Processor

3rd party company

Payment gateway

Insurance Company

Payment handling

fee


fee

Revenue, Users data

fee





Behavior analysis


Client,Exposure Exposure,

Logistic service


Pay-as-you-goInsurance


Partners

Restaurant

Online stores

Offline stores

House cleaner

Massage therapist

Movie theaterConcert, sport, and art ticketing office

Beautician

Saving accounts

Transjakarta

Figure 2.5. Gojek value network

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Gojek’s Privacy policies

The privacy policy is only explained on the company’s website. Aside from information provided di-

rectly when signing up to the service (such as: name, email address, and phone number), the company

will also collect information about the IP address and MAC address of the device, geo-location, and

browser used by the users and its cookies (Gojek, 2016a). The company will use those data to provide

the service to the customer (Gojek, 2016a). However, the company may also transfer these data to

the third party, even though it claims that it will not sell, rent, or lease the information (Gojek, 2016a).

Furthermore, the company will retain the data as long as the users do not cancel or unsubscribe to

the application. If there is any update to the privacy policy, Gojek will announce the change (at least)

one day before the effective date of the new policy (Gojek, 2016a).

2.4.2 GrabBike GrabBike is another motorcycle ridesharing service company in Indonesia. The GrabBike is one of the

ridesharing products of Grab Company, which consists of: GrabCar (a P2P car rental service), GrabTaxi

(a B2P taxi service), GrabBike (a P2P Ojek online service), and GrabExpress (a package delivery service).

Grab Company is a Malaysian startup company founded by Anthony Tan and Tan Hooi Ling in 2012

(Cosseboom, 2015). Initially, Grab Company entered Indonesia with GrabTaxi product in June 2014,

while later in May 2015 GrabBike was introduced to the Indonesian market (Cosseboom, 2015;

Iskandar, 2015). However, to make comparable assessment with Gojek, only GrabBike will be further

elaborated in the following paragraphs.

Like Gojek, Grab also claims itself as a technology company which does not provide any transportation

and delivery service (Grab, 2016e). Furthermore, the company also claims that the negotiation and

transaction are up to the peer providers and the users independently without any interference from

the company (Grab, 2016e). The GrabBike business model is acting as a marketplace in which motor-

cycle drivers and users meet via its mobile application. Furthermore, the tariff policy is also similar

with Gojek, which are: minimum tariff, per kilometer tariff, per minute tariff for waiting, and a surge

tariff in the busy hours (Gojakgojek.com, 2016a). However, the revenue sharing between drivers and

the company is slightly different with Gojek. The GrabBike drivers will get the 90% of the revenue

while the company will only receive 10% of the revenue (Fajrina, 2015). Nevertheless, the number of

orders in GrabBike will be fewer as there are no other direct partners involved in the ecosystem other

than the peer drivers.

Similar to Gojek, GrabBike also wants to control the drivers. The GrabBike is also adopting for-profit

peer-to-peer type ridesharing platform. However, as its service offering is not as varied as Gojek, Grab-

Bike has less control over their users. The only business partner of GrabBike in Indonesia is Mataha-

riMall.com (an e-commerce platform). MatahariMall.com uses GrabBike drivers as its official courier

service in Jakarta (Grab, 2016a).

GrabBike also provides insurance for both drivers and users. The insurance provided by GrabBike only

covers the medical insurance for accidents but not for deathly accidents (Grab, 2016c). However, it is

not clearly stated, specifically for the drivers, whether the insurance covers only the journey or at-

tached to the drivers even though they do not drive for the customers.

GrabBike offerings

The GrabBike application is integrated into the Grab application, whilst the offering is not as diverse

as Gojek. With other ridesharing service offerings listed in the Grab application, such as: GrabCar and

GrabTaxi, Grab only offers two motorcycle based products, i.e., GrabBike and GrabExpress (Grab,

Page | 29

2016b). In March 2016, the company announced that it pursues a strategic partnership with Mataha-

riMall.com and provide GrabExpress as the official courier for the goods bought in the MatahariM-

all.com website (Grab, 2016a). Figure 2.6 displays the main page of the Grab application, especially

GrabBike.

Figure 2.6. Grab application main page (GrabBike page)

GrabBike ecosystem

Since the GrabBike offerings are very simple, so is the ecosystem. The direct actors only consist of the

motorcycle drivers and the peer users. However, the indirect actor may vary due to the fact that Grab

is a company that operates in several countries, thus the customers’ data may be transferred to the

other countries as well. Figure 2.7 and Figure 2.8 portray the GrabBike ecosystem and value network.

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GRAB PLATFORM

Peer providers

Peer users

Adjacent actors

Data Processor

Adjacent Users3rd Party company

Insurance Company

Other Grab subsidiary

Motorcycle owner and drivers

Direct interaction Indirect interaction

Partners

Online stores: MatahariMall.com

Figure 2.7. Grab ecosystem

GRABPeer

providers

Peer users

Data Processor

3rd party company

Other Grab subsidiary

Payment gateway

Insurance Company

Payment handling

fee


fee

fee

Revenue, Users data

fee





Behavior analysis

Behavior analysis


Client,Exposure

Logistic service


Pay-as-you-goInsurance


Online stores

Saving accounts

Figure 2.8. Grab value network

Page | 31

GrabBike privacy policy

It is mentioned on the Grab website (but not in the application) that the company collects, stores,

uses, and processes users’ personal information that is provided directly when the users register to

use the service, such as (but not limited to): name, identification number, birth certificate number,

passport number, nationality, address, telephone number, credit or debit card details, race, gender,

date of birth, and email address (Grab, 2016e). However, the information is not limited to those di-

rectly provided data, but also other sensitive personal data, such as: data related to health, religious,

and other similar beliefs (Grab, 2016d, 2016e).

The company also explains the purpose of data collection, such as: to perform service fulfilment and

maintain communication with the customer (Grab, 2016e). Furthermore, the company explicitly states

that it will also transfer all those types of data to the third party for advertising and marketing purpose

(Grab, 2016e). Moreover, since the company also has presence in other countries, the stored data

may be transferred to, stored, used, and processed in other jurisdictions than Indonesia (Grab, 2016d).

However, the company also provides the users opportunity to opt out on the use of their data from

any purposes other than service fulfilment by contacting the company by email or simply by clicking

the unsubscribe link in the company’s marketing email (Grab, 2016d).

2.5 Conclusion As previously mentioned in the introductory paragraph, this chapter discusses the contextual factors,

domain, as well as the scope of the study. Specifically, the chapter aims to answer SQ1. (What is the

peer-to-peer ridesharing service and how is the elaboration of the concept and its application in the

Indonesian market?).

We elaborate the ridesharing service as a derivation of the collaboration consumption concept, in

which from the discussion, we cannot give collaborative consumption a shared definition. Yet, we can

still conclude that the collaborative consumption is a concept of sharing which has certain character-

istics, i.e.: (1) the distributed power of actors who can provide and distribute their goods to another

based on a shared value and trust without the role of centralized institution (Botsman, 2013); (2) the

changing experience of the consumer from the passive role to the active and connected collaborators,

producers, creators, providers, and even financers (Botsman, 2013); and (3) the use of technology as

an efficient mediation tools (Botsman, 2013).

Furthermore, there are four drivers of collaborative consumption, which are: (1) the consumerization

and the ubiquity of digital technology; (2) shift concept of value, that promote access to use goods

rather than ownership to goods; (3) a new thinking of the way assets create wealth from economic

lenses; and (4) environmental awareness (Botsman, 2013; Sundararajan, 2014).

In general, there are three categories of collaborative consumption, which are (according to Botsman

and Rogers (2011)):

1. Redistribution markets, is the activities to redistribute unused or underutilized assets; for exam-

ple: tweedehands.nl, www.half.ebay.com,

2. Collaborative lifestyle, is the new ways to do an exchange process of tangible and intangible as-

sets, such as: skill, time, space, and money; for example: skillshare.com,

3. Product service system (PSS), which means to purchase the use/access to the benefit of product

without owning the ownership; for example: Uber and Lyft.

The ICT development has elevated the business potential of collaboration consumption concept. The

digital platform, as the most prominent use of technology development in collaborative consumption,

Page | 32

has created further business innovation. Together with social media communities, global positioning

service, and mobile application, they have produced a more efficient market environment.

The specific collaborative consumption discussed is the ridesharing as a PSS type of collaborative con-

sumption implemented in the transportation field. The ridesharing categorization can be distinguished

by both market orientation and the organizational form. The above mentioned Table 2.1 details the

categorization of ridesharing. Furthermore, the motives of participating in the ridesharing system is

also varied, in which only the service providers may have the optimistic motives to make the world a

better place while the other participating actors are only motivated by the hedonistic and utilitarian

motives.

The general ecosystem of P2P ridesharing mainly focused on the (digital) platform owner as the me-

diator of transaction when the users use the service. Also, with the loosely-coupled potential of digital

platform, the platform owner can further extend the ecosystem by inviting adjacent actors to collab-

orate in the ridesharing system. When a user uses the service, his/her personal data might be involved

in the value exchange process. The data not only limited to the basic data to provide the service (such

as: pick up and drop off location and the mobile phone number), but also more sensitive data (such

as: behavior of users when using the service).

Going deeper to the two cases of Indonesian P2P ridesharing implementation, our study concludes

that: like Uber, the Indonesian ridesharing providers do not want to be identified as transportation

service providers. Instead, they claim themselves as technology companies that provide a marketplace

to perform negotiation and transaction between the peer providers and the users. Nevertheless, both

companies still want to control the quality over the peer providers to ensure the sustainability of their

service. Furthermore, the complexity of the ridesharing ecosystem depends on the service offerings

and the involved actors. In the study, we also found out that even though both companies provide a

clear privacy policy, this privacy policy is not easily accessible from the application.

2.6 Discussion From the above mentioned elaboration, we may conclude that P2P ridesharing service is actually an

implementation of collaboration consumption concept which translates the product service system in

transportation field. This concept heavily bases the operation at the digital platform as the intermedi-

ary infrastructure to provide the service to its users. The recent innovation of P2P ridesharing service

in Indonesia is in the form of Ojek online service, in which the ridesharing ecosystem has been devel-

oped to other adjacent actors/partners through the use of technology development (specifically the

digital platform).

ICT becomes a vital aspect in the development of a ridesharing service due to its ability to create a

more competitive markets and to remove information asymmetry. Moreover, by using a digital plat-

form, a ridesharing company can exploit the loosely coupled capability of a digital platform to further

expand its ecosystem.

Also, the ICT development has made data capture become more feasible. The users’ personal data,

including not only the basic data (such as: name, phone number, and address), but also more sensitive

data (such as: spending behavior, traveling behavior, and user interests) are easily captured by the

tracking capability bestowed by the technology when they are using the service. The basic personal

data (such as pick up and drop off location) are used to provide the basic service. Besides, more ad-

vanced personal data that can be gathered and mined by the use of ICT (such as behavioral and pref-

erences information) can be used to create additional values on top of the basic service. Thus, these

potentials will invite other actors to be part of a more complicated ridesharing ecosystem.

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As the ridesharing ecosystem becomes more complicated, the use of personal data may raise issue of

privacy concerns. The users who disclose personal data may not know what data, what for, and to

whom their data will be distributed and who will use the data. At such, the business practice can create

another information asymmetry as the users may be unaware of the harm and/or risk of disclosing

their personal data, before the privacy violation is occurred. Because of this information asymmetry,

the ecosystem will be unsustainable as the users do not fully realize the real cost they are paying in

order to use the service. On the other hand, the RPSs can also have the risk of being sued by the

customer if they do not explain completely to their customers related to the use of the users’ personal

data. Therefore, before the RSPs start to expand their ecosystem (and create their multisided ecosys-

tem), they have to prepare privacy right assurance measures and/or the additional value to be given

to the users in return to their disclosed personal data.

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Page | 35

3 Theoretical Framework: The

privacy and Privacy decision

making theory

This chapter specifically answers the sub-question 2 (What is privacy, what are the constructs that

build up the privacy decision making theory, and how can the company prepare the implementation

of privacy mitigation strategy?). By answering this question, we develop our conceptual model, in-

cluding the type of privacy involved in the peer-to-peer ridesharing service as well as the privacy mit-

igation strategies that can be applied in such service. With the aim to structure the chapter, the fol-

lowing sub-division questions will be answered in this chapter:

SQ2a. How is privacy defined?

SQ2b. How can the concept of privacy be categorized and what types of privacy are related to

the context of peer-to-peer ridesharing service?

SQ2c. How is the privacy decision making theory elaborated?

SQ2d. How are the previous studies about privacy mitigation approaches concluded?

SQ2e. What can the company do to prepare the implementation of privacy mitigation strate-

gies?

SQ2f. How is privacy valued?

To answer those questions, a literature reviews was conducted to identify the knowledge gap and to

explore the underlying theories. Furthermore, the previous studies related to the privacy concerns

were also explored to ensure all variables and important discussions related to the theories were not

left out in the study. The literature was collected from textbooks, journals, peer-reviewed journals,

conference proceedings, theses, reports, business white papers, industry expert reviews, and the in-

ternet.

This chapter is structured as follows. This first section explains the subdivisions of sub-research ques-

tion, the research strategy of this chapter, and the chapter organization. Next, the definition of privacy

will be elaborated in paragraph 3.1. There are two approaches to define privacy, i.e., as a right (para-

graph 3.1.1) and as an interest (paragraph 3.1.2). Moreover, the categorization of privacy will be dis-

cussed in paragraph 3.2. Next, the privacy decision making theory will be elaborated on in paragraph

3.4 to study about privacy concerns measurement and their mitigation strategies, whereas the hy-

potheses development based on the privacy decision making is formed as the base line of the survey

and statistical analyses that we wish to develop are presented in paragraph 3.5. Next, the privacy

mitigation strategies implementation preparation is explained in paragraph 3.6, while the privacy val-

uation theory is elaborated in paragraph 3.7. Finally, the conclusion of the chapter will be formulated

in paragraph 3.8.

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3.1 What is Privacy? Nowadays, personal data become more valuable and “threatening”. Waldo et al. (2007), in their book

that discusses privacy engagement practice in the digital age and extensive information technology,

portray the organization holding personal data is like Damocles who is partying under the sword.

Moreover, a workshop of World Economic Forum (WEF) concludes that personal information can now

both create and destroy value (WEF, 2012). Personal data and privacy have been a concern since com-

puters began to be used in public sector, even long before the internet era (Dutta et al., 2011). Nev-

ertheless, the massively available literature on privacy is still not able to ensure the users in the aspect

of privacy protection practice, especially in the information age (Waldo et al., 2007). Moreover, recent

research shows that privacy concern is still become the biggest thread to innovations that harness the

digitalization (Lee & Kwon, 2015; Li et al., 2010).

Even though privacy has been studied for a long time, the concept, however, is still ambiguous with

multiple meanings, interpretations, and value judgments (Xu et al., 2012). The concept of privacy is

vague but yet well-understood (Waldo et al., 2007). Privacy must be portrayed like a terrain in a land-

scape because there is no predominant concept of it (Solove, 2008), while the other scholars prefer

to use the “umbrella term” to explain privacy, such as: problems that arise from information collec-

tion, processing, dissemination, and invasion activities (Xu et al., 2012).

On the contrary, Waldo et al. (2007) try to conceptualize privacy in a broader concept rather than on

the individual level, they define privacy as “a set of social concern related to the control of, access to,

and use of (personal) information” (Waldo et al., 2007, p. 84). Even though the concept of privacy is

pluralistic, it is also well-understood at the same time, as people who use this term share their own

definition and are valuing the privacy in the same direction (Waldo et al., 2007). Moreover, the term

of privacy has many connotations; e.g., control over personal information, right to be left alone, or

access to person and property. In general, privacy is seen as the necessary condition needed by indi-

viduals to separate their personal and public lives and to outline what should be kept as personal

information and what should be available for public (Waldo et al., 2007).

Additionally, Warnier et al. (2015) argue that privacy is associated with the three main points, i.e.: (1)

the right to be left alone and freedom of intrusion from others, or the negative liberty; (2) the ability

to control the information about oneself; and (3) freedom of surveillance, which includes the right not

to be monitored, tracked, or followed. Furthermore, they also argue that the degree of privacy is rel-

ative and dependent to the context and the data subject (Warnier et al., 2015). For example, ones

might have occupation that requires them to have a limited (or even not having) privacy, such as ce-

lebrities and royalties.

Moreover, other privacy scholars also make definition of privacy in the economic dimension. Noting

that individuals are economic actors, economists define the economics of privacy as: the control over

protection and disclosure of one’s personal data that will produce trade-offs with tangible and intan-

gible economic benefits (Acquisti, Taylor, et al., 2015). For example: Culnan and Bies (2003) make the

economic conception of privacy, in which they define the privacy as an instrument to make a second-

ary exchange transaction, such as: a non-monetary exchange transaction of their personal data for

additional value (for example: a higher level quality of service).

Based on the above mentioned explanation, Table 3.1. Summary of the privacy conception summa-

rizes the scholars’ conception of privacy.

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Table 3.1. Summary of the privacy conception

Privacy conception and definition Scholars

Problems that arise from information collection, processing, dissemination, and invasion activities

(Xu et al., 2012).

A set of social concern with many connotations (such as: control over ones’ personal information and the right to be left alone) that related to the control of, access to, and use of (personal) information.

(Waldo et al., 2007).

Privacy is associated with right to be left alone, control over ones’ personal data, and freedom of surveillance.

(Warnier et al., 2015)

“the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others” (p. 7)

(Westin, 1968)

“The ability (i.e., capacity) of the individual to control personally (vis-à-vis other individuals, groups, organizations, etc.) information about one's self” (p. 460)

(Stone et al., 1983)

Control over personal data that is able to generate trade-offs with economic benefits.

(Acquisti, Taylor, et al., 2015)

An instrument for non-monetary secondary exchange in order to “purchase” additional value added service

(Culnan & Bies, 2003)

Seeing the ground purpose, the privacy disclosure is a mandatory condition in some cases, specifically

in the activity related to the emergency and governmental activities. It creates trade-offs between the

privacy right invasion and the societal benefit of privacy disclosure. For example: the need to track the

users’ cell phone vs. the ability to instantly locate the dialer in an emergency dialing service, or the

need to install closed-circuit-television (CCTV) that able to locate and monitor individuals’ activities

vs. the increase of the public safety. Moreover, in other cases, the privacy disclosure will also be useful

to offer value added service. A lot of business innovation opportunities are coming from the use of

personal data, for example: Facebook adverts with its targeted marketing audience (facebook.com,

2016), personalization services (Chellappa & Sin, 2005; Xu et al., 2011; Xu et al., 2009), and privacy

protection as the business value proposition (Liu et al., 2011).

One might see privacy as part of his/her fundamental right. However, due to the development of

technology, the perception about privacy is evolving in recent days. People start to see privacy as an

interest that exchangeable for certain benefits, and “sell” it to the company – or the data service pro-

viders (DSPs) - that collects, uses, processes, disseminates, stores, and/or aggregates the personal

information and that offer services, both to the users and third party in the ecosystem (Roosendaal et

al., 2014). In the next two sub-sections, we will explain the definition of privacy in two contradicting

approaches, i.e., as a right and as a tradable interest.

3.1.1 Privacy as a right Privacy is traditionally defined as a right. Scholars in the earlier days define privacy as part of the fun-

damental right (for example: Westin (1968), Altman (1976), and Parent (1983)). Furthermore, a lot of

laws and conventions also state privacy as part of the basic human right that need to be protected.

For example, the article 12 of Universal Declaration of Human Right explicitly states that “No one shall

be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks

upon his honor or reputation” (Assembly, 1948, p. n.a.). More recent law, the article 8 of European

Convention of Human Right also states privacy as one of the fundamental rights. It explicitly says

“[e]veryone has the right to respect for his private and family life, his home and his correspondence”

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(Europe, 1950, p. 10). In addition, the Privacy International Organization also defines privacy as “[a]

fundamental right, (that) essential to autonomy and the protection of human dignity, serving as the

foundation upon which many other human rights are built” (privacyinternational.org, 2016, p. n.a.). It

enables the people to create barriers and manage the boundaries between private and public space

(privacyinternational.org, 2016). They all support the connotation of privacy as the right to be left

alone.

Seeing privacy as a fundamental human right means that the DSPs have to ensure the protection of

the personal data. A lot of laws, directions, guidelines, and frameworks have been formed to support

this notion. One of most well-known privacy frameworks is the OECD Privacy Framework (OECD, 1980)

that has been updated in 2013 to keep up with the development of digitalization with some updates

on volume, range of analytics, threats, value, actors, interactions, and global availability (OECD,

2013b). The OECD privacy framework has 8 principles, i.e.:

1. Collection limitation principle, the collection of personal data should be limited to the pur-

pose of the collection and any such data should be obtained by lawful and fair means and with

consent of the personal data subjects.

2. Data quality principle, the collected personal data should be complete, accurate, and kept

up-to-date.

3. Purpose specification principle, the purpose of which personal data are collected has to be

specified no later than the collection process is taking place, and the use of this data is limited

to this purpose.

4. Use limitation principle, the personal data should not be disclosed and used other than the

purpose that stated in the collection process, except with the consent from the data owner or

under the direction of law.

5. Security safeguards principle, DSPs should protect the personal data against risks of loss, un-

authorized access, modification, destruction, use, or disclosure of data.

6. Openness principle, DSPs has to be transparent about the practices, policies, and the devel-

opment of the collected personal data.

7. Individual participation principle, the personal data subjects have the right to have control

over their personal data.

8. Accountability principle, DSPs should be accountable related to the seven principles stated

above.

Additionally, APEC7 region has also developed a privacy framework for its economic members. The

APEC Privacy Frameworks aims to balance and promote the information privacy protection across its

economic members without creating unnecessary barriers of information flow among them (APEC,

2005). This framework is also consistent with the core principles of OECD Privacy Framework.

APEC Privacy Framework is developed to address the specific issues related to the APEC countries and

other diversities in the members. For example: most of the countries in APEC are emerging countries

that just recently embrace the development of internet in which the privacy awareness (sometimes)

is minimal (Dutton et al., 2014), therefore the depth of the framework implementation may vary from

one country to another. However, the APEC Privacy framework is not far different with the OECD Pri-

7 The members of APEC Countries are: The United States; Australia; Brunei Darussalam; Canada; Chile; China; Hong Kong, China; Indonesia; Japan; Malaysia; Mexico; New Zealand; Papua New Guinea; Peru; The Philippines; Russia; Singapore; Re-public of Korea; Chinese Taipei; Thailand; and Viet Nam

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vacy Framework. The major differences between OECD Privacy Framework and APEC Privacy Frame-

work lie on the practical basis, where the members are allowed to justify the level of adoption accord-

ing to their needs. The principles are:

1. Preventing harm principle, DSPs should protect the personal data subjects from the harm

resulting from the wrong collection or misuse of their personal data.

2. Notice principle, DSPs should ensure that the personal data subjects are aware and under-

stand what information is collected, the purpose of it being collected, to whom the personal

data will be disclosed, and the choice and control over the collected personal data. With this

notice, the personal data subjects are able to make more informed decision.

3. Collection limitation, the collection of personal data is limited to the data that is relevant to

the purpose of collection, and it has to be obtained by lawful and fair means, with notice and

consent from the personal data subjects.

4. Uses of personal data, the collected personal data should be used only for the purpose of

collection or related purpose, except with consent from the data subjects, in necessity condi-

tions to provide product or service requested by the data subjects, or by the authority of laws.

This principle also governs the transfer or disclosure of personal information to a third party.

5. Choice, where appropriate, DSPs should provide the data subjects clear, understandable, easy

to understand, and affordable mechanisms to exercise the choices related to the collection,

use, transfer, and disclosure of the personal data.

6. Integrity of personal information, DSPs have to maintain the completeness and accuracy of

the data and keep the data up-to-date.

7. Security safeguards, DSPs should provide proportional safety and protection against risks of

loss, unauthorized access, modification, destruction, use, or disclosure of data. Such safe-

guards have to be reviewed and reassessed periodically.

8. Access and correction, DSPs should empower the data subjects with the ability to access and

correct their personal data in a reasonable manner and form.

9. Accountability, DSPs must be accountable to comply with the principles above. In the case of

the data are transferred to another party, DSPs must ensure that the recipient will protect the

data consistently to those principles.

Furthermore, on the national level, the Indonesian Government also prepares a law related to per-

sonal data handling. This law governs the specific process of collecting, processing, analyzing, storing,

presenting, transferring, publishing, and deletion of personal data, and also noticing to the data sub-

ject (MCIT, 2015). However, the detailed level of governance is still lacking compared to the APEC and

OECD Privacy framework. It neither addresses the implementation mechanism (for example: the

standards used and the assessors) nor audit mechanism of the regulation. The principles of personal

data handling according to this law are:

1. Purpose, the data collection should be accurate and the analysis should be limited to the rel-

evant purpose. This data should also be verified by the data subject.

2. Choice and control, the data subjects have choices whether the collected data are treated as

private data or publicly available data. DSPs must provide access for data subjects to modify,

add/delete, or update the collected data.

3. Consent, DSPs must obtain the consent from the data owner related to the data collection

process.

4. Protection, DSPs must be credible and able to perform protection measures to the collected

data. The protection measures have to be audited regularly.

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5. Storing, the physical data centers and disaster recovery center used to store the data have to

be located in Indonesia.

6. Presenting, transferring, publishing the data are not allowed unless with the data subjects’

consent or under the directive of law.

The three above mentioned frameworks (and law) have similar general principles, which are: Personal

data must be collected only for specific purposes; they can be processed only if the data subjects have

given their consent; they must be processed fairly and lawfully; they must be protected from any risks;

and control over the data has to be given to the data subjects.

Taking into account the development of technology which cannot be separated from the privacy right,

the concept of Privacy by Design (PbD) has been developed. It is derived by the notion that the tech-

nology is fundamentally neutral, the operator, however, is the one who can use the technology to

protect as well as to invade the privacy (Irani et al., 2013). Additionally, the technology development

also contributes to systemic risk of privacy in which the data collection becomes possible to be inferred

without the users’ consent and knowledge (Dhar et al., 2011).

The PbD approach aims to protect the privacy right proactively up-front in the design phase of arti-

facts, systems, business processes, physical and networked infrastructure, and the entire ecosystem

and governance model (Cavoukian, 2006; Cavoukian & Jonas, 2012). The principles of PbD are:

1. Proactive not reactive; Preventative not remedial. It begins with a recognition of value and

benefit of proactive implementation of strong privacy practice, early and consistently. It an-

ticipates and prevents privacy invasion from happening.

2. Privacy as the default. PbD aims to deliver maximum degree of privacy protection, therefore,

it is built into the system by default.

3. Privacy embedded into design. PbD is embedded within the design of the artifacts, systems,

business processes, and not as add-ons.

4. Full functionality – Positive sum, not Zero sum. PbD aims to accommodate all actors to pro-

tect their interests and to realize the benefits of privacy protection in a positive sum, not zero

sum where the unnecessary trade-offs are present.

5. End-to-end security – Lifecycle protection. PbD aims to ensure the security or personal data

throughout the entire lifecycle of the artefact in which personal data is involved.

6. Visibility and transparency. PbD aims to assure all actors operating according to the promises,

subject to independent audit, and it has to be visible and transparent to both users and pro-

viders.

7. Respect for user privacy. PbD aims to keep the operation user-centric.

Additionally, Warnier et al. (2015) argue PbD has been studied for a long time, especially in the field

of computer science, derived from value sensitive design approach. In summary, there are three dif-

ferent methods to design a privacy preserving (computer) system, i.e.: (1) never store any personal

data, (2) develop strict policies when storing and processing personal data, and (3) only use anony-

mization of personal data (Warnier et al., 2015). However, not all the methods are fit to be imple-

mented in the today’s technology development, especially the first method. Nowadays, many of or-

ganizations store and process personal data as their source of marketing intelligent activities. Never-

theless, Warnier et al. (2015) also argue that many data directives and PbD approaches are still vague

and lack of common understanding between actors, especially in the implementation phase. They also

suggest that there is a need to develop the detailed PbD guidance.

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Despite the availability of privacy frameworks and approaches, all those available frameworks and

approaches are only applicable to the DSPs from an organizational point of view. On the contrary,

from the data subjects’ point of view, the empirical analysis related to the data subjects’ ability to

control their right is still lacking (Rohunen et al., 2014).

Xu (2007) argues that to ensure the individuals’ privacy right, the data subjects can exercise control

over their personal data. The privacy control consists of both individual control and proxy control

based on the notion that when the possibility to exercise of individual control is unavailable, users will

renounce their control to the available proxy control, which is defined as the attempt of individuals to

gain control through powerful others when they do not have adequate resource or power to exercise

their individual control (Xu, 2007; Yamaguchi, 2001).

Xu et al. (2012) develop the privacy assurance approaches to cover those abovementioned privacy

controls. This approach focuses on two types of control, i.e., the individual control and the proxy con-

trol (Xu et al., 2012). Firstly, the individual control, which is developed based on Individual Privacy

Protection Responses (IPPR) taxonomy by Son and Kim (2008). The IPPR is divided into three nomo-

logical terms, i.e.: (1) Information provision (refusal & misrepresentation of information); (2) Private

action (removal & negative word-of-mouth); (3) Public action (complaining directly to the company &

complaining indirectly to third party) (Son & Kim, 2008). Secondly, the proxy control, in which the

other actors acts as the control agents, it includes industry self-regulation (such as: third party privacy

auditor & privacy protection seal) and the government legislation in this group (Xu et al., 2012).

3.1.2 Privacy as an interest In contrast to the previous theories, Clarke (1997) defines privacy as an interest. He argues that defin-

ing privacy as a right is problematic as it does not have an absolute standard and it is also cloudy

between the legal rights terminology and moral rights terminology (Clarke, 2006). He specifically de-

fines privacy as something that people like to have, or using his original word: “the interest that indi-

viduals have in sustaining a 'personal space', free from interference by other people and organiza-

tions”(Clarke, 2006, p. n.a.). Defining privacy as an interest means viewing privacy through economic

lenses that can be given up for utilitarian interest. In this case, the privacy protection measure means

balancing the interest between actors (Clarke, 2013), i.e., the data subjects’ interest to protect their

privacy versus the DSPs’ interest to acquire the data for their own benefit.

Furthermore, Clarke also details the privacy with many dimensions (Clarke, 2013). First, the privacy of

the person, which is also referred as “the bodily privacy”. This privacy protects the information of the

human body structure and information. The example of privacy intrusion in this type of privacy is:

when the company asks a candidate to submit a compulsory sample of DNA and/or body tissue to be

analyzed before the candidate is joining the company. Second, the privacy of personal behavior, which

controls the information related to all aspect of behavior, such as: religion, sexual preference, and

political views, both in private and public spheres. Third, the privacy of personal communication,

which explains the individual interest in being able to communicate in any means without being mon-

itored by others. Last, the privacy of personal data, which explains the individuals claim to prevent

their data to become available without their consent.

Additionally, he also forms another dimension from the couple of two previously developed privacy

dimensions, i.e., privacy of personal communication and privacy of personal data. This new dimension

is formed due to the development of communication technology, particularly in the development of

the internet, which increases the use of public attention. He termed this new dimension as “infor-

mation privacy” (Clarke, 2006).

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Furthermore, because of the new disturbing development has occurred, where individuals like to “rec-

ord” the experience they are getting from meeting people or buying goods or services in both online

and offline form (for example: diary, blogs, and social media), he further developed these dimensions

by adding another dimension, i.e., the privacy of personal experience, which limits the “recorded ex-

perience” from becoming automatically available for exploitation and being exploited (Clarke, 2013).

For example: people can write anything in their micro blogging page or application. However, the ap-

plication should empower the users to control and moderate to whom the blog-posting will be avail-

able. Examples of applications that use this approach are Facebook, Google+, and Path. In addition,

Solove (2007) also develops his own taxonomy of privacy based on the privacy problems and harms

due to the conflicting interests between actors. His taxonomy includes: (1) the information collection,

e.g., CCTV surveillance and interrogation; (2) information processing, such as: identification, aggrega-

tion, and secondary use; (3) information dissemination, including disclosure to other parties, and

blackmailing; (4) and invasion, e.g., decisional interference and intrusion (Solove, 2007).

To conclude the section, the privacy can be defined both as a right as well as an interest. Defining

privacy as a right means the DSPs have to make the privacy right properties are assured when they

acquire the users’ personal data. On the contrary, defining privacy as an interest means the privacy

can be traded over certain (additional) benefits expected by the users. Therefore, the DSPs can pre-

pare the “benefits” that might be wanted by the data subjects before trying to acquire the users’

personal information.

3.2 Categorization of privacy In the previous section, we presented Clarke’s version of privacy dimensions (Clarke, 2013). However,

this categorization of privacy does not fully fit to the recent development of technology. For example:

with the development of unmanned aerial vehicle (such as drone), people can have the risk of privacy

intrusion. Based on this case, Clarke’s privacy dimension fails to define what kind of privacy intrusion

it is, particularly in the segregation of public and private space. Furthermore, the categorization of

privacy involved in the recent development of e-commerce, in which the e-commerce website/appli-

cation is able to monitor the users’ behavior in making purchase (famuharan, 2015); or the use of

cookies in the web browser, which enables the web browser to monitor the “click-and-browse” be-

havior of the users (bigcommerce, 2015); is failed to be defined by Clarke’s privacy dimension. More-

over, the categorization of privacy involved in the practice of personal data market and the data driven

marketing (for example: Deighton and Johnson (2013), Spiekermann et al. (2015), & Roosendaal et al.

(2014)) are also failed to be defined in the Clarke’s privacy dimension. In other words, the Clarke’s

categorization of privacy is still too general to be applied in the context of the recent developments

of technology. Therefore, Finn et al. (2013) try to expand the dimensions so that they can fit to these

development of technology. The dimensions are:

1. Privacy of the person that includes the right or interest to keep the human body information

(such as: DNA code and biometric identity) and characteristics private. This privacy dimension

is shared the same definition with Clarke’s privacy of the person.

2. Privacy of behavior and action that includes the intimate issues such as: religion, sexual pref-

erence and habit, and political views. It also includes the ability to act or behave in public,

semi-public, and personal space without being monitored and/controlled by others.

3. Privacy of communication, which includes the concerns related to the “sniffing” of communi-

cation activities, including the use of bugs, email tapping, and telephone or wireless commu-

nication interception.

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4. Privacy of data and image that contains the concerns about assurance that “the individuals’

data are not automatically available to other individuals and organizations and that people

can exercise a substantial degree of control over that data and its use” (Finn et al., 2013, p. 8).

5. Privacy of thought and feeling includes the concerns of the individuals’ right to think and feel

whatever they like. They also have the right not to have their thoughts and feelings revealed.

This type of privacy is important to balance the power of creative freedom of individuals and

the control power of the state.

6. Privacy of location and space comprises the concerns of individuals to have the right to move

and travel without being identified, monitored, and/or tracked. It also includes the right of

privacy in the different spaces, such as: in the office, in the moving car, or in the home.

7. Privacy of association (including group privacy) includes the concerns of individuals’ right to

associate themselves with whomever they wish without being monitored by other individuals

or organizations. This dimension of privacy is closely related to the freedom of speech that

has been associated with democratic live of society.

3.2.1 Privacy in peer-to-peer ridesharing service Based on Finn’s privacy categorization, combined with the explanation in chapter 2 (specifically in the

paragraph 2.4), we can conclude that in the use of personal data analytics in the peer-to-peer rides-

haring service, only three types of privacy are involved and will be explored. Firstly, the privacy of

location and space; because of the mobile app ordering model of P2P ridesharing will use the device’s

geolocation information, the users of this service have to disclose their location to the company. Fur-

thermore, the ending point (and its correlated track) will also be recorded to calculate the transpor-

tation fee. In some cases, the company (with the proxy of the mobile application) also gives the op-

portunity for the user to name the certain location as “home” or “office” to make the next ordering

process becomes easier. In this case, the user’s location and space information are disclosed alto-

gether.

Secondly, the privacy of behavioral and action will also be studied in this research. The opportunity to

offer a personalization service based on personal data analytics requires the buying and traveling be-

havior of the users. This behavioral information is mined to predict the users’ preferences toward

certain products and services. The mined results are presented in the form of suggestions and recom-

mendations to the users.

Finally, the privacy of data and image will also be analyzed. With the notion that users’ personal data

are valuable, the company could resell the users’ behavioral information, or the analysis of the behav-

ior, to other parties so the other parties will have this information in order to upsell their product

through the company’s application (both as a bundling product or a separate product). In this case,

the data subjects will have the risk of losing their privacy of data and image.

3.3 Privacy decision making theory The privacy mitigation strategy is developed using the justice theory, especially two forms of justice,

i.e., the wealth distribution justice and the procedural justice. The sub-section 3.3.1 will focus on the

wealth distribution justice that sees privacy as utilitarian tool to get certain benefits whereas the pro-

cedural justice that sees privacy as a right will be elaborated in the next sub-section (paragraph 3.3.2

The outcome of the analysis is the foundation for the privacy decision making constructs.

3.3.1 The privacy calculus In the process of making decisions to disclose personal information, the data subjects often calculate

the trade-off of wealth, i.e., trade-off between the privacy cost and the privacy benefit. Privacy calcu-

lus theory is one of the theories in the privacy decision making based on this cost-benefit analysis. It

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infers the privacy interests as an exchange tool when data subjects demand for certain benefit as a

return for disclosing their personal information (Xu et al., 2009). The consumers (or the data subjects)

would perform the analysis to assess whether the benefit they are getting offsets the perceived cost

they have to pay (in the form of disclosing their personal data), and respond accordingly.

Privacy calculus theory is developed by Culnan and Armstrong (1999) based on the fairness theory.

They argue that customers will be willing to disclose their personal information if the company pro-

vides a fair procedure to the customers to address their privacy concerns (Culnan & Armstrong, 1999).

When DSPs collect personal data from data subjects, they will see the collection of personal data as

social contracts with the data subjects. Furthermore, the data subjects will keep participating in this

social contract as long as the benefits they receive exceed the costs and the risks associated to the

disclosure (Culnan & Armstrong, 1999). Moreover, Culnan and Bies (2003) also propose the concept

of “secondary exchange” to explain the privacy calculus, in which they argue that the utilitarian users

will perform the exchange of personal information for utilitarian benefits as by-product (secondary

exchange) of the products and services exchange (the primary exchange) (Culnan & Bies, 2003).

Additionally, Dinev and Hart (2006) also extend the privacy calculus model. They add the perceived

risk factor as the antecedent of privacy concerns in the privacy calculus model. Using data gathered

from 369 samples, a structural equation model was performed to confirm the relation. Moreover, Xu

et al. (2009) also developed the model further by incorporating justice theory in the privacy calculus

model. They include compensations, industry-self regulation, and government regulation as part of

the privacy intervention approach (Xu et al., 2009). By incorporating those approaches, the data sub-

jects will be able to evaluate the fairness of the distribution outcome in one hand as well as the fairness

of procedure of information collection and use in the other hand (Xu et al., 2009).

More recent, Kehr et al. (2015) improved this model by developing an extension to the privacy calculus

model. They propose the situational-specific privacy calculus, in which the cost and benefit assess-

ment is influenced by pre-existing attitude, e.g., the data subjects’ general privacy concerns and their

institutional trust to the system they are involved in, and the bounded rationality of the data subjects

(Kehr et al., 2015). They also argue that when users are presented with positive effect of privacy dis-

closure, they often underestimate the risk of information disclosure (Kehr et al., 2015). Their research

suggests that the users are facing a bounded rationality in doing an evaluation of privacy decision

making. Furthermore, Gu et al. (2015) also try to expand the privacy calculus model by studying the

interaction effects of the contextual factors to the privacy calculus model. Their research suggests that

contextual factors; specifically the application popularity, permission sensitivity, and the permission

explanation; are influencing the users’ calculus behavior (Gu et al., 2015).

3.4.1.1 The cost In the privacy calculus model, the cost part of the privacy calculus is often represented by the privacy

concerns (for example: Lee and Kwon (2015), Dinev and Hart (2006), Xu et al. (2009), Morosan and

DeFranco (2015), Keith et al. (2013), Kehr et al. (2015), Sun et al. (2015), and Guo et al. (2012)). Many

scholars have tried to operationalize the privacy concern. Smith et al. (1996) conceptualize the privacy

concerns with four dimensions of construct. Firstly, the collection, this construct measures the con-

cerns that the data subjects experience when the company collects and stores their personally iden-

tifiable data. Secondly, the error, which operationalizes the data subjects’ concerns related error that

might happen to the personal data. Thirdly, the unauthorized secondary use, which measures the data

subjects’ concerns that information collected is used for secondary purpose other than the initial col-

lection purpose. Fourthly, the improper access, related to the data subjects’ concerns that their data

are automatically available to others. This construct is often called concern for information privacy

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(CFIP). However, Smith et al. (1996) test these dimensions only use exploratory analysis without doing

the next step, i.e., confirmatory analysis.

In addition to that, Smith et al. (1996) also develop two more rarely used constructs, i.e.: reduced

judgment and combining data. The reduced judgment explains the concerns related to the use of data

collected as (inadequate) automated decision making source (Smith et al., 1996). For example, the

improper use of personal data to automate the decision about the creditworthiness of a person in

applying a loan from a bank. This construct, however, is not used often because scholars seldom iden-

tify reduced judgment as privacy concern (Smith et al., 1996). The second construct, the combining

data, explains the concerns related to the use of personal data that are combined with other database

to create a “mosaic effect” – single information become meaningful if it is combined with other data

– (Smith et al., 1996). This concern is rarely used because it is similar to the collection and unauthorized

secondary use construct (Smith et al., 1996). These two constructs are later empirically proven insig-

nificant.

More recent, Stewart and Segars (2002) try to improve the CFIP construct with second-order factors

using the scale from Smith et al. (1996). They drew a sample of 355 consumers and developed a con-

firmatory factor analysis framework to improve the parsimoniousness of CFIP constructs (Stewart &

Segars, 2002). They conclude that their research results are consistent with the prior findings and

suggest that the CFIP construct is better analyzed using confirmatory analysis to avoid the risk of mul-

ticollinearity (Hair et al., 2013; Stewart & Segars, 2002). This suggestion concludes that CFIP is a solid

construct to measure privacy concerns because it shows consistent result between exploratory and

confirmatory analysis.

Focusing on internet privacy concern, Malhotra et al. (2004) develop another privacy concern con-

struct called internet users’ information privacy concerns (IUIPC). They argue that IUIPC will better

explain privacy concerns in the online environment which has two-ways interaction between data

subjects and the DSPs (Malhotra et al., 2004). They propose three dimensions of construct, i.e., con-

trol, awareness, and collection (Malhotra et al., 2004). Malhotra et al. (2004) argue that the control is

important because of two reasons, i.e., (1) the consumers have taken high risk in submitting their

personal information, and (2) the previous studies (e.g.: Phelps et al. (2000) and Nowak and Phelps

(1995)) suggest that the majority of consumers perceive control as one of the important things to

reflect privacy concerns. If control is the active dimension part of the consumer, the awareness, more-

over, is also important to reflect the passive dimension of the privacy concerns. It explains the degree

to which consumers are aware about the organization’s policy related to the information privacy prac-

tice (Malhotra et al., 2004).

Additionally, Dinev and Hart (2004) also try to develop the privacy concerns dimension for the e-com-

merce model. By means of regression analysis to develop the privacy concerns construct, they con-

clude that the internet privacy concerns are measured by: (1) abuse (as the result of access vulnera-

bility), in which the users concerns that their submitted personal data are misused by the DSPs; and

(2) finding, explains the users’ concerns related to the risk of exposure of their personal data (Dinev &

Hart, 2004). These two constructs are also aligned with the CFIP constructs, i.e., the unauthorized sec-

ondary use (analogue with abuse) and the improper access (analogue with finding).

3.4.1.2 Privacy concerns constructs and its relation to the Privacy Frameworks From the abovementioned analysis, we can conclude that privacy concerns can be measured using

CFIP and IUIPC constructs. Furthermore, if we take a look to these constructs and confront them to

the privacy principles in the mainstream privacy frameworks (paragraph 3.2.1); i.e.: OECD and APEC

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privacy framework; the privacy concerns dimensions are tightly related to the principles in the frame-

work. In the CFIP construct, the collection is representing the collection limitation principle in both

privacy frameworks, while the improper access is representing security safeguard principle also in

both frameworks. Moreover, the error is representing the data quality principle in the OECD privacy

framework and integrity of personal data principle in APEC privacy framework. Furthermore, the un-

authorized secondary use is representing the use limitation principle in both principles.

Additionally, the framework is also represented by the IUIPC privacy construct. First, the control is

representing the individual participation principle in the OECD privacy framework and choice, and ac-

cess and correction principle in the APEC privacy framework. And second, the awareness as repre-

sentative of openness and purpose specification principle in the OECD privacy framework and notice

principle in the APEC privacy framework. In summary, Table 3.2 summarizes the privacy concern and

its mapping to the privacy right frameworks.

Table 3.2. Privacy concern mapping

Privacy Concerns OECD Privacy frame-work principles

APEC Privacy frame-work principles

CFIP IUIPC

Collection Collection Collection limitation Collection limitation

Unauthorized/im-proper access

Security safeguards - Security safeguards - Preventing harm

Unauthorized second-ary use

Use limitation Use of personal infor-mation

Error Data quality Integrity of personal information

Control Individual participa-tion

- Choice - Access and correc-

tion

Awareness - Openness - Purpose specifica-

tion

Notice

Applicable only to the internal organization Accountability Accountability

3.4.1.3 The benefit The second part of the calculus, i.e., the benefit part, is explored by many privacy scholars as the

utilitarian measures to mitigate the privacy concerns (the cost part of the privacy calculus). Utilitarian

scholars often brake down the benefits into two types of benefits, i.e., tangible and intangible benefits.

In the tangible benefit part, Li et al. (2010) suggest that the monetary reward is dependent on the

fairness information exchange. They also argue that the “situation specific”, or contextual factor, will

influence (or have moderating effect to) the effectiveness of the monetary reward; for example: the

monetary reward would undermine the personal information disclosure if the fairness of information

exchange has not been achieved (Li et al., 2010).

Additionally, Xu et al. (2009) try to measure the effectiveness of monetary benefits to mitigate the

privacy concerns in the context of location based services (push and pull based information providing).

By using a quasi-experimental survey on the data from 528 respondents, they suggest that providing

monetary benefit is more important in push based location based services rather than pull based (Xu

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et al., 2009). In more recent research, Derikx et al. (2016) try to measure the buy-off value of certain

type of personal information based on Finn et al. (2013) privacy categorization. Their research con-

cludes that privacy behavior and action, which is operationalized by registration of driving behavior,

is perceived as the most substantial privacy type by the mobile insurance users (Derikx et al., 2016).

In practice, the monetary reward could be a form of discount and coupon (Li et al., 2010) or specific

to the context of the personal data collection, such as: calling time and rebate of a monthly phone bill

in mobile based location based service (LBS) (Xu et al., 2009), or discount in monthly insurance in the

context of M-insurance (Derikx et al., 2016).

On the other hand, the intangible benefit is also explored thoroughly to mitigate the privacy concern.

Li et al. (2010) use usefulness to measure the intangible benefits that can be applied to mitigate the

privacy concerns. They suggest that the perceived usefulness of products and services will effectively

mitigate the privacy concerns (Li et al., 2010). Furthermore, in a more recent survey, Derikx (2014)

suggest that the perceived usefulness compensates the privacy concerns more effectively rather than

the expected monetary benefit.

Compared to the perceived usefulness, the concept of personalization has been investigated more

often to overcome the privacy concerns. Personalization is defined as “the ability to provide contents

and services that are tailored to individuals based on knowledge about their preferences and behav-

iors” (Adomavicius & Tuzhilin, 2005, p. 84). The personalization is often juxtaposed with privacy dis-

closure, because the consumers’ personal information is the company’s main source to provide per-

sonalization (Taylor et al., 2009). Because of this reason, the privacy-personalization paradox has been

studied in many contexts.

Chellappa and Sin (2005) explore the mitigation ability of personalization to the privacy concerns in a

general online environment. Aligned with the previous studies, they conclude that personalization will

effectively mitigate the privacy concerns (Chellappa & Sin, 2005). They also suggest that the company

can mitigate the users’ privacy concerns by conducting trust building activities, because their research

empirically concludes that that trust in the company will have significant negative effect (albeit small)

to the users’ privacy concerns (Chellappa & Sin, 2005). In addition, Awad and Krishnan (2006) also

empirically confirm the that personalization is able to effectively mitigate the privacy concerns. Based

on a survey to 400 online consumers, they suggest that the consumers who have a desire to higher

level of information transparency will be more reluctant to be profiled online (Awad & Krishnan, 2006).

In the context of media communication, Li and Unger (2012) explore the extent of personalized news

recommendations to mitigate privacy concerns. They suggest that the quality of recommendation is

one of the important things to mitigate privacy concerns (Li & Unger, 2012).

In the context of LBS, research by Xu et al. (2011) confirms that personalization is one of the anteced-

ents of privacy benefit. Furthermore, the personalization services will effectively mitigate the privacy

concern in the context of location aware marketing. In the same context, Liu et al. (2011) also study

the effect of personalization in the user payoff perception in using location based service. Using a field

experiment, they conclude that the amount of personalization available will effectively mitigate (di-

rectly and indirectly) the privacy concerns (Liu et al., 2011).

Morosan and DeFranco (2015) also explore personalization as a privacy concerns mitigation measure

in mobile applications, specifically in a hotel application. In addition to the benefit of personalization,

they conclude that the trust to the mobile application effectively increases the information disclosure

(Morosan & DeFranco, 2015). Furthermore, they incorporate the role of emotion in their study, which

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suggests that only positive emotion will influence the personal information disclosure (Morosan &

DeFranco, 2015).

Moreover, Guo et al. (2012) study the personalization-privacy paradox in the context of m-health.

Their study confirms the ability of personalization in mitigating privacy concerns (Guo et al., 2012).

Furthermore, they discover the mediation role of trust in the adoption of personalized m-health ser-

vices (Guo et al., 2012). Additionally, Lee and Kwon (2015) explore the ability of personalization to

mitigate privacy concerns in the m-health. They suggest that the service accuracy, as the operational-

ization of personalization, will effectively mitigate the privacy concern (Lee & Kwon, 2015). Table 3.3

summarizes the previous studies of concepts that can be part of the privacy calculus.

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Table 3.3. Previous studies of privacy mitigation efforts

No Author Context Method Privacy cost construct Privacy benefit con-struct

Note

1 Li et al. (2010) e-commerce Experiment Perceived risk Monetary rewards, per-ceived usefulness

The effectiveness of monetary reward is dependent on the context of the service

2 Xu et al. (2009) Location based ser-vices

Quasi experi-mental

Perceived risk Compensation Compensation is more effective to mitigate privacy concern in push-based LBS than in pull-based LBS

3 Derikx et al. (2016)

m-insurance Conjoint anal-ysis

Information of data and image, information of location and space, in-formation of behavior and action

Discount in monthly in-surance

Users perceive the information of behavior and actions as the most substantial personal infor-mation

4 Derikx (2014) m-insurance Survey, Multi-ple regression

General privacy con-cern

Perceived usefulness The usefulness is more effec-tive than monetary benefits to overcome privacy concerns

5 Li and Unger (2012)

e-news Experiment Perceived risk Personalized news rec-ommendation

Previous experience may also influence the privacy concerns

6 Xu et al. (2011) Location based ser-vices

Experiment Perceived risk Personalized advertise-ment

Explicit personalization has stronger impact to the privacy concerns

7 Liu et al., (2011) Location based ser-vices

DSRM Personal data disclosed

Personalization Presented that the privacy pro-tection can be a new value proposition for mobile busi-ness.

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No Author Context Method Privacy cost construct Privacy benefit con-struct

Note

8 Morosan and DeFranco (2015)

Hotel mobile application

Survey, SEM Perceived risk Personalization Trust to the application is more effective to mitigate privacy concerns than trust to the com-pany

9 Chellappa and Sin (2005)

General online envi-ronment

Survey, SEM General privacy con-cern

Personalization General trust can give moderat-ing effect to the privacy con-cerns

10 Awad and Krish-nan (2006

General online envi-ronment

Survey, SEM General privacy con-cern

Personalization There are minority people who never be willing to be profiled online, no matter what the mit-igation strategy done by the company

11 Guo et al. (2012) m-health Survey, SEM Privacy concern (adopted from Cocosila and Archer (2009))

Personalization Trust is the mediating factor to the privacy concerns, personal-ization, and behavioral inten-tion to use m-health service.

12 Lee and Kwon (2015)

m-health Experiment Behavior information Service accuracy They suggest the m-health pro-viders to prepare a set of choices of personalization ser-vice with various disclosure level to address the m-health users

In conclusion, Table 3.3 above summarizes that various privacy benefits are tested to mitigate the users’ privacy concerns. Both tangible and

intangible benefits are able to efficiently mitigate the privacy concerns. However, there is no silver bullet that can be applied as we cannot gener-

alize those strategies in all types of contexts. For example, Li et al. (2010) find that the monetary rewards shows its inefficiency to mitigate privacy

concerns when the collected information has low relevancy to the e-commerce transaction. Another example is research by Xu et al. (2009) con-

firms that compensation only works on the push based advertisement. In contrast, the compensation is less efficient to mitigate the privacy con-

cerns in the mobile insurance service (Derikx, 2014). This conclusion becomes the basis of our empirical research to investigate the type of miti-

gation strategy that will efficiently mitigate the privacy concerns in the peer-to-peer ridesharing service.

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3.3.2 Beyond the privacy calculus In the previous sub-section, we have discussed the privacy mitigation strategy in respect to the wealth

distribution justice, i.e., the privacy calculus theory. However, the privacy calculus has potential draw-

backs. Mainly due to the fact that this theory is developed based on the cost benefit analysis theory,

which assumes all individuals are utilitarian agents that have perfect foresight about the future con-

sequences in every decision related to the privacy concern. Also, this theory requires the actors to

show utility-maximizing behavior, to be fully informed about the possibilities based on random prob-

ability, and act as “utility-maximizing Bayesian updaters” (Acquisti & Grossklags, 2005).

The privacy calculus concept only measures the privacy concern and the privacy benefit as tangible

value. However, the complexity to measure the value of privacy concern and the immateriality of ben-

efits become the main limitations of this theory (Rohunen et al., 2014). Moreover, the risk and uncer-

tainty that might appear because of the complexity of the business model and the ecosystem (see

chapter 2 paragraph 2.4) of the DSPs are often unknown. It entails that the subjects do not necessarily

know when the data collection process is performed or may not know the consequences of such col-

lection and intrusion (Acquisti, Brandimarte, et al., 2015). Also, the benefits and costs associated with

this trade-off are complex and often bundled with the product or services, in which the real costs are

only revealed after violation of the personal data has occurred (Acquisti, Brandimarte, et al., 2015).

From the users’ perspective, many empirical researches discover several other psychological factors

that support the critics to the privacy calculus. For example, a research by Acquisti et al. (2009) which

tries to find out the value of willingness to accept (WTA) (in order to accept a decrease in privacy) and

willingness to pay (WTP) (in order to increase the control over the privacy). Their research concludes

that there are endowment effects, in which people tend to put a higher value to what they own com-

pared to what they do not own (Kahneman et al., 1991), and order effect (the order of offerings influ-

ences behavior of people in making decision). Furthermore, people also tend to overvalue the benefits

and undervalue the losses (Acquisti & Grossklags, 2004)(i.e., hyperbolic discounting, (Rabin, 2013)) as

well as are likely to trade long term privacy risks over the immediate rewards (Acquisti & Grossklags,

2005) (i.e., instant gratification (O'Donoghue & Rabin, 1999)).

Acquisti, Brandimarte, et al. (2015) argue that the privacy calculus concept is problematic because of

three main reasons, i.e.: (1) the users’ uncertainty about the consequences of they are disclosing their

personal data (the primary uncertainty) and the uncertainty about their response to these conse-

quences (the secondary uncertainty), (2) the context dependence that would amplify those uncertain-

ties, (3) the manipulability of privacy concerns.

The first critic to the privacy calculus theory is the uncertainty that always exists in the process. The

uncertainty is mainly caused by: the incomplete information, bounded rationality, and deviation from

rational strategy that always take place in the decision making process (Acquisti & Grossklags, 2005;

Waldo et al., 2007). Because of the complexity of the business ecosystem where they are involved in,

data subjects are rarely fully informed, nor completely understand about what, when, how, and what

for the data collection is required (Tsai et al., 2011). This full information is often only known by the

DSPs, which creates incomplete information for the data subjects and information asymmetry be-

tween data subjects and DSPs.

Even if people have access to this information, they cannot process all of the information due to limi-

tation of the human ability to process the data (Simon, 1982). Furthermore, the privacy calculus is

based on social contract theory, in which the major assumption is bounded rationality that may limit

the users’ ability to process immense amounts of information in a limited time (Acquisti & Grossklags,

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2005). Therefore, users often only make satisfactory decisions and are unable to maximize the utility

they may get.

Even if the data subjects are able to get and calculate all information, the psychological distortion (i.e.,

emotions) is often taking place (Roeser, 2012). For example: people seem overconfident in assessing

the risks involved in privacy disclosure. Furthermore, the lack of available substitute to the bundle (of

privacy disclosure and products and services) that are offered by the DSPs also amplify the psycholog-

ical distortion (Grossklags & Acquisti, 2007). Often, the consumer (data subjects) are faced in the

“take-it or leave-it” condition which may amplify the psychological distortion. In a social experiment,

Grossklags and Acquisti (2007) also discovered the paradox between users privacy behavior and their

privacy attitude. Their research suggests that even though people perceive their privacy is important,

a limited efforts are done to protect their privacy (Grossklags & Acquisti, 2007). They argue that one

of the causes of this phenomenon is the lack of substitutability of the products and services offered

by the service providers that are intertwined with the requirement to disclose users’ personal data

(Grossklags & Acquisti, 2007).

Those three reasons (incomplete information, bounded rationality, and deviation from rational strat-

egy) make the Pareto frontier unknown. The data subjects will never be able to behave rationally to

maximize their utility and become the victim of unequal wealth distribution. It creates the first uncer-

tainty – the primary uncertainty. This reason also stimulates individuals to underestimate the possible

future consequences of disclosing personal information and to choose instantaneous rewards as a

return of their disclosed personal information (Acquisti & Grossklags, 2005). Their choice to choose

instantaneous rewards is one of the examples of the secondary uncertainty.

The second and third critics to the privacy calculus model are the lack of context dependencies and

malleability of privacy concerns. A couple of scholars already explored the contextual factor effect to

the privacy calculus (see Table 3.3). However, they still fail to explain the effect of the societal contex-

tual factors, such as: euphoria that are manipulable by commercial, industry self-regulation, and gov-

ernment regulation (Acquisti, Brandimarte, et al., 2015). In their research, Acquisti, Brandimarte, et

al. (2015) argue that when individuals are uncertain about their preferences toward privacy concerns,

they will look to social guidance and see what other people might respond to this uncertainty. Fur-

thermore, they argue that the social guidance is also a function of context and social behavior which

can be influenced by commercial, industry self-regulation, and government regulation too (Acquisti,

Brandimarte, et al., 2015).

Based on those aforementioned critics, we can argue that exploring privacy mitigation only from the

privacy calculus perspective, which bases the rationale in the privacy definition as an interest, is prob-

lematic. Therefore, we should include the analysis of privacy mitigation strategies that bases the the-

ory on the privacy definition as a right.

Seeing privacy as a right, users can exercise the control over their personal data to ensure their privacy

right (Xu et al., 2012), because control is associated with the perceived privacy risks of the users

(Olivero & Lunt, 2004). Additionally, Xu (2007) argues that the notion of privacy intrusion can be cap-

tured by the loss of control of personal data. He therefore argues that the control can be increased by

using two control approaches developed by (Yamaguchi, 2001), i.e., individual control and proxy con-

trol (Xu, 2007), in which Xu et al. (2012) define those exercise of controls as the privacy assurance

approaches.

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In respect to privacy mitigation strategy, Xu and Teo (2004) argue that the users’ perceived control

over their disclosed personal data has positive influence to mitigate their privacy concerns. As previ-

ously discussed in the paragraph 3.2.1, the control can be exercised by using privacy assurance ap-

proaches (Xu et al., 2012). Firstly, it can be exercised by using the individual control, which is devel-

oped based on Individual Privacy Protection Responses (IPPR) taxonomy by Son and Kim (2008). IPPR

consists of three categories, which are: (1) Information provision, includes refusal to disclose personal

information completely and misrepresentation of information by using several approaches (for exam-

ple: by using a pseudoname and anonymity); (2) Private action, includes removal of submitted per-

sonal information and negative word-of-mouth about the mishandling of the personal information;

(3) Public action, which includes complaining directly to the company and complaining indirectly to a

third party (usually the regulatory body) (Son & Kim, 2008).

Secondly, the privacy assurance approach can also be implemented by using the proxy control, includ-

ing the institutional industry proxy control, which includes the establishment of industry self-regula-

tion (such as: third party privacy auditor & privacy protection seal) (Xu et al., 2012), and the institu-

tional regulatory proxy control, for example the formation of law and authority office by the govern-

ment (Xu et al., 2012). Table 3.4 summarizes the privacy assurance approaches to protect the privacy

right.

Table 3.4. Privacy assurance approach (Xu et al., 2012)

Level Privacy assurance approach

Individual Information Provision Refusal

Misrepresentation of infor-mation

Private Action Removal of information

Negative WoM

Public Action Complain directly to the com-pany

Complaining indirectly to third party

Industry self-regulation Third party auditor

Privacy protection seal

Government Government legislation and au-thority

In conclusion, as the privacy is defined both as an interest and a right, the privacy mitigation strategy

should follow these conceptions as well. In the concept of privacy as an interest, the privacy mitigation

strategy can be explored using the privacy calculus theory, in which the utilitarian approaches are

used. On the other hand, in the concept of privacy as a right, the privacy mitigation strategy is exer-

cised using the control over privacy, in which the privacy assurance approaches can be applied. Figure

3.1 draws the summary of the previous studies of privacy concerns.

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Mitigation strategy

Privacy concerns

Benefits

Privacy assurance approach

IUIPC

CFIP

Tangible

Intangible

Individual self-protection (ISP)

Industry self-regulation (ISR)

Govt regulatory and legislation (GRL)

Utilitarian approach(Privacy calculus)

Non-utilitarian approach

Figure 3.1. Summary of privacy mitigation strategy

3.4 Hypotheses development From the previous mentioned theories, we understand that privacy is undeniably important for indi-

viduals as well as the companies. The equally distributed justice has to be assured to ensure the sus-

tainability of the business ecosystem as well as the positive sum value. However, people often behave

contradictory; for example, even though individuals perceive that privacy is important and should be

guarded, almost none of them are willing to spend anything to protect it (Grossklags & Acquisti, 2007).

For the organizational perspective, such phenomenon is like “taking a candy from a baby”. A very

minor effort can influence the individuals’ perception toward the value of privacy. Thus, taking only

the utilitarian approach to assure the distributive justice will only portray incomplete picture of privacy

mitigation strategy and may lead to unsustainable business practice. Therefore, in this study we will

try to explore the complete picture of privacy mitigation practice, i.e., from the utilitarian approaches

(wealth distribution justice) as well as the non-utilitarian approaches (procedural justice).

There are various operationalization methods to measure privacy concerns. In this study, we will use

both of privacy concern constructs developed by Smith et al. (1996), i.e., the CFIP, and by Malhotra et

al. (2004), i.e., the IUIPC, to measure the individuals privacy concerns. We use both of these constructs

because, altogether, they are fully fit to the mainstream privacy frameworks, i.e., the OECD Privacy

framework and the APEC Privacy framework. From the previously developed privacy decision making

theory, in which the privacy concerns are representing the cost part of the privacy calculus theory, we

hypothesize that the privacy concerns will have negative effect to the willingness to disclose personal

data.

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H1a. In the setting of the Indonesian market, the users’ concerns of information privacy (CFIP) will

negatively correlate to the willingness to disclose personal data.

H1b. In the setting of the Indonesian market, the internet users’ privacy concerns (IUIPC) will nega-

tively correlate to the willingness to disclose personal data.

The justice theory is the main foundation on the secondary exchange of personal data (Culnan & Bies,

2003). It consists of equally distributed wealth (distributed justice) and procedural fairness (Xu et al.,

2009). The higher degree of fairness that the data subjects might perceive will reduce their privacy

concerns (Son & Kim, 2008). Furthermore, to ensure the distributed fairness, the DSPs can offer ben-

efits to the data subjects, which can be broken down to the two types of benefits, i.e., the tangible

benefits and intangible benefits. Deriving from this notion and our previously developed privacy deci-

sion making theory, we argue that both tangible and intangible benefits that DSPs offer will increase

the personal information disclosure as they represent the benefit part of the privacy calculus theory.

H2a. In the setting of the Indonesian market, the intangible benefits will positively correlate to the

willingness to disclose personal data.

H2b. In the setting of Indonesian market, the tangible benefits will positively correlate to the will-

ingness to disclose personal data.

Aside from the distributed justice assurance efforts (utilitarian view of privacy), the DSPs also have to

ensure the procedural justice to overcome the limitations of the privacy calculus theory. The proce-

dural justice is operationalizable by exercising privacy control that consists of individual control and

proxy control (Xu et al., 2012). Moreover, Xu and Teo (2004) argue that the users’ perceived control

over their disclosed personal data has positive influence to mitigate their privacy concerns. The exer-

cise of controls are then conducted by performing the privacy assurance approaches (Xu et al., 2012).

The privacy assurance approaches are broken down into three types of approaches. The individual

approach, which is developed based on IPPR (Son & Kim, 2008), reflects the personal control measures

of the individuals. It gives a “way-out” from the social contract if they feel the exchange does not meet

their perceived procedural justice. Therefore, a higher degree of individuals’ willingness to take this

approach will reduce the degree of personal information disclosure.

For the DSPs perspective, providing proxy control could also reflect the DSPs accountability level of

the privacy assurance approach. They can form an external third party auditor to ensure the suffi-

ciency of organization’s privacy assurance practice. Moreover, the DSPs can also use the privacy pro-

tection seal to communicate whether the company is adopting a sufficient privacy assurance practice

to the data subjects. In addition to the self-industrial standards, the governmental regulation could

also help to reduce the societal uncertainty regarding the use of personal information and its protec-

tion approach (Acquisti, Brandimarte, et al., 2015). We believe users who demand a higher self-indus-

trial standard and the presence of governmental regulation regarding the use of personal data will

also have a high privacy concerns. Therefore, they will also be less willing to disclose their personal

data. Consequently, we propose the third hypotheses as follows:

H3a. The extent to perform individual privacy protection responses regarding the use of personal

data and their protection will negatively correlate to the willingness to disclose personal data.

H3b. The extent of the individuals’ prerequisite about the presence of industrial self-regulation re-

garding the use of personal data and its protection will negatively correlate to the willingness to

disclose personal data.

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H3c. The extent of the individuals’ precondition about the presence of governmental regulation re-

garding the use of personal data and its protection will negatively correlate to the willingness to

disclose personal data.

In addition to the previously mentioned relationships, we also want to include several demographic

variables to check the consistency of the results as well as to test the significant moderating effects to

the results. These demographical variables are: gender, age, salary level, education level, previous

privacy experience, and efficacy of using smartphone. The aforementioned demographic variables are

known to have effects to the privacy decision making rationale in prior research, such as: gender (Sun

et al., 2015), age (Awwal, 2012), education level (Phelps et al., 2000), previous experience of the pri-

vacy intrusion (Li & Unger, 2012) and efficacy of using smartphone (Keith et al., 2015). In our study,

we also want to test whether those demographic variables influence the users’ privacy decision mak-

ing rationale.

Based on the abovementioned set of hypotheses, Figure 3.2 is drawn to summarize this research’s

theoretical constructs, i.e.: (1) the negative effects of privacy concerns to the willingness to disclose

personal information, (2) the positive effects of benefits to the willingness to disclose personal infor-

mation, (3) the mixed effects of privacy assurance approaches to the willingness to disclose personal

information, and (4) the moderating effects of demographic and individual factors.

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Privacy concerns

Benefits

Privacy assurance approach

IUIPC

CFIP

Tangible

Intangible

Individual self-protection

Industry self-regulation

Govt regulatory and legislation

Willingness to disclose personal

information+

-

-

+

-

Demographic factors

Previous Experience Self efficacy Age Education Sex Salary

-

-

Figure 3.2. Conceptual model of Privacy decision making

3.5 Preparing the implementation of privacy mitigation strategies Based in the previously mentioned privacy mitigation strategies (discussion in paragraph 3.4), the pri-

vacy concerns can be alleviated by performing both utilitarian approaches as well as right assurance

approaches (non-utilitarian approach) (see Figure 3.1). These mitigation strategies represent the strat-

egies demanded (for benefit type strategies, ISR and GRL) and likely performed (for ISP strategy) by

the users. Furthermore, based on these strategies, the company can define the type of investments

that need to be prepared in order to acquire the users’ personal data, which can be broken down as

follows:

1. Tangible benefit. Offering tangible benefits strategy implies that the company should prepare

investments in the form of cash. The cash can be distributed to its users in the form of direct

tangible benefits (such as: sign up bonus (Li et al., 2010; Xu et al., 2009)) or indirect tangible

benefits (such as: discount in monthly fee (Derikx et al., 2016)).

2. Intangible benefits. On the contrary, a company that offers intangible benefits to mitigate the

users’ privacy concerns should prepare investments in the form of technology that can offer

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additional values to its customers. The possible additional values that can be offered are: per-

sonalization service (Li & Unger, 2012; Liu et al., 2011; Xu et al., 2011) and accuracy (and effi-

ciency) of the service (Lee & Kwon, 2015).

3. Individual self-protection (ISP). To mitigate the privacy concerns of the users who have a high

likelihood to perform ISP, the company should prepare an excellent customer service depart-

ment, because the users who have high level of ISP tend to make complaints often both di-

rectly to the company or indirectly to other parties via any medias available (email, phone call,

and social media) (Son & Kim, 2008).

4. Industry self-regulation (ISR). On the other hand, the company that wants to target users with

high level of ISR needs to prepare a proven privacy policy and governance and hire 3rd party

auditor to regularly audit its privacy practice, because users in this group regard the privacy

audit and privacy policy is the important thing to mitigate their privacy concerns (Xu, 2007; Xu

et al., 2011; Xu & Teo, 2004; Xu et al., 2009).

5. Government regulation and legislation (GRL). On the contrary, the company that wants to

target users with high level of GRL cannot do anything other than urge the government to

issue adequate personal data protection law (Xu, 2007; Xu et al., 2011; Xu & Teo, 2004; Xu et

al., 2009).

We therefore define the target market based on the users’ perception towards the appropriate pri-

vacy mitigation strategy according to them (i.e., utilitarian type strategy or non-utilitarian type strat-

egy). To simplify the analysis, we divide the perception of mitigation strategy into nine clusters that

are derived according to their perception, whether they want a complete total solution or only partial

solution of each type of strategy. Based on this classification, we develop a nomological approach to

profile each class, as follows:

1. The total solution seeker cluster. The members of this groups perceive all strategy as effective

and important strategy to mitigate their privacy concerns and increase their willingness to

disclose personal information.

2. The strong utilitarian cluster. The members of this group perceive all privacy benefits strategy

and part of the privacy assurance strategy as effective and important strategy to mitigate their

privacy concerns and increase their willingness to disclose personal information.

3. The pure utilitarian cluster. The members of this group only perceive the utilitarian approach,

i.e., providing privacy benefits as the effective and important strategy to mitigate their privacy

concerns and increase their willingness to disclose personal information.

4. The strong privacy right seeker cluster. The privacy concerns of the members of this group

can be mitigated by providing all privacy assurance approach and partial privacy benefits.

5. The combined strategy seeker cluster. The members of this group perceive part of the privacy

benefits item and privacy assurance approach item as the effective and important privacy mit-

igation strategy to mitigate their privacy concerns as well as to increase their willingness to


6. The weak utilitarian cluster. The members of this group perceive only part of the utilitarian

strategy as the effective and important strategy to mitigate their privacy concerns and in-

crease their willingness to disclose personal information.

7. The pure privacy right seeker cluster. The members of this group perceive that ensuring the

privacy right is a non-bargain condition to mitigate their privacy concerns and increase their

willingness to disclose personal information.

8. The weak privacy right seeker cluster. The members of this group perceive only part of the

privacy assurance approach as the effective strategy to mitigate their privacy concerns and

increase their willingness to disclose personal information.

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9. Indifferent group, in which the members of this group perceive neither providing privacy ben-

efits nor providing privacy assurance approach will effectively mitigate their privacy concerns

and increase their willingness to disclose personal information.

In summary, Table 3.5 portrays the cluster profiling according to the privacy mitigation strategy.

Table 3.5. Cluster profiling

Providing privacy assurance approach

All assurance ap-

proach Part assurance ap-

proach None

Pro

vid

ing

pri

vacy

ben

-ef

its

All benefits Total solution seeker Strong utilitarian Pure utilitarian

Partial benefits Strong privacy right

seeker Combined strategy

seeker Weak utilitarian

None Pure privacy right

seeker Weak privacy right

seeker Indifferent

Legend: Green shading: equally combined type of strategy; Blue shading: utilitarian dominant strategy; Brown

shading: privacy right assurance dominant strategy

We argue that the companies’ need to know whether they want to make an effort to mitigate the

users’ privacy concerns from the utilitarian perspective and/or non-utilitarian perspective those

abovementioned strategies. On the other hand, to perform a focused effort, the performed strategies

are closely related to the target market that the companies want to capture.

3.6 Personal data valuation The growing perception of privacy as a utilitarian good that can be traded for additional value moti-

vates privacy advocates and scholars to study the privacy valuation. This valuation is also important

to quantify the investment needed by the company to acquire the users’ personal data. Nevertheless,

there is no consensus on the absolute value (each type) of privacy (Huberman et al., 2005; Ward,

2001), due to the differences in the contextual factor where the privacy is involved and the method

to value privacy (Acquisti et al., 2009; Acquisti, Taylor, et al., 2015; Roosendaal et al., 2014).

The urgency to quantify the value of privacy is to give better insight for the companies whether they

want to offer incentive directly to the users of to invest in an efficient technology, building an excellent

customer service department, or implementing privacy enhancing technology and privacy govern-

ance. On the other hand, the government can utilize this information to build and regulate the per-

sonal data market for specific type of information, for example: Varian (2009) who proposes a regula-

tion of privacy by using market mechanisms to value privacy for secondary exchange.

There are two main perspectives in quantifying the value of personal data, i.e., market perspective

and individual perspective.

3.6.1 Market perspective The value of personal data from market perspective can be assessed by using various ways. OECD

suggests that valuing personal data from market perspective can be assessed by three ways: (1) cal-

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culating the stock price of the company that operates in personal data market, (2) measuring the rev-

enue of such company, and (3) calculating the price of personal data sold in personal data market

(OECD, 2013a).

The stock value of a company is a (fractional) trust that the shareholders attach to a firm’s capacity to

be profitable (Berk & DeMarzo, 2011). It can be used as a proxy to measure the value of a firm. The

stock itself, however, does not directly reflect the value of personal data traded by the company. Ra-

ther, the stock only measures the (anticipated) performance of the firm valued by the shareholders

(Berk & DeMarzo, 2011).

Likewise, measuring revenue of a company that plays in personal data market will not directly reflect

the personal data value. Even though this mechanism provides better proxy to value the company,

the revenue of the company may be the result of its ability to strategically win the personal data mar-

ket and does not quantify the each personal data record owned by the company (Roosendaal et al.,

2014).

Additionally, measuring personal data from the price of personal data traded in personal data market

will not directly measure the real value of personal data. This is mainly because there are some per-

sonal data that can be used several times without losing its original value (such as name and date of

birth). This type of data is traded not because of its exclusiveness (such as behavioral data), but rather

because of it is being available (Roosendaal et al., 2014).

3.6.2 Individual perspective Measuring personal data value from individual perspective means we need to investigate the per-

ceived value of personal data directly from the data subjects. The mechanism to get the “real” per-

ceived value may be varied depends on the context and the type of personal data that need to be

valued.

Hann et al. (2002) promote privacy valuation from the individual mechanism. By using conjoint exper-

iment to 84 US respondents and 184 Singaporean respondents, they quantify the privacy; which they

define as their willingness to pay (WTP) to get the ability to review of error, restriction against im-

proper access, and un-allowed unauthorized secondary use; as USD 7.98 to USD 16.58 for US respond-

ent and USD 10.45 to USD 26.93 for Singaporean respondents (Hann et al., 2002). However, their

research is still unable to quantify each of privacy type.

A more recent study by Danezis et al. (2005) tries to quantify the privacy of location value. By using

the economics of anonymity theory8 (see Acquisti et al. (2003)), they design an auction experimental

design to Cambridge computer science undergraduate students. They discover that £ 10 is the proper

value of location privacy (Danezis et al., 2005). Additionally, Huberman et al. (2005) also perform an

auction experimental model to quantify privacy, specifically to the demographical information that

valued as more than USD 100. Nevertheless, since the auction method does not promote trade-offs

in making evaluation, the data subjects can put as highest value as possible to value their privacy,

which limits the reliability of the valuation result.

Additionally, Krasnova et al. (2009) try to measure the value of privacy in the online social network

context. They present the valuation of privacy in the form of WTP to enjoy certain type online social

network with varied privacy assurance level (Krasnova et al., 2009). Their research concludes that the

8 Economics of anonymity bases the rationale on the incentive that the participants might get when they participate in the provision of the service that gives them additional benefit in the form of more anonymity.

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users are ready to pay from 0.67 euro to 0.81 euro for the possibility to control the access of infor-

mation to their social network page.

The most recent survey by Derikx et al. (2016) is conducted to study the privacy buy-off value in the

context of m-insurance. By using a conjoint experiment, their research concludes that the privacy can

be valued differently. The privacy of location and space is valued at 2.27 euro per month, privacy of

behavior and action at 2.98 euro per month, whereas the privacy of data and image is valued differ-

ently according to the actor who use the data, i.e., for internal use: -2.91 euro (negative) and external

use 2.77 euro (Derikx et al., 2016).

In conclusion, valuing privacy from market perspective does not directly reflect the real value of per-

sonal data. Therefore, we argue that personal data will be better valued by using individual’s perspec-

tive as it reflects the real value perceived by the data subjects. Additionally, the aforementioned anal-

yses also conclude that the contextual setting has strong influence to the privacy valuation. This mo-

tivates our study to quantify privacy specific to our context of study.

Moreover, the privacy valuation is also bound to human rationale limitations. For example, Acquisti

et al. (2009) discover the endowment effect9 and order effect10 are in place when they try to measure

the WTA and WTP of privacy. Therefore, we motivate our study to calculate the WTA of privacy (be-

cause the WTA can reveal the real value of privacy perceived by the users) in the specific context of

peer-to-peer ridesharing service.

Based on the two abovementioned analyses, we develop our privacy valuation study by using the Finn

et al. (2013) privacy classification that is applied in the context of P2P ridesharing service. We include

the three privacy type in the study, i.e., privacy of location and space (PLS), privacy of behavior and

action (PBA), and privacy of data and image (PDI) (see paragraph 3.3.1). Additionally, to have a uniform

measurement, we include the monetary money to measure the value of privacy. Since this part of our

study only incorporates the utilitarian aspect of privacy, we base our study to the cost-benefit analysis

only. We present the PLS, PBA, and PDI as the cost part of the analysis, whereas the monetary as the

benefit part of the analysis.

3.7 Conclusion The aforementioned analyses discus the privacy definition, privacy categorization, privacy decision

making theory, and what the company can do to prepare the mitigations action. Paragraph 3.1 discuss

the definition of privacy, particularly to answer the SQ2a. (How is privacy defined?). Privacy can be

defined both as a right (as part of the fundamental of human right) and interest that individuals like

to have to segregate their personal sphere and public sphere. From the literature reviews, we can

conclude that traditionally, individuals interpret privacy as their fundamental right. However, due to

the emergence of the technology, in which creates the separation between personal and public space

becomes as thin as rake, individuals start to treat privacy as tradable interest.

Furthermore, scholars also argue that define the privacy multiple dimensions (such as (Clarke, 2006)

and (Finn et al., 2013)). We follow Finn et al. (2013) privacy categorization in this research due to its

fitness to the recent technology development. Finn et al. (2013) categorize privacy to seven types of

privacy, i.e., privacy of the person, privacy of behavior and action, privacy of communication, privacy

of data and image, privacy of thought and feeling, privacy of location and space, and privacy of asso-

ciation (including group privacy).

9 Endowment effect: people tent to put higher value to what they already own compared to what they do not. 10 The order effect: the order of presenting choices influences the behavior of the respondents in making choice.

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Taking into account the context of the study, in which we will use Ojek online and its opportunity to

offer personalization service to the users, we use only three privacy types are relevant to the context

of peer-to-peer transportation service, which are:

1. Privacy of behavior and action. The right and interest of individuals to protect the intimate

issues such as: religion, sexual preference and habit, political views, and the ability to act or

behave in public, semi-public, and personal space without being monitored and/controlled by

others. This privacy type is used related to the users’ behavioral analysis in using the Ojek

online service.

2. Privacy of data and image. The right and interest of individuals to the assurance that “the

individuals’ data is not automatically available to other individuals and organizations and that

people can exercise a substantial degree of control over that data and its use” (Finn et al.,

2013, p. 8). This privacy type is used in relation to the personal data transfer between compa-

nies in the Ojek online ecosystem.

3. Privacy of location and space. The right and interest of individuals to move and travel without

being identified, monitored, and/or tracked, and to have separation between different spaces,

such as: in the office, in the moving car, or in the home. This privacy type is used related to

the disclosure of pickup and drop-off location when using the basic Ojek online service.

Particularly, the aforementioned Finn et al. (2013) privacy categorization and the relevant privacy type

in the context of the study answer the SQ2b. (How can the concept of privacy be categorized and what

types of privacy are related to the context of peer-to-peer ridesharing service?)

To answer the SQ2c (How is the privacy decision making theory is elaborated?), we need to understand

the users’ rationale in disclosing personal information. The privacy decision making theory is elabo-

rated in paragraph 3.3. The basic theory is built based on the cost benefit analysis theory, which named

as privacy calculus theory. In this theory, the data subjects are only willing to disclose their personal

data as long as the utilitarian benefits they may get outweigh the costs they have to disclose.

In this theory the cost part of the calculus is represented by the privacy concerns, which is measured

by two types of constructs, i.e., concerns for internet privacy (CFIP) (Smith et al., 1996) and internet

users’ individual privacy concerns (IUIPC) (Malhotra et al., 2004). The constructs altogether are able

to represent the mainstream privacy frameworks, such as OECD Privacy framework and APEC Privacy

framework. On the other hand, the benefits part of the privacy calculus theory is represented by the

privacy benefits. Prior privacy calculus studies has elaborated two types of benefits, which are: the

tangible benefits (such as: Xu et al. (2009) and Derikx (2014)) and intangible benefits (such as: Xu et

al. (2011) and Chellappa and Sin (2005)).

However, letting the utilitarian approach alone to elaborate the privacy decision making will be prob-

lematic. The privacy calculus theory has received several critics, mainly due to this theory bases the

analysis on the cost-benefit analysis approach, such as: (1) the assumption of all users are utilitarian

actors who have perfect foresight, especially to the risks and harms of disclosing personal information,

is ill-advised (Acquisti & Grossklags, 2005); (2) disability of the theory to calculate a complex product

or service which intertwines the privacy concerns and the (immaterial) benefits (Rohunen et al., 2014);

and (3) failure to incorporate the psychological factors of the users in making calculation of costs and

benefits (Acquisti et al., 2009). We propose the extension of privacy calculus theory by incorporating

the privacy assurance approaches in the privacy decision making theory.

We therefore develop our privacy decision making framework by combining the utilitarian view of

privacy as well as the right view of privacy. To incorporate the right view of privacy, we develop our

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model by involving privacy right assurance approach in the users’ privacy disclosure rationale as a

complement to the privacy calculus theory. The privacy assurance approach is developed by exercising

the control mechanism of privacy, in which the control mechanism can be divided into two parts, i.e.,

active approach and passive approach (Xu et al., 2012). The active control approach can be done by

performing individual self-protection (Son & Kim, 2008), whereas the passive control is applied by

using industry self-regulation and government regulation and legislation (Xu et al., 2012).

Various mitigation strategies are studied in the prior studies. Those mitigation strategies consist of:

(1) offering both tangible and intangible benefits to the data subjects, and (2) ensure the data subjects

are empowered by using the privacy assurance approach. We then develop the hypothesized relation-

ships of the aforementioned privacy mitigation strategies to the willingness to disclose personal data,

which are detailed in Table 3.6.

Table 3.6. Table of hypotheses

Hypotheses

H1 H1a. In the setting of the Indonesian market, the users’ concerns of information privacy (CFIP) will negatively correlate to the willingness to disclose personal data. H1b. In the setting of the Indonesian market, the internet users’ privacy concerns (IUIPC) will negatively correlate to the willingness to disclose personal data.

H2 H2a. In the setting of the Indonesian market, the intangible benefits will positively correlate to the willingness to disclose personal data.

H2b. In the setting of Indonesian market, the tangible benefits will positively correlate to the willingness to disclose personal data.

H3 H3a. The extent to perform individual privacy protection responses regarding the use of per-sonal data and their protection will negatively correlate to the willingness to disclose per-sonal data. H3b. The extent of the individuals’ prerequisite about the presence of industrial self-regula-tion regarding the use of personal data and its protection will negatively correlate to the willingness to disclose personal data. H3c. The extent of the individuals’ precondition about the presence of governmental regu-lation regarding the use of personal data and its protection will negatively correlate to the willingness to disclose personal data.

To prepare the action to mitigate the users’ privacy concerns, the company can prepare specific action

in respect to the specific mitigation strategy. Since the strategy consists of the utilitarian approach and

non-utilitarian approach, the company’s action can be broken down into: (1) preparing an investment

in the form of cash to pursue tangible benefit utilitarian strategy, (2) prepares a technology to follow

intangible benefit utilitarian strategy, (3) preparing an excellent customer focus department to pre-

pares the privacy assurance approach from users who have high likelihood to perform ISP, (4) prepar-

ing a proven privacy policy and governance to pursue industry self-regulation approach, and (5) urging

the government to issue an adequate personal data protection law.

Furthermore, since the use of privacy as utilitarian goods to get additional value from the company,

the urgency to quantify privacy is emerged. The privacy valuation is useful to give better insight for

the companies whether they want to offer incentive form directly to the users of to invest in an effi-

cient technology, building an excellent customer service department, or implementing privacy en-

hancing technology and privacy governance.

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The privacy valuation can be conducted by using both market perspective and individuals’ perspective.

In contrast to the market perspective which is unable to quantify the real personal data value per-

ceived by the users, the individuals’ perspective valuation performs better to quantify the real value

of privacy perceived by the users as this method gathers data directly from the data subjects. Never-

theless, the individuals’ perspective valuation is still bound to contextual factors and humans’

bounded rationality, such as: endowment effect and order effect.

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4 Survey design

In this chapter, we explain the research methodology, including sampling and respondents acquisition

strategy and the survey instruments development that will be presented in the survey questionnaire.

The data as the result of the instruments responses are then analyzed by using quantitative analysis

(statistical tools). Referring to the research introduction, we will develop a questionnaire with two

parts of survey, followed by three types of statistical analysis. Therefore, the explanation into two

parts will be divided into two parts, which are:

Firstly, the explorative and confirmative assessment. In this part, we explain the survey questionnaire

development and data preparation to test the hypotheses related to the privacy benefits and privacy

assurance approaches to increase the users’ willingness to disclose personal information. Moreover,

as the constructs used are measured by many instruments, we will also perform factor analyses to

summarize these instruments so that each construct can be represented by a single summarization

value. Particularly, this test will answer the SQ3: To what extent are the privacy decision making con-

structs influencing the users’ willingness to disclose their personal information? Furthermore, based

on those responses, we also segment the users based on the most effective mitigation strategy(ies)

according to them. This segmentation aims to answer SQ4: How can the users be segmented based on

the privacy concern mitigation effort?

Secondly, the experimental assessment. In this part, we explain the conjoint questionnaire develop-

ment and data preparation to measure the buy-off value of the privacy types related to the use of

personalization in the peer-to-peer (P2P) transportation service. Conjoint assessment is used because

the services in our study are constructed with a complex set of attribute, in which we will not be able

to measure the effect of each isolated attribute in a realistic way. Thus, we want to create a “market-

like” environment to incorporate the consumers’ trade-off evaluation related to the set of attributes

in the services. Specifically, this experimental assessment will answer the question: To what extent do

the users rank the value of each personal information relative to the other type of personal information

and how much is the buy-off value for each of their personal information? (SQ5).

This chapter is organized as follows. Firstly, the sampling strategy will be elaborated in the paragraph

4.1. Furthermore, the survey questionnaire and instruments development and the preparation for the

respected statistical analysis (the multiple regression and cluster analysis) will be explained in the par-

agraph 4.2. Next, the choice sets questionnaire with the following conjoint analysis experiment prep-

aration will be discussed in the paragraph 4.3.

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4.1 Sampling strategy and data collection method The main aim of this research is to explore the privacy concerns and the privacy mitigation strategies

that are implemented in the context of Indonesian P2P ridesharing. Jakarta was chosen as the target

of study because the P2P ridesharing concept is already well known by Jakarta population. Further-

more, as the researcher is coming from Jakarta, he would be able to understand the contextual and

cultural factors better.

In particular, we target the population of the people who are familiar to the concept of Ojek online

aged 14-55+ years old, including the current users of Ojek online and the people who never use the

Ojek online but still understand how the Ojek online service works. As of December 2015, the current

users of Ojek online has reached 95% of Jakarta population (based on the number of app download)

(GooglePlay, 2015). This number, however, might be biased by some people who have two or more

mobile phones installed with Ojek online app. Nevertheless, the high level of download number indi-

cates that the majority of Jakarta people have used the Ojek online service. Yet, we do not understand

the composition and the population statistics of these users. Thus, we use the general Jakarta popu-

lation as the proxy of our target population.

The information about Jakarta’s demography is gathered from the most recent available Jakarta report

developed by the Central Bureau of Statistic (BPS) year 2014 (Jakarta, 2015a). Since one of the re-

search questions aims to make a segmentation of users, the representativeness of the sample taken

from the entire population of Jakarta will be important. Therefore, we conduct a stratified sampling

strategy to ensure the representativeness of the sample as well as to improve the generalizability of

the research findings. We use the age level as our strata. We stratify the age into 10 years of range

and then assign the datum according to this level. After the stratification is conducted, we perform a

random sampling strategy to recruit the respondents for each sample.

The main challenge in the sampling phase is to define the number of sample as just right. On the one

hand, we understand that the higher number of sample will help us to get the sufficient statistical

power as too narrow a sample will have high risks of non-normality and become insensitive (Hair et

al., 2013). On the other hand, an oversized sample will work against us as the analysis becomes overly

sensitive (Hair et al., 2013). We assign one sample for each 50000 people in each age-range. However,

we exclude the age-range 0-14 years old from the research as we understand that the people (chil-

dren) in this age are not legally allowed to enter a binding agreement with a company (in this case to

use the Ojek online service) (Indonesia, 2014). In total, we expect to have about 157 respondents to

completely fill in the questionnaires. Table 4.1 summarizes the stratified sampling result. In this table,

“P” explains the population and “S” explains the sample.

Table 4.1. Stratified Sampling of Jakarta Population

Age Sex

Total (P) Total (S) Male (P) Male (S) Female (P) Female (S)

15-24 789931 16 850774 18 1640705 34

25-34 1067468 22 1037192 21 2104660 43

35-44 860631 18 813708 17 1674339 35

45-55 581401 12 572751 12 1154152 24

55+ 494868 10 516736 11 1011604 21

TOTAL 3794299 78 3791161 79 7585460 157

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The two aforementioned assessments require different approaches and techniques. The samples and

the respondents, however, will be the same. Therefore, the data collection activity for both question-

naires will be conducted at the same time. To collect the data, we handed out the invitations to fill in

the questionnaire at:

1. Two universities: University of Indonesia (public university) (28 March 2016) and Atma Jaya

University (private university) (29 March 2016), to target respondents with age range 15 – 24

years old.

2. Bus stops and terminal: Ragunan Terminal (rural hub) (4 April 2016), Halte Karet (urban hub)

(5 April 2016), and Halte Harmoni (urban hub) (7 April 2016), to target respondents with age

range 25 – 44 years old.

3. Train Stations: Sudirman station (urban hub) (30 March 2016) and Tanah Abang station (rural

hub) (31 March 2016), to target respondents with age range 25 – 44 years old.

4. Mall and shopping center: Pejaten Village (rural mall) (26 March 2016) and ITC Kuningan (ur-

ban shopping center) (27 March 2016), to target respondents with age range 45+ years old.

We chose those sites to ensure both the urban and rural populations would get the invitation to give

their responses.

To increase the response rate, we used an invitation in the form of mini poster advertisement which

stated that the respondents would have a chance to get IDR 200K MAP voucher (a claimable voucher

that can be redeemed in various department stores, restaurants, and fashion stores), by browsing to

the mentioned URL (QR Code) in the poster. We handed the invitation out to the every second (even)

persons who were getting into the main entrance of the survey location. When respondents tried to

claim the voucher, they would be redirected to the survey webpage to complete the questionnaire

before filling in their contact to claim the voucher. We checked the survey dashboard daily to monitor

the quota sample of the age segment. Once we had reached the quota for age range, we stopped the

data collection in the site respected to that age range.

After two weeks of data collection, we found out that there was under-representativeness to the peo-

ple aged 45+. The likely possible reasons for this condition might be: (1) people of these age ranges

might not be interested to the utilitarian incentive to fill in the questionnaire; (2) people of these age

ranges might not be fully familiar with the concept of Ojek online. Therefore, we decided to use per-

sonal face-to-face approach to address these people in the above mentioned selected locations. After

several personal approaches and informal conversations with the senior respondents, we concluded

that the second reason was more valid to explain the under representativeness of these age ranges.

Most of the senior respondents argued that even though they often used the Ojek online service, they

rarely used their own smartphone (and their own account) to order the service. Instead, they asked

their children or younger relatives to order the service for them. However, after another week exten-

sion of data collection (until 16 April 2016), the under representativeness of sample in this age range

was still found. This would become one of our limitations in this study.

4.2 Explorative and predictive assessment There are two explorative analyses performed in this study. Firstly, we used multiple regression to

analyze the extent that the consumers have in respect to mitigate their privacy concerns. The multiple

regression has two main functions, i.e., explorative ability and predictive ability (Hair et al., 2013). The

explorative ability provides explanation of the model (the sign, magnitude, and significance) whereas

the predictive ability provides prediction to which extent the independent variable(s) can predict the

change in dependent variable.

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As the privacy decision making constructs will be built by many instruments, we will also perform

factor analysis to eliminate the non-representative instruments as well as to summarize the repre-

sentative instruments into a single summarized value. This summarized value is also known as the

factor score that sufficiently represents the original set of the initial instruments (Hair et al., 2013).

Each summarized value is formed by the factor loadings of the instruments that build the construct.

Secondly, we would also conduct cluster to search and build a group based on the individual’s natural

characteristics. The main objective of cluster analysis is maximizing the homogeneity of individuals

within groups while, at the same time, maximizing heterogeneity of individuals between the groups

(Hair et al., 2013). However, this analysis is often criticized because of several reasons, e.g.: (1) it only

provides descriptive atheoretical analysis, (2) this analysis will always forms clusters even though there

is no actual structure in the data, and (3) it is hardly generalizable as the analysis is only based on the

gathered samples (Hair et al., 2013). Therefore, the high degree of sample representativeness is im-

portant for this analysis.

In this paragraph, we explain our approach in building our survey instruments (sub-paragraph 4.2.1).

Furthermore, the initial data analysis, including the data cleansing and testing the preliminary assump-

tions for the explorative test, are explored in the paragraph 4.2.2.

4.2.1 Survey questionnaire and instruments development A survey for the explorative assessment was developed based on the hypotheses that have been

formed in section 3.5. (see Table 3.6).

To test the hypotheses, quantitative analyses were conducted in the study. Hence, operationalization

of variables was needed to put them into instrument questions. The following subsections will explain

the operationalization of variables and instrument development to measure those variables.

We then translated those instruments and presented them in Bahasa to make them understandable

for the local respondents. To measure the respondents’ attitude to each instrument, the instruments

were presented in seven point likert scales. The detailed presentation of the questionnaire is pre-

sented in Appendix 4.1. Survey Questionnaire.

The following sub-paragraphs will explain the method used to derive the questionnaire instruments.

4.2.1.1 Privacy concerns The instruments to measure the privacy concerns were developed based on both CFIP and IUIPC con-

structs. These constructs together were completely fit to the mainstream privacy frameworks, i.e., the

OECD privacy framework and the APEC privacy framework, which were explained in paragraph 3.4.1.2.

We used measurement instruments from previous research, i.e.: Smith et al. (1996), Stewart and

Segars (2002), and Malhotra et al. (2004), in our study. The overview of the constructs definition for

privacy concerns is explained in Table 4.2.

Table 4.2. Privacy concerns construct definition

No Construct Definition Source

1 Collection The concerns that the data subject are experiencing when the company collects and stores their personally identifiable data.

(Malhotra et al., 2004; Smith et al., 1996; Stewart & Segars, 2002)

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No Construct Definition Source

2 Improper access

The concerns of data subjects have that their data become au-tomatically available to others.

(Smith et al., 1996; Stewart & Segars, 2002)

3 Unau-thorized second-ary use

The concerns related to the use of personal data other than specified in the initial collection purpose.


4 Error The concerns related to the error that might happen to the sub-mitted personal data


5 Control The concerns that the data subjects have related to the choice among responses that can produce different outcome.

(Spiekermann, 2005)

6 Aware-ness

The concerns about the data subjects’ awareness to the organi-zational information privacy policy and practice.

(Malhotra et al., 2004)

Based on these definitions, we developed the operationalization instruments to measure the effect of

each construct. Table 4.3 presents the operationalization of the abovementioned constructs.

Table 4.3. Privacy concerns operationalization

No Con-struct

Instruments Source

1 Control The online company has to provide access for the users to con-trol over decisions about how their information is collected, used, and shared


2 Control Consumer control of personal data is the most important thing in privacy

3 Control I believe that my privacy is invaded when control is lost or un-willingly reduced as a result of a marketing transaction

4 Aware-ness

Companies seeking information online should disclose the way the data are collected, processed, and used.

5 Aware-ness

A good consumer online privacy policy should be clear and un-derstandable.

6 Aware-ness

It is very important to me that I am aware and knowledgeable about how my personal data will be used.

7 Collec-tion

It usually bothers me when online companies ask me for per-sonal data.


8 Collec-tion

When online companies ask me for personal data, I think twice before providing it.

9 Collec-tion

It bothers me to give personal data to online companies

10 Collec-tion

I’m concerned that online companies are collecting too much personal data about me

11 Im-proper Access

Online companies should devote more time and effort to pre-venting unauthorized access to personal data.

(Smith et al., 1996)

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No Con-struct

Instruments Source

12 Im-proper Access

Computer databases that contain personal data should be pro-tected from unauthorized access—no matter how much it costs.

13 Im-proper Access

Online companies should take more steps to make sure that un-authorized people cannot access personal data on their comput-ers.

14 Unau-thorized Second-ary Use

Online companies should not use personal data for any purpose unless it has been authorized by the individuals who provided the information.


When people give personal data to an online company for some reason, the online company should never use the information for any other reason


Online companies should never sell the personal data in their databases to other companies.



Online companies should never share personal data with other companies unless it has been authorized by the individuals who provided the information.

18 Error All the personal data in computer databases should be double-checked for accuracy—no matter how much this costs

19 Error Online companies should do their outmost steps to ensure that the personal data in their files is accurate.

20 Error Online companies should have solid procedures to correct errors in personal data.

Initially, we used all those aforementioned instruments in our questionnaire. However, after pretest-

ing the questionnaire, we concluded that the respondents might experience fatigue after filling in the

questionnaire. The pretest among 11 persons, aged between 19 to 51 years old, showed that people

needed 20 minutes (in average, SD=2.5) to fill in the questionnaire. Specifically, they were experienc-

ing boredom after filling in the privacy concern section. Therefore, we decided to reduce the privacy

concern instruments. We use only the highest correlated instruments from Malhotra et al. (2004),

Smith et al. (1996), and Stewart and Segars (2002) for each construct in our study. Furthermore, we

also put the contextual factor, i.e., the Ojek online to the instruments by using the “Ojek online com-

pany” as a replacement to the “online company”. The final instruments used are presented in Table

4.4.

Table 4.4. Final instruments used to measure privacy concerns.

No Con-struct

Instrument Source

1 Control The Ojek online company has to provide access for the users to control over decisions about how their information is collected, used, and shared


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No Con-struct

Instrument Source

2 Aware-ness

It is very important to me that I am aware and knowledgeable about how my personal data will be used.

3 Collec-tion

I’m concerned that Ojek online companies are collecting too much personal data about me


4 Im-proper Access

Ojek online companies should devote more time and effort to preventing unauthorized access to personal data.



Ojek online companies should never share personal data with other companies unless it has been authorized by the individuals who provided the information.

6 Error Ojek online companies should take more steps to make sure that the personal data in their files is accurate.

4.2.1.2 Privacy benefits In contrast to the construct of the privacy concern, we newly developed the instruments for the pri-

vacy benefits. The privacy benefits construct was developed based on previous studies (see paragraph

3.4), i.e., the tangible benefits and intangible benefits. We used direct monetary benefits and indirect

monetary benefits as the operationalization of the tangible benefits. Furthermore, we also used the

personalization and the usefulness as the operationalization of intangible benefits. However, since the

context of the study (i.e., the Ojek online) is quite new, we modified the instruments accordingly.

Table 4.5 explains the overview of operationalization and the instruments of the privacy benefits.

Table 4.5. Privacy benefits operationalization

No Con-struct

Operationali-zation

Instruments Source

Tangible Direct mone-tary Benefit

I like it when the company gives me direct compensation (such as: sign up credit or shopping voucher)

Li et al. (2010)

Indirect mone-tary Benefit

I perceive monthly transportation discount as important to me

Xu et al. (2009)

Intangi-ble

Personalization It is important for me if the company is able to offer me a high quality of discount recom-mendation that suits me perfectly

Li and Unger (2012)

Usefulness Usefulness of application is one of the im-portant factors for me to use the application

Li and Unger (2012)

4.2.1.3 Privacy assurance approaches Likewise, the operationalization of privacy assurance approach was developed based on the previous

studies (Son and Kim (2008) & Xu et al. (2012)). However, we modified the instruments according to

the contextual factor in the object of study. Table 4.6 explains the overview of the operationalization

and the instruments.

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Table 4.6. Privacy assurance approaches instruments

No Construct Operationali-zation

Instruments

1 Individual Self-Pro-tection

Refusal to dis-close

I will never give out my personal data Son and Kim (2008) & Xu et al. (2012). 2 Anonymous

user The anonymity is important when I inter-act with the Ojek online company

3 Removal I will remove my personal data from the Ojek online company if my data is not properly handled by the company

4 Negative WoM I will speak to my friends/relatives about my bad experience with the Ojek online company mishandling my personal data when my personal data is not properly treated

5 Complain di-rectly to com-panies

I will call or write the Ojek online com-pany to complain about the way it uses my personal data when it is not properly treated

6 Complaint indi-rectly to a 3rd party

I will call or write the public authority to complain about the way an Ojek online company uses my personal data when it is not properly treated

7 Industry self-regula-tion

3rd party audi-tor

It is important that the Ojek online com-pany has been audited by a 3rd party se-curity auditor

Xu et al. (2012)

8 Protection seal It is important if the Ojek online company shows a protection seal (such as: TRUSTe) in the application

9 Govern-ment regu-lation

National It is important if the government regu-lates the privacy assurance practice for the companies

4.2.1.4 Willingness to disclose personal information We measured the willingness to disclose personal information as our dependent variable. However,

since the context of this research was quite new in the domain of privacy studies, we newly developed

all the instruments. We argued that the data subjects can share their personal data both directly and

indirectly. The direct disclosure is occurred when the data subjects disclose their personal data directly

to the company by filling in their personal data using any media provided by the company, whereas

the indirect disclosure is occurred when the data subjects use other forms of personal data; such as:

data in social media, GPS, or data stored in a personal data vault; to be disclosed to the company.

Table 4.7 presents the overview of the operationalization of the variable willingness to disclose per-

sonal data.

Table 4.7. Willingness to disclose personal data instruments

No Construct Operationaliza-tion

Instruments

1 Willingness to disclose

Direct disclosure

I always use my complete personal information when I sign up to personalized advertisement

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No Construct Operationaliza-tion

Instruments

2 I will be truthfully provide my name, date of birth, home address, personal email address, and personal phone number to the online company

3 I will honestly fill in my interests list when the Ojek online company asks for my interests

4 Indirect disclosure If available, I would like to use a “sign in with social me-dia” button when I sign up to use an application

5 When available, I always turn my mobile phone GPS on when I order services from the Ojek online ordering ap-plication

4.2.1.5 Demographic variables In addition to the hypothesized variables, we also proposed demographic variables in the analysis of

the relationships between privacy calculus constructs, including: age, gender, salary, education level,

efficacy in using smart phone, and previous privacy intrusion experience. These variables are derived

from the previous privacy concerns and mitigations studies (i.e.: Sun et al. (2015), Awwal (2012),

Phelps et al. (2000), (Li & Unger, 2012), and (Keith et al., 2015)).

In addition to the straight forward demographic question (such as: age, gender, education, and salary),

we develop additional instruments to measure the rest demographic variables. The first variable is the

previous privacy experience. We operationalize this variable by using a question “How often you re-

ceive unwanted advertisement email and/or phone call even though you never sign up to this adver-

tisement?”. The second variable is the self-efficacy level. We use the number of year experience in

using a smart phone as the proxy to the users’ smartphone efficacy level based on prior research by

Li and Unger (2012). The instrument question is formed as follows: “How long have you used a smart

phone? (A smart phone is a phone with Android, iOS, Blackberry, or Windows operating system)”.

After we developed the instruments to measure the constructs/variables, we assigned the coding to

those variables. Table 4.8 summarizes the coding mechanism of variables.

Table 4.8. Variables and coding summary

No Construct Sub construct Coding

1 Dependent Variable Willingness to disclose = wtd

Direct disclosure wtd1

2 wtd2

3 wtd3

4 Indirect disclosure wtd4

5 wtd5

Independent Variables

6 Privacy concerns 1 = IUIPC Control Picon

7 Awareness Piaw

8 Collection Picol

9 Privacy Concerns 2 = CFIP Improper access pcima

10 Unauthorized sec-ondary use

pcusu

11 Error Pcerr

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No Construct Sub construct Coding

12 Tangible benefits = tang_pben Direct monetary ben-efit

btmb1

13 Indirect monetary benefit

btmb2

14 Intangible benefits = intang_pben Personalization bipe

15 Usefulness bius

16 Individual self-protection = isp Refusal to disclose aisp1

17 Anonymous user aisp2

18 Removal aisp3

19 Negative WoM aisp4

20 Complain directly to companies

aisp5

21 Complaint indirectly to a 3rd party

aisp6

22 Industrial self-regulation = isr 3rd party auditor aisr1

23 Protection seal aisr2

24 Government regulation = grl Grl

Demographic Variables

25 Previous experience privacy intrusion dpre

26 Self-efficacy dsel

27 Age dage

28 Education level aedu

29 Salary dsal

30 Gender dsex

4.2.2 Data cleansing and preparation After three weeks of data collection, we managed to get 301 filled in questionnaires. However, we

found out that there were 36 incomplete cases. After examining the incomplete cases one-by-one, we

concluded that the missing cases might be resulted from fatigue of the respondents in filling in the

questionnaire. Furthermore, as the questionnaire could not be submitted unless all responses had

been filled, all incomplete questionnaire responses resulted from drop out from the questionnaire

(not from data error). Therefore, we continued the analysis using the listwise method (only using the

complete data) to clean the data due to the comprehensiveness of the analysis. Finally, we only used

265 valid and complete cases (11.9% dropout ratio).

In general, we have an imbalance in the sample representativeness. Firstly, we have over representa-

tiveness of the sample especially in the age ranges 15-24 and 25-34. In contrast, we get under repre-

sentativeness of sample in the age ranges 45+. The logical explanation to this is: the younger age

ranges might appreciate utilitarian incentive more than the older age range, which is also aligned with

several findings from marketing research (for example: Saleh et al. (2013), Roy Dholakia and Uusitalo

(2002)). Secondly, we also get over representativeness of female respondents in this study. This also

might be caused by the fact that women enjoy utilitarian incentives more than their male counterpart

(Saleh et al., 2013; Sun et al., 2015). Table 4.9 summarizes the comparison of demographics between

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the population and the sample. Additionally, Figure 4.1 shows the overview of the respondents’ de-

mography.

Table 4.9. Respondents’ comparison to the population

Age

Sex

Male (% Population)

Male re-spondents

Male (% re-spondents)

Female (% population)

Female re-spondents

Female (% respond-

ents)

15-24 10% 54 20% 11% 63 24%

25-34 14% 29 11% 13% 57 22%

35-44 11% 17 6% 11% 23 9%

45-55 8% 7 3% 8% 7 3%

55+ 6% 4 2% 7% 4 2%

TOTAL 50% 111 42% 50% 154 58%

Figure 4.1. Respondents’ demography dispersion

The next data cleansing phase was detecting the outliers. However, since we designed the question-

naire response with likert scale (the only possible answers were 1-7, except the age and the self-effi-

cacy), the number of outliers in the cases would be minimal. Furthermore, after we explored both

variable age and self-efficacy, we did not find any outliers in the cases.

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4.2.2.1 Descriptive statistic of the demographic variables After we looked into the age/gender distribution of the respondents, we explore the descriptive sta-

tistic of the moderating variables. Firstly, the education level. The majority of the respondents are

people who have access to a higher education system (e.g., university and academy), which is por-

trayed by the high percentage of respondents who have graduated from senior high school (80.3%).

This number, however, overly-represents the higher education level population in the statistic of Ja-

karta (i.e., 51.9%) (Jakarta, 2015a). This might be resulted from the choice of the respondent invitation

places, which are located in the area where most higher education people socialize themselves.

Secondly, the monthly income level. On average, the salary of the respondents is in the middle income

level, i.e., IDR 5 mio – IDR 10 mio (≈ €350 - €700) per month, in which 55.3% respondents are in this

level and only 7.1% respondents are within the high level of income (> IDR 20mio ≈ €1350 per month).

This finding is also aligned with the high Gini coefficient index of Jakarta, which is 0.436 that means

6.4% of population owns 50% of the total income in the city (Jakarta, 2015a).

Thirdly, the efficacy in using a smartphone which is measured by the experience in using a

smartphone. The average of respondents’ experience in using smart phone is 5.71 years. This number,

however, does not directly reflect the efficacy level of using a smart phone. Most of the users now use

smart phones because of either the hedonistic purpose or the unavailability of basic feature phones

in the market (Chun et al., 2012). Therefore, although they use a smart phone, it does not mean that

they understand all the smart features of a smart phone. Thus, they may only use the basic feature of

a smart phone on a daily basis. Nevertheless, the number of years using a smart phone is still positively

correlated to the smart phone efficacy (Li & Unger, 2012).

Lastly, we also explored the consumers’ previous privacy intrusion experience. Our research concludes

that the majority of the respondents are people who often become the victim of privacy invasion

(mean = 3.9, out of 5 with neutral value = 3). The statistic shows that 71.7% of respondents have

negative experiences with privacy intrusion, such as: they often receive unknown emails or phone calls

from marketing agencies. The detailed demographic variables statistic information is shown in Appen-

dix 4.2. Respondents’ demography.

4.2.2.2 Data Screening Testing the assumptions of multivariate analysis were needed before we performed the explorative

analysis. Firstly, the normality test. Normality test was required because if the deviation from the nor-

mal distribution is sufficiently large, then all statistical test will be invalid (Hair et al., 2013). We con-

ducted the normality test to the residuals in the multiple regression analysis. To do that, we plotted

the standardized residual of each statistical test and see the symmetry of this residual. The plots for

the residuals show there are no outliers (no value below -3 and above +3), which indicate that the

standardized errors are quite normal.

Secondly, the homoscedasticity test. Testing homoscedasticity was needed to test the homogeneity

of the error (residuals) across the range of the independent variables (IV). It also showed the equality

of errors variance across all values of the predicted value of the DV The homoscedasticity was im-

portant because it reflects the models’ ability to predict a DV is consistent across range of that DV

(Hair et al., 2013). Put it in simpler words, if a model is able to predict both low values of DV and high

value of DV in a consistent accuracy, then this model satisfies the homoscedasticity assumption. We

tested the homoscedasticity by using both P-P plots of standardized residual and scatter plots of the

standardized residual vs. predicted value of DV. The homoscedasticity test of the cases showed that

minor heteroscedasticity was present. Even though the heteroscedasticity would not bias the anal-

yses, we might expect the increase of the standard error in the analyses. However, any violation to

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this assumption would not be problematic to the estimation results. The detail test results are shown

in Appendix 4.3 Normality and homoscedasticity test.

The next data screening test was the linearity test. This test was required to explore whether the

relationships between IVs and DV meet the “linear” properties before we performed the multiple re-

gression linear analysis. We used statistical tests for linearity to find the un-linearity of variables. Then

we performed scatter plot analysis to add both linear and quadratic trend lines to visually detect the

linearity of the variables, especially to the variables that did not meet the linearity assumption. If the

differences of the explained variance between linear line and quadratic line did not differ significantly,

we could then consider the distribution as linear. We found out there are two variables that did not

satisfy the linearity test. However, after we performed scatter plot analysis, we found out that there

was no significant difference in the explained variance between linear plot line and the quadratic plot

line. Thus, we considered all the variables met the linearity assumption. Appendix 4.4 Linearity test

shows the linearity test.

The final assumption test was the absence of correlated error. As the data collected were not time

series data, we could assume that the correlated error would be absent.

4.2.2.3 Data preparation for multiple regression analysis Recalling the discussion in paragraph 4.2.1, we used several variables to measure each construct to

avoid measurement errors that might be caused by bias(es) in the instruments. To perform the multi-

ple regression analysis, a single number estimate that could represent the variables under the con-

struct was needed. Therefore, we performed factor analysis to summarize the data and to select which

variable(s) that could build up the construct but still represent the multiple aspects of construct in a

single scale (Hair et al., 2013). Furthermore, we also used factor analysis to compute the factor score

of each construct based on the contributing loading factor of each variables to the respective con-

struct.

Since we already had hypothesized the variable structures, we performed the factor analysis only to

the group of variables under each construct. For example, we only performed factor analysis to wtd1,

wtd2, wtd3, wtd4, and wtd5 to measure the factor score of wtd and so on. We used 0.6 measured of

sampling adequacy (MSA) as the degree of intercorrelation among variables (Hair et al., 2013). This

MSA requirement resulted two IPPR variables were taken out from the analysis (aisp1 and aisp2) as

both variables failed to meet the MSA adequacy requirement. As the final result, Table 4.10 summa-

rizes the overview of the factor analysis results with the corresponding factor loadings, whereas the

detailed results of the factor analysis is explained in Appendix 4.5 Factor Analysis.

Table 4.10. Factor analysis results and the corresponding variables

Construct Corresponding variables Corresponding factor loadings

Willingness to disclose = FAC_WTD wtd1 Direct disclosure .791

wtd2 .791

wtd3 .733

wtd4 Indirect disclosure .612

wtd5 .670

Privacy concerns = FAC_IUIPC Picon Control .748

Piaw Awareness .830

Picol Collection .731

Privacy concerns = FAC_CFIP pcima Improper access .716

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Construct Corresponding variables Corresponding factor loadings

pcusu Unauthorized secondary use

.854

Pcerr Error .570

Tangible benefits = FAC_TANG_BEN

btmb1 Direct monetary benefit .921

btmb2 Indirect monetary benefit .921

Intangible benefits = FAC_IN-TANG_BEN

bipe Personalization .838

bius Usefulness .838

Individual self-protection = FAC_IPPR

aisp3 Removal .612

aisp4 Negative WoM .721

aisp5 Complain directly to com-panies

.849

aisp6 Complaint indirectly to a 3rd party

.792

Industrial self-regulation = FAC_ISR aisr1 3rd party auditor .892

aisr2 Protection seal .892

Government regulation = FAC_AGRL

agrn

Demographic Variables

Previous experience privacy intru-sion

dpre

Self-efficacy dsel

Age dage

Education level aedu

Salary dsal

Gender dsex

In addition to the factor analysis to perform summarization of variables, we also incorporate the de-

mographic variables in the analysis. For each demographic variable, we spilt the cases into two groups

of cases as follows. For gender variable, we split the cases into male group and female group, whereas

for the salary variable, we split the cases into two groups, i.e., low salary (IDR 5 mio – IDR 10 mio per

month) and high salary (> IDR 10 mio per month). Additionally, we also include cases of people who

reject to disclose their salary information to the analysis.

Furthermore, we also incorporate the previous privacy intrusion experience as the moderating varia-

ble. We group the respondents who score lower than 3 (from never to rarely experiencing privacy

intrusion) to the positive experience group, whereas people who score higher than 4 (often to very

often experiencing privacy intrusion) will be included in the negative experience group.

We also split the cases based on respondents’ education level. Respondents who have high school as

their highest education level will be grouped into low education group, whereas people who have

access to higher education level (e.g., university and academy) will be grouped into high education

group. In the age division, we grouped people who aged lower than 35 years old to the young group,

whereas the rest are grouped to the old group. To split the self-efficacy variable, we divide the re-

spondents’ smartphone use experience into two equal ranges by using the median value as the group

separator. We put people who have lower than 5 years’ experience in using smart phone as the low

self-efficacy group, whereas the rest are grouped to the high self-efficacy group. Table 4.11 displays

the composition of cases in each group.

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Table 4.11. Number of cases in each segregation based on demographic variables

All

Gender Salary Privacy experi-

ence Education Age Self-efficacy

Male Fe-

male Low High

Reject to dis-close

Posi-tive

Nega-tive

Low High Young Old Low High

265 111 154 166 60 39 75 190 52 213 212 53 146 119

4.2.2.4 Data preparation for cluster analysis The main assumption to conduct a cluster analysis was the absence of multicollinearity (Hair et al.,

2013). We tested the multicollinearity by looking at the correlation matrix between variables. We

found out that no variables are significantly correlated with more than 0.6 correlation value (see Ap-

pendix 4.6. Correlation matrix). This result indicates that our variables are free from multicollinearity

risks.

Before we conducted the segmentation to the cases, we recomputed the value responses of the pri-

vacy mitigation strategy variables, i.e., tangible benefits, intangible benefits, individual self-protection,

industry self-regulation, and government regulation and legislation. We assigned -1 value to any re-

sponse valued from 1.00 to 3.00, 0 value to any response valued 3.001 to 4.99, and +1 value to any

response valued 5.00 to 7.00. By using this re-computation, we could interpret that recomputed mit-

igation strategy variable as follows: a recomputed variable with negative value means the mitigation

strategy is less preferred by the respondents, thus performing this strategy will ineffectively increase

their willingness to disclose personal data. Likewise, a recomputed variable with positive value means

that the mitigation strategy is more preferred by the respondents and performing this strategy will

effectively increase their willingness to disclose personal data, whereas 0 value means that the re-

spondents indifferently valued the mitigation strategy.

However, we should note that we perform cluster analysis based on instruments presented in the

multiple regression analysis, in which all privacy mitigation statements are presented in positive state-

ments. Therefore, we could expect that the respondents may choose all those mitigation strategies as

important because there are no trade-offs involved in the choice mechanism. To incorporate trade-

offs in the users’ choice mechanism, we present additional questionnaire in the form of conjoint anal-

ysis that will be elaborated in next paragraph (4.3).

4.3 Conjoint assessment of the privacy buy-off value The second part of the analysis is to measure the buy-off value and the part-worth utility of each

privacy dimension related to the others by using the conjoint analysis. The conjoint analysis is a re-

search method used in the evaluation of complex products or services while maintaining realistic con-

texts of the respondents (Hair et al., 2013). It is developed based on the premise that consumers eval-

uate the utility value of products and services by combining the amount and the combination of at-

tributes in those products and services (Hair et al., 2013). A more detailed about of conjoint analysis

is further explained in Appendix 4.7. A more detailed explanation about Conjoint Analysis.

In this research, we use the stated choice CBC method. The reason is twofold: (1) we want to analyze

a hypothetical product or service which can be satisfied by the stated choice CBC, and (2) we want to

avoid the risk of multicollinearity as well as minimize the amount of noise that might happen in the

revealed choice model.

The main assumption in the stated choice CBC is the respondents are rational actors who intend to

choose a profile from a choice set in which their utility value is maximized. In a stated choice CBC, each

profile will have a different composition of attribute and attribute level which then will be evaluated

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and chosen by the respondents. By choosing the profiles, the respondents assign the part-worth utility

value of each attributes level. Then, the utility value can be derived based on the sum of the part-

worth utility of each attribute.

We use the multinomial logit (MNL) model to design the choice model as well as estimate the utility

function of the model. This model is the most commonly used model to design and analyze the dis-

crete choice model due to its ability to simplify the model estimation in a quick way and availability in

the most statistical software package (Louviere et al., 2000; Louviere; et al., 2003; MacDonald et al.,

2012). By using this model, the assumption of independence of irrelevant alternative, which assumes

that the respondents’ choice for a specific profile is independent from the presence or absent of other

alternatives, are implied (Louviere et al., 2000). This assumption, however, has both strength and

weakness. The strength of the assumption is that the model is able to provide convenient choice

model which allows introduction and/or elimination of alternative without re-estimation to the whole

model, whereas the weakness is that the model might be able to bias the estimation as the involved

attributes may be correlated one another (Louviere et al., 2000).

In the following subsections, we explain the design methodology of the stated choice CBC in our re-

search.

4.3.1 Contextual setting: personalization service in peer-to-peer ridesharing ser-

vice The contextual setting of this research is developed based on the knowledge we get from the Chapter

2, particularly 2.4. In this research we present the personalization as a new unbranded hypothetical

service offered by a hypothetical P2P ridesharing provider in Indonesia (called GOPEK) to our respond-

ents. This personalization service would be able to give push recommendations to the consumers re-

lated to the promotion of goods and services. By using this personalization service, the consumers

would be able to save their monthly transportation costs due to the efficiency that the application

provides in using the transportation service. However, this service would require the users to disclose

their personal information, specifically: their traveling patterns and buying patterns.

The GOPEK’s personalization engine would capture and store those personal data and perform data

mining to them to analyze and predict the users’ preferences and interests. Furthermore, the infor-

mation would be cross-matched with the users’ demographical data in order to give recommendations

(for example: people with the same profile and interest as you also buy from store X). Moreover, the

preferences and interests’ data could also be sold to third parties who might be interested to upsell

or cross-sell their product and service offerings.

The use-case example of this personalization service that was presented in the questionnaire is pre-

sented in Textbox 4.1:

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4.3.2 Personal data disclosure definition and attribute selection From the above mentioned hypothetical service, we defined the types of personal data included to

offer the personalization service are:

1. The privacy of location and space, which is defined as “the right to move about in public or

semi-public space without being identified, tracked, or monitored” (Finn et al., 2013, p. 9).

This privacy type can be operationalized by using the users’ traveling pattern. Both pick up and

drop off points. Furthermore, the users can also name the saved location with a certain name,

such as: office or home, to simplify the ordering process. As such, the users will also disclose the

“space” of this location. Since this is the mandatory information (even if the user does not use the

personalization service), this type of privacy is set as the default setting of the design.

2. The privacy of behavior and action, which is defined as “the ability to behave in public, semi-

public or one’s private space without having actions monitored or controlled by others” (Finn

et al., 2013, p. 8).

This type of privacy dimension is perfectly operationalized by the disclosure of the consumers’

buying pattern, such as: what time the consumer buys certain goods, what kind of meal he/she

ordered last night, what time he/she usually orders a dinner, and any other activity that can be

related to the behavior of the individuals. In the conjoint design, we give the respondent an op-

portunity to choose whether they are willing to disclose this information or not since it is not a

default setting of the service.

3. The privacy of data and image. This privacy explains the users “concerns about making sure

that individuals’ data is not automatically available to other individuals and organizations and

that people can exercise a substantial degree of control over that data and its use” (Finn et al.,

2013, p. 8).

This privacy type is operationalized by the willingness of users receiving advertisement from other

companies through the company’s application. If the consumers chose to enable this option, they

Textbox 4.1. Ojek online personalization use-case

Abu (male, single) is a loyal customer of GOPEK that offers not only basic transportation services

(point-to-point pick up and drop off – named as GoNumpang), but also several additional services,

such as: a groceries shopping service (GoBelanja) and restaurant home-delivery service (GoMakan).

Initially, he uses only GoNumpang in daily basis from home to the office, and return. Occasionally,

he also uses several other services offered by the company.

Just recently, the company is offering a personalization service that gives information about what

certain restaurant (his favorite one) that gives promotion that is located on his daily route. Further-

more, the company can also predict when the time he will run out of grocery items, and inform him

that a groceries shop located in his route also sells this item with promotion. By using this person-

alization service, he has the opportunity to save his transportation cost by buying this product at

the same time he uses the basic transportation service.

In addition to that, he is also presented with several recommendations of products or services that

he has never used. The recommendation is presented in the form: “people who have the same

profile as you also buy food Y from restaurant B”. The recommendation is comparably fit with his

interest and the restaurant located nearby to his location.

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indirectly allow the company to transfer their personal data to other companies. In our design,

the users are able to choose whether they are willing to make their personal information available

to other companies or not.

In summary, Table 4.12 summarizes the privacy types, operationalization attributes, and their levels.

Table 4.12. Privacy type operationalization attributes

No Privacy type Attribute Level

Privacy harm

No privacy harm

1 Privacy of location and space Traveling pattern disclosure Yes (de-fault)

2 Privacy of behavior and action Buying pattern disclosure Yes No

3 Privacy of data and image Receiving advertising from other company

Yes No

Furthermore, since we also wanted to measure the buy-off value of these personal data, the monetary

attribute was introduced in the analysis in the form of expected monthly transportation cost saving.

By using this attribute, we can compare those personal data to the money scale to measure the part-

worth of each personal data.

The value of expected monetary benefit was derived from the average daily transportation cost using

existing Ojek online service providers. The existing Ojek online service providers charges IDR 20.000

(in average) for a single trip (gojakgojek.com, 2016b). Therefore, we could suggest that the monthly

average transportation cost is IDR 880.000. We presented the expected monthly transportation saving

in four levels, i.e.: IDR 0,00 (0%), IDR 88.000,00 (10%), IDR 176.000,00 (20%), and IDR 264.000,00

(30%).

The privacy disclosures then were coded with dummy coding while the expected transportation saving

was coded with effect coding. The overview of the attributes, their levels, and level coding is presented

in Table 4.13 and Table 4.14.

Table 4.13. Attribute dummy coding

No Privacy type Attribute Dummy Coding

Privacy harm

No privacy harm

1 Privacy of location and space (PLS)

Traveling pattern disclosure Yes (de-fault)

2 Privacy of behavior and action (PBA)

Buying pattern disclosure 1 0

3 Privacy of data and image (PDI) Receiving advertising from other company

1 0

Table 4.14. Attribute effect coding

No Expected transportation saving (ES) Level Effect Coding s0 s1 s2

1 IDR 270.000 (≈ 30% saving) 1 0 0

2 IDR 180.000 (≈ 20% saving) 0 1 0

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No Expected transportation saving (ES) Level Effect Coding s0 s1 s2

3 IDR 90.000 (≈ 10% saving) 0 0 1

4 IDR 0 (0% saving) -1 -1 -1

4.3.3 Choice set design After we defined the attributes used and the levels, we developed an experimental design using the

above mentioned attributes. The full profile of those attributes and attribute levels would produce 16

choices. However, we did not want to present all these choices as it would present information over-

load to the respondents. Consequently, we used fractional profile method to choose which choice that

would represent the full profile as well as eliminate the multicollinearity in the design. The Ngene

software was employed to discover this design.

Ngene is software to generate experimental design in state-choice experiments (Choice-Metrics,

2012). We can specify the number of options (alts), number of rows in the design (rows), the algorithm

to construct the design (orth), and the utility function of each option (model). Figure 4.2 describes

the syntax to produce this design in Ngene software.

Figure 4.2. Ngene syntax

We used orthogonal design to eliminate multicollinearity in the design. Firstly, we referred to the basic

plan ruling to decide the number of choices in our (fractional) design, in this regard, we refer to the

Louviere et al. (2000). From this document, we concluded that we need 8 choices to ensure the rep-

resentativeness and orthogonality of the design. Hence, we used Ngene to look for the profiles that

fitted to the design.

As we only wanted to measure the part-worth utility of each attribute, we presented the alternative

1 and alternative 2 by using unlabeled generic attributes. Thus, we were allowed to use sequential

construction for the algorithm to look for the orthogonal design (orth=seq). In this method, Ngene

would look for the choices first, then randomly assigned the choices to the choice set. However, this

approach would imply correlations existed between alternatives, even though the zero correlation

was achieved within alternative. However, this would not be a problem since the alternatives are un-

labeled – generic attributes (Choice-Metrics, 2012).

Furthermore, we also needed to define the utility function of the design. The utility function was de-

rived based on the previously mentioned attributes and attribute levels. In addition, the constant b0

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(in alt3) was introduced to measure the effect of the default attribute (the privacy of location and

space and the general utility value) in the utility function.

We propose the utility function of using personalized Ojek online service as:

𝑈 = 𝑎𝑠𝑐1 + 𝐵_𝑝𝑑𝑖 ∗ 𝑝𝑑𝑖 + 𝐵_𝑝𝑏𝑎 ∗ 𝑝𝑏𝑎 + 𝐵_𝑒𝑠 ∗ 𝑒𝑠

Equation 4.1. Utility function

Where:

U = Utility value

asc1 = constant which will capture the value of privacy of location and space (pls) and the

general level of the service’s utility value

B_pdi = coefficient for privacy data and image (pdi) value

B_pba = coefficient for privacy behavior and action (pba) value

B_es = coefficient for expected saving (es)

However, we need to modify Equation 4.1 as we have coded the variable es into effect coded variables

(Table 4.14). The modified equation according to the effect coded variable is:

𝑈 = 𝑎𝑠𝑐1 + 𝐵_𝑝𝑑𝑖 ∗ 𝑝𝑑𝑖 + 𝐵_𝑝𝑏𝑎 ∗ 𝑝𝑏𝑎 + 𝐵_𝑒𝑠0 ∗ 𝑒𝑠0 +𝐵_𝑒𝑠1 ∗ 𝑒𝑠1 + 𝐵_𝑒𝑠2 ∗ 𝑒𝑠2

Equation 4.2. Utility function with effect coding for variable es

4.3.4 Questionnaire design and development After we run the simulation, Ngene produced 8 set of choices as the most efficient orthogonal design

for this conjoint experiment. The overview of the choice sets and its respected coding is explained in

Appendix 4.8. Choice sets configuration. Based on those choice sets, we developed the contextual

setting before we typed in those choice sets into the questionnaire. Finally, we merged these choice

sets questions with the previously developed questionnaire (questionnaire to discover the effective

mitigation strategy to increase the willingness to disclose personal information (in paragraph 4.2)).

We presented each choice sets in a two steps of questioning. The first question asked the respondent’s

preference over two alternatives, i.e., alternative 1 (alt1) and alternative 2 (alt2). After the respondent

chose the alternative, we presented the second question, in which we asked whether the respondent

will actually subscribe to the service if the service is available in the market. If the respondent chose

“yes” then we saved the initial choice, however, if the respondent chose “no” we could get the re-

spondent truly preference, i.e., the third alternative (alt3), and saved this choice. By using this method

of questioning, we could avoid the risk of not having any valid response (i.e., if all respondents chose

alternative 3) that might cause us failed to measure the PLS, PDI, and PBA.

4.3.5 Data processing and analysis After we get the survey questionnaire result, we employed Biogeme software to perform the data

analysis for the conjoint analysis. Biogeme (Bierlaire Optimization toolbox for GEv Model Estimation)

is freeware software developed by Michel Bierlaire to perform the estimation of likelihood in discrete

choice models (Bierlaire, 2003). The inputs of the program are: the design specification (based on

Ngene’s design with transformation of the effect coded variable – ES) and the dataset from the ques-

tionnaire.

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Based on the Equation 4.2, we create a syntax for Biogeme to run the estimation for the attribute.

Figure 4.3 displays the Biogeme syntax.

Figure 4.3. Biogeme syntax

We will conduct the analysis to the all cases as well as segregated cases based on the moderating

variables and the initial perception of each privacy construct items (i.e., the initial level of willingness

to disclose, privacy concerns, privacy benefits, and the privacy assurance approaches).

An important parameter to measure the validity of the estimate is Rho-square (ρ2) which explains the

goodness-of-fit of the model to the cases. The ρ2 is calculated as:

𝜌2 = 1 −𝐿0

𝐿∗

Where

L0 = the Log likelihood of the initial estimation where all coefficients are estimated at 0

L* = the Log likelihood of the estimated model from the cases

Based on the equation, the higher ρ2 (closer to 1) suggests that the fitness of the estimation to the

cases is higher, and vice versa. The models with ρ2 values between 0.2 and 0.4 are considered as the

indicative of a good model fits (Louviere; et al., 2003). The result of the analysis will be thoroughly

elaborated on in the next chapter (Chapter 5).

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5 Survey Results, Data Analyses,

and Discussion

As previously mentioned in chapter 1, this chapter discusses the survey result, data analyses, and the

findings of the research, including the test results of the hypotheses formulated in chapter 3, the clas-

sification of users based on the appropriate privacy mitigation strategy, the analysis of the relative

importance of each privacy type based on its part-worth utilities, and the willingness to accept (or the

buy-off value) of privacy.

The first section of this chapter (paragraph 5.1) briefly explores the descriptive analysis of the survey

result. The analysis includes the initial statistics for willingness to disclose, privacy concerns, ac-

ceptance of privacy benefits, and the extent to perform privacy assurance approaches.

Next, the second section (paragraph 5.2) elaborates the hypotheses testing results of the multiple

regression analysis. The generic multiple regression of the complete cases is performed to give the

complete picture of the privacy mitigation strategy that can be applied in the population as a whole.

Furthermore, the demographic variables are also included in the more detailed analyses to test the

consistency of the results across the population as well as to test whether the control variables influ-

ence the analyses results. The analyses in this section yields the answer to the SQ3 (To what extent

are the privacy decision making constructs influencing the users’ willingness to disclose their personal

information?).

The third section of this chapter (paragraph 5.3) analyzes the classification of users by using the cluster

analysis to perform segmentation to the users according to their preferred privacy mitigation strategy.

The main aim of the segmentation is to help the company to prepare and apply an appropriate privacy

mitigation strategy to the specific target market, such as: the specific (niche) market and the type of

investment specific to the chosen target to acquire personal data from the people in this cluster. Ad-

ditionally, the prediction of the market size of each target market is also calculated based on the com-

position of each cluster. Similarly, we also include the demographic variables to test whether they will

influence the formed segmentation of users. The conclusion in this section provides the answer of the

SQ 4 (How can the users be segmented based on the privacy concern mitigation efforts?).

Finally, the last section (paragraph 5.4) elaborates the experimental assessment of the research to

measure the part-worth utilities as well as the buy-off value of privacy by using the conjoint analysis.

Also, we include the demographic variables to the analysis to test whether these variables influence

the buy-off value of privacy. Additionally, we also include the initial perception of the willingness to

disclose personal information, the privacy concerns as well as the privacy mitigation strategies (privacy

benefits and privacy assurance approaches) as the moderating variables to make deeper analyses. The

aforementioned analyses produce the answer to the SQ5 (To what extent do the users rank the value

of each personal information relative to the other type of personal information and how much is the

buy-off value for each of their personal data?). All those three analyses will be followed by a discussion

to comprehensively elaborate the empirical test result (paragraph 5.5).

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5.1 Descriptive Statistics

5.1.1 Willingness to disclose (WTD) In general, a quite high level of willingness to disclose personal information is shown in the population.

This finding is portrayed by the descriptive statistic result that shows 4.44 as the mean value of the

willingness to disclose (we set 4.00 as neutral value -either agree or disagree- of disclosing personal

information). Furthermore, the statistic also shows negative skew in the result which explains that the

population is more concentrated in the higher level value.

In addition to the overall analysis of the willingness to disclose personal information, we also elaborate

per item attribute of the willingness to disclose, by breaking down the construct into two sub-con-

structs, i.e., direct and indirect disclosure (see Table 5.1). The indirect disclosure presents the higher

mean value than the direct disclosure, in which the disclosure via GPS when ordering Ojek online ser-

vice – wtd5 (When available, I always turn my mobile phone GPS on when I order services from Ojek

online ordering application) has the highest mean value and the providing interest list – wtd3 (I will

honestly fill in my interests list when the online company ask for my interest) to the company has the

lowest value. By this result, the statistic suggests the company to use an indirect approach when col-

lecting its customers’ personal information. Table 5.1 displays the summary of the descriptive statistic

of the WTD.

Table 5.1. Willingness to disclose descriptive statistic

WTD Statistics 95% Confidence Interval

of the Difference

Mean Std. Devia-tion

Skew-ness

Kurtosis Lower Upper

Average WTD 4.44 1.21 -.33 -.38 4.30 4.59

Average direct disclosure 4.30 1.38 -.35 -.59 4.13 4.46

Average indirect disclosure 4.66 1.43 -.41 -.55 4.49 4.84

Complete personal infor-mation

4.29 1.72 -.37 -1.02 4.09 4.50

Honest personal information 4.65 1.61 -.66 -.71 4.45 4.84

Honest interest list 3.94 1.67 -.09 -1.25 3.74 4.15

Sign-in with social media 4.09 1.93 -.08 -1.45 3.85 4.32

GPS use 5.24 1.49 -1.28 .89 5.06 5.42

Likert scale 1 – 7 is used in the operationalization of variable with 4 as the neutral value.

5.1.2 Privacy concerns The first view of the privacy concern statistic shows that the population has a high level of privacy

concerns, which is shown by 6.2 (out of 7) as the mean value of privacy concerns. Furthermore, this

high concerns have become a consensus in the population as shown in the small Standard deviation

(SD) value. The result also suggests that the population is concentrated in a higher level value of pri-

vacy concerns as shown by a negative skewness as well as the positive kurtosis in the descriptive sta-

tistic.

Furthermore, a more detailed analysis shows that the unauthorized secondary use of personal infor-

mation (Ojek online companies should never share personal data with other companies unless it has

been authorized by the individuals who provided the information) has been the highest customers’

privacy concern. The low value of SD also concludes that a big portion of population agrees that the

disclosed personal data must only be used for the specified purpose of collection without any second-

ary unauthorized use. Additionally, the statistic also shows that the consumers have least concern to

the collection of data collected by the company (I’m concerned that online companies are collecting

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too much personal data about me). Table 5.2 displays the descriptive statistic for privacy concerns

variable.

Table 5.2. Privacy concerns descriptive statistic

Privacy concerns Statistics 95% Confidence Interval of

the Difference


Skewness Kurtosis Lower Upper

Average privacy con-cerns

6.21 0.58 -1.08 2.95 6.14 6.28

Control 6.12 0.95 -1.73 4.43 6.01 6.24

Awareness 6.33 0.85 -2.41 9.65 6.23 6.43

Collection 5.60 1.32 -1.21 1.25 5.44 5.76

Improper access 6.43 0.68 -1.52 4.32 6.35 6.52

Unauthorized sec-ondary use

6.68 0.63 -3.95 27.50 6.60 6.76

Error 6.08 1.13 -1.76 3.55 5.94 6.22


5.1.3 Privacy benefits Similar to the privacy concerns result, the perception of privacy benefits is also very high. The mean

value of the privacy benefits shows 6.14 which also becomes a consensus in the population with a

small value of SD. Furthermore, the result also shows a negative skewness and positive kurtosis value.

This result suggests that the population is concentrated in a higher level extent of receiving privacy

benefits in order to disclose the personal information.

Breaking down the type of benefits, we find that people value the tangible benefit more than the

intangible benefit, although the difference is very minimal. We also found that providing indirect ben-

efit as the most attractive privacy benefit (I perceive monthly transportation discount is very interest-

ing for me) and offering personalization (It is important for me if the company is able to offer me a

high quality of recommendation that suits me perfectly) as the least attractive benefit chosen by the

respondents. Nevertheless, the personalization still produces a significantly higher value from neutral

value. Table 5.3 summarizes the descriptive statistic of privacy benefits.

Table 5.3. Descriptive statistic of privacy benefits

Privacy benefits Statistics 95% Confidence Inter-val of the Difference

Mean Std. Devi-ation


Average privacy benefits 6.14 0.69 -1.24 2.64 6.06 6.22

Average Tangible benefits 6.18 0.81 -1.48 3.56 6.09 6.28

Average Intangible benefits 6.10 0.75 -1.08 1.93 6.01 6.19

Direct monetary benefit 6.14 0.95 -1.72 4.10 6.03 6.25

Indirect monetary benefit 6.23 0.80 -1.49 3.91 6.13 6.32

Personalization 5.98 1.13 -1.95 4.67 5.85 6.12

Usefulness 6.21 0.64 -0.92 3.06 6.13 6.29


5.1.4 Privacy assurance approaches

5.1.4.1 Individual self-protection (ISP) The individual self-protection is the active action part of consumers to ensure their privacy right. The

average value of 5.9 as the Individual self-protection suggests that, in general, the respondents are

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more likely to perform defensive actions to react to any privacy invasion activity. Yet, the SD is quite

high which shows that the level of likelihood reaction may vary. In a more detailed analysis, the highest

likelihood of performing self-protection is the removal of data (the highest mean = 6.29), whereas the

least likelihood reaction is the refusal to disclose personal information (mean = 3.9). This result sug-

gests that the respondents are not willing to collaborate with the company completely if they feel the

company mistreated their data. On the contrary, the respondents do not have a tendency to decline

if the company asks them to give their personal information. The detailed descriptive of ISP is drawn

in Table 5.4.

Table 5.4. Descriptive statistic of Individual self-protection

Individual self-protection Statistics 95% Confidence Interval of

the Difference



Average ISP 5.93 0.84 -.69 .28 5.82 6.03

Refusal 3.92 1.57 .01 -1.11 3.73 4.11

Anonymity 4.49 1.46 -.27 -.84 4.31 4.67

Removal 6.29 0.86 -1.78 4.32 6.19 6.40

Negative WOM 5.94 1.18 -1.50 2.61 5.79 6.08

Direct complain 5.91 1.10 -1.28 1.70 5.77 6.04

Indirect complain 5.57 1.31 -1.01 .65 5.41 5.73


5.1.4.2 Industry self-regulation (ISR) On average, the mean value of ISR 6.12 suggests that the consumers’ demand to the presence of

industry self-regulation is quite high. Furthermore, the self-regulation is divided into two approaches,

i.e., the regular privacy audit by a 3rd party auditor and the presence of a privacy seal logo in the

company’s branding (such as: in the mobile application and the website). The descriptive statistic

shows that both approaches have a high mean value (6.09 and 6.16 respectively) that suggest that

both approaches are equally appreciated by the consumers when implemented. Table 5.5 displays the

detail statistic of ISR.

Table 5.5. Descriptive statistic of Industry self-regulation

Industry self-regulation Statistics 95% Confidence Interval of

the Difference



Average ISR 6.12 0.82 -1.33 2.64 6.03 6.22

3rd party auditor 6.09 0.95 -1.50 3.04 5.97 6.20

Protection seal 6.16 0.87 -1.45 2.96 6.06 6.27


5.1.4.3 Government regulatory and legislation (GRL) In addition to the industry self-regulation, the consumers are able to perform the passive action by

using the government legislation and regulation to regulate the company’s privacy and personal data

practice. The analysis of descriptive statistic shows the mean value of 6.50 and low SD which conclude

that the consumers’ demand for government regulation to regulate the company’s privacy practice

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and personal data use is high and has been a consensus. Table 5.6 displays the detail statistic of GRL,

in which Likert scale 1 – 7 is used in the operationalization of variable with 4 as the neutral value.

Table 5.6. Descriptive statistic of Government regulation and legislation

Government regulation and legislation Statistics 95% Confidence

Interval of the Dif-ference



Govt regulation and legislation

6.50 0.69 -1.79 5.05 6.41 6.58

5.2 Multiple Regression Analysis In order to answer SQ.3, i.e., To what extent are the privacy decision making constructs influencing the

users’ willingness to disclose their personal information?, we test the hypotheses formed in chapter 3

by using multiple regression analysis. The initial correlation test is conducted prior to the multiple

regression analyses in order to investigate the potential variables that influence the willingness to

disclose personal data. The cross-correlation test shows that only privacy benefits (both tangible and

intangible benefit) have a direct significant correlation with willingness to disclose, in which the tangi-

ble benefit holds the highest correlation. However, we need to investigate further the partial correla-

tion once these variables are included to the analysis to get the complete estimation. Therefore, the

stepwise method is chosen as the estimation procedure in our multiple regression analyses. Table 5.7

displays the detailed correlation test among variables.

Table 5.7. Correlation test of variables

WTD IUIPC CFIP TANG_BEN

INTANG_ BEN

ISP ISR GRL

WTD Pearson Correla-tion

1 -0.04 0.005 .342** .248** 0.086 -0.023 -0.056

Sig. (2-tailed)

0.517 0.931 0 0 0.163 0.706 0.365

IUIPC Pearson Correla-tion

-0.04 1 .402** 0.086 .179** .320** .338** .316**

Sig. (2-tailed)

0.517 0 0.165 0.003 0 0 0

CFIP Pearson Correla-tion

0.005 .402** 1 .184** .170** .444** .381** .502**

Sig. (2-tailed)

0.931 0 0.003 0.005 0 0 0

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Paragraph 5.2.1 explains the generic multiple regression analysis applied to the whole cases. Next,

paragraph 5.2.2 explains the analyses to the segregated cases based on contextual variables to inves-

tigate the consistency of the analysis results. Finally, the conclusion of the multiple regression analyses

are formed in paragraph 5.2.3.

5.2.1 Generic multiple regression analysis We set a 90% significance threshold for the variables to be included to the regression model. We

define the general linear relationship model of our regression analysis as follows:

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𝑊𝑇𝐷 = 𝐵0 + 𝐵1 ∗ 𝐼𝑈𝐼𝑃𝐶 + 𝐵2 ∗ 𝐶𝐹𝐼𝑃 + 𝐵3 ∗ 𝑇𝐴𝑁𝐺𝐵𝐸𝑁 + 𝐵4 ∗ 𝐼𝑁𝑇𝐴𝑁𝐺𝐵𝐸𝑁 + 𝐵5 ∗ 𝐼𝑆𝑃 + 𝐵6∗ 𝐼𝑆𝑅 + 𝐵7 ∗ 𝐴𝐺𝑅𝐿

Where:

WTD = willingness to disclose personal data

IUIPC = internet users’ information privacy concerns (Malhotra et al., 2004)

CFIP = concern for information privacy (Smith et al., 1996)

TANGBEN = privacy tangible benefit

INTANGBEN = privacy intangible benefit

ISP = individual self-protection (Xu et al., 2012) (based on IPPR by Son and Kim (2008))

ISR = industry self-regulation (Xu et al., 2012)

AGRL = government regulation and legislation (Xu et al., 2012)

The regression analysis aims to estimate the coefficients of the model, i.e., B0 through B7.

In general, the test result shows only tangible benefit and the governmental regulation and legisla-

tion have direct significant correlation to the willingness to disclose. The tangible benefit has direct

positive significant relationship with the willingness to disclose, whereas the governmental regulation

and legislation has direct negative significant with the willingness to disclose. The Beta of tangible

benefit scores higher than the Beta of governmental regulatory and legislation (see Table 5.8), which

means that the tangible benefit has higher predictive power than the extent of governmental regula-

tory and legislation demand. Any increase/decrease of one SD of the tangible benefit will increase/de-

crease 0.357 SD of the willingness to disclose personal information, while keeping the other predictors

constant. On the other hand, every increase/decrease of one SD of the extent governmental regula-

tory and legislation demand will decrease/increase the willingness to disclose personal information by

0.105 SD, ceteris paribus. Table 5.8 displays the output of the multiple regression result.

Table 5.8. Multiple regression analysis output

Variables Beta t Sig

Constant NA 0 1

Tangible benefit 0.357 6.125 0

Government Regulatory and Legislation

-0.105 -1.8 0.073

Model summary

Number of cases 265

Adjusted R square 0.121

F value 19.223 significant at .000

Looking at the model summary, the adjusted R2 value is quite low. Nevertheless, the F value is higher

than the acceptable F value (in which the statistic shows 19.223 as the F value of the model whereas

the F-distribution table with df=7 and 200 df at 0.05 significant level yields value 2.06), thus we can

conclude that the model is a not a result of a chance occurrence (Hair et al., 2013). This result builds

the generic model of our multiple regression analysis.

A noteworthy finding is found in the relationship between privacy assurance approaches variables

(i.e., Individual self-protection, industry self-regulation, and government regulation and legislation)

and the privacy concerns variables (i.e., IUIPC and CFIP). We identified strong correlations between

Page | 93

IUIPC and privacy assurance approaches and between CFIP and privacy assurance approaches in the

correlation table (see Table 5.7). We then conduct multiple regression analyses with IUIPC and CFIP

as the dependent variables and ISP, ISR, and GLR as the independent variables.

The multiple regression analyses results show that IUIPC is significantly explained (direct, positive re-

lationships) by the whole privacy assurance approach construct, even though the test fails to meet the

normality test assumption as the P-P plot of the residual deviates from the normal plot line. Never-

theless, a theoretical investigation and explanation are needed to investigate this relationship further.

On the other hand, the CFIP is significantly explained (direct, positive relationships) only by the ISP

and GRL. This finding partially shares the result with the research by Xu et al. (2012), in which they

investigate the relationship between privacy assurance approach constructs and the context specific

CFIP with the mediating effect of perceived control. Likewise, this test fails to meet the normality test

as the residuals significantly deviates from the normal line plot. The summary of the multiple regres-

sion outputs is displayed in Table 5.9, whereas the detail results of both multiple regression analyses

are detailed in Appendix 5.1 Multiple regression analysis of IUIPC and CFIP.

Table 5.9. IUIPC and CFIP multiple regression outputs

Dependent Variables

IUIPC CFIP

Independent Variables Beta t Sig Beta t Sig

Individual self-protection .170 2.579 .010 .283 5.044 .000

Industry self-regulation .184 2.693 .008 NA NA NA


.155 2.329 .021 .383 6.817 .000

Model summary

Number of cases 265 265

Adjusted R square 0.156 0.313

F value 17.285 significant at .000 61.026 significant at .000

5.2.2 Consistency test based on demographic variables After we perform multiple regression to all of the cases, we perform multiple regression analyses to

each of groups segregated by the previously defined demographic variables (see paragraph 4.2.1.5).

In the gender grouping, we discover that the IUIPC, both privacy benefits (tangible and intangible),

and the industrial self-regulation have significant direct relationship to the willingness to disclose per-

sonal information in the male group. The highest Beta is owned by the tangible benefit, followed by

the intangible benefit. Moreover, the male population sees the IUIPC as significant predictor to the

willingness to disclose personal information. On the other hand, this population sees providing indus-

try self-regulation as the significant measures to increase the willingness to disclose personal infor-

mation. This result suggests, for the male group, that utilitarian approaches are more interesting than

the presence of privacy assurance measures by the company. Furthermore, a moderate adjusted R

square score suggests that this model is a good fitting model.

On their female counterpart, the willingness to disclose personal information is only influenced by

providing tangible benefit. The tangible benefit scores Beta 0.212 with 0.05 confidence level. Yet, the

model only explains very small variance.

The abovementioned two analyses suggest that gender is influencing the human rationale in disclosing

personal information, particularly in the perception of privacy concerns, intangible benefits, and pri-

vacy assurance approach. The detail of the multiple regression output is displayed in Table 5.10.

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Table 5.10. Multiple regression output based on gender segregation

Male Female


Privacy Concerns (IUIPC) -.174 -1.940 .055 - - -

Privacy Concerns (CFIP) - - - - - -

Tangible Benefits .335 3.499 .001 .212 2.679 .008

Intangible Benefits .318 3.188 .002 - - -

Individual self-protection - - - - - -

Industry self-regulation -.216 -2.423 .017 - - -


- - - - - -

Model summary


Adjusted R square .308 0.039


In the grouping based on salary, we discover that both low salary and high salary groups share a same

direct significant predictor to the willingness to disclose personal information, i.e., the tangible benefit.

In the low salary group, the tangible benefit has Beta 0.336, whereas in their high salary counterpart

it has Beta 0.357. Both model, however, have low adjusted R square value (i.e., 10.7% and 11.2% re-

spectively). These findings suggest that salary does not influence the users’ privacy decision making

rationale.

An interesting finding is found in the group of people who reject to disclose their salary information,

in which we could also assume that these people have a slightly higher privacy concerns level. The

intangible benefit and governmental regulation and legislation appear to have direct significant rela-

tionship to the willingness to disclose personal information with similar predictive magnitude (Beta =

0.394 and -0.351 respectively). The model of this group also has higher explained variance level than

the former two groups (13.7%). However, we cannot conclude whether the initial privacy concerns

level becomes the antecedent of the outcome difference in this group, as the privacy concerns mean

values (both IUIPC and CFIP) do not differ significantly with the privacy concerns level in both low

salary and high salary group. Table 5.11 summarizes the regression analysis based on salary segrega-

tion.

Table 5.11. Multiple regression output based on salary segregation

Low salary High salary Reject to disclose salary

Independent Variables Beta t Sig Beta t Sig Beta t Sig

Privacy Concerns (IUIPC) - - - - - - - - -

Privacy Concerns (CFIP) - - - - - - - - -

Tangible Benefits .336 4.569 .000 .357 2.908 .005 - - -

Intangible Benefits - - - - - - .394 2.453 .019

Individual self-protection - - - - - - - - -

Industry self-regulation - - - - - - - - -


- - - - - - -.351 -2.182 .036

Model summary

Number of cases 166 60 39

Adjusted R square 0.107 0.112 0.137

F value 20.873 significant at .000 8.457 significant at .005 4.016 significant at .027

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In the analysis to groups based on previous privacy experience, an astonishing finding is found in the

positive experience group (people who never or seldom experience privacy intrusion), in which the

tangible benefit and the individual self-protection show direct significant relationships to the willing-

ness to disclose personal information. The tangible benefit scores Beta 0.375, whereas the individual

self-protection, surprisingly, scores Beta 0.182 (positive sign), which scores a reversed sign from the

hypothesized relationship. This model explains 18.6% variance. This finding suggests that people who

have positive privacy experience are willingly to disclose personal information in the “alert” mode,

which means that they will directly perform self-protection actions whenever the company fails to

treat their personal information correctly.

On the contrary, the analysis in the negative experience group shows that the willingness to disclose

is predicted by two predictors, i.e., the CFIP and tangible benefit. The CFIP scores Beta -0.131 whereas

the tangible benefit scores Beta 0.334. This numbers suggests that the utilitarian benefits outweigh

the privacy concerns to increase the willingness to disclose personal information. The model, however,

only explains 9.6% variance. From the two aforementioned analyses, we can conclude that the previ-

ous privacy experience influences the user privacy disclosure rationale. The detailed multiple regres-

sion output is drawn in Table 5.12.

Table 5.12. Multiple regression output based on privacy experience segregation

Positive experience Negative experience


Privacy Concerns (IUIPC) - - - -.131 -1.828 .069



Intangible Benefits - - - - - -

Individual self-protection .182 1.681 .097 - - -

Industry self-regulation - - - - - -


- - - - - -

Model summary




In the grouping based on education level, we discover that tangible benefit and industry self-regulation

have been the significant predictors for the willingness to disclose personal information in low educa-

tion group. The tangible benefit scores Beta = 0.365 whereas the industry self-regulation scores Beta

-0.283. This model explains 16.1% variance. On their older group counterpart, both tangible and in-

tangible benefit are the significant predictors to increase the willingness to disclose personal infor-

mation. The tangible benefit appears to have higher influence rather than the intangible benefit (Beta

0.272 and 0.136 respectively). This model explains 12.7% variance. Based on these findings, we may

suggest that education level influences the users’ rationale in disclosing personal information, partic-

ularly in the perception of intangible benefits and industry self-regulation. Table 5.13 summarizes the

multiple regression output.

Page | 96

Table 5.13. Multiple regression output based on education level segregation

Low education High education


Privacy Concerns (IUIPC) - - - - - -



Intangible Benefits - - - .136 1.720 .087




- - - - - -

Model summary




The next grouping is based on age. In the young group, both types of benefits and industry self-regu-

lation appear to have direct significant relationship to the willingness to disclose personal information.

The tangible benefit has the highest predictive magnitude with Beta = 0.284, followed by the intangi-

ble benefit with Beta = 0.165 and the industry self-regulation with Beta = -0.166. This model explains

14.4% variance. In their old group counterpart, the tangible benefit appears as the only significant

predictor that influences the willingness to disclose personal information with Beta = 0.336. This

model, however, only explains 9.6% variance in the population. These findings suggest that the age

level of the respondents influences their rationale in disclosing personal information, specifically in

the perception of intangible benefits and industry self-regulation. Table 5.14 summarizes the multiple

regression output.

Table 5.14. Multiple regression output based on age level segregation

Young Old

Independent Variables Beta T Sig Beta t Sig

Privacy Concerns (IUIPC) - - - - - -



Intangible Benefits .165 2.089 .038 - - -




- - - - - -

Model summary




The last demographic variable is the self-efficacy, which has been grouped into low and high self-effi-

cacy. In the low self-efficacy group, the tangible benefit appears as the only direct significant predictor

for the willingness to disclose personal information. The test shows Beta 0.339 as the prediction mag-

nitude. Nonetheless, the model has a low explained variance level. In the high self-efficacy group, the

model concludes that IUIPC and tangible benefit appear to have direct significant relationship to the

Page | 97

willingness to disclose personal information. IUIPC has Beta -0.176 whereas tangible benefit has Beta

0.353. This finding suggests that the self-efficacy level influences the human rationale in disclosing

personal information, particularly the privacy concerns. Table 5.15 summarizes the multiple regres-

sion analysis output of the data based on self-efficacy level.

Table 5.15. Multiple regression output based on self-efficacy level segregation

Low self-efficacy High self-efficacy


Privacy Concerns (IUIPC) - - - -.176 -2.062 .041



Intangible Benefits - - - - - -


Industry self-regulation - - - - - -


- - - - - -

Model summary




Table 5.16 summarizes the multiple regression analyses test result as well as the explained variance

of the models.

Page | 98

Table 5.16. Multiple regression analyses summary

Independent Variables Generic

Gender Salary Previous Experience of

privacy intrusion Education Age Self-efficacy

Male Female Low High Reject to disclose

Positive Negative Low High Young Old Low High

Privacy Concerns (IUIPC) NA -0.174* NA NA NA NA NA NA NA NA NA NA NA -0.176**

Privacy Concerns (CFIP) NA NA NA NA NA NA NA -0.131* NA NA NA NA NA NA

Tangible benefit 0.357**** 0.335*** 0.212** 0.336**** 0.357** NA 0.375*** 0.334**** 0.365** 0.272*** 0.284**** 0.336** 0.339**** 0.353****

Intangible benefit NA 0.318** NA NA NA 0.394** NA NA NA 0.136* 0.165** NA NA NA

Individual Self-protection NA NA NA NA NA NA 0.182* NA NA NA NA NA NA NA

Industry Self-regulation NA -0.216** NA NA NA NA NA NA -0.283** NA -0.166** NA NA NA


-0.105* NA NA NA NA -0.351** NA NA NA NA NA NA NA NA

Model Summary

Number of cases 265 111 154 166 60 39 75 190 52 213 212 53 146 119

Adjusted R square 0.121 .308 0.039 0.107 0.112 0.137 0.186 0.096 0.161 0.127 0.144 0.096 0.109 0.138

F value 19.223**** 13.234**** 7.176**** 20.873**** 8.457* 4.016* 9.475**** 11.057**** 5.885**** 16.393**** 12.854**** 6.494* 18.742**** 10.411****

Significant level: **** = 0.0001; *** = 0.001; ** = 0.5; and * = 0.1 Dependent variable: Willingness to disclose

Page | 99

5.2.3 Conclusion From the previous multiple regression analyses, we can conclude that the tangible benefit has been

the major predictor to significantly influence the willingness to disclose personal information, by ap-

pearing consistently in the generic model and (almost) all group divisions. The only one group which

does not have the tangible benefit as predictor is the reject to disclose salary information group. Yet,

this group still regards the intangible benefit as the influential predictor to increase the willingness to


On the other hand, the items of the privacy assurance, i.e., individual self-protection, industry self-

regulation, and governmental regulatory and legislation, are pointed up significantly in only 6 groups

from the total 14 groups model available. The privacy assurance items are shown in the generic model,

male group, reject to disclose salary information group, positive privacy experience group, low educa-

tion group, and the young population. This also suggests that seeing privacy as an interest, in which

the consumers regard their privacy as a means to get utilitarian benefits from the company, has be-

come more prevailing than seeing privacy as a right.

Surprisingly, the privacy concerns, in which these items have been the main predictors in most prior

research (such as: Dinev and Hart (2006), Awad and Krishnan (2006), Kehr et al. (2015), Taylor et al.

(2009), Li and Unger (2012), and Liu et al. (2011)), do not show a major contribution to the willingness

to disclose personal information in our empirical results. The privacy concerns items, i.e., IUIPC and

CFIP, only appear in the male group, negative privacy experience group, and the high self-efficacy

group.

An unexpected result is found in the positive privacy experience group. We notice that the willingness

to disclose personal information is positively influenced by the likelihood to perform individual self-

protection, which consists of removal of data, negative WoM, direct complaint to the company, and

indirect complaint to the 3rd party or authority, when their privacy is invaded. This suggests that the

members in the positive privacy experience group is willing to increases the personal data disclosure,

but they also incline to perform individual self-protection in the same time. In other words, this group

will always be in the “alert” mode when disclosing the personal information.

Furthermore, we also found significant difference in the number of predictors after we segmented the

cases into groups. In the segmentation based on male gender, we discover four variables, which con-

sist of IUIPC, tangible benefit, intangible benefit, and industry self-regulation as the significant predic-

tors to influence the willingness to disclose personal information. On the contrary, the female group

has only the tangible benefit as the significant predictor to influence the willingness to disclose per-

sonal information. These findings suggest that, in respect to the privacy disclosure, male population

has more complicated rationale than their female counterpart.

This conclusion is also found in the age segmentation, in which the young group has more complicated

rationale than their older counterpart. This suggestion is supported by the finding that the young

group has three significant predictors to influence the willingness to disclose personal information,

i.e., tangible benefit, intangible benefit, and industry self-regulation. On the other hand, the older

group has much simpler rationale by having only one predictor (i.e., tangible benefit) as the significant

influencer of the willingness to disclose personal information. Moreover, from the analyses of data

segregated based on demographic variables, we can conclude that the only variables influence the

human rationale in disclosing personal information are: gender, previous privacy experience, educa-

tion level, age, and self-efficacy level.

Page | 100

With the aim to test the hypotheses formed in Chapter 3, we recall the formed hypotheses in Table

3.6. From the aforementioned analyses, we can conclude that our hypotheses are partially supported

by our empirical study. The most complete supported hypotheses are found in the male group. De-

spite the privacy concerns level, the tangible benefit has been the major predictor that appears in

almost all analyses (except the reject to disclose salary information group). From the above mentioned

statistical analyses, Table 5.17 draws the conclusion of our hypotheses testing.

Table 5.17. Hypotheses testing summary

Hypothesis Generic

Gender Salary Previous Experi-

ence of privacy in-trusion

Education Age Self-effi-

cacy

Male Female Low High Reject to dis-close


H1a

H1b

H2a

H2b

H3a

H3b

H3c

Dependent Variable: Willingness to disclose personal information Green shading: accept the hypothesis; white shading: reject the hypothesis Red shading: the finding shows a significant but reversed direction result from the hypothesis

Page | 101

5.3 Cluster Analysis To answer the SQ.4, i.e., How can the users be segmented based on the privacy concern mitigation

effort?, the general segmentation is performed to all of the cases to identify the possible market seg-

mentation in the population. Recalling the privacy decision making discussion in chapter 3.4, in which

we want to mitigate the users’ privacy concerns using both utilitarian approach and non-utilitarian

approach (see Figure 3.1), we include all privacy mitigation strategies as the group properties of the

cluster, which are:

1. Privacy benefits strategy, which consists of providing tangible benefits and intangible bene-

fits, and

2. Privacy assurance approach strategy, which consist of the likelihood of users to perform indi-

vidual self-protection (ISP) (which is also known as Individual privacy protection responses –

IPPR), demand for industry self-regulation (ISR), and demand for governmental regulation

and legislation (GRL). This strategy aims to ensure the users’ privacy right.

Due to the atheoriecal property of cluster analysis in deciding the number of cluster formed, we per-

form an iterative method to set the number of cluster in our analysis. Firstly, we set 9 clusters (see

paragraph 3.5 for the cluster nomological structure) as the initial possible cluster as we understand

that forming more than 9 clusters will be hard to profile. When the analysis forms a cluster with less

than 2 members OR less than 1% size, we regard that finding as the indication of an overestimated

number of clusters formed in the analysis. Then, we reduce the possible cluster by one, and so on,

until we have no formed cluster that has less than 2 members OR 1% size.

The next paragraph (paragraph 5.3.1) explains the generic cluster analysis that is applied to the whole

cases to segment the population. Furthermore, the clusterization based on the demographic variables

segregation are elaborated on in the paragraph 5.3.2. Finally, the conclusion of the cluster analysis is

formed in the last paragraph (paragraph 5.3.3).

5.3.1 Generic cluster analysis The first clusterization is performed to the whole cases to make segmentation to the population. We

find 4 significant clusters in the segmentation. The first cluster is profiled as Pure utilitarian as the

people in this segment regard only utilitarian type of strategies (both tangible and intangible) to be

able to mitigate their privacy concerns. The second cluster is profiled as Total solution seeker because

this cluster regards all mitigation strategies are important to mitigate the privacy concerns. The third

cluster is profiled as Strong utilitarian. This cluster regards all utilitarian privacy mitigation strategies

are important, whereas only partial privacy assurance approaches are regarded as important, i.e., the

industry self-regulation and government regulation and legislation, as equally important means to

mitigate the privacy concerns. Finally, the fourth cluster is profiled as Total solution seeker because

the people in this cluster perceive all type of privacy mitigation strategies are important. Figure 5.1

displays the graphical properties of each cluster.

Page | 102

Figure 5.1. Final cluster properties

From the graphical depiction of cluster properties, we understand that the second and the fourth

cluster has the same profile, albeit they have different importance magnitude on each strategy. To

calculate the potential market segment, we combine the second and fourth cluster into a single Total

utilitarian cluster, and we therefore have three final clusters in total, which will be analyzed as follows:

Firstly, the pure utilitarian group forms the smallest potential market share with 1.1% size. This may

suggest if a company provides only utilitarian type of strategy only (both tangible and intangible ben-

efits), it may only capture 1.1% of total population.

Secondly, the strong utilitarian group accounts for 10.2% potential market share. This finding suggests

that a company that performs a type of privacy assurance strategy in addition to utilitarian strategies

will have the opportunity to capture (at least) 10.2% market share in the market. Finally, the total

solution seeker, which has been the largest cluster formed in the analysis. This cluster forms 88.7%

potential market share in the population. This finding suggests that the company that wants to target

the largest fish should prepare all type of strategies prior to the users’ personal data acquisition. Figure

5.2 summarizes the potential market share composition in a pie chart.

Page | 103

Figure 5.2. Groups’ potential market share

5.3.2 Cluster analysis to the segregated data based on demographic variables After we conducted the general cluster analysis to all of the cases and produced the generic clusters,

we performed the cluster analyses specific to the segregated data based on the demographic varia-

bles. The clusterization results of the segregated data are useful to help a company that wants to

target a specific demographic market to prepare an appropriate privacy mitigation strategy to this

market. For example: a company that markets male specific products and personalization services to

male population can make use of the clusterization result of male population in order to help the

company to prepare an appropriate privacy mitigation strategy; or a company that markets innovative

products and services that offers privacy enhancing technology as value differentiator (targeted to

high privacy concerned population) can use the clusterization result of the population with a negative

privacy experience.

We use the same grouping method as we did on the multiple regression analyses to group the cases

into segregated groups. Next, we perform the cluster analysis, cluster profiling, and cluster composi-

tion analysis to each formed group. Table 5.18 summaries the clusterization based on control variables

segregations results, while the detail profile of each cluster is elaborated in Appendix 5.2 Cluster

properties of data segregated based on demographic variables.

Pure utilitarian, 1.1%

Total solution seeker, 88.7%

Strong utilitarian, 10.2%

Generic cluster analysis result

Page | 104

Table 5.18. Potential market size

Cluster name Ge-

neric model

Gender Salary Previous Experi-

ence of privacy in-trusion

Education Age Self-efficacy

Average Size

ranking Male Female Low High

Reject to dis-close


Total solution seeker

88.7% 90.1% 100% 90.4% 98.3% 94.9% 88.0% 95.3% 98.1% 92.5% 98.6% 92.5% 100% 96.6% 94.6% 1st

Strong utilitar-ian

10.2% 6.3% 1.8% 4.7% 6.1% 7.5% 3.4% 5.7% 3rd

Pure utilitarian 1.1% 1.7% 1.4% 1.4% 1.4% 6th

Strong privacy right seeker

1.8% 7.8% 12.0% 9.4% 7.8% 2nd

Combined strategy seeker

1.9% 1.9% 5th

Weak utilitar-ian

NA NA

Pure privacy right seeker

1.8% 5.1% 3.5% 4th

Weak privacy right seeker

NA NA

Indifferent group

NA NA

Statistic summary

Number of cases

265 111 154 166 60 39 75 190 52 213 212 53 146 119

Legend: Green shading: equally combined type strategy; Blue shading: utilitarian dominant strategy; Brown shading: privacy right assurance dominant strategy

Page | 105

5.3.3 Conclusion The specific question aimed to be answered in this section is SQ.4, i.e., How can the users be segmented

based on the privacy concern mitigation efforts? The previously analyzed cluster profiling matrix suggest

us to form 9 clusters, which are named and defined as portrayed in Table 3.5. However, there are only 6

significant clusters formed from the analyses, which are:

1. Total solution seeker,

2. Strong utilitarian,

3. Pure utilitarian,

4. Strong privacy right seeker,

5. Combined strategy seeker, and

6. Pure privacy right seeker.

Our findings, which is summarized in Table 5.18, conclude that the total solution seeker appears as the

most dominant cluster in population. This cluster always holds the biggest potential market share, not

only in generic clusterization, but also in every segregated cases (based on the demographic variables).

This finding suggests that the data acquirer company must be ready to prepare the all mitigation strategies

if it wants to get the biggest market share in the market (regardless which segment it wants to target).

However, we must note that this finding is logical as the presentation of question may induce the respond-

ents to choose all the strategy as the appropriate strategy due to the absence of trade-offs when making

any choice. From this limitation, we performed the conjoint analysis to incorporate trade-offs and present

a better outcome as the complement to the clusters formed.

Also, the finding suggests that the majority of the population demands their privacy to be assured as well

as to be “bought” (both directly using tangible benefit or indirectly using intangible benefit) before they

share their personal information to the data acquirer company. It also concludes that the majority of the

people still perceive their privacy as a right in accompaniment to their privacy perception as an interest.

Furthermore, the average size of the privacy right assurance dominant strategy is formed bigger than the

utilitarian dominant strategy. This suggests that more people are willing their privacy to be “assured” only

rather than to be “bought” only.

Noteworthy results are found in the finding, in which no weak utilitarian cluster and weak privacy right

seeker cluster are formed. This finding concludes that no one wants only partial type of strategy only

(partial utilitarian type strategy without any right assurance strategy, or partial right assurance strategy

without any utilitarian type strategy) to applied to mitigate the privacy concerns. Furthermore, the finding

also concludes that no indifferent group is formed, which suggests that everyone in the population wants

their privacy concerns are mitigated before disclosing his/her personal information.

5.4 Conjoint Analysis After we have the answer of how to increase personal data disclosure and to which group a certain miti-

gation strategy is applied from the prior analyses, we now want to answer the how much question, par-

ticularly related to the tangible type of mitigation strategy. By employing Biogeme (Bierlaire, 2003), we

estimate the coefficients of each variable/attribute involved in the conjoint analysis. This estimation result

is formed as Betas which then are multiplied by their respective variable and summed up to calculate the

total utility value. Additionally, the general level utility is also calculated by capturing the constant/inter-

cept in the estimates. However, as we have set the PLS disclosure in the default value (see section 4.3.2

Page | 106

Personal data disclosure definition and attribute selection), the constant/intercept value will also describe

the part-worth utility value of PLS.

Recalling the discussion in chapter 4.3.3 Choice set design, we propose the effect coded utility function of

using personalized Ojek online service as:

𝑈 = 𝑎𝑠𝑐1 + 𝐵_𝑝𝑑𝑖 ∗ 𝑝𝑑𝑖 + 𝐵_𝑝𝑏𝑎 ∗ 𝑝𝑏𝑎 + 𝐵_𝑒𝑠0 ∗ 𝑒𝑠0 + 𝐵_𝑒𝑠1 ∗ 𝑒𝑠1 + 𝐵_𝑒𝑠2 ∗ 𝑒𝑠2

Equation 5.3. Utility function with effect coding for variable es

After we run the estimation and produce all estimated coefficients, we translate back the effect coded

estimation to its initial (original) utility function, which is defined as:

𝑈 = 𝑎𝑠𝑐1 + 𝐵_𝑝𝑑𝑖 ∗ 𝑝𝑑𝑖 + 𝐵_𝑝𝑏𝑎 ∗ 𝑝𝑏𝑎 + 𝐵_𝑒𝑠 ∗ 𝑒𝑠

Equation 5.4. Utility function

Where:

U = Utility value

asc1 = constant which will capture the value of privacy of location and space (PLS) and the general

level of the service’s utility value

B_pdi = coefficient for privacy data and image (PDI) value

B_pba = coefficient for privacy behavior and action (PBA) value

B_es = coefficient for expected saving (es)

The following paragraphs explore the estimated coefficients as the output of the Biogeme software. The

generic estimated coefficients are elaborated in paragraph 5.4.1. Furthermore, we also estimate the co-

efficients according to the segregation of cases based on the control variables, which are elaborated in

the paragraph 5.4.2. Moreover, we also want to test the consistency of the coefficients to the segregated

cases based on initial perception of the willingness to disclose personal data as well as their perception to

each privacy mitigation constructs which will be explained in paragraph 5.4.3. Finally, the conclusion of

the analysis is formed in the paragraph 5.4.4.

5.4.1 Generic estimated coefficient In the generic model, we analyze all data without doing any segregation to the cases. The estimated MNL

model produces ρ2 square = 0.209, which indicates the model is a good fit model. All the estimated coef-

ficients show significance at the level of 95%. Table 5.19 summarizes the generic model estimation result.

Page | 107

Table 5.19. Estimated coefficients for generic model

Attribute Estimated coef-

ficients

B_es0 0.795

B_es1 0.97

B_es2 -0.508

B_pba -1.62

B_pdi -0.345

asc1 0.943

observation 2120

ρ square 0.209

Before we conduct the analysis to the estimates, we recode the above-mentioned estimated coefficients

to the original utility function (Equation 5.4. Utility function).

As we have set the disclosure of PLS as the default value (see Table 4.12. Privacy type operationalization

attributes), the general level of utility of the service will be intertwined with the part-worth utility value

of PLS. Thus, we cannot clearly segregate the part-worth value of PLS and the initial general level of Ojek

online service utility. Figure 5.3 depicts the estimated coefficients as the result of the recoded estimation.

Figure 5.3. Graphical presentation of the estimated coefficients

Page | 108

After we estimate the coefficients, we calculate the importance level of each attribute compared to an-

other. We use their range value of the part-worth utility and compare them to the overall utility value of

the service. Table 5.20 summarizes the recoding variables, estimates, importance level and the im-

portance ranking of the conjoint analysis.

Table 5.20. Recoded coefficients and the calculation of the importance

Attribute Estimated coeffi-

cients Range of part-

worth value Importance

Importance ranking

B_pba -1.62 1.62 32% 2nd

B_pdi -0.345 0.345 7% 4th

asc1 0.943 0.943 18% 3rd

Exp

ect

ed

sav

ing

(ID

R/m

on

th)

0 -1.257 2.227 43% 1st

90 -0.508

180 0.97

270 0.795

From the above mentioned coefficients and the graphical presentation, we understand that the part-

worth utility value of expected saving is saturated at IDR 180. Therefore, we use 180 value as the maxi-

mum range of providing monetary saving. (Later in the analyses, we also found that this finding is con-

sistent to the whole analyses on segregated cases based on both control variables and the initial percep-

tion of privacy mitigation strategies – supported by ANOVA test to the part-worth utilities of the 180 and

270 value that shows no significant difference between them).

After we calculate the part-worth importance of each attributes, we conclude that the expected monetary

saving has been the most important predictor deciding the utility value of the Ojek online personalization

service. Among the privacy attributes, we also found that PBA is the most important privacy type to pre-

dict the utility value of using a personalized Ojek online service. With its negative value, PBA appears as

the significant predictor to measure the utility value in providing a personalization service to the Ojek

online users. Furthermore, the positive sign is found in the asc1 value, which captures the general level of

the service’s utility value and the part-worth utility of PLS. This finding suggests that disclosing a location

information only still gives a positive overall utility value of using the service (ceteris paribus).

Additionally, from those coefficients, we want to calculate the buy-off value of each privacy type. To do

that, we calculate the Rupiah money per utility value of the ES variable, by dividing the range of WTA (IDR

180 – IDR 0) with the range part-worth utility value of the expected saving for the respected rupiah money

(0.97 – (-1.257)), which equals to IDR 80.83/utility value. By using this conversion, we can calculate the

buy-off value of each type of privacy.

From the calculation, we understand that the most “expensive” privacy type is the PBA, which equals to

IDR 131K (≈ 5 times average Ojek online trip). The PDI scores IDR 28K which equals to 1-time average Ojek

online trip. As the PLS part-worth utility and the general level utility of the service is entangled, we cannot

calculate the buy-off value of PLS. Nevertheless, we found a negative buy-off value of PLS and general

level utility of the service which concludes that by offering the basic ridesharing service (pickup and drop

Page | 109

off only), the company can acquire the users’ PLS information without performing any other additional

measures. Table 5.21 summarizes the buy-off value of each privacy type.

Table 5.21. Buy-off value of each privacy type

Item Buy-off Value (IDR)

Rupiah per utility

value

80.826

PBA 130.938

PDI 27.885

PLS + general level

utility (GLU)

-76.219

The above mentioned result suggests that the data acquirer company should prepare IDR 130.938 (≈

€9.03) per month to “buy” the users’ behavioral information and IDR 27.885 (≈ €1.92) per month to “per-

suade” the users to allow the company to use their personal data as secondary uses.

5.4.2 Estimation to the segregation data based on demographic variables In addition to the estimates to the overall population, we also conduct segregations based on our previ-

ously defined demographic variables. This segregation is useful to measure the consistency as well as

understand the perception of the specific population related to their privacy ranking and its buy-off value.

In certain group segregations, we found the model could not determine the coefficient of the PDI due to

insignificance in the estimation results. These groups are: high salary group and old group. We then ex-

clude this attribute from the further calculation in those groups. Likewise, after we estimate all the coef-

ficients, we calculate the buy off value of each privacy type. Table 5.22 and Table 5.23 summarize the

estimation results and the buy-off value calculation from the cases that are segregated based on control

variables.

Page | 110

Table 5.22. Coefficient estimation based on demographic variables segregations

Beta11

Gender Salary Previous experience Education Age Self-efficacy

Male Female Low High Reject to dis-close


B_pba -1.48 -1.75 -1.61 -1.51 -1.89 -1.18 -1.8 -1.77 -1.59 -1.64 -1.56 -1.54 -1.71

B_pdi -0.51 -0.207* -0.369 NA -0.618 -0.521 -0.276 -0.39* -0.336 -0.405 NA -0.441 -0.24

asc1 0.861 1.01 1.1 0.555 0.947 0.742 1.03 0.951 0.943 1.04 0.556 1.24 0.617

Exp

ect

ed

savi

ng

0 -0.983 -1.473 -1.38 -0.988 -1.242 -1.045 -1.339 -1.522 -1.198 -1.281 -1.176 -1.45 -1.05

90 -0.385 -0.621 -0.506 -0.456 -0.598 -0.656 -0.455 -0.568 -0.494 -0.508 -0.494 -0.429 -0.623

180 0.785 1.12 1.07 0.684 1.02 0.83 1.03 1.09 0.944 1.03 0.724 1.01 0.932

270 0.583 0.974 0.816 0.76 0.82 0.871 0.764 1 0.748 0.759 0.946 0.869 0.741

Estimation data

observation 888 1232 1328 480 312 600 1520 416 1704 1696 424 1168 952

ρ square 0.169 0.248 0.23 0.163 0.239 0.182 0.224 0.247 0.201 0.22 0.186 0.237 0.198

Table 5.23. Buy-off value of privacy based on control variables segregations

Buy-off value (IDR .000)

Gender Salary Previous experience Education Age Self-efficacy

Male Female Low High Reject to disclose


PBA 151 121 118 163 150 113 137 122 134 128 148 113 155

PDI 52 14 27 NA 49 50 21 27 28 32 NA 32 22

PLS + GLU -88 -70 -81 -60 -75 -71 -78 -66 -79 -81 -53 -91 -56

11 * indicates significant at 0.1; NA indicates the estimate is not significant at 0.1

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5.4.3 Estimation to segregated data based on initial perception of WTD, privacy

concerns, and the privacy mitigation construct In addition to the analyses of demographic variables’ influence to the conjoint analysis results, we also

wanted to investigate whether the initial perceptions of the willingness to disclose level and the privacy

concerns level are influencing the importance perception of the users’ privacy type as well as its buy-off

value. Additionally, we also want to investigate whether the users’ perception towards certain privacy

mitigation strategy influence their perception of privacy rankings and its buy-off value.

To do that, we re-classified the cases by calculating the standardized privacy mitigation strategy variables,

then grouped the cases that scores lower than 0 (zero) to the relatively low perception group and higher

than 0 to relatively high perception group. By this ruling, we can have (almost) equal number cases for

each estimation.

After we re-classify the cases, we run the program to estimate the coefficients in each class. Next, we

calculate the buy-off value of each privacy. Table 5.24 and Table 5.25 summarizes the estimation results

and the buy-off value of privacy calculations.

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Table 5.24. Coefficient estimations based on privacy constructs segregations

Beta Privacy concern Perception to benefits Willingness to disclose

Individual self-pro-tection

Industry self-regula-tion

Government regulation

Low High Low High Low High Low High Low High Low High

B_pba -1.74 -1.5 -1.66 -1.6 -1.49 -1.76 -1.7 -1.55 -1.65 -1.6 -1.7 -1.57

B_pdi -0.379 -0.316 -0.324 -0.372 -0.302 -0.39 -0.388 -0.317 -0.366 -0.327 -0.3 -0.381

asc1 1.05 0.833 1.01 0.891 0.902 0.989 1.02 0.883 1.07 0.807 1.07 0.858

Exp

ect

ed

savi

ng

0 -1.344 -1.173 -1.461 -1.077 -1.152 -1.364 -1.42 -1.132 -1.475 -0.991 -1.51 -1.085

90 -0.465 -0.557 -0.576 -0.446 -0.411 -0.612 -0.446 -0.555 -0.565 -0.439 -0.452 -0.552

180 0.936 1.01 0.987 0.961 0.893 1.05 1.05 0.907 1.04 0.884 1.03 0.932

270 0.873 0.72 1.05 0.562 0.67 0.926 0.816 0.78 1 0.546 0.932 0.705

Estimation data

observation 1096 1024 1056 1064 1024 1096 944 1176 1216 904 896 1224

ρ square 0.223 0.197 0.238 0.187 0.181 0.238 0.227 0.196 0.243 0.174 0.236 0.194

Table 5.25. Buy-off value of privacy based on privacy constructs segregations

Buy-off value (IDR .000)

Privacy concern Perception to benefits Willingness to disclose Individual self-protec-

tion Industry self-regula-

tion Government regula-

tion

Low High Low High Low High Low High Low High Low High

PBA 137 124 122 141 131 131 124 137 118 154 120 140

PDI 30 26 24 33 27 29 28 28 26 31 21 34

PLS + GLU -83 -69 -74 -79 -79 -74 -74 -78 -77 -77 -76 -77

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5.4.4 Conclusion This section has analyzed how the users rank their privacy and how much they are willing to accept to give

out this privacy. From the abovementioned analyses, we can conclude that the potential saving generated

by the personalization service has been the most significant predictor to measure the total utility value of

the service. This finding suggests that the utility value of personalization service is mainly generated by

the utilitarian aspect of the service and not the personal data involved in generating this service.

Focusing on the personal data involved in offering the service, we analyze each type of privacy. Firstly, the

privacy of behavioral and action, i.e., “the ability to behave in public, semi-public or one’s private space

without having actions monitored or controlled by others” (Finn et al., 2013, p. 8), which is operationalized

by submitting the users’ buying behavior to the company. The users value PBA as the most valuable pri-

vacy with 32% predictive power to measure the total utility value of the personalization service. Further-

more, the PBA part-worth utility value is higher than the highest possible part-worth utility value of the

expected saving. This finding is consistent not only in the generic estimation, but also in all over segrega-

tion analysis. To acquire users’ behavioral data, the data acquirer company should prepare (on average)

IDR 133K (≈ €9.2) per month.

The next valuable privacy type is the PDI, i.e., the “concerns about making sure that individuals’ data is

not automatically available to other individuals and organizations and that people can exercise a substan-

tial degree of control over that data and its use” (Finn et al., 2013, p. 8), which is operationalized by the

willingness of receiving advertisement from other companies. The finding suggests that PDI can be ac-

quired by (on average) IDR 29K (≈ €2) per month. Lastly, the privacy of location and space, i.e., “the right

to move about in public or semi-public space without being identified, tracked, or monitored” (Finn et al.,

2013, p. 9). As we have set the disclosure of PLS as the default value of using the transportation service,

the value of PLS cannot be derived individually without incorporating the general level utility of the ser-

vice. Thus, we can only compare the PLS with the general perception with the basic utility service (i.e.,

pickup and drop off points). Our findings conclude that the PLS can be acquired by the use of the basic

transportation service only, without offering any additional effort. This might be caused by the needs of

users to enjoy the ride service as accurate as possible (related to the pickup and drop off location).

From the findings in the control variable cases segregation, we discover that there is no outlier found in

the findings. The findings suggest that the privacy buy-off values are quite consistent among the grouping

results. Therefore, we may conclude that the demographic properties of users do not influence the privacy

valuation. The graphical representation of this analysis is portrayed in Figure 5.4.

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Figure 5.4. Graphical presentation of privacy buy-off value to the segregated data based on control variables

Looking into estimation results in the segregated cases based on initial perception of WTD, privacy con-

cerns, and the privacy mitigation construct, we could not find any outlier in the findings. The findings

suggest that the privacy buy-off values are consistent across the segregations based on the privacy miti-

gation strategy constructs. Therefore, we can also conclude that the items in the privacy mitigation strat-

egy constructs do not influence the WTA of the privacy. The graphical representation of this analysis is

portrayed in Figure 5.5.

Figure 5.5. Graphical presentation of privacy buy-off value to the segregated data based on privacy mitigation strategies

However, it should be noted that the operationalization of each privacy type is using a very specific con-

text in the P2P transportation service (i.e., pick up and destination point for PLS, buying behavior for PBA,

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and receiving advertisement from other company for PDI), thus the ranking of the privacy and its buy-off

value might be dissimilar if applied to other type of context.

5.5 Discussion From the above mentioned analyses, we found a contradictory behavior of users in disclosing their per-

sonal information. Even though the level of privacy concerns is high, the respondents do not mind to

disclose their personal information, regardless the presence of the security measures and privacy practice

performed by the company. This finding shares the same finding in prior research by Rohunen et al. (2014)

in the context of disclosing users’ driving behavior for the European open data initiative to Finnish popu-

lation. This argumentation is also strengthened by the exclusion of two ISP items from the analysis due to

their failure to meet the statistical sufficiency requirement (i.e., the refusal to disclose and anonymity

which do not pass the MSA threshold – see paragraph 4.2.2.3). By these exclusions, we can suggest that

even though people have high privacy concerns level, they still bluntly disclose their personal data to the

company that wants to acquire these data without even masking their personal data. Therefore, the pri-

vacy concerns do not significantly influence the willingness to disclose personal information.

Combining the multiple regression analysis results with the descriptive statistics, we can conclude that

giving intangible benefit partly contributes to the willingness to disclose personal information. Further-

more, the usefulness of the application is a more preferred intangible benefit than the personalization,

which in fact is the least preferred benefits chosen by the customers. Yet, the personalization is still per-

ceived as an important benefit of disclosing their personal data (suggested by the 5.96 mean value).

Surprising results are found in our study. Compared to the privacy empirical studies in developed coun-

tries, the predictors of the willingness to disclose of personal information in the emerging country is a lot

different. In the developed countries, the privacy concerns were the major predictor to the willingness to

disclose personal information (for example: Dinev and Hart (2006), Awad and Krishnan (2006), Kehr et al.

(2015), Taylor et al. (2009), Li and Unger (2012), and Liu et al. (2011)); whereas our findings suggest that

even though the privacy concerns level is high in general, they do not become the major predictor for the

willingness to disclose personal information. In other words, the high level of privacy concerns indiffer-

ently influences the users to disclose their personal information to the data acquirer company.

Additionally, our research also concludes that people with high self-efficacy level do not have high level

of willingness to disclose personal data. In fact, their willingness to disclose is lower, albeit insignificant,

than their low self-efficacy counterpart. This conclusion is contradictive with Keith et al. (2015), in which

they find the self-efficacy level positively correlates to the willingness to disclose personal data with the

mediating effect of perceived risk. The most probable explanation on this finding is the users who have

higher level of efficacy are more aware about the risks and harms of disclosing personal information than

their lower efficacy counterpart.

Moreover, the users’ previous privacy experience also does not influence the users’ willingness to disclose

information. In fact, only the salary variable has a significant effect on the willingness to disclose personal

information. People who have a higher level of income are less likely to disclose their personal infor-

mation. This result is supported by the ANOVA table in Appendix 5.3 Mean difference of the WTD vs.

demographic Variables. However, we should note that the context of the study is a P2P ridesharing ser-

vice, in which high salaried people seldom become the loyal user of this service. Furthermore, this finding

might be biased as we limit the population to the people who are aware of the P2P ridesharing concept.

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In general, we may suggest that the insignificance result of privacy concerns and privacy experience to

predict the users’ willingness to disclose personal information might be caused by technophilia and eu-

phoria of users to try an innovative services offered by the company. We base this suggestion on Schor

and Fitzmaurice (2015), in which they present that technophilia and euphoria is one of the users’ motive

to collaborate in the ridesharing service. Additionally, we may also suggest that the users’ perception of

privacy concerns is already influenced by the power of persuasion and marketing. After the service was

introduced in the market in 2014, the mainstream communication medias often advertise the Ojek online

service in their prime spots (for example: liputan6.com (2016) and cnnindonesia.com (2015). This may

also elevate the users’ perception to the usefulness of the service which makes the users ignore their

initial privacy concerns. We base this suggestion to the research by Acquisti et al. (2009) and Acquisti,

Brandimarte, et al. (2015) which suggest that the privacy can be malleable by using persuasion of social

guidance (which can be influenced by the communication media marketing and word of mouth).

A noteworthy finding is found in the relationship between privacy assurance approaches variables (i.e.,

Individual self-protection, industry self-regulation, and government regulation and legislation) and the

privacy concerns variables, i.e., Internet users’ individual privacy concerns (IUIPC) and concerns for infor-

mation privacy (CFIP). We identify strong correlations between IUIPC and privacy assurance approaches

and between CFIP and privacy assurance approaches in the cross correlation table. We then conduct mul-

tiple regression analyses with IUIPC and CFIP as the dependent variables and ISP, ISR, and GLR as the

independent variables. However, the test results indicate only IUIPC is significantly explained (direct, pos-

itive relationships) by the whole privacy assurance approach construct. Nevertheless, a theoretical inves-

tigation and explanation are needed to investigate this relationship further

Looking into both multiple regression results and the cluster analysis results, we understand that even

though the average size of the privacy right assurance dominant strategy is formed bigger than the utili-

tarian dominant strategy, the dominant influential strategy to increase the users’ willingness to disclose

personal data is the utilitarian type strategy. It may suggest us that assuring the privacy right (by using

privacy assurance approaches) is not used to increase the users’ personal data disclosure, but it should be

the “default mode strategy” offered by the company.

The abovementioned suggestion is also strengthened by the finding in the multiple regression analyses,

in which we found only some privacy mitigation strategies that significantly influence the users’ willing-

ness to disclose personal information, but yet, the dominant cluster formed in the cluster analysis is the

total solution seeker. By this findings, we conclude that: in general, the population want their privacy is

assured as the prerequisite condition before the company acquires their personal data (regardless what

strategy is used to mitigate the users’ privacy concerns).

However, we must note that the presentation of instrument questions to perform the cluster analysis is

tendentious to make the respondents to choose all appropriate strategy without any expense (no trade-

offs are presented when making choice). Therefore, we perform the conjoint analysis as the complement

to the previously elaborated cluster analysis, particularly to present the trade-offs to the respondents.

Turning now to the elaboration of the conjoint analysis results, we concluded that privacy of behavior and

action (PBA) is the most precious privacy type between PBA, privacy of data and image (PDI), and privacy

of location and space (PLS) according to the population. From the descriptive statistic data and the con-

joint analysis result, however, we discovered that even though the unauthorized secondary use of per-

sonal information holds the highest privacy concerns value, a very minimal amount of money (IDR 29K ≈

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€2) is required to buy the PDI. Furthermore, this conclusion is consistent across the segregation based on

control variables as well as the initial perception of WTD, privacy concerns, and the privacy mitigation

construct. This contradictory behavior might also be related to the technophilia (Schor & Fitzmaurice,

2015) and the malleability of privacy concerns (Acquisti, Brandimarte, et al., 2015; Acquisti et al., 2009).

To recap the discussion, we create Table 5.26 to summarize the abovementioned analyses. This table

combines the results of the hypotheses testing, the dominant cluster formed in the cluster analysis, and

the privacy buy-off values calculated from the conjoint analysis. Additionally, we also incorporate the total

utility values of personalization (generated by calculating the part-worth utility of PBA, PLS + GLU, and the

maximum part-worth expected saving), to capture the users’ perception to the personalization service in

P2P ridesharing.

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Table 5.26. Research strategic summary12

Items Generic model (100%)



Male (42%)

Female (58%)

Low (62.6%)

High (22.6%)

Reject to disclose (14.7%)

Positive (28%)

Negative (72%)

Low (20%)

High (80%)

Young (80%)

Old (20%) Low (88%)

High (12%)

Biggest cluster and size

Total so-lution seeker 86.8%







Total solu-tion seeker 88.4%







Influential variable(s) to increase WTD

Tangible benefit GRL

IUIPC Tangible benefit Intangible benefit ISR

Tangible benefit

Tangible benefit

Tangible benefit

Intangible benefit GRL

Tangible benefit ISP

CFIP Tangible benefit

Tangible benefit ISR

Tangible benefit Intangible benefit

Tangible benefit Intangible benefit ISR

Tangible benefit

Tangible benefit

IUIPC Tangible benefit

Privacy buy-off value

PBA 131 151 121 118 163 150 113 137 122 134 128 148 113 155

PDI 28 52 14 27 NA 49 50 21 27 28 32 NA 32 22

Total utility value of using P2P personalization service without reselling the data (using only PBA, PLS + GLU, and 180K monetary saving)

Utility value

0.293 0.166 0.38 0.56 -0.271 0.077 0.392 0.26 0.271 0.297 0.43 -0.28 0.71 -0.161

12 WTD = willingness to disclose personal information; IUIPC = internet user’s information privacy concerns (Malhotra et al., 2004); CFIP = concerns for information privacy (Smith et al., 1996); ISP = individual self-protection (or IPPR (Son & Kim, 2008)); ISR = industry self-regulation (Xu et al., 2012); GRL = government regulation and legislation (Xu et al., 2012).

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From this table, we may suggest that the personalization service should not be offered to the high

salary group, the seniors group, or the high self-efficacy group, as the sum utility value of personaliza-

tion service in these groups shows a negative value. Moreover, we also found that the privacy buy-off

value of these groups is higher than their other counterparts. Thus, we may conclude that the people

on these groups value their privacy more than their other counterparts.

Also, the Table 5.26 also shows that the most-welcome groups to enjoy the personalization service

are the low salary group and the young group, in which the salary is strongly correlated to the age.

Both of these groups value personalization services with the highest level of utility value among the

others. As the personalization service requires the users’ behavioral data as its “raw material”, the

company should acquire these data before offering the personalization service. From the table, we

understand that both groups have the total solution seeker as the dominant cluster with more than

90% potential market share, thus it suggests the company to prepare a utilitarian strategy as well as

a privacy assurance strategy prior to the users’ data acquisition. However, to significantly increase the

groups’ willingness to disclose personal data, the company should prepare (at least) a tangible form

of benefit as a complement to the personalization service.

Moreover, the table shows the buy-off value of privacy (or the users’ WTA) in the form of the tangible

benefit. For the young users as example, with benefits valued as IDR 128K (≈ €8.82) per month, the

young users will willingly disclose their behavioral data to the company. Based on the descriptive sta-

tistic result, the most enjoyed benefit type is providing monthly discounts in using transportation ser-

vice. However, it does not close the possibility to offer the benefit in the form of bonus balance of

using P2P ridesharing service as well as a claimable voucher that can be redeemed at the partner

company in the P2P ridesharing ecosystem as these type of benefits is perceived positively by the

users. Additionally, if the company is also willing to resell the young users’ personal data, it only needs

to prepare an additional tangible form of benefit by IDR 32K (≈ €2.1) per month.

Furthermore, the table also serves additional purpose for a company who wants to offer a personali-

zation service that targets a specific type of customer. For example, if a company wants to offer per-

sonalization targeted at the high-school population (low educated population), it should prepare a

tangible form of benefit (with IDR 122K ≈ €8.4 value) as the complement to the personalization to

increase their willingness to disclose behavioral data. While, to further increase the users’ willingness

to disclose personal data, the company should also put a privacy seal in its mobile application.

Additionally, the abovementioned privacy valuation can be used to quantify the investment that need

to be prepared by multiplying these values to the market share targeted by the company. For example,

if a P2P ridesharing company in Indonesia wants to target 50% market share of Jakarta’s young popu-

lation (± 1.500.000 people) (Jakarta, 2015a), the company needs to prepare investment ± IDR 180

billion (≈ € 12.8 million) per month to acquire the users’ behavioral data. This number can be the

indication of how much the required investment in order to buy the raw material data to offer per-

sonalization to the young users.

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6 Concluding chapter

The final chapter of this thesis presents the results of our study. The first paragraph (6.1) provides the

conclusion of the research, which is written based on the summarization of the conclusion in the pre-

vious chapters. Also, this conclusion serves as the answer of our main research question. Next, para-

graph 6.2 elaborates the implications of this study to the previously built theory of privacy decision

making. Furthermore, the societal implications as well as managerial recommendation derived from

our findings are also provided in this paragraph. Next, paragraph 6.3 explains the limitations of our

study, which resulted from our choice of research strategy, statistical method preference, and popu-

lation and sample targeting. Finally, the recommendations for future research as well as possible im-

provements of our research are provided in paragraph 6.4.

6.1 Conclusion Information and communication technology (ICT) development has made previously unthinkable in-

novations become possible. Especially for the internet as a general purpose technology, it lowers the

barrier to entry of internet based business. Take a look at the P2P ridesharing service, which bases its

operations on the internet and the ubiquity of smartphones. The number of P2P ridesharing (digital)

platforms has been spreading exponentially lately as a proof that internet has lowered the barrier to

entry for such business opportunity. On the other hand, it will also make the competition become

tighter. Hence, this tense competition urges the platform owners to innovate further so it can sustain

and grow its business in the market. One of the opportunities to do that is by offering personalization,

which heavily uses personal data as the “raw material”.

With the development of tracking devices, social media, and positioning systems, ICT makes users’

personal data captures become easier. Thus, the use of personal data has also been increasing by this

data capture easiness, which indeed will increase the privacy concerns of the users and resist users

from giving out their personal data. On the one hand, the company needs to have the users’ personal

data as the raw material to offer additional value. On the other hand, the users have the right to reject

any personal data disclosure performed by the company. Nevertheless, the recent ICT development

has also opened another view of privacy as tradable interest which can be bought by the company by

giving additional benefits.

From this rationale, this research is conducted with the objective to make recommendations to the

peer-to-peer ridesharing platform provider in the effort of mitigating the privacy disclosure issue, by

investigating the general model of privacy mitigation strategy, segmenting the users, and matching

the mitigation strategy to each segment with respect to the view of privacy as a right as well as an

interest.

To support the objective, we develop the following main research question:




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The aforementioned objective is explored under the context of the P2P ridesharing service in the In-

donesian market. Therefore, to provide a clear understanding of the context, the elaboration of P2P

ridesharing service and its ecosystem and implementation in the emerging markets are provided.

Furthermore, to provide a solid answer to the main research question, we structure the RQ into sev-

eral sub-questions, in which the answers of these sub-question will build up the answer of the main

research question.

SQ1. What is the peer-to-peer ridesharing service and how is the elaboration of the concept and

its application in the Indonesian emerging market?

Chapter 2 has elaborated the P2P ridesharing concept as the derivative form of collaborative con-

sumption concept implemented in the personal transportation field. Initially, the motive of collabo-

rating in the system was the utopian motivation, such as: to create a greener environment and benev-

olent to help the others. However, in the recent days, the motivations of collaborating in the system

have shifted toward more utilitarian and hedonistic motives.

The Indonesian P2P ridesharing concept has emerged exponentially by the help of ubiquity of

smartphone and development of internet access, which make the concept becomes widely available.

ICT development has elevated the innovative potential of a ridesharing concept. By using a digital

platform, the ridesharing ecosystem can enjoy the loosely coupled potential so it can extend the eco-

system without the need to modify the overall ecosystem. However, as the ecosystem becomes more

complex, the users’ personal data involved in the ecosystem will be threatened, and it will elevate the

privacy concerns of the users. Therefore, before the company starts acquiring and exploiting the users’

personal data, it needs to prepare appropriate privacy assurance measures as well as additional value

benefit to the users in return to their disclosed personal data.

To further explore the privacy and types of personal data involved in the ridesharing as well as the

users’ privacy decision making rationale, we formed the following SQ2 as follows:

SQ2: What is privacy, what are the constructs that build up the privacy decision making theory,

and how can the company prepare the implementation of privacy mitigation strategy?

Chapter 3 elaborates the definition of privacy, the privacy decision making theory as well as the con-

structs that build this theory. We conclude that privacy can be defined both as a right and as an inter-

est. Seeing privacy as a right means that the data subjects still see the privacy assurance is the right

they need to have, whereas seeing privacy as an interest means that data subjects start to see privacy

as a tradable interest that can be used as a means to get certain benefits. The mainstream privacy

decision making theory bases the rationale in the cost and benefit analysis. The cost part of the ra-

tionale is represented by the privacy concerns and the associated privacy risks that need to be given

up by the data subjects, whereas the benefit part is represented by (additional) benefit that they may

get from their disclosed personal data.

The cost and benefit analysis presumes that the cost and the benefit are free from uncertainty. How-

ever, this condition never be the case in the real products/services available in the market. The real

market always introduces incomplete information, bounded rationality, and deviation from rational

strategy in the process of cost and benefit evaluation (Acquisti & Grossklags, 2005; Waldo et al., 2007).

Furthermore, the context dependencies and malleability of perception of privacy further complicate

the individuals’ decision making process (Acquisti, Brandimarte, et al., 2015). All these conditions may

limit the individuals’ from getting the maximum utility they may have.

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Based on the aforementioned analyses, we argue that putting the utilitarian approach only to mitigate

the privacy concerns is problematic. Therefore, we develop our privacy decision making framework

by combining the utilitarian approach and non-utilitarian approach as the base rationale in order to

disclose personal data. We include the privacy assurance approaches as the non-utilitarian approach,

including: (1) individual self-protection which is developed from individual privacy protection re-

sponses (Son & Kim, 2008), (2) industry self-regulation, and (3) government regulation and action (Xu

et al., 2012).

In regard to the privacy decision making rationale, we hypothesized that the users’ willingness to dis-

close personal data will be influenced by (1) the users’ privacy concerns, in which we elaborate this

construct into two sub-constructs, i.e., the Internet users’ individual privacy concerns (IUIPC) and con-

cerns for internet privacy (CFIP); (2) the privacy benefits offered by the company, in which we detail

the construct into two type of benefits, i.e., tangible and intangible benefit; (3) the privacy assurance

approach that accentuate the privacy control, in which we detailed the measures into individual self-

protection as the active control part and industry self-regulation and government regulation and leg-

islation as the passive control part.

Furthermore, with regard to the abovementioned mitigation strategies, the company can prepare

their actions which can be broken down as follows: Firstly, by offering tangible benefits the company

needs to prepare cash to “buy” the users’ personal data. This cash can be distributed directly as: sign

up bonus (Li et al., 2010; Xu et al., 2009), or indirectly as: discount in monthly fee (Derikx et al., 2016).

Secondly, by offering intangible benefits, the company should prepare an investment in technology to

offer a more efficient and useful service to its customer, in order to acquire their personal data. This

technology is developed in order to offer additional value that can be in a form of: personalization

service (Li & Unger, 2012; Liu et al., 2011; Xu et al., 2011) and service accuracy (Lee & Kwon, 2015).

Thirdly, to ensure the individual users control (ISP), the company should prepare an excellent customer

service department, since the users with high ISP level incline to make complaints often both directly

or indirectly via any medias available (email, phone call, and social media) (Son & Kim, 2008). Fourthly,

to implement ISR, the company needs to prepare a proven privacy policy and governance and hire 3rd

party auditor to audit and standardize the organization’s privacy practice (Xu, 2007; Xu et al., 2011;

Xu & Teo, 2004; Xu et al., 2009). Finally, the company should also urge the government to issue an

adequate personal data protection law (Xu, 2007; Xu et al., 2011; Xu & Teo, 2004; Xu et al., 2009).

Nevertheless, the privacy valuation is also important to quantify and measure the investment needed

or should to be prepared in order to perform the aforementioned strategies.

Additionally, we explored the multi-dimensional aspects of privacy. We argue that, in respect to the

recent technology developments, the multi-dimensionality of privacy is well fitted to Finn et al. (2013)

privacy categorization. In summary, Finn et al. (2013) privacy categorization consists of: Privacy of

the person, Privacy of behavior and action, Privacy of communication, Privacy of data and image,

Privacy of thought and feeling, Privacy of location and space, and Privacy of association (including

group privacy).

Based on the aforementioned context of study, we identify that there are three types of privacy that

are closely associated with the P2P ridesharing service. Firstly, the privacy of behavior and action

(PBA) which is related to the effort to provide personalization to the customers. Secondly, the privacy

of location and space (PLS) which is needed to the service fulfilment, i.e., the user has to disclose the

pickup and drop-off location when using the service. And finally, the privacy of data and image (PDI)

which is related to the providing 3rd party advertisement to the customers.

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After we have a complete understanding of the context of study as well as the theoretical framework

that build up the thesis, we test the aforementioned privacy decision making theory in the context of

P2P ridesharing service by using empirical data gathered from 265 Jakarta population by using a survey

questionnaire. By conducting the hypotheses testing, we aim to answer the following sub-question:

SQ3. To what extent are the privacy decision making constructs influencing the users’ willingness

to disclose their personal information?

Chapter 5.2 has concluded that the tangible form of benefit, such as: sign up credit or shopping

voucher (direct type) or monthly transportation discount (indirect type) has been the dominant con-

struct in predicting and influencing the users’ willingness to disclose personal data. Furthermore, the

privacy concern constructs, which have a strong influence on the willingness to disclose personal data

in prior studies; such as: Dinev and Hart (2006), Awad and Krishnan (2006), Kehr et al. (2015), Taylor

et al. (2009), Li and Unger (2012), and Liu et al. (2011); do not significantly influence the willingness to

disclose personal data in our case. Moreover, only one construct of privacy assurance measures (i.e.,

the government regulation and legislation) significantly influences the willingness to disclose personal

data. Even though the findings vary to different segregated of data, the tangible benefits still become

the dominant predictor to increase the willingness to disclose personal data.

Despite we now have the knowledge to increase the willingness to disclose personal data, we need to

make a classification of users based on the appropriate privacy mitigation strategy, which will explain

the “default-prerequisite” condition preferred by the customer as well as the “catalyst” to increase

the personal data disclosure. Furthermore, this classification can also be used to predict the market

size of specific target markets as well as the focused privacy mitigation strategy prepared by the com-

pany to acquire the users’ personal data. For those reasons, the following SQ4 is formed.

SQ 4: How can the users be segmented based on the privacy concern mitigation efforts?

Chapter 5.3 elaborates the findings of the cluster analysis to answer the question SQ4. We conclude

that the majority of the population is formed by the total solution seeker cluster, which means they

want their privacy is assured as well as traded with both tangible and intangible benefits. The total

solution seeker group has dominantly captured the biggest potential market size in the market which

occupies more than 80% of market in the general segmentation as well as segregated data segmenta-

tion. Additionally, our findings conclude that no one wants only partial type of strategy only (partial

utilitarian type strategy without any right assurance strategy, or partial right assurance strategy with-

out any utilitarian type strategy) to applied to mitigate the privacy concerns. Furthermore, our findings

also suggest that everyone in the population wants their privacy concerns are mitigated before dis-

closing his/her personal information.

From the answers to aforementioned analyses, we have the knowledge of what strategy can be ap-

plied to increase the personal data disclosure and to which group this strategy is applied. However,

we still lack of knowledge of the magnitude of the strategies, especially of the strategies that are re-

lated to tangible type of benefit. To have this knowledge, we developed an experimental study to

measure the willingness to accept (WTA) of users in return for the personal data disclosure. Specifi-

cally, we develop the SQ5 as follows:

SQ 5: To what extent do the users rank the value of each personal information relative to the

other type of personal information and how much is the buy-off value for each of their personal

information?

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In Chapter 5.4 we conclude that privacy of behavior and action has been the most valued privacy type

in the context of P2P ridesharing service, with the WTA value of (on average) IDR 133K (≈ €9.2) per

month. Furthermore, a different valuation is found in the other type of privacy. Privacy of data and

image, which is operationalized by accepting advertisements from 3rd party company, is valued (on

average) IDR 29K (≈ €2) per month. And finally, the privacy of location and space, in which we set this

privacy as the default disclosure in using the service, can be acquired by usefulness of the service only,

without performing any mitigation strategy. These findings also conclude that the multi-dimensional-

ity of privacy is conclusive, as the users valued different types of privacy differently.

From the elaboration of the context and the recent development of technology, in the chapter 2.2,

and compounded by the ridesharing business ecosystem in the chapter 2.4, we understand how the

competition in the ridesharing looks like and how the ridesharing company can innovate further to

sustain its market share and grow their profit by means of offering value added services on the basis

of personal data analytics, we can conclude that it is very likely that the users’ privacy concerns are

elevated. In the perspective of the ridesharing company, however, the question still remains. What

should P2P ridesharing companies which uses personal data analytics based innovation do to acquire

their users’ data? And how much is the investment needed to acquire those personal data? We argue

that these companies should employ a focused mitigation strategy in the process of acquiring their

users’ personal data. Thus, to answer the main research question and the research objective, we pro-

vide the following Table 6.27 is yielded from the answers of SQ3, SQ4, and SQ5.

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Table 6.27. Recommendations for company related to privacy concerns mitigation strategy13

Items Generic model (100%)



Male (42%)

Female (58%)

Low (62.6%)

High (22.6%)

Reject to disclose (14.7%)

Positive (28%)

Negative (72%)

Low (20%)

High (80%)

Young (80%)

Old (20%) Low (88%)

High (12%)

Biggest cluster and size








Total solu-tion seeker 88.4%







Influential variable(s) to increase WTD

Tangible benefit GRL

IUIPC Tangible benefit Intangible benefit ISR

Tangible benefit

Tangible benefit

Tangible benefit

Intangible benefit GRL

Tangible benefit ISP

CFIP Tangible benefit

Tangible benefit ISR

Tangible benefit Intangible benefit

Tangible benefit Intangible benefit ISR

Tangible benefit

Tangible benefit

IUIPC Tangible benefit

Privacy buy-off value

PBA 131 151 121 118 163 150 113 137 122 134 128 148 125 217

PDI 28 52 14 27 NA 49 50 21 27 28 32 NA 30 NA

13 WTD = willingness to disclose personal information; IUIPC = internet user’s information privacy concerns (Malhotra et al., 2004); CFIP = concerns for information privacy (Smith et al., 1996); ISP = individual self-protection (or IPPR (Son & Kim, 2008)); ISR = industry self-regulation (Xu et al., 2012); GRL = government regulation and legislation (Xu et al., 2012).

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6.2 Contributions The aforementioned results contribute to the prior privacy decision making study as well as provide critics

to the prior research. Furthermore, our empirical finding would also produce societal implications and

managerial (and policy) recommendations in respect to the use of privacy practice and personal data an-

alytics.

6.2.1 Academic contribution

6.2.1.1 Assuring privacy right as the default and prerequisite condition Our study is the first attempt to combine the utilitarian view of privacy and the right assurance view of

privacy in one context of study. From the cluster analysis results, which produce the total solution seeker

as the dominant cluster, we may suggest that even though the privacy starts to be seen as a means to get

utilitarian benefits, scholars should not forget/disregard the fact that privacy is initially seen as a right.

Thus, assuring the privacy right still becomes the mandatory and prerequisite condition before starting to

explore the utilitarian aspect of privacy. Therefore, future scholars who study privacy should also consider

the holistic view of privacy, i.e., from the right aspect as well as from the utilitarian aspect.

6.2.1.2 Multi dimensionality of privacy This study concludes that data subjects put different value for each type of privacy. Therefore, seeing

privacy as a single dimension will be problematic and inappropriate. As the context of our research only

allows us to include three types of privacy, i.e., privacy of behavior and action, privacy of location and

space, and privacy of data and image; further studies using different types of context should be performed

to further investigate the value of the other types of privacy.

6.2.1.3 Contextual settings influence the privacy calculus rationale and the value of privacy Our research concludes that the contextual settings are also influencing the users’ privacy calculus ra-

tionale, which is derived by their cost-benefit perception. This mechanism is operable by the difference of

privacy valuation involved in the specific context of use that is valued by the users. In our study, the privacy

of data and image (PLS) and privacy of behavior and action (PBA) are valued differently, in which the PLS

can be bought by the usefulness of the service only, whereas the PBA needs additional tangible form ben-

efits to be bought. We may suggest that the privacy type (in this case: PLS) that is mandatory needed to

provide a certain service (in this case: ridesharing service), is valued lower than the privacy that is not

directly related to the service fulfillment. Therefore, future research about the privacy calculus and privacy

valuation should also consider the conclusion that contextual setting influence the users’ privacy calculus

rationale and how the users value privacy.

6.2.2 Societal implication and managerial recommendation

6.2.2.1 Privacy right assurance is not an option, rather a mandatory condition From the conclusion derived from cluster analysis and multiple regression analysis, we have found that

the privacy assurance approaches are chosen by most respondents. However, these variables do not sig-

nificantly influence the users’ willingness to disclose personal information. Furthermore, the results from

cluster analysis have also concluded that respondents are incline to choose the right assurance dominant

approaches rather than utilitarian dominant approaches. Those findings suggest that company should per-

form privacy assurance approach as the prerequisite condition prior to personal data acquisition.

6.2.2.2 Not everyone can be a target for personal data analytic based personalization For the companies that are willing to offer personalization series based on personal data analytics, it

should be noted that not all market segments are willing to enjoy personalization or realize the positive

utility value of the personalization service. Thus this company should choose the target market carefully

and prepare the privacy mitigation strategy accordingly. Our strategic summary in Table 5.26 presents

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suggestions to this matter, particularly to which market the P2P ridesharing personalization service could

be applied, and by what strategy and by how much money their personal data can be acquired.

6.2.2.3 Data subjects value forms of privacy differently. Our study concluded that users value each privacy type differently. Therefore, the company which wants

to acquire a specific type of privacy should prepare different efforts and “prices”. Based on our summary

in Table 6.27, we can quantify the investment needed to target each group of customer. For example, the

company that wants to use 30% Jakarta female population as the target for personalization should prepare

cash investment = 1.470.000 (number of people) x IDR 121.000 = IDR 177.870.000.000 (± € 11.8m) to

acquire the female populations’ behavioral data. This value of investment might be accurate as female

population regards only the tangible benefits as the efficient means to mitigate their privacy concerns.

However, we should note that this value might differ for each group according to the (projected) size of

the target market and the perceived efficient means to mitigate its privacy concerns.

6.2.2.4 Be aware of the risk of storing and using personal data Even though the tangible benefits type of mitigation strategy is more preferred by the majority of people,

the effort to assure privacy rights should not be left out. This recommendation is implied based on the

high mean value of the intention to share and the negative experience in our finding, which could produce

a quantifiable risk of damaging the company’s brand image. Furthermore, this risk is amplified as the ma-

jority of Indonesian people are active social media users, which is concluded by the high daily amount

average of using social media (almost 3 hours daily use of social media, in which more than 85% of these

users are people aged 13 to 39 years old – which has been the most attractive age range according to our

finding) (Kemp, 2016; Sadowski, 2016).

6.2.2.5 The need of using advanced privacy enhancement technologies As we have concluded in the analysis, assuring privacy rights is the default condition as well as prerequisite

before conducting personal data capture. Therefore, with recent technology developments which can be

used to guard as well as to invade privacy, a data acquirer company should prepare an advanced privacy

enhancing technology. This technology should allow the data subjects to perform (but not limited to) ac-

tivities as follows:

1. Increase control over their data. To date, the only measure provided by the Indonesian companies

is the “unsubscribe” link from the email marketing mailing list. However, as a higher degree of

personal data may be involved, the company should employ a technology that is able to increase

the extent of control for the users over their disclosed data, for every type of personal data. One

example of this tool is the web/mobile app based dashboard setting where users are able to per-

form measurable control directly over their disclosed data, such as: Google’s my activity (myactiv-

ity.google.com), which allows the users to control whether they want to be the subject for tar-

geted marketing activity and administer which data can be used in such activity.

2. Perform substantial degree of anonymity. As we have seen in our finding, not everyone is willing

to be profiled plainly. Therefore, providing certain measures to perform anonymity or giving

pseudo name to such users may be appropriate.

3. Perform fully informed consent. This tool should help the users to be fully informed about the

company’s privacy practice, in the easiest and cheapest possible way before they start using the

service(s).

4. Negotiate the terms and conditions directly, which may involve a reward and other type of

measures.

5. Perform audit to the use of their data.

6. Remove and edit the accuracy of their data.

https://myactivity.google.com/

https://myactivity.google.com/

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6.2.2.6 Urge the government of Indonesia to create an adequate privacy protection law Our research has concluded that the majority of the respondents are people who have been experiencing

privacy intrusion in their live. Yet, they still easily disclose personal data to a company that wants to ac-

quire their data. This might be a result of “take-it-or-leave-it” conditions offered to them. As personal data

use has been increasing, the government of Indonesia needs to create an adequate privacy protection

law. The current (draft of) privacy law still explains and regulates the personal data use on a very general

level, without providing the detailed use-case regulation and the audit and control mechanisms to the

company’s privacy practice. Therefore, a more detailed policy needs to be prepared, at least in accordance

to the APEC privacy framework. Furthermore, this issue becomes urgent due to the fact that ASEAN Eco-

nomic Community14 is initiated, thus might involve customers’ personal data transfer across the ASEAN

countries.

The privacy law should regulate the company’s privacy practice, including:

1. Harm prevention to the users’ personal data must be prioritized

2. Promotion of the users’ awareness of the company’s privacy practice, including the type of data

collected in the process, the purpose of the collection, the users of the data, and to whom the

data will be disclosed, should be explained in easiest way possible.

3. Requirement to have a fully informed consent (written or digitally) from the users.

4. Providing access to the users to control their data.

5. Providing choice to the users, even though the users do not want to disclose their personal data,

they should still be able to use the (limited) service. This condition is necessary to minimize the

take-it-or-leave-it situation.

6. The presence of an audit policy and punishment mechanism to the company.

7. Data transfer regulation, within country and between countries (transborder).

6.3 Limitations We understand that our research is far from perfection. This research is bound to a number of limitations

which are caused by: Firstly, the limitation of the literature review. The abundance literature of privacy

constructs forced us to limit the literature reviews by only using literature from 2010 onward and followed

by the snow ball method. Thus some important findings might be (accidentally) left out from the study.

Secondly, the context of study as Ojek online is minimally understandable by the general reader. Moreo-

ver, there is no scientific publication explaining the business model, business ecosystem, and business

operation of this context yet. Thus, we might not have a complete and scientific picture of the contextual

factors of the study. It is suggested to scientifically study the Ojek online concept by using an interview

approach to the actors involved in the ecosystem.

Thirdly, limitation related to the target population selection. We limited our respondents to people who

are familiar with the P2P ridesharing only, this choice may exclude the voice of people who are not familiar

with the concept. Therefore, the generalizability of the research may be limited only to the context of P2P

transportation users only. We may suggest the next study to use a broader selection of population to study

the overall perception of privacy. This might be done by using mobile phone users, for example, as the

target population; as currently Indonesian mobile operators start to build their own e/m-commerce to

also capture the benefit of the ubiquity of smartphones and the internet.

14 ASEAN economic community (AEC) is a major milestone to achieve economic integration within ASEAN countries, which consist of: Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.

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Fourthly, our sampling method is using stratified sampling based on Jakarta population. This method may

not capture other demographics which have different properties to Jakarta people. Therefore, the gener-

alizability of this research might only be applied to a population which has similar demographic properties

as Jakarta, such as other metropolitan cities in emerging countries.

Fifthly, limitation related to the data collection method. We only differentiated the location based on rural

or urban type of locations, yet, we did not identify the composition of people who might appear in these

locations beforehand. This may result in an overly representation of highly educated people, which may

result in bias in the statistical analysis. Furthermore, the choice of using the voucher form invitation might

be biased and only interactive to the people who have a high utilitarian mindset.

Finally, limitation related to the statistical method, including:

1. We did not run structured equation modeling (SEM) to the cases, especially to assess the relation-

ship between privacy concerns constructs and the privacy assurance approaches construct.

Whereas we understand that by using SEM, we can produce better estimates to draw the com-

plete relationship between constructs.

2. Instead of using latent class analysis (LCA), which bases the estimation to the model based proba-

bilistic (top-down) approach (Magidson & Vermunt, 2002); we chose the cluster analysis (which

bases the estimation to the distance – bottom up – approach) to produce the classification of

users, even though the cluster analysis is criticized as an atheoretical method (Hair et al., 2013).

Furthermore, as we do not have any prior models to estimate the number of formed clusters, we

limited the formed cluster from each analysis by only using the composition of the users and our

own interpretation to the graphical presentation of the cluster properties (to see the significant

different between formed clusters). We believe that conducting LCA will provide better insight

into the users’ classification since it can identify the latent properties of the members in one

group.

3. We use a very specific contextual illustration in our conjoint analyses, which may not be general-

izable to the other type of context. Moreover, the main assumption in the conjoint analysis is the

absence of collinearity between the tested attributes. Yet, we did not test the collinearity diagno-

sis of the attributes; especially in the relationship between PDI vs. PBA, PDI vs. PLS, and PDI vs.

PBA + PLS, which we believe they might be correlated in a certain extent; prior to the conjoint

analysis survey design.

6.4 Future research recommendations Given our limitations of study, we give several recommendations to the future research, which are:

1. Test the relationship of IUIPC, CFIP, and privacy assurance approaches by using SEM approach. By

conducting SEM analysis, we can have better insights into the relationships between IUIPC, CFIP,

and the privacy assurance approaches constructs.

2. Run the LCA to estimate the classification of users. Because LCA is a more powerful tool than

cluster analysis as it could estimate the confirmatory results of the cluster, and include moderating

variables in the analysis (Magidson & Vermunt, 2004).

3. Conduct an experimental assessment by using conjoint analysis which includes the interaction

effects of all attributes in our study.

4. Scientific analysis of the Indonesian P2P ridesharing business ecosystem. Collaborative consump-

tion scholars can start to explore the scientific analysis of the Indonesian P2P ridesharing ecosys-

tem, including but not limited to: the business model of the company, value network analysis,

Value – information – process analysis (VIP analysis), and the state-of-the-art implementation of

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the business model in the company. Because these companies rely heavily on the technology in-

frastructure, the STOF framework can be applied to assess the company’s business ecosystem.

Additionally, scholars can also conduct a roadmapping and stress testing analysis to the existing

business model as well as the hypothetical personalization service that can be applied in the near

future.

5. Conduct privacy valuation study to measure other types of privacy. The examples of setting to

value different types of privacy are:

a. Privacy of the person. The suitable context of this privacy might be in the situation at

which people want to apply for a certain type of job which requires a specific informa-

tional background of the candidate.

b. Privacy of communication. This privacy may appear in the situation when national secu-

rity is compromised, but yet, people still want their trails undetected when they go online.

One example of setting to quantify privacy of communication is: a study of the willingness

to pay to use the advanced feature of an Onion routing network (for example: Tor Net-

work (Dingledine et al., 2004)).

c. Privacy of thought and feeling, which may only appear in the psychological therapy situ-

ation in which the “trust” is built among the parties who share the feeling. However, since

all psychologists always work under a vow to not reveal any communication and infor-

mation of the patients, this type of privacy is difficult to be quantified.

d. Privacy of association (including group privacy). This privacy type may only appear in the

condition when people belong to a certain group, thus makes this privacy type is difficult

to be measured.

6. Conduct design science research (see: Peffers et al. (2007)) to prepare the fit implementation of

personalization service in the P2P ridesharing service, including identify the latent values that

need to be preserved by the service.

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7 Critical reflections

“With great power comes great responsibility” (Lee, 1962)

It has been three years after the World Economic Forum concluded that new internet adopter countries

are less aware of privacy concerns (Dutton et al., 2014). But yet, our study concludes that the condition

has not changed, at least not in Indonesia. People with high privacy concerns level still easily disclose their

personal data to the company, regardless the privacy practice conducted by the company, which suggests

that they are not fully aware of the risks of disclosing their personal data. Nevertheless, Indonesia, as a

country with tremendous growth of internet penetration (Dutta; et al., 2015), still need to prepare an

adequate privacy protection law to be applied in the country.

I am living in the era when big data is newly emerged. Everyone talks about it. Technology market research

reports predict the hype of it. Everyone is so excited about it and embraces it. From the telecom infra-

structure vendors (such as: Cisco, Alcatel, Nokia, Huawei, Juniper, etc.), telecom operators, FMCG indus-

tries, market research companies, to the end users who start to explore the open IoT platforms; basically,

everyone. The vendors build the Internet of Things (IoT) devices, cloud computing infrastructure providers

build the server infrastructure, telecom operators sell the network infrastructure services, software com-

panies develop the tools to process the unstructured data. All of them collaborate to tap the tremendous

potential generated by IoT and the Big data, thus they create a euphoria in the ICT society. But sometimes

they forget about the other side of the coin. They need to be aware of the risks and the harm of this

technology. Therefore, every marketing activity of IoT should present balanced information to the society;

not only sell its benefits, but also present the potential risks and harms produced by IoT. Especially, to the

end users (and society) who are not fully informed about how this technology does work but yet too en-

thusiastic to use the technology.

I must suggest that every technology innovation should be started with a grand vision of a perfect world,

which I would define as: a world where the people are equally wealthy; a world where no conflict and

discrimination of religion and race exist; a world where an infinite term (not only a long-term) of sustain-

ability is achieved. All of these could be achieve when the technology can always ensure all the fundamen-

tal rights of people have, without any opportunity for others to intrude this right.

With this vision, technology innovators should think carefully to decide the ability and capability of their

artifact. Von Schomberg (2013) in his paper argues that:

“For modern innovations, responsibility for the consequences of implementation is primarily re-

lated to the properties and characteristics of the products or the technology and less to the priv-

ileged owners and creators of the technology. … all informed citizens should be able to make (safe

and responsible) use of it” (p. 4)

However, not everyone is responsible enough to operate and use the artifact. Therefore, technology in-

novators should be wise when designing a perfect artifact, in order to avoid and limit the potential of

misuse/abuse of this artifact. He/she needs to be socially sensitive to measure whether the society is ready

to accept his/her invention responsibly. Because when his/her invention reaches the market, the use and

improvement of this invention are decided by the market mechanism (Von Schomberg, 2013).

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In every developed country, there will (almost) always be an adequate national innovation system and

regulatory that “incubate” the technological innovation and assess the sustainability potential of technol-

ogy innovation. But in Indonesia, where the people tend to replicate the success technology in the devel-

oped country to be applied there, this may not (if not never) be the case. It has been concluded that the

fragmented innovation policies and the absence of proper innovation system in Indonesia has hampered

the “formal” innovation performance (Vang, 2006). Therefore, most innovative products and services in

Indonesia are resulted from out-of-the-system innovation, either technology push (replica of the success-

ful technology in developed countries) or policy/market pull, which sometimes neglect the fundamental

ethic principles and lack of precautionary measures. Quite often, innovators in Indonesia release an inno-

vative product with arguments: “because we can” or “because there are good demands of it”, and aban-

don the long term effect of their invention.

I must present my research findings to Indonesian government as an empirical base to prepare an ade-

quate personal data protection law, and push the Indonesian government to build a proper national inno-

vation and regulatory system. Because technology cannot merely be seen as a tool, as it can transform the

way we live, the way we move, the way we work; in general, it can transform our society. We cannot stay

seeing technology as a black-box without putting ethical values in it. Therefore, the value sensitive design

(Van den Hoven, 2013) must be implemented in the technology invention, and the stakeholders should be

involved as early as possible and in every aspect of technology innovation, from the proof-of-concept of

the service to the acceptance test of the final artifact; from the dumb devices manufactured by the tech-

nology manufacturer to the technology services offered by the service providers; especially to the tech-

nology innovations that utilizes users’ personal data.

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Those who plan do better than those who do not plan even though they rarely stick to their plan.

~ Winston Churchill

With regard to the research process, I have several reflections in my concern. This research project was

initially designed to be finished within 6 months of duration. Here and there adjustments were done dur-

ing the proposal development phase. Until the time I had the kick-off schedule, I realized that all those

plans were based on my plan, without considering the other stakeholders’ plan (my committee). There-

fore, minor deviations had to be made related to the summer break plan. In summary, I had additional a

one-month extension to finish this project.

Additionally, some deviations were also made during the project. The first deviation was because I had to

go back to Indonesia earlier due to unforeseen circumstances. Thus, to made my time efficient, I made

some adjustments in the plan, which was to perform the data collection earlier than it was initially

planned. This adjustment, however, had consequences. I had to finalize my chapter 3 (theoretical frame-

work) and chapter 4 (survey design) 3 weeks earlier. This was the moment when the maximum sleepless

nights in place.

The second deviation was related to the data collection process, mainly because my choice to use a

voucher as incentive to fill in the questionnaire. I did not realize that this choice also influences the re-

spondents’ composition and might possibly bias the analysis. This choice caused me to dedicate one-week

extension to perform face to face data collection, during that time I could not do other things in plan.

With the earlier data collection process, I can enjoy more flexible time in the later phase. Yet, this ample

time did not make me became more productive. I realized that I was experiencing the common novice

project manager’s problems in managing a project, i.e., student syndrome15 and Parkinson’s law16, in

which both problems might possibly cause delays to the project.

I am quite lucky though. Because I have taken a project management course during my study. Thus, I

quickly realized the problem and adjusted my timeline tighter, so I did not have time to procrastinate.

From this experience, I would suggest the program manager of Management of Technology to include

Project management course (at least the basic one) in the core module of MOT curriculum.

Nonetheless, I seeked satisfactory steps only in choosing my statistical analysis method, particularly

related to clusterization and segmentation of users. If I was asked what would I do differently if I had a

chance to re-do the research, I would dedicate more time in studying advance statistical methods before

deciding which method I would use in my analysis.

15 Student syndrome is a behavioral issue in project management, especially in the theory of constraint (TOC). The student syn-drome states that the more time a project has does not improve the quality and the quantity of the project because the project manager tends to leave everything in the last minutes (Blackstone Jr et al., 2009; Rand, 2000) and wastes the time buffer mar-gins (Lechler et al., 2005). 16 Parkinson’s law is also a behavioral issue in the TOC, which means that individuals tend not to finish the tasks ahead, even though they have an opportunity to do so (Blackstone Jr et al., 2009; Lechler et al., 2005).

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Successful and unsuccessful people do not vary greatly in their abilities. They vary in their desires to

reach their potential. ~ John Maxwell

This thesis is developed adhered to the strong foundation of the management of technology (MOT) cur-

ricula, particularly the ICT Management specialization, in which the core objective of MOT program is to

prepare the future technology manager as a competent, responsible, and ethical manager that can apply

the knowledge compiled from MOT curricula into meaningful and measurable artifact that will be able to

contribute to the sustainable society development.

The foundational of ethical value that need to be preserved by any (technology) artifact is generated by

studying the social value, whereas the implementation of this value is studied in technology dynamic

course. Additionally, the basic knowledge of digital platform to elaborate the role of technology in the

peer-to-peer (P2P) ridesharing service is synthesized from ICT design and valorization and mobile apps

course. Furthermore, the foundation of knowledge to analyze the business ecosystem of such service as

well as the potential expansion of the ecosystem are produced from e-Business course in ICT specializa-

tion.

Moreover, the pillars of conducting this scientific research are also gathered from the foundational

knowledge in social and scientific value course which then is compounded by scientific method course.

Nevertheless, other fundamental core curricula also build up my foundational logical analysis, such as:

Financial management, High – tech marketing, and economic foundation.

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Appendixes

Appendix 2.1. Digital platform categorization There are numerous platform types with a very strong heterogeneity between them. It creates a difficulty

to explain how (a specific) platform works (Walravens & Ballon, 2009). Noting this argumentation and with

regard to the collaborative consumption context, we argue that the important classification of a platform

can be done from (1) its ability to control the users and the providers (control mechanism), in which we

can measure the monopolistic potential of a platform, and (2) its assets distribution mechanism, in which

we can measure the scalability potential of a platform.

In the control mechanism, Walravens and Ballon (2009) introduce a topology of platforms in their paper.

They argue that there are four type of platforms based on the control over the customers and the asset

providers, i.e., system integrator, enabler, broker, and neutral (Walravens & Ballon, 2009). Firstly, the sys-

tem integrator platforms that have control over the assets providers as well as the customers of those

assets. This type of platform will also provide customer services as the mediator between customers and

the providers. Secondly, the enabler; it is a platform that has control over the assets but not the relation-

ship over the customers. This type of platforms only acts as a foundation architecture of a system and the

marketplace for the partner providers who provide the assets. Thirdly, the broker platforms that have

control over the customers but do not control the assets providers. And lastly, the neutral platforms that

do not have any control over assets providers and customers. This type of platforms acts only as market-

place of services. Table Ap.2.7.1 summarizes Walravens and Ballon (2009) platform categorization.

Table Ap.2.7.1. Platform categorization based on control over users and providers (Walravens & Ballon, 2009)

Control over the customers

Yes No

Co

ntr

ol o

ver

the

asse

t p

ro-

vid

ers

Yes

System integrator: the platform owner controls the assets from the providers to ensure the value crea-tion. It also manages relationship

with its customers.

Enabler: the platform controls the assets of the assets providers, how-

ever, the providers themselves manage their customer relation-

ship.

No

Broker: the platform has control over their loyal customers, however, it

does not have any control over the assets that build the value proposi-

tion.

Neutral: the platform does not have any control over their asset

providers and customers.

In the categorization based on asset distribution, Hill and Wellman (2011) offer a classification of platforms

based on the assets providers or rental mechanism (centralized and decentralized) and the transfer mech-

anism of the assets to the customers (centralized and decentralized). Firstly, the centralized rental and

centralized transfer, in which the platform owner has the assets and dispatches the assets to the customer

centrally. Secondly, the decentralized rental and centralized transfer, in which the assets are decentralized

without being controlled by the platform owner and the transfers of the asset to the customer are directed

Page | 148

by the platform centrally. Normally, if there is a customer that asks for a service, the platform owner will

announce the request to the providers, and if the providers are interested to take the request, they will

place a bid to it. Thirdly, the centralized rental and decentralized transfer, in which the platform owner

has all the assets while the booking and the transfers of the assets are done indirectly via the platform

between the peer customers. The Zipcar (see Textbox 2.1. ZipCar) service is a fit example of this type of

platform. Lastly, the decentralized assets and decentralized transfer or the “peer-to-peer”, in which all the

assets and the transfers of assets are done indirectly via the platform. In this type of platform, the platform

owner only provides the marketplace to perform the transaction. Table Ap.2.7.2 summarizes the platform

classification based on Hill and Wellman (2011).

Table Ap.2.7.2. Platform categorization based on asset distribution (Hill & Wellman, 2011)

Asset rental mechanism

Centralized Decentralized

Ass

et t

ran

sfer

mec

han

ism

Centralized The platform owner owns the as-

sets and distribute the assets centrally to the customers.

The platform relies to the com-munity to provide the assets,

however, it controls the assets distribution to the customers.

Decentralized

The platform owner owns the as-sets. Users who want to use the asset have to book and pick the

asset up in the pick-up spots.

The platform relies to the com-munity to provide the assets, the users have to negotiate the pick-

up with the asset owner indi-rectly via the platform that is

provided by the platform owner.

Based on those previous mentioned classification, we may conclude that the biggest monopolistic poten-

tial of a platform is owned by the system integrator platform, whereas the least monopolistic potential is

owned by the neutral platform. The company that wants to maximize the platform monopolistic potential

should strategically design its architecture towards maximizing the control to both users and providers.

This can be done by ensuring the sustainability of the value networks between the actors participating in

the platform (Tiwana, 2013). Furthermore, we may also conclude that the platform with the highest scala-

bility level is owned by the peer-to-peer (P2P) type. By using P2P type, the biggest challenge of a platform

owner is deciding the potential users who have the strongest network effect (the penguin dilemma)

(Tiwana, 2013), and performing the “divide and conquer” strategy to acquire these users.

Page | 149

Appendix 4.1. Survey Questionnaire

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Appendix 4.2. Respondents’ demography

Page | 151

Page | 152

Appendix 4.3 Normality and homoscedasticity test

Standardized Residual total case

Page | 153

Standardized Residual gender split

Page | 154

Standardized Residual salary split

Page | 155

Standardized Residual previous privacy experience split

Page | 156

Standardized Residual education split

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Standardized Residual age split

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Standardized Residual self-efficacy split

Page | 159

Appendix 4.4 Linearity test

Page | 160

Appendix 4.5 Factor Analysis

Willingness to disclose = FAC_WTD

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .774

Bartlett's Test of Sphericity

Approx. Chi-Square 329.625

df 10

Sig. .000

Anti-image Matrices

wtd1 wtd2 wtd3 wtd4 wtd5

Anti-image Covariance

wtd1 .585 -.211 -.161 -.144 -.031

wtd2 -.211 .569 -.177 .021 -.169

wtd3 -.161 -.177 .665 -.064 -.035

wtd4 -.144 .021 -.064 .777 -.213

wtd5 -.031 -.169 -.035 -.213 .731

Anti-image Correlation

wtd1 .769a -.367 -.259 -.213 -.048

wtd2 -.367 .745a -.288 .032 -.262

wtd3 -.259 -.288 .811a -.088 -.050

wtd4 -.213 .032 -.088 .771a -.283

wtd5 -.048 -.262 -.050 -.283 .784a

a. Measures of Sampling Adequacy(MSA)

Communalities

Initial Extraction

wtd1 1.000 .625

wtd2 1.000 .626

wtd3 1.000 .538

wtd4 1.000 .375

wtd5 1.000 .449

Extraction Method: Principal

Component Analysis.

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.613 52.262 52.262 2.613 52.262 52.262

2 .840 16.806 69.069

3 .644 12.888 81.957

Page | 161

4 .507 10.138 92.095

5 .395 7.905 100.000

Extraction Method: Principal Component Analysis.

Component Matrixa

Component

1

wtd1 .791

wtd2 .791

wtd3 .733

wtd4 .612

wtd5 .670

Extraction Method:

Principal Component

Analysis.

a. 1 components ex-

tracted.

Page | 162

Component Score

Coefficient Matrix

Component

1

wtd1 .303

wtd2 .303

wtd3 .281

wtd4 .234

wtd5 .257

Extraction Method:

Principal Component

Analysis.

Component Score Co-

variance Matrix

Component 1

1 1.000

Extraction Method: Princi-

pal Component Analysis.

Page | 163

Privacy concerns = FAC_IUIPC & FAC_CFIP





df 15

Sig. .000

Anti-image Matrices

picon piaw picol pcima pcusu pcerr


picon .592 -.292 .026 -.095 .052 -.078

piaw -.292 .505 -.214 -.062 -.069 -.030

picol .026 -.214 .806 -.082 .048 .006

pcima -.095 -.062 -.082 .625 -.311 -.005

pcusu .052 -.069 .048 -.311 .683 -.135

pcerr -.078 -.030 .006 -.005 -.135 .912


picon .663a -.535 .037 -.157 .081 -.106

piaw -.535 .665a -.335 -.110 -.117 -.045

picol .037 -.335 .711a -.116 .065 .008

pcima -.157 -.110 -.116 .703a -.477 -.007

pcusu .081 -.117 .065 -.477 .626a -.171

pcerr -.106 -.045 .008 -.007 -.171 .808a


Communalities

Initial

picon 1.000

piaw 1.000

picol 1.000

pcima 1.000

pcusu 1.000

pcerr 1.000

Extraction Method:

Principal Component

Analysis.


Component Initial Eigenvalues Rotation Sums of Squared Loadings

Page | 164


1 2.510 41.828 41.828 1.910 31.827 31.827

2 1.094 18.232 60.059 1.694 28.232 60.059

3 .882 14.692 74.752

4 .741 12.357 87.108

5 .441 7.353 94.461

6 .332 5.539 100.000


Component Ma-

trixa

a. 2 components

extracted.

Rotated Component Matrixa

Component

1 2

picon .748 .232

Page | 165

piaw .830 .268

picol .731 -.038

pcima .344 .716

pcusu .061 .854

pcerr .058 .570


Component Analysis.

Rotation Method: Varimax with

Kaiser Normalization.

a. Rotation converged in 3 itera-

tions.

Component Transformation Matrix

Component 1 2

1 .759 .651

2 -.651 .759

Extraction Method: Principal Compo-

nent Analysis.

Rotation Method: Varimax with Kaiser

Normalization.

Page | 166

Component Score Coefficient

Matrix

Component

1 2

picon .402 -.029

piaw .444 -.025

picol .461 -.213

pcima .030 .410

pcusu -.180 .579

pcerr -.110 .382


Component Analysis.

Rotation Method: Varimax with

Kaiser Normalization.

Component Scores.

Component Score Covariance Matrix

Component 1 2

1 1.000 .000

2 .000 1.000


nent Analysis.

Rotation Method: Varimax with Kaiser

Normalization.

Component Scores.

Tangible Benefits = FAC_TANG_BEN





df 1

Sig. .000

Anti-image Matrices

btmb1 btmb2

Anti-image Covariance btmb1 .515 -.359

btmb2 -.359 .515

Anti-image Correlation btmb1 .500a -.696

btmb2 -.696 .500a


Page | 167

Communalities

Initial Extraction

btmb1 1.000 .848

btmb2 1.000 .848

Extraction Method: Principal Com-

ponent Analysis.




1 1.696 84.823 84.823 1.696 84.823 84.823

2 .304 15.177 100.000


Component Matrixa

Component

1

btmb1 .921

btmb2 .921

Extraction Method: Prin-

cipal Component Analy-

sis.

a. 1 components ex-

tracted.

Intangible Benefits = FAC_INTANG_BEN





df 1

Sig. .000

Anti-image Matrices

bipe bius

Anti-image Covariance bipe .837 -.338

bius -.338 .837

Page | 168

Anti-image Correlation bipe .500a -.403

bius -.403 .500a


Communalities

Initial Extraction

bipe 1.000 .702

bius 1.000 .702


Component Analysis.




1 1.403 70.156 70.156 1.403 70.156 70.156

2 .597 29.844 100.000


Component Matrixa

Component

1

bipe .838

bius .838

Extraction Method:

Principal Component

Analysis.

a. 1 components ex-

tracted.

Individual self-protection = FAC_IPPR

A. Initial





df 15

Sig. .000

Page | 169

Anti-image Matrices

aisp1 aisp2 aisp3 aisp4 aisp5 aisp6


aisp1 .761 -.332 -.028 .069 .022 -.098

aisp2 -.332 .758 -.133 .034 .035 -.028

aisp3 -.028 -.133 .800 -.080 -.157 -.039

aisp4 .069 .034 -.080 .725 -.181 -.119

aisp5 .022 .035 -.157 -.181 .543 -.267

aisp6 -.098 -.028 -.039 -.119 -.267 .602


aisp1 .542a -.437 -.036 .093 .034 -.144

aisp2 -.437 .544a -.170 .046 .055 -.041

aisp3 -.036 -.170 .786a -.104 -.238 -.056

aisp4 .093 .046 -.104 .777a -.288 -.180

aisp5 .034 .055 -.238 -.288 .671a -.466

aisp6 -.144 -.041 -.056 -.180 -.466 .702a


Communalities

Initial Extraction

aisp1 1.000 .699

aisp2 1.000 .715

aisp3 1.000 .424

aisp4 1.000 .583

aisp5 1.000 .729

aisp6 1.000 .635


Component Analysis.




1 2.285 38.086 38.086 2.285 38.086 38.086

2 1.499 24.985 63.072 1.499 24.985 63.072

3 .738 12.299 75.371

4 .591 9.845 85.216

5 .510 8.499 93.715

6 .377 6.285 100.000

Page | 170


Component Matrixa

Component

1 2

aisp1 .240 .801

aisp2 .243 .810

aisp3 .637 .133

aisp4 .675 -.356

aisp5 .822 -.231

aisp6 .795 -.055


Component Analysis.

a. 2 components extracted.

Page | 171

Component Score Coefficient

Matrix

Component

1 2

aisp1 .105 .534

aisp2 .106 .540

aisp3 .279 .089

aisp4 .295 -.238

aisp5 .360 -.154

aisp6 .348 -.036


Component Analysis.

Component Score Covariance Matrix

Component 1 2

1 1.000 .000

2 .000 1.000


nent Analysis.

B. After deletion of variable aisp1 & aisp2





df 6

Sig. .000

Anti-image Matrices

aisp3 aisp4 aisp5 aisp6


aisp3 .837 -.066 -.153 -.064

aisp4 -.066 .738 -.192 -.108

aisp5 -.153 -.192 .547 -.270

aisp6 -.064 -.108 -.270 .624

Anti-image Correlation aisp3 .825a -.084 -.226 -.089

Page | 172

aisp4 -.084 .792a -.302 -.159

aisp5 -.226 -.302 .674a -.462

aisp6 -.089 -.159 -.462 .712a


Communalities

Initial Extraction

aisp3 1.000 .375

aisp4 1.000 .520

aisp5 1.000 .721

aisp6 1.000 .627


Component Analysis.




1 2.243 56.081 56.081 2.243 56.081 56.081

2 .761 19.028 75.109

3 .608 15.190 90.299

4 .388 9.701 100.000


Page | 173

Component Matrixa

Component

1

aisp3 .612

aisp4 .721

aisp5 .849

aisp6 .792

Extraction Method:

Principal Component

Analysis.

a. 1 components ex-

tracted.

Component Score

Coefficient Matrix

Component

1

aisp3 .273

aisp4 .321

aisp5 .379

aisp6 .353

Extraction Method:

Principal Component

Analysis.

Rotation Method: Obli-

min with Kaiser Nor-

malization.

Component Score Co-

variance Matrix

Component 1

1 1.000

Page | 174



Rotation Method: Oblimin

with Kaiser Normalization.

Industry self-regulation = FAC_ISR





df 1

Sig. .000

Anti-image Matrices

aisr1 aisr2

Anti-image Covariance aisr1 .649 -.384

aisr2 -.384 .649

Anti-image Correlation aisr1 .500a -.593

aisr2 -.593 .500a


Communalities

Initial Extraction

aisr1 1.000 .796

aisr2 1.000 .796


Component Analysis.




1 1.593 79.642 79.642 1.593 79.642 79.642

2 .407 20.358 100.000

Page | 175


Component Matrixa

Component

1

aisr1 .892

aisr2 .892

Extraction Method:

Principal Component

Analysis.

a. 1 components ex-

tracted.

Page | 176

Component Score

Coefficient Matrix

Component

1

aisr1 .560

aisr2 .560

Extraction Method:

Principal Component

Analysis.

Component Score Co-

variance Matrix

Component 1

1 1.000



Page | 177

Appendix 4.6. Correlation matrix

FAC_WTD FAC_IUIPC FAC_CFIP FAC_TANG_BEN FAC_INTANG_BEN FAC_IPPR FAC_ISR FAC_AGRL dpre dsel dage dedu dsal

FAC_WTD

Pearson Correlation 1 -.040 .005 .342** .248** .086 -.023 -.056 .046 -.037 -.052 -.071 -.121

Sig. (2-tailed) .517 .931 .000 .000 .163 .706 .365 .453 .545 .397 .249 .070

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_IUIPC

Pearson Correlation -.040 1 .402** .086 .179** .320** .338** .316** .103 -.050 .004 .010 .016

Sig. (2-tailed) .517 .000 .165 .003 .000 .000 .000 .095 .414 .949 .873 .814

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_CFIP

Pearson Correlation .005 .402** 1 .184** .170** .444** .381** .502** .006 .008 .069 .005 .068

Sig. (2-tailed) .931 .000 .003 .005 .000 .000 .000 .928 .899 .263 .939 .308

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_TANG_BEN

Pearson Correlation .342** .086 .184** 1 .565** .229** .203** .137* .081 -.034 -.053 -.114 -.124

Sig. (2-tailed) .000 .165 .003 .000 .000 .001 .025 .188 .578 .386 .064 .063

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_INTANG_BEN

Pearson Correlation .248** .179** .170** .565** 1 .290** .255** .199** .139* -.029 .123* -.002 .008

Sig. (2-tailed) .000 .003 .005 .000 .000 .000 .001 .024 .634 .046 .977 .906

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_IPPR

Pearson Correlation .086 .320** .444** .229** .290** 1 .463** .418** .001 -.003 .100 -.090 -.024

Sig. (2-tailed) .163 .000 .000 .000 .000 .000 .000 .986 .963 .103 .143 .719

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_ISR

Pearson Correlation -.023 .338** .381** .203** .255** .463** 1 .485** .064 .011 .068 -.050 -.076

Sig. (2-tailed) .706 .000 .000 .001 .000 .000 .000 .302 .864 .267 .417 .255

N 265 265 265 265 265 265 265 265 265 265 265 265 226

FAC_AGRL

Pearson Correlation -.056 .316** .502** .137* .199** .418** .485** 1 .012 .003 .043 .078 -.021

Sig. (2-tailed) .365 .000 .000 .025 .001 .000 .000 .851 .967 .483 .203 .750

N 265 265 265 265 265 265 265 265 265 265 265 265 226

dpre

Pearson Correlation .046 .103 .006 .081 .139* .001 .064 .012 1 .016 .041 -.013 .035

Sig. (2-tailed) .453 .095 .928 .188 .024 .986 .302 .851 .798 .509 .836 .601

N 265 265 265 265 265 265 265 265 265 265 265 265 226

dsel

Pearson Correlation -.037 -.050 .008 -.034 -.029 -.003 .011 .003 .016 1 .172** .219** .282**

Sig. (2-tailed) .545 .414 .899 .578 .634 .963 .864 .967 .798 .005 .000 .000

N 265 265 265 265 265 265 265 265 265 265 265 265 226

dage

Pearson Correlation -.052 .004 .069 -.053 .123* .100 .068 .043 .041 .172** 1 .263** .575**

Sig. (2-tailed) .397 .949 .263 .386 .046 .103 .267 .483 .509 .005 .000 .000

N 265 265 265 265 265 265 265 265 265 265 265 265 226

dedu

Pearson Correlation -.071 .010 .005 -.114 -.002 -.090 -.050 .078 -.013 .219** .263** 1 .254**

Sig. (2-tailed) .249 .873 .939 .064 .977 .143 .417 .203 .836 .000 .000 .000

N 265 265 265 265 265 265 265 265 265 265 265 265 226

dsal

Pearson Correlation -.121 .016 .068 -.124 .008 -.024 -.076 -.021 .035 .282** .575** .254** 1

Sig. (2-tailed) .070 .814 .308 .063 .906 .719 .255 .750 .601 .000 .000 .000

N 226 226 226 226 226 226 226 226 226 226 226 226 226

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Page | 178

Appendix 4.7. A more detailed explanation about Conjoint Analysis The conjoint analysis is an experimental research method used in the evaluation of complex products

or services while, in the same time, maintaining the realistic contexts (Hair et al., 2013). It is developed

based on the assumption that consumers evaluate the value of products and services by combining

the value of the combination of attributes attached in the products and services (Hair et al., 2013). By

using this method, a “market-like” environment is achieved by incorporating the consumers’ trade-off

evaluation related to the set of attributes in the products or services that have varied into sets of

choices (Kanetkar, 2002).

The general postulate of a conjoint analysis method is the utility that the customer get from a product

is derived from the product properties, not the product per se, in which the product is compounded

by a single or combination of those properties (Louviere; et al., 2003). Particularly, the conjoint anal-

ysis aims to understand how the respondents will react to the changes in levels and/or attributes that

embodied in certain product or service, and not in how the utility function is formed (Louviere; et al.,

2003).

The terminologies used in the conjoint analysis are:

1. Utility, which represents the value or the measure of preference over a product and/or ser-

vice.

2. Attributes, which represents the characteristic of the product or service, for example: sweet-

ness and caffeine.

3. Levels, which represents the possible value of each attribute, for example: unsweetened, low

sugar and medium sugar, or decaf and caffeinated.

4. Profile, which represents the object/product/service that consist of selection of attributes and

their level, for example: Coffee A [low sugar, decaf], or coffee B [medium sugar, decaf].

5. Designs, which represents a number of profiles that are presented to and evaluated by the

respondents. It could be full profile, in which all choices based on a full factorial design are

presented, or a fraction of profile, in which only part of the full profile choices will be pre-

sented.

There are three kinds of conjoint methodology, i.e., traditional approach, adaptive approach, and

choice based approach (CBC). In the traditional approach, the respondents are presented with a full

set of profiles and asked to perform the choice. The main drawback of this approach is: if the products

or services have a lot of attributes and/or levels, the respondents will have a lot of profiles to be eval-

uated in the same time because they have to give rating/ranking for each profile. Hence, the respond-

ents might experience an information overload and be unable to evaluate the design in detail (Rao,

2014). The second approach, i.e.: the adaptive approach, has been developed to answer this chal-

lenge. This approach is able to perform the conjoint analysis of product or services with more than 10

attributes. In this approach, the presented set of profiles are varied and adapted based on the re-

spondents’ previous answers. In each section, only one or a few attributes were presented, therefore,

the respondents will not be overwhelmed by a lot of profiles at once (Orme, 2009).

In between those two approaches, the CBC analysis is often used to determine customer preferences.

It performs a unique form to present the profiles in a set of choices (Hair et al., 2013). The respondents

then are asked to choose one profile from this set of profiles, one of which their utility is maximized

(Rao, 2014). This approach has the most realistic approach to what happens in the real market, in

which the consumers do not always evaluate the complete competing products or services before

choosing the selected product or service. For this reason, the CBC analysis is used in this study. How-

ever, this approach also is far more complex in the process of generating the “efficient” designs that

are presented to the respondents. To overcome this complexity, statistical software is often employed

to determine the most efficient design.

Furthermore, there are two types of CBC. The first method is the stated choice CBC. This method is

able to present the choices into experimental condition to estimate the utility value of certain hypo-

thetical conditions (Fifer et al., 2014). Thus, even though the presented choices are not yet available

in the market, we can predict the possible users’ acceptance once the choices are available in the

market. The second method of CBC is the revealed choice CBC. This method aims to “reveal” the users’

utility value of the real and known conditions. In contrast to the stated choice model, in which only

captures what the respondents intend to do, this method is effectively able to produce the actual data

based on the respondents’ actual behavior (Louviere et al., 2000). As choices formed in the revealed

choice CBC are based on the real condition in the market, constructing a complete and exhaustive

revealed choice study sometimes needs a huge number of choice sets due to the attributes involved

in a real product might be varied. Moreover, in the real life condition, the attributes that build up a

product are often correlated, thus it will complicate the analysis and produce significant amount of

noise (Brownstone et al., 2000; Louviere et al., 2000).

Appendix 4.8. Choice sets configuration

Choice situa-tion

alt1.pdi

alt1.pba

alt1.es

alt1.es effect coding alt2.pdi

alt2.pba

alt2.es

alt2.es effect coding

alt1.s.e0

alt1.s.e1

alt1.s.e2

alt2.s.e0

alt2.s.e1

alt2.s.e2

1 0 0 270 1 0 0 1 1 270 1 0 0

2 1 1 270 1 0 0 1 0 180 0 1 0

3 0 1 180 0 1 0 0 1 90 0 0 1

4 1 0 180 0 1 0 1 0 90 0 0 1

5 0 1 90 0 0 1 0 0 270 1 0 0

6 1 0 90 0 0 1 0 0 0 -1 -1 -1

7 0 0 0 -1 -1 -1 1 1 0 -1 -1 -1

8 1 1 0 -1 -1 -1 0 1 180 0 1 0

Page | 180

Appendix 5.1 Multiple regression analysis of IUIPC and CFIP

IUIPC

Model Summaryd

Model R R Square Adjusted R Square Std. Error of the Es-

timate

1 .338a .114 .111 .94289579

2 .385b .148 .142 .92633185

3 .407c .166 .156 .91860837

a. Predictors: (Constant), FAC_ISR

b. Predictors: (Constant), FAC_ISR, FAC_IPPR

c. Predictors: (Constant), FAC_ISR, FAC_IPPR, FAC_AGRL

d. Dependent Variable: FAC_IUIPC

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 30.179 1 30.179 33.945 .000b

Residual 233.821 263 .889

Total 264.000 264

2

Regression 39.180 2 19.590 22.830 .000c

Residual 224.820 262 .858

Total 264.000 264

3

Regression 43.757 3 14.586 17.285 .000d

Residual 220.243 261 .844

Total 264.000 264

a. Dependent Variable: FAC_IUIPC

b. Predictors: (Constant), FAC_ISR

c. Predictors: (Constant), FAC_ISR, FAC_IPPR

d. Predictors: (Constant), FAC_ISR, FAC_IPPR, FAC_AGRL

Coefficientsa

Model Unstandardized Coeffi-

cients

Standardized

Coefficients

t Sig. Correlations Collinearity Statistics

B Std. Error Beta Zero-order Partial Part Tolerance VIF

1 (Constant) -1.001E-013 .058

.000 1.000

FAC_ISR .338 .058 .338 5.826 .000 .338 .338 .338 1.000 1.000

2

(Constant) -1.001E-013 .057

.000 1.000

FAC_ISR .242 .064 .242 3.760 .000 .338 .226 .214 .786 1.272

FAC_IPPR .208 .064 .208 3.239 .001 .320 .196 .185 .786 1.272

3

(Constant) -1.005E-013 .056

.000 1.000

FAC_ISR .184 .068 .184 2.693 .008 .338 .164 .152 .683 1.464

FAC_IPPR .170 .066 .170 2.579 .010 .320 .158 .146 .737 1.357

FAC_AGRL .155 .067 .155 2.329 .021 .316 .143 .132 .717 1.395


Excluded Variablesa

Model Beta In t Sig. Partial Correla-

tion

Collinearity Statistics

Tolerance VIF Minimum Toler-

ance

1

FAC_IPPR .208b 3.239 .001 .196 .786 1.272 .786

FAC_AGRL .199b 3.040 .003 .185 .765 1.307 .765

2 FAC_AGRL .155c 2.329 .021 .143 .717 1.395 .683


b. Predictors in the Model: (Constant), FAC_ISR

c. Predictors in the Model: (Constant), FAC_ISR, FAC_IPPR

Page | 182

CFIP

Model Summaryc

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change df1 df2 Sig. F Change

1 .502a .252 .249 .86676837 .252 88.397 1 263 .000

2 .564b .318 .313 .82909950 .066 25.441 1 262 .000

a. Predictors: (Constant), FAC_AGRL

b. Predictors: (Constant), FAC_AGRL, FAC_IPPR

c. Dependent Variable: FAC_CFIP

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 66.411 1 66.411 88.397 .000b

Residual 197.589 263 .751

Total 264.000 264

2

Regression 83.900 2 41.950 61.026 .000c

Residual 180.100 262 .687

Total 264.000 264

a. Dependent Variable: FAC_CFIP

b. Predictors: (Constant), FAC_AGRL

c. Predictors: (Constant), FAC_AGRL, FAC_IPPR

Coefficientsa

Model Unstandardized Coeffi-

cients

Standardized

Coefficients

t Sig. Correlations Collinearity Statis-

tics

B Std. Error Beta Zero-or-

der

Partial Part Tolerance VIF

1 (Constant) -1.010E-013 .053

.000 1.000

FAC_AGRL .502 .053 .502 9.402 .000 .502 .502 .502 1.000 1.000

2

(Constant) -1.007E-013 .051

.000 1.000

FAC_AGRL .383 .056 .383 6.817 .000 .502 .388 .348 .825 1.212

FAC_IPPR .283 .056 .283 5.044 .000 .444 .298 .257 .825 1.212


Excluded Variablesa

Model Beta In t Sig. Partial Correla-

tion

Collinearity Statistics

Tolerance VIF Minimum Toler-

ance

1 FAC_IPPR .283b 5.044 .000 .298 .825 1.212 .825

FAC_ISR .180b 3.001 .003 .182 .765 1.307 .765

2 FAC_ISR .094c 1.529 .127 .094 .683 1.464 .683


b. Predictors in the Model: (Constant), FAC_AGRL

c. Predictors in the Model: (Constant), FAC_AGRL, FAC_IPPR

Page | 184

Appendix 5.2 Cluster properties of data segregated based on demographic

variables

Page | 186

Appendix 5.3 Mean difference of the WTD vs. demographic Variables

wtd vs. self-efficacy

Report

FAC_WTD

split_sel Mean N Std. Deviation

1.00 .0054460 233 .99091280

2.00 -.0396535 32 1.07972631

Total .0000000 265 1.00000000

ANOVA Table

Sum of Squares df Mean Square F Sig.

FAC_WTD * split_sel

Between Groups (Combined) .057 1 .057 .057 .811

Within Groups 263.943 263 1.004

Total 264.000 264

wtd vs. previous experience

Report

FAC_WTD

split_pre Mean N Std. Deviation

1.00 -.0767909 75 1.02554382

2.00 .0303122 190 .99084270

Total .0000000 265 1.00000000

ANOVA Table


FAC_WTD * split_pre


Within Groups 263.383 263 1.001

Total 264.000 264

wtd vs. education

Report

FAC_WTD

split_edu Mean N Std. Deviation

1.00 -.0820690 52 .92200533

2.00 .0200356 213 1.01917807

Total .0000000 265 1.00000000

Page | 188

ANOVA Table


FAC_WTD * split_edu


Within Groups 263.564 263 1.002

Total 264.000 264

wtd vs. salary

Report

FAC_WTD

split_sal Mean N Std. Deviation

1 .1339180 166 .92735477

2 -.1576006 60 1.15912922

Total .0565237 226 .99980966

ANOVA Table


FAC_WTD * split_sal

Between Groups (Combined) 3.745 1 3.745 3.793 .053

Within Groups 221.169 224 .987

Total 224.914 225

wtd vs. age

Report

FAC_WTD

age_split Mean N Std. Deviation

0 .0174011 212 .98997974

1 -.0696045 53 1.04592981

Total .0000000 265 1.00000000

ANOVA Table


FAC_WTD * age_split


Within Groups 263.679 263 1.003

Total 264.000 264

wtd vs. dsex

Report

FAC_WTD

dsex Mean N Std. Deviation

0 -.0990818 111 1.05755228

1 .0714161 154 .95349033

Total .0000000 265 1.00000000

ANOVA Table


FAC_WTD * dsex

Between Groups (Combined) 1.875 1 1.875 1.881 .171

Within Groups 262.125 263 .997

Total 264.000 264

Page | 190

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