東海大學企業管理學系碩士論文 -...

東海大學企業管理學系

碩士論文

印尼網路使用者對於 O2O模式的認知研

究-知曉度與知覺風險之調節效果

Internet User’s Perceptions towards O2O

(Online-to-Offline) in Indonesia: The

Moderation Effect of Awareness and

Perceived Risk

指導教授：王本正博士

研究生：黄女鈴撰

中華民國一○五年二月

-i-

Title of Thesis: Internet User’s Perceptions towards O2O (Online-to-Offline) in

Indonesia: The Moderation Effect of Awareness and Perceived Risk

Name of Institute: Master of Business Administration, Tunghai University

Graduation Time: February, 2016

Student Name: Melys Wijaya Oey Advisor Name: Wang, Ben-Jeng

Abstract:

Online shopping has become well-known these days for both in developed countries

and developing countries, like Indonesia for example. One of the E-commerce business

model that is now starting to enter Indonesia market is called O2O. This study is aimed to

find out the perceptions of O2O in Indonesia from its consumer by adding moderator

variable to the model. Technology Acceptance Model (TAM) is conducted in this study,

with additional of awareness and perceived risk as the moderator variables. Using

quantitative method, this study has gathered 379 respondents collected from questionnaire

distribution through the social media and messenger application such as Facebook and LINE.

Results from this study showing that the O2O business model is accepted by Indonesia’s

internet user with both moderator variables don’t have significant relationship with the

perceived ease of use, awareness is significantly related with the perceived usefulness and

the perceived risk is only partially moderates the perceived usefulness.

Keywords: E-commerce, O2O, TAM, Awareness, Perceived Risk, Moderation Analysis

-ii-

論文名稱：印尼網路使用者對於O2O模式的認知研究-知曉度與知覺風險之調節效果

校所名稱：東海大學企業管理學系研究所

畢業時間：2016 年 2 月

研究生：黃女鈴指導教授：王本正

中文摘要:

近年網路購物在以發展過家已被廣為使用，然而這樣的購物模式也廣泛的在發展中

國家被使用，像是印度尼西亞。目前一種電子商務模式稱為 O2O 剛進入印尼市場，

本研究目的是找出印尼消費者對此 O2O 模式的看法，並進一步加入調節變數解釋消

費者的看法之影響。本研究是科技接受模式（Technology Acceptance Model, TAM）

為基礎，並加入知曉度與知覺風險作為調節變數。本研究採用量化的方法，分別透

過社群媒體與通訊應用程式，如 Facebook與 LINE，回收 379份問卷。研究結果顯示

O2O 商業模式是被印尼的網路使用者所接受的，而在調節變數『感知易用性』

（perceived ease of use, PEOU）與『知覺風險』及『知曉度』並無顯著的調節效果；

『知曉度』與『感知有用性』（perceived usefulness, PU）是具有顯著的，只有『知

覺風險』與『感知有用性』則具有部份調節的效果。

關鍵詞：電子商務，O2O，TAM，知曉度，知覺風險，調節分析

-iii-

Table of Contents

Abstract .............................................................................................. i

中文摘要 ............................................................................................ ii

Table of Contents ............................................................................. iii

List of Table ...................................................................................... v

List of Figure .................................................................................... vi

Chapter 1 – Introduction ................................................................. 1

1.1 Background ........................................................................................ 1

1.2 Research Purpose ............................................................................... 4

1.3 Research Problem Statement ............................................................. 5

1.4 Significance of Study ........................................................................ 5

1.5 Thesis Structure ................................................................................. 5

Chapter 2 – Literature Review ....................................................... 7

2.1 E-Commerce ...................................................................................... 7

2.2 Online-to-Offline (O2O) ................................................................... 9

2.3 Technology Acceptance Model (TAM) ........................................... 10

2.4 Awareness ........................................................................................ 12

2.5 Perceived Risk ................................................................................. 12

2.6 Past Research ................................................................................... 15

Chapter 3 – Methodology .............................................................. 17

3.1 Research Method ............................................................................. 17

3.2 Research Model and Research Hypothesis ..................................... 17

3.3 Population and Sample .................................................................... 19

3.4 Data Collection Technique .............................................................. 20

3.5 Operational Definition Variable ...................................................... 23

3.6 Analyzing Data Technique .............................................................. 24

-iv-

Chapter 4 – Analysis and Result ................................................... 29

4.1 Questionnaire Results and Analysis ................................................ 29

4.2 Research Variables Descriptive Analysis ........................................ 45

4.3 Reliability Analysis ......................................................................... 46

4.4 Validity Analysis .............................................................................. 49

4.5 Multicollinearity Analysis ............................................................... 53

4.5 Moderation Regression Analysis ..................................................... 53

4.7 Hypotheses Testing Result............................................................... 55

Chapter 5 – Conclusion ................................................................. 59

5.1 Conclusion ....................................................................................... 59

5.2 Managerial Implications .................................................................. 60

5.3 Suggestion ....................................................................................... 61

References ....................................................................................... 63

Appendices ...................................................................................... 68

-v-

List of Table

TABLE 1-1 INDONESIA INTERNET USERS IN YEAR 2000-2015 ................................................ 2

TABLE 3-1 LIST OF VARIABLE’S CONSTRUCTS FOR THE RESEARCH MODEL AND SOURCES ..... 21

TABLE 4-1 GENDER OF THE RESPONDENTS ........................................................................... 31

TABLE 4-2 AGE OF THE RESPONDENTS .................................................................................. 32

TABLE 4-3 OCCUPATION OF THE RESPONDENTS .................................................................... 33

TABLE 4-4 RELIGION OF THE RESPONDENTS ......................................................................... 34

TABLE 4-5 LIVING PLACE OF THE RESPONDENTS .................................................................. 35

TABLE 4-6 EDUCATION OF THE RESPONDENTS ...................................................................... 36

TABLE 4-7 MONTHLY INCOME OF THE RESPONDENTS ........................................................... 36

TABLE 4-8 INTERNET USING EXPERIENCE OF THE RESPONDENTS .......................................... 37

TABLE 4-9 INTERNET USAGE OF THE RESPONDENTS (PER WEEK) .......................................... 38

TABLE 4-10 INDONESIA’S O2O (E-COMMERCE) WEBSITE..................................................... 39

TABLE 4-11 ITEMS BOUGHT IN THE WEBSITE ........................................................................ 40

TABLE 4-12 EXPENSE FROM PURCHASE HISTORY .................................................................. 42

TABLE 4-13 PAYMENT METHODS .......................................................................................... 43

TABLE 4-14 MAIN REASON TO USE O2O ............................................................................... 44

TABLE 4-15 DESCRIPTIVE ANALYSIS FOR THE VARIABLES ..................................................... 45

TABLE 4-16 BIVARIATE CORRELATION IN EACH VARIABLE .................................................... 45

TABLE 4-17 PERCEIVED USEFULNESS (PU) RELIABILITY TEST RESULT ................................. 46

TABLE 4-18 PERCEIVED USEFULNESS (PU) ITEM-TOTAL TEST RESULT ................................. 46

TABLE 4-19 PERCEIVED EASE OF USE (PEOU) RELIABILITY TEST RESULT ........................... 47

TABLE 4-20 PERCEIVED EASE OF USE (PEOU) ITEM-TOTAL TEST RESULT ............................ 47

TABLE 4-21 PERCEIVED RISK (PR) RELIABILITY TEST RESULT .............................................. 48

TABLE 4-22 PERCEIVED RISK (PR) ITEM-TOTAL TEST RESULT .............................................. 48

TABLE 4-23 BEHAVIORAL INTENTION TO USE (BI2U) RELIABILITY TEST RESULT ................. 49

TABLE 4-24 BEHAVIORAL INTENTION TO USE (BI2U) ITEM-TOTAL TEST RESULT .................. 49

TABLE 4-25 INDICATORS VALIDITY RESULTS ......................................................................... 50

TABLE 4-26 MULTICOLLINEARITY ANALYSIS RESULT............................................................ 53

TABLE 4-27 MODERATION REGRESSION ANALYSIS 1ST AND 2ND

LAYER ................................ 54

TABLE 4-28 MODERATION REGRESSION ANALYSIS 3RD LAYER ............................................. 55

TABLE 4-29 HYPOTHESES TESTING RESULT ......................................................................... 56

-vi-

List of Figure

FIGURE 1.1 NUMBER OF INDONESIA’S INTERNET USER OVER POPULATION IN 2000-2014 ...... 2

FIGURE 2.1 FLOW PROCESS OF O2O BUSINESS MODEL (DU & TANG, 2014) ....................... 10

FIGURE 2.2 THE ORIGINAL TECHNOLOGY ACCEPTANCE MODEL (GUNAWAN, 2013) ............ 11

FIGURE 2.3 THE EXTENSION OF TECHNOLOGY ACCEPTANCE MODEL (WU & WANG, 2005) 11

FIGURE 2.4 CONCEPTUAL MODEL AND RESEARCH HYPOTHESES OF PAVLOU ....................... 16

FIGURE 3.1 CONCEPTUAL MODEL AND RESEARCH HYPOTHESES .......................................... 18

FIGURE 3.2 CONCEPTUAL MODEL OF MODERATION .............................................................. 26

FIGURE 3.3 STATISTICAL MODEL OF A MODERATOR EFFECT (RO, 2013)................................. 27

FIGURE 4.1 SHOPPING EXPERIENCE IN INDONESIA’S O2O WEBSITE PIE CHART ..................... 30

FIGURE 4.2 SHOPPING EXPERIENCE IN INDONESIA’S E-COMMERCE WEBSITE PIE CHART ....... 30

FIGURE 4.3 GENDER OF THE RESPONDENTS’ PIE CHART ........................................................ 31

FIGURE 4.4 AGE OF THE RESPONDENTS’ PIE CHART .............................................................. 32

FIGURE 4.5 OCCUPATION OF THE RESPONDENTS’ PIE CHART ................................................. 33

FIGURE 4.6 RELIGION OF THE RESPONDENTS’ PIE CHART ...................................................... 34

FIGURE 4.7 LIVING PLACE OF THE RESPONDENTS’ PIE CHART ............................................... 35

FIGURE 4.8 EDUCATION OF THE RESPONDENTS’ PIE CHART ................................................... 36

FIGURE 4.9 EDUCATION OF THE RESPONDENTS’ PIE CHART ................................................... 37

FIGURE 4.10 INTERNET USING EXPERIENCE OF THE RESPONDENTS’ PIE CHART ..................... 38

FIGURE 4.11 INTERNET USAGE OF THE RESPONDENTS’ PIE CHART ......................................... 39

FIGURE 4.12 INDONESIA’S O2O (E-COMMERCE) WEBSITE PIE CHART ................................... 40

FIGURE 4.13 ITEMS BOUGHT IN THE WEBSITE PIE CHART ....................................................... 41

FIGURE 4.14 EXPENSE FROM PURCHASE HISTORY PIE CHART ................................................ 42

FIGURE 4.15 PAYMENT METHODS PIE CHART ........................................................................ 43

FIGURE 4.16 MAIN REASON TO USE O2O PIE CHART ............................................................. 44

-1-

Chapter 1 – Introduction

Because of the technology development, internet has become the most needed thing

by the people in the world. Internet gives many convenience for the user by providing

information, speed, and the simplicity to access it. Nowadays many company already utilized

the use of internet to develop their businesses through what we know by the term E-

Commerce.

In Asia, China is leading the market of economy and one of them is online shopping

(E-Commerce) using the technology of internet. This make the other country in Asia wants

to follow China’s success in E-commerce, like Indonesia. Indonesia is known as a big

country consists of many islands and diversity, making it a unique country. The number of

internet user in Indonesia from time to time is rising and it creates the potential for some

local company to adapt and create the E-commerce business in hope that it will become a

success business for Indonesia like what has been happened in China and other countries

outside Asia.

1.1 Background

The number of people who like to shop via online is increasing these days, made

many businesses realized that there are many advantages could be gained through E-

commerce (Shen & Wang, 2014). Montague (2011) claimed that there are many different

new forms of E-commerce successively appear because of the significant growth of E-

commerce every year. This phenomenon affecting not only in big cities and developed

countries such as in America and Europe, but also affects many developing countries in Asia

and welcomed by their people. The rapid development of technology and Internet also give

significant impact to the online purchasing behavior happening in their country. Indonesia,

as one of developing countries in Asia also showing an increase at their Internet using as

-2-

noted by Internet Live Stats, the number of Internet users in Indonesia by the year 2014 is

predicted by 42,258,824 people with the percentage of 16.72% from the population of

252,812,245 people, comparing to the year of 2009 where the population is 237,486,894

people and the number of internet users is 16,434,093 people with the percentage of 6.92%.

Table 1.1 and Figure 1.1 below is describing about the number of Indonesia’s Internet users

from the year 2000.

Table 1-1 Indonesia Internet Users in Year 2000-2015

Figure 1.1 Number of Indonesia’s Internet User over Population in 2000-2014

Source: http://www.internetlivestats.com/internet-users/indonesia/ [cited 2014/05/15]

-10,000,000

0

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013* 2014*

Indonesia Internet Users

-3-

Because of the increasing in internet usage happening in Indonesia, it is believed that

this can make a good start for some company to expand their business into E-Commerce by

using this data. Up till now, there are already some companies that using the internet to create

E-commerce business. E-commerce becomes a new kind of business where Internet becomes

the media of doing the transaction. By that, people aren’t needed to leave their house if they

want to buy things, especially if the things they want to buy is not in the same place as they

live, or very far from their house. People also don’t need to experience the traffic jam like in

some big cities like Jakarta, Indonesia if they want to buy some things.

As the E-Commerce is showing great increase in the past few years and get good

welcome from the people in Indonesia, there’s this new type of E-commerce that also starting

to enter the market in Indonesia which what we called O2O. O2O comes from the term

Online to Offline, a business that comes from using the type of online payment and service

offline. Using the internet, businesses seek for the customer via Internet and after that take

those customer to physical outlet (store). Shen & Wang (2014) in their journal describe that

many customers define O2O as an online “discovery mechanism” for activities offline. O2O

development in Asia has been hold by China as the fastest growing business, and already

been adopted since 2010. Knowing that China has succeed with their O2O commerce,

Indonesia in the other part are now trying to do the same.

Awareness in the other way also give important role for the O2O commerce.

Awareness, as in Najafi (2012) said that it is a condition or capability to feel, to perceive, or

being sensible of objects, events, or forms of sensory. As in E-commerce, awareness

especially in O2O will give effects in the development of O2O in the future which lead us to

know whether it is being accepted or not.

There’s always risk in every kind of business. In E-commerce, the risk is considered

bigger than any other kind of business. The customers fear from various associated elements,

such as risk in financial transaction, online security risk, social image risks, time loss risk,

-4-

privacy risk, psychological risk or product performance risk (Kumar & Dange, 2014). Thus,

perceived risk is considered to be one of the variable that is used to determine the perception

of O2O commerce.

This thesis is going to research about whether the O2O is accepted and welcomed by

the people in Indonesia, as well as the E-commerce has been approved. This study is using

Indonesia as the object because seeing the great development of Indonesia recently has

drawing attention to the world and also the researcher has better understanding and familiar

with Indonesia’s situation which ease the researcher to examine this research. Technology

Acceptance Model (TAM) will be used in this research and using consumer awareness and

risk perception as the external variables to be used in the TAM model as its moderation

variable. Therefore, this research is proposed to be Internet User’s Perceptions towards O2O

(Online-to-Offline) in Indonesia: The Moderation Effect of Awareness and Perceived Risk.

1.2 Research Purpose

This thesis has some purpose which stated as follow:

a) To study about Indonesian Internet users’ perception about O2O (Online-to-

Offline) business model as a new concept in Indonesia’s E-Commerce

Business.

b) To find out how the O2O business model is accepted based on consumer’s

perception which emphasized in awareness and perceived risk as the

moderation variable combined using the technology acceptance model used

in this research.

-5-

1.3 Research Problem Statement

This research problem statement is how the Indonesian Internet users, especially

those who like to purchase things via online percept the O2O as a new model of E-

Commerce coming into Indonesia since O2O is now starting to grow and influencing

Indonesia’s E-Commerce business model.

1.4 Significance of Study

The internet users in Indonesia will be the main focus in this study, especially those

who like to purchase things via internet, using the E-commerce business particularly

in O2O business model which recently started to enter Indonesia’s E-commerce

market. The results can be used to find out whether the O2O business model is

accepted by Indonesia’s internet user as the customer.

1.5 Thesis Structure

This thesis structure will be divided into five chapter which described as follows:

CHAPTER 1: INTRODUCTION

For the first chapter, like as been described in this chapter, it contains the general

overview of the thesis, including the background and motivation of how this research

is made, and also about the research purpose, research problem statement, the

significance of study, and the thesis structure.

CHAPTER 2: LITERATURE REVIEW

Chapter two is describing about all theories and literature reviews that support this

research, from many resources and journals. The literature reviews that used in this

chapter are all related to the research topic and also help the writer to do the

methodology.

-6-

CHAPTER 3: METHODOLOGY

The contents of chapter three is describing about the methodology that is used for

this research, how the research is being conducted, and also contains the research

model and hypotheses used in this thesis.

CHAPTER 4: ANALYSIS AND RESULT

Chapter four is describing about the result of the research and analyzing the data into

a result and lead into a discussion.

CHAPTER 5: CONCLUSION

The last chapter, which is chapter five, is describing about the final conclusion and

recommendations about the research that has been made through chapter four’s result.

-7-

Chapter 2 – Literature Review

The successful of E-Commerce business is depending on how the customer can

accept the internet technologies as viable transaction and the recognition of Web retailers as

reliable merchants (Pavlou, 2003). There will be some risks rise from this business, like the

security and trustworthy of using internet as the media to do the transaction. Therefore the

consumer awareness and perceived risk from the customer to the retailer is assessed as

important part in making the customer accepting this business model.

2.1 E-Commerce

According to Turban (2011), Electronic Commerce or usually we called by E-

Commerce is the process of many transactions such as selling, exchanging, buying, or

transferring information, products, and/or services through computer networks, typically the

Internet and intranets. In many standpoints, E-commerce is described in Kalakota and

Whinston in Ngai (2002) journal as follows:

From communications standpoint, E-Commerce is defined as delivery process of

payments, information, or products/services that using computer networks, telephone

lines, or any other media.

From business process standpoint, E-Commerce is defined as the practice of

technology in the direction of the automation of workflow and business transactions.

From service standpoint, E-Commerce is defined as a device that expresses the

craving of management, companies, and clients to reduce service expenses by

refining the product quality while the service delivery speed is increased.

From online standpoint, E-Commerce delivers the ability of selling and buying

information and products happening in the Internet and other online services.

E-Commerce is mostly classified into their base of the transactions and the connections

-8-

among participants. Below is the classification of E-Commerce based on Turban (2011):

Business-to-Business (B2B)

Business-to-business e-commerce, similarly identified as eB2B (electronic B2B), or

B2B, is using the Internet, intranets, extranets, or private networks to make

transactions between businesses that is made electronically. Either the businesses or

organizations are counted as the participants of B2B e-commerce.

Business-to-Consumer (B2C)

Business-to-consumer is an e-commerce model that contains transactions of services

or products retail from businesses to individual shoppers.

Consumer-to-Business (C2B)

Consumer-to-Business (C2B) is an e-commerce model where the person is using the

Internet to market their services or products to organizations or business, and those

persons who look for vendors to bid on services or products for them.

Consumer-to-Consumer (C2C)

Consumer-to-consumer (C2C) e-commerce is also known as peer-to-peer (P2P)

networks or exchanges. This type of e-commerce is involving all transactions made

from and by the individual consumers. Third parties can also be included in these

transactions, mostly formed as those who facilitate the marketplace, like eBay or

social network site.

E-Government

E-Government is generally using the information technology, and particularly e-

commerce, by giving more easy and handy access from organizations and citizens to

the government services and information, and also giving public services delivery to

business associates, citizens, and persons who works in public area. Managing

government business transactions with businesses, citizens, and within governments

themselves is also an efficient and effective method of E-government. Some major

-9-

categories in E-government are Government-to-Business (G2B), Government-to-

Employees (G2E), Internal Efficiency and Effectiveness (IEE), Government-to-

Citizens (G2C), and Government-to-Government (G2G).

2.2 Online-to-Offline (O2O)

Online-to-Offline, mostly known as O2O, is managing the offline services between

the customers and companies via online website or mobile terminals (Shen & Wang, 2014).

The concept is the customers finishing the payment via online, afterwards they get the

services by offline. Du & Tang (2014) said that O2O becomes a new business model by

combining online shopping with the front line transactions. Alex Rempel in Shen & Wang

(2014) journal proposed O2O as a combination of online and offline channels creating a new

kind of e-commerce. It is an online marketplace with online procurements, and also able to

handle numerous businesses offline. O2O commerce also known as a precise form of

multichannel combination, focusing on doing online advertising, like using social media, an

also enhancing the physical store’s sales (Gong and Maddox in Phang, et. al., 2014). Zhixin

(2012) also said O2O is enabling the Internet to become the leader of transactions offline,

means the Internet technology combines together with business opportunity offline.

The core concept of O2O business model is on-line prepayment (Wang & Lai, 2014),

which also defined by Rampel in Shen & Wang (2014) journal as a combination between

store traffic and payment mode to achieve a service offline. The companies find consumers

using the Internet at first then carry them to the physical store. Customers is affected by O2O

commerce in both online and offline which provides dealers opportunities: in the online area,

dealers attempt to encourage content generation for their products, such as product

observations, that can develop awareness and knowledge of online product; in the offline

area, dealers can bring clients to their physical store to make purchases (Phang, et. al., 2014).

The advantage of on-line prepayment is that every transaction is on-line so that every

-10-

transaction is traceable and stores can make use of Internet channel to promote their products

or service. By means of O2O, users could view, select products or service on-line, after they

complete payment process on-line, users could enjoy these services off-line (Wang & Lai,

2014). Below is the flow process of O2O business model.

Figure 2.1 Flow Process of O2O Business Model (Du & Tang, 2014)

2.3 Technology Acceptance Model (TAM)

Based on Fred Davis theory in 1989, the technology of acceptance model was first

established as the adoption of the Theory of Reasoned Action (TRA). TAM was developed

to predict and describe computer-usage behavior and in 1975 Fishbein and Ajzen’s theory of

reasoned action (TRA) contains TAM theoretical grounding which mention that attitudes

stimulates beliefs, that lead to intentions, and lastly to behaviors (Klopping & McKinney,

2004). Davis in Suhendra, et. al. (2009) journal also said that providing foundation for

determining influences of external factors on attitude, trust, and objectives of information

technology end-users was the main objective of TAM. In Gunawan, et. al. (2013) journal, it

is stated that TAM has replaced many of TRA’s attitude measurement by using two

technology acceptance measures – usefulness, and ease of use.

The original TAM as stated in Wu & Wang (2005) be made up with perceived

usefulness, perceived ease of use, attitude toward using, behavioral intention to use, and

actual system use. Legris, et. al., in Wu and Wang (2005) journal proposed that to prepare an

even stronger model, additional variables is required to be given in TAM. An extension of

TAM proposed by Venkatesh and Davis in Wu and Wang (2005) journal consists of cognitive

-11-

instrumental processes and social influence processes, but attitude toward using is omitted

because of weak predictors of either actual system use or behavioral intention to use.

Previous research finding by Taylor and Todd in Wu and Wang (2005) journal made it

consistent because both social influence processes and cognitive instrumental processes

significantly influenced user acceptance and that perceived usefulness and perceived ease of

use implicitly influenced actual system use by way of behavioral intention to use.

Figure 2.2 The Original Technology Acceptance Model (Gunawan, 2013)

Figure 2.3 The Extension of Technology Acceptance Model (Wu & Wang, 2005)

Perceived usefulness is “to what extent an individual believes that the use of a system will

improve his/her performance”, and perceived ease of use is “to what extent an individual

believes that the use of a system will be uncomplicated” (Davis in Klopping and McKinney,

2004). Jonas and Norman in Gunawan (2013) journal define perceived usefulness as how far

a student believes that using the technology will improve his or her performance. Perceived

usefulness might be able to be a strong determinant of intention to use the technology. They

also define perceived ease of use as to what extent a student believes that using the

technology will be relatively effortless.

-12-

2.4 Awareness

Najafi (2012) define awareness as the condition or capability to feel, to perceive, or

to being sensible of objects, events, or forms of sensory. Broadly speaking, it is the quality

or state of being conscious of something. Awareness in biological psychology is defined as

cognitive reaction and a human's or an animal's opinion to an event or condition. As for the

consumer awareness regarding to the use of Internet and E-commerce, Chang, et. al. (2012)

described as it is the existent possibility of the seller (in this term is the e-retailers) perceived

by consumers. Najafi (2012) in his journal defines consumer awareness as a buyer's

knowledge of a specific company or product that allows them to get the greatest from what

he / she buys.

2.5 Perceived Risk

Risk is defined by Mitchell in Mohamed, et. al. (2011) journal as the variation in the

allocation of possible results, their subjective values and their likelihood. Featherman &

Pavlou (2003) also commonly thought perceived risk as felt of uncertainty in using a product

or service about potential negative consequences. Peter & Ryan in Featherman & Pavlou

(2003) formally defined perceived risk as ‘‘the expectancy of losses related to acts and

purchase as an inhibitor to purchase behavior’’ and Bauer in Featherman & Pavlou (2003)

defined perceived risk as ‘‘a combination of seriousness plus uncertainty of outcome

involved’’. In Kumar & Dange (2014) journal, Baurer and Michael Laroche stated that there

are two components of perceived risk which are consequences (the importance of a loss) and

uncertainty (the likelihood of unfavorable outcomes). Adobor (2005) said that the

individual’s personal feeling of certainty that the outcomes will be not favorable, and the

number that would be lost if the outcomes of an act were unfavorable are defined as

perceived risk. Perceived risk is also defined by Staelin in Kumar & Dange (2014) as “the

buyer’s opinion of the hesitation and related unfavorable expenses of purchasing a service

-13-

or product”. Perceived risk in online shopping perception is defined as “the opportunities of

any negative consequences or any loss as the online shopping’s result” (Hassan, et. al., 2006).

Based on Wu & Wang (2005) perception, since online transactions became popular, the

perceived risk definition has changed. Ben-Ur and Forsythe in Wu & Wang (2005) journal

explained that product quality and deception were mainly regarded as perceived risks in the

past. Nowadays, perceived risk is known as certain forms of social, product performance,

physical, time risks, psychological, or financial when customers make online transactions.

In context of online shopping, some types below which described from many

researchers taken from the journal of Kumar & Dange (2014) can describe perceived risk is

a multidimensional construct, as also in Featherman & Pavlou (2003) journal:

1. Financial Risk

According to Laroche (2004), the potential loss of money related with the item

purchase is defined as financial risk. When bad purchase circumstances happened,

Cases (2002) defines financial risk is connected to the money loss. Financial risk is

the shopping activity resulting in the perceived financial concern (ÇENGEL, 2012).

2. Product Performance Risk

Ueltschy (2004) in his journal stated that when a product or brand does not give the

intended performance, the loss incurred from it is defined as product performance

risk ÇENGEL (2012) also said that performance risk is stated as the risk of not

covering the intended performance standards.

3. Physical Risk

Based on ÇENGEL (2012), physical risk are the risk associated with its usage, for

example security and health concerns. As for Ueltschy (2004) and Cases (2002) in

their journal said that the relations between individual’s health and safety is defined

as physical risk.

-14-

4. Psychological Risk

Ueltschy (2004) stated that psychological risk describes a person’s dissatisfaction in

her/him in the situation of a bad choice of product/service. Psychological risk is also

described as self-concept or self-image possibility of harm as the outcome of the item

bought (Laroche, 2004). Psychological risks in terms of their personal image are the

risks that are performed by the customers as the outcome of the product not being

recognized by them (ÇENGEL, 2012).

5. Social Risk

According to Laroche (2004), the definition of social risk is the potential loss given

to the customer by other individuals about respect, friendship, and/or esteem and is

expected to happen with services because of the service encounter. When poor

product/service choice situation occurs, Ueltschy (2004) said social risk also reflects

the dissatisfaction by his friends in the individual.

6. Time/ Convenience Risk

Cases (2002) said that the used of time to purchase a product and the time wasted in

a bad purchase situation is defined as time risk. The potential loss of time and energy

related to procurement of the item is defined as time risk said by Laroche (2004).

7. Source Risk

Source risk is concerning several other potential sources and credibility of perceived

risk (Mohamed, et. al., 2011).

-15-

8. Privacy Risk

When information about one individual is used without his/her permission or

knowledge, the possibility of losing control over those personal information is

defined as privacy risk.

9. Overall Risk

When all criteria are evaluated together, it can be counted as general measure of

perceived risk.

2.6 Past Research

It is found that past research related and similar to this study is titled “Consumer

Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology

Acceptance Model” from the author Paul A. Pavlou (2003, l01 – 134). This research is

studying about forecasting acceptance from consumer about E-Commerce by offering

a series of main supports for attracting customers in online transactions. Divided into

two studies, using variables from Technology Acceptance Model (TAM) as the main

supports of E-commerce acceptance, adding trust and perceived risk as the external

variable. This research model is provided in figure 2-4 below. The result indicates that

both studies have strong support in accepting the E-Commerce model.

-16-

Figure 2.4 Conceptual Model and Research Hypotheses of Pavlou

Source: Pavlou, Paul A. (2003). Consumer Acceptance of Electronic Commerce: Integrating

Trust and Risk with the Technology Acceptance Model. International Journal of Electronic

Commerce, Vol. 7, No. 3, pp. l01 – 134.

-17-

Chapter 3 – Methodology

Methodology is defined as applying theoretical and systematic analysis of the

methods to a field of study. This chapter will describes which research method will be used

in this study, the research model and hypotheses, how the population and sample will be

obtained, data collection technique will be used, the operational definition variable used in

this study, and the analyzing data technique used for this study.

3.1 Research Method

Quantitative approach will be used as the research method in this study. Based from

John W. Creswell (2003), a quantitative approach is developing knowledge primarily by the

investigator using post-positivist claims (i.e., reduction to specific questions and variables

and hypotheses, experiment of theories, cause and effect thinking, and the use of observation

and measurement), using experiments and surveys as the strategies of inquest and collects

data on prearranged tools that produce statistical data.

The data from this research is gathered by using online questionnaire as the research

sample. The result from these questionnaire will be analyzed with statistical method by using

SPSS as the instrument for the data analyzing process.

3.2 Research Model and Research Hypothesis

Figure 3.1 below is showing the research model for this study which has been used

in similar study by Pavlou (2003). Technology Acceptance Model (TAM) will be used in this

research and for the data processing and analyzing data technique, moderation regression

analysis will be conducted.

-18-

There are five hypotheses that formed from this model and described below.

H1: Consumer usage intentions have significant influence on actual usage of O2O.

H2: Consumer intentions to use have significant relations to perceived usefulness of

the Website interface in O2O.

H3: Consumer intentions to use have significant relations to perceived ease of use of

the Website interface in O2O.

H4a: Consumer intention to use have significant relations to the moderation of

awareness to perceived usefulness in O2O.

H4b: Consumer intention to use have no significant relations to the moderation of

perceived risk to perceived usefulness in O2O.

H5a: Consumer intention to use have significant relations to the moderation of

awareness to perceived ease of use in O2O.

H5b: Consumer intention to use have no significant relations to the moderation of

perceived risk to perceived ease of use in O2O.

Figure 3.1 Conceptual Model and Research Hypotheses

-19-

3.3 Population and Sample

Population defined by Sugiyono (2014) is generalized area which consist of

object/subject that has certain quality and characteristic that established by the researchers

to be learned and deduct a conclusion. The population of this research is all of Indonesian

people that are also internet users.

Based on Cooper (2014), by choosing several factors in a population, the whole

population may be drawn into conclusions that becomes the basic idea of sampling. A sample

examines a piece of the target population, and to represent that population, the piece must be

selected carefully. Sampling design is majorly divided by two, which is non-probability and

probability sampling. In probability sampling, the selection is done by random – a supervised

procedure that endures that every population factor is given a known zero opportunity of

preference. As in non-probability sampling, it is subjective and arbitrary, which means that

in doing the sampling, it is done with a pattern or scheme. The main difference between these

two sampling is the term random.

In this research, we will use probability sampling as the sampling design, and will

use the simple random sampling for the method. It is said Simple because the taking sample

process was randomly selected without regarding the level that exist in the population

(Sugiyono, 2014). Sample size in this study is adapting Harsandi (2013) with the number of

population 55 million internet users, 95% confidence level, and 5% confidence interval,

using PHStat statistic add-in system for Microsoft Excel, the minimum sample size that was

obtained is 384 sample.

-20-

3.4 Data Collection Technique

In this research, the data collection technique will be separated into two major

categories, which are the primary and secondary data. Primary data according to Cooper

(2014) is the research question—data the researcher gathers to find the nearest particular

problem. For this research, the primary data that will be used is the questionnaire. The

questionnaire will be formed using Survey Monkey online survey software and then the

questionnaire link will be distributed to the internet users in Indonesia via email, social media

such as Facebook, and by sending personal message directly to the respondents using

messenger application. There are four parts in the survey questionnaire. First part is about

filtering the respondents whether they have shopping experience in Indonesia’s O2O website

or not. If they have, then they can proceed to the next part, if they don’t then they will be

asked whether they have shopping experience in Indonesia’s E-commerce website or not. If

they have then they can use their experience to answer the rest of the questions, if they don’t

then they can’t fill the questionnaire anymore; the second part of the questionnaire will be

asking about their experience in using O2O as their online shopping method; the third part

is recording the consumer’s perception from each variable in the model. The questionnaire

will also use the 5-point Likert Scale as the rating scale with the scale value of 1 indicating

the strongly disagree, 2 for disagree, 3 for neither agree nor disagree, 4 for agree, and 5 for

strongly agree; the fourth—which is the last part will be recording the subject’s demographic

information. Below is the list of items about each variable’s constructs used in the

questionnaire.

-21-

Table 3-1 List of variable’s constructs for the research model and sources

Construct

Variable Indicator Description References

Consumer

Awareness

(CA)

CA1

According to the description above, I am

completely aware of the existence about this

platform

Bailey

(2005)

Perceived

Usefulness

(PU)

PU1 Overall, I find this platform is useful.

Pavlou

(2003)

PU2 I think this platform is valuable to me.

PU3 The workflow process in this platform is useful to

me.

PU4 This overall platform is functional

PU5 Using this platform enables me to find products I

want more quickly. Benlian,

A., et. al

(2012)

PU6 Using this this platform enhances my

effectiveness in finding suitable products.

PU7 If I use this platform, I will increase the quality of

my judgments.

Perceived

Ease of

Use

(PEOU)

PEOU1 My interaction with this platform is clear and

understandable.

Pavlou

(2003)

PEOU2 Interacting with this platform does not require a

lot af mental effort.

PEOU3 I find it easy to locate the information that I need

in this platform.

PEOU4 I find this platform easy to use.

PEOU5 Learning to apply this platform would be easy for

me.

Benlian,

A., et. al

(2012)

-22-

Perceived

Risk (PR)

PR1 I think this platform will be able to make itself

clearly understood.

Mohame

d, F. A.,

et. al

(2011)

PR2 I doubt this platform will be able to make this type

of business model work for Indonesian people.

PR3 I am concerned about the accessibility of this

platform through online approach.

PR4 I’m concerned that the technology used in this

platform won’t be reliable.

PR5

I’m not sure I’ll have the time needed to

successfully complete my purchasing process in

this platform.

PR6 I am concerned about the availability of products I

want to buy in a timely basis.

PR7 I’m afraid that this platform will take too much

time away from my daily activities.

PR8 I don’t think this purchasing process in this

platform would interfere with my regular schedule.

PR9 I am worried about keeping myself motivated to

purchase in this purchase.

PR10 I have a feeling that purchasing in this platform are

less important than the traditional-way purchasing.

PR11 Just the thought of purchasing in this platform

causes me to feel stressed.

PR12 It is difficult to determine the credibility of some

retailers offering this platform.

PR13 It is not hard to ascertain the expertise of some


PR14 It’s not difficult to learn the reputation of retailers

offering this platform.

PR15 I’m concerned about the credibility of some


PR16 I think that retailers that offer this platform are just

as good as traditional or online retailers.

-23-

Behavioral

Intension

To Use

(BI2U)

BI2U1 Given the chance, I intend to use this platform.

Pavlou

(2003)

BI2U2 Given the chance, I predict that I should use this

platform in the future.

BI2U3 It is likely that I will transact with this platform in

the near future (range of weeks).

BI2U4 If the opportunity arises, I’ll make transaction in

this platform

BI2U5 I would never even consider purchasing in this

platform.

BI2U6

There’s a very good chance that I’ll purchase in

this platform in the future (range of months or

years).

Actual

Usage

(AU)

AU1

I have frequently used this platform to conduct

product purchases or monetary transactions during

the last six months.

Pavlou

(2003)

The secondary data, based on Cooper (2014) said that it is the results of studies

completed by others and for different purposes than the one for which the data are being

reviewed. The secondary data that is used in this research are gathered from journals, books,

and literatures that related to this research.

3.5 Operational Definition Variable

Defining the operational variable is a step that needed in order to conduct a research.

Operational definition is a concept to provide the estimated variable that accomplished by

observing the behavioral dimensions, properties or facts indicated by the concept. The

variables are then interpreted into noticeable and measureable elements so as to create an

index of measurement of a concept (Cavana, 2001).

The operational definition of variable that is used in this research will be described as below:

1. Consumer Awareness

-24-

Consumer awareness in this research is defined as the level of awareness from

the internet user (consumer) towards the online-to-offline commerce in Indonesia.

2. Perceived Risk

The perceived risk in this research is defined as how the consumers will think of

the use of O2O will give some risks that resulting the consumers don’t want to

use and/or buy products via O2O.

3. Perceived Usefulness

Perceived usefulness definition variable in this research is the assumption that

the use of Online-to-Offline commerce is giving good contribution (useful) to

users.

4. Perceived Ease of Use

The operational definition of perceived ease of use is the assumption of the O2O

commerce will be easy to use.

5. Behavioral Intension to Use

Operational definition for the behavioral intension to use is the probability of the

internet users will use O2O to do transaction in buying some products.

6. Actual Usage

The actual usage in this research is defined as the level of actual use in using O2O

as the media for doing E-commerce by the internet users / consumers.

3.6 Analyzing Data Technique

Data Quality Test (Validity and Reliability)

Pretest will be done before doing the field research, it is to ensure that the

-25-

research instrument has already been understood, accurate, and consistent. The

pretest of data quality test can be evaluated through validity and reliability test. Hair

(2010) described validity as how far a measure or set of measures can represents the

concept of study correctly–extent to which it is free from any nonrandom or

structured error. The validity test that will be used is the Pearson product moment

which comparing the correlation coefficient with 0.3. If the correlation value of an

item statement is smaller of equal to 0.3 then the statement is not valid and has to be

deleted from the test. Only the items that has correlation value greater than 0.3 that

valid and will be included in the test (Sugiyono, 2014).

The reliability test is a valuation of the level of consistency between

numerous dimensions of a variable (Hair, 2010). This test will using the most

common type of measuring the reliability is the reliability coefficient with

Cronbach’s alpha. Based on Hair (2010), it is agreed that in general, the lower limit

for Cronbach’s alpha is 0.7, even though in exploratory research it may decrease to

0.6. These means that the coefficient value of alpha is greater than 0.6 then it is

concluded the research instrument is reliable.

Multicollinearity Test

Multicollinearity according to Hair (2010) is the degree to which a construct

can be described by other constructs in the research. In identifying the

multicollinearity, there are two most common measures which are tolerance and the

variance inflation factor (VIF). We will use the VIF measures to test the

multicollinearity. Diamantopoulos and Winklhofer in Schiavon (2012) reported that

VIF>10 implies relevant problems, this means that VIF below than 10 is acceptable

and don’t have multicollinearity. Hair (2010) also suggests that the higher the number

of tolerance is then the lower the VIF will be.

-26-

Moderation Regression Analysis

A moderator defined by Baron and Kenny in Ro (2012) and Karazsia, et. al.

(2014) journal is a third variable that affect the association between a predictor and

criterion—or so called independent variable and outcome (dependent) variable—and

offers helpful information regarding when, why, or how an event occurs. It is also

can be said that in different stages of the moderator variable, the independent variable

has stronger or weaker association with the outcome variable. The influence of

moderator is not just in the predictor or criterion alone, but it is more in the relation

between them as it is explained in figure 3-2 below. A moderator acts to change the

direction or strength a connection between predictor and criterion. It is also known

that based on terms of interaction, the level of the moderator itself affects the outcome

of a predictor on the criterion (Karazsia, et. al., 2014).

Figure 3.2 Moderation’s conceptual model (Schwebel & Barton in Karazsia, 2014)

The same research conducted by Fairchild (2014) also stated that the

regression of the predictor on the result in moderation varies from across levels of

the moderating variable. Different regression relationships over varying ranks of the

moderator is caused by a non-additive connection between the outcome variable and

predictor created by this dependency. The primary predictor at different levels creates

differential prediction of the outcome that explained as moderator effects. Based on

Ro (2012), using hierarchical multiple regression analysis is the most common

technique to check the moderator effect. As the first step of the regression,

Predictor

Moderator

Criterion

-27-

independent variable (X) as predictors and the moderator variable (Mod) as outcome

variable (Y) is entered into the model. Both the Mod and/or X no need to be important

predictors of the result variable when testing for an interaction. The interaction period

is the next step which added the moderator effect by multiplying the independent and

the moderator variables (X×Mod). The procedure is illustrated in figure 3-3 below.

Figure 3.3 Statistical model of a moderator effect (Ro, 2013)

For basic moderation model, the multiple regression as stated by Fairchild

(2014) is:

Y = β0 + β1X + β2Z + β3XZ

Where Y is the outcome’s expected value, β1 is the outcome of the program (X)’s

effect which controls another variables in the model, β2 is the moderator variable’s

outcome on the result which controls another variables in the model, and β3 is the

interaction’s outcome between the moderator and the program on the result. A

moderator effect is considered present if the outcome variable in the interaction

period and also the interaction period added model according to the change in R2 has

statistically significant amount of variance (Ro, 2013).

Occasionally a single regression model is used by researchers which means

Moderator

Variable (Mod)

Independent

Variable (X)

Outcome

Variable (Y)

Step 1

Step 2

Add

Independent

* Moderator

(X * Mod)

-28-

“all” of the variables, which includes the interaction period, are simultaneously

entered in a singular step. Unless the variables are entered in a distinct step, this will

resulting as the predictors’ main effects cannot be seen. It is because in the same steps

the interaction period’s presence modifies the variance described by the independent

variables itself. Therefore it still decided by using a multi-step hierarchical multiple

regression as the usual procedure. The first step of hierarchical multiple regression is

by seeing the separation of main effects—independent variable and the moderator

itself—and the next step is allowing the researcher to see the separation of

moderation effects.

-29-

Chapter 4 – Analysis and Result

After all of the research process that has been conducted and described in chapter 3,

this chapter will describe the result of the analysis based from the questionnaire and the

statistics results from SPSS. The questionnaire is divided by four parts, with total of 25

questions and have 36 items of indicator measurements spread in six variables used for the

purpose of this research.

4.1 Questionnaire Results and Analysis

The questionnaire are gathered in the period of one month, started from October 22nd

2015 to November 22nd 2015, trough social media and personal message like Facebook,

Line, and other social media applications. During this period, for about 751 respondents are

collected, leaving 463 respondents’ data are valid. There is also missing data from this valid

data, which is about 84 data (18.14%) leaving only 379 data can be used for the data analysis.

Here are the descriptive analysis for the questionnaire. The researcher wants to know first

whether the respondents already have shopping experience in Indonesia’s O2O website or

not, the results came that only 28% from total of 751 respondents already have shopping

experience in Indonesia’s O2O website as shown in figure 4-1. The other 72% who doesn’t

have any shopping experience in Indonesia’s O2O website are asked for the alternative

questions about shopping experience in Indonesia’s E-commerce website.

As in figure 4-2, the results came in for about 50% from the 72% who don’t have

shopping experience in Indonesia’s O2O website do have shopping experience in

Indonesia’s E-commerce website. This means that whether there still little respondents have

shopping experience in Indonesia’s O2O website, but those who have shopping experience

in Indonesia’s E-commerce website are also a lot.

-30-

Figure 4.1 Shopping experience in Indonesia’s O2O website pie chart

Figure 4.2 Shopping experience in Indonesia’s E-commerce website pie chart

For the demographic analysis, the researcher divide it into nine questions that are described

below.

1. Gender

Based from table 4-1 below, the respondents for this questionnaire is dominated

by female with 51.5% while the male is as much as 48.5%, but the difference

with the male respondents is not quite large so basically the questionnaire is filled

equally by both male and female.

Yes28%

No72%

Do you have shopping experience in Indonesia's O2O website? (Apakah anda

memiliki pengalaman berbelanja di website O2O Indonesia?)

Yes No

Yes50%

No50%

Do you have shopping experience in Indonesia's E-commerce website? (Apakah anda memiliki pengalaman berbelanja di

website E-commerce Indonesia?)

Yes No

-31-

Table 4-1 Gender of the respondents

Figure 4.3 Gender of the respondents’ pie chart

2. Age

For the age of the respondents, table 4-2 below describes the number of

respondents who has filled the questionnaire is mostly by the range of 21-25 years

old with the percentage of 70%. The others are followed by the 26-30 years old

by 12.7% and 16-20 years old by 12.4%.

Male51%

Female49%

Gender

Male Female

-32-

Table 4-2 Age of the respondents

Figure 4.4 Age of the respondents’ pie chart

3. Occupation

Half of the respondents have the occupation as an employee with 51.1%, the next

is followed by 27.4% as a student. Table 4-3 below showing the number of

respondents with their occupation.

-33-

Table 4-3 Occupation of the respondents

Occupation

Answer Options Response Percent Response Count

Student 27.4% 84

Employee 51.1% 157

Entrepreneur 14.7% 45

Housewife 2.3% 7

Teacher 2.3% 7

Other (please specify) 2.3% 7

Figure 4.5 Occupation of the respondents’ pie chart

4. Religion

The researcher asked about the religion of the respondents in order to find

whether there is influence between the shopping experience and the religion of

the respondents. Result from table 4-4 shows that 40.4% of the respondents are

Christians, followed by 23.5% are Buddhist, 18.6% are Catholics, and 15.3% are

Moslems. Though the majority of the respondents are Christians, the other

religions are also quietly have the similar portion and that means that religions

somehow have relationships with the online shopping experience.

27.4%

51.1%

14.7%

2.3%

2.3%

2.3%

Student

Employee

Entrepreneur

Housewife

Teacher

Other (please specify)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%

Occupation

-34-

Table 4-4 Religion of the respondents

Religion


Moslem 15.3% 47

Christian 40.4% 124

Catholic 18.6% 57

Hindhu 1.0% 3

Buddhist 23.5% 72


Figure 4.6 Religion of the respondents’ pie chart

5. Living Place

Table 4-5 below describes that most of the respondents as much as 71.3% are live

in Java Island, the second is from Kalimantan Island as much as 23.1%. As for

the others, the respondents are very little comparing to those two islands. It is

believed that most of the respondents are come from this two islands because the

infrastructure in those two places are more well developed than the other island

in Indonesia. Java Island is where the capital city is located, and many trades are

happening in this island that makes Java is the center of many business happening

in Indonesia. As for Kalimantan, it is one of the basis of Indonesia’s economic

15.3%

40.4%

18.6%

1.0%

23.5%

1.3%

Moslem

Christian

Catholic

Hindhu

Buddhist


0.0% 20.0% 40.0% 60.0%

Religion

-35-

strength, with its contribution in gas and coal sector for nation’s income.

Table 4-5 Living place of the respondents

Places (Island) where you live in Indonesia


Java 71.3% 219

Sumatra 2.0% 6

Kalimantan 23.1% 71

Sulawesi 1.6% 5

Bali 1.0% 3


Figure 4.7 Living place of the respondents’ pie chart

6. Last Education

Many of the respondents from this questionnaire have their last education for

undergraduate or mostly known as bachelor degree. As shown in table 4-6 below,

it has 72% for the bachelor degree, following by 13.4% for master degree, and

12.4% for senior high school. 21-25 years old are the highest numbers from all

of the responses collected, which means that they might have just graduated from

university or already working with their bachelor degree as their last education.

71.3%

2.0%

23.1%

1.6%

1.0%

1.0%

Java

Sumatra

Kalimantan

Sulawesi

Bali

Other (pleasespecify)

0.0% 20.0% 40.0% 60.0% 80.0%

Places (Island) where you live in Indonesia

-36-

Table 4-6 Education of the respondents

Figure 4.8 Education of the respondents’ pie chart

7. Monthly income

The respondents’ monthly income is described in table 4-7 below. It is showed

that the income range in Rp 3,000,000.- to Rp 6,000,000.- has the most

respondents as much as 32.9%. The second highest is 24.4% for the income of

Rp 6,000,000.- to Rp 12,000,000.-.

Table 4-7 Monthly Income of the respondents

-37-

Figure 4.9 Education of the respondents’ pie chart

8. Experience in using the Internet

Based on the results from table 4-8 below, more than half of the respondents

(51.8%) have more than 9 years’ experience of using the internet. The others as

much as 30.6% have the number of 6-9 years’ experience in using the internet,

and 16.3% for 3-6 years’ experience in using the internet.

Table 4-8 Internet using experience of the respondents

-38-

Experience in Using the Internet


< 3 years 1.3% 4

3-6 years 16.3% 50

6-9 years 30.6% 94

> 9 years 51.8% 159

Figure 4.10 Internet using experience of the respondents’ pie chart

9. Internet usage per week

As for the respondents’ internet usage per week, more than half of the respondents

as much as 62.2% have spent more than 9 hours per week in using the internet as

shown in table 4-9 below. The other 40% are separating in 3-5 hours (11.4%), 5-

7 hours (11.1%), 7-9 hours (9.8%), and less than 3 hours (5%) internet usage per

week.

Table 4-9 Internet usage of the respondents (per week)

1.3%

16.3%

30.6%

51.8%

< 3 years

3-6 years

6-9 years

> 9 years

0.0% 20.0% 40.0% 60.0%

Experience in Using the Internet

-39-

Figure 4.11 Internet usage of the respondents’ pie chart

The next part is about the respondents’ experience in using Internet as the media for

online shopping, especially in O2O (E-commerce) business model. This part is divided into

five questions which will be described below.

1. Indonesia’s O2O (E-commerce) website that mostly used to buy goods

Based from table 4-10 below, 42.8% of the respondents mostly use Lazada as

their O2O (E-commerce) website to buy their goods, following by 29.4% of the

respondents use other website not mentioned in the provided options such as

Zalora, Bhinneka, and Tokopedia.

Table 4-10 Indonesia’s O2O (E-commerce) website

1.3%

4.2%

11.4%

11.1%

9.8%

62.2%

< 1 hour

1-3 hours

3-5 hours

5-7 hours

7-9 hours

> 9 hours

0.0% 20.0% 40.0% 60.0% 80.0%

Internet Usage per Week

-40-

Which Indonesia’s O2O (E-commerce) website do you mostly use to buy

your goods? (Situs O2O (E-commerce) Indonesia apa yang paling sering

anda gunakan untuk membeli barang?


MatahariMall.com 3.0% 11

GroupOn Disdus 19.3% 71

Jakarta Notebook 3.3% 12

Enter Komputer 2.2% 8

Lazada 42.8% 157


Figure 4.12 Indonesia’s O2O (E-commerce) website pie chart

2. Items that mostly bought in the website

Many of the respondents use the website to buy fashion stuffs, like clothes, shoes,

bags, etc. It is supported by the result showed in table 4-11 below as much as

38.1%, the second highest is buying the gadgets and accessories stuffs as much

as 28.3%, the third highest is using the online website to buy food and beverages

vouchers as much as 15.8%.

Table 4-11 Items bought in the website

3.0%

19.3%

3.3%

2.2%

42.8%

29.4%

MatahariMall.com

GroupOn Disdus

Jakarta Notebook

Enter Komputer

Lazada


0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

Which Indonesia's O2O (E-commerce) website do youmostly use to buy your goods? (Situs O2O (E-commerce) Indonesia apa yang paling sering andagunakan untuk membeli barang?

-41-

Which items from the following that you mostly buy from this Indonesia's

O2O (E-commerce) website? (Barang apa saja yang paling sering anda

beli di situs O2O (E-commerce) ini?)


Fashion (Clothes, shoes, bag, etc) 38.1% 140

Gadgets and

Accessories (Camera, Notebook,

Tablet, Mobile Phone, etc.)

28.3% 104

Books 0.8% 3

Food & Beverages Vouchers 15.8% 58

Travel or Leisure Activities 6.8% 25

Sport Utilities 3.3% 12


Figure 4.13 Items bought in the website pie chart

3. Expense from purchase history

38.1%

28.3%

0.8%

15.8%

6.8%

3.3%

6.8%

Fashion (Clothes, shoes, bag, etc)

Gadgets and Accessories (Camera,…

Books

Food & Beverages Vouchers

Travel or Leisure Activities

Sport Utilities


0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

Which items from the following that you mostly buy fromthis Indonesia's O2O (E-commerce) website?(Barang apasaja yang paling sering anda beli di situs O2O (E-commerce)ini?)

-42-

Based on the results provided in table 4-12 below, as much as 65.4% of the

respondents mostly spend around Rp 50,000,- to Rp 500,000,- for their purchase

history. It followed by the amount around Rp 500,000,- to Rp 1,500,000,- as much

as 20.2%.

Table 4-12 Expense from purchase history

Figure 4.14 Expense from purchase history pie chart

4. Payment methods used when making purchase

-43-

Based on the results provided in table 4-13 below, it is shown that more than half

of the respondents (69.5%) prefer using Bank Transfer as their payment method

when make the purchasing in the website. The results also given the Credit Card

payment method in the second highest by 23.4%.

Table 4-13 Payment methods

What kind of payment methods do you mostly use in buying goods in this

Indonesia's O2O (E-commerce) website? (Jenis pembayaran apa yang

paling sering anda gunakan dalam membeli barang di situs O2O (E-

commerce) Indonesia ini?)


Bank Transfer 69.5% 255

Credit card 23.4% 86

Paypal 1.4% 5

On Site Payment 5.7% 21

Figure 4.15 Payment methods pie chart

5. Main reason to use O2O as choice to purchase goods

69.5%

23.4%

1.4%

5.7%

Bank Transfer

Credit card

Paypal

On Site Payment

0.0% 20.0% 40.0% 60.0% 80.0%

What kind of payment methods do you mostlyuse in buying goods in this Indonesia's O2O (E-commerce) website? (Jenis pembayaran apayang paling sering anda gunakan dalam membelibarang di situs O2O (E-commerce) Indonesia ini?)

-44-

The respondents were asked about their main reason of using O2O (E-commerce)

as their way to purchase goods. The results came in with nearly half of the

respondents (49.9%) giving the reason as the website often offers discount that

can only be obtained when they purchase it online rather than directly buy in the

physical store. Followed by the second most-answered reason as it is more

efficient to buy it via online (43.9%). The other reasons can be seen in table 4-14

provided below.

Table 4-14 Main reason to use O2O

What is the main reason you want to use this O2O (E-commerce) website

as your choice to purchase goods? (Apa yang menjadi alasan utama anda

menggunakan situs O2O (E-commerce) Indonesia ini sebagai pilihan

anda dalam membeli barang?)


They often offers discount (special

price) which we can't get it if we

directly buy to the physical store.

49.9% 183

More efficient (can make the

purchase anywhere, anytime).

43.9% 161

Much safer. 3.0% 11


Figure 4.16 Main reason to use O2O pie chart

49.9%

43.9%

3.0%

3.3%

They often offers discount…

More efficient (can make the…

Much safer.


0.0% 20.0% 40.0% 60.0%

What is the main reason you want to use this O2O (E-commerce) website as your choice to purchasegoods?(Apa yang menjadi alasan utama andamenggunakan situs O2O (E-commerce) Indonesia inisebagai pilihan anda dalam membeli barang?)

-45-

4.2 Research Variables Descriptive Analysis

In this part, it will describes about all of the variables used in this research and

conduct the descriptive analysis to obtain the mean and standard deviation. Table 4-15 below

is the outcome of the descriptive analysis.

Table 4-15 Descriptive analysis for the variables

The next part is the researcher wants to see the correlations between each variables

using the Pearson’s correlation coefficient. The result is described in table 4-16 below.

Table 4-16 Bivariate correlation in each variable

-46-

4.3 Reliability Analysis

To measure the reliability of the indicators in each variables used in this study, the

reliability analysis is conducted. There are many ways to conduct the reliability analysis, one

of them is by using the Cronbach’s alpha coefficient to count the reliability. According to

Hair (2010), the minimum number of reliability is 0.7, but it is still agreeable in 0.6. This

reliability measurements is only conducted for variables more than one construct, as in this

research there are two variables that only have one construct—awareness and actual usage—

so that the reliability measurement is only conducted for four variables in this research which

described below.

1. Perceived Usefulness (PU)

As provided in table 4-17 below, the overall Cronbach’s alpha for Perceived

usefulness is 0.903. This means that variable PU is reliable as its number is

greater than 0.7. As for the item total statistics, none of each construct’s

Cronbach’s alpha is larger than the overall Cronbach’s alpha, so none of them is

necessary to be deleted.

Table 4-17 Perceived Usefulness (PU) reliability test result

Table 4-18 Perceived Usefulness (PU) item-total test result

-47-

2. Perceived Ease of Use (PEOU)

In table 4-19 below is the result of Perceived Ease of Use reliability test. The

overall Cronbach’s alpha is 0.893. Again the result is higher than the minimum

coefficient of 0.7, which means the construct PEOU variable is reliable. The item

statistic in table 4-20 also shown that all of the construct’s Cronbach’s alpha after

item deleted is below the overall Cronbach’s alpha. This means that there is no

need to delete any construct in this variable.

Table 4-19 Perceived Ease of Use (PEOU) reliability test result

Table 4-20 Perceived Ease of Use (PEOU) item-total test result

3. Perceived Risk (PR)

Below is the Perceived Risk variable reliability result provided in table 4-21. The

number of overall Cronbach’s alpha is 0.868 which means the PR variable is

reliable because it is greater than 0.7. The item statistic for this variable is

provided in table 4-22 and showed that there is no need to delete any of the

construct because all of the Cronbach’s alpha after item deleted is still under the

overall Cronbach’s alpha.

-48-

Table 4-21 Perceived Risk (PR) reliability test result

Table 4-22 Perceived Risk (PR) item-total test result

4. Behavioral Intention to Use (BI2U)

According to table 4-23 below, the Behavioral Intention to Use (BI2U) variable

has the result of overall Cronbach’s alpha for 0.807. This also means that the

variable is reliable because the coefficient is greater than the minimum number

of 0.7. The item total statistics in table 4-24 resulting in one of the construct has

the Cronbach’s alpha after item deleted a little greater than the overall Cronbach’s

alpha as much as 0.899. This number is giving less significance to the overall

Cronbach’s alpha if the construct is deleted so the researcher decided not to delete

-49-

the construct.

Table 4-23 Behavioral Intention to Use (BI2U) reliability test result

Table 4-24 Behavioral Intention to Use (BI2U) item-total test result

4.4 Validity Analysis

The validity analysis is using the Pearson correlation value as described in chapter 3.

Based on the result provided in table 4-25 below, all of the constructs are valid because the

Pearson coefficient for the total is greater than 0.3. The significant value also in number

0.000 which means that the constructs are equal or less than 0.05, resulting there is

significant correlations between the constructs.

-50-

Table 4-25 Indicators validity results

Construct Description Awareness PU1 PU2 PU3 PU4 PU5 PU6 PU7 PEOU1 PEOU2 PEOU3 PEOU4 PEOU5 PR1 PR2 PR3 PR4 PR5 PR6 PR7 PR8 PR9 PR10 PR11 PR12 PR13 PR14 PR15 PR16 BI2U1 BI2U2 BI2U3 BI2U4 BI2U5 BI2U6 AU total

Awareness Pearson Correlation 1 .558** .426** .515** .543** .401** .389** .306** .522** .449** .446** .482** .498** .283** -0.072 0.036 -0.021 -.118* 0.068 -0.054 0.074 -0.063 -0.012 -.163** 0.053 0.097 0.07 .278** .186** .456** .389** .313** .355** -0.015 .323** .284** .511**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.161 0.481 0.682 0.021 0.188 0.298 0.148 0.222 0.818 0.001 0.3 0.06 0.175 0 0 0 0 0 0 0.766 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU1 Pearson Correlation .558** 1 .670** .711** .739** .509** .506** .415** .640** .529** .540** .636** .526** .335** -.115* -0.017 -.105* -.216** -0.004 -.133** .132** -0.064 -0.087 -.257** 0.059 .123* .167** .303** .232** .508** .497** .350** .436** 0.05 .434** .352** .597**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.025 0.741 0.041 0 0.935 0.009 0.01 0.211 0.09 0 0.255 0.017 0.001 0 0 0 0 0 0 0.335 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU2 Pearson Correlation .426** .670** 1 .692** .676** .476** .441** .541** .549** .502** .491** .571** .496** .342** -0.06 0.036 -0.062 -.135** -0.003 -0.063 .150** -0.039 -0.016 -0.052 -0.011 .245** .208** .256** .285** .501** .505** .460** .467** -0.023 .407** .408** .627**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.24 0.485 0.228 0.008 0.953 0.222 0.004 0.448 0.753 0.31 0.834 0 0 0 0 0 0 0 0 0.657 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU3 Pearson Correlation .515** .711** .692** 1 .762** .556** .534** .521** .619** .550** .553** .647** .553** .326** -.107* 0.019 -0.08 -.139** 0.036 -.182** .135** -0.066 -0.055 -.178** 0.071 .134** .190** .296** .189** .509** .479** .336** .403** 0.026 .396** .353** .620**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.037 0.718 0.118 0.007 0.488 0 0.008 0.198 0.286 0 0.169 0.009 0 0 0 0 0 0 0 0.619 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU4 Pearson Correlation .543** .739** .676** .762** 1 .551** .548** .501** .636** .570** .576** .650** .570** .363** -.126* 0.003 -.125* -.187** 0.02 -.135** .182** -0.025 -0.035 -.159** 0.055 .158** .177** .320** .233** .532** .509** .403** .476** 0.023 .430** .383** .648**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.014 0.954 0.015 0 0.699 0.009 0 0.624 0.502 0.002 0.285 0.002 0.001 0 0 0 0 0 0 0.655 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU5 Pearson Correlation .401** .509** .476** .556** .551** 1 .748** .471** .466** .418** .538** .515** .436** .338** -0.059 0.026 -0.052 -.117* 0.082 -.158** 0.076 -0.075 -0.027 -.175** 0.029 .136** .151** .326** .253** .440** .403** .283** .351** -0.075 .348** .307** .547**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.249 0.614 0.309 0.023 0.111 0.002 0.142 0.147 0.604 0.001 0.576 0.008 0.003 0 0 0 0 0 0 0.146 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU6 Pearson Correlation .389** .506** .441** .534** .548** .748** 1 .517** .542** .418** .528** .556** .533** .331** -0.021 0.039 -0.036 -0.085 0.052 -0.088 .128* -0.013 -0.032 -.148** 0.09 .124* .134** .299** .268** .396** .386** .250** .350** -0.017 .331** .311** .573**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.681 0.445 0.484 0.098 0.314 0.089 0.013 0.798 0.535 0.004 0.079 0.016 0.009 0 0 0 0 0 0 0.744 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PU7 Pearson Correlation .306** .415** .541** .521** .501** .471** .517** 1 .452** .424** .435** .454** .457** .293** 0.05 .103* -0.054 -0.004 0.015 -0.01 .167** 0.006 0.014 0.031 0.003 .229** .217** .233** .235** .396** .436** .307** .367** -0.058 .342** .312** .569**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.334 0.045 0.295 0.931 0.772 0.844 0.001 0.907 0.78 0.549 0.955 0 0 0 0 0 0 0 0 0.258 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PEOU1 Pearson Correlation .522** .640** .549** .619** .636** .466** .542** .452** 1 .605** .646** .719** .628** .408** -0.078 -0.022 -0.043 -.141** 0.053 -.117* .110* -0.082 -0.032 -.171** .131* .113* .218** .297** .275** .470** .435** .289** .350** -0.037 .343** .360** .613**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.127 0.674 0.403 0.006 0.306 0.022 0.032 0.109 0.529 0.001 0.011 0.028 0 0 0 0 0 0 0 0.472 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PEOU2 Pearson Correlation .449** .529** .502** .550** .570** .418** .418** .424** .605** 1 .569** .602** .518** .429** -0.09 0.034 -0.028 -.146** .111* -0.093 0.084 -0.089 0.013 -.133** 0.091 .158** .221** .258** .244** .481** .469** .344** .384** 0.005 .359** .275** .584**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.082 0.511 0.583 0.004 0.03 0.069 0.102 0.085 0.802 0.01 0.077 0.002 0 0 0 0 0 0 0 0.919 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PEOU3 Pearson Correlation .446** .540** .491** .553** .576** .538** .528** .435** .646** .569** 1 .699** .644** .407** -.104* -0.061 -.109* -.103* 0.017 -.140** .111* -0.085 -0.036 -.171** 0.015 .212** .251** .292** .293** .499** .464** .395** .425** -0.057 .411** .360** .601**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.044 0.238 0.033 0.045 0.741 0.006 0.031 0.097 0.483 0.001 0.778 0 0 0 0 0 0 0 0 0.269 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PEOU4 Pearson Correlation .482** .636** .571** .647** .650** .515** .556** .454** .719** .602** .699** 1 .674** .437** -0.087 0.007 -0.013 -.155** 0.051 -.130* .135** -0.069 -0.018 -.138** .105* .153** .213** .315** .317** .514** .464** .337** .423** -0.039 .400** .397** .651**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.092 0.895 0.803 0.002 0.324 0.011 0.009 0.18 0.724 0.007 0.041 0.003 0 0 0 0 0 0 0 0.45 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PEOU5 Pearson Correlation .498** .526** .496** .553** .570** .436** .533** .457** .628** .518** .644** .674** 1 .415** -0.046 -0.032 -0.003 -0.055 -0.022 -0.029 0.093 -0.022 -0.032 -.132** .149** .229** .242** .287** .240** .509** .446** .362** .415** -0.009 .352** .350** .623**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.368 0.54 0.961 0.288 0.671 0.578 0.069 0.664 0.531 0.01 0.004 0 0 0 0 0 0 0 0 0.861 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

Correlations

-51-


PR1 Pearson Correlation .283** .335** .342** .326** .363** .338** .331** .293** .408** .429** .407** .437** .415** 1 0.036 0.06 0.03 0.009 .123* 0.06 .156** 0.017 0.042 0.027 .127* .309** .243** .317** .369** .449** .380** .331** .356** 0.011 .351** .245** .553**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0.481 0.242 0.557 0.86 0.017 0.242 0.002 0.748 0.417 0.597 0.013 0 0 0 0 0 0 0 0 0.834 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR2 Pearson Correlation -0.072 -.115* -0.06 -.107* -.126* -0.059 -0.021 0.05 -0.078 -0.09 -.104* -0.087 -0.046 0.036 1 .610** .606** .568** .352** .486** .281** .403** .473** .461** .310** .248** .212** 0.083 .164** -.102* -.137** -0.073 -0.08 -.319** -0.081 -0.051 .307**

Sig. (2-tailed) 0.161 0.025 0.24 0.037 0.014 0.249 0.681 0.334 0.127 0.082 0.044 0.092 0.368 0.481 0 0 0 0 0 0 0 0 0 0 0 0 0.108 0.001 0.048 0.008 0.155 0.12 0 0.115 0.32 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR3 Pearson Correlation 0.036 -0.017 0.036 0.019 0.003 0.026 0.039 .103* -0.022 0.034 -0.061 0.007 -0.032 0.06 .610** 1 .642** .518** .420** .442** .287** .318** .467** .362** .357** .110* .129* .154** .114* 0.011 -0.012 0.008 -0.023 -.257** -0.096 0.042 .378**

Sig. (2-tailed) 0.481 0.741 0.485 0.718 0.954 0.614 0.445 0.045 0.674 0.511 0.238 0.895 0.54 0.242 0 0 0 0 0 0 0 0 0 0 0.033 0.012 0.003 0.026 0.824 0.82 0.883 0.657 0 0.061 0.41 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR4 Pearson Correlation -0.021 -.105* -0.062 -0.08 -.125* -0.052 -0.036 -0.054 -0.043 -0.028 -.109* -0.013 -0.003 0.03 .606** .642** 1 .597** .501** .535** .223** .448** .466** .459** .456** .146** .137** .150** .158** -0.077 -.116* -.106* -.113* -.313** -.174** -.108* .327**

Sig. (2-tailed) 0.682 0.041 0.228 0.118 0.015 0.309 0.484 0.295 0.403 0.583 0.033 0.803 0.961 0.557 0 0 0 0 0 0 0 0 0 0 0.004 0.008 0.003 0.002 0.136 0.024 0.039 0.027 0 0.001 0.036 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR5 Pearson Correlation -.118* -.216** -.135** -.139** -.187** -.117* -0.085 -0.004 -.141** -.146** -.103* -.155** -0.055 0.009 .568** .518** .597** 1 .392** .594** .162** .419** .477** .544** .338** .173** .203** .116* .102* -.170** -.179** -.132* -.156** -.380** -.184** -.142** .238**

Sig. (2-tailed) 0.021 0 0.008 0.007 0 0.023 0.098 0.931 0.006 0.004 0.045 0.002 0.288 0.86 0 0 0 0 0 0.002 0 0 0 0 0.001 0 0.024 0.046 0.001 0 0.01 0.002 0 0 0.006 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR6 Pearson Correlation 0.068 -0.004 -0.003 0.036 0.02 0.082 0.052 0.015 0.053 .111* 0.017 0.051 -0.022 .123* .352** .420** .501** .392** 1 .410** .214** .402** .447** .346** .433** 0.095 .147** .264** .150** 0.089 0.052 0.024 -0.026 -.141** 0.018 -0.018 .395**

Sig. (2-tailed) 0.188 0.935 0.953 0.488 0.699 0.111 0.314 0.772 0.306 0.03 0.741 0.324 0.671 0.017 0 0 0 0 0 0 0 0 0 0 0.065 0.004 0 0.003 0.084 0.316 0.638 0.613 0.006 0.723 0.726 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR7 Pearson Correlation -0.054 -.133** -0.063 -.182** -.135** -.158** -0.088 -0.01 -.117* -0.093 -.140** -.130* -0.029 0.06 .486** .442** .535** .594** .410** 1 .231** .542** .448** .576** .347** .231** .194** .188** .160** -0.042 -.123* 0.003 -0.051 -.250** -0.074 -0.043 .313**

Sig. (2-tailed) 0.298 0.009 0.222 0 0.009 0.002 0.089 0.844 0.022 0.069 0.006 0.011 0.578 0.242 0 0 0 0 0 0 0 0 0 0 0 0 0 0.002 0.414 0.017 0.948 0.323 0 0.148 0.399 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR8 Pearson Correlation 0.074 .132** .150** .135** .182** 0.076 .128* .167** .110* 0.084 .111* .135** 0.093 .156** .281** .287** .223** .162** .214** .231** 1 .258** .250** .182** .272** .258** .158** .121* .211** .191** .135** .146** .166** -0.085 .150** .171** .408**

Sig. (2-tailed) 0.148 0.01 0.004 0.008 0 0.142 0.013 0.001 0.032 0.102 0.031 0.009 0.069 0.002 0 0 0 0.002 0 0 0 0 0 0 0 0.002 0.018 0 0 0.009 0.004 0.001 0.1 0.004 0.001 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR9 Pearson Correlation -0.063 -0.064 -0.039 -0.066 -0.025 -0.075 -0.013 0.006 -0.082 -0.089 -0.085 -0.069 -0.022 0.017 .403** .318** .448** .419** .402** .542** .258** 1 .456** .505** .329** .205** .210** .173** .234** 0.006 0.016 0.007 0.045 -.232** -0.006 -0.01 .332**

Sig. (2-tailed) 0.222 0.211 0.448 0.198 0.624 0.147 0.798 0.907 0.109 0.085 0.097 0.18 0.664 0.748 0 0 0 0 0 0 0 0 0 0 0 0 0.001 0 0.91 0.761 0.892 0.387 0 0.902 0.843 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR10 Pearson Correlation -0.012 -0.087 -0.016 -0.055 -0.035 -0.027 -0.032 0.014 -0.032 0.013 -0.036 -0.018 -0.032 0.042 .473** .467** .466** .477** .447** .448** .250** .456** 1 .577** .387** .194** .183** .167** .121* -0.039 -0.06 0.018 -0.056 -.336** -0.089 -0.066 .340**

Sig. (2-tailed) 0.818 0.09 0.753 0.286 0.502 0.604 0.535 0.78 0.529 0.802 0.483 0.724 0.531 0.417 0 0 0 0 0 0 0 0 0 0 0 0 0.001 0.018 0.445 0.242 0.725 0.277 0 0.082 0.197 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR11 Pearson Correlation -.163** -.257** -0.052 -.178** -.159** -.175** -.148** 0.031 -.171** -.133** -.171** -.138** -.132** 0.027 .461** .362** .459** .544** .346** .576** .182** .505** .577** 1 .311** .244** .255** 0.028 .136** -.139** -.159** 0.021 -.132* -.418** -.123* -0.016 .235**

Sig. (2-tailed) 0.001 0 0.31 0 0.002 0.001 0.004 0.549 0.001 0.01 0.001 0.007 0.01 0.597 0 0 0 0 0 0 0 0 0 0 0 0 0.593 0.008 0.007 0.002 0.683 0.01 0 0.016 0.751 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR12 Pearson Correlation 0.053 0.059 -0.011 0.071 0.055 0.029 0.09 0.003 .131* 0.091 0.015 .105* .149** .127* .310** .357** .456** .338** .433** .347** .272** .329** .387** .311** 1 -0.024 0.041 .286** 0.061 .102* 0.025 -0.023 -0.002 -.162** -0.058 0.039 .369**

Sig. (2-tailed) 0.3 0.255 0.834 0.169 0.285 0.576 0.079 0.955 0.011 0.077 0.778 0.041 0.004 0.013 0 0 0 0 0 0 0 0 0 0 0.643 0.432 0 0.24 0.047 0.63 0.655 0.974 0.002 0.261 0.45 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

Correlations

-52-


PR13 Pearson Correlation 0.097 .123* .245** .134** .158** .136** .124* .229** .113* .158** .212** .153** .229** .309** .248** .110* .146** .173** 0.095 .231** .258** .205** .194** .244** -0.024 1 .638** .268** .495** .275** .271** .330** .311** -.165** .269** .164** .482**

Sig. (2-tailed) 0.06 0.017 0 0.009 0.002 0.008 0.016 0 0.028 0.002 0 0.003 0 0 0 0.033 0.004 0.001 0.065 0 0 0 0 0 0.643 0 0 0 0 0 0 0 0.001 0 0.001 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR14 Pearson Correlation 0.07 .167** .208** .190** .177** .151** .134** .217** .218** .221** .251** .213** .242** .243** .212** .129* .137** .203** .147** .194** .158** .210** .183** .255** 0.041 .638** 1 .307** .443** .244** .223** .271** .296** -.155** .196** .125* .482**

Sig. (2-tailed) 0.175 0.001 0 0 0.001 0.003 0.009 0 0 0 0 0 0 0 0 0.012 0.008 0 0.004 0 0.002 0 0 0 0.432 0 0 0 0 0 0 0 0.002 0 0.015 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR15 Pearson Correlation .278** .303** .256** .296** .320** .326** .299** .233** .297** .258** .292** .315** .287** .317** 0.083 .154** .150** .116* .264** .188** .121* .173** .167** 0.028 .286** .268** .307** 1 .345** .447** .354** .245** .349** 0.031 .282** .179** .559**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.108 0.003 0.003 0.024 0 0 0.018 0.001 0.001 0.593 0 0 0 0 0 0 0 0 0.548 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

PR16 Pearson Correlation .186** .232** .285** .189** .233** .253** .268** .235** .275** .244** .293** .317** .240** .369** .164** .114* .158** .102* .150** .160** .211** .234** .121* .136** 0.061 .495** .443** .345** 1 .281** .323** .241** .281** -0.052 .287** .169** .525**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.001 0.026 0.002 0.046 0.003 0.002 0 0 0.018 0.008 0.24 0 0 0 0 0 0 0 0.312 0 0.001 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

BI2U1 Pearson Correlation .456** .508** .501** .509** .532** .440** .396** .396** .470** .481** .499** .514** .509** .449** -.102* 0.011 -0.077 -.170** 0.089 -0.042 .191** 0.006 -0.039 -.139** .102* .275** .244** .447** .281** 1 .795** .550** .696** 0.062 .606** .353** .655**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.048 0.824 0.136 0.001 0.084 0.414 0 0.91 0.445 0.007 0.047 0 0 0 0 0 0 0 0.227 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

BI2U2 Pearson Correlation .389** .497** .505** .479** .509** .403** .386** .436** .435** .469** .464** .464** .446** .380** -.137** -0.012 -.116* -.179** 0.052 -.123* .135** 0.016 -0.06 -.159** 0.025 .271** .223** .354** .323** .795** 1 .565** .695** 0.091 .639** .400** .611**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.008 0.82 0.024 0 0.316 0.017 0.009 0.761 0.242 0.002 0.63 0 0 0 0 0 0 0 0.077 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

BI2U3 Pearson Correlation .313** .350** .460** .336** .403** .283** .250** .307** .289** .344** .395** .337** .362** .331** -0.073 0.008 -.106* -.132* 0.024 0.003 .146** 0.007 0.018 0.021 -0.023 .330** .271** .245** .241** .550** .565** 1 .653** -0.071 .582** .487** .534**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.155 0.883 0.039 0.01 0.638 0.948 0.004 0.892 0.725 0.683 0.655 0 0 0 0 0 0 0 0.169 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

BI2U4 Pearson Correlation .355** .436** .467** .403** .476** .351** .350** .367** .350** .384** .425** .423** .415** .356** -0.08 -0.023 -.113* -.156** -0.026 -0.051 .166** 0.045 -0.056 -.132* -0.002 .311** .296** .349** .281** .696** .695** .653** 1 0.079 .690** .453** .591**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 0.657 0.027 0.002 0.613 0.323 0.001 0.387 0.277 0.01 0.974 0 0 0 0 0 0 0 0.125 0 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

BI2U5 Pearson Correlation -0.015 0.05 -0.023 0.026 0.023 -0.075 -0.017 -0.058 -0.037 0.005 -0.057 -0.039 -0.009 0.011 -.319** -.257** -.313** -.380** -.141** -.250** -0.085 -.232** -.336** -.418** -.162** -.165** -.155** 0.031 -0.052 0.062 0.091 -0.071 0.079 1 0.041 -0.014 -.147**

Sig. (2-tailed) 0.766 0.335 0.657 0.619 0.655 0.146 0.744 0.258 0.472 0.919 0.269 0.45 0.861 0.834 0 0 0 0 0.006 0 0.1 0 0 0 0.002 0.001 0.002 0.548 0.312 0.227 0.077 0.169 0.125 0.431 0.787 0.004

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

BI2U6 Pearson Correlation .323** .434** .407** .396** .430** .348** .331** .342** .343** .359** .411** .400** .352** .351** -0.081 -0.096 -.174** -.184** 0.018 -0.074 .150** -0.006 -0.089 -.123* -0.058 .269** .196** .282** .287** .606** .639** .582** .690** 0.041 1 .415** .525**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.115 0.061 0.001 0 0.723 0.148 0.004 0.902 0.082 0.016 0.261 0 0 0 0 0 0 0 0 0.431 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

AU Pearson Correlation .284** .352** .408** .353** .383** .307** .311** .312** .360** .275** .360** .397** .350** .245** -0.051 0.042 -.108* -.142** -0.018 -0.043 .171** -0.01 -0.066 -0.016 0.039 .164** .125* .179** .169** .353** .400** .487** .453** -0.014 .415** 1 .467**

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.32 0.41 0.036 0.006 0.726 0.399 0.001 0.843 0.197 0.751 0.45 0.001 0.015 0 0.001 0 0 0 0 0.787 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

total Pearson Correlation .511** .597** .627** .620** .648** .547** .573** .569** .613** .584** .601** .651** .623** .553** .307** .378** .327** .238** .395** .313** .408** .332** .340** .235** .369** .482** .482** .559** .525** .655** .611** .534** .591** -.147** .525** .467** 1

Sig. (2-tailed) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.004 0 0

N 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379 379

Correlations

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

-53-

4.5 Multicollinearity Analysis

Collinearity diagnostics is held in order to find if there is multicollinearity occurred

between the independent variables in this research. Result in table 4-26 below indicating that

all of the independent variables are in good form of not having multicollinearity, because

their VIF are all below 10. This means that all of the independent variables used in this

research are not having multicollinearity problem.

Table 4-26 Multicollinearity analysis result

Coefficientsa

Model Collinearity Statistics

Tolerance VIF

1 Awareness .639 1.566

PU .335 2.985

PEOU .341 2.933

PR .983 1.018

BI2U .644 1.552

a. Dependent Variable: Actual Usage

4.5 Moderation Regression Analysis

In this moderation regression analysis, we use Baron and Kenny moderation

regression model which is the hierarchical multiple regression technique to conduct this

research. There are three steps performed in order to achieve the result. The first one is

calculating the main effect amongst the dependent variable and independent variable. The

second is calculate the moderate variable to the dependent variable, and the last step

calculating the moderation effect to the dependent variable by multiplying the moderator

variable and the independent variable. Because of this research using model that has three

layers, it will separated into two times of hierarchical multiple regression analysis. The first

one is calculating the main effect and moderation effect of independent variables to BI2U as

the dependent variable, the second is calculating the relationship between BI2U to the Actual

Usage as dependent variable.

-54-

From table 4-27 provided below, the first model is calculating the relation between

variable PU and PEOU as independent variable to BI2U as dependent variable. The result

gives the number of 0.000 in significant value with β = 0.378 for the PU, and 0.000 of

significance value with β = 0.244 for the PEOU. The multiple R value for this model is 0.591

with the R Square of 0.349, and the F ratio of 100.751.

The second model is calculating the Awareness and PR as moderation variable

towards the BI2U with the results showing that the awareness have significant value of 0.099

and β = 0.085, and the PR for 0.305 in significance value with β = -0.043. Their multiple R

is showing in 0.596 with the R square of 0.356 and F ratio of 51.577.

Table 4-27 Moderation Regression Analysis 1st and 2nd Layer

Hierarchy

Variables

Predictive variables

in the Hierarchy

Model 1 Model 2 Model 3

β t β t β t

Main Effect

PU 0.378 5.570*** 0.355 5.121*** 0.420 6.073***

PEOU 0.244 3.601*** 0.219 3.121** 0.175 2.477*

Awareness 0.085 1.652 0.099 1.880

PR -0.043 -1.026 -0.006 -0.131

Moderation

Effect

PUXAWA 0.369 3.771***

PEOUXAWA -0.081 -0.810

PUXPR -0.236 -1.747

PEOUXPR -0.037 -0.269

Regression

Model

Summary

F value 100.751*** 51.577*** 32.673***

R 0.591 0.596 0.643

R2 0.349 0.356 0.414

ΔR2 0.349*** 0.007 0.058***

p＜.10 *p＜.05 **p＜.01 ***p＜.001

Dependent variable : BI2U

The third model is conducting the regression between the moderation variable

together with independent variable towards the BI2U as dependent variable. The number of

significance level for the awareness moderates the PU is 0.000 with β = 0.369; awareness

moderates the PEOU creates the significance value of 0.418 with β = -0.081. As for the PR

-55-

moderates the PU, it has 0.081 in its significance value with β = -0.236; PR moderates PEOU

has the level of significance 0.788 with β = -0.037. For this model’s multiple R value is 0.643

with R square as much as 0.414 with the number 32.673 in its F ratio.

The next part is calculating the last model of this regression, which is the relationship

between BI2U as independent variable and AU as dependent variable. Result in table 4-28

below indicates that the significance level is in 0.000 with β = 0.472. This model’s multiple

R value is 0.472 with the R square of 0.223 and F ratio of 107.921.

Table 4-28 Moderation Regression Analysis 3rd Layer

Hierarchy Variables Predictive variables within the

Hierarchy

Model 1

β t

Independent Variable BI2U 0.472 10.389***

Regression Model

Summary

F value 107.921***

R 0.472

R2 0.223

ΔR2 0.223***

p＜.10 *p＜.05 **p＜.01 ***p＜.001

Dependent variable : Actual Usage

4.7 Hypotheses Testing Result

Based on the moderation regression analysis that has been established above, the

hypotheses are now can be tested based on the result obtained from the regression. Table 4-

29 below indicated the hypotheses result. The result will be described further below.

-56-

Table 29 Hypotheses Testing Result

No. Hypotheses P-Value Result

H1 Consumer usage intentions have significant

influence on actual usage of O2O

0.000

(β = 0.472, p < 0.05)

Supported

H2 Consumer intentions to use have significant

relations to perceived usefulness of the

Website interface in O2O

0.000

(β = 0.378, p < 0.05)

Supported

H3 Consumer intentions to use have significant

relations to perceived ease of use of the

Website interface in O2O

0.000

(β = 0.244, p < 0.05)

Supported

H4a Consumer intention to use have significant

relations to the moderation of awareness to

perceived usefulness in O2O

0.000

(β = 0.369, p < 0.05)

Supported

H4b Consumer intention to use no significant

relations to the moderation of perceived risk

to perceived usefulness in O2O.

0.081

(β = -0.236, p < 0.10)

Partially

Supported

H5a Consumer intention to use have significant

relations to the moderation of awareness to

perceived ease of use in O2O.

0.418

(β = -0.081, p > 0.05)

Not

Supported

H5b Consumer intention to use have no

significant relations to the moderation of

perceived risk to perceived ease of use in

O2O.

0.788

(β = -0.037, p > 0.05)

Not

Supported

1. Behavioral intention to use effect to the actual usage

H1: Consumer usage intentions have significant influence actual usage of

O2O

Based on the regression analysis result provided in table 4-28 above, the

significance value of the BI2U to actual usage is 0.000. This means that the

consumer usage intention is positively related to the actual usage (β = 0.472, p <

0.05). Thus, hypothesis 1 is supported.

-57-

2. Perceived usefulness effect to the behavioral intention to use

H2: Consumer intentions to use have significant relations to perceived

usefulness of the Web interface in O2O.

The regression analysis results in table 4-27 above shows that the significance

value of PU to BI2U is 0.000. This result indicates that there is positive relation

between the PU and BI2U (β = 0.378, p < 0.05). Therefore, it is concluded that

hypothesis 2 is supported.

3. Perceived ease of use effect to the behavioral intention to use

H3: Consumer intentions to use have significant relations to perceived ease of

use of the Web interface in O2O.

Based on the result provided table 4-27 above, there is also obtained that the

significance value of PEOU to BI2U is 0.000. It is indicated that the PEOU is

positively related to BI2U (β = 0.244, p < 0.05) which means that hypothesis 3 is

supported.

4. Awareness moderation effect to the behavioral intention to use

H4a: Consumer intention to have significant relations to the moderation of

awareness to perceived usefulness in O2O.

As provided in table 4-27 above, the regression between PU and BI2U, with

awareness as the moderation variable is resulting 0.000 in its significance value.

This means that awareness moderates PU is positively related to the BI2U (β =

0.369, p < 0.05). Thus, hypotheses 4a is supported.

H5a: Consumer intention to use have significant relations to the moderation

of awareness to perceived ease of use in O2O.

The other is to analyze the moderation of awareness to PEOU related to BI2U.

Table 4-27 above shows that the significance value from this analysis is 0.418.

This result explains that awareness moderates the PEOU is negatively related to

-58-

BI2U (β = -0.081, p > 0.05) which means that hypothesis 5a is not supported.

5. Perceived risk moderation effect to the behavioral intention to use

H4b: Consumer intention to use have no significant relations to the moderation

of perceived risk to perceived usefulness in O2O.

According to table 4-27 above, it is obtained that the significance value of PR

moderates PU in its relationship between BI2U is 0.081. This number indicates

that PR moderates PU is negatively related to the BI2U (β = -0.236, p < 0.10)

which is marginally significant. Therefore, hypothesis 4b is partially supported.

H5b: Consumer intention to use have no significant relations to the moderation

of perceived risk to perceived ease of use in O2O.

Table 4-27 above giving the result of the moderation of PR to PEOU with the

relationship between BI2U. The significance value is 0.788, indicates that the

moderation of PR to PEOU is non-significant with the BI2U (β = -0.037, p >

0.05). Therefore, hypothesis 5b is not supported.

-59-

Chapter 5 – Conclusion

After going through many process and several steps needed to make this research

from the introduction up to the results and analysis, it reached the last step to conclude and

reveal the implication from this research. This chapter will explain more about the

conclusion and implication obtained from this research, and also will giving some suggestion

for future research.

5.1 Conclusion

After going through the process on this research, results are obtained and conclusion

could be formed for this research. With the help of past research similar with this (Pavlou,

2003), this research is formed using awareness and perceived risk as the external variables

who moderates the TAM concept, the perception of internet users towards O2O in Indonesia

is considered good. Although almost half of the valid respondents don’t have any experience

in using O2O website, instead the researcher using respondents’ experience in online

shopping experience like basic E-commerce website to represents the O2O as it is known to

be have quite similar in the purchasing process and also is a part of the E-commerce business.

However there are some hypotheses that are not supported, thus creating no relationship

between them that will be explained further below.

For the basic model of TAM itself, both PEOU and PU showing significant

relationship with the BI2U and the BI2U also has significant relationship with the actual

usage. It is concluded that TAM has successfully represent the internet users’ acceptance to

the O2O business model in Indonesia. The basic model of TAM is resulting that the O2O

business model in Indonesia is accepted by Indonesia’s internet users.

As for the moderation variable, it is concluded that awareness have positive

significant relationship with the BI2U when it is moderating the PU. On the contrary,

-60-

awareness indicating negative relationship with the BI2U when moderating the PEOU. The

PR variable showing it is marginally significant with the BI2U when it is moderating the PU,

whereas it is completely non-significant when it is moderating the PEOU. So both awareness

and PR don’t have any relationship with the BI2U when it is moderating the PEOU, and PR

moderating PU to the relationship with BI2U is partially supported.

Based on the data obtained from the questionnaire, it is known that more than half of

all the respondents gathered don’t have any experience both in E-commerce nor O2O, which

can be concluded that the distribution of promotion and information for both E-commerce

and O2O business in Indonesia is uneven. It can be seen as most of the respondents collected

are mostly live in Java Island, where the capital city of Indonesia is located and the

infrastructure and development is more rapidly comparing to other islands.

5.2 Managerial Implications

Some managerial implications that can be obtained from this research are the uneven

distribution of the respondents in the questionnaire and the form of state in Indonesia itself.

It is known that Indonesia is a big country which consists of many big and small islands that

separate by straits and seas. As for the capital city of Indonesia, Jakarta is located in Java

Island where most of the infrastructure there is well developed comparing to other islands in

all over Indonesia. Another fact, E-commerce and O2O business model are using good

logistics as one of their main concern to support their performance and satisfies the

customers. This will be one of the problem and implication that appear based on this results

of the study because most of the respondents are live in Java Island which means the logistics

part also not good in serving for other islands outside Java. Because the basic form of E-

commerce is convenience and quick response in serving and sending their goods to the

customer, the importance of good logistics also need to be considered. This might be the

reason why the development of E-commerce and O2O is not good in Indonesia.

-61-

Internet speed and connection also play important role in this study. Because

Indonesia is a country with many islands, make the infrastructure growth unevenly thus

impacting in the development of internet to outside of Java Island. It is known that the

internet service provider growing in outside Java Island is still a few and the speed they give

to the customer is still counted as slow connection makes the internet user outside Java Island

is much fewer than Java Island. This makes the customer prefer on shopping directly to the

physical store rather than shopping via online since it can’t give the customer more benefits.

5.3 Suggestion

Through all of the research findings, there are some implications that could be

obtained. First of all, the result shows that PEOU has negative and non-significant

relationship with the BI2U if it moderated with the awareness, and so does with PEOU with

PR as its moderating variable. It is suggested to find another variable that suits and could

give positive and significant relationship to the TAM concept such as trust level.

Secondly, the perceived risk is proven only have marginal significant and leads to

partial relationship to BI2U when moderated PU. It is assumed that the PR is not good when

it is used as the moderation variable, unlike those in past research who has significant

relationship between risk and intention to use (Pavlou, 2003). Next research is suggested to

use perceived risk as external variable like the one in past the past research as if it is

conducting the same TAM.

The third is from the results obtained, most of the respondents are from the bachelor

degree background education, and already work as employees, with the income of average

level. Their purchase goods are also majorly below Rp 1,000,000,- (about NTD 2,500),

meaning that the consumers are still in lower level of purchasing. It may suggests for the

O2O (E-commerce) vendor to make more attraction and giving more approach in making

the costumer want to purchase more products and increase the purchasing amount.

-62-

Just like what has been said in the conclusion above, most of the respondents from

this research are come from Java Island where the development growth is rapidly comparing

to other places in Indonesia. This can be assumed that the promotion and infrastructure of

the O2O or E-commerce business model is not distributed evenly. It is suggested that for

further development in this business there may be more effort in making more promotion

and giving information especially in places outside Java Island so that it could be a perfect

even distribution.

This research might give some contributions for those who want to start opening O2O

or E-commerce business in Indonesia by seeing the logistic problem and find solutions in

how to make a good logistic process by serving high speed delivery with affordable prices.

It is a good option for new comers who wants to start business by seeing this problem as a

good opportunity to create their own business in logistics section. Once the logistic problem

is solved and Indonesia have a better and good logistics, then the development of E-

commerce and O2O will be increasing rapidly and there will be more customer attract in

using and purchase goods via internet.

-63-

References

A. Journal

Adobor, H. (2005). Trust as sense making: the micro dynamics of trust in inter firm alliances.

Journal of Business Research 58(3), 330-337.

Benlian, A., Ryad T., Thomas H. (2012). Differential Effects of Provider Recommendations

and Consumer Reviews in E-Commerce Transactions: An Experimental Study.

Journal of Management Information Systems 29(1), 237-272.

Cases, A.-S. (2002). Perceived Risk and Risk Reduction Strategies in Internet Shopping. The

International Review of Retail, Distribution and Consumer Research Volume 12, Issue

4, Page 375–394.

Çengel, F. Y. (2012). The Perceived Risk and Value Based Model of Online Retailing. Online

Academic Journal of Information Technology Vol.3 (9), 7 – 21.

Chang, Yaping, Hu Shaolong, and Zhang Geng. (2012). Price Dispersion Formative

Mechanism Research on Pattern of C2C. Affective Computing and Intelligent

Interaction, AISC 137, pp. 491–497.

Du, Yingsheng, and Youchun Tang. (2014). Study on the Development of O2O E-commerce

Platform of China from the Perspective of Offline Service Quality. International

Journal of Business and Social Science Vol. 5, No. 4 Page 308 – 312.

-64-

Fairchild, A. J. and David P. Mackinnon. (2014). Using Mediation and Moderation Analyses

to Enhance Prevention Research. Advances in Prevention Science, Vol.1 Defining

Prevention Science, pp. 537 – 555.

Featherman, Mauricio S., and Paul A. Pavlou. (2003). Predicting e-services adoption: a

perceived risk facets perspective. Int. J. Human-Computer Studies 59, 451–474.

Gunawan, Wilson, Wei Wei Goh, and Jer Lang Hong. (2013). Exploring Students’

Perceptions of Learning Management System: An Empirical Study Based on TAM.

IEEE International Conference on Teaching, Assessment and Learning for

Engineering (TALE), 367 – 372.

Harsandi, B., Purnama, J.,Soetomo, M.A.A., and Galinum, M. (2013). Internet User Trust

Measurement Analysis towards E-Commerce System in Indonesia. ICACSIS, 225-230.

Hassan, A. M., Michele B. K., Allison W. P., Fatma A. M. (2006). Conceptualization and

measurement of perceived risk in online shopping. Marketing Management Journal

16(1), 138-147.

Karazsia, B. T, Kristoffer S. B., Bridget A., David M. J., Katherine E. D. (2014). Integrating

Mediation and Moderation to Advance Theory Development and Testing. Journal of

Pediatric Psychology 39(2) pp. 163–173.

Klopping, Inge M., and Earl McKinney. (2004). Extending The Technology Acceptance

Model And The Task-Technology Fit Model To Consumer E-Commerce. Information

Technology, Learning, and Performance Journal, 22(1), 35 – 48.

-65-

Kumar, Vinay, and Dr. Ujwala Dange. (2014). A study on perceived risk in online shopping

of youth in Pune: A factor analysis. Acme Intellects International Journal of Research

in Management, Social Sciences & Technology Vol.8, Page 1 – 9.

Indonesia Internet User statistic 2015 [cited 2015 05/15]; Available from:

http://www.internetlivestats.com/internet-users/indonesia/.

Laroche, Michel, Gordon H. G. M., Jasmin B., Zhiyong Y. (2004). Exploring How

Intangibility Affects Perceived Risk. Journal of Service Research, Volume 6, No. 4, p.

373 – 389.

Mohamed, F. A., Ahmad M. H., Barbara S. (2011). Conceptualization and Measurement of

Perceived Risk of Online Education. Academy of Educational Leadership Journal,

Vol. 15, No. 4, Page 1 – 16.

Montague, David A. (2011). Essentials of Online Payment Security and Fraud Prevention.

Hoboken, NJ: John Wiley & Sons.

Najafi, Issa. (2012). The Role of e-Commerce Awareness on Increasing Electronic Trust. Life

Science Journal Vol. 9 (4), Page 1487 – 1494.

Ngai, E. W. T., and F. K. T. Wat. (2002). A Literature Review and Classification of Electronic

Commerce Research. Information & Management 39, pp. 415-429.

http://www.internetlivestats.com/internet-users/indonesia/

-66-

Pavlou, Paul A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust

and Risk with the Technology Acceptance Model. International Journal of Electronic

Commerce, Vol. 7, No. 3, pp. l01 – 134.

Phang, Chee Wei, Chuan-Hoo Tan, Juliana Sutanto, Fabio Magagna, and Xianghua Lu.

(2014). Leveraging O2O Commerce for Product Promotion: An Empirical

Investigation in Mainland China. IEEE Transactions on Engineering Management,

Vol. 61, No. 4, pp. 623 – 632.

Ro, Heejung. (2012). Moderator and Mediator Effects in Hospitality Research. International

Journal of Hospitality Management 31, 952 – 961.

Schiavon S., and Lee K. H. (2012). Dynamic Predictive Clothing Insulation Models Based

On Outdoor Air and Indoor Operative Temperatures. Building and Environment.

http://dx.doi.org/10.1016/j.buildenv.2012.08.024

Shen, Chentao, and Wang, Yongle. (2014). Online to Offline Business Model: Comparative

Study of Chinese O2O Companies. Halmstad University.

Suhendra, E. Susy, Budi Hermana, and Toto Sugiharto. (2009). Behavioral Analysis of

Information Technology Acceptance in Indonesia Small Enterprises. Anadolu

International Conference in Economics, Turkey.

Ueltschy, Linda C., Robert F. K., Peter Y. (2004). A Cross-National Study of Perceived

Consumer Risk towards Online (Internet) Purchasing. The Multinational Business

Review, Vol. 12(2), p.59 - 82.

http://dx.doi.org/10.1016/j.buildenv.2012.08.024

-67-

Wang, Feng-Sheng, and Lai Gu-Hsin. (2014). Empirical Study to Design Field Applications

for O2O (Online to Offline) Business Model in Tourism with Mobile Computing and

Cloud Service Supports. 産研論集 No. 46-47, 193 – 199.

Wu, Jen-Her, and Shu-Ching Wang. (2005). What drives mobile commerce? An empirical

evaluation of the revised technology acceptance model. Information & Management

42, 719–729.

Zhixin. (2012). Analysis of O2O commerce model. Journal of Science and Technology

(Social Sciences Edition). No.13.

B. Books

Cavana, R.Y. (2001). Applied business Research: qualitative and quantitative methods.

Australia, John Wiley and Sons, Inc.

Cooper, Donald R., and Pamela S. Schindler. (2014). Business Research Methods, Twelfth

Edition. New York: McGraw-Hill.

Hair, J. F., Black, W.C., Babin, B. H., and Anderson, R. E. (2010). Multivariate Data Analysis

(seventh edition). New Jersey: Pearson Prentice Hall.

Sugiyono. (2014). Metode Penelitian Pendidikan (Pendekatan Kuantitatif, Kualitatif dan

R&D). Bandung: Alfabeta.

Turban, Efraim, David King, and Judy Lang. (2011). Introduction to Electronic Commerce.

New Jersey: Pearson Prentice Hall.

-68-

Appendices

Questionnaire:

東海大學企業管理學系碩士論文 -...

Documents

Transcript of 東海大學企業管理學系碩士論文 -...

東海大學企業管理學系 碩士論文 -...

Documents

Transcript of 東海大學企業管理學系 碩士論文 -...

東海大學企業管理學系碩士論文 -...

Transcript of 東海大學企業管理學系碩士論文 -...