Eindhoven, December 2012

BSc Industrial Engineering and Management Science – 2010

Student identity number 0610320

in partial fulfilment of the requirements for the degree of

Master of Science

in Innovation Management

Supervisors Dr J.P.M. Wouters, TU/e, Innovation Technology Entrepreneurship and Marketing (Department of

Industrial Engineering and Innovation Sciences, School of Industrial Engineering)

Dr M.C. Willemsen, TU/e, Human Technology Interaction (Department of Industrial Engineering and

Innovation Sciences, School of Innovation Sciences)

J. Hes, BCom, Docdata Commerce, Marketing Department

Validating the e-Servicescape

An explanatory study towards web shop

conversion optimisation

by

Maarten van Haperen

TUE. School of Industrial Engineering

Series Master Theses Innovation Management

I

Executive Summary

Growth in e-commerce occurs at a tremendous rate. Over the past decade the continuing development

in electronic and mobile capabilities has led to increasingly more opportunities for e-commerce sales.

European e-commerce retail sales in 2011 were at 96 billion Euros, of which 9 billion in the

Netherlands (Thuiswinkel.org, 2012), and are expected to grow by 12.2% per year, topping 172

billion Euros in 2016 (Gill, 2012). Within the operation of web shops, maximizing turnover can be

seen as a key characteristic of success. As such increased research efforts have focussed on factors

determining the buying behaviour of online customers. Trust has been identified as the main driver of

purchase intentions online (Harris & Goode, 2010), which is in turn influenced by several factors.

These factors are summarized under the term e-servicescape: “the online environment factors that

exist during service delivery” (Harris & Goode, 2010).

What made the implementation and design of these factors challenging, is the notion that web shop

visitors are not homogeneous. Not only do different visitors have different intentions for visiting

based on utilitarian versus hedonic purposes, visitors are also heterogeneous in the sense that they are

in one of several stages part of the decision making process underlying online consumer purchase

behaviour (Butler & Peppard, 1998; Teltzrow & Berendt, 2003; Van der Heijden, Verhagen, &

Creemers, 2003). As such, different pages and sections of a web shop are oriented towards supporting

one or multiple of these stages and goals. A major deficit of the e-servicescape model as presented by

Harris and Goode (2010) is not taking into account these different phases. Furthermore, this

theoretical view opposed to the practical nature of web shop operation in which specific pages are

optimized in order to maximize revenue and purchases. The main goal of web shop operators is thus

to optimise cart value and conversion, defined as “the amount of purchasing visitors as opposed to the

total amount of visitors having expressed an interest towards a product” (Teltzrow & Berendt, 2003).

In order to further validate the e-servicescape model and in order to include the different stages of the

consumer decision making process, the following research question was developed:

Which e-servicescape factors and design rules can be used during different

stages of the consumer decision making process to optimise web shop conversion?

Based on a literature review encompassing 87 quality academic sources, 37 design rules were

extracted. These design rules were part of the e-servicescape factor “aesthetic appeal”, “layout and

functionality” or “financial security” and subsequently coupled to one or more of the consumer

decision making stages part of this research: search for information, evaluation of alternatives and

choice / purchase. Next, 13 semi-structured interviews were held with employees at a web shop

operator focussed on web shops of large retailers and brands in order to validate the model. This

resulted in the updating of 7 design rules and the adding of 10 additional design rules. Given that the

interviews were held at a single company, generalizability of the model is somewhat limited and

requires future research. The final model is depicted at the end of the main text of this thesis.

Next to the validated e-servicescape model, additional knowledge on two design rules part of the

model was created by implementing these rules at a lingerie retailer’s web shop using two field

experiments. The first experiment focussed on the inclusion of cross-selling functionality on the cart

page and the use of unique selling points (USPs) on the cart page. The results showed a negative

II

impact of cross-selling functionality on cart-to-purchase conversion rates, but not on profitability.

Furthermore the USPs did not prove to have an effect on cart-to-purchase conversion, but this might

have been a result of the sale that was active during the experiment. The second experiment focussed

on the effects of using a single-page versus a multi-page checkout on checkout-conversion. The

dataset was however too limited to draw scientific conclusion.

The limited amount of data available was not only a limitation for the checkout experiment, but also

for the cart page experiment. In made it difficult in both cases to correctly identify the effect of time

based covariates. Additionally the research setup and field nature of the experiments did not allow for

the measuring of concepts such as trust and purchase intentions, as were some technical restrictions

present.

The main scientific contributions of the research are threefold. First of all the e-servicescape model is

expanded to include consumer decision making process stages and specific and detailed design rules.

Second the field validation of the model by means of interviews allowed for the inclusion of field

knowledge, best practices and sentiment. Finally future research opportunities regarding specific

design rules and topics were identified.

The main managerial implications of the research are also threefold: the validated e-servicescape

model allows for the day-to-day use of a practical model with design rules in order to influence and

optimise visitors purchase intentions. Furthermore cross-selling was identified as having a negative

effect on cart-to-purchase conversion but no impact on revenues in the specific case of the lingerie

retailer under discussion. It implies the careful consideration of where and when to use cross-selling

to increase cart values due to the potential negative effects on conversion. The final implication is

formed by the experiment execution: it is important to carefully consider experiment setups, data

collection methods and data analysis methods as well as the way specific implications are established.

A solid academic approach and well thought-out plan are needed to derive significant and meaningful

results.

III

Preface

After six years of education, I can finally say that I do not for a second have doubts about my choice

for the education field of Industrial Engineering and specifically Innovation Management at

Eindhoven University of Technology. Balancing ‘hard’ engineering skills and knowledge and more

‘soft’ marketing and psychological skills encompassed what I was looking for and have found. The

combination of different fields of interests has both provided me with challenges and opportunities;

challenges in prioritising and in time and stress management on the one hand and opportunities for

expanding knowledge and both personal and professional enrichment on the other hand.

It feels both strange and relieving that this report marks the end of my six year education at

Eindhoven University of Technology. However it is with confidence that I now enter the career

market being the next step lying in front of me. I would like to express my gratitude to those that have

helped me in the realization of this thesis report as the ending of my Master’s Education. First of all to

my first TU/e supervisor Joost Wouters, for kindling my enthusiasm for marketing during his courses

and for his useful advice and aid in providing structure and simplicity where there first was

complexity. I also would like to express my gratitude to my second supervisor Martijn Willemsen for

supporting me and keeping me on the correct path of providing academic value in this thesis.

My thesis research would not have been possible without the support of and opportunities made

available by Docdata Commerce. I owe gratitude to my company supervisor Jurriaan Hes for

providing me with insights and his support during the realisation of my experiments and to all

Docdata Commerce employees for their support and willingness to cooperate with both my thesis

project in general and specifically the interviews I held with them.

The past six years of education and the completion of my thesis would not have been as successful or

at least triple as difficult without the continuous support of my parents Rob and Ineke, not-so-little

brother Wouter and my loving girlfriend Meike. I thank them for their criticism, support and faith.

To all those that I have not seen the chance of thanking here, please know that I am grateful for your

support, enthusiasm and spare time company. Were it during my study projects, my part time jobs, my

countless hours of practicing Irish dancing or during any other wonderful activity, I have learned that

practice makes perfect as longs as you keep an open, creative mind.

Maarten van Haperen

December 13, 2012

Scribendi recte sapere est principium et fons. - Horatius, ‘De Arte Poëtica’ 309

IV

V

Table of contents

Executive Summary ............................................................................................................................ I

Preface ................................................................................................................................................ III

1. Introduction

Research question and contribution ...................................................................................... 1 1.1.

Research approach ................................................................................................................ 2 1.2.

Research method ................................................................................................................... 2 1.3.

Thesis outline ........................................................................................................................ 2 1.4.

2. Validating and extending the e-servicescape model

Online trust............................................................................................................................ 5 2.1.

E-servicescape....................................................................................................................... 7 2.2.

Consumer decision making process ...................................................................................... 9 2.3.

Integrating the e-servicescape and the consumer decision making process ........................ 11 2.4.

Validating and extending the e-servicescape model ........................................................... 11 2.5.

3. Experiment design based on the e-servicescape model

Improving cart page conversion .......................................................................................... 17 3.1.

Improving checkout conversion .......................................................................................... 18 3.2.

4. Experiment method

Data collection .................................................................................................................... 19 4.1.

Data analysis ....................................................................................................................... 21 4.2.

Research quality .................................................................................................................. 23 4.3.

5. Experiment results and discussion

Experiment ‘Cart page’ ....................................................................................................... 25 5.1.

Experiment ‘Checkout’ ....................................................................................................... 30 5.2.

6. Conclusion ................................................................................................................................. 33

7. Reflection

Limitations .......................................................................................................................... 35 7.1.

Theoretical contributions and future research opportunities ............................................... 37 7.2.

Managerial implications ...................................................................................................... 39 7.3.

8. References ................................................................................................................................. 43

VI

Appendix A. Literature review results .................................................................................... 47

Appendix B. Company structure .............................................................................................. 49

Appendix C. Case study interview questions ....................................................................... 51

Appendix D. Case study results ............................................................................................... 57

Appendix E. Clickstream variables recorded ....................................................................... 65

Appendix F. Experiment design ............................................................................................... 69

Appendix G. Experiment results ............................................................................................... 77

1

1. Introduction

Growth in e-commerce occurs at a tremendous rate. Over the past decade the continuing development

in electronic and mobile capabilities has led to increasingly more opportunities for e-commerce sales.

European e-commerce retail sales in 2011 were at 96 billion Euros, of which 9 billion in the

Netherlands (Thuiswinkel.org, 2012), and are expected to grow by 12.2% per year, topping 172

billion Euros in 2016 (Gill, 2012). Not only the rise of fully internet-based companies is a major

development, e-commerce has also become increasingly important for retailers that operate brick-and-

mortar shops. This is due to the fact that the growth in e-commerce sales also implies the diminishing

of offline spending. A web shop is hence not merely an opportunity but has become a necessity.

Within the operation of web shops, maximizing turnover can be seen as a key characteristic of

success. As such increased research efforts have focussed on factors determining the buying

behaviour of online customers. Trust has been identified as the main driver of purchase intentions

online (Harris & Goode, 2010), which is in turn influenced by several factors. These factors are

summarized under the term e-servicescape: “the online environment factors that exist during service

delivery” (Harris & Goode, 2010). This term stemmed from research on designing the servicescape in

physical environments, with a focus on all factors during a service delivery in for example a shop or

hospital (Bitner, 1992).

What made the implementation and design of these factors challenging, is the notion that web shop

visitors are not homogeneous. Not only do different visitors have different intentions for visiting

based on utilitarian versus hedonic purposes, visitors are also heterogeneous in the sense that they are

in one of several stages part of the decision making process underlying online consumer purchase

behaviour (Butler & Peppard, 1998; Teltzrow & Berendt, 2003; Van der Heijden, Verhagen, &

Creemers, 2003). As such, different pages and sections of a web shop are oriented towards supporting

one or multiple of these stages and goals. A major deficit of the e-servicescape model as presented by

Harris and Goode (2010) is not taking into account these different phases. This opposes the practical

nature of web shop operation in which specific pages and sections are optimized in order to maximize

revenue and purchases. The main goal of web shop operators is thus to optimise cart value and

conversion, defined as “the amount of purchasing visitors as opposed to the total amount of visitors

having expressed an interest towards a product” (Teltzrow & Berendt, 2003).

Research question and contribution 1.1.

In order to further validate the e-servicescape model and in order to include the different stages of the

consumer decision making process, the following research question was developed:

Which e-servicescape factors and design rules can be used during different

stages of the consumer decision making process to optimise web shop conversion?

By providing an answer to the research question, the contributions of this research are both theoretical

and practical. The theoretical contributions are twofold. First they consist of validating the e-

servicescape model as proposed by Harris and Goode (2010) by incorporating knowledge from

different academic publication regarding e-servicescape factors and knowledge available in the field.

Second the e-servicescape model is extended to include the different stages of the consumer decision

making process and different purposes of web shop pages and sections, a criticism on the original

model that only looked at a web shop in general.

2

Research approach 1.2.

In order to answer the research question a multi-step approach was considered, as depicted in Figure

1.1. The first step in the research approach consisted of validating and detailing e-servicescape design

rules on the basis of academic publications and where applicable assigning them to specific stages in

the consumer decision making process. This resulted in a theoretical e-servicescape design rule model

adapted towards the consumer decision making process.

The second step in the research consisted of validating the theoretical framework with knowledge

available in the field of web shop operations. This resulted in a validated and extended e-servicescape

design rule model adapted towards the consumer decision making process. Two of the design rules

were investigated further in order to generate more knowledge on their implementation.

Research method 1.3.

The research approach outlined was established on the basis of three research methodologies: a

systematic literature review, a single embedded case study and a field experiment. The systematic

literature review was based on 35 quality academic sources. It established the e-servicescape model,

the consumer decision making process and a theoretical framework incorporating the design rules

based on the combination of the two. Next a series of interviews was conducted on the basis of a

single embedded case study at a web shop operator, in order to validate and extend the framework.

Two design rules were selected from the framework for further research, which was done using an

explanatory field experiment at the web shop of a large lingerie retailer.

Thesis outline 1.4.

Based on the research question and corresponding research approach, the remainder of this thesis is

outlined as depicted in Table 1.1: chapter two depicts the literature review and single embedded case

study on which the e-servicescape design rule model adapted towards the consumer decision making

process was based and validated. Chapter three focuses on establishing a set of hypotheses based on

two design rules selected for further investigation using field experiments. The experiment design and

method are described in chapter four after which the results from the experiment are presented and

discussed in chapter five. Chapter six subsequently focusses on providing conclusions drawn on the

basis of the entire research, before discussing its limitations and identifying opportunities for future

research as well as theoretical and managerial implications.

Figure 1.1 Research approach

Academic publications

Literature review

Design rule investigation

Field experiment

E-servicescape design

rule model

Knowledge from field

Case study

validate

3

Table 1.1 Thesis outline per chapter

Chapter Topic Research method

1 Introduction

2 Establishing, validating and extending the design rule model. Literature review, case study

3 Establishing hypotheses based on contradicting design rules. Experiment

4 Experiment design and method Experiment

5 Experiment results and discussion. Experiment

6 Research conclusions

7 Limitations, opportunities and implications.

4

5

2. Validating and extending the e-servicescape model

Online trust 2.1.

Due to the low search costs and effort involved in online shopping, visits without purchase intentions

will be more common online as opposed to offline in the future (Moe & Fader, 2004). Over the past

decade increasing research was performed with regards to stimulating the intention to purchase by

means of different drivers. One of the main drivers for purchase intentions as posed by several

researchers in the early 2000’s is trust (Van der Heijden et al., 2003). Other studies have shown this to

be true: “trusting beliefs regarding the web site had a significant positive effect on intention to buy

from it” (Stewart, 2003: 5) and “In particular, the emotion of trustworthiness has been emphasized as

one of the most important factors for the successful completion of commercial transactions” (Jinwoo

Kim & Moon, 1998: 2). Additionally, research has shown that poorly functional designed web shops

result in large amount of lost customers (Silverman, Bachann, & Al-Akharas, 2001; J. Song, Jones, &

Gudigantala, 2007).

2.1.1. Defining trust

In order to be able to identify the roles of trust on purchase intentions, a definition was needed:

“Online trust can be defined as an Internet user’s psychological state of risk acceptance, based upon

the positive expectations of the intentions or behaviours of an online merchant” (Y. D. Wang &

Emurian, 2005: 42). This view was supported by Lee & Turban, (2001: 79): “the willingness of a

consumer to be vulnerable to the actions of an internet merchant in an Internet shopping transaction,

based on the expectation that the Internet merchant will behave in certain agreeable ways, irrespective

of the ability of the consumer to monitor or control the Internet merchant.” The definitions show three

aspects part of the trust definition: it involves two parties (trustor and trustee) that need each other for

mutual benefits, it involves risk and it involves the trustor believing that the trustee will behave

according to the risk involving behaviour (Siau & Shen, 2003).

Shopping online brings risks that do not exist in traditional shopping, such as the absence of a

physical quality check before purchase and difficulties in safeguarding financial and privacy

information once handed over to the merchant (Lee & Turban, 2001). Both M. Lee and Turban (2001)

and Siau and Shen (2003) constituted three factors as main elements of online trustworthiness that

build on the previous definitions: ability, benevolence and integrity. Ability deals with the merchant’s

skills and competencies in performing online. Benevolence focuses on whether the consumer is

convinced that the merchant wants to do things right over merely maximizing profit. The final factor

integrity relates to the perception of the consumer that the trusted party is honest and acts in

correspondence with acceptable principles. Online trust is a combination of these three factors and is

furthermore context and situational specific.

In case of e-commerce a challenge lay in the fact that trust issues not solely involve the consumer and

the web shop merchant. There are also trust issues between the consumer and the computer system

through which that consumer makes transactions (Lee & Turban, 2001). In this research focus was put

on the type of trust mentioned first, as it was expected that interaction between consumers and

computer systems became more mainstream. This makes the web shop merchant trust issues more

relevant. Important to consider in this perspective is also the propensity to trust of individual

consumers; the way cues related to trustworthiness are magnified or reduced based on the personality

of the consumer (Lee & Turban, 2001).

6

2.1.2. Influencing trust

There are several aspects that influence a consumer’s trust in a web shop. It has to be taken into

account that trust is not merely the outcome of technical requirements but involves communication as

well (Chadwick, 2001). Hence trust can be influenced both implicit and explicit on both emotional

and cognitive levels. It can take place before visiting a web shop, during the visit and after an online

transaction (Egger, 2001). Three important aspects for influencing trust are past experience, external

factors and the e-servicescape.

The first aspect involves past experiences, influencing a consumers attitude towards and trust in web

shops in the future (Salam, Iyer, Palvia, & Singh, 2005). Bad experiences may negatively affect trust,

whilst good experiences may make it easier for a consumer to trust. The experiences do not only

constitute experiences related to the same web shop in the past, but also include experiences at other

web shops. A study by Wang and Emurian (2005) also showed that respondents with bad previous

experiences with web shops, such as being cheated on, gave comparatively lower ratings when asked

to value overall trustworthiness of a new web shop.

The second aspect involves external factors. Trust can be influenced by factors inside the scope of

control of the web shop that moderate the impact of both good and bad previous experiences as

described earlier. Such factors include a well-established offline reputation, the consumer’s perceived

size of the company, where bigger is better, and communications such as press releases,

advertisements and promotions, (L. H. Kim, Qu, & Kim, 2009; Lee & Turban, 2001; Salam et al.,

2005; Van der Heijden et al., 2003). The same goes for external factors outside the direct scope of

control of the web shop, such as news reports, evaluations and product recalls, but also guarantees,

legal rules and procedures. The latter factors influence what is called institution based trust, which in

turn will also affect purchase intentions of consumers (Salam et al., 2005). It can even lead to a

situation where a consumer does not trust a web shop, but makes a purchase nonetheless as there is

trust in the control systems and (legal) procedures in place (Van der Heijden et al., 2003).

The third and final important aspect is based on the e-servicescape model, see Figure 2.1 (Harris &

Goode, 2010). The e-servicescape was defined as “the online environment factors that exist during

service delivery” (Harris & Goode, 2010, p. 231). Hence by identifying and changing trust-

influencing factors on a web shop, both trust and correspondingly purchase intentions can be

Figure 2.1 E-Servicescape, factors and sub-factors (Harris & Goode, 2010)

7

influenced. The importance of web shop design was also stated by Wang and Emurian (2005, p. 43):

“In other words, one key consideration in fostering online trust is to build a trust-inducing e-

commerce interface.” Examining the web shop by looking whether it is from a well-known brand,

reading product information and looking for symbols of security approval are important risk-

reduction strategies by consumers (L. H. Kim et al., 2009) and are an integral part of the web shop

design. By designing the e-servicescape of a web shop in such a manner that trust and subsequently

purchase intentions are maximized, will have a direct impact on conversion. If users have higher

purchase intentions than can be converted into purchases, a web shop owner can increase revenues on

the basis of its current visitor set, without the direct necessity to attract additional visitors to gain more

visitors with a high enough purchase intention.

E-servicescape 2.2.

Once visiting a web shop consumers immediately form quick impression of the web shop, which they

seek to confirm with additional information and impressions they receive from the web shop. This

goes as far as interpreting information to suit the initial impression: on the same web shop, consumers

with a negative first impression interpret information negatively, whilst consumers with a positive

initial impression interpret the same information in a positive way (Stewart, 2003). The initial

impression can be influenced by the design of the e-servicescape. Before going into detail on the e-

servicescape, the origin and background of the concept are reviewed.

2.2.1. Concept

Bitner (1992, p. 58) has conceptualised the term servicescape as being “the built environment (i.e., the

manmade, physical surroundings as opposed to the natural or social environment)” and has posed that

it has a major impact on both consumers and employees in service organisations. Important aspects

involve ambient conditions, space and function, and signs and symbols. Although factors for brick-

and-mortar stores and web shops converge, there are some major differences as the online aspect

creates unique challenges at different transaction steps. The e-servicescape (also termed cyberscape or

virtual servicescape) was thus described as “the online environment factors that exist during service

delivery” (Harris & Goode, 2010: p. 231). This definition was widely supported by other researchers

(Jeon & Jeong, 2009; Vilnai-yavetz & Rafaeli, 2006; Williams & Dargel, 2004).

Several main factors form the e-servicescape of web shops. Aspects such as brand name cannot be

directly influenced and are therefore not part of the e-servicescape, although indicated by Fang &

Salvendy (2003) as a major factor impacting a shopper’s trust in web shop. The influential aspect that

is part of the e-servicescape is the placement and the number of displays of the brand name. The same

goes for the mention that people prefer to shop online over shopping at brick-and-mortar stores

because it is more convenient. Although this is most likely the case, the aspect by itself does not

improve purchase intentions. However, the way it is made clear in the e-servicescape to consumers

why online shopping should be preferred and is more convenient is, as well as providing a picture of a

physical building to induce trust in the online shop (Stewart, 2003).

As a starting ground for an e-servicescape literature review, the model by Harris & Goode (2010)

was used. According to their model, the e-servicescape consists of three factors as depicted in Figure

2.1. The first factor is aesthetic appeal, the second factor is layout and functionality and the third

factor is financial security. With regards to aesthetic appeal the focus lies on online ambient

conditions that create an attractive and alluring servicescape from the customer perspective. Layout

and functionality focuses on a contrasting aspect. Layout is focussed on the arrangement,

8

organisation, structure and adaptability of web shops. Functionality focuses on the extent to which

these aspects facilitate the conversion and service goals. The final factor, financial security deals with

the way in which consumers executing their purchase intention perceive the payment process as

secure and feel safe to complete it.

2.2.2. E-servicescape factor ‘Aesthetic appeal’

The first of the three factors part of the e-servicescape is aesthetic appeal. Y. D. Wang and Emurian

(2005: 51) stated that “design is more than an artistic interface.” This was supported by other

research, stating that aesthetics “deals with the sensory experience elicited by an artefact, and the

extent to which this experience tallies with individual goals and attitudes” (Vilnai-yavetz & Rafaeli,

2006: 248). The definition was supported by Cai and Xu (2011: 161) who stated that aesthetics is “a

holistic perception of design principles and individual objects (on a web shop), (…) closely connected

to attention and understanding, (…) that significantly affect human affect and emotion.” Good visual

design not only provides visual pleasure, but also comfortable reading and ease of use which (Y. D.

Wang & Emurian, 2005). Three sub-factors are part of aesthetic appeal, as seen in Figure 2.1:

originality of design, visual appeal and entertainment value (Harris & Goode, 2010).

Aesthetic appeal plays an important role in today’s consumers’ online consumption style, which

shifted from utilitarian purposes to a combination of utilitarian and hedonic purposes in which

recreation and entertainment have become more important aspects (Van der Heijden et al., 2003; J. Y.

Wang, Minor, & Wei, 2011). This shift and the different goals of users need to be taken into account

during the different purchase related stages in a web shop, moreover as the initial brief exposure of a

consumer to a web shop page immediately results in an aesthetic impression. This impression

correlates with the consumer attitude to that page and the entire web shop (Cai & Xu, 2011).

2.2.3. E-servicescape factor ‘Layout & functionality’

The second of the three factors part of the e-servicescape is layout and functionality. It encompasses

which design aspects are included on a web shop and the placement of these aspects. Four sub-factors

are part of layout and functionality, as seen in Figure 2.1: usability, relevance of information,

customisation and interactivity. The overall goal is to create a web shop with “easy-to-use navigation,

frequent updating, minimal download times, relevance to users and high quality content” (Palmer,

2002: 153).

The importance of layout and functionality was underlined further by Cai and Xu (2011: 162): “When

a web site is intuitively understandable in its design, it facilitates users’ interaction with the web site

and gives them a strong sense of control, knowledge of where to focus their attention and deep

cognitive enjoyment. As a result users may experience a state of flow whereby they have a distorted

sense of the passage of time and achieve an intrinsically enjoyable experience.”

2.2.4. E-servicescape factor ‘Financial security’

The third and final factor part of the e-servicescape is financial security. It encompasses security as

experienced while making (or planning to make) an electronic or internet payment. Financial security

is an important factor of the e-servicescape (Siau & Shen, 2003), moreover as in 2001 over 80% of

online shoppers abandoned their shopping carts before completing a transaction (Hausman & Siekpe,

2009). Although partly explainable by the fact that many web shop visitors use the shopping cart as a

wish list and comparison tool between web shops, reducing this percentage by increasing financial

security, results in a direct increase in revenue.

9

Although the penetration of internet payment is increasing, there will still be customers that have not

made (many) internet payments. Hence it is important to consider factors that determine the potential

adoption and usage of payment methods by customers. He and Mykytyn (2007) found that customer’s

willingness to adopt and use depends mostly on the overall design quality of the web shop, on

perceived risks and perceived benefits and on the payment features offered.

Liang and Lai (2002) state the importance of functional support using a good design to meet the

customer’s needs online. This is supported by Ranganathan & Ganapathy (2002) who state that the

overall quality of the web shop design is important for the performance of the web shop. Next to the

overall design quality, two factors part of the e-servicescape aspect financial security that can

influence customer’s willingness to adopt and use online payment methods: perceived security based

on perceived risk and ease of payment, as seen in Figure 2.1.

Consumer decision making process 2.3.

A major deficit of the e-servicescape by Harris and Goode (2010) is that the model focusses on web

shop at an abstract level. It does not take into consideration that consumers proceed through various

phases when making a purchase decision and that different pages and sections of the web shop

facilitate one or several of these phases.

Before focussing on the consumer decision making process it has to be noted that consumers can be

placed on a continuum of two extreme values based on behavioural characteristics (Carmel, Crawford,

& Chen, 1992; Tomes, 2000): goal-directed behaviour, focussed on making a purchase, and

experiential behaviour, focussed on browsing. The goal-directed and experiential behaviour are

respectively characterised by extrinsic versus intrinsic motivation, utilitarian benefits versus hedonic

benefits and directed versus non-directed search (Hong, Thong, & Tam, 2004).

The phases a consumers proceeds through when looking to buy a product which requires limited to

extensive problem solving behaviour, were depicted in Figure 2.2 (Butler & Peppard, 1998; Miles,

Howes, & Davies, 2000). Although the process has been set up as being linear, iterations and

feedback loops are very important as it is unlikely the consumer will follow a strictly linear approach

in his decision behaviour. The first phase constitutes a consumer realising or being attended to the fact

that a new product or service is required. Next follows the search for information in which different

alternatives are derived, followed by a phase in which these alternatives are evaluated and a phase in

which a choice on the product and purchase location are made. The final stage focuses on satisfaction

and loyalty behaviour resulting from a purchase, leading either to future purchases or disappointed

customers. As the thesis focused on conversion optimisation, focus was placed on the information

search phase, evaluation phase and purchase phase. Post-purchase behaviour was taken into thought

however, as converting existing customers to repeat buyers has proven to be over six times cheaper

than converting new customers (Silverman et al., 2001).

Figure 2.2 Online consumer purchase and decision behaviour (Butler & Peppard, 1998)

Information search

Evaluation of alternatives

Post-purchase behaviour

Choice /

purchase

Problem recognition

10

The approach towards viewing the consumer purchase and decision process as an iterative, but

generally linear process view, was coupled to the intention based view identified by Song and Zahedi

(2005), stating that a consumer visits a web shop with the intention to make a current purchase

decision, revisit the web shop in the future or to repurchase in the future. Consumers in the

information search phase will likely have the intention to revisit the web shop in the future, not

necessarily purchasing in their current visit, whilst consumers in the choice and purchase phase are

likely to have a direct purchase intention. The phase focussing on the evaluation of alternatives could

indicate both web shop visit intentions. It may be the case that customers are looking to immediately

buy after evaluating alternatives or that they may postpone the purchase action to a later visit,

indicating that there could both be a return visit or current purchase intention. The final intention by

Song and Zahedi (2005), the intention to repurchase, was coupled to the post-purchase behaviour

stage after the actual purchase. As customers evaluate their purchase on a product and on a web shop

level in this phase (Butler & Peppard, 1998), this is the moment that determines whether or not a

buyer is likely to visit the web shop again in the future with the intention to purchase again.

Focussing on the current purchase intention in the model by Song and Zahedi (2005), the consumer

buying and decision making process was linked to the business oriented customer life cycle

perspective as seen in Figure 2.4 (Teltzrow & Berendt, 2003). From a business point of view, a

company tries to get suspects (targeted customers) to visit the web shop, making them prospects.

Once on the web shop, the prospects are converted to customers, which will have different loyalty

behaviours based on purchase satisfaction. The suspects to prospects process can be coupled to the

problem recognition and information search phases by Butler and Peppard (1998), whilst the

prospects to customer process can be coupled to the evaluation and choice phase.

It was interesting to investigate the conversion process of consumers more closely, as these are

consumers who have already expressed an interest in buying and only need to complete their

purchase. The purchase process consists of four steps, as seen in Figure 2.3: seeing a product

impression, performing a product click-through, effecting a basket placement and making a product

purchase (Teltzrow & Berendt, 2003). A product impression is for example a product image on the

category overview page. Clicking on this product in order to get the product page is the following

step. If a consumer decides to buy, the product will be placed in the shopping basket. This stage poses

challenges as research by Close and Kukar-Kinney (2010) indicated that consumers also use a

shopping cart to compare products between web shops. The final stage is completing the purchase by

providing credentials and a shopping address and by paying for the product. At this time price

negotiation options such as vouchers or coupons, (multiple) shipping options and payment options

become important in the decision making by the consumer (Silverman et al., 2001).

A similar view was oriented on sequential Nominal User Tasks (NUTs). NUTs are in this case tasks a

customer has to perform in order to place an order on a web shop (Sismeiro & Bucklin, 2004). Three

tasks were identified. First a customer has to complete the product configuration, for example

selecting the product colour, desired size and quantity, and then place the product in the basket. Next

the purchase stage as identified by Teltzrow and Berendt (2003) is split in two tasks. The customer

first has to provide a complete set of personal information. This can be done by either providing all

details or by logging in if an account was made in the past. The final task is confirming the order by

providing payment data or making a payment.

11

Integrating the e-servicescape and the consumer decision making process 2.4.

Taking both the e-servicescape factors and sub-factors and the consumer decision making process, a

list of design rules to maximise purchase intentions was established on the basis of an academic

literature review including 35 quality academic sources, as depicted in the final model in Table 2.1 at

the end of this chapter and in the detailed model depicted in Appendix A. The design rules were

coupled to the consumer decision making process phases ‘information search’, ‘evaluation of

alternatives’ and ‘choice / purchase’ as these phases are most prominent on a web shop. The design

rules were selected to be applicable to one or multiple of these phases on the basis of either the article

they were extracted from (i.e. ‘provide a link back to shopping’ occurs in the cart and as such

automatically only in the ‘choice / purchase’ phase) or on the basis of common sense (i.e. ‘display

out-of-stock products and sizes’, which can only be applicable during the ‘information search’ phase).

Validating and extending the e-servicescape model 2.5.

A single embedded case study at a large web shop operator was used to validate and the model

established on the pervious pages. The company was founded in 2000, originally focussing on

providing system management hardware and services until 2005 an order fulfilment company took a

minority stake. In 2010 that company extended its share and gained sole proprietorship in the e-

commerce operations organisation that is currently employing 28 FTEs, as seen in Appendix B. The

organisation operates web shops for large brands and retailers and its portfolio includes a lingerie

retailer, a luxury leather products brand and a company selling printing supplies.

Figure 2.4 Online consumer purchase and decision behaviour (Butler & Peppard, 1998)

No sale

nC Not acquired

nP Not reached

nS

Choice /

purchase

Prospects Customers Suspects Repeat

customers

Rep.customers elsewhere

One-time customers

nM4

= nC

Figure 2.3 Online consumer purchase and decision behaviour (Butler & Peppard, 1998)

nM3 = nC

nM2

= nC

nM1

= nC

Prospects

M2 Clicked-through

M3

Placed in basket

M1 Saw impression

Customer made purchase

12

2.5.1. Case study method

The method by which the best practices were derived was a single embedded exploratory case study.

A single case study was used as only employees at the e-commerce fulfilment company were

interviewed to extract knowledge, as the case study was exploratory in nature as it was aimed at

identifying design rules and hypothesis regarding factors that influence conversion in web shops (Yin,

2009).

The unit of analyses was best practices concerning web shop conversion optimisation present within

the e-commerce company. The best practices were based on four specific cases of web shops that

made over 100,000 Euros revenue per year or processed over a million visitors per year, next to

comments that were made regarding other web shops. Using web shops with more traffic reduced the

risk of missing or overstating on optimisation techniques that are too small to measure and do not or

unintended got significant outcomes. In selecting cases a choice was made to only investigate projects

of web shops that have were recently launched on the basis of a well-known, strong and established

offline brand, or web shops that have existed for over a year, in order to be able to analyse the

servicescape factors in general. It was expected that there would be substantial moderating influences

for brands and web shops that had yet to establish themselves.

The case study data was acquired using the data collection principles of Yin (2009) in order to ensure

a basic level of reliability and validity. Reliability deals with the repeatability of a study where equal

results should follow in case of a study reproduction under the same conditions. Validity deals with

concepts, measurements and conclusions being well-founded and consists of construct validity,

whether a measurement tools measures what is intended for, internal validity, dealing with the

causality of findings and external validity, generalizability beyond the current study.

The first principle included maintaining a chain of evidence by being clear about subsequent steps in

the case study and having readers understand the structure of the research in order to make clear on

what grounds conclusions were based. The second principle was creating a case study database. It

ensured reliability by allowing other investigators to review the evidence used in the case study

report, being notes, documents, interviews and other materials, although access to this database was

limited and confidential. The third and final principle was using multiple data sources, also known as

triangulation, and ensured construct validity.

The data was collected by interviews that were held with staff that was directly involved with

operating aspects of web shops and could have impact or knowledge on how to design the e-

servicescape. The interviewees consisted of three customer support employees, four shop managers,

two marketers, the managing director, one intern and an interview with a representative of the

company that provided the payment interface, one of the largest payment service providers globally.

Two customer support employees were included to test the interview format and because they

received direct consumer feedback in their daily operations. Four shop managers were included due to

their involvement in web shop operations, with two shop managers focussing on shop management of

web shops for retailers and brands and two shop managers focussing on web shops that were fully

oriented on SAPOS, Sales At Point Of Service. Two marketing engineers were included as they

support the shop managers, the managing director was included because of his knowledge on the e-

commerce business environment and one academic intern was included due to his focus on predicting

online purchase intentions. The software engineers were not interviewed as they stated they did not

have any knowledge on web shop optimisation and were fully focussed on technology, being able to

build nearly anything required by other departments. The representative of the payment company was

included to shed light specifically on the payments section in the e-servicescape model.

13

The interviews were semi-structured, see 0, based on the e-servicescape (sub-)factors mentioned

earlier (aesthetic appeal, layout & functionality and financial security) in order to validate and confirm

knowledge from the literature based model, whilst at the same time leaving room to include other

factors not included in the model (Van Aken, Berends, & Van der Bij, 2007). As the interviews were

semi-structured based on open questions, there was ample possibilities to venture into specifics

regarding literature based and field based factors. This prevented over-focussing and ensured a clear,

broad view on the matters discussed. The results of the audio-taped interviews were reviewed by the

interviewees themselves to ensure construct validity (Yin, 2009).

2.5.2. Case study results

The results of the case study were analysed as follows: every design rule was declared either

supported or commented on per interviewee in case the interviewee had knowledge regarding the

design rule and it was discussed in the interview. After generalising these results over all

interviewees, a decision was made whether the design rule was supported or needed revision. As there

were no design rules that were contradicted by a large portion of the interviewees, no drop decision

were made. A support or comment decision was made on the basis of two characteristics: support

ratio and individual expertise.

The first characteristic is the ratio of the amount of individual supports compared to the total amount

of supports and comments. The general rule was established that at design rule should have supports

or comments in least six of the eleven interviews and that there should be a 67% majority of support

statements to come to a support decision. In case of a lower ratio, the design rule was revised to

include the comments that arose during the interviews.

The second decision characteristic is the expected knowledge of the individual interviewees,

compensating that a designer is expected to be better informed about design rules regarding aesthetics

than for example the representative of the payment service provider. This made it possible to support

or revise a design rule going against the outcome of the first decision characteristic.

Next to supporting or revising design rules, it was also possible to add new design rules. Again the

decision characteristics above were taken into account, considering that at least three interviewees

should independently mention a new decision rule in order for it to be included. In case of adding

design rules, the corresponding decision making process stages are based on common logic. The

overall model with revised and added was discussed with the marketing department and found

appropriate.

The anonymised, individual and aggregate level interview results have been included in 0.

Consecutively the e-servicescape model was updated and validated, as can be seen in 0, resulting in

the overall model as depicted on the following pages in Table 2.1.

14

Table 2.1 Validated e-servicescape model

D

ec

isio

n m

akin

g

pro

ce

ss

sta

ge

Choice / purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

Inclu

de o

rig

inal desig

ns a

nd s

igns s

uch a

s lo

gos.

Anim

ate

lo

gos f

or

incre

ased e

ffectiveness a

nd im

pact, b

ut sparin

gly

to a

void

dis

tractio

n.

Whils

t adherin

g t

o s

tandard

and c

om

mon d

esig

n r

ule

s, m

ake s

ure

the d

esig

n fits t

he w

eb s

hop a

nd b

rand p

ropositio

n.

If p

ossib

le, im

ple

me

nt re

fere

nces to a

n o

fflin

e b

rand a

nd r

eta

iler.

Desig

n s

hould

be c

olo

urf

ul by a

good s

ele

ctio

n,

pla

cem

ent

and c

om

bin

atio

n o

f colo

urs

.

Desig

n s

hould

be d

ivers

e,

by v

isual richness,

dynam

ics, novelty a

nd c

reativity.

Desig

n s

hould

be s

imp

le b

y s

how

ing u

nity,

hom

ogeneity, cla

rity

, ord

erlin

ess a

nd b

ala

nce.

Desig

n s

hould

show

cra

ftsm

anship

by m

odern

ity a

nd in

tegra

tin

g s

implic

ity,

div

ers

ity a

nd c

olo

urf

uln

ess.

Pro

vid

e la

rge s

ize h

igh q

ualit

y p

roduct im

ages s

upport

ed b

y s

chem

atic p

roduct chara

cte

ristics.

Pro

vid

e lo

gos, cert

ific

ate

s a

nd o

ther

vis

ual cues e

arly o

n to e

nhance f

eelin

gs o

f tr

ust.

Do n

ot

dis

tract users

with a

esth

etic d

esig

ns d

urin

g c

heckout.

Th

e c

are

ful use o

f people

on p

ictu

res c

an p

rovid

e c

onte

xt

and tra

nsfe

r em

otio

n a

nd f

eelin

g.

Cre

ate

a d

esig

n t

hat flo

ws f

luently fro

m h

om

e p

age t

o c

heckout

with focus o

n s

upport

for

decis

ion a

nd t

ransactio

n p

rocesses

Use a

consis

tent to

ne o

f voic

e t

hat suits t

he t

arg

et audie

nce.

Be s

carc

e w

ith v

ivid

ente

rtain

me

nt as it decre

ases s

hoppin

g c

art

use.

Cre

ate

ente

rtain

me

nt

by p

rovid

ing thoughtf

ul use o

f co

lour

and typogra

phy b

ased o

n f

unctio

nalit

y.

Cre

ate

ente

rtain

me

nt

by s

ocia

l aspects

, in

tera

ctive e

lem

ents

and in

spiratio

nal desig

n.

Pro

vid

e e

nte

rtain

me

nt

by r

egula

rly u

pdatin

g the w

eb s

hop s

o c

onsum

ers

get th

e f

eelin

g it

evolv

es.

Ori

gin

ality

of

de

sig

n

Vis

ual

ap

pe

al

En

tert

ain

men

t

valu

e

Aesthetic appeal

15

Table 2.1 Validated e-servicescape model (continued)

D

ec

isio

n m

akin

g

pro

ce

ss

sta

ge

Choice / purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

Build

mu

ltip

le w

ays o

f navig

atio

n b

ased o

n e

ase

-of-

use b

y d

iffe

rent ty

pes o

f consum

ers

and the a

ctio

ns it fa

cili

tate

s t

hat contin

uously

show

s

the b

readth

and d

epth

of th

e w

eb s

hop.

Consid

er

that th

e s

ize a

nd locatio

n o

f te

xt

and g

raphic

s d

ete

rmin

e u

sers

’ attentio

n b

ased o

n F

-shaped s

cannin

g p

att

ern

s.

Cre

ate

a c

lean a

nd u

nclu

ttere

d d

esig

n, w

ithout unnecessary

text

and g

raphic

s a

nd m

inim

um

lo

adin

g t

ime

s a

nd s

yste

m c

rashes, th

at

behaves a

s u

ser

expect.

Pro

vid

e c

lear

org

anis

atio

n a

nd layout

without

dis

tractio

ns.

Pro

vid

e a

lin

k b

ack t

o s

hoppin

g.

Cre

ate

a c

onsis

tent

and lo

gic

al user

flo

w fro

m h

om

e p

age to c

heckout.

Pro

vid

e c

onta

ct in

form

atio

n,

pre

fera

bly

inclu

din

g a

(fr

ee)

num

ber,

to r

each t

he c

onsum

er

support

depart

me

nt.

Sta

te c

om

petitive a

dvanta

ges r

ega

rdin

g t

he q

ualit

y o

f pro

duct offerin

gs a

nd s

erv

ices c

learly t

hro

ughout

the w

eb s

hop.

Sta

te in

form

atio

n r

egard

ing p

rice, fe

atu

res, in

vento

ry in

form

atio

n a

nd o

rder

rela

ted c

harg

es a

s e

arly o

n a

s p

ossib

le.

Pro

vid

e in

form

atio

n that is

accura

te,

consis

tent

and s

pecific

, support

ed b

y full

siz

e p

ictu

res.

Pro

vid

e in

form

atio

n that is

accura

te,

consis

tent

and s

pecific

.

Dis

pla

y o

ut-

of-

sto

ck s

izes,

but re

move p

erm

anent out-

of-

sto

ck p

roducts

and c

olo

urs

.

Pro

vid

e in

form

atio

n fro

m a

consum

er

poin

t of vie

w w

hils

t keepin

g t

hem

in

a c

ontin

uous f

low

.

Th

e lo

catio

n, ty

pe a

nd im

ple

me

nta

tio

n o

f cro

ss-s

elli

ng,

especia

lly in c

ase o

f lim

ited d

ata

and b

usin

ess r

ule

, should

be c

onsid

ere

d d

ue t

o

conflic

tin

g r

esults.

Specify c

usto

mis

atio

n t

ow

ard

s d

ecis

ion a

nd t

ransactio

n p

rocesses.

Add f

eatu

res s

upport

ing d

irect

inte

ractivity b

etw

een v

isitors

and s

ale

s o

r support

em

plo

yees.

Add in

tera

ctive functio

nalit

y t

hat

is p

ote

ntia

lly u

sefu

l or

influ

ences s

ite u

sage a

nd n

avig

atio

n.

Change t

ext and c

olo

urs

when h

overin

g o

ver

actio

nable

text

and im

age

s.

Usab

ilit

y

Rele

van

ce o

f

info

rmati

on

Cu

sto

mis

ati

on

Inte

racti

vit

y

Layout & functionality

16

Table 2.1 Validated e-servicescape model (continued)

D

ec

isio

n m

akin

g

pro

ce

ss

sta

ge

Choice / purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

Dis

pla

y t

ruste

d a

nd in

dependent seals

and c

ert

ific

ate

s o

f appro

val th

roughout

the w

eb s

hop.

Ask o

nly

str

ictly n

ecessary

in

form

atio

n a

nd e

xclu

de m

ark

etin

g q

uestions.

Explic

itly

sta

te w

hat in

form

atio

n is s

tore

d a

nd n

ot sto

red.

Dis

pla

y t

ruste

d a

nd in

dependent seals

and c

ert

ific

ate

s o

f appro

val.

Cre

ate

a c

onsis

tent

and lo

gic

al user

flo

w fro

m h

om

e p

age to c

heckout.

Allo

w for

checkout com

ple

tio

n w

ithout

regis

tratio

n o

r usin

g a

n a

ccount.

Pro

vid

e a

ctio

nable

feedback a

nd e

rror

me

ssages a

nd o

nly

if str

ictly n

ecessary

.

Pro

vid

e in

form

atio

n r

egard

ing t

he d

iffe

rent checkout ste

ps a

s w

ell

as t

he c

urr

ent

locatio

n.

Pro

vid

e the o

ptio

n o

f cre

dit c

ard

paym

ents

, re

gula

r paym

ent ty

pes a

nd p

aym

ent ty

pes that fu

nctio

n a

s the e

xte

nsio

n o

f exis

tin

g m

eth

ods.

Ta

ke th

e pro

ducts

sold

and diffe

rent

targ

et

audie

nces in

to account

when desig

nin

g s

ingle

or

multi-page checkouts

both

for

speed and

confirm

atio

n.

Perc

eiv

ed

secu

rity

Ease o

f u

se

Financial security

17

3. Experiment design based on the e-servicescape model

On the basis of the validated e-servicescape model, two design rules were selected for further

investigation by means of field experiments. The selection was based both on academic value to the

field and on practical opportunities available to execute experiments in order to generate knowledge.

Because of the nature of the field experiments, it was not possible to measure purchase intentions and

other psychological concepts at a visitor level. Instead conversion, “the amount of purchasing visitors

as opposed to the total amount of visitors having expressed an interest towards a product” (Teltzrow

& Berendt, 2003), was used to identify the effects of experiment variations. This was based on the

logic that by increasing trust, consumers will have higher purchase intentions, subsequently resulting

in overall higher web shop purchase rates.

The first design rule selected focussed on cross-sell functionality: “The location, type and

implementation of cross-selling, especially in case of limited data and business rule, should be

considered due to conflicting results.” Considering that the topic of recommendation engines is a

highly active research field, a decision was made to focus on generating on the effects of cross-selling

at one particular page of the web shop: the shopping cart. The cart page was selected as cross-selling

is an important variable for the cart page influencing the balance of getting visitors to enter checkout

and complete their order on the one hand and stimulating higher cart values on the other end.

Hypotheses regarding the design rule are established in paragraph 3.1.

The second design rule selected focussed on the type of checkout used: “Take the products sold and

different target audiences into account when designing single or multi-page checkouts both for speed

and confirmation.” Little academic research has been done on the topic of single-page and multi-page

checkouts. As such a field experiment is used in order to create a first indication towards the effect

size and direction of different checkout variations on checkout conversion rates. The hypothesis

regarding the design rule is established in paragraph 3.2.

Improving cart page conversion 3.1.

When comparing to a clean and minimised cart page design, the main benefit of adding cross-selling

would be to increase revenue and as such web shop profitability. Several interviewees however also

pointed out critical remarks. These remarks focussed on situations with little or insufficient data and

resources to successfully implement cross-selling on the cart page, in which cases cross-selling could

have a negative impact on cart to purchase conversion rate due to the offering of non-matching

products, subsequently leading to declining web shop revenue. As such a hypothesis was stated to

investigate the effect of cross-selling in the cart compared to a transaction oriented cart design on web

shop revenue and conversion rate. It was hypothesised that the extended revenue from cross-selling

counterweighs the decrease in conversion rate:

Hypothesis 1A A clean cart page design oriented on completing a transaction performs equal

to a cart page design oriented on enhancing cart value when compared on

cart-to-purchase conversion rate and cart value.

Next to cross-selling the effect of an additional design rule was investigated. Several design rules

focused on providing important information regarding ordering as early on in processes as possible so

that customers are informed beforehand and are not to be brought in doubt regarding the order

conditions late in the process. As such it was thought to be beneficial to again explicitly state unique

18

selling points (USPs) of the web shop in the cart, with both the goals to inform and persuade potential

buyers. Given the nature of the situation described, a hypothesis was stated to compare the cart page

design enhanced with USPs with a clean transaction oriented cart page design. It was hypothesised

that the USPs would have a positive effect on the cart-to-purchase conversion compared to the

transaction oriented cart page design:

Hypothesis 1B A transaction oriented cart page design supported by USPs has a higher cart-

to-purchase conversion rate than a transaction oriented cart page design

without USPs.

Improving checkout conversion 3.2.

Four factors were addressed in the ease of use design rule focussed on the type of checkout to be used:

speed, confirmation, product and target audience. In the setting in which this experiment was able to

run, product and target audience were already set. The products were lingerie articles in the low-to-

medium price range and the target audience consisted of females between the ages of sixteen and

fifty. The need for the remaining two factors, speed and confirmation, were investigated using two

checkout designs: a single-page and a multi-page checkout. It is expected that the target audience in

this specific case has sufficient knowledge with purchasing and paying online and with the internet in

general. Furthermore it is expected that once they have selected the products of their choice and are

ready to proceed to checkout, they want to pay swiftly with less confirmation rather than in a more

time and click consuming manner with more confirmation. The latter is supported by Bucklin and

Sismeiro (2003) stating that operators should consider pages with more information on each page to

reduce the number of page views needed to complete a transaction. As such it was hypothesised that a

single-page checkout outperforms a multi-page checkout when it comes to checkout-to-purchase

conversion rates:

Hypothesis 2 A single-page checkout has a higher checkout-to-purchase conversion rate

than a multi-page checkout.

19

4. Experiment method

The hypotheses established in the previous chapter were tested in two separate on-site field

experiments. This chapter details the method used for collecting data in paragraph 4.1 and the method

of data analysis in paragraph 4.2. The quality safeguarding of the experiment data is also discussed, in

paragraph 4.3.

Data collection 4.1.

The on-site field experiments were run at a large lingerie retailer’s web shop. The lingerie shop

formula, operated in the Netherlands by an e-commerce fulfilment partner, focuses on being personal,

service oriented and stocking high quality lingerie products with a decent price/quality balance. The

web shop was launched after three months of developing in February 2012. At the time of the

experiment nearly 500 products with over 4000 SKU’s (different sizes and colours) of three brands

were sold online, whilst the web shop processed over 130.000 unique visitors each month.

The web shop pages and page variations that were part of the field experiment were identified and

(re)designed according to the design rules under investigation. After approval by the retailer and the

fulfilment partner following design iterations, the experiments were executed. In the case of the cart

page and checkout experiments, A/B software tools were used to respectively equally assign visitors

to different cart page variations and to randomly assign visitors to different checkout variations in a

one (single-page) to four (multi-page) ratio. An open source web analytics software package was used

to record and anonymously store individual click stream data on a page view level, as depicted in 0.

4.1.1. Experiment ‘Cart page’

In order to test the hypotheses that providing USPs on the cart page positively influences conversion

and that orienting the cart page towards increasing shopping cart value influences conversion and cart

value, two variations were designed. These variations were based on a control condition, which is

depicted version next to the other variations in the conceptual design in Figure 4.1 and the actual

design in 0.

The first variation was the ‘Control’ variation. It encompassed no signs or functionality of either

Figure 4.1 Cart page designs; Left: variation 1 (Clean, control), middle: variation 2 (USPs), right: variation 3 (Cross sell)

20

cross-selling or additional USPs and was considered the most functional oriented cart page design.

The variation was based on the design rules not to distract users with aesthetics and to provide clear

organisation and layout without distractions.

The second variation ‘USPs’ focuses on clearly depicting unique selling points and other important

information on the web shop early on in the checkout process. This way consumers should be less

distracted and perceive less risk later on in the checkout process, as based on the design rules to state

advantages regarding the services of the web shop and providing checkout and order related

information as early on in the process as possible. The USPs used were delivery time, return policy,

the ability to pay in a secure way and kind service offered by the web shop. Furthermore the logos of

several banks were depicted. These specific USPs were used as they are promoted throughout the web

shop, hence providing consistency and not providing too much new information to the consumer, as

the remainder of the cart page already requires the processing of new information.

The third and final variation ‘Cross sell’ is focussed on providing the opportunity of recommending

articles to consumers in order to stimulate cart value, as such investigating the effect of cross-selling

in the cart based on relative low amounts of relational data between products available . It showed the

label ‘matching articles’ on top with two matching products below, based on a product-based

predefined set of matching products that related on the topic of whether or not a product is from the

same designer line. In case of several products, the recommendations were selected randomly

(although persistent in the case of a page refresh) from the set of recommendations available. The

recommendations were displayed with a product image, brand name, product name and price. In case

the product depicted was part of a promotion, all prices including mark-offs were shown. When

clicking on one of the products the product popped out and showed again the product image, brand

and product name, but this time supported by detailed product information, the article number and the

option to choose a colour and size as well as a button to directly add the product to the shopping cart.

4.1.2. Experiment ‘Checkout’

In order to test the influence of minimising the steps and actions a consumer must complete to place

an order whilst balancing confirmation of information, two checkout designs were tested: a multi-

page and a single-page checkout. These checkouts included the checkout steps as depicted in Figure

4.2. The first step focussed on acquiring the personal information of the consumer, albeit that it was

proceeded in the multi-page checkout by acquiring the consumers’ e-mailadress to check whether an

account already existed or not. The second step was focussed on determining the invoice address and

the shipping address. The third and final step aimed at completing the purchase by selecting a

payment method and subsequently either entering payment information or temporarily leaving the

retailer’s web shop to do so. After successful submission of the personal and shipment information

and completing the payment procedure, a success page with order information was shown.

The first design was a multi-page based checkout, focussed on confirming at every step the

Success

Order confirmed

Cart Step 1

Login / register

Step 2

Select adress

Step 3

Choose payment

Step 1*

Enter e-mailadress

Step 3*

Cancel payment

Figure 4.2 Checkout flow

21

information users entered in preceding steps. As depicted in Table 4.1 and shown in 0, the different

checkout steps were spread out over several pages. Next to the required information, the right hand

side of the page showed an overview of the order to be placed, as well as several unique selling points

for the web shop and a clickable DigiCert security seal. Furthermore all information not strictly

necessary for completing the checkout was removed from both the header and the footer.

The second design was a single-page based checkout, focussed on letting users complete the checkout

process as quickly as possible. As depicted in Table 4.1 and shown in 0, the different checkout steps

were shown on a single-page below one another. Due to this design no e-mailadress was required to

be entered by consumers to enter the checkout and as such consumers were only able to register for an

account after completion of their order. As with the multi-page checkout, the right hand side of the

page showed an overview of the order and additional information, whilst the header and footer were

stripped of non-vital information.

Table 4.1 Implementation of checkout steps

Checkout step Multi-page Single-page

Enter e-mailadress Page 1 Not included

Login / register Page 1 Login: 1st page section

Register: 2nd

page section

Select shipment and invoice address Page 2 3rd

page section

Choose payment method Page 3 4th

page section

Success Success-page Success-page

Data analysis 4.2.

The experiment design by Montgomery and Runger (Montgomery & Runger, 2007), shown in Figure

4.3, requires the identification of a dependent variable, determined on the basis of a hypotheses,

controllable (independent) variables, being the different design variations based on the e-servicescape

factors playing a role in conversion optimisation, and finally uncontrollable factors. One of these

uncontrollable factors was considered to be the day of the week on a working day (Monday to

Thursday) versus weekend level (Friday until Sunday), as it not was not possible to gather enough

longitudinal data to conduct a viable analysis on the factor. An additional uncontrollable factor was

Input

Visitors

Web shop

conversion process

Output

Transactions (y)

z1 z2 zn

Uncontrollable (noise) factors

…

Controllable factors

x1 x2 xn

…

Figure 4.3 Experiment design (Montgomery & Runger, 2007)

22

considered to be the time of day, which was split into morning (from 6.00 AM until 12.00 PM),

afternoon (from 12.00 PM until 6.00 PM) and evening / night (6.00 PM until 6.00 AM), as one would

expect that visitors behave differently during morning and evening hours. A final uncontrollable

factor considered was cart value.

Based on the click stream data several significant variables were calculated focussed both on the

general data and the specific individual experiments as explained in the subsequent sub-paragraphs.

After collection, corrupt data was removed from the dataset, specifically mobile visitors as after the

experiments it showed that a cookie bug was preventing half the mobile users from paying. Next the

data was analysed on the basis of Logistic Regression and Analysis of Co-Variance (ANCOVA)

techniques, suitable for analysing A/B/n- and multivariate experiments, using the statistical software

package SPSS. The choice for the specific method is dependent on whether the dependent variable is

dichotomous or continuous. On the basis of the analyses the hypotheses were tested and conclusions

were drawn.

4.2.1. Analysis experiment ‘Cart page’

As implied in the hypotheses regarding the cart design, the aim of the cart experiment was to optimise

conversion, defined as “a consumer that visits the cart page and the success page” (based on (Butler &

Peppard, 1998)). As such all the consumers completing their purchase received value 1, whilst others

received value 0.

{ ( )

( )

The combination of the dependent variable, the independent variable being the cart variation and the

uncontrollable factors, formed up the model as depicted in Table 4.2.

4.2.2. Analysis experiment ‘Checkout ‘

As implied in the hypotheses regarding the checkout design, the aim of the checkout experiment was

to optimise conversion, defined as “a consumer that enters the checkout and reaches the success page”

(based on (Butler & Peppard, 1998)). As such all the consumers completing their purchase received

value 1, whilst others received value 0. A limitation in the measurement possibilities of the single-

page checkout was neglecting the situation in which consumers started entering personal data on the

single-page checkout page without completing all information.

{ ( )

( )

The combination of the dependent variable, the independent variable being the checkout variation,

and the uncontrollable factors, formed up the experiment model as depicted in Table 4.2.

Table 4.2 Experiment models

Variable Experiment

Cart page Checkout

Dependent Complete purchase Complete purchase

Independent Cart variation Checkout variation

Uncontrollable Weekend Time of day Cart value

Weekend Time of day Cart value

23

Research quality 4.3.

Using an experiment as a means to identify factors and their magnitude requires taking several quality

dimensions into account, being reliability and construct, internal and external validity. Reliability

deals with the repeatability of a study. If the study were to be repeated under the same conditions, the

same results should follow. Securing reliability was done by using a structured, documented way of

working both whilst setting up the experiment and whilst performing the data analysis.

Construct validity deals with whether the measurement tool actually measures the concept being

studied. This is one of the major issues in the field experiments, as the field setup did not allow for the

measuring of characteristics and psychological concepts such as trust and purchase intention at a

visitor level. Instead key performance indicators focussing on conversion were identified based on

academic literature. Future research should focus on measuring the underlying concepts that likely

resulted in the outcome of the current experiment.

Next to construct validity, internal validity deals with the causality of results. This was safeguarded

and made open to discussion by identifying the conversion process in the literature review and using

indicators to measure several steps in this process during the experiments, even though the experiment

did not allow for the measuring of characteristics and psychological concepts as discussed before.

Additionally, external validity deals with the generalizability of the study, which was controlled for

by keeping the uncontrollable factors at a minimum. In order to do so, these factors and their potential

effects on the output variables were identified as much as possible and included as covariates. Clearly

describing the context of the experiment furthermore makes clear to what level the results are

extendable to different contexts.

24

25

5. Experiment results and discussion

On the basis of experiments designed in the previous chapters, data was gathered and analysed. This

chapter details the results of the individual independently run analyses, starting with the cart page

experiment in paragraph 5.1, followed by the checkout experiment in paragraph 5.2.

For all experiments it goes that data generated by the web shop operators both directly and via means

of additional software tools was excluded. Furthermore visitors using a mobile device were excluded

due to technical issues on mobile platforms influencing the behaviour of visitors. Additionally,

analysis assumptions regarding independence of observations were considered tenable, as visitors

shown more than one experiment variation were excluded from the dataset.

Experiment ‘Cart page’ 5.1.

Data for the cart page experiment was gathered between June 20, 2012 and July 6, 2012. In this 17

day time period 58,937 visitors visited the web shop of which 2,736 visitors (4.7%) was displayed one

of the cart page variations. This is taking into account the exclusion of visitors that were shown

multiple variations and visitors that showed anomalies in their cart and checkout pattern. Additionally,

inspection of the data and potential outliers resulted in the identification of one outlier in the ‘cross

sell’ variation where a cart value of over 300 Euros occurred, whereas throughout all variations the

next maximum values were all around 200 Euros. Even though the purchase has been validated, the

case was excluded from the dataset in the ‘value’-base model as there was a clear impact on the

results threatening generalizability. The main characteristics of the main variables in the cart

experiment dataset can be found in Table 5.1 and in 0 for the covariates day of the week, time of day

and cart value.

Table 5.1 Descriptive data cart experiment

Variation Visitors

Final step completed Conversion

Cart

Lo

gin

/

reg

iste

r

Sh

ipm

en

t

Pa

ym

en

t

me

tho

d

Can

cel

Su

cce

ss

Can

cel &

su

ccess

Mean Std. dev.

Total 2,763 1129 304 117 159 25 1012 17 37.2% .484

Clean 923 352

38,1% 99

10,7% 43

4,7% 63

6,8% 10

1,1% 348

37,7% 8

0,9% 38.6% .487

Unique selling points

899 380

42,3% 88

9,8% 38

4,2% 46

5,1% 9

1,0% 332

36,9% 6

0,7% 37.6% .485

Cross selling

941 397

42,2% 117

12,4% 36

3,8% 50

5,3% 6

0,6% 332

35,3% 3

0,3% 35.6% .479

The descriptives depict that a large portion of users do not get past the cart page. The ‘clean’ page

appears to have the highest cart-to-checkout conversion rate, but shows the largest portions of visitors

leaving the checkout on the shipment- and payment method-page when compared to the other

variations. This is opposed to the cross-selling variation that sees most checkout visitors drop out at

the login/register-page.

26

5.1.1. Baseline conversion model - Logistic regression

The analysis performed is a logistic regression. Three models were analysed. The first model only

included the purchase variable as dependent variable and the cart variation as the independent

variable. Next cart value was entered as independent value and finally day of the week and time of the

day. Before conducting the regression the accompanying assumptions are discussed. Due to the

logistic nature of the dataset, we assume a linear relationship between the logit of the outcome

variable and the combined independent variables. Additionally we assume that no important variables

are omitted and no extraneous variables are included. Based on the dataset and variables available

these assumptions are tenable. Two additional assumptions are that the observations are independent

and that the independent variables are measured without error, which are both tenable due to the

experiment setup. A final assumption is the absence of multicollinearity in the independent variables.

Given the VIF values, included in 0, for the time of day dummy variables (morning and afternoon)

and a condition index for a dimension that is slightly larger than others, there appears to be

multicollinearity between the variables. Given the nature of the variable this was to be expected,

however the implication of erratic changes in the coefficient estimates in case of small changes in the

model and the data should be taken into mind.

The results of the logistic regression can be found in Table 5.2. Starting by comparing the

performance of the different models, the constant predicted chance of purchase is with values of 60%

to 70% drastically too high in all models when compared to the descriptive conversion rate of 37.2%.

Furthermore the adding of the covariates did improve the performance of the model, even though the

value of the chi2-test did not approximate a significant result.

Table 5.2 Results logistic regression experiment ‘cart page’

Model 1 Model 2 Model 3

Variable B Wald

Odds-

ratio B Wald

Odds-

ratio B Wald

Odds-

ratio

Experiment

variation

USP 0.030 0.088 1.030 0.039 0.149 1.039 0.037 0.134 1.037

Cross-selling -0.103 1.093 0.902 -0.110 1.246 0.895 -0.108 1.183 0.898

Cart value Not included 0.04 10.185 1.004* 0.004 10.221 1.004*

Day of week: Weekend Not included Not included -0.101 1.526 0.904

Time of day Morning

Not included Not included -0.051 0.206 1.053

Afternoon -0.010 0.013 0.990

Constant -0.0336 23.391 0.715* -0.511 33.167 0.600* -0.573 25.705 0.628*

Model performance Model 1 Model 2 Model 3

Hosmer and Lemeshow

chi2-test

0.000 (p = 1.000) 6.722 (p = 0.567) 10.804 (p = 0.213)

R2 Nagelkerke 0.001 0.007 0.008

Cox & Snell 0.001 0.005 0.006

* = p < 0.001

Interpreting the results from the logistic regression is done by looking at the odds ratio rather than at

the coefficient, as it provides an intuitive interpretation: for example a constant odds ratio of 0.3

implies a 30% predicted chance and a variable odds-ratio of 1.1 implies an increase of 10% per 1

increase in the variable, meaning that a variable value of 3 results in a predicted chance of 39%. When

looking at the second model (including cart value as a significant covariate), it shows that the

predicted chance of completing an order increases with 0.4% per Euro of cart value on the constant,

which is relatively large given the average order value of 38.41 Euros (implying 9.2% base point

27

average increase in predicted chance of purchase). Time of day and weekday versus weekend does not

significantly influence the predicted chance of completing a purchase.

Taking into account all models with all covariates, there were no significant influences of the cart

page designs on conversion. The approximate 3% to 4% increase over the 60% to 70% baseline

predicted chance of completing a purchase is highly insignificant with p-values around 0.72. The

stronger negative influence of the cross-selling enabled cart page design of approximately 10% over

the 60% to 70% baseline predicted chance of completing purchase is insignificant at the p-value of

0.27. More data is needed to investigate the effects of the cart page designs and more longitudinal

data is needed to get more insights into the effects of the covariates.

5.1.2. Linear conversion model - ANCOVA

Given the fact that the cart page design involving cross-selling variation did not significantly differ

from the clean cart page design at a small enough α-level, and given that the descriptive analysis of

the dataset showed the cart variation likely having an influence on the completion behaviour at

different checkout steps, an additional model was created in which all checkout steps were weighted

equally. As such a value of 0.00 means that a consumer did not get past the cart page, whilst a value

of 0.25 implied the visitor reaching the login / register page, a value of 0.50 implied reaching the

select address page, a value of 0.75 implied reaching the payment method selection page and a value

of 1.00 implied a consumer completing the purchase. Additionally the ‘payment cancel’ step was

considered to be half way the third step and the success page, as such having a value of 0.875.

An Analysis of Co-Variance was run on the dataset with the covariates weekday versus weekend,

time of visit and cart value as covariates and the conversion variable as dependent variable. The

covariates were included in the model as they improved the quality of the model during the logistic

regression analysis. The ANCOVA implied the testing of four assumptions. Next to the assumption of

independence of the observations, which is considered tenable as discussed before, the assumption of

a normally distributed population was violated as expected due to the nature of the conversion

variable. This does however not necessarily result in issues with the analysis, as ANCOVA is fairly

robust to violation of the normality assumption and as such the non-normal distribution only had a

small effect on the Type I error rates. Additionally the assumption of homogeneity of variance was

tenable, pcart_conv_linear = 0.334 for Levene’s Test of Equality of Error Variances (α = 0.05). The

assumption of homogeneity of regression slopes was not tenable for the cart value variable (p < 0.05),

but no suitable dummy-coding scheme was identified that both resolved the violation and kept the

model easily interpretable. As such it has to be taken into account during the discussion that the cart

value covariate may display different effect types at different variable levels.

The result of the ANCOVA can be found in 0. Neither the covariate weekday versus weekend nor

time of day was significantly related to the linear conversion rate at any level or showed F-values that

were close to or larger than the critical F-value (F(1, 2,496) = 5.02). The covariate cart value did

prove to be significantly related to the linear conversion rate at F(1, 2,496) = 12.89, p = 0.000,

displaying F-values far above critical levels. The effect size proved to be relatively high with B =

0.001 (t(2,496) = 3.590; p = 0.000), which given the average order value of 38.41 Euros implies an

average increase in conversion of nearly 4%.

After controlling for the covariates, the effect of the cart page design on the linear conversion rate was

still not significant at the α = 0.05 level, but did show significance near the α = 0.1 level with F(2,

2,496) = 2.090, p = 0.124. Still the critical F-value (F(2, 2,490) = 3.69) was not reached. The result

did however give an indication towards the direction of the cart page design effects. Contrasts

28

revealed that the results again point towards small or no differences between the clean cart page

design and the cart page design incorporating USPs (p = 0.139) Looking at the differences between

the clean cart page design and the cross-selling enabled cart page design, a nearly significant

difference was found with p = 0.051, also depicted in Figure 5.1.

5.1.3. Including cart value – ANCOVA

As stated in the hypothesis, the focus should not only be on conversion as well as on overall revenue.

The experiment influenced cross-selling options available and as such potentially influences cart

values. In order to identify those effects, an additional model was created in which the values for the

linear conversion model, showing more significant results than the baseline model, were multiplied by

the cart value of the visitor.

An ANCOVA was performed with the valued conversion as dependent variable, the cart page

variation as independent variable and time of the day and weekday versus weekend as covariates.

Given the dependent variable, cart value was no longer included as covariate. Running an ANCOVA

implied the testing of four assumptions. Next to the assumption of independence of the observations,

which is considered tenable as discussed earlier, the assumption of a normally distributed population

was violated as expected due to the nature of the conversion variable. This does however not

necessarily result in issues with the analysis, as ANCOVA is fairly robust to violation of the normality

assumption and as such the non-normal distribution only had a small effect on the Type I error rates.

Additionally the assumption of homogeneity of variance was tenable, pcart_conv_linear_value = 0.213 for

Levene’s Test of Equality of Error Variances (α = 0.05), as was the assumption of homogeneity of

regression slopes, which was tested during the running of the models (p < 0.05).

The result of the ANCOVA can be found in 0. Of the covariates, only time of day, specifically

morning, was found significantly related to the valued linear conversion at F(1, 2,497) = 4.066, p =

0.044, which is close but not above the critical F-value (F(1, 2,497) = 5.02). The effect size proved to

be high with B = 3.317 (t(2,497) = 2.016, p = 0.044). The other time of day covariates and weekday

Figure 5.1 Mean values and confidence intervals (α = 0.05) of conversion variables

0.532

0.523

0.489

0.502

0.491

0.459

0.563

0.554

0.520

0,45

0,49

0,53

0,57

Clean USP Cross-selling

Cart experiment 'Linear conversion'

23.40

21.67

22.84

21.40

19.61

20.84

25.39

23.74

24.83

18,00

22,00

26,00

Clean USP Cross-selling

Cart experiment 'Linear valued conversion'

0.57

0.53

0.49

0.45

26

22

18

29

versus weekend variables were not significantly related to linear conversion rate at any day of the

week, as were not F-values depicted that were larger or close to the critical F-value.

After controlling for the covariates, the effect of the cart page design on the valued linear conversion

rate was found not significant with F(2, 2,491) = 0.178, p = 0.488, lower than the critical F-value

(F(2, 2,497) = 3.69). Contrasts also revealed only non-significant differences, as seen in Figure 5.1.

5.1.4. Discussion

The first hypothesis underlying the cart page experiment was stated as follows: “A cart page design

oriented on completing a transaction performs equal in revenue based on cart-to-purchase

conversion rate and cart value as a cart page design oriented on enhancing cart value.”

On the basis of the experiment results the first hypothesis was partly rejected (p = 0.051). Looking

merely at cart-to-purchase conversion, no statistically significant effect of the cart page design

variation was found using a logistic regression. However, when taking into account the different steps

part of the checkout using a linear conversion value model, a statistically significant effect was found

using an ANCOVA: the clean cart page design outperformed the cart page design providing cross-

selling functionality. This rejects the hypothesis on the cart-to-purchase conversion rate aspect.

It was noted that in both the general and detailed model the cart value proved to be a significant

covariate. However, when using a model in which the linear conversion value was multiplied by the

cart value, no significant effects of the cart page designs were found and only the time of day

covariate morning was found significant. The cart pages designs as such performed equal on the

revenue aspect (p = 0.488).

From the results one can deducted that it is important to find a balance between the two designs which

can be dependent on moderating and environmental business factors. On the one hand conversion is

important, but web shop operators should also take into account the revenue made from orders.

Higher value orders result in higher profits given product margins and shipping costs, whilst on the

other hand a high conversion rates provide the opportunity to achieve economies of scale or to clear

out old stock.

The second hypothesis underlying the cart page experiment was stated as follows: “A transaction

oriented cart page design supported by USPs has a higher cart-to-purchase conversion rate than a

transaction oriented cart page design without USPs.”

On the basis of the experiments result the second hypothesis is rejected (p = 0.139). There was no

statistically significant effect of providing USPs in the cart page design on cart-to-purchase

conversion rates.

Three important aspects influenced the results. First of all the presence of only approximately 900

data points per cart variation made measuring clear differences between the two variations difficult,

given there was a difference in cart-to-purchase conversion rate of only 1%. More data is needed to

clearly measure the effects of providing USPs.

The second aspect influencing results was the fact that the experiment was run during a sale period. It

was expected that the sales had a larger impact on purchase intentions than the USPs. As such the

USPs did no longer have an additional effect on purchase intentions and subsequently conversion

rates. This also correlates to the third and final aspect influencing the results, which was the content of

the USPs. The USPs were considered relatively weak, being more selling points in general than being

30

unique to the web shop. Depicting stronger USPs, which was not approved for this experiment, or

moving the USPs to a location on the cart page with more central focus would likely resolve in

different results.

Experiment ‘Checkout’ 5.2.

The experiment time range was set from August 24, 2012 to September 2, 2012. During the ten day

period 21,699 visitors visited the web shop, of which 548 visitors (2.5%) was displayed one of the

cart page variations (NCheckout ‘Multi-page’ = 450, NCheckout ‘Single-page’ = 98). In total 364 orders NCheckout ‘Multi-

page’ = 296, NCheckout ‘Single-page’ = 68) were placed, resulting in a checkout conversion of 66.4 % (MCheckout

‘Multi-page’ = 0.66, MCheckout ‘Single-page’ = 0.69). Inspection of the data and did not result in the

identification of outliers.

5.2.1. Results

The conversion rates of both checkout variations were close to one another with a difference of only

3% and a small amount of data points. As such first a chi-square test was performed, which equalled p

= 0.493. Therefore it was expected that further analysis would not result in a model with significant

variables. However, in order to get an estimate towards the size of the effect and to analyse the role of

cart value and time of day, a logistic regression was run. The model included the dependent variable

as the purchase variable, the checkout variation as the independent variable, as well as the time of the

day and cart value. Due to the short experiment run time, the covariate day of the week was excluded

from the model.

Before conducting the regression the accompanying assumptions are discussed. Due to the logistic

nature of the dataset, we assume a linear relationship between the logit of the outcome variable and

the combined independent variables. Additionally we assume that no important variables are omitted

and no extraneous variables are included. Based on the dataset and variables available these

assumptions are tenable. Two additional assumptions are that the observations are independent and

that the independent variables are measured without error, which are both tenable due to the

experiment setup. A final assumption is the absence of multicollinearity in the independent variables.

Given the relatively high VIF values, included in 0, for the time of day dummy variables (morning,

and afternoon) and a condition index for a dimension that is substantially larger than others, there

appears to be multicollinearity between the variables. Given the nature of the variable this was to be

expected, but not considered a major property given the spurious nature of the dataset.

The results of the three logistic regression runs, using no covariates, cart value as a covariate and with

time of the day as well as cart value as covariates, can be found in 0. Interpreting the results from the

logistic regression is done by looking at the odds ratio rather than at the coefficient, as it provides an

intuitive interpretation. Furthermore, given the low value for N, focus was on identifying both

significant results and relatively large odds ratios that provide an indication of the effect to be

investigated in future research. All three models did not perform extremely well. The experiment

variation was not significant in all models. The model including all variables provided significant

results that were in line with the cart experiments on the topic of cart value and morning hours being

significant. However for all models and interpretations, there is a high risk of over fitting the data

given the relatively low amount of data points. This also suits the predicted baseline chance of

completing purchase which is higher than 100%.

31

5.2.2. Discussion

The hypothesis underlying the checkout experiment was stated as follows: “A single-page checkout

has a higher checkout-to-purchase conversion rate than a multi-page checkout.”

Given the spurious nature of the dataset, one can only state that more data needs to be gathered

including data on covariates, as they appear to have a clear impact on conversion. No significant

results can be extracted regarding the single-page versus multi-page discussion other than carefully

stating they point, as expected from the descriptive data, towards a positive effect of the single-page

checkout design on checkout-to-purchase conversion compared to a multi-page checkout design. It

has to be noted that these preliminary unsupported results were specific to this case and the specific

checkout designs of this experiment.

Despite the inability to draw conclusion from the experiment, it is worthwhile to mention that

potential positive results perceived by companies redesigning their multi-page checkout into a single-

page checkout are caused by the mere fact that they are working on building new and optimised

checkouts. It could very well be that the improved results would also have been achieved by radically

optimising their multi-page checkout. In order to identify the relevance of academic research into

checkout design and checkout types, additional practical and laboratory research over multiple web

shops is needed.

32

33

6. Conclusion

Effective design of web shops is a key web shop success factor. The e-servicescape model by Harris

and Goode (2010) provides a good starting point for building web shops that increase consumer

purchase intentions and as such revenues and profitability. A major deficit of the model is however

that it does not take into account the different goals and behaviours of individual web shop visitors.

Past research has shown consumer to proceed through different phases in a consumer decision making

process before actually making an online purchase. As such the following research question was

posed:

Which e-servicescape factors and design rules can be used during different

stages of the consumer decision making process to optimise web shop conversion?

Three factors were established on a literature review and a validation oriented single embedded case

study: aesthetic appeal, layout and functionality, and perceived security. In total 44 design rules were

placed under these factors that were coupled to the applicable consumer decision making process

stages of search for information, evaluation of alternatives and choice / purchase. The final model is

depicted in Table 7.1 at the end of the next chapter.

With regards to the e-servicescape factor visual appeal, the main conclusion was drawn that

originality is not a necessity and that it is more important to provide a design that adheres to

consumers’ expectations based on (existing) brand values and that flows fluently from homepage to

checkout. Furthermore the importance of product images was stated several times as it can provide

context to images and can even transfer emotions and feelings regarding a web shop and specific

products. Although not researched often in the past, the role of product photography appears to be one

of vital importance to the success of a web shop. More specifically even, discussion focussed towards

the effect on conversion rates of using product photography displayed on models.

With regards to the e-servicescape factor layout and functionality, two main conclusions were drawn.

First of all it showed important to continuously take the end user into account when designing these e-

servicescape aspects in a web shop and adhere to expectations of consumers in order to create a

logical continuous flow from homepage to checkout. Secondly the role of cross-selling in a web shop

was discussed. As in academic literature, contradicting findings were found on the type, location and

implementation of cross-selling in a web shop. The discussion revolved around the type of cross-

selling and the way it should be presented throughout the web shop on the one hand and specifically

on the usage of cross-selling on cart page. Both potential benefits, such as an increase in cart value,

and potential disadvantages, a decrease in cart-to-purchase conversion due to consumers brought into

doubt, were mentioned.

With regards to the final e-servicescape factor financial security one of the main conclusion was that

it consumers should feel as safe as possible by invoking feelings of trust and security using logos,

certificates and statements. At the same time the effect of these cues is limited in case of existing

brands and retailers and they can even undermine feelings of trust if they are to prominent and distract

the user from entering his personal information and focussing on checking the security of the web

shop instead. The second conclusion was that a checkout should be made as easy to complete as

possible and that it should provide every payment method that a consumer could potentially want to

use, as long as it is well known and don’t make other consumers doubt the security of the web shop.

Regarding ease of use, the interviewees also focussed on the usage of a single-page or a multi-page

34

checkout. In literature little research has been done in this are as of yet, even though getting

consumers to complete the checkout process can be seen as a core activity of web shop owners.

The validated e-servicescape model shows that different design rules and approaches should be

considered for different stages in the consumer decision making process and as such on different

pages. In order to generate more academic knowledge on two topics covered by the design rules, two

field experiments were performed. Even though conclusions were drawn from these results, notion

should be made that the experiments featured a relatively low amount of data points and are as such

tentative and specific to the case of the lingerie retailer under discussion.

The first experiment focussed on the role of cross-selling on the cart page and its effect on revenue

and cart-to-purchase conversion. On the basis of the results it was concluded that elements on the cart

page that potentially distract users from proceeding to checkout including, though not exclusively,

cross-selling functionality, have a negatively influence on the cart-to-purchase conversion rate. Focus

on conversion should however be balanced out against higher cart values and as such revenues which

may be increased by means of cross-selling functionality.

The second experiment focussed on a recent development in the field: the testing and usage of single-

page and multi-page checkouts. The dataset regarding this experiment was highly spurious, making it

impossible to draw definitive conclusions. More research is needed into the topic of checkout design

and one should strongly take into account the presence of covariates, of which at least time of day and

cart value were identified as important.

35

7. Reflection

The aim of theoretical research is to contribute to science. However, as with every research there are

limitations to the current research that need to be taken into account, which is done in paragraph 7.1.

Taken these limitations into account, the academic implications of the research are discussed in

paragraph 0, including the identification of future research opportunities. The chapter ends with the

identification of managerial implications in paragraph 7.3.

Limitations 7.1.

This thesis research had several limitations to it that are important to consider when establishing

generalisations and implications. The limitations can be divided into three categories: limitations due

to the research design, limitations due to the research execution and technical limitations.

7.1.1. Research design

The research design, using a combination of academic literature, field based interviews and two field

experiments mainly provided limitations on the aspect of generalizability. Although an external case

study was part of the original research design, contact with twenty e-commerce companies did not

result in the opportunity of interviews. Reasons varied from a lack of interest in cooperating in the

research, to insufficient resources partly due to the summer period in which the research was

executed, to declining cooperation due to the competitive position of the respondents to the company

where the thesis internship was performed. The remaining in-company interviews limit the

generalizability of the e-servicescape model as it only focusses on the knowledge of employees in one

company, albeit that the interviewees come from different departments of a company that has

operated different types of web shops both in the past and at this point in time. As a result the model

and design principles established in this case study should be tested, confirmed and deepened out

further both at other companies and in different industries than the online apparel and fashion retail

industry. Although web shops with fast moving consumer goods, being printer supplies, were

covered, the interviewees showed that different design rules may apply based on the web shop owners

goal of a web shop: purchase and retention or solely purchase.

Next to the theoretical and field work in order to establish the e-servicescape model, two experiments

were executed. Three main limitations were present in the experiment design, of which the first was

focussed on the limited time span of the experiment. This resulted in the difficult interpretation of the

data due to the inability to correctly measure time-based covariates. More data was needed to gain

more insights into these covariates, which should also provide the opportunity to investigate

interaction effects between variables and the opportunity to design models that perform better on an

overall scale.

Next to the limitations due to a limited time span, generalizability is threatened by only including a

single web shop. In order to be able to better generalize the results, the experiments will need to be

repeated on different web shops both in similar and different industries, in order to establish the effect

of different design rule interpretation on consumer purchase intentions on a web shop. Moreover,

specific attributes, characteristics and propositions of web shops may result in very different results

amongst web shops that at first sight appear to be equal. The experiment results do however provide

guidelines on the direction of effects and aspects to consider when designing and optimising different

web shop pages and clusters of pages.

36

Perhaps even the most important limitation regarding the experiment design is however that due to the

field nature of the experiments, it was not possible to measure trust and purchase intentions and that

instead conversion was chosen as a measurement. This threatened construct validity and internal

validity and provides a strong recommendation for future research

7.1.2. Research execution

The academic literature review and validating interviews provided two main limitations. The first

limitation is that, given the scope of the master thesis research, the academic literature needed to

balance between being high-level and generic and being detailed. A choice was made to assume the

model of Harris and Goode (2010) in the interpretation that three specific e-servicescape factors and

sub-factors influence trust and subsequently purchase intentions, and to focus this research on

identifying design rules that influence these (sub-)factors. As such the validated e-servicescape model

should be tested in a laboratory setting to identify the causal relationships of the design rules and

measure their influence on both trust and purchase intentions directly.

A second limitation is in the interview execution. The interviews focussed on identifying aspects

influencing e-servicescape sub-factors and the testing of design-rules without literally depicting the

design rules but by incorporating them into the style of questioning. This was done in order not to

direct the interviewees and to gain as much data as was possible. However, this also implies a small

limitation to the validity of the e-servicescape model validation. Given the fact that the design rules

were however incorporated into the questioning, this was not considered an issue

The execution of the experiments resulted in three additional limitations. The first limitation focusses

on the translation of the hypotheses into experiments. Given the fact that the experiments were

executed at an actual web shop, being operated by a third party company, not all proposed and desired

experiment designs could be tested. The necessary approval of both the web shop owner and operator

resulted in more conservative experiment designs. This limited the potential effects of experiment

variations as they showed more resemblance to one another and as such made it more difficult to

derive statistically significant results and conclusions. This was enhanced by the second limitation,

also discussed during the discussion of the experiment results, which was the restricted period of time

the experiments were allowed to run. This lead to small amounts of data points available that

hampered the results analyses and drawing of conclusions: models used were of low performance and

interpretation was challenging given the expected role of covariates for which too little data was

available on the one hand and given the small differences between the different experiment variations

on the other hand. Although both limitations results in limited construct validity and external validity

of the research, the results and conclusion do provide directions towards the effects that can be

expected when making e-servicescape design decisions as well as the direction of the results of

comparable experiments.

The time period in which the experiments were conducted created the third limitation; in case of the

cart page experiment a sale period occurred and a new collection of swim wear was made available.

During the checkout experiment a sale occurred as well. In order to enhance validity, similar

experiments should be executed once more during periods with new collection, during sale periods

and during periods where there is no strong marketing campaign active. It is expected that the

different marketing campaigns attract different types of consumers, for example oriented towards

bargains during sale periods, which might result in a preference for displaying a specific type of cart

page or checkout during that period. The current results as such provide a direction for expected

effects and future research.

37

7.1.3. Technical limitations

The tools available at the time of the experiment as well as the implementation of the tools provided

several limitations to the experiment results It is tenable that the observed results are influenced

largely by the (absence of a) marketing campaign active during the experiment period. Therefore it is

important to replicate the results of this experiment at other web shops during comparable time frames

in order to establish where the balance lies between the design rules focussing on providing

navigational functionality and inspirational design on the one hand and guiding users to products of

their interest as direct and with as few clicks as possible on the other hand.

With respect to the cart page and checkout experiment there were some technical limitations in

measuring the amount of information entered by consumers in the case of the checkout experiment

and with measuring the cart value in case of both experiments. The inability to measure the amount of

data entered in the case of the single-page experiment variation limited the options of building a more

refined model next to the high level checkout-to-purchase conversion model, in order to analyse the

effect of the checkout variation on entering checkout information and completing a purchase. The cart

value was measured at the final page a web shop visitor visited, instead of when the consumers

entered the checkout. This implied that inaccurate data might exist where consumers entered the

checkout with a cart filled with products, exited the checkout, changed the cart contents and

subsequently left the web shop. It was however expected these cases were at a minimum as consumers

that exited checkout and cleared out their entire cart were excluded from the dataset based on their

cart value of zero. Furthermore the likelihood of this limitation having a major impact on the

experiments results was found relatively small and as such does not provide further implications for

generalisation and applicability of the conclusion.

Theoretical contributions and future research opportunities 7.2.

The contributions of this research are threefold. First of all the theoretical e-servicescape model

provided an overview of knowledge available in the academic research field on optimising conversion

using an e-servicescape perspective. The model provided can both be used to identify research fields

that yet require more theoretical investigation and as a starting point for quantifying the effects of

certain e-servicescape characteristics, for which this study was too limited.

The second contribution of this research lies in the subsequent step of including field data. The field

of e-commerce is changing rapidly, leading to past research results that are no longer fully accurate or

at worst even obsolete. Although there were limitations to the results of the case study, it did provide

insights into the current sentiment and knowledge available in the field regarding the implementation

of e-servicescape design rules. This leads towards the identification of future research opportunities

and directions both in confirming the results and performing additional explanatory research to further

identify the effects of the design rules.

The third theoretical contribution of this research was formed by the experiments. They identified the

potential strong impact of marketing campaigns on conversion rates at different web shop stages,

which requires further explanatory research in case of both experiments. Furthermore, although the

scale was of the experiment was too small to provide results, a first step was made in research

regarding single-page and multi-page checkouts which provides a first step towards further

investigation in the research field. Future research should focus on extending the experiment at

different web shops in different industries during different types of marketing campaigns over longer

periods of time, in order to provide more insights into factors and moderators playing a role in

checkout conversion rates. At the same time future research should focus on further identifying the

38

usage of cross-selling in the checkout. Additional dependent variables, types of cross-selling and

design choices are needed to gain more insights on the influence of both cross-selling and moderators

on conversion rates.

As stated, the most important future research opportunities lay in the identification of factors

influencing the success of cross-selling in the cart and the use of multi-page and single-page

checkouts based on the experiments. In general this research should focus on acquiring additional data

to ensure reliability and on quantifying effects and confirming effects identified in this study, and the

identification of moderating factors such as time of day, day of week and different marketing

campaigns. On the basis of the validated e-servicescape model future research opportunities were also

identified as being the identification of the role of using product on model photography over sole

product photography and the identification of the role of the category page (whether it should be

functional and oriented on navigation, or whether it should be focussed on inspiring visitors).

Additionally purchasing online by consumers via mobile communication devices such as smartphones

and tablets is becoming more mainstream, which also leads to a need of more research towards the

experiment results and on the implementation of design rules in a so-called ‘mobile environment’.

39

Managerial implications 7.3.

Next to theoretical contributions, the research also provided several managerial implications. First of

all the e-servicescape model combining theory and practice may be used by managers as a tool and

guideline on the tactical level when designing or optimising the e-servicescape of a web shop.

Although large web shop operators may find the model beneficial, web shops with limited resources

or relatively small amounts of visitors that are limited testing abilities could find the model to be a

starting point to optimise their web shop on the basis of theoretically and practical grounded

knowledge regarding web shop aspects. The final model is depicted in Table 7.1 at the next pages.

Additional managerial implications were created by the experiment results regarding the cart page

design. Even though intuition and the analogy to offline checkout bargains might lead to the inclusion

of cross-selling on the cart page, the functionality may prove detrimental to conversion rate. Careful

considerations regarding the implementation of the functionality should be taken into account, as well

as multiple dependent variables such as conversion rate and average cart value in order to establish

which version works best for a specific web shop.

Finally next to the specific implications from the experiments, the importance of both extensive

testing and deliberate experiment designs was shown. On the one hand, results might very well not be

as expected, but more important the experiment implications should be established meticulously. The

experiments part of this research are a clear example of the latter. Small amounts of data lead to

difficult analyses and interpretations of data and results which might, in case of wrong types of

analyses, result in spurious conclusions based on insufficient or inadequate data or might at least lead

to limited generalizability of conclusions even within a single web shop.

40

Table 7.1 Validated e-servicescape model

De

cis

ion

ma

kin

g

pro

ce

ss

sta

ge

Choice / purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

Inclu

de o

rig

inal desig

ns a

nd s

igns s

uch a

s lo

gos.

Anim

ate

lo

gos f

or

incre

ased e

ffectiveness a

nd im

pact, b

ut sparin

gly

to a

void

dis

tractio

n.

Whils

t adherin

g t

o s

tandard

and c

om

mon d

esig

n r

ule

s, m

ake s

ure

the d

esig

n fits t

he w

eb s

hop a

nd b

rand p

ropositio

n.

If p

ossib

le, im

ple

me

nt re

fere

nces to a

n o

fflin

e b

rand a

nd r

eta

iler.

Desig

n s

hould

be c

olo

urf

ul by a

good s

ele

ctio

n,

pla

cem

ent

and c

om

bin

atio

n o

f colo

urs

.

Desig

n s

hould

be d

ivers

e,

by v

isual richness,

dynam

ics, novelty a

nd c

reativity.

Desig

n s

hould

be s

imp

le b

y s

how

ing u

nity,

hom

ogeneity, cla

rity

, ord

erlin

ess a

nd b

ala

nce.

Desig

n s

hould

show

cra

ftsm

anship

by m

odern

ity a

nd in

tegra

tin

g s

implic

ity,

div

ers

ity a

nd c

olo

urf

uln

ess.

Pro

vid

e la

rge s

ize h

igh q

ualit

y p

roduct im

ages s

upport

ed b

y s

chem

atic p

roduct chara

cte

ristics.

Pro

vid

e lo

gos, cert

ific

ate

s a

nd o

ther

vis

ual cues e

arly o

n to e

nhance f

eelin

gs o

f tr

ust.

Do n

ot

dis

tract users

with a

esth

etic d

esig

ns d

urin

g c

heckout.

Th

e c

are

ful use o

f people

on p

ictu

res c

an p

rovid

e c

onte

xt

and tra

nsfe

r em

otio

n a

nd f

eelin

g.

Cre

ate

a d

esig

n t

hat flo

ws f

luently fro

m h

om

e p

age t

o c

heckout

with focus o

n s

upport

for

decis

ion a

nd t

ransactio

n p

rocesses

Use a

consis

tent to

ne o

f voic

e t

hat suits t

he t

arg

et audie

nce.

Be s

carc

e w

ith v

ivid

ente

rtain

me

nt as it decre

ases s

hoppin

g c

art

use.

Cre

ate

ente

rtain

me

nt

by p

rovid

ing thoughtf

ul use o

f colo

ur

and typogra

phy b

ased o

n f

unctio

nalit

y.

Cre

ate

ente

rtain

me

nt

by s

ocia

l aspects

, in

tera

ctive e

lem

ents

and in

spiratio

nal desig

n.

Pro

vid

e e

nte

rtain

me

nt

by r

egula

rly u

pdatin

g the w

eb s

hop s

o c

onsum

ers

get th

e f

eelin

g it

evolv

es.

Ori

gin

ality

of

de

sig

n

Vis

ual

ap

pe

al

En

tert

ain

men

t

valu

e

Aesthetic appeal

41

Table 7.1 Validated e-servicescape model (continued)

De

cis

ion

ma

kin

g

pro

ce

ss

sta

ge

Choice / purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

Build

mu

ltip

le w

ays o

f navig

atio

n b

ased o

n e

ase

-of-

use b

y d

iffe

rent ty

pes o

f consum

ers

and the a

ctio

ns it fa

cili

tate

s t

hat contin

uously

show

s

the b

readth

and d

epth

of th

e w

eb s

hop.

Consid

er

that th

e s

ize a

nd locatio

n o

f te

xt

and g

raphic

s d

ete

rmin

e u

sers

’ attentio

n b

ased o

n F

-shaped s

cannin

g p

att

ern

s.

Cre

ate

a c

lean a

nd u

nclu

ttere

d d

esig

n, w

ithout unnecessary

text

and g

raphic

s a

nd m

inim

um

lo

adin

g t

ime

s a

nd s

yste

m c

rashes, th

at

behaves a

s u

ser

expect.

Pro

vid

e c

lear

org

anis

atio

n a

nd layout

without

dis

tractio

ns.

Pro

vid

e a

lin

k b

ack t

o s

hoppin

g.

Cre

ate

a c

onsis

tent

and lo

gic

al user

flo

w fro

m h

om

e p

age to c

heckout.

Pro

vid

e c

onta

ct in

form

atio

n,

pre

fera

bly

inclu

din

g a

(fr

ee)

num

ber,

to r

each t

he c

onsum

er

support

depart

me

nt.

Sta

te c

om

petitive a

dvanta

ges r

ega

rdin

g t

he q

ualit

y o

f pro

duct offerin

gs a

nd s

erv

ices c

learly t

hro

ughout

the w

eb s

hop.

Sta

te in

form

atio

n r

egard

ing p

rice, fe

atu

res, in

vento

ry in

form

atio

n a

nd o

rder

rela

ted c

harg

es a

s e

arly o

n a

s p

ossib

le.

Pro

vid

e in

form

atio

n that is

accura

te,

consis

tent

and s

pecific

, support

ed b

y full

siz

e p

ictu

res.

Pro

vid

e in

form

atio

n that is

accura

te,

consis

tent

and s

pecific

.

Dis

pla

y o

ut-

of-

sto

ck s

izes,

but re

move p

erm

anent out-

of-

sto

ck p

roducts

and c

olo

urs

.

Pro

vid

e in

form

atio

n fro

m a

consum

er

poin

t of vie

w w

hils

t keepin

g t

hem

in

a c

ontin

uous f

low

.

Th

e lo

catio

n, ty

pe a

nd im

ple

me

nta

tio

n o

f cro

ss-s

elli

ng,

especia

lly in c

ase o

f lim

ited d

ata

and b

usin

ess r

ule

, should

be c

onsid

ere

d d

ue t

o

conflic

tin

g r

esults.

Specify c

usto

mis

atio

n t

ow

ard

s d

ecis

ion a

nd t

ransactio

n p

rocesses.

Add f

eatu

res s

upport

ing d

irect

inte

ractivity b

etw

een v

isitors

and s

ale

s o

r support

em

plo

yees.

Add in

tera

ctive functio

nalit

y t

hat

is p

ote

ntia

lly u

sefu

l or

influ

ences s

ite u

sage a

nd n

avig

atio

n.

Change t

ext and c

olo

urs

when h

overin

g o

ver

actio

nable

text

and im

age

s.

Usab

ilit

y

Rele

van

ce o

f

info

rmati

on

Cu

sto

mis

ati

on

Inte

racti

vit

y

Layout & functionality

42

Table 7.1 Validated e-servicescape model (continued)

De

cis

ion

ma

kin

g

pro

ce

ss

sta

ge

Choice / purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

Dis

pla

y t

ruste

d a

nd in

dependent seals

and c

ert

ific

ate

s o

f appro

val th

roughout

the w

eb s

hop.

Ask o

nly

str

ictly n

ecessary

in

form

atio

n a

nd e

xclu

de m

ark

etin

g q

uestions.

Explic

itly

sta

te w

hat in

form

atio

n is s

tore

d a

nd n

ot sto

red.

Dis

pla

y t

ruste

d a

nd in

dependent seals

and c

ert

ific

ate

s o

f appro

val.

Cre

ate

a c

onsis

tent

and lo

gic

al user

flo

w fro

m h

om

e p

age to c

heckout.

Allo

w for

checkout com

ple

tio

n w

ithout

regis

tratio

n o

r usin

g a

n a

ccount.

.Pro

vid

e a

ctio

nable

feedback a

nd e

rror

me

ssages a

nd o

nly

if str

ictly n

ecessary

Pro

vid

e in

form

atio

n r

egard

ing t

he d

iffe

rent checkout ste

ps a

s w

ell

as t

he c

urr

ent

locatio

n.

Pro

vid

e the o

ptio

n o

f cre

dit c

ard

paym

ents

, re

gula

r paym

ent ty

pes a

nd p

aym

ent ty

pes that fu

nctio

n a

s the e

xte

nsio

n o

f exis

tin

g m

eth

ods.

Ta

ke th

e pro

ducts

sold

and diffe

rent

targ

et

audie

nces in

to account

when desig

nin

g s

ingle

or

multi-

page checkouts

both

for

speed and

confirm

atio

n.

Perc

eiv

ed

secu

rity

Ease o

f u

se

Financial security

43

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Appendices - 47

Appendix A. Literature review results

Table A.1 Literature based e-servicescape model

Lit

era

ture

So

urc

e

(Fin

k &

La

up

ase

, 2

00

0;

Harr

is &

Go

od

e,

201

0)

(Fa

ng

& S

alv

end

y,

20

03

; F

ink &

La

up

ase

, 2

00

0)

(Mo

sh

age

n &

Th

iels

ch

, 2

01

0)

(Fa

ng

& S

alv

end

y,

20

03

;

Mo

sh

ag

en &

Th

iels

ch

, 2

01

0)

(Child

ers

, C

arr

, &

Pe

ck,

200

2;

La

vie

& T

ractin

sky,

20

04;

Mo

sh

ag

en &

Th

iels

ch

, 2

01

0)

(Mo

sh

age

n &

Th

iels

ch

, 2

01

0)

(Ag

arw

al &

He

dg

e, 2

00

8;

Bla

nco

,

Sa

rasa

, &

Sa

ncle

me

nte

, 2

010

;

Fa

ng

& S

alv

en

dy, 2

00

3;

Y.

D.

Wa

ng

& E

mu

rian

, 20

05

)

(Hau

sm

an

& S

iekp

e, 2

00

9; L

. H

.

Kim

et

al.,

20

09

; Y

. D

. W

an

g &

Em

uri

an

, 2

00

5)

(Cai &

Xu

, 2

01

1;

Clo

se

& K

ukar-

Kin

ne

y,

20

10

; Y

. J.

Wan

g,

Hern

an

de

z, &

Min

or,

20

10)

(Clo

se

& K

uka

r-K

inn

ey, 2

01

0)

(Harr

is &

Go

od

e,

201

0)

(Cai &

Xu

, 2

01

1;

Y.

J.

Wa

ng

et

al.,

20

10)

(Eg

ge

r, 2

00

1; F

an

g &

Sa

lven

dy,

20

03

; P

alm

er,

20

02

; S

ilve

rma

n

et

al.,

20

01

; T

ucke

r, 2

00

8;

Zh

an

g,

Vo

n D

ran

, B

lake

, &

Pip

ith

su

ksu

nt,

20

01

)

(Ag

arw

al &

He

dg

e, 2

00

8; F

an

g &

Sa

lven

dy,

20

03)

(Cra

ve

n,

Jo

hn

so

n, &

Bu

tte

rs,

20

10

; F

an

g &

Sa

lven

dy, 2

00

3;

Lin

g,

Sa

lven

dy,

& P

urd

ue

Univ

ers

ity,

20

06;

Pa

lme

r, 2

00

2;

Silv

erm

an

et

al.,

20

01)

(J.

Kim

& M

oo

n, 1

99

8)

(Fa

ng

& S

alv

end

y,

20

03

)

(Fa

ng

& S

alv

end

y,

20

03

)

(Ja

ng

& B

urn

s, 2

00

4;

Silv

erm

an

et

al.,

20

01

; Y

an

, 2

00

9)

(Clo

se

& K

uka

r-K

inn

ey, 2

01

0;

Eg

ge

r, 2

001

)

(Fa

ng

& S

alv

end

y,

20

03

;

La

roch

e, M

cD

ou

ga

ll, B

erg

ero

n,

&

Ya

ng

, 2

00

4; Z

ha

ng

et

al., 2

00

1)

(Eg

ge

r, 2

00

1)

(Fa

ng

& S

alv

end

y,

20

03

)

De

cis

ion

ma

kin

g

pro

ce

ss

sta

ge 3*

2*

1*

Des

ign

ru

le

Inclu

de o

rig

inal desig

ns a

nd s

igns s

uch a

s lo

gos

Anim

ate

lo

gos f

or

incre

ased e

ffectiveness a

nd im

pact, b

ut sparin

gly

to a

void

dis

tractio

n.

Desig

n s

hould

be c

olo

urf

ul by a

good s

ele

ctio

n,

pla

cem

ent

and c

om

bin

atio

n o

f colo

urs

.

Desig

n s

hould

be d

ivers

e,

by v

isual richness,

dynam

ics, novelty a

nd c

reativity.

Desig

n s

hould

be s

imp

le b

y s

how

ing u

nity,

hom

ogeneity, cla

rity

, ord

erlin

ess a

nd b

ala

nce.

Desig

n

should

show

cra

ftsm

anship

by

mo

dern

ity

and

inte

gra

ting

sim

plic

ity,

div

ers

ity

and

colo

urf

uln

ess.

Pro

vid

e la

rge s

ize h

igh q

ualit

y p

roduct im

ages s

upport

ed b

y s

chem

atic p

roduct chara

cte

ristics.

Pro

vid

e lo

gos, cert

ific

ate

s a

nd o

ther

vis

ual cues e

arly o

n to e

nhance f

eelin

gs o

f tr

ust.

Do n

ot

dis

tract users

with a

esth

etic d

esig

ns d

urin

g c

heckout.

Be s

carc

e w

ith v

ivid

ente

rtain

me

nt as it decre

ases s

hoppin

g c

art

use.

Cre

ate

ente

rtain

me

nt

by p

rovid

ing thoughtf

ul use o

f colo

ur

and typogra

phy b

ased o

n f

unctio

nalit

y.

Cre

ate

ente

rtain

me

nt

by s

ocia

l aspects

, in

tera

ctive e

lem

ents

and in

spiratio

nal desig

n.

Build

mu

ltip

le w

ays o

f navig

atio

n b

ased o

n e

ase

-of-

use b

y d

iffe

rent

types o

f consum

ers

and t

he

actio

ns it fa

cili

tate

s t

hat contin

uously

show

s the b

readth

and d

epth

of

the w

eb s

hop.

Consid

er

that

the s

ize a

nd l

ocation o

f te

xt

and g

raphic

s d

ete

rmin

e u

sers

’ att

entio

n b

ased o

n F

-shaped s

cannin

g p

att

ern

s.

Cre

ate

a cle

an and unclu

ttere

d desig

n,

without

unnecessary

te

xt

and gra

phic

s and m

inim

um

loadin

g t

ime

s a

nd s

yste

m c

rashes, th

at behaves a

s u

ser

expect.

Pro

vid

e c

lear

org

anis

atio

n a

nd layout

without

dis

tractio

ns.

Pro

vid

e a

lin

k b

ack t

o s

hoppin

g.

Pro

vid

e c

onta

ct in

form

atio

n in

clu

din

g a

(fr

ee)

num

ber

to r

each t

he c

onsum

er

support

depart

me

nt.

Sta

te com

petitive advanta

ges re

gard

ing th

e qualit

y of

pro

duct

offerin

gs and serv

ices cle

arly

thro

ughout

the w

eb s

hop.

Sta

te i

nfo

rma

tio

n r

egard

ing p

rice,

featu

res,

invento

ry i

nfo

rmatio

n a

nd o

rder

rela

ted c

harg

es a

s

early o

n a

s p

ossib

le.

Pro

vid

e in

form

atio

n that is

accura

te, consis

tent

and s

pecific

, support

ed b

y full

siz

e p

ictu

res.

Pro

vid

e in

form

atio

n that is

accura

te, consis

tent

and s

pecific

.

Dis

pla

y o

ut-

of-

sto

ck p

roducts

and s

izes.

Ori

gin

ality

of

de

sig

n

Vis

ual

ap

pe

al

En

tert

ain

men

t

valu

e

Usab

ilit

y

Rele

van

ce o

f

info

rmati

on

Aesthetic appeal Layout &

functionality *

1 =

In

form

atio

n s

ea

rch

, 2

= E

va

lua

tio

n o

f a

lte

rna

tive

s, 3

= C

ho

ice

/ P

urc

ha

se

Appendices - 48

Table A.1 Literature based e-servicescape model (continued)

Lit

era

ture

So

urc

e

(Th

irum

ala

i &

Sin

ha

, 2

01

1;

Ze

ith

am

l, P

ara

su

ram

an

, &

Ma

lho

tra,

20

02)

(Th

irum

ala

i &

Sin

ha

, 2

01

1)

(Viln

ai-

ya

ve

tz &

Ra

fae

li, 2

00

6)

(J.

H.

So

ng

& Z

inkha

n,

200

8;

Wu

, H

u,

& W

u,

20

10)

(Wu

et

al.,

20

10

)

(L.

H.

Kim

et

al.,

20

09;

Y.

D.

Wa

ng

& E

mu

rian

, 20

05

) (F

an

g &

Sa

lvend

y,

20

03

;

Ga

rba

rino

& S

tra

hile

vitz,

20

04

)

(Kw

on

& L

ee

, 2

003

)

(L.

H.

Kim

et

al.,

20

09;

Y.

D.

Wa

ng

& E

mu

rian

, 20

05

)

(Eg

ge

r, 2

00

1; F

an

g &

Sa

lven

dy,

20

03

)

(Eg

ge

r, 2

00

1;

Laro

ch

e e

t a

l.,

20

04

)

(Eg

ge

r, 2

00

1;

Laro

ch

e e

t a

l.,

20

04

) (G

efe

n,

Kara

ha

nna

, &

Str

au

b,

20

03

; H

e &

Mykyty

n,

20

07

;

Lia

ng

& L

ai, 2

00

2)

De

cis

ion

ma

kin

g

pro

ce

ss

sta

ge

3

2

1

Des

ign

ru

le

Consid

er

where

to p

lace c

usto

mis

atio

n a

s t

here

are

conflic

tin

g r

esults.

Specify c

usto

mis

atio

n t

ow

ard

s d

ecis

ion a

nd t

ransactio

n p

rocesses.

Add f

eatu

res s

upport

ing d

irect

inte

ractivity b

etw

een v

isitors

and s

ale

s o

r support

em

plo

yees.

Add in

tera

ctive functio

nalit

y t

hat

is p

ote

ntia

lly u

sefu

l or

influ

ences s

ite u

sage a

nd n

avig

atio

n.

Change t

ext and c

olo

urs

when h

overin

g o

ver

actio

nable

text

and im

ages.

Dis

pla

y t

ruste

d a

nd in

de

pendent seals

and c

ert

ific

ate

s o

f appro

val th

roughout

the w

eb s

hop.

Ask o

nly

str

ictly n

ecessary

in

form

atio

n a

nd e

xclu

de m

ark

etin

g q

uestions.

Explic

itly

sta

te w

hat in

form

atio

n is s

tore

d a

nd n

ot sto

red.

Dis

pla

y t

ruste

d a

nd in

dependent seals

and c

ert

ific

ate

s o

f appro

val.

Allo

w for

checkout com

ple

tio

n w

ithout

regis

tratio

n o

r usin

g a

n a

ccount.

Pro

vid

e a

ctio

nable

feedback a

nd e

rror

me

ssages a

nd o

nly

if str

ictly n

ecessary

.

Pro

vid

e in

form

atio

n r

egard

ing t

he d

iffe

rent checkout ste

ps a

s w

ell

as t

he c

urr

ent

locatio

n.

Pro

vid

e th

e optio

n of

cre

dit card

paym

ents

, re

gula

r paym

ent

types and paym

ent

types th

at

functio

n a

s t

he e

xte

nsio

n o

f exis

ting m

eth

ods.

Cu

sto

mis

ati

on

Inte

racti

vit

y

Perc

eiv

ed

secu

rity

Ease o

f u

se

Layout & functionality

Financial security

*

1 =

In

form

atio

n s

ea

rch

, 2

= E

va

lua

tio

n o

f a

lte

rna

tive

s, 3

= C

ho

ice

/ P

urc

ha

se

*

1 =

In

form

atio

n s

ea

rch

, 2

= E

va

lua

tio

n o

f a

lte

rna

tive

s, 3

= C

ho

ice

/ P

urc

ha

se

Appendices - 49

Appendix B. Company structure

Figure B.1 Company structure (Docdata N.V., 2012)

Appendices - 50

Appendices - 51

Appendix C. Case study interview questions

The interview questions were held in Dutch. An English translation is printed in italic.

C.1 Welkom / Welcome

o Het interview is vertrouwelijk, dus er kan vrij (zowel lovend als kritisch) gesproken worden.

The interview is confidential, so you can speak freely in both positive and negative

statements.

o Het interview wordt opgenomen (audio) om later uit te kunnen werken en in geval van twijfel

over de uitwerking terug te kunnen luisteren.

The interview will be recorded (audio) in order to write a transcript and listen to the

interview again in case of the need for clarification.

o De opnames worden na afloop van het onderzoek vernietigd.

The recordings will be destroyed at the end of the thesis research.

o De uitwerking / inzichten worden ter goedkeuring voorgelegd aan de geïnterviewde.

The findings taken from the interview will be shown to the interviewee with a request for

approval.

o Heb je vooraf vragen of opmerkingen?

Are there any questions before we begin?

o Ben je akkoord met bovenstaande punten?

Do you acknowledge the points mentioned above?

C.2 Inleiding / Introduction

o De achtergrond van het onderzoek wordt uitegelegd: Afstudeeronderzoek aan de TU

Eindhoven naar conversie optimalisatie.

The background of the research is discussed: TU Eindhoven thesis research on conversion

optimisation.

o Doel van het interview is het verzamelen van best practices en validatie van het model.

Aim of the interview is to gather best practices and validate the e-servicescape model used.

o Voorbeelden tijdens het interview graag gericht op grote cases.

The examples in the interview should preferably be focused on large cases.

C.3 Functie / Function

o Wat is je achtergrond (opleiding, werkervaring)?

What is your background (education, experience)?

o Wat is je rol binnen Docdata Commerce?

What is your position at Docdata Commerce?

o Wat zijn je verantwoordelijkheden?

What are your responsibilities?

C.4 Optimalisatieproces / Optimisation process

o Hoe wordt op dit moment conversie geoptimaliseerd?

What are the current practices of conversion optimisation?

Appendices - 52

o Idee

Idea generation

o Uitwerking

Working out the idea in detail

o Voorstel

Proposing a web shop change

o Processtappen

Which steps are followed from proposal to execution?

o Implementatie

Implementing the idea

o Testen

Testing

o Feedback

Feedback

o Hoe wordt succes / falen bepaald?

How are success and failure identified?

o Wat vindt je van dit proces?

What is your view on this process?

o Welke sterke punten en verbeterpunten zijn er?

What are the strong and weak points of the process?

C.5 Aankoopproces / Consumer decision making process

o Wat zijn de verschillende fases die een consument doorloopt bij het maken van een keuze?

What are the different customer decision making process phases?

o Welke concrete acties op een website horen hierbij?

Which visitor actions on a web shop coincide with these phases?

C.6 Optimalisatiefactoren algemeen / Optimisation factors in general

o Gebruik hierbij eventueel het aankoopproces als richtlijn, maar gebruik het e-servicescape

model niet.

Use the customer decision making process as a guideline, but don’t use the e-servicescape

model.

o Welke web shop factoren spelen volgens jou een rol bij het optimaliseren van conversie?

Which web shop factors play a role when optimising conversion?

o Op welk psychologisch proces heeft deze factor invloed?

Which psychological process is influenced by this factor?

o Wat is de grootte van het effect van deze factor op de conversie?

What is the effect size of the factor on conversion?

o Kun je een voorbeeld geven?

Can you give an example?

C.7 E-Servicescape / E-servicescape

o Uitleggen wat de e-servicescape inhoudt.

Explain the e-servicescape concept.

o Gebruik het algemene model (zonder subfactoren)

Appendices - 53

Use the abstract model (without sub-factors)

o Kun je vertellen wat je onder ieder van deze factoren vindt vallen in het algemeen?

Can you explain what the factors encompass in general?

o Ontbreken er nog factoren?

Are there factors missing?

o Focus op factor 1, laat de subfactoren niet zien.

Focus on factor 1, but don’t show sub-factors.

o Welke sub-factoren vallen hier onder?

Which sub-factors are part of this factor?

o Laat subfactoren zien.

Show the sub-factors.

o Kun je per subfactor vertellen wat je daar onder verstaat?

Please explain for every sub-factor what it encompasses.

o Kun je een voorbeeld noemen van hoe je die subfactor in de praktijk gebruikt (hebt)?

Can you name an example of how you use(d) that sub-factor in practice?

o Welke theorie zit hier achter?

What theory is behind you explanation?

o Wat is het effect van deze subfactor?

What is the effect of the sub-factor?

o Mis je nog factoren?

Do you miss any sub-factors?

o Herhaal het voorgaande proces voor factor 2.

Repeat the above process for factor 2.

o Herhaal het voorgaande proces voor factor 3.

Repeat the above process for factor 3.

o Zijn er nog factoren die je mist overall?

Do you miss any factors or sub-factors?

C.8 Afsluiting / Final remarks

o Zijn er nog andere zaken die van belang zijn bij conversieoptimalisatie?

Are there any other (non-e-servicescape) factors important when optimising for conversion?

o Zijn er nog andere dingen die je aan wil dragen?

Is there anything else you would like to add?

o Heb je nog vragen of opmerkingen over het algemeen?

Do you have any question or remarks in general?

o Wil je een terugkoppeling van de algemene resultaten?

Would you like to receive feedback on the generic research results?

Appendices - 54

C.9 Figures

Figure C.1 E-servicescape, trust and purchase intention

Figure C.2 E-servicescape factors

Figure C.3 E-servicescape factor ‘Aesthetic appeal’

Appendices - 55

Figure C.4 E-servicescape factor ‘Layout & functionality’

Figure C.6 Full E-servicescape model

Figure C.5 E-servicescape factor ‘Financial security’

Appendices - 56

Appendices - 57

Appendix D. Case study results

D.1 Individual results Table D.1 Individual case study results

Appendices - 58

Table D.1 Individual case study results (continued)

Appendices - 59

Table D.1 Individual case study results (continued)

Appendices - 60

Table D.1 Individual case study results (continued)

Appendices - 61

D.2 Aggregated results Table D.2 Validated e-servicescape model

D

es

ign

ru

le

Inclu

de o

rig

inal desig

ns a

nd s

igns s

uch a

s lo

gos.

Anim

ate

lo

gos f

or

incre

ased e

ffectiveness a

nd im

pact, b

ut sparin

gly

to a

void

dis

tractio

n.

Whils

t adherin

g t

o s

tandard

and c

om

mon d

esig

n r

ule

s, m

ake s

ure

the d

esig

n fits t

he w

eb

shop a

nd b

rand p

ropositio

n.

If p

ossib

le, im

ple

me

nt re

fere

nces to a

n o

fflin

e b

rand a

nd r

eta

iler.

Desig

n s

hou

ld b

e c

olo

urf

ul by a

good s

ele

ctio

n,

pla

cem

ent

and c

om

bin

atio

n o

f colo

urs

.

Desig

n s

hould

be d

ivers

e,

by v

isual richness,

dynam

ics, novelty a

nd c

reativity.

Desig

n s

hould

be s

imp

le b

y s

how

ing u

nity,

hom

ogeneity, cla

rity

, ord

erlin

ess a

nd b

ala

nce.

Desig

n s

hould

show

cra

ftsm

anship

by m

odern

ity a

nd i

nte

gra

tin

g s

implic

ity,

div

ers

ity a

nd

colo

urf

uln

ess.

Pro

vid

e

larg

e

siz

e

hig

h

qualit

y

pro

duct

ima

ges

support

ed

by

schem

atic

pro

duct

chara

cte

ristics.

Pro

vid

e lo

gos, cert

ific

ate

s a

nd o

ther

vis

ual cues e

arly o

n to e

nhance f

eelin

gs o

f tr

ust.

Do n

ot

dis

tract users

with a

esth

etic d

esig

ns d

urin

g c

heckout.

Th

e c

are

ful use o

f people

on p

ictu

res c

an p

rovid

e c

onte

xt

and tra

nsfe

r em

otio

n a

nd f

eelin

g.

Cre

ate

a d

esig

n t

hat flo

ws f

luently fro

m h

om

e p

age t

o c

heckout

with focus o

n s

upport

for

decis

ion a

nd tra

nsactio

n p

rocesses

Use a

consis

tent to

ne o

f voic

e t

hat suits t

he t

arg

et audie

nce.

Be s

carc

e w

ith v

ivid

ente

rtain

me

nt as it decre

ases s

hoppin

g c

art

use.

Cre

ate

ente

rtain

me

nt

by

pro

vid

ing

thoughtf

ul

use

of

colo

ur

and

typogra

phy

ba

sed

on

functio

nalit

y.

Cre

ate

ente

rtain

me

nt

by s

ocia

l aspects

, in

tera

ctive e

lem

ents

and in

spiratio

nal desig

n.

Pro

vid

e e

nte

rtain

me

nt

by r

egula

rly u

pdatin

g the w

eb s

hop s

o c

onsum

ers

get th

e f

eelin

g it

evolv

es.

Cas

e s

tud

y D

ec

isio

n

Support

Support

Add

Add

Support

Support

Support

Support

Support

Support

Support

Add

Add

Add

Support

Support

Support

Add

Ex

pe

rtis

e

Desig

n

Ma

rke

tin

g

Ma

rke

tin

g

Desig

n

Ma

rke

tin

g

Desig

n

Ma

rke

tin

g

Pa

ym

en

ts

Desig

n

Pa

ym

en

ts

Desig

n

Ma

rke

tin

g

Desig

n

Rati

o

7 /

7

3 /

3

7

2

6 /

6

6 /

6

6 /

6

5 /

5

8 /

8

5 /

5

8 /

8

3

2

3

5 /

5

6 /

6

7 /

7

1

Dec

isio

n m

akin

g

pro

ce

ss

sta

ge

Choice / Purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

1.1

.1

1.1

.2

New

New

1.2

.1

1.2

.2

1.2

.3

1.2

.4

1.2

.5

1.2

.6

1.2

.7

New

New

New

1.3

.1

1.3

.2

1.3

.3

New

Ori

gin

ality

of

de

sig

n

Vis

ual

ap

pe

al

En

tert

ain

men

t valu

e

Aesthetic appeal

Appendices - 62

Table D.2 Validated e-servicescape model (continued)

De

sig

n r

ule

Build

mu

ltip

le w

ays o

f navig

atio

n b

ased o

n e

ase

-of-

use b

y d

iffe

rent ty

pes o

f consum

ers

and

the a

ctio

ns it fa

cili

tate

s t

hat continuously

show

s the b

readth

and d

epth

of

the w

eb s

hop.

Consid

er

that th

e s

ize a

nd locatio

n o

f te

xt

and g

raphic

s d

ete

rmin

e u

sers

’ attentio

n b

ased o

n

F-s

haped s

cannin

g p

att

ern

s.

Cre

ate

a c

lean a

nd u

nclu

ttere

d d

esig

n, w

ithout unnecessary

text

and g

raphic

s a

nd

min

imum

lo

adin

g t

ime

s a

nd s

yste

m c

rashes, th

at behaves a

s u

ser

expect.

Pro

vid

e c

lear

org

anis

atio

n a

nd layout

without

dis

tractio

ns.

Pro

vid

e a

lin

k b

ack t

o s

hoppin

g.

Cre

ate

a c

onsis

tent

and lo

gic

al user

flo

w fro

m h

om

e p

age to c

heckout.

Pro

vid

e c

onta

ct in

form

atio

n,

pre

fera

bly

inclu

din

g a

(fr

ee)

num

ber,

to r

each t

he c

onsum

er

support

depart

me

nt.

Sta

te c

om

petitive a

dvanta

ges r

egard

ing t

he q

ualit

y o

f pro

duct offerin

gs a

nd s

erv

ices c

learly

thro

ughout

the w

eb s

hop.

Sta

te in

form

atio

n r

egard

ing p

rice, fe

atu

res, in

vento

ry in

form

atio

n a

nd o

rder

rela

ted c

ha

rges

as e

arly o

n a

s p

ossib

le.

Pro

vid

e in

form

atio

n that is

accura

te,

consis

tent

and s

pecific

, support

ed b

y full

siz

e p

ictu

res.

Pro

vid

e in

form

atio

n that is

accura

te,

consis

tent

and s

pecific

.

Dis

pla

y o

ut-

of-

sto

ck p

roducts

and s

izes,

but re

move p

erm

anent out-

of-

sto

ck p

roducts

and

colo

urs

.

Pro

vid

e in

form

atio

n fro

m a

consum

er

poin

t of vie

w w

hils

t keepin

g them

in

a c

ontin

uous

flo

w.

Consid

er

where

to p

lace c

usto

mis

atio

n a

s t

here

are

conflic

tin

g r

esults.

Th

e lo

catio

n, ty

pe a

nd im

ple

me

nta

tio

n o

f cro

ss-s

elli

ng,

especia

lly in c

ase o

f lim

ited d

ata

and b

usin

ess r

ule

, should

be c

onsid

ere

d d

ue t

o c

onflic

tin

g r

esults.

Specify c

usto

mis

atio

n t

ow

ard

s d

ecis

ion a

nd t

ransactio

n p

rocesses.

Add f

eatu

res s

upport

ing d

irect

inte

ractivity b

etw

een v

isitors

and s

ale

s o

r support

em

plo

yees.

Add in

tera

ctive functio

nalit

y t

hat

is p

ote

ntia

lly u

sefu

l or

influ

ences s

ite u

sage a

nd

navig

atio

n.

Change t

ext and c

olo

urs

when h

overin

g o

ver

actio

nable

text

and im

ages.

Cas

e s

tud

y D

ec

isio

n

Support

Support

Support

Support

Support

Add

Revis

e

Support

Support

Support

Support

Revis

e

Add

Revis

e

Support

Support

Support

Support

Ex

pe

rtis

e

Desig

n

Ma

rke

tin

g

Ma

rke

tin

g

Su

pp

ort

Desig

n

Ma

rke

tin

g

Ma

rke

tin

g

Mg

t. d

ire

cto

r

Com

me

nts

all

inte

rvie

we

es

Desig

n

Ma

rke

tin

g

Desig

n

Ma

rke

tin

g

Rati

o

6 /

6

8 /

8

6 /

7

8 /

8

4 /

4

3

7 /

9

8 /

9

9 /

10

7 /

8

7 /

8

2 /

3

2

8 /

8

8 /

8

1 /

3

4 /

6

4 /

4

Dec

isio

n m

akin

g

pro

ce

ss

sta

ge

Choice / Purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

2.1

.1

2.1

.2

2.2

.3

2.1

.4

2.1

.5

New

2.2

.1

2.2

.2

2.2

.3

2.2

.4

2.1

.5

2.1

.6

New

2.3

.1

2.3

.2

2.4

.1

2.4

.2

2.4

.3

Usab

ilit

y

Rele

van

ce o

f

info

rmati

on

Cu

sto

mis

ati

on

Inte

racti

vit

y

Layout & functionality

Appendices - 63

Table D.2 Validated e-servicescape model (continued)

De

sig

n r

ule

Dis

pla

y tr

uste

d and in

dependent

seals

and cert

ific

ate

s of

appro

val

thro

ughout

the w

eb

shop.

Ask o

nly

str

ictly n

ecessary

in

form

atio

n a

nd e

xclu

de m

ark

etin

g q

uestions.

Explic

itly

sta

te w

hat in

form

atio

n is s

tore

d a

nd n

ot sto

red.

Dis

pla

y t

ruste

d a

nd in

dependent seals

and c

ert

ific

ate

s o

f appro

val.

Cre

ate

a c

onsis

tent

and lo

gic

al user

flo

w fro

m h

om

e p

age to c

heckout.

Allo

w for

checkout com

ple

tio

n w

ithout

regis

tratio

n o

r usin

g a

n a

ccount.

Pro

vid

e a

ctio

nable

feedback a

nd e

rror

me

ssages a

nd o

nly

if str

ictly n

ecessary

.

Pro

vid

e in

form

atio

n r

egard

ing t

he d

iffe

rent checkout ste

ps a

s w

ell

as t

he c

urr

ent

locatio

n.

Pro

vid

e t

he o

ptio

n o

f cre

dit c

ard

paym

ents

, re

gula

r paym

ent

types a

nd p

aym

ent

types t

hat

functio

n a

s t

he e

xte

nsio

n o

f exis

ting m

eth

ods.

Ta

ke t

he p

roducts

sold

and d

iffe

rent

targ

et

audie

nces i

nto

account

when d

esig

nin

g s

ingle

or

mu

lti-page c

heckouts

both

for

speed a

nd c

onfirm

atio

n.

Cas

e s

tud

y D

ec

isio

n

Support

Support

Support

Support

Add

No s

upp

ort

,

kee

p b

ase

d

on litera

ture

Support

Support

Revis

e

Add

Ex

pe

rtis

e

Desig

n

Ma

rke

tin

g

Ma

rke

tin

g

Pa

ym

en

ts

Su

pp

ort

Rati

o

9 /

11

1 /

2

2 /

2

8 /

10

5

1 /

1

3 /

3

9 /

9

8 /

12

7

Dec

isio

n m

akin

g

pro

ce

ss

sta

ge

Choice / Purchase

Evaluation of alternatives

Information search

Des

ign

ru

le

3.1

.2

3.2

.3

3.1

.4

New

2.2

.1

2.2

.2

2.2

.3

2.2

.4

New

Perc

eiv

ed

secu

rity

Ease o

f u

se

Financial security

Appendices - 64

Appendices - 65

Appendix E. Clickstream variables recorded

Table E.1 Clickstream variables recorded

Re

ma

rks

No

t d

isp

layed

at

“Bra

nds”

and

“Tre

nd

” m

enu

ite

ms

On

ly a

va

ilable

at p

rod

uct

pa

ges a

nd

pro

du

ct-

pop

ups

Ava

ilab

le w

he

n t

he c

art

has a

min

imum

of 1

pro

duct

Ava

ilab

le o

n t

he

ho

me

pa

ge

an

d L

ing

eri

e-c

ate

go

ry p

age

s

an

d -

pro

duct

pa

ges

Pa

ge

relo

ad a

fte

r clic

k

Pa

ge

relo

ad a

fte

r clic

k

On

ly m

easu

red w

he

n v

isito

r is

log

ged

in

Pro

du

ct

de

tails

dis

pla

ye

d in

po

pu

p

Me

asu

red

at

Eve

ry p

age

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Appendices - 66

Table E.1 Clickstream variables recorded (continued)

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Appendices - 67

Table E.1 Clickstream variables recorded (continued)

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Appendices - 68

Appendices - 69

Appendix F. Experiment design

F.1 Cart page

Figure F.1 Web shop cart variation ‘Clean’

Appendices - 70

Figure F.2 Web shop cart variation ‘USP’

Appendices - 71

Figure F.3 Web shop cart variation ‘Cross sell’

on click

Appendices - 72

F.2 Checkout

on click

Figure F.4 Web shop checkout variation ‘Multipage – Step 1’

Appendices - 73

Figure F.5 Top: Web shop checkout variation ‘Multipage – Step 2’ Bottom: Web shop checkout variation ‘Multipage – Step 3’

Appendices - 74

Figure F.6 Web shop checkout variation ‘Single-page’

Appendices - 75

Figure F.7 Checkout success page

Appendices - 76

Appendices - 77

Appendix G. Experiment results

G.1 Descriptive statistics covariates Table G.1 Descriptive statistics of covariates

Variable

Experiment ‘Cart page’ Model 3 Experiment ‘Checkout’

Clean USP Cross- selling Multi-page Single-page

Time of the day

Morning 189

22.1% 153

19.1% 155

18.2% 112

24.9% 21

21.4%

Afternoon 350

40.9% 328

41.1% 375

44.0% 182

40.4% 49

0.50%

Evening / Night 315

36.9% 316

39.6% 322

37.8% 156

34.7% 28

28.6%

Day of the week

Weekday 396

46.4% 397

49.8% 391

45.9% n/a n/a

Weekend 458

53.6% 400

50.2% 461

54.1% n/a n/a

Mean cart value (Euros) 42.20 40.09 43.82 28.88 30.83

G.2 Logistic regression: Collinearity statistics

Table G.2 Collinearity statistics experiments ‘cart page’ and ‘checkout’

Variable Experiment ‘Cart page’ Experiment ‘Checkout’

Tolerance VIF Tolerance VIF

Experiment variaton 0.998 1.002 0.993 1.007

Cart value 0.998 1.002 0.996 1.004

Time of the day: Morning 0.818 1.223 0.764 1.309

Time of the day: Afternoon 0.820 1.220 0.762 1.312

Day of the week: Weekend 0.999 1.001 n/a n/a

Appendices - 78

G.3 Cart page: Linear model

Table G.3 ANCOVA ‘Cart page’, Dependent: Conversion linear, Test of Between-subjects effects

Source Type III

sum of squares Df

Mean square

F Sig. Observed

powerb

Corrected model 4.022 a 6 0.670 3.284 0.003 0.936

Intercept 106.136 1 106.136 519.894 0.000 1.000

Cart value 2.631 1 2.631 12.887 0.000 0.431

Day of the week: Weekend

0.267 1 0.267 1.307 0.253 0.208

Time of day: Morning

0.149 1 0.149 0.731 0.393 0.137

Time of day: Afternoon

0.033 1 0.033 0.160 0.689 0.069

Cart variation 0.853 2 0.427 2.090 0.124 0.948

Error 509.557 2,496 0.204

Total 1,176.474 2,503

Corrected total 513.579 2,502

a

R Squared = 0.008 (Adjusted R Squared = 0.005)

b Computed using alpha = 0.05

Table G.4 ANCOVA ‘Cart page’, Dependent: Conversion linear, Parameter estimates

Parameter B Std. Error t Sig.

95% Confidence interval

Observed power

b Lower

bound Upper bound

Intercept 0.456 0.025 18.270 0.000 0.407 0.505 1.000

Cart value 0.001 0.000 3.590 0.000 0.000 0,002 0.948

Day of the week: Weekend

-0.021 0.018 -1.143 0.253 -0.056 0,015 0.208

Time of day: Morning

0.021 0.025 0.855 0.393 -0.028 0,071 0.137

Time of day: Afternoon

-0.08 0.020 -0.400 0.689 -0.048 0,032 0.069

Cart variation 1 -0.043 0.022 -1.956 0.051 -0.086 -0.000 0,498

Cart variation 2 -0.010 0.022 -0.441 0.659 -0.054 0,034 0.316

Cart variation 3 0.00a . . . . . .

a

This parameter is set to zero because it is redundant

b Computed using alpha = 0.05

Appendices - 79

Table G.5 ANCOVA ‘Cart page’, Dependent: Conversion linear, Pairwise comparison

Cart page variation Mean difference (I - J)

Std. error Sig.b

95% Confidence interval For difference

b

(I) (J) Lower bound Upper bound

1 2 3

-0.033 -0.043

0.022 0.022

0.417 0.152

-0.086 -0.095

0.020 0.010

2 1 3

0.033 -0.010

0.022 0.022

0.417 1.000

-0.020 -0.063

0.086 0.044

3 1 2

0.043 0.010

0.022 0.022

0.152 1.000

-0.010 -0.044

0.095 0.063

Based on estimated marginal means

b Adjustment for multiple comparisons: Bonferroni.

G.4 Cart page: Valued model

Table G.6 ANCOVA ‘Cart page’, Dependent: Conversion linear valued, Test of Between-subjects effects

Source Type III

sum of squares Df

Mean square

F Sig. Observed

powerb

Corrected model 5,254.202a 5 1050.840 1.191 0.311 0.429

Intercept 317,318.064 1 317,318.064 359.631 0.000 1.000

Day of the week: Weekend

62.399 1 62.399 0.071 0.790 0.058

Time of day: Morning

3,587.725 1 3,587.725 4.066 0.044 0.522

Time of day: Afternoon

138.452 1 138.452 0.157 0.692 0.068

Cart variation 1,266.847 2 633.423 0.718 0.488 0.172

Error 2,203,211.685 2,497 882.343

Total 3,493,262.227 2,503

Corrected total 2,208,465.887 2,502

a

R Squared = 0.012 (Adjusted R Squared = 0.007)

b Computed using alpha = 0.05

Table G.7 ANCOVA ‘Cart page’, Dependent: Conversion linear valued, Parameter estimates

Parameter B Std. Error t Sig.

95% Confidence interval

Observed

powerb Lower

bound Upper bound

Intercept 22.683 1.434 15.821 0.000 19.871 25.494 1.000

Day of the week: Weekend

-0.316 1.190 -0.266 0.790 -2.650 2.017 0.058

Hour – Morning 3.317 1.645 2.016 0.044 0.091 6.542 0.522

Hour – Afternoon 0.526 1.329 0.396 0.692 -2.079 3.132 0.068

Cart variation 1 -0.559 1.440 -0.389 0.698 -3.382 2.263 0.067

Cart variation 2 -1.725 1.464 -1.178 0.239 -.4.597 1.146 0.218

Cart variation 3 0.00a . . . . . .

a

This parameter is set to zero because it is redundant

b Computed using alpha = 0.05

Appendices - 80

Table G.8 ANCOVA ‘Cart page’, Dependent: Conversion linear valued, Pairwise comparison

Cart page variation Mean difference (I - J)

Std. error Sig.b

95% Confidence interval For difference

b

(I) (J) Lower bound Upper bound

1 2 3

1.166 -0.559

1.465 1.440

1.000 1.000

-2.344 -4.008

4.675 2.889

2 1 2

-1.166 -1.725

1.465 1.464

1.000 0.716

-4.675 -5.233

2.344 1.783

3 1 2

0.559 1.725

1.440 1.464

1.000 0.716

-2.889 -1.783

4.008 5.233

Based on estimated marginal means

b Adjustment for multiple comparisons: Bonferroni.

G.5 Checkout Table G.9 Results logistic regression experiment ‘checkout’

Model 1 Model 2 Model 3

Variable B Wald

Odds-

ratio B Wald

Odds-

ratio B Wald

Odds-

ratio

Experiment variation:

Single-page 0.165 0.470 1.179 0.147 0.371 1.159 0.155 0.404 1.167

Cart value Not included 0.010 4.806 1.010* 0.010 0.004 5.256*

Hour of visit Morning

Not included Not included 0.494 0.250 1.638*

Afternoon 0.112 0.288 1.118

Constant 0.653 43.248 1.922* 0.388 6.352 1.474* 0.211 1.094 1.235

Model performance Model 1 Model 2 Model 3

Hosmer and Lemeshow

chi2-test

0.000 (p = .) 12.734 (p = 0.121) 15.520 (p = 0.050)

R2 Nagelkerke 0.001 0.014 0.025

Cox & Snell 0.001 0.010 0.018

* p < 0.05