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Perceived Performance Risk and its Influence on Abandoned Cart Syndrome (ACS) – An Exploratory Study
by Simon Scott Moore
School of Advertising, Marketing and Public Relations Faculty of Business
Queensland University of Technology Brisbane, Queensland, Australia
Submitted for qualification towards a Master of Business (Research)
2004
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Statement of Original Authorship
The work contained in this thesis has not been previously submitted for a degree or diploma at any higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Sign: …………………………………………. Date: …../……/……
Acknowledgements
For some, the art of writing is as natural as taking a breath, for others it can be as much a struggle as walking through a darkened room. This acknowledgement goes to those who helped turn on the lights. I would like to thank Charles Patti, my supervisor, manager, my mentor at times and always a dear friend. You took a chance on me, even though you knew the risks that lay ahead, and for that I am truly honoured. To Jim Everett, thank you for always keeping your door open, for stretching my vocabulary and giving me so much encouragement along the way. To my corridor, thank you for adopting me as an honorary member of the PR team. Each of you uniquely managed to keep my spirits high, my mind focused and my heart in the right place. To Jenny, thanks for all the advice and you were right, it is all about the journey. To my dearest friend Elizabeth (Lizzy) Macpherson, without whom this work could not have been possible. The words on this page will never be enough to express my thanks to you and your family. You are truly one of a kind. To my Mum, thank you for always knowing I could do it. Finally I acknowledge the three most important things in my life. To the Lord my God for answering the many prayers late at night in front of the computer. To my wife and best friend, Kaori, for all your love and patience, standing by me through the good times and bad, for that I am truly blessed. To my children, Jake and Tiana, it took a while but I’m back!
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This work is dedicated to my Dad
“I know you’re proud”
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Key words
Internet shopping, abandoned cart syndrome, performance risk, extrinsic cues, purchase behaviour, performance evaluation, projective techniques, vignettes, qualitative, exploratory.
Abstract
Despite predictions of Internet shopping reaching 6.9 trillion dollars by the end of 2004, research is now suggesting many online consumers are still very reluctant to complete the online shopping process. A number of authors have attributed consumers’ reluctance to purchase online to apparent barriers, however, such barriers have not been fully examined within a theoretical context. While most studies of consumers’ decision to shop on the Internet have focussed on key shopping determinants, this thesis builds a conceptual model grounded in consumer behaviour theory. In particular, this thesis explores the application of the perceived risk theoretical framework, specifically looking at one dimension of perceived risk theory – performance risk and the influence it has on the phenomenon of Internet Abandoned Cart Syndrome (ACS). To explore this phenomenon, a number of extrinsic cues are identified as playing a major role in the performance evaluation process of online purchases. The combination of these elements enabled the researcher to develop a conceptual model from which a series of propositions were drawn. To acquire pertinent data and investigate each proposition, this study used a combination of indirect and direct techniques, namely projective techniques in the form of a third-person vignette, a structured tick-box questionnaire and finally semi-structured interviews. The results suggest that collectively the extrinsic cues of brand, reputation, design and price have an overall impact on the performance evaluation process just prior to an online purchase. Varying these cues either positively or negatively had a strong impact on performance evaluation. The conclusion of this study suggests consumers are often unable to measure the full extent of risk-taking directly. In the majority of cases, consumers are guided by numerous factors, some intrinsic, others extrinsic. E-tailers with an established reputation, a well designed web site with known brands and a balanced pricing strategy reduce the perceived performance risks associated with purchasing online, thus reducing the occurrence of ACS.
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Table of Contents Page
Chapter One – Introduction to the Research ..........................................................10
Background ..................................................................................................................10
Problem Discussion .....................................................................................................13
Purpose.........................................................................................................................17
Research Problem ........................................................................................................17
Research Questions......................................................................................................17
Research contribution ..................................................................................................17
Restrictions ..................................................................................................................18
Key Definitions............................................................................................................19
Outlook of the Thesis...................................................................................................21
Conclusion ...................................................................................................................22
Chapter Two – Literature Review and Model Development.................................23
Conceptual Framework................................................................................................23
Perceived Risk Theory.................................................................................................24
Performance Risk.........................................................................................................30
Performance Evaluation...............................................................................................31
The Importance of Extrinsic Cues ...............................................................................33
The significance of brand.........................................................................................36
The importance of price. ..........................................................................................39
The power of design.................................................................................................41
The importance of reputation...................................................................................44
Propositions..................................................................................................................48
Conclusion ...................................................................................................................49
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Chapter Three - Research Design ............................................................................51
Research Purpose .........................................................................................................51
Research Approach ......................................................................................................52
Research Design...........................................................................................................55
Research Methodology ................................................................................................58
Data Collection Methods .............................................................................................61
Sampling Procedure .....................................................................................................67
Data Analysis ...............................................................................................................69
Methodological Limitations.........................................................................................71
Ethical Considerations .................................................................................................74
Conclusion ...................................................................................................................75
Chapter Four - Results ..............................................................................................76
Research Results ..........................................................................................................76
Emerging Patterns........................................................................................................78
Reasons for the Choices Made.....................................................................................80
Case scenario one.....................................................................................................80
Case scenario two. ...................................................................................................82
Case scenario three. .................................................................................................84
Case scenario four....................................................................................................86
Factors That Influenced Their Decisions.....................................................................87
The Risks Identified.....................................................................................................89
Summary of Results.....................................................................................................91
Brand........................................................................................................................91
Price. ........................................................................................................................92
Design. .....................................................................................................................92
Reputation. ...............................................................................................................92
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Performance Evaluation...........................................................................................93
Performance Risk.....................................................................................................93
Conclusion ...................................................................................................................94
Chapter Five – Conclusions and Implications for Future Research .....................95
Performance Evaluation...............................................................................................95
Brand........................................................................................................................97
Price. ......................................................................................................................100
Design. ...................................................................................................................102
Reputation. .............................................................................................................104
Answering the Research Questions ...........................................................................106
Answering research question one. .........................................................................107
Answering research question two. .........................................................................108
The Conceptual Model Revisited...............................................................................109
Implications to Propositions ......................................................................................110
Proposition one. .....................................................................................................110
Proposition two. .....................................................................................................110
Proposition three. ...................................................................................................111
Proposition four. ....................................................................................................112
Proposition five. .....................................................................................................112
Conclusion about the Research Problem ...................................................................113
Contribution of the Research .....................................................................................115
Implications to Theory...............................................................................................115
Cue-utilisation theory.............................................................................................116
Multi-dimensional risk theory................................................................................116
Consumer decision-making. ..................................................................................117
Implications for Practitioners.....................................................................................117
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Limitations .................................................................................................................119
Suggestions for Future Research ...............................................................................121
Conclusion .................................................................................................................122
Appendix A – Summary Results of Interview Data ..................................................124
Appendix B – Project Concent Form.........................................................................129
Appendix C – Participant Information Package ........................................................130
Appendix D – Vignette (Male Version).....................................................................132
Appendix E – Tick-Box Questionnaire (Male Version) ............................................133
Appendix F – Vignette (Female Version)..................................................................134
Appendix G – Tick-Box Questionnaire (Female Version) ........................................135
Appendix H – Interview Questions............................................................................136
References..................................................................................................................137
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List of figures Figure 1. Conceptual model of perceived performance risk........................................24
Figure 2. A classification of qualitative research procedures ......................................56
List of Tables
Table 1. Online Product Related Shopping: Advantages and Disadvantages .............15
Table 2. Adaptation of Risk Dimensions to the Internet Environment........................29
Table 3. Basic Belief Systems of Alternative Enquiry Paradigms ..............................53
Table 4. Data Collection Methods Employed..............................................................58
Table 5. Summary Results to the Tick-box Questionnaire ..........................................77
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Chapter One – Introduction to the Research
Background
Few could argue that the Internet is a phenomenon. It has been described as
the next ‘age’, equal to the industrial or agricultural age, only faster in terms of
growth (Gates, 2000). Less than a quarter of a century ago, the Internet was an
obscure network of large computers used only by a small community of researchers.
The majority of computers were found in corporate information technology (IT)
departments or research laboratories, and hardly anyone imagined the Internet would
play an important role in our lives. The very idea of a ‘personal computer’, much less
millions of them connected by a global network seemed absurd to all but a handful of
enthusiasts.
Today, the Internet is far from obscure. It is the focus of attention for
businesses, governments and individuals around the world. It has spawned entirely
new industries, transformed existing ones, and become a global phenomenon. Despite
its impact, today's Internet is still roughly where the automobile was during the era of
Henry Ford's Model T. “We've seen a lot of amazing things so far, but there is much
more to come. We are only at the dawn of the Internet Age” (Gates, 2000, p.1).
While it is true we are only at the very beginning of the online innovation
revolution, it is fair to conclude that the Internet has become a major intermediate for
accessing a wide range of product information, is used extensively for purchasing
various products and services, and has become an integral tool for communicating
with customers. An increasing number of retail businesses are discovering the Internet
is fundamental to conduct daily business. Businesses of all sizes are attempting to
harness the power of the Web to communicate with current and potential customers
from all over the world. Sometimes referred to by marketers as the “fifth medium”,
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with newspapers, magazines, radio, and television the other four, the Web provides
affordable, accessible technology that brings together buyers and sellers on a global
scale (Kiani, 1998 cited in Huuva & Sannerborg, 2003). The Web is no longer
considered just a network of computers sending bytes of data back and forth; it is now
a true sales and distribution channel, considered part of everyday life.
The process of buying and selling over this digital media is often labelled as
electronic commerce, or e-commerce and the companies who trade in this
environment are often referred to as the business-to-consumer market or electronic
retailers (Kalakota & Robinson, 1999; Huuva & Sannerborg, 2003). Although the
word e-commerce appears simple enough to understand in terms of stipulating a
transactional procedure, it is not a single piece of technology or solitary procedure, it
is a combination of technologies, applications, processes, business strategies, and
practices, all necessary to conduct business electronically (McIvor, Humphreys &
Huang, 2000 cited in Huuva & Sannerborg, 2003).
Over the past five years, we have seen e-commerce grow and it is certainly not
expected to ease any time in the near future (Ward & Lee, 2000). In 1999, e-
commerce transactions accounted for over $150 billion in sales and it is now predicted
that this amount will increase to $6.9 trillion by the end of 2004 (Forster Research,
2002).
In terms of marketing, the Web has some imitable characteristics that make it
fundamentally different from traditional marketing communication. A shift has
occurred from one-way to two-way communication between retailers and consumers
with a further shift from the more predictable one-to-many communication models of
the past to the many-to-many model of the present. This has altered the way
consumers align themselves with retailers and how they are persuaded to buy products
as a consequence (Hoffman & Novak, 1996).
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Because the Web presents a significantly different atmosphere for marketing
and consumer activities; traditional, more conventional activities need to be
transformed to entice consumers to purchase (Hoffman & Novak, 1996).
A considerable number of authors have documented the Internet in terms of its
commercial viability (Hoffman & Novak, 1996; Crisp, Jarvenpaa, & Todd, 1997;
Jones & Biasiotto, 1999; Radosevich & Tweney, 1999; Balabanis & Reynolds, 2001;
Forsythe & Shi, 2003). In one study of user habits, shopping via the Internet has been
found to be one of the fastest growing uses of the medium with over half of all
Internet users indicating shopping as a primary use (GVU 10th WWW User Survey,
1998). An early study by the Australian Bureau of Statistics (ABS) provides a further
perspective on what is perceived to be a continuation of the online success story. This
is a positive viewpoint of substantial growth and enormous global reach. According to
the ABS, 14% of 25 - 39 year olds had shopped online in the past 12 months to
November 2000, compared to 11% of 18 – 24 year olds, 10% of 40 – 45 year olds and
4% of those aged 55 years or more. According to popular press, the Web is set to
revolutionise the way purchases are made. It offers the future in home shopping, with
convenience and a broader range of merchandise available, making the online
environment an alluring prospect for many retailers (Van Beveren & Wilson, 2002).
Some evidence suggests the more time we spend online the more likely we are
to use the medium to shop. According to a study by Jupiter Communications (2001),
20.5% of people online for less than one year are shoppers, 28.8% of people online
for less than two years are shoppers and finally 42.5% of people online for more than
two years are shoppers.
The purpose of this chapter is to introduce and discuss the background to the
problem area under investigation. This is followed by a discussion of the specific
investigation including the overall purpose and goal of the study. A preliminary
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research question is posed, helping shape the investigation. This is followed by a
series of more refined secondary questions. Finally, the delineations and overall
outlook of the thesis is presented.
Problem Discussion
Despite predictions of Internet sales reaching into the trillions of dollars
(Forrester Research, 2002), many online consumers are still reluctant to complete the
online shopping process. A large percentage of online consumers are still window
shoppers using information gathered online to make their purchases in more
traditional brick and mortar stores (GVU 10th WWW User Survey, 1998).
Surprisingly, these same shoppers often place items into their virtual shopping carts,
only deciding to abandon the cart just prior to the purchase.
Some market researchers suggest the rate of shopping cart abandonment is
25% at the low-end of the scale (Anderson Consulting, 2002) and as high as 78%
(Bizrate.com, 2002) at the top end of the scale. The abandonment of shopping carts by
more than one third of website visitors (Enos & Conlin, 2000; Fenech & Cass, 2001)
means many companies fail to even cover the set-up and maintenance costs of their
online retail sites let alone make reasonable profits. The level of cart abandonment has
given rise to the phenomenon now recognised by researchers as Abandoned Cart
Syndrome (ACS), (Fenech, 2002). By way of explanation, ACS is defined as being
when an online consumer visits an e-commerce website with the view to purchasing a
product, however he or she chooses not to make a purchase, thereby abandoning both
the purchase process and the site in which the purchase was going to be made. In
some instances, consumers can even go so far as to placing items into the actual
electronic shopping cart or basket, only deciding to leave the site before processing
the final transaction. But, what do we know about the causes of ACS?
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Many authors (Hubscher et al., 2002; Ranganathan & Grandon, 2002;
Helander, 2000; Jarvenpaa & Tractinsky, 1999; Hoffman, Novak, & Peralta, 1999)
have identified links between consumers’ reluctance to shop online and apparent
barriers, such as slow load times, trouble finding products, lack of trust, credit card
issues, privacy issues, and many others. Although this information may help to
improve the online retail marketplace, there appears to be little to suggest these
barriers have been fully examined within a specific theoretical context. Thus, the
nature of these barriers and their potential impact on consumers’ decision to purchase
online is still unclear. It is possible however to associate each of these apparent
barriers to some dimension of risk perception. Each barrier has a direct or indirect
association to each of the identified dimensions of risk. For example, credit card
concerns can be associated to perceived financial risk, slow load times have an
obvious association to perceived time-loss risk, or trouble finding products could
arguably be associated with performance risk.
A considerable amount of marketing literature focuses on the Internet as a
marketing medium, primarily looking at its advantages and disadvantages (e.g.,
Pallab, 1996 cited in Forsythe & Shi, 2003). For example, numerous studies have
investigated Internet users and shoppers identifying characteristics and behavioural
patterns in an attempt to show association to the behaviour of shopping itself
(Henrichs, 1995; Mehta & Sivadas, 1995; Donthu & Garcia, 1999). Other studies
have addressed consumers’ specific concerns such as privacy issues and credit card
security (Hoffman Novak & Peralta, 1999; Jacobs, 1997). A number of possible
advantages and disadvantages of online product-related shopping are identified in
Table 1.
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Table 1. Online Product Related Shopping: Advantages and Disadvantages
Advantages Disadvantages
Greater product choice. Security and privacy risks.
More availability. Purchasing process breakdowns.
Large number of sellers. Delivery risks.
Rapid product comparisons can be done. No touch, feel, smell, taste cues.
Direct delivery of products to local area. Product pictures poor.
Greater access to product information. Products’ colours can be distorted.
Many product sales are untaxed. Quality is difficult to assess.
Access for shoppers in remote areas. Product returns can be complicated.
- Difficult and lengthy search.
- Too much product information.
(Source: Seigel, 2003, p.225, adapted for this study)
These studies appear limited in their contribution to any specific theory
(Forsythe & Shi, 2003). Surprisingly, no studies have been found that specifically
examine the many types of perceived risk associated with Internet shopping and its
relationship to ACS. However, perceived risk has been used to explain traditional
shopping behaviour in other environments such as the supermarket (Dunn et al.,
1986), telephone shopping (Cox & Rich, 1964), and in-home catalogue shopping
(McCorkle, 1990; Mitchell, 1999). These retail environments share similarities to
Internet shopping, yet the application of perceived risk as a theoretical framework has
not been applied fully to the online environment.
All marketers face a challenge of trying to determine what might influence
consumers’ behaviour. Although the ultimate goal is to influence consumers’
purchase behaviour to generate successful transactions, most marketers know the final
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purchase decision is only one component of the purchase process. A great deal of
consumer evaluation takes place prior to the final purchase decision. Therefore,
research needs to focus on what influences the consumer’s decision making process
(Belch & Belch, 2003). From the perspective of science, consumer behaviour is a rich
environment in which to investigate many theories (Arnould, Price & Zinkham,
2002). Understanding online consumers and what influences purchase decisions is
considered important because online shopping is only just beginning to move into
mass markets and as such is now attracting the attention of researchers. (Rowley,
2000).
The challenge for researchers is there is no single theory within consumer
behaviour that explains why consumers act the way they do, or in the case of e-tailing,
choose to abandon their virtual shopping carts or proceed with the purchase.
This thesis considers three theories relevant for understanding online
consumers’ purchase decision behaviour. The first is the multi-dimensional perceived
risk theory (Brooker, 1984; Jacoby & Kaplan, 1972; Peter & Tarpey, 1975; Garner,
1986; Mitchell, 1992; Stone & Gronhaug, 1993; Ho et al., 1994). From this theory the
surrogate dimension of perceived performance risk is considered as a major
influencing factor (Mitchell, 1992). The second theory investigates the decision-
making process. This provides the focus for this study, specifically looking at the
performance evaluation (of alternatives) stage of the purchase decision (Mitchell,
1992). The third theory is cue-utilisation theory (Cox, 1967; Olson, 1972 cited in
Chen & Dubinsky, 2003) which examines the extrinsic cues that guide consumers in
making risk assessment decisions (Arnould et al., 2002). It is within these theoretical
frameworks that the model for this study is developed and explored.
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Purpose
The purpose of this thesis is:
To explore the role perceived performance risk has on Internet Abandoned Cart
Syndrome (ACS).
This study is based on two objectives:
1. To build a model which explores the role of brand, price, design and
reputation on performance risk perception on ACS.
2. To explore what influence performance risk has on ACS with the view to
broadening our understanding of the phenomenon.
Research Problem
The central research problem is:
What influence does perceived performance risk have on Abandoned Cart Syndrome?
Research Questions
The research questions that help to elaborate on the central research problem are:
1) What influence do the extrinsic cues of brand, price, website design and
reputation have on performance evaluation?
2) What influence does performance evaluation have on perceived performance
risk leading to shopping cart abandonment?
Research contribution
This exploratory research study is designed to extend the literature within the
theoretical framework of risk theory, cue-utilisation theory and the performance
evaluation stage of decision making behaviour. At a practical level, this study has
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implications for online marketers and developers of Internet Business-to-Consumer
(B2C) e-commerce strategies.
Theory contribution.
The aim of this research is to provide further evidence supporting the “multi-
dimensional perceived risk theory” school of thought, specifically looking at
performance risk and its surrogate powers over consumer behaviour. By conducting
this study it is hoped the outcome will demonstrate the influence perceived
performance risk has on purchase decisions at the point of the online checkout.
Practical contribution.
At a practical level this study assists marketers and online retailer developers
in designing more efficient, risk reducing strategies for online retailers.
Perhaps the following observation from Zikmund (1973 cited in Ho, et al.,
1994) best summarises the intended contributions of the model proposed to both
marketing practitioners and marketing scholars:
…the marketer would gain more useful information on why a product (service) is perceived to be risky and, therefore, be in a better position to reduce consumers' risk perception (Ho, et al., 1994, p.7)
Restrictions
Due to time limitations set for this thesis, and the many characteristics of the
topic under investigation, an attempt to narrow the focus is made. The study focuses
only on perceived performance risk associated with physical products rather than
services. This is because a lot of product information found online is mainly focused
on physical products.
As the aim is to explore what influence performance risk has on consumers at
the online checkout, little attention is given to e-tailers. Finally, caution must be
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observed in determining any generalisation of results presented because of the
exploratory and qualitative nature of this research.
Key Definitions
To ensure this study adheres to the framework on which it is based, the following
definitions are provided for key terms used throughout.
The internet.
Sometimes referred to as "the Net," the Internet is a worldwide system of
physical computer networks which users at any one computer can acquire
information, transfer messages and conduct transactions from any other computer
(Chaffey, et.al., 2003).
The world wide web.
Also referred to as “the Web”, the World Wide Web is a medium for
publishing information and content on the Internet and allows users to access this
information from any Internet connected computer around the world (Strauss, El-
Ansary & Frost, 2003).
Online shopping.
The process of online shopping involves images or listings of goods and
services that are viewed remotely via electronic means, e.g. a vendor's Web site. Items
are selected for purchase, and the transaction is completed electronically with a credit
card or an established credit account (Atis.org, 2004).
Shopping carts.
On a Web site that sells products or services online, the shopping cart is a
common metaphor (from the original grocery store shopping cart) for a piece of
software that acts as an online store's catalogue and ordering process. Typically, a
shopping cart is the interface of a company's Web site. It allows consumers to select
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merchandise; review what they have selected; make necessary modifications or
additions; and purchase the merchandise (Seigel, 2003; Webopedia.com, 2004).
E-tailing.
E-tailing refers to retailing of goods and services over the Internet. For the
purpose of this study, an e-tailer is a Business to Consumer (B2C) business that
executes a transaction online with the final consumer (Learnthat.com, 2004).
Perceived risk.
Perceived risk is the risk of uncertainty, in the customer’s view, attached to the
purchase of a product not meeting the relevant or expected needs (Neal, Quester &
Hawkins 2002, p. 542). Perceived risk can also be related to product performance or
to the social, personal, or financial costs of poor product performance (Arnold, Price
& Zinkhan, 2002, p. 603).
Perceived performance risk.
Perceived performance risk is the way a product or service delivers benefits, as
perceived by a consumer (Neal, Quester & Hawkins, 2002, p. 542). Performance risk
is defined as a fear of loss that may be incurred when a brand, product or supplier
does not perform as expected (Horton, 1976 cited in Ha, 2002).
Brand.
A brand helps to uniquely identify the products or services of a seller and
furthermore helps to differentiate them from those of its competitors (Aaker, 1996;
Keller, 1998; Kotler, Brown, Adam, & Armstrong, 2003). A brand can also be viewed
as an inherent promise made from a company or product to the consumer that reflects
what a consumer can expect in terms of overall quality (Miletsky, 2002, p. 224).
Reputation.
Reputation can be defined as “a distribution of options (the overt expressions
of a collective image) about a person or other entity, in a stakeholder or interest
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group” (Bromley, 2001, p.316). Quality store and/or brand reputation can be defined
as a collective of favorable past actions and results and the ability to deliver valued
outcomes to multiple stakeholders (Harris & de Chernatony, 2001, p. 441).
Price.
Price is the amount of money an individual is prepared to pay to acquire a
product or service. Price can also be viewed more broadly as the sum of the values
consumers exchange for the benefits of having or using the product or service (Kotler,
et al., 2004, p. 921).
Website design.
Nielsen (2000 cited in Chaffey, et al., 2004) defines website design according
to three main areas: 1) site design and structure – the overall structure of a site; 2)
page design – the layout of individual pages within a site; and 3) content design – how
the text and graphic content of each page is designed (p. 293).
B2C.
B2C is an acronym for Business-to-Consumer. In terms of the World Wide
Web, a B2C site is a place where the exchange of services, information and/or
products from a business to a consumer takes place (Webopedia.com, 2004).
Outlook of the Thesis
This thesis is divided into five chapters. This chapter demonstrates an insight
into the field of study, the research problem under review and a presentation of the
overall purpose. The research questions are presented and some restrictions
acknowledged. In the second chapter, the theoretical model for this research is
discussed, supportive literature is presented and a series of research propositions are
put forward for further investigation. In the third chapter, the research design used to
collect and analyse data is outlined. Chapter Four details the results of the study and
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the final chapter provides interpretations, puts forward a number of conclusions and
future research recommendations are presented.
Conclusion
Chapter One has provided key foundations from which the central research
question can be explored. The major problem area under investigation has been
presented and justification for the study provided. To ensure a tight framework is
followed, a number of restrictions are presented. Finally, an outlook of the overall
thesis has been offered.
In the next chapter the model developed for this thesis is discussed, key
literature is reviewed and support for the model is presented. This is followed by a
series of key propositions which help guide the exploration.
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Chapter Two – Literature Review and Model Development
This chapter builds a conceptual framework designed to ground this study and
provide an outline of the proposed model. A review of supportive literature is
conducted, helping sustain the development of propositions discussed in Chapter
Three of this thesis.
Conceptual Framework
A great deal of effort is shown by consumers prior to making actual purchases
(Wilkie, 1994). Some research suggests perceived performance risk has a
considerable influence on consumers’ purchase intent before making a transaction
(Ha, 2002; Horton, 1976; Mitchell, 1992; Mitchell, 1998; Pope, Brown & Forrest,
1999). Thus, “identifying factors that are critical for converting browsers into buyers,
acquiring new customers, and retaining old customers should be of great interest to e-
marketers” (Chen & Dubinsky, 2003, p. 325).
The model presented in Figure 1 identifies several antecedents that influence
perceived performance risk in an online e-commerce setting. At the core of this model
is performance risk and four key antecedents known as extrinsic cues. It is believed
these cues affect the severity of perceived performance risk. While many extrinsic
cues may influence perceived performance risk, the four discussed in this study are
considered by many as vitally important (Chen & Dubinsky, 2003; Ha, 2002; Horton,
1976; Mitchell, 1992; Mitchell, 1998; Pope, Brown & Forrest, 1999). To assist in
exploring the influence perceived performance risk has on purchase intent, the
conceptual model and review of literature is provided.
The study of current literature focuses on two key areas. Firstly, literature on
perceived risk theory is analysed. This analysis identifies and evaluates what primary
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contributing risk factors influence the purchase decision at the very point of the
electronic checkout. Secondly, literature that focuses specifically on performance risk
is reviewed and extrinsic cues that influence this dimension are explored. Further, this
exploration evaluates the natural linkage between these components.
Figure 1. Conceptual model of perceived performance risk in an e-commerce context.
(Source: developed for the purpose of this research project)
Perceived Risk Theory
“The importance of understanding a theory such as perceived risk theory,
which has one of the longest research traditions in consumer behaviour, should not be
underestimated” (Mitchell, 1992, p. 26).
A number of different definitions of risk can be found in the literature. This
study begins with a review of literature commonly used in modern decision theory,
employed by Tversky and Kahneman (1992), Kilka (1997), Dowling and Staelin
(1994) and Mitchell (1999). When defining risk these researchers also refer to
previous research by Knight (1921), Bauer (1960), Cox (1967) and Cunningham
(1967). From this research, two schools of thought about risk are identified (Ho, et al.,
1994).
Performance evaluation
Perceived Performance Risk
Abandoned Cart Syndrome
Brand Price Site reputation
P5Perceived Risk
Web design
P1 P2 P3 P4
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Raymond Bauer (1960) first introduced the marketing community to the
concept of risk. He argued that consumer behaviour is risk-taking behaviour because a
consumer’s actions produce unanticipated consequences, some of which may be
unpleasant (Bauer, 1960, cited in Kim & Lennon, 2000). An interesting point made in
his first paper stated:
I have neither confidence nor anxiety that my proposal will cause any major stir. At most, it is to be hoped that it will attract the attention of a few researchers and practitioners and at least survive through infancy (Bauer, 1960, p.389 cited in Mitchell, 1999).
After forty years, the concept of risk and risk perception has survived well beyond
Bauer’s consideration of risk in its infancy and has established a tradition of research
unparalleled in consumer behaviour research (Mitchell, 1999).
To understand the framework for this study, the following definition of risk
and uncertainty is provided. In general, a decision can be deemed too risky when the
probability that a certain outcome will occur in the future is precisely known
(Camerer & Weber, 1992; Knight, 1921; Tversky & Kahneman, 1992; Kilka, 1997).
In the case of uncertainty, probabilities are not precisely known but people can form
more or less vague beliefs about probabilities when faced with the outcome of a
decision (Camerer & Weber, 1992; Knight, 1921; Tversky & Kahneman, 1992; Kilka,
1997).
Cunningham (1967, cited in Mitchell, 1992) was one of the first to suggest that
risk comprises two dimensions by conceptualising perceived risk in terms of
uncertainty and consequences. Mitchell (1992) then shows how Peter and Ryan
(1976) modified this original model slightly and in its simplest form can be
represented as:
Risk = probability of consequences occurring x negative consequences of poor brand choice.
26
“The notion to multiply these two dimensions is likely to stem from
probability theory where utility is measured by multiplying the probability by
expected value. This is one of the simplest models of perceived risk mainly drawing
from economics theory and has been used for the past 25 years by many researchers”
(Mitchell, 1992, p. 27).
Other early work in the marketing discipline included books on risk taking and
information handling in consumer behaviour (Cox, 1967 cited in Dowling & Staelin,
1994) followed by several conceptual models of consumer risk perception and
handling (see, Markin, 1974; Stem, Lamb & McLachlan, 1977; Taylor, 1974, cited in
Dowling & Staelin, 1994). Dowling and Staelin (1994) suggest that perceived risk
contains both a cognitive and affective component. Dowling (1986) define perceived
risk as both a situational and personal consumer behavioural construct influencing the
decision-making process. Dowling and Staelin (1994) define risk in terms of “the
consumers’ perception of uncertainty and adverse consequences of buying a product
or service” (p. 119). This appears to be the most common definition of risk by
consumer researchers. Risk is perceived in virtually all purchase decisions to the
extent that a consumer cannot always be certain that all buying goals will be achieved
when making a purchase decision (Cox, 1967 cited in Tan, 1999).
Over the past forty years, many scholars have argued that consumer behaviour
is essentially risk-taking behaviour; that actions of a consumer produce unanticipated
consequences, some of which may be deemed to be unpleasant. This sense of
unpleasantness has led to considerable research examining the impact of risk on
traditional consumer decision-making. For example Forsythe and Shi, (2003) suggest
consumers are apprehensive when they cannot be sure that purchases will allow them
to achieve their buying goals citing Cox and Rich, (1964) as support. Therefore,
perceived risk can be considered a function of uncertainty about potential outcomes of
27
a behaviour and possible unpleasantness of these outcomes. Specific studies
concerning the Web have found that consumers perceive online shopping of higher
risk than in-store shopping (Tan, 1999; Donthu & Garcia, 1999), perhaps for many of
the same reasons that apply to other modes of in-home shopping (Forsythe & Shi,
2003). This leads to the question of what types of risks Internet shoppers face and
what potential impact such risks have on the actual purchase decision perceived by
these shoppers? To consider this, the issue of risk must be addressed in more detail.
Two schools of thought.
Ho, et al. (1994) identified two basic approaches that help define or measure
the concept of perceived risk: the uncertainty-consequences approach and the risk-
component approach. The uncertainty-consequences approach (e.g. Cunningham,
1967; Cox, 1967; Dowling & Staelin, 1994 cited in Ho, et al., 1994) measures
perceived risk as a function of the uncertainty of purchase outcomes in terms of
subjective probability and the consequences associated with unfavourable purchase
outcomes. Knight (1921 cited in Mitchell, 1999) defines the concept of risk in terms
of uncertainty. Knight proposes that risk has a known probability while uncertainty
exists when knowledge of a precise probability is lacking. Consequently, Knight
believes we should be talking about perceived uncertainty rather than risk.
Cunningham (1967 cited in Mitchell, 1999) notes that uncertainty and
consequences might involve either known or unknown probabilities in relation to
overall loss and suggests that it makes little difference in relation to probability other
than a subjective view that loss exists (Mitchell, 1999). “However, this approach to
defining risk, which is based on prior work in economics and statistical decision
theory, has been viewed by some as inappropriate in consumer behaviour research”
(Bettman, 1975; Sjoberg, 1980; Stone & Gronhaug, 1993 cited in Ho, et al. 1994,
p.5).
28
Several studies show that the major dimensions of perceived risk can account
for a substantial fraction of overall perceived risk (e.g., Brooker, 1984; Jacoby &
Kaplan, 1972; Peter & Tarpey, 1975; Garner, 1986; Mitchell, 1992; Stone &
Gronhaug, 1993; Ho et al., 1994). Overall perceived risk can be predicted by
combining several functionally independent dimensions of risk—that is, risk results
from the interaction of a linked set of dimensions that combine to produce overall
perceived risk in buying behaviour, ultimately affecting behaviour (Ho et al., 1994).
Jacoby and Kaplan (1972) were amongst the first to study these dimensions in a bid to
identify or measure overall perceived risk. They specifically looked at five kinds of
risk: (1) financial; (2) performance; (3) physical; (4) psychological; and (5) social.
They concluded that these five dimensions can predict overall perceived risk
accurately, suggesting a sixth dimension, time-loss, be included in future research.
These six commonly reviewed risk dimensions have been further adapted to the
Internet environment and are defined in Table 2.
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Table 2. Adaptation of Risk Dimensions to the Internet Environment
Risk Dimension Definition Adapted to Internet Environment Physical Risk
Physical risk is defined as a perceived (and sometimes real) sense of physical pain caused by a level of anxiety associated with the negative outcome of a purchase decision that at the time of the purchase decision is real to the individual (Salam et al., 1998 cited in Fenech, 2000).
Performance Risk
Performance risk is defined as a fear of loss that may be incurred when a brand, product or supplier does not perform as expected (Horton, 1976 cited in Ha, 2002).
Psychological Risk
Psychological risk broadly describes instances where product consumption may harm the consumer's self-esteem or self-perceptions. Psychological risk perception is defined as the experience of anxiety or psychological discomfort arising from anticipated post-behavioural affective reactions such as worry and regret from the purchase decision made (Perugini & Bagozzi, 1999; Dholakia, 2001 cited in Ha, 2002).
Social Risk Social risk is where individuals are concerned with what others such as reference or peer groups may think. Peer groups exert a large amount of pressure to conform to the rest of the group beliefs (Mitchell, 1992). The social risk that if the shopping process outcome is negative in some way the perceived image of the consumer from others' viewpoints will be negative and as such consumers affected by this pressure abandon their carts.
Financial Risk Financial risk is defined as a net financial loss to a customer, including the possibility that the product may need to be repaired, replaced or the purchase price refunded (Horton, 1976 cited in Ha, 2002). Where the loss of money is an important consideration, financial risk is said to be high (Ha, 2002).
Time-Loss Risk Time loss risk may refer to the loss of time incurred due to difficulty of navigation and/or submitting an online order, finding appropriate web pages to purchase from, or delays receiving products after purchase (slow delivery times) (Forsythe & Shi 2002). Two leading causes of dissatisfying online experiences that may be thought of as a time loss risk include a disorganized or confusing web site and pages that are too slow to download (GVU 9th WWW User Survey, 1998 cited in Forsythe & Shi, 2002). Additionally, potential delays or difficulties in receiving ordered merchandise are a concern for some online shoppers.
(Source: Developed for the purpose of this study)
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Performance Risk
A rich stream of literature provides support for the usage of these risk factors
to help better understand consumer product evaluation and purchase (Featherman &
Pavlou, 2002). While these definitions show overall risk is a major influence on
consumer behaviour, more recent research suggests performance risk has the most
influence on consumers’ decision to purchase (Mitchell, 1992).
Expanding on the definition of performance risk put forward by Horton
(1976), the abandonment of online purchases can occur as a result of concerns that the
product, brand or website might not perform as expected, thus not delivering
anticipated benefits.
Pope et al. (2001) include quality risk in their definition of performance risk.
Like other researchers, Pope et al. (2001) support the notion that performance risk is
based on the belief that a product may not perform as expected or not provide the
benefits desired. This leads to the perception that the purchase has a degree of risk
attached. To further build on the definition of performance risk, Arnould et al. (2002)
notes the following when discussing quality,
…perceived quality, whether in reference to a product or service, has been defined as the consumer’s evaluative judgement about an entity’s overall excellence or superiority in providing desired benefits (p. 327).
Mitchell (1998) suggests performance risk can be regarded in two ways. First,
performance relates to concerns that chosen products or stores might not perform as
expected and will not deliver the benefits promised. He suggests most researchers
treat performance risk in this fashion, which is evident by the other definitions
provided. Mitchell’s (1998) second view is that performance can be seen as a
surrogate for overall perceived risk. This results from the combination of other losses
found in multidimensional risk theory. In this sense, where a retailer fails to meet
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consumers desired benefits, some or all of the risk dimensions will ultimately be faced
(Mitchell, 1998).
In terms of multidimensional risk studies that influence purchase behaviour,
this study is designed to expand on Mitchell’s view. Perceived performance risk has a
number of characteristics requiring further exploration if we are to better understand
the overall influence perceived performance risk has on decision-making. The
exploration begins with a review of performance evaluation and the effect extrinsic
cues may have in determining evaluation alternatives.
Performance Evaluation
There are several models of consumer decision-making where evaluation takes
place and from these models performance evaluation is drawn (Mitchell, 1992).
Mitchell (1992) notes that for many years marketing scholars have investigated the
role of performance evaluation in the consumer-buying process, citing Howard and
Sheth (1969), Nicosia (1966) Engel et al. (1978) as the most prominent scholars.
Although the models vary in specific detail, all have five stages in the decision-
making process. The five stages are: problem recognition, information search,
evaluation of alternatives, purchase decision and post-purchase behaviour. This study
looks at the evaluation of alternatives stage of decision-making as it is believed this is
where consumers review both internal and external motivational cues. The evaluation
of these cues leads to evaluating perceived performance risk and purchase intention.
Evaluation of alternatives.
Mitchell (1992) maintains the following when discussing the evaluation of
alternatives.
…this stage is essentially concerned with how the consumer chooses between alternative products and brands. The first concept is that buyers see products
32
as bundles of attributes, e.g. a hi-fi system is seen in terms of; sound quality, number of speakers, style, reliability, warranty, price, etc (p. 28).
Due to the subjective nature of decision-making, different consumers are
likely to require different attributes when evaluating choice (Mitchell, 1992). Mitchell
goes on to suggest this is the first source of uncertainty for consumers when making a
purchase decision. That is, which criteria or attributes should consumers use to judge
a product? A consumer may be uncertain about which attributes to use, some may be
completely unaware of certain attributes until an information search makes them
aware. This suggests consumers attach importance weights to the attributes they
choose. Mitchell (1998) cites this as the second source of uncertainty in the
consumer’s mind. The consumer is not fully aware of the importance placed on each
attribute. Assigning importance weights to attributes is therefore not something the
most informed consumers can be certain about, especially for new or infrequent
purchases. Cox (1967) has suggested that each information cue, such as an attribute,
has a predictive value. This predictive value is defined as how well the attribute
predicts the future performance of the product. The consumer however, is not always
sure about the usefulness of these predictive cues; for example, will a warranty help
predict future performances of a product better than a brand name or reputation
(Mitchell, 1998)?
Neal, Quester and Hawkins (2002) suggest there is a strong association
between the evaluation process and perceived performance.
A particular alternative, such as a product, brand or retail outlet, is selected because it is thought to be a better overall choice than other alternatives that were considered in the purchase process. Whether consumers select that particular item because of its presumed superior functional performance… there is a certain expectation that the item will offer a certain level of performance. The expected level of performance can range from quite low (‘this brand isn’t very good but it’s the only one, and I’m in a hurry’) to quite high. In general we tend to perceive performance to be in line with our expectations (p.151).
33
As Ray (2001) notes, online shoppers appear to have high expectations when it
comes to performance and retailers need to provide customers with a service that
matches or exceeds expectation, otherwise failure is the most obvious outcome.
Understanding what consumers anticipate when evaluating an outcome is
necessary as expectations provide a standard of comparison against which consumers
judge perceived performance (Walker & Baker, 2000). Research proposes that
consumer judgments result from a comparison of expectations and perceptions of
performance and traditionally rely on predicted expectations (i.e., what consumers
predict or think will occur is what they perceive will occur) (Swan & Trawick, 1980;
Oliver, 1981; Zeithaml et al., 1993 cited in Walker & Baker, 2000, p. 414).
The Importance of Extrinsic Cues
According to cue-utilization theory, products consist of an assortment of cues
that serve as surrogate indicators of product quality or performance (Cox, 1967;
Olson, 1972 cited in Chen & Dubinsky, 2003, p 329). These cues are grouped into
two categories, extrinsic and intrinsic (Arnould et al., 2002). Extrinsic cues are
product-related attributes that are not part of the physical product, for example, price,
brand name, country-of-origin, reputation, design, and many others. Intrinsic cues
represent product-related attributes that cannot be manipulated without changing the
physical properties of the product itself (Chen & Dubinsky, 2003, p. 329).
Chen & Dubinsky (2003) suggest,
…a consumer’s perception of quality is different from objective quality. The latter describes the actual technical superiority or excellence of the product that is measurable or verifiable according to some predetermined standards (Monroe & Krishman, 1985), as judged from intrinsic cues (p. 330).
Perceived performance risk on the other hand, is a higher-level construct, and
highly subjective owing to the specific consumption setting (Zeithaml, 1988). This is
34
especially the case with on-line shopping. Consumers generally have no intrinsic
attributes to generate objective judgments about the performance of a product (Chen
& Dubinsky, 2003). In an e-commerce setting consumers have a lower level of
tangibility because of the lack of demonstrable proof about the performance of a
product (Vijayasarathy & Jones, 2000 cited in Chen & Dubinsky, 2003). Under these
conditions, extrinsic attributes often have a stronger authority on consumers’
perception of quality and product performance (Teas & Agarwal, 2000).
Based on previous research, there are conceivably four extrinsic cues
associated with perceived performance risk in an online consumer shopping
environment. The four extrinsic cues identified for this study are brand, price, website
design and the e-tailer’s reputation.
Forsythe and Shi (2003) suggest when a consumer buys a product in a
traditional retail setting they primarily use intrinsic cues such as the five human senses
to assist in product performance evaluation. For example, a consumer purchasing fruit
from a market will often touch test for freshness, smell the item to determine its
ripeness, look for any blemishes and where possible attempt to taste a sample of the
product before placing the item in their cart or shopping basket.
However, in an online setting the inability to use intrinsic cues to judge the
performance of products ultimately results in increased performance risk (Forsythe &
Shi, 2003). A heightened level of performance risk results in the abandonment of the
purchase.
Agarwal and Teas (2001) found in their study of traditional shopping that
consumers use extrinsic cues such as price, brand name and store name to assign
quality perceptions. Dawar and Parker (1994) also studied the importance of extrinsic
cues on consumers’ performance evaluation across geographical and cultural
boundaries and found that these cues transcend the boundaries. This research is vitally
35
important to this study due to the global reach of the Internet. Agarwal and Teas
(2001) note the importance of further research and examination of these complex
consumer behavioural models and the need for further exploration into these cues and
their effects on consumers’ willingness-to-buy.
Overall risk perception is reduced through non-physical characteristics such as
extrinsic cues. To reduce risk consumers use these cues. Bearden and Shimp’s (1982)
study found that physical intrinsic characteristics only help mitigate risk perceptions.
They argue that extrinsic cues are particularly valuable when products’ intrinsic cues
have low confidence and predictive values (Cox, 1962 cited in Bearden & Shimp,
1982). Further, “when consumers cannot tell how well a product will perform, how
safe it is, how socially acceptable it might be, etc., they tend to depend on… extrinsic
cues to enhance confidence by predicting performance” (P. 229). This highlights the
importance performance risk has on the other dimensions of risk, e.g. how safe it is –
physical risk, social acceptability – social risk, and so on.
The importance of performance risk and the extrinsic cues used to evaluate
performance are more obvious with Internet shopping. Consumers require greater
assurance that the product ordered is of the quality anticipated especially when they
are not able to physically evaluate the product. The consumer must be confident that
the goods ordered are the right goods, delivered to the right place and in sound
working order. Ang and Lee (2000) argue that the final fulfilment process itself plays
a significant role in performance evaluation, not just the quality of the goods promised
by the web retailer.
Essentially, the literature suggests online consumers use extrinsic cues when
making purchase decisions to help reduce perceived risk. For example, a study by
Perlusz and Sorensen (2001) showed that online consumers may not differ in their
evaluation of extrinsic product attributes regardless of their experience in the past.
36
This is especially important as it demonstrates consumers place a significant value on
performance despite their experience in an online purchase setting (Jupiter
Communications, 2001). Perlusz and Sorensen (2001) found that performance risk
appears to influence online consumers’ decision to shop substantially. The results of
Perlusz and Sorensen reveal differences between online consumers who believe the
web is associated with some levels of risk. Overall it seems online consumers put
more emphasis on assessing extrinsic attributes compared to other types of shoppers.
This is supported by Bearden and Shimp’s (1992) previous research in physical
settings such as traditional ‘Brick and Mortar’ stores. It seems consumers use extrinsic
attributes to reduce risk perceptions when they are unable to assess the intrinsic
performance of the product under consideration (Agarwal & Teas, 2001; Lee et al.,
2000).
The significance of brand.
A brand is made up of several components, e.g. logo, colour, tagline and
shape. These components help to uniquely identify products and assist in
differentiating them from those of their competitors (Aaker, 1991; Keller, 1998;
Kotler, Brown, Adam, & Armstrong, 2003; Miletsky, 2002). Brands are considered
valuable because they can influence consumers’ perceptions. A good brand can signal
product superiority to customers, which may lead to favourable evaluations of
performance (Aaker & Jocobson, 2001; Erdem & Swait, 1998). In terms of
performance, this signifies that a brand is also seen as a company’s promise to its
consumer. The promise a brand makes in an online environment is even more
significant as the online domain is still considered to be uncertain (as previously
cited).
37
The power of a brand helps differentiate the many online retailers available to
consumers and assists in establishing some degree of credibility (Harvin, 2000).
Online retailers need to gain sound credibility from the market to create a positive
image for the products being sold. According to Carton (2001) a strategy employed by
consumers to minimize the risk of buying online is to select credible brands, as these
brands communicate trust, reducing the level of uncertainty felt by the consumer (Van
Beveren & Wilson, 2002). Van Beveren and Wilson (2002) assert “Consumers use
risk-reducing strategies in choice situations where they perceive risk and to reduce the
consequences of the decision, consumers might employ brand loyalty” (p. 3).
In a survey conducted by Ward and Lee (2000) it was found that brands
positively influence over half of all online buying decisions. Their study found that
although the cost associated with information seeking online is considered low,
established brands are better positioned than their newer online counterparts. They
found that customers still rely on familiar brands when making a decision in the
purchasing process (Ward & Lee, 2000). It was found consumers rely heavily on well-
known brands as a short cut in evaluating different products (Ward & Lee, 2000).
Some researchers view brand names as summary constructs (Han, 1989;
Johansson, 1989) or shorthand cues (Zeithaml, 1988) for quality and performance.
Consumers can sometimes make product quality assumptions based on brand names
(Agarwal & Teas, 2001). The process can be explained using the affect-referral
process discussed by Wright (1975). This suggests consumers do not examine brand
attributes every time they choose a brand, instead they simplify their decision-making
by basing their judgments on brand attitudes or cues rather than on product attribute
information (Agarwal & Teas, 2001). With an increased level of uncertainty and a
heightened level of expectation online, recognised brands can be a good
representation of quality and are evaluated positively. That is, brands become a
38
performance risk reliever for many online consumers (Ward & Lee, 2001). The level
of acceptance of familiar brands is founded by the perception of credibility and trust
for the user and helps to relieve the association of risk linked with the Internet (Davis,
Buchanan-Oliver & Brodie, 1999). Tan (1999) acknowledges brand image strategies
are effective risk relievers for potential Internet shoppers. Having a strong online
brand reputation is strategically important in reducing risk perception, creating a
positive influence on performance risk.
Simeon (1999) suggests in any market be it traditional or virtual, successful
brand development relies heavily on customer recognition. Brands have a number of
extrinsic attributes that are considered intangible including performance, quality and
price.
Reynolds’ (2000) study of brand values found that years of positive brand
experience give brands equity recognition and thus the power to prompt consumers to
follow those brands online. Customers rate familiar brands highly as this helps ease
the choice individuals make at the time of purchasing (Reynolds, 2000). Familiar
brands are even more important online as there are so many competing e-tailers vying
for business (Reynolds, 2000). A study by Allen & Fjermestad (2001) identified that
new Internet users tend to use sites that have familiar brands first with 40% of new
online shoppers preferring to purchase from online merchants who they have
previously purchased from offline.
If a consumer is evaluating the performance risk of a product and they have
had a positive experience with the brand, they are more likely to be positive about the
performance risks associated with that brand. Furthermore, consumers are more
willing to trying new products from brand names they have grown to trust and
evaluate positively. This creates a lower level of risk when extending the brand into
new product lines or into new channels (Harvin, 2000). Single products and other
39
lesser-known brands often lack credibility needed to develop consumer trust.
Consequently, significant limitations and restricted opportunities exist for newly
created online brands. These limitations need to be identified and explored before
successfully conducting online business (Allen & Fjermestad, 2001).
It is therefore proposed that a negative assessment of brand is likely to have a
negative influence on consumers’ evaluation of perceived performance risk. This is
likely to increasing the potential Abandoned Cart Syndrome occuring.
The importance of price.
Price is the second extrinsic cue requiring further exploration. “Marketing has
long been considered a functional business activity directly responsible for generating
revenue and price is a key marketing mix tool used to achieve revenue goals” (Siegel,
2003, p. 250). Most consumers use price as a determining factor in deciding whether
or not to purchase a product (Siegel, 2003). Price has often been seen as an important
measurement for quality (Olson, 1977) sometimes described as “the best known
extrinsic indicator of quality” (Ophuis & Van Trijp, 1995, p 179).
Dodds and Monroe (1985) note that price has often been considered as an
extrinsic attribute that is repeatedly used by consumers to assess quality. Dodds et al.
(1991) also suggest consumers use price as a quality indicator as it reflects a belief
that supply and demand forces lead to a natural ordering of products on a price scale.
As a result of this scale a positive relationship exists between price and product
quality. Other researchers also support the notion that price continues to be a quality
cue when placed alongside other extrinsic cues such as brand name or reputation (e.g.,
Rao & Monroe, 1989; Teas & Agarwal, 2001). This suggests that price be considered
a positive indicator of perceived quality due to the absence of intrinsic cues in online
shopping (Chen & Dubinsky, 2003).
40
Sweeney et al. (1999) argue that price has positive association with perceived
product quality; however, in terms of overall risk perception this is likely to lead to
greater level of financial uncertainty (Sweeney et al., 1999). Consumers who pay a
higher price for products are more likely to suffer from financial loss than those who
pay a lower price. Furthermore Sweeney et al. (1999 cited in Chen & Dubinsky, 2003)
suggests, as price level increases, the risk of an incorrect product assessment also
increases, thus affecting the performance evaluation.
Siegel (2003) suggests “most buyers will not make a purchase if they think a
price is not honest or fair. The likelihood of a purchase depends on how badly they
need or want the product, their perception of price fairness and whether or not they
have the means to complete the purchase” (p. 251). This is often true when consumers
lack product familiarity because they purchase the product infrequently or they buy
from unfamiliar stores or mediums such as the Internet (Rao & Monroe, 1989). For
the vast majority the concept of online shopping is still a new experience, therefore,
high prices may generate a greater degree of perceived performance risk.
Price has been found to have a positive impact on perceived product quality
however, as a financial sacrifice; price contributes negatively to value (Dodds et al.,
1991; Monroe, 1990; Zeithaml, 1988 cited in Chen & Dubinsky, 2003). It is suggested
that price sensitive shoppers often identify price as being an important component of
the purchase decision, often compare prices between one alternative and another
(Zeithaml, 1988). Chen & Dubinsky (2003) suggest that as buying online is most
often considered cheaper than purchasing through regular channels; price becomes an
important extrinsic cue in evaluating performance. Their research shows that seeking
the best price is a major motivation of online shoppers (Korgaonkar & Wolin, 1999,
cited in Chen & Dubinsky, 2003). It is believed the transaction costs for e-commerce
should typically be lower than traditional settings. This is apparently because of the
41
reduced operational and infrastructural costs often associated with the online domain
(Strader & Shaw, 1999).
It is therefore proposed that a negative assessment of the price offering of a
product is likely to have a negative influence on consumers’ evaluation of the
perceived performance risk. This increases the likelihood of Abandoned Cart
Syndrome.
The power of design.
Greenberg and Garfinkle (1963) are the first to recognise the role imagery
(design) plays in shaping consumer response. It was another 20 years before research
began to focus more attention on visual persuasion and even later before it was
recognised on the Internet (Woods, 1981; Foggin, 1991; Babin & Darden, 1996 as
cited in Winn & Beck, 2002).
Because online shopping is almost exclusively a visual experience with strong
links to design, there is a need to look closely at literature connecting persuasion to
visual imagery and its power as an extrinsic cue.
In terms of Internet sites, Ranganathan and Grandon (2002) define design as
the way in which the content of web sites is presented to customers.
The design of a website has been studied in diverse contexts. For instance, Wan
(1999) studied features of web site design and placed these features in a matrix of
business functions versus customer values (Wan, 1999 as cited in Ranganathan &
Grandon, 2002).
McCarthy and Aronson (2001) propose there is a direct relationship between
site design and a consumer's intention to return to the site and purchase. This
relationship is defined through a model that explores the influence of customer
satisfaction, particularly when the Web site design is inline with the expectations of
42
the consumer (McCarthy and Aronson, 2001, p.1). “When performances exceed
expectations, positive disconfirmation occurs and the likelihood of consumer
satisfaction increases. When expectations are not met, negative disconfirmation
occurs and the likelihood of consumer dissatisfaction increases” (Arnould et al., 2002,
p. 625). It is believed that the more a user revisits a site, the more likely their
expectations have been met, the more satisfied they are, and the more likely they are
to purchase. It is also believed that design plays a major role in influencing consumers
to re-visit the same site (McCarthy & Aronson, 2001). This would indicate that the
quality of design influences the trust and loyalty a consumer has with a site.
According to McCarthy and Aronson (2001) a well-designed website will result in the
development of a loyal customer base that will be more likely to purchase goods and
services from the online retailer. By satisfying consumers’ needs and meeting
expectations, loyalty is developed.
Ranganathan and Grandon (2002) found that poor design was a significant
variable that impacted negatively on online sales. According to their research, to
improve online sales there is an imminent need to understand the factors that
influence online purchases. Among the various factors cited by these authors, the
design of Web sites was repeatedly mentioned as imperative in impacting on the
decision to make an online purchase. It is expected that customers will purchase more
due to effective presentation than many other factors (Jefferson, 1997).
Balabanis and Reynolds (2001) on the other hand believe that while research
has considered the many virtues of the Internet, it is still limited in scope (Berthon,
Pitt & Watson, 1996; Liang & Huang, 1998 as cited in Balabanis & Reynolds, 2001).
They argue that little attention has been given to how consumer differences affect the
evaluation of web sites, believing that online retailers need to design web sites that
43
sustain the interest of consumers, especially those who hold opinion leadership status
(Mowen & Minor, 1998).
Crisp, Jarvenpaa and Todd (1997) also recognise the potential linkage between
constructs of consumer behaviour (i.e., motivation and perception) and web design.
Crisp et al. (1997) suggest,
…improving the store fronts in relation to the site’s design, and thereby affecting consumers’ beliefs about web shopping, should be a greater concern for retailers than simply waiting for Internet shopping (and therefore customer attitude and intention) to mature (p. 12).
To ensure the long-term development of Internet-based retailing environments,
studies need to consider how different user segments respond to alternative site design
strategies (Crisp et al., 1997). In a study by Winn and Beck (2002), an emphasis was
placed on design and its link to consumer persuasion. Arnould et al. (2002) defines
persuasion as “an active attempt to change individual consumer behavioural attitudes”
(p. 475). Winn and Beck (2002) argue that while many approaches contribute to a
better understanding of site design, researchers have not addressed a vital element of
visual design – its persuasive power and affect on purchase behaviour. Winn and
Beck (2002) believe that e-commerce Web design also serves a classic rhetorical
function of persuading shoppers to buy. That is, appealing to customers' reason can
build credibility creating positive feelings about the site. These factors increase the
likelihood of consumers making a purchase (Winn & Beck, 2002).
In fact, e-commerce sites and the design elements used to build them serve a
relevant function. They are a means of persuading potential customers to explore the
site’s content, interacting with the site and ultimately purchasing (Winn & Beck,
2002).
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According to Winn and Beck (2002) a site design that aids customers by
structuring product information in the ‘best possible way’ has certain persuasive
powers. The product information supports the customer's decision-making process in
two ways. The first is the amount of information the site provides and the other is the
way the information is displayed. Both of these are considered key factors to effective
design, especially with the absence of intrinsic cues normally found in traditional
shopping environments such as retail stores and shopping centres.
Customers’ willingness to purchase is ultimately affected by the design of
store environments (Helander, 2000). Although Helander (2000) focuses primarily on
design to help support his argument, the actual perception of the store environment
links this argument to consumer behaviour. The following quote highlights the
association between design and performance, especially when navigating within an
online store:
A store with great performance affordances invites purchase decisions and thereby commits the customer. Thus a customer may decide to visit a section of a store by following a well-designed link, which offers high performance affordance. Following the decision to visit, a customer may decide to navigate in the store to find the item in question. Errors in Navigation are common, and they discourage customers. Store design variables, such as Number of Clicks to destination, must be considered. A store that lacks performance affordances will discourage sales (Helander, 2000, p. 770).
It is therefore proposed that a negative assessment of the design of a website is
likely to have a negative influence on consumers’ evaluation of perceived
performance risk. This increases the likelihood of Abandoned Cart Syndrome.
The importance of reputation.
The final extrinsic cue is reputation. When choosing amongst competing
brands, consumers often find themselves facing the uncertainty of product
performance.
45
Marketing research has found consumers use buying cues mostly when
assessing product quality (Dawar & Parker, 1994). This is especially true when
consumers need to reduce the perceived risk of a purchase (Jacoby, Olson, &
Haddock, 1971; Olson, 1977). Because consumers are usually unable to trial products
being purchased via the Internet, risk perceptions are often exacerbated. One of the
more prevalent extrinsic cues in the literature is product and/or the retailers’
reputation (Agarwal & Teas, 2001; Cooper & Ross, 1985; Emons, 1988 cited in
Dawar & Parker 1994; Olson, 1977; Rao & Monroe, 1989; Tan, 1999). The Internet’s
infinite shelf-space has lowered the barriers to entry for many potential e-tailers
ultimately increasing the online clutter and consumer choice. Under those conditions
reputation is the “one plausible defence against competitive attacks” (Baker, Warner
& Dawley, 1998, p. 48). In terms of risk, the primary function of reputation is to
reduce the risk of transacting parties and helps improve the evaluation of products and
services offered by e-tailers to the market.
Bearden and Shimp (1982) believe extrinsic cues have risk reducing qualities
that help consumers during product evaluation. Supporting previous research,
purchasing a well-known product with a quality reputation is considered a risk-
reduction strategy (Bearden & Shimp, 1982). Due to past experiences consumers rely
on a collective of information regarding reputation and rely heavily on extrinsic cues
during the purchase process (Olson, 1977 cited in Bearden & Shimp, 1982).
A great deal of a company’s reputation is obtained by the way it presents itself
to the public in much the same way a store presents itself to customers. On the
Internet, a consumer’s perception of an e-tailer’s reputation is partly attained from the
content and technologies employed in the design of the site. The perception formed
from the site has the ability to either heighten consumers’ perceptions of risk or
diminish such perceptions and is to some extent based on the reputation of the
46
storefront (Van Beveren & Wilson, 2002). Tan (1999) argues that a retailer’s
reputation is an essential risk reliever in an online shopping environment. This
position builds on the earlier work of Bauer (1960), which suggests that consumers
use extrinsic cues such as reputation, to form perceptions of risks, which in turn lead
them to form perceptions of quality (Bearden & Shimp, 1982).
In a study by Chen and Dubinsky (2003) reputation is given key
acknowledgement. While they suggest the reputation of the e-tailer is garnered from
various sources such as word-of-mouth communication, level of advertising, and
brand equity (citing Bolton & Drew, 1991; Teas & Agarwal, 2000; Zeithaml, 1988)
they advocate that reputation has been found to directly affect consumers’ quality
perceptions (Gardner, 1971 cited in Chen & Dubinsky, 2003). Chen and Dubinsky
(2003) further propose reputation serves as a surrogate for quality and acts as a
dominant choice by providing consumers with a bundle of information about
performance (citing Dawar & Parker, 1994; Hoyer & Brown, 1990; Jacoby, Szybillo,
& Busato-Schach, 1977; Rao & Monroe, 1989 for examples). This suggests the
reputation of e-tailers is positively related to performance perceptions. Chen and
Dubinsky (2003) state that consumers are likely to perceive an e-tailer with a good
reputation as more trustworthy and credible than one with a poor reputation and an
increase in trust leads to a greater likelihood of purchase. Consequently, as an
extrinsic cue, an e-tailer’s good reputation should foster lower performance risk for
online shoppers increasing purchase intent (Hendrix, 1999; Chen & Dubinsky, 2003).
Consumers also favour more prestigious retailers offering premium products
as another risk reducing strategy. Specifically, “Consumers reduce risks by
purchasing products from a store with a quality reputation” (Thorelli, Lim & Ye,
1989, p. 37 cited in Agarwal & Teas, 2001). Agarwal and Teas (2001) suggest
consumers often pay a premium for products that have strong reputable brand names
47
because reputable brands are perceived to stand for quality. This helps reduce the risk
of product performance failure. Likewise a higher store reputation serves to increase
quality perceptions and is expected to reduce performance risk (Cox 1962; Hisrich,
Dornoff, & Kernan, 1972; Leavitt, 1967, cited in Agarwal & Teas, 2001).
Finally, a further analysis of the marketing literature (e.g., Tan 1999) shows
the use of certain risk reduction strategies such as brand or store reputation are
successful in reducing the risk perception of consumers (e.g. Roselius, 1971; Shimp &
Bearden, 1982; Innis & Unnava, 1991; Boulding & Kirmani, 1993 cited in Tan,
1999). The results of Tan’s (1999) study show consumers perceive Internet shopping
to be of higher risk than in-store shopping, and only those who are less risk averse are
more likely to shop on the Internet. Tan (1999) recognises one of the more popular
risk relievers as being the e-tailer’s reputation. An e-tailer known by the consumer
and with an established reputation is more effective in reducing the risk perception
than an e-tailer without an established reputation (Tan, 1999).
The consumer who decides to purchase a product is making a number of
purchase decisions at the time of the transaction (Mitchell, 1992). For example, the
many product attributes provide just one of the many decisional cues used by
consumers when making purchase decisions. Mitchell (1992) identifies store
reputation as one factor important in the overall decision process. For different
products the purchase decision carries with it different levels of risk. For example,
some stores may have a good reputation for after-sales service; others may have a
reputation for being the store many opinion leaders go to to acquire such products.
Ultimately these offer some degree of risk reduction. That is, to risk choosing another
store to buy the same brand may be deemed unacceptable by the majority of
consumers (Mitchell, 1992).
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As reported in prior research there are many factors that influence consumers’
choice to purchase online, however reputation appears to be an important decision-
making cue worthy of further exploration (Phau & Poon, 2000). Reputation is
therefore considered an essential ingredient in an online shopping environment.
It is proposed that a negative assessment of an e-tailer’s reputation is likely to
have a negative influence on consumers’ evaluation of the perceived performance
risks. This increases the likelihood of Abandoned Cart Syndrome.
Propositions
Having reviewed current literature and justified each of the components within
the conceptual model proposed, the final stage in this chapter presents a number of
essential propositions derived from this model.
Pandit (1996) best describes propositions as generalised relationships between
a category and its concepts and between discrete categories. These were originally
called 'hypotheses' by Glaser and Strauss (1967 cited in Pandit, 1996). It is felt the
term 'proposition' is more appropriate as Whetten (1989, p. 492 cited in Pandit, 1996)
correctly points out, “propositions involve conceptual relationships whereas
hypotheses require measured relationships”. Since this approach produces conceptual
and not measured relationships, the former term is preferred for this study.
Based on the conceptual model presented in this study, the following propositions are
offered:
P1. A negative evaluation of the brand is likely to heighten consumers’
level of perceived performance risk thereby increasing the likelihood of
Abandoned Cart Syndrome.
49
P2. A negative evaluation of the product’s price is likely to heighten
consumers’ level of perceived performance risk thereby increasing the
likelihood of Abandoned Cart Syndrome.
P3. A negative evaluation of the website design is likely to heighten
consumers’ level of perceived performance risk thereby increasing the
likelihood of Abandoned Cart Syndrome.
P4. A negative evaluation of the e-tailer’s reputation is likely to heighten
consumers’ level of perceived performance risk thereby increasing the
likelihood of Abandoned Cart Syndrome.
P5. A negative assessment of performance risk is likely to have a negative
impact on purchase intention thereby increasing the likelihood of Abandoned
Cart Syndrome.
Conclusion
The literature reviewed in this study has provided a theoretical foundation for
developing this research study. A series of propositions were presented as an outcome
to the proposed conceptual model.
A more comprehensive review of literature covering risk theory and consumer
behavioural theory identified a host of potential research opportunities. In particular,
intrinsic and extrinsic cues used in product purchase evaluation were considered.
From these, the extrinsic cues of brand, price, design and reputation were identified as
vital in evaluating online purchase opportunities in terms of performance risk and
purchase intentions. The uncertain environment of the Internet has compounded the
need for further exploratory research to determine the impact these characteristics
have on performance evaluation and perceived performance risk. While several
50
researchers have begun to investigate the online purchase setting, the phenomenon of
ACS still remains largely unexplored.
51
Chapter Three - Research Design
This chapter identifies suitable research methods to explore the phenomenon
of Abandoned Cart Syndrome. This begins by providing an understanding of the
research design used, supported by specific definitions and key discussion points
established by others in the field of social research. The chosen data collection
methods are discussed in detail helping justify the model outlined in this thesis. To
ensure reliability issues are appropriately addressed, a number of advantages and
disadvantages of methods employed for this study are presented. To conclude this
chapter, the implementation procedure for collecting data and the research sample
used is acknowledged. Finally, research design limitations are summarised and ethical
considerations addressed.
Research Purpose
According to Blaikie (2000), research refers to the process that links research
questions, data, and research conclusions. Yin (1989) advocates “research is an action
plan for getting from here to there” (Blaikie 2000, p. 35).
When establishing a research study the design of the study begins with the
process of selecting a general topic of interest. In this case, perceived risk and Internet
Shopping Cart Abandonment (ACS) is chosen. Then the researcher establishes a
suitable approach to investigate the topic. This selection helps guide the researcher’s
view of reality, assists the researcher in defining the relationship between the
researcher and reality, and finally helps determine the appropriate methodology to be
used (Gummesson, 2000).
The primary objectives of basic research are to explore, describe, explain,
understand or predict. These five basic research objectives can be grouped into
52
specific classifications, that is, exploratory, descriptive and explanatory (Blaikie,
2000). Before a research project can commence it is essential to select the appropriate
classification best suited to the objectives of the study.
The objective of this study is to obtain a better understanding of a domain still
in its earlier stages of development. To meet this objective an exploratory research
design is used. An exploratory research design is most appropriate when the primary
objective is to identify and understand a phenomenon or problem, define the problem
more precisely, or when uncertainty exists regarding the most suitable models to use
to better understand the phenomenon (Berg, 2004; Czinkota & Kotabe, 2001;
Neuman, 2003).
Exploratory research is a useful approach when the researcher wishes to gain
an initial insight into a new environment, as is the case with online shopping and
consumer purchase behaviour (Czinkota & Kotabe, 2001). Flexibility is also another
important characteristic of an exploratory study. According to Yin (1989), the
research design used in an exploratory study needs to be as flexible as possible and
conducted in a way that provides guidance for procedures to be engaged during future
research stages or other studies about the topic.
Research Approach
Perry, Riege and Brown (1999) advocate, “researchers operate within a
scientific paradigm that is either explicit or implicit” (p. 19). A paradigm can be
regarded as the “basic belief system or world view that guides the investigator” (Guba
& Lincoln, 1994, p. 105). This is an overall conceptual framework that researchers
work in. As Perry et al. (1999) suggest in Table 3, the paradigm selected by the
researcher helps guide the researcher’s view of reality (ontology), assists the
researcher in defining the relationship between the researcher and reality
53
(epistemology), and finally helps determine the appropriate technique used to discover
that reality (methodology).
Table 3. Basic Belief Systems of Alternative Enquiry Paradigms
Positivism Paradigm
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As recommended by Perry et al. (1999), the realism paradigm is the most
appropriate research paradigm for studies using an exploratory research design with a
qualitative methodological research approach.
The realism paradigm is more appropriate here as one of the objectives of this
research is to look for and obtain a better understanding of the common reality in
which many people operate independently. This is a subjective rather than objective
view of the world (Perry et al., 1999). That is, risk is considered by many as
subjective in nature (Cunningham, 1967; Cox, 1967; Dowling & Staelin, 1994 cited in
Ho et al., 1994). Realists believe there is a “real” world to discover even if this world
is considered imperfect (Guba & Lincoln, 1994; Tsoukas, 1989 cited in Perry et al.,
1999). In contrast to constructivists and critical theorists who believe perception is not
reality, perception for a realist is merely “a window onto reality from which a picture
of reality can be triangulated with other perceptions” (Perry et al., 1999, p. 21).
Specifically, realists acknowledge the difference between the world and particular
perceptions of it, and consider there is only one reality although several perceptions of
reality exist, while constructivists and critical theorists believe there are simply many
realities (Perry et al., 1999).
Based on the description by Perry et al. (1999), the realism paradigm is
considered the most suitable for this study especially given the complex nature of
social science and that this research is attempting to explore this complexity in an
inductive fashion.
The research approach is the next issue for discussion. Qualitative research is
described as “the non-numerical examination and interpretation of observations, for
the purpose of discovering underlying meanings and patterns of relationships”
(Babbie, 1992 cited in Casebeer & Verhoef, 1997, p. 2).
55
What can be discovered by qualitative research is not sweeping generalisations
but related findings. This process of discovery is basic to the theoretical underpinning
of the qualitative approach (Creswell, 1994). It is the discovery characteristics of
qualitative research that makes it suitable for an exploratory study into relatively
unknown phenomena. As the primary objective of this study is to explore the
phenomenon of shopping cart abandonment and map out further research projects, the
qualitative approach is deemed the most appropriate choice for this study. The aim is
not to make generalised statements about online user behaviour. Instead the aim is to
establish a closer connection with users and create a deeper understanding of the
participants’ perceptions relating to their specific behaviour at the point of the online
checkout. Finally, the qualitative approach was selected to explore and uncover the
most detailed information possible to better understand the phenomenon of shopping
cart abandonment.
Research Design
Research design is defined by Easterby-Smith et al. (1991, p. 21 cited in
Pandit, 1996) as, “... the overall configuration of a piece of research: what kind of
evidence is gathered from where, and how such evidence is interpreted in order to
provide good answers to the basic research question[s]”.
A combined data collection approach has been applied given the exploratory
nature of this study and the social complexity of the subjective domain. It is hoped
greater reliability is obtained by combining the procedures used by Malhotra et al.
(2002), that is, a ‘direct and indirect approach’ (p. 192). In a direct approach to
qualitative methodologies, the nature of the product is disclosed to participants
making the desired outcome of the study obvious. The indirect approach however
disguises the true purpose of the study (Malhotra et al., 2002). One of the more
56
common data collection techniques used in an indirect research design is projective
techniques. In contrast, when a direct approach is needed a popular technique in
qualitative research is in-depth interviews. It is hoped a greater reliability is achieved
by combining these two procedures in the research design, especially in a phased
approach as this study has. Figure 2 is a visual representation of the qualitative
research procedures used in this study.
Figure 2. A classification of qualitative research procedures
(Source: Malhotra et al., (2002, p. 193) adapted for the purpose of this study)
The following information introduces each of the data collection methods
employed in this study comprising a direct and indirect approach. As an outline, an
introduction to projective techniques is provided. This is followed by a justification
for the story completion technique method, which is well supported by many other
Data collection procedures
Phase 2
Step 3 Semi-structured
In-depth interview
Projective Technique
Step 1 Short Story (Completion technique)
Step 2 Tick-box
Questionnaire
Phase 1
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researchers (McDaniel & Gates, 2002; Marshall & Rossman, 1995; Malhotra et.al,
2002).
The next step in data collection is a tick-box questionnaire that participants
completed after reading the short story (also known as a vignette). This structured yet
projective form of questioning is a technique that asks the respondents to answer
structured questions from the perspective of another person or group (Anderson,
1978; Clader & Burnkrant, 1977; Robertson & Joselyn, 1974; cited in Fisher, 1993).
This requires the story and questionnaire is to be presented in the third-person.
Having read the vignette about a shopper’s decision to purchase a video
camera via the Web, this study’s the respondents are asked to project themselves into
the role of the shopper and decide what the shopper does next. Using the
questionnaire, each participant is asked tick one box in each of the four case
scenarios. To some degree, this limits the final response to only the specifics of each
case scenario. In each case scenario the participant is asked to decide what the story’s
character should do based on the nature of the extrinsic cues presented. This is
designed to help explore what impact these cues have on the outcome of each
purchase and whether these cues influence the consumer’s decision-making at the
checkout.
For example, in case scenario one the price of each product offered is set at the
recommended retail price (RRP). In all three purchase options the consumer is buying
from a well-designed, reputable site. In this scenario and remaining three, the fourth
option is “none of the above” representing abandonment. In case scenario two, the
same three brands are offered from a site that is still well designed, however this time
the price has been dropped slightly under the RRP. Also vital is the lack of reputation
which has been removed from the scenario. The final two case scenarios continue to
alter the mix of extrinsic cues of brand, price, reputation and design. The participant is
58
asked to complete the vignette by picking one of the purchase options provided in
each of the four case scenarios.
The final stage in data collection is to interview and record each participant’s
responses to a series of semi-structured questions. An in-depth approach to
interviewing is used. The three elements that make up the research design are visually
presented in Table 4.
Table 4. Data Collection Methods Employed
Step Method
One Projective story (vignette)
Two Checklist based questionnaire
Three Semi-structured in-depth interview
(Source: Developed for the purpose of this study)
Research Methodology
When directly questioned, a typical behavioural pattern of humans is to often
portray one’s self in the best possible light. This can cause a distortion to the results of
such questioning and lead to bias. That is, the resulting data is biased towards
respondents’ perceptions of what is correct or socially acceptable rather than true to
their feelings and attitudes towards a given situation (Maccoby & Maccoby, 1954
cited in Fisher, 1993). An increasingly important technique used by researchers to
limit social bias is to use more indirect methods of questioning such as projective
questioning (Fisher, 1993).
Born from clinical psychology as a means of assessment and psychoanalytic
treatment, projective techniques have gained increasing acceptance in other
disciplines such as consumer and market research since the end of World War II
59
(Bellak, 1992; Graham & Lilly, 1984; Kassarjian, 1974; cited in Donoghue, 2000).
Definitions of projective techniques can be found in marketing literature as far back as
the 1950’s. For example Haire’s (1950) study (cited in Will et al., 1996, p. 38)
suggests that “when we approach a consumer directly with questions about his
reaction to a product we often get false and misleading answers to our questions”.
Through the use of projective techniques however, we can encourage respondents to
reveal their true attitudes and feelings about a product (Dichter, 1960 cited in Will et
al., 1996).
Since the Haire (1950) ‘shopping list’ study cited above, marketing
researchers have given increasing attention to using projective techniques (Miller,
1991; Piirto, 1990 cited in Fram & Cibotti, 1991; McDaniel & Gates, 2002; Malhotra
et al., 2002; Marshall & Rossman, 1995). In essence, projective techniques enable
respondents to display their true, sometimes subjective views and feelings regarding a
subject. This is especially the case when presented with case scenarios enabling them
to project their thoughts onto another subject of discussion.
These views suggest projective techniques can assist in overcoming self-
censorship and help encourage true expression. These techniques can help inspire
personal emotion, essential when the aim is to explore such subjective topics as
perceived performance and risk perception (Malhotra et al., 2002). In many cases a
decision can be made without a full appreciation of the exact reason, citing ‘I’m not
sure why, it just seemed too great a risk to take at the time’. No admission is made as
to why it was considered risky or specifically what influenced the decision in terms of
risk. It is this lack of understanding that makes the use of projective techniques so
unique when exploring what influences online consumer behaviour at the point of the
checkout. By using projective techniques, participants in this study are encouraged to
60
express factors that influenced their decision to abandon the shopping process rather
than present general statements about risk.
Theoretical foundations of projective techniques.
The basic premise for projective techniques involves the research subject
“projecting” his or her personality, attitudes, opinions, perceptions and self-concepts
onto another subject (Burns & Lennon, 1993; Webb, 1992; cited in Donoghue, 2000).
The concept of projection commonly found in psychoanalytic literature, is viewed as
“a defense mechanism with which the ego protects itself from anxiety by externalising
unpleasant feelings” (Gordon & Langmaid, 1988, p. 95, cited in Donoghue, 2000).
Neal, Quester and Hawkins (2002) use the term ‘motivation research’ when
describing projective techniques. They suggest projective techniques help describe
latent motives, that is, motives which are either unknown to the consumer or the
consumer may be reluctant to admit at the time of questioning. Projective techniques
are an essential research technique that helps explore such motives. This is especially
true when the aim is to better understand factors influencing consumers’ purchase
decisions.
Design and structure of the research method.
The design and structure of projective techniques can vary across a broad
spectrum. The stimuli used can range from structured clearly defined stimuli to very
ambiguous completely unstructured at the other extreme. This study employs a middle
ground approach to its design, similar to that used by Donoghue (2000). An additional
design adaptation has been applied to this approach, which is taken from a study by
Fisher (1993). Specifically, a semi-ambiguous structure is used. To more effectively
extract the respondents’ decisions and thoughts, Fisher’s study uses an indirect but
structured approach to questioning. By combining these approaches, the respondents
are made to feel more at ease and are more likely to provide a clear view of their
61
thoughts and feelings. This still allows the researcher to extract pertinent data for
analysis, however it is in a controlled manner.
Type of projective technique used.
The use of projective techniques is well documented within the marketing
discipline (Donoghue, 2000; Fisher, 1993; McDaniel & Gates, 2002; Malhotra, et.al,
2002; Marshall & Rossman 1995; Neal, Quester & Hawkins, 2002). For this study the
projective technique chosen is the completion technique. It is ideal as it is commonly
used in marketing research and is therefore well tested (Donoghue, 2000; Fisher,
1993).
In completion techniques the respondent is asked to complete an unfinished
sentence or story (Malhotra et al., 2002). In this study a vignette is used rather than a
sentence. As a short story, the vignette is tailored to provide the reader with sufficient
enough information to help make a purchase decision. The vignette however does not
provide any conclusion, thus the reader is left to make the final purchase decision. Of
all the projective techniques available to researchers, completion tests are considered
the most useful and reliable in qualitative research (McDaniel and Gates, 2002).
Malhotra et al. (2002) suggests, “projective techniques should be used for exploratory
research to gain initial insights and understanding” (p. 212). As this study’s objective
is to better understanding what influence perceived performance risk has on shopping
cart abandonment, this method was considered an ideal choice.
Data Collection Methods
Using vignettes.
Hughes (1998) describes vignettes as “stories about individuals and situations
which make reference to important points in the study of perceptions, beliefs and
attitudes” (p. 381). Finch (1987) describes them as “short stories about hypothetical
62
characters in specified circumstances, to whose situation the interviewee is invited to
respond” (p. 105).
The central feature of this method is that it helps explore participants’
subjective belief systems (Renold, 2002). This exploratory and subjective feature of
vignettes makes this method suitable for use with projective techniques.
For this study participants are presented with a purchase dilemma requiring a
decision they are asked to respond to the purchase situation presented by stating what
they would do, or how they imagine the story’s character would behave to certain
situations or occurrences (Renold, 2002). This data collection technique therefore has
strong links to indirect or projective techniques because of the ‘third-person’ found
within this method.
At the beginning of the data collection process the vignette was employed to
facilitate a discussion around each participant’s opinion (Hazel, 1995 cited in Barter
& Renold, 2000). This technique incorporates the ‘third-person’ approach and is an
ideal method of developing rapport (Hazel, 1995 cited in Barter & Renold, 2000).
More often it is found the third person technique within vignettes elicits deep-seated
opinions held by the reader that would normally be perceived as reflecting negatively
on the individual involved in the study (Barter & Renold, 2000). That is, people often
see virtues in themselves while seeing vices in others (Renold, 2002). Allowing
participants to speak freely about their perceptions towards a purchase situation
permits the researcher to explore the participants’ own definitions and evaluations
(Renold, 2002). This makes vignettes suitable for exploring the perceptions of risk of
online shoppers.
The use of vignettes in a staged approach to data collection is quite common.
MacAuley (1996) used the same approach to exploring participants’ perceptions and
experiences, using vignettes to achieve an ‘insider’ position on perceptions and value
63
systems, as was described in Renold (2002). Barter and Renold (2000) also used
vignettes in conjunction with semi-structured interviews. By asking all participants to
respond to a range of vignettes independently from the interview process, a
comparison of individual responses to different behaviours was generated. Finch
(1987) was another researcher who found by combining vignettes with a
questionnaire, she was able to provide a less static and more interactive realistic
environment for respondents. This increased the reliability of the overall research
design.
In this study a single vignette has been designed with gender alterations made
to the story according to each participant (see Appendix D & F). The vignette is
designed as a complementary data collection technique alongside the other collection
methods in use (Hazel 1995; Hughes, 1998). This single story provides readers with
all relevant information considered necessary to evaluate the online buying
environment in relation to a high involvement product purchase. Contained within the
vignette are the four extrinsic cues discussed in the model development of this study.
The vignette does not contain any leading or overly suggestive information that forces
the reader to choose one purchase option over another. Instead, the interpretation of
potential risks involved in the purchase decision is purely subjective and based on the
reader’s own perceptions. Vignettes serve to activate respondents’ imagination and
interest, helping elicit their written statements on a checklist that follows the vignette
(Poulou & Norwich, 2001). The first phase of data collection concludes with the
participants projecting their views in a structured questionnaire in the form of a
checklist questionnaire. The questionnaire presents a series of case scenarios based on
variations to each of the extrinsic cues discussed in the conceptual model.
64
Checklist based questionnaire.
One of the most widely used research tools in data collection is the
questionnaire (Czinkota & Kotabe, 2001). “A questionnaire… is a formalised set of
questions for obtaining information from respondents” (Malhotra et al., 2002, p. 272).
While questionnaires are typically the primary data collection method used in
quantitative research, qualitative researchers use questionnaires to learn more about
the distribution of characteristics, attitudes, or beliefs of the subject (Marshall &
Rossman, 1995). In terms of projective methods, this study follows Fisher’s (1993)
use of questioning in a bid to reduce social bias.
As previously revealed, the second step in data collection is a checklist-based
questionnaire using a series of tick boxes across four separate case scenarios. This is
designed to place the reader into an ‘as near to real-life online purchase decision
experience’ as possible. The primary focus of each case scenario is on evaluating
performance risk of online shopping. Using this style of questionnaire does not allow
participants to independently complete the story; instead the reader is guided down a
number of possible paths while still allowing them to make the final decision. This
means the respondent can complete the story in a projective sense, just using a more
controlled methodology. The questionnaire presented to each participant contains four
case scenarios, each scenario containing four tick-boxes options (see Appendix E &
G). From one case scenario to the next a number of variations are made, such as a loss
of reputation, poor website design, or a reduction in product prices as a possible trade
off to other cues. As a means of explanation, the loss of reputation is presented to
participants in the questionnaire using the term ‘unknown’, that is, a site is unknown
to the participant, therefore it lacks any recognisable reputation as a consequence.
Additional, the reference to ‘poor website design’ is noted in the questionnaire as
being ‘poor design’. Design can be defined as the art of using elements to convey
65
information to the viewer. This can include typography, images, layout, positioning,
navigational structure and all other visual cues presented to the website user. A poorly
designed site is one that does not adequately convey information to the user thereby
negatively affecting their perception of the sites quality.
After reading the vignette, each respondent is asked to choose one option from
each of the four case scenarios. This rigid and controlled technique of combining
vignettes with a questionnaire is taken from Gavrilidou et al. (1993), who used 15
descriptive variations of a short story to provide very brief yet concrete episodes for
respondents to focus their attention on (Poulou & Norwich, 2001). Coleman and
Gilliam (1983) also used a similar technique in their survey of teachers’ attitudes,
where the participants were asked to read a total of seven vignettes, each having slight
variations before an attitudinal style questionnaire was presented (Poulou & Norwich,
2001). In a study by Kalafat et al. (1993 cited in Hughes, 1998), four vignettes were
placed in front of a self-completion questionnaire and participants were asked to note
their responses to variations found in each of the scenarios (Hughes, 1998).
Malhotra et al., 2002 notes a questionnaire is only one element of a data
collection package and is often combined with other methods to increase reliability.
Having discussed projective style vignettes supported by a questionnaire, the third
phase in the research design is the semi-structured in-depth interviews.
Semi-structured in-depth interview.
The interview technique is considered one of the major sources of primary
data collection in qualitative research (Blaikie, 2000). Sometimes defined as a
conversation with a purpose, interviews can be used on their own or alternatively, can
be one of several methods employed to collect data (Marshall & Rossman, 1995). For
this study, the interview was chosen as the primary data collection method because of
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its strength in focusing directly on the topic under investigation (Marshall &
Rossman, 1995; Neuman, 2003; Patton, 1990).
The interview technique is selected because it provides sufficient flexibility
while remaining focused on the aims and research questions of the study. Using
interviews, the researcher creates a high level of interactivity and the responsive
nature of respondents’ comments matches the objectives of this study (Berg, 2004;
Marshall & Rossman, 1995). These characteristics are essential when exploring
unknown phenomena (Malhotra et al., 2002). The qualitative in-depth style of
interviewing employed in this study allows the researcher to get closer to the
participants’ meanings and interpretations of the projective story provided (Blaikie,
2000). Interviewing, in combination with other collection methods provides a more
reliable alternative to one single method.
Interviews were identified as the most suitable option for this study to gain an
insight into consumers’ perception of risk and Abandoned Cart Syndrome. Each
interview also helps the researcher gain an insight into experiences and ideas of the
participants to compare with other data collection methods to be used.
Although there are different interviewing techniques, such as open-ended,
semi-structured and structured; this study uses the semi-structured interview approach
employed by Carter (1999). Rather than using a fully structured set of interview
questions which may inhibit responses, or an ambiguous approach to questioning that
provides irrelevant data, the researcher uses a semi-structured interview procedure.
This approach elicits unpredictable responses from participants while maintaining a
level of control over the process (Carter, 1999). Although the interviews are not fully
structured, questions put to each respondent are still directed at the identified research
issues discussed in Chapter Two.
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To obtain an accurate representation of respondents’ views, all interviews
were recorded using an audio tape recorder. This technique provides a more accurate
portrayal of evidence provided by each participant (Patton, 1990). Using a tape
recorder allows the researcher to accurately refer to previous interviews providing a
greater level of reliability (Patton, 1990). All recordings are transcribed to paper
verbatim to give a physical and accurate account of each interview. This aids in
highlighting applicable issues, identifying key themes, and minimising bias (Patton,
1990).
Sampling Procedure
Because this study is exploratory and qualitative, the aim is to form a map of
relevant characteristics of the population rather than mirror the number of people who
share those characteristics. Therefore a small sample was chosen to collect data.
In this instance purposive sampling was the technique employed. This type of
sampling technique is common within qualitative studies and more favoured than the
typical random sampling often used in quantitative studies (Miles & Huberman,
1994). Purposive sampling helps facilitate a consistent and comprehensive map of
circumstances, attitudes, behaviours and experiences, which begin to provide answers
to the research questions posed. It was felt this type of sampling selection process
provides a much needed, deeper and richer source of data.
The sample for this study is structured around a number of criteria that form
the basis for selecting and recruiting participants. The criteria used required that all
participants to be over the age of 18 years, own a valid and current credit card and
have made a recent purchase of a product or service via the Internet. It is essential that
each participant has made a purchase via the Internet to ensure they understand the
third-person buying situation presented to them. A further screening criteria item in
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use is the type of products purchase by the participants in the past, in terms of high
versus low involvement, and their monetary value. This is important to ensure the
product characteristics of product type and price range selected for the vignette were
representative of participants previous online purchase experiences. This further
ensured participants could identify with the purchase decision required in the vignette.
Each participant is selected because they have experienced the decision making
process and potential risks associated with purchasing high priced, high involvement
products online. This experience furthermore helps project a real life view of their
motives, attitudes and feelings. Another important criterion for sample selection is
each respondent’s online purchase was recent and their recall of the purchase does not
suffer any memory loss because of time.
This study’s sample is drawn from the student body of a university in
Brisbane, Australia. For convenience, this sample is localised making it easy for
participants and the researcher to conduct the interviews over a short period of time.
While the sample respondents are sourced solely from within the student body of the
university, thereby potentially limiting any generalisation of results, it is believed the
participants resemble a typical online shopper. This is because of the selection criteria
used. The participants in this study all have the means to make a purchase online and
have made a recent purchase via the Internet. As these students were familiar with the
many concepts of Internet shopping they are regarded as suitable.
To obtain participants, notices were placed on electronic bulletin boards in a
number of undergraduate classes with emails sent to groups of students matching the
selection criteria. Based on this process eleven participants were recruited for the
study.
The final size of the sample was reached when additional interviews ceased to
add additional value to this study. This is known as ‘adequacy’, which is when
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sufficient data is collected leading to saturation (Neuman, 1997, p. 419). The data for
this study was collected over a three week period with each individual participant’s
involvement spanning a total of 25 – 30 minutes.
Data Analysis
In qualitative data analysis, the role of the researcher is to take raw data, place
it into categories and manipulate the data to identify key patterns and themes to
provide a better understanding of the phenomenon being studied (Neuman, 1997;
2003). When using a small sample size, as was the case here, the aim is not to arrive
at generalisations across a wider population (Patton, 1990). The key desire is to show
that a theory or interpretation is conceivable, and in doing so, the researcher opens the
door for future studies across the greater community.
In qualitative studies, typically the focus on data is in the form of words rather
than numbers (Miles & Huberman, 1994). In this study, words are derived from in-
depth interviews conducted at the final phase of data collection. These words, in
conjunction with participants’ purchase selections were analysed. The transcribed
words and corresponding tick-boxes required processing or coding, which in
qualitative research is a form of data analysis (Miles & Huberman, 1994; Neuman,
1997; 2003).
Special consideration is required because the analysis took place after data
collection instead of during data collection as is often the case in qualitative studies
(Sarantakos, 1998). Apart from the transcription process, this study uses four other
steps to analyse the interview data as recommended by Sarantakos (1998). The final
data analysis in this thesis strictly adheres to this five-step model of qualitative data
analysis for interviews (Sarantakos, 1998, p. 321).
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Step 1. The researcher compiled each interview transcript ensuring a full and
accurate account of each interview was obtained. This enabled the researcher to
immerse himself in the discussions held, without distraction. A key function of the
transcription phase is to also ensure the text produced is cleaned and edited,
eliminating typographical errors and removing any possible contradictions (Ibid).
Step 2. After rereading all eleven interview transcripts several times, the
researcher began breaking down and scrutinising each transcript. The primary aim
was to identify possible themes and patterns to match the propositions discussed in
Chapter Two. Each transcript was colour-coded using three colours to represent each
interview question. A final colour was used for standout themes such as important
words, sentences and phrases best describing participants’ feelings, motives and
attitudes towards each purchase decision. This procedure aided in data reduction. By
highlighting only the crucial data associated with each tick-box selected, a great deal
of day-to-day conversational text was ignored. This helped minimise distraction and
meant the researcher purely focused on the data relevant to the study. Referred to as
coding, this was an important step in the data analysis process (Neuman, 1997, 2003;
Miles & Huberman, 1994; Sarantakos, 1998; Patton, 1990; Denzin & Lincoln, 1994).
Step 3. This step in the model involves the development of categories and the
identification of key trends, themes and sub-themes found in the data. For this study
the researcher began by only writing down key comments made by each participant
when choosing an option from all four case scenarios. Using a table, all eleven
participants were placed in separate columns alongside each case scenario, and the
tick boxes were presented in four rows from tick box one through to four. The
combined four case scenarios produced 44 segments with corresponding participant
comments.
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Displaying the data from the four case scenarios and corresponding tick-boxes
was important when looking for patterns in the transcribed data. When analysing the
data the researcher is looking for themes and patterns that emerge from each case
scenario. This is best described as a collection of thoughts relevant to each case
scenario using the four tick-boxes as the primary guide instead of each respondent.
The final groups of words and phrases were placed under specific headings matching
each critical construct discussed in the literature review. Finally, key terms, words and
phrases used to describe the factors and risks influencing participants’ decisions were
combined with themes and patterns identified in each case scenario. Miles and
Huberman (1994) refer to this process as data display.
Step 4. With the data reduced and displayed in an orderly fashion, the next
step is to analyse the grouped themes across the four case scenarios. Sarantakos
(1998) describes this step as a process whereby the researcher begins to find
similarities and common patterns amongst the data.
The most important aim of analysis is to address the study’s central problem
and answer any research questions. It is also hoped the interpretation of data provides
a deeper insight into the influence performance risk has on ACS.
Step 5. Verification is the final step used in analysing the interview data. By
going back over the original transcripts once more, the strength of interpretations are
rechecked and verified as true (Ibid).
Methodological Limitations
With any research project, there are going to be inherent limitations found in
any methodology used. The following discussion of the methodological limitations of
this study focuses primarily on two factors: 1) the interactive nature and plausibility of
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the data collection methods; and 2) the lack of generalisability, often a criticism of
exploratory and qualitative research methods in general.
Vignettes.
A vignette needs to contain sufficient enough information, presented in a
realistic manner, to help the reader understand all the facts presented. Given the third-
person nature of vignettes, it must present both a believable scenario and contain
lifelike characters to attract the interest of the respondent and stimulate their
imagination sufficiently (Poulou et al., 2001).
A limitation of vignettes is the risk of them being written in a biased fashion.
This usually occurs when the researcher avoids certain issues, stays away from raising
problem situations, or when respondents give socially acceptable responses (Miles,
1990 cited in Poulou et al., 2001). Another limitation of vignettes lies in the area of
the interpretation and generalisability. This technique is not able to account for all the
possible environmental or personal factors that may influence a consumer’s decision
in real life circumstances (Poulou et al., 2001). By combining other techniques with
the vignette, these limitations were avoided.
Projective techniques.
Like the other methods employed in this study, the risk of researcher bias is
the primary limitation with projective techniques. This technique is an open-ended
approach to data collection making the interpretation and analysis subjective. While
this technique is commonly used as a third-person method to elicit deeper views of
participants, the results may not reflect the views of the general population (Malhotra
et al., 2002, p. 211).
Interviewing.
There are several limitations associated with interviews. One such limitation is
data generalisability. That is, the limited number of participants a researcher can
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effectively interview for a study makes the results difficult to extend across a broader
community. Unlike surveys, where each respondent answers the same question, in the
same way using the same language, interviews are more often at the opposite end of
the spectrum (Sudman & Blair, 1998, p. 201). This causes the interview results to be
subjective in nature. The researcher’s role is to interpret themes and patterns emerging
from the interview transcripts and because of the subjective interpretation required,
the outcome can sometimes lead to researcher bias. This occurs when the researcher
influences each participant’s responses during the interview forcing desired patterns
to emerge (Malhotra et al., 2002). These limitations were avoided by using the tick-
box questionnaire before interviewing and using a semi-structured approach.
To overcome this study’s limitations a combined direct and indirect method
was employed. This form of triangulation aids in minimising limitations.
Finally, this research is an exploratory, qualitative study with limited
generalisability. This is mainly because of the small number of participants used to
collect data. Also noteworthy was that eight of the eleven participants were female
suggesting a gender limitation may exist. The screening process and criteria used to
identify and appoint participants eliminates gender bias. This study is not looking at
specific gender issues in performance risk evaluation. The sample size used to acquire
data is also consistent with other qualitative studies.
All research methods have some form of limitation and criticism; this study is
no different. What is essential to the reliability of this study is that limitations have
been sufficiently addressed. The ethical considerations of the study are now
addressed.
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Ethical Considerations
As Merriam (2002, p. 29) suggests, “a good qualitative study is one that has
been conducted in an ethical manner”. All efforts have been made to eliminate any
ethical concerns associated with this study. This is achieved by adhering to the strict
ethical guidelines set out by the Queensland University of Technology Human Ethics
Committee, which provided the ethical clearance for this study to commence. Prior to
data collection, each participant was provided with an information kit containing a
detailed outline of the nature, aims and objectives of this study. This kit also covered
issues of involvement risk, confidentiality and terms of consent (see Appendix C for
details).
Furthermore, a written guarantee from the researcher was provided to each
participant stating that his or her identity would remain strictly confidential.
Assurances in writing were made to each respondent that all tape recordings of
interviews would be destroyed at the completion of the data analysis stage of this
research project, further providing a guarantee of anonymity and a level of ethical
assurance. All participants signed an authority giving consent at the beginning of the
data collection process (see Appendix B). This consent provides permission for the
researcher to use any data gathered for the purpose of this research and allows for that
data to be used for any further publications. Merriam (2002) notes “although
qualitative researchers can turn to guidelines… for dealing with some ethical
concerns… the burden of producing a study that has been conducted and disseminated
in an ethical manner lies with the individual investigator” (p. 30). The ethical conduct
used while performing this research is principal to the moral framework this
researcher lives his day-to-day life by, making the ethical burden Merriam refers to all
the greater.
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Conclusion
The research approach used in this study was acknowledged with a
comprehensive outline of the methods employed to collect data. In particular, the
research design which employed a combined indirect and direct approach to acquiring
data was discussed. Having examined the specifics of the design used and considered
the process of data analysis, the final stage of this chapter was to discuss the sample
procedure used, highlight the methodological limitations and note the ethical
considerations of this study.
The next chapter works through the results gained from this research approach to
move towards an understanding of the phenomenon of Abandoned Cart Syndrome.
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Chapter Four - Results Chapter Four presents the results from this study using the following
framework. First, a summary of participants’ purchase choices to each case scenario is
presented. These results are displayed using the qualitative tradition of ‘visual
mapping’, a technique often employed in the qualitative tradition of presenting
research data (Miles & Huberman, 1994). The choices made by each participant are
presented and summarised with key patterns emerging from these results displayed in
Table 5. This is followed by a summary of findings from the interview process with
emerging themes from the in-depth interviews compared against data obtained during
stage one of the collection process.
The interview data is presented in three parts. First, the summative responses
to why each participant made their purchase choice across all four case scenarios are
presented. Next, the influential factors that affected participants’ purchase decisions
are revealed. Finally, the risks each participant perceived when making the purchase
decision are discussed. To conclude Chapter Four, an overall summary of results is
provided.
This triangulation of data analysis was designed to increase the reliability of
the findings as discussed in Chapter Three.
Research Results
As discussed in Chapter Three, participants were first given a vignette written
in a projective, third-person format with a structured ending. As a means of
concluding the vignette, each participant was presented with a tick-box questionnaire
and asked to select one purchase option from each of four case scenarios. This
projective approach to data collection is known as a story completion technique and is
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well documented as a qualitative research method in exploratory studies (McDaniel &
Gates, 2002; Marshall & Rossman, 1995; Malhotra et al., 2002).
Each participant was then interviewed. During the semi-structured interview
process each participant was asked to respond to the following: 1) explain your reason
for choosing each option in each of the four case scenarios, 2) what factors influenced
your decision in each of the case scenarios, and 3) what risks did the character in the
vignette face when making their decision? An outline of the interview process can be
seen in Appendix H.
Table 5. Summary Results to the Tick-box Questionnaire
(Source: Developed for the purpose of this study)
Case Scenario 1 Brand Price Design Reputation P
1 P2
P3
P4
P5
P6
P7
P8
P9
P 10
P 11
Sony $899 Good Known √ √ √ √ √ √ √
LG $799 Good Known √ √ √ √
Palsonic $699 Good Known Purchase Choices
Abandon N/A N/A N/A
Case Scenario 2 Brand Price Design Reputation P
1 P2
P3
P4
P5
P6
P7
P8
P9
P 10
P 11
Sony $799 Good Unknown √ √ √
LG $750 Good Unknown √ √ √
Palsonic $650 Good Unknown Purchase Choices
Abandon N/A N/A N/A √ √ √ √ √
Case Scenario 3 Brand Price Design Reputation P
1 P2
P3
P4
P5
P6
P7
P8
P9
P 10
P 11
Sony $799 Poor Known √ √ √ √ √ √ √
LG $750 Poor Known √
Palsonic $650 Poor Known Purchase Choices
Abandon N/A N/A N/A √ √ √
Case Scenario 4 Brand Price Design Reputation P
1 P2
P3
P4
P5
P6
P7
P8
P9
P 10
P 11
Sony $699 Poor Unknown
LG $599 Poor Unknown
Palsonic $550 Poor Unknown Purchase Choices
Abandon N/A N/A N/A √ √ √ √ √ √ √ √ √ √ √
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Emerging Patterns
The data displayed in Table 5 moves towards category development.
Categories, created when a researcher groups or clusters the data, become the basis for
the organisation and conceptualisation of data (Dey, 1993 cited in Dye, Schatz,
Rosenberg & Coleman, 2000).
The first stages of pattern identification begin to emerge, which is vital in
discovering recurring decision-making patterns amongst the participants of this study.
This is recognised as a process of identifying, coding, and categorising the primary
patterns in data (Patton, 1990). "The qualitative analyst's effort at uncovering patterns,
themes, and categories is a creative process that requires making carefully considered
judgments about what is really significant and meaningful in the data” (Patton, 1990,
p. 406, cited in Dye, Schatz, Rosenberg, & Coleman, 2000). Patton (1990) further
notes these patterns, themes, and categories of analysis "emerge out of the data rather
than being imposed on them prior to data collection and analysis" (p. 390).
The most consistent pattern of participants’ purchase choices is identified in
case scenario four. Under the conditions described in this scenario, all participants
opted to abandon the online shopping cart. When participants are presented with an
unknown, poorly designed website the result is 100% abandonment of the shopping
cart. This is despite two of the three brands being well-known to participants and price
being set substantially below the pre-determined budget, which had no impact on
prompting a purchase.
The next pattern is observed in case scenario one, with the opposite pattern
occurring to case scenario four. When the extrinsic cues of design and reputation are
presented positively all participants purchased a camera. This was despite a higher
pricing strategy employed in this scenario which had little effect on many participants.
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More notable was the favouritism towards Sony, with the same brands being used in
all case scenarios. Over 60% of participants decided to exceed budget and purchase
the Sony camera, even though it was described in the vignette as an older superseded
model. The Sony was also the only product that was above the predetermined budget.
The remainder of participants purchased the LG camera choosing to remain within
budget.
The third pattern emerged in case scenario three in which all cameras were
priced below the pre-set budget, the e-tailers’ reputation was known to the purchaser,
however the site design was poor. In this scenario, almost 70% of participants
purchased a camera with all but one opting to purchase the Sony camera. The
remainder abandoned their shopping carts.
The fourth pattern was found in case scenario two. In this scenario the design
of the site was of a high standard, however, the reputation of the e-tailer was
unknown. This case scenario provided the most inconsistent purchasing pattern
amongst participants. An even number of participants chose to purchase either the
Sony or LG showing no particular brand preference, with the remainder opting to
abandon their cart.
In comparing the results across the four case scenarios, the highest level of
abandonment took place (100%) when the e-tailer had no reputation, the website was
poorly designed, and the product prices were displayed much lower than expected. In
contrast, the greatest number of purchases occurred (100%) when the sites were
reputable, well designed and the prices were equal or higher than the recommended
retail price. The predetermined budget had no impact on these two case scenarios.
Whether under budget or over budget there was little impact on the decision to make a
purchase or abandon the cart. The other extrinsic cues of brand, reputation and design
appear to have played more important roles in influencing purchase intent.
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A more notable comparison can be found with case scenarios two and three.
Here budget issues were removed. In both scenarios the maximum purchase price for
a camera was identical in both case scenarios and set just under the predetermined
budget. The variations took place with site reputation and web design. These cues
were presented both positively and negatively in each scenario. The results show
under these conditions lowering positive perception of reputation had greater effect on
abandonment than lowering positive perception of design, suggesting reputation has
greater influence on perception than design.
In terms of consistent patterns across all case scenarios the most obvious was
the lack of interest in Palsonic. In all four case scenarios, no participant opted to
purchase the Palsonic camera, despite the price of the camera being well below the
budget. Even when placed within well designed websites with sound reputations, the
lure of substantial savings was not sufficient to cause a transaction to occur.
The factors that influenced the participants’ choices and what risks were
perceived during decision-making are outlined next.
Reasons for the Choices Made
Case scenario one.
In case scenario, one all three cameras (Sony, LG and Palsonic) were priced at
a recommended retail price, with Sony priced above the predetermined budget
allocated in the vignette. The LG was fractionally under budget, and Palsonic was
well under budget. The purchase options available included buying from a well
designed and reputable website. Therefore, reputation and design were presented
positively. Under these conditions all participants opted to purchase a video camera.
The vast majority of participants chose Sony over the other brands noting the primary
reason as being trust in the brand, thus the brand cue was also positively perceived.
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Price was not as important to these participants as was trust in the brand. Participants
were prepared to exceed budget to acquire trust, reducing the performance risk of the
purchase.
Interviewee: I place brand over price. I’d rather buy reputation. Design helps but brand and reputation are higher. (Participant one)
For some participants, price was a key determinant in the evaluation process. These
participants opted for the LG.
Interviewee: Price was the key. With good reputation and good design meant I didn’t worry. (Participant eight)
This comment also suggests that in some instances when reputation and design were
of a high standard the determining factor became price.
Interviewee: Price was the main thing. If it’s too cheap it’s got to be crap! Quality and performance are important but price is number one. (Participant 9) Interviewee: Reputation was good, design was good and LG was closest to my budget. That was my reasoning. (Participant 10)
When brands were presented alongside well designed and reputable websites,
purchase choice became a subjective decision based on brand trust and in some cases
price preference. While price did have some effect on consumer choice, it appears to
be based on individual budgetary preferences. Under the purchase conditions of this
study, greater predilection was towards Sony as a trusted brand of choice.
Supporting this position was participant eleven who said, “I simply pay more
for a trusted brand.” Participant one also acknowledged brand over price stating, “I
place brand over price any day,” Participant three reinforced this by saying, “I pay the
extra for a brand I trust. First I looked at brand and paid more for peace of mind.”
Participants four and five acknowledge brand preferences saying respectively, “better
the brand equals more trust” and “better brand means better quality, that’s why I
purchased the Sony”. Participant six also exceeded budget based on brand trust
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expressing her views with the following, “you pay more for trust in the brand. The
brand plus reputation and a good design means trust”. No participants purchased
Palsonic with participant nine noting, “I never buy a brand I know nothing about.”
Case scenario two.
When participants were asked to purchase in case scenario two, the reputation
of the e-tailers’ sites was presented negatively. The majority of respondents agreed
that good design is no substitute for reputation. That is, the change made to reputation
in case scenario two created a different overall result to case scenario one and
provided the first insight into a deeper understanding of what might influence ACS.
By presenting reputation negatively, each brand was placed in a purchase
environment of uncertainty. This affected the decision to purchase, despite the website
design being of high quality and the price of each camera set lower than the previous
scenario. The results showed for a number of the participants, brands had little impact
on the choice to abandon the shopping process, opposite to case scenario one where
all participants purchased a camera.
A price reduction on all cameras had little effect on purchase intent when
reputation was presented negatively, with only participant seven suggesting that price
was more important than reputation. This suggests ‘reputation’ has a greater effect on
consumer choice to abandon. In comparison to brand, reputation has a greater
influence over performance evaluation.
Interviewee: I wouldn’t buy. With no reputation you never know if you’re ever going to get what you purchased no matter what the brand. (Participant ten)
Of those participants who did opt to purchase a camera there was an equal
preference between Sony and LG, suggesting personal favouritism towards one brand
or another. Participant one stated, “Design is an important key and it’s a brand I trust,
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that’s why I purchased the Sony.” Participant six also connected design and brand
saying, “With a good brand like Sony, if I see a well-designed site I feel some thought
went into the company. This helps with trust.” Participant eight seemed to stay with a
trusted brand suggesting that design helped with gaining trust. This is noted in her
comment, “I stick with brands. So long as it’s well laid out, easy to follow, step by
step, I’ll buy even if I don’t know them”. Participant four, who also purchased the
Sony, believed that combining a trusted brand with a quality designed site created the
reputation needed to feel safe. This is backed by his comment “Better brand equals
more trust. With a good design I’ll use it more. More use increases the reputation and
therefore trust”.
Those participants who opted to abandon the shopping cart discussed
reputation as a strong influential factor in performance evaluation. Price has little
effect over performance evaluation and purchase intent with no reputation in play.
The results suggest design as an extrinsic cue had practically no impact on the
intention to make a purchase with reputation removed. The following results support
this:
Interviewee: Decreased reputation equals no trust. I wouldn’t put my credit card into a site I didn’t know. Price was an afterthought. (Participant two) Interviewee: With no reputation I wouldn’t buy. Design was not a factor. Reputation is very important. (Participant three) Interviewee: No reputation, no purchase. That’s it, it doesn’t matter how well it’s designed, I wouldn’t buy because I don’t know who it is. (Participant five) Interviewee: I wouldn’t buy. With no reputation you never know if you’re ever going to get what you purchased no matter what the brand. (Participant ten) Interviewee: I wouldn’t give my credit card details to a site I didn’t know. You wouldn’t know if you’re ever going to get the product. (Participant eleven)
Noteworthy are the comments made by participants ten and eleven. These
summary comments regarding abandonment provide strong connections between
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reputation and performance evaluation. An association was identified between no
reputation, non-delivery of the goods purchased and perceived performance risk. This
is regardless of how well the site was designed; how well the brands were known to
participants and prices in this scenario set within or under the predetermined budget.
The urgency to make a purchase as described in the vignette had no effect on
participants’ decisions. Finally, no participants elected to purchase Palsonic despite
the price being set almost $150 less than the Sony and $100 less than the LG.
Case scenario three.
In case scenario three site reputation was reintroduced positively and design
was presented negatively. This premeditated test was designed to look at the effects
web design has on performance evaluation. In this scenario, the brands were presented
to each participant in the same fashion as previous scenarios and price was identical to
case scenario two. The results showed that while a poorly designed website did have
an impact on some participants, causing abandonment; the results were lower than
case scenario two. More notable was that with the reintroduction of reputation and
reduction in website design quality, almost all participants who did purchase, chose
Sony as the preferred brand. Only one participant chose LG.
The results show reputation was weighted higher than website design in the
minds of participants. When participants were faced with the decision to purchase
from a reputable but poorly designed site, design had little impact on performance
evaluation and the decision to purchase or not.
Interviewee: I place reputation over design any day. I don’t need the pretty pictures. It’s still a reputable dealer and it’s under budget, that’s all I need. (Participant one)
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Reputation was a strong overriding influence in evaluating performance, much
greater than design. Perhaps the strongest views were those of participants six and
eleven who said respectively:
Interviewee: I know a lot of reputable companies online with horrible designs and they’re okay. Reputation overrides design. (Participant six) Interviewee: I don’t really care what the web site looks like. If it’s reputable that I’m buying from I don’t care if it’s a great design. (Participant eleven)
The subsequent comment by participant nine highlights strong support towards
trusted brands and its influence over performance evaluation, more so than design.
Interviewee: Sony has a history, a better one. I go with the brand and a site with a reputation. A few broken links, so what, look at eBay. (Participant nine)
When brand is combined with reputation a powerful combination of extrinsic
cues occurs. Participants two, four and ten abandoned their shopping carts with the
consensus being poor design lowers consumer trust. This heightens perceived risk and
creates a sense of poor performance in terms of delivery expectations. These results
are supported by the following participant comments.
Interviewee: Design is linked to reputation but it’s still not enough to make me buy. It’s too risky. (Participant two) Interviewee: Why would I spend money on a site that looks bad? It can’t be trusted even if they have a reputation with others. (Participant four) Interviewee: I wouldn’t buy anything from a site that I either didn’t know or was poorly designed. If they haven’t designed it well you don’t know if you’re ever going to get what you purchased. (Participant ten)
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Case scenario four.
Case scenario four’s results are to some extent, self-explanatory with all
participants opting to abandon their shopping carts. It would seem when the e-tailer’s
reputation is removed or acknowledged as unknown and website design quality is
negatively presented, the outcome is cart abandonment. What is noteworthy in this
scenario is that these results occurred despite the dramatic reduction in price of all
products offered. Price seemed to have had little to no impact on participants’
decision to purchase, in some cases the price reduction aided in cart abandonment.
Most noteworthy in this study is the repeated acknowledgement of the risks
involved in purchases that lack reputation. In almost every case there were references
made to the issue of performance. More specifically, under these conditions there was
the likelihood that the system, the store, the delivery service or the product itself
would not perform.
Participants made ongoing reference to the importance of reputation. For
example, participant five noted, “Poor design, good design, it didn’t matter. I just
didn’t know them. I pay extra for the peace of mind. It’s all about reputation.” This
suggests that reputation continues to be a stronger extrinsic cue. Price also has a
substantial effect on abandonment when lowered considerably.
Interviewee: I wouldn’t buy on price. I wouldn’t buy anything from a site I didn’t know. (Participant eleven)
A consistent concern of many participants was products not being delivered
after the purchase process. This suggests a strong relationship between the delivery
process as a component of performance evaluation and perceived performance risks of
online purchases. Brand choice has little impact on the decision when options are
limited to e-tailers with an unknown reputation and poor site design. Even when lower
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prices are added to the mix, the result is an increased perception of poor performance
and abandonment occurred in every case.
Factors That Influenced Their Decisions
When asked what factors might influence participants’ decision to purchase or
not, a number of specific concerns were identified. Voiced as influential factors, these
concerns are highlighted in the summary table (see Appendix A) and identified as
prominent in the decision making process of each participant. Furthermore, these
factors are directly linked to perceived performance risk and its effects on ACS.
While expressed in different ways, the most common factor influencing
participants’ decision to purchase was identified as reputation, both in terms of the e-
tailer and the brand. All participants voiced strong views about this cue.
Interviewee: Definitely reputation. That’s the big one. If I’m going to buy I’m going with a company that I know. Price is also important but I’m always wary of being ripped off. (Participant seven) Interviewee: Reputation is what influences me. The fact that it’s well known and established. The reputation of the company I’m buying from is more of an influence than the product I’m buying. (Participant eleven)
Participant five expressed the most salient comment in favour of reputation as a potent
extrinsic cue:
Interviewee: If it were just any old ‘Joe Bloggs’ I wouldn’t deal with them. It’s all about reputation, reputation equals accountability. Design is not nearly as important. I’ll pay the price to get what I want. (Participant five)
Participant nine’s suggestion that reputation and design were influential
factors in decision-making was representative of common views however reputation
was a more dominant cue.
Interviewee: Site layout is a big one but I prefer to go with a reputable site. Budget is the third big factor I look at but it’s the last point. (Participant eight)
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The effects of reputation were expressed in numerous ways, such as, the
failure to deliver goods, the e-tailer’s return policies, and its association with financial
risk. Such comments have a connection to reputation and are therefore related to
performance evaluation.
Interviewee: One of the biggest factors is the return policy. When buying, I want to feel it, touch it, and play with it. If something breaks then what? Will the site perform? (Participant six)
For many participants, the thought of entering their credit card details into
certain sites, especially those sites with unknown reputations or that are poorly
designed, was sufficient for abandonment to occur. For many a connection exists with
financial risks, non-delivery anxiety and reputation.
Interviewee: I’m not going to hand over my credit card to anyone on the Net. Reputation, that’s the biggest thing. Goods not showing up, that’s another factor. Risk and reputation are two very big issues. (Participant three)
Participant four also acknowledged the relationship between financial and
performance risk, noting concern with increased complications should something go
wrong with delivery.
Interviewee: The biggest factor is putting in an order on my credit card and then getting nothing back or there are complications that increase the cost of buying. (Participant four)
Participant nine captured the views of many participants by stating the following:
Interviewee: The two big factors are reputation and design, in that order. Unknown and poor design makes it a no brainier. Unknown but well designed is an even greater risk. Putting my credit card in over the Web, that’s a factor. Finally, there are delivery concerns, times and schedules, and then the cost of it all. (Participant nine)
In this study, price and budget were only mentioned by participants two and eight as
important influential factors affecting the purchase decision. Participant two
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suggested that price has a direct correlation to better or more trusted (reputable)
products.
Interviewee: Prices I suppose are important when it comes to brand. More expensive, it’s got to be better. (Participant two)
Participant eight merely suggested that budget is considered as a factor, however it is
placed lower down the line of importance.
Interviewee: Budget is the third big factor I look at but it’s the last point. (Participant eight)
In summary, all participants were influenced by each extrinsic cue in varying
degrees, with a number of participants noting the effects from several cues. These
cues seemed to affect the outcome of the purchase decision with participants ranking
the order of importance in terms of their influential power. The majority of those
involved in the study noted reputation as being very important.
The Risks Identified
During the interview, the third question posed to participants related to precise
issues of risk. This was articulated in the following way, “what risks did John or Jane
face when making the purchase decisions?” This was posed in the third-person
projective format, consistent with the methods used to collect data for the study.
Noteworthy are the consistencies and similarities between the results of this interview
question and the previously presented results. This provides further internal validity to
the results obtained.
While reputation was highlighted as a dominant cue in decision-making, the
third interview question relating to risk itself, enabled participants to discuss their
feelings with more depth.
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The first risk factor discussed by participants was the financial risk of
shopping online using a credit card. For almost all participants in this study, financial
risks were front of mind.
Interviewee: There is always the risk of putting your credit card in. (Participant two) Interviewee: Another risk is someone steals your money and the goods don’t show up and your credit card is stolen. (Participant three)
These remarks suggest a connection exists between financial and performance risk.
Participant four also noted the connection between these two risk factors with the
observation,
Interviewee: The big risk is putting your credit card in and then nothing shows up. It’s all about the return policy. (Participant four)
Participant six was one of the few not concerned with overall financial risk, however
she does suggest that reputation and quality design are vital to removing the financial
risks associated with an online purchase.
Interviewee: I’m not concerned about the credit card thing; if they’re reputable and well designed they’re secure. (Participant six)
Participants eight and nine continue this theme by noting the surrogate power of
performance risk.
Interviewee: You could lose your money; the goods don’t show up, and there’s no way of contacting them, that’s a big risk. (Participant eight) Interviewee: Putting your credit card number on a site that you may not know and the site doesn’t deliver. That’s a risk. (Participant nine)
Of all participant comments and views, participant eleven best captures the feelings of
all participants in relation to financial risk and its association with performance.
Interviewee: The main risk for me would be putting my credit card number online and it goes astray and someone else gets hold of it. This is a huge risk. Also the fact that you’re spending quite a lot of money (in this instance) and
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you might not actually receive the goods. It might not arrive in one piece or there might be something wrong with it, then a huge payment to send it back. That’s it, first credit card, then not actually getting what you want. (Participant eleven)
In summarising the results of question three, the majority of participants
interviewed were most concerned with the risk of financial loss both in terms of credit
card theft and the goods not being delivered after the purchase has been made. Both of
these two factors are identified in the literature as having a direct relationship with
performance risk.
Summary of Results
The shared views of participants suggested the power of each extrinsic cue as
an independent variable does exist, however, the perception of risk varies
substantially from one participant to the next. Next, a closer look is taken at each of
the cues in terms of their overall effect on performance evaluation and its effect on
perceived performance risk.
Brand.
In some cases brand was sufficient enough to cause many participants to
purchase beyond their predetermined budget. When presented at the opposite end of
the spectrum, such as Palsonic, the same cue was just as powerful a factor in
performance evaluation. It should be noted that brand only had positive effects on
performance evaluation when the other extrinsic cues of design and reputation were
also presented positively to participants.
Almost all participants identified well-known brands as having a strong
influence in evaluating performance risk, especially when shopping online. When
placed alongside other powerful cues such as reputation, the influence became greater.
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Price.
For some participants price was an important influential factor in making a
purchase decision, particularly when choosing between one known brand and another.
Price appears to have affected a number of participants. When price was lowered to a
perceived unacceptable level, the performance risk of both the product and vendor
was also negatively affected. This suggests that price is an influential cue in
evaluating performance risk when measured separately from the other cues. When
placed alongside positively presented extrinsic cues it ceased to be influential for most
participants.
Design.
Under certain circumstances design did have some effect on the evaluation
process of performance risk assessment. The majority of participants however placed
design as secondary to brand and reputation. When placed beside the other cues, it
became a factor in evaluating the performance risks of a purchase only when other
cues were presented positively.
The results also showed that when the design of the website is poor, the
participants turn to reputation as the surrogate cue both with brand and website
reputation.
Reputation.
In online purchase settings every participant identified reputation as the
strongest individual extrinsic cue affecting the performance evaluation process.
Reputation was mentioned repeatedly as having an effect on the decision to abandon
the shopping cart. It was also mentioned by many participants as having a direct
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correlation to other cues such as brand, that is, the reputation of the brand was often as
important as the reputation of the e-tailer.
It was found that when the reputation of e-tailers was noticeably removed
from case scenarios, the majority of participants opted to abandon the cart.
Maintaining a sound reputation and removing design however had a lesser impact on
abandonment rates.
Performance Evaluation
The results show collectively extrinsic cues of brand, reputation, design and
price have an overall impact on the performance evaluation process just prior to a
purchase. Varying these cues either positively or negatively had a strong impact on
performance evaluation. This was equal in both the positive and negative settings
(e.g., case scenario one – all positive and case scenario four – all negative). When
reputation and design cues were altered independently, either negatively or positively,
inconsistencies began to appear in the results. Participants identified reputation as the
stronger of the two cues.
Performance Risk.
Participants viewed the greatest risk associated with shopping online as the
combination of performance, in terms of the goods not showing up and the financial
risks associated with the purchase itself. Participants identified the fear of having their
credit card stolen and products not being delivered as most concerning.
These results were evident where no reputation existed, a poorly designed
website was presented, and when the prices of items were set well under the
recommended retail price. No participants identified the same performance risks when
all the cues were positively presented. When reputation and design were separately
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affected, the loss of reputation had a greater impact on perceived performance risk
than design.
Conclusion
Throughout this chapter the results from the three-stage data collection process
have been presented. This was supported by the extensive use of participants’
comments.
A number of patterns that emerged from the results were identified and
subsequently discussed. A detailed analysis of the summative responses obtained from
interviewing participants was also presented. The triangulation of data obtained from
this study was used to compare and contrast these findings and finally, the results of
this process were presented and discussed in detail.
In the final chapter the results of this study are compared against literature
reviewed in Chapter Two of this thesis. The purpose is to identify and discuss any
major gaps emerging from within the theories discussed. Conclusions are drawn in
response to the research problem and research questions. Finally, the propositions
developed in Chapter Two are assessed and future research recommendations are
presented.
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Chapter Five – Conclusions and Implications for Future Research
This chapter revisits the literature from which the conceptual model was
developed to compare it against the results obtained from this study. It is hoped this
strengthens the conceptual model proposed in Chapter Two and contributes to multi-
dimensional risk theory, cue-utilisation theory and the study of online buyer
behaviour. It is also hoped a better understanding of what may influence online
shopping cart abandonment syndrome is obtained. The following discussion unlocks
the door for future research opportunities.
One of the key foundations identified from the literature is cue-utilisation
theory. This theory suggests that products consist of an assortment of cues that serve
as surrogate indicators of performance (Cox, 1967; Olson, 1972; Chen & Dubinsky,
2003). The four cues of brand, price, reputation and design extracted from the
literature have a recognised effect on the consumer’s performance evaluation process.
The measurable effect of performance evaluation is perceived performance risk.
At the point of a purchase decision, the more positive presentation of these
cues to consumers, the lower the perceived performance risk exists. It is therefore less
likely that abandoned cart syndrome would occur.
Performance Evaluation
The issue of performance evaluation was fundamental to this study’s
conceptual model into performance risk. From the literature on the five-step model of
consumer buying behaviour, the role of performance evaluation was identified as a
vital stage in understanding performance risk. This evaluation stage is particularly
relevant when consumers examine motivational cues or attributes that lead to
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purchase intent (Mitchell, 1998; Neal, Quester & Hawkins, 2002; Walker & Baker,
2000).
Performance evaluation is not merely a process whereby a consumer chooses
between alternative products and brands. When evaluating a purchase decision,
consumers consider different attributes associated with that purchase (Mitchell, 1998).
It appears the outcome of a purchase decision depends greatly on how the consumer
evaluates these different attributes. This suggests that during the performance
evaluation process consumers may attach different levels of importance to each of the
attributes identified and place those attributes in a specific order of priority.
Mitchell (1998) suggests consumers are uncertain how important each
attribute is. Therefore, assigning importance to each individual attribute or cue is not
something that even the most rational and informed consumers can do accurately.
The results of this study do not reflect Mitchell’s observations. Every
participant in this study was able to make an evaluative decision based on the product
attributes based on the four extrinsic cues provided. The participants rated each cue in
order of importance, placing reputation higher than design and design higher than
price in the evaluation process of a single purchase decision. The importance or
weight placed on each cue helped participants determine the purchase outcome prior
to making the final decision. The results of this study showed that the combined
evaluation of cues may contribute substantially to the decision to abandon the cart or
proceed with a purchase.
When it comes to evaluating the performance of a purchase prior to making
the actual transaction, this study shows that online shoppers have high performance
expectations and that the extrinsic cues presented to consumers play a considerable
role in the decision making process.
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An important outcome obtained from this study is the support for Walker and
Baker’s (2000) earlier study into expectation versus performance evaluation.
Expectations appear to provide a standard of comparison for consumers to judge the
performance outcome. Understanding what consumers anticipate in terms of
evaluating an outcome is therefore vital (Walker & Baker, 2000). Their research
proposes that consumer judgements result from a comparison of expectations and
perceptions of performance. Consumers traditionally rely on predicted expectations,
that is, what they predict or think will occur is what they perceive will occur (Swan &
Trawick, 1980; Oliver, 1981; Zeithaml et al., 1993 cited in Walker & Baker, 2000).
This study achieved a similar result suggesting the same outcome can be applied
whether the consumer is offline or online. What was not identified in Walker and
Baker’s (2000) study was the collective influence these extrinsic cues have on the
predictive expectations of consumers. The results relating to the four extrinsic cues of
brand, price, design and reputation are now examined.
Brand.
Brands play a considerable role as an extrinsic cue. Brands also appear to have
an impact on other extrinsic cues in addition to having an influence on overall
performance evaluation. For example, participants in this study perceived the brand
Sony, as also having a positive brand reputation. The literature suggests this same
positive perception translates across into the site from which the product is purchased.
This only happens when presented alongside a site with an equally well-known
reputation as results from this study suggest. The combination of brand and store
reputation helps build trust in the consumer’s mind. This trust minimises the
perceived performance risk associated with the purchase. The opposite occurred when
the same well-known brand (Sony) was placed in a site that had no reputation. Under
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these conditions, almost 50% of participants became suspicious of the offering and
abandoned their shopping carts.
To minimise the risk of buying, online consumers often select well-known
brands as these brands communicate trust and help reduce the level of uncertainty felt
at the time of purchase (Carton, 2001). This strategy was also utilised by the
participants of this study with every participant opting to purchase the well-known
brand. As justification for their choices the participants cited issues of trust,
heightened levels of security in the performance of the product and less risk as a
consequence. The results of this study digress from Carton’s (2001) position. While
brand has a direct relationship with trust and operates as a risk reducing cue, other
cues including reputation, design and even price are inter-connected and perform a
role in the evaluative process. It is the collective influence of these cues that
determines the decision to purchase. As a means to minimise uncertainty however,
brands are highly influential in the evaluation process.
Consumers use risk-reducing strategies in choice situations where there is
perceived risk, and consumers employ brand loyalty to reduce the consequences of a
risky decision (Van Beveren & Wilson, 2002). As a means of investigation, the
participants of this study were given the option to purchase an unknown brand of
camera, at a significantly lower price than other cameras. The participants of this
study opted to purchase the known brand of camera regardless of the cost savings of
the lesser known alternatives.
These findings support the view that consumers rely heavily on well-known
brands as a short cut in evaluating different products. This is especially true when
placed alongside products that were either new to the market or unknown to the
consumer. This supports the findings of Ward and Lee (2000), who found that well-
known brands influence over half of all online buying decisions made.
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As was discussed earlier, the outcome of this study found that online
consumers do view brand names as summary constructs (Han, 1989; Johansson, 1989)
or shorthand cues for quality performance (Zeithaml, 1988). As was argued in
Agarwal & Teas’ (2001) study, consumers do appear to make product quality
inferences based on positive brand names. In addition to brand, consumers also rely
heavily on the other attributes of a purchase setting. Also considered at the time of the
purchase is the product’s price, the retailer’s reputation and in the case of online
settings, the website’s design. This conflicts with Agarwal & Teas’ (2001) opinion
that consumers do not examine other attributes every time they make a brand choice.
Instead they simplify their decision making process by basing their judgments on
brand cues alone. With an increased level of uncertainty and a heightened level of
expectation, recognised brands are a good representation of quality and are therefore
evaluated positively. Brands are not, however, the only factor evaluated by consumers
at the time of a purchase. Evidence of this exists in the results of case scenario four
where the brands were presented positively to participants, while the other cues were
presented negatively. The outcome of case-scenario four was abandonment by all
participants. Even when reputable brands are offered, if the other cues are evaluated
negatively, shopping cart abandonment occurs.
This study further supported Reynolds’ (2000) findings that customers rate
familiar brands highly as this makes the choice easier for the individual at the time of
purchase. All participants rated well-known brands as vitally important when making
a purchase online. Participants however also relied equally on the positive evaluation
of the other extrinsic cues before the perceived risks were reduced enough for them to
make a final choice.
The results of this study are in keeping with much of the literature on the
importance and power of brand in the process of evaluating performance risk. The
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results suggest, however, that brands are not necessarily the most influential cue in
overall performance risk assessment. Regardless of the urgency placed on making a
purchase, the brand name alone does not have the power to influence a purchase.
Price.
The results document that price is shown to be an effective cue in assisting
participants with risk reduction. For example, the vast majority of participants in case
scenario one were content to pay extra for a brand that provided peace of mind. Over
65% of the participants chose to purchase the Sony camera, exceeding the
predetermined budget set by the researcher by almost $100. This suggests that price
was secondary to brand reputation and the subsequent trust provided. In case scenario
four, where the price of each camera was heavily reduced below the predetermined
budget, all participants opted to abandon their shopping carts. Some participants even
suggested that the performance of the retailer was anticipated as riskier when the
prices were so low.
When comparing the results of case scenario one and four there seems to be a
fine line between paying extra for a product as a means of reducing the level of
perceived uncertainty, and paying too little for the same product, increasing the
perceived risks of poor performance.
As Siegel (2003) argues, most consumers use price as a determining factor in
their decision to purchase a product or not. When measuring product quality the
literature identifies price as an important extrinsic cue (Dodds & Monroe, 1985;
Olson, 1977 cited in Chen & Dubinsky, 2003). While true, like the other cues
investigated in this study, price is not nearly as effective on its own as it is when
presented alongside the other extrinsic cues.
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The results of this study run counter to Siegel’s (2003) view that many
purchases depend greatly on how badly the consumer needs a product and whether
they have the means to complete the transaction. All participants in this study had the
ability and means to make a purchase and had a justifiable reason for making a
purchase, however, the participants were all equally prepared to abandon their
shopping cart. The lower price had no effect on reducing consumers’ perceptions of
performance risk, especially when the other cues were presented negatively.
Sweeney et al. (1999) argued that price has a positive association with
perceived product quality, however, this association is likely to lead to a greater level
of financial uncertainty in terms of overall risk perception. The results of this study
challenge this view. Consumers who paid a higher price for the same product did not
suffer an increased perception of financial loss as compared to those who paid a lower
price. The opposite was found to be the case. As the price levels were increased, the
perceived performance risk for many participants decreased. This suggests that a
higher price for a well-known brand reduces the perception of poor performance for
both the product purchased and the e-tailer.
The literature suggests that higher prices are likely to generate a greater degree
of perceived performance risk for on-line shoppers, however the results from this
study suggest the opposite. Many participants were prepared to spend more to reduce
the perceived performance risk of the purchase. It is important to recognise that the
results from this study may not be true in all online shopping circumstances. In some
situations, consumers are prepared to pay more to increase positive performance
evaluation, thereby lowering the associated risk perception of the purchase. For those
who have had some experience within the domain of online shopping and know what
to expect, lower prices in an e-commerce environment may not necessarily be a
positive performance risk reliever.
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Design.
According to the study by McCarthy and Aronson (2002), a well-designed
website aids in the development of a loyal customer base. For e-commerce sites this
loyalty translates into increased purchases of goods and services. The results from this
study support McCarthy and Aronson’s view that increased usage of the same site
does aid in the development of loyalty toward that site, thereby increasing the
likelihood of a purchase.
An important result of this study is the challenge to the views of Ranganathan
and Grandon (2002). According to their research, the design of a website was
repeatedly mentioned as influential on the decision to make an online purchase.
While many participants of this author’s study identified good website design
as reducing performance risk anxiety, it was not given the same level of importance as
for previous researchers. Design, while being an important extrinsic cue, was
considered secondary to the reputation of the e-tailer and the value placed on the
brand of the product. When evaluating performance, some participants even gave
price greater significance over design as an influential cue.
Balabanis and Reynolds (2001) argue that online retailers need to design web
sites that sustain the interest of consumers by using innovative and pleasing visual
elements. This study contradicts this view. While it is conceded that design is vital in
helping guide the shopper through the purchase process itself, usually in terms of
navigational design, the participants cited other performance evaluation cues such as
reputation and brand preference as holding greater importance, especially when a
purchase is the final consideration.
Crisp et al.’s (1997) study four years earlier also gives recognition to the
potential linkage between motivation and perception, and the association to extrinsic
cues such as Web design. Crisp et al. (1997, p. 12) suggests, “improving the store
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front in relation to the site’s design, and thereby affecting consumers’ beliefs about
Web shopping, should be a greater concern for retailers than simply waiting for
Internet shopping (and therefore customer attitude and intention) to mature”.
The linkage identified by Crisp et al. (1997) was not evident in the findings of
this study. Regardless of the poor design of the site, over 70% of participants were
content to purchase a camera. Many participants they merely substituted the loss of
design with other positive cues such as reputation and brand. Others just ignored
design altogether giving it little consideration when assessing the purchase options
available.
Winn and Beck (2002) place a high emphasis on design and its link to
consumer persuasion. Like Crisp et al. (1997), Winn and Beck’s study (2002) argues
that design factors readily translate into persuasion to purchase.
This view was not supported by this study. E-commerce sites and the design
elements from which they are built do not necessarily persuade potential customers to
purchase. The other cues of brand, reputation and in some instances, even price, have
much greater persuasive powers over consumers’ decision to purchase than design.
The findings of this study suggest customers’ willingness to purchase is not
necessarily affected by the design of the store environment alone as many previous
studies would advocate (Balabanis & Reynolds, 2001; Crisp et al. 1997; Helander,
2000; Winn & Beck, 2002). Instead, the combination of the extrinsic cues, including
design as a contributing factor, collectively helps to persuade a consumer to purchase.
When viewed independently, design only has a small amount of influence on the
choice to purchase from an online retailer. When presented as part of the collective
cues, design plays a greater role in the performance evaluation process.
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Reputation.
The marketing and consumer behaviour literature suggests that consumers use
buying cues. This is especially true under buying situations where there is a greater
need to reduce the perceived risk of a purchase (Jacoby, Olson, & Haddock, 1971;
Olson, 1977; Chen & Dubinsky, 2003).
Reputation is shown to be a substantial risk reduction cue of all those
considered in this study, supporting the work of Agarwal and Teas, 2001; Cooper and
Ross, 1985; Emons, 1988; Olson, 1977; Rao and Monroe, 1989; and Tan, 1999.
Reputation helps improve the overall evaluation of products and services
offered by the e-tailer creating a positive evaluation of performance. A substantial
risk-reduction strategy used by the participants of this study was to purchase a well-
known product from a well-known online retailer.
Bearden and Shimp (1982) suggest that in almost all purchase situations,
consumers use a variety of factors in relation to reputation and rely heavily on this
extrinsic cue especially in the absences of intrinsic cues of the online environment.
The results of this study support this view.
Earlier, online store design and its influence on the consumer’s perception of
performance was examined. What remains in question is whether reputation is
attained by the way a company presents itself visually to its public. Bearden and
Shimp (1982) suggest that a consumer’s perception of an e-tailer is attained from the
content and technologies employed in the design of the site, however under the
conditions employed in this study, it is uncertain whether this is true. The perception
formed from the site has the ability to either heighten the consumers’ perception of
risk or diminish such perceptions. This is based mainly on the store’s reputation.
Of all the extrinsic cues investigated, reputation has the strongest effects on
performance evaluation. The literature notes reputation is a powerful evaluative cue
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that directly affects consumers’ quality perceptions (Agarwal & Teas, 2001). Further,
these same quality perceptions have been found to have direct influence over the
decision to proceed with a purchase or abandon the shopping process. Chen and
Dubinsky’s (2003) study proposed that reputation serves as a surrogate for quality and
a dominant choice heuristic by providing consumers with a bundle of information
about the performance of a product. This study supports that view. The participants
continually stated that reputation of the e-tailer is positively related to performance
perceptions of the product purchased. The results advocate consumers are likely to
perceive an e-tailer with a good reputation as more trustworthy (Hendrix, 1999). An
increase in trust reduces the perceived performance risks associated with the purchase.
Consequently, an e-tailer’s reputation should foster a lower performance risk for
online shoppers thereby increasing purchase intent. This occurs so long as the
reputation is perceived positively.
It was found consumers are prepared to pay a premium for a product with a
strong reputable brand name as proposed by Agarwal and Teas (2001). In case
scenario one for example, the majority of participants chose to exceed the
recommended budget to acquire a more reputable brand. To help reduce the risk
associated with poor performance, participants purchased what they considered to be
reputable brands citing that they stood for quality. Further, this study showed when
the reputation of the retailer is presented positively, even when other conditions such
as design are presented negatively, participants used reputation as the surrogate cue
for performance evaluation. Participants considered a positive reputation as an
essential ingredient in online shopping. Reputation was also consistently used as a risk
reduction strategy by participants. This suggests that an e-tailer with an established
reputation is far more effective in reducing the perceived performance risk than an e-
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tailer without an established reputation. The power of reputation appears to work in
isolation to all other cues.
Although the literature identifies many factors that may influence a purchase,
none appear to be stronger than the e-tailer’s reputation. Reputation acts as a surrogate
over the other cues investigated in this study in much the same way as Mitchell’s
(1999) study showed that performance risk acts as a surrogate over all other
dimensions of perceived risk. This is an important outcome and worthy of further
investigation.
Answering the Research Questions
The central research problem of this study is, “what influence does perceived
performance risk have on Abandoned Cart Syndrome”. As a means of addressing this
problem, the following research questions were developed:
1) What influence do the extrinsic cues of brand, price, website design and
reputation have on performance evaluation of an online shopper?
2) What influence does performance evaluation have on perceived performance
risk leading to shopping cart abandonment?
The research design and methodology outlined in Chapter Three was
developed as a means to adequately address each of these questions. The research
questions were developed from literature to aid in acquiring a deeper understanding of
what influence perceived performance risk may have on ACS. By answering these
questions it is hoped the researcher contributes further to academic knowledge
surrounding cue-utilisation theory, risk theory, and online buyer behaviour.
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Answering research question one.
The results suggest the extrinsic cues of brand, price, design and reputation
influence the evaluation of performance. However, the independent effects each cue
has on online purchase intent are not as substantial as first theorised. This study’s
discovery that some participants grouped the cues together and placed them in some
order of importance is important, and worthy of further, more empirical investigation.
While each cue was found to autonomously impact on the evaluation process,
with some cues having a greater effect on the purchase outcome than others (e.g.
reputation and brand), this study seems to suggest that it is the collective power of
these cues that has a major impact on the purchase decision. While this suggestion
requires considerably more empirical evidence to support such a claim, in the form of
future quantitative studies it is worth noting all the same. The participants of this
study did appear to rate the importance of each cue as a collective, placing them in an
order of importance pertaining to the perceived performance risk. This was the major
theme to develop from this exploratory study and requires further investigation in the
future.
The results show that the study’s participants consciously placed the four
extrinsic cues into the following order of importance: 1) reputation, 2) brand, 3)
design and then 4) price. This is supported by comments made by participants during
the interview process, where they repeatedly grouped the cues together when
discussing the purchase evaluation process. This grouping of cues seemed to help
guide the final decision to abandon the cart or proceed with the purchase. While it
remains uncertain as to what extent each individual cue can affect the overall
performance evaluation of a purchase decision, it does seem that the collective power
of these cues can help towards determining the outcome of a purchase.
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Answering research question two.
Having noted and recognised the key attributes that contribute to the
performance evaluation process, the next step is to consider what influence
performance evaluation has on consumers’ perception of performance risk. The
results suggest a close relationship between the evaluation stage of decision-making
and the cues that aid in guiding the process. This is noted with caution as this study
was not causal in nature. The results suggest that performance evaluation has a
influence over the level of performance risk perceived by a consumer.
The consumer considers the overall collective influence of extrinsic cues when
evaluating the performance risk associated with a purchase and uses both reputation
and brand as guiding cues to aid in the final decision. This study’s participants
considered the financial risks of a purchase and noted concerns associated with
product delivery and return policies when considering the retailer. Both these
dimensions are identified in the multi-dimensional risk theory discussed in Chapter
Two of this thesis. This outcome supports Mitchell’s (1998) belief that the other
dimensions of risk relate to performance risk, noting its surrogate effects. The
majority of participants linked these attributes to overall perceived performance risk
as part of their evaluation of performance. This is best summarised by participants
nine and eleven.
Interviewee: Putting your credit card number on a site that you may not know and the site doesn’t deliver. That’s a risk. I’m less confident in the ability to actually get the product at your doorstep when it’s supposed to be there and in the condition it should be. The quality of service rather than the product I’m buying. There are all kinds of risks, maybe you don’t get the product at all, and maybe it’s a complete fabrication, a box with a picture on the front. Reputation and design helps lower the risks. (Participant nine) Interviewee: The main risk for me would be putting my credit card number online and it goes astray and someone else gets hold of it. This is a huge risk. Also the fact that you’re spending quite a lot of money (in this instance) and you might not actually receive the goods. It might not arrive in one piece or there might be something wrong with it, then a huge payment to send it back.
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That’s it, first credit card, then not actually getting what you want. (Participant eleven)
Prior to completing the transaction, consumers give a considerable amount of
thought to evaluating performance. For both theorists and practitioners, further
consideration of these factors is needed if we are to reduce the performance risks
perceived by consumers.
The Conceptual Model Revisited
At the very core of the conceptual model developed for this thesis is
performance risk. The literature suggests this dimension can often act as a surrogate
over all other risk dimensions making it ideal for investigation. Cue-utilisation theory
was also identified from the reviewed literature. This theory suggests when consumers
are making a purchase decision they are evaluating both intrinsic and extrinsic
attributes or cues relating to the product and purchase process.
When consumers shop online they pay close attention to extrinsic cues whilst
evaluating the performance of a purchase. This is most often done prior to making a
purchase. In an online setting the traditional intrinsic cues of smell, touch, taste and
even sight are limited or missing. The extrinsic cues therefore play an important role
in the evaluation process of the online shopper.
The extrinsic cues of brand, price, website design and e-tailer’s reputation
were placed within the performance evaluation stage of the conceptual model. Based
on previous literature, each of these cues has the capacity to independently affect the
level of perceived performance risk felt by the consumer. It is then proposed that the
level of negative impact each of these cues has on performance risk ultimately
determines the outcome of the purchase intent.
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Implications to Propositions
Proposition one.
A negative evaluation of the brand is likely to heighten the consumer’s level of
perceived performance risk thereby increasing the likelihood of ACS.
This study suggests the brand offered to consumers has an effect on the
perception of performance risk; however it also appears dependant on other evaluative
factors under consideration at the time of the purchase assessment. Therefore, it is true
that a negative evaluation of the brand does heighten the consumer’s level of
performance risk, although it is questionable whether or not this individual cue is
likely to increase ACS. The only exception to this is when the brand is completely
unknown to the purchaser.
The first example of brand’s negative effect on performance evaluation was
with Palsonic. Presented to participants as an unknown brand, the Palsonic could be
purchased from a reputable well-designed site, and priced lower than the other
products offered. Despite this, a negative view was taken during the evaluation
process. This lead to a consistent abandonment in all shopping situations presented to
participants. At the opposite end of the brand spectrum, the Sony was positively
perceived by participants. The findings of this study suggest a negative evaluation of
brand is likely to heighten the consumer’s level of perceived performance risk
increasing the likelihood of Abandoned Cart Syndrome.
Proposition two.
A negative evaluation of the product’s price is likely to heighten the consumer’s level
of perceived performance risk thereby increasing the likelihood of ACS.
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The results of this study place less weight on price as an extrinsic cue in
Internet shopping. While this doesn’t alter the outcome of the proposition posed, it
does reduce its impact. As an independent variable, price has an impact on decision-
making, acknowledged by participants as an important factor when deciding what
product to purchase. However price did not have a major impact on the decision to
abandon the shopping cart in an online setting. Price was found to be a balancing cue
in the evaluation process. The lower the price, the more negative the perception,
especially when placed alongside other cues like brand, reputation and design. In
terms of the evaluation process, price helps balance the relationship amongst other
cues. If set too high, it was considered by participants as a swindle; if set too low, it
was considered too much of a risk. In that sense, the negative evaluation of price
heightens the level of perceived performance risk, however, it is questionable whether
it has the power alone to increase ACS.
Proposition three.
A negative evaluation of the website design is likely to heighten the consumer’s level
of perceived performance risk thereby increasing the likelihood of ACS.
Of all extrinsic cues examined in this study, website design was found to be
less effective as an influential cue on performance evaluation than the literature lead
the researcher to believe. A number of participants viewed design as a ‘nice-to-have’
cue instead of an essential ingredient in evaluating performance. Design (especially
within a typical B2C retail setting) was identified by some participants as an
important factor in the overall assessment of purchase situations. However, the
majority interviewed felt it was not a key motivator in evaluating the performance
outcome of a purchase, more a superficial factor on the perceived risks involved in a
purchase. It was found that a negative evaluation of website design is not likely to
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sufficiently heighten the consumer’s level of perceived performance risk increasing
the likelihood of ACS.
Proposition four.
A negative evaluation of the e-tailer’s reputation is likely to heighten the consumer’s
level of perceived performance risk thereby increasing the likelihood of ACS.
The fourth proposition centres on reputation. A negative evaluation of an e-
tailer’s reputation is likely to increase the level of performance risk perceived by a
consumer. This study found a negative evaluation of the e-tailer’s reputation does
have some effect on the evaluation of performance. Moreover, the increase in risk
anxiety associated with negative reputation was found to be strong enough to increase
the likelihood of abandonment. Several participants revealed strong views on the
power of reputation, especially in online retail settings. The discovery that reputation
has a surrogate effect on the other cues investigated was an unexpected outcome. In
almost every instance it was found that reputation had some involvement in
performance evaluation and perceived performance risk. To be more precise,
reputation is the most dominant cue in terms of the relationship amongst extrinsic
cues investigated in this study.
In all but one instance, reputation was noted by participants as playing a
fundamental role in their decision to purchase. This finding is considered worthy of
further investigation.
Proposition five.
A negative assessment of performance risk is likely to have a negative impact on
purchase intention thereby increasing the likelihood of ACS.
A consumer’s intention to purchase is deeply affected by a negative
assessment of performance risks perceived just prior to making a purchase. The belief
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is that the greater the negative evaluation perceived during performance evaluation,
the more likely we will see an increase in shopping cart abandonment. This outcome
is substantially affected by the evaluation of cues (in this instance, the extrinsic cues
of brand, price, reputation and design) used as measurements of performance. For
example, a poor reputation of product and/or brand, a poorly designed website and the
lowering of price increases the likelihood of shopping cart abandonment. As each cue
is affected positively, a diminishing negative evaluation on performance risk occurs,
decreasing abandonment. The question remains as to the amount of negative or
positive perception required from each external cue to affect perceived performance
risk positively, increasing the likelihood of a purchase.
While it seems logical that a negative assessment of performance risk is likely
to have a negative impact on purchase intention increasing the likelihood of
Abandoned Cart Syndrome, the factors determining this outcome are more complex
than first considered and require further investigation.
Conclusion about the Research Problem
As stated, the research problem under investigation is – What influence does
perceived performance risk have on Abandoned Cart Syndrome?
The primary objective of this study was to gain a deeper understanding of a
relatively new domain. This exploratory study has revealed sufficient evidence to
support the notion that perceived performance risk does have an effect on ACS.
This discovery is noted with caution. This study does not suggest any type of
definitive answers have been obtained from the results or any causal relationships
have been tested to any extent.
The investigation of previous literature and subsequent gathering of primary
data explored factors that may contribute to the existence of ACS. It is important to
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note that risk, specifically performance risk, is measured not only by what is being
purchased, but also how and from where the purchase is made. This supplementary
consideration directed the researcher to further explore the influential factors that
might affect the nature of performance and the evaluative process a consumer
undertakes when making a purchase.
The findings suggest that online consumers recognise and are affected by
extrinsic cues associated with performance evaluation. The more negative these cues
are presented, the greater the likelihood of consumers abandoning the shopping cart.
What was unforeseen at the beginning of this investigation was reputation
emerging as a dominant cue over others investigated. As individual elements the
effects of the four extrinsic cues on ACS appear limited, at least in terms of
understanding what may influence consumers to abandon their online shopping carts.
However, collectively the effects of these cues becomes substantial in evaluating the
performance of the product purchased (the brand) and the environment (reputation,
price and design) in which the purchase is made.
It seems logical that while the influence the individual extrinsic cues have on
performance evaluation is more unassuming than expected, collectively their overall
impact on perceived performance risk was enough to help explain some online
shopping behaviour.
The findings of this exploratory study have provided a better understanding of
some factors causing ACS. This study has helped to understand what influences
consumer behaviour in an online shopping environment at the point of the online
checkout. Furthermore, the potential future research opportunities identified
throughout this investigation contribute to further enhancements of theory
development into perceived risk.
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Contribution of the Research
This study’s primary objective was to explore the relatively unknown
phenomenon of ACS. This was achieved by investigating factors which might
influence the consumer purchase process. The conceptual model proposed in Chapter
Two identified a number of key attributes believed to influence consumers’ perception
of performance risk. The model was designed to explore relationships amongst
attributes in an attempt to isolate key variables for further examination.
Theoretically this thesis has two specific contributions. First, the conceptual
model developed helps extend perceived risk literature by identifying the relationship
between extrinsic cues, performance evaluation and performance risk.
Assessing previous literature on cue-utilisation, multi-dimensional risk, and
purchase decision-making theories and integrating them into one theoretical
framework helped deepen our understanding of the phenomena in question. Second, it
is believed that the model is one of the first to explore perceived performance risk and
its influence on ACS in an Internet based retail environment.
Implications to Theory
The findings of this study have a number of important implications both
academically and for industry practitioners. For scholarly researchers, the results
emphasise the need for empirical testing of relationships between cue-utilisation
theory, performance evaluation and associated risks perceived at the point of the
online checkout. To date, research in this area has largely been at a conceptual level.
This study is vital to our understanding and further development of theory.
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Cue-utilisation theory.
The first theory used to construct the proposed model was cue-utilisation
theory. Consumers use the power of extrinsic cues to assist in making evaluative
decisions regarding the expectations of performance, both with the product and
retailer under consideration. In online settings the additional cue of website design
was introduced and evaluated.
The outcome of the expectation of performance is what actually determines
the final effect cue-utilisation has on perceived performance risk. In this study, each
individual cue was a factor in the evaluation of alternatives leading to performance
evaluation, however an unexpected outcome was the effect the combination of these
cues have on abandonment. The potential existence of some order or grouping to
extrinsic cues is important to the future theory development in this area of study.
Furthermore, reputation was found to act as a surrogate over other cues, highlighting
the need for further research to fully understand the collective power of extrinsic cues
and the potential dominant affect reputation may have.
Multi-dimensional risk theory.
One aim of this research was to broaden our understanding of multi-
dimensional risk theory (Brooker, 1984; Ho et al., 1994; Jacoby & Kaplan, 1972;
Peter & Tarpey, 1975; Garner, 1986; Mitchell, 1992; Stone & Gronhaug, 1993).
Specifically this study focused on just one dimension of risk theory, performance risk,
and looked at its surrogate power over consumer behaviour (Mitchell, 1998). This was
done by considering the influential nature of extrinsic cues in evaluating alternatives
and the subsequent performance assessment.
It is doubtful that this study has provided any substantially new insights into
multi-dimensional risk theory and the surrogate influence performance risk has over
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the other dimensions contained within the theory. As identified, this sits outside the
scope of exploration of this study. This theory did contribute substantially to the
development of the proposed model, however a future study that impacts on a greater
understanding of risk theory itself is required. By conducting this study, the results
show perceived performance risk does influence the purchase decision made by a
consumer at the point of the online checkout, acknowledging its importance to this
study.
Consumer decision-making.
Of the five stages in consumer decision-making discussed by Mitchell (1998,
1999), the evaluation of alternatives and subsequent performance evaluation process
is vitally important in our understanding of perceived performance risk and online
shopping. This stage in decision-making is found to be most critical in terms of our
understanding of what might influence cart abandonment. While only at a
rudimentary level, the implication to this theory is still believed important and
warrants further investigation. This is especially true with online shopping, which is
still a relatively new domain for researchers.
In summary, this study has extended the pre-existing theories of cue-
utilisation, multi-dimensional risk, and purchase decision-making while relating them
to a new domain, the online shopping market.
Implications for Practitioners
This study has only begun to broaden our understanding of consumers’
decision to abandon online shopping carts. A number of implications to practitioners
are worth noting.
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This study has provided an understanding of the importance of each variable
within the proposed model and their role in decision-making. Due to the preliminary
nature of the study this is noted with caution. However, marketers serious about
developing online sales strategies must first address reputation as a key construct to be
successful. When evaluating a purchase alternative and potential performance risks
associated with a purchase, the results of this study suggest reputation is the one cue
that stands out from the others. If practitioners wish to reduce levels of abandonment,
a sound reputation for both product and store must first be established.
With further research, online marketers will begin focusing their attention on
the combined influential power these four cues have in the evaluation phase of a
purchase decision. An area of interest would be to test the relationship between
reputation and the other extrinsic cues.
Due to the large number of online retail sites and purchase opportunities now
available to consumers, forward thinking practitioners need to modify strategies to
meet the needs of the online consumer. Addressing the relationship between the brand
and store reputation, in conjunction with a sensible approach to site design and
pricing, over time, will aid in reducing cart abandonment. Behind this strategy is trust,
a term most often cited by participants of this study when considering performance
risk. To be precise, participants need to trust the store, trust the brand, trust the return
policy, trust that prices are fair and their expectations are met. These same consumers
want assurances that goods purchased are delivered on time and in sound working
order, highlighting the need for trust in third-party operators involved in the
transaction. This suggests a strong correlation between performance expectations and
reputation of the retailer and their relationships with other vendors. From a financial
perspective, participants noted concerns with credit card misuse, which highlights the
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role reputation plays in third party financial institutions and the transactional systems
offered.
Marketers must address these issues if they intend to be successful within the
competitive domain of the Internet. Simply designing a good looking website and
filling it with product appears to be unsatisfactory, yet this seems to have been the
strategy employed by many practitioners in the past.
Limitations
Although problems associated with overcoming consumer barriers to online
shopping are well recognised (Hubscher et al., 2002; Ranganathan & Grandon, 2002;
Helander, 2000; Jarvenpaa & Tractinsky, 1999; Hoffman, Novak, & Peralta, 1999),
there are few studies that have attempted to explore individual dimensions of
perceived risk within an online context. Equally unchartered is the identification of
what might influence purchase decisions at the online checkout. By examining one
dimension and its influence on purchase decisions, it was hoped a better
understanding of ACS would be achieved.
A number of limitations to this study do exist. To begin, the method used to
collect data for this study was exploratory. Although the methodology was justified in
Chapter Three, it remains a possible limitation.
Furthermore, the outcome of online purchases is potentially affected by a
number of other influential factors. For example, marketing efforts of the retailer
might influence the decision to purchase a product online. This is especially true if the
promotional offer is highly influential. Additionally, the use of persuasive advertising,
increased brand awareness campaigns, the individual consumer’s attitude and
adoption to this innovation might influence the decision to purchase (Ranganathan &
Grandon, 2002). While true, these factors have been excluded from this study. It is
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believed by this researcher that they add too much complexity to this current study
and require individual investigation. Future studies could consider each of these
elements against the proposed model, helping to further advance the model.
Next, the types of products sold online need to be considered in the context of
consumers’ perception of risk. Future studies should focus on a range of products to
help provide more generalisable results.
While the literature discusses numerous extrinsic cues often considered during
purchase decisions, this study only looked at four of these cues. These four were
identified in the literature as most relevant to the online shopping environment.
Conceivably, other external attributes might also have a role to play in affecting the
outcome of a shopping cart purchase however they were not investigated as part of
this study.
This study is limited to just one product, which by its very nature and price
range was high involvement. That is, a video camera may have played a role in the
participants’ interpretation of quality and performance expectations causing some bias
in their decision. The same may not be as important for low involvement products,
thus potentially limiting findings of this study for this product group.
Due to the exploratory nature of this study, any generalisation of results
obtained would be inappropriate. This research was not causal by nature. Therefore
the research does not suggest a certain amount of negative or positive performance
risk is required to cause shopping cart abandonment to occur. Nor does this study
provide any empirical evidence to show what degree each of the extrinsic cues affect
the evaluation process.
The results did find that the collective influence of extrinsic cues has an effect
on the evaluation process and the collective effect has an influence on ACS.
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Suggestions for Future Research
A considerable amount of further work is required to look at causal
relationships amongst the attributes proposed in this study. Of specific interest to
researchers should be a greater identification of extrinsic cues and what causes
differences in purchase outcomes across diverse product categories and broader
consumer markets within the online setting. This should include other high
involvement products as well as low involvement products such as general household
items which are purchase on a more regular basis.
This thesis was exploratory in nature and has only generated some
introductory findings. Further research is necessary to determine whether the
conceptual model proposed receives empirical support, thereby adding to theory
development. These additional investigations require thorough testing of the variables
within the proposed model and require a much larger sample base for such an
empirical exercise. The focus would be more on the combined use of vignettes and
questionnaires instead of relying on data obtained primarily from in-depth interviews.
This would entail a quantitative study perhaps using conjoint statistical analysis
techniques.
Given this study only focused on the pre-purchase phase of buyer decision-
making, future research could expand on this work by exploring and testing
consumers’ post-purchase evaluation process. Also vital is our understanding of how
this post-purchase experience might influence the same individual online shopper.
This study did not explore the different types of online consumers and product
classes available online such as low involvement products. Later research could
segment and empirically test these differences and extend our understanding of
perceived performance risk in a variety of different markets. Future work should
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investigate the various expectations consumers have of both on-line and off-line
shopping outlets. For example, do traditional store brands and reputations extend into
the online environment and vice-versa? What is the relationship amongst these
variables within both contexts?
Finally, future research needs to evaluate the notable relationship each
extrinsic cue has with one another, with extensive testing of the surrogate powers
reputation may have over other cues.
From a strategic standpoint, it is important to both academic and industry
practitioners that we examine why ACS occurs and to test the causal relationship
amongst the variables of this study’s model. This is vitally important to theory
development within an online retail context.
Conclusion
Consumers are often unable to measure the full extent of risk taking directly.
In the majority of cases, consumers are guided by numerous factors, some intrinsic,
others extrinsic. E-tailers with established reputations, offering quality performance,
known brands, assisted by quality site design and a balanced pricing strategy, reduce
the perceived performance risks associated with purchasing online. While several
limitations have been identified with this study, until a complete appreciation of what
affects online consumers’ decision making is fully tested, the high percentage of cart
abandonment will prevail. The portrait of the online consumer is far from complete.
This study has only begun to consider some pre-existing theories, developed to better
understand consumer behaviour. This study has identified future research
opportunities in the area of online consumer behaviour and with further investigation,
researchers can develop models that guide the online business-to-consumer marketer.
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It is important to note this researcher only explored one dimension within the
multidimensional risk theory and only four extrinsic cues influential in the
performance evaluation process. Future studies should extend the proposed model
across other dimensions of perceived risk.
It is held that this study has made an important contribution to academic
research in this relatively unchartered territory and has some substantial practical
applications for online marketers. To quote Clift (1997), “only by identifying and
confronting buyers' perceived risks can we truly begin to overcome purchasing
resistance that is all too often silent”.
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Appendix A
Table 1. Summary Results of Interview Question One, Scenario One
Interview Question One Case Scenario One Participant 1 I place Brand over Price. I’d rather buy reputation.
Design helps but brand and reputation are higher. Participant 2 Reputation and Design are the two things I look for. If
you decrease the reputation you decrease the trust. Participant 3 I pay the extra for brand. First I looked at brand, then
design and paid more for peace of mind. Participant 4 Good reputation and good design equals trust. I blew
the budget because of safety. Participant 5 Pay extra for trust. Good reputation, well designed that
equals safety. Better brand means better quality. Participant 6 You ay more for trust in the brand. The brand plus
reputation and a good design means trust. Participant 7 Palsonic was too cheap which was a bit dodgy, Sony
was overpriced, and the LG was midrange and in budget.
Participant 8 Price was the key. Good reputation and good design meant I didn’t worry.
Participant 9 Price was the main thing. If it’s too cheap it’s got to be crap. Quality and performance are important but price is No 1.
Participant 10 I never buy from a site I don’t know that is poorly designed. Reputation was good, design was good and LG was closest to my budget. That was my reasoning.
Participant 11 I simple pay more for a trusted brand. There were no worries with the decision because of the design and reputation were both good.
Table 2. Summary Results of Interview Question One, Scenario Two.
Interview Question One Case Scenario Two Participant 1 Look for 3rd party safety net. This provides reputation
and security. Design is the key and it’s a brand I trust. Participant 2 Decreased reputation equals no trust. I wouldn’t put my
credit card in to a site I didn’t know. Price was an after thought.
Participant 3 With no reputation I wouldn’t buy. Design was not a factor. Reputation is very important.
Participant 4 Better brand equals more trust. With a good design I’ll use it more. More use increases the reputation and therefore trust.
Participant 5 No reputation, no purchase. That’s it, it doesn’t matter
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how well it’s designed, wouldn’t buy because I don’t know who it is.
Participant 6 With a good brand, if I see a well-designed site I feel some thought went in to the company. This helps with trust.
Participant 7 Price (Budget) was important so I went with LG. Reputation was not as important.
Participant 8 I stick with brands. So long as it’s well laid out, easy to follow, step by step, I’ll buy even if I don’t know them.
Participant 9 I didn’t want to max out my budget. I never buy a brand I know nothing about. Brand is still important.
Participant 10 I wouldn’t buy. With no reputation you never know if you’re ever going to get what you purchased no matter what the brand.
Participant 11 I wouldn’t give my credit card details to a site I didn’t know. You wouldn’t know if you’re ever going to get the product.
Table 3. Summary Results of Interview Question One, Scenario Three.
Interview Question One Case Scenario Three Participant 1 I place reputation over design any day. I trust Sony. Participant 2 Design is linked to reputation but it’s still not enough to
make me buy. It’s too risky. Participant 3 As long as it’s reputable that’s okay but if it looks
unsafe that’s a worry. Participant 4 Why would I spend money on a site that looks bad? It
can’t be trusted even if they have a reputation. Participant 5 I don’t need the pretty pictures. It’s still a reputable
dealer and it’s under budget, that’s all I need. Participant 6 I know a lot of reputable companies online with
horrible designs and they’re okay. Reputation overrides design.
Participant 7 Reputation is far more important than design. That’s why I went with Sony.
Participant 8 Because I needed the camera I would have bought because of reputation but I wouldn’t have been impressed. No effort.
Participant 9 Sony has a history, a better one. I go with the brand and a site with reputation. A few broken links, so what, look at ebay!
Participant 10 I wouldn’t buy anything from a site that I either didn’t know or was poorly designed. If they haven’t designed it well you don’t know if you’re ever going to get what you purchased.
Participant 11 I don’t really care what the web site looks like. If it’s reputable that I’m buying from I don’t care if it’s a great design.
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Table 4. Summary Results of Interview Question One, Scenario Four.
Interview Question One Case Scenario Four Participant 1 Too suspicious. I’d be worried if they were going to
send me the stuff or not, no matter how cheap. You hear about it a lot.
Participant 2 You don’t know what you’re going to get. I started to think unknown, poor design, low price, and high risk.
Participant 3 Con artists. That’s it, that’s why. Participant 4 Prices started to get down to the lower levels. You gotta
question why it’s so cheap. Lower price, higher risks. Participant 5 Poor design, good design, it didn’t matter. I just didn’t
know them. Pay extra for the peace of mind. All about reputation
Participant 6 No reputation and bad design, forget it! Significant lower price, it’s more suspicious. I’m going to ask why. No trust.
Participant 7 The risks in this case were too high. Participant 8 I just go to a site that’s reputable. I know I’ll get the
product I ordered. It’s too risky otherwise. Participant 9 It’s a decent investment in time and money. I would
give my credit card over. Will I get the product on time in one piece?
Participant 10 Risks were just too high. Will I get what I wanted or get ripped off? With the price down you would have to wonder why? Can you trust them to deliver?
Participant 11 I wouldn’t buy on price. I wouldn’t buy anything from a site I didn’t know.
Table 5. Summary Results of Interview Question Two.
Interview Question Two Summary of participants responses Participant 1 Reputation could be one of the most important things,
absolutely. Design is big, but not the biggest. Participant 2 Prices I suppose are important when it comes to Brand.
More expensive, it’s got to be better. Participant 3 I’m not going to hand over my credit card to anyone on
the net. Reputations, that’s the biggest thing. Goods not showing up, that’s another factor. Risk and reputation are two very big issues.
Participant 4 The biggest factor is putting in an order and my credit card and then getting nothing back or there are complications that increase the cost of buying.
Participant 5 If it were just any old ‘Joe Bloggs’ I wouldn’t deal with them. It’s all about reputation, Reputation equals accountability. Design is not nearly as important. I’ll pay the price to get what I want.
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Participant 6 One of the biggest factors is the return policy. When buying, I want to feel it, touch it, and play with it. If something breaks then what? Will the site perform?
Participant 7 Definitely reputation. That’s the big one. If I’m going to buy I’m going with a company that I know. Price is also important but I’m always wary of being ripped off.
Participant 8 Site layout is a big one but I prefer to go with a reputable site. Budget is the third big factor I look at but it’s the last point.
Participant 9 “The two big factors are reputation and design, in that order. Unknown and poor design makes it a no brainier. Unknown but well designed is an even greater risk. Putting my credit card in over the web, that’s a factor. Finally, there are delivery concerns, times and schedules, and then the cost of it all”.
Participant 10 It’s mainly the brand, not so much on price. It’s all about brand history and the security of the transaction. I like payment options that give me peace of mind.
Participant 11 Reputation is what influences me. The fact that it’s well known and established. The reputation of the company I’m buying from is more of an influence than the product I’m buying.
Table 6. Summary Results of Interview Question Three.
Interview Question Three Summary of participants responses Participant 1 A big risk is that the items don’t show up. You need
time to research it, shop around for prices, or you increase the risks. Another risk is if things go wrong you don’t have any retail shop to take the camera back to. That’s a problem. It’s a lot easier to get things fixed with a reputable brand. They have more at stake, it reduces the risks.
Participant 2 There is always the risk of putting your credit card in. You need to check out how secure it is before you buy. The other thing is time, wasting time if things go wrong, getting your money back. The risk of delivery, not getting the goods.
Participant 3 In this case (buying a camera), ordinarily it’s an item you like to touch, to hold, see how it actually works, that’s a big thing for me. At least with a book or CD you know that’s it, you know what you’re getting. Another risk is someone steals your money and the goods don’t show up and your credit card is stolen. If I’m buying from a reputable site I don’t give it a second thought. If I don’t know them I’m a bit anxious about it coming. Dollars come into it too.
Participant 4 The big risk is putting your credit card in and then nothing shows up. It’s all about the return policy.
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Participant 5 If he buys it and he doesn’t get it or it doesn’t live up to expectations, that’s a risk. I think, sometimes you put it in the cart then you go ‘oh hang on, I can just go down to the shop and get it’.
Participant 6 One of the risks is the return policy. You need to be able to send it back. You need to be able to get your money back or get a replacement. If there is reputation there is probably a policy. If they come from some place I’ve never heard of, that’s a problem. I’m not concerned about the credit card thing; if they’re reputable and well designed they’re secure. Unknown, poor design, you wouldn’t know.
Participant 7 There’s the risk of credit cards. And the risk of not getting the product because you haven’t seen it. Can you give it back if it doesn’t meet your expectations?
Participant 8 Internet shopping is different to doing anything else online. You could loose your money; the goods don’t show up, no way of contacting them, that’s a big risk.
Participant 9 Putting your credit card number on a site that you may not know and the site doesn’t deliver. That’s a risk. I’m less confident in the ability to actually get the product at your doorstep when it’s supposed to be there and in the condition it should be. The quality of service rather than the product I’m buying. There are all kinds of risks, maybe you don’t get the product at all, and maybe it’s a complete fabrication, a box with a picture on the front. Reputation and design helps lower the risks.
Participant 10 If I bought the camera would it work, that’s a risk. I mean if it didn’t work how would I send it back? History and reputation are important as is the security and privacy.
Participant 11 The main risk for me would be putting my credit card number online and it goes astray and someone else gets hold of it. This is a huge risk. Also the fact that you’re spending quite a lot of money (in this instance) and you might not actually receive the goods. It might not arrive in one piece or there might be something wrong with it, then a huge payment to send it back. That’s it, first credit card, then not actually getting what you want.
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Appendix B
Project Consent Form Project Title: Perceived Performance Risk and its influence on Abandoned
Cart Syndrome (ACS). Project Contacts: Mr Simon Moore Masters Research Student Faculty of Business School of Advertising, Marketing and Public Relations, QUT 10th Floor, Z Block, Gardens Point GPO Box 2434, BRISBANE 4001 Ph: (07) 3864 1354 Fax: (07) 3864 1811 Email: [email protected]
By completing the section below you indicate that you: 1. have read and understood the information package provided to you; 2. have had any questions about this research project explained to your satisfaction; 3. have been informed that the confidentiality of the information you provide will be
maintained, safeguarded and no identifying information will be released without your consent;
4. have been assured that you are free to withdraw from this project at any time,
without comment or penalty; and 5. have agreed to participate in this research project. Name ............................................................... Signature ............................................................... Date ......./........./.........
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Appendix C
Participant Information Package Simon Moore Principal Supervisor Masters Research Student, Faculty of Business Professor Charles Patti Queensland University of Technology Faculty of Business, QUT School of Advertising, Marketing School of Advertising, Marketing & Public Relations & Public Relations Mobile: 0413 833 353 Phone: 07 3864 2972 Email: [email protected] Email: [email protected] Re: Master of Business Research Project Project Title: Perceived Performance Risk and its influence on Abandoned Cart
Syndrome (ACS). Description: This research project aims to investigate the influence performance risk has on online shoppers. The project is being conducted as part of the Master of Research studies of Mr Simon Moore at Queensland University of Technology and may form part of future publication/s by Mr Moore. It is anticipated that your involvement in this project will take approximately 30 – 45 minutes to complete. Expected Benefits: Your involvement in this project will not directly benefit you. However, it is hoped that by advancing understanding of perceived risk in an online setting, this project will contribute to the development of more effective marketing strategies for online retailers in the future. Confidentiality: Participants will not be identifiable in any way by the data collected. Only the research team will have access to the information you provide, and there are no details recorded (written or taped) by which you can be identified. Once transcribed, all taped responses will be destroyed, so it will be impossible for any individual to be identified in any way. As such, your anonymity and confidentiality is ensured in the event of any publication of this study’s results. Voluntary Participation: Your participation in this project is completely voluntary. You may decline to participate at any time before or during the study, or may choose not to answer any individual question. You will not face any negative comment, penalty or consequence as a result of deciding not to participate, and such a decision will not affect any current or future involvement you have with QUT (e.g. your grades).
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Questions / Further Information: If you have any questions or wish to seek further information, please feel free to contact the Chief Investigator, Simon Moore on [email protected] or the Principal Supervisor, Professor Charles Patti on 3864 2972. Concerns / Complaints If you have any concerns or complaints about the ethical conduct of this project please contact the Secretary of the University Human Research Ethics Committee on 3864 2902. Instructions: Phase one of this research project is for you as a participant to read the attached short story. Once you’ve digested the information contained in the story, please turn to the brief questionnaire. This questionnaire contains four scenarios. You are required to tick one box in each scenario that best supports your purchase decision. At the completion of the questionnaire you will be then interviewed discussing your responses to the scenarios.
Thank you for your participation in this project. Your time and thoughts are greatly appreciated.
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Appendix D
Vignette – Male Version John buys a video camera John is a 28-year-old marketing executive for Mining Corp Ltd based in the outback township of Longreach, several hundred kilometres from Brisbane. With a trip back to his hometown of Sydney looming, John has decided to videotape the sights in and around Longreach to show everyone. All he needs now is a video camera. Because John typically works long hours, usually well after the only electrical store in town has closed, he decides the best alternative is to surf the Internet for possible camera options. Although John does prefers to test-drive most products he acquires, (i.e. feel the weight, push buttons, and physically look at all the colour choices available) and admits he likes to have the salesperson sell him on all the benefits of a product he’s considering, buying items such as airline tickets, books, music and software over the web is not a new experience for him. After reading through countless information websites on video cameras John has narrowed his choice down to three, all of which are the based models in their categories and all have the same product features. The first on his list is the Sony Video-8 Handicam, which has been successfully sold on the market for a number of years. His second option is the LG-10 Videocam which is a first for LG, a company normally known for its whitegoods, and finally there’s the somewhat unknown but extremely low-priced camera by Palsonic called the Digicam-1000. “It’s time to make a decision” John decides, and with a credit card limit of $2,000.00 but a predetermined budget of $800.00, he logs on to his favourite search engine (www.google.com.au), types in the three brand names with the added words ‘to buy’ and is presented with an endless supply of online stores from which he can make his purchase. With some recognisable (e.g., www.ebay.com) and some not so familiar websites (e.g., www.max-camera-discounts.com.au) to choose from, many that seem to have been professionally designed, while still others that have not given much thought to page layout and functionality (e.g., small, poorly presented product images and somewhat limited content), John’s admits that one of his fear is the loss that may be incurred things don’t go as planned. What does John do next?
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Appendix E
Tick-Box Questionnaire Johns Options If you were John, which option from each of the scenarios would you pick? (N.B. Please only tick one box from each scenario) Scenario One Buy the Sony @ $899.00 (rrp) from a reputable, well designed site? Buy the LG @ $750.00 (rrp) from a reputable, well designed site? Buy the Palsonic @ $500.00 (rrp) from a reputable, well designed site? None of the above Scenario Two Buy the Sony @ $799.00 from an unknown but well designed site? Buy the LG @ $699.00 from an unknown but well designed site? Buy the Palsonic @ $450.00 from an unknown but well designed site? None of the above Scenario Three Buy the Sony @ $799.00 from a reputable but poorly designed site? Buy the LG @ $699.00 from a reputable but poorly designed site? Buy the Palsonic @ $450.00 from a reputable but poorly designed site? None of the above Scenario Four Buy the Sony @ $750.00 from an unknown, poorly designed site? Buy the LG @ $650.00 from an unknown, poorly designed site? Buy the Palsonic @ $400.00 from an unknown, poorly designed site? None of the above
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Appendix F
Vignette – Female Version Jane buys a video camera Jane is a 28-year-old marketing executive for Mining Corp Ltd based in the outback township of Longreach, several hundred kilometres from Brisbane. With a trip back to her hometown of Sydney looming, Jane has decided to videotape the sights in and around Longreach to show everyone. All she needs now is a video camera. Because Jane typically works long hours, usually well after the only electrical store in town has closed, she decides the best alternative is to surf the Internet for possible camera options. Although Jane does prefers to test-drive most products she acquires, (i.e. feel the weight, push buttons, and physically look at all the colour choices available) and admits she likes to have the salesperson sell her on all the benefits of a product she’s considering, buying items such as airline tickets, books, music and software over the web is not a new experience for her. After reading through countless information websites on video cameras Jane has narrowed her choice down to three, all of which are the based models in their categories and all have the same product features. The first on her list is the Sony Video-8 Handicam, which has been successfully sold on the market for a number of years. Her second option is the LG-10 Videocam which is a first for LG, a company normally known for its whitegoods, and finally there’s the somewhat unknown but extremely low-priced camera by Palsonic called the Digicam-1000. “It’s time to make a decision” Jane decides, and with a credit card limit of $2,000.00 but a predetermined budget of $800.00, she logs on to her favourite search engine (www.google.com.au), types in the three brand names with the added words ‘to buy’ and is presented with an endless supply of online stores from which she can make his purchase. With some recognisable (e.g., www.ebay.com) and some not so familiar websites (e.g., www.max-camera-discounts.com.au) to choose from, many that seem to have been professionally designed, while still others that have not given much thought to page layout and functionality (e.g., small, poorly presented product images and somewhat limited content), Jane’s admits that one of her fear is the loss that may be incurred if things don’t go as planned. What does Jane do next?
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Appendix G
Tick-box Questionnaire Jane’s Options If you were Jane, which option from each of the scenarios would you pick? (N.B. Please only tick one box from each scenario) Scenario One Buy the Sony @ $899.00 (rrp) from a reputable, well designed site? Buy the LG @ $750.00 (rrp) from a reputable, well designed site? Buy the Palsonic @ $500.00 (rrp) from a reputable, well designed site? None of the above Scenario Two Buy the Sony @ $799.00 from an unknown but well designed site? Buy the LG @ $699.00 from an unknown but well designed site? Buy the Palsonic @ $450.00 from an unknown but well designed site? None of the above Scenario Three Buy the Sony @ $799.00 from a reputable but poorly designed site? Buy the LG @ $699.00 from a reputable but poorly designed site? Buy the Palsonic @ $450.00 from a reputable but poorly designed site? None of the above Scenario Four Buy the Sony @ $750.00 from an unknown, poorly designed site? Buy the LG @ $650.00 from an unknown, poorly designed site? Buy the Palsonic @ $400.00 from an unknown, poorly designed site? None of the above
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Appendix H
Interview Questions
Questions to be answered by participants: Name
Age
Gender
Income
What experience does the participant have with internet shopping?
Can you recall the last item you purchased online and what its dollar amount was?
1. Explain why you made your decision in each of the scenarios?
2. What factors influenced your decision in each of the scenarios?
3. What risks do you think Jane faces?
137
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