Kavala 2007
Greenwich University
MSc Finance and Financial Information Systems
‘Electronic Commerce,
Customers’ satisfaction in the Greek on line shopping context’
By Konstantinos Theodoridis
Supervision by Dr Dimitrios I. Maditinos
Special Thanks to Dr Dimitrios I. Maditinos
Table of contents
ABSTRACT ............................................................................................................................... 4
1. INTRODUCTION .................................................................................................................. 5
1.1 Defining e-commerce ................................................................................................................................... 5
1.2 E-commerce evolution ................................................................................................................................. 5
1.3 E-commerce in Greece ................................................................................................................................. 7
1.4 Summary ...................................................................................................................................................... 7
2. LITERATURE REVIEW ....................................................................................................... 8
2.1 introduction .................................................................................................................................................. 8
2.2 Customer satisfaction .................................................................................................................................. 9
2.3 On line shopping attributes ....................................................................................................................... 11
2.4 Satisfaction indexes .................................................................................................................................... 15
2.5 Loyalty and trust ........................................................................................................................................ 17
2.6 Summary .................................................................................................................................................... 20
3. METHODOLOGY ............................................................................................................... 20
3.1 Introduction................................................................................................................................................ 20
3.2 Relevant methodologies ............................................................................................................................. 21
3.3 Conceptual framework .............................................................................................................................. 29
3.4 Hypotheses development ........................................................................................................................... 31
3.5 Instrument development ........................................................................................................................... 34
3.6 Summary .................................................................................................................................................... 36
4. EMPIRICAL RESULTS ...................................................................................................... 37
4.1 Introduction................................................................................................................................................ 37
4.2 Sample selection ......................................................................................................................................... 38
4.3 Respondent’s profile .................................................................................................................................. 39
4.4 Satisfaction index ....................................................................................................................................... 40
4.5 Construct validity and reliability analysis ............................................................................................... 41
4.6 Hypotheses tests ......................................................................................................................................... 44
4.7 Regression analysis .................................................................................................................................... 48
4.8 Discussion ................................................................................................................................................... 52
4.9 Summary .................................................................................................................................................... 54
5. CONCLUDING REMARKS ............................................................................................... 54
6. REFERENCES ..................................................................................................................... 57
APPENDIX A. QUESTIONNAIRE ........................................................................................ 64
APPENDIX B. SPSS OUTPUT TABLES ............................................................................... 71
Abstract
This study explores the relationship of several web shopping characteristics and
electronic customers’ purchasing behavior. Results of a survey with 359 Greek on-line
customers indicated that product information quality is the most significant determinant of
satisfaction, while user interface quality, service information quality, purchasing process
convenience, security perception and product attractiveness have significant impact on overall
satisfaction with variable importance weights. Furthermore in this study we investigate the
effect of overall satisfaction on customer’s post purchase behavior and loyalty which are
expressed with their repurchase and revisit intentions. This relationship reveals that a
generally satisfied customer not only is likely to revisit and repurchase from a specific web
store but also to increase his repurchasing and revisiting frequency in the future. Therefore
overall satisfaction significantly increases loyalty.
1. Introduction
1.1 Defining e-commerce
The trading of goods and services over computer mediated networks is a quite
descriptive definition for electronic commerce. In this context, payment and/or delivery of
products is not necessarily conducted over such a network. Additionally, e-commerce is
mainly based on a graphic interface comprised of pictures, images and video clips (Lohse and
Spiller, 1998). The primary distinction between electronic commerce and traditional
commerce is the way in which information is exchanged and processed. This means that
instead of being exchanged through direct personal contact, information is transmitted via a
digital network, or some other electronic channel. Finally, the internet based commerce
enables consumers to make an extended search for product information but also to purchase
products or services through direct interaction with the on-line store.
1.2 E-commerce evolution
The great advent of the World Wide Web enormously increased the development
possibilities of e-commerce. This Business to Customer (B2C) interaction through an online
virtual store offers many advantages mainly concerning the ability of on-line commerce
practitioners to trace customers’ purchasing behavior. Apart from that however, e-commerce
gives its practitioners the opportunity to collect valuable information about their customers’
profile (Moe and Fader, 2002). Bucklin et al. (2002) conclude: ”The detailed nature of the
information tracked about internet usage and e-commerce transactions, presents an
enormous opportunity for empirical modelers to enhance the understanding and prediction of
choice behavior”.
Quelch and Klein (1996) suggest that the internet would revolutionize international
marketing. The years that followed, the usage of the World Wide Web increased sharply and
became accessible to all market segments and target groups. The internet, at that time, was a
promising technology with unlimited applications that was expected to transform modern day
life. As a consequence, the e-commerce shakeout was impending and managers claimed that
the internet would help firms to reach vast numbers of customers all around the globe,
minimise storage and transaction costs and eliminate information asymmetries between
buyers and sellers. The great revolution challenged many successful businesses to enter the
new economy.
In the last decade however, things turned out to be much more pessimistic than the
preceding years, since the e-commerce revolution did not prove to happen as originally
envisioned. Nielsen (1999) predicts a web usability meltdown for many e-businesses because
of their rush to deploy web sites that don’t meet the needs of the targeted user groups. From
the very beginning of the millennium, internet based firms begin to go bankrupt in a
“domino” fashion and the most sizeable bubble of the modern years suddenly bursts
(Lightner, 2003). The causes of this major failure are complex and many researchers ever
since are trying to identify them. Thus, the last decade in particular a key issue in e-commerce
literature is the explanation and measurement of customer satisfaction. Subsequently the most
crucial consideration is the exploration of the determinants (and the relationships between
them) of on-line customers’ satisfaction, loyalty, trust and post purchase behavior (see: Delone
and Mclean, 1992; Jarvenpaa and Todd, 1996; Kim and Park, 1997; Ho and Wu, 1999; Kim,
1999). Therefore, analyzing consumers’ level of satisfaction becomes of special interest for
businesses, academics and marketing experts (Oliver, 1980) because it is closely related to the
level of customer loyalty and therefore to intentions for repeated purchase (Anderson, Fornell
and Lehmann, 1994).
1.3 E-commerce in Greece
E-commerce in Greece has started receiving attention only in the late nineties mainly
due to the slow development of internet and its low infusion rate until that time (Sungbin,
Byun and Sung, 2003). This is the most serious suspending factor for the development and
implementation of e-commerce in Greece. The late nineties however, there seems to be a
tendency for adjustment to the average European e-commerce usage level. Specifically,
according to AGB Nielsen and the Information Society Observatory’s1 (E-metrics, 2006)
research about the use of internet and its applications in Greece, about 69.5% of Greek
internet users have established at least one on-line purchase in the year 2006, increased by 2%
from the previous year and by 14% from the year 2004. Furthermore, 91% of the Greek
internet users that have established at least one on-line purchase intent to repeat their purchase
within a six month time. Finally, there seems to be a slight precedence in favor of Greek on-
line stores versus foreign ones, as 53.5% of respondents that have established at least one on-
line purchase mostly prefer Greek on-line stores in contradiction to the 46.5% that mostly
prefer foreign ones. E-commerce activity however is still limited to the high technology
products and books, while a very small amount of web stores also offers tickets booking
services.
1.4 Summary
The great advent of the internet led to a vast development of electronic commerce
worldwide. The great failure however of electronic companies in the late nineties triggered
many academics and businesses to examine the causes of the failure emerging an extending
1 AGB Nielsen is one of most sizeable market research corporations and specializes in mass and electronic
media research. E-metrics is an annual survey of Greek internet users and is conducted with the support of the
Information society observatory. E-metrics of 2006 was conducted at a sample of 31,889 internet users from
October 2006 to November 2006.
literature on e-customers’ satisfaction. Furthermore, the low internet infusion in Greece
slowed down the development of electronic commerce even more. Based on a literature
review, that is presented in the next section, we explore the main determinants of customers’
satisfaction and its impact on customers’ post purchase behavior in the Greek on-line
shopping context.
2. Literature Review
2.1 Introduction
Comprehension and measurement of customers’ satisfaction has started receiving
attention in literature from the initiation of information systems applications (see: Ein-Dor
and Segev, 1978; Ives and Olson, 1984, among others). Scholars worldwide give various
definitions of satisfaction and a plethora of studies are dealing only with this specific issue.
Other studies are exploring antecedents for a web store success in general and try to identify
the web stores’ attributes that would lead to a successful performance. Moreover, trust and
loyalty are considered strong determinants for customers’ post purchase behaviour and for
this reason a significant number of studies examine associations between trust, loyalty,
satisfaction, repurchase and profitability (Wang, Tang and Tang, 2001). Furthermore, some
metrics were developed in order to give an algebraic measurement of satisfaction which can
be produced not only to study differences across various marketplaces but also to give an
absolute measurement for a single marketplace in different periods of time. Also, some
metrics are dealing with satisfaction measurement for a single web store in order to produce
useful and comparable conclusions. Several studies and their main purposes and findings
concerning e-commerce customers’ satisfaction are presented in this chapter. These studies
are the base for the conception of our research framework.
2.2 Customer satisfaction
Current literature on customer satisfaction converge that the most direct determinant
of satisfaction is expectation followed by perceived performance (Kim, 2005). Two principal
interpretations of satisfaction prevail: satisfaction as a process and satisfaction as an outcome
(Parker and Mathews, 2001). The value percept theory views satisfaction as an emotional
response triggered by a cognitive evaluative process (Parker and Mathews, 2001). Earlier
concepts however, define satisfaction as an evaluative judgement concerning a specific
purchasing decision (Oliver, 1997). Swan and Combs (1976) were among the first to argue
that satisfaction is associated with performance that fulfils expectations, while dissatisfaction
occurs when performance falls below expectations.
Traditional models concerning satisfaction implicitly assume that customer
satisfaction is the result of cognitive processes, while more recent conceptual developments
suggest that affective processes may also contribute substantially to the explanation and
prediction of consumer satisfaction (Westbrook and Oliver, 1991). Kotler (2000) states that
satisfaction is a person’s feelings of contentment or disappointment resulting from comparing
a product’s perceived performance, in relation to his or her expectations (Kotler, 2000). The
hypothesis however that satisfaction affects customers’ future behaviour (revisit frequency
and repeated purchase) not only is intuitively strong but also empirically supported by studies
that explore a link between satisfaction, loyalty and profitability (see: Fornell and Wernerfelt,
1987; Anderson, Fornell and Lehmann, 1994, among others).
Many scholars in the field of electronic commerce and information systems regard the
work of Delone and Maclean (1992) as a major breakthrough. Molla and Licker (2001)
recognise the existing similarities between e-commerce systems and other information
systems and are stimulated to exploit the possibilities of extending the theories about
information systems success to the e-commerce context. With a relevant study they make an
attempt to apply and extend Delone and Maclean’s (1992) established model to e-commerce
success. To do so, they define an independent variable called customer e-commerce
satisfaction (CES) that should be treated as a product of a continuous process of satisfaction
and reformulation. Continuous assessments enable the identification of trends and the
evaluation of customer e-commerce satisfaction in depth of time. Furthermore, customer e-
commerce satisfaction has to be regarded cross-culturally, because there are many companies
that do business globally, thus satisfaction may be depicted on different dimensions in various
cultures (Molla and Licker, 2001) (Molla and Licker, 2001).
In the e-commerce context, there is a great gap between customer needs and the way
that a company perceives them (Cox and Dale, 2001). When management in particular
misinterprets the customer needs, the customer’s evaluation for service quality will not be
objective. Heskett et al. (1994) exploit this fact and highlight the importance of high
customer satisfaction for a good financial performance. Lin (2003) in an attempt to address
this gap argues that providing the highest delivered value by e-commerce can be considered
as a real contribution to customers and identifies three dimensions that significantly
influences customer satisfaction which are: customer need, customer value and customer cost.
Boyer, Tomas and Hult (2006) with their study for the British on-line groceries market
reveal that customer perceptions of overall satisfaction gets better as they gain experience
with the new method of ordering and receiving groceries. Furthermore, the choice of picking
method seems to have a large impact on overall customer satisfaction in particular for the
experienced users. Service and product quality as well as time savings also affect significantly
customers’ purchasing intentions. Boyer, Tomas and Hult (2006) base their study on the
groceries market which is one of the most universal commodities and the major initiative
(called efficient consumer response) to modernise the supply chain, whose volatility and
uncertainty is responsible for a large portion of hidden costs for trading (Frankel, Goldsby and
Whipple, 2002).
Poel and Buckinx (2005) finally use different types of predictors to forecast
purchasing behavior. Their study incorporates predictors that were used in past studies while
they also introduce some new ones. This way the scholars incorporate data concerning not
only web site visiting frequency but also the kind of web stores, historical purchases and
detailed customer demographics which increase the performance of the model. Their model is
a powerful on-line purchasing behavior instrument which offers a better way to classify
customers concerning their future on-line purchase behavior.
2.3 On-line shopping attributes
Many studies are exploring the online shopping attributes for a successful
performance of a web store (see: Jarvenpaa and Todd, 1997; Lohse and Spiller, 1998;
Syzmanski and Hise, 2000; Liu and Arnett, 2000, among others). These studies make a
general classification of on-line stores’ attributes into four categories: a) merchandise, which
includes product related characteristics such as assortment, variety and product information
(Jarvenpaa and Todd, 1997); b) customer service and promotions, that is careful, continuous
and useful communication with customers across geographic barriers, (Lohse and Spiller,
1998); c) navigation and convenience, which is closely related to the user interface, store
layout, organisation features and ease of use (Szymanski and Hise, 2000); d) security
perception, which mainly deals with customers’ trust and safety of transactions (Elliot and
Fowell, 2000; Szymanski and Hise, 2000).
Based on the four attribute categories, Park and Kim (2003) examine consumer’s
relational purchasing behaviour in an on-line shopping context and find that user interface
quality, service information quality, security perception and site awareness have significant
effects on consumer’s on-line store commitment. The most important factor, however, among
the four, is service information, the quality of which enables consumers to reduce costs of
information search and processing (Alba et al., 1997) although some factors that affect
consumer purchasing behaviour (see: Jarvenpaa and Todd, 1997; Lohse and Spiller, 1998;
Syzmanski and Hise, 2000; Liu and Arnett, 2000, among others) such as price and promotion
are excluded from Park and Kim’s (2003) research.
Segmenting consumers into four categories according to their involvement and
whether they are innovators or adaptors Park and Kim (2003) reveal a robust causal link
between consumers’ brand loyalty and website loyalty as well as a close link between
consumers’ cognitive style or involvement type and their website loyalty. Justified by the fact
that consumers’ brand loyalty gives companies a sustainable competitive advantage (Gounaris
and Stathakopoulos, 2004) other scholars propose that website managers can effectively
enhance consumers’ website loyalty by targeting their underlying cognitive involvement as
each segment will respond to internet activities in a different way.
In literature there are different dimensions of shopping on-line and various tests about
their relationship with satisfaction (see: Kim, 2005; Javenpaa and Todd, 1996; Alba et al.,
1997; Raymond, 1985; Baroudi and Orlikowski, 1988; Doll and Torkzadeh, 1988; Davis,
1989, among others). Furthermore, there are several characteristics of an online shopping
experience that may be related to the users’ demographic data (Bellman, Lohse and Johnson,
1999). Preferences in e-commerce sites are differentiated by age, education and income
(Lightner, 2003). In Lightner’s (2003) study as respondents increase in age, income or
education the preferences impact of on-line shop characteristics become less important, while
reputation of the vendor rise. Lightner (2003) summarises that preferences are much clearer
for the mature affluent customers whose sensory impact is not affected by complicated and
fancy design elements. This customer group is more concerned about products that meet their
needs regardless of price. On the other side, younger and less affluent target groups are more
concerned on product information and their sensory impact is more affected with sense
invoking design. Price in this study doesn’t seem to be a major issue for this target group
(Lightner, 2003).
Internet is being widely used for commercial activity (Liu and Arnett, 2000; Robbins
and Stylianou, 2003). In light of this fact many e-commerce firms are developed and they
highly depend on customers’ visits to their web stores, purchases and, more importantly,
customers’ post purchase behaviour (revisit and repurchase) (Smith and Merchant, 2001).
Cao, Zhang and Seydel (2005) make an effort to pool together a set of factors that they were
proved to affect the quality of an on-line store, following Liu and Arnett’s (2000) crucial
factors that lead to e-commerce success (information quality, system use, playfulness and
system design quality). These factors are proved to affect customers’ preferences and
intentions and finally make them repeat customers. Thus, Cao, Zhang and Seydel (2005) view
the on-line store’s quality from a customer’s perspective and transform their web-site quality
attributes in functionality, content, service and attractiveness. Their research model is built
upon technology acceptance model (TAM), (Davis, 1989), information systems success
model (Parasuraman, Zeithaml and Berry, 1988), and the trust concept (Delone and McLean,
1992). They finally conclude that, information quality, system quality and service quality of
an on-line store, plays an important role in affecting customers’ perceptions although
attractiveness is less critical in the business to business (B2B) context in which their research
takes place.
A research that is conducted by Zviran, Glezer and Avni (2006), incorporates the
parameter of web sites type differentiation in the concept of user satisfaction. The World
Wide Web hosts web sites of variable types with great differences in target audiences, making
it difficult to classify them. However, several attempts to do such a classification are made.
Hoffman, Novak and Chatterjee (1995) for instance propose a classification of commercial
web sites into six categories: online storefront, internet presence, content, mall, incentive, and
search agent. Additionally, Cappel and Myerscough (1996) classify the business use of the
World Wide Web into marketplace awareness, customer support, sales, advertising, and
electronic information services.
Zviran, Glezer, Avni (2006) adopt the compact IBM (1999)2 classification of web sites
which is based to the volume of traffic and finally propose a classification of five types of
high-volume web sites: publish/subscribe, online shopping, customer self-service, trading,
and B2B, from which they exclude the last one for overlapping the other four. Finally, they
empirically investigate the effect of user-based design and web site usability on user
satisfaction across the four proposed types of commercial web sites. The study’s findings
indicate that web sites have a great range of hidden and subjective factors that act beyond the
process of user and system interaction and affect overall user satisfaction which could serve
the development and maintenance phases of web site creation. By refining other recent studies
the authors conclude that Web site success is not related only to usability measures but also
incorporates the user-based design construct (Zviran, Glezer and Avni, 2006).
2 IBM, Summary of high-volume Web site classifications, 1999.
2.4 Satisfaction indexes
A variety of metrics are developed for the evaluation of e-commerce success such as
page hits, views and conversion rates. Quaddus and Achjari (2005) incorporate in their
research both operational and strategic measures differentiating driving and impeding factors
according to their contribution to e-commerce success. The results indicate that organisations
usually take into consideration the advantages of information technology but ignore the
factors that may stop their achievement. The main purpose of e-commerce use by
organisations is to achieve benefits from its use such as cost savings and customer
relationship management services for customers (Quaddus and Achjari, 2005). Besides e-
commerce apart from the electronic buying and selling purchasing process includes all the
other activities that support the sale process (Applegate et al., 1996). Quaddus and Achjari
(2005) suggest that increased benefits from the use of e-commerce can predict the perceived
expected success of its use although lowering constraints does not significantly affect the
success of e-commerce.
Kim (2005) applies the concept of ‘satisfaction’ in three different perspectives:
management information systems (MIS), marketing and e-commerce. In his study he
develops an index using a weighted sum model to measure satisfaction, viewed from these
three different aspects. Kim (2005) views e-customers not only as computer users but also as
consumers. Kim’s (2005) study is considered by its author as the first step to integrate
satisfaction literature, as he identifies a large set of variables retrieved by a large collection of
studies (see: Bailey and Pearson, 1983; DeLone and McLean, 1992; Anderson, Fornell and
Lehmann, 1994; Arnott and Bridgewater, 2002, among others). Kim in his study makes an
attempt to produce an instrument for measuring e-commerce end-user satisfaction based on
similar models that use weighted sum indexes to measure satisfaction (see: Raymond, 1985;
Baroudi and Orlikowski, 1988; Doll and Torkzadeh, 1988; Davis, 1989).
Cho and Park (2001) in their study for the development of electronic commerce user-
satisfaction index (ECUSI) make an effort to produce a way of measuring the overall end-user
satisfaction. Trying to illustrate various existing patterns within the on-line shopping
environment they view e-customers from two different perspectives. To measure their
satisfaction they regard them as both customers of a retail business and users of information
technology. The development of their satisfaction index is closely related to the test of the
underlying theoretical relationship among related constructs (Bagozzi, 1994). The score of
this instrument is directly related to the future purchasing intention of on-line customers,
because it is directly influenced by the research model constructs (Cho and Park, 2001). Cho
and Park’s (2001) study is very similar to Kim’s (2005) in calculation of the satisfaction
index.
Finally Wang, Tang and Tang (2001), present another e-commerce satisfaction
measurement tool tested on member customers of web sites that market digital products and
services. Considering existing satisfaction measurement models inapplicable as they referred
to conventional data processing or the end-user computing environment, they produce a
generally applicable instrument providing a common framework for the comparative analysis
of results from various other studies (see: Churchill, 1979), with the use of advanced
statistical techniques. In specific, this tool can be used to compare customer information
satisfaction for different websites incorporating specific factors (customer support, security,
ease of use, digital products/services, transaction and payment, information content, and
innovation).
2.5 Loyalty and trust
As the base of potential online customers increases, consumer loyalty and trust, is
regarded as the core of brand equity (Aaker, 1991), a basis for a price premium (Aaker, 1996)
and an essence of the relationship between business and consumer (Reichheld, 1996).
Following these facts, literature concerning commercial trust and especially web trust has
started to become richer. The internet era however, created a brand new B2C e-commerce
market which is so new that today there is a lack of extended literature investigating
consumer loyalty and trust in this market.
Wang et al. (2005) propose a model to describe how consumers transfer their existing
brand loyalty in the traditional retail market to the same brand’s website in an on-line
shopping context and how their perceived risk at the brand’s website intervenes with this
loyalty transformation. Wang et al. (2005) make an effort to fill the gap to the limited
literature investigating consumer loyalty in the e-commerce context following the study of
Jarvenpaa and Todd (1997).
As Internet is becoming an essential business tool for trading, distributing and selling
products between organisations and consumers (Barnes and Vidgen, 2000), the interest for
building strong relationships with e-customers is increasing. It is known that trust is a
fundamental principle for every business relationship (Hart, Saunders and Power, 1997) and
therefore a crucial stimulator for electronic purchasing (Quelch and Klein, 1996). This is
illustrated in Keen’s (1997) study in which he argues that the lack of consumer trust is the
most significant long-term barrier for the development of internet shopping and
comprehending the impact that internet marketing have.
Corbitt, Thanasankit and Yi (2002) try to identify the key trust-related factors in the
B2C context as well as to propose a framework based on relationships among these factors by
testing a set of hypotheses. They find trust, to be caused predominately by three sources: e-
commerce reputation in general, the consumers, and the specific e-commerce web site.
Findings of the research also suggest that the likelihood of purchasing on-line is positively
related to the perceived trust in e-commerce and to experience in internet usage.
The issue of security in particular is both of a short and long term concern. Furnell and
Karweni (1999) imply that not only future but also current web customers are concerned
about security problems. They try to examine the requirement of all stakeholders for
technologies that will provide a basis for trust in an e-commerce context. They conclude that,
although there is a significant concern among on-line consumers about the security of their
on-line purchases, the benefits offered by the medium, diminishes them. Furnell and Karweni
(1999) also reveal that there is a lack of awareness or understanding of the available security
technologies and this is a major problem because it prevents the establishment of a wider
foundation of trust based on the new technology.
Another major barrier for further e-commerce growth is the lack of consumer
confidence in web stores (Kaplan and Niescwietz, 2003). This fact leads many researchers to
focus on e-commerce trust and its impact on purchasing intentions. Kaplan and Niescwietz
(2003) examine two factors that may minimise trust barriers on customers’ on-line purchasing
intentions. These factors are whether a web trust seal is displayed on an on-line store and the
popularity of the site. They propose that each one of these factors significantly affect future
purchasing intentions. This proposition is supported by their research model, while statistic
tests reveal that both web trust seal as well as popularity of an on-line store have a significant
effect on purchasing intentions. The scholars however, regard the fact that benefits of web
trust are driven by changes in assurance beliefs, as the most important finding of this study.
E-commerce nowadays generates huge revenues for modern firms (Modahl, 2000) and
offers the capability to purely domestic firms to trade globally (Quelch and Klein, 1996).
However, there is very confined literature on how antecedents to internet-based purchasing
affect the customer intentions to shop on-line and interact with each other. For instance,
purchasing on-line may be inhibited by the suspicions and risks connected to the use of
technology, mostly within international transactions (Gefen, 2000). This fact is identified as a
major factor that affects consumer purchasing decisions (see: Bauer, 1960; Dowling and
Staelin, 1994; Weber, Blais and Betz, 2002, among others). Kuhlmeier and Knight, (2003)
emphasise the need to overcome the negative image that on-line shopping may present to
consumers in global markets, although their research’s findings do not apply cross-culturally
due to different distribution rates of internet technology around the world (Kuhlmeier and
Knight, 2003). Their study is based on other studies with similar goals (see: Verhage, Yavas
and Green, 1990; Mitchell and Vassos, 1997; Weber and Hise, 1998; Makhija and Stewart,
2002).
Lancastre and Lages (2005) find that trust and commitment are the main factors for e-
customer cooperation, which is positively affected by termination costs, supplier relationship
policies and practices, communication and information exchange, while it is negatively
affected by product prices and opportunistic behavior. Furthermore, their findings support that
trust is a prerequisite of commitment development. Researchers reinforce the idea that
customer relationship process should be viewed as a long term rewarding process (Lancastre
and Lages, 2005). Based on the concept that in the electronic market context, marketing is
required to perform new roles, such as customer support service (Kalyanam and Mcintyre,
2002) which is closely associated with trust, commitment, and cooperation, they encourage
suppliers to view customers’ position more closely before taking relationship management
decisions.
2.6 Summary
In this section some of the most representative studies about satisfaction and the
prerequisites that an on-line store should fulfill to meet satisfaction criterions, were presented.
Some of the dominant satisfaction determinants that were presented in this section are
accordingly modified and incorporated to our approach of e-customer satisfaction in the
Greek on-line shopping context. Consequently, some of the prevalent methodologies that
were developed to address the issue of measuring satisfaction and its impact on post purchase
behavior are presented in the next section.
3. Methodology
3.1 Introduction
The common objective in most of the studies about customer satisfaction is to define
the factors that mostly affect customers’ experience when shopping on-line. This objective is
approached from different points of view by many scholars across the world, since there is a
plurality of factors that may satisfy this convention. Although different methodological
approaches are used to address this problem, most of the literature presents surveys on
customers to test sets of hypotheses and explore relationships between the satisfaction
determinants, based on theoretically conceived research models. However, there are also
some studies that produce an algebraic score of overall satisfaction using a sum weighted
formula such as Kim’s (2005) ECUSI (E-commerce User Satisfaction Index) that was
presented in chapter two. The prevailing studies that are used as drivers for the development
of our study’s methodology are those of Zviran Glezer and Avni (2006), Wang et al. (2005),
Kim (2005), Kuhlmeier and Knight (2003), Park and Kim (2003), Cho and Park (2001),
Wang and Strong (1996) where a set of on-line shopping attributes are checked for
influencing customers’ overall satisfaction and repurchase intention. Furthermore the
calculation of the algebraic satisfaction score is also used in our study. Other studies (see:
Kuhlmeier and Knight, 2003; Delone and Maclean, 1992; Gwinner, Gremmler and Bitner,
1998 among others) help in reaching the main objective of our research, which is to test the
relationship between a set of on-line shopping attributes to the overall customer satisfaction in
the Greek web shopping context. Moreover, these studies also provide a list of satisfaction
items for the development of our research instrument but also present various methodological
views, which help in conceiving the conceptual framework of this research.
3.2 Relevant methodologies
In an attempt to identify the key factors that can affect on-line customer purchasing
behavior, Park and Kim (2003) develop a research model to test whether some selected on-
line shopping attributes alter customers’ perception of an on-line store. They use two
constructs, “information satisfaction” (see: Delone and Maclean, 1992; Wang and Strong,
1996) and “relational benefit” (see: Gwinner, Gremmler and Bitner, 1998) as mediating
factors between the main constructs and consumers’ purchasing behaviour (Crosby and
Stephens, 1987). In their study, Park and Kim (2003) conceptualise information satisfaction
as “an emotional reaction to the experience provided by the overall information service”
following the definition of Westbrook (1983). The set of factors they use in order to build
their research model were proved to affect customers’ purchasing behaviour.
First of all “information quality” (Delone and McLean, 1992), which is divided into
product information quality and service information quality, refers to the information
provided by the on-line store for product characteristics and service rendering respectively.
They identify six components to determine information quality which are: relevancy, recency,
sufficiency, playfulness, consistency and understandability (see: Delone and McLean, 1992;
Wang and Strong, 1996), which are adopted, after slight adaptations, by our study’s
methodology. Park and Kim (2003) develop their first hypothesis as follows:
H1: There is a positive relationship between information satisfaction and information quality.
“User interface quality” is another factor which refers to site design and layout,
information search convenience and easy navigation sequence (Spiller and Lohse, 1997). Park
and Kim (2003) use four items to measure user interface quality which are: convenience for
ordering and searching products, ease of navigation and user friendliness, which are also
adapted to be used in this study’s methodology. Thus their second hypothesis is as follows:
H2: There is a positive relationship between information satisfaction and user interface
quality.
“Security perception” is the last factor tested for affecting information satisfaction by
Park and Kim (2003). Gefen (2000) proves that consumers are concerned about payment
security, reliability and privacy policy. Based on Gefen (2000), Park and Kim (2003) identify
three items to describe security perception which are: payment security, sufficient privacy
policy information and reliable private information management. Therefore their third
hypothesis is the following:
H3: There is a positive relationship between information satisfaction and security.
Relational benefit is defined as “the benefit that customers receive from long term
relationships above and beyond the core service performance” (Gwinner, Gremmler and
Bitner, 1998). Based on this definition Park and Kim (2003) test the following hypotheses:
H4: There is a positive relationship between information quality and relational benefit
H5: There is a positive relationship between security perception and relational benefit
“Site awareness” is also included in their research and is retrieved from Aaker (1991)
who defines site awareness as “the ability of a buyer to recognise or recall that a site is a
member of a certain service category”. The hypothesis that Park and Kim (2003) test was the
following:
H6: There is a positive relationship between site awareness and relational benefit.
Finally Park and Kim (2003) prove that both “information satisfaction” and “relational
benefit” are positively related to site commitment which is positively related to purchasing
behaviour. This is a hypothesis that is investigated by many other studies (see: Garbarino and
Johnson, 1999; Hocutt, 1998 among others) who argue that a committed customer will revisit
an on-line store and make repeated purchases. The hypothesis that Park and Kim (2003) test
are the following:
H7: Information satisfaction is positively related to site commitment.
H8: Relational benefit is positively related to site commitment.
Park and Kim’s (2003) study is based on a web based questionnaire hyperlinked to
selected on-line bookstores. Respondents were Korean members of the specific bookstores.
The survey period was from three to four weeks for each selected bookstore and the number
of valid and usable questionnaires was 602. The validity of each construct is assessed with
principal component factor analysis using VARIMAX rotation. The result of this study is that
user interface quality, product and service information quality as well as security perception
and site awareness have significant effects on site commitment, while information satisfaction
and relational benefit have a significant mediating effect between the selected satisfaction
determinants and consumers’ purchasing behaviour.
Kim (2005), in his attempt to develop an index of measuring satisfaction, collects a
large set of variables from an extended literature review on satisfaction. This way, he comes
up with 126 variables related to customer satisfaction of which only 52 are finally used due to
overlapping and similarities among them. In his proposed research model he examines ten
factors and associates them with e-commerce overall satisfaction, repurchase frequency and
repurchase intention. Kim’s research model is actually a weighted average sum model,
advanced by extending the number of independent satisfaction constructs and by linking
satisfaction with two more independent variables, repeated purchase intention and purchase
behavior. Richnis (1983), Ho and Wo (1999), Lee (1999) and Vijayasarathy and Johnson
(2000) are some of the main and most completed studies that Kim relies on to retrieve
variables and incorporate them into his research.
Kim’s (2005) research model is tested in a sample of respondents consisting of 40 per
cent on-line Korean shoppers that are employed and of 60 per cent Korean student shoppers.
His research model produces an overall satisfaction index for each respondent which is
calculated from the equation (where Rij is Rating of item j, Wij is
importance of item j and ECCSIi is electronic commerce customer satisfaction index for
respondent i). Kim (2005) performs discriminate validity tests using principal component
factor analysis with VARIMAX rotation. Based on Kim’s (2005) study we adjust and
incorporate his overall satisfaction index in the Greek on-line shopping context.
Cho and Park (2001) in a similar study developed an index measuring electronic
customers’ satisfaction. Based on previous literature and by interviewing MIS and marketing
researchers, they identify ten constructs that were proved to affect customer satisfaction. The
constructs that they used were: quality of product information (Delone And Maclean, 1992;
Baroudi and Orlikowski, 1988), level of consumer services (Baroudi and Orlikowski, 1988),
satisfaction with purchase results and delivery (Delone and Maclean, 1992), goodness of site
design (Delone and Maclean, 1992), satisfaction with purchasing process (Tanner, 1996),
quality of product merchandising and portfolio (Fornell et al., 1996; Jarvenpaa and Todd,
1997), satisfaction with delivery time and charge (Tanner, 1996), convenience of payment
methods (Jarvenpaa and Todd, 1997), ease of use (Delone and Maclean, 1992), and provision
of additional information services (Baroudi and Orlikowski, 1988).
Cho and Park (2001) use 51 items suggested by marketing and electronic commerce
experts, to explore the constructs they conceive. They measure those items on a seven point
Likert scale using a sample of 435 usable responses out of 2,000 questionnaires that were
initially distributed. They perform principal components factor analysis that proves a
consistent factor structure. Reliability tests produce acceptable results, while their proposed
index score was calculated as the summation of all the respondents’ responses, which is
algebraically described with the equation: (where ECUSI is Electronic
Customer-User Satisfaction Index, Rij is Reaction to factor j by user i).
In order to examine the relationship between the index and the level of consumers’
purchasing intention, Cho and Park (2001) perform regression analysis, results of which
revealed that consumer service, purchase result and delivery, site design, purchasing process,
product sales, delivery time and charge are significantly contributing variables to the
dependent variable. Construct “site use” however produce a relatively low β coefficient
showing weak contribution to the predictive power of this model. This index is considered, by
the researchers, efficient since both validity and reliability measures are within acceptable
levels. Finally the index score is closely related to the level of consumers’ purchasing
intention. Some of the items Cho and Park (2001) use are incorporated to our model because
they were proved that they significantly affect satisfaction. Besides, this study could be
regarded as an ancestor of Kim’s (2005) because its objective is very similar and Kim’s
(2005) index is actually an extension of Cho and Park’s (2001) index.
Wang et al. (2005) conduct a study in order to test whether innovativeness and
involvement are determinants of website loyalty. They conceptualise a framework to describe
how consumers transfer their existing brand loyalty in the traditional retail market to the same
brand’s Website in the B2C e-commerce market and how their perceived risk at the brand’s
Website mediates this loyalty transformation. They split customers into four segments: less
involved adaptors, more involved innovators, more involved adaptors, less involved
innovators.
The constructs they examine are: brand loyalty in the traditional market, website
loyalty to the brand’s website and actual website purchasing frequency. Also, they use
perceived risk when purchasing at the brand’s website as a mediator between dependent and
independent variables. Consumers’ perceived risk when buying at the brand’s website is used
as a mediator between brand loyalty and website loyalty. However, only the more-involved
customers’ segments demonstrate a positive casual link between the website loyalty and the
actual website buying frequency.
The instrument they use to test the set of hypotheses is a web based questionnaire and
the responses are collected via e-mail invitations to Taiwan internet buyers of a well-known
brand’s Website. This way they finally collected 1,044 valid responses.
Applying factor analysis with the use of principal components method with
VARIMAX rotation Wang et al. (2005) test the validity of their instrument. Also, correlation
tests reveal that all the hypotheses are strongly supported except one that is only weakly
supported (only the more-involved segments will demonstrate a positive casual link between
the website loyalty and the actual website buying frequency). The study’s results reveal that
consumers’ cognitive style and involvement level lead to distinct loyalty transformation
model between the four consumer segments.
Zviran, Glezer and Avni (2006) make an effort to empirically test user satisfaction in
different types of web sites in relation with usability and user based design. Based on previous
studies, they presume that the better the site design fit consumers’ preferences, the higher the
satisfaction attributed to the site and the higher the loyalty. This leads to the formulation of
their first hypothesis:
H1: Web sites exhibiting a higher degree of usability will be associated with greater
perceived user satisfaction.
Zviran, Glezer and Avni (2006) based on Hansen’s (1981) user based design
principles, (knowing the user, minimising memorisation, optimising operations, engineering
for errors) develop the second hypothesis considering that when a site satisfies these
principles it can achieve greater perceived satisfaction. Their hypothesis is the following:
H2: Web sites adhering to user-based design principles will result in greater perceived user
satisfaction.
Finally, the great heterogeneity of web sites and the indulgence to categorise web sites
influences usability and perceived satisfaction depending on the type of the web site. Thus,
the third and fourth hypotheses in Zviran, Glezer and Avni (2006) study are as follows:
H3: The type of a Web site influences the relationship between the Web site’s usability and
perceived user satisfaction.
H4: The type of a Web site influences the relationship between the Web site’s user-based
design capabilities and perceived user satisfaction.
The instrument that Zviran, Glezer and Avni (2006) use three prior research
instruments (see: Doll, Xia and Torkzadeh, 1994; Brooke, 1996; Abels, White and Hahn,
1998) as frame of reference. The questionnaire is comprised of 39 questions including basic
demographic information. It is tested for construct validity by performing principal
components factor analysis with VARIMAX rotation which produces significant loadings. A
number of 359 valid responses were received through the web based questionnaire of which
58 per cent male and 42 per cent female. There is a quota placement in order to distill the
sample according to IBM’s web site classification, which according to the authors is the only
acceptable site categorization. The responses are categorised accordingly: publish/subscribe
(90 responses), online shopping (90 responses), customer self-service (90 responses), and
trading (89 responses).
Zviran, Glezer and Avni (2006) also performed regression analysis in order to
estimate the model’s coefficients using the following equation:
(Equation 3.1) Satisfaction = a + X*(usability) + Y*(content) + Z*(search)
The results of regression analysis indicated strong support for both first and second
hypotheses since it yielded high β coefficients. Regression results also indicated strong
support for both third and fourth hypotheses, while multicollinearity tests revealed that their
predictors do not autocorrelate.
Kuhlmeier and Knight (2003) propose a research model where internet proclivity and
experience are positively related to on-online purchasing likelihood (Goldsmith, 2002).
Internet proclivity in this study is defined as the frequency, in hours per week, that someone
uses the internet while experience is defined as the amount of time, in years, that someone has
used the internet (Miyazaki and Fernandez, 2001). In our study user’s experience is
determined in the same way. Parameter of risk is also assessed in Kuhlmeier and Knight
(2003) study and perceived risk is considered to be negatively related to purchasing likelihood
(Shimp and Bearden, 1982). Perceived risk is used as a mediator between internet proclivity
and purchasing likelihood as well as between internet experience and purchasing likelihood.
They formed their set of hypotheses which are tested through an on-line consumers survey.
The instrument of this survey is a questionnaire addressed to business students in three
different countries, France, Macao and the US. The survey scales are assessed for construct
validity by performing confirmatory principal components factor analysis with VARIMAX
rotation and for reliability (see: Nunnally, 1978) for each of the three national samples
separately. The results of the hypotheses tests suggest that internet proclivity is not of great
importance as an antecedent of risk perception. Internet experience on the other hand seems to
have a significant negative relation to the perception of risk. Furthermore both internet
proclivity and internet experience as well as perceived risk appear to affect significantly
consumers’ purchasing likelihood. The examined constructs however, seem to have great
variations among the different countries that the survey took place, due to the great
differences in technological infusion (Kuhlmeier and Knight, 2003).
3.3 Conceptual framework
In our study we examine the effect of some on-line shopping attributes on overall user
satisfaction as well as the impact of satisfaction on customers’ post purchase behavior in.
More specifically, based on literature, a set of on-line shopping attributes are identified and
used to test whether and to what extent they affect on-line customers’ satisfaction. Initially,
twelve constructs are selected from which we narrowed to seven due to similarities in
meaning and lack of applicability in the Greek context. The attributes that we finally ended up
to are the following: (a) User interface quality (Cho and Park, 2001) refers to graphic layout
of a web store, playfulness, convenience and easiness to use menus and controls. This
attribute explores in general the easiness for the user to navigate wanted pages in an elegant
and tasteful environment; (b) Product and service information quality (Cho and Park, 2001)
refers to how sufficient, updated, easy to understand and consistent information the site
provides about its products and services; (c) Security perception (Cho and Park, 2001), deals
with safety issues in general like personal information management and payment security; (e)
User’s participation (Kim, 2005) is based on the measurement of user’s experience in
shopping on-line in combination with the time that the user spends on-line; (f) Purchasing
process convenience (Kim, 2005) refers to how easy and convenient it is for the user to
purchase a product and if the site provides detailed information on how to do that and (g)
Product attractiveness (Kim, 2005) which examines how desired is the product that a site
sells, how many product categories can a user find in this site and what percentage of
availability is there in the web store. Using these constructs the following research model is
developed:
Figure 3.1. The research model
3.4 Hypotheses development
In order to examine the relationships between the selected on-line store attributes and
their effect on overall customer satisfaction we are testing a set of twelve hypotheses. The
hypotheses conceptualisation is mainly based on the literature review that was presented in
chapter 2 with some adjustments were necessary.
Accuracy, update and consistency of information in a web store about its products
increase overall customer satisfaction. This fact is strongly supported in Park and Kim’s
(2003) study. Although product and service information quality is examined in many other
studies (see: Ho and Wu, 1999; Kim, 1999; Cho and Park, 2001 among others), in most of the
cases it is presented with a different label (for example, in Kim’s study it is presented as
product information). Hence, the first hypothesis is the following:
H1: Product Information Quality is positively related to E-commerce customer satisfaction.
A pleasant, tasteful and playful layout of a web store as well as the convenience for a
customer to navigate across its pages increases the overall customer satisfaction. Many
studies are dealing with issues concerning the interface of an on-line store and it is considered
as a primary determinant of customer satisfaction (see: Park and Kim, 2003; Kim, 2005;
Zviran, Glezer and Avni, 2006, among others). Thus, the second hypothesis is formulated as
follows:
H2: User interface quality is positively related to E-commerce customer satisfaction.
Consistency, accuracy and update are also essential for the services that an on-line
store provides (Fornell et al., 1996). In the same way as product information quality, service
information quality is also tested for positively affecting overall satisfaction. Thus, the third
hypothesis is as follows:
H3: Service Information Quality is positively related to E-commerce customer satisfaction.
It is imperative that an on-line store has simplified, easy and quick purchasing process.
The more convenient for the user this process is, the highest the level of satisfaction attributed
to the web store (see: Kim, 2005; Ho and Wu, 1999; Zviran, Glezer and Avni (2006), among
others). After this, fourth hypothesis is formed as follows:
H4: Purchasing process convenience is positively related to E-commerce customer
satisfaction.
Security is one of the most important concerns of on-line customers worldwide.
Security credentials provided by an on-line store, privacy policy and trust are only some of
the parameters of security issue. The more these parameters are developed in a web store the
highest the level of customer satisfaction. Security is considered of such importance that some
studies are dealing only with this issue (see: Corbitt, Thanasankit and Yi, 2003). Most of
studies however, incorporate this issue in their research framework among other examined
variables (see: Park and Kim, 2003). From all the above the fifth hypothesis is formed as
follows:
H5: Security perception is positively related to e-commerce customer satisfaction.
It is more convenient for a user to find a variety of product categories in a single web
store for which he has a formed attitude. This way the user does not have to look in different
stores for different products. The most popular the product categories and the bigger the
product categories amount in an on-line store the highest the level of overall satisfaction (see:
Kim, 2005; Javenpaa and Todd, 1996; Kim, 1999). So sixth hypothesis is the following:
H6: Product attractiveness is positively related to E-commerce customer satisfaction.
Users’ participation which refers to the frequency of on-line purchasing, and the
amount of total purchases shows an experienced user and also increase trust and consequently
overall satisfaction by the on-line shopping experience (Corbitt, Thanasankit and Yi, 2003).
The more experienced and involved a user is with internet and e-commerce the more satisfied
he is from his on-line purchasing experience (Lee, 1999). Thus, the seventh hypothesis is the
following:
H7: User’s participation, in e-commerce, is positively related to E-commerce customer
satisfaction.
High overall satisfaction levels from an on-line shopping experience lead in increase
to revisit frequency and to repurchase intention, subsequently it lead in increase to repurchase
frequency. This hypothesis is strongly supported by the literature and this is the reason for
most of studies examine constructs that affect satisfaction (see: Kim, 2005; Park and Kim,
2003 among others). So the eighth, ninth, tenth and eleventh hypotheses are as follows:
H8: Overall satisfaction level from a web store is positively related to revisit frequency.
H9: Overall satisfaction level from a web store is positively related to repurchase frequency.
H10: Overall satisfaction level from a web store is positively related to revisit intention
H11: Overall satisfaction level from a web store is positively related to repurchase intention.
Finally, as it is expected that revisiting intention increases repurchase intention an
extra hypothesis is tested, so the twelfth hypothesis is as follows:
H12: Revisit intention is positively related to repurchase intention.
3.5 Instrument development
The instrument that is used to investigate the above proposed model is an on-line
questionnaire consisting of 31 satisfaction and 7 demographic questions. These questions are
primarily used to test the hypotheses that were developed and presented earlier in the analysis.
The response structure for each question is a seven point Likert scale. Furthermore, the
importance of every item for the user is also measured on a seven point Likert scale.
Measuring the importance of each item for each respondent we are able to produce a score for
each respondent the sum of which is an estimation of the overall e-commerce customer
satisfaction, following Kim’s (2005) index which has already been presented. Five more items
are used to measure, purchase behavior which refers to revisit and repurchase intention and
frequency. One of this item measures overall satisfaction as it is reported from respondents
and this is used in order to compare the index score with the self reported satisfaction.
This instrument is developed using a combination of items mainly retrieved from the
studies of Kim (2005) and Park and Kim (2003), after adjusting many of them to fit the Greek
respondents’ attitude. Lists of items from other studies are also employed to enrich the
primary list of items. Also some new questions are added where necessary. The questionnaire
was pre-tested through a pilot survey among the students of the Msc in Finance and Financial
Information Systems at the campus of TEI of Kavala. After this test some adjustments are
made in several items, while some unnecessary items were eliminated as they were conceived
by the respondents as similar in meaning with others or because they were not fully applicable
in the Greek language. Also several misinterpretations of specific items by the respondents
led to their elimination or correction. In table 3.1 a brief description of the questionnaire is
presented. For more detailed presentation refer to appendix A.
Table 3.1 Questionnaire description
Product Information quality
Updated
Sufficient
Easy to understand
Consistent
Playful
Relevant
Reliable
User interface quality
Convenient to order a product in evaluated store
Convenient navigation in evaluated store
Appropriate use of color in evaluated store’s interface
Convenient to search for a product in evaluated store
Tasteful screen layout and design of evaluated store
Service information quality
Updated
Sufficient
Consistent
Playful
Relevant
Purchasing process
Convenient arrangement of products
Easy to manage shopping basket
Sufficient usage directions
E-commerce participation On line purchases
Value of on line purchase
Percentage of electronic to total purchase
Security perception Effective guidance to correct entry errors
Protection of payment information
Proper management of private information
Product attractiveness Satisfactory product categories amount
Satisfactory availability percentage
3.6 Summary
Several methodological approaches that are used as drivers for the development of our
conceptual framework and methodology were outlined in this section. Furthermore, a set of
twelve hypotheses is presented in full detail. The set of hypotheses test the influence of
specific factors on overall customers’ satisfaction. Also the test of the impact that overall
satisfaction have on post purchase behavior is outlined. The instrument that is used to test
these relationships is finally presented. Appendix A shows the original version of the
questionnaire as was sent to the respondents. Further in the analysis some specific statistic
procedures are employed to test the validity of this research framework. In the next section
the specific statistic measures and the main findings of this study are presented. Appendix B
shows all the statistic measures that are used to produce the results and findings of this
research.
4. Empirical Results
4.1 Introduction
Satisfaction is a complicated concept and needs thorough study of literature as well as
careful and precise implementation of theory to conceive and measure it. Based on previous
literature and detailed study of the research methodologies that were presented in chapters two
and three we conceived a conceptual framework and constructed a research model comprised
of seven factors that were empirically proved to affect e-customers’ satisfaction. In this
chapter the proposed research framework is empirically tested in the Greek on-line shopping
context, through an on-line survey. The respondents’ body is comprised by on-line customers,
members of Greek on-line stores and its’ profile is described in full detail in table 4.1. The
dataset of responses is tested for validity and reliability and then the set of hypotheses is
tested using standarised statistics. Finally, regression analysis helps us to validate the
methodology, examine the impact of each factor on overall satisfaction and extract useful
conclusions for electronic commerce in Greece. The statistic mechanisms are conducted using
the Statistical Package for Social Sciences (SPSS v. 12.0).
4.2 Sample selection
Greek residents, who have established at least one on-line purchase in their life time
from a Greek on-line store, were asked to evaluate their most recent on-line shopping
experience. Data were collected through 1,826 e-mail invitations that were sent to member
customers of distinguished Greek on-line stores. Also e-mail invitations were sent to members
of specific forums and blogs about on-line commerce and internet usage in general. These e-
mail invitations were sent out in July 2007 and reminder e-mails were sent in August 2007.
Finally, from the total of 1,826 e-mail invitations that were sent out we received 390
responses, 359 of which were usable and valid (response rate 20 per cent). Responses were
categorised according to the respondents’ residence, age, working status, monthly income and
education level (Table 4.1). A quota placement on age and residence was set in order to
follow the typical Greek internet user profile (AGB Nielsen and the Information Society
Observatory, 2006), at the extent that this was possible. Respondents were asked to evaluate
the web store that they made their more recent purchase from. This way, seven well known
Greek on-line stores were finally evaluated in order to produce the most possible objective
satisfaction measurement. Specifically, 32.3 per cent of respondents evaluated e-shop.gr
which is an almost exclusively on-line store, trading technology products and electronics.
Next mostly evaluated store is aegeanair.gr with a percentage of 27.6 per cent. This web store
belongs to the largest aviation company in Greece. The third mostly evaluated on-line store is
plaisio.gr, which also trades technology products and electronics and was evaluated by the 24
per cent of the respondents. Far from the third mostly evaluated on-line store, t-bar.gr, an on-
line store which trades custom made T-shirts, gathered the 9.5 per cent of the evaluations.
Finally, two on-line bookstores, papasotiriou.gr and books.gr were both evaluated by the 2.8
per cent of the respondents.
A dataset was formed, by the responses that were finally collected, which was
processed using the Statistical Package for Social Sciences (SPSS) v.12.0. A missing value
analysis was performed using the series mean method in order to replace all missing values
and avoid bias. Also a questionnaire tracking method was used in order to edit and check the
coding process and minimise coding errors. The tracking was performed by assigning the
unique case number to the corresponding questionnaire.
4.3 Respondents’ profile
Respondents’ residence is in all the regions of Greece and the amount of required
responses, according to the quota placement, from each region was selected as a proportion of
its total internet users (AGB Nielsen and the Information Society Observatory, 2006). The
resulting sample consists of 66.6 per cent male respondents and of 33.4 per cent female
respondents. Age categorisation indicated that 23.4 per cent of the respondents belong at the
15-24 age bracket, 36.3 per cent at the 25-34 age bracket, 32 per cent at the 35-44 and the rest
8.3 per cent is older than 45 years old. Additionally, 15.8 per cent have a monthly income of
401-800€, while 18.6% belongs to the 801-1200€ income bracket, 33.8 per cent to the 1201-
1600€ and 27.5 per cent have a monthly income of more than 1600€. The working status of
respondents is 16.5 per cent self employed, 40.3 per cent work in an office working position
and 32.1 per cent work in an out of office working position. A 9.4 per cent of the respondents
are students while 1.7 per cent are unemployed. Additionally, experience of the respondents
with internet is measured using two parameters, time spent per logon and amount of logons
per week. Specifically, 7.5 per cent of the respondents spend less than an hour per logon,
while 21.7 per cent spend 1-2 hours per log on, 27.3 per cent 2-3 hours, 24.8 per cent 3-5
hours and 18.7 per cent of the respondents spend more than 5 hours per log on. Finally, 5.6
per cent of the respondents logs on less than 2 times per week, 13.1 per cent 3-4 times, 21.4
per cent 5-14 times and 30.9 percent 14-21 times. A significant percentage of respondents, 29
percent, log on more than 21 times per week (Table 4.1).
Table 4.1 Respondents' Profile
Gender Age
Male Female 15-24 25-34 35-44 45-54 55-64 65+
66.6% 33.4% 23.4% 36.3% 32% 5.3% 2.1% 0.9%
Income/month
-400€ 401€-800€ 801€-1200€ 1201€-1600€ 1601€-2000€ 2001€+
4.3% 15.8% 18.6% 33.8% 21.8% 5.7%
Working Status
Self Employed Employees
(in office)
Other employees
(not in office)
Student Unemployed
16.5% 40.3% 32.1% 9.4% 1.7%
Time spent per logon (in hours) Times per week
-1 1-2 2-3 3-5 5+ -2 3-4 5-14 14-21 21+
7.5% 21.7% 27.3% 24.8% 18.7% 5.6% 13.1% 21.4% 30.9% 29%
4.4 Satisfaction index
Two different variables are used to measure satisfaction. The first one is the degree of
satisfaction that each respondent reports from his own web shopping experience and it is
measured on a seven point Likert scale (self reported satisfaction). The second variable
follows Kim’s (2005) proposed index and uses a score which is calculated for each
respondent as a sum weighted average of all the respondent’s answers (index based
satisfaction). This score is divided by seven, so as to be consistent with the seven point Likert
scale. More specifically, every item that is examined in the questionnaire is weighted by each
respondent according to its importance. The weights of each item are also measured on a
seven point Likert scale. In both cases point 1 means that the specific item is not important at
all and point 7 means that the specific item is absolutely important.
The mean of the index based satisfaction for the total of the 359 responses is 4.79
while the mean of the self reported satisfaction is 5.76. The correlation between self reported
satisfaction and index based satisfaction is .929 and is significant at the 1 per cent level,
which is a strong evidence that Kim’s (2005) satisfaction measurement is reliable and
consistent with the self reported satisfaction scores. Moreover, we consider Kim’s (2005)
measurement of satisfaction reliable, because its standard deviation is .582 while self reported
satisfaction’s score standard deviation is .621, thus index based satisfaction contains less bias.
The reliability of this index is also proved by the t-value test that was performed in order to
examine the equality of the means (t358=77.898, p>.000). Results of this test are shown in
table 4.2.
Table 4.2 Means Comparison
Satisfaction N Mean S.D. t-value Sig.(2-
tailed)
Paired samples test
Index Based 359 4.79 .582 155.932 .000 t Sig. Mean S.D
Self Reported 359
59
5.74 .621 175.118 .000 77.89
8
.000 .944 .230
4.5 Construct validity and reliability analysis
The ratio between the amount of responses and the amount of variables is 12:1 and is a
primary evidence about the sample size adequacy (Parasuraman, Zeithaml and Berry, 1988).
Furthermore an acceptable Kaiser-Meyer-Olkin measure of sampling adequacy of .803 as
well as acceptable Bartlett sphericity test statistics (p =.000) also validate the sample size.
With acceptable results of all the tests concerning sample adequacy, the 29 items were
submitted to a principal components factor analysis using VARIMAX rotation in order to
assess the discriminate validity and convergence was achieved in 5 iterations. The resultant
seven factors produced strong factor loadings. However, two items were dropped because
they extracted very low communalities of .309 and .419 respectively which were not accepted
since they were significantly lower than the least accepted .5 value. Also construct “site
awareness” containing two items was dropped because it produced unacceptable Cronbach
Alpha reliability test (α = .345<.5). The remaining seven factors proved reliable since they
yielded Cronbach Alpha values greater than .549 (Table 4.3), while five out of seven factors
yielded values greater than .71. Finally, the seven resultant factors explain a 64.731 per cent
of the total variance. The seven resultant factors are: Product information quality, User
interface quality, Service information quality, Purchasing process, E-commerce participation,
Security perception and Product attractiveness. Further details on the factor analysis and
reliability results are shown in table 4.3.
Table 4.2 Factor and reliability analysis
Factor Name Items Factor
loadings
Cronbach
Alpha
Variance
Explained
Communalities
extraction
Factor 1 Product Information quality
Updated .757
.923 17.322
.594
Sufficient .936 .877
Easy to understand .891 .796
Consistent .630 .513
Playful .881 .783
Relevant .857 .742
Reliable .814 .683
Factor 2 User interface quality
Convenient to order a product in evaluated store .680
.830 11.269
.532
Convenient navigation in evaluated store .866 .735
Appropriate use of color in evaluated store’s interface 715 .607
Convenient to search for a product in evaluated store .829 .707
Tasteful screen layout and design of evaluated store .709 .537
Factor 3 Service information quality
Updated .791
.839 10.864
.650
Sufficient .858 .753
Consistent .648 .520
Playful .845 .728
Relevant .754 .593
Factor 4 Purchasing process
Convenient arrangement of products .758
.705 7.043
.586
Easy to manage shopping basket .811 .685
Sufficient usage directions .781 .650
Factor 5 E-commerce participation On-line purchases .783
.645 6.883
.687
Value of on-line purchase .747 .574
Percentage of electronic to total purchase .689 .504
Factor 6 Security perception Effective guidance to correct entry errors .664
.585 6.108
.563
Protection of payment information .758 .595
Proper management of private information .722 .581
Factor 7 Product attractiveness Satisfactory product categories amount .843 .549 5.242
.747
Satisfactory availability percentage .752 .602
4.6 Hypotheses tests
In chapter three, a set of twelve hypotheses was developed concerning the degree at
which each examined construct affects overall satisfaction and the relationship between
overall satisfaction and post purchase behaviour. In this section correlation tests are
conducted in order to examine the validity of this set of hypotheses. For this purpose
Pearson’s correlation coefficient is employed, the significance of which indicates the degree
of dependence between the resulting factors and the overall satisfaction variable. The validity
of the hypotheses is tested for both the index based satisfaction and the self reported
satisfaction with the bivariate correlations method of SPSS v. 12.0 (Table 4.4).
The first hypothesis about the relationship of product information quality and overall
satisfaction is as follows:
H1: Product information quality is positively related to e-commerce customers’ satisfaction.
This hypothesis is strongly supported by the data and is valid for the relationship of product
information quality with both the index based satisfaction and the self reported satisfaction.
Pearson’s correlation coefficient scores is .544 for the index based and .503 for the self
reported satisfaction respectively while correlations are significant at the 1 per cent level. This
fact illustrates that overall satisfaction strongly depends on product information quality.
The second hypothesis is the following:
H2: User interface quality is positively related to e-commerce customer satisfaction.
The second hypothesis is also supported by the data at the 1 per cent significance level with
Pearson’s correlation coefficient scores of .297 for the correlation of user interface quality
with self reported satisfaction and .315 for the correlation of user interface quality with index
based satisfaction. These scores indicate that interface quality is also a significantly affecting
factor for overall satisfaction.
The third hypothesis is as follows:
H3: Service information quality is positively related to e-commerce customer satisfaction.
Third hypothesis shows that service information quality affects overall satisfaction since it is
also supported by the data at the 1 per cent significance level with a Pearson’s score of .228
for the correlation of service information quality with self reported satisfaction and .254 for
the correlation of service information quality with index based satisfaction. Therefore overall
satisfaction also depends on service information quality.
The fourth hypothesis is the following:
H4: Purchasing process convenience is positively related to e-commerce customer
satisfaction.
This hypothesis shows that the purchasing process convenience is a determinative factor of
overall satisfaction. This hypothesis is supported by the data at the 1 per cent significance
level with .209 Pearson coefficient score for the correlation with the self reported satisfaction
and .205 for the correlation with the index based satisfaction. Thus overall satisfaction also
depends on the convenience of the purchasing process.
Fifth hypothesis is the following:
H5: Users’ e-commerce participation is positively related to e-commerce customer
satisfaction.
This hypothesis is not supported by the data since there is no significant correlation
coefficient score. This means that user’s experience in electronic commerce do not affect
overall satisfaction. Therefore more experienced customers are not necessarily more satisfied
than less experienced customers.
The sixth hypothesis is the following:
H6: Security perception is positively related to e-commerce customer satisfaction.
This hypothesis is supported at the 1 per cent significance level with Pearson’s scores of .180
for the correlation with self reported satisfaction and .209 for the correlation with the index
based satisfaction. Therefore it proves that customers’ satisfaction depend on security
perception.
Seventh hypothesis is the following:
H7: Product attractiveness is positively related to e-commerce customer satisfaction.
Seventh hypothesis is also supported by the data at the 1 per cent significance level with
Pearson’s scores of .179 for the correlation with the self reported satisfaction and .176 for the
correlation with the index based satisfaction. The relatively low coefficient scores indicate
that although overall satisfaction depends on product attractiveness the support of this
hypothesis is relatively weak in relation to the six first hypotheses.
Hypotheses eighth, ninth, tenth and eleventh are examining the influence that overall
satisfaction has on revisit frequency, repurchase frequency, revisit intention and repurchase
intention. These four factors are the main components of loyalty, the relationship of which
with overall satisfaction is of special interest for all on-line retailers (Wang, Chia-Yi, Pallister
and Foxall, 2005).
More specifically eighth hypothesis is the following:
H8: Overall satisfaction level from a web store is positively related to revisit frequency.
This means that a satisfied on-line customer will revisit more often the specific web store in
the future. This hypothesis is strongly supported at the 1 per cent significance level with high
correlation coefficients of .837 for the correlation with self reported satisfaction and .898 for
the correlation with the index based satisfaction.
Ninth hypothesis is as follows:
H9: Overall satisfaction level from a web store is positively related to repurchase frequency.
The strong support of this hypothesis shows that a satisfied customer is likely to increase his
repurchase frequency in the future. Ninth hypothesis is supported at the 1 per cent
significance level with also high correlation coefficients of .834 for the correlation with self
reported satisfaction and .894 for the correlation with the index based satisfaction.
Tenth hypothesis is the following:
H10: Overall satisfaction level from a web store is positively related to the revisit intention.
This hypothesis is supported at the 1 per cent significance level with high correlation scores.
For the correlation with the self reported satisfaction, in particular, Pearson’s coefficient score
is .740, while for the correlation with the index based satisfaction it is .788. These scores
provide strong support for this hypothesis and indicate that a satisfied customer strongly
intents to revisit the specific web store.
The eleventh hypothesis is the following:
H11: Overall satisfaction level from a web store is positively related to the repurchase
intention.
This hypothesis is also strongly supported by the data with correlation scores of .640 for the
correlation with self reported satisfaction and .663 for the correlation with index based
satisfaction. These scores illustrate the strong intention of a satisfied customer for repurchase
in the future.
A final test which is conducted between two dependent variables, the validity of which
could emerge valuable results, is whether revisit intention affects repurchase intention.
Therefore twelfth hypothesis is formed as follows:
H12: Revisit intention is positively related to repurchase intention.
This hypothesis is supported by the dataset of responses at the 1 per cent significance level
with a correlation coefficient of .567. The relatively strong support of this hypothesis means
that repurchase intention generally follows revisit intention.
All the tests that concern the validation of the set of the hypotheses as well as
coefficient scores for each hypothesis for both index based and self reported satisfaction are
shown in table 4.4.
Table 4.3 Hypotheses Tests
Hypotheses Pearson Coefficient for Satisfaction
Outcome
All Correlations are significant at
the .01 level (2-tailed)
Index Based Self Reported
H1 .544 (.000) .503 (.000) Supported
H2 .315 (.000) .297 (.000) Supported
H3 .254 (.000) .228 (.000) Supported
H4 .205 (.000) .209 (.000) Supported
H5 -.050 (0.643) -.025 (0.341) Not Supported
H6 .209 (.000) .180 (.000) Supported
H7 .176 (.000) .179 (.000) Supported
H8 .898 (.000) .837 (.000) Supported
H9 .894 (.000) .834 (.000) Supported
H10 .788 (.000) .740 (.000) Supported
H11 .663 (.000) .640 (.000) Supported
H12 .567 (.000) Supported
4.7 Regression analysis
In order to observe the impact of each construct to overall satisfaction, several
regression analyses were performed, first using the index based satisfaction and then the self
reported satisfaction as the dependent variable. In both cases the seven constructs that
emerged from the factor analysis are used as independent variables. Regressions were
performed using the enter method and a significant model emerged (F7.351=68.030, p<.0005),
with an adjusted R square = .567 (Table 4.5). The regression with the self reported
satisfaction as dependent variable produced less predictive power (F7.351=50.442, p<0.0005),
although results are not significantly different. Adjusted R square in the case of self reported
satisfaction model is .492. Furthermore, multicollinearity diagnostics revealed that there is no
autocorrelation between the constructs. More specifically, tolerance scores3 for all the
3 The closest to zero the tolerance score is the stronger the relationship with other variables.
variables are greater than .159, while there are no extremely large VIF scores4 for both index
based and self reported satisfaction. In addition, Durbin-Watson scores are also acceptable for
both satisfaction variables (index based and self reported). Specifically Durbin-Watson scores
are 1.750 (d>du) for index based satisfaction and 1.811 (d>du) for self reported satisfaction
which also indicates no evident autocorrelation in both cases. The same conclusion can be
extracted from the correlation matrix, since there are no significant correlations between
predictors, again in both index based satisfaction and self reported satisfaction.
Table 4.4 Model Summary
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Durbin-
Watson
Index based .759 .576 .567 .383 1.750
Self Reported .708 .501 .492 .442 1.811
The constructs that seem to significantly influence overall satisfaction (for both index
based and self reported satisfaction) are product information quality (β = .544, t = 15.6, p =
.000), user interface quality (β = .315, t = 9.0, p = .000), service information quality (β = .254,
t = 7.2, p = .000), purchasing process (β = .205, t = 5.8, p = .000), security perception (β =
.203, t = 5.8, p = .000) and product attractiveness (β = .176, t = 5.0, p = .000) which has a
weaker impact. E-commerce participation is not significant and has no impact at all on overall
satisfaction (β = -.050, t = -1.4, p = .148) (Table 4.6).
4 An extremely large VIF value of a variable related to the other variables indicates multicollinearity.
Table 4.6 Coefficients
Index Based Satisfaction Self Reported Satisfaction
Constructs
Unstandardized Standardized T
Sig.
Unstandardized Standardized t Sig.
B
Std.
Error Beta
B Std.
Error
Beta
(Constant) 4.791 .020 237.0 .000 5.735 .023 245.5 .000
Product Information
Quality .317 .020 .544 15.6 .000 .312 .023 .503 13.3 .000
User Interface
Quality .183 .020 .315 9.0 .000 .184 .023 .297 7.8 .000
Service Information
Quality .148 .020 .254 7.2 .000 .141 .023 .228 6.0 .000
Purchasing Process .119 .020 .205 5.8 .000 .130 .023 .209 5.5 .000
E-commerce
Participation -.029 .020 -.050 -1.4 .148 -.015 .023 -.025 -.65 .515
Security Perception .118 .020 .203 5.8 .000 .111 .023 .180 4.7 .000
Product
Attractiveness .103 .020 .176 5.0 .000 .111 .023 .179 4.7 .000
Finally, table 4.6 and β standardized coefficients for both index based and self
reported satisfaction as well as their significance, gives another strong validation prove for the
set of hypotheses apart from the preceding correlation analysis. In specific, H1 (β = .544,
p<.0005), H2 (β = .315, p<.0005), H3 (β = .254, p<.0005), H4 (β = .205, p<.0005), H6 (β =
.203, p<.0005) and H7 (β = .176, p<.0005) are supported by the data, while H5 (β =.-050, p =
-1.448) is not supported by the data.
Table 4.7 ANOVA
Index Based Satisfaction Self Reported Satisfaction
Sum of
Squares df
Mean
Square F Sig.
Sum of
Squares df
Mean
Square F Sig.
Regression 69.848 7 9.978 68.030 .000 69.135 7 9.876 50.442 .000
Residual 51.483 351 .147 68.725 351 .196
Total 121.331 358 137.861 358
The results from the regression of index based satisfaction and self reported
satisfaction with revisit frequency as dependent variable revealed a significant predictive
power of this regression model (F1.357=833.003, p<.0005). These results are also a strong
indication for the validity of hypothesis H8 for both index based (β=.898, p<.0005) and self
reported satisfaction (β=.837, p<.0005).
Table 4.8 Regression satisfaction vs. revisit frequency
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Durbin-
Watson
Index based .898 .807 .806 .423 1.941
Self Reported .837 .700 .699 .527 2.137
Furthermore, regression of index based satisfaction and self reported satisfaction with
repurchase frequency as dependent variable also revealed a high predictive power
(F1.357=1423.382, p<.0005). The results of this regression are strong evidence for the validity
of hypothesis H9 for both index based (β=.894, p<.0005) and self reported (β=.834, p<.0005)
satisfaction.
Table 4.9 Regression satisfaction vs. repurchase frequency
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Durbin-
Watson
Index based .894 .799 .799 .411 1.965
Self Reported .834 .696 .695 .507 2.171
Regression of index based satisfaction and self reported satisfaction with revisit
intention as dependent variable revealed a high predictive power (F1.357=433.359, p<.0005).
This result provide strong support to hypothesis H10 for both index based (β=.788, p<.0005)
and self reported (β=.740, p<.0005) satisfaction.
Table 4.10 Regression satisfaction vs. revisit intention
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Durbin-
Watson
Index based .788
.621 .620 .823 1.903
Self Reported .740 .548 .547 .898 2.055
Finally, regression of index based satisfaction and self reported satisfaction with
repurchase intention as dependent variable revealed a high predictive power (F1.357=247.558,
p<.0005). This regression validated hypothesis H11 for both index based (β=.663, p<.0005)
and self reported (β=.640, p<.0005) satisfaction.
Table 4.11 Regression satisfaction vs. repurchase intention
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Durbin-
Watson
Index based .663
.440 .439 .473 1.937
Self Reported .640 .409 .408 .486 1.956
4.8 Discussion
Results of this analysis show that product information quality is highly related to the
customers’ overall satisfaction. Overall satisfaction is also highly affected by user interface
quality. Finally, service information quality, purchasing process convenience, security
perception and product attractiveness have a positive, but relatively weaker, impact to overall
satisfaction. All the above lead to the conclusion that Greek on-line customers are much more
concerned about the product itself when buying on-line and so they pursue detailed, updated
and sufficient information about it. Moreover, Greek users look for a convenient and
tastefully designed interface and the web stores’ designers should take this fact under
consideration. Information about services that a web store provides, which mainly concerns
delivery, after sales service, return policy etc., also seem to influence customers. This means
that Greek on-line customers need to know the provided services by a web store and to be
confident that this information is updated and valid but their satisfaction level cannot
significantly change only by this parameter. Security issues are a significant determinant for
overall satisfaction but not of the utmost importance in relation to the other constructs. In
contrast with other studies (see: Corbitt, Thanasankit and Yi, 2003) where security is a
primary concern, Greek customers seem to have other priorities about their needs and they are
less concerned about security issues. Products availability or amount of product categories
also seem to influence on-line customers in their purchasing decision. On the other hand,
experience from prior on-line purchases is not at all a determinant for the satisfaction of
Greek on-line customers. That means that an experienced user is not necessarily a more
satisfied one.
The extended correlation and regression analyses also produced some implications
about customers’ post purchase behavior that are also worth mentioning. Specifically, a
satisfied on-line customer seems to have the intention to revisit the web store and furthermore
to increase his revisiting frequency. This fact is evident by the strong relationship between
overall satisfaction and revisiting intention or frequency. Also, a generally satisfied customer
not only has the intention to repurchase from the specific web store but also to increase his
purchasing frequency in the future. Finally it is evident from the analysis, that there is a high
likelihood that revisiting leads to repurchasing. All the above lead to the profound conclusion
that was also examined by other studies, that the first step to loyalty is satisfaction (Wang,
Chia-Yi, Pallister and Foxall, 2005).
Our findings are very similar to Park and Kim’s (2003) who also found that product
information quality, service information quality, interface quality and security perception are
strong determinants of satisfaction. In our study, however product information quality has
precedence than Park and Kim’s (2003). Also, our findings has great similarities with Kim’s
(2005) study where again product information, site design (user interface quality in our case),
process convenience and product attractiveness are found to strongly affect satisfaction.
4.9 Summary
In this section all the theoretical framework that was developed in previous sections is
empirically tested and examined through the dataset of 359 responses. After discriminate
validity and sample adequacy tests, the set of hypotheses that was developed earlier in the
analysis is tested and regression analyses produce valuable conclusions for the Greek on-line
shopping context. In the next chapter detailed conclusions from this analysis are extracted and
limitations of our research are outlined. Also some suggestions for further research and
analysis are presented.
5. Concluding Remarks
In this study a set of on-line satisfaction characteristics derived from literature are
examined for their possible effect on customers’ overall satisfaction based on a research
framework. Furthermore, we examine the effect of satisfaction on customers’ post purchase
behavior. These relationships are examined through the validation of a set of twelve
hypotheses. The research was performed using a sample of 359 Greek on-line customers. The
respondents are members of seven different Greek on-line stores and were reached by e-mail
invitations. All the numeric findings of the statistical procedures and tests are used to extract
implications about the Greek on-line shopping context and to identify determinants of
satisfaction.
Specifically, we found that information about products in a web store is highly related
to customers’ overall satisfaction and should be of the utmost importance for on-line shopping
practitioners. User interface quality is also found to positively affect overall satisfaction at a
high level and to significantly increase customers’ perception of a web store quality. This
means that Greek on-line customers are highly influenced by a tastefully designed and
convenient to use interface, thus it is important for interface designers to implement these
concepts in their projects. Information about the services a web store provides and the
convenience to purchase from a web store are also important determinants of satisfaction,
nevertheless at a relatively smaller degree. Furthermore, although the amount of product
categories and the product availability percentage do not seem to be the primary concern of
customers in a web store, it is still directly related to overall satisfaction. On the other hand e-
commerce participation, which describes someone’s experience as an on-line customer, do
not seem to affect overall satisfaction at all. This means that a more experienced user is not
necessarily more confident in using a web store and thus, does not necessarily feel more
satisfied than a less experienced one. These results illustrate customers’ perceptual weights of
each examined satisfaction factor and we propose that they are taken under serious
consideration by on-line retailers.
Furthermore, findings of this study describe some of the most important effects of
satisfaction on customer’s post purchase behavior. A thorough analysis of the collected data
revealed some expected but still useful results on this issue. Specifically it seems that high
levels of overall satisfaction not only lead to a significant increase in revisit and repurchase
intention, but also to an increase in revisit and repurchase frequency. This fact should also be
taken under serious consideration by the on-line retailers because repurchase frequency is the
main ingredient for loyalty, which after all is the ultimate goal of every business. Thus a
satisfied customer is more likely to be a loyal customer in the near future that a less satisfied
one. Finally, we believe that findings of this research meet our initial objective, to define
some of the causes of positive on-line shopping experiences and therefore to help web
shopping practitioners in Greece to improve web stores.
However, although this study’s findings provide important and useful implications for
the web shopping context in Greece, there were several limitations that may have caused
minor errors. At first, the significantly low internet usage and especially on-line shopping
infusion rate in Greece made it quite difficult to gather enough valid responses so as to form
an absolutely representative dataset. The relatively few on-line stores, limited to the trading of
books, high technology products and air services, made it quite difficult to reach a significant
amount of on-line customers from different contexts. This fact compelled us to accept
responses from customers that made their most recent purchase a long time ago and their
judgment about the web store evaluation, may have been faded out. Second, refusal of on-line
stores to forward the questionnaire to their members’ or at least to confirm the validity of the
respondents’ membership may have caused bias due to false purchase declaration by some
respondents. Third, the use of self reported Likert scales includes the possibility of a common
method bias and slight distraction of the results since the conception of Likert scales may vary
by each respondent. Besides, this is illustrated by the comparison of self reported satisfaction
and index based satisfaction means which are slightly different.
For future research we propose a focused analysis on consumer behavior in specific
product categories and services or even industrial sectors. This kind of research would reveal
any differences in customers’ behavior and satisfaction perception into a widely diversified
market. Furthermore, in spite of the fact that a wide literature review was used in this study,
extraction and measurement of more items and constructs, that wasn’t incorporated, is
imperative for further understanding of the satisfaction concept in the Greek on-line context.
Finally, a study of satisfaction determinants in the off line market would provide further
ammunition for further understanding of on-line satisfaction and its related concepts. After
all, even an on-line customer is primarily a customer with different interaction with the
retailer and as such he should primarily be confronted.
In the end, it is more than certain that the rapid growth of internet will finally be
infused in the Greek market and so e-commerce is expected to follow the great worldwide
development in the near future. We consider it very important that an electronic marketplace
is built on standarised principles and rules because, that would gear its further development.
6. References
Aaker, D.A. (1991), Managing Brand Equity: Capitalizing on the Value of a Brand Name,
New York, Free Press.
Aaker, D.A. (1996), Building Strong Brands, New York, Free Press.
Abels, E., M.D. White, and K. Hahn (1998), ‘A user-based design process for Web sites’,
Internet Research: Electronic Networking Applications and Policy, 8(1), pp. 39-48.
Alba, J., J. Lynch, B. Weitz, C. Janiszewski, R. Lutz, A. Sawyer and S. Wood (1997),
‘Interactive home shopping: consumer, retailer and manufacturer incentives to participate in
electronic marketplaces’, Journal of Marketing, 61(8), pp.38-53.
Anderson, E. W., C. Fornell and D. R. Lehmann (1994), ‘Customer satisfaction, market share,
and profitability: Findings from Sweden’, Journal of Marketing, 58(3), pp. 53–66.
Applegate, L. M., C. W. Holsapple, R. Kalakota, F. J. Radermacher and A.B. Whinston
(1996), ‘Electronic commerce: building blocks of new business opportunity’, Journal of
Organizational Computing and Electronic Commerce, 6(1), pp.1–10.
Arnott, D. C. and S. Bridgewater (2002), ‘Internet, interactions and implications for
marketing’, Marketing Intelligence & Planning, 20(2), pp. 86–95.
Bagozzi, R.P. (1994), ‘Measurement in marketing research: basic principles of questionnaire
design’, Principles of Marketing research, 25(3), pp.1-49.
Bailey, J. E. and S. W. Pearson (1983), ‘Development of a tool for measuring and analysing
computer user satisfaction’, Management Science, 29(5), pp. 531-545.
Baroudi, J. J. and W. J. Orlikowski (1988), ‘A short form measure of user information
satisfaction: A psychometric evaluation and notes on use’, Journal of Management
Information Systems, 4(4), pp. 44-59.
Bauer, R. (1960), ‘Consumer behavior as risk taking’, in R. Hancock, (Ed.), Dynamic
Marketing for a Changing World, Chicago, IL.: American Marketing Association
Bellman, S., G. Lohse and E. Johnson (1999), ‘Predictors of online buying behavior’,
Communications of the ACM, 42(5), pp. 32-38.
Boyer, K.K., G. Tomas and M. Hult (2006), ‘Customer behavioral intentions for online
purchases: An examination of fulfillment method and customer experience level’, Journal of
Operations Management, 24(3), pp.124-147.
Brooke, J. (1996), ‘SUS: a quick and dirty usability scale’, in: PP.W. Jordan, B. Thomas, B.A.
Weerdmeester, I.L. McClelland (Eds.), Usability Evaluation in Industry, London, UK: Taylor
& Francis.
Bucklin, R.E., J.M. Lattin, A. Ansari, S. Gupta, D. Bell, E. Coupey, J.D.C. Little, C. Mela, A.
Montgomery and J. Steckel (2002), ‘Choice and the internet: From clickstream to research
stream’, Marketing Letters, 13(3), pp. 245–258.
Cao, I., M. Zhang and K. Seydel (2005), ‘B2C e-commerce web site quality: an empirical
examination’, Industrial Management & Data Systems, 105(5), pp. 645-661.
Cappel, J.J. and M.A. Myerscough (1996), ‘World Wide Web uses for electronic commerce:
towards a classification scheme’, Proceedings of the 1996 Second AIS Conference, Phoenix,
Arizona.
Cho, N. and S. Park (2001), ‘Development of electronic commerce user-consumer satisfaction
index (ECUSI) for internet shopping’, Industrial Management and Data Systems, 101(8), pp.
400-409.
Churchill, G. A. (1979), ‘A Paradigm for Developing Better Measures of Marketing
Constructs’, Journal of Marketing Research, 16(1), pp.64-73.
Corbitt, B. J., T. Thanasankit and H. Yi (2003), ‘Trust and e-commerce: a study of consumer
perceptions’, Electronic Commerce Research and Applications, 2(2), pp. 203-215.
Cox, J. and B.G. Dale (2001), ‘Service quality and e-commerce: an explanatory analysis’,
Managing Service Quality, 11(2), pp.121-131.
Crosby L.A. and N. Stephens (1987), ‘Effects of relationship marketing on satisfaction,
retention and prices in the life insurance industry’, Journal of Marketing Research, 24(8),
pp.404-411.
Davis, F.D. (1989), ‘Perceived usefulness, Perceived ease of use and user acceptance of
information technology’, Management Information Systems Quarterly, 13(3), pp. 319-339.
Delone, W.H. and E.R. Mclean (2003), ‘The Delone and Mclean model of information
systems success: a ten-year update’, Journal of Management Information Systems, 19(4), pp.
9-30.
Delone, W. H. and Mclean, E. R. (1992), ‘Information systems success: The quest for the
dependent variable’, Information Systems Research, 35(3), pp. 211–225.
Doll, W. J. and G. Torkzadeh (1988), ‘The measurement of end-user computing satisfaction’,
Management Information Systems Quarterly, 12(2), pp. 259-274.
Doll, W.J., W. Xia and G. Torkzadeh (1994), ‘A confirmatory factor analysis of the end-user
computing satisfaction instrument’, MIS Quarterly, 18(4), pp. 453–461.
Dowling, G.R. and R. Staelin (1994), ‘A model of perceived risk and intended risk-handling
activity’, Journal of Consumer Research, 21(9), pp. 119-34.
Ein-Dor, PP. and E. Segev (1978), ‘Organizational context and success of management
information systems’, Management Science, 24(10), pp. 1064-1077.
Elliot, N. and H. Fowell (2000), ‘Expectations versus reality: a snapshot of consumers
experiences with internet retailing’, International Journal of Information Management, 20(4),
pp. 323-36.
Fornell, C. and B. Wernerfelt (1987), ‘Defensive marketing strategy by customer complaint
management: A theoretical analysis’, Journal of Marketing Research, 24(4), pp. 337–346.
Frankel, R., T.J. Goldsby and J.M. Whipple (2002), ‘Grocery industry collaboration in the
wake of ECR’, International Journal of Logistics Management, 13(1), pp. 57-72.
Fung, R.Y.K., A.C. Pereira and W.H.R. Yeung (2000), ‘Performance evaluation of a Web-
based information system for laboratories and service centers’, Logistics Information
Management, 13(4), pp. 218-227.
Furnell, S.M. and T. Karweni (1999), ‘Security implications of electronic commerce: a survey
of consumers and businesses’, Internet Research: Electronic Networking Applications and
Policy, 9(5), pp. 372-382.
Garbarino, E. and M.S. Johnson (1999), ‘The different roles of satisfaction, trust and
commitment in customer relationships’, Journal of Marketing, 63(24), pp.70-87.
Gefen, D. (2000), ‘E-Commerce: the role of familiarity and trust’, Omega, 28(2), pp. 725-37.
Goldsmith, R.E. (2002), ‘Explaining and predicting consumer intention to purchase over the
internet: an exploratory study’, Journal of Marketing Theory and Practice, 10(2), pp. 22-8.
Gounaris, S. and V. Stathakopoulos (2004), ‘Antecedents and consequences of brand loyalty:
an empirical study’, Journal of Brand Management, 11(4), pp. 283–306.
Gwinner, K.P., D.D. Gremmler and M.J. Bitner (1998), ‘Relational benefits in services
industries: The customer’s perspectives’, Journal of the Academy of Marketing Science,
26(2), pp. 555-562.
Hansen, W.J. (1981), ‘User engineering principles for interactive systems’, in: Proceedings of
the Fall Joint Computer Conference, Montvale, NJ: AFIPS Press, pp. 523-532.
Hart, P.P., C. Saunders and B. Power (1997), ‘Power and trust: critical factors in the
adoptions and use of electronic data interchange’, Organizational Science, 8(1), pp. 23-42.
Heskett, J.L, T.O. Jones, G.W. Loverman, W.E. Saiser and L.A. Schlesinger (1994), ‘Putting
the service profit chain to work’, Harvard business review, 11(5), pp.164-74.
Ho, C.F. and W.H. Wu (1999), ‘Antecedents of customer satisfaction on the internet: An
empirical study of online shopping’, Proceedings of the 32nd Hawaii International Conference
on System Sciences.
Hocutt, M.A. (1998), ‘Relationship dissolution model: antecedents of relationship
commitment and the likelihood of dissolving a relationship’, International Journal of Service
Industry Management, 9(2), pp. 189-200.
Hoffman, D.L., T.P. Novak and PP. Chatterjee (1995), ‘Commercial scenarios for the web:
opportunities and challenges’, Journal of Computer-Mediated Communication, 1(3), pp.254-
259.
Ives, B. and M.H. Olson (1984), ‘User involvement and MIS success: a review of research’,
Management Science, 30(5), pp. 586-603.
Jarvenpaa, S.L. and P.A. Todd (1996), ‘Customer reactions to electronic shopping on the
World Wide Web’, International Journal of Electronic Commerce, 11(2), pp. 59–88.
Kalyanam, K.S. and S. Mcintyre (2002), ‘The e-marketing mix: A contribution of the e-tailing
wars’, Journal of the Academy of Marketing Science, 30(4), pp. 487– 499.
Kaplan, E.S. and J.R. Niescwietz (2003), ‘An Examination of the effects of Web trust and
company type on consumer’s purchasing intentions’, International Journal Of Auditing, 7(2),
pp.155-168.
Keen, P.G.W. (1997), ‘Are you ready for ‘trust’ economy?’, Computer World 31, 16(80), pp.
58-65.
Kim, H.R. (1999), ‘A study of the evaluation of electronic commerce customer satisfaction’,
Master’s Thesis, Hanyang University, Korea.
Kim, H.R. (2005), ‘Developing an index of online customer satisfaction’, Journal of
Financial Services Marketing, 10(6), pp.149–64.
Kotler, PP. (2000), Marketing management international edition, Englewood Cliffs, NJ,
Prentice Hall.
Kuhlmeier, D. and G. Knight (2005), ‘Antecedents to internet-based purchasing: a
multinational study’, International Marketing Review, 22(4), pp. 460-473.
Lancastre, A. and L.F. Lages (2005), ‘The relationship between buyer and a B2B e-
marketplace: Cooperation determinants in an electronic market context’, Industrial Marketing
Management, 28(6), pp.354-367.
Lightner, N.J. (2003), ‘What users want in e-commerce design: effects of age, education and
income’, Ergonomics, 46(1-3), pp.153-168.
Lin, C.C. (2003), ‘A critical Appraisal of customer satisfaction and e-commerce’, Managerial
Auditing Journal, 18(3), pp.202-212.
Liu, C. and K.P. Arnett (2000), ‘Exploring the factors associated with Web site success in the
context of electronic commerce’, Information and Management, 38(7), pp.23-33.
Liu, C. and K.P. Arnett (2000), ‘Exploring the factors associated with web site success in the
context of electronic commerce’, Information & Management, 38(1), pp. 23-33.
Lohse, G.L. and PP. Spiller (1998), ‘Electronic Shopping’, Communications of ACM, 41(7),
pp.81-9.
Makhija, M. and A. Stewart (2002), ‘The effect of national context on perceptions of risk: a
comparison of planned versus free-market managers’, Journal of International Business
Studies, 33(4), pp. 737.
Mitchell, V.W. and V. Vassos, (1997), ‘Perceived risk and risk reduction in holiday
purchases: a cross-cultural and gender analysis’, Journal of Euromarketing, 6(3), pp. 47-79.
Miyazaki, A.D. and A. Fernandez (2001), ‘Consumer perceptions of privacy and security
risks for online shopping’, The Journal of Consumer Affairs, 25(1), pp. 27-44.
Modahl, M. (2000), Now or Never: How Companies Must Change Today to Win the Battle for
Internet Consumers, New York, HarperCollins.
Moe, W., P.S. Fader (2002), ‘Dynamic Conversion Behavior at e-Commerce Sites’, Wharton
Marketing Department Working Paper.
Molla, A. and S.P. Licker (2001), ‘E-commerce systems success: an attempt to extend and
respecify the Delone and Maclean model of IS success’, Journal of Electronic Commerce
Research, 2(4), pp. 35-49.
Nielsen, J. (1999), Designing Web Usability: The Art of Simplicity, Indianapolis, New Riders
Publishing.
Nunnally, J.C. (1978), Psychometric Theory, New York, McGraw-Hill.
Oliver, S. (1997), ‘A model for the future of electronic commerce’, Information Management
and Computer Security, 5(5), pp.166-9.
Parasuraman, A., V.A. Zeithaml and L.L. Berry (1988), ‘SERVQUAL: a multiple-item scale
for measuring consumer perceptions of service quality’, Journal of Retailing, 64(1), pp. 12-
40.
Parker C, and B.P. Mathews (2001), ‘Customer Satisfaction: Contrasting academic and
consumers’ interpretations’, Marketing Intelligence and Planning, 19(1), pp.38-44.
Park, C.H. and Y.C. Kim (2003), ‘Identifying key Factors affecting consumer purchase
behavior in an online shopping context’, International Journal of retail and distribution
management, 31(1), pp. 16-29.
Quaddus, M. and D. Achjari (2005), ‘A model for electronic commerce success’,
Telecommunications Policy, 29(5), pp. 127–152.
Quelch, J.A. and L.R. Klein (1996), ‘The internet and international marketing’, Sloan
Management Review, 60(75), pp.122-137.
Raymond, L. (1985), ‘Organizational characteristics and MIS success in small businesses,
Management Information Systems Quarterly, 9(1), pp. 37–52.
Reichheld, F.F. (1996), The Loyalty Effect: The Hidden Force Behind Growth, Profits, and
Lasting Value, US, Harvard Business School Press.
Richnis, M. L. (1983) ‘Negative word-of-mouth by dissatisfied consumers: A pilot study’,
Journal of Marketing, 46(1), pp. 68–78.
Robbins, S. and A. Stylianou (2003), ‘Global corporate web sites: an empirical investigation
of content and design’, Information & Management, 40(3), pp. 205-12.
Shimp, T. and W. Bearden (1982), ‘Warranty and other extrinsic cue effects on consumers’
risk perceptions’, Journal of Consumer Research, 9(4), pp. 38-46.
Smith, B.A and E.J. Merchant (2001), ‘Designing an attractive web site: variables of
importance’, Proceedings of the 32nd Annual Conference of the Decision Sciences Institute,
San Francisco, CA.
Sungbin C., J.H. Byun and M. Sung (2003), ‘Impact of the high speed internet on user
behaviors: case study in Korea’, Internet Research: Electronic Networking Applications and
Policy, 13(1), pp.46-60.
Swan, J.E. and L.J. Combs (1976), ‘Product performance and consumer satisfaction: a new
concept’, Journal of marketing, 40(7), pp.25-33.
Syzmanski, T. and P. Hise (2000), ‘E-satisfaction: an initial examination’, Journal of
Retailing, 76(3), pp.309-22.
Van den Poel, D. and W. Buckinx (2005), ‘Predicting online-purchasing behavior’, European
Journal of Operational Research, 166 (12), pp. 557–575.
Verhage, B.J., U. Yavas and R.T. Green (1990), ‘Perceived risk: a cross-cultural
phenomenon?’, International Journal of Research in Marketing, 10(2), pp. 297-303.
Vijayasarathy, L. R. and J. M. Johnson (2000), ‘Intentions to shop using internet catalogues:
Exploring the effects of product types, shopping orientations, and attitudes towards the
computer’, Electronic Markets, 10(1), pp. 1–10.
Wang, H.C., M.H. Chia-Yi, J.G. Pallister and G.R. Foxall (2005), ‘Innovativeness and
involvement as determinants of website loyalty: II. Determinants of consumer loyalty in B2C
e-commerce’, Technovation, 5(21), pp. 321-329.
Wang, H.C. and D.M. Strong (1996), ‘Beyond accuracy: what data quality means to data
consumers’, Journal of Management Information Systems, 12(4), pp.5-34.
Wang, Y.S., T.I. Tang and J.E. Tang (2001), ‘An instrument for measuring customer
satisfaction toward web sites that market digital products and services’, Journal of Electronic
Commerce Research, 2(3), pp. 52-64.
Weber, E.U. and C. Hise (1998), ‘Cross-cultural differences in risk perception, but similarities
in attitudes towards perceived risk’, Management Science, 44(9), pp. 1205-18.
Weber, E.U., A.R. Blais, and N.E. Betz (2002), ‘A domain-specific risk-attitude scale:
measuring risk perceptions and risk behaviors’, Journal of Behavioral Decision Making,
15(4), pp. 263-272.
Westbrook, R.A. and R.L. Oliver (1991), ‘The dimensionality of consumption emotion
patterns and consumer satisfaction’, Journal of Consumer research, 18(14), pp. 84-91.
Westbrook, R.A. (1983), ‘Value-Percept disparity: an alternative to the disconfirmation of
expectations theory of consumer satisfaction’, Advances of consumer research, 10(1), pp.
256-61.
Zviran, M., C. Glezer, and I. Avni (2006), ‘User satisfaction from commercial web sites: The
effect of design and use’, Information & Management, 43(1), pp.157–178.
Appendix A. Questionnaire Part A. E-commerce Participation
1A. How many times have you ever made a purchase over the internet?
2A. What is the value of your total on-line purchase?
Part B. Product attractiveness - How may product categories did the evaluated web store have?
- What is the ideal amount of product categories that you wish to find in a web store?
(THESE QUESTIONS ARE USED ONLY FOR COMPARISON) 1B.Actual amount of
product categories
2B. Ideal amount of
product categories
One Product category 1 1
Two to Five Product Categories 2 2
Five to Ten Product Categories 3 3
Ten to Fifteen Product Categories 4 4
Fifteen or more 5 5
DK/NA 99
- What availability percentage did the evaluated web store have?
- What is the ideal availability percentage of new products for a web store?
(THESE QUESTIONS ARE USED ONLY FOR COMPARISON) Actual availability
percentage
Ideal availability
percentage
Less than 20% 1 1
21-40% 2 2
41-60% 3 3
61-80% 4 4
81% or more 5 5
DK/NA 99 99
1A. Times of on-line purchase
None X go to demographics
One 1
Two-Five 2
Five-Ten 3
Ten or more 4
2A. Value of on-line purchase
20 € or less 1
21€-50€ 2
51€-150€ 3
151€-500€ 4
501€-1000€ 5
1001€ or more 6
3A. What percentage of your total purchase is established over the internet?
Less than 1% 1
1%-5% 2
6%-10% 3
11%-15% 4
16%-20% 5
21%-25% 6
26% or more 7
1B. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AD “1”
REPRESENTS STRONGLY DISAGREE):
I believe it is important for a web store to:
Str
on
gly
dis
agre
e 2 3
Nei
ther
ag
ree
no
r
dis
agre
e 5 6
Str
on
gly
agre
e
Have a sufficient number of product categories e.g.
books, electronics, gadgets etc.
1 2 3 4 5 6 7
Have a sufficient availability percentage of new
products.
1 2 3 4 5 6 7
The evaluated web store has:
Str
on
gly
dis
agre
e 2 3
Nei
ther
ag
ree
no
r
dis
agre
e 5 6
Str
on
gly
agre
e
Has a sufficient number of product categories e.g.
books, electronics, gadgets etc.
1 2 3 4 5 6 7
Has a sufficient availability percentage of new
products.
1 2 3 4 5 6 7
Part C. Purchasing Process convenience 1C. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AD “1”
REPRESENTS STRONGLY DISAGREE):
Str
on
gly
dis
agre
e
2 3 Nei
ther
ag
ree
no
r
dis
ag
ree
5 6 Str
on
gly
agre
e
The arrangement of products in the evaluated web store is
convenient for me
1 2 3 4 5 6 7
It is important that a web store has convenient product arrangement 1 2 3 4 5 6 7
The shopping basket of the evaluated web store is easy to mage 1 2 3 4 5 6 7
It is important that the shopping basket of a web store is easy to
manage
1 2 3 4 5 6 7
The evaluated web store, provides sufficient directions and
information about how to use it
1 2 3 4 5 6 7
It is important that a web store provides sufficient directions and
information about how to use it
1 2 3 4 5 6 7
Part D. Security Perception 1D. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AND “1”
REPRESENTS STRONGLY DISAGREE):
Str
on
gly
dis
agre
e
2 3
Nei
ther
Ag
ree
No
r
Dis
ag
ree
5 6
Str
on
gly
agre
e
I believe that payment information is
appropriately protected by the evaluated web
store
1 2 3 4 5 6 7
It is important that a web store protects the
payment information of my purchases
1 2 3 4 5 6 7
The evaluated web store, guides me
effectively to correct entry errors
1 2 3 4 5 6 7
It is important that a web store offers effective
guidance to correct entry errors
1 2 3 4 5 6 7
I believe that the evaluated web store, will not
use my private information in a unwanted
manner
1 2 3 4 5 6 7
Part E. Product information quality 1E. Please mark the point that best approaches the characteristic of the evaluated web store:
The product information of the web store I made my most recent on-line purchase
from, is
Not Updated 1 2 3 4 5 6 7 Updated
Insufficient 1 2 3 4 5 6 7 Sufficient
Difficult to understand 1 2 3 4 5 6 7 Easy to understand
Inconsistent 1 2 3 4 5 6 7 Consistent
Not Playful 1 2 3 4 5 6 7 Playful
Irrelevant 1 2 3 4 5 6 7 Relevant
Unreliably represented 1 2 3 4 5 6 7 Reliably represented
2E. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AD “1”
REPRESENTS STRONGLY DISAGREE):
It is important that product information of a
web store is: Str
on
gly
dis
agre
e
2 3
Nei
ther
agre
e n
or
dis
agre
e
5 6
Str
on
gly
agre
e
Updated 1 2 3 4 5 6 7
Sufficient 1 2 3 4 5 6 7
Easy to understand 1 2 3 4 5 6 7
Consistent 1 2 3 4 5 6 7
Playful 1 2 3 4 5 6 7
Relevant 1 2 3 4 5 6 7
Reliable(reliably represented) 1 2 3 4 5 6 7
Part F. Service Information Quality 1F. Please mark the point that best approaches the characteristic of the web store that you
made your most recent on-line purchase from:
The information about the services of the web store that I made my most
recent purchase from is:
Not up-dated 1 2 3 4 5 6 7 Up-dated
Insufficient 1 2 3 4 5 6 7 Sufficient
Difficult to understand 1 2 3 4 5 6 7 Easy to understand
Inconsistent 1 2 3 4 5 6 7 Consistent
Not playful 1 2 3 4 5 6 7 Playful
irrelevant 1 2 3 4 5 6 7 Relevant
Unreliably represented 1 2 3 4 5 6 7 Reliably represented
2F. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AD “1”
REPRESENTS STRONGLY DISAGREE):
It is important that the service
information of a web store is:
Str
on
gly
dis
agre
e
2 3
Nei
ther
agre
e n
or
dis
agre
e 5 6
Str
on
gly
agre
e
up-dated 1 2 3 4 5 6 7
sufficient 1 2 3 4 5 6 7
easy to understand 1 2 3 4 5 6 7
consistent 1 2 3 4 5 6 7
playful 1 2 3 4 5 6 7
Relevant 1 2 3 4 5 6 7
Reliable(reliably represented) 1 2 3 4 5 6 7
Part G. User interface quality 1G. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AD “1”
REPRESENTS STRONGLY DISAGREE):
Str
on
gly
dis
agre
e 2 3
Nei
ther
agre
e n
or
dis
agre
e 5 6
Str
on
gly
agre
e
The evaluated web store’s interface is
convenient to order a product 1 2 3 4 5 6 7
It is important that a web store has convenient
user interface 1 2 3 4 5 6 7
The evaluated web store’s interface is
convenient to navigate wanted pages 1 2 3 4 5 6 7
It is important that I can easily navigate
wanted pages in a web store 1 2 3 4 5 6 7
The evaluated web store makes appropriate
use of colour in its design 1 2 3 4 5 6 7
It is important that a on-line makes
appropriate use of color in its interface 1 2 3 4 5 6 7
It was convenient for me to search for a
product in the evaluated web store 1 2 3 4 5 6 7
It is important that it is easy for a non-
experienced user to search for a product in a
web store
1 2 3 4 5 6 7
The Screen design / layout is tasteful in the
evaluated web store 1 2 3 4 5 6 7
It is important that the design/layout of a web
store is tasteful 1 2 3 4 5 6 7
The evaluated web store makes appropriate
use of animation 1 2 3 4 5 6 7
It is important that a web store makes
appropriate use of animation 1 2 3 4 5 6 7
Part H. Purchase behavior 1H. Please mark to what extend do you agree or not with each of the following statements
(USE A 7-POINT SCALE WHERE “7” REPRESENTS STRONGLY AGREE AD “1”
REPRESENTS STRONGLY DISAGREE):
Str
on
gly
dis
agre
e 2 3
Nei
ther
agre
e n
or
dis
agre
e 5 6
Str
on
gly
agre
e
I would definitely purchase again from the evaluated
web store in the future
1 2 3 4 5 6 7
I would definitely visit again the evaluated web store 1 2 3 4 5 6 7
I will definitely increase the frequency that I visit the
evaluated web store in the future
1 2 3 4 5 6 7
I will definitely increase the frequency that I purchase
products or services from the evaluated web store in the
future
1 2 3 4 5 6 7
In general I am completely satisfied by my on-line
shopping experience from the evaluated store
1 2 3 4 5 6 7
Demographics
Gender Male 1
Female 2
Age -15 1
15-24 2
25-34 3
35-44 4
45-54 5
55-64 6
65+ 7
Residence Thrace 1 Sterea Ellada 7
East Macedonia 2 Peloponesus 9
Central Macedonia 3 Ionia Islads X
West Macedonia 4 Aegean Islads Ψ
Thessaly 5
Epirus 6
Educational Level
No school at all 1
3rd Class Elementary School-3rd Class High School 2
3rd Class Senior High School or other school of similar level 3
T.E.I. or other higher education of similar level 4
University Graduate or other similar institution graduate 5
Working Status
Self-Employed Employees Farmers 1 Scientists (Doctors, lawyers, etc.) 1
Owners of small businesses (no employees) 2 Managers 2
Owners of small family businesses (1-2 empl.) 3 Supervisors 3
Owners of small businesses (up to 50 empl.) 4 Office employees 4
Owners of businesses (50+ empl.) 5 Employees (not in office) 5
Scientists (Doctors, etc.) owners of businesses 6 Technicians 6
Housekeepers 7
Retired X Students 8
Unemployed 9
Household’s monthly income
Less the 400€ 1
401€-800€ 2
801€-1200€ 3
1201€-1600€ 4
1601€-2000€ 5
2001€ or more 6
Refuse to answer 99
Internet Usage Time spent per logon Times per week
Less the 1 hour 1 Less the 2 times per week 1
1-2 hours 2 3-4 times per week 2
2-3 hours 3 5-14 times per week 3
3-5 hours 4 14-21 times per week 4
5+ hours 5 21+times per week 5
Appendix B – SPSS OUTPUT TABLES
Factor Analysis
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .803
Bartlett's Test of Sphericity
Approx. Chi-Square 4242.545
df 378
Sig. .000
Communalities
Initial Extraction
q.1a on-line purchases 1.000 .687
q.2a value of on-line purchase 1.000 .574
q.3a percentage of electronic to total purchase 1.000 .504
q.1b satisfactory product categories amount 1.000 .747
q.2b satisfactory availabilty percentage 1.000 .602
q.1c convenient arrangement of products of evaluated store 1.000 .586
q.2c easy to manage shopping basket in evaluated store 1.000 .685
q.3c sufficient directions of usage in evaluated store 1.000 .650
q.1d effective guidance to correct entry errors 1.000 .563
q.2d protection of payment information in evaluated store 1.000 .595
q.3d private information will not be used in an unwanted manner 1.000 .581
q.1e not updated-updated 1.000 .594
q.2e insufficient-sufficient 1.000 .877
q.3e difficult to understand-easy to understand 1.000 .796
q.4e inconsistent-consistent 1.000 .513
q.5e not playfull-playfull 1.000 .783
q.6e irrelevant-relevant 1.000 .742
q.7e unreliably represented-reliably represented 1.000 .683
q.1f not updated-updated 1.000 .650
q.2f insufficient-sufficient 1.000 .753
q.3f inconsistent-consistent 1.000 .520
q.4f not playfull-playfull 1.000 .728
q.5f irrelevant-relevant 1.000 .593
q.1g convenient to order 1.000 .532
q.2g convenience to navigate wanted pages 1.000 .735
q.3g appropriate use of color in evaluated store 1.000 .607
q.4g convenience to search for a product 1.000 .707
q.5g tasteful sreen layout design in evaluated store 1.000 .537
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance Cumulative %
1 4.926 17.593 17.593 4.926 17.593 17.593 4.850 17.322 17.322
2 3.483 12.439 30.032 3.483 12.439 30.032 3.155 11.269 28.590
3 2.809 10.034 40.066 2.809 10.034 40.066 3.042 10.864 39.455
4 2.111 7.540 47.605 2.111 7.540 47.605 1.972 7.043 46.498
5 1.936 6.915 54.521 1.936 6.915 54.521 1.927 6.883 53.381
6 1.504 5.370 59.891 1.504 5.370 59.891 1.710 6.108 59.489
7 1.355 4.840 64.731 1.355 4.840 64.731 1.468 5.242 64.731
8 .879 3.138 67.869
9 .829 2.961 70.830
10 .734 2.620 73.450
11 .666 2.377 75.827
12 .647 2.312 78.139
13 .606 2.165 80.304
14 .562 2.006 82.311
15 .542 1.937 84.247
16 .519 1.852 86.100
17 .479 1.711 87.810
18 .453 1.617 89.428
19 .437 1.562 90.990
20 .416 1.487 92.476
21 .358 1.280 93.757
22 .356 1.270 95.027
23 .331 1.183 96.210
24 .288 1.030 97.240
25 .254 .907 98.147
26 .213 .762 98.909
27 .191 .682 99.591
28 .114 .409 100.000
Extraction Method: Principal Component Analysis.
Rotated Component Matrix(a)
Component
1 2 3 4 5 6 7
q.1a on-line purchases
.783
q.2a value of on-line purchase
.750
q.3a percentage of electronic to total purchase
.692
q.1b satisfactory product categories amount
.849
q.2b satisfactory availabilty percentage
.757
q.1c convenient arrangement of products of evaluated store
.754
q.2c easy to manage shopping basket in evaluated store
.820
q.3c sufficient directions of usage in evaluated store
.784
q.1d effective guidance to correct entry errors
.678
q.2d protection of payment information in evaluated store
.765
q.3d private information will not be used in an unwanted manner
.745
q.1e not updated-updated .757
q.2e insufficient-sufficient .936
q.3e difficult to understand-easy to understand .891
q.4e inconsistent-consistent .631
q.5e not playfull-playfull .880
q.6e irrelevant-relevant .857
q.7e unreliably represented-reliably represented .814
q.1f not updated-updated
.794
q.2f insufficient-sufficient
.860
q.3f inconsistent-consistent
.642
q.4f not playfull-playfull
.844
q.5f irrelevant-relevant
.756
q.1g convenient to order
.707
q.2g convenience to navigate wanted pages
.854
q.3g appropriate use of color in evaluated store
.744
q.4g convenience to search for a product
.831
q.5g tasteful sreen layout design in evaluated store
.698
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Component Transformation Matrix
Component 1 2 3 4 5 6 7
1 .980 .171 -.001 .001 -.098 .024 -.010
2 .147 -.703 .657 -.035 .216 -.034 .057
3 -.107 .659 .708 .182 .080 .069 .093
4 .061 -.020 -.224 .417 .696 .524 .116
5 .009 -.125 -.043 .865 -.223 -.428 .048
6 -.032 -.114 .005 .040 -.485 .450 .739
7 .042 .113 -.121 -.207 .411 -.578 .652
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Correlations – Hypotheses Test
Hypothesis H1
Descriptive Statistics
Mean Std. Deviation N
q.5h self reported overall satisfaction 5.74 .621 359
q.6h Index based overall satisfaction 4.79 .582 359
FAC1_1 REGR factor score 1 for analysis 1 .0000000 1.00000000 359
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC1_1 REGR factor
score 1 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) .503(**)
Sig. (2-tailed) . .000 .000
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 .544(**)
Sig. (2-tailed) .000 . .000
N 359 359 359
FAC1_1 REGR factor
score 1 for analysis 1
Pearson
Correlation .503(**) .544(**) 1
Sig. (2-tailed) .000 .000 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Hypothesis H2
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC2_1 REGR factor
score 2 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) .297(**)
Sig. (2-tailed) . .000 .000
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 .315(**)
Sig. (2-tailed) .000 . .000
N 359 359 359
FAC2_1 REGR factor
score 2 for analysis 1
Pearson
Correlation .297(**) .315(**) 1
Sig. (2-tailed) .000 .000 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Hypothesis H3
Descriptive Statistics
Mean Std. Deviation N
q.5h self reported overall satisfaction 5.74 .621 359
q.6h Index based overall satisfaction 4.79 .582 359
FAC3_1 REGR factor score 3 for analysis 1 .0000000 1.00000000 359
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC3_1 REGR factor
score 3 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) .228(**)
Sig. (2-tailed) . .000 .000
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 .254(**)
Sig. (2-tailed) .000 . .000
N 359 359 359
FAC3_1 REGR factor
score 3 for analysis 1
Pearson
Correlation .228(**) .254(**) 1
Sig. (2-tailed) .000 .000 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Hypothesis H4
Descriptive Statistics
Mean Std. Deviation N
q.5h self reported overall satisfaction 5.74 .621 359
q.6h Index based overall satisfaction 4.79 .582 359
FAC4_1 REGR factor score 4 for analysis 1 .0000000 1.00000000 359
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC4_1 REGR factor
score 4 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) .209(**)
Sig. (2-tailed) . .000 .000
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 .205(**)
Sig. (2-tailed) .000 . .000
N 359 359 359
FAC4_1 REGR factor
score 4 for analysis 1
Pearson
Correlation .209(**) .205(**) 1
Sig. (2-tailed) .000 .000 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Hypothesis H5
Descriptive Statistics
Mean Std. Deviation N
q.5h self reported overall satisfaction 5.74 .621 359
q.6h Index based overall satisfaction 4.79 .582 359
FAC5_1 REGR factor score 5 for analysis 1 .0000000 1.00000000 359
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC5_1 REGR factor
score 5 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) -.025
Sig. (2-tailed) . .000 .643
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 -.050
Sig. (2-tailed) .000 . .341
N 359 359 359
FAC5_1 REGR factor
score 5 for analysis 1
Pearson
Correlation -.025 -.050 1
Sig. (2-tailed) .643 .341 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Hypothesis H6
Descriptive Statistics
Mean Std. Deviation N
q.5h self reported overall satisfaction 5.74 .621 359
q.6h Index based overall satisfaction 4.79 .582 359
FAC6_1 REGR factor score 6 for analysis 1 .0000000 1.00000000 359
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC6_1 REGR factor
score 6 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) .180(**)
Sig. (2-tailed) . .000 .001
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 .203(**)
Sig. (2-tailed) .000 . .000
N 359 359 359
FAC6_1 REGR factor
score 6 for analysis 1
Pearson
Correlation .180(**) .203(**) 1
Sig. (2-tailed) .001 .000 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Hypothesis H7
Descriptive Statistics
Mean Std. Deviation N
q.5h self reported overall satisfaction 5.74 .621 359
q.6h Index based overall satisfaction 4.79 .582 359
FAC7_1 REGR factor score 7 for analysis 1 .0000000 1.00000000 359
Correlations
q.5h self reported
overall satisfaction
q.6h Index based
overall satisfaction
FAC7_1 REGR factor
score 7 for analysis 1
q.5h self reported
overall satisfaction
Pearson
Correlation 1 .929(**) .179(**)
Sig. (2-tailed) . .000 .001
N 359 359 359
q.6h Index based
overall satisfaction
Pearson
Correlation .929(**) 1 .176(**)
Sig. (2-tailed) .000 . .001
N 359 359 359
FAC7_1 REGR factor
score 7 for analysis 1
Pearson
Correlation .179(**) .176(**) 1
Sig. (2-tailed) .001 .001 .
N 359 359 359
** Correlation is significant at the 0.01 level (2-tailed).
Reliability
Factor 5
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.645 3
Reliability
Factor 7
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.549 2
Reliability
Factor 4
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.705 3
Reliability
Factor 6
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.585 3
Reliability
Factor 1
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.923 7
Reliability
Factor 3
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.839 5
Reliability
Factor 2
Case Processing Summary
N %
Cases
Valid 359 100.0
Excluded(a) 0 .0
Total 359 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.830 5
Regression Analysis
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .759(a) .576 .567 .383 1.750
a Predictors: (Constant). FAC7_1 REGR factor score 7 for analysis 1 . FAC6_1 REGR factor score 6 for analysis 1 .
FAC5_1 REGR factor score 5 for analysis 1 . FAC4_1 REGR factor score 4 for analysis 1 . FAC3_1 REGR factor
score 3 for analysis 1 . FAC2_1 REGR factor score 2 for analysis 1 . FAC1_1 REGR factor score 1 for analysis 1
b Dependent Variable: q.6h Index based overall satisfaction
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 69.848 7 9.978 68.030 .000(a)
Residual 51.483 351 .147
Total 121.331 358
a Predictors: (Constant). FAC7_1 REGR factor score 7 for analysis 1 . FAC6_1 REGR factor score 6 for analysis 1 .
FAC5_1 REGR factor score 5 for analysis 1 . FAC4_1 REGR factor score 4 for analysis 1 . FAC3_1 REGR factor
score 3 for analysis 1 . FAC2_1 REGR factor score 2 for analysis 1 . FAC1_1 REGR factor score 1 for analysis 1
b Dependent Variable: q.6h Index based overall satisfaction
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) 4.791 .020
237.030 .000
FAC1_1 REGR factor score 1 for
analysis 1 .317 .020 .544 15.642 .000
FAC2_1 REGR factor score 2 for
analysis 1 .183 .020 .315 9.046 .000
FAC3_1 REGR factor score 3 for
analysis 1 .148 .020 .254 7.298 .000
FAC4_1 REGR factor score 4 for
analysis 1 .119 .020 .205 5.892 .000
FAC5_1 REGR factor score 5 for
analysis 1 -.029 .020 -.050 -1.448 .148
FAC6_1 REGR factor score 6 for
analysis 1 .118 .020 .203 5.827 .000
FAC7_1 REGR factor score 7 for
analysis 1 .103 .020 .176 5.067 .000
a Dependent Variable: q.6h Index based overall satisfaction
Casewise Diagnostics(a)
Case Number Std. Residual q.6h Index based overall satisfaction
37 -3.088 4
a Dependent Variable: q.6h Index based overall satisfaction
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.25 5.54 4.79 .442 359
Residual -1.183 1.022 .000 .379 359
Std. Predicted Value -3.486 1.697 .000 1.000 359
Std. Residual -3.088 2.670 .000 .990 359
a Dependent Variable: q.6h Index based overall satisfaction
Regression Analysis
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .708(a) .501 .492 .442 1.811
a Predictors: (Constant). FAC7_1 REGR factor score 7 for analysis 1 . FAC6_1 REGR factor score 6 for analysis 1 .
FAC5_1 REGR factor score 5 for analysis 1 . FAC4_1 REGR factor score 4 for analysis 1 . FAC3_1 REGR factor
score 3 for analysis 1 . FAC2_1 REGR factor score 2 for analysis 1 . FAC1_1 REGR factor score 1 for analysis 1
b Dependent Variable: q.5h self reported overall satisfaction
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 69.135 7 9.876 50.442 .000(a)
Residual 68.725 351 .196
Total 137.861 358
a Predictors: (Constant). FAC7_1 REGR factor score 7 for analysis 1 . FAC6_1 REGR factor score 6 for analysis 1 .
FAC5_1 REGR factor score 5 for analysis 1 . FAC4_1 REGR factor score 4 for analysis 1 . FAC3_1 REGR factor
score 3 for analysis 1 . FAC2_1 REGR factor score 2 for analysis 1 . FAC1_1 REGR factor score 1 for analysis 1
b Dependent Variable: q.5h self reported overall satisfaction
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) 5.735 .023
245.587 .000
FAC1_1 REGR factor score 1 for
analysis 1 .312 .023 .503 13.343 .000
FAC2_1 REGR factor score 2 for
analysis 1 .184 .023 .297 7.880 .000
FAC3_1 REGR factor score 3 for
analysis 1 .141 .023 .228 6.042 .000
FAC4_1 REGR factor score 4 for
analysis 1 .130 .023 .209 5.553 .000
FAC5_1 REGR factor score 5 for
analysis 1 -.015 .023 -.025 -.651 .515
FAC6_1 REGR factor score 6 for
analysis 1 .111 .023 .180 4.764 .000
FAC7_1 REGR factor score 7 for
analysis 1 .111 .023 .179 4.744 .000
a Dependent Variable: q.5h self reported overall satisfaction
Casewise Diagnostics(a)
Case Number Std. Residual q.5h self reported overall satisfaction
20 -3.901 4
99 -3.012 4
a Dependent Variable: q.5h self reported overall satisfaction
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 4.24 6.50 5.74 .439 359
Residual -1.726 1.101 .000 .438 359
Std. Predicted Value -3.400 1.749 .000 1.000 359
Std. Residual -3.901 2.489 .000 .990 359
a Dependent Variable: q.5h self reported overall satisfaction
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.1h Repurchase intention 6.32 .631 359
q.6h Index based overall satisfaction 4.79 .582 359
Correlations
q.1h Repurchase
intention
q.6h Index based overall
satisfaction
Pearson
Correlation
q.1h Repurchase intention 1.000 .663
q.6h Index based overall
satisfaction .663 1.000
Sig. (1-tailed)
q.1h Repurchase intention . .000
q.6h Index based overall
satisfaction .000 .
N
q.1h Repurchase intention 359 359
q.6h Index based overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .663(a) .440 .439 .473 1.937
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.1h Repurchase intention
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 62.719 1 62.719 280.587 .000(a)
Residual 79.799 357 .224
Total 142.518 358
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.1h Repurchase intention
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) 2.878 .207
13.895 .000
q.6h Index based overall
satisfaction .719 .043 .663 16.751 .000
a Dependent Variable: q.1h Repurchase intention
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 5.04 7.19 6.32 .419 359
Residual -.754 .527 .000 .472 359
Std. Predicted Value -3.077 2.077 .000 1.000 359
Std. Residual -1.596 1.114 .000 .999 359
a Dependent Variable: q.1h Repurchase intention
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.2h revisit intention 5.02 1.335 359
q.6h Index based overall satisfaction 4.79 .582 359
Correlations
q.2h revisit
intention
q.6h Index based overall
satisfaction
Pearson
Correlation
q.2h revisit intention 1.000 .788
q.6h Index based overall
satisfaction .788 1.000
Sig. (1-tailed)
q.2h revisit intention . .000
q.6h Index based overall
satisfaction .000 .
N
q.2h revisit intention 359 359
q.6h Index based overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .788(a) .621 .620 .823 1.903
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.2h revisit intention
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 396.205 1 396.205 585.221 .000(a)
Residual 241.695 357 .677
Total 637.900 358
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.2h revisit intention
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) -3.641 .361
-
10.100 .000
q.6h Index based overall
satisfaction 1.807 .075 .788 24.191 .000
a Dependent Variable: q.2h revisit intention
Casewise Diagnostics(a)
Case Number Std. Residual q.2h revisit intention
125 -3.891 4
171 -3.891 4
202 -3.891 4
a Dependent Variable: q.2h revisit intention
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 1.78 7.20 5.02 1.052 359
Residual -3.201 2.220 .000 .822 359
Std. Predicted Value -3.077 2.077 .000 1.000 359
Std. Residual -3.891 2.698 .000 .999 359
a Dependent Variable: q.2h revisit intention
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.3h revisit frequency 6.44 .961 359
q.6h Index based overall satisfaction 4.79 .582 359
Correlations
q.3h revisit
frequency
q.6h Index based overall
satisfaction
Pearson
Correlation
q.3h revisit frequency 1.000 .898
q.6h Index based overall
satisfaction .898 1.000
Sig. (1-tailed)
q.3h revisit frequency . .000
q.6h Index based overall
satisfaction .000 .
N
q.3h revisit frequency 359 359
q.6h Index based overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .898(a) .807 .806 .423 1.941
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.3h revisit frequency
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 266.442 1 266.442 1488.632 .000(a)
Residual 63.898 357 .179
Total 330.340 358
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.3h revisit frequency
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) -.663 .185
-3.574 .000
q.6h Index based overall
satisfaction 1.482 .038 .898 38.583 .000
a Dependent Variable: q.3h revisit frequency
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.78 8.23 6.44 .863 359
Residual -1.229 .253 .000 .422 359
Std. Predicted Value -3.077 2.077 .000 1.000 359
Std. Residual -2.905 .598 .000 .999 359
a Dependent Variable: q.3h revisit frequency
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.4h repurchase frequency 5.45 .917 359
q.6h Index based overall satisfaction 4.79 .582 359
Correlations
q.4h repurchase
frequency
q.6h Index based overall
satisfaction
Pearson
Correlation
q.4h repurchase frequency 1.000 .894
q.6h Index based overall
satisfaction .894 1.000
Sig. (1-tailed)
q.4h repurchase frequency . .000
q.6h Index based overall
satisfaction .000 .
N
q.4h repurchase frequency 359 359
q.6h Index based overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .894(a) .799 .799 .411 1.965
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.4h repurchase frequency
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 240.561 1 240.561 1423.382 .000(a)
Residual 60.335 357 .169
Total 300.897 358
a Predictors: (Constant). q.6h Index based overall satisfaction
b Dependent Variable: q.4h repurchase frequency
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) -1.295 .180
-7.189 .000
q.6h Index based overall
satisfaction 1.408 .037 .894 37.728 .000
a Dependent Variable: q.4h repurchase frequency
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 2.93 7.15 5.45 .820 359
Residual -1.153 .255 .000 .411 359
Std. Predicted Value -3.077 2.077 .000 1.000 359
Std. Residual -2.806 .619 .000 .999 359
a Dependent Variable: q.4h repurchase frequency
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.4h repurchase frequency 5.45 .917 359
q.5h self reported overall satisfaction 5.74 .621 359
Correlations
q.4h repurchase
frequency
q.5h self reported overall
satisfaction
Pearson
Correlation
q.4h repurchase frequency 1.000 .834
q.5h self reported overall
satisfaction .834 1.000
Sig. (1-tailed)
q.4h repurchase frequency . .000
q.5h self reported overall
satisfaction .000 .
N
q.4h repurchase frequency 359 359
q.5h self reported overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .834(a) .696 .695 .507 2.171
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.4h repurchase frequency
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 209.309 1 209.309 815.865 .000(a)
Residual 91.588 357 .257
Total 300.897 358
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.4h repurchase frequency
Coefficients(a)
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error
Beta
1 (Constant) -1.616 .249
-6.493 .000
q.5h self reported overall satisfaction 1.232 .043 .834 28.563 .000
a Dependent Variable: q.4h repurchase frequency
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.31 7.01 5.45 .765 359
Residual -1.009 1.455 .000 .506 359
Std. Predicted Value -2.796 2.038 .000 1.000 359
Std. Residual -1.993 2.872 .000 .999 359
a Dependent Variable: q.4h repurchase frequency
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.3h revisit frequency 6.44 .961 359
q.5h self reported overall satisfaction 5.74 .621 359
Correlations
q.3h revisit
frequency
q.5h self reported overall
satisfaction
Pearson
Correlation
q.3h revisit frequency 1.000 .837
q.5h self reported overall
satisfaction .837 1.000
Sig. (1-tailed)
q.3h revisit frequency . .000
q.5h self reported overall
satisfaction .000 .
N
q.3h revisit frequency 359 359
q.5h self reported overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .837(a) .700 .699 .527 2.137
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.3h revisit frequency
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 231.238 1 231.238 833.003 .000(a)
Residual 99.102 357 .278
Total 330.340 358
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.3h revisit frequency
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) -.991 .259
-3.827 .000
q.5h self reported overall
satisfaction 1.295 .045 .837 28.862 .000
a Dependent Variable: q.3h revisit frequency
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 4.19 8.08 6.44 .804 359
Residual -1.190 1.515 .000 .526 359
Std. Predicted Value -2.796 2.038 .000 1.000 359
Std. Residual -2.258 2.876 .000 .999 359
a Dependent Variable: q.3h revisit frequency
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.2h revisit intention 5.02 1.335 359
q.5h self reported overall satisfaction 5.74 .621 359
Correlations
q.2h revisit
intention
q.5h self reported overall
satisfaction
Pearson
Correlation
q.2h revisit intention 1.000 .740
q.5h self reported overall
satisfaction .740 1.000
Sig. (1-tailed)
q.2h revisit intention . .000
q.5h self reported overall
satisfaction .000 .
N
q.2h revisit intention 359 359
q.5h self reported overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .740(a) .548 .547 .898 2.055
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.2h revisit intention
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 349.764 1 349.764 433.359 .000(a)
Residual 288.135 357 .807
Total 637.900 358
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.2h revisit intention
Coefficients(a)
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error
Beta
1 (Constant) -4.119 .441
-9.331 .000
q.5h self reported overall satisfaction 1.593 .077 .740 20.817 .000
a Dependent Variable: q.2h revisit intention
Casewise Diagnostics(a)
Case Number Std. Residual q.2h revisit intention
125 -3.374 4
171 -3.374 4
202 -3.374 4
a Dependent Variable: q.2h revisit intention
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 2.25 7.03 5.02 .988 359
Residual -3.031 2.155 .000 .897 359
Std. Predicted Value -2.796 2.038 .000 1.000 359
Std. Residual -3.374 2.398 .000 .999 359
a Dependent Variable: q.2h revisit intention
Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
q.1h Repurchase intention 6.32 .631 359
q.5h self reported overall satisfaction 5.74 .621 359
Correlations
q.1h Repurchase
intention
q.5h self reported overall
satisfaction
Pearson
Correlation
q.1h Repurchase intention 1.000 .640
q.5h self reported overall
satisfaction .640 1.000
Sig. (1-tailed)
q.1h Repurchase intention . .000
q.5h self reported overall
satisfaction .000 .
N
q.1h Repurchase intention 359 359
q.5h self reported overall
satisfaction 359 359
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .640(a) .409 .408 .486 1.956
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.1h Repurchase intention
ANOVA(b)
Model
Sum of Squares df Mean Square F Sig.
1
Regression 58.359 1 58.359 247.558 .000(a)
Residual 84.159 357 .236
Total 142.518 358
a Predictors: (Constant). q.5h self reported overall satisfaction
b Dependent Variable: q.1h Repurchase intention
Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error
Beta
1
(Constant) 2.592 .239
10.864 .000
q.5h self reported overall
satisfaction .651 .041 .640 15.734 .000
a Dependent Variable: q.1h Repurchase intention
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 5.19 7.15 6.32 .404 359
Residual -.845 1.155 .000 .485 359
Std. Predicted Value -2.796 2.038 .000 1.000 359
Std. Residual -1.740 2.380 .000 .999 359
a Dependent Variable: q.1h Repurchase intention
