investigating customer satisfaction dimensions with service quality of online auctions: an empirical...

ORI GIN AL ARTICLE

Investigating customer satisfaction dimensionswith service quality of online auctions: an empiricalinvestigation of e-Bay

Eugenia Papaioannou • Costas Assimakopoulos •

Christos Sarmaniotis • Christos K. Georgiadis

Received: 31 January 2012 / Revised: 13 September 2012 / Accepted: 19 September 2012 /

Published online: 5 October 2012

� Springer-Verlag Berlin Heidelberg 2012

Abstract Over the recent years, C2C (consumer-to-consumer) electronic market

has increased rapidly, and has become the most active segment of e-markets today.

Despite the growth and popularity of online auctions and especially of e-Bay, which

has been considered to be one of the most profitable e-commerce companies, the

online auction environment is risky and transactions are complex due to the fact that

buyer and seller do not know each other and they are not familiar. Although many

studies have examined the impact of e-service quality on customer satisfaction in

B2C (business-to-consumer) or B2B (business-to-business) commerce, the literature

is insufficient in the case of C2C commerce. However, a number of authors have

conducted research in C2C and particularly in e-Bay but in all of the cases, data

source from e-Bay’s feedback system was employed in order to investigate service

quality issues. Nevertheless, in this study a field research was conducted in order to

collect the necessary data and thus reducing possible bias. The purpose of this paper

is to examine some determinants of satisfaction with service quality dimensions in

the online auction environment. More specifically, this paper is examining the case

of e-Bay, in order to have a better understanding of buyers’ satisfaction in this risky

environment. The paper’s aim is accomplished through an empirical investigation of

a sample of 2,099 buyers of e-Bay, examining buyers’ expectation and perception

levels towards service quality of e-Bay. Gap analysis was first employed in order to

E. Papaioannou � C. K. Georgiadis (&)

Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

e-mail: [email protected]

E. Papaioannou


C. Assimakopoulos � C. Sarmaniotis

Department of Marketing, Alexander TEI of Thessaloniki, Thessaloniki, Greece


C. Sarmaniotis


123

Inf Syst E-Bus Manage (2013) 11:313–330

DOI 10.1007/s10257-012-0202-z

identify users’ disconfirmation dimensions in the online auction market place.

Some research hypotheses are formulated to find out whether these dimensions

have a positive disconfirmation. In most of the cases a positive disconfirmation is

tracked down. Secondly, factor analysis was utilized to identify broad determi-

nants of e-Bay service quality. The pool of the initial items deduced to four

factors. These factors are, to a degree, differentiated in the Greek environment

from factors found in other studies. A second round of research hypotheses are

formulated again to find out whether those factors are related to the users’ dis-

confirmation dimensions. Finally, a new theoretical model is proposed based on

the above mentioned hypotheses.

Keywords e-Service quality � Satisfaction � Customer’s perception and

expectation � Gap analysis � C2C � e-Bay

1 Introduction

Over the recent years, C2C (Consumer-to-Consumer) electronic market has

increased rapidly and has become the most active segment of e-markets today

(Rauniar et al. 2009). For instance, e-Bay is the world’s foremost online

marketplace. Through reliable customer service, efficient information exchange,

and by utilizing the latest technology, e-Bay has created an auction-based market

community of efficiency unheard of prior to the internet revolution.

e-Bay, Inc. provides online market places for the sale of goods and services as

well as other online commerce or e-commerce, platforms and online payment

solutions to a diverse community of individuals and businesses. The company

brings together millions of buyers and sellers every day on a local, national and

international basis through an array of websites.

Despite the unstable but challenging environment in 2009, e-Bay delivered $8.7

billion in revenues, a 2 % increase from the prior year. Moreover, e-Bay connected

more buyers and sellers than ever before through its core payments and e-commerce

businesses in 2009. PayPal had an exceptional 2009, ending the year with a net total

payment volume (TPV) of $71 billion—a 19 % increase over 2008—and 81 million

active registered accounts globally. e-Bay’s marketplaces business expanded its

customer base to 90 million active users worldwide (e-Bay 2009).

e-Bay expects annual revenue to grow up to $10–$12 billion in 2011. The online

auction site expects significant growth from its payment service PayPal and Internet

telecom service Skype over the next 3 years. e-Bay expects PayPal revenue to reach

$4–$5 billion in 2011, compared with $2.4 billion in 2008, and Skype revenue to

more than double to over $1 billion in 2011(e-Bay 2010).

However, not every online auction has been able to attract the above number of

bidders into the auction process. According to Rupak et al. (2009) effectiveness

measures of online auctions should incorporate a successful website design, which

can play a significant role in the overall marketing communication mix. Anderson

and Srinivasan (2003) underline the necessity for improving bidder’s loyalty in

online auctions. Loyal customers are considered essential to business survival. As

314 E. Papaioannou et al.

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competition increases, the need for customer loyalty will become increasingly

important (Trasorras et al. 2009). Moreover, many studies have provided empirical

evidence supporting that satisfaction has a significant influence on repurchase

intention and loyalty (Pavlou 2002; Tsai et al. 2006; Stefanou et al. 2003). Huang

and Finch (2010) study was designed to extend the understanding of customer

satisfaction determinants in high-risk online auction environments. Yen and Lu

(2007, 2008) aim to shed light on e-service quality in online auctions and investigate

the determinants of buyer’s loyalty intension in online auctions. All of the above

argue that e-service quality is related to user satisfaction and little is known as to

which dimensions of service quality are important to customer satisfaction in the

online auction context. Although many studies have examined the impact of

e-service quality on customer satisfaction in B2C (business-to-consumer) com-

merce, the literature is poor in the case of C2C commerce.

Despite the growth and popularity of online auctions and especially of e-Bay,

which has been considered as one of the most profitable e-commerce companies, the

online auction environment is risky and transactions are complex due to the fact that

buyer and seller do not know each other and they are not familiar. Specifically, this

risk mainly stems from incomplete security regarding seller authentication (Tams

2010). Buyer could be afraid of paying for but never receiving the product or

receiving products that were not as expected. The seller could be afraid of charging

products because buyers can be dishonest concerning their payment behavior (Lin

et al. 2006).

Consequently, there is a need for reducing uncertainty and fostering trust for

exchange among strangers. Notably, online auction houses usually use reputation

systems in order to mitigate the treat of opportunistic behavior and also to

communicate a seller’s performance history to potential customers.

Many authors have used the data source from e-Bay’s feedback system in order to

investigate service quality issues. In contrast, this paper’s purpose is accomplished

through an empirical investigation of a sample of 2,099 buyers of e-Bay.

The objectives of the research reported in this paper were:

• to investigate buyers’ expectation and perception levels towards service quality

of e-Bay and to analyze the discrepancy gap between buyers’ expectation and

perception

• to propose determinants of e-Bay service quality, which is one of the

contributions of this paper. The possible impact on user disconfirmation of

these broad determinants is investigated in the Greek environment and this is

another contribution of the paper

• to examine consumption attitudes and behavior of Greek buyers and finally to

investigate the extent of the usage of e-Bay services by buyers and identify

possible relationships with demographic variables.

The rest of this paper is organized as follows: next section describes the

reputation mechanism that e-Bay uses. Then we review the relevant literature on

customer satisfaction and service quality in e-commerce context. We then describe

our data collection and research methodology. Next demographics of the sample are

depicted and significance tests are performed between variables. Moreover, research

Investigating customer satisfaction dimensions 315

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hypotheses, data analysis and discussion are presented. Some research hypotheses

are formulated to see whether some dimensions (My e-Bay Service, Design

Functionality, Products, Services towards the Customers, Privacy Security, Overall

Satisfaction) of users’ disconfirmation are positive. To achieve this, gap analysis is

employed between perceived and expected performance. Then, factor analysis is

utilized to identify broad determinants of e-Bay service quality. Then a second set of

research hypotheses are formulated to find out whether these factors (System

usability and effective marketing, Financial transactions and trust, Communication,

Customer and sales support) are related to the users’ disconfirmation dimensions. A

new theoretical model is proposed based on the above mentioned hypotheses.

Finally, we conclude with managerial implications, limitations and propositions for

further research issues.

2 Analysis and evaluation of e-Bay mechanism system

e-Bay’s reputation systems are feedback reports of a trader’s reputation in terms of

several different components that reflect the trader’s behavioral performance in

previous online transactions (Lin et al. 2007; Dellarocas and Wood 2008). On

e-Bay, both the seller and the buyer of an object can rate each other after a

transaction. As the auction is over, the seller and the buyer can leave feedback on

their trading partner regarding this transaction. A trader’s reputation is measured by

an overall rating, which is counted and saved as a number (the feedback score).

Moreover, the feedback score, which is a ‘‘positive’’, ‘‘neutral’’, or ‘‘negative’’

rating, is accompanied by a textual comment. The ratings a user receives are used to

calculate his/her feedback score. A positive rating increases the user’s feedback

score by one point, a negative rating decreases it by one point and a neutral rating

leaves it unchanged. There are rules that are applied if a user tries to rate more than

once and generally e-Bay’s feedback forum is the most popular and successful

online reputation system because of its design and the innovation that it uses. Due to

that, many authors have used the data source from e-Bay’s feedback system in order

to investigate service quality issues, or customer loyalty and satisfaction because

those feedback reports provide the sellers opportunities to identify and understand

the determinants of customer satisfaction and dissatisfaction.

However, though it is true that reputation system is of central importance in

electronic markets and this is something that has been substantiated by many

studies, it has been argued that this exchange of rates between seller and buyer

might lead to strategic reciprocation and retaliation. Leaving a positive rating could

be driven by expectations to receive a positive rating in return. On the other hand,

the fear of retaliatory negative feedback makes users avoid leaving a negative

feedback even when they are dissatisfied (Klein et al. 2009). Consequently, the

rating behavior would bias the reputation index towards more positive evaluations

(Resnick and Zeckhauser 2002). Furthermore, buyers have the chance to rate four

parameters in a five scale measurement, i.e. how accurate the item description was,

how satisfied buyers were with the seller’s communication, how quickly the seller

shipped the item and how reasonable the shipping and handling charges were. It is


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obvious that a feedback mechanism reduces the high risk exposure in online

auctions and gives important information for potential buyers, but this is not an

evidence of the overall buyer’s satisfaction with e-Bay’s services.

3 Literature review

One of the most important features which are essential for the survival of a company

is customers satisfaction. Most researchers agree that satisfaction is an attitude or

evaluation that is formed by the customer comparing their pre-purchase expectation

of what he/she expected to receive from the product to their subjective perceptions

of the performance they actually did receive (Oliver 1989). Thus, customer

satisfaction is defined as a result of customer’s evaluation with the consumption

experience with the services (Rene et al. 2009). However, the customers have

different levels of satisfaction as they have different attitudes and perceived

performance from products/services. Due to that it is very important for the

companies to know their customers’ expectations as these expectations influence

customers’ satisfaction. When expectations are met or exceeded, customers report

higher levels of satisfaction (Jones et al. 2003). If a customer does not get what he/

she expected, the expectation is said to be disconfirmed (Hunt 1991). A negative

disconfirmation, results in dissatisfaction.

Based on disconfirmation theory, which emerges as the primary foundation for

satisfaction models, satisfaction is mainly defined by the gap between perceived

performance, expectations and desires (Oliver 1993; Bhattacherjee 2001). This

theory was proposed that satisfaction is affected by the intensity and direction

(positive or negative) of the gap (disconfirmation) between expectations and

perceived performance (Khalifa and Liu 2002). Expectation disconfirmation

occurs in three forms. Positive disconfirmation: occurs when perceived perfor-

mance exceeds expectations. Confirmation: occurs when perceived performance

meets expectations and Negative disconfirmation, that is, when actual is not as

good as expected (Hunt 1991; Abedniya 2011). At this point, we should stress the

importance of the perceived performance. A number of studies has also shown a

strong direct link from perceived performance to satisfaction (Oliver 1993).

Perceived performance is defined as customer’s perception of how service/product

performance fulfills their needs, wants and desire (Cadotte et al. 1987).

Customers’ perception depends on the quality of the services/products that are

provided for them by a company (Parasuraman et al. 1988). One of the important

factors to measure perceived service quality is service quality dimension.

Parasuraman et al. (1988) defined the service quality in terms of five dimensions:

Reliability, Empathy, Tangible, Responsiveness and Assurance. In the SERV-

QUAL model service quality is the gap between customers’ expectations (E) and

their perception of the service provider’s performance (P). Hence the service

quality can be measured by subtracting the customer’s expectations from the

customer’s perception.

The concept of Quality of Service (QoS) has been very important in the

e-commerce environment. Traditionally, online service providers measure the QoS


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and guarantee service-related characteristics from the service provider’s point-of-

view. In that case, QoS metrics have been based on system components

characteristics not taking into account human perception (Touradj 2010). Nowa-

days, quality of service is concerned with the overall experience the user has when

accessing and using provided services (ITU-T 800 2008). Prior studies have

discovered that in the online context some traditional service quality dimensions

may not be relevant and additional ones may be necessary (Torkzadeh and Dhillon

2002; Burke 2002; Lee and Lin 2005; Parasuraman et al. 2005). The service quality

in e-commerce is different from the traditionally studied service quality in

marketing research, which has been focusing on the services in organizations

(Bhattacherjee 2001). Marketing research does not usually consider the technolog-

ical antecedents of quality. Online transactions is a complex process that can be

divided into various sub-processes such as navigation, information availability, ease

of use, fulfillment, online payment (Yen and Lu 2008). Thus, e-service quality

contains many components, which reflects two attributes in its measurement.

Reviewing the literature, we can realize that there are a lot of studies that focus on

service quality dimensions in online contexts showing that quality dimensions can

be classified into two categories. The first category captures the quality of the web

site interface, which includes web site design and security/privacy. The second

category goes beyond the web site interface and emphasizes the service delivery

process. Among these dimensions are fulfillment reliability, fulfillment speed, and

customer service (Huang and Finch 2010).

Fortunately, some studies have taken into account both categories in order to set

criteria for measuring e-service quality. Among others, Zeithaml (2000; 2002),

Zeithaml et al. (2002), Parasuraman et al. (2005), Bauer et al. (2006) introduced

e-service quality scale and they addressed important statements regarding e-service

quality in the online shopping transactions. Their studies provide us a good

theoretical framework to apply them in C2C environment.

In light of the above in the next section we formulate some hypotheses related, on

the one hand, to the discrepancies of user’s expected and perceived performance and

on the other, to determinants of service quality.

4 Research methodology

4.1 Questionnaire

The survey instrument was an e-SERVQUAL type questionnaire, consisting of 9

parts. In the first part, general questions related to the personal online behavior of

the respondents’ were asked. In the second part there were questions specifically

related to users’ behavior towards e-Bay. In the ninth part, demographic questions

were asked. In the middle parts (third to eighth), items, modified from the

e-SERVQUAL model, were used in order to measure buyers’ expectation and

perception levels with service quality of the e-Bay provided products/services. Most

of the items that are used for measuring perceived service quality and buyers’

expectations were measured on an interval scale using Likert 5-point measurement,


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except for the personal online behavior and demographic questions which were

measured on ordinal and/or nominal scales. Measures included in the fourth to the

sixth part of the questionnaire establish the extraction of gap scores. Finally, we

should stress that the questionnaire was pretested with a sample size of 60 experts,

in order to improve it. The respondents provided comments of some items and

possible misunderstandings over a few terms which were taken under consideration.

The questionnaire was in Greek in order to be more comprehensive to the

respondents.

4.2 Sample

The study investigates the buyers’ satisfaction in the context of e-Bay environment.

In collaboration with colleagues from other universities (Technological Educational

Institutes of Crete, Athens, Patras, Serres, Larissa, Kozani, Kastoria and Thessa-

loniki and University of Macedonia), we constructed a population of e-Bay users in

Greece. Due to the nature of that type of research the final composition of the

population was mainly students (undergraduate-postgraduate) of the above institu-

tions. The total population was 4,002. The population members were invited by

emails to participate in the research. Due to the fact that most of the population

members were students, there was a willingness to participate. Approximately 2,500

(2,487) members participated in the study. The response rate was 62.14 %.

All the population members willing to participate were approached by

interviewers personally. Therefore, it is clear that we did not choose a sample but

we contacted the whole population. The interviews were conducted by trained

students of the aforementioned institutions, attending e-business courses. A credit

was given to the interviewers for the course they attended as a motive to conduct

satisfactorily the interviews.

Obviously, not all the questionnaires received were valid. After careful editing,

2,099 valid questionnaires were left. The most common reason for dropping a

questionnaire was that the respondent had no experience on e-Bay transactions as

revealed by the respondents’ answers and evaluated by the authors. We should

underline that in our research, a necessary condition that users should meet in order

to be included in the sample, was the necessity that respondents should have buying

experience from e-Bay at least once. It was not enough that the respondents should

have known about e-Bay or should have visited e-Bay’s website. When a respondent

did not have this kind of experience he/she could not answer the majority of the

questions. Consequently, there were no responses to the specific questions and thus

those questionnaires dropped as invalid.

4.3 Demographics

The majority of the respondents were males. More specifically, 61 % of the sample

were males and 39 % were females. It seems that males tend to visit and buy

products more frequently than females. 66.9 % had an age between 18 and 24 years

old, 22.9 % were aged between 25 and 30 years old and 10.2 % of the sample were


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older than 30 years. It is obvious that young people are the majority of the e-Bay

customers.

Regarding the education level, 10.5 % graduated from primary school, whereas,

8.6 and 4.5 % graduated from secondary and high school, respectively. The

undergraduate students were the 58.3 % of the sample and diploma/degree holder

were the 18.1 % of the respondents.

In respect to occupation, university students (undergraduate and post graduate)

were the 60.5 % of the sample, 10.2 % private employees, 8.3 % freelancers, 7.3 %

entrepreneurs and 6.6 % civil servants. The rest of the sample declared several other

occupations.

The questionnaire included questions about general consumption attitudes in the

use of internet. The places where people access internet are at home (95.8 %), at job

(19.3 %), at internet cafe shops (13 %), at school (30.7 %) and anywhere while

using their mobile phone (38.8 %).

The reasons for accessing internet are to read the news (83.9 %), to exchange

e-mails (82.3 %), to search for products and services in order to buy (72.7 %), for

hobbies (66.2 %), entertainment (63.1 %), for web banking (22 %), for download-

ing scientific material for their studies (57.4 %), to buy products and/or services

(60.7 %).

Concerning the frequency of buying products or services using internet, the

overall majority (56.8 %) responds that they buy less than 1–2 times per month.

34.4 % buy more frequently than 2 times per month, 6 % do not buy at all and

2.8 % refused to answer to that question.

In the second part of the questionnaire people were asked questions focused on

behaviors, attitudes and intentions towards the e-Bay.

Regarding the frequency people visit the e-Bay, 4 % of the sample visits e-Bay

everyday, 5.3 % 3–4 times a week, 17.5 % 1–2 times a week, 37 % 1–2 times a

month and 30.9 % rarely.

Visitors of e-Bay are divided mainly into two categories. Those that intend to buy

products (39.2 %) and those that intend to be informed about new products

available, compare prices etc. (59.3 %). There is another 1.5 % that visits e-Bay for

other reasons.

Someone can learn about e-Bay through many channels. Friends and relatives is

the most common channel (88.7 %) for information related to e-Bay. Internet is

another popular channel (44.9 %). Other means are magazines (8 %), television

(3.3 %), newspapers (2.3 %) and radio (2.2 %).

It would be quite interesting, conducting a survey for e-Bay, to write down the

products mostly preferred to be bought by visitors. Clothes, computer hardware and

electronic appliances are the most popular product categories.

Considering the payment method, 23.7 % prefer to pay cash on delivery, 28.8 %

by credit card, 7 % by bank deposit, 5.6 % by bank transfer and 34.9 % by PayPal.

Finally, 90.5 % could suggest e-Bay to a friend. This is very important and

especially in the internet era, where, the effect and distribution of word of mouth

(WOM) have been further enhanced, as individuals can now make their opinions

easily accessible to other internet users (Dellarocas 2003).


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Moreover, it is very interesting to examine through significance testing some

profile characteristics of the users like demographics or behaviors and annual

turnover through internet. As mentioned previously, the majority of the sample that

has at least bought once through internet is young males currently being university

students. Nevertheless, it is under investigation whether demographics have an

effect on annual turnover through internet. Using Anova, it is depicted that the

turnover is related to the gender (sig. \ 0.01). However, age, education level and

occupation are not related (sig. [ 0.1).

On the other hand, the frequency that someone visits e-Bay is related to the

annual turnover (sig. \ 0.001) but the method of payment is not related

(sig. [ 0.05). Finally, the method of payment and their aspect on the security of

the financial transactions are found to be related (sig. \ 0.05).

5 Research hypotheses, data analysis and discussion

5.1 Dimensions of users’ disconfirmation

The focus of the current study is on the buyers’ overall satisfaction from e-Bay

usage. Based on the literature, it should be noted that quality is an imperative factor

of service dimension in relation to the evaluation of customers’ satisfaction.

As described in the literature, disconfirmation is the subjective judgments

resulting from comparing the buyers’ expectations and their perception of

performance experience. Positive disconfirmation is when the perceived experience

is greater than expected. The dimensions used in order to measure disconfirmation

are design and functionality of the web site, products, services towards customers,

privacy/security and overall satisfaction. These criteria were chosen from the

relevant literature (Parasuraman et al. 2005) and ‘‘My e-Bay’’ dimension (tool) was

added by the authors. Thus, the hypotheses formulated are the following:

H1a ‘‘My e-Bay Service’’ has a positive user disconfirmation.

H1b ‘‘Design Functionality’’ has a positive user disconfirmation.

H1c ‘‘Products’’ has a positive user disconfirmation.

H1d ‘‘Services towards the Customers’’ has a positive user disconfirmation.

H1e ‘‘Privacy Security’’ has a positive user disconfirmation.

H1f ‘‘Overall Satisfaction’’ has a positive user disconfirmation.

In the literature the efficiency of the web site, the system availability and privacy

are primary components of e-service quality (Yen and Lu 2008). Those components

are a little bit differentiated in the Greek environment according to our investigation.

In this research there have been used thirty items in order to identify some broad

determinants of e-Bay’s e-service quality. The specific hypotheses relating these

determinants to the user’s disconfirmation will be formulated and tested below.

Gap analysis (perceived perception minus expectation) was employed to measure

the service quality of the e-Bay. In services through websites, generally, quality can be

measured by the perception of the customer on how well the service has been delivered

(Lewis 1993; Hampton 1993). It is important to test the customers of e-Bay


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perceptions (actual experience) to see whether quality of services through the website

met, exceeded or followed the expectations. Additionally, Gap analysis could be a tool

for measuring the satisfaction/dissatisfaction of the customer. Therefore, the study of

the Gap could be a useful tool for the location of the weak aspects of a website that

provide services and, hence, contribute to the improvement of the services.

The variables that were examined measured satisfaction/dissatisfaction consid-

ering design and functionality of the e-Bay website, the products, services towards

customers, privacy and security of the transactions, satisfaction/dissatisfaction of

the specific service of ‘‘My e-Bay’’ and finally, the overall satisfaction.

In Sect. 5.2 a number of hypotheses have been formulated. These hypotheses are

tested.

Paired samples T test (see Table 1) revealed that the gap scores were statistically

significant (sig. \ 0.05) for all of the positive and negative gaps with the exception

of one pair of variables regarding the security/privacy satisfaction. In that case

sig. [ 0.1 and hence the result can be considered not statistically significant.

Therefore, hypotheses H1b, H1c, H1d and H1f are supported (statistically significant

and positive gap). On the other hand, hypothesis H1a regarding the ‘‘My e-Bay

service’’ although having statistically significant result it has a negative disconfir-

mation. Hence, H1a is not supported, whereas hypothesis H1e is not supported due

to the fact that the result is not statistically significant.

Analyzing the ‘‘gaps’’, it could be noted that e-Bay services towards the

customers, functionality and design of the e-Bay web site as well as provided

products do have satisfied customers. The gaps although positive, are short, meaning

that perceived performance and expected performance are close to each other

(Fig. 1). This can be interpreted as a lack of impressive positive surprise by the

website to the customers. On the other hand, customers really know what to expect

from such a website which means that details make the difference. There is always

room for improvement as customers’ demands and competition are increasing.

The maximum positive gap is depicted in the design and functionality variables,

whereas, the minimum positive gap is shown at the variables examining the

products. The negative gap has the largest absolute value compared to all other gaps

(Table 1). However, the distance between perceived and expected satisfaction still

remains short.

Table 1 Gap analysis of 6 dimensions measuring perceived and expected performance

Examined dimension Mean

perceived

SD

perceived

Mean

expected

SD

expected

Gaps Sig.

My e-Bay Service 3.19 1.31 3.41 1.137 –.218 .000

Design functionality 3.68 0.661 3.51 0.762 .164 .000

Products 3.68 0.671 3.60 0.744 .077 .000

Services towards the

customers

3.60 0.679 3.50 0.730 .103 .000

Privacy security 3.74 1.148 3.69 1.384 .051 .136

Overall satisfaction 3.72 0.656 3.61 7.18 .111 .000


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Efforts should be made in order to follow the increasing expectations of the

customers in order to maintain the gaps to a positive level. Nevertheless, it is also of

marginal effort to change ‘‘My e-Bay’’ service from negative to positive gap as the

latter is extremely short.

5.2 Determinants of e-Bays service quality

In order to identify some factors influencing the user’s satisfaction over e-Bay

experience, factor analysis was utilized on the related 30 sentences included in the

questionnaire. Kaiser–Meyer–Olkin measure for sampling adequacy was of an

acceptable magnitude (KMO 0.901) (see Table 2). Moreover, Bartlett’s sphericity

test gave a significance level of 0.000. Hence, all assumptions for carrying out factor

analysis are met. The extraction technique chosen was principal components and the

rotation method was varimax (Hair et al. 1998).

Initially, six factors were extracted out of 32 sentences. After rejecting those

factors that have insufficient loadings, we deduced to four factors and thirty items.

Factor analysis converged after 6 iterations. These factors explain 52 % of the total

variance (Table 3). The cut-off point for accepting sufficient loadings was 0.3. The

factors are named as follows:

Fig. 1 Schematic representation of the gap measurements among perceived and expected satisfactionover 6 dimensions proposed in the literature

Table 2 KMO and Bartlett’s

test for data adequacy

Based on correlations

Kaiser–Meyer–Olkin measure of sampling

Adequacy. .901

Bartlett’s test of sphericity

Approx. Chi-square 14,646.376

df 496

Sig. .000


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1. System usability and effective marketing

2. Financial transactions and trust

3. Communication

4. Customer and sales support

Factor 1 includes variables that have to do with functionality of the e-Bay

website and marketing of products, i.e. ‘‘structure of the web page’’, ‘‘products

Table 3 Total variance explained by the extracted factors

Component Initial Eigenvaluesa Extraction sums of squared

loadings

Rotation sums of squared

loadings

Total % of

variance

Cumulative

%

Total % of

variance

Cumulative

%

Total % of

variance

Cumulative

%

1 6.679 17.941 17.941 6.679 17.941 17.941 4.339 11.656 11.656

2 5.211 13.756 31.697 5.211 13.756 31.697 3.319 8.914 20.571

3 4.985 11.949 43.646 4.985 11.949 43.646 6.665 18.845 39.416

4 2.486 8.577 52.223 2.486 8.577 52.223 5.01 12.807 52.223

5 1.751 4.763 56.986

6 1.662 4.466 61.452

7 1.369 3.679 65.131

8 1.332 3.578 68.708

9 1.283 3.445 72.154

10 1.224 3.288 75.441

11 0.988 2.653 78.094

12 0.818 2.197 80.291

13 0.79 2.123 82.414

14 0.719 1.932 84.345

15 0.63 1.694 86.039

16 0.595 1.599 87.638

17 0.571 1.533 89.172

18 0.504 1.353 90.525

19 0.421 1.132 91.657

20 0.382 1.025 92.682

21 0.36 0.966 93.648

22 0.34 0.914 94.562

23 0.308 0.827 95.389

24 0.287 0.77 96.159

25 0.279 0.749 96.908

26 0.275 0.739 97.647

27 0.251 0.674 98.321

28 0.249 0.668 98.989

29 0.194 0.522 99.51

30 0.182 0.49 100

Extraction method: principal component analysis


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presentation’’, ‘‘prices’’, ‘‘products quality’’, ‘‘products delivery’’, ‘‘products

comparison module’’, ‘‘products search engine’’ etc. Since all those variables have

to do with the website’s structure, usability and marketing of products, it was named

as ‘‘System usability and effective marketing’’.

Factor 2 has variables like the ‘‘payment methods’’, ‘‘products insurance’’,

‘‘security of the paying transactions’’, ‘‘privacy of the financial transactions’’, ‘‘after

sales service’’ and ‘‘terms of paying and security’’ hence the factor name is

‘‘Financial transactions and trust’’.

Factor 3 entails variables such as ‘‘renewal frequency of the products

information’’ and ‘‘communication for the customers support’’. These variables

can be joined under the umbrella of ‘‘Communication’’.

Finally, factor 4 incorporates variables such as ‘‘products availability’’,

‘‘monitoring the customer’s account’’, and ‘‘behaviors of the sellers’’. Since those

variables have to do with the sales procedure and customer support the factor was

named as ‘‘Customer and sales support’’.

In Table 4 variables distribution to the factors are shown.

Further, Pearson correlation analysis was used between the identified factors and

the users’ disconfirmation dimensions (see Table 1).

Therefore, the hypotheses to be tested are the following:

H2a ‘‘System usability and effective marketing’’ is associated with user

disconfirmation.

H2b ‘‘Financial transactions and trust’’ is associated with user disconfirmation.

H2c ‘‘Communication’’ is associated with user disconfirmation.

H2d ‘‘Customer and sales support’’ is associated with user disconfirmation.

The correlation analysis used showed that there are statistically significant

relations between factor 1 and products (p \ 0.1) and design functionality

(p \ 0.05). Factor 2 is related with services towards the customers (p \ 0.05).

Factor 3 is related to design and functionality (p \ 0.05) and finally, factor 4 is

related to both design/functionality (p \ 0.05) and services towards the customers

(p \ 0.05).

Based on the above we propose the following theoretical model (see Fig. 2).

The factor analysis resulted in a number of factors that influence user’s

satisfaction over e-Bay experience. In fact the influence is indirect due to the fact

that disconfirmation is one stage and then satisfaction is resulted. The proposed

model presented in this paper is constructed after the hypotheses H2a, H2b, H2c and

H2d formulated. Figure 2 is the graphical representation of the proposed model.

Disconfirmation of the user is defined as the difference between perceived and

expected performance and located in 5 out of 6 dimensions shown in Table 1 of

Sect. 5. Nevertheless, ‘‘Privacy and Security’’ dimension is not included for the

reason that the significance level of the calculated gap is high (p [ 0.1) and thus this

dimension is rejected. It is worth to mention that the ‘‘My e-Bay Service’’, that is

also examined in the questionnaire (and included as a disconfirmation dimension in

Fig. 1), is not associated with any of the factors. Thus, ‘‘My e-Bay Service’’ can be

excluded from the disconfirmation model. The remaining 4 dimensions are all

included in the model. It should be noted that a factor is associated with user


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disconfirmation if there is at least one of the disconfirmation dimensions that is

associated with that factor. Hence, hypothesis H2a through H2d are supported due to

the fact that there is at least one dimension with statistically significant association

with each one of the dimensions (weak though) (Fig. 2).

Table 4 Rotated component matrix of the factor analysis

Rescaled component System

usability

and effective

marketing

Financial

transaction

and trust

Communication Customer

and sales

support

Rotated component matrix

Design of the website 0.487 0.188 0.103 0.188

Structure of the front page information 0.447 0.08 0.052 -0.031

Easiness of surfing to the web pages 0.502 0.13 0.064 0.162

Easiness for auditing new auctions 0.418 0.07 0.1 -0.007

Renewal frequency of the products

information

0.262 0.049 0.638 0.069

Presentation of preferred products 0.476 0.13 0.116 0.061

Presentation of new arrivals of products 0.460 0.134 0.1 0.116

Products variety 0.457 0.011 -0.022 0.041

Products information adequacy 0.524 0.135 0.076 0.148

Validity of products information 0.490 0.175 0.071 0.191

Products availability 0.213 0.035 0.06 0.978

Products prices 0.506 0.127 0.03 0.224

Offers 0.461 0.179 0.074 0.194

Product’s quality 0.507 0.192 0.072 0.225

My e-Bay service 0.458 0.3 0.052 0.234

Easiness of registration 0.430 0.276 0.055 0.181

Methods for products delivery 0.501 0.359 0.044 0.087

Time of products receipt by customers 0.427 0.353 0.06 -0.024

Monitoring the customers account 0.133 0.091 0.046 0.995

Communication for the customers support 0.046 0.139 0.959 0.079

After sales service 0.204 0.325 0.122 0.011

Behaviors of the sellers 0.121 0.104 0.016 0.978

Products search engine 0.477 0.07 -0.027 -0.122

Product’s comparison service 0.419 0.262 0.092 0.134

Time of products delivery 0.412 0.339 0.077 -0.022

Products insurance 0.294 0.356 0.096 0.085

Paying methods -0.171 0.903 -0.046 0.043

Security of the paying transactions 0.336 0.494 0.054 0.212

Privacy of the financial transactions 0.356 0.479 0.06 0.191

Terms of paying and security 0.270 0.38 0.036 0.023

Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalizationa Rotation converged in 6 iterations (With bold loadings of the specific item belonging to the factor of the

respective column)


123

Summarizing, factor analysis, partially, verified the already known results from the

literature, deviating simultaneously from the latter, in order to describe the Greek

environment. In comparison to other studies which have identified determinants of

auctioneer’s e-service quality we notice that in our study marketing oriented items are

prominent and more specifically included in the first factor named as ‘‘System usability

and effective marketing’’. Probably, these items and the deduced factor have to do with

the Greek environment. Studying carefully Table 4 it is obvious that web design issues

are included in the first factor of our study (design of the website, structure of the front

page information). Moreover, functionality issues that are indissolubly connected to the

web site interface are also included (My e-Bay service, easiness of registration,

presentation of preferred products, presentation of new arrivals of products, easiness of

surfing to the web pages, easiness for auditing new auctions). In contrast marketing

dimensions are identified in the first factor (presentation of preferred products,

presentation of new arrivals of products, products variety, products information

adequacy, products prices, products quality, products delivery etc.).

On the other hand, the rest of the factors are almost similar with those found in

the relevant literature concerning C2C environment (Yen and Lu 2008; Huang and

Finch 2010). Therefore, it is obvious that our research extents existing theory in the

C2C context.

6 Conclusions, further research, managerial implications and limitations

Rapid growth of C2C market, suggests that this area needs extensive and systematic

investigation. At the same time, a literature review indicates that online auction

Fig. 2 Graphical representation of the proposed model connecting the factors affecting e-Bays servicequality and user’s disconfirmation


123

buying and selling has not been studied to the extent that other areas of the internet

have been investigated. Indeed, most of the information regarding online auction

has been presented in the social networks. Empirical research slightly appears in the

academic literature concerning customers’ satisfaction issues in this type of

marketplace.

In this paper a modification of the well known e-SERVQUAL model concerning

the evaluation of the customer’s satisfaction with e-Bay, is used. Thus, a research

model is proposed consisted of the determinants of e-Bays service quality (four

factors) as depicted by the discrepancies (disconfirmation) of the dimensions named

‘‘Products’’, ‘‘Design Functionality’’ and ‘‘Services towards the Customers’’.

Moreover, gap analysis has been employed to determine the difference that exists

between users’ perceived and expected performance. It is found that gap is positive,

however, perceived performance and expected performance are close to each other.

Furthermore, the extent of the usage of e-Bay services by Greek buyers and their

relationship with demographic variables are investigated in order to find out their

consumption attitudes. Some significant relationships have been found.

Finally, a new theoretical model is proposed based on the one hand on some

dimensions of users’ disconfirmation and on the other hand on the factors

influencing the users’ satisfaction over e-Bay experience. The model is established

after the verification of the research hypotheses that were confirmed.

Although this study makes a significant contribution in extending past research it

gives directions for further research studies. More specifically, additional auction

sites should be included in future research in order to have more generalized and

representative results. Further, comparison studies should be conducted among

different consumer/sellers of different countries. Comparison studies between data

of e-Bay’s feedback mechanism and data collected through field studies as ours

should also be conducted. Additional research is needed concerning the effects of

the type of auctioned items to customer satisfaction.

The paper’s results contribute to improving the conduct of management at

e-Bay’s and other similar companies. The gap scores show that gap is positive;

however perceived performance and expected performance are close to each other.

e-Bay should have a mechanism detecting such small differences in perceived/

expected performance and strategies to cope with.

e-Bay vendors in the Greek market should do their best to emphasize and take

advantage of word-of-mouth. Findings show that 90.5 % could suggest e-Bay to a

friend. By doing so, vendors can build a more trustworthy image and gain more

valuable customers.

Finally, e-Bay could be assisted by taking into account the determinants of

auctioneer’s service quality and the aforementioned dimensions of the user

disconfirmation identified in this research.

It is obvious that in the C2C market the online sellers are customers as well.

Buyers and sellers play the same important role in C2C e-Commerce. Seller’s

perceptions and expectations to the vendor remain untested. This study has not

addressed this problem and could be considered as a limitation.

Furthermore, it should be noted that as e-Bay is the world’s foremost online

auctions, this study has concentrated only on it. Nevertheless, e-Bay is only one of


123

the several online auction business models that exist in the online marketplace.

Consequently, any generalizations should be made with caution.

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