2004:266 shu master's thesis1018947/fulltext01.pdf · buyers reap the benefits from having...

2004:266 SHU

M A S T E R ' S T H E S I S

Perception of Service Qualityin E-commerce

An Analytical Study of Internet Auction Sites

Atanu Nath Liu Zheng

Luleå University of Technology

MSc Programme in Electronic CommerceDepartment of Business Administration and Social Sciences

Division of Industrial marketing and e-commerce

2004:266 SHU - ISSN: 1404-5508 - ISRN: LTU-SHU-EX--04/266--SE

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Abstract

E-commerce as we know today has burgeoned with the advent of the World Wide Web and the subsequent mass acceptance and usage of it. Its huge potential for commercial and transactional purposes has not yet been fully tapped. One of the newer areas of marketing theory application in the context of the Internet, or more specifically E-Commerce is Service Marketing. In this paper, we explore the possibility of extending the existing tools for assessing and measuring service quality as perceived by customers to the context of purely virtual enterprises. Our aim is to see whether there are differences perceived by customers in service quality across online service based companies; and identify the attributes responsible for any such perceived differences; and the areas in which customers do not distinguish between providers. Confirmation and disconfirmation of a set of hypotheses is used to determine the distinctions based on variances between the identified attributes. Our findings indicate customers today set purely internet based service companies apart based on tangibility, reliability, and responsiveness of the companies rather than their perceived credibility and security in services offered. This indicates a blurring down of the divisions between online and offline service providing companies in the customer’s mind. Our interpretation of this blurring down of division is that e-commerce and transactions online has gained maturity and customers no longer feel trepidation regarding the often touted issues of credibility and security exclusive to online entities. Rather, today they are more concerned with who is able to provide them with better service with respect to their product centric worries that are more traditional in nature. Our recommendation for marketers would be to stop viewing themselves as purveyors of online or off-line services, but rather as purveyors of simply “services” and ensure quality therein. The customer has stopped caring whether service is delivered online, or off-line; for them the novelty of receiving services online has worn out, with the resultant expectations that quality in the more traditional aspects of service delivery would match at least that received offline. It is time the marketers stopped worrying where to position their selves along the click and mortar spectrum, and follow the customer’s lead.

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Acknowledgements

First we would like to acknowledge our parents, who have been the lifelong source of inspiration in any and all of our endeavors. We would also like to express our gratitude to our supervisor, Professor Lennart Persson, who guided us along the way and helped us achieve our goal; and Professor Esmail Salehi-Sangeri. We would also like to extend our warmest thanks to our families and to those that helped us in any way in the writing of this thesis. Luleå. December 2004. Atanu Nath Liu Zheng

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Table of Contents

Chapter 1.........................................................................................................................8 1.1 Introduction ...........................................................................................................8 1.2 Background ...........................................................................................................8

1.2.1 The increasing importance of services industries and its characteristics ...........9 1.2.2 Web based services .........................................................................................9 1.2.3 Web Service quality dimensions....................................................................10 1.2.4 The central role of quality in web based services:..........................................10

1.3 Problem Discussion & Justification......................................................................11 1.4 Research Statement:.............................................................................................11 1.5 Research questions:..............................................................................................12 1.6 Hypothesis formulation:.......................................................................................12 1.7 Implication of the research:..................................................................................12

Chapter 2....................................................................................................................... 14 Literature review: .......................................................................................................... 14

2.1 Services ...............................................................................................................14 2.2 Service quality: ....................................................................................................15 2.3 Service quality dimensions and measurement.......................................................17 2.4 Web service quality: ............................................................................................20

2.4.1 Information availability and content: .............................................................20 2.4.2 Ease of use (Usability): .................................................................................21 2.4.3 Privacy/Security:...........................................................................................21 2.4.4 Graphic style:................................................................................................22 2.4.5 Fulfillment/reliability: ...................................................................................22 2.4.6 Other Web service qualities:..........................................................................22

2.5 Dimensions of web service quality.......................................................................23 2.6 Measuring web service quality.............................................................................25 2.7 e-Servqual as a tool for measuring web service quality: .......................................27 2.8 Customer Satisfaction and service quality ............................................................28 2.9 Models showing the link between service quality measurement and satisfaction: .30 2.10 Conceptual model for web service quality:.........................................................32 2.11 Emerged Theoretical framework: .......................................................................34

Chapter 3....................................................................................................................... 36 Methodology................................................................................................................. 36

3.1 Research Purpose.................................................................................................36 3.1.1 Exploratory Research ....................................................................................37 3.1.2 Descriptive Research.....................................................................................37 3.1.3 Explanatory Research....................................................................................37

3.2 Research Approach..............................................................................................38 3.2.1 Deductive vs. Inductive.................................................................................39

3.3 Research strategy.................................................................................................39 3.4 Data Collection Methods .....................................................................................40

3.4.1. Online Survey vs. Offline(traditional) ..........................................................40

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3.4.2 Using Secondary data....................................................................................41 3.5 Sample selection ..................................................................................................41 3.6 Data Analysis Methods ........................................................................................42

3.6.1 Instrument for data analysis...........................................................................42 A. SERVQUAL for electronic assistive technology services ..............................42 B. Anova Single Factor Analysis .......................................................................43

3.7 The validity and reliability of research findings....................................................43 3.8 Methodology Summary........................................................................................45

Chapter 4....................................................................................................................... 46 Test subjects and Data collection................................................................................... 46

4.1 Chosen Test sites for analysis ..............................................................................46 4.1.1 Tradera.com..................................................................................................46 4.1.2 Ebay.com......................................................................................................47

4.2 The population sample .........................................................................................48

Chapter 5....................................................................................................................... 49 Data Analysis and Results ............................................................................................. 49

5.1 Analysis of perception of tangibility ....................................................................49 5.2 Analysis of perception of reliability .....................................................................50 5.3 Analysis of perception of responsiveness .............................................................51 5.4 Analysis of perception of credibility ....................................................................52 5.5 Analysis of perception of Security .......................................................................53 5.6 Analysis of cumulative perception of Tradera and Ebay.com ...............................54

Chapter 6....................................................................................................................... 57 Cross Analysis of data and Results ................................................................................ 57

6.1 Defining the analytical tool and setting parameters: .............................................57 6.1.1 Analysis of Variance (ANOVA): ..................................................................57

Assumptions: .....................................................................................................57 6.1.2 F Distribution:...............................................................................................57

6.2 Analytical Constructs:..........................................................................................58 6.3 ANOVA testing on the population samples and hypothesis testing:......................59

6.3.1 Tangibility Data set:......................................................................................59 6.3.2 Reliability data set:........................................................................................59 6.3.3 Responsiveness data set: ...............................................................................60 6.3.4 Credibility data set: .......................................................................................60 6.3.5 Security data set:...........................................................................................61

6.4 ANOVA testing within population between the groups:.......................................61 For Tradera:.......................................................................................................61 For Ebay: ...........................................................................................................61

Chapter 7....................................................................................................................... 63 Summary of Analytical Findings and Conclusions......................................................... 63

7.1 Implications of the findings: ................................................................................64 7.2 Directions for further research: ............................................................................65

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References..................................................................................................................... 66

Appendix I .................................................................................................................... 70 Ebay Online Questionnaire ........................................................................................70

Appendix II ................................................................................................................... 73

Tradera Online Questionnaire ....................................................................................73

Appendix III.................................................................................................................. 76 Categorization by Attributes in Database: A ..............................................................76

Ebay ......................................................................................................................76 Categorization by Attributes in Database: B...............................................................77

Tradera ..................................................................................................................77

Appendix IV.................................................................................................................. 78 A. Tangibility data set................................................................................................78 B. Reliability data set.................................................................................................79 C. Responsiveness data set: .......................................................................................80 D. Credibility data set:...............................................................................................81 E. Security data set: ...................................................................................................82

Appendix V................................................................................................................... 84

A. ANOVA testing within population between the groups .........................................84 For Tradera............................................................................................................84

B. ANOVA testing within population between the groups .........................................85 For Ebay................................................................................................................85

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List of Figures Figure 1: Drivers of customer satisfaction _________________________________________ 18 Figure 2: Measuring e-Commerce Effectiveness using a combination of User Satisfaction and Service Quality Theories__________________________________________ 30 Figure 3: Conceptual model for understanding web service quality_________________ 32 Figure 4: Perception of Tangibility _________________________________________________ 49 Figure 5: Perception of Reliability__________________________________________________ 50 Figure 6: Perception of Responsiveness ___________________________________________ 51 Figure 7: Perception of Credibility _________________________________________________ 52 Figure 8: Perception of Security ___________________________________________________ 53 Figure 9: EBay Attributes Perception ______________________________________________ 54 Figure 10: Tradera Attributes Perception __________________________________________ 55 Figure 11: Averaged Attributes scores_____________________________________________ 56 List of Tables Table 1: Difference between products and services.............................................................. 15 Table 2: Relevant Situations for different Research Strategies ......................................... 39 Table 3: Reliability and validity for quantitative designs ..................................................... 44 Table 4: Research Methods adopted of this paper................................................................. 45 Table 5: Overall comparative scores.......................................................................................... 55

Perception of Service Quality in E-Commerce: Introduction

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Chapter 1 In this first chapter, we shall discuss some background information about internet’s evolution into a marketing and purchasing channel. This will be followed by problem discussion, motivation for the research, and identification of research problem and research questions. 1.1 Introduction Internet came into being in the early 70s, yet for the majority of the population it was virtually inaccessible until the early 90’s. Electronic commerce, or e-commerce, as we know it today, was made possible through the advent of the world wide web, and the browser(s). E-commerce is the “Business activities conducted using electronic data transmission via the Internet and the WWW.” (Schneider & Perry, 2000). E-commerce has proven to be beneficial to sellers and buyers alike. Through the usage of E-commerce, sellers can now access narrow market segments that may be widely distributed geographically, thereby extending accessibility globally (Napier, 2001). Buyers reap the benefits from having access to global markets and access to a much larger product catalog from a wider and varied range of sellers. As a fallout effect, improvements have also been made in product quality, and newer and more creative ways of selling existing products have also been devised. The conversion of the Internet into a shopping destination was very rapid indeed. The dot com domain grew from being just 16% of the overall top level domains in 1995 to nearly 84% by 1997. However, consumer adoption of the Internet as a viable store front, so to speak, was delayed, primarily due to the relative novelty of the concept to the laymen, and security concerns for both the laymen and the informed buyers. As the early reluctance gradually dissipated, the nature of company-customer interactions has gone through fundamental transformations. Since online shoppers interact with a digital system rather than personnel, firms that fail to understand the needs of their “virtual” customers and tailor services accordingly will falter (Chen, 2003). As such, for firms, listening to customer voices is the initial step in planning service quality improvement endeavours. In turn, identifying customer perceived service quality dimensions and their roles in customer satisfaction and dissatisfaction provides a frame of reference for online service providers to assess their service performance (Yang and Fang, 2004). 1.2 Background Since the focus of this paper is the customer’s perception of the attributes which they believe are important in the delivery of service quality online, thus a brief explanation on services, web services and service quality components is in order. This is also because online services invariably share some common territory in relation to traditional interpersonal services (Yang and Fang, 2004).


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1.2.1 The increasing importance of services industries and its characteristics Service industries are playing an increasingly important role in the overall economy, and interest in the measurement of service quality is thus understandably high (Cronin and Taylor, 1992), and the delivery of higher levels of service quality is the strategy that is increasingly being offered as a key to service providers’ efforts to position themselves more effectively in the marketplace (Brown and Swartz, 1989; Rudie and Wansley, 1985; Parasuraman, Zeithaml, and Berry, 1988). Superior service quality measurably increases a firm’s overall profitability, its price premium, and its perceived and actual market share (Zeithaml et al., 1996). Hoffman et al (1997) observed that the trend today is moving towards an increase in services as share of the gross domestic product (GDP). There has also been a shift from a purely exchange perspective to a relational perspective (Grönroos, 1996). According to Haas (1995), the ability to create and maintain relationships has become an important skill in contemporary business marketing. Zeithaml (1996) further asserts that customer satisfaction paves the way for customer retention and increased profits, which in turn leads to employee loyalty and quality services. To have a meaningful discourse on service quality later on, we need to first understand the basic characteristics of services. Zeithaml, Parasuraman, and Berry (1985) identify three characteristics of services – intangibility, heterogeneity, and inseparability. Since services are performances rather than objects, precise manufacturing specifications concerning uniform quality can rarely be set (Zeithaml, Parasuraman, & Berry, 1985). Because of this intangibility, firms find it difficult to understand how consumers perceive their services and evaluate service quality (Zeithaml, 1981). The second characteristic identifies by Zeithaml et al (1985) is the heterogeneity of services – with performances varying from producer to producer, from customer to customer and from day to day. Also consistency of behavior from service personnel is difficult to assure (Booms and Bitner, 1981). Third, Zeithaml et al (1985) point out that production and consumption of many services are inseparable (Carmen and Langeard, 1980; Gronröös, 1978). They highlight that quality occurs during service delivery specially in labor intensive services, as opposed to being engineered at a manufacturing plant. Quality is in the interaction between the client and the contact person from the service firm (Lehtinen and Lehtinen, 1982). 1.2.2 Web based services Web based services, or online services offer customers a panoply of benefits such as enhanced control, ease of use, and reduced transaction charges (Scullion and Nicholas, 2001; Zeithaml, 2002). As such, online services have grown rapidly and have emerged as the leading edge of the service industry (Yang and Fang, 2004). Fang and Yang (2004) identify two important characteristics of online services. The first being online services invariably share some common territory in relation to traditional interpersonal services. Second characteristic they identify is that web sites function as well-defined information systems – in that, web based technologies have been used to


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automate product distribution and customer services, including transaction and payment systems, customer relationship management systems, as well as the underlying analytics, reporting, and operations of these systems. 1.2.3 Web Service quality dimensions A number of studies have been undertaken to identify quality dimensions and detailed aspects of online services and their relationships with customer satisfaction (Zeithaml and Bitner, 2003). Parasuraman, Zeithaml, and Berry (1985) reveal ten detailed dimensions through focus group studies: tangibles, reliability, responsiveness, communication, credibility, security, competence, courtesy, understanding the customer, and access. They further narrow down these ten dimensions to five: tangibles, reliability, responsibility, assurance, and empathy. Further, since the internet is a relatively new information technology, research in information systems along with technology adoption provides useful insights into identifying some important dimensions in service quality online (Yang and Fang, 2004). According to the technology adoption model (TAM), users’ decisions to adopt a new information technology are principally determined by their attitudes toward two overreaching factors related to the technology: ease of use; and usefulness (Davis, 1989). Perceived ease of use refers to “the degree to which a person believes that using a particular system will be free of effort” and perceived usefulness refers to “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989). Since consumers’ use of internet based services can be viewed as similar to the adoption of new technology, ease of use and usefulness are important factors in evaluating online service quality (Yang and Fang, 2004). Lin and Wu (2002) found that several key dimensions of online service quality such as information content, customization, reliability, and response have significant effects on perceived ease of use and perceived usefulness, which in turn influence attitude towards usage and reusage of internet portal sites. 1.2.4 The central role of quality in web based services: In the early days of e-commerce, web presence and low price were believed to be the drivers of success (Parasuraman, Malhotra, and Zeithaml, 2002). However, today having a simple web presence and low prices do not guarantee success any more, instead the service quality issues have become prevalent: issues such as consumers’ inability to complete transactions, products not delivered on time or at all, non-responsiveness to e-mails, and non-accessibility to desired information. Under such circumstances, Web or e-service quality has entered the picture as a differentiating strategy. To encourage repeat purchases and build customer loyalty, companies are needed to shift the focus of ebusiness from e-commerce (the transactions) to e-service (Parasuraman, Malhotra, and Zeithaml, 2002). Moreover, as online firms often possess limited resources, priorities must therefore be determined among alternative service attributes when making investment decisions related to achievement of superior services (Brandt, 1997). As such,


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it is desirable for online service providers to uncover what attributes consumers utilize in their assessment of overall service quality and satisfaction and which attributes are more important. 1.3 Problem Discussion & Justification Blackwell (2001) states that “The winners in “clicks and order” retailing, like their predecessors in “bricks and mortar” retailing will be those who know how to take care of their customer better than competitors and provide better solutions than were available to consumers in the past”. Thus, to achieve competitive advantage and differentiating strategies by providing superior service quality, it is desirable for online service providers to uncover what attributes customers utilize in their assessment of overall service quality and satisfaction and which attributes are important (Yang and Fang, 2004). Yet, quality remains an elusive and indistinct construct (Parasuraman, Zeithaml, and Berry, 1985) and is often mistaken for imprecise adjectives like goodness, or luxury, or shininess, or weight (Crosby, 1979). Also quality and its requirements are not easily articulated by customers (Takeuchi and Quelch, 1983). When it comes to measuring online service quality, we run into even bigger obstacles due to the service providers’ lack of experience in streamlining operations and their limited understanding of online customers’ usage behaviors (Yang and Fang, 2004). Thus attributes of service quality need to be prioritized for achieving superior service quality with limited resources. A number of researchers have made the point that the purpose of measuring service quality and customer satisfaction is to provide information to enhance customer loyalty and improve overall financial performance of the firm (Oliver 1997; Rust, Zahorik, and Keiningham 1994). This crucial role of customer satisfaction paving the way for retention and profits may prove even more crucial in the context of online or internet based service companies. This is because of the ease with which customers can switch between providers on the internet. Thus it is imperative to identify the components, that lead to superior service quality and as a result customer satisfaction online, and prioritize them. To do so and thereby deliver superior service quality, managers of companies with Web presences must first understand how customers perceive and evaluate online customer service. This involves defining what web service quality (also referred to as e-SQ) is, identifying its underlying dimensions, and determining how it can be conceptualized and measured (Zeithaml, Parasuraman, and Malhotra, 2002). As such, the first step in establishing a reliable customer measurement system is to define service quality and customer satisfaction and to link these constructs to objective measures of performance. This paper explores the service quality literature and formulates a comparative study to measure customer responses to service quality constructs applied in an online context and to see how the customer measurement system reflect customer perception of quality online. 1.4 Research Statement: The above discussion leads us to identify the following research statement:


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• To gain a better understanding of service quality as it applies online by doing

a comparison study through measuring the attributes behind customer’s perceived service quality across online sites within a particular service area.

1.5 Research questions: The emerged research questions are:

1. Do customers perceive differences in service quality among internet companies that offer similar services?

2. Which factors strongly influence consumers’ perception of service quality in

online service companies? We propose hypothesis testing in trying to find the answers to our first research question. Through literature review we identify a set of attributes or factors responsible for formulating customer perception of service quality and formulate our questionnaire for finding the relative weightage for the second research question. 1.6 Hypothesis formulation: In trying to answer our first research question, we formulate the following two hypotheses: Ho (Null Hypothesis) = There is no significant difference in service quality components between the companies as perceived by customers. To express in a formulaic form: H0: µ1 = µ2 H1 = There is significant difference in service quality components between the companies as perceived by customers. To express in a formulaic form: H1: Not all µi are equal. If we find a statistically significant difference for the components analysis, it would reject the null hypothesis; meaning for those components the perception about the two cases differ. 1.7 Implication of the research:


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As is apparent from our formulation of research objective and question, we are focused on finding whether customers perceive significant differences between service quality offered by the Internet based service providers, and what are the factors responsible for such perceived quality of services. Our exploration will have to start with tools that have proved to be valid in analyzing the traditional market place data. It remains to be seen how or whether the traditional tools for measuring customer perception of service quality can be extended towards measuring pure e-commerce ventures and its clientele. We hope to get a clearer understanding of how customer behavior digresses in the virtual or e-marketplace compared to the brick and mortar places, or even the clicks and mortal market places; and whether many commonly held beliefs about e-commerce are still valid or not (RQ1). In the process, we shall also embark on the path of identifying which are the variables that are responsible for causing variance among customer perception (RQ2). In the following section, we shall proceed with the literature review to discuss more in depth the relevant concepts for our study.

Perception of Service Quality in E-Commerce: Literature Review

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Chapter 2 This chapter details the literature and research that has been conducted on service, service quality and web based services. We give a brief outline of service and what is meant by service quality, followed by the definitions of web services and outline the dimensions of web service quality and the various measurement tools of the dimensions. This gives us a broad outline of the underlying theoretical concepts used in this research. We also highlight the significance of service quality and its impact on satisfaction as a precursor to customer’s perception of the services, and the frameworks linking customer perception of service quality and satisfaction. Finally, in our frame of reference we choose a model that fits our chosen variables the best as the basis of conducting empirical studies for our research. Literature review: 2.1 Services Services have traditionally been categorized as being either consumer services or professional services (Fitzsimmons and Sullivan, 1982; Sasser, Olsen, and Wyckoff, 1978). On a product-service continuum, professional services (e.g. medical care, legal services) come close to being pure services (Woodside, Frey, and Daly, 1989). The characteristics of such services are frequently no tangible good is exchanged, the service is produced and consumed relatively simultaneously for each consumer, and the consumer is an important part of what is actually delivered (Woodside, Frey, and Daly, 1989). Customers consider a different sets of variables when evaluating products as opposed to services (John Groth, Richard Dye, 1999). The following table outlines some of the important differences between the nature of products and services: Source: Constructed on John Groth, Richard Dye, 1999 Nature of difference Product Services Tangibility Yes X Definable/Observable Perception dependent X yes Time dependent Maybe Maybe Returnability Yes X Definable match with needs Yes X Centrality of core product Yes X Centrality of human interaction X Yes Impersonal Yes X Psychic factor X Yes Utilitarian factor Yes X


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Table 1: Difference between products and services We should however keep in mind that there are often no definitive yes or no answers to the responses generated by the above variables. There may be a certain degree of ambiguity involved, as well as overlaps. As Parasuraman et al. (1985) confirms, when purchasing goods, the consumer employs many tangible cues to judge quality: style, hardness, color, label, feel, package, fit etc. However, when purchasing services, fewer tangible cues exist; with tangible evidence limited to the service provider’s physical facilities, equipment, and personnel. 2.2 Service quality: The delivery of higher levels of service quality is the strategy that is increasingly being offered as a key to service providers' efforts to position themselves more effectively in the marketplace (cf. Brown and Swartz 1989; Parasuraman, Zeithaml, and Berry 1988; Rudie and Wansley 1985; Thompson, DeSouza, and Gale 1985). However, the problem inherent in the implementation of such a strategy has been eloquently identified by several researchers: service quality is an elusive and abstract construct that is difficult to define and measure (Brown and Swartz 1989; Carman 1990; Crosby 1979; Garvin 1983; Parasuraman, Zeithaml, and Berry 1985, 1988; Rathmell 1966). Service quality has been described as a form of attitude, related but not equivalent to satisfaction, that results from the comparison of expectations with performance (Bolton and Drew 1991a; Parasuraman, Zeithaml, and Berry 1988). Service Quality is founded on a comparison between what the customer feels should be offered and what is provided (Parasuraman, Zeithaml and Berry, 1985). “Service Quality” can be assessed by measuring customer’s expectations and perceptions of perfoemance level for a range of service attributes (Parasuraman et al., 1985, 1988, 1991; Zeithaml et al., 1990). It is “An attitude formed by a long-term, overall evaluation of a firm’s performance” (Kalakota and Whinston, 1996). Operationally, service quality is represented by answers to such questions as: is the service delivered to customer what they expected or different from what they expected? Was the service they received approximately what they expected or better or worse then expected (Woodside, Frey & Daly, 1989). Service quality is a measure of how well the service level delivered matches customer expectations. Delivering quality service means conforming to customer expectations on a consistent basis (Lewis and Booms, 1983). Gronröös contended that consumers compare the service they expect with perceptions of the service they receive in evaluating service quality. Olshavsky (1985) views quality as a form of overall evaluation of a product, similar in many ways to attitude. Parasuraman et al. (1985) also support this view and have tried to explore the meaning of quality in the context of services, and into the characteristics the


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service and its provider should possess in order to project a high quality image, and the criteria customers use in evaluating service quality. They found that customers used the same general criteria in arriving at evaluative judgement about service quality. Zeithaml, Parasuraman, and Berry (1985) strongly suggest that service quality, as perceived by customers, stems from a comparison of what they feel service firms should offer with their perceptions of the performance of firms providing the service. Perceived service quality is therefore viewed as the degree and direction of discrepancy between consumers’ perceptions and expectations; where expectations are viewed as desires or wants of consumers, or what they feel a service provider should offer rather than would offer. Perceived quality is the consumer’s judgement about an entity’s overall excellence or superiority (Zeithaml, 1987). It differs from objective quality (as above) in that it is a form of attitude, related but not equivalent to satisfaction, and results from a comparison of expectations with perceptions of performance (Parasuraman, Zeithaml, and Berry, 1988). On another note, a study by Cohen, Fishbein, and Ahtola (1972) shows an individual's attitude is defined by his or her importance-weighted evaluation of the performance of the specific dimensions of a product or service, also known as the “adequacy-importance model”. Mazi, Ahtola, and Klippel (1975) indicate that the performance dimension alone predicts behavioral intentions and behavior. Their findings suggest using only performance perceptions as a measure of service quality.

Researchers suggest that service quality and satisfaction are distinct constructs (Bitner 1990; Bolton and Drew 1991; Parasuraman, Zeithaml, and Berry 1988). An explanation of the difference between the two given is that perceived service quality is a form of attitude, a long-run overall evaluation, whereas satisfaction is a transaction-specific measure (Bitner 1990; Bolton and Drew 1991a; Parasuraman, Zeithaml, Berry 1988). Parasuraman, Zeithaml, and Berry (1988) state that in measuring perceived service quality the level of comparison is what a consumer should expect, whereas in measures of satisfaction the appropriate comparison is what a consumer would expect. However, such a differentiation appears to be inconsistent with Woodruff, Cadotte, and Jenkins' (1983) suggestion that expectations should be based on experience norms--what consumers should expect from a given service provider given their experience with that specific type of service organization.

Thus, “The service literature has left confusion as to the relationship between consumer satisfaction and service quality. This distinction is important to managers and researchers alike because service providers need to know whether their objective should be to have consumers who are "satisfied" with their performance or to deliver the maximum level of "perceived service quality." (Cronin and Taylor, 1992). The confusion is further deepened by the fact that Parasuraman, Zeithaml, and Berry (1985, 1988) proposed that higher levels of perceived service quality result in increased consumer satisfaction, but other


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research evidence suggests that satisfaction is an antecedent of service quality (cf. Bitner 1990; Bolton and Drew l991).

In their study, Bolton and Drew (1991 a) uses the common assumption that service quality is analogous to an attitude as a basis to suggest that satisfaction is an antecedent of service quality. Specifically, Bolton and Drew posit that perceived service quality (ATTITUDEt) is a function of a consumer's residual perception of the service's quality from the prior period (ATTITUDEt-1) and his or her level of (dis)satisfaction with the current level of service performance (CS/D1

t) or (customer satisfaction / dissatisfaction). This notion suggests that satisfaction is a distinct construct that mediates prior perceptions of service quality to form the current perception of service quality. As Cronin and Taylor (1992) suggest, a major problem in the literature is the hesitancy to call perceived service quality an attitude. The literature's position is typified by Parasuraman, Zeithaml, and Berry's (1988) description of service quality as "... similar in many ways to an attitude". Oliver suggests that consumers form an attitude about a service provider on the basis of their prior expectations about the performance of the firm, and this attitude affects their intentions to purchase from that organization. This attitude then is modified by the level of (dis)satisfaction experienced by the consumer during subsequent encounters with the firm. The revised attitude becomes the relevant input for determining a consumer's current purchase intentions (Oliver, 1980). To follow Cronin and Taylor’s (1992) interpretation of the findings of Oliver, they conclude based on Oliver’s study that if service quality is considered to be an attitude, then (1) in the absence of prior experience with a service provider, expectations initially define the level of perceived service quality, (2) upon the first experience with the service provider, the disconfirmation process leads to a revision in the initial level of perceived service quality, (3) subsequent experiences with the service provider will lead to further disconfirmation, which again modifies the level of perceived service quality, and (4) the redefined level of perceived service quality similarly modifies a consumer's purchase intentions toward that service provider. Thus Cronin and Taylor (1992) conclude that service quality and consumer satisfaction are distinct constructs, yet are related in that satisfaction mediates the effect of prior-period perceptions of service quality to cause a revised service quality perception to be formed. Satisfaction thus rapidly becomes part of the revised perception of service quality. Oliver (1997) and Taylor and Baker (1994) also conclude that service quality and customer satisfaction are different constructs. This is further supported by Hurley and Estelami (1998) who conclude that while service quality and satisfaction are distinct constructs, a causal relationship exists between the two, and that perceptions of service quality affect feelings of satisfaction which, in turn, influence future purchase behavior. 2.3 Service quality dimensions and measurement


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Service quality dimensions are aspects that consumers evaluate to form overall judgements about the service (Parasuraman et al, 1985, 1988, Zeithaml, 1996). There has been considerable debate as to what these dimensions are, in an effort to reach a consensus regarding the dimensions. In general, two dimensions have been identified (Grönroos, 1985; Macdougall and Levesque, 1992; Morgan and Piercy, 1992; Parasuraman, 1991; Dabholkar, 1996):

• Core (Outcome) aspects • Relational (Process) aspects

The Core service quality dimension centers around “What is delivered”; whereas the Relational service quality dimension is defined based on “How it is delivered”. “Core quality” is the basic service contracted for or promised. “Relational quality” lies in the way in which the service is delivered (Grönroos, 1985, Morgan and Piercy, 1992). The convergence point of these two factors compose the overall assessment of the service provider. It also contributes to the stated likelihood of the customer returning to the service provider (Macdougall, Levesque, Journal of Services Marketing, 2000). The following figure shows the relational context among perception of quality, and resultant satisfaction.

Figure 1: Drivers of customer satisfaction

Source: (Macdougall and Levesque, Jnl of Svc Mkt, 2002). Mcdougall and Levesque (2002) based the above model on a set of hypothesis:

• H1: customer satisfaction is directly related to core service quality • H2: customer satisfaction is directly related to relational service quality • H3: customer satisfaction is directly related to perceived value • H4: future intentions to switch are inversely related to customer satisfaction, and • H5: future intentions to remain loyal is directly related to customer satisfaction.

The scope of this paper is limited to the discussion of the set of the first two hypothesis. Since the perception of quality varies regardless of the core set of services performed, for services firms, It is crucial to identify the critical factors that determine customer satisfaction and loyalty (Macdougall, Levesque, 2000). In the real world, it is all the more


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imperative, as customer loyalty is a prime determinant of long-term financial performance of firms (Jones and Sasser, 1995), and because for service oriented firms increased loyalty can substantially increase profits (Reichheld and Sasser, 1990). Johnston has provided 18 service dimensions and their definitions, they are: access, aesthetics, attentiveness, availability, care, cleanliness / tidiness, comfort, commitment, communication, competence, courtesy, flexibility, friendliness, functionality, integrity, reliability, responsiveness, and security (Johnston, 1995). In the traditional service quality literature, the research model presented by Parasuraman et al. (1988) has been a popular way of measuring service quality. They chose four lines of business for research: bank, credit card company, securities company, and products maintenance company. In-depth interviews with employees of these companies and focus group interviews with their customers were conducted. This research found there are five gaps of service quality between customers’ expectation and actual performances, and suggested ten determinants of service quality. They are tangibles, reliability, responsiveness, communication, credibility, security, competence, courtesy, understanding the customer, and access. They further narrowed down these ten dimensions to five: tangibles, reliability, responsibility, assurance, and empathy. This five dimensional model is referred to as Servqual (Parasuraman et al., 1988). In the Parasuraman model:

• Tangibles refer to physical facilities, equipment, and appearance of personnel. • Reliability means ability to perform the promised service dependably and

accurately. • Responsiveness means willingness to help customers and provide prompt service. • Assurance indicated knowledge and courtesy of employees and their ability to

inspire trust confidence. • Empathy refers to caring, individualized attention the firm provides its customers

(Parasuraman et al.,1988). Here, the last two dimensions contain items representing seven original dimensions which are communication, credibility, security, competence, courtesy, understanding / knowing customers, and access. These last mentioned dimensions did not remain distinct in Parasuraman, Berry, and Zeithaml’s experiment, as such they were assimilated in the five major dimensional categories. However, this paper tries to see the fit by bringing into the picture some of the assimilated variables from within the last two dimensions in designing the data collection instrument, and proceeds with the experiment thenceforth. Van dyke et al. criticized that SERVQUAL suffers from a number of conceptual and empirical difficulties (Thomas P. Van Dyke, Leon A. Kappelman nad Victor R. Prybutok 1997). Conceptual difficulties include the operationalization of perceived service quality as a difference or gap score, the ambiguity of using a single measure of service quality across different industries. Empirical problems, which may be linked to the use of


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different scores, include reduced reliability, poor convergent validity, and poor predictive validity. Recently, Jiang et al.’s empirical study among 168 users and 168 professionals concluded that the SERVQUAL measure is a valuable analytical tool for IS managers (James J. Jiang, Gary Klein and Christopher L. Carr 2002). The study found high convergent validity for the reliability, responsiveness, assurance, and empathy of the SERVQUAL scales and found acceptable levels of reliability and discriminant validity among the reliability, responsiveness, and empathy scales. 2.4 Web service quality: We categorize the extant research on online service quality into studies focusing on online retailing services (e.g. Wolfinbarger and Gilly, 2002. For more comprehensive review, see Zeithaml et al., 2002; Yang and Jun, 2002); Web site design quality; and narrowly defined online services other than retailing (e.g. portal services, online travel services). These studies reflect various aspects of online service quality, which facilitate the development of the coding scheme in our study. Malhotra, Parasuraman and Zeithaml (2002) point out that some differences exist in the meaning of Web site service quality. Some researchers (such as Lociacono, Watson, and Goodhue 2000) focus their operationalizations on the technical quality of theWeb site itself rather than the service quality provided to customers through the Web site. Some conceptualizations are limited to the interactions with the site itself, while others include the post–Web site services of fulfillment and returns. Malhotra, Parasuraman and Zeithaml (2000) defined web service quality (or e-SQ) as the extent to which a Web site facilitates efficient and effective shopping, purchasing, and delivery of products and services. In their definition, the meaning of service is comprehensive and includes both pre– and post–Web site service aspects. Malhotra, Parasuraman and Zeithaml (2002) identified five criteria that customers use in evaluating Web sites in general and service quality delivery through Web sites in particular. These include (1) information availability and content, (2) ease of use or usability, (3) privacy/security, (4) graphic style, and (5) fulfillment. 2.4.1 Information availability and content: The availability and depth of information are frequently mentioned as an important reason for shopping online (Li, Kuo, and Russell 1999; Wolfinbarger and Gilly 2001). Consumers seek general information, product/service information, customer support information, customer service information (Aladwani and Palvia 2002), complete information on particular products, and information to compare across alternatives (Ranganaghan and Ganapathy 2002). On the Internet, consumers do not have an


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opportunity to feel or touch the product or talk with sales people. Therefore, if they have unusual questions, they will leave the shopping site and visit other sites (or stores) (Song and Zinkhan, 2003). In terms of shopping online versus offline, online buyers perceive a benefit in obtaining information directly from a site rather than having to go through salespeople in an offline store (Malhotra, Parasuraman and Zeithaml, 2002). The reduction in search costs for products and product-related information has been identified as one of the key benefits of online shopping (Alba et al. 1997; Ariely 2000; Bakos 1997; Lynch and Ariely 2000). When used as criteria for a particular Web site, having sufficient information to compare products and to make a selection appear to be important factors. In terms of information content, the ability to search price and quality information increases satisfaction with both the experience and product purchased and improves intentions to revisit and repurchase from a Web site (Lynch and Ariely 2000). 2.4.2 Ease of use (Usability): Given that Internet-based transactions might seem complex and intimidating to many customers, it is reasonable to expect the ease of use (EOU) of Web sites to be an important determinant of perceived e-SQ (Parasuraman, Malhotra, and Zeithaml, 2003). Davis (1989); Davis, Bagozzi, and Warshaw (1989) defined ease of use as “the degree to which the prospective user expects the target system to be free of effort”. Ease of use has often been termed usability in the online context (Swaminathan et al. 1999). A site’s search functions, download speed, overall design, and organization are among the key elements that affect usability (Jarvenpaa and Todd 1997; Lohse and Spiller 1998; Montoya-Weiss, Voss, and Grewal 2000; Nielsen 2000; Novak, Hoffman, andYung 2000; Spiller and Lohse 1997- 1998). Usability is affected by type of consumers and type of tasks (Song and Zinkhan, 2003). Usable system must be compatible with users’ cognitive skills in communication, understanding, memory, and problem solving (Goodwin 1987; Benbunan-Fich 2001). Usability could be measured as following five attributes: learnability, efficiency, memorability, low errors, and subjective satisfaction (Nielson 1996). Loiacono et al. (2002) consider two distinct aspects of ease of use when it is applied to Web: ease of understanding and ease of navigation. On the commercial Web site, most users are consumers and their main purpose of visiting Web site is to purchase products/services. Therefore, usability on the Internet shopping site should be understood in terms of not only userapplication but also transaction system (Song and Zinkhan, 2003). 2.4.3 Privacy/Security: Privacy and security are key evaluative criteria in online services (Culnan 1999; Culnan and Armstrong, 1999). Privacy involves the protection of personal information - not sharing personal information collected about consumers with other sites (as in selling lists), protecting anonymity, and providing informed consent (Friedman, Kahn, and Howe


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2000). Phelps et al. (2001) argue that privacy concerns are negatively related to purchase behavior and purchase decision. Thus, a number of Web sites have developed and displayed consumer privacy policies, and a number of companies have also emerged to verify, audit, and certify the privacy policies of various Web sites (Ranganathan and Ganapathy 2002). Security, on the other hand, involves protecting users from the risk of fraud and financial loss from the use of their credit card or other financial information (Malhotra, Parasuraman and Zeithaml, 2002). Security of monetary transaction, which is a key customer concern, influences Web site quality (Song and Zinkhan, 2003). 2.4.4 Graphic style: Researchers have studied the impact that graphic style of the site has on customer perceptions of online shopping (Lynch and Ariely, 2000). Graphic style involves such issues as color, layout, print size and type, number of photographs and graphics, and animation (Malhotra, Parasuraman and Zeithaml, 2002). 2.4.5 Fulfillment/reliability: Fulfillment is the whole process between placing the order and delivering the products (or service). Fulfillment policy is a key feature of site design. To provide complete fulfillment service, orders should be entered correctly, processed rapidly, sent to the appropriate location, and delivered on time. From the consumers’ point of view, fulfillment is related to issues such as correct order procedure, fast and low-cost delivery, and interactive customer support (Song and Zinkhan, 2003). Consumers are concerned about being unable to return an item if it fails to meet their approval (Bhatnagar et al. 2000). Consumers are re-assured when they are informed about the progress of the order process, step-by-step. For example, many sites provide an order tracking service that keeps consumers informed via email. Wolfinbarger and Gilly (2002) found that reliability/fulfillment ratings were the strongest predictor of customer satisfaction and quality and the second strongest predictor of loyalty/ intentions to repurchase at a site. Reliability online means on-time and accurate delivery, accurate product representation, and other fulfillment issues and it may also reflect technical reliability, such as the proper functioning of the site (Malhotra, Parasuraman and Zeithaml, 2002). 2.4.6 Other Web service qualities: Online retailing service quality: Drawing upon the traditional SERVQUAL scale, Zeithaml et al. (2001, 2002) have developed e-SERVQUAL for measuring e-service quality. In a series of focus group interviews, Zeithaml et al. (2001) have identified eleven dimensions of online service quality: access, ease of navigation, efficiency,


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flexibility, reliability, personalization, security/privacy, responsiveness, assurance/trust, site aesthetics, and price knowledge. The further comparison of e-service quality and traditional service quality indicates that consumers use some common dimensions such as reliability, responsiveness, access, and assurance to evaluate both traditional service quality and e-service quality. The study, however, was based on consumer experience of online retailing services. In the same vein, Wolfinbarger and Gilly (2002), through focus group interviews and an online survey, have uncovered four online retailing experience factors: Web site design, reliability, privacy/security, and customer service. Yang and Jun (2002) have uncovered six prominent factors – reliability, access, ease of use, personalization, security, and credibility – employed by Internet purchasers to evaluate e-tailers’ service quality and seven principal service attributes – security, responsiveness, ease of use, reliability, availability, personalization, and access – that could prevent visitors from purchasing. These retailing services oriented studies have certain limitations in terms of the generalization of their findings to other settings. Web site design quality: From the information systems perspective, Yoo and Donthu (2001) have developed an instrument for measuring online retailers’ Web sites, SITEQUAL, with four dimensions: ease of use, aesthetic design, processing speed, and security. Another highly cited instrument, WEBQUAL (Web quality) developed by Loiacono et al. (2002) consists of 12 dimensions: information fit to task, interaction, trust, response time, design, intuitiveness, visual appeal, innovativeness, flow, integrated communication, business process, and substitutability. Liu and Arnett (2000), through Web and e-mail surveys of 689 Webmasters of Fortune 1,000 companies’ Web site, have discovered four major factors leading to the success of a Web site, i.e. information and service quality, system use, playfulness, and system design quality. These scales, however, have one major limitation as they are “geared toward helping Web site designers to better design Web sites to affect the interaction perceptions of the users. Therefore, the scale is more pertinent to interface design than service quality measurement” (Zeithaml et al., 2002, p. 366). 2.5 Dimensions of web service quality Some studies develop online service quality framework based on literature review. Cox and Dale (2001) have noted that traditional service quality dimensions, such as competence, courtesy, cleanliness, comfort, and friendliness, are not relevant to online retailing; whereas other factors, such as accessibility, communication, credibility, and appearance, are critical to its success. Madu and Madu (2002) proposed the following 15 dimensions of online service quality based on literature review: performance, features, structure, aesthetics, reliability, storage capacity, serviceability, security and system integrity, trust, responsiveness, product/service differentiation and customization, Web store policies, reputation, assurance, and empathy. Zeithaml et al. (2002) conducted a critical review of extant e-service quality literature and identified seven dimensions for e-service quality – efficiency, reliability, fulfillment, privacy, responsiveness,


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compensation, and contact. These seven dimensions were further classified into two categories: a core service scale for measuring the customers’ perceptions of e-tailers’ service quality when customers experience no problems with the site (efficiency, reliability, fulfillment, and privacy) and a recovery service scale for measuring e-tailers’ service recovery when problems occur (responsiveness, compensation, and contact). It should be noted that researchers have expressed concern about applying traditional service quality dimensions to online service quality arena. Cox and Dale (2001) note that traditional service quality dimensions such as competence, courtesy, cleanliness, comfort, and friendliness are not relevant to online retailing, whereas factors such as accessibility, communication, credibility, and appearance are critical to its success. Madu and Madu (2002) proposed 15 online service quality dimensions; they are performance, features, structure, aesthetics, reliability, storage capacity, serviceability, security and system integrity, trust, responsiveness, product / service differentiation and customization, web store policies, reputation, assurance, and empathy. Zeithaml et al. (2002) identified seven dimensions for e-service quality; which are efficiency, reliability, fulfillment, privacy, responsiveness, compensation, and contact. These seven dimensions were further classified into two categories: a core service scale for measuring the customers’ perceptions of e-tailers’ service quality when customers experience no problems with the site (efficiency, reliability, fulfillment, and privacy) and a recovery service scale for measuring e-tailers’ service recovery when problems occur (responsiveness, compensation, and contact) (Yang and fang, 2004). Other studies have identified key dimensions of online service quality in the context of narrowly defined online businesses, such as portal services and travel agencies. In each of these online services, some different dimensions emerged. For instance, Van Riel et al. (2001) have derived three key portal service quality attributes affecting consumers’ overall satisfaction with the portal: core service, support service, and user interface. Kaynama and Black (2000) have identified the following dimensions for online travel agencies: content and purpose, accessibility, navigation, design and presentation, responsiveness, background, and personalization and customization. Alongside marketing literature, we need to take into consideration literature from information systems in conducting any research in online service quality (Fang and Yang, 2004). This provides us with the dimensions of ease of use and usefulness (Davis, 1989). Lin and Wu (2002) identified key dimensions of online service quality to be information content, customization, reliability, and response rate. They contend that these attributes have significant effects on perceived ease of use and perceived usefulness which in turn influence attitude towards continued usage of the sites. The need to consider information systems approaches is further supported by the fact that online customers are end-users of both information and networked systems (DeLone and McLean, 1992; Stockdale and Standing, 2002). Doll and Torkzadeh (1998) came up with a 12 item scale to measure five service quality dimensions of content, accuracy, format, ease of use, and timeliness. These were further verified by Hendrickson and Collins (1996).


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A distinct feature of the e-Commerce environment is that the product or service that the business sells is delivered primarily through the use of information technology i.e. the web-site and the associated applications that run on both the client and server side. This implies that an assessment of the e-customer’s interaction of the business through its web-site is inherently also an assessment of the quality of service e.g. the electronic shop-front, the mechanism in place for online transacting, communication channels for pre and post sale service etc. In other words assessing these business functions is similar to what instruments like SERVQUAL were developed to assess (Pather, Erwin, and Remenyi, 2003). 2.6 Measuring web service quality Measurement of service quality delivery through Web sites is in its early stages and some measures of e-SQ are ad hoc and include only a few factors (Parasuraman, Zeithaml, and Malhotra, 2002). Rice (1997) measured web service quality in a survey of 87 web sites through measures that included (1) good content/information, whichwas the primary driver of revisit, and (2) enjoyable experience on the first visit, which was the second most important driver. Liu and Arnett (2000) in their survey of Fortune 1000 companies used five key dimensions to measure service quality. They are:

1. Quality of information: it consists of relevant, accurate, timely, customized, complete information presentation.

2. Service: it involves quick response, assurance, empathy, and follow-up. 3. System use: it includes security, correct transaction, customer control on

transaction, order-tracking facility, and privacy. 4. Playfulness: it is determined by customers’ sense of enjoyment, interactivity,

attractive features, and enabling customer concentration. 5. Design of the Web site system/ interface: it involves organized hyperlinks,

customized search functions, speed of access, and ease of correcting errors.

Yang, Peterson, and Huang (2001) identified and measured six dimensions of consumer perceptions of service quality:

1. ease of use, which includes user friendliness, loading/ transaction speed, search capability, and easy navigation;

2. content contained on the Web site, particularly information that matches the needs of the consumer;

3. accuracy of content; 4. timeliness of response; 5. aesthetics involving attractiveness of the site and catalog pictures; and 6. privacy.


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However, as Parasuraman, Malhotra, and Zeithaml (2002) point out, a lot of the measurement of e-SQ in these research studies is rather arbitrary, to the extent that the scales and dimensions used to measure e-SQ have not been empirically validated. Also, several businesses such as Bizrate.com have developed their own methodologies to measure service quality provided by online retailers. Measurement tool employed by site such as gomez.com measure the performance of the site and assign ratings based on whether various criteria are satisfied. Measurement involves direct examination of the Web site, performance monitoring of various pages on the site, pricing transactions, mock transactions (account opening), interaction with customer service representatives, and a questionnaire filled out by each company. The index obtained from this procedure is then multiplied by the importance profile of a customer The categories measured by Gomez are broadly classified into the following:

1. ease of use (functionality of the Web site, consistency of design and navigation, smoothness of interactions),

2. efficient access to information (signifying back-end integration of data), 3. customer confidence (breadth and depth of customer service options, including

channels of interactions, 4. promptness and accuracy of e-mail response, privacy policies, guarantees), 5. reliability (load times and security), 6. years the web site or company has been in business, 7. on-site resources (availability of products, availability of online response to

requests, detailed information on each product line), 8. relationship services (online help/tutorials, recommendations, personalization of

information, reuse of customer information to facilitate future interactions, incentive programs), and

9. Overall cost (total cost of ownership of typical offering baskets, added fees for shipping and handling, minimum balances and interest rates—for financial services companies). (Source: Parasuraman, Malhotra, and Zeithaml, 2002).

Lociacono et al. (2000) established a scale called WEBQUAL with 12 dimensions: informational fit to task, interaction, trust, response time, design, intuitiveness, visual appeal, innovativeness, flow (emotional appeal), integrated communication, business processes, and substitutability. Wolfinbarger and Gilly (2002) used online and offline focus groups, a sorting task, and an online survey of a customer panel to develop a scale called .comQ. The scale contains four factors: Web site design (involving the expected attributes associated with design as well as an item dealing with personalization), reliability (involving accurate representation of the product, on-time delivery, and accurate orders), privacy/security (feeling safe and trusting of the site), and customer service (combining interest in solving problems, willingness of personnel to help and prompt answers to inquiries).


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2.7 e-Servqual as a tool for measuring web service quality: Zeithaml, Parasuraman, and Malhotra (2000, 2002) developed e-SERVQUAL that identifies seven dimensions - efficiency, reliability, fulfillment, privacy, responsiveness, compensation, and contact—that form a core service scale and a recovery service scale. Four dimensions—efficiency, reliability, fulfillment, and privacy - form the core e- SERVQUAL scale that is used to measure the customers’ perceptions of service quality delivered by online retailers. Core service scale in e-SERVQUAL

• Efficiency refers to the ability of the customers to get to theWeb site, find their desired product and information associated with it, and check out with minimal effort.

• Fulfillment incorporates accuracy of service promises, having products in stock, and delivering the products in the promised time.

• Reliability is associated with the technical functioning of the site, particularly the extent to which it is available and functioning properly.

• Privacy includes assurance that shopping behavior data are not shared and that credit card information is secure

Recovery service scale in e-SERVQUAL The three other dimensions - responsiveness, compensation, and contact come into effect only when customers run into problems. They constitute the recovery e-SERVQUAL scale.

• Responsiveness measures the ability of e-tailers to provide appropriate information to customers when a problem occurs, have mechanisms for handling returns, and provide online guarantees.

• Compensation is the dimension that involves receiving money back and returning shipping and handling costs.

• Contact points to the need of customers to be able to speak to a live customer service agent online or through the phone.

Parasuraman, Zeithaml, and Malhotra (2002) comment that half of the dimensions of SERVQUAL (reflected both in the original 10-dimension conceptualization and in the five SERVQUAL factors identified empirically in subsequent research) (Parasuraman, Zeithaml, and Berry 1985, 1988) are also used by consumers when they evaluate web service quality; and that Many of the perceptual attributes pertaining to Web site service quality remain the same as in SERVQUAL—honoring promises, being available when the customer wants to do business, having a reputable name, and knowing customers. However, some of the perceptual attributes of reliability and access dealt with online-specific issues such as system crashes and operation and availability of the network—attributes not present in SERVQUAL (Parasuraman, Zeithaml, and Malhotra; 2002).


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2.8 Customer Satisfaction and service quality As we have discussed earlier that there is a causal relationship between service quality and satisfaction, a brief explanation of satisfaction is needed to address service quality issues. Satisfaction is a person’s feelings of pleasure or disappointment resulting from comparing a product’s perceived performance (or outcome) in relation to his or her expectations (Kotler, 2000). Oliver (1981) states that satisfaction is a summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with the consumer’s prior feelings about the consumption experience. He differentiates it from attitude saying attitude is the consumer’s relatively enduring affective orientation for a product, store, or process (customer service) while satisfaction is the emotional reaction following a disconfirmation experience which acts on the base attitude level and consumption specific. Thus attitude is more general to a product or store compared to satisfaction which is more situationally oriented. Satisfaction is important to the individual consumer because it reflects a positive outcome from the outlay of scarce resources and/or the fulfillment of unmet needs (Ho and Wu, 1999). There is a definitive relationship between customer satisfaction and future intentions (Bearden and Teel, 1983, Oliver, 1980). However, not much empirical evidence exists concerning the relationship between future intentions and its determinants; which are service quality dimensions (core and the relational) and perceived value (Bolton and Drew, 1991). Also, it is not clear if future intentions and customer satisfaction are driven by the same set of factors, because customer satisfaction is a necessary but not a sufficient condition for future intentions (Macdougall and Levesque, 2000). There are two principal interpretation of satisfactions within the literature – satisfaction as a process and satisfaction as an outcome (Parker and Matthews, 2001; Lin, 2003). Early satisfaction research defined satisfaction as a post-choice evaluative judgement concerning a specific purchase decision (Oliver, 1980; Churchill and Suprenant, 1992). Oliver (1997) stated satisfaction as a function of disconfirmation, which in turn is a function of both expectations and performance. The expectation disconfirmation theory suggested that satisfaction is determined by the intensity (size) and direction (positive/negative) of the gap (disconfirmation) between expectations and perceived performance (Khalifa and Liu, 2002). Expectations are shaped by personal experience and understanding of the environment, taking into account practical feasibility based on the expectancy theory (Tolman, 1932; Khalifa and Liu, 2002). Perceived performance is a less biased evaluation of performance based on objective judgements rather than emotional reactions (Swan and Combs, 1976). Expectation disconfirmation occurs in three forms (Khalifa and Liu, 2003):

1. positive disconfirmation: when perceived performance exceeds expectations 2. confirmation: when perceived performance meets expectations 3. negative disconfirmation: when perceived performance falls below expectations


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Parasuraman also brings expectation and confirmation of expectation into the context of satisfaction. According to the Disconfirmation Paradigm (Parasuraman et al, 1988), customer’s feeling of satisfaction is a result of a comparison process between perceived performance and one or more comparison standards, such as expectations. The customer is anticipating a confirmation of his expectations, and performance of the product either confirms his expectations or not, leading to satisfaction or dissatisfaction. If performance exceeds expectations it leads to a positive disconfirmation. A below expectation performance leads to negative disconfirmation. Recent researches have proposed the usage of desires rather than expectation as the comparison standard in the disconfirmation process (Spreng, Mackenzie, and Olshavsky, 1996; Suh and Lee, 1994). Desires are labeled as internalized and desired ideal standard (Spreng, Mackenzie, and Olshavsky, 1996). The formation of desires is not based on realistic prediction of actual performance, but on inner emotional needs or wants that are not necessarily constrained by rational cognitive understanding of the situation or practical feasibility (Khalifa and Liu, 2003). Under the desire disconfirmation theory, low performance, although meeting the individual’s expectations, can fall below the desired performance (negative disconfirmation) and is more likely to lead to dissatisfaction (Khalifa and Liu, 2003). Satisfaction arises from a cognitive process that compares perceived performance against a set of comparison standards (Eggert and Ulaga, 2002). Yet, the feeling of satisfaction clearly denotes a state of mind, hence the term ‘feeling’. Thus some satisfaction scales are based on cognitive dimensions that are measurable or quantifiable or at least distinct. Against which, some satisfaction scales will be based on the affective nature of mind, an unmeasurable and unquantifiable state. Trying to draw a generalized model may well be fruitless for this reason. However, ‘the extent to which a satisfaction scale focuses on the cognitive or the affective dimension should have an impact in terms of both the antecedents that affect satisfaction and the consequences fostered by satisfaction’ (Eggert and Ulaga, 2002). Gross points out that construct of satisfaction in a business market is misleading since purchasing managers buy for economic reasons rather than emotional reasons. Gross (1997) has emphasized the need for replacement of the satisfaction construct with a value construct. Eggert states that customer satisfaction measures how well a supplier is doing with his/her present market offering as perceived by existing customers. Thus it provides scope for improvements in current products and services. Also, a direct linkage between customer satisfaction and return rate would presumably be reflected in industry performance indicators, i.e. a larger market share, high profit and growth rate, increase in earnings per share (eps) etc. Yet, market surveys have often surfaced with companies with high satisfaction rates but declining market shares (Gale, 1994; Jones and Sasser, 1995). Gale (1994) has ventured the reason for this being non-integration of potential customers, non-customers, and competitors in the set of analysis. The customer’s perception of price also needs to be taken into account (Eggert and Ulaga, 2002).


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2.9 Models showing the link between service quality measurement and satisfaction: Pather, Erwin, and Remenyi (2003) have proposed the following model to measure the quality of the information systems product. The model incorporates both measurement of user satisfaction and measurement of service quality in trying assess the effectiveness and quality of the IS product. The relationship lies in that in delivering an online service, the customer is essentially being delivered an IS product.

Figure 2: Measuring e-Commerce Effectiveness using a combination of User Satisfaction

and Service Quality Theories (Source: Pather, Erwin, and Remenyi, 2003).

According to the auithors, the model provides an appropriate basis to investigate a relevant scale to measure effectiveness in the e-Commerce environment by providing a basis for an evaluation of how relevant the imensions of traditional service quality scales are e.g. the five dimensions used in the SERVQUAL scale [Parasuraman et al. 1988] are Tangibles, Reliability, Responsiveness, Assurance, and Empathy; and secondly, other independent variables identified in exploratory e-Commerce studies (for example Molla & Licker [2001]) such as Trust, Content-quality etc. also need to be verified through empirical evidence.


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Ho and Wu (1999) have proposed the following model to encompass the antecedents of customer satisfaction online.

Figure 3: A research model for antecedents of customer satisfaction online (Source: Ho and Wu, IEEE proceedings, 1999)

In here, they identify five antecedents of customer satisfaction: (1) logistical support, (2) technological characteristics, (3) information characteristics, (4) homepage presentation, and (5) product characteristics. Logistical support contained quick response to customers’ needs, providing communication channels (i.e., e-mail or fax), quickly delivering goods for customers, and providing after service. Technological factors contained modern computer and network facilities and well-structured information systems. Information factors included reliable output information and secure transaction. Homepage presentation contained providing ease to use interface and detail information of goods. Product characteristics included variety of goods and lower prices for goods. The above model indicates that logistical support, technological characteristics, information characteristics, homepage presentation, and product characteristics, are each held to influence customer satisfaction.


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2.10 Conceptual model for web service quality: Parasuraman, Zeithaml, and Malhotra (2002) have proposed the following model for understanding web service quality.

Figure 3: Conceptual model for understanding web service quality The model builds a framework for understanding customer assessment of web service quality (e-SQ) and its consequences (top half of the model) and organizational deficiencies that could contribute to poor assessment of web service quality (the bottom half of the model). The company side shows three potential disconnects - depicted as the information, design, and communication gaps - that can occur in the process of designing, operating, and marketing Web sites. These gaps collectively contribute to the “fulfilment gap” on the customer’s side, triggering a chain of adverse effects on perceived web service quality among other effects. Our study and the questionnaire are closely related to the above mentioned gaps in the model. These gaps are explained briefly below. Design gap: The design gap represents the failure to fully incorporate knowledge about customer requirements into the structure and functioning of the Web site. The initial design of a Web site should be informed by the company’s knowledge about features desired by customers. Likewise, the ongoing operations of the Web site should undergo appropriate adjustments in response to customer feedback.


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Communication gap: This gap reflects a lack of accurate understanding on the part of marketing personnel about a Web site’s features, capabilities, and limitations. Under the ideal scenario, the marketing of theWeb site will be based on sound knowledge about what it can and cannot offer, with those responsible for designing and operating the Web site communicating regularly with marketing personnel to ensure that promises do not exceed what is possible. The lack of effective communication between marketing and operations documented in traditional SQ contexts (Parasuraman et al. 1985; Zeithaml et al. 1988) is likely to be present in web service quality contexts as well. Fulfilment gap: The fulfillment gap, occurring on the customer side of the model in Figure 1, represents the overall discrepancy between a customer’s requirements and experiences. Shortfalls such as a customer’s inability to complete an e-purchase transaction are also manifestations of the fulfillment gap in that they reflect unfulfilled customer desires. These kinds of customer frustrations are not a result of exaggerated external promises but rather are due to deficiencies in the design and operation of theWeb site in terms of their failure to fully incorporate customers’ desires. This type of fulfillment gap stems from the cumulative effect of the information and design gaps, just as the fulfillment gap triggered by inflated promises is a consequence of the communication gap. In the model, 1, the fulfillment gap and customer experiences are both shown as key determinants of perceived web service quality. In our methodology chapter, we shall be formulating our online survey following the “e-SERVQUAL” structure in light of the above model along with the multiple dimensions identified in the literature survey; and see whether they provide a fit for our study purposes.


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2.11 Emerged Theoretical framework: The following model by Parasuraman, Zeithaml and Malhotra (2002) is taken as the basis for forming this study.

The model has been explained earlier in literature review. We are restricting ourselves within the model to issues related to communication gap, design gap, and subsequent fulfilment gap and their influence over perception of web service quality of customers. The rectangular box identifies the delimited section of the model that is the focus of our study. It includes all of the company side, and on the customer side excludes perceived value and purchase/repurchase decisions which are outside the purview of our paper. Based on this model as the framework, we employ the variables identified in both servqual and e-servqual for proceeding further. Each of these variables fall within one of the gaps (e.g. communication, design, or information gap) identified above, and are responsible together for creating the fulfilment gap leading to variance in perception as has been outlined in the literature review. The variables are as below:

• Efficiency referring to the ability of the customers to get to theWeb site, find their desired product and information associated with it, and check out with minimal effort.


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• Fulfillment incorporating accuracy of service promises, having products in stock, and delivering the products in the promised time.

• Reliability associated with the technical functioning of the site, particularly the extent to which it is available and functioning properly.

• Privacy including assurance that shopping behavior data are not shared and that credit card information is secure

• Responsiveness measuring the ability of e-tailers to provide appropriate information to customers when a problem occurs, have mechanisms for handling returns, and provide online guarantees.

• Compensation being the dimension that involves receiving money back and returning shipping and handling costs.

• Contact pointing to the need of customers to be able to speak to a live customer service agent online or through the phone; and additionally,

• Graphic style We have re-classified the above components into the following mentioned segments for analytical study. This, we believe gives us a better match between traditional service quality measurement literature while incorporating the unique dimensions present in the online environment.

• Tangibility: Efficiency and fulfilment, graphic style • Reliability: Reliability • Responsiveness: responsiveness and contact • Credibility: Compensation • Security: privacy and assurance

The methodology chapter provides us with a detailed reasoning behind selection of the analytical methods, selection of the test cases, and the analytical tools used in context of the above chosen variables.

Perception of Service Quality in E-Commerce: Methodology

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Chapter 3 In this chapter, we outline the methodology to be used in our research and the theoretical basis behind the approaches and their definitions for the understanding of the reader. We start by identifying the differences between the exploratory, descriptive, and explanatory research approaches and identify our research in this category. We also highlight the difference between deductive vs. inductive research, identify our research strategy. We also compare the benefits of conducting an online survey versus offline survey, and justify our adoption of the online survey technique. Data analysis methods and instruments are chosen and defended. A brief discussion about the validity and reliability of the study is included. Methodology We have outlined our research purpose and research questions in Chapter One. The aim of this chapter are to:

• Discuss our research purpose in relation to other purposes • Expound our research approach and strategy, followed with the data collection

and sample selection methods being adopted • Introduce the research techniques that we have utilised in the pursuit of our goals • Present the data analysis methods and the credibility of the research findings

In trying to explore this theme, we shall try to identify factors that were found to be significant in prior research. We have already discussed in brief the role and influence of traditional service quality dimensions, online service quality dimensions, and the role of perceived ease of use and usefulness. Thus the emergent questionnaire would include technology adoption, perceived ease of use, and perceived Usefulness or Utility, while taking Servqual as the basis for designing the tool. It is viewed that the collection of data will take place in geographically varied markets. We are doing so from the customer’s point of view, that is judging the customer’s perception of the service quality offered by e-businesses (B2C). Discovering the relative weightage between the service components will help us identify which factors motivate customers more, or which factors play an important role in the customer’s perception about the service quality of an internet based service provider.

3.1 Research Purpose

Every researcher has his/her own personal motivation to perform a scientific study while in general according to Yin (1994), Wiedersheim-Paul and Eriksson (1999) the types of research purpose can be classified in three categories: Exploratory research, Descriptive research and Explanatory(or Causal) research.


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3.1.1 Exploratory Research Exploratory research is characterized by its flexibility. When a problem is broad and not specifically defined, the researchers use exploratory research as a preliminary step (Gail, 2003). By an exploratory study we mean a study of a new phenomenon (Yin 1994). Exploratory studies are a valuable means of finding out “what is happening; to seek new insights; to ask questions and to assess phenomena in a new light”(Robson, 1993). Exploratory research has the goal of formulating problems more precisely, clarifying concepts, gathering explanations, gaining insight, eliminating impractical ideas, and forming hypotheses. It can be performed using a literature search, surveying certain people about their experiences, focus groups, and case studies. For instance, when surveying people, exploratory research studies would not try to acquire a representative sample, but rather, seek to interview those who are knowledgeable and who might be able to provide insight concerning the relationship among variables. Case studies can include contrasting situations or benchmarking against an organization known for its excellence. Exploratory research may develop hypotheses, but it does not seek to test them.

3.1.2 Descriptive Research When a particular phenomenon of a nature is under study, it is understandable, that research is needed to describe it, to explain its properties and inner relationships (Huczynski and Buchanan 1991). The object of descriptive research is “to portray an accurate profile of persons, events or situations”(Robson, 1993). In academic research, descriptive research is more rigid than exploratory research. When conducting a management or business research, it seeks to describe users of a product or service, determine the proportion of the population that uses a product or service, or predict future demand for a product or service. As opposed to exploratory research, descriptive research should define questions, people surveyed, and the method of analysis prior to beginning data collection. In other words, the who, what, where, when, why, and how aspects of the research should be defined. Such preparation allows one the opportunity to make any required changes before the process of data collection has begun. However, descriptive research should be thought of as a means to an end rather than an end in itself (Saunders, Lewis and Thornhill, 2000)

3.1.3 Explanatory Research

The study can be explanatory when the focus is on cause-effect relationships, explaining what causes produced what effects (Yin 1994). Explanatory (or Causal) research seeks to find cause and effect relationships between variables. It accomplishes this goal through laboratory and field experiments.

Our research purpose and research questions reveal that this study is primarily descriptive. Large-scale survey studies are conducted on the online customers’ value


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perception aspect. The related secondary data is collected and analyzed from other literature or previous research to verify the hypothesis or result as well. Especially, in the analysis and conclusion stage, the study becomes slightly explanatory, as the research questions of this study will suppose to be inferred in the final chapter and conclusions will be drawn.

3.2 Research Approach Qualitative, quantitative or multiple?

The qualitative and quantitative methods refer to the means through which one chooses to discuss and analyze the selected data (Patel & Davidson, 1994).

Qualitative research is the search for knowledge that is supposed to investigate, interpret, and understanding the phenomena by the means of an inside perspective (Patel & Tebelius, 1987). Furthermore, Yin (1994) states that qualitative methods are often related to case studies, where the aim is to receive thorough information and thereby obtain a deep understanding of the research problem. Qualitative research is softer, and explores why people act or think the way they do, and is most effective when 'open ended', as in focus groups or in-depth interviews. A quantitative approach implies the search for knowledge that will measure, describe, and explain the phenomena of our reality (Patel & Tebelius, 1987). Quantitative research is often formalized and well structured. Quantitative research is concerned with measurement of numbers, such as the number of people who would buy a particular product, the percentage of people who agree with a certain statement, or the satisfaction levels of your customers. Online research is well-suited to this kind of work.

We are interested in knowing more about how different factors relate to a customer value perception to an online auction portal; for example, if different service or technique to adding customer loyalty to the service provider ‘make any difference’. These ambitions may indicate that we should use quantitative methods and address a larger population in order to compare similarities and differences across different customer groups in different geographic location. In fact, many research projects start with quantities, and then dig a little deeper by deploying a bit of quality. It is quite usual for a single study to combine quantitative and qualitative methods and to use primary and secondary data(Saunders, Lewis and Thornhill, 2000) There’re two major advantages to employing multi-methods in the same study. First, different methods can be used for different purposes in a study. Second, multi-methods enables triangulation to take place. Triangulation refers to the use of different data collection methods within one study in order to ensure that the data are telling you what you think they are telling you. In order to help us gain a deeper understanding of online auction customer perception phenomenon, quantative approach will also be adopted throughout the research process.


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3.2.1 Deductive vs. Inductive According to Saunders(2000), The research should use the inductive approach, where the author would collect data and develop theory as a result of the data analysis; While the deductive approach where the authors develop a theory and hypothesis(or hypertheses) and design a research strategy to test the hypothesis. Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a "top-down" approach; Inductive reasoning works the other way, moving from specific observations to broader generalizations and theories. Informally, we sometimes call this a "bottom up" approach (Trochim 2002).

In this study we begin with thinking up a theory about our topic of interest. Then we try to narrow that down into more specific hypotheses that we can test. We narrow down even further when we collect related data to address the hypotheses. This ultimately leads us to be able to test the hypotheses with specific data -- a confirmation or verification of our original theories. So we draw on our research approach with deductive trait. 3.3 Research strategy

Research strategy will be a general plan of how you will go about answering the research question(s) you have set(the importance of clearly defining the research question cannot be over-emphasised)( Saunders et al. 2000) If you are not only interested in theoretical matters, you must select a suitable empirical research strategy. We shall in this section firstly show to which aspects Yin (1994) pays attention in selecting strategy. Thereafter our own classification will be derived.

Strategy Form of research questions Requires control of Focuses on

contemporary behavioral events? events Experiment How, why? Yes Yes Survey Who, what, where, how many, how No Yes

much? Archival Who, what, where, how many, how No Yes/No analysis much? History How, why? No No Case study How, why? No Yes

Table 2: Relevant Situations for different Research Strategies Source: (Yin, 1994). Yin (1994) compares the case study with experiments, surveys, histories, and the analysis of archival information. Each strategy has peculiar advantages and disadvantages, depending on three conditions: 1) the type of research question, 2) the control an investigator has over actual behavioral events, and 3) the focus on contemporary as opposed to historical phenomena.


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Since the aim of this study was to collect the answers form a large scales of online auction user and formulate the best possible answers of the investigated problems we have mainly chosen a comparison survey from two user groups in two major online auction portal as our research strategy. This choice is also partly determined by our research approach, which to most extent is of quantitative nature. Furthermore, documentation, such as academic articles and previous studies on the research area, has been used as a form of secondary data. As stated by Yin (1994), documentation offers the advantage of being static and thus may be re-examined when necessary, also, documentation is exact, contains accurate references and other details, and has a broad coverage over time.

3.4 Data Collection Methods “If you wish to know the road up the mountain, you must ask the man who goes back and forth on it.” –Zenrinkusi(1993)

3.4.1. Online Survey vs. Offline(traditional) A survey is an appropriate strategy due to the fact that the aim is to answer who, where, how many, or how much questions. There is no faster, more affordable way to conduct a survey irrespective of size. Furthermore, due to the quantitative nature of this study, a survey is appropriate because of its quantitative character.

When should we consider using online survey, and when are other methods more appropriate? By this, we’ve consulted below the criteria concluded by Suzi Bentley(2004) to ensure our decision of using online survey(two online questionnaires) as our primary methods of data collection.

What are online questionnaires good for?

• Measuring perception of different products or brands • Measuring satisfaction • Evaluating websites • Measuring awareness • Obtaining feedback about future plans • Gathering thoughts on sensitive data • Gaining business-to-business feedback

When should the study be stuck to traditional methods?

• When non-written feedback is required • When conducting large-scale national studies • For tracking market share or revenue data • When trying to reach older audiences • If audio or video stimuli are present


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The advantages of online survey are that it is cost effective, great for quantitative research, quick to deploy and can provide a good breadth of responses. While it is not so good if one need to collect in-depth responses, or conduct qualitative research. Since our research area is defined online auction, and the primary data we collected is also from online customers group, obviously the online questionnaire methods seems more natural and effective to be adopted.

3.4.2 Using Secondary data As a supplementary of online questionnaires, secondary data from websites and literatures is collected. As we have mentioned before, the data collected from survey were quantitative. Secondary qualitative data were used to present the companies within whose customer base we conducted our empirical research. We place our study in both the positivist and the interpretive camps, utilising a mixture of survey, and secondary data. Previous literature describing survey is valuable in identifying the salient points of the survey methodology, as well as illustrating the weaknesses associated with earlier research. Rich materials concerning such research are readily available, thus our research includes a substantial literature review, the enhancement of earlier research models and the development of an instrument. Research operations, means the instrument we’ve adopted on conducting the survey, will be discussed and explained in much greater detail in the data presentation and analysis parts in the thesis.

3.5 Sample selection

For many research questions and objectives it will be impossible for researcher either to collect or to analyse all the data available due to the time, money and often access. Many researchers, for example Moster and Kalton(1986) and Henry(1990), argue that using sampling enables a higher overall accuracy than a census due to the time saving, designing According to Saunders et al.(2000), Sampling techniques can be divided into two types:

• Probability or representative sampling • Non-probability or judgmental sampling

Probability sampling is most commonly associated with survey-based research where researcher needs to make inferences from the sample about a population to answer the research questions or to meet the research objectives. Based on our data collection methods, probability sampling is the most suitable type of the subsequence when selecting research samples.


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In general, the main criteria for the sample selection in this study are:

• Two major online auction customer groups from North America and Scandinavia • Two major online auction company –Ebay and Tradera, based in USA/UK and

Sweden • From the user/customer perspective

3.6 Data Analysis Methods

Here we detail the operationalization of the practical side of the thesis – the survey - recounting how we have identified suitable groups for study and detailing our detailed procedures for technique adoption and data analysis. To judge the difference in service quality, an instrument called SERVQUAL is used as a basis. User feedback is taken on five key categories of customer perception, Tangibility, Reliability, Responsiveness, Credibility, and Security. An Anova Single Factor Analysis is done to measure the variances and to prove or disprove two hypothesis: Ho (Null Hypothesis) = There is no significant difference between the companies (grouped by individual components). H1 = There is significant difference between the companies (grouped by individual components).

3.6.1 Instrument for data analysis

A. SERVQUAL for electronic assistive technology services The SERVQUAL instrument was developed by Parasuraman et al. in 1988 to quantify the gap between customer expectations and perceptions. They define expectations as predictions made by consumers about what is likely to happen during an impending transaction or exchange. SERVQUAL is a 22-item Likert scale survey, which compares customer expectations and perceptions regarding five attributes of service quality:

1) Tangibles i.e. physical facilities, equipment and appearance of personnel 2) Reliability i.e. the ability to perform the promised service dependably and

accurately 3) Responsiveness i.e. the willingness to help customers and provide a prompt

service 4) Assurance i.e. knowledge and courtesy of employees and their ability to inspire

trust and confidence 5) Empathy i.e. caring, individualised attention provided to customers.


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According to Parasuraman et al. (1988), assurance and empathy contain items representing other dimensions such as communication, credibility, security, competence, courtesy and understanding / knowing customers. In this study, credibility and security are selected as the core variables in B to C setting measurement.

The idea of SERVQUAL is for the user of the service to complete the questionnaire twice. The first version of the questionnaire would contain questions that refer to the electronic assistive technology services in general in order to measure the user´s expectations of the service. The second version of the questionnaire would contain questions that refer to the electronic assistive technology services when, or after, the user has experienced the service.

Therefore a negative gap result in the SERVQUAL survey would indicate that the user's perceptions of the service is lower than his/her expectations and would indicate a problem in that particular area.

B. Anova Single Factor Analysis A factor is a categorical predictor variable. Factors are composed of levels. For example, treatment is a factor with the various types of treatments comprising the levels. The levels should be exclusive, that is, a subject should appear under only one level which in this case means given a single type of treatment(Gerard E. Dallal). The term Single Factor ANOVA, One Way Analysis of Variance, and One Way ANOVA are used interchangeably to describe the situation where a contiuouse response is being described in terms of a single categorical variable or factor composed of two or more categories. It is a generalization of the test for independent samples to situations with more than two groups.

The detailed implementation of the SERVQUAL and ANOVA analysis methods control and manipulations in our study will be presented in the following data presentation and analysis chapters.

3.7 The validity and reliability of research findings The table below demonstrates parallel concepts that come under the general umbrella of reliability and validity for quantitative designs. (Guba, E. & Y. Lincoln 1989) Common Understanding of this Concept Quantitative methods Consistency (Consistency sets limits on the truth value and applicability of your research.)

Reliability - get the same results on repeated tries. Types:

• stability over time • representativeness across samples • intercoder


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Truth value Internal/Construct Validity - the items used to

measure the concept form a single, coherent whole. Content validity - the measure covers the range of meaning included in the concept.

Applicability External Validity/Generalizability - the measurement of the concept actually measures what you intend it to. If you took this measurement or these findings to another situation they would still apply.

Neutrality Objectivity - the researcher maintains the proper distance from the subject to avoid biasing the research.

Table 3: Reliability and validity for quantitative designs

Validity measures if the information received is the information meant to be received (Merriam, 1998). If a question can be misunderstood, the information is said to be of low validity. In order to avoid this situation, the questions for the online survey were planned carefully and optimized before putting onto the web. In order to make sure the respondents gave the valid answers, the questions also have been reviewed and further clarified during a trial stage. While the external validity can be low due to the limited research objectives selections, our sampling groups are from two accredited online auction websites in the world, this can guarantee to a great extent for the final conclusion applicability. Reliability is synonymous with the consistency of a test, survey, observation, or other measuring device. Because our research is of quantitative trait, the reliability of the findings can be ensured by ensuring the survey time scale, stability, equalization questionnaires design and even the objectivity of the measurement instrument itself.


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3.8 Methodology Summary When we talk about "studies," we might actually be referring to any of a number of different things, using such diverse techniques as surveys, interviews, experiments, case studies, and field research. These different techniques vary widely not only in the way they are conducted, but also what their results mean, and especially what we can conclude from them. In conclusion, our general methods of research and the corresponding effectiveness comparing the other two research purpose categories are is demonstrated in the table below.

Purpose Topic Observation Technique Validity Reliability

Exploratory Research

New and unknown topic

Qualitatively rich, but cannot be generalized. Leads to future studies.

Field research High Low

Case Studies High Low

Intensive Interviews

High Low

Descriptive Research

Familiar topic that needs to be better described. We want to know the extent of the phenomena.

Less rich data than exploratory research, but results can be better generalized to help us determine the extent of the effects.

Surveys Low High

Available data High High

Explanatory or Casual Research

Familiar topic that is well described but required better understanding and prediction

Rich qualitative data. Descriptions suppressed to help control factors in order to better understand the qualitative and causal mechanisms.

Experiments Low High

Games and Models

Low High

Table 4: Research Methods adopted of this paper

Perception of Service Quality in E-Commerce: Data Collection

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Chapter 4 Test subjects and Data collection In this section, we discuss the two sample populations. These are two internet based companies who provide auction services, these are Tradera.com and ebay.com. we shall give a brief overview of the companies here. This will be followed by the results obtained, and analysis of the data. 4.1 Chosen Test sites for analysis For the purpose of collecting data, we have chosen two major internet auction sites: Tradera.com and ebay.com. Tradera.com is the premier internet auction site operating out of Sweden, its operations are also restricted to Sweden. However, participants in the auction can operate on a worldwide basis. Below, we give a brief introduction about the sites. 4.1.1 Tradera.com Tradera of today is the result of the merger between the three auction websites: Bid2Day, mrBid and Tradera. A community of registered users buy and sell a variety of goods in auction style on the site. The trade is foremost between private persons, but companies also sell goods on the website. Tradera’s buyers and sellers are spread all around Sweden. Today it has over 200.000 members and hundreds of thousands of visitors each month. An evaluation system is used in Tradera, where sellers and buyers give each other evaluations about how the deals are done. The system makes the trade safe. Serious seller and buyers receive positive evaluations and those who do not live upto commitments receive negative ones and may be suspended. All the members’ evaluations are easily available to all members, both buyers and sellers. Tradera AB (Inc.), organisationsnummer 55 65 69-46 42, is founded by Kaplan’s Auktioner AB. The company is running websites on the domain-names: � Bid2Day.com � mrBid.se � Tradera.se/Tradera.com Tradera Scandinavia’s operations are supported by technical support from IT Provider, Eniro, New Media Spark, Kaplans Auktioner, Spray and TIME Vision. Tradera.com is built and is continuously developed by the staff of Tradera.com and Genuity that responsible for running and maintenance. Personnel at Tradera

• Tobias Adolfsson - Key Account & Content Manager


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• Daniel Larsson - VP - Marketing and Sales Manager • Mattias Danielsson - Working member of the board • Jonas Nordlander - CEO

4.1.2 Ebay.com Ebay was formed in September 1995 for the sale of goods and services by a diverse community of individuals and businesses. It has registered members from all over the world, and has grown to be one of the most popular online auction and shopping destinations on the internet. Its motto is “The World’s Online Marketplace”. Mission eBay's mission is to provide a global trading platform where practically anyone can trade practically anything. (source: Operations Ebay lists millions of items for sale or auction on their site every day. The categories include antiques and art, books, business & industrial, cars & other vehicles, clothing & accessories, coins, collectibles, crafts, dolls & bears, electronics & computers, home furnishings, jewelry & watches, movies & DVDs, music, musical instruments, pottery & glass, real estate, sporting goods & memorabilia, stamps, tickets, toys & hobbies and travel etc. Currently, eBay has local sites that serve Australia, Austria, Belgium, Canada, France, Germany, Ireland, Italy, Korea, the Netherlands, New Zealand, Singapore, Spain, Sweden, Switzerland, Taiwan and the United Kingdom. In addition, eBay has a presence in Latin America and China through its investments in MercadoLibre.com and EachNet, respectively. eBay offers a wide variety of features and services that enable members to buy and sell on the site quickly and conveniently. Buyers have the option to purchase items in auction-style format or items can be purchased at fixed price through a feature called Buy-It-Now. In addition, items at fixed price are also available Half.com, an eBay company. Payments for executed transactions are conducted through Paypal, the most well known online payment service.


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4.2 The population sample The questionnaire was built in an electronic format. To maintain consistency, users/bidders were selected from the same category of “desktop computers” both Tradera.com and ebay.com. This was selected due to two reasons. First, since our questionnaire involves a base level degree of knowledge and familiarity with the internet and internet sites. It was presumed that internet bidders of computers online would tend to possess average knowledge to give an informed answer to the questionnaire, and would require little clarification of any questions. Second, the category of desktop computers and peripherals were found to be at the same category level across both sites. Thus, it gives us more uniformity in our study across the two sites. Potential respondents were selected randomly from within the same category. While the selection was random, yet it was tried to select potential respondents who had at least a few feedbacks. This ensured a higher level of involvement and familiarity with the system on the part of the respondents. Two hundred respondents were approached both from Tradera.se and Ebay.com. The respondents were approached via email, which contained a hyperlink to the web-based questionnaire page. A total of 200 emails for each site were sent to randomly selected users within a particular product category of computers and peripherals. Total responses received were 110, or 27.5 percent. This is in line, or even above average with the usual response rate for such a survey. Thus it shows we have a means for getting a reliable answer to our queries for the scope of this work. The questionnaire was formulated taking a modified e-ServQual instrument as the basis. A few modifications were made to the original instrument. This includes replacing Assurance and Empathy with Credibility and Security as the primary attributes. The rationale and the selection of the variable was done based on the literature review, and it was an objective of the paper to explore how well these variables respond in an online setting instead of the traditional variables used in the e-SERVQUAL model.

Perception of Service Quality in E-Commerce: Data Analysis and Results

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Chapter 5 Data Analysis and Results For our analysis, we have focused on five components: tangibility, reliability, responsiveness, credibility, and security. We shall discuss the relative position of customer perception of the two subject companies in each of these five component areas below. In the charts, the X-axis is the number of respondents, with each number identifying a single respondent. The Y- Axis shows the cumulative score of a respondent in a particular category, and is the cumulative score of his responses to all the questions in the survey in that category.

5.1 Analysis of perception of tangibility

Tangibility perception

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Figure 4: Perception of Tangibility

As we can see from the chart, the tangibility perception among the customers proceeds in a more or less sinusoidal manner. The tangibility factor concerns itself with primarily the visual appeal and usability of the websites. The chart is based on the cumulative response of a particular respondent in that category. It is interesting to note is that while there are variations, the range of variation falls generally within 12 to 17. Only three customers have expressed grave dissatisfaction with the tangible aspects of the Tradera site, and two have done so for ebay. The ebay site has ranked slightly higher than tradera over the charting the course. This means the customers surveyed of ebay rate it higher than tradera in general. However, we need to remember that these are two different respondent groups.


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5.2 Analysis of perception of reliability

Reliability perception

02468

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Figure 5: Perception of Reliability

We have a more pronounced variation in customer perception when it comes to reliability. It may be commonly assumed that a massive brand name site like ebay would perform well in this category, simply because customers may be more trusting of what is equivalent to a brand name. we see a pattern supportive of that notion, with ebay’s perception curve, while fluctuating, varies between 8 and 18. This is in contrast to Tradera, which registers 6 customers whose perception is unfavourable to the reliability component of Tradera’s services, with ranges lying between 0 and 19. However, there are also 6 customers whose perception of Tradera’s reliability reaches 18 or above, while only 4 customers perceive such a high rating for ebay.


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5.3 Analysis of perception of responsiveness

Responsiveness perception

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Figure 6: Perception of Responsiveness

We have a clear breach from the patterns apparent from the other two categories discussed so far. Tradera ranks consistently higher than ebay across the users surveyed. Responsivness includes such factors as availability of technical support, approachability of stuff, sympathetic response from the stuff in case of problem transactions etc. five customers have ranked Tradera 18 in its cumulative score, while the highest rating achived by ebay was 16, with most of its respondents ranking ebay somewhere between 8 and 6. It may be debated whether the size of the company and the number of transactions it concludes every day, which are many times over that of Tradera, has anything to do with the relatively poor performance in this category by ebay. In contrast, in the same line of thought, it may be hazarded that the smaller size of Tradera allows the company to engage itself in a more intimate relationship with its customer base. However, these are simply possible explanations of this response behaviour, which need to be examined further.


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5.4 Analysis of perception of credibility

Credibility perception

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Figure 7: Perception of Credibility

In the credibility aspect, we go back to a somewhat similar pattern generated by the tangibility and reliability factors. The range of responses varies between 6 and 14. The lines represent a more or less identical pattern for both Tradera and ebay. Only one customer has rated Tradera’s credibility at 2. Overall, the mean score is closer to 10 or above for both the companies, implying a relatively high credence led to both the companies by their customers.


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5.5 Analysis of perception of Security

Security perception

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Figure 8: Perception of Security

Tradera.se has performed better than ebay in perceived security by its customers. The highest score was obtained (27) in this category, while two other customers also ranked it at 25. We also see a more pronounced variation, with only a couple of customers having ranked Tradera at less than 15. In contrast, ebay registers a less varied pattern of fluctuations, with the response range varying between 15 and 25. Cumulatively, Tradera performs higher than ebay in this category.


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5.6 Analysis of cumulative perception of Tradera and Ebay.com Next we take a look at the individual companies, and the cumulative perception score registered through the responses of their customers. This also helps us to get an idea of the relative emphasis placed by the customers in forming a perception about the company’s services.

Eaby component perception

0200400600800

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Figure 9: EBay Attributes Perception

From the chart it is apparent that tangibility and reliability are emphasised upon by the customers, along with security. Responsiveness receives the lowest cumulative score. This may mean a negative perception of the responsiveness of ebay as a whole. The nature of the questions related to responsiveness are to judge the customer’s impression of how ebay might react if he/she faces a problem, and also to judge actual responsiveness for problems faced. Either way, the cumulative effect of one fails to balance out the effect of another, leading us to believe in the customer’s overall low perception of the responsiveness of the company.


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Tradera component perception

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Figure 10: Tradera Attributes Perception

We face a slightly different picture in case of Tradera. There is no marked fall in responsiveness compared to the other perception components. Whether this may be attributed to the smaller size of the firm is subject to further investigations. Since we are concerned with perceptions, we need to explore the psychological assumptions of smaller firms being able to provide a more intimate service experience. The security aspect is also paramount here, as in the case of Ebay. Security scores the highest, which also shows the customers have much less concern regarding the adequacy of the software security underlying the sites. The table below shows the overall comparative picture of customer perception of the two companies. It takes the mean value of the five perception components. Averaged values Tradera Ebay Tangibility 12,76 15 Reliability 12,92 14,94 Responsiveness 10,34 6,9 Credibility 10,52 11,04 Security 19,02 19,36

Table 5: Overall comparative scores


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0

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Figure 11: Averaged Attributes scores

Here we see while Ebay performs better than Tradera in tangibility and reliability, it scores lower than Tradera in responsiveness. The perception regarding credibility and security is almost equal for ebay and Tradera, with ebay leading Tradera slightly.

Perception of Service Quality in E-Commerce: Cross Analysis of Data and Results

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Chapter 6 Cross Analysis of data and Results In this chapter, we shall analyse the data in three parts. In the first part, we shall analyse the result sets obtained using statistical tools. A short overview of the statistical processes employed will also be given along with the justification for using them in the analysis. In the end, we shall prove or disprove our hypotheses. In the second part of this chapter, we shall draw comparisons and present general conclusions from our findings. We shall also draw a comparison between the results and the theoretical frame of reference. These two parts will allow us to identify implications of our findings, and reach our conclusions. 6.1 Defining the analytical tool and setting parameters: 6.1.1 Analysis of Variance (ANOVA): We have shown a comparative scenario between our findings in the results section. To further analyze the data obtained in our results and ascertain the statistical validity of our findings, we chose the ANOVA method. It is an advanced statistical method. ANOVA is a statistical method for determining the existence of differences among several population means (Complete Business Statistics, Amir Aczel, 4th ed.). The statistical technique requires the analysis of different forms of variance associated with the random samples under study. Assumptions: There are two assumptions which are required for conducting ANOVA testing, which are:

1. Independent random sampling from each of the populations is assumed. 2. It is assumed that the populations under study are normally distributed, with

means that may or may not be equal, but will have equal variances. The method is used for testing the validity of hypotheses. The hypothesis test of analysis of variance is as follows: H0: µ1= µ2 = µ3 = …. = µr H1: Not all µi (I = 1, 2, 3, …, r) are equal. Here, H0 is the null hypothesis, and H1 is the alternative hypothesis. Here r signifies the number of populations under study. An independent random sample is drawn from each of the r populations. The size of the sample from population i (i = 1, 2, 3, …; r) is ni, and the total sample size then would be: n = n1 + n2 + n3 + … + nr 6.1.2 F Distribution:


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From the r samples we can compute several different quantities which leads to a computed value of a test statistic that follows a known F distribution when the null hypothesis is true. From the value of the statistic and the critical point for a given level of significance, it is possible to make a determination of whether the r population means are equal (Complete Business Statistics, Amir Aczel, 4th ed.). Based on the assumptions mentioned above, the test statistic of analysis of variance follows an F distribution when the null hypothesis is true. The F distribution has two kinds of degrees of freedom: degrees of freedom for the numerator, and degrees of freedom for the denominator. In the analysis of variance, the numerator degrees of freedom are r-1 and the denominator degrees of freedom are n-r. the value of the test statistic is: ANOVA test statistic = F ( r-1, n-r) A rejection of the null hypothesis through ANOVA testing means there is statistical evidence that not all means are equal. However, it will not tell us in which way they are different. Thus, if the null hypothesis is rejected in some cases, then we need to conduct further statistical tests. This is beyond the scope of this paper. The level of significance or the right-tailed critical point (α) for our analysis = 0.05. 6.2 Analytical Constructs: We need a few more constructs to understand the treatment of ANOVA factor analysis. The F distribution is used in determining differences between two population variances, and the appropriate degrees of freedom for the numerator and the denominator of F come from the degrees of freedom of the sample variance in the numerator and the sample variance in the denominator of the ratio. Here, the numerator is MSTR (mean square treatment) and has r-1 degrees of freedom. The denominator is MSE (mean square error) and has n-r degrees of freedom. Thus, the test statistic in analysis of variance is: F(r-1, n-r) = MSTR / MSE When the null hypothesis of ANOVA is true, all r population means are equal. In terms of the expected values of the mean squares we have E (MSE) = σ2 And E(MSTR) = σ2 + Σni (µi – µ)2 / (r-1) Where µi is the mean of population i and µ is the combined mean of all r populations. If the null hypothesis is not true and differences do exist among the r population means, then MSTR will tend to be larger than MSE.


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6.3 ANOVA testing on the population samples and hypothesis testing: In this section, we run the ANOVA test on all five categories grouped by the two populations to find variance among them, and ascertain the significance of the variance. Here, we shall present the data sets and the corresponding results of the statistical analyses; and describe the significance of the results. 6.3.1 Tangibility Data set: The statistical test has been run through the Excel Data analysis set. Here: Refer: Appendix IV A Sources of variation: The sum of squares for treatment (SSTR) = 125.44 The sum of squares for error (SSE) = 1111.12 Total (SST) = 1236.56 Mean Squares (MSTR) = SSTR / (r-1) = 125.44 MSE = SSE / (n-r) = 11.33796 This gives us the F Ratio = MSTR / MSE = 125.44 / 11.33796 = 11.06372 The corresponding critical value for F is 3.938112. Therefore, we see that the F ratio is higher than the F critical value. This signifies a rejection of our null hypothesis. In terms of our research question, this would indicate that there is significant difference in the way customers perceive the tangibility aspect of the services provided by the two companies in our research. 6.3.2 Reliability data set: Refer: Appendix IV B Sources of variation: The sum of squares for treatment (SSTR) = 102,01 The sum of squares for error (SSE) = 1222,5 Total (SST) = 1324,51 Mean Squares: (MSTR) = SSTR / (r-1) = 102,01 MSE = SSE / (n-r) = 12,47449 This gives us the F Ratio = MSTR / MSE = 102,01 / 12,47449 = 8,177489


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The corresponding critical value for F is 3.938112. Therefore, we see that the F ratio is higher than the F critical value. This signifies a rejection of our null hypothesis. In terms of our research question, this would indicate that there is significant difference in the way customers perceive the reliability aspect of the services provided by the two companies in our research. 6.3.3 Responsiveness data set: Refer: Appendix IV C Sources of variation: The sum of squares for treatment (SSTR) = 295,84 The sum of squares for error (SSE) = 1141,72 Total (SST) = 1437,56 Mean Squares: (MSTR) = SSTR / (r-1) = 295,84 MSE = SSE / (n-r) = 11,6502 This gives us the F Ratio = MSTR / MSE = 295,84 / 11,6502 = 25,39355 The corresponding critical value for F is 3.938112. Therefore, we see that the F ratio is higher than the F critical value. This signifies a rejection of our null hypothesis. In terms of our research question, this would indicate that there is significant difference in the way customers perceive the responsiveness aspect of the services provided by the two companies in our research. 6.3.4 Credibility data set: Refer: Appendix IV D Sources of variation: The sum of squares for treatment (SSTR) = 6,76 The sum of squares for error (SSE) = 480,4 Total (SST) = 487,16 Mean Squares: (MSTR) = SSTR / (r-1) = 6,76 MSE = SSE / (n-r) = 4,902041 This gives us the F Ratio = MSTR / MSE = 6,76 / 4,902041 = 1,379017 The corresponding critical value for F is 3.938112. Therefore, we see that the F ratio is lower than the F critical value. This signifies an acceptance of our null hypothesis.


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In terms of our research question, this would indicate that there is no significant difference in the way customers perceive the credibility aspect of the services provided by the two companies in our research. 6.3.5 Security data set: Refer: Appendix IV E Sources of variation: The sum of squares for treatment (SSTR) = 2,89 The sum of squares for error (SSE) = 758,5 Total (SST) = 761,39 Mean Squares: (MSTR) = SSTR / (r-1) = 2,89 MSE = SSE / (n-r) = 7,739796 This gives us the F Ratio = MSTR / MSE = 2,89 / 7,739796 = 0,373395 The corresponding critical value for F is 3.938112. Therefore, we see that the F ratio is lower than the F critical value. This signifies an acceptance of our null hypothesis. In terms of our research question, this would indicate that there is no significant difference in the way customers perceive the security aspect of the services provided by the two companies in our research. 6.4 ANOVA testing within population between the groups: Here we run a further ANOVA test within the same population between the five service attributes. This will help us identify whether there is any significant co-relation between the components themselves which would indicate an influencing behavior. Refer: Appendix V A For Tradera: As we see from the test, the F ratio is 50.34068 which is higher than the F critical value of 2.40864. Thus we conclude there is no significant relationship within the groups. Refer: Appendix V B For Ebay:


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The F ratio for ebay is 159.869; significantly higher than the F critical value of 2.408491. Thus, we conclude that here also there is no significant co-relation between the components within the group. One point to be noted is that the F ratio for Ebay is significantly higher (about 3 times) than that of Tradera. This may indicate a more cohesiveness within the group attributes in case of Tradera compared to Ebay.

Perception of Service Quality in E-Commerce: Summary of Analytical Findings and Conclusions

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Chapter 7 Summary of Analytical Findings and Conclusions In this section we shall summarize our analytical findings with respect to the research questions and present our conclusions, as well as outline a path for further research on the issues. To recap, our research questions were: 1. Do customers perceive a difference in service quality between companies for exclusively internet based services? 2. If there is a perceived difference, then which factors are contributing to the difference?Finally we summarize the possible implications of these perceived differences and draw our conclusion; and also outline directions for further research. Our findings and conclusions drawn thereof are presented below: From our analysis, it is clear that customers do perceive a difference in service quality along some attributes, while they do not do so for some other attributes. We found significant variance between the two sample populations (Tradera and Ebay) in attributes Tangibility, Reliability, and Responsiveness. In tangibility, our questions were centred on visual appeal and usability of the websites. The customer response to reliability perception was more varied than in the case of Tangibility. Ebay ranks higher than Tradera in this regard. This finding is consistent with the conception that a massive brand name would garner more support in people’s perception of reliability. In responsiveness category, Tradera, a smaller company, has ranked higher than Ebay. This finding would indicate that in a customer’s perception, a smaller enterprise is apt to be more responsive than a larger one. In credibility and security, both companies have a mean score of above 10, indicating a high level of credence on the part of the customers. Perceived security ranks even higher than perceived credibility score for both companies. The above findings are further refined and reconfirmed through further statistical tests that we conducted. Through ANOVA testing, we confirmed acceptance of our null hypothesis in case of Credibility and Security; indicating there is no significant difference in the way customers perceive credibility and security aspects of the two companies. The null hypothesis was rejected in case of the other three attributes, Tangibility, reliability, and responsiveness; confirming customers perceive there to be significant differences in the three attributes between the two companies. In order to explore any further relationships, we ran ANOVA test within each populations between the five groups. Our test results failed to show any significant co-relation between the attributes themselves within a single population. This would indicate that


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there is no clear influence of one attribute over another in forming a customer’s perception regarding a company’s service quality; but rather the perception is a resultant vector of all its service attributes. 7.1 Implications of the findings: We have conducted our analysis on two internet auction sites Tradera and Ebay that are not only representatives but also trend setters in internet auction sites which occupies a mass participated and important segment today in e-commerce. Consequently, the opinions and user behavior trends in the auction sites are a good indicator of the consumer psyche engaged in transaction over the internet today. Through our study, limited as it may be, we were able to reach two conclusions. With regard to the first research question of whether customers perceive a difference in the service quality offered by internet auction sites, the answer is yes. We had identified five factors: Tangibility, reliability, responsiveness, security, and credibility through which we had carried out our investigations. We were able to confirm significant perceived difference in tangibility, reliability, and responsiveness, and found no significant perceived difference in security and credibility perceptions. Our second research question stemmed from the first. Which factors strongly influence these perceived differences? As has been said, the strong difference lies in the factors of tangibility, reliability, and responsiveness. Here in lies the key to our findings, because the factors of security and credibility are much more relevant for an e-commerce or online company than it is for an offline company. Yet we have seen customers are finding no significant difference between the companies when it comes to the factors restricted to an online setting. What does this finding tell us? Internet has been available to the public for over a decade, and e-commerce started its journey over 8 years ago. During this time, and even today, beliefs persist that customers are scared of conducting transactions online, that they are concerned about the security of their transactions, or disturbed over not knowing who is at the other end of the terminal. These beliefs have also been aided and abetted by the popular press dealing with e-commerce issues. Yet, its not often the customer has been asked whether these beliefs today are facts or simply myths, handed down and broadcast without checking the reality. Our interpretation of the findings is that e-commerce has reached its maturity at last. Consumers are familiar enough with internet based services to a degree today that they do not see purchasing or auctioning on the internet as a novelty transaction, but rather just another chore. The reason security and credibility perceptions do not differ is not because they are unimportant to the customer, they still are; but because customers today assume there would be a standard followed by whoever does business on the internet, and the practices would be ethical by default than otherwise. Thus customers do not differentiate


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online companies based on their security measures offered or their online pedigree, these factors are now taken for granted. Instead, what they focus upon are how the companies differentiate themselves on the more traditional of off-line service components: tangibility, responsiveness, reliability. Their questions today are not “will my credit card number be safe in your hands?”, but rather “if I run into trouble will I be able to get in touch with you?” or “can I return my product?”. Thus business online has come a full circle at last, and the customers have stopped caring whether a business is virtual or not. The marketers need to pay heed to that message. The final contribution of this thesis is the thought that maybe it is time for us to stop thinking of companies and the services they offer in terms of whether they are online or off-line, but rather how well they are providing the service. 7.2 Directions for further research: Customer perception of internet services is still a relatively new area of knowledge. In this paper, we have tried to embark upon a journey to see how well the traditional service marketing concepts can be applied to marketing over the internet. In doing so, we have tried to adapt traditional measurement tools in marketing and statistics to explain customer’s perceptional behavior over the internet. Undoubtedly, ours has been a limited effort constrained by a small sample size; the possibility of bias in the responses, and even the validity of some of our assumptions. Establishing the validity of our adaptation and findings would be the subject of further research. More specifically, the replacement of empathy and assurance in the e-Servqual scale by credibility and security is one instrumental and conceptual issue that remains subject to validation. It was our belief that the changed context of trading on the internet as opposed to the traditional marketplaces warranted the replacement. Further studies need to be conducted to confirm or disconfirm the validity of the assumption.

Perception of Service Quality in E-Commerce: Reference List

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References Bitner, M.J., (1990), Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses, Journal of Marketing, Vol. 54 (April), pp. 69-82. Bitner, M.J., Booms, B.H., and Tetreault, M.S., (1990), The Service Encounter: diagnosing favorable and unfavorable, Journal of Marketing, Vol. 54, January, pp. 71-84. Bolton, R.N., and Drew, J., (1991), A Longitudinal Analysis of the Impact of Service Changes on Customer Attitudes, Journal of Marketing, Vol. 55 (January), pp. 1-9. Brown, S.W., and Swartz, TA; (1989), A gap analysis of professional service quality, Journal of Marketing, 1989 Brown, S.W., and Swartz, TA; (1989), Consumer and provider expectations and experiences in evaluating professional service quality, Journal of the Academy of Marketing Science, 1989 Cohen, J., Fishbein, M., and Ahtola, O., (1972), The Nature and Uses of Expectancy Value Models in Consumer Attitude Research, Journal of Marketing Research, Vol. 9 (November), pp. 456-460. Cronin, J.J. & Taylor, S.A. (1994); Servperf Versus Servqual : Reconciling Performance-Based And Perceptions-Minus-Expectations Measurement Of Service Quality, Journal Of Marketing, Vol. 58, Pp 125-131 Davis, F.D., (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, Vol. 13, No. 3, pp. 319-40. Dabholkar, P.A., (1996), Consumer Evaluations of New Technology based Self-Service Options: An Investigation of Alternative Models of SQ, International Journal of Research in Marketing, Vol. 13 No. 1, pp. 29-51. Dodds, W.B. & Monroe, K.B., (2001), The Effect Of Brand And Price Information On Subjective Product Evaluations, Working Paper At The Virginia Polytechnic Institute Dodds, W.B., Monroe, K.B. & Grewal, D., (1991), Effects Of Price, Brand, And Stre Information On Buyers’ Product Evaluations, Journal Of Marketing Research, Vol. XXVIII, Pp. 307-319. Doll, W.J., and Torkzadeh, G., (1988), The measurement of end-user computing satisfaction, MIS Quarterly, Vol. 12 No. 2, pp. 259-74. Eggert, A. & Ulaga, W., (2002), Customer Perceived Value: A Substitute For Satisfaction In Business Markets?, Journal Of Business & Industrial Marketing, Vol. 17 No. 2/3, Pp. 107-118.


67

Fitzsimmons, JA, and RS Sullivan; (1982), Service Operations Management, McGraw-Hill, Inc., New York, NY. Goodman, M.R.V., (1999), The Pursuit Of Value Through Qualitative Market Research, Qualitative Market Research: An International Journal, Vol. 2, No. 2, Pp. 111-120. Gordon H.G., Terrence L.(2000) Customer Satisfaction With Services: Putting Perceived Value Into The Equation, Journal Of Service Marketing, Vol. 14 No. 5 Groth, J.C. & Dye, R.T, (1999), Service Quality: Perceived Value, Expectations, Shortfalls, And Bonuses, Managing Service Quality, Vol. 9 No. 4, Pp. 274-285 Gronröös, C., (1990), Service Management and Marketing: Managing the Moments of Truth in Service Competition, Lexington, M.A.: Lexington Books. Guba, E. & Y. Lincoln (1989) Fourth Generation Evaluation, Newbury Park, Sage Publications. Hendrickson, A.R., and Collins, M.R., (1996), An assessment of structure and causation of IS usage, The database for advances in information systems, Vol. 27, No. 2, pp. 61-7. Huczynski And Buchanan (1991) Organisational Behaviour – An Introductory Text. Hemel Hempstead, UK, Prentice Hall Lewis, R.C., and Booms, B.H., (1983), The Marketing Aspects of Service Quality, Emerging Perspectives on Services Marketing. Lin, C.C., (2003), A Critical Appraisal Of Customer Satisfaction And E-Commerce, Managerial Auditing Journal 18/3, Pp. 202-212. Lin, C.S., and Wu, S., (2002), Exploring the impact of online service quality on portal site usage, Proceedings of the 35th Hawaii International Conference on System Science. Mark Saunders, Philip Lewis, Adrian Thornhill (2000) Research Methods For Business Students Second Edition, UK, Financial Times, Prentice Hall Mcdougall, G. & Levesque, T., (2000), Customer Satisfaction With Services: Putting Perceived Value Into The Equation, Journal Of Services Marketing, Vol. 14 No. 5, Pp. 392-410. Merriam, S. B., (1998). Qualitative Research And Case Study Applications In Education. San Francisco, Jossey-Bass, Inc.


68

Minocha, S., Dawson, L., Roberts, D., and Petre, M., (2004), E-SEQUAL: A customer centered approach to providing value in e-commerce environments, Technical Report No. 2004/12, Faculty of Mathematics and Computing, The Open University, UK. Napier, H.A., Judd, P.J., Rivers, O.N., and Wagner, S. W. (2001), Create A Winning E-business, Thomson, Learning, Boston, MA Olshavsky, R. W., (1985), Perceived Quality in Consumer Decision Making: An Integrated Theoretical Perspective, In J. Jacoby & J. Olson (Eds.), Perceived Quality (pp. 3-29). Lexington, MA: Lexington Books. Patel, R. And Tebelius, U. (Eds.). (1994), The Basic Of Research Methodology. Lund University, Studentlitteratur. Pather, S., Erwin, G., and Remenyi, D., (2003), Measuring E-commerce effectiveness: A conceptual model, Proceedings of SAICSIT, pp. 143.152. Parasuraman, A., Zeithaml, V.A. & Berry, L., (1985), A Conceptual Model Of Service Quality And Its Implications For Future Research, Journal Of Marketing, Vol. 49, Pp. 41-50. Parasuraman, A., Zeithaml, V.A. & Berry, L., (1994), Reassessment Of Expectations As A Comparison Standard In Measuring Service Quality: Implications For Further Research, Journal Of Marketing, Vol. 58, Pp. 111-124. Parasuraman, A.; Zeithaml, Valerie A.; Berry, Leonard L.(1998) SERVQUAL: A Multiple Item Scale For Measuring Consumer Perceptions Of Service Quality. Journal Of Retailing, Vol. 64, No. 1 Parasuraman, A., Berry, l. L., & Zeithaml, V. A. (1991). Refinement and Reassessment of SERVQUAL Scale. Journal of Retailing, Vol. 67 No. 4, 420-450 Rao, A.R. & Monroe, K.B. (1998), The Moderating Effect Of Prior Knowledge On Cue Utilization In Product Evaluations, Journal Of Consumer Research, Vol. 15. Rathmell, J.M., (1966), What is Meant by Services?, Journal of Marketing, Vol. 30 (October), pp. 32-36. Rudie, M.J., and Wansley, H., (1985), The Merrill Lynch Quality Program, Services Marketing in a Changed Environment, Chicago, American Marketing Association. Sasser, W.E., Olsen, P., Wyckoff, D.D., (1978). Designing the service firm organization. In: Management of Service Operations. Allyn & Bacon, Boston, MA. Schneider, G.P., and J.T.Perry. Electronic Commerce. Course Technology, 2000


69

Song, J.H., and Zinkhan, G. M., (2003), Features of web site design, perceptions of web site quality, and patronage behavior, ACME proceedings, pp. 106-114. Spreng, Richard A. and Olshavsky, Richard W., (1993) A desires congruency model of consumer satisfaction, Journal of the Academy of Marketing Science Vol. 21(3):169177. Stockdale, R., and Standing, C., (2002), A framework for the selection of electronic marketplaces: a content analysis approach, Internet Research: Electronic Networking Applications and Policy, Vo. 12, No. 3, pp. 221-34. Teas, R.K, (Oct 1993), Expectations, Performance, Evaluation, And Consumer’s Perceptions Of Quality; Journal Of Marketing, Vol. 57, Pp 18-34. Teas, R.K, (1994), Expectations As A Comparison Standard In Measuring Service Quality: An Assessment Of A Reassessment; Journal Of Marketing, Vol. 58, Pp 132-139 Wilder, C., (May 29, 2000), The Wilder Side: Beware The Hype And The Anti-Hype, Informationweek.Com Woodside A.G., Frey L.L., and Daly R.T., (1989), Linking service quality, customer satisfaction, and behavioral intention, Journal of Health Care Marketing, Vol. 9 No. 4, pp. 5-17. Yang, Z., and Fang, X., (2004), Online Service Quality Dimensions and their Relationships with Satisfaction, International Journal of Service Industry Management, Vo. 15, No. 3. Yin, R.K. (1994), Case Study Research: Design And Methods. Thousand Oaks, Sage Publications Zeithaml, V.A. (1998), Consumer Perceptions Of Price, Quality, And Value: A Means-End Model And Synthesis Of Evidence; Journal Of Marketing, Vol. 52, Pp 2-22 Zeithaml, V.A., Parasuraman, A., and Malhotra, A., (2002), Service Quality Delivery through Web Sites: A Critical Review of Extant Knowledge, Journal of the Academy of Marketing Science, Vol. 30 No. 4, pp. 362-375. Zhan Chen & Alan J. Dubinsky(2003) A Conceptual Model Of Perceived Customer Value In E-Commerce: A Preliminary Investigation, Wiley Periodicals, Inc. Gerard E. Dallal 2003, Http://Www.Tufts.Edu/~Gdallal/Anova1.Htm Single Factor Analysis Of Variance William M.K. Trochim 2002 Http://Www.Socialresearchmethods.Net/ The Web Center For Social Research Methods

Perception of Service Quality in E-Commerce: Appendices

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Appendix I Ebay Online Questionnaire

Ebay Survey User survey on ebay.com Page 1 of 2 The following set of questions relate to your feelings about Ebay.com. For each statement, please show the extent to which you believe Tradera has the feature described. Rate 5 if you strongly agree with the statement, and rate 1 if you strongly disagree. There are no correct answers, simply indicate your feelings according to your perceptions about the company.

Section A 1.

1 2 3 4 5 Ebay uses the latest available technology on their site.

The Ebaya site is visually appealing. The layout of the site is clutter free and professionally designed.

The categories are easily understood and clear. Sufficient explanation of bidding process is provided.

Section B 2.

1 2 3 4 5 Ebay gives you bidding information on time. Ebay’s system is dependable. If you have problems in the bidding process, Ebay will be sympathetic.

User records and feedbacks are kept accurately. Services are provided on time.


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Section C 3.

1 2 3 4 5 Ebay does not tell you clearly what service it gives you. You do not receive prompt support (technical or otherwise) from Tradera.

Employees are not easy to approach. Emploeeys are too busy to respond to customer queries.

Section D 4.

1 2 3 4 5 You can trust the employees of Ebay. You feel safe in transactions through Ebay. Ebay will be co-operative if buyer or seller does not perform. It does not give enough information about users to boost confidence.

Section E 5.

1 2 3 4 5 Ebay does not offer enough individual customization at their site.

Ebay does not know what your needs are. Page 1 of 2

Next Page


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Ebay Survey User survey on ebay.com Page 2 of 2 The following set of questions relate to your feelings about Ebay.com. For each statement, please show the extent to which you believe Tradera has the feature described. Rate 5 if you strongly agree with the statement, and rate 1 if you strongly disagree. There are no correct answers, simply indicate your feelings according to your perceptions about the company.

Section F 6.

1 2 3 4 5 I trust a seller based on reviews. The number of seller reviews is very important to me.

Detailed product image is important to me. I need detailed product information for bidding.

Section G 7.

1 2 3 4 5 I feel insecure in using this technology. The service is too complicated to actually purchase. The possibilities of getting help in case of problems is important.

The fees charged is reasonable. Page 2 of 2

Submit Survey


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Appendix II

Tradera Online Questionnaire

Tradera Survey User survey on Tradera.com Page 1 of 2 The following set of questions relate to your feelings about Tradera.com. For each statement, please show the extent to which you believe Tradera has the feature described. Rate 5 if you strongly agree with the statement, and rate 1 if you strongly disagree. There are no correct answers, simply indicate your feelings according to your perceptions about the company.

Section A 1.

1 2 3 4 5 Tradera uses the latest available technology on their site.

The Tradera site is visually appealing. The layout of the site is clutter free and professionally designed.

The categories are easily understood and clear. Sufficient explanation is provided both in Swedish in English.

Section B 2.

1 2 3 4 5 Tradera gives you bidding information on time. Tradera’s system is dependable. If you have problems in the bidding process, Tradera will be sympathetic.

User records and feedbacks are kept accurately. Services are provided on time.


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Section C 3.

1 2 3 4 5 Tradera does not tell you clearly what service it gives you. You do not receive prompt support (technical or otherwise) from Tradera.

Employees are not easy to approach. Emploeeys are too busy to respond to customer queries.

Section D 4.

1 2 3 4 5 You can trust the employees of Tradera. You feel safe in transactions through Tradera. Tradera will be co-operative if buyer or seller does not perform. It does not give enough information about users to boost confidence.

Section E 5.

1 2 3 4 5 Tradera does not offer enough individual customization at their site.

Tradera does not know what your needs are. Page 1 of 2

Next Page


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Tradera Survey User survey on Tradera.com Page 2 of 2 The following set of questions relate to your feelings about Tradera.com. For each statement, please show the extent to which you believe Tradera has the feature described. Rate 5 if you strongly agree with the statement, and rate 1 if you strongly disagree. There are no correct answers, simply indicate your feelings according to your perceptions about the company.

Section F 6.

1 2 3 4 5 I trust a seller based on reviews. The number of seller reviews is very important to me.

Detailed product image is important to me. I need detailed product information for bidding.

Section G 7.

1 2 3 4 5 I feel insecure in using this technology. The service is too complicated to actually purchase. The possibilities of getting help in case of problems is important.

The fees charged is reasonable. Page 2 of 2

Submit Survey


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Appendix III Categorization by Attributes in Database: A Ebay Tangibility T1_EBAY_USES_LATEST_AVAILABLE_TECHNOLOGY_ON_THEIR_SITE T1_EBAYA_SITE_VISUALLY_APPEALING T1_LAYOUT_SITE_CLUTTER_FREE_AND_PROFESSIONALLY_DESIGNED T1_CATEGORIES_EASILY_UNDERSTOOD_AND_CLEAR T1_SUFFICIENT_EXPLANATION_BIDDING_PROCESS_PROVIDED Reliability R1_EBAY_GIVES_BIDDING_INFORMATION_ON_TIME R1_EBAY_S_SYSTEM_DEPENDABLE R1_IF_HAVE_PROBLEMS_IN_BIDDING_PROCESS_EBAY_WILL_BE_SYMPATHETIC R1_USER_RECORDS_AND_FEEDBACKS_KEPT_ACCURATELY R1_SERVICES_PROVIDED_ON_TIME Responsiveness R2_EBAY_DOES_NOT_TELL_CLEARLY_SERVICE_GIVES_YOU R2_NOT_RECEIVE_PROMPT_SUPPORT_TECHNICAL_OR_OTHERWISE_FROM_TRADERA R2_EMPLOYEES_NOT_EASY_TO_APPROACH R2_EMPLOEEYS_TOO_BUSY_TO_RESPOND_TO_CUSTOMER_QUERIES E1_EBAY_DOES_NOT_OFFER_ENOUGH_INDIVIDUAL_CUSTOMIZATION_AT_THEIR_SITE E1_EBAY_DOES_NOT_KNOW_NEEDS_ARE Credibility A1_CAN_TRUST_EMPLOYEES_EBAY A1_FEEL_SAFE_IN_TRANSACTIONS_THROUGH_EBAY A1_EBAY_WILL_BE_CO_OPERATIVE_IF_BUYER_OR_SELLER_DOES_NOT_PERFORM A1_DOES_NOT_GIVE_ENOUGH_INFORMATION_ABOUT_USERS_TO_BOOST_CONFIDENCE Security F1_I_TRUST_SELLER_BASED_ON_REVIEWS F1_NUMBER_SELLER_REVIEWS_VERY_IMPORTANT_TO_ME F1_DETAILED_PRODUCT_IMAGE_IMPORTANT_TO_ME F1_I_NEED_DETAILED_PRODUCT_INFORMATION_FOR_BIDDING G1_I_FEEL_INSECURE_IN_USING_THIS_TECHNOLOGY G1_SERVICE_TOO_COMPLICATED_TO_ACTUALLY_PURCHASE G1_POSSIBILITIES_GETTING_HELP_IN_CASE_PROBLEMS_IMPORTANT G1_FEES_CHARGED_REASONABLE


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Categorization by Attributes in Database: B Tradera Tangibility T1_TRADERA_USES_LATEST_AVAILABLE_TECHNOLOGY_ON_THEIR_SITE T1_TRADERA_SITE_VISUALLY_APPEALING T1_LAYOUT_SITE_CLUTTER_FREE_AND_PROFESSIONALLY_DESIGNED T1_CATEGORIES_EASILY_UNDERSTOOD_AND_CLEAR T1_SUFFICIENT_EXPLANATION_BIDDING_PROCESS_PROVIDED Reliability R1_TRADERA_GIVES_BIDDING_INFORMATION_ON_TIME R1_TRADERA_S_SYSTEM_DEPENDABLE R1_IF_HAVE_PROBLEMS_IN_BIDDING_PROCESS_TRADERA_WILL_BE_SYMPATHETIC R1_USER_RECORDS_AND_FEEDBACKS_KEPT_ACCURATELY R1_SERVICES_PROVIDED_ON_TIME Responsiveness R2_TRADERA_DOES_NOT_TELL_CLEARLY_SERVICE_GIVES_YOU R2_NOT_RECEIVE_PROMPT_SUPPORT_TECHNICAL_OR_OTHERWISE_FROM_TRADERA R2_EMPLOYEES_NOT_EASY_TO_APPROACH R2_EMPLOEEYS_TOO_BUSY_TO_RESPOND_TO_CUSTOMER_QUERIES E1_TRADERA_DOES_NOT_OFFER_ENOUGH_INDIVIDUAL_CUSTOMIZATION_AT_THEIR_SITE E1_TRADERA_DOES_NOT_KNOW_NEEDS_ARE Credibility A1_CAN_TRUST_EMPLOYEES_TRADERA A1_FEEL_SAFE_IN_TRANSACTIONS_THROUGH_TRADERA A1_TRADERA_WILL_BE_CO_OPERATIVE_IF_BUYER_OR_SELLER_DOES_NOT_PERFORM A1_DOES_NOT_GIVE_ENOUGH_INFORMATION_ABOUT_USERS_TO_BOOST_CONFIDENCE Security F1_I_TRUST_SELLER_BASED_ON_REVIEWS F1_NUMBER_SELLER_REVIEWS_VERY_IMPORTANT_TO_ME F1_DETAILED_PRODUCT_IMAGE_IMPORTANT_TO_ME F1_I_NEED_DETAILED_PRODUCT_INFORMATION_FOR_BIDDING G1_I_FEEL_INSECURE_IN_USING_THIS_TECHNOLOGY G1_SERVICE_TOO_COMPLICATED_TO_ACTUALLY_PURCHASE G1_POSSIBILITIES_GETTING_HELP_IN_CASE_PROBLEMS_IMPORTANT G1_FEES_CHARGED_REASONABLE


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Appendix IV A. Tangibility data set

Tradera Ebay 13 12 Anova: Single Factor 12 11 8 18 SUMMARY

15 15 Groups Count Sum Average Variance 17 18 Tradera 50 638 12,76 13,77796 10 16 Ebay 50 750 15 8,897959

16 17 11 18

11 12 ANOVA

16 12 Source of Variation SS df MS F P-value F crit

14 17 Between Groups 125,44 1 125,44 11,06372 0,00124 3,938112 15 17 Within Groups 1111,12 98 11,33796 10 16

16 11 Total 1236,56 99

12 18 15 16 12 13 12 15 16 18 12 18 14 16 5 17 19 14 8 15 13 6 11 17 9 13 9 16 5 16 9 16 17 17 18 20 11 13 13 17 9 10 11 15 10 14 8 17 17 11 13 16 15 19 12 15 5 17 18 15


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15 15 17 6 10 13 18 12 19 17 17 17

B. Reliability data set

Tradera Ebay 17 17 Anova: Single Factor 13 16 8 17 SUMMARY 15 14 Groups Count Sum Average Variance 18 17 Tradera 50 646 12,92 17,13633 7 15 Ebay 50 747 14,94 7,812653 16 16 14 16 15 13 ANOVA 16 13 Source of Variation SS df MS F P-value F crit 15 18 Between Groups 102,01 1 102,01 8,177489 0,005183 3,938112 12 12 Within Groups 1222,5 98 12,47449 9 16 13 10 Total 1324,51 99 14 18 0 17 14 13 10 15 18 17 13 17 9 13 5 15 18 11 10 15 18 7 13 19 13 17 13 13 5 18 9 15 13 17 18 19 11 13 17 15 9 10 10 15 10 14 11 17 17 9 13 15 13 17


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14 16 7 18 15 13 18 17 15 8 10 16 19 14 17 18 19 16

C. Responsiveness data set:

Tradera Ebay 10 8 Anova: Single Factor 16 6 18 7 SUMMARY 11 7 Groups Count Sum Average Variance 8 8 Tradera 50 517 10,34 14,2698 18 8 Ebay 50 345 6,9 9,030612 9 9 9 4 9 14 ANOVA 9 14 Source of Variation SS df MS F P-value F crit 8 7 Between Groups 295,84 1 295,84 25,39355 2,14E-06 3,938112 8 6 Within Groups 1141,72 98 11,6502 8 4 6 12 Total 1437,56 99 10 8 6 3 6 3 15 6 4 5 10 4 6 8 18 7 2 8 11 3 9 12 9 3 15 6 13 8 18 3 11 7 8 6 7 4 16 7 7 6 12 12 11 7 9 5 13 6


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11 9 11 7 10 8 10 3 18 6 12 6 11 8 7 16 8 4 8 8 10 4 8 5

D. Credibility data set:

Tradera Ebay 11 13 Anova: Single Factor 13 12 8 10 SUMMARY 10 12 Groups Count Sum Average Variance 12 13 Tradera 50 526 10,52 6,785306 10 11 Ebay 50 552 11,04 3,018776 14 13 9 14 11 12 ANOVA 12 10 Source of Variation SS df MS F P-value F crit 12 12 Between Groups 6,76 1 6,76 1,379017 0,243114 3,938112 11 12 Within Groups 480,4 98 4,902041 9 10 13 8 Total 487,16 99 13 14 12 12 12 13 10 12 12 13 8 13 6 11 8 12 13 10 2 11 12 7 11 11 7 11 12 10 7 12 8 12 12 7 13 12 6 10 13 10 8 8


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8 12 10 12 6 13 11 7 10 11 12 11 12 10 7 11 13 10 13 12 14 8 12 11 12 9 12 11 14 11

E. Security data set:

Tradera Ebay 21 20 Anova: Single Factor 24 16 25 19 SUMMARY 17 14 Groups Count Sum Average Variance 17 20 Tradera 50 951 19,02 9,734286 23 22 Ebay 50 968 19,36 5,745306 19 21 17 18 18 19 ANOVA 18 21 Source of Variation SS df MS F P-value F crit 18 18 Between Groups 2,89 1 2,89 0,373395 0,542574 3,938112 20 18 Within Groups 758,5 98 7,739796 20 18 19 22 Total 761,39 99 19 20 12 19 18 20 24 16 17 20 20 19 19 17 28 23 22 17 19 22 19 25 17 20 18 21 21 15 25 19 16 18 19 17 19 22


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20 19 15 17 18 24 16 20 16 18 13 19 16 16 19 20 19 20 16 20 26 21 19 20 18 19 19 26 21 19 18 17 16 20 18 17


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Appendix V A. ANOVA testing within population between the groups For Tradera

Tangibility Reliability Responsiveness Credibility Security Tradera 13 17 10 11 21 Anova: Single Factor 12 13 16 13 24 8 8 18 8 25 SUMMARY 15 15 11 10 17 Groups Count Sum Average Variance 17 18 8 12 17 Tangibility 50 638 12,76 13,77796 10 7 18 10 23 Reliability 50 646 12,92 17,13633 16 16 9 14 19 Responsiveness 50 517 10,34 14,2698 11 14 9 9 17 Credibility 49 512 10,44898 6,669218 11 15 9 11 18 Security 50 951 19,02 9,734286 16 16 9 12 18 14 15 8 12 18 15 12 8 11 20 ANOVA

10 9 8 9 20 Source of Variation SS df MS F P-value

16 13 6 13 19 Between Groups 2484,95 4 621,2375 50,34068

7,3773E-31

12 14 10 13 19 Within Groups 3011,122 244 12,34067 15 0 6 12 12 12 14 6 12 18 Total 5496,072 248 12 10 15 10 24 16 18 4 12 17 12 13 10 8 20 14 9 6 6 19 5 5 18 8 28 19 18 2 13 22 8 10 11 2 19 13 18 9 12 19 11 13 9 11 17 9 13 15 7 18 9 13 13 12 21 5 5 18 7 25 9 9 11 8 16 17 13 8 12 19 18 18 7 13 19 11 11 16 6 20 13 17 7 13 15 9 9 12 8 18 11 10 11 8 16 10 10 9 10 16 8 11 13 6 13 17 17 11 11 16 13 13 11 10 19 15 13 10 12 19 12 14 10 12 16 5 7 18 7 26


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18 15 12 13 19 15 18 11 13 18 17 15 7 14 19 10 10 8 12 21 18 19 8 12 18 19 17 10 12 16 17 19 8 18

B. ANOVA testing within population between the groups For Ebay

Tangibility Reliability Responsiveness Credibility Security Ebay 12 17 8 13 20 Anova: Single Factor 11 16 6 12 16 18 17 7 10 19 SUMMARY 15 14 7 12 14 Groups Count Sum Average Variance 18 17 8 13 20 Tangibility 50 750 15 8,897959 16 15 8 11 22 Reliability 50 747 14,94 7,812653 17 16 9 13 21 Responsiveness 50 345 6,9 9,030612 18 16 4 14 18 Credibility 50 552 11,04 3,018776 12 13 14 12 19 Security 50 968 19,36 5,745306 12 13 14 10 21 17 18 7 12 18 17 12 6 12 18 ANOVA

16 16 4 10 18 Source of Variation SS df MS F

P-value F crit

11 10 12 8 22 Between Groups 4413,064 4 1103,266 159,869

4,53E-67 2,408

18 18 8 14 20 Within Groups 1690,76 245 6,901061 16 17 3 12 19 13 13 3 13 20 Total 6103,824 249 15 15 6 12 16 18 17 5 13 20 18 17 4 13 19 16 13 8 11 17 17 15 7 12 23 14 11 8 10 17 15 15 3 11 22 6 7 12 7 25 17 19 3 11 20 13 17 6 11 21 16 13 8 10 15 16 18 3 12 19 16 15 7 12 18 17 17 6 7 17 20 19 4 12 22 13 13 7 10 19 17 15 6 10 17 10 10 12 8 24 15 15 7 12 20 14 14 5 12 18 17 17 6 13 19


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