Relationships between process quality, outcome quality ... - CiteSeerX

The Service Industries Journal Vol. 00, No. 0, Month 2009, 1 –12

Relationships between process quality, outcome quality, satisfaction, and behavioural intentions for online travel agencies – evidence from Taiwan Ching-Fu Chen and Ya-Ling Kao Department of Transportation and Communication Management Science, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan, Republic of China (Received 5 February 2009; final version received 17 July 2009) Instead of using the SERVQUAL-type scale, this study conceptualizes e-service quality by two dimensions – process quality and outcome quality – and explores the relationships between process quality, outcome quality, satisfaction, and behavioural intentions in the context of online travel agencies. Using an empirical survey consisting of 240 Taiwanese respondents who have purchased online travel agents’ products, the results reveal that process quality and outcome quality have significantly direct and positive effects on satisfaction. In addition, there exists a significant influence of satisfaction on behavioural intentions. While supporting the quality– satisfaction–behavioural intentions relationship overall, this study specifically provides more insights into the construction and effects of e-service quality.

CE: VLD

QA: KJK

Coll: RP

Keywords: process quality; outcome quality; satisfaction; behavioural intentions; online travel agency

Introduction According to the World Tourism Organization in 2000, tourism has become the primary source of international trade for many countries (Tourism Bureau, 2002), with tourist receipts accounting for over 8% of global foreign exchange income. With the development of the Internet, online travel agencies facilitating e-commerce have emerged as a powerful and efficient intermediary within the travel industry. Shopping via the Internet affords consumers the opportunity to experience convenience through reduced shopping costs vis-a`-vis physical effort. Online travel agencies have thus become an alternate powerful distribution channel in the travel industry, and therefore it is very important that they, like any other retailer, learn how to promote customer loyalty and raise customer retention rates. Service quality and customer satisfaction have been widely confirmed as influential antecedents of customer loyalty (Cristobal, Flavian, & Guinaliu, 2007; Cronin, Brady, & Hult, 2000; Imrie, Durden, & Cadogan, 2000; Petrick & Backman, 2002). Providing high quality service is recognized as a critical factor in the success of travel and tourism industry firms (Caro & Garcia, 2008). Previous studies on online service quality have primarily focused on the interaction of the customer and the website and contributed various Internet service quality or e-service quality scales to measure website interactivity, such as WebQual (Barnes & Vidgen, 2000, 2002; Lociacono, 2000; Lociacono, Watson, & Goodhue, 2002), SITEQUAL (Yoo & Donthu, 2001),

Corresponding author. Email: [email protected]

ISSN 0264-2069 print/ISSN 1743-9507 online # 2009 Taylor & Francis DOI: 10.1080/02642060903191108 http://www.informaworld.com

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C.-F. Chen and Y.-L. Kao

and e-SERVQUAL (Zeithaml, Parasuraman, & Malhotra, 2002). However, the scope of e-service is broader than just looking at how a consumer interacts with a website. E-service quality also relates to customer perceptions of the outcome of the service, along with recovery perceptions if a problem should occur (Collier & Bienstock, 2006). This study aims to explore the relationships between service quality, satisfaction, and behavioural intentions in the context of online travel agents. Additionally, focusing on e-service features, e-service quality is specifically conceptualized to encompass both dimensions of the interactive process (order placement) and outcome (order receipt). By doing this, we can examine how e-service quality affects customer satisfaction and behavioural intentions, with respect to different components (i.e. the process and outcome). Theoretical background and hypotheses Internet customer loyalty is difficult and costly to maintain, and requires a level of service that satisfies the customer (Cristobal et al., 2007). When modelling consumers’ behavioural intentions in the e-service context, the key variables normally considered include service quality, satisfaction, and behavioural intentions. Higher levels of service quality produce higher levels of customer satisfaction, which in turn lead to higher levels of customer patronage and sales revenue (Chow, Lau, Lo, Sha, & Yun, 2007). However, most studies that conceptualize e-service quality focus on the interaction of the customer and the website. Yoo and Donthu (2001) developed the SITEQUAL scale with four dimensions to measure the perceived quality of an online store. In addition, Barnes and Vidgen (2000, 2002) proposed a WebQual scale with five dimensions to measure e-commerce quality, while Lociacono (2000) and Lociacono et al. (2002) also developed the WebQualTM scale with 12 dimensions to measure website quality. Cristobal et al. (2007) and Collier and Bienstock (2006) provided a summary review of the main studies on e-service quality measurement. Due to the unique characteristics of online services, such as server problems, outages for backing up information and connectivity issues, it is more appropriate to differentiate e-service quality into two subsequent dimensions, the interactive process and outcome (Collier & Bienstock, 2006), which are both involved in this study. Note that apart from the interactive process and outcome, Collier and Bienstock (2006) also included a recovery dimension to capture the manner of service failures in their e-service quality conceptualization. However, considering that service failures do not happen to all customers, we did not include this dimension in the current study in order to avoid respondents’ bias. A multidimensional and hierarchical service model for the travel agency industry was also provided by Caro and Garcia (2008) based on concepts contained in Brady and Cronin (2001). In their service quality model, service quality is conceptualized by three dimensions: personal interaction, physical environment, and outcome. The personal interaction (process) dimension includes three factors (conduct, expertise, and problem solving), the physical environment dimension includes two factors (equipment and ambient conditions), and the outcome dimension includes two factors relating to waiting time and valence. Since the context in which an online travel agency operates is different from that of a physical travel agency, the physical environment dimension is not considered in constructing the term of e-service quality in this study. Therefore, this study conceptualizes the process quality to measure the quality evaluation based on the interactive process taking place online when the customer is researching and then purchasing the service, while the outcome quality is related to the how the service is delivered. This section first discusses the definitions of both quality constructs and subsequently hypothesizes the relationships between the variables used in the current study.

The Service Industries Journal

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Process quality The process quality dimension is defined as a customer’s evaluation of his or her interaction with a website. Several researchers have indicated the importance of this factor in the delivery of services, and have identified it as having the most significant effect on service quality perception in both offline and online services (Caro & Garcia, 2008; Collier & Bienstock, 2006). According to Collier and Bienstock (2006), the process quality is represented by five dimensions: privacy, design, information accuracy, ease of use, and functionality. Privacy is related to companies not sharing information with third parties, unless the customer gives permission, as well as the security of sensitive information between the customer and the company. Design pertains to the visual appearance and audible elements of a site which includes factors such as the use of colour, animation, video, pictures, text, format, and sound. Information accuracy refers to the information that is presented about the products and services being offered. Ease of use represents the ability of a customer to find information or enact a transaction with the least amount of effort. In addition, ease of use includes issues such as site navigation and an effective search engine, the ability to easily change or cancel an order, and the ability to inform customers of missing information in forms they have to complete. Functionality is when a website operates or executes the commands of the customer, and this also refers to the ability to appeal to a universal audience by using multilingual translations and functions that allow access for the disabled. Outcome quality Outcome quality is what a customer is left with at the end of the transaction, and it plays an incredibly influential role in the evaluation of overall service quality. The outcome of a service is the ultimate reason why a customer goes to a website. The outcome quality is conceptualized as the fulfilment dimension in the E-S-QUAL model, proposed by Parasuraman, Zeithaml and Malhotra (2005) to capture the outcome of the service experience. However, Collier and Bienstock (2006) argued that a uni-dimensional conceptualization of outcome quality is inappropriate, and instead proposed a tri-dimensional model that includes order timeliness, order accuracy, and order condition. Order timeliness is receiving the service within an expected amount of time. Order accuracy is processing the online order to the exact specifications of the customer, which includes the place of receipt, quantity, and agreed price. Order condition refers to the product being free from damage and of the promised quality. However, due to the special characteristics of travel services, such as intangibility and perishability, the dimensions of order timeliness and order condition are not applicable in an online travel agency context, and are hence not taken into account in this study. Relationships between the variables Past studies have established the antecedent, mediating, and consequent relationships among customer perceptions of service quality, customer satisfaction, and post-purchase behavioural intentions (Chen, 2008). It has been widely accepted that service quality directly influences satisfaction (Baker & Crompton, 2000), and satisfaction is a direct antecedent of behavioural intentions (Cronin et al., 2000; Dodds, Monroe, & Grewal, 1991; Petrick & Backman, 2002). Since service quality is conceptualized as process quality and outcome quality in this study, it is reasonable to hypothesize that process quality directly and positively influences outcome quality, and both process quality and outcome quality,

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Figure 1. The conceptual model.

working as the integrated service quality in past studies, directly and positively influence satisfaction. Based on the review of the aforementioned past studies, this study proposes the conceptual model shown in Figure 1, and the hypotheses are stated as follows: H1: E-service process quality has a positive influence on e-service outcome quality. H2: E-service process quality has a positive influence on satisfaction. H3: E-service process quality has a positive influence on behavioural intentions. H4: E-service outcome quality has a positive influence on satisfaction. H5: E-service outcome quality has a positive influence on behavioural intentions. H6: Satisfaction has a positive influence on behavioural intentions.

Methodology The purpose of this study is to examine a relationship model incorporating four dimensions, namely, process quality, outcome quality, satisfaction, and behavioural intentions, in the context of online travel agents in Taiwan. This section provides an overview of the research methods that are used to answer the proposed research hypotheses. The questionnaire design is described first, followed by an explanation of the data collection method and a profile of the respondents, as well as the data analysis approach. Measurement The questionnaire was designed as a survey instrument, including all constructs of the proposed model, to investigate the hypotheses of interest. The questions in the questionnaire were based on a review of the literature and specific travel industry characteristics. A pre-test was carried out with randomly selected postgraduate students at National Kung University in Southern Taiwan. Based on a feedback from a pilot sample of 30 respondents, the survey instrument was revised and finalized to improve clarity and readability, and subsequently the content validity was deemed to be adequate. The survey questionnaire consists of five parts: process quality, outcome quality, satisfaction, behavioural intentions, and respondent information. Part 1 deals with the measurement of process quality using a 12-item scale underlying four dimensions, including design (four items), information accuracy (three items), ease of use (two items), and functionality (three items), adapted from Zeithaml et al. (2002) to fit the online travel agent context. The items for each dimension are shown in Table 1. For example, the item ‘It is easy to get anywhere on this e-retailer’s website’ was modified to ‘It is easy to get anywhere on this online travel agent’s website’. Part 2 deals with the measurement of outcome quality using a single-item scale to measure the overall evaluation of an online travel agent’s performance, with special respect to core service delivery. This item is ‘My orders from this online travel agent rarely contain the wrong items’, which was adapted from Collier and Bienstock (2006).


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Table 1. CFA of process quality. Item reliability Factor/items PQ1: Functionality This online travel agent provides various payment options This online travel agent provides secure payment systems This online travel agent provides a confirmation of items ordered This online travel agent’s website has a running total of purchases as the order progresses PQ2: Ease of use It is easy to get anywhere on this online travel agent’s website I do not get lost on this online travel agent’s website This online travel agent provides a site map PQ3: Design This online travel agent’s website design is innovative I am able to see the graphics clearly on this online travel agent’s website PQ4: Information accuracy Prices are shown with the items on the screen This online travel agent provides accurate information This online travel agent updates information immediately

Factor loading

Measure error

t-Value

0.67

0.55

11.32

0.68

0.54

11.48

0.90

0.19

17.11

0.86

0.26

15.89

0.79

0.38

13.15

0.83

0.31

14.03

0.60

0.64

9.46

0.61

0.63

8.55

0.77

0.41

10.18

0.69

0.53

10.88

0.77

0.41

12.54

0.69

0.53

10.93

Construct reliability

Average variance extracted

0.863

0.615

0.787

0.557

0.647

0.481

0.759

0.512

p , 0.05.

Part 3 deals with the measurement of satisfaction using two overall satisfaction items from Tax and Brown (1998). These two items are ‘In general I am happy with the service experience’ and ‘In general I am satisfied with the service this online travel agent provided’. Part 4 deals with the measurement of behavioural intentions using two items – namely, ‘I am willing to recommend this online travel agent to my friends’ and ‘It is likely that I will re-purchase from this online travel agent in the future’ – adapted from Tax and Brown (1998) and Mathwick (2002). Respondents were asked to indicate their level agreement with each item in the first four sections on a five-point Likert scale, anchored by ‘strongly disagree/dissatisfy (¼1)’ to ‘strongly agree/satisfy (¼5)’. Finally, Part 5 collects the respondents’ demographic information and online shopping behavioural information, using 13 items, including age, education level, martial status, salary, use tenure, purchase experience, and so on, via a categorical scale.

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Data collection The questionnaire survey was conducted at the Taipei International Travel Fair in Taiwan during one week in December 2007. Before answering the questionnaire, the potential respondents were approached by trained interviewers at the exit of the Travel Fair, in order to ensure their past shopping experience with online travel agents (namely at least once in the past 3 months) and subsequently they were asked to take part in the survey after a brief introduction of its aims. The potential respondents were chosen based on a convenience sampling method, as this allows a large number of respondents to be interviewed in a relatively short time (Hair, Bush, & Ortinau, 2003), and most visitors to the Travel Fair visitors were in a hurry to leave the event. However, we also recognize the disadvantage of such sampling, in that the raw data and results obtained from this method are not generalized to the defined target population with any measure of precision (Hair et al., 2003). A total of 300 questionnaires were distributed. After deleting incomplete responses, 240 usable samples were obtained, resulting in a response rate of 80.0%. This relatively high response rate was based on the aforementioned premise, that is, we asked respondents’ willingness to answer the questionnaire before the questionnaire survey took place. In other words, only those who are willing to take part in this study are viewed as potential respondents. Females made up the majority of the respondents (57.5%), along with those aged 20–30 (67.1%), and those holding a bachelor’s degree or higher (66.7%). Most of the respondents (84.6%) were single and around half had a monthly income of NT$20,001– 50,000 (or US$625–1562). Most of the respondents (95%) had at least five related purchase experiences over the previous year. The three most frequently purchased products through an online travel agent were accommodation (26.3%), international airline tickets (17.5%), and flight and hotel packages (16.2%).

Data analysis In line with the two-step approach proposed by Gerbing and Anderson (1988), a measurement model was tested before testing the structural model. Confirmatory factor analysis (CFA) and structural equation modelling analysis were used to check construct validity and the goodness-of-fit indices for the measurement and structural models and further examine the relationships among constructs under investigation.

Results CFA of process quality This study implements a CFA using LISREL 8.30 (Joreskog & Sorbom, 1993) to assess the uni-dimensionality of each item to its first-order dimension of process quality scale. Adapted from Zeithaml, Parasuraman and Malhotra (2000), we take a four-dimension scale of process quality, including design (four items), information accuracy (three items), ease of use (two items), and functionality (three items). Table 1 reports the CFA results of the process quality scale. To examine the scale convergent validity, high loadings on a factor should be expected. According to Hair, Anderson, Tatham and Black (1998), the item should be removed if its associated standardized factor loading is less than 0.5. As seen in Table 1, all standardized factor loadings are larger than 0.5 and significant with a t-value at 5%, indicating the convergent validity of process quality scale. These items are then summed up to form the first-order formative indicators of


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the functionality (PQ1), ease of use (PQ2), design (PQ3), and information accuracy (PQ4) underlying process quality scale. Measurement model Once the summated indicators of process quality are created, another CFA is used to develop and test a measurement model for the three constructs, namely, process quality, satisfaction, and behavioural intentions. Note that the single-item scale of outcome quality is not included in the measurement. Table 2 shows CFA results of the measurement model. According to Hair et al. (1998), the convergent validity of the CFA results should be supported by item reliability, construct reliability, and average variance extracted. Item reliability indicates the amount of variance in an item due to the underlying construct, while t-values for all the standardized factor loadings of items are found to be significant (p , 0.05) and all loadings are larger than 0.5, assuring item reliability (Table 2). Construct reliability estimates range from 0.76 to 0.89, satisfying the threshold value for acceptable reliability of 0.7 as suggested by Hair et al. (1998). The variance-extracted measure reflects the overall amount of variance in the indicators accounted for by the latent construct. The average variance extracted lies between 0.45 and 0.80. Compared with the cut-off value of 0.5, all constructs are generally satisfactory, except for process quality, which is slightly lower. These results indicate that the measurement items have moderate to high reliability and validity. Structural model Having established a reliable and valid measurement model, a structural model is performed to test the predictive relationships between constructs of the proposed conceptual model. The simultaneous maximum-likelihood-estimation procedures are used to examine the hypothesized relationships among process quality, outcome quality, satisfaction, and behavioural intentions. Figure 2 reports the goodness-of-fit indices of the final estimated structural model. The chi-square statistic (x2 ¼ 35.18, df ¼ 22) is significant (p ¼ 0.037), however, the ratio of the chi-square value to degrees of freedom (x2/df ¼ 1.6) is less than 3. Other fit indices, including AGFI (0.94), CFI (0.99), NFI Table 2. Convergent validity. Item reliability Factor loading

Measure error

t-Value

PQ1 PQ2 PQ3 PQ4

0.71 0.63 0.71 0.62

0.50 0.60 0.50 0.62

SAT1 SAT2

0.91 0.88 0.81 0.78

Constructs

Items

Process quality

Satisfaction

Behavioural intentions BI1 BI2

Construct reliability

Average variance extracted

11.35 9.82 11.32 9.55

0.76

0.45

0.17 0.23

17.51 16.52

0.89

0.80

0.34 0.40

13.87 13.13

0.77

0.63

Note: x 2 ¼ 35.18 (p ¼ 0.037), x 2 /df ¼ 1.6, RESEA ¼ 0.05, GFI ¼ 0.97, AGFI ¼ 0.94, NFI ¼ 0.98, CFI ¼ 0.99. p , 0.05.

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Figure 2. Estimated model. Notes: The values in the parentheses are t-values. Solid lines denote significance at the 5% level, while dashed lines denote insignificance.

(0.98), and RMSEA (0.05), indicate that the structural model has a reasonable explanation of the observed covariance among the constructs of interest. Regarding the hypothesis tests, as shown in Table 3, four of the six hypothesized relationships are supported in the estimated structural model, except for H3: process quality ! behavioural intentions, and H5: outcome quality ! behavioural intentions. As shown in Figure 2, process quality has significant positive effects on both outcome quality (g1 ¼ 0.51, t-value ¼ 7.46) and satisfaction (g2 ¼ 0.70, t-value ¼ 8.96). Hence, H1 and H2 are supported. Outcome quality is found to have a significant positive effect only on satisfaction (g4 ¼ 0.13, t-value ¼ 2.00), but not on behavioural intentions, indicating that H4 is supported. Finally, satisfaction is also found to have a significant positive effect on behavioural intentions (g6 ¼ 0.80, t-value ¼ 7.00), and thus H6 is supported. Table 4 reports the direct, indirect, and total effects of independent variables on the behavioural intentions. The total effect of individual variables on the behavioural intentions is calculated from the sum of direct and indirect effects. The total effect of satisfaction on behavioural intentions is 0.80, equalling its direct effect due to no indirect effect. The total effect of process quality on behavioural intentions is 0.69, consisting of a direct effect of 0.03 and an indirect effect of 0.66. Finally, the total effect of outcome quality on behavioural intentions is 0.20, including a direct effect of 0.09 and an indirect effect of 0.11. These results reveal that satisfaction is the most influential determinant of behavioural intentions, as it has the largest total effect. Additionally, both process quality and outcome quality show their effects indirectly through the mediation of satisfaction. The effect of process quality on behavioural intentions is greater than

Table 3. Hypothesis tests. Path H1: E-service process quality ! E-service outcome quality H2: E-service process quality ! satisfaction H3: E-service process quality ! behavioural intentions H4: E-service outcome quality ! satisfaction H5: E-service outcome quality ! behavioural intentions H6: Satisfaction ! behavioural intentions

Estimate

t-Value

Hypothesis test

0.51 0.70 0.03 0.13 0.09 0.80

7.46 8.96 0.22 2.00 1.48 7.00

Supported Supported Rejected Supported Rejected Supported

The Service Industries Journal Table 4.

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Direct, indirect, and total effects of behavioural intentions.

Path

Effect

Estimates

t-Value

Process quality ! behavioural intentions

Indirect effect Direct effect Total effect

0.66 0.03 0.69

6.23 – 8.62

Outcome quality ! behavioural intentions


0.11 0.09 0.20

1.93 – 2.59

Satisfaction ! behavioural intentions


– 0.80 0.80

– 7.00 7.00

p , 0.05.

the outcome quality, indicating the importance of process quality with respect to quality in the process of creating behavioural intentions. To sum up, given the setting of online travel agents in Taiwan, the results overall confirm the quality – satisfaction – behavioural intentions relationship model that has been widely confirmed by previous studies. In addition, the influence of quality on behavioural intentions is mediated by satisfaction. However, with regard to the main purpose of this study, service quality is differentiated into process quality and outcome quality. Interestingly, both process quality and outcome quality appear to have positive direct influences on satisfaction, as expected, while no direct effects on behavioural intentions are discovered. In addition, the causal relationship between process quality and outcome quality is also supported in this study, i.e. process quality is an antecedent of outcome quality. These findings can only be explored by conceptualizing service quality into sub-scales of quality based on the process-outcome specification.

Conclusions This study proposes and empirically tests a relationship model for online travel agents based upon service quality, satisfaction, and behavioural intentions. Rather than employing a widely used service quality scale, such as SERVQUAL, this study specifically differentiates two quality components of e-travel services: process quality and outcome quality. Structural relationship analysis confirms the service quality – satisfaction – behavioural intentions model in general, and reveals the effects of both process quality and outcome quality on satisfaction and behavioural intentions in particular. First, the process quality – which encompasses the four dimensions of ease of use, design, information accuracy, and functionality – shows its positive influences on outcome quality, satisfaction, and eventually on behavioural intentions via the mediation of satisfaction. It implies the importance of process quality to build customer loyalty as represented by positive behavioural intentions. Although intangible, process quality, when customers have their first contact with the e-travel agent, serves much the same role as that of front-line employees in tangible, face-to-face service contexts. The process quality customers’ evaluation is mainly based on the interactivity between customers and an e-travel agent’s website. Consequently, to improve this, e-travel agent marketers should focus on the four dimensions outlined above to ensure that service items underlying each of these meet the customers’ needs. Once good process quality is perceived by customers, positive perceptions towards

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outcome quality, satisfaction, and behavioural intentions are likely to occur. In addition, the role of satisfaction is also crucial in fostering in customer loyalty. Both process quality and outcome quality have positive effects on behavioural intentions. However, their effects are indirect and mediated by satisfaction, rather than direct. This implies that the perceived performance of an e-travel agent must match or even surpass the customers’ expectations in order to inspire satisfaction and loyalty. In other words, while providing customers with efficient and satisfactory service is important, high process quality is essential if customer loyalty is to be ensured. If high service quality does not lead to customer satisfaction, then customer loyalty is still uncertain. Moreover, service recovery is extremely important in relation to online service quality, because consumers are just one click away from a competitor’s website (Collier & Bienstock, 2006). However, this study has not examined this issue, and future research should thus consider recovery and examine its relationship between service quality, satisfaction, and behavioural intentions in the e-travel agent context. In addition, future research should attempt to replicate this study in different cultural settings in order to generalize the conceptual framework of e-service quality that it proposes.

Acknowledgements The authors would like to thank Shao-Yuan Lee and Wen-Chieh Cheng for their assistance in this study. The usual disclaimer applies. References Baker, D.A., & Crompton, J.L. (2000). Quality, satisfaction and behavioural intentions. Annals of Tourism Research, 27(3), 785–804. Barnes, S.J., & Vidgen, R.T. (2000). WebQual: An Exploration of Web Site Quality. Paper presented at the meeting of the Eighth European Conference on Information Systems, Vienna, Austria. Barnes, S.J., & Vidgen, R.T. (2002). An integrative approach to assessment of e-commerce quality. Journal of Electronic Commerce Research, 3(3), 114–127. Brady, M.K., & Cronin, J.J. (2001). Some new thoughts on conceptualizing perceived service quality: A hierarchical approach. Journal of Marketing, 65(3), 34–49. Caro, L.M., & Garcia, J.A.M. (2008). Developing a multidimentional and hierarchical service quality model for the travel agency industry. Tourism Management, 29(4), 706–720. Chen, C.F. (2008). Investigating structural relationships between service quality, perceived value, satisfaction, and behavioural intentions for air passengers: Evidence from Taiwan. Transportation Research Part A, 42(4), 709–717. Chow, I.H.-S., Lau, V.P., Lo, T.W.-C., Sha, Z., & Yun, H. (2007). Service quality in restaurant operations in China: Decision- or experiential-oriented perspectives. International Journal of Hospitality Management, 26(3), 698–710. Collier, J.E., & Bienstock, C.C. (2006). Measuring service quality in e-retailing. Journal of Service Research, 8(3), 260–275. Cristobal, E., Flavian, C., & Guinaliu, M. (2007). Perceived e-service quality (PeSQ): Measurement validation and effects on consumer satisfaction and web site loyalty. Managing Service Quality, 17(3), 317–340. Cronin, J.J., Brady, M.K., & Hult, G.T.M. (2000). Assessing the effects of quality, value and customer satisfaction on consumer behavioural intentions in service environments. Journal of Retailing, 76(2), 193–218. Dodds, W.B., Monroe, K.B., & Grewal, D. (1991). Effects of price, brand and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307–319. Gerbing, D.W., & Anderson, J.C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186–192. Hair, J.F.J., Anderson, R.E., Tatham, R.L., & Black, W.C. (1998). Multivariate data analysis with readings (5th ed.). Englewood Cliffs, NJ: Prentice Hall.


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Hair, J.F., Bush, R.P., & Ortinau, D.J. (2003). Marketing research: Within a changing information environment (2nd ed.). Boston: McGraw-Hill. Imrie, B.C., Durden, G., & Cadogan, J.W. (2000). Towards a conceptualization of service quality in the global market arena. Advances in International Marketing, 10(1), 143–162. Joreskog, K.G., & Sorbom, D. (1993). LISREL VIII: User’s reference guide. Chicago: Scientific Software International. TM Lociacono, E.T. (2000). WebQual : A web site quality Instrument. Unpublished doctoral dissertation, University of Georgia, Athens, GA. TM Lociacono, E.T., Watson, R.T., & Goodhue, D.L. (2002). WebQual : A Measure of Web Site Quality. Paper presented at the meeting of the American Marketing Association on the Marketing Theory and Applications, Chicago. Mathwick, C. (2002). Understanding the online customer: A typology of online relational norms and behavior. Journal of Interactive Marketing, 16(1), 40–55. Parasuraman, A., Zeithaml, V.A., & Malhotra, A. (2005). E-S-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. Petrick, J.F., & Backman, S.J. (2002). An examination of the construct of perceived value for the prediction of golf travelers’ intentions to revisit. Journal of Travel Research, 41(1), 38–45. Tax, S.S., & Brown, S.W. (1998). Recovering and learning from service failure. Sloan Management Review, 40(1), 75–88. Tourism Bureau. (2002). White paper tourism policy. Taipei, Taiwan: Ministry of Transportation and Communications. Yoo, B., & Donthu, N. (2001). Developing a scale to measure the perceived quality of an internet shopping site (Sitequal). Quarterly Journal of Electronic Commerce, 2(1), 31–46. Zeithaml, V.A., Parasuraman, A., & Malhotra, A. (2000). E-service quality: Definition, dimensions, and conceptual model (Working Paper). Cambridge, MA: Marketing Science Institute. Zeithaml, V.A., Parasuraman, A., & Malhotra, A. (2002). Service delivery through web sites: A critical review of extant knowledge. Journal of Academy of Marketing Science, 30(4), 362–375.

Appendix

Table A1.

The descriptive statistics of scale items.

Dimension Process quality

Scale items Functionality This online travel agent provides various payment options This online travel agent provides secure payment systems The online travel agent provides a confirmation of items ordered This online travel agent’s website has a running total of purchases as the order progresses Ease of use It is easy to get anywhere on this online travel agent’s website I do not get lost on this online travel agent’s website This online travel agent provides a site map Design This online travel agent’s website design is innovative I am able to see the graphics clearly on this online travel agent’s website Information accuracy Prices are shown with the items on the screen This online travel agent provides accurate information This online travel agent updates information immediately

Mean

S.D.

3.97 0.64 3.855 3.76 074 3.87 0.67 3.82

0.72

3.92

0.60 3.787

3.76 3.68

0.63 0.68

3.37 3.57

0.67 3.47 0.75

3.74 3.73 3.70

0.73 3.723 0.65 0.72 (Continued)

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Table A1. Continued. Dimension

Scale items

Outcome quality My orders from this online travel agent rarely contain the wrong items Satisfaction In general I (am/was) happy with the service experience I was satisfied with the service this online travel agent provided Behavioural I will recommend this online travel agent to my friends intentions I intend to purchase from this online travel agent in the future

Mean

S.D.

3.72

0.73 3.72

3.67 3.63

0.62 3.65 0.68

3.63 3.71

0.67 3.67 0.68