Examining a hybrid model for e-satisfaction and e-loyalty to e-ticketing on airline websites

Journal of Air Transport Management 37 (2014) 36e44

Contents lists available at ScienceDirect

Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman

Examining a hybrid model for e-satisfaction and e-loyalty to e-ticketing on airline websites Naeimeh Elkhani a, *, Sheida Soltani a, Mir Hadi Moazen Jamshidi b,1 a b

Department of Information System, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310 Johor Bahru, Johor, Malaysia Department of Management, Payame Noor University, Tehran

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 13 March 2014

In the air transportation industry, web-based marketing has already been widely applied to service frequent customers as well as to attract new ones. The importance of attracting new customers and keeping existing ones loyal to e-ticketing on airline websites is crucial. Accordingly, this study proposes an integrated model for evaluating the effectiveness of airlines’ websites from a customer point of view. This model is based on the three perspectives of the marketing mix 4Ps, E-SERVQUAL and Expectancy Disconfirmation Theory. E-marketing and E-SERVQUAL features are divided into three dimensions, specifically information, system and service disconfirmations. The methodology was applied based on Structural Equation Modeling (SEM) and was administered to online customers who carry out e-ticketing via an airline website. The results show that customer disconfirmations have a positive significant impact on overall customer e-satisfaction. The significance of this relationship was more considerable in the service dimension of e-marketing, as well as the system dimensions of E-SERVQUAL and e-marketing. Moreover, overall e-satisfaction was found to mediate the relationship between customer disconfirmations and consumer e-loyalty. Ó 2014 Published by Elsevier Ltd.

Keywords: Airline website quality E-SERVQUAL Marketing mix 4Ps Expectancy Disconfirmation Theory E-satisfaction E-loyalty

1. Introduction Information Communication Technologies (ICTs) have revolutionized the whole business world. They can provide powerful strategic and tactical tools for organizations which, if properly applied and used, lead to great advantages in promoting and strengthening their competitiveness. The airline industry in particular has fostered a dependency on ICTs since a number of airline functions rely greatly on them. The popularity of the Internet and e-commerce technologies have provided a platform for air transportation companies to bypass intermediaries such as travel agents and transact with their customers directly (Nyshadham, 2000; Tsai et al., 2005). One of the most valid solutions for attracting more customers and enhancing business values is to sell low-fare air tickets and facilitate boarding processes, such as eticketing and online check-in, through an airlines’ own website (Wei and Ozok, 2005). Airlines are increasingly resorting to the Web, not only as a useful tool by which to obtain information but

* Corresponding author. Tel.: þ60 1114247210. E-mail addresses: [email protected] (N. Elkhani), Iranjamshidi.hadi@ gmail.com (M.H.M. Jamshidi). 1 þ98 91412246209. 0969-6997/$ e see front matter Ó 2014 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.jairtraman.2014.01.006

also for the provision of electronic/paperless tickets, as well as transparent and clear pricing led by both proactive and reactive yield management, financial incentives for self-booking online, online promotions, powerful customer relationship management systems and online advertising strategies (Yu, 2008). A direct relationship with customers causes airlines to pay more attention to determine what customers want and do not want (Chen and Chang, 2005). Accordingly, this allows airlines to gain a closer relationship with their customers. In this regard, companies have long viewed striving for customer satisfaction as an important goal. The extant literature substantiates the positive effects of customer satisfaction on desirable outcomes. Most importantly, satisfaction is seen as being the major determinant of subsequent loyalty (Anderson and Sullivan, 1993; Szymanski and Henard, 2001; Fassnacht and Köse, 2007; Blattberg et al., 2009). Therefore, as online retailing is increasing in importance, analysis is required to understand the drivers of satisfaction and loyalty within website equality. Evaluating website quality in previous research has been applied based on the three approaches, namely information systems (IS), marketing and a mixture of these two (Chiou et al., 2008; Chiou et al., 2010). The E-SERVQUAL model which was proposed by Parasuraman et al. (2005) is the most well-known model with the IS approach for analyzing website quality. In the context of airline

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44

websites, prior studies (e.g., Lee and Wu, 2011; Llach et al., 2012; Forgas et al., 2012) have used the adapted criteria of original ESERVQUAL for evaluating customer satisfaction and loyalty. Moreover, in recent years, a marketing approach, namely the WebMarketing Mix (WMM) has been used to evaluate website quality. With this approach, Chiou et al. (2010) proposed a strategic evaluation framework for website quality including information, agreement and settlement phases. Here the criteria for evaluating website quality are categorized into four factors, product, promotion, price and place (the ‘4Ps’), and are used in the case of online travel agencies to evaluate website quality. As regards a mix of IS and marketing approaches for evaluating airlines’ websites, Tsai et al. (2011) proposed a model which was applied to analyze the websites of five air transportation companies in Taiwan based on the point of view of experts in the industry. However an assessment of previous research dealing with the evaluation of airline website quality shows that there is still the existence of a significant gap. This mainly relates to the evaluation of airline website quality based on the mixed or integrated ESERVQUAL and 4Ps model considering the customer point of view. For evaluating the customer point of view, this study integrates Expectancy Disconfirmation Theory (EDT) with the proposed mixed model; this theory still remains unexplored in the literature. EDT has five measurement methods for evaluating customer satisfaction; among them is the Additive Difference Model (ADM) which is used in this research. Accordingly, this study aims to: first, propose a hybrid model to define the airline website quality criteria from both 4Ps and ESERQUAL perspectives in three e-quality dimensions covering information, system and service respectively. Following that, EDT will be mixed with the marketing mix 4Ps and E-SERVQUAL to measure customer disconfirmations separately in each dimension of airline website e-quality criteria. This subsequently leads to the creation of a multi-dimension model to measure customer satisfaction and loyalty regarding website e-service and e-marketing criteria. Second; the proposed model will be examined with an airline website to determine whether each of the disconfirmation dimensions have a positive effect on overall customer e-satisfaction. In addition, it will seek to establish whether overall e-satisfaction plays a mediating role between customer disconfirmations and e-loyalty. This paper is organized as follows. First, a literature review is provided on the concept of E-SERVQUAL, the marketing mix 4Ps and EDT, in the context of airline website quality. Second, a conceptual framework is built by formulating hypotheses on the effects of customer disconfirmations from E-SERVQUAL and e-marketing criteria on customer overall e-satisfaction and e-loyalty. Thereafter, the empirical study is described by presenting the methodology, followed by the hypotheses testing. Finally, the paper closes with a discussion of the results, implications and conclusions. 2. Literature review 2.1. The perspective of e-quality and E-SERVQUAL The term e-quality is a requirement for effective web-based marketing. Marketing performance of an e-commerce website relies on its capability to deliver quality customer service. A great deal of effort has been devoted to assessing the e-quality of websites according to various dimensions. A review by Balfagih et al. (2008) shows that six models designed to evaluate the quality of e-commerce websites have been used commonly in previous studies. These are the: DeLone & McLean E-commerce Model (DeLone and McLean, 2004); ISO 9126 Quality Model (ISO/IEC, 2001); WebQual 4.0 Model (Barnes and Vidgen, 2003); Palmer’s Model (Palmer, 2002); Stefani & Xenos Quality Model (Stefani and Xenos, 2001),

37

and SERVQUAL model (Parasuraman et al., 1988). However, according to previous studies such as Ladhari (2009), it seems that the most widespread criteria used to assess service quality in an electronic context is E-SERVQUAL by Parasuraman et al. (2005). This is an adaptation of the well-known SERVQUAL criteria designed to assess quality of services in general. Originally, E-SERVQUAL was structured into 22 items grouped into four dimensions, namely efficiency (the ease and speed of accessing and using the site), fulfillment (the extent to which the site’s promises about order delivery and item availability are fulfilled), system availability (the correct technical functioning of the site) and privacy (the degree to which the site is safe and protects customer information). In the context of airline websites, prior studies (e.g., Lee and Wu, 2011; Llach et al., 2012; Forgas et al., 2012) have used the adapted criteria of the original E-SERVQUAL model for the purposes of evaluating customer satisfaction and loyalty. 2.2. The marketing mix and the 4Ps The marketing mix and 4Ps are useful to highlight some unique aspects of e-marketing (Kalyanam and McIntyre, 2002). The definition of 4Ps in e-marketing, and particularly in the airline website context, can be explained in the following manner: Product: air transportation companies provide an array of travel information (e.g., product, destination, flight) on their websites to enhance the product offerings, and facilitate web-based marketing (Ho and Lee, 2007). The perceived uniqueness of information concerning product detail and variety, product comparison and ease of cancellation or modification of ordered products are differentiated features that would potentially increase the interests of online customers (Chiou et al., 2010). Price: the e-marketing environment enables online differentiated pricing strategies. Differentiated pricing includes last minute discounts, web-only prices and other online-only deals for various tourism products (Han and Mills, 2006). Price information includes all relevant charge details, price comparisons and competitive prices which are the criteria for website evaluation of pricing (Chiou et al., 2010). Promotion: in an e-commerce environment, the virtual value chain can offer several venues to advertise products and services. For example, a company may advertise discounts to customers through its website. With airlines, special travel packages or discounted packages are offered to attract the interest of internet users. Criteria such as advertising promotion and discounts, as well as reputation and credibility of the site, are important (Chiou et al., 2010). Place: the basic function of place is to provide a transactional channel, such as online reservations, reservations tracking and online payment (Ho and Lee, 2007). Chiou et al. (2010) identified the criteria for place evaluation of websites as ease of finding target information, ease of understanding and reading, up to date content, content relevancy and usefulness, ease of navigation, product search, loading speed and processing, efficient and quick linkage, ease of access, ease of online transaction, as well as a convenient payment method. 2.3. Expectancy Disconfirmation Theory (EDT) In order to measure customer satisfaction, EDT is introduced as an important theory which can measure customer satisfaction from the perceived quality of products or services (Oliver, 1980; Patterson and Johnson, 1997; Spreng and Page, 2003). EDT has two well-known variables, expectation or desire, and experience or perceived performance. These variables are defined in two different time periods. Expectation or desire are related to the pre-purchase

38

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44

time period, and experience or perceived performance are related to the after-purchase time period. The difference between initial expectation and perceived experience is known as disconfirmation of expectation (Bhattacherjee and Premkumar, 2004; Oliver, 1980; Spreng and Page, 2003). The result of this difference is that disconfirmation of expectation can be either positive or negative. When customer perceived performance about the quality of a specific product or service is better than customer expectation, positive disconfirmation will occur. Conversely, if the customer perceives the performance as being worse than they expected about the quality of a specific product or service, negative disconfirmation will emerge. According to Yi (1990) positive disconfirmation will lead to customer satisfaction, while negative disconfirmation means perceived performance of products or services have not succeeded in attracting customer satisfaction. EDT has been applied by many researchers in different fields to acquire better understanding of customer expectations and requirements in relation to attracting their satisfaction. These fields include marketing, tourism, information technology, repurchase behavior and retention (Oliver, 1980; Fallon and Schofield, 2003; Bhattacherjee and Premkumar, 2004; Hsu et al., 2006; Patterson and Johnson, 1997) and airline websites (Chen, 2008; Finn et al., 2009). EDT has five measurement methods for evaluating customer satisfaction, among them is the Additive Difference Model (ADM). This model provides a more distinct assessment in comparison with other methods. Prior studies in the field of evaluating airline websites, for instance Chen (2008), used one of the measurement methods of EDT based on Parasuraman et al. (1994) known as Difference score (DIFF) for measuring customer satisfaction. In addition, Finn et al. (2009) used another measurement method of EDT, based on Churchill and Surprenant (1982), known as Better than/worse than (BTWT) for measuring airline customer satisfaction of website quality. To the best of our knowledge, none of the studies of airline websites have used ADM for assessing customer satisfaction concerning airline website quality. Due to the problems associated with the use of difference scores such as low reliability (which assumes pre-use expectations are the same as retrieved expectations), the difference component of the operationalized ADM is a subjective assessment of the difference between standard and performance and is not actually a difference score. Thus the operationalizations of the constructs are as follows:

DC ¼

X

ei ðSDCi Þ

where DC ¼ desires congruency for the product, ei ¼ a weighting parameter that is the consumer’s evaluation of the goodness or poorness of the difference between the expected and the actual performance. SDCi ¼ the consumer’s subjective perception of the congruency between their desires (D) regarding attribute i and the performance of the product (PP) on attribute i. The ADM method has appeared to work well in both a service setting and in a product setting (Spreng and Page, 2003). In addition, this operationalization is a more general form of other combinatorial methods such as the ideal point model (e.g., Teas’s 1993 “evaluative performance” model).

3. The proposed model and hypothesis For the purposes of evaluating customer e-satisfaction, we need to know the customer point of view in relation to combinationoriented features related to IS and marketing factors. To evaluate website quality, we used E-SERVQUAL features as an IS approach with the four dimensions being efficiency (EF), system availability (SysA), fulfillment (FUL), and privacy (PRI). This was adapted from

Evaluating airline’s website

E-SERVQUAL

Information

System

Service

Efficacy Fulfillment Privacy System Availability

E-marketing

Information

System

Service

Place Price Product Promotion

Fig. 1. Proposed model comprised of E-SERVQUAL and e-marketing measures.

the original criteria using airline website studies. We then categorized E-SERVQUAL features into three dimensions of e-quality, namely information quality, system quality and service quality (DeLone and McLean, 2004; Lee and Wu, 2011; Llach et al., 2012) as described below: (1) Information quality is the quality of the information produced and delivered by a system as perceived by the user (Lee and Kozar, 2006; Negash et al., 2003). In our proposed model, the information quality dimension includes information efficiency with two items of E-SERVQUAL, as follows: EF1: easy-to-find information that is needed on the site including business-related (e-ticketing) and general information. EF2: helpful information: business-related (e-ticketing) and general information. (2) System quality is not only a measure of the information processing system itself but also an engineering-oriented performance characteristic (Ahn et al., 2007; Negash et al., 2003). A high level of system quality may provide users with more convenience, privacy and faster responses. In our proposed model, system quality includes system efficiency with three items, system availability with two items, and privacy with two items, as follows: EF3: ease of getting anywhere on the site (ease of navigation). EF4: fast loading of pages. EF5: ability to get on site quickly. SysA1: availability of site. SysA2: the site does not crash. PRI1: the site does not share personal information with other sites. PRI2: site adequately protects credit card information. (3) Service quality refers to the overall support delivered by the website (i.e., how well a delivered service level matches customer expectations) (Ahn et al., 2007; Lee and Kozar, 2006). In the current study, service quality comprises service efficiency with one item, and fulfillment with two items: EF6: ability to complete a transaction quickly. FUL1: ease of canceling or modifying a reservation. FUL2: searching mechanism (flight search). From the perspective of e-marketing, our model categorized the criteria of Chiou et al. (2011) for the 4Ps in the three dimensions as shown below: (1) Information quality Price: price detail including all relevant charge details; Place: up-to-date content, relevance of information; Promotion: advertising promotion and discount; and Product: product detail and variety (flight selection, seat selection, meal selection).

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44

(2) System quality Price: price comparison; Place: convenient payment method; Promotion: reputation and credibility of the site; Product: product comparison (offered flights).

39

dimensions of customer disconfirmations and e-loyalty. With the six dimensions of customer disconfirmations including information, system, and service disconfirmation, we conjecture as follows: H5: customer overall e-satisfaction mediates the effects of disconfirmations on consumer e-loyalty.

(3) Service quality Price: competitive price; Place: ease of online transaction, online assistant and help function; Promotion: promotion and discount to members and nonmembers; Product: possibility of canceling or modifying flight. The proposed model for evaluating airline website quality is shown in Fig. 1. EDT is added to Fig. 1 to evaluate customer disconfirmation. However, a single stage disconfirmation for all ESERVQUAL features or for all e-marketing features is unlikely to adequately account for the complexity of quality evaluation of websites. Our model will measure customer disconfirmation from the specific features in each e-quality dimension using both ESERVQUAL and e-marketing criteria. If a customer realizes that the perceived information of a product or service satisfies their expectations, then this positive disconfirmation successfully leads to customer satisfaction. Thus, we hypothesize that customer positive information quality disconfirmation in both E-SERVQUAL and emarketing aspects exert a positive influence on customer satisfaction. Hence, in both aspects of E-SERVQUAL and e-marketing, we hypothesize as follows: H1: customer information disconfirmation has a positive direct influence on overall customer e-satisfaction. (H1a and H1b) H2: customer system disconfirmation has a positive direct influence on overall customer e-satisfaction. (H2a and H2b) H3: customer service disconfirmation has a positive direct influence on overall customer e-satisfaction. (H3a and H3b) Past studies have suggested that service quality satisfaction affects loyalty and post-purchase behavior (Oliver, 1980; Anderson and Sullivan, 1993). Also, e-satisfaction has been found to be the principal antecedent of e-loyalty (Chiou, 2004) and intention to recommend (Finn et al., 2009). In order for satisfaction to affect loyalty, frequent and accumulated satisfaction is necessary, such that episodes of individual satisfaction are aggregated and mixed. In our study, overall e-satisfaction is the result of customer disconfirmation aggregations in the aspects of E-SERVQUAL and emarketing. We anticipate that overall customer e-satisfaction from disconfirmations will lead to customers being encouraged to create new business with the airline websites in the future and retain customer loyalty to e-ticketing. Hence, on the basis of Flavian et al. (2006) we relate overall customer e-satisfaction to e-loyalty, by determining specifically: H4: overall customer e-satisfaction with an airline website has a positive influence on customer e-loyalty to the airline company’s website.

The proposed model for analyzing the hypotheses is shown in Fig. 2. 4. Methodology 4.1. Measures As discussed, the model in this study was adapted from the original E-SERVQUAL. The measures of E-SERVQUAL comprise four dimensions including efficacy, fulfillment, privacy, and system availability for airline websites. From the perspective of E-SERVQUAL, the model is based on 12 items categorized in three dimensions, namely information quality, system quality, and service quality. From the perspective of e-marketing, our model categorized 17 criteria for 4Ps in the same three dimensions. For the measurement criteria of overall e-satisfaction, the first question has two set answers which are based on Spreng and Page (2003); while the second question is adapted from Finn et al. (2009). In relation to e-loyalty, two questions of Lee and Wu (2011) and Llach et al. (2012) have been adapted. Table 1 provides a detailed summary of items measured through multi-item criteria for the measurement of the constructs in which responses from the participants were measured. 4.2. Sample and data collection The study was carried out with the online customers of the AirAsia airline website. These customers comprised a selection of students, lecturers and staff members from Universiti Teknologi Malaysia, who had carried out online ticketing booking at least once on the website. The respondent sample was drawn from two cultural groups of online customers, specifically Malaysian residents and international customers. 357 questionnaires were handed out personally and a total of 309 questionnaires were returned, which made a response rate of 86 percent. Of the returned questionnaires, nine were incomplete and these were excluded from the data analysis. The remainder comprised 300 completed questionnaires. Table 2 presents the demographic information. 4.3. Analysis The two components of the ADM measure of desires disconfirmation were captured by first asking the subject the following E-serv Disc 1 H1a E-serv Disc 2

Moreover, there is evidence in literature studies which shows how customer e-satisfaction mediates the relationship between customer disconfirmations and loyalty components. For example, the model proposed by Finn et al. (2009) indicates that overall satisfaction has a mediating effect between system disconfirmation and intention to recommend. Further, there is a link between offering disconfirmation and intention to recommend. In the context of airline website, we defined e-loyalty explicitly by the intention to re-purchase and intention to recommend. We hypothesize that overall e-satisfaction mediates the relationship between six

H2a E-serv Disc 3

E-mark Disc 1 E-mark Disc 2

H3a

H5 Overall E-satisfaction

H4

E-loyalty

H1b H2b H3b

E-mark Disc 3

Fig. 2. Disc 1: information disconfirmation, Disc 2: system disconfirmation, and Disc 3: service disconfirmation.

40

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44

Table 1 Measurement scales. Constructs

Measures

ESD1

E-S-D-1: easy to find information I need on the site including business-related (e-ticketing) and general information. E-S-D-2: helpful information (business-related (e-ticketing) and general information). E-S-D-3: easy to get anywhere on the site (ease of navigation). E-S-D-4: loads its pages fast. E-S-D-5: get on site quickly. E-S-D-6: availability of site for business. E-S-D-7: site does not crash. E-S-D-8: site does not share personal information with other sites. E-S-D-9: site protects information about credit card. E-S-D-10: complete a transaction quickly. E-S-D-11: easy to cancel or modify reservation. E-S-D-12: ease of searching mechanism (flight search) E-M-D-1: price detail including all relevant charges details E-M-D-3: up-to-date business-related (e-ticketing) content E-M-D-4: relevance of business-related (e-ticketing) information E-M-D-5: advertising promotion and discount E-M-D-6: product detail and variety (flight selection) E-M-D-7: product detail and variety (seat selection) E-M-D-8: product detail and variety (meal selection) E-M-D-9: price comparison E-M-D-10: ease of online transaction E-M-D-11: convenient payment method E-M-D-12: reputation and credibility of the business website E-M-D-13: product comparison (offered flights with different price and flight-time for specific demand) E-M-D-14: competitive price E-M-D-15: online assistant and help in business (eticketing) E-M-D-16: promotion and discount to members and nonmembers E-M-D-17: possibility of canceling or modifying flight I am. with my use of airline website. Overall, how would you rate your experience on this site? I will do more business with the website in the future. I encourage friends and relatives to do business with the website.

ESD2

ESD3

EMD1

EMD2

EMD3

Overall e-satisfaction E-loyalty

question: “In comparison to the quality level of each aspect that you desired, how big was the difference between what you wanted and what the airline website actually provided?” A 7-likert scale for each of the 29 attributes was provided and anchored by the following: 1 ¼ “exactly as I desired” and 7 ¼ “extremely different than I desired.” Second, following each of these scales was a measure that asked: “How good or bad is this difference?” This was directed on an 11-point scale anchored by responses of: 5 ¼ “very bad” and þ5 ¼ “very good,” as well as 0 ¼ “neither bad nor good.” For the first two questions of overall e-satisfaction the measures of a 5-likert scale (1 ¼ “not at all positive” and 5 ¼ “extremely positive”; 1 ¼ “extremely displeased” and 5 ¼ “extremely pleased”) were used. Further, in relation to the third question of overall esatisfaction, responses ranging from 1 ¼ “extremely dissatisfied” and 5 ¼ “extremely satisfied” were considered. For e-loyalty, two questions were adapted on the 5-likert scale and ranged from 1 ¼ “strongly agree” to 5 ¼ ”strongly disagree”. Moreover, we used a Partial Least Squares (PLS) analysis as implemented in SmartPLS to estimate both the measurement model and the structural model (Hansmann and Ringle, 2004). This technique is considered adequate since the distribution of the data deviates from normality and PLS makes no distributional assumptions (Fornell and Cha, 1994). Thus, the following measures were implemented: first, the measurement model was assessed, in

which construct validity and reliability of the measures are key. Second, the structural model with hypotheses is tested. In addition, for assessing goodness-of-fit measures, the model fit was tested in LISREL 8.7.1. 5. Results 5.1. Validity and reliability Convergent and discriminant validity was used to assess the construct validity of the instruments used. Composite reliability (CR) values should be greater than 0.7 while the average variance extracted (AVE) should be above 0.5. The results of CR and AVE are shown in Table 3. All AVEs were 0.778 or higher and all CRs were 0.925 or higher. These results supports the convergent validity of each of the constructs. As shown in Table 4, all AVE square roots surpass the correlations with the other constructs which means discriminant validity of the scales is affirmed. Internal consistency of the measures exists if Cronbach alpha, composite reliability (CR) and item loadings of each criterion all exceed 0.7 (Fornell and Larcker, 1981). As shown in Table 5, the coefficients of Cronbach’s alpha for the six constructs were in excess of 0.70, ranging from 0.804 to 0.915. These results are acceptable, with all composite reliability (CR) findings being 0.824 or higher. Similarly, all factor loadings were 0.737 or higher for both company. Therefore, internal consistency is met by this model. 5.2. Disconfirmation result To operationalize the ADM measure of desires disconfirmation, we allocated customers’ responses to two scores as follows: SDCi ¼ How big was the difference between what you wanted and what the airline website actually provided? (1 ¼ “exactly as I desired” and 7 ¼ “extremely different than I desired”) and ei ¼ How good or bad is this difference? (5 ¼ “very bad” and þ5 ¼ “very good,” with 0 ¼ “neither bad nor good”). Accordingly, these data were multiplied for each attribute of the ADM formula. Thus, the range of data which resulted from the multiple of ei in SDCi was [35, þ35] for each attribute. The data were normalized in a specific range [C, D] ¼ [1, 5] for each attribute A to become a range corresponding with the response of overall e-satisfaction and eloyalty. The normalization was undertaken with the one technique of normalization known as Min Max Normalization (appearing as the formula below) and then used for data analysis.

 ðA  Minimum value of AÞ *ðD  CÞ þ C ðMaximum value of A  Minimum value of AÞ

 B¼

The result of disconfirmations is demonstrated in six dimensions as shown in Table 6. It indicates that, after normalization of data in the range of [1, 5], all disconfirmations show a mean of more than three which indicates positive customer disconfirmations in all six dimensions. Among the disconfirmations’ mean, EMD3 and EMD2 show a significantly positive disconfirmation, followed by ESD2 and EMD1 respectively. 5.3. Model fit The overall fit of the structural model was checked initially by examining c2, the goodness of fit index (GFI), the adjusted goodness of fit index (AGFI), the normalized fit index (NFI), the comparative fit index (CFI), as well as the root mean square error of approximation (RMSEA) applied to a test model fit. The recommended acceptance of a good fit to a model requires that the obtained GFI, AGFI, NFI and CFI values should be greater than or equal to 0.90. In

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44 Table 2 Demographics of information.

18e24 25e34 35e40 Total Male Female Total Local International Total

Frequency

Percentage

100 117 83 300 176 124 300 113 187 300

33.33 39 27.67 100 58.67 41.33 100 37.67 62.33 100

Table 3 Convergent validity. Constructs

AVE

Composite reliability

EMD1 EMD2 EMD3 ESD1 ESD2 ESD3 LOY SAT

0.632 0.669 0.656 0.701 0.641 0.676 0.778 0.693

0.923 0.866 0.884 0.824 0.925 0.862 0.875 0.871

41

step-1: the relationship between independent variables (six disconfirmations) and mediator (overall e-satisfaction); step-2: the relationship between independent variables (six disconfirmations) and dependent variable (e-loyalty); and step-3: the relationship among independent variable (six disconfirmations), mediator (overall e-satisfaction) and dependent variable (e-loyalty) respectively. In order to demonstrate whether overall e-satisfaction mediates the effects of the six disconfirmations on e-loyalty, the standardized beta value for the six disconfirmations and e-loyalty in step-2 needs to be substantially larger than the same variable displayed in step-3. Table 8 illustrates the three steps involved when assessing the mediation effect of overall e-satisfaction. The results indicate that after the addition of overall e-satisfaction, all the b in step-3 are less than in step-2. However, most of them are still meaningful. Thus, overall e-satisfaction partially mediates the relationship between ESD1, ESD3, ESD4, ESD6 and e-loyalty. However, the results of b show that, after adding overall e-satisfaction, the b in step-3 has become less than is required to account for a meaningful path between ESD2, ESD5 and e-loyalty. Thus, in the case of ESD2 and ESD5, overall e-satisfaction fully mediates the relationship between them and e-loyalty. Table 9 explains the result of t-value and b for the mediator concerning overall esatisfaction. 6. Discussion

addition to that, an acceptable value of RMSEA should range from 0.05 to 0.08 (Hair et al., 1998). By using a correlation matrix among 29 measurement variables, SEM analysis is performed against the proposed conceptual model. The SEM results depicted are: c2 ¼ 515.57 (p ¼ <0.001), df ¼ 231, GFI ¼ 0.96, AGFI ¼ 0.90, NFI ¼ 0.96, CFI ¼ 0.93 and RMSEA ¼ 0.71 respectively. The results indicate a good fit for the structural model.

5.4. Hypothesis testing Regarding the evaluation of the model, we estimated that path coefficients (the coefficients of the relationships between variables) would confirm the research hypotheses. We performed hypothesis testing by following the recommendation of Chin (1998) that the significance of each path’s coefficient can be estimated by t- tests using bootstrapping with 500 subsamples. The result of hypothesis testing including the mean, standard deviation, t-value and b are indicated in Table 7. Our results supported the hypotheses with six paths from disconfirmations positively influencing overall esatisfaction. The suggested hypotheses, H1a, H1b, H2a, H2b, H3a, H3b, have been confirmed based on the resulting b and t-value. Moreover, the results showed the customer overall e-satisfaction to have a positive direct influence on e-loyalty (b ¼ 0.270, t-value ¼ 4.59) supporting H4. This is carried out by means of three steps as follows:

The empirical analysis has led to several findings. First, our results gained from analyzing the influence of customer disconfirmations on overall e-satisfaction are consistent with general EDT literature (e.g., Oliver, 1980; Patterson and Johnson, 1997; Spreng and Page, 2003). Also, they are consistent with research in the context of airline websites such as Finn et al. (2009). These studies found that customer system disconfirmation (including the criteria of home page, order, and offering disconfirmation) has a positive direct impact on satisfaction levels. Thus our results indicate that the positive result of customer perception is quite different between what they expect from the quality of (website and e-business) services and what they perceived as the direct influence on customer e-satisfaction. The result of measured disconfirmations by the formulation of ADM shows that the perception of the customer is positive in all dimensions of disconfirmations. Accordingly, it was estimated that the positive disconfirmation results produced a positive impact in e-satisfaction as confirmed by the b in the path analysis. Second, the result of the path analyses conducted between overall e-satisfaction and e-loyalty indicates that overall customer e-satisfaction from multiple disconfirmations has a positive influence on e-loyalty. We defined e-loyalty explicitly as the intention to re-purchase and intention to recommend the service. Thus our result is consistent with past research studies which have shown that overall e-satisfaction has a positive effect on the intention to recommend the service (e.g., Finn et al., 2009). It is also consistent

Table 4 Discriminant validity. R- square for satisfaction and loyalty is 0.82 and 0.68 , respectively. AVE EMD1 EMD2 EMD3 ESD1 ESD2 ESD3 LOY SAT a

0.632 0.669 0.656 0.701 0.641 0.676 0.778 0.693

EMD1

EMD2

EMD3

ESD1

ESD2

ESD3

LOY

SAT

0.817a 0.690 0.688 0.771 0.695 0.693 0.711

0.810a 0.725 0.772 0.658 0.682 0.648

0.837a 0.738 0.570 0.573 0.688

0.801a 0.662 0.721 0.763

0.822a 0.797 0.788

0.882a 0.761

0.832a

a

0.794 0.715 0.633 0.723 0.642 0.651 0.629 0.630

Diagonal elements are the square roots of average variance extracted (AVE).

42

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44 Table 5 Internal consistency. Items ESD1 (CR [ 0.923; a [ 0.903) E-S-D-1 E-S-D-2 ESD2 (CR [ 0.866; a [ 0.804) E-S-D-3 E-S-D-4 E-S-D-5 E-S-D-6 E-S-D-7 E-S-D-8 E-S-D-9 ESD3 (CR [ 0.883; a [ 0.825) E-S-D-10 E-S-D-11 E-S-D-12 EMD1 (CR [ 0.824; a [ 0.875) E-M-D-1 E-M-D-2 E-M-D-3 E-M-D-4 E-M-D-5 E-M-D-6 E-M-D-7 E-M-D-8 EMD2 (CR [ 0.925; a [ 0.905) E-M-D-9 E-M-D-10 E-M-D-11 E-M-D-12 E-M-D-13 EMD3 (CR [ 0.861; a [ 0.860) E-M-D-14 E-M-D-15 E-M-D-16 E-M-D-17 SAT (CR [ 0.875; a [ 0.915) SAT1 SAT2 SAT3 LOY (CR [ 0.871; a [ 0.879) LOY1 LOY2

Factor loading 0.818 0.856 0.785 0.781 0.887 0.737 0.796 0.887 0.811 0.754 0.848 0.859 0.765 0.813 0.823 0.836 0.808 0.775 0.751 0.781 0.805 0.848 0.811 0.781 0.776 0.799 0.851 0.853 0.816 0.799 0.851 0.830 0.882 0.850

with studies showing overall e-satisfaction has a positive impact on behavioral intention, including the intention to recommend as well as the intention to repurchase (e.g., Chen, 2008). Third, as regards the results indicated in Table 8 step-3, overall e-satisfaction plays a partial mediator role in ESD1, ESD3, ESD4, and ESD6 respectively. In these cases, adding overall e-satisfaction to the relationship between ESD1, ESD3, ESD4, ESD6 and e-loyalty does not result in the elimination of a direct relationship between ESD1, ESD3, ESD4, ESD6 and e-loyalty. However, ESD2 and ESD5, with full mediation impact, do so. In total, mediation results indicate that enhancing customer e-satisfaction regarding these six dimensions of disconfirmation would be significantly helpful to retain customers’ loyalty when using the airline website for eticketing. Although the mediation role of e-satisfaction was more significant in ESD2 and ESD5, it has also made a meaningful role in the mediation of rest disconfirmations. These results are consistent with previous studies. In particular, they emphasize that airlines should pay special attention to meeting customers’ needs and expectations regarding E-SERVQUAL and e-marketing attributes which result in increased customer satisfaction with websites. Consequently, more satisfied customers lead to more loyal ones. This research has two major theoretical contributions. First, we blended EDT with E-SERVQUAL and e-marketing with the theories

of psychology, information systems and marketing. Second, we differentiated between customer information disconfirmation, system disconfirmation and service disconfirmation with both ESERVQUAL and e-marketing. Therefore, the proposed multidimension model of disconfirmation has the advantage of examining the interaction between customer and airline websites in a more dynamic fashion by tracing customer disconfirmation at each level. In addition, two practical implications can be drawn. First, it is apparent that online consumer satisfaction is formed by the customer disconfirmation of both E-SERVQUAL and e-marketing attributes. The results indicate that some disconfirmation aspects have a more significant impact on overall customer e-satisfaction. Thus, the quality-base response of airline websites in such aspects plays an important role in gaining customer satisfaction and ultimately retaining their loyalty. For example, disconfirmation of emarketing in the service dimension has the most significant direct impact on overall e-satisfaction. Also, the second and third important disconfirmation dimensions which have relatively more importance are system dimensions of E-SERVQUAL and e-marketing. Therefore, website developers and decision-making managers should ensure that the website back-end (system quality) is capable of providing prompt, complete and secure transactions and replies to customer queries (e-business). Moreover, the results confirm the mediation role of customer e-satisfaction between customer (ESERVQUAL and e-marketing) disconfirmations and customer eloyalty. Thus, our findings have significant implications for airline website practitioners who are seeking to increase the volume of online customers and are eager to know how to satisfy online consumers and retain their loyalty to their website. 7. Conclusions This research has provided insight into differentiating factors that are significant antecedents in the multi dimensions of customer disconfirmation. These dimensions may not capture all the features that would be useful to explain customer disconfirmation in each dimension. However, disconfirmation stages remain to be investigated by further research in order to extend more related features. In addition, since the model was only conducted on one airline website, future studies should focus on investigating the proposed model on different airline websites. Also, since this survey was conducted on those who were educated to university level, future studies need to extend the sample of other respondents in order to confirm the validity of the model. Overall, the current study followed three empirical objectives. First, the direct impact of online customer disconfirmations on overall e-satisfaction was examined; second, the direct impact of overall e-satisfaction on e-loyalty was investigated, and third, the mediating role of overall e-satisfaction between disconfirmations and e-loyalty was considered. The results of the first and third objectives were consistent with previous studies such as those of Finn et al. (2009). These confirmed a positive direct impact of customer disconfirmations on customer overall e-satisfaction, as well as the mediating role of overall customer e-satisfaction existing between customer disconfirmations and e-loyalty. However, from a theoretical aspect, the model provided a better understanding of the category of customer disconfirmations in both aspects of E-SERVQUAL and e-marketing. The result of the second objective was consistent with a study by Baia et al. (2008) which confirmed that customer satisfaction from website quality has a positive impact on purchase intention. Finally, airline websites are powerful tools designed to attract new customers to airline companies, as well as seeking to retain the loyalty of existing customers. To reach this aim, proper

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44

References

Table 6 The result of disconfirmations’ mean.

ESD1 ESD2 ESD3 EMD1 EMD2 EMD3

Minimum

Maximum

Mean

1.00 1.00 1.00 1.00 1.00 1.00

5.00 5.00 5.00 5.00 5.00 5.00

3.03 3.22 3.08 3.10 3.57 3.95

More than 3 ¼ positive disconfirmation, Less than 3 ¼ negative disconfirmation. Table 7 Path coefficients. Paths

Sample mean (M)

Standard deviation (STDEV)

t Statistics (jO/STERRj)

b

ESD1->SAT ESD2->SAT ESD3->SAT EMD1->SAT EMD2->SAT EMD3->SAT SAT->LOY

0.537 0.410 0.336 0.523 0.444 0.352 0.365

0.060 0.067 0.066 0.070 0.070 0.078 0.078

2.03 6.25 4.83 4.59 6.40 7.37 4.59

0.270 0.321 0.270 0.319 0.451 0.516 0.83

Significant of ESD1 to SAT is p-value less than 0.5 and for the rest of paths p-value is less than 0.1. Table 8 Mediating effect of overall e-satisfaction. Independent Steps ESD1 ESD2 ESD3 EMD1 EMD2 EMD3

Criteria Predictors

Step1 SAT

Step2 LOY

Step3 LOY

43

(b)

ESD1 0.270 ESD2 0.321 ESD3 0.270 EMD1 0.319 EMD2 0.451 EMD3 0.516 ESD1 0.545 ESD2 0.725 ESD3 0.517 EMD1 0.840 EMD2 0.343 EMD3 0.654 ESD1 SAT 1.05 ESD2 0.09 ESD3 1.24 EMD1 1.19 EMD2 0.03 EMD3 3.43

Result

EMD1 EMD2 EMD3 EMD4 EMD5 EMD6

(partially mediate) (fully mediate) (partially mediate) (partially mediate) (fully mediate) (partially mediate)

development of a website considering both quality aspects of ESERVQUAL and e-marketing is significantly helpful. A high quality website impacts on the airline companies’ strategic goals in both the short-term and long-term. In the short-term, they gain some benefits, such as increase of e-ticketing on the website, which could improve financial rates. In the long-term, an airline company may achieve superior performance, such as delivering better e-service to customers, keeping customers loyal to e-ticketing on the website, enhancing their competitiveness and maintaining their position in the market.

Table 9 Path coefficient of mediators. Paths

t Statistics (jO/STERRj)

b

ESD1->SAT->LOY ESD2->SAT->LOY ESD3->SAT->LOY EMD1->SAT->LOY EMD2->SAT->LOY EMD3->SAT->LOY

2.78 2.49 3.29 2.86 4.34 4.16

0.343 0.454 0.520 0.399 0.566 0.518

Ahn, T., Ryu, S., Han, I., 2007. The impact of Web quality and playfulness on user acceptance of online retailing. Inf. Manag. 44 (3), 263e275. Anderson, E.W., Sullivan, M.W., 1993. The antecedents and consequences of customer satisfaction for firms. Mark. Sci. 12 (Spring), 125e143. Baia, B., Lawb, R., Wen, I., 2008. The impact of website quality on customer satisfaction and purchase intentions: evidence from Chinese online visitors. Int. J. Hosp. Manag. 27, 391e402. Balfagih, Zain, Mohamed, N., Mahmud, M., 2008. In search of a model for evaluating the quality of e-commerce web sites. In: 2008 International Symposium on Information Technology, vol. 1, pp. 1e6. Barnes, S.J., Vidgen, R., 2003. Measuring web site quality improvements, a case study of the forum measuring web site quality improvements: a case study of the forum on strategic management knowledge exchange. Ind. Manag. Data Syst. 103 (5), 297e309. Bhattacherjee, A., Premkumar, G., 2004. Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Q. 28 (2), 229e254. Blattberg, R.C., Malthouse, E.C., Neslin, S.A., 2009. Customer lifetime value: empirical generalizations and some conceptual questions. J. Interact. Mark. 23, 157e168. Chen, F.Y., Chang, Y.H., 2005. Examining airline service quality from a process perspective. J. Air Transp. Manag. 11, 79e87. Chen, C.F., 2008. Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: evidence from Taiwan. Transp. Res. A 42, 709e717. Chin, W., 1998. The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (Ed.), Modern Business Research Methods. Lawrence Erlbaum Associates, Mahwah, NJ, pp. 295e336. Chiou, Jyh-Shen, 2004. The antecedents of consumers’ loyalty toward internet service providers. Inf. Manag. 41 (6), 685e695. Chiou, W.C., Perng, C., Tsai, J.T., Lin, C.C., 2008. The review of the website evaluation framework in IS and marketing journals. Int. J. Inf. Syst. Change Manag. 3 (1), 81e104. Chiou, W.C., Lin, C.C., Perng, C., 2010. A strategic framework for website evaluation based on a review of the literature from 1995e2006. Inf. Manag. 47, 282e290. Chiou, W.C., Lin, C.C., Perng, C., 2011. A strategic website evaluation of online travel agencies. Tour. Manag. 32, 1463e1473. Churchill, G.A., Surprenant, C., 1982. An investigation into the determinants of consumer satisfaction. J. Mark. Res. 19 (November), 491e504. DeLone, W.H., McLean, E.R., 2004. Measuring e-commerce success: applying the DeLone and McLean information systems success model. Int. J. Electron. Commer. 9 (1), 31e47. Fallon, P., Schofield, P., 2003. First-timer versus repeat visitor satisfaction: the case of Orlando, Florida. Tour. Anal. 8 (2), 205e210. Fassnacht, M., Köse, I., 2007. Consequences of web-based service quality: uncovering a multi-faceted chain of effects. J. Interact. Mark. 21 (3), 35e54. Finn, A., Wang, L., Frank, T., 2009. Attribute perceptions, customer satisfaction and intention to recommend e-services. J. Interact. Mark. 23, 209e220. Flavian, Carlos, Guinaliu, Miguel, Gurrea, Raquel, 2006. The role played by perceived usability, satisfaction and consumer trust on website loyalty. Inf. Manag. 43 (January), 1e14. Forgas, S., Palau, R., Sánchez, J., Huertas-García, R., 2012. Online drivers and offline influences related to loyalty to airline websites. J. Air Transp. Manag. 18, 43e46. Fornell, C., Larcker, D.F., 1981. Evaluating structural equations models with unobservable variables and measurement error. J. Mark. Res. 18 (39), 50. Fornell, C.R., Cha, J., 1994. Partial least squares. In: Bagozzi, R.P. (Ed.), Advanced Methods of Marketing Research. Blackwell, Oxford, pp. 57e78. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998. Multivariate Data Analysis, fifth ed. Prentice Hall International Inc, Upper Saddle River, NJ. Han, J.H., Mills, J.E., 2006. Zero acquaintance benchmarking at travel destination websites: what is the first impression that national tourism organizations try to make? Int. J. Tour. Res. 8 (6), 405e430. Hansmann, K.W., Ringle, C.M., 2004. SmartPLS Manual. University of Hamburg, Hamburg. Ho, C.I., Lee, Y.L., 2007. The development of an e-travel service quality scale. Tour. Manag. 28 (6), 1434e1449. Hsu, M.H., Yen, C.H., Chiu, C.M., Chang, C.M., 2006. A longitudinal investigation of continued online shopping behavior: an extension of the theory of planned behavior. Int. J. Hum.-Comput. Stud. 64 (9), 889e904. ISO/IEC, 9126, 2001. Software Product Evaluation e Quality Characteristics and Guidelines for the User. International Organization for Standardization, Geneva. Kalyanam, K., McIntyre, S., 2002. The e-marketing mix: a contribution of the retailing wars. J. Acad. Mark. Sci. 30 (4), 483e495. Ladhari, R., 2009. A review of twenty years of SERVQUAL research. Int. J. Qual. Serv. Sci. 1 (2), 172e198. Lee, F.H., Wu, W.Y., 2011. Moderating effects of technology acceptance perspectives on e-service quality formation: evidence from airline websites in Taiwan. Expert Syst. Appl. 38, 7766e7773. Lee, Y., Kozar, K.A., 2006. Investigating the effect of website quality on e-business success: an analytic hierarchy process (AHP) approach. Decis. Support Syst. 42 (3), 1383e1401. Llach, J., Marimon, F., Alonso-Almeida, M. d. M., Bernardo, M., 2012. Determinants of online booking loyalties for the purchasing of airline tickets. Tour. Manag., 1e9.

44

N. Elkhani et al. / Journal of Air Transport Management 37 (2014) 36e44

Negash, S., Ryan, T., Igbaria, M., 2003. Quality and effectiveness in web-based customer support system. Inf. Manag. 40 (8), 757e768. Nyshadham, E.A., 2000. Privacy policies of air travel web sites: a survey and analysis. J. Air Transp. Manag. 6 (3), 143e152. Oliver, R.L., 1980. A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 17 (November), 460e469. Palmer, J.W., 2002. Web site usability, design, and performance metrics. Inf. Syst. Res. 13 (2), 151e167. Parasuraman, A., Zeithaml, V.A., Berry, L.L., 1994. Alternative scales for measuring service quality: a comparative assessment based on psychometric and diagnostic criteria. J. Retail. 70 (3), 201e230. Parasuraman, A., Zeithaml, V.A., Berry, L.L., 1988. SERVQUAL: a multi-item scale for measuring consumer perceptions of service quality. J. Retail. 64 (Spring), 13e40. Parasuraman, A., Zeithaml, V.A., Malhotra, A., 2005. A multiple-item scale for assessing electronic service quality. J. Serv. Res. 7 (3), 213e233. Patterson, P.G., Johnson, L.W.,1997. Modeling the determinants of customer satisfaction for business-to-business professional services. J. Acad. Mark. Sci. 25 (1), 4e17. Spreng, R.A., Page Jr., T.J., 2003. A test of alternative measures of disconfirmation. Decis. Sci. 34 (1), 31e62.

Stefani, A., Xenos, M., 2001. A model for assessing the quality of e-commerce systems. In: Proceedings of the PCHCI, pp. 105e109. Szymanski, D.M., Henard, D.H., 2001. Customer satisfaction: a meta-analysis of the empirical evidence. J. Acad. Mark. Sci. 29 (1), 16e35. Teas, R.K., 1993. Expectations, performance evaluation, and consumers’ perceptions of quality. J. Mark. 57 (October), 18e34. Tsai, H.T., Huang, L., Lin, C.G., 2005. Emerging e-commerce development model for Taiwanese travel agencies. Tour. Manag. 26 (5), 787e796. Tsai, W.H., Chou, W.C., Leu, J.D., 2011. An effectiveness evaluation model for the web-based marketing of the airline industry. Expert Syst. Appl. 38, 15499e 15516. Wei, J., Ozok, A., 2005. Development of a web-based mobile airline ticketing model with usability features. Ind. Manag. Data Syst. 105 (9), 1261e1277. Yi, Y., 1990. A critical review of consumer satisfaction. In: Zeithaml, V.A. (Ed.), Review of Marketing 1990. America Marketing Association, Chicago, pp. 68e 123. Yu, S.F., 2008. Price perception of online airline ticket shoppers. J. Air Transp. Manag. 14 (2), 66e69.