Understanding post-adoption behaviors of e-service users in the context of online travel services

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INFMAN-2745; No. of Pages 10 Information & Management xxx (2014) xxx–xxx

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Information & Management journal homepage: www.elsevier.com/locate/im

Understanding post-adoption behaviors of e-service users in the context of online travel services Hongxiu Li a,*, Yong Liu b a b

Turku School of Economics, University of Turku, Rehtorinpellonkatu 3, 20500 Turku, Finland University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany

A R T I C L E I N F O

A B S T R A C T

Article history: Received 2 January 2012 Received in revised form 16 April 2014 Accepted 7 July 2014 Available online xxx

We developed a model to investigate the factors influencing two different post-adoption behaviors of e-service users based on the Post-Acceptance Model of IS Continuance (IS continuance model): (1) continuance intention to use e-services; and (2) Word of Mouth (WOM) behavior. We tested the research model using a survey of 543 usable responses in China. Our findings show that satisfaction and perceived usefulness positively affect continuance intention, which, together with perceived usefulness, positively influences the WOM behavior. The two different post-adoption behaviors of e-service users, continuance intention and WOM, are closely related. Implications for theory and practice are also discussed. ß 2014 Elsevier B.V. All rights reserved.

Keywords: Post-adoption behaviors Continuance intention WOM Recommendation ECT

1. Introduction The post-adoption behaviors of information system (IS) users have recently attracted a great deal of attention among both researchers and practitioners. Prior post-adoption research in the IS domain has primarily focused on two post-adoption behaviors, namely, continuance intention and continuance usage. The prior research posits that the continuous use of IS is vital to the success of IS implementation among firms in the competitive marketplace because benefits from organizations’ IS investments can only realized through continuous IS usage [1]. In the marketing literature, other behavioral outcomes, such as word of mouth (WOM), are said to be important, particularly in the highly competitive online environment [2]. Prior marketing research on the effect of WOM suggests that it is positively associated with sales [3–5]. Trusov et al. [6] have studied the effect of WOM marketing in attracting new users in the context of an online social network site and compared WOM with traditional marketing vehicles. They have found that WOM can help to attract new users, has longer carryover effects and produces substantially higher response elasticities than traditional marketing actions. The impact of WOM on sales and in

* Corresponding author. Tel.: +358 50 3722471. E-mail addresses: hongxiu.li@utu.fi (H. Li), [email protected] (Y. Liu).

attracting new users has even encouraged some companies to compensate consumers for reviews of their products or services [7]. An apt comment on WOM marketing, which was published in the Wall Street Journal, states that ‘‘Instead of tossing away millions of dollars on Superbowl advertisements, fledgling dotcom companies are trying to catch attention through much cheaper marketing strategies such as blogging and [WOM] campaigns’’ [8, p. B2A]. There are several reasons for the appeal of using the Internet to spread WOM. First, the Internet provides numerous sites where consumers produce user-generated content (UGC) and share their reviews, preferences and experiences with others via chat rooms, discussion forums, bulletin boards, blogs, newsgroups, email, personal Web pages, social networks and virtual community blogs [9,10]. The Internet facilitates the rapid spread of WOM to different users at significantly lower costs [6]. Second, WOM has a significant effect on consumer purchasing behavior because consumers prefer to rely on WOM from experienced consumers instead of advertising, especially when attempting to reduce the perceived risks and uncertainties associated with purchasing decisions [11]. Thus, WOM can be viewed as an efficient method of online marketing. Mangold et al. [11] and Murray [12] both find that the influence of WOM is stronger on those who have purchased services than on those who purchased products because one characteristic of services is that they are experience goods for which quality is easier to ascertain.

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Though WOM has been argued to affect sales and help attract potential users, less research attention has been paid to the drivers of WOM in IS research. Prior IS research on post-adoption behavior has primarily focused on IS continuance and has not explored WOM behavior in the IS domain. Furthermore, the relationship between IS continuance and WOM has received minimal attention from IS researchers. Accordingly, there is a need to study the factors that drive the continuance intention to use an IS, the WOM behavior of individuals during the post-adoption stage and the need to clarify the relationship between the continuous use of an IS and WOM behavior. Thus, this paper addresses the following research questions: RQ1: What are the factors motivating individuals’ IS continuance intention and their WOM with respect to an IS? RQ2: What is the relationship between IS continuance intention and WOM? To answer the research questions, this study proposed a postadoption model that incorporated WOM into Bhattacherjee’s [1] IS Continuance Model and then empirically validated the research model in the context of online travel services. In this research, empirical data about online travel services were collected via a survey questionnaire and assessed using structural equation modeling. The objective of this study is to develop and assess a model that captures not only the factors that motivate an individual’s IS continuance intention and WOM behavior but also the relationship between IS continuance intention and WOM in relation to an IS. In doing so, we offer several contributions. First, we linked IS continuance intention directly to WOM. The investigation of the relationship between the two different post-adoption behaviors should help explain the strategies that IS companies employ to retain IS users while promoting their IS via users’ WOM behavior. Individual users are not limited to users of the IS: they can also be recruited as a marketing channel for IS services [7]. Second, this research investigated the WOM behavior of e-service users from IS adoption behavior perspectives. The findings of this study are expected to provide insights into how user satisfaction and cognition influence e-service users’ continuance intention and WOM behavior in the post-adoption stage and to show how continuance intention influences WOM behavior in the context of B2C e-services. Third, this research investigated the impact of users’ IS usefulness perceptions on different IS post-adoption behaviors. The remainder of this article is organized as follows: the two post-adoption behaviors of IS users (IS continuance and WOM) and the IS Continuance Model proposed by Bhattacherjee [1] are introduced. Next, the research model and relevant hypotheses are presented, followed by a description of the study design and research methodology. After discussing the research findings, the paper highlights implications for both research and practice. Finally, the limitations of this study and suggestions for future research are presented. 2. Literature review and research background

WOM is also a form of post-adoption behavior; the term refers to a consumer-dominated channel for broadcasting product, service or company information in which senders are independent of the market [9,18]. Consumers often value WOM information more highly than that provided by a company because WOM information is perceived to be more reliable, credible and trustworthy than firm-provided information [18]. According to Grewal et al. [19], WOM senders have neither an underlying motive nor an incentive for their referral. Thus, WOM is argued to have a powerful influence on consumer behavior, for example, when consumers search for and assess product information and subsequent decision-making and purchasing behavior [18]. In the online environment, users continue or discontinue using online services based on their prior experience of e-service use. In addition, their prior e-service use experience might also result in other post-adoption behavior, such as WOM, complaints, channel switching or willingness to pay. For example, e-service users may continue with previously used e-services and recommend those eservices to others—if their needs were met during the prior use. However, they may discontinue using the e-service, switch to an alternative and spread negative WOM to others. Clearly, both IS continuance and WOM are important when investigating e-service users’ post-adoption behaviors. 2.2. IS Continuance Model Recently, a new school of thought has been used to explore IS continuance, namely, the IS Continuance Model developed by Bhattacherjee [1]. The IS Continuance Model originates from the Technology Acceptance Model (TAM) [20] and Expectation Confirmation Theory (ECT) [21] and blends the IS domain and the marketing field. Bhattacherjee [1] integrates perceived usefulness from TAM into ECT, together with user satisfaction, to explain users’ IS continuance intentions (see Fig. 1). In the IS Continuance Model, IS continuance intention is expected to depend on three variables: user satisfaction; the confirmation of expectations through prior IS use; and post-adoption expectations, in which perceived usefulness represents post-adoption expectations. Together, user satisfaction and perceived usefulness determine IS continuance intention, whereas the confirmation of expectations exerts a positive influence on perceived usefulness and user satisfaction. User satisfaction was found to be a salient predictor of the IS continuance intention, according to the empirical test results obtained by Bhattacherjee [1]. The IS Continuance Model proposed by Bhattacherjee [1] has a solid theoretical foundation and focuses on the motivations for individual users’ IS continuance intentions that emerge in the IS post-adoption stage. It has been widely applied in IS research to examine IS users’ post-adoption behavior in different research contexts, such as e-learning [22–24], e-banking [25], e-government [13], e-commerce [26,27], virtual communities [28] and blogs [63]. The IS Continuance Model examines the effect of user cognition (perceived usefulness) and satisfaction on individual users’ post-adoption behavior. Prior IS research based on the IS Continuance Model has found that perceived usefulness and user

2.1. Post-adoption behaviors: IS continuance and WOM IS continuance was suggested as a primary behavioral outcome of the post-adoption stage that includes both the continuance intention and the continuous use of an IS [1,13,14]. According to Bhattacherjee [1], IS continuance refers to an IS user’s decision to continue using a particular IS for a long period [1,13,15]. IS continuance is a form of post-adoption behavior, and in IS research, the term ‘‘IS continuance’’ is often used as a synonym for postadoption behavior [16,17].

Fig. 1. ECT-based IS Continuance Model.

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satisfaction are the primary determinants of IS users’ continuance intentions [22,29]. For example, based on their research results related to the IS Continuance Model, Liao et al. [22] have found that user satisfaction and perceived usefulness are the primary determinants of individuals’ continuance intention to use online courses, and satisfaction is an antecedent of continuance intention superior to perceived usefulness for all user groups included in their research, including initial adopters, short-term users and long-term users. Similarly, Stone et al. (2013) have validated the IS Continuance Model in the context of electronic textbooks, finding that satisfaction is a stronger predictor than perceived usefulness of students’ continuance intention to use an electronic textbook, which is consistent with the findings of Bhattacherjee [1]. Recently, the IS Continuance Model has been further extended in the context of predicting other types of IS post-adoption behaviors, such as WOM and continuance behavior, or exploring other possible factors that may influence users’ IS continuance intention or behavior, such as perceived ease of use or perceived enjoyment (c.f. [13,23,24,27,30]. For instance, Lin et al. [27] have extended the IS Continuance Model by adding perceived playfulness as a predictor of continuance intention to use a Web portal and have found that individuals’ continuance intention to use a Web portal is primarily predicted by user satisfaction, followed by the Web portal’s perceived usefulness and finally, by its perceived playfulness. Shiau and Chao [63] have added perceived ease of use to the IS Continuance Model as a predictor of continuance intention and have compared three models (TAM, the IS Continuance Model and the integrated model) to explain continuance intention. They have found that the IS Continuance Model provides a better explanation of the continuance intention to use blogs. Their study also has reported that user satisfaction is the primary determinant of users’ continuance intention to use a blog, but perceived usefulness only influences continuance intention indirectly, via user satisfaction. Bhattacherjee et al. [13] have extended the IS Continuance Model by including continuance behavior and contingent factors such as IT selfefficacy and facilitating conditions as part of the model. They have found that the extended model provides a good explanation of IS users’ continuance behavior. Limayem and Cheung [23] have extended the IS Continuance Model by adding the moderating effect of IS habits on IS continuance intention and IS continuance behavior and by postulating direct links from satisfaction and prior behavior to IS continuance behavior. They have found that the IS Continuance Model is good at explaining IS continuance intention, and that the strength of intention in predicting continuance behavior is weakened if a user has a strong IS habit. In the marketing literature, the two constructs included in the IS Continuance Model—satisfaction and perceived usefulness—have also been argued to be salient predictors of WOM behavior [11,31– 34,50]. The widespread use of the IS Continuance Model to explore the post-adoption behavior of IS users in different research contexts makes it reasonable to use the IS Continuance Model as the theoretical framework to explore the two post-adoption behaviors (continuous intention and WOM behavior) of individual e-service users in this research.

3. Research model and hypotheses 3.1. IS Continuance Model based hypotheses Because the current research employs the IS Continuance Model proposed by Bhattacherjee [1] as its basic research framework, the following hypotheses—posited in the IS Continuance Model—are also suggested in this study:

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H1. User satisfaction positively affects continuance intention to use an e-service. H2. Perceived usefulness positively affects continuance intention to use an e-service. H3. Perceived usefulness positively affects user satisfaction with an e-service. H4. The extent of the confirmation of user expectations positively affects a user’s satisfaction with an e-service. H5. The extent of the confirmation of user expectations positively affects the perceived usefulness of an e-service. 3.2. Incorporating WOM into the IS Continuance Model Much of the prior IS research has paid particular attention to studying the continuance intention and continuance behavior of IS users. Though WOM has been suggested as an important postadoption behavior, the prior research has rarely explored the interdependence of WOM on IS continuance intention or behavior in a single model. This research attempted to explore IS users’ continuance intention and WOM from the ECT paradigm by incorporating WOM into the IS Continuance Model. Satisfaction has been analyzed in-depth in the marketing literature and has been found to play a critical role in predicting various consumer behavior outcomes, such as continuous purchasing and WOM [31–36]. Broadly speaking, we can say that satisfied customers are more likely to engage in or generate WOM and to become effective promoters of a product or service [37]. The prior research provides a variety of theoretical reasons that support a positive relationship between satisfaction and WOM, such as altruism, instrumentalism, ego defense and the reduction of cognitive dissonance [38,39]. The positive relationship between satisfaction and WOM has also been empirically validated in IS research [2,40,41]. Chea and Luo [40] have investigated three post-adoption behaviors of online service users and have found that satisfaction is positively associated with WOM behavior. Chen et al. [41] have validated the association between satisfaction and WOM in the Web 2.0 context. Taken together, these findings imply that in the online service environment, satisfied eservice customers are more likely to promote an e-service to others. Thus, the following hypothesis is proposed: H6. User satisfaction positively affects a user’s WOM behavior regarding an e-service.

Prior marketing literature argues that user satisfaction is not the only motivator of WOM behavior: other stimuli that motivate WOM behavior include loyalty and repurchase intention [42–45]. Prior marketing research shows that a retained customer is loyal due to his or her commitment to the product or service provider; that customer will recommend that others purchase the same product or service [46,69]. The relationship between repurchase intention and WOM has been empirically validated in the marketing research in different research contexts. Petrick [45] investigates repurchase intention and WOM behavior of cruise passengers and finds that repurchase intention is positively related to WOM behavior. Cruise passengers with higher repurchase intentions are more likely to positively recommend the cruise service to others than are those with lower repurchase intention. Olaru et al. [47] validate repurchase intention and WOM behavior in the context of organizational R&D services. Oh [44] investigates individuals’ post-purchase decision-making process in the context

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of luxury hotels and finds that repurchase intention strongly influences WOM. In the IS domain, research on post-adoption behavior primarily investigates IS users’ continuance intention to use an IS or the continuous use of an IS. Only a few marketing research studies explore the relationship between IS users’ continuance intention and WOM behavior. Chen et al. [41] suggest WOM as a predictor of IS users’ continuance intention and validate it in the context of Web 2.0. Their research suggests that Web 2.0 users who recommend Web 2.0 to others are more likely to continue their usage of Web 2.0. Taking into consideration the conclusions in the marketing literature concerning the positive relationship between repurchase intention and WOM, it is reasonable and rational to propose that e-service users who have the intention to continue their usage of an e-service will recommend that e-service to others. If the users themselves have no intention to continue using the e-service, it is very unlikely that they will recommend the e-service to others. Therefore, we argued that continuance intention should be a prerequisite of WOM. Thus, the following hypothesis is proposed: H7. Users’ continuance intention to use an e-service positively affects their WOM behavior regarding that e-service. Prior research findings in the marketing field suggest that a variety of product or service factors—including perceived usefulness or performance, perceived risks, and product and service complexity—are the primary drivers of an individual’s likelihood to spread WOM [48–51]. Mangold et al. [11] argue that individual consumers always recommend the most useful service options to others. Moldovan et al. [50] examine the affect of perceived usefulness on WOM in the research context of online shopping and find that perceived usefulness is positively related to WOM. The higher the level of usefulness, the more positively valenced the WOM. Conversely, lower levels of usefulness lead to a more negatively valenced WOM. In the IS literature, perceived usefulness is defined as users’ expectancy with respect to IS performance. It is argued that perceived usefulness is a salient attribute of IS services. Drawing on these findings, we propose that perceived usefulness affects the WOM behavior of e-service users, thus suggesting the following hypothesis: H8. Perceived usefulness positively affects users’ WOM behavior regarding an e-service. The research model in this study is presented in Fig. 2. 4. Research methodology 4.1. Research setting Online travel services were chosen as the empirical setting for this research primarily for the following two reasons. First, online

Fig. 2. The proposed research model.

travel services are among the most widely used e-services. Increasingly, individuals have been using online travel services such as searching for travel information, making travel plan and booking travel. Given that our proposed research model was specifically developed to explore the post-adoption behaviors of individual e-service users, online travel services are an appropriate setting in which to test it. Second, WOM is popular among individual online travel service users because UGC has become an important information source of travel-related information [52]. Accordingly, it is possible for us to examine online travel service users’ WOM behavior, which has been studied in the IS domain quite less than other post-adoption behaviors (i.e., continuance intention and continuance use). Thus, online travel services appear to offer an appropriate empirical setting for testing our research model. 4.2. Measurement Five constructs are included in the research model, including continuance intention, WOM, satisfaction, perceived usefulness and confirmation. All of the constructs were measured using multiple observed items, and the measurement items of the five constructs are primarily adapted from existing IS literature to ensure construct validity. Some modification and rewording has been conducted to specifically relate the research context to that of online travel services. A five-point Likert scale anchored from strongly disagree (1) to strongly agree (5) was used to measure each item of the instrument. IS continuance intention was measured using three items adapted from Bhattacherjee [1] and Bhattacherjee et al. [13]. The four items for the WOM construct were modified from Dolen et al. [37] and Maxham [53]. The user satisfaction items were adapted from Oliver [21] and Spreng et al. [54]. The construct confirmation was taken from Bhattacherjee [1]. Perceived usefulness was taken from Davis [20] and Bhattacherjee [1] and modified to reflect the utility of the online travel service. 4.3. Data collection In the IS domain, the survey approach has been widely adopted as one of the most popular research methods of studying human behavior and IS usage. The survey approach is particularly beneficial when research uses ‘‘what’’ and ‘‘how’’ questions [55]. The research questions in this study are typical ‘‘what’’ questions. Thus, we adopt a survey approach to explore our research questions. We developed a questionnaire to collect empirical data from Chinese online travel service users. Because the respondents are Chinese, the questionnaire was developed in Chinese. The questionnaire was first developed in English and then translated into Chinese by a professional translator at a translation company and one of this study’s researchers, whose mother tongue is Chinese. The questionnaire was primarily developed based on the prior literature on IS users’ IS continuance and post-adoption behaviors. To assess the validity and reliability of the research instrument, a pilot test was conducted among 15 individuals using the initial Chinese version of the questionnaire. Eleven of those individuals responded to the questionnaire with valuable feedback and comments. Their comments were used to further refine the questions in the questionnaire. The empirical data were collected by surveying the customers of a large travel agency in China. Several considerations led to the choice of company. First, the chosen travel agency has been in business for more than 20 years and has provided online travel services to its customers for approximately 10 years. Compared to

Please cite this article in press as: H. Li, Y. Liu, Understanding post-adoption behaviors of e-service users in the context of online travel services, Inf. Manage. (2014), http://dx.doi.org/10.1016/j.im.2014.07.004

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4.4. Validity and reliability

Demographic profiles

Category

Frequency

Percentage

Gender

Male Female Total

336 207 543

61.9 38.1 100.0

Age

18–25 26–35 36–45 46–55 56–65 Total

172 165 152 34 20 543

31.6 30.4 28.0 6.3 3.7 100.0

Education

College student Bachelor’s level Master’s level Ph.D. level Total

104 287 114 38 543

19.2 52.9 21.0 7.0 100.0

Online travel service booking experience

1–5 times More than 5 times Total

232 311 543

42.7 57.3 100.0

other Chinese travel service organizations, this company’s online travel service launched earlier and was more successful. Second, this travel agency has a database of 20,000 individual customers, thus making it possible to obtain a sufficiently large sample. One thousand five hundred customers were randomly selected from the company’s customer base and invited to participate in the research. The customers were asked to indicate what motivated their continuance intention regarding the use of the online travel service and their WOM behavior regarding the online travel service in relation to their prior experience using online travel services. Each customer received a letter containing a questionnaire from the online travel service company asking for their participation. Five hundred and forty-three usable responses were returned, providing the sample for this study. The respondent group ranged from 18 to 65 years of age; 61.9% were male and 38.1% were female. All of the respondents had used the Internet for more than 2 years; 42.7% had used online travel services one to five times and 57.3% had used such services six to ten times. The majority of respondents (90%) were between 18 and 45 years of age. Their demographic information is presented in Table 1.

Structural equation modeling (SEM) has been used to evaluate this study’s research model and hypotheses. Partial Least Squares (PLS) was employed to obtain estimates for both the measurement and structural parameters of the proposed model [56]. An algorithm procedure in PLS was employed to test the measurement model. Convergent validity indicates the degree to which the items on a scale that are assumed theoretically associated are related in reality. Convergent validity is evaluated using the following three criteria: (i) the estimates of the factor loadings of the measurements on the respective constructs must be significant and exceed 0.7; (ii) the composite reliability (CR) of all the constructs must be over 0.8; and (iii) the average variance extracted (AVE) by each construct must be above the cut-off value of 0.5 [57–59]. The test results in this study showed that most of the factor loadings of the measurement items in the research model are satisfactory and have a cut-off value above 0.7, except for three items whose results are acceptable with cut-off values between 0.5 and 0.7. The values of composite reliability (CR) and average extracted variance (AVE) satisfy the threshold values of 0.7 and 0.5, respectively (see Table 2). The Cronbach’s alpha values range from 0.700 to 0.819, thus reaching the 0.7 level. The test results indicated a good internal consistency and confirmed the reliability of the research instrument, supporting the convergent validity of the research data [57–59]. Testing discriminant validity involves checking whether the measurements reflect the construct in question or whether they reflect another related construct of the research. Discriminant validity can be verified by testing the estimates of the square root of the AVE for each construct. If the variance of the square root of the AVE for each construct is larger than any correlation between the tested construct and any other construct, then discriminant validity is supported [60]. The test results in this study show that the square root of the AVE for each construct is greater than the correlation estimates with other constructs (see Table 3), thus meeting the discriminant validity criteria [60]. A cross-loading method was further used to assess the discriminant validity of the measures employed in this study. As indicated in Table 4, the loadings and cross-loadings of all of the items in the proposed research model satisfied the two criteria for discriminant validity suggested by Chin [57, p. 321], who states

Table 2 The measurement model. Construct

Items

CR

AVE

Cronbach’s alpha

Factor loading

St. error

t-value

Perceived usefulness

PU1 PU2 PU3 PU4

0.806

0.512

0.700

0.753 0.785 0.738 0.570

0.026 0.030 0.032 0.054

29.684 25.707 22.682 10.437

Confirmation

CON1 CON2 CON3

0.840

0.638

0.729

0.701 0.867 0.814

0.067 0.026 0.052

9.826 32.959 14.900

Satisfaction

SAT1 SAT2

0.890

0.687

0.820

0.867 0.789

0.021 0.028

40.611 27.570

Continuance intention

CI1 CI2 CI3

0.892

0.734

0.819

0.875 0.859 0.837

0.013 0.018 0.018

66.346 47.322 47.022

Word of mouth

WOM1 WOM2 WOM3 WOM4

0.814

0.531

0.747

0.831 0.864 0.587 0.589

0.019 0.018 0.065 0.068

43.144 48.582 9.039 8.663

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6 Table 3 Correlation Matrix and Discriminant Assessment.

PU CON SAT CI WOM

PU

CON

SAT

CI

WOM

0.715 0.128 0.429 0.416 0.415

0.798 0.321 0.101 0.161

0.827 0.508 0.371

0.856 0.610

0.728

Note: the diagonal elements represent the square roots of the AVE; the off-diagonal elements are the correlation estimates.

that ‘‘If an indicator loads higher with other Latent Variables (LVs) than the one it is intended to measure, the researcher may wish to reconsider its appropriateness because it is unclear which construct or constructs it is actually reflecting. Furthermore, we should expect each block of indicators to load higher for its respective LV than indicators for other LVs.’’ The test results indicate that each construct in the research model is more closely associated with its own measurements than with those of any other construct. Thus, the discriminant validity of this study is supported [60]. 4.5. Research model and hypotheses test The effects and the statistical significance of the parameters in the SEM were tested using a bootstrapping procedure in PLS. A graphical description of the research results is presented in Fig. 3, including both the path coefficients and the variances explained. The test results indicate that almost all of the proposed hypotheses are significant, except for H6 (SAT to WOM). IS continuance intention is found to be positively influenced by both perceived usefulness (b = 0.240, p < 0.001) and satisfaction (b = 0.392, p < 0.001). WOM is positively associated with both IS continuance intention (b = 0.523, p < 0.001) and perceived usefulness (b = 0.189, p < 0.001). User satisfaction is found to be influenced by perceived usefulness (b = 0.404, p < 0.001) and confirmation (b = 0.222, p < 0.001), and confirmation has significant effects on perceived usefulness (b = 0.108, p < 0.001). The proposed intention model explains 29.7% of IS continuance intention and 40.1% of WOM. Perceived usefulness and confirmation account for 23.7% of satisfaction, whereas confirmation accounts for only 1.4% of perceived usefulness.

Table 4 Loadings and cross-loadings for the measures in the research model. Items

PU

CON

SAT

CI

WOM

PU1 PU2 PU3 PU4 CON1 CON2 CON3 SAT1 SAT2 CI1 CI2 CI3 WOM1 WOM2 WOM3 WOM4

0.753 0.785 0.738 0.570 0.005 0.193 0.505 0.407 0.331 0.403 0.319 0.341 0.297 0.467 0.124 0.135

0.090 0.129 0.052 0.120 0.701 0.867 0.814 0.376 0.053 0.142 0.100 0.040 0.202 0.189 0.156 0.158

0.403 0.392 0.248 0.208 0.180 0.265 0.189 0.867 0.789 0.404 0.409 0.432 0.218 0.415 -0.029 0.285

0.379 0.261 0.374 0.152 0.044 0.134 0.037 0.426 0.403 0.875 0.859 0.837 0.556 0.580 0.113 0.242

0.224 0.390 0.278 0.302 0.191 0.128 0.105 0.301 0.305 0.506 0.420 0.525 0.831 0.864 0.587 0.589

Fig. 3. Structural analysis of the research model. Note: ***: p-value < 0.001; significant path; not significant path.

5. Discussion This study investigates two different post-adoption behaviors of IS users: continuance intention and the WOM behavior, both in the research context of online travel services. In this study, perceived usefulness and satisfaction are found to be the two factors that motivate the continuance intention to use online travel services. In addition, perceived usefulness and continuance intention are the two factors that determine online travel service users’ WOM behavior. These findings answer our research questions about which factors motivate individuals’ IS continuance intention and WOM behavior and about the relationship between continuance intention and WOM behavior. The research results show that the factors that motivate the continuance intention and WOM behavior of online travel service users in the post-adoption stage are different, although those behaviors are positively associated. Satisfaction was found to be the stronger factor influencing continuance intention (b = 0.392). Perceived usefulness loses its saliency when motivating continuous intention to use online travel services in the post-adoption stage compared to its strong saliency in the initial adoption stage. In prior IS continuance research based on the IS Continuance Model, inconsistent findings on the impact of perceived usefulness and satisfaction on continuance intention have frequently been reported. Some research has found that perceived usefulness is a stronger predictor than satisfaction of continuance intention [23,61,62], whereas other research has found that satisfaction is the stronger predictor [24,29,30]. However, Shiau and Chau [63] have found that satisfaction maintains its saliency when motivating individuals’ continuance intention to use blogs, whereas perceived usefulness is not a significant predictor of continuance intention. The inconsistent findings of prior research raise concerns about the impact of postadoption usefulness perception on continuance intention. This study’s findings are consistent with the contention in the IS Continuance Model that satisfied users are essential for motivating IS continuance intention. In practice, user satisfaction is based on experiences with prior online travel service use. Compared to users’ cognitive beliefs about online travel service usage, such as the perceived usefulness of online travel services, user satisfaction is much more realistic and unbiased—and less susceptible to change—because user perceptions of the usefulness of online travel services change before and after their use of those services. Thus, users decide whether to continue or discontinue their use of online travel services by relying more on their satisfaction rather than on their cognitive beliefs (such as perceived usefulness) about the use of online travel services. A comparison of the results of our findings with Bhattacherjee [1] shows that in our research, satisfaction (b = 0.392 here, b = 0.567 in Bhattacherjee) and perceived usefulness (b = 0.240 here, b = 0.294 in Bhattacherjee) have a weaker impact on

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continuance intention. This might be due to different research contexts. Bhattacherjee [1] investigated users’ continuance intention in the context of utilitarian-oriented online banking services, and perceived usefulness and confirmation were found to explain 33% of user satisfaction. This research investigates users’ continuance intention with respect to online travel services and found that perceived usefulness and confirmation explain 23.7% of user satisfaction. According to Chung and Buhalis [64], online travel services are multi-purpose-oriented IS. Online travel services can be used to acquire information for the purpose of making travel decisions or for hedonic and socio-psychological reasons such as collecting pictures for fun, sharing travel information and obtaining knowledge for social presence. Thus, there might be other constructs that influence online travel service users’ satisfaction and usage decisions, such as perceived ease of use and perceived enjoyment. This can help clarify why perceived usefulness and confirmation explain the smaller covariance of the construct of satisfaction and why perceived usefulness and satisfaction exert a weaker influence on continuance intention in our research model. The study results also specify perceived usefulness and continuance intention as the two motivators of online travel service users’ WOM behavior. Continuance intention exerts the stronger influence on users’ WOM behavior (b = 0.523), whereas perceived usefulness (b = 0.189) influences the WOM behavior of online travel service users both directly and indirectly via continuance intention. In other words, online travel services users will give positive WOM about a service when they perceive it as useful and intend to continue using it. Unexpectedly, user satisfaction was found to influence the WOM behavior of online travel service users not directly, but indirectly via continuance intention. This finding contrasts with the argument that user satisfaction is a salient motivator behind WOM behavior [40,41]. Chea and Luo [40] have applied the IS Continuance Model to examine users’ continuance intention and two other post-adoption behaviors—recommendation and complaint—and have found that user satisfaction has a positive influence on both continuance intention and recommendation behavior. Chen et al. [41] have found that satisfaction is a strong predictor of WOM. To help explain the non-significant relationship between user satisfaction and WOM, we deleted the mediator of continuance intention between user satisfaction and WOM and conducted a model test. The test results show that user satisfaction affects WOM (b = 0.224) significantly, and perceived usefulness exerts stronger influence on WOM (b = 0.329) than user satisfaction. User satisfaction and perceived usefulness explains 22.4% of the variance in WOM, whereas in the extended IS Continuance Model, perceived usefulness and continuance intention account for 40.1% of the variance in WOM. This difference indicated that the extended IS Continuance Model performs better on the construct of WOM than the latter one because the addition of continuance intention provides additional explanatory power to WOM. The explanation for this result is that perceived usefulness has been suggested as being crucial in predicting users’ acceptance of an IS. Perceived usefulness refers to users’ subjective perceptions of how IS use will improve their performance [65], and it captures the instrumentality or the rational component of users’ decisions [66]. When online travel service users use WOM to recommend an online travel service to potential users, they should first feel that the online travel service would be useful for others. Thus, they rely on their subjective perceptions of the usefulness of the IS after they use it. In other words, they would like to make the WOM decision rationally. Although our finding on the relationship between user satisfaction and WOM contradicts some prior research, it is

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explainable. In general, satisfied users are more likely to make recommendations to others. According to Bhattacherjee [66], the affective component is embodied in satisfaction. The relative strengths of the affective and rational components determine users’ decision process. For example, online travel service users may recommend online travel services to others, even though they are not satisfied with their prior IS use. In their decision-making process, the rational component (perceived usefulness) is much more relevant than their subjective attitude (satisfaction) when making the WOM decision. Thus, when referring to online travel services, satisfaction may not be a very critical factor predicting WOM. Customers primarily use online travel services for decisionmaking purposes. Most online travel service providers can provide good customer service. Making online travel service more useful could contribute more to customers’ WOM behavior. Although user satisfaction is not found to be directly associated with WOM, its significant association with continuance intention remains unchanged. Continuance intention mediates the influence of user satisfaction on WOM behavior. This finding corresponds to the conclusions of prior research. Obviously, user satisfaction cannot be ignored in examining online travel service users’ postadoption behaviors. The extended IS Continuance Model could provide a better understanding of how perceived usefulness and user satisfaction predict online travel service users’ post-adoption behaviors. The study results find perceived usefulness and confirmation to be the two drivers of user satisfaction, which is consistent with prior research results about IS users’ post-adoption behavior. In addition, it was assumed that confirmation would affect perceived usefulness to a significant extent. However, it only explained 1.4% of perceived usefulness. A comparison of the results of our findings with the results of Bhattacherjee [1] shows that confirmation (b = 0.108 here, b = 0.451 in Bhattacherjee) has a weaker impact on perceived usefulness in our research. A possible explanation of the weak connection between confirmation and perceived usefulness in our research might also be due to the different research contexts. Bhattacherjee [1] has argued that users may have low initial usefulness perceptions of a new IS because they are not sure what to expect from its use. Users might still use the IS and form more concrete perceptions of the usefulness of the IS from their usage experience. Their low initial perceptions of usefulness can be improved through their actual usage behavior as a confirmation process. In other words, users’ perceptions of usefulness will be adjusted upward because of their confirmed IS usage experience. With the travel industry’s widespread deployment of the Internet, online users can easily initiate high perceptions of the usefulness of online travel services, and they know what to expect from their usage of online travel services. Users initiate high usefulness perceptions of online travel services, which cannot be drastically improved by their confirmed experience of using online travel services. Therefore, confirmed usage of online travel services results in a limited change to users’ perceptions of the usefulness of those services. Bhattacherjee [1] has conducted an empirical study in the context of online banking services. In general, users cannot test an online banking service without opening an account. Therefore, whereas users can intuitively see the value of online travel services, they cannot do so with online banking services, which might result in low usefulness perceptions of online banking services. 6. Theoretical and managerial implications 6.1. Theoretical implications The goal of this research was to identify the salient determinants of IS users’ two post-adoption behaviors (IS

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continuance intention and WOM behavior) and to examine the possible interdependence of those behaviors. To achieve this goal, the IS Continuance Model was adopted as the basic research framework and was integrated with WOM to theorize a model that explores post-adoption behaviors. In this research, the IS Continuance Model was found to be a good model to explore IS users’ WOM behavior and IS continuance intention. Prior IS research has also employed the IS Continuance Model to explore other post-adoption behavior, such as IS continuance behavior [13,23]. We arrive at the conclusion that the IS Continuance Model is a good model to investigate different post-adoption behaviors, including IS continuance intention, IS continuance behavior and WOM behavior. WOM affects both the diffusion and sale of technology [67]. In the online environment, WOM campaigns have become popular methods of promoting technology diffusion. However, less is known about what causes IS users to promote an IS through WOM. This study examines IS users’ WOM and continuance intention. This study also attempts to examine the role of continuance intention in IS users’ WOM behavior at the post-adoption stage, at least in the context of e-services, and sheds light on the drivers of WOM from the perspective of technology usage. Our research results indicate that WOM behavior is primarily determined by IS users’ continuance intention. The stronger the IS continuance intention, the more likely users are to use WOM to promote that IS. The above association between IS continuance intention and WOM suggests not only that continuance intention is important in predicting continuous use behavior regarding an IS but also that continuance intention also strongly affects other post-adoption behaviors, such as WOM. Retaining IS users can indirectly help attract other potential IS users. Thus, IS continuance intention and WOM are closely associated. Perceived usefulness was found to have a positive influence on WOM behavior and continuance intention. Perceived usefulness predicts e-service users’ WOM behavior, both directly and indirectly, via continuance intention. The results imply that a user’s cognition of the usefulness of an IS remains important factor in predicting IS users’ post-adoption behaviors. The effect of user satisfaction was found to exert a strong influence on e-service users’ continuance intention and perceived usefulness. This suggests that both the affect and cognitions of IS are important motivators for IS users’ continuance intention. The result also supports prior findings, which argue that IS users’ postadoption behavior is influenced by those users’ affect and cognition and that there is an interplay between the two [40]. In the online environment, competitors are capable of emulating a competitor’s technological service attributes and features. However, it is more difficult to create an emotional bond or connection, although doing so is a clear goal because an emotional bond or connection creates long-term customer trust and develops healthy customer relationships. Unexpectedly, user satisfaction was found not to be a determinant of e-service users’ WOM behavior. This turns out that the relationship between satisfaction and WOM is asymmetrical: satisfaction does not ensure IS users’ WOM behavior, and user satisfaction influences WOM behavior only indirectly via IS continuance intention. 6.2. Managerial implications Chung and Darke [68] argue that e-service providers should not only try to retain their existing users but also motivate existing users to promote their e-services via positive WOM. In that respect, this study offers new insights into both retaining existing e-service users and developing positive WOM in the context of online travel services.

First, this study provides online travel service companies’ marketing managers with strong evidence about the positive value of selecting retained customers for WOM marketing campaigns. Thus, they should attempt to recruit retained users for those campaigns. Second, online travel service providers should try to maximize user satisfaction. The results show that user satisfaction can not only enhance the continuance intention of online travel service users but also influence their WOM behavior indirectly via continuance intention. When the expectations of online travel service users are met, user satisfaction is generated. Therefore, online travel service providers should try to meet the needs of their online customers, thus generating a cycle of satisfaction and eservice improvement. Improved user satisfaction will motivate online customers to continue using the service, giving them reasons to promote it to others. Consequently, the retention rate of online travel services’ customers will be increased and their positive WOM behavior will be indirectly encouraged, thus helping online travel service providers realize service marketing benefits in the electronic marketplace via different channels. Our results show that user satisfaction is not a significant determinant of WOM behavior. Thus, online travel service providers should motivate online travel service users to generate WOM by improving their service (such as different aspects of service quality), not user satisfaction. Third, online travel service providers should realize the importance of the perceived usefulness of online travel services in the post-adoption stage. To improve user perception of the usefulness of their services, online travel companies should try to improve the utilitarian aspects of their websites. Perceived usefulness, as an extrinsic motivation for online travel service users can help online travel service providers improve user satisfaction, motivate users’ continuance intention and encourage their WOM behavior.

7. Limitations and implications for future research There are some limitations to this present study. First, this study was conducted in China. It is recommended that the study be replicated in other nations to produce an international sample and to investigate possible differences in online travel users’ postadoption behavior in different nations and cultures. Second, the study explored the relationships between IS users’ cognition and satisfaction and their post-adoption behaviors, such as continuance intention and WOM. However, the influence of other aspects on IS users’ post-adoption behaviors, such as service quality, perceived reputation, trust, social presence, mood and emotion, has not been considered. Thus, further research should also examine different aspects of IS users’ post-adoption behavior. Meanwhile, we only investigated two post-adoption behaviors (continuance intention and WOM), leaving other post-adoption behaviors unexplored. Thus, it is necessary to investigate whether the IS Continuance Model is also a good model to explore other post-adoption behaviors, such as repurchasing behavior, and whether the model can be used to explore the associations among various post-adoption behaviors of IS users, such as continuance intention, WOM and repurchasing intention. Finally, this study was conducted in the context of online travel services. The research context is different from the research settings of prior studies based on the IS Continuance Model, as discussed in the literature review section. Thus, the results of this study can be generalized to the online travel service sector, but not to other sectors. Therefore, future research should expand the research model to other contexts to validate the research model empirically and to achieve the goal of obtaining generalizable research results.

Please cite this article in press as: H. Li, Y. Liu, Understanding post-adoption behaviors of e-service users in the context of online travel services, Inf. Manage. (2014), http://dx.doi.org/10.1016/j.im.2014.07.004

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Acknowledgements We would like to thank the anonymous reviewers for their constructive comments. This research was partially funded by the China MOE Project of Humanities and Social Sciences (No. 13YJC630228), the National Natural Science Foundation of China (No. 71362027) and the Finnish Foundation for Economic Education. An earlier version of this paper was published in the Proceedings of the 19th European Conference on Information Systems. Appendix Questionnaire Construct

Item

Source

PU

PU1: It is possible to obtain cheaper prices using online travel services PU2: Online travel services are convenient PU3: Using online travel services can save me time PU4: For me, online travel services are useful

[1,20]

CON

CON1: My past online travel service encounter was better than I had expected CON2: Most of my expectations about using online travel services were confirmed CON3: On the whole, my prior online travel service encounter experience was positive

[1]

SAT

SAT1: I am satisfied with my past experience of using online travel services SAT2: I am very pleased with my past experience of using online travel services

[21,54]

CI

CI1: I intend to use online travel services in the future CI2: I intend to use online travel services more in the future CI3: I intend to use similar competing online travel services rather than other alternatives, such as traditional travel agencies

[1,13]

WOM

WOM1: I would recommend online travel services to people I know WOM2: I intend to recommend online travel services to people I know WOM3: I would recommend online travel services in an Internet discussion forum WOM4 I intend to recommend online travel services in an Internet discussion forum

[37,53]

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Please cite this article in press as: H. Li, Y. Liu, Understanding post-adoption behaviors of e-service users in the context of online travel services, Inf. Manage. (2014), http://dx.doi.org/10.1016/j.im.2014.07.004