L’ENGAGEMENT DANS LE BOUCHE A OREILLE ELECTRONIQUE : POURQUOI ET COMMENT LES TOURISTES PARTAGENT-ILS LEURS EXPERIENCES SUR LES PLATEFORMES D’OPINION EN LIGNE ? Wafa Hammedi* Université de Namur - CeRCLe (Centre de Recherche sur la Consommation et les Loisirs) [email protected] Alain Decrop Université de Namur - Cercle [email protected] Justine Virlée Université de Namur, CeRCle [email protected] * [email protected] Résumé : Toutes les études antérieures ont démontré que sur les plateformes en ligne peer-to peer, la plupart des participants sont des lecteurs passifs plutôt que des contributeurs actifs. Seulement 11 à 28% des voyageurs partagent leurs opinions sur Internet après une expérience. En dépit de leurs importantes contributions à comprendre les facteurs moteurs de bouche- àoreille électronique, la plupart des recherches antérieures ont porté sur les lecteurs passifs plutôt que sur les fournisseurs d’information ou donneurs d'opinion. Cette recherche vise à combler cette lacune en ayant comme objectif de comprendre en profondeur l'intention de partager d’expériences en ligne. La théorie du comportement planifié sera utilisée en tant que cadre théorique. Les données récoltées auprès d’un large panel de vacanciers belges ont été utilisées pour tester notre modèle. Des analyses de clusters, équations structurelles et analyses multi-groupes ont permis ont été mobilisés pour valider nos hypothèses. Mots clef : E-wom , engagement en ligne, plateformes peer-to peer E-WOM ENGAGEMENT: WHAT DRIVES TOURISTS TO SHARE EXPERIENCES IN ONLINE OPINION PLATFORMS? Abstract : Previous studies have shown that in the peer-to peer platforms, most of participants are passive readers rather than active contributors.. Only 11 to 28% of the travellers share their opinions on Internet after an experience. Despite their huge contribution to understand the key drivers of e-WOM, most of previous research has focused on information and opinion readers rather than on information providers or opinion givers. This paper aims to fill this gap to some extent by focusing on the latter and investigating the drivers of the intention to share experiences online. Such an understanding is vital for the tourism industry in order to increase the proportion of opinion givers among tourists. The theory of planned behaviour will be used as a framework to investigate four major antecedents of the intention to share experiences online. Our model was tested using data collected by the means of web-based survey among a large panel of French-speaking Belgian vacationers. Clusters analysis, Path modelling analysis and Multi-group analysis have been used to test the model. Keywords : EWOM ; Online Engagement ; Peer-to Peer Platforms ; Lurkers ; Posters E-WOM ENGAGEMENT: WHAT DRIVES TOURISTS TO SHARE EXPERIENCES IN ONLINE OPINION PLATFORMS? Introduction The digital revolution has brought foundational changes in marketing, customer relationship, and brand management (Kaplan and Haenlein, 2010). Today, consumers increasingly use digital media not just to search for products or services but also to engage with the companies they buy from and with other consumers in order to share insights or experiences (Kaplan and Haenlein, 2010, Kaplan and Haenlein, 2011). ICT has engendered radically new ways of interacting (Malthouse and Hofacker, 2010) with thousands of digital platforms including social networks, blogs, discussion groups, and podcasts. Nowadays, consumers are able to share their opinions more easily, more quickly and with a larger number of persons than in an offline context (Dellarocas, 2003). This phenomenon of customer-to-customer or peer-to-peer interactions in the cyberspace is called “e-word-ofmouth” (e-WOM). Hennig-Thurau and Walsh (2003) defined e-WOM as “any positive or negative statement made by potential, actual, or former customers about a product or company, and which is made available to a multitude of people and institutions”. Interpersonal influence is particularly powerful in the tourism industry because tourism services such as the online booking of a hotel is considered as an “experience good” in the sense that the quality of the service is unknown before its consumption (Klein, 1998). Given the high-involvement nature of the decision for a vacation, customers are increasingly relying on consumers’ opinion (Bronner and de Hoog, 2011). Therefore, many consumers consult tourists’ reviews shared on dedicated online platforms because this information is perceived as more credible and up to date than the information from the firm (Litvin et al., 2008, Gretzel and Yoo, 2008). Some researchers even consider that interpersonal influence and e-WOM are among the most important sources of information (Litvin et al., 2008). E-WOM can be beneficial for companies (Litvin et al., 2008). Compete (2007) estimated that e-WOM generates more than US$ 10 billion in online travel purchases every year. Moreover, e-WOM improves consumer awareness of brands, products or services, especially for lesser-known products (Vermeulen and Seegers, 2009). E-WOM also gives companies the opportunity to be aware of what is said by customers about their offerings, and to take these comments into account in order to continually improve their offerings (Litvin et al., 2008). The challenge for tourism providers is to ensure that their customers will share their experiences (van Doorn et al., 2010). A substantial part of the tourism and hotel decision-making process occurs online. According to a survey by TNS-Sofres (2010), 69% of European consumers search for online information while planning their trip; 44% visit official tour-operators’ websites before making their purchase, and 68% of Web users prefer to use search engines that provide access to varied sources of information (forums, price comparators, personal blogs, brand communities etc.), the content of which is beyond the direct reach of marketers. Finally, 30% of vacationers seek the advice of other consumers while searching for information, and such recommendations appear to have a deciding influence in 87% of cases. Previous studies have shown that in the peer-to peer platforms, most of participants are passive readers rather than active contributors (Bronner and de Hoog, 2011, N'Goala and Morrongiello, 2014). Only 11 to 28% of the travelers share their opinions on Internet after an experience (Bronner and de Hoog, 2011, N'Goala and Morrongiello, 2014). A large proportion of consumers are thus influenced by a small segment of consumers who actively engage in a e-WOM (Moe et al., 2011). Despite their huge contribution to understand the key drivers of e-WOM, most of previous research has focused on information and opinion readers rather than on information providers or opinion givers (Bronner and de Hoog, 2011, Liang et al., 2013). This paper aims to fill this gap to some extent by focusing on the latter and investigating the drivers of the intention to share experiences online. Such an understanding is vital for the tourism industry in order to increase the proportion of opinion givers among tourists (N'Goala and Morrongiello, 2014). The theory of planned behavior will be used as a framework to investigate four major antecedents of the intention to share experiences online, i.e.: (1) consumers’ attitude towards opinion sharing on a peer-to-peer platform, (2) subjective norms, (3) consumers’ perceived self-efficacy, and (4) perceived ease of use. Literature review Previous studies show that word-of-mouth significantly influences consumer purchases of goods and services, and e-WOM is a form of this communication (Engel et al., 1969, Katz and Lazarsfeld, 1955). According to Litvin et al. (2008), e-WOM includes both conversations between firms and customers, and conversations among consumers themselves. E-WOM can be characterized by two dimensions: the communication scope and the level of interactivity (Litvin et al., 2008). It means that e-WOM can take the form of a one-to-one conversation (i.e. emails), a one-to-many (i.e. review sites), or a many-to-many conversation (i.e. virtual communities). Furthermore, e-WOM can be asynchronous, which implies a low level of interactivity (i.e. emails, review sites, blogs) or synchronous, which means a high level of interactivity (i.e. chatrooms, instant messaging). Existing academic research has primarily considered readers and the few studies focusing on opinion givers devote their attention to the antecedents of e-WOM related to the service experience such as customer satisfaction (Liang et al., 2013, Yang, 2013), service quality (Jeong and Jang, 2011), atmosphere (Jeong and Jang, 2011), price fairness (Jeong and Jang, 2011). Other studies focus on the motives for sharing experience such as egoism (Yang, 2013, Cheung and Lee, 2012), altruism(N'Goala and Morrongiello, 2014, Cheung and Lee, 2012), social benefits (Yap et al., 2013), exerting power (N'Goala and Morrongiello, 2014), revenge (N'Goala and Morrongiello, 2014), or helping the company (N'Goala and Morrongiello, 2014). However, most of the variance of customers’ (intention to) use e-WOM communication remains unexplained (Liang et al., 2013, N'Goala and Morrongiello, 2014). This suggests that the drivers of e-WOM in the tourism sector may not only be related to the service experience itself. Given e-WOM’s ability to strongly affect the performance of firms, especially in the tourism sector, researchers are increasingly studying e-WOM in this context (Cantallops and Salvi, 2014). For example, Bronner and de Hoog (2011) aim to understand what kind of vacationers post their opinion on review sites, with which motivations, on what kind of websites, and what are the message characteristics. Many researchers, including N'Goala and Morrongiello (2014), Cantallops and Salvi (2014), and Casaló et al. (2010) focus on the motivations for travelers to actively engage in WOM and to share their experience about a stay or a flight with other consumers. Then, Litvin et al. (2008) study the phenomenon of interpersonal influence and WOM in tourism and hospitality, and suggest strategies to manage e-WOM in this sector. A final example is related to Ye et al. (2009)’s research, which aims at quantifying the impact of e-WOM on the booking of hotel rooms. Hypotheses The theory of planned behavior (Ajzen, 1991) suggests two major antecedents to the individual’s intention to perform a particular behavior: (1) attitude towards the behavior and (2) subjective norms. Attitude towards the behavior is defined as “the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” (Ajzen, 1991). Subjective norms are defined as “the person perceived social pressure to perform or not to perform the behavior” (Ajzen, 1991). Moreover, Ajzen (1991) suggests that the behavior may be influenced by the “perceived behavioral control” referring to “people’s perception of the ease or difficulty of performing the behavior of interest”. In this study, the TPB is adapted to the context of e-WOM. On the one hand, we translate “attitude towards the behavior” into the attitude towards using an opinion platform. On the other hand, “subjective norms” is related to the pressure the individual receives from relative people to use such an opinion platform. Finally, the antecedent “perceived control” is divided in two variables: perceived self-efficacy and the perceived ease of use. Perceived selfefficacy is “a form of self-evaluation that influences decisions about what behaviors to undertake, the amount of effort and persistence to put forth when faced with obstacles, and finally, the mastery of the behavior“ (Hsu et al., 2007). It has often been used within the TPB (Conner and Armitage, 1998) and in studies on the usage of computer (Igbaria and Iivari, 1995) and the information system literature (Hsu et al., 2007). Perceived ease of use refers to the cost of using the opinion platform (Casaló et al., 2010); it is thus related to consumers’ resources and opportunities (Ajzen, 1991). Based on the TPB framework as adapted in our context, we formulate the following hypotheses: H1: The attitude towards using an opinion platform has a positive effect on the tourist’s intention to share experiences online H2: Subjective norms about sharing opinions online have a positive effect on the tourist’s intention to share experiences online H3: Perceived self-efficacy has a positive effect on the tourist’s attitude towards using an opinion platform H4: Perceived ease-of-use has a positive effect on the tourist’s attitude towards using an opinion platform Figure 1: Research model Perceived Self-efficacy H3 Perceived Ease of use H4 Attitude towards using an opinion platform Subjective Norms H1 H2 Intention to share experiences Finally, our study considers the different behaviors displayed by the users of opinion platforms. These include a series of active practices such as posting reviews or sharing pictures or and more passive activities such as reading others’ comments or getting general information (Tonteri et al., 2011). Of course, we may expect different weights in the path model of Figure 1 depending on whether users are more active (“posters”) or more passive (“lurkers”). Methods Data were collected by means of a web-based survey among a large panel of Frenchspeaking Belgian vacationers who are members of an international vacationer’s panel of about over 8 million members. Data collection was performed between June and July 2015. To be included in the study, participants should have visited Booking or TripAdvisor in the last 6 months. Respondents were asked to consider one of these two most popular online opinion platforms in France and in Belgium (AFP, 2015) to fill in the survey. The questionnaire was extensively pretested and revised. We obtained in total 475 valid responses. Participants were on average 38 years old; 46,5% of the respondents were female and 53,5% were male. All variables are measured using multidimensional scales, which were selected and adapted from existing literature and previously validated. The reliability of these scales was assessed by using Cronbach’s alphas. The alphas for all the constructs are above the 0.7 threshold. An exploratory factor analysis was conducted to explore data structures (Payan and McFarland, 2005). The results supported the 5 factors solution. No significant cross-loadings were found. The factors account for 80% of total variance. We also tested for the constructs’ discriminant validity. In our study, none of the intercorrelations between the constructs exceed the square root of the AVE and all values of AVE exceed 0.5 (Fornell and Larcker, 1981) Data analysis was conducted in three steps. First, a two-step cluster analysis was performed in order to identify groups of users based on their behaviors on the platform. The first step was performed using the SPSS software. Secondly, we tested our conceptual model with structural equation modeling (SEM). We used partial least squares (PLS) regression (White et al., 2003) as implemented in SmartPLS (Ringle et al., 2005). PLS generates estimates of standardized regression coefficients (beta values) for the model’s paths and factor loadings for the measurement items. To test the effects of the parameters in the model, a bootstrapping procedure (5,000 resamples and construct-level correction) has been used (Chin, 1998). Finally, to deeply understand how these relationships differ according to level of activity on the platform, we compared the path model for both passive vs. active members. SmartPLS Multi-group analysis has been used. The multi-group analysis allows to test if predefined data groups have significant differences in their group-specific parameter estimates (e.g., outer weights, outer loadings and path coefficients) (Sarstedt et al., 2011). Results Cluster analysis As expected, two groups of users were obtained based on their behaviors on opinion platforms: active (47%) and passive (53%) users. The first group, i.e. “active users”, scored relatively higher on activities such as sharing content, writing reviews, interacting with other members through comments, responding to questions from other members etc. In contrast, the second group, i.e. “passive members” scored high on activities such as reading, getting answer, getting information and scored lower on behaviors related to sharing experience, photos, videos etc. Path model testing The model of Figure 2 explains 90,1% (p-value=0.000) of the intention to share experience on online opinion platforms and 82,5% (p-value=0.000) of the consumers’ attitude towards sharing experience. The data were tested for nonresponse bias by comparing early respondents (25% of the sample) with late respondents (25% of the sample) as recommended by Armstrong and Overton (1977). We find support for the 5 hypotheses at a significant level (α) of 0.05 (one-tailed). The consumers’ intention to share experience on online opinion platforms is highly impacted by the consumer’s attitude towards sharing experience (β=0,471, p-value=0,000) and subjective norms (β=0,284, p-value=0,000). The most influential variable on attitude towards opinion platform is the member’s perceived self-efficacy (β=0,51, p-value = 0,000). It is followed by the perceived ease-of-use (β=0,264, p-value=0,000). Figure 2: Path model for the whole sample (n=475) SE 0.51(0.00) ATT 0.47(0.00) R2=0.83 INTENT R2=0.91 0.26 (0.00) PEOU SN 0.28 (0.00) Multi-groups analysis To understand deeply how these relationship differ according to the level of activity. We compared the path model for passive vs. active users. Table 1 displays the relevant estimates of means and weights resulting from SmartPLS Multi-group analysis as well as a significance test for difference in the estimates. Regarding the passive group, it appears that the model explains 38% (p-value = 0.000) of the attitude towards sharing experience and 21,5% (p-value = 0.000) of the intention to share experience. In this group, the most impacting variable is the perceived self-efficacy (β= 0.495, p-value =0.000) followed by subjective norms (β= 0.190, p-value = 0.020) and the perceived ease-of-use (β= 0.180, pvalue = 0.016). The intention to share experience is impacted by the attitude towards sharing experience (β=0.348, p-value=0.000). For the active group, the explanative power of the path model is even higher (54% of the attitude and 52% of the intention to share experience). Intention to share experience is strongly impacted by the attitude towards sharing experience (β=0.570, p-value=0.000), which itself is more strongly influenced by perceived self-efficacy (β= 0.500, p-value =0.000) than by perceived ease-of-use (β= 0.330, p-value = 0.000). In the two last columns of Table 1 we can observe if these differences between the two groups are significant. On the one hand, it appears that the impact of attitude on the intention to share experience is more important for active than for passive users. On the other hand, the model seems to explain more variance for active users than passive ones. Table 1: Weights for active vs. passive groups ATT INT PEOU ATT SN INTENT SE ATT Difference active-passive 0.220 0.152 0.039 0.006 P-value 0.007* 0.068 0.332 0.474 R2 ATT R2 INTENT 0.158 0.312 0.050* 0.000* Conclusion The development of interactive digital media has fundamentally altered marketing’s ecosystem of influence. The social web is currently reshaping branding, and dramatic change is underway. Social media technologies have engendered radically new ways of interacting (Malthouse and Hofacker, 2010). To this end, literally hundreds of digital platforms exist (e.g., social networks, blogs, discussion groups, and podcasts). In this paper, we have explored the determinants of tourists’ intention to share experiences on such platforms. Based on the theory of planned behavior framework, we have shown that the intention of the tourist’s intention to share experiences online is both positively influenced by the attitude towards using an opinion platform and subjective norms about sharing opinions online. Moreover, our results indicate that perceived self-efficacy (personal variable) and perceived ease-of-use (situational variable) both have a positive effect on the tourist’s attitude towards using an opinion platform. Finally, the model fit appears to be better for active (i.e. “posters”) than for passive (i.e. “lurkers”) platform users. Such results contribute to the literature on e-WOM in the tourism area by identifying antecedents of the intention to share experiences online beyond experience-related drivers and consumers’ motives (N'Goala and Morrongiello, 2014, Liang et al., 2013). The intention to share experiences online appears to be a new potential predictor of the e-WOM and online interaction behaviors. The explanation power of this construct relies on the fact that it is supposed to vary between individuals but it is supposed to be rather stable across situations (Zuroff, 1986). This implies that the engagement in the e-WOM could be explained not only by the tourism experience itself (Shoda et al., 1994). This research also contributes to the research on e-WOM by identifying the drivers of tourists’ intention to share their experience in online opinion platforms (Casaló et al., 2010, Malinen, 2015). This new era involves participatory, collaborative, and socially linked behaviors whereby consumers are taking an increasingly active role in co-creating everything from product design to promotional messages (Fournier and Avery, 2011). References AFP. 2015. Tripadvisor fête ses 15 ans avec une communauté toujours plus large. Challenges.fr, p.08/2015. 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