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)
Alain Decrop
Université de Namur - Cercle
Justine Virlée
Université de Namur, CeRCle
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- à-
oreille 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-of-
mouth” (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 self-
efficacy 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
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
Perceived
Self-efficacy
Attitude
towards using
an opinion
platform
Subjective
Norms
Intention to
share
experiences
Perceived
Ease of use
H4
H1
H2
H3
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 French-
speaking 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 pre-
defined 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
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