and the sales of certain product and services can be increased significantly
via online reviews. In the context of
restaurant industry, it seems that WOM plays a determined role in every
effective marketing strategy for restaurants specifically those with limited
promotion budgets. This paper aims
to apply the theory of planned behavior to investigate the impact of e-WOM on
the customer when choosing restaurant amongst alternatives.
WOM refers to sharing opinions from one consumer to another.
Baloglu and Macclary (1999) considered that WOM has a significant impact on
customer’s perceived image. The spread of internet decreases the personal
communication and made a transfer from WOM to e-WOM.
E-WOM is defined as “any positive or negative statement made by
potential, actual, or former customers about a product or company which is made
available to multitude of the people and institutes via the internet.
Based on previous studies, the popularity and the sales of certain
product and services can be increased significantly via online reviews. Sen and
Lerman (2007) found that the valence of the reviews positively versus
negatively significantly affects consumer’s attitude toward the reviewed
product. In the context of restaurant industry which is highly competitive, the
restaurateurs try to have a deep understanding of the wants, needs and
perception of customers to attract them and retain a long term relationship. It
seems that WOM plays a determined role in every effective marketing strategy
for restaurants specifically those with limited promotion budgets. Furthermore,
the intangibility of the service and the difficulty to evaluate it before the
purchase bring risk, so the customers are more dependent on the interpersonal
influence of e-WOM.
Increasing studies have shown that consumers are more interested in
products discussed online, then those marketed traditionally. These studies
have proven that e-WOM has a significant effect on consumer behavior. However
few researchers have examined the impact of WOM on behavioral intention in the
The aim of the research is to examine the influence of e-WOM on
customers when choosing a restaurant. In this research, the theory of planned
behavior was used to examine the influence of e-WOM on customer’s behavioral intention
to select a restaurant.
The TPB by Ajzen’s described
how the behavior is formed and affected by three factors which are the
attitude, subjective norms and perceived behavioral control. A large number of
researches in the social science have used Ajzen’s model and address many areas
as smoking behavior (Babrow et al., 1990),and ethical behavior (Flamnery and
Mary, 2000). These studies support the usefulness of Ajzen’s theory. However,
few studies that examines Ajzen’s model
in the context of hospitality are present.
The TPB as discussed by Ajzen (1991) is a development of the theory of reasoned
action developed by Ajzen and Fishbein (1980) and considered as one of the
famous conceptual groundwork in all research related to the human action
(Ajzen, 2001). According to the theory, there is a link between the belief of
individuals and their behavior.
Mcguire (1969) provides a definition of attitude that is widely
held by psychologists. He stated that an attitude is an evaluative response to
an antecedent stimulus or attitude object. Ajzen (1988) elaborates on Mcguire’s
definition by describing attitudes as a predisposition to respond favorably or
unfavorably to an object. Based on Ajzen’s model, attitude is the degree to
which a person has a favorable or unfavorable evaluation or appraisal of the
behavior in question (Ajzen, 1991). In this study, we restrict the term “attitude”
to a customer’s evaluation of a restaurant.
Subjective norm is an original construct from TRA. It deals with
the influence of social environment or social pressure on the individuals and
thus on behavioral intention (Fishbein and Ajzen, 1975). Subjective norm is defined as “the perceived
social pressure to perform or not to perform the behavior” by the individual
(Ajzen, 1991, p. 188). The role of subjective norm as a determinant of
intention is well documented in situations where the actual behavior entails
tangible and beneficial consequences for the consumer (Taylor and Todd, 1995).
Applied to this study, subjective norms reflect consumer perceptions of whether
the feeling of choosing restaurant amongst others is accepted, encouraged and
implemented by the consumer’s circle of influence.
Perceived Behavioral Control
PBC is defined as, given the
presence or absence of requisite resources and opportunities, the individual’s
perception of the ease or difficulty in performing the behavior of interest
(Ajzen, 1991). In another word, the behavior is correlated to the confidence of
the individual in hisher ability of performing that behavior.
Behavioral Intention (BI)
Intention is defined as the perception of an individual towards
performance of a particular behavior (Fishbein and Ajzen, 1975). The intention
is predicted by attitude and subjective norm. Behavioral intention represents
the extent of the individual’s intentions to perform or not to perform one
certain behavior (Ajzen, 1991).
e- WOM in Restaurant Industry
WOM is a form of interpersonal communication amongst consumers. Research
also revealed that WOM is a consequence of customer’s emotional responses to consumption
experiences. Yet, researchs still lack in the domain of restaurant industry.
The intangibility and the higher risk associated to the service drive the
customer to rely on other’s opinions to evaluate the service before purchase.
The intention toward eating out in a particular place increases when positive
recommendations are made, affecting referent beliefs. These beliefs seem to
have important weight in the decision-making process. According to Cousins et
al., (2002), there are various motives that drive customers to talk about
restaurant. They classify the elements of the restaurant offer in order of
importance as: food and drink, service, cleanliness-hygiene, value for money
and ambiance. Additionally, the type of restaurant moderated the relationship
between restaurant service and ambience quality and customer behavioral. The advanced technology spreads the e-WOM and
makes it accessible to millions of people. According to the study of Kasabov
(2016) in the Chinese context, he found that the information is a key
motivation to seek e-WOM in social networks, information relevance and
usefulness motivate customers to solicit e-WOM and information quantity
significantly affects customer’s behavior. Moreover, Baber et al., (2016) found
that attitude mediates the relationship between online WOM communication and
customer purchase intention.
Based on these previous studies, there is a high potential impact
of e-WOM on the consumer decisions process. In the next section, Ajzen’s TPB
will be described to provide a frame work to develop research hypotheses.
The theoretical model employed in this research is based on the
theory of planned behavior. It offers a comprehensive yet parsimonious
psychological theory that identifies a causal structure for explaining a wide
range of human behavior (Morris et al., 2005). Attitude, subjective norms and
perceived behavioral control influence an individual’s intention to perform a
given behavior. However, many researchers criticized TPB is that the theory is
purely rational, not taking account of cognitive and affective factors that are
known to bias human judgments and behavior. “In reality, the theory draws a
much more complex and nuanced picture, and the emotions result from beliefs and
affect intention and behavior” (Ajzen, 2011, p. 1116).
In general, the more favorable the attitude toward the behavior,
the stronger will an individual’s intention to perform the behavior. Moreover,
WOM is acknowledged to play a considerable role in influencing and forming
consumer attitudes and behavioral intentions (Chatterjee, 2001; Chevalier and Mayzlin, 2006; Sen and Lerman,
2007; Smith and Vogt, 1995; Xia and Bechwati, 2008). Ying and Chung (2007)
stated that positive WOM leads to more favorable attitude toward a specific
product. In sum, the literature indicates that e-WOM has a significant impact
on attitude. Thus, it is hypothesized that:
Ø H1: e-WOM has a significant impact
on customer’s attitude.
Ø H1a: Attitude has a significant
impact on customer’s intention to choosing restaurant offers Lebanese food in
TPB views the role of social pressure to be more important when the
motivation to comply with that pressure is greater (Mathieson, 1991). Pavlov and Fygensen (2006) found that subjective
norms affect user’s intention to make online purchase. The previous studies
differ in the results. Davis et al stated that there is no significant relationship
between subjective norms and intention. However, Taylor and Todd have shown
significant relationship between Subjective norms and intention. It is assumed
in the literature that the model using TPB framework shows that subjective
norms have a significant relationship with intention. Further, Guoqing et al.,
(2009) in their study of Chinese consumers found that WOM has a positive
influence on the receiver’s objective norms. Thus, it is hypothesized that:
Ø H2: e-WOM has a significant impact
on subjective norms.
Ø H2a: Subjective norms have a
significant impact on customer’s intention to choosing restaurant offers
Lebanese food in Beirut.
Perceived behavioral control is an important factor predicting
behavior. Previous studies have shown that perceived behavioral control affects
the intention to purchase Halal food products, and intention to consume soft
drink. Also, Cheng et al., (2006) found that negative WOM communication is
positively related to perceived behavioral control. Thus, it is hypothesized
Ø H3: e-WOM has a significant impact
Ø H3a: P.B.C has a significant impact
on customer’s intention to choosing restaurant offers Lebanese food in Beirut.
2.1 Proposed Conceptual Framework
Perceived Behavioral control
Intention to select restaurant
Electronic Word of Mouth
The target population for this study is the restaurant that offered
Lebanese food located in Beirut. In this city, there are a plenty of shopping
malls, restaurants, bars and hotels that attract people to visit that area, and
consequently, the customers will confront many alternatives for a specific
object (e.g. restaurant).
Non-probability sampling technique will adopt in this study as
there is an inaccessibility to gain sufficient information for a sampling
frame. The research will base on Zomato. It is an online application; Zomato
covers more than 3,800 restaurants in Beirut and is available on web and mobile
(iOS, Android, Windows Phone and Blackberry). Through Zomato, users can browse
through restaurant information, read and write restaurant reviews, share
pictures and build a personal network of people whose trusted opinions. The 10
selected top restaurants in Beirut are: El Denye Hek, Loris, Furn Beaino, Babel
Bay, Diwan Beirut, Socrate, Em Charif, Abd El Wahab, Le pêcheur and Nasma.
The sampling elements are the
restaurant patrons who have selected a restaurant amongst the top 10 mentioned
At a 95% confidence level
based on a 5% margin of error, a population of
100,000 requires 383 samples, while a 10,00,000 will need 384
samples (Saunders et al., 2009). Thus, a
total of 500 questionnaires will be distributed to the target respondents
A self-administered pilot test will conduct, and a survey questionnaires
will be used in this research because this is the most commonly used method to
obtain data from a huge amount of respondents. Besides that, it is quick,
efficient, less costly and accurate in assessing information from the target
Firstly, demographic details will be asked to gather data about the
respondent’s age, education level achieved, the monthly income, and the
frequency of using online application to select restaurant. Secondly, the
survey questionnaires will consist questions about the independent variables (e-WOM)
and the dependent variable (attitude, subjective norms, and perceived
behavioral control), then the independent variables (attitude, subjective
norms, and perceived behavioral control), and the dependent variable (intention
to select restaurant).
and Fishbein, M. (1980), Understanding Attitudes and Predicting Social
Behavior, Prentice-Hall, Englewood Cliffs, NJ.
(1991), “The theory of planned behavior”, Organizational Behavior and Human
Decision Process, Vol. 50 No. 2, pp. 179-211
(2001), “Attitudes”, Annual Review of Psychology, Vol. 52 No. 1, pp. 27-58.
(2011), “Theory of planned behaviour”, in van Lange, P.A.M., Kruglanski, A.W.
and Higgins, E.T. (Eds), Handbook of Theories of Social Psychology: Volume One,
Sage Publication, pp. 438-459.
A., Thurasamy, R., Malik, M.I., Sadiq, B., Islam, S. and Sajjad, M. (2016),
“Online word-of-mouth antecedents, attitude and intention-to purchase
electronic products in Pakistan”, Telematics and Informatics, Vol. 33 No. 2,
A.S., Black, D.R. and Tiffany, S.T. (1990), “Beliefs, attitudes, intentions,
and a smokingcessation program: a planned behavior analysis of communication
campaign development”, Health Communication, Vol. 2 No. 3, pp. 145-63.
S. and McCleary, K.W. (1999), “A model of destination image formation”, Annals
of Tourism Research, Vol. 35 No. 4, pp. 11-15.
P. (2001), “Online reviews: do consumers use them?”, Advances in Consumer
Research, Vol. 28 No. 1, pp. 129-33.
S., Lam, T. and Hsu, C.H.C. (2006), “Negative word-of-mouth communication
intention: an application of the theory of planned behavior”, Journal of
Hospitality & Tourism Research, Vol. 30 No. 1, pp. 95-116.
J., Foskett, D. and Gillespie, C. (2002), Food and Beverage Management, 2nd
ed., Prentice-Hall, Harlow
F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer
technology: a comparison of two theoretical models”, Management Science, Vol.
35 No. 8, pp. 982-1003
M. and Ajzen, I. (1975), Belief, Attitude, Intention, and Behavior: An
Introduction to Theory and Research, Addison-Wesley, Reading, MA.
B.L. and May, D.R. (2000), “Environmental ethical decision making in the US
metal-finishing industry”, Academy of Management Journal, Vol. 43 No. 4, pp.
G., Zhongke, Z., Kai, C. and Xiaofan, W. (2009), “The influence of WOM on
consumers’ intention of brand switching: the mediate role of subjective norms”,
paper presented at the Summit International Marketing Science and Management
Technology Conference, December, Beijing, December 26-29, available at:
www.seiofbluemountain.com/upload/ product/200911/2009scyxhy01a8.pdf (accessed
February 28, 2011).
E. (2016), “Unknown, surprising, and economically significant: the realities of
electronic word of mouth in Chinese social networking sites”, Journal of
Business Research, Vol. 69 No. 2, pp. 642-652.
K. (1991), “Predicting user intentions: comparing the technology acceptance
model with the theory of planned behavior”, Information Systems Research, Vol.
2 No. 3, pp. 173-91.
M.G., Venkatesh, V. and Ackerman, P. (2005), “Gender and age differences in
employee decisions about new technology: an extension to the theory of planned
behavior”, IEEE Transactions on Engineering Management, Vol. 52 No. 1, pp.
P.A. and Fygenson, M. (2006), “Understanding and predicting electronic commerce
adoption: an extension of the theory of planned behavior”, MIS Quarterly, Vol.
30 No. 1, pp. 115-43.
S. and Todd, P. (1995), “Decomposition and crossover effects in the theory of
planned behaviour: a study of consumer adoption intentions”, International
Journal of Research in Marketing, Vol. 12 No. 2, pp. 137-55.
M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. (5th Edition.). Essex: Pearson Education Limited
and Lerman, D. (2007), “Why are you telling me this? An examination into
negative consumer reviews on the web”, Journal of Interactive Marketing, Vol.
21 No. 4, pp. 76-94.
H.L. and Chung, C.M.Y. (2007), “The effects of single-message single-source
mixed word-ofmouth on product attitude and purchase intention”, Asia Pacific
Journal of Marketing, Vol. 19 No. 1, pp. 75-86.