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2018 Cambridge Business & Economics Conference ISBN : 9780974211428
The Effect of Positive and Negative E–WOM on Consumer Decisions in Jordanian Tourism Industry
Khalid Nasser AlzubiAl-Balqa Applied University
Salt, [email protected]
Abstract
The purpose of this study is to examine the effect of electronic word of mouth (E-
WOM) on making customer decisions in Jordanian tourism services. The study uses
quantitative techniques such as descriptive analysis and hypothesis testing analysis.
The study develops questionnaire to collect data from the study participants. The
study employs the convenient sample as non-probability sample to select its
participants. 420 questionnaires were distributed personally customer of Jordanian
tourism services. only 305 questionnaires were returned with 5 invalid
questionnaires, therefore, 300 valid questionnaires are considered for data analysis
with response rate is 72%.
The study concludes that both ways of positive and negative electronic word of mouth
has a significant effect on customer decisions. More specifically, the effect of negative
E-WOM on customer decisions is more than the effect of positive E-WOM on
customer decisions. The study concludes that E-WOM is a credible source of
consumer opinions, feedback, and experience that can be used by tourism businesses
in making corrective actions or improving measures for their products and services
Keywords: Electronic word of mouth, Customer decisions, Online tourist, Tourism
industry, Questionnaire, Jordan.
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Introduction
Electronic business and e-marketing grow in a dramatic and
dynamic environment (Al-Weshah, 2018). Word of mouth (WOM) is
defined as information transfer from certain customer to another customer in order to
change their preferences, purchase behaviour, or customers interact with others (Fox
and Longart, 2016). Customer relationship and communication are
valuable issues to enhance marketing performance (Al-Weshah,
2017). Many researchers such as Cheung and Thadani, (2012) Lerrthaitrakul and
Panjakajornsak (2014) stated that there are different ways for customers to exchange
their opinions using electronic media such as E-WOM. Consumers can write down
their feedback, comments, or suggestions of improvements on products or services.
Internet technologies support different businesses to enhance their
competitive positions to a greater level than enables by old
technologies (Al-Weshah and Alzubi, 2012). E-WOM channels are
considered in three different ways; one to one, one to many, and many to many. One-
to one channel can be used to send messages from one customer to another when a
person sends an email or instant message to another. One-to-many channel can be
used to send message from one person to other persons such as online comments
about products or services or online chat rooms. Many-to-many can be used to send
messages from group of persons to other groups of people like virtual communities
and online communities (Litvin, Goldsmith and Pan, 2008)
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WOM is important in tourism destination choice, however, previous research in
tourism area is still limited. Research attention has been paid by researchers to
adoption of electronic WOM (E-WOM) like online travel reviews, tourism blogs, or
tourism information searches. E-WOM employs the internet and provides a new way
of reaching, analyzing, explaining, and managing the role of communication tools in
hospitality and tourism (Litvin et al., 2008).
Travel industry is a service-oriented industry. It offers tourist with
different services in terms of tourism and travel planning (Al-
Weshah, 2018). Technology usage by customers to share their opinions about
products or services (E-WOM) can be taken as a priority for tourism business
especially when E-wom is out of businesses control (Yang, 2017). Recently, E-WOM
has become an effective communication tool in social-media marketing (Hussain et
al., 2017). In Middle East region, Almana and Mirza (2013) stated that there is limited
studies on the effect of internet consumer reviews on customer decisions. Therefore,
this study examine the effect of E-WOM on consumer decisions in Jordanian tourism
industry.
Theoretical ground of E-WOM
WOM was defined as a communication way among consumers about a product,
service (Litvin et al., 2008). The latest technologies can deal with customers problems
(Al-Weshah et al. 2018). Generally, businesses consider both kinds of
communication (WOM and E-WOM) as a new way to assess customers’ needs and
adapt product promotion in order to meet these needs (Yang, 2017). Online
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community users appear to spend efforts to evaluate the information credibility, in
addition to virtual communities (Brown et aI., 2007).
A new form of online WOM communication has been considered and it is known as
electronic word-of-mouth or E-WOM (Yang, 2017). This kind of communication
mainly depends on internet platforms, which is classified as one of the most important
sources of information on the Web. Brown et al (2007) reported that E-WOM
exchanges may affect consumer behavior thorough different three factors; source
credibility, tie strength, and homophily. They stated that in order to assess the nature
of E-WOM interaction, it is necessary to understand how these three factors such as
credibility, tie strength, and homophily are different from traditional WOM.
Brown et al. (2007) stated that online communities or online review for websites can
create some kinds of authority, which would provide online information to
customers. The authoritativeness of any website can affect E-WOM differently
compared to the impact of traditional WOM. However, few studies conducted on
consumer perceptions of both positive and negative E-WOM. Jalilvand et al
(2011) concluded that business can participate in online
communities of customers and they also disseminate all types of the
necessary information about their products. Providing the most
important information to customers may result in high adoption of
information.
Litvin et al. (2008) suggested all informal communication with customers via the
Internet such as E-WOM in terms of characteristics of products or services. The
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major advantage of E-WOM that is available to all types of consumers, who will
access online communities to share their feedback, comments, opinions, and reviews
with other customers especially most consumers may trust WOM from their friends
and families. Nieto et al. (2014) concluded that most customers seek to online
comments (E-WOM) for getting information about products.Jalilvand et al
(2011) stated that E-WOM enables consumers to obtain information
from a vast and geographically scattered group of people who have
personal experience with particular products or services over the
world.
Sen and Lerman (2007) examined the negative effect of E-WOM consumer reviews
about utilitarian and hedonic products. The outcomes indicated that readers consider
that negative reviews about hedonic products were related to internal reasons of the
reviewers. In the case of utilitarian product reviews, readers consider that the
reviewer's negative opinions due to external reasons.
Broadly, negative reviews of customers are more useful than positive reviews of
customers; however, each product item has a different level of negative E-WOM
impact on their trends (Sen and Lerman, 2007). Consumers consider that negative
comments of reviewers about products is to inform other buyers about true experience
or feelings. However, negative reviews of consumers may not based on product
quality and that they are motivated by their internal reasons (Sen and Lerman, 2007).
One form of E-WOM is online consumer reviews, it includes all customers comments
created and posted by the final customer of products who have bought the product.
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(Almana and Mirza, 2013). Moreover, E-WOM provides businesses with a way to
assess customers’ needs and perceptions about the product. E-WOM is a cost-
effective way to communicate among customers and businesses (Nieto et al., 2014).
Recently, E-WOM has become an effective tool for social-media marketing in the
business environment (Hussain et al., 2017). Therefore, this study investigates the
both sides of positive and negative E-WOM and their contribution in consumer
decisions in Jordanian tourism industry.
E-WOM and consumer decision making
Cheung and Thadani (2012) stated that when customers seek information, the quality
of information sources may influence consumers acceptance of E-WOM as
communication channels. Senecal and Nantel (2004) found that the several E-WOM
tools can affect consumers' buying decision processes. Many tools like online blogs,
chat rooms, websites reviews, and social network reviews and comments are
considered as important sources of information that consumers can follow before and
during a buying decision making for products and services. In their study in Saudi
Arabia, Almana and Mirza (2013) concluded that about 80% of their study
participants have stated that they follow and read internet reviews before making
purchasing process.
Frambach, Roest, and Krishnan (2007) stated that consumers who seek online
information about products and services will divide the buying processes of products
into three stages; the pre-purchase stage, purchasing stage, and post-purchasing stage.
Their study results stated that many online search for comments about products and
service is taken place during pre-purchasing stage, moreover, at the post-purchasing
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stages, most consumers share both online positive and negative comments about their
personal experience about the products.
Future purchase intentions are influenced by negative or positive attitudes toward the
product or service by enabling customer to compare actual performance of the product
or service’s with their expectations (Yang, 2017). Online consumer review as a
form of E-WOM consists of positive or negative customer’s
statements about products. customer information is useful for
buying decision making because it provides consumers with indirect
experiences about usage of the product (Jalilvand et al. 2011). Park
and Lee (2009) stated that the effect of negative E-WOM on a product buying is
greater than positive effect of E-WOM. Hence, type of the product is associated with
E-WOM messages. Specifically, the effect of a negative E-WOM is more significant
when E-WOM communication is used for experienced products (Park and Lee, 2009).
Some studies have been conducted about E-WOM effectiveness
such as (Lee and Lee, 2009). E-WOM studies started from complex
customer activities of the E-WOM. These studies can be categorized
into two levels of research; market level and individual level. The
difference between market level and individual level relies on
information view. The market level of E-WOM identifies the product
information process through viewing E-WOM as accumulated
customer opinion and its relationship with other market level
signals. On the other hand, the individual level of E-WOM identifies
the customer’s decision-making process by viewing the E-WOM as
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informational issues, focusing on how the information affects a
customer’s decision-making process (Lee and Lee, 2009).
Jalilvand et al (2011) concluded that businesses can participate in
online communities in order to provide customers with all necessary
information. When businesses provide relevant information to
customers, this will lead to higher adoption of information. The new
trend of electronic communication does not provide the face-to-face
contact with customers. To cover the shortage of personal
relationship, E-business sites can offer certain website or pages
that offer customer reviews about products. Hence, different
sources of E-WOM play an effective role in the consumer decision-
making. other studies such as (Hussain et al., 2017) concluded that consumers use
E-WOM in order to minimize risk during consumer decision-making process. In
Middle East context, Almana and Mirza (2013) investigated the effects of online
reviews on Saudi citizens' purchasing decisions. The results show that Saudi online
customers are influenced by E-WOM, and most customers depend on online forums
during making decisions to buy different products. Therefore, this study examine the
effect of the both positive and negative E-WOM on consumer decisions in Jordanian
tourism services.
The study aim and objectives
The aim of the study is to examine the effect of electronic word of mouth (E-WOM)
on making customer decisions in Jordanian tourism services
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To examine the effect of positive electronic word of mouth on making positive
customer decisions in Jordanian tourism services.
To examine the effect of negative electronic word of mouth on making negative
customer decisions in Jordanian tourism services.
To suggest recommendations and implications to Jordanian tourism customers.
Based on the critical review of the literature, the current study can develop particular
hypotheses, the hypotheses are formulated as in the following forms:
H: Electronic word of mouth (E-WOM) has a significant effect on making customer
decisions in Jordanian tourism services.
The main hypothesis can divided into two sub-hypotheses
H1: Positive electronic word of mouth has a significant effect on making positive
customer decisions in Jordanian tourism services
H2: Negative electronic word of mouth has a significant effect on making negative
customer decisions in Jordanian tourism services.
Methodology of the study
The study uses the quantitative design as a research methodology such as descriptive
analysis and hypothesis testing approaches. Particularly, mean, standard deviation,
and simple and multiple regression are calculated to examine the cause and effect
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relationship between electronic word of mouth and customer decisions in tourism
services. The study develops a self-administrated questionnaire to collect data using
five-item Likert scale with a range from (1) strongly disagree to (5) strongly agree.
The study selects respondents based on the convenient sample.
For purposes of the questionnaire validity, the questionnaire is initially tested by
seven Jordanian academics to get their opinions and feedback that are considered in
developing final copy of the questionnaire. For the purposes of reliability of the
questionnaire, Cronbach’s alpha is calculated as internal consistency measurement
based on statistical packages for social sciences (SPSS).
The Cronbach's Alpha for the questionnaire is 77%. This ratio reveals that there is a
high internal consistency in the study instrument. 420 questionnaires were distributed
personally customer of Jordanian tourism services. Only 305 questionnaires were
returned with 5 invalid questionnaires, therefore, 300 valid questionnaires are
considered for data analysis with response rate is 72%.
Respondents of the questionnaire have different demographic characteristics in term
of gender, education level, and ages. These characteristics are shown in table 1.
Table (1) Sample characteristics
Category Frequency PercentageGenderMale 160 53.3Female 140 46.7 Respondents ageLess than 18 to 22 years 92 30.7From 23 to 29 years 84 28.0From 30 to 40 years 66 22.0
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40 years or more 58 19.3EducationDiploma or less 66 22.0Bachelor 169 56.4Master 43 14.3Doctorate 22 07.3As noted from the table 1, 53% of respondents are male. In regarding to respondents
age, 31% of respondents age are classified between (18 to 22 yrs). In regarding to
respondents education, 56% of respondents have a bachelor degree.
Analysis of data
Positive electronic word of mouth and customer decisions
To assess the positive electronic word of mouth and customer decisions in Jordanian
tourism services, responses were analyzed using different frequencies analysis such as
mean, standard deviation, agreement level, and importance for each statement. The
analysis results are shown in table 2.
Table (2): Frequencies analysis positive electronic word of mouth and customer decisions
Statements Mean
Standard
Deviation
Agreemen
t level
Importance
I consider good experience for online customer towards
tourism services.3.82 0.521 High
3
Online reviews of satisfied customer affect my decisions 3.61 0.430 High 6
Online comments of satisfied customer affect my decisions 4.24 0.389 High 1
Online check in for customers encourage me 3.64 0.412 High 5
Iive- videos for customer from tourism place encourage me 3.81 0.521 High 4
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Positive online feedback from customers affect my
decisions4.18 0.482 High
2
Social media photos for customers enhance my decisions 3.32 0.471 Moderate 7
The table 2 shows that the means for positive electronic word of mouth and customer
decisions items are ranged between (3.32 - 4.24), with high agreement level by
respondents, the results show that the item (Online comments of satisfied customer
affect my decisions) has the highest mean with (4.24) and standard deviation (0.389).
The statement that (Social media photos for customers enhance my decisions) has
the lowest rank with mean (3.32) with standard deviation (0.471). These results show
that satisfied customer and their positive comments mainly affect customers
decisions.
Negative electronic word of mouth and customer decisions
To assess the negative electronic word of mouth and customer decisions in Jordanian
tourism services, responses were analyzed based on different frequencies analysis
such as mean, standard deviation, agreement level, and importance for each statement.
The results of analysis are shown in table 3.
Table (3): Frequencies analysis negative electronic word of mouth and customer decisions
Statement Mean
Standard
Deviation
Agreemen
t level
Importance
I consider bad experience for online customer towards
tourism services.4.05 0.411 High
3
Online reviews of dissatisfied customer affect my
decisions 4.02 0.383 High
4
Online comments of dissatisfied customer affect my
decisions4.31 0.312 High
1
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Online complaints of customers affect my decisions 3.94 0.430 High 5
Online suggestions for service improvements by
customers affect my decisions3.41 0.522 Moderate
6
Negative online feedback from customers affect my
decisions4.25 0.311 High
2
The table 3 shows that the means for negative electronic word of mouth and customer
decisions items are ranged between (3.41 - 4.31), with high agreement level by
respondents, the results show that the item (Online comments of dissatisfied customer
affect my decisions) has the highest mean with (4.31) and standard deviation (0.312).
The statement that (Online suggestions for service improvements by customers affect
my decisions) has the lowest rank with mean (3.41) with standard deviation (0.522).
These results show that dissatisfied customer and their negative comments mainly
affect customers decisions.
The effect of E-WOM on customer decisions
In order to test the study hypotheses, the linear regression can be applied where
customer decision is the dependent variable and positive electronic word of mouth
and negative electronic word of mouth are the independent variables. The results of
regression analysis are summarized in the table 4.
Table (4): Multiple regression for the effect of E-WOM on customer decisions
Variables Beta T Sig level
Positive E-WOM 1.110 4.124 0.015Negative E-WOM 1.341 4.610 0.021
R2 0.583F 50.241
Sig level 0.001
As noted from the table 4, The Beta value for positive E-WOM shows that (1.110) is
the expected change rate in customer decision when positive electronic word of mouth
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is changed by one unit. On the other hand, The Beta value for negative E-WOM
shows (1.341) is the expected change rate in customer decisions when negative
electronic word of mouth is changed by one unit. Thus, the Beta values shows that the
expected change rate in customer decision is more in the case of negative electronic
word of mouth than positive electronic word of mouth
R2 value shows that 0.583 of change in customer decisions can be interpreted by E-
WOM. Significant level is less that 5%. Hence, the main hypothesis that states
“Electronic word of mouth has a significant effect on customer decisions” is accepted.
The effect of positive E-WOM on positive customer decisions
In order to test the study first sub-hypotheses, the simple linear regression can be
applied where customer decision is the dependent variable and positive electronic
word of mouth is the independent variable. The results of regression analysis are
summarized in the table 5.
Table 5: Simple regression for the effect of positive E-WOM on customer decisions
Variables Customer decisions
Beta R R2 Significant level
Positive E-WOM 1.61 0.611 0.373 0.002
As noted from the table 5, The Beta value for Positive E-WOM shows that (1.61) is the
expected change rate in customer decision when positive electronic word of mouth is
changed by one unit. R value (0.611) states that there a positive relationship between
positive customer decision and positive electronic word of mouth. Moreover, R2 value
shows that 0.373 of change in customer decisions can be interpreted by E-WOM.
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Significant level is (0.002) and less that 5%. Hence, the first sub-hypothesis that states
“Positive electronic word of mouth has a significant effect on positive customer
decisions” is accepted.
The effect of negative E-WOM on negative customer decisions
In order to test the second sub-hypotheses, the linear regression can be applied where
negative customer decision is the dependent variable and negative electronic word of
mouth and negative electronic word of mouth is the independent variable. The
findings of regression analysis are summarized in the table 6.
Table 6: Simple regression for the effect of negative E-WOM on negative customer decisions
Variables Customer decisions
Beta R R2 Significant level
Negative E-WOM 1.83 0.651 0.423 0.01
As noted from the table 5, The Beta value for Positive E-WOM shows that (1.83) is the
expected change rate in customer decision when negative electronic word of mouth is
changed by one unit. R value (0.651) states that there a positive relationship between
negative customer decision and negative electronic word of mouth. Moreover, R2
value shows that 0.423 of change in customer decisions can be interpreted by E-
WOM. Significant level is (0.001) and less that 5%. Hence, the second sub-hypothesis
that states “Negative electronic word of mouth has a significant effect on negative
customer decisions” is accepted.
Results of the study
The study results can be classified into two categories; results of descriptive analysis
and results of hypothesis testing methods. In terms of the descriptive analysis and
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regarding to positive electronic word of mouth and customer decisions, the results
show that satisfied customer and their positive comments mainly affect customers
decisions. On the other hand, in regarding to negative electronic word of mouth and
customer decisions, the results show that dissatisfied customer and their negative
comments mainly affect customers decisions. However, the results show that
dissatisfied customer has more effect on customers decisions that satisfied customers.
Based in multiple regression in regarding to the effect of E-WOM on customers
decisions, the results show that electronic word of mouth has a significant effect on
customer decisions. Therefore, the main hypothesis is accepted. These results are
consistent with Lee and Lee (2009) who focused on E-WOM and how
the information affects a customer’s decision-making. The result is
also supported by Jalilvand et al. (2011) who considered that
consumer information is useful for decision making because it
provides different consumers with indirect experiences about the
product. The result is also supported by Senecal and Nantel (2004) who
found that the several E-WOM tools can affect consumers' buying decision processes.
In regarding to simple regression and the effect of positive E-WOM on customers
decisions, the results show that positive electronic word of mouth has a significant
effect on positive customer decisions. Thus, the first sub-hypothesis is accepted. On
the other hand, In regarding to simple regression the effect of negative E-WOM on
customers decisions, the results show that negative electronic word of mouth has a
significant effect on negative customer decisions. Thus, the second sub-hypothesis is
accepted. These results are consistent with Yang (2017) who stated that negative or
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positive attitudes toward the product or service may affect customers’ future purchase
intentions by enabling customers to compare actual performance of the product with
customers expectations.
Moreover, the result shows that the effect of negative E-WOM on customer decisions
is more than the effect of positive E-WOM on customer decisions. This result is
supported by Park and Lee (2009) who stated that the effect of negative E-WOM on
products purchasing decision is greater than positive E-WOM. The result is also
supported by Sen and Lerman (2007) who reported that consumers take into account
the negative comments of reviewers about products in order to inform true experience
or feelings of other buyers.
Practical recommendations for tourism businesses
Based on the study results, some recommendations are suggested for tourism business
in Jordan.
First of all, tourism businesses and their marketers have to recognize that
customers are going online in terms of searching, selecting, and
purchasing different tourism products. Therefore, tourism business
have to develop programs of social media marketing to interact with
their online customers.
Second, consumers are exposed to many positive and negative
reviews and comments about tourism products. Therefore, they are
likely influenced by many websites that are devoted to discuss the
features and characteristics of product or service.
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Third, E-WOM is a credible source of consumer information in terms of customer
feedback, opinions, and experience that can be used by tourism businesses in making
corrective actions or improving measures for their products and services
The study value can be considered by its contribution to the understanding of tourists
behaviour and their interaction in Jordan towards e-communities. In the light of
practices, the study provides insights into the E-WOM as communication patterns and
their different advantages that lead to E-WOM adoption by tourists. The study is also
provides practical lessons for marketers in recognizing the key features of E-WOM in
terms of positive and negative comments or reviews that effect decision making.
Limitations and recommendations for future studies
In the light of the study limitations, some research recommendations are suggested for
future studies. The current study focused on tourism services customers. In future
studies, more research may be conducted to investigate other industries. In the study
methodology, this study uses quantitative research, future studies may use different
techniques such as qualitative studies to investigate the different elements of E-
WOM .
List of referencesAlmana, A. & Mirza, A. (2013) The impact of electronic word of mouth on consumers’ purchasing decisions. International Journal of Computer Applications, 82 (9), 23-31.
Al-Weshah, G. (2018). E-Marketing practices from Jordanian tourism agencies perspectives: a qualitative evidence. International Journal of Online Marketing, 8 (1), 21-36.
July 2-3, 2018Cambridge, UK 18
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Al-Weshah, G., Al-Manasrah, E., & Al-Qatawneh, M. (2018) Customer relationship management systems and organizational performance: a quantitative evidence from Jordanian telecommunication industry. Journal of Marketing Communications, Vol. 24. ,No.4.
Al-Weshah, G. (2017). Marketing intelligence and customer relationships: empirical evidence from Jordanian banks. Journal of Marketing analytics, 5 (34), 141-152.
Al-Weshah, G. & Al-Zubi, K. (2012). E-business Enablers and Barriers: Empirical Study of SMEs in Jordanian Communication Sector. Global Journal of Business Research (GJBR). 6 (3), 1-15.
Brown, 1., Broderick, A. J., & Lee, N. (2007). Word of mouth communication withinonline communities: Conceptualizing the online social network. Journal ofInteractive Marketing 21(3),2-20.
Cheung, C. & Thadani, D. (2012) The impact of electronic word of- mouth communication: A literature analysis and integrative model, Decision Support Systems, 54 (1), 461-470.
Frambach,R., Roest, H. & Krishnan, T. (2007) The impact of consumer Internet experience on channel preference and usageintentions across the different stages of the buying process,‖ Journal of Interactive Marketing, 21 (2), 26-41,
Fox, G. & Longart P. (2016) electronic word- of- mouth: successful communication strategies for restaurants. Tourism and Hospitality Management, 22 (2), 211-223,
Hussain, S., Ahmed, W., Jafar, R. M. S., Rabnawaz, A., & Jianzhou, Y. (2017). eWOM source credibility, perceived risk and food product customer’s information adoption. Comput. Hum. Behav. 66, 96–102.
Jalilvand, M., Esfahani, S. & Samiei, N. Electronic word-of-mouth: challenges and opportunities. Procedia Computer Science. 3, 42-26
Lee, J. & Lee, J. (2009) Understanding the product information inference process in electronic word-of-mouth: An objectivity–subjectivity dichotomy perspective, Journal of Information & Management, 46, 302–311.
Lerrthaitrakul, W. & Panjakajornsak, V. (2014) The impact of electronic word-of-mouth factors on consumers’ buying decision-making processes in the low cost carriers: a conceptual framework. International Journal of Trade, Economics and Finance, 5 (2), 142-146.
Litvin, S., Goldsmith, R. & Pan,B. (2008)Electronic word-of mouth in hospitality and tourism management,‖ Tourism Management, (29)3, 458-468.
July 2-3, 2018Cambridge, UK 19
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Nieto, J., Hernández-Maestro, R. M., & Muñoz-Gallego, P. A. (2014). Marketing decisions, customer reviews, and business performance: the use of the Toprural website by Spanish rural lodging establishments. Tour. Manage. 45, 115–123.
Park, C., and Lee, T. M. (2009) Information direction, website reputation and eWOMeffect: A moderating role of product type. Journal of Business Research, 62, 61-67.
Sen, S. & Lennan, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the web. Journal of Interactive Marketing, 21(4), 76-94.
Senecal, S. & Nantel, J. (2004) The influence of online product recommendations on consumers’ online choices, Journal of Retailing, 80 (2) 159-169,
Yang, F. X. (2017) Effects of restaurant satisfaction and knowledge sharing motivation on eWOM intentions: the moderating role of technology acceptance factors. Journal of Hospitality and Tourism Research. 41, 93–127.
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