The Effect of Positive and Negative E–WOM on Consumer ... Nasser Alzubi.doc · Web viewThe...

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.

July 2-3, 2018Cambridge, UK 1


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)



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


https://www.frontiersin.org/articles/10.3389/fpsyg.2017.01256/full#B27






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





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.




(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





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




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




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


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


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



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



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



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


1



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


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



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.



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



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




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.



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.



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.



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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