A Study on the Impact of Online Word-of-Mouth for Airlines on Customer Behavior



Yong-Sook Kim, Jin-Woo Park*
School of Business, Korea Aerospace University, 76 Hanggongdaehak-ro, Deokyang-gu, Goyang-si, Gyeonggi-do, South Korea, 421-791


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© 2017 Kim and Park.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Business, Korea Aerospace University, School of Business, Korea Aerospace University, 76 Hanggongdaehak-ro, Deokyang-gu, Goyang-si, Gyeonggi-do, South Korea, 421-791, Tel: 82-2-300-0354, Fax: 82-2-300-0225; E-mail: jwpark@kau.ac.kr


Abstract

Introduction:

This study analyzes the impact of online word-of-mouth for airlines on the behavioral intention of airline customers through information acceptance and satisfaction.

Methods:

A survey is carried out with customers who use airline social media and websites. A total of 270 questionnaires is analyzed via structural equation modeling.

Results:

The results indicate that online word-of-mouth has a significant impact on acceptance and satisfaction, and satisfaction has a significant impact on behavioral intention.

Conclusion:

This research has practical implications in that it can provide the basic data necessary to establish an efficient online communication strategy for airline online word-of-mouth. It also has academic implications in that it examines the impact of airline online word-of-mouth on customer behavior.

Keywords: Online word-of mouth, Information acceptance, Satisfaction, Behavioral intention, Online platforms, Purchasing behavior.



1. INTRODUCTION

At present, most consumers go online to obtain information on products and services and share their personal experiences and feelings. Online platforms are an important means of communication for businesses and consumers, and they play a significant role in consumer decision making processes. Product evaluations such as purchase reviews can be carried out immediately online, and this can directly affect a company’s sales and image. As such, online reviews provide important information and purchase cues to potential customers [1]. When purchasing products online, 79% of consumers refer to online reviews. The credibility of online reviews is fairly high at 71.3% and, when these reviews are negative, seven out of 10 consumers avoid purchasing the product [2].

The importance of online word-of-mouth about airlines has been continuously emphasized. With the diversification of information sharing routes among consumers via a range of communication channels on the internet, the influence of such information seminated by online word-of-mouth has brought about dramatic changes. In the same vein, to be responsive to the changing behavior of passengers choosing airlines by referring to online word-of-mouth, customer relationship management is definitely needed, in the sense that the customer-oriented marketing fundamentally enables strategic responses leading to the increase in the management performance of airlines. Despite the importance of online word-of-mouth in the airline industry as part of the service sector, online word-of-mouth per se about airline services and the effects of online word-of-mouth conducive to understanding and predicting the behavior of passengers, in particular, have not been well-documented. With the rapid growth of air transport market, and the low-cost airlines adding to the already tough competition, passengers have enhanced their relationships with airlines by gaining information through online word-of-mouth and clarified their needs, which provide airlines with opportunities for service improvement, cost savings and profit creation by means of linked products [3].

Although the importance of online word-of-mouth has continued to increase, existing studies on the subject focus primarily on purchasing behavior toward internet shopping malls for clothing and fashion [4]. Also, previous studies on online word-of-mouth mostly focused on the effects of corporate images and consumer attitudes on word-of-mouth or emphasized the importance of images and attitudes [5]. Customer word-of-mouth can also play an important role for airlines when establishing strategies, but systematic research on its impact is still lacking. Despite the practical worth of online word-of-mouth from airline customers, empirical research on airline online reviews and word-of-mouth has not been implemented to any appreciable degree. Yet, research on the effects of online word-of-mouth on behavior intention by the medium of information reception and satisfaction has not been conducted. Accordingly, the present study examines the impact that online word-of-mouth for airlines has on the behavioral intention of airline customers through information acceptance and satisfaction.

2. THEORETICAL BACKGROUND

2.1. Online Word-of-Mouth

Social media marketing, which is an indispensable component of the business in the 21st century, is used as an effective communication strategy across the board. Social media marketing is a medium of creating value for stakeholders in order to achieve the strategic organizational goals [6]. Online word-of-mouth, which refers to word-of-mouth communication that exists on the internet and social media marketing, denotes all positive and negative statements made by multitudes of people, including both potential customers and actual customers. Hence, the expression of diverse experiences via online reviews and comments has an important impact on purchase decisions because these experiences complement the direct experiences of those seeking this online content. Whereas off-line word-of-mouth content is delivered verbally, online word-of-mouth content exists in text form, which can be easily processed and stored. As such, this text-based information is more easily propagated via the internet, providing ordinary customers with easier, faster access to a more diverse array of word-of-mouth content [7]. Because the characteristics of delivery facilitate convenience, a greater number of people can access useful online word-of-mouth information with greater ease. Furthermore, the internet now makes it possible to deliver personalized word-of-mouth information, both in terms of quantity and quality [7]. As a result of this approach, online consumers now tend to exhibit more efficient decision making [8].

Chatterjee [9] claimed that online word-of-mouth information promoted the purchase intention of consumers because it had a large impact on consumer purchases. In addition, she asserted that online word-of-mouth information had a greater impact than traditional off-line word-of-mouth information because it approached innumerable information accommodators simultaneously. Lee [10] confirmed that the characteristics of online word-of-mouth used when purchasing products from an internet shopping mall affected the benefits sought from sales promotions. These characteristics were important factors that led to purchase satisfaction, re-purchase intention and word-of-mouth intention. Kim [11] investigated the expectations derived from word-of-mouth communication. He divided the primary effects (i.e. information accommodation) from the secondary effects (i.e. word-of-mouth activities) and concluded that certain causal relationships existed between these variables. In other words, through word-of-mouth accommodation, the process accelerated when the existing information receivers began playing the role of new information deliverers. Online reviews override any other types of online word-of-mouth as the source of information, referring to informing other consumers of positive or negative experiences related to companies, products or services [12]. Reviews that the consumers perceive as helpful before they purchase goods or services, play critical roles. Chen et al. [13] reported that online reviews communities regarded as helpful catalyzed the purchase, while McKnight et al. [14] shed light on the reliability of information as an important element influencing the adoption of the information in online reviews. To sustain and grow a successful online business, it is important to provide reviews that sound helpful to those who search reviews. Many researchers demonstrated that online reviews exerted significant effects on sales growth [15]. Senecal and Nantel (2004) [16] reported that consumers tend to be influenced by others’ reviews online when making decisions, and that their acceptance of word-of-mouth changed their attitudes towards goods and services, emphasizing the importance of encouraging consumers to engage in word-of-mouth activities as the senders of word-of-mouth. The foregoing previous findings underlie the following hypothesis on the acceptance in online word-of-mouth.

H1. Online word-of-mouth will have a positive effect on acceptance.

Satisfaction is the pleasure or disappointment a person feels when comparing the performance that he or she perceives on a product in relation to expectation, whereas the level of satisfaction refers to the function of difference between performance and expectation [17]. Customer satisfaction is very important in a market where corporate profit should be generated because it is a major determinant of repeat sales, the attraction of loyal customers, effective word-of-mouth marketing and customer preference [18]. In a study surveying the tourists visiting Mallorca in Spain, Kozak [19] confirmed the fact that tourists satisfied with tour activities are highly likely to return and engage in positive word-of-mouth. These previous findings lay the foundation for the following hypothesis on the satisfaction in online word-of-mouth.

H2. Online word-of-mouth will have a positive effect on satisfaction.

In that information passed from person to person through word-of-mouth is perceived as highly reliable, word-of-mouth was reported to have strong effects on corporate reputation and on people’s decision to purchase goods or services [20, 21]. Chevalier and Mayzlin [22] empirically proved the online book reviews posted by consumers on Amazon and Barnes & Noble impacted upon their book sales. Positive reviews effectively promoted books to potential buyers and influenced consumers’ purchase activities. Thus, based on the aforementioned findings, the following hypothesis was set up concerning the behavioral intention in online word-of-mouth.

H3. Online word-of-mouth will have a significant effect on behavioral intention.

2.2. Information Acceptance and Customer Satisfaction

Acceptance denotes ‘the degree to which consumers accept a particular opinion or attitude’, and information acceptance denotes a behavior type that consumers assume as they access information [23]. Acceptance in this research denotes the acceptance of information by passengers and their assumption of a positive attitude toward an airline. Harrison-Walker [24] defined word-of-mouth acceptance as the formation of a favorable attitude toward word-of-mouth information and purchase intention. Chatterijee [9] suggested that the acceptance of online word-of-mouth delivered via social media outlets such as websites, blogs, bulletin boards and reviews impacted consumer behavior, including purchase decisions. She also noted that this acceptance affected corporate image. Word-of-mouth acceptance can become an important variable of measurement to grasp consumer attitudes toward online word-of-mouth [25]. Based on the findings of preceding studies regarding acceptance, the following hypothesis is derived.

H4. Acceptance will have a positive effect on behavioral intention.

Customer satisfaction can be defined as “a value judgment that customers recognize for an airline before they use it, while they are using, after they use it and as a continuing response” [26]. Anderson & Sullivan [27] stated that, if satisfied with the product purchased, consumers would continue purchasing that product, and there was a good possibility that they would share their positive product preference with others. On the other hand, when not satisfied with a product purchased, consumers would shift to another product, manufacturer or retailer, or they would complain about the product to other consumers. Dwyer et al. [28] supposed that the final goal of a company was to elicit repurchases through customer satisfaction, thereby generating profit. In their study, they clarified the positive relationship between customer satisfaction and repurchase intention. Kim and Lim [29] established the satisfaction of users on hotel websites exerted significant effects on their recommendation to others and actual offline visits. The authors demonstrated that customer satisfaction was dependent on information provision, design and communication among the quality components of hotel websites, led to the behavioral intention and had significant effects on word-of-mouth and actual visits. Based on the findings of preceding studies related to satisfaction, the following hypothesis is derived.

H5. Satisfaction will have a significant effect on behavioral intention.

2.3. Research Model

This study investigates the effect that online word-of-mouth for airlines has on information acceptance, satisfaction and behavioral intention. To implement this objective, a research model is developed based on preceding studies, as shown in Fig. 1.

Fig. (1). Research model.

3. METHODOLOGY

The questionnaire used in this research was prepared after implementing a preliminary survey with modifications and supplementation based on the findings of existing studies. The questionnaire consisted of questions on online word-of-mouth, acceptance, satisfaction and behavioral intention. A scale for the questionnaire was prepared using a 7-point Likert scale, in which 7 points were given to ‘Very much so’ and 1 point was given to ‘Not at all’. The measurement questions used in this research are shown in Table 1.

Table 1. Measurement items.
Measures Variablesa Preceding Studies
Online word-of-mouth I often read online reviews to determine which airline makes a good impression on passengers.
I often utilize online reviews to check whether the airline I selected is appropriate.
I often refer to online reviews to choose an attractive airline.
I collect information using online reviews before choosing an airline.
I feel more at ease if information is obtained from an online site when choosing an airline.
Information obtained from an online site is very important to me when choosing an airline.
Bambauer and
Mangold (2011) [30]
Jalilv and Samiei
(2012) [31]
Bearden et al.
(1989) [32]
Thoumrungroje (2014) [33]
Acceptance Online reviews are helpful when I evaluate this airline.
I tend to accept online reviews rather easily.
I tend to be affected by online reviews.
Wathen and Burkell (2002) [34]
Teng et al. (2015) [35]
Satisfaction I am satisfied overall with this airline.
I am satisfied with the airline from which I obtained online reviews.
Choi (2014) [36]
Behavioral intention Given an opportunity, I am willing to use this airline.
There is a greater possibility that I will use this airline rather than other airlines.
I will recommend this airline to other people.
I will speak of this airline positively.
Mohammed & MustafaIlkan (2015) [37]
Note: a = Cronbach’ α

The questionnaire was given to Koreans who had accessed online word-of-mouth through an airline social media platform or website during the past year. The survey was conducted from December. 12, to 24, 2016. 300 copies of the questionnaire were distributed, and 292 copies were recovered. Excluding 18 copies that contained insincere or inappropriate answers, a total of 270 copies were used for the final analysis. The socio-demographic characteristics and general characteristics are shown in Table 2. In terms of gender distribution, 75.6% (204 persons) were female, and 24.4% (66 persons) were male. This matched the results of preceding studies that indicated that more women sought online reviews when using airlines than men. In terms of age, the number of airline users in their 40s was the highest at 104 (38.5%), followed by users in their 30s at 63 (23.3%), users in their 50s at 61 (22.6%), users in their 20s at 29 (10.7%), and users in their 60s at 13 (4.8%). In terms of online sources, airline websites accounted for the highest total at 142 persons (52.6%), followed by blogs at 71 persons (26.3%), ‘use all’ at 23 persons (8.6%), online communities at 15 persons (5.6%), SNSes at 14 persons (5.2%), and photo-share apps at 5 persons (1.9%)

Table 2. Socio-demographic characteristics of respondents.
Division Frequency (Persons) Rate (%)
Gender Male 66 24.4
Female 204 75.6
Age 20s 29 10.7
30s 63 23.3
40s 104 38.5
50s 61 22.6
60s or older 13 4.8
Frequency of online search Search every time 51 18.9
Search occasionally 134 49.6
Almost no search 76 28.1
No search at all 9 3.3
Online source Online community 15 5.6
Blog 71 26.3
Airline website 142 52.6
SNS 14 5.2
Photo-share app 5 1.9
Use all 23 8.6
Number of airline information searches 1~2 times 85 31.5
3~4 times 80 29,6
5 times or more 105 38.9

A confirmatory factor analysis was carried to verify the validity of the research hypotheses. The results are shown in Table 3. The standardized regression coefficient of each measurement variable was 0.7 or higher, securing convergent validity. The overall model fitness provided values of χ2=219.395, df=81, CMIN/DF=2.709, p=0.000, GFI=0.905, NFI=0.945, IFI=0.965, CFI=0.965, RMR=0.144, and RMSEA=0.08. Although the GFI did not achieve acceptance level fitness, the remaining indices were fit, thus satisfying the overall acceptance level. In addition, as a result of calculating each latent variable of the measurement model, the AVE values of constructs other than the AVE values of online word-of-mouth were 0.5 or higher, proving that convergent validity existed among the measurement variables used in this study.

Table 3. Confirmatory factor analysis.
Construct Measurement variable SMC Regression Coefficient
(C.R.)
Standardized Regression Coefficient Reliability a
Online word-of-mouth WOM1 0.627 1.064 (13.026) 0.792 0.936
WOM2 0.806 1.254 (14.964) 0.898
WOM3 0.812 1.233 (15.031) 0.901
WOM4 0.827 1.219 (15.177) 0.909
WOM5 0.601 1.024 (17.949) 0.775
WOM6 0.535 1.000 (Fix) 0.732
Acceptance A1 0.741 0.908 (21.127) 0.861 0.927
A2 0.849 0.963 (24.624) 0.921
A3 0.849 1.000 (Fix) 0.921
Satisfaction S1 0.921 1.000 (Fix)) 0.959 0.830
S2 0.548 0.79 (15.065) 0.74
Behavioral intention B1 0.784 1.000 (Fix) 0.885 0.936
B2 0.611 0.943 (20.187) 0.782
B3 0.794 1.056 (21.638) 0.891
B4 0.912 1.13 (25.256) 0.955
Note: a = Cronbach’ α

To verify the research hypotheses, a structural equation model analysis was carried out. The fitness index values of the model were χ2=219.395 df=81 p <0.001, GFI=0.905, AGFI=0.859, RMR=0.144, CFI=0.965, TLI=0.954, and RMSEA=0.08, satisfying the required fitness acceptance level. Accordingly, the structural model presented in this research can be judged as fitting. The results of the hypothes is verification are shown in Fig. 2.

Fig. (2). The testing results of the hypothetical model.

Online word-of-mouth turned out to have a significant effect on acceptance (β = 0.762, p < 0.001) and (β = 0.205, p < 0.05). This means that online word-of-mouth played a very important role in the passengers’ acceptance of word-of-mouth and airline satisfaction because it had an effect on the overall evaluation of the airline. In addition, satisfaction had a positive effect on behavioral intention (β = 0.903, p < 0.001). Thus, it was possible to know that online word-of-mouth had an effect on behavioral intention through satisfaction. This means that positive online word-of-mouth for an airline enhanced airline satisfaction. At the same time, it enhanced the intention of passengers to use and recommend an airline. On the other hand, information acceptance did not have a significant effect on behavioral intention (β = 0.063, p > 0.05). As a result of the research hypothesis verification, hypotheses H1, H2 and H4 were adopted whereas hypotheses H3 and H5 were rejected.

4. DISCUSSION

The foregoing analysis results have the following implications. First, this research has academic implications in that it approached the effect of online word-of-mouth on passenger behavior with an emphasis on acceptance and satisfaction. This enhanced the explanatory power of airline online word-of-mouth marketing. This research content has not been dealt with in existing studies. Second, previous studies on online word-of-mouth mostly focused on the effects of corporate images and consumer attitudes on word-of-mouth or emphasized the importance of images and attitudes, whereas the present study highlighted the effects of passengers’ reception of online word-of-mouth about airlines and their satisfaction on their behavior intention. Third, research on airline marketing was mostly concerned with loyalty, with some recent studies exploring the characteristics of their SNS, whilst it is hard to find research on online word-of-mouth about airlines. This is the first study that shed light on the effects of online word-of-mouth about airlines and delved into the effects of the reception of online word-of-mouth and satisfaction on potential passengers. Notably, the present findings indicate the importance of voluntary word-of-mouth of potential passengers and the effects of online word-of-mouth. Fourth, given the consensus on the fast-paced responsiveness of airline services to the ever diversifying needs and demands of knowledgeable passengers, this study notably suggested the importance of online word-of-mouth in tandem with the need for customer-centered marketing approaches taking into account the characteristics of individual passengers. Fifth, this research has practical implications in that through this type of empirical analysis, basic data can be mined and used to establish an efficient online communication strategy for airline online word-of-mouth. If passengers promote an airline voluntarily online, that airline can reduce its advertisement costs and improve its marketing activities by adopting an online communication strategy to communicate with customers. Finally, the present findings on the factors relevant to the effects of online word-of-mouth about airlines on passengers’ purchase behavior provide some practical reference data conducive to developing clear marketing strategies focused on such factors. Taken together, airlines can increase passengers’ satisfaction and use intention by analyzing the attributes of online word-of-mouth and reinforcing their specialties.

The limitations and future research tasks of this research can be stated as follows. Although it would have been more desirable to collect samples without considering the nationality of airline users, this study targeted only Koreans due to time, cost and data collection considerations. This caused undue limitations regarding generalization. This problem could be resolved by implementing questionnaire surveys that include non-Koreans in the future. Moreover, in this study, behavioral intention was viewed from the perspective of use intention, recommendation intention and word-of-mouth intention. However, if diverse variables that affected behavioral intention were taken into consideration, it would be possible to grasp the effect that online word-of-mouth had on the diverse behavioral intentions of passengers. In future research, therefore, to overcome the limitations suggested above, it is necessary to increase the survey time and carry out more systematic and refined analyses.

CONCLUSION

This study analyzed the effect of online word-of-mouth behavior on the behavioral intention of passengers through acceptance and satisfaction. The results of the empirical analysis can be summarized as follows. First, online word-of-mouth had a significant effect on acceptance, but it did not have a significant effect on behavioral intention through acceptance. Thus, although passengers accepted information through online word-of-mouth, the accepted information ultimately did not have an effect on behavioral intention. Second, online word-of-mouth had significant effects on satisfaction. Also, the effects of online word-of-mouth on information reception outstripped those on satisfaction. Yet, online word-of-mouth had indirect effects on passengers’ behavior intention by the medium of satisfaction. That is, online word-of-mouth about airlines could contribute to increasing the satisfaction with airlines, which in turn increases the intention to use and recommend those.

CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

Declared none.

REFERENCES

[1] Gretzel U, Yoo KH. Use and impact of online travel reviews Infor commun technol tourism 2008; 2: 35-46.
[2] Harris D. "Overview of korea economy and franchise business", [Online] Available from: http://ezinearticles.com/ ?Overview-of-KoreaEconomy-and-Franchise-Business&id=9049306, 2015. Accessed Aug. 23, 2017
[3] Shin SY, Yoo SY, Shin HY, Chung MS. A study on differenciated online marketing plans of air transport products based on internet use orientation. Korean Acad Soc Busin 2009; 2009: 1-18.
[4] Seo WH. Influence Factors of Online Re-WOM Intention: Focused on Reviews about Fashion Items MS thesis. Daegu, Republic of Korea: Keimyung University 2008.
[5] Cho JW. A Study on Effects of Airline CSR Activities on Corporate Image, Attitude and Behavior Intention MS thesis. Seoul, Republic of Korea: Kyung Hee University 2013.
[6] Felix R, Rauschnabel PA, Hinsch C. Elements of strategic social media marketing: A holistic framework. J Bus Res 2017; 70: 118-26.
[7] Peterson RA, Merion MC. Consumer Information Search Behavior and the Internet. Psychol Mark 2003; 20: 99-121.
[8] Talarzyk WW, Widing RE. Direct marketing and online consumer information services (OLCISs): Implications and challenges. J Direct Mark 1994; 8: 6-17.
[9] Chatterjee P. Online Reviews: Do Consumers Use Them. Adv Consum Res 2001; 28: 129-33.
[10] Lee SJ. The Effect of Purchase Reviews of Fashion Products of Internet Shopping mall on Benefits Sought of Sales Promotion, Fashion Customer’s Purchase Satisfaction, Repurchase Intention, and Word-of-Mouth Intention MS thesis. Seoul, Republic of Korea: Seoul Women's University, 2014.
[11] Kim CH. A study on the online words of mouth behavior: Structural analysis of the online WOM information searching, website visit, purchase and WOM information delivery. J Distribut Manage Res 2010; 13: 55-75.
[12] Park C, Jeong SY. Content Analysis of Online Consumer WOM. Adv Res 2006; 70: 91-118.
[13] Chen Y, Xie J. Third-party production review and firm marketing strategy. Mark Sci 2005; 24: 218-40.
[14] McKnight H, Kacmar C. Factors of information credibility for an internet advice site Proceedings of the 39th Annual Hawaii International Conference 2006; 6: 113b-b.
[15] Duan W, Gu B, Whinston AB. Do online reviews matter? An empirical investigation of panel data. Decis Support Syst 2008; 45: 1007-16.
[16] Senecal S, Nantel J. The influence of online product recommendations on consumers’ online choices. J Retailing 2004; 80: 159-69.
[17] Kotle P. Marketing Management: The Millennium Edition Upper Saddle River. NJ: Prentice-Hall 2000.
[18] Beardon W O, Teel J E. Selected determinants of consumer satisfaction and complaint reports J market Res 1983; 80: 21-8.
[19] Kozak M. Comparative analysis of tourist motivations by nationality and destinations. Tour Manage 2002; 23(3): 221-32.
[20] R.L. Day, "Research perspectives on consumer complaining behavior" In: Theoretical Development in Marketing, C.W. Lamb, jr. and P.M. Dunne, Eds., American Marketing Association: Chicago, 1980, pp. 211-215.
[21] Zeithamal VA, Berry LL, Parasuman A. The nature and determinants of customer expectations of service. J Acad Mark Sci 1993; 21: 1-12.
[22] Chevalier JA, Mayzline D. The effects of word of mouth on sales: online book reviews National bureau of economic research working paper #10148 2003.
[23] Dong YH. The Effects of the Characteristics of E-WOM Information on E-WOM Acceptance and Purchase Intentions-Focused on Online Purchase of Books MS thesis. Gyeong Sang NamDo, Republic of Korea: Gyeongsang National University 2015.
[24] Harrison-Walker LJ. The measurement of word-of-mouth communication and an investigation of service quality and customer commitment as potential antecedents. J Serv Res 2001; 4: 60-75.
[25] Song HJ. The influence of source characteristics of SNS on online CSR e-WOM acceptance and attitudes to the company MS thesis. Seoul, Republic of Korea: Seoul National University 2016.
[26] Kim ST. (A) study on the effects of the delayed cost and the service quality of the airline corporation on the trust, the satisfaction of the customers, the image, and the intention of the behavior: centered around the comparison between FSC and LCC Doctoral Dissertation. Kyonggi do, Republic of Korea: Kyonggi University 2013.
[27] Anderson EW, Sullivan MW. The antecedent and consequences of customer satisfaction for firms. Mark Sci 1994; 12: 125-43.
[28] Dwyer RF, Schurr PH, Oh S. Developing Buyer-Seller Relationship. J Mark 1987; 51: 11-27.
[29] Kim JM, Im ST. Hotel web services quality affects customer satisfaction, word of mouth and visits effect. Tour Res 2004; 29: 255-70.
[30] Bambauer-Sachse S, Mangold S. Brand equity dilution through negative online word-of-mouth communication. J Retailing Consum Serv 2011; 18: 38-45.
[31] Jalilvand MR, Samiei N. The effect of electronic word of mouth on brand image and purchase intention: An empirical study in the automobile industry in Iran. Mark Intell Plann 2012; 30: 460-76.
[32] Bearden WO, Netemeyer RG, Teel JE. Measurement of consumer susceptibility to interpersonal influence. J Consum Res 1989; 15: 473-81.
[33] Thoumrungroje A. The influence of social media intensity and EWOM on conspicuous consumption. Procedia Soc Behav Sci 2014; 148: 7-15.
[34] Wathen CN, Burkell J. Believe it or not: Factors influencing credibility on the Web. J Assoc Inf Sci Technol 2002; 53: 134-44.
[35] Teng S, Khong KW, Goh WW. Persuasive communication: a comparison of major attitude-behaviour theories in a social media context. J Internet Commerce 2015; 14: 42-64.
[36] Choi SA. The Study on the Effect of the Trust and Satisfaction Regarding the Quality of Online Service of Internet duty-free shop MS thesis. Kyonggi do, Republic of Korea: Korea Aerospace University 2014.
[37] Abubakar AM, Ilkan M, Sahin P. eWOM, eReferral and gender in the virtual community. Mark Intell Plann 2015; 34: 692-710.