Next Article in Journal / Special Issue
The Relationship between CSR Communication on Social Media, Purchase Intention, and E-WOM in the Banking Sector of an Emerging Economy
Previous Article in Journal
Understanding the Adoption of Incentivized Word-of-Mouth in the Online Environment
Open AccessArticle

Creating Electronic Word of Mouth Credibility through Social Networking Sites and Determining Its Impact on Brand Image and Online Purchase Intentions in India

1
Department of Business Administration, Integral University, Lucknow 226026, India
2
Department of Business Management, School of Management Science, Lucknow 226001, India
3
Department of Accountancy, College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia
4
Department of Business Administration, College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia
5
Department of Business Administration, Aligarh Muslim University, Aligarh 202002, India
*
Authors to whom correspondence should be addressed.
Academic Editor: Shib Sankar Sana
J. Theor. Appl. Electron. Commer. Res. 2021, 16(4), 1008-1024; https://doi.org/10.3390/jtaer16040057
Received: 19 January 2021 / Revised: 2 March 2021 / Accepted: 3 March 2021 / Published: 13 March 2021
(This article belongs to the Special Issue The New Era of Digital Marketing)

Abstract

The aim of this study is to identify the factors affecting the credibility of electronic word-of-mouth (eWOM) stimulation through Social Networking Sites (SNSs) through an empirical model providing both theoretical understandings and practical implications. The proposed framework explicates the consumer’s use of SNSs as a tool for information sharing and its effect on brand image and online purchase intentions. The consumer survey was done through a structured questionnaire developed in accordance with the literature. Data was collected from 256 respondents, using both offline and online modes from 4 different cities of India. Structural Equation Modeling was employed to estimate the proposed model and determine the antecedents of consumer eWOM credibility and in turn its effect on brand image leading to consumer purchase intentions. The results show SNS activities play a significant role in creating eWOM credibility, which leads to shaping the brand image and purchase intentions. The findings would help companies to create a positive brand image to enhance their purchase intentions through eWOM aroused via SNSs.
Keywords: eWOM credibility; brand image; purchase intention; social network sites (SNSs); social media marketing; word-of-mouth marketing eWOM credibility; brand image; purchase intention; social network sites (SNSs); social media marketing; word-of-mouth marketing

1. Introduction

Communication through word-of-mouth usually plays a vital role in effecting and shaping consumer attitudes and behavior intentions [1,2].But today with the help of social media, messages can be sent across the globe within seconds without a delay. Social media has become a platform of communication and acquaintance for community. Social media on a common rostrum brings together two members for example clients and web distributers where these people associate and trade data [3]. While Evans [4] characterized social media as a medium of communication where individuals of similar personalities interface and associate with one another to share their background.
Building brand awareness [5,6] in competitive markets can assume a functioning job in the advanced marketing condition. Companies have recognized that solid brand awareness will give a tough competition to other products in the market. So in this view [6,7] advertising is the medium which catches and develops an image and picture of a brand or product in the consumer’s mind that holds solid positions and assembles information about brands that could push consumers to mindful attitudes. Advertising, whether it is on an around the world, national, or close by basis, is critical, as it can penetrate audiences with large numbers of consumers by exhorting, engaging or helping them to recollect the continuation of a brand or rather by persuading or serving them to isolate a thing or relationship from others in the market so that they get motivation to purchase a product or service [8,9].
In the past, there were few companies in the market and so were the numbers of products which were manufactured to fulfill the needs of the people. But today the word “need” has become broad spectrum and there is tough competition. Therefore, in this propelled development-driven time, brands produce the products as indicated by their consumers needs. Consumers request the products and afterward brands deal with carrying consumers’ request into common sense product solutions [10,11].
Presently such a large number of tools of advertisement like television (TV), newspapers, word of mouth, magazine, radio etc. are available, which make the consumer more aware and impact them to purchase. Amongst these tools TV is the important source to contact the greatest crowd but now the consumers do not believe what they view in TV advertisements. They accept when someone tells (implies, word of mouth) them about the product or brand. Word-of-mouth (WOM) conversations help the dispersion of novelties and brand-related evidence amongst amenable crowds. Experienced consumers take part in word-of-mouth trades to impart their encounters to and deliver recommendations for beginner consumers [12].
Presently, in the unique environment, people do not have more time to interact with each other, so they are examining or transferring information via electronic word-of-mouth (eWOM) by utilizing social networks through the internet. Since its initiation, the internet has become a popular marketing channel. Most organizations presently see the internet as an appealing medium to connect with clients. Advertisers are putting considerable assets in web-based displays, joined by rapidly expanding utilization of the web by the clients [13].
Advertisement through word-of-mouth communication currently takes place online including through online conversation gatherings, newsgroups, web journals, review sites, and SNSs [14], which are at present popularly known as electronic word-of-mouth. eWOM is an assertion made by potential, real, or prior customers about a product or organization, which is made accessible to a huge number of individuals and institutions by means of the internet.
Since eWOM has become a major tool of advertisement, therefore companies must study the factors affecting eWOM and must try to engage their potential customers and audience via SNSs. The brands are now rewarding and giving exciting offers to their customers if they spread their products in the market and if the buying is promoted. Such positive examples [15] are mainly predominant on SNSs, for example, Facebook, WhatsApp, Instagram, Twitter, LinkedIn, Pinterest, Skype etc. However, the success of their accomplishments rely upon their general credibility [16,17].
Messages of eWOM spread on SNSs are open and straightforward as the message shows on the user’s individual profile page as well as on the friends’ newsfeeds associated with that user [11]. Inside this online space, brands are progressively attempting to use their ”fan” and “followers” associations in order to appeal to different and new customers [18].
There have been various studies that explored the credibility of word-of-mouth (WOM) in the offline world [19,20,21,22]; these studies investigated eWOM credibility and consumer online purchase intention. However, there is dearth of studies explicating the role of specific eWOM aspects in creating eWOM credibility, specifically in the Indian context. Along with these investigations there is also a need to contemplate the relationship of eWOM credibility with brand awareness leading to consumer purchase intention.
The purpose of the study is to find out how do consumers view a product when featured through social networking sites (SNSs). The research aims at discovering the reliability of eWOM amongst buyers and to what extent these eWOMs affect the brand image as well as the consumers purchase intentions. The specific research questions are:
  • What is the role of eWOM aspects—high involvement, trust, recommendation and message content in creating eWOM credibility?
  • Does eWOM credibility lead to brand awareness and purchase intention?
Thus, keeping in mind the need of the research this paper has been constructed to empirically determine factors affecting eWOM credibility on social networking sites (SNSs) and brand image and consumer’s online purchase intentions in India.
The research will have both academic and practical implications. It will contribute to the body of literature by producing evidence of relationships between considered eWOM aspects and eWOM credibility leading to brand intention and purchase intention. Practically it will give insights to the marketers in creating better strategies to create successful eWOM campaigns.
The following sections give some highlights of the past literature and professional methodology used in data collection; finally, based on the discoveries of the study, conclusion and recommendations have been suggested.

2. Theoretical Background and Hypothesis Formulation

2.1. Present and Practical Digital Word

A report of Sannam S4 [23] indicated that India has 574 million dynamic internet consumers starting in 2019. After China, India is the second-biggest online market. It is anticipated that by December 2025 there will be around 974 million internet users in India and internet penetration is ascending all through India. Indians form the largest user base of Facebook users across the world. In India YouTube and Facebook are well-known social networks [23]. Reports additionally stated that the passage of WhatsApp into India’s internet market helped the application use, through an increase in use in rural parts of India as of late. Statistics display that the spread of the messaging service covers more broad areas than simply metropolitan (urban) areas. It is also indicated that the augmented obtainability of internet networks and access as of late, impelled by the central government’s Digital India initiative, was straightforwardly comparative to the development of social media consumers.
The Indian buyer is progressively devouring the substance on digital platforms. This tendency is noticed for a wide range of substance including music (audio), or video, and news (text). Expanding internet infiltration and cell phone multiplication has prompted solid elements boosting more noteworthy utilization of content on the internet in India.
Jamie [15] discloses that, individuals presently do not need close gatherings or handshakes. Similarly, in this time of digitization, individuals have seen habits as socially influential on the internet, which is credible with the appearance of the numerous long range informal communication stages or social networking platforms and applications.

2.2. Why eWOM

Hence, Gilly et al. [24] expressed that, the relative expertise of the electronic word-of-mouth (eWOM) beneficiary should likewise be thought of as important influencers. It remained previously recognized that as consumers acquired additional knowledge with products and services their dependence on WOM waned [25]; however, higher purchaser mastery may make certain eWOM communications further easily reached. This can be ascribed to the degree of participation the beneficiary needs to precisely interpret the electronic word-of-mouth (eWOM) message.
Electronic word-of-mouth (eWOM) is for the most part observed as beneficial in the prospect that it is efficiently reachable and available to every individual who can exploit the internet [26]. Moreover, the internet provides an ideal mix of user demographics, which allows purchasers to reach a blend of conclusions that help them form their own thoughts about a product or service [27,28].
These days’ consumers mostly depend on the advice and suggestions that their friends give about the products on social networks. Around 21% of consumers find the performance of the products, which have been reviewed by their peers on the social networking sites, as pretty good [29].
To add more to the studies Reichelt et al. [30] in his findings said that currently the craze of the internet is exponentially increasing among consumers and thus due to its manifold popularity consumers are exchanging and posting more and more product information on SNSs. Due to the extensive use of SNSs by consumers, large volumes of suggestions and information is available online with almost free access to anyone. The use of digital media has completely changed the perspective of buying and selling. These days just by few mouse clicks a consumer can study the reviews of a product by its old and present users and then make a decision to purchase or not. Thereby, seeing its vast interference and a major decisive factor in buying of the product, eWOM is at present counted as a crucial constituent of the consumer purchase decision-making process [31].
Bhat [32] mentioned that information quality and information quantity [33], which is available to consumers in the form of reviews, significantly influences consumers’ purchasing intentions. eWOM information helps in buying decisions of consumers. Further, Mehyar et al. [33] examined how consumers’ purchasing intention toward products may vary depending on, credibility, and quantity, which would yield different behaviors in purchasing intentions. Thus, communication through eWOM is commonly familiar to assume an observable job in affecting and making consumer perspective and behavioral intentions [1]. It has for quite some time been perceived to influence consumer decision making, and the assessments and recommendations of others have been believed to have a significant impact on purchase decisions [19,34].
When people share information about a brand or product through social networking sites, then along with the awareness of the people, credibility also increases and this awareness and credibility influences them to change purchase intention towards a brand.

2.3. Factors Affecting eWOM Credibility and Purchase Intention

Banerjee, Dutta, and Dasgupta [13] examined how the accessibility of wide and recent information, intermittent household earnings, and involvement of consuming the internet are the significant viewpoints impacting Indian consumers’ disposition towards online purchase, yet buyers worry over online safety. This is the main reason behind consumers not participating in online purchasing.
Customers who effectively have attachments with brands on SNSs are overwhelmingly the brands’ loyal consumers [35], whose commitment shows a passion similar to a psychological and behavioral connection to the brand [36].
Credibility was hence assumed as an intrinsic element or factor in word-of-mouth (WOM) messages from strong-tie sources [37]. Researchers tested and reviewed different eWOM variables influence on consumers’ purchase intentions; among them the variables, information usefulness, information quality, information adoption, and information credibility are identified as prevalently tested variables. The effects of eWOM on consumers’ perception of credibility might be better explained by adding more dimensions of eWOM in social media found in other studies. These dimensions may include Review Consistency [31], Recommendation Rating [38] and Task Attraction [38,39]. In the detailed analysis, Trust, Self-efficacy, Altruism, and Moral Obligation have been identified as relevant to customers that needs to give a special consideration. Farzin and Fattahi [40] additionally analyzed other correlative factors that are aligned with motivational and psychological differences, and their attraction to technology, market Mavenism and Self-presenting.
Cheung et al. [41] highlighted that if the receiver of eWOM benefits from the information and has no basis to doubt that information, it will be viewed as credible and the recommendation treated with equal credibility, while the restricting camp states that eWOM is spread by obscure individuals with obscure thought processes who can cover their actual personalities and post secretly [42]. Additionally, positive eWOM is potentially spread by advertisers posing online as pleased customers in an attempt to strengthen brand statuses [43]. However, the factors leading to the increased popularity of eWOM are yet locked in the chamber of secrets and are tough to encrypt; consumers might be rewarded for posting good and positive reviews of the product [17].
SNS provides a medium and aides the exchange of ideas and opinions among a known group of friends as well as among unknown groups of people. Currently most of the famous and widely used SNS platforms like WhatsApp, Facebook, Instagram, LinkedIn, and Twitter are keen on the implementation of a Real User Policy, that eradicates the cloud of ambiguity by revealing the true identities which further enhances the trustworthiness of SNS among its users. In addition to this, an SNS also promotes online friends to connect with their offline friends, thereby keeping relations lively and also empowering the SNS-based connections [44]. These features of an SNS make it advantageous over other sources of information and has helped in reviving the opinion of source credibility which was once considered as the only legacy and heritage of offline WOM.
It has been studied and revealed in the study of consumer perceptions of online reviews by Lee and Ma [45], that the greater the degree of interpersonal influence in a consumer the greater is the dependence of the consumer on the online eWOM as a more reliable source of collecting product information. The conclusions by Chu and Kim [46] also confirm the findings that the susceptibility characteristic and group influence are directly proportional to each other, which in turn increase the authenticity of eWOM as it has a larger influential group available.
Earlier there was a concept that people not disclosing their identities were taken into fake consideration and their online reviews were not fully accepted. But the trend has changed as revealed by the study of [47,48] that shows that consumers now have an increased level of trust in the online reviews posted by anonymous people rather than the reviews on radio, TV, magazines, or newspapers. According to Nieto et al. [49], consumers show a higher level of faith in the online reviews posted by other consumers as compared to the reviews of the same product posted by the sellers or manufacturers of the product. Ultimately these trusts in online reviews have greater contribution in the purchase intentions of the consumer in buying a product [47,50].
Potency or influence of a bond amongst various individuals of a social network is referred to as tie strength [51]. The tie strength might be weak or strong [52]. Pigg and Crank [53] observed that strong tie strengths are among relatives, friends, or family members since they concern relations that are personal and are the wellspring of meaningful and emotional help to different individuals.
Usually, word-of-mouth (WOM) communications in the offline atmosphere happened amongst individuals who showed strong tie associations as between close friends or family members [12]. WOM, in this case, was viewed as believable, truthful, relevant, and honest as the message was originating from reliable sources about their particular involvements with brands and products [54].
According to Elaboration Likelihood Model [55] and the Heuristic-Systematic Model [56], involvement is viewed as an important factor. In the two models the higher the beneficiary’s contribution with an item or service, for example, the more weightage and significance one puts on the buying choice and the more intricate the psychological handling of the message contentions that will happen. It is indicated that if there is low involvement then beneficiary trust of the message content is less, in which case there is dependence on more reachable or heuristic/intuitive cues, for example, source qualities as an intermediary for credibility, bringing about appropriation/non-reception.
Hutter et al. [57] further stated that active buyers, who associated with the brand, enhance the brand page view, which promotes more brand awareness. One of the important tasks of marketers is to build brand awareness because brands construct their online social outlines so as to appeal to their target market, to involve their target market as well as convert those market members into sales. Brand awareness is made by building up customer relationships and acquiring their trust in the brand’s products. Each brand makes its own marketing content program through which they measure their brand awareness on social media; such measures include brand mentions, search volume, blog shares and the most important of all is the social media reach [58,59].
Over the range of recent decades, promoting approaches and theories have encountered variations, and exhibiting authorities these days base their practices on social drivers rather than standard measures for appealing to more purchasers or making a brand picture in the mind of customers.
A study on online retailing conducted by Mintel [29] appealed that respondents concurred with the way that brands’ comments and consumer reviews regard to an item or a product helps them in overcoming their interests and stimulated them in making the online buying.
SNSs and social media permit marketers to impact straightforwardly their intended target group. Various researchers [60] have investigated the comparative significance of the two media for the consumers however research is absent in the writing on the forerunners that assume a basic part in electronic word-of-mouth (eWOM) behavior and its importance of research subsequent effect on the Brand image and purchase intention.
Researchers also indicated that trust is the inspiration of a person to follow up on or follow the information and guidance given by another individual [53]. Trust has a fundamental role in inspiring individuals to exchange their judgment or perspectives about a brand or product [61].
Self-efficacy involves an individual’s trust in one’s abilities [62,63], and it involves individual arbitration with respect to one’s ability to execute and organize the game-plan needed for certain well-defined natures of presentations [64], and significantly affects people’s motivating, feeling, behaving, thinking, and decision making [62].
For deciding the purchase intention of the consumers [65] brand equity and brand image are the two important variables that play a vital role. Brand Image embraces attributes and benefits related with a brand that make the brand unique and differentiate the firm’s offer from that of its opponents. Keller [66] defined brand image benefits as “what consumers think the brand can do for them”, and defined brand image as the perception of buyers when they are exposed to a brand and is reflected by brand associations in their evoked set. Now purchase behavior of consumers has been altered, before making a purchase decision they frequently make inquiries about the brand quality. Thus, prior to settling on a purchase decision to get themselves the most suitable product for their consumption consumers tend to watch blogger reviews [67]. Ansari et al. [11] further examined that the virtual societies of brands on social media are producing purchaser involvement and trust towards brands. Consumers place their trust in the guidance of friends, as individual buyers are not expected to have any motivation to deceive them [41,68]. Such sort of trust that is created through evidence and familiarity sharing by the customers about the particular brand helps the imminent purchasers in strengthening their interests with respect to the product and hence prompts purchase desire.
Gillian Laurent Muzellec [69] discloses that numerous credibility contemplations can be organized into two general categories. The first category incorporates audience factors [46,70], tie strength relationships [19,52], receiver processing, motivation, involvement levels [56,71], and ability [55], however, another category contains message characteristics or message content [16,41].
The purchase or buying decision [11] by buyers is the decision-making procedure for an exchange of services or products being offered in the market. Researchers also defined that purchase intention additionally alludes [72] to the procedure of setting a decision of purchase, during which the buyer reflects numerous circumstances and bases. Cheung and Thadani [73] indicated that purchase intention of an individual is straightforwardly allied with the attitudes, behaviors and perceptions of the end user toward the service or product itself or even the retailer and seller.
The marketplace conveys a broad group of brands present nearby similar products with various qualities in order to appeal to customers. Since this is an innovative and technology-driven period, thus, social media is the platform for brands to demonstrate their contributions alongside the product information to save buyers valuable time and pull them towards the brand [44].
An organization’s long-haul cash flow and future income can be affected by many factors like brand image (BI), a buyer’s willingness to follow through on premium costs, sustainable competitive advantage, stock prices, strategic decision making, and marketing practices [66,74].
Ansari et al. [11] further examined in her study that, propagation of brand and making it socially popular and contents of social media marketing are important components that affect the consumer purchase decision.
In entirety for High Involvement (HI), Trust (TR), Recommendation (RC), Message Content (MC) and its impact on eWOM credibility (EW), we hypothesize the following:
  • H1—High involvement with the SNSs has a positive and significant effect on eWOM credibility among customers.
  • H2—Trust on the SNSs has a positive and significant effect on eWOM credibility among customers.
  • H3—Recommendations of SNS members have a positive and significant effect on eWOM credibility among customers.
  • H4—Message content has a positive and significant effect on eWOM credibility among customers.
Where HI, TR, RC and MC are considered as independent factors while EW as a dependent factor.
To test the impact of eWOM credibility on the brand image and the purchase intentions of consumers, the accompanying hypotheses were proposed:
  • H5—eWOM credibility of the customers has positive and significant effect on the brand image.
  • H6—Brand image has positive and significant effect on the purchase intention.
On the basis of presumed connections among the variables, as framed by the above formulated hypothesis, the conceptual structure of research is constructed in Figure 1.

3. Methodology

The aim of the current study is to examine the impact of eWOM on brand image and consumer buying behavior in the Indian context, therefore the data was collected from multiple cities (Lucknow, Delhi, Mumbai and Sitapur), including females and males from the age groups of less than 25 to more than 45 years.
Primary data was collected from September to November 2020 through a questionnaire developed for the purpose of this study, administered personally as well through online mode. The first section of the questionnaire recorded the demographic details of the respondents. The next section asked the names of four social networks regularly used by consumers. The last section consisted of the psychographic instruments to measure eWOM aspects, SNS Credibility, Brand Image and Purchase Intentions. Pre-validated scales were adapted to measure various constructs of interest as shown in the Appendix A. Each construct was measured through a set of instruments recorded on a five-point Likert type scale. A reliability analysis was conducted through Cronbach’s Alpha on a pilot basis prior to full-fledged data collection so that the instruments could be tested for their internal consistency. It was found that all the scales showed an acceptable level of internal consistency.
Quantitative analysis of the empirical data was done to diagnose the relationships among variables. Since sampling frames are not available, the India Purposive Sampling Technique was adopted in selecting only those individuals involved in online buying or having exposure to social media campaigns of companies. More than 300 questionnaires were distributed, 270 were collected back, while only 256 responses were found to be eligible for data analysis. The hypothesis testing and model estimation was done through a two step Structural Equation Modeling—the measurement model and structural model through SPSS AMOS.

4. Results

4.1. Sample Profile and Descriptive Analysis

The demographic characteristics are provided in Table 1. According to the results, 166 respondents were male and 86 were female (58.3% vs. 41.7%). Most of the people sampled were young people; with 39.8% of the respondents between 25 to 35 years and 35.8% between 18 to 25 years of age. Most of them are graduate and post-graduate students who are taking part in decision making.
The descriptive analysis yields that the maximum number of respondents (92.9%) had access to the SNSs. Of the 256, 92.9% of respondents are frequent user of social networking sites. In which 76.4% are Facebook, 66.9% are YouTube while 50.4% of the respondents are LinkedIn users. It may be assumed that the collected sample is a proper mix of various demographics, extensive users of social media and good representative of the target population.
Further, the proposed model was tested and estimated through SEM [75] in two steps involving the measurement model or confirmatory factor analysis (CFA) and structural model or path analysis.

4.2. Measurement Model

CFA was done to ascertain the structure of the latent variables or constructs considered in the study. Model fit was established first, followed by the model estimation and adequacy through assessing the internal consistency, convergent and discriminant validity. The absolute fit indices—Chi square value of CMIN 221.091 was significant at the 0.000 level with 114 DOF. Though it should be insignificant, however for big samples it is rarely insignificant Therefore to assess the model, another statistic, CMIN/DOF was observed; its upper limit is 5 as recommended by Carmines and McIver [76]. The observed value was 1.939, falling into an acceptable limit.
The GFI for the model is 0.918 (values > 9 are very good), the RMR is 0.05 (a value less than equal to 0.05 is desirable [75]) and RMSEA for the model is 0.060 (desirable range 0.03—0.08 and recommended by Byrne [75]). The relative fit indices were NFI—0.905, CFI—0.951 and TLI—0.934, again very much in the desirable range. The parsimonious were AGFI—0.877 and—PNFI—0.708, though a value of 0.9 or greater is desirable a value > 0.8 is also quite acceptable [77]. The AGFI was acceptable whereas PNFI was on the lower side. Overall the 10 fit indices were analyzed and it was found that 8 were in an acceptable range, therefore the measurement model was considered to be a good fit of the data. After establishing the model fit the measurement model estimated parameters were analyzed for the internal consistency, convergent and discriminant validity as suggested by Hair Jr. et al. [78] for reflective models. Cronbach’s Alpha was estimated for internal consistency and it was observed that all the constructs had a value greater than 0.7(as shown in Table 2).
Convergent validity was established in accordance with Fornell and Larcker [79], suggesting retaining only those indicators having a minimum loading of 0.7. Also the average variance extracted (AVE) was calculated for each construct and it was observed that AVEs for all constructs were > 0.5 (desirable for convergent validity as recommended by Bagozzi and Yi) [80]. For discriminant validity, the average squared correlations of each construct with other constructs were calculated (as shown at the bottom of each column in below Table 3); Fornell and Larcker [79] suggest that AVE of each construct (as shown in the diagonal of the below Table 3) should be greater than the average squared correlation of the construct, as observed and shown in the above, and the results shown in Figure 2 also show the correlation among the latent variables along with the factor loading.

4.3. Structural Model

The measurement model determines the adequacy of the latent variables in measuring the constructs through observed variables. Once it is established we proceeded to the structural model for estimating the proposed relationships and hypothesis testing. First the model fit was assessed, followed by path analysis. The absolute fit indices—chi-squared value of CMIN 255.909 was significant at the 0.000 level with 123 DOF. Though it should be insignificant, however for big samples it is rarely insignificant, therefore to assess the model another statistic CMIN/DOF was observed to be 2.081, falling in the acceptable limit as suggested by Carmines and McIver [76].
The GFI for the model is 0.905 (values > 9 are very good), RMR is 0.61 (a value less than equal to 0.05 is desirable [75]) and RMSEA for the model is 0.065 (desirable range 0.03—0.08 and recommended by Byrne) [75]. The relative fit indices were NFI—0.890, CFI—0.939 and TLI—0.924. The parsimonious were AGFI—0.868 and—PNFI—0.715, though a value of 0.9 or greater is desirable, a value > 0.8 is also quite acceptable [77]. Here also out of 10 analyzed fit indices it was found that 8 were in the acceptable range, therefore we can proceed to the path analysis (as shown in Figure 3) and draw conclusions.

5. Discussion and Conclusions

From the path analysis, shown in Table 4, it was found that there is sufficient evidence that high involvement with SNS, trust on SNS, recommendation by SNS members and message content has significant impact on eWOM credibility. Among the four factors, recommendation was found to have the strongest effect on eWOM credibility, followed by high involvement, message content and trust, respectively. Together these variables explained 26.2% of the variance in eWOM credibility. It means there are several other factors that create eWOM credibility not considered in this study. The coefficient of relationship between eWOM credibility and brand image was found to be very strong, indicating a strong impact of eWOM credibility on the creation of a brand image. The path analysis also confirmed significant relationship between the brand image and the purchase intentions of customers. It may be concluded that brand image is an important precursor of purchase intentions and the considered SNS characteristics play an important role in shaping the eWOM credibility and brand image.
The present study focused primarily on factors creating eWOM credibility. From the obtained results it may be concluded that the considered four factors are significant predictors of eWOM credibility. These results support the findings of earlier researches [53,81,82]. Recommendation was found to be most important factor to create credibility of messages sent through SNS. It implies that the more consumers recommend a product to others through SNS the more credibility of SNS is created and recommendations also have significant impact on purchase decisions [4,19]. The study also produces evidence of the important role of involvement with the SNS. The ELM Model [55] and the HSM Model [56] established the importance of involvement, emphasizing that low involvement leads to less trust on picking of message content. This also brings out the role of involvement in creating trust or credibility of the SNS. Message content in itself is an important component of social media marketing [11] and it interacts with the other two eWOM aspects of involvement and recommendation. It is a well-established fact that interesting and engaging content increase involvement and may also lead to recommendation. Although Trust was found to have weakest impact on credibility, it is an important one as trust has greater contribution in purchase intentions [47,50]. Hence the importance of these four characteristics (HI, TR, RC, MC) in creating eWOM may be established, leading to formation of positive brand image and ultimately resulting in purchase intentions.

6. Practical Implications

The findings throw some light on the importance of various factors that create eWOM credibility. Companies may identify the antecedents and their relative importance to create eWOM credibility. The results of this study would help companies to create a positive image of their brands in the mind of consumers to enhance their purchase intentions through eWOM through SNSs. The study emphasizes that marketers for a successful campaign through SNSs must focus on these four characteristics because these characteristics generate credibility and credibility creates a brand image which leads towards purchase intention.

7. Limitations and Proposed Future Research

Obviously, respondents of a few cities don’t actually speak to the entire population of SNSs members, so that eWOM credibility and its impact can be investigated in different cities and geographical areas also. The authors concede that, notwithstanding the factors considered in this study, there are other similarly key factors related to eWOM experiences that can be considered. With respect to the significance of eWOM, different aspects of brand loyalty, brand equity, repeat purchase, etc., could be further explored and investigated.

Author Contributions

Conceptualization, M.S.S., U.A.S. and M.A.K.; Resources, M.S.S., U.A.S., M.A.K. and J.H.S.; Investigation, U.A.S., A.K.S. and J.H.S.; methodology, M.S.S., U.A.S., M.A.K., and A.K.S.; writing—original draft preparation, M.S.S., U.A.S., M.A.K., I.G.A. and J.H.S.; writing—review and editing, M.S.S., U.A.S., M.A.K., I.G.A., and A.K.S.; supervision, M.S.S., A.K.S. and I.G.A.; project administration, U.A.S., I.G.A., A.K.S. and J.H.S. All authors have read and agreed to the published version of the manuscript

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

  • High Involvement [83,84]
  • HI 1—My interaction with members of SNSs of which I am a member is high
  • HI 2—If I leave the social network that I was a member of and join another social network, it is important to me that my friends accompany me
  • HI 3—I am always very motivated to share everything with my friends or family members through social networking sites (SNS)
2.
Trust [46]
  • TR 1—I trust most of my contacts in my friends list in the social networks I am a member of
  • TR 2—In my view, members of social networks trusts each other and shares their information regarding products and brands with each other
  • TR 3—Members of social networks of which I am a member in giving advice on products and brands are competent and effective
3.
Recommendation [85]
  • RC 1—Recommendation increases credibility and I generally purchase those brands that I think others will recommend to me
  • RC 2—If I want to be like someone, I often try to buy the same brands that they buy
  • RC 3—I often identify with other people by purchasing the same products and brands they purchase and recommend to me
4.
Message Content [86]
  • MC 1—I have a strong concern about the type of content, especially the emotional contents on SNS.
  • MC 2—I enjoy the entertaining content and would also share it to my fellow members so that they can also enjoy it
  • MC 3—The interestingness of content is important and helps create more engagement with the SNS
5.
Electronic word-of-mouth [40,87]
  • eWOM1—To make sure that I buy the right products or brands, I often read online reviews of products and brands written by other fellow members in social networks
  • eWOM 2—To choose the right products or brands, I often consult online reviews of products and brands provided by other fellow members in social networks
  • eWOM 3—I always publish my experiences with products and brands in social networks on request of other members
6.
Brand image [88,89]
  • BI 1—Information credibility, that is, the products or brands introduced by my friends in social networks, creates a brand image of products
  • BI 2—Credibility on SNS creates a brand image in our mind
  • BI 3—Consumer’s online review creates a brand image in our mind
7.
Purchase intention [88,90]
  • PI 1—I would like to purchase the products or brands introduced by my friends in social networks
  • PI 2—I would like to purchase those products or brands whose information is provided by my credible social network
  • PI 3—I would like to purchase the products or brands based on online reviews by consumers in social networks

References

  1. Chevalier, J.A.; Mayzlin, D. The effect of word of mouth on sales: Online book reviews. J. Mark. Res. 2006, 43, 345–354. [Google Scholar] [CrossRef]
  2. Xia, L.; Bechwati, N.N. Word of mouth: The role of cognitive personalization in online consumer reviews. J. Interact. Advert. 2008, 9, 3–13. [Google Scholar] [CrossRef]
  3. Touchette, B.; Schanski, M.; Lee, S.L. Apparel Brands ‘Use Of Facebook: An Exploratory Content Analysis Of Branded Entertainment. J. Fash. Mark. Manag. 2015, 19, 107–119. [Google Scholar] [CrossRef]
  4. Evans, D. An Hour A Day. In Social Media Marketing; Wiley Publishing: Indianapolis, IN, USA, 2008. [Google Scholar]
  5. Karamian, H.; Nadoushan, M.A.; Nadoushan, A.A. Do Social Media Marketing Activities Increase Brand Equity? Int. J. Econ. Manag. Soc. Sci. 2015, 4, 362–365. [Google Scholar]
  6. Latif, W.B.; Islam, M.A.; Noor, I.M. Building Brand Awareness in the Modern Marketing Environment: A Conceptual Model. Int. J. Bus. Technopreneurship 2014, 4, 69–82. [Google Scholar]
  7. Higie, R.A.; Sewall, M.A. Using recall and brand preference to evaluate advertising effec-tiveness. J. Advert. Res. 1991, 31, 56–63. [Google Scholar]
  8. Fill, C. Marketing Communications-Contexts, Strategies and Applications, 3rd ed.; Prentice Hall: Englewood Cliffs, NJ, USA; Pearson Education Ltd.: Harlow, UK, 2002. [Google Scholar]
  9. Kim, K.R. The Effects of Advertising and Publicity on Corporate Reputation and Sales Revenue: 1985–2005. Ph.D. Thesis, The University of Texas, Austin, TX, USA, 2007. [Google Scholar]
  10. Ahmed, W.; Mahmood, Z.; Ahmad, A. Does Advertising Exposure Level Matter? Implications for Experimental Research in Advertising. Bus. Econ. Rev. 2016, 8, 23–34. [Google Scholar] [CrossRef]
  11. Ansari, S.; Ansari, G.; Ghori, M.U.; Kazi, A.G. Impact of Brand Awareness and Social Media ContentMarketing on Consumer Purchase Decision. J. Pub. Value Adm. Insights 2019, 2, 5–10. [Google Scholar] [CrossRef]
  12. Dichter, E. How word-of-mouth advertising works. Harv. Bus. Rev. 1966, 44, 147–166. [Google Scholar]
  13. Banerjee, N.; Dutta, A.; Dasgupta, T. A study on customers’ attitude towards online shopping-An Indian perspective. Indian J. Mark. 2010, 40, 36–42. [Google Scholar]
  14. Weinberg, B.D.; Davis, L. Exploring the WOW in online-auction feedback. J. Bus. Res. 2005, 58, 1609–1621. [Google Scholar] [CrossRef]
  15. Jamie. 65+ Social Networking Sites You Need to Know about. 2020. Available online: https://makeawebsitehub.com/social-media-sites/ (accessed on 8 October 2020).
  16. Ryu, G.; Feick, L. A penny for your thoughts: Referral reward programs and referral likelihood. J. Mark. 2007, 71, 84–94. [Google Scholar] [CrossRef]
  17. Schmitt, P.; Skiera, B.; Van den Bulte, C. Referral programs and customer value. J. Mark. 2011, 75, 46–59. [Google Scholar] [CrossRef]
  18. LaPointe, P. Measuring Facebook’s Impact on Marketing: The Proverbial Hits the Fan. J. Advert. Res. 2012, 52, 286–287. [Google Scholar] [CrossRef]
  19. Brown, J.J.; Reingen, P.H. Social ties and word-of-mouth referral behavior. J. Consum. Res. 1987, 14, 350–362. [Google Scholar] [CrossRef]
  20. Duhan, D.F.; Johnson, S.D.; Wilcox, J.B.; Harrell, G.D. Influences on Consumer Use of Word of Mouth Recommendation Sources. J. Acad. Mark. Sci. 1997, 25, 283–295. [Google Scholar] [CrossRef]
  21. Gatignon, H.; Robertson, T.S. An Exchange Theory Model of Interpersonal Communication. Adv. Consum. Res. 1986, 13, 534–538. [Google Scholar]
  22. Leonard-Barton, D. Experts as Negative Opinion Leaders in the Diffusion of a Technological Innovation. J. Consum. Res. 1985, 11, 914–926. [Google Scholar] [CrossRef]
  23. Sannam, S. Digital Trends & 2019 Social Media Landscape in India. 2020. Available online: https://Sannams4.Com/Digital-And-Social-Media-Landscape-In-India/ (accessed on 10 October 2020).
  24. Gilly, M.C.; Graham, J.L.; Wolfinbarger, M.F.; Yale, L.J. A Dyadic Study of Interpersonal Information Search. J. Acad. Mark. Sci. 1998, 26, 83–100. [Google Scholar] [CrossRef]
  25. Herr, P.M.; Kardes, F.R.; Kim, J. Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective. J. Consum. Res. 1991, 17, 454–465. [Google Scholar] [CrossRef]
  26. Evans, C.; Erkan, I. The Impacts of Electronic Word of Mouth in Social Media on ConsumersPurchase Intentions; The International Institute of Knowledge Management (TIIKM): Colombo, Sri Lanka, 2014; pp. 9–14. Available online: http://tiikm.com/publication/ICODM-2014Online-Proceeding-Book.pdf (accessed on 20 September 2020).
  27. Hennig-Thurau, T.; Gwinner, K.P.; Walsh, G.; Gremler, D.D. Electronic word-of-mouth viaconsumer-opinion platforms: What motivates consumers to articulate themselves on the internet? J. Interact. Mark. 2004, 18, 38–52. [Google Scholar] [CrossRef]
  28. Litvin, S.W.; Goldsmith, R.E.; Pan, B. Electronic word-of-mouth in hospitality and tourismmanagement. Tour. Manag. 2008, 29, 458–468. [Google Scholar] [CrossRef]
  29. Mintel. Social Networking—Ireland. 2015. Available online: http://academic.mintel.com/display/739944/ (accessed on 10 October 2020).
  30. Reichelt, J.; Sievert, J.; Jacob, F. How Credibility AffectseWOM Reading: The Influences of Expertise, Trustworthiness, and Similarity on Utilitarian and Social Functions. J. Mark. Commun. 2014, 20, 65–81. [Google Scholar] [CrossRef]
  31. Moran, G.; Muzellec, L.; Nolan, E. Consumer Moments of Truth in the Digital Context. J. Advert. Res. 2014, 54, 200–204. [Google Scholar] [CrossRef]
  32. Bhat, N. The influence of Electronic word of mouth (Ewom) on Consumers Purchase Intention: A review and analysis of the existing literature. IOSR J. Eng. 2020, 10, 27–36. [Google Scholar] [CrossRef]
  33. Hamzah, M.; Saeed, M.; Baroom, H.; Aljaafreh, A.; Al-Adaileh, R. The Impact Of Electronic Word Of Mouth On Consumers Purchasing Intention. J. Theor. Appl. Inf. Technol. 2020, 98, 183–193. [Google Scholar]
  34. Engel, J.F.; Kegerreis, R.J.; Blackwell, R.D. Word-of-mouth communication by the innovator. J. Mark. 1969, 33, 15–19. [Google Scholar] [CrossRef]
  35. Nelson-Field, K.; Riebe, E.; Sharp, B. What’s Not to ‘Like?’ Can a Facebook Fan Base Give a Brand The Advertising Reach It Needs? J. Advert. Res. 2012, 52, 262–269. [Google Scholar] [CrossRef]
  36. Hollebeek, L.D. Demystifying Customer Brand Engagement: Exploring the Loyalty Nexus. J. Mark. Manag. 2011, 27, 785–807. [Google Scholar] [CrossRef]
  37. Kozinets, R.V.; de Valck, K.; Wojnicki, A.C.; Wilner, S.J.S. Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities. J. Mark. 2010, 74, 71–89. [Google Scholar] [CrossRef]
  38. Fang, Y.-H. Beyond the Credibility of Electronic Word of Mouth: Exploring eWOM Adoption on Social Networking Sites from Affective and Curiosity Perspectives. Int. J. Electron. Commer. 2014, 18, 67–102. [Google Scholar] [CrossRef]
  39. Dash, A.K. Use of online social networking sites by college students and its implications for marketing: A case study in Tripura. Indian J. Mark. 2011, 41, 68–76. [Google Scholar]
  40. Farzin, M.; Fattahi, M. eWOM through social networking sites and impact on purchase intention and brand image in Iran. J. Adv. Manag. Res. 2018. [Google Scholar] [CrossRef]
  41. Cheung, M.Y.; Luo, C.; Sia, C.L.; Chen, H. Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations. Int. J. Electron. Commer. 2009, 13, 9–38. [Google Scholar] [CrossRef]
  42. Dellarocas, C. The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms. Manag. Sci. 2003, 49, 1407–1424. [Google Scholar] [CrossRef]
  43. Godes, D.; Mayzlin, D. Firm-Created Word-of-Mouth Communication: Evidence from a Field Test. Mark. Sci. 2009, 28, 721–739. [Google Scholar] [CrossRef]
  44. Chatterjee, P. Drivers of New Product Recommending and Referral Behaviour on Social Network Sites. Int. J. Advert. 2011, 30, 77–101. [Google Scholar] [CrossRef]
  45. Lee, H.-H.; Ma, Y.J. Consumer Perceptions of Online Consumer Product and Service Reviews. Focusing on Information Processing Confidence and Susceptibility to Peer Influence. J. Res. Interact. Mark. 2012, 6, 110–132. [Google Scholar] [CrossRef]
  46. Chu, S.-C.; Kim, Y. Determinants of Consumer Engagement in Electronic Word-of-Mouth (eWOM) in Social Networking Sites. Int. J. Advert. 2011, 30, 47–75. [Google Scholar] [CrossRef]
  47. Lee, M.; Youn, S. Electronic word of mouth (eWOM) how eWOM platforms influence consumer product judgment. Int. J. Advert. 2009, 28, 473–499. [Google Scholar] [CrossRef]
  48. Zhang, J.Q.; Craciun, G.; Shin, D. When does electronic word-of-mouth matter? A study of consumer product reviews. J. Bus. Res. 2010, 63, 1336–1341. [Google Scholar] [CrossRef]
  49. Nieto, J.; Hernandez-Maestro, R.M.; Munoz-Gallego, P.A. Marketing decisions, customer reviews, and business performance: The use of the Toprural website by Spanish rural lodging establishments. Tour. Manag. 2014, 45, 115–123. [Google Scholar] [CrossRef]
  50. Tham, A.; Croy, G.; Mair, J. Social media in destination choice: Distinctive electronic wordof-mouth dimensions. J. Travel Tour. Mark. 2013, 30, 144–155. [Google Scholar] [CrossRef]
  51. Mittal, V.; Huppertz, J.W.; Khare, A. Customer complaining: The role of tie strength and information control. J. Retail. 2008, 84, 195–204. [Google Scholar] [CrossRef]
  52. Granovetter, M.S. The strength of weak ties. Am. J. Sociol. 1973, 78, 1360–1380. [Google Scholar] [CrossRef]
  53. Pigg, K.E.; Crank, L.D. Building community social capital: The potential and promise of information and communications technologies. J. Commun. Inform. 2004, 1, 58–73. [Google Scholar] [CrossRef]
  54. Wirtz, J.; Chew, P. The Effects of Incentives, Deal Proneness, Satisfaction and Tie Strength on Word-of-Mouth Behaviour. Int. J. Serv. Ind. Manag. 2002, 13, 141–162. [Google Scholar] [CrossRef]
  55. Petty, R.; Cacioppo, J.T.; Schumann, D. Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement. J. Consum. Res. 1983, 10, 135–146. [Google Scholar] [CrossRef]
  56. Chaiken, S. Heuristic versus Systematic Information Processing and the Use of Source versus Message Cues in Persuasion. J. Personal. Soc. Psychol. 1980, 39, 752–766. [Google Scholar] [CrossRef]
  57. Hutter, K.; Hautz, J.; Dennhardt, S.; Füller, J. The impact of user interactions in social media on brand awareness and purchase intention: The case of MINI on Facebook. J. Prod. Brand Manag. 2013, 22, 342–351. [Google Scholar] [CrossRef]
  58. Hines. 4 Ways to Measure Brand Awareness. 2017. Available online: https://www.fronetics.com/4-ways-measure-brand-awareness/ (accessed on 8 October 2020).
  59. Lin, C.; Wu, Y.S.; Chen, J.C.V. Electronic Word-of-Mouth: The Moderating Roles of Product Involvement and Brand Image. In Proceedings of the TIIM 2013, Phuket, Thailand, 29–31 May 2013; pp. 39–47. [Google Scholar]
  60. Drury, G. Opinion piece: Social media should marketers engage and how can it be done effectively? J. Direct Data Digit. Mark. Pract. 2008, 9, 274–277. [Google Scholar] [CrossRef]
  61. Huang, J.H.; Hsiao, T.T.; Chen, Y.F. The effects of electronic word of mouth on product judgment and choice: The moderating role of the sense of virtual community. J. Appl. Soc. Psychol. 2012, 42, 2326–2347. [Google Scholar] [CrossRef]
  62. Geissler, G.L.; Edison, S.W. Market mavens’ attitudes towards general technology: Implications for marketing communications. J. Mark. Commun. 2005, 11, 73–94. [Google Scholar] [CrossRef]
  63. Wallace, E.; Buil, I.; de Chernatony, L. Facebook ‘Friendship’ and Brand Advocacy. J. Brand Manag. 2012, 20, 128–146. [Google Scholar] [CrossRef]
  64. Cheung, M.Y.; Sia, C.L.; Kuan, K.Y. Is this Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective. J. Assoc. Inf. Syst. 2012, 13, 618–635. [Google Scholar] [CrossRef]
  65. Tumer, D. A research on effectiveness of Facebook advertising on enhancing purchase intention of consumers. Comput. Hum. Behav. 2015, 49, 597–600. [Google Scholar]
  66. Keller, K.L. Strategic Brand Management. Building, Measuring and Managing Brand Equity; Prentice Hall: Englewood Cliffs, NJ, USA, 1998. [Google Scholar]
  67. Kim, D. Vlog as a Branding Tool—How to Build a Brand with a Video Blog in Social Media. Bachelor’s Thesis, Helsinki Metropolia University of Applied Sciences, Helsinki, Finland, 2017. [Google Scholar]
  68. Fong, J.; Burton, S. Electronic Word-of-Mouth: A Comparison of Stated and Revealed Behavior on Electronic Discussion Boards. J. Interact. Advert. 2006, 6, 61–70. [Google Scholar] [CrossRef]
  69. Moran, G.; Muzellec, L. eWOM credibility on social networking sites: A framework. J. Mark. Commun. 2017, 23, 149–161. [Google Scholar] [CrossRef]
  70. Hovland, C.I.; Weiss, W. The Influence of Source Credibility on Communication Effectiveness. Pub. Opin. Q. 1951, 15, 635–650. [Google Scholar] [CrossRef]
  71. Park, D.H.; Lee, J.; Han, I. The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. Int. J. Electron. Commer. 2007, 11, 125–148. [Google Scholar] [CrossRef]
  72. Shah, S.; Aziz, J.; Jaffari, A.R.; Waris, S.; Ejaz, W.; Fatima, M.; Sherazi, S. The impact of brands on consumer purchase intentions. Asian J. Bus. Manag. 2012, 4, 105–110. [Google Scholar]
  73. Cheung, C.M.K.; Thadani, D.R. The Effectiveness of Electronic Word-of-Mouth Communication: A Literature Analysis. In Proceedings of the 23rd BLED eConference 2010, Bled, Slovenia, 20–23 June 2010; pp. 329–345. Available online: http://aisel.aisnet.org/bled2010/18 (accessed on 5 October 2012).
  74. Brakus, J.J.; Schmitt, B.H.; Zarantonello, L. Brand experience: What is it? How is it measured? Does it affect loyalty? J. Mark. 2009, 73, 52–68. [Google Scholar] [CrossRef]
  75. Byrne, B.M. Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming; Lawrence Erlbaum: Mahwah, NJ, USA, 1998. [Google Scholar]
  76. Carmines, E.G.; McIver, J.P. Analyzing models with unobserved variables: Analysis of covariance structures. Soc. Meas. 1981, 19, 65–110. [Google Scholar]
  77. Maiti, J.; Khanzode, J.J. Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India. Saf. Sci. 2009, 47, 1068–1076. [Google Scholar] [CrossRef]
  78. Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 2014, 26, 106–121. [Google Scholar] [CrossRef]
  79. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 28, 39–50. [Google Scholar] [CrossRef]
  80. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  81. D’Rozario, D.; Choudhury, P.K. Effect of assimilation on consumer susceptibility to interpersonal influence. J. Consum. Mark. 2000, 17, 290–307. [Google Scholar] [CrossRef]
  82. Trusov, M.; Bucklin, R.E.; Pauwels, K. Effects of Word-of-Mouth versus Traditional Marketing: Findings from an Internet Social Networking Site. J. Mark. 2009, 73, 90–102. [Google Scholar] [CrossRef]
  83. Norman, A.T.; Russell, C.A. The pass-along effect: Investigating word-of-mouth effects on online survey procedures. J. Comput.-Mediat. Commun. 2006, 11, 1085–1103. [Google Scholar] [CrossRef]
  84. Gilbert, E.; Karahalios, K. Predicting Tie Strength with Social Media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 4–9 April 2009; pp. 211–220. [Google Scholar]
  85. Bearden, W.O.; Netemeyer, R.G.; Teel, J.E. Measurement of consumer susceptibility to interpersonal influence. J. Consum. Res. 1989, 15, 473–481. [Google Scholar] [CrossRef]
  86. Barger, V.; Peltier, J.W.; Schultz, D.E. Social media and consumer engagement: A review and research agenda. J. Res. Interact. Mark. 2016, 10, 268–287. [Google Scholar] [CrossRef]
  87. Cheung, C.M.; Lee, M.K. What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Dec. Support Syst. 2012, 53, 218–225. [Google Scholar] [CrossRef]
  88. Jalilvand, M.R.; 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. Plan. 2012, 30, 460–476. [Google Scholar] [CrossRef]
  89. Davis, D.F.; Golicic, S.L.; Marquardt, A. Measuring brand equity for logistics services. Int. J. Log. Manag. 2009, 20, 201–212. [Google Scholar] [CrossRef]
  90. Shukla, P. Impact of interpersonal influences, brand origin and brand image on luxury purchase intentions: Measuring interfunctional interactions and a cross-national comparison. J. World Bus. 2011, 46, 242–252. [Google Scholar] [CrossRef]
Figure 1. Conceptual structure of the study.
Figure 1. Conceptual structure of the study.
Jtaer 16 00057 g001
Figure 2. Measurement model of the study.
Figure 2. Measurement model of the study.
Jtaer 16 00057 g002
Figure 3. Structural model of the study.
Figure 3. Structural model of the study.
Jtaer 16 00057 g003
Table 1. Sample profile.
Table 1. Sample profile.
VariablesCategoryPercentVariableCategoryPercent
GenderMale 58.3Employment TypePrivate41.3
Female41.7Government33.1
Age Group<25 Years13.0Others25.6
25–35 Years38.6Income Level<$3438.7
35–45 Years28.0$343–$41135.8
>45 Years20.5$411–$54931.9
EducationBelow UG6.3$549–$68613.4
Graduate38.6>$68610.2
Post Graduate46.1Experience Level<2 years25.2
Above PG9.12–5 years24.0
Nature of JobDecision Making42.55–10 years20.5
Non Decision Making57.5>10 years30.3
Table 2. Measurement model—indicator loadings, reliability and AVE.
Table 2. Measurement model—indicator loadings, reliability and AVE.
Indicators<---ConstructsLoadingAlphaAVE
v1<---HIHigh Involvement0.8090.8060.684
v2<---HIHigh Involvement0.845
v5<---TRSNS Trust 0.6970.7720.650
v6<---TRSNS Trust 0.902
v7<---RCRecommendation 0.7190.8190.609
v8<---RCRecommendation 0.829
v9<---RCRecommendation 0.79
v10<---MCMessage Content0.7830.7240.517
v11<---MCMessage Content0.841
v12<---MCMessage Content0.479
v13<---EWEWOM Credibility0.8370.7990.577
v15<---EWEWOM Credibility0.733
v16<---EWEWOM Credibility0.703
v18<---BIBrand Image0.7390.7950.584
v19<---BIBrand Image0.79
v20<---BIBrand Image0.763
v22<---PIPurchase Intention0.8240.7710.607
v24<---PIPurchase Intention0.731
Table 3. Constructs correlation.
Table 3. Constructs correlation.
ConstructsHITRRCMCEWBIPI
HI0.684------
TR0.6220.650-----
RC0.6350.6070.609----
MC0.4440.440.450.517---
EW0.650.580.6550.5090.577--
BI0.6520.6660.6820.590.8670.584
PI0.4350.5040.4730.5630.5170.6030.607
Avg. R20.3370.3300.3490.2530.4110.4660.269
All the correlations were significant, diagonal values show the AVE.
Table 4. Structural model—path analysis.
Table 4. Structural model—path analysis.
Dependent RelationshipsPath Coefficients
EWOM Credibility<---HIHigh Involvement0.262 *
EWOM Credibility<---TRSNS Trust 0.192 *
EWOM Credibility<---RCRecommendation 0.317 **
EWOM Credibility<---MCMessage Content0.238 **
Brand Image<---EWEWOM Credibility0.929 **
Purchase Intention<---BIBrand Image0.614 **
* significant at 0.01, ** significant at 0.000.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Back to TopTop