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Examining the Effects of eWOM, Trust Inclination, and Information Adoption on Purchase Intentions in an Accelerated Digital Marketing Context

Faculty of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan
Azman Hashim International Business School, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Endicott College of International Studies (ECIS), Woosong University, Daejeon 300-718, Korea
Author to whom correspondence should be addressed.
Information 2020, 11(10), 478;
Received: 13 September 2020 / Revised: 4 October 2020 / Accepted: 7 October 2020 / Published: 13 October 2020


The study focuses on the canvas of online information transmission that has expanded exponentially. Especially due to social media networks, consumers have been exposed to significant amounts of disinformation, misinformation and actual information. Electronic word-of-mouth (eWOM) on social media networks has been facilitating swift information spread. Henceforth, it has become increasingly problematic for consumers to adopt authentic information and differentiate between marketers-generated content and user-generated content. The study aims to unfold the factors that lead to the information adoption that consequently motivates consumers to purchase products and services. The research study provides a comprehensive framework to re-configure factors that lead to consumers’ purchase intentions in the digital economy. Respondents of the study were those individuals who have been buying products online. The theoretically knitted causal relationships were estimated using a structural equation modelling (SEM) technique. The results indicate that trust inclination and information adoption sequentially mediate relationships between information quality, information usefulness, perceived risk and argument quality with purchase intentions.
Keywords: information adoption; social media; eWOM; trust; online buying; purchase intentions information adoption; social media; eWOM; trust; online buying; purchase intentions

1. Introduction

Modern marketing endeavors have been significantly affecting consumer psyche as the outcomes in the form of escalating online sales are promising for the businesses [1,2]. In this regard, Electronic word-of-mouth (eWOM) is an influential marketing tool since it has the propensity to reach a larger audience in a limited time [3,4]. Multinational giants have even started focusing on eWOM communications rather than spending hefty amounts of money on conventional advertisements [4,5]. Not only does the reach of the customers become wider, but the companies can also ensure that the message has been seen by the consumers [6]. These developments in the field of marketing are beneficial for the companies, entrepreneurs and marketers. Previously, eWOM was configured on diverse platforms such as blogs, shopping websites, discussion forums and review websites [7,8]. Nevertheless, empirical evidence has indicated social media to be the most powerful eWOM source [9,10,11].
User-generated comments and opinions on social media are considered organic [12]. For that matter, the profiles are often visited by individuals in order to verify the authenticity of the message. However, due to marketer’s proactive involvement on the social media websites, the organic eWOM communications are now becoming faded [8]. Firm-generated and marketer-generated content has been posted extensively, which is having colossal impact on the opinion making of the users [13]. Moreover, the users are inclined towards online buying because of the impulsive eWOM messages posted on the social media websites [14,15,16]. Prior studies have focused on eWOM firm-generated and user-generated aspects in different ways [17,18]. Meanwhile, it remains eminent to explore what factors of eWOM build customers’ trust, which eventually leads them to adopt the information, and consequently buy products and services [16].
The social media augmentation in the Asian markets has been phenomenal. Especially emerging markets such as Pakistan, which has 44.61 million internet users and 37 million social media users [19]. A total of 44% of the millennials and generation Z of the country are involved in online buying through their smartphones [20]. Nielsen reports that almost 82% of the urban population in Pakistan have been involved in online buying [21]. According to State Bank of Pakistan, the online sales in the country have reached 40.1 billion rupees in fiscal year(FY)18, which shows a substantial growth of 93.7% compared to 20.7 billion rupees of online sales in FY17 [22]. These facts and figures suggest the increasing online buying in the Pakistani consumer market and social media being one of the leading factors in the decision making of the consumers.
The study aims to discover the evolving role of electronic word-of-mouth (eWOM) by unfolding its dimensions and determining how it builds the trust of the customers. The theoretical model is based on the theory of planned behavior. The attitudinal, subjective norms, intentions and behaviors are precisely discussed in the theoretical model. Consequently, six hypotheses of the study are evolved from the theoretical knitting. The trust building sphere eventually leads to the adoption of information and purchase intentions. These theoretical foundations are conceived through systematic review of the literature. The study is conducted in the Pakistani context as online buying in the country is augmenting impressively. The study would help marketers to effectively evolve their marketing strategies as per the market needs and engage with the consumers in a vibrant manner.

2. Literature Review

2.1. Electronic Word of Mouth (eWOM)

Numerous researchers have pointed out the supremacy and augmenting power of electronic word-of-mouth in the marketing context [10,23]. The individuals’ buying pattern has been significantly revamped after the inception of the Internet. Prior methods of asking from others about product reviews are changing since individuals find it convenient to consult an internet search about a product query [4,6]. The alterations in the eWOM platforms have been beneficial for the customers too. Previously, platforms with one-way eWOM communications dominated, but after the inception of social media, individuals have now access to actually inspect the individual who is providing his/her experience about the product or service [3,13].
One of the advantages which social media has over other platforms is the two-way interactive communications. Not only can reviews from random individuals for buying or searching for a specific product or service be attained, but it provides the perfect opportunity to interact with friends, peers, family members and colleagues on a vibrant digital platform [8]. The transmission of information is swift and people tend to let each other know about their preferences, likes and dislikes in an abrupt manner [10,24]. Because of these aspects, the information processed on social media not only has a swift reach, but individuals also mostly believe in the information being circulated [23,25]. However, this reliability sphere has led to serious issues regarding fraudulent information processing on social media networks [5].
Contemporary research on eWOM has indicated information usefulness, information quality and argument quality to be the core eWOM antecedents [13,23]. The quality of information on the social media platforms needs to be of substantial value and worth. As per the literary foundations, it is envisioned that the quality of information passed on the online platforms builds trust [15,16]. The following hypothesis is thus formulated:
Hypothesis 1 (H1).
Information quality has a significant positive effect on trust inclination.
The effects of eWOM are quite difficult to control (such as traditional WOM), although it provides opportunity for marketers to make effective product sales and tap consumers precisely [12,13,26]. eWOM continues to be one of the most powerful marketing tools; nevertheless, it has the propensity to negatively portray company’s image on the Internet too [8]. The transmission of useful information has been a matter of concern for the users too. The decision-making is based on the essence of the message. If the message does not have any credible and authentic information, it is considered to be of no worth and value [15,16]. The following hypothesis is formulated considering this perspective:
Hypothesis 2 (H2).
Information usefulness has a significant positive effect on trust inclination.
Prior studies have identified how eWOM has been facilitating increasing product and service sales of organizations. The volume of interactions by company officials with customers is another critical feature to be determined [14,27]. Researchers have highlighted that reviews being posted by the consumers on the company pages or websites decide the future sales of the companies. In cases where there are many negative comments made by the customers the sales of the firm would definitely decline [17]. However, if there are only positive comments made by the customers and there are not been any neutral comments present, it also makes customers skeptical at times [3]. Argument quality is henceforth argued to be another indispensable eWOM antecedent.
Hypothesis 3 (H3).
Argument quality has a significant positive effect on trust inclination.

2.2. Perceived Risk

The literature has identified perceived risk to be subjective as its enormity varies from context to context. Khwaja et al. [28] argued that behaviors of the consumers are sometimes risk taking which helps the firms to grow. Mahmood et al. [10] explained that the amount of stake and subjective certainty feelings can be configured through perceived risk. The risk connected with the class of the product is known as inherent risk. Importance of the product in the customers mind, pricing, functioning and characteristic determination is known to be inherent risk [29]. For example, the element of inherent risk is certainly higher in buying a German automobile than in buying a chocolate. On the other hand, handled risk is an unavoidable risk which is connected with the product or service. It is importamt to note that with the prevalence of requisite information, handled risk can be managed [30]. Thus, configuring perceived risk effects on trust inclination would be a thought-provoking dimension to be explored:
Hypothesis 4 (H4).
Perceived risk has a significant positive effect on trust inclination.

2.3. Trust Inclination

According to the degree of trust on eWOM information, consumers tend to act differently. The factor effecting consumer trust behavior is perceived risk. The chances of entering into a risky situation are avoided by the consumers. When the consumers seek a high element of risk, they tend to opt for more inquiry through eWOM. The utilization of eWOM is at its zenith when consumers feel that they are entering into risky situations [16]. Similarly, Khwaja et al. [28] elucidated that the seller’s information is less credible for individuals as they seek consumers reviews. The reviews of consumers are considered to be of a credible nature as they provide product insights without any bias. From the company’s end, there is always a positive portrayal of the product and services; hence, an element of perceived risk remains in the potential customer’s mind [3]. Trust inclination is hence hypothesized to positively impact the information adoption spectrum.
Hypothesis 5 (H5).
Trust inclination has a significant positive effect on information adoption.

2.4. Information Adoption Model (IAM)

In eWOM communications, a basic level of information is being transferred [31]. Nevertheless, the impact of that information would vary among people. The same content can alarm certain individuals while the others would receive it causally [23,32]. The questionable nature of eWOM information processed on social media networks is challenging for individuals to digest [33]. The quality and credibility of the content is certainly unclear as it cannot be understood whether the information is user-generated or marketer-generated [18]. For this scrutiny, the information adoption model (IAM) is regarded as an appropriate model.
Computer mediated platforms are easily inspected using the respective model [34,35]. The adoption of information is often conceded as a notion which would eventually make the decision in favor or against something. The underlying doctrine of the information adoption process glorifies that information cannot be attained without the absence of certain core metrics i.e., argument quality, source credibility and information usefulness [32,33]. The systematic deployment of all these three components would eventually lead to the information adoption protocols.
Hypothesis 6 (H6).
Information adoption has a significant positive effect on purchase intentions.

3. Materials and Methods

As per the objectives of the study and to empirically examine relationships presented in Figure 1, the positivism research philosophy was deployed using a deductive approach. The study was cross-sectional and the data were collected using a structured questionnaire. The construct items were adapted, i.e., information quality [36], information usefulness [37], perceived risk [38], argument quality [34], trust inclination [39], information adoption [40] and purchase intentions [28] (Appendix A). According to Digitz [19], the number of active mobile phone subscribers is at 154.3 million, meanwhile, around 37 million users are active social media users in the country. A simple random sampling technique was used for the collection of data. The population of the study was ascertained through the conducting of secondary research. Considering the data had to be collected from the respondents involved in online buying, random sampling was found to be an appropriate method. A sizeable representation of the population can be found using this technique. The data were extracted from active social media users. Muthen and Muthen [41] have illustrated that, for conducting structural equation modelling on a normalized data set, a sample size of 150 respondents is reasonable. In addition, Hair et al. [42] as well as Lowry and Gaskin [43], identified a sample size of more than 200 to be prudent for structural equation modelling. Henceforth, 400 questionnaires were circulated among the respondents and, after the removal of outliers, missing value treatment and data cleansing, a sample size of 342 respondents was considered for data analysis. The demographic outcomes of the survey sample are provided in Table 1.

4. Results

4.1. Model Estimation Using Structural Equation Modelling (SEM)

The Structural Equation Modelling (SEM) technique was deployed for the testing of hypotheses. Initially, preliminary tests were executed for the fulfillment of assumptions of regression. Analysis of Moment Structures (AMOS) 22.0 software was used for the conduction of SEM. As per the central limit theorem, a large number of samples ensures data normality [6]. Additionally, researchers such as Hair et al. [44] clarified that a sample size of more than 200 is ample for using the structural equation modelling technique. Henceforth, a sample size of 342 was processed by conducting data normality and screening tests, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and path model analysis.

4.2. Assumptions of Structural Equation Modelling

4.2.1. Data Screening

Data screening is a critical aspect in the data analysis phase as, in the case of outliers, missing values and absence of linearity, the data are considered unfit for statistical analysis. There were no cases of outliers and missing values in the data set. Table 2 provides the results of the data normality. The standard deviation of the constructs was less than two, which is acceptable. Furthermore, the skewness values should be between + 2, and kurtosis values should be among + 3 [23]. The results indicate that the standard deviation, skewness and kurtosis values were in the acceptance range, hence no data normality concerns were surfaced.

4.2.2. Exploratory Factor Analysis (EFA)

The conduction of exploratory factor analysis (EFA) is mandatory in co-variance based structural equation modelling. EFA provides an evident path that demonstrates how items are precisely loaded based on their respective factors. The tests of EFA were conducted on the Statistical Package for Social Sciences (SPSS) 24.0. According to Gaskin [45], the individual items’ factors loadings must be greater than 0.4 in EFA. Furthermore, the Kaiser-Meyer-Olkin (KMO) and Bartlett’s values should be greater than 0.6 [28]. Table 3 reveals the EFA outcomes (denoted by Ρ). The individual items loadings greater than 0.4 reveal that the values were in the acceptable range and all the items loaded on their respective factors.

4.2.3. Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis (CFA) is considered the backbone of covariance structural equation modelling. One of the differentiating factors between covariance-based SEM and variance-based SEM is the conducting of CFA. In order to conduct CFA, diligence in EFA is required. The detailed results of CFA are provided in Table 3. The factor loadings of items of CFA are denoted with λ. According to Gaskin [45], CFA items should be greater than 0.3 and must be loaded on their respective factors. The results indicate that all the items were way above the threshold value. The constructs’ reliability was configured too as it remains eminent to check whether the items were reliable.
Cronbach’s alpha (α) and Composite Reliability (C.R) values should be greater than 0.7 and less than one. Similarly, the constructs’ validity was examined through average variance extracted (AVE). Lowry and Gaskin [43]; note that the AVE values must be greater than 0.5 and less than 1. It remained critical to investigate measurement model fit indices too, for which absolute and incremental fit indices were determined. The chi square/degree of freedom (χ2/df) value was 1.244 which is between the acceptance range of 1–5. Furthermore, Standardised Root Mean Residual (SRMR) and Root Mean Squared Error of Approximation (RMSEA) values were 0.036 and 0.027, respectively, which are also in the tolerable range. The normed fit index (NFI), comparative fit index (CFI) and tucker lewis index (TLI) values were 0.939, 0.985 and 0.987, respectively, which is less than 1 and greater than 0.8; hence, the measurement model was statistically sound. Similarly, goodness of fit index (GFI) and adjusted goodness of fit index (AGFI) values were 0.933 and 0.913 respectively, which are also in the acceptable range. The detailed results in Table 3 indicate that there were no concerns in the confirmatory factor analysis.

4.2.4. Discriminant Validity

The emergence of multicollinearity is one of the major concerns while configuring linear relationships. Discriminant validity was estimated among the constructs to identify if there are high correlations among the constructs. Multicollinearity was measured in two ways: using the variance inflation factor (VIF) and tolerance values, and through a diagonal correlations pattern. The acceptance range of VIF values is between one and five, and the tolerance value must be less than one [42]. Maximum shared variance (MSV) values are also less than 1 which is acceptable. Table 4 indicates the discriminant validity results. The table shows VIF, tolerance values and diagonal correlations values to be in the permissible range.

4.2.5. Path Analysis Outcomes

The attainment of affirmative results of data normality, explorative factor analysis, confirmatory factor analysis and discriminant validity leads to the testing of hypotheses, for which path modelling was conducted. The depiction of the structural path model is provided in Figure 2. The outcomes of the respective paths are presented in Table 5. The first hypothesis referenced a positive relationship among information quality (IQ) and trust inclination (TI). The path coefficient value of 0.131 and significance value of 0.00 confirm a positive causal relationship. Similarly, hypotheses two, three and four referenced a positive effect of information usefulness (IU), perceived risk (PR) and argument quality (AQ) on trust inclination (TI). The path coefficients values of 0.317, 0.134 and 0.404 highlight a strong relationship among the constructs. Hypothesis five referenced the positively inclined relationship of trust inclination (TI) and information adoption. The beta values of 0.544 and significance value of 0.00 accept the said hypothesis. Lastly, the sixth hypothesis envisioned a strong relationship of information adoption with purchase intentions. The results indicate the acceptance of the respective relationship as the beta value emerged as 0.307. The detailed outcomes are presented in Table 5.

5. Discussion and Conclusions

The inspection of electronic word of mouth (eWOM) antecedents along with factors such as perceived risk was necessary to be explore, as the sphere of online communications is growing. Electronic word of mouth has revamped the entire sphere of discussions as digitally recorded conversations have been a colossal impact on consumers’ purchase decision making [7]. The consumers have been however not clearly understood the aspect of firm generated and user generated content [9]. The study accentuated three core dimensions of eWOM communications, i.e., information quality, information usefulness and argument quality, and determined their effects upon trust inclination. Similarly, perceived risk was also measured, and its effect estimated upon trust inclination as it remains an evolving aspect in the eWOM acceptance spectrum [10,23,34].
Prior studies have examined eWOM effects on purchase intentions in different research settings. Erkan and Evans [7] explored eWOM antecedents’ effect and presented information acceptance model by blending the core dimensions of an information adoption model and a technology acceptance model. Similarly, Khwaja et al. [23] pointed out the source credibility dimensions of eWOM and researched information adoption. The extended meta-analysis of Babic et al. [4] pointed out the core merits of eWOM and its phenomenal impacts on purchase intention spheres in various studies [12,13]. The present study meanwhile attained contemporary eWOM dimensions, extracting from them an information adoption model and configuring its proclivity on purchase intentions [3,7]. The theoretical model was developed from the preceding research studies and it was thoroughly investigated how eWOM and perceived risk would impact trust inclination initially, and eventually lead to purchase intentions [9,29,31].
The detailed review of the literature pointed out the different eWOM antecedents and settings in which eWOM has been discussed or studied. The contextual and theoretical gap presented the viewpoint of illustrating information quality, information usefulness and argument quality as the core eWOM dimensions, and further engaged perceived risk with this prevalent notion. Subsequently, measuring their effects on trust inclination remained a necessary factor as the building of trust further leads to information adoption and purchase intentions. The theoretical model was constructed on the aforementioned factors and further statistical modelling was conducted. The data collection was done precisely from the individuals involved in the online buying. The online buying aspect in emerging markets such as Pakistan has been increasing. Due to massive electronic revolution globally, Pakistan happens to be one of those countries which adopted the Internet and electronic communications. The online buying sphere has hence escalated quickly as the majority of the country’s population are adults. The data were collected from the individuals engaged in online buying and, subsequently, statistical analysis was conducted on the collected data set.
The structural equation modelling (SEM) technique has been proven to affirm causal relationships among the constructs [4,43]. As per the study’s objective, SEM was deployed on the collected data set and all the pre-requisite tests for identification of outliers, missing value analysis etc. were conducted in the first phase. The detection of data normality was further conducted for ensuring that there would not be any concerns regarding abnormality in the data. Exploratory factor analysis was conducted with the goal that the item and factor knitting would emerge clearly, and the researcher would have a specific insight into the items’ validity. The positive results of the exploratory factor analysis led to the determination of confirmatory factor analysis in which factor loadings, reliability, composite reliability and construct validity were estimated. The measurement model fit indices provided confirmatory outcomes which motivated the execution of path analysis. The path analysis orientation remained for the testing of hypotheses. The theoretically established relationships were henceforth statistically estimated and the outcomes clearly indicated regarding the strong association among the constructs.
The study would be beneficial for marketers as the context of social media has unfolded in a diverse manner. The framework provides an overarching message to marketers for developing effective marketing initiatives on electronic platforms [8,28]. Electronic word of mouth on social media platforms has helped marketers to boost their sales [9,31]. Through this study, they would be able to further identify that what factors of eWOM are considered by the consumers while making a purchase decision [13,28]. Furthermore, features such as information quality, useful information and argument quality happen to be essential factors while developing eWOM marketing messages [8].

6. Future Research Directions

The research study has clearly examined eWOM positive dimensions; however, it remains pertinent to explore the negative aspects of eWOM communications [14]. The study was conducted in the emerging market of Pakistan and the data were collected cross-sectionally. As per the regional context, the survey must be translated and circulated among the respondents. Through this approach, holistic understanding of the subject matter can be attained. Future studies should determine the respective framework in a different cultural setting and conduct longitudinal research study. The examination of eWOM source credibility dimensions should be incorporated in future studies too, as this focuses on eminent factors such as homophily, trustworthiness, objectivity and expertness [23]. Furthermore, the socio-economic background of the respondents must be taken into consideration and used as a control variable. Furthermore, perceived risk should be taken as a multi-facet construct and its dimensional effects should be unfolded [30,38]. Lastly, the intentional part of consumers, i.e., purchase intentions, are configured in the respective study. Further explorations should be on consumer purchase behaviors and post-purchase behaviors [6,30,31].

Author Contributions

Conceptualization, M.G.K., S.M., and U.Z.; methodology, M.G.K., U.Z.; software, M.G.K.; validation, M.G.K.; formal analysis, M.G.K.; investigation, M.G.K.; resources, M.G.K., S.M.; data curation, M.G.K., and S.M.; writing—original draft preparation, M.G.K., S.M., and U.Z.; writing—review and editing, M.G.K., U.Z.; project administration, M.G.K., and U.Z. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Survey Items
Constructs and Items
Information Quality
 i. The information about products which are shared by my friends in social media are understandable.
 ii. The information about products which are shared by my friends in social media are clear.
 iii. In general, I think the quality of them is high.
Information Usefulness
 i. The information on social media platforms is valuable and informative
 ii. The information on social media platforms is informative
 iii. The information on social media platforms is helpful
Perceived Risk
 i. Reading the reviews helps me reduce the uncertainty of shopping online.
 ii. Reading the reviews decreases my concerns about unpleasant experiences that may happen when I shop online.
 iii. Reading the reviews increases my confidence in online shopping choices
Information Adoption
 i. I closely followed the suggestions of the positive comments and went to the recommended online retailing stores further
 ii. I agree with the opinion suggested regarding online retailing stores in the comments on the internet.
Trust Inclination
 i. My online shopping website is reliable
 ii. My online shopping can be trusted; there are no uncertainties.
 iii. Anyone trusting my online shopping is not asking for trouble.
Argument Quality
 i. The arguments provided on the social media are relevant
 ii. The arguments provided on the social media are appropriate
 iii. The arguments provided on the social media are applicable
 iv. The arguments provided on the social media are up-to-date
 v. The arguments provided on the social media are reliable
 vi. The arguments provided on the social media are sufficiently complete your need
 vii. The arguments provided on the social media are include all necessary values
Purchase Intentions
 i. It is very likely that I will buy the product.
 ii. I will purchase the product next time I need a product.
 iii. I will definitely try the product.
 iv. I will recommend the product to my friends


  1. Yao, B.; Shanoyan, A.; Peterson, H.H.; Boyer, C.; Baker, L. The use of new-media marketing in the green industry: Analysis of social media use and impact on sales. Agribus 2019, 35, 281–297. [Google Scholar]
  2. Zhang, S.; Pauwels, K.; Peng, C. The impact of adding online-to-offline service platform channels on firms’ offline and total sales and profits. J. Int. Mktg 2019, 47, 115–128. [Google Scholar] [CrossRef]
  3. Abedi, E.; Ghorbanzadeh, D.; Rahehagh, A. Influence of eWOM information on consumers’ behavioral intentions in mobile social networks. J. Adv. Manag. Res. 2019, 17, 84–109. [Google Scholar]
  4. Babic, A.; Sotgiu, F.; De Valck, K.; Bijmolt, T.H. The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. J. Mark. Res. 2016, 53, 297–318. [Google Scholar]
  5. Lkhaasuren, M.; Nam, K.D. The Effect of Electronic Word of Mouth (eWOM) on Purchase Intention on Korean Cosmetic Products in the Mongolian Market. J. Int. Trade Commer. 2018, 14, 161–175. [Google Scholar] [CrossRef]
  6. Hamdani, N.A.; Abdul, G.; Maulani, F. The influence of E-WOM on purchase intentions in local culinary business sector. Int. J. Eng. Technol. 2018, 7, 246–250. [Google Scholar] [CrossRef]
  7. Erkan, I.; Evans, C. Social media or shopping websites? The influence of eWOM on consumers’ online purchase intentions. J. Mark. Commun. 2018, 24, 617–632. [Google Scholar]
  8. Kala, D.; Chaubey, D.S. The effect of eWOM communication on brand image and purchase intention towards lifestyle products in India. Int. J. Serv. Econ. Manag. 2018, 9, 143–157. [Google Scholar]
  9. Evgeniy, Y.; Lee, K.; Roh, T.W. The Effect of eWOM on Purchase Intention for Korean-brand Cars in Russia: The Mediating Role of Brand Image and Perceived Quality. J. Korea Trade 2019, 23, 102–117. [Google Scholar] [CrossRef]
  10. Mahmood, S.; Khwaja, M.G.; Jusoh, A. Electronic word of mouth on social media websites: Role of social capital theory, self-determination theory, and altruism. Int. J. Space Based Situat. Comput. 2019, 9, 74–89. [Google Scholar] [CrossRef]
  11. Roy, G.; Datta, B.; Mukherjee, S. Role of electronic word-of-mouth content and valence in influencing online purchase behavior. J. Mark. Commun. 2019, 25, 661–684. [Google Scholar] [CrossRef]
  12. Ek Styven, M.; Foster, T. Who am I if you can’t see me? The “self” of young travellers as driver of eWOM in social media. J. Tour. Futures 2018, 4, 80–92. [Google Scholar] [CrossRef]
  13. Rao, K.S.; Rao, B. Examining eWOM Credibility-Consumer Purchase Intention Relationship in Facebook: A Mediation Analysis. Ind. J. Mark. 2019, 49, 7–22. [Google Scholar] [CrossRef]
  14. Wang, J.J.; Wang, L.Y.; Wang, M.M. Understanding the effects of eWOM social ties on purchase intentions: A moderated mediation investigation. Electron. Commer. Res. Appl. 2018, 28, 54–56. [Google Scholar] [CrossRef]
  15. Zhang, K.Z.K.; Cheung, C.M.K.; Lee, M.K.O. Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. Int. J. Inf. Manag. 2014, 34, 89–98. [Google Scholar] [CrossRef]
  16. Zhang, X.J.; Wu, Y.; Li, Y. The Tendency of Trust in A Distrustful Environment: The Mediation Role of Contextual Perceptions in eWOM. J. Mark. Dev. Compet. 2019, 13, 45–63. [Google Scholar]
  17. Poulis, A.; Rizomyliotis, I.; Konstantoulaki, K. Do firms still need to be social? Firm generated content in social media. Inf. Technol. People 2019, 32, 387–404. [Google Scholar]
  18. Yang, Z.; Zheng, Y.; Zhang, Y.; Jiang, Y.; Chao, H.T.; Doong, S.C. Bipolar influence of firm-generated content on customers’ offline purchasing behavior: A field experiment in China. Electron. Commer. Res. Appl. 2019, 35, 100844. [Google Scholar] [CrossRef]
  19. Digitz. Digital Trend Pakistan—Industry Research Report. Available online: (accessed on 27 December 2019).
  20. Khan, S. The Booming Trend of Online Shopping in Pakistan. Available online: (accessed on 18 August 2019).
  21. Farooq, M. 82% of Pakistani Urban Consumers Made an Online Purchase in 2018: Nielsen. Pakistan Today 2020. Available online: (accessed on 18 August 2019).
  22. Hanif, U. Pakistan’s changing e-commerce landscape. The Express Tribune. Available online: (accessed on 3 October 2019).
  23. Khwaja, M.G.; Jusoh, A.; Nor, K.M. Does Electronic word-of-mouth (eWOM) on Social Media leads to Information Adoption? Empirical Evidence from the Emerging Markets! Int. J. Recent Technol. Eng. 2019, 8, 3281–3288. [Google Scholar]
  24. Bashir, S.; Khwaja, M.G. Pakistani Hotels Industry: A New Paradigm to Investigate Visitors Decision-Making Process. Asia Proc. Soc. Sci. 2018, 2, 219–223. [Google Scholar] [CrossRef]
  25. Oussous, A.; Benjelloun, F.Z.; Lahcen, A.A.; Belfkih, S. ASA: A framework for Arabic sentiment analysis. J. Inf. Sci. 2020, 46, 544–559. [Google Scholar] [CrossRef]
  26. Bashir, S.; Khwaja, M.G.; Turi, J.A.; Toheed, H. Extension of planned behavioral theory to consumer behaviors in green hotel. Heliyon 2019, 5, 29–74. [Google Scholar] [CrossRef]
  27. Dashtipour, K.; Gogate, M.; Li, J.; Jiang, F.; Kong, B.; Hussain, A. A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing 2020, 380, 1–10. [Google Scholar] [CrossRef]
  28. Khwaja, M.G.; Jusoh, A.; Nor, K.M. Does online social presence lead to purchase intentions? Int. J. Econ. Policy Emerg. Econ. 2019, 12, 198–206. [Google Scholar] [CrossRef]
  29. Pelaez, A.; Chen, C.W.; Chen, Y.X. Effects of perceived risk on intention to purchase: A meta-analysis. J. Comput. Inf. Syst. 2019, 59, 73–84. [Google Scholar] [CrossRef]
  30. Dabrynin, H.; Zhang, J. The investigation of the online customer experience and perceived risk on purchase intention in China. J. Mark. Dev. Compet. 2019, 13, 16–30. [Google Scholar]
  31. Erkan, I.; Evans, C. The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Comput. Hum. Behav. 2016, 61, 47–55. [Google Scholar]
  32. Zhang, J.; Ito, N.; Wu, W.; Li, Z. Exploring travel information adoption on social media in the Chinese context: An extended elm model with users’ social presence. Int. J. Mark. Soc. Policy 2017, 1, 41–54. [Google Scholar]
  33. Cho, V.; Chan, D. How social influence through information adoption from online review sites affects collective decision making. Enterp. Inf. Syst. 2019, 1–25. [Google Scholar] [CrossRef]
  34. Hussain, S.; Guangju, W.; Jafar, R.M.S.; Ilyas, Z.; Mustafa, G.; Jianzhou, Y. Consumers’ online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Comput. Hum. Behav. 2018, 80, 22–32. [Google Scholar] [CrossRef]
  35. Shu, M.L.; Scott, N. Influence of social media on Chinese students’ choice of an overseas study destination: An information adoption model perspective. J. Travel Tour. Mark. 2014, 31, 286–302. [Google Scholar] [CrossRef]
  36. 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]
  37. Bailey, J.E.; Pearson, S.W. Development of a tool for measuring and analyzing computer user satisfaction. Manag. Sci. 1983, 29, 530–545. [Google Scholar] [CrossRef]
  38. Bettman, J.R. Perceived risk and its components: A model and empirical test. J. Mark. Res. 1973, 184–190. [Google Scholar] [CrossRef]
  39. Corritore, C.L.; Marble, R.P.; Wiedenbeck, S.; Kracher, B.; Chandran, A. Measuring online trust of websites: Credibility, perceived ease of use, and risk. AMCIS Proc. 2005, 370, 2419–2427. [Google Scholar]
  40. 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]
  41. Muthen, L.K.; Muthen, B.O. How to use a Monte Carlo study to decide on sample size and determine power. Struct. Equ. Mod. 2002, 9, 599–620. [Google Scholar] [CrossRef]
  42. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis; Pearson Prentice Hall.: Upper Saddle River, NJ, USA, 2010; Volume 5, pp. 207–219. [Google Scholar]
  43. Lowry, P.B.; Gaskin, J. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Trans. Prof. Commun. 2014, 57, 123–146. [Google Scholar]
  44. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
  45. Gaskin, J. Confirmatory Factor Analysis; Gaskination’s StatWiki, 2016. Retrieved 15 November 2019. Available online: (accessed on 18 August 2019).
Figure 1. Conceptual Framework.
Figure 1. Conceptual Framework.
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Figure 2. Structural Path Analysis on Analysis of Moment Structures (AMOS).
Figure 2. Structural Path Analysis on Analysis of Moment Structures (AMOS).
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Table 1. Sample demographics (N = 342).
Table 1. Sample demographics (N = 342).
Age18 years and below185.3%
19–28 years27881.3%
29–39 years3610.5%
Above 40 years102.9%
EducationHigh School329.4%
Social Media UsageFacebook16247.4%
Other Social Networking Sites SNS144.1%
Table 2. Constructs Composite Normality (N = 342).
Table 2. Constructs Composite Normality (N = 342).
Table 3. Confirmatory factor analysis (CFA)/Exploratory factor analysis (EFA) factor loadings, reliability, and validity of the measurement model (N = 342).
Table 3. Confirmatory factor analysis (CFA)/Exploratory factor analysis (EFA) factor loadings, reliability, and validity of the measurement model (N = 342).
Constructs and ItemsΡλαC.RAVE
Information Quality
Information Usefulness
Perceived Risk
Information Adoption
Trust Inclination
Argument Quality
Purchase Intentions
Measurement model fit statistics: a. Absolute fit indices (χ2 = 310.967, df = 250, P = 0.000, χ2/df = 1.244, AGFI = 0.913, GFI = 0.933, RMSEA = 0.027, SRMR = 0.036); b. Incremental fit indices (NFI = 0.939, CFI = 0.987 and TLI = 0.985).
Table 4. Discriminant validity of constructs and correlations (N = 342).
Table 4. Discriminant validity of constructs and correlations (N = 342).
PI_al 0.0310.0380.837
Table 5. Results of Hypotheses (N = 342).
Table 5. Results of Hypotheses (N = 342).
Independent VariablesModel 1Model 2Model 3Results
Hypothesized direct effect paths
H1. IQ0.131 ** Supported
H2. IU0.317 ** Supported
H3. PR0.134 * Supported
H4. AQ0.404 ** Supported
R-Square0.401 **
Hypothesized indirect effect paths
H5. TI 0.544 ** Supported
H6. IA 0.190 **Supported
R-Square 0.307 **0.049
* p < 0.05, ** p < 0.01.
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