Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2021 Vol: 20 Issue: 4

User-Generated Content-Consumer Buying Intentions Nexus: The Mediating Role of Brand Image

Nawras M. Nusairat, Applied Science Private University (ASU)

Mohammad A. Alroale, Applied Science Private University (ASU)

Marzouq Al Qeed, Al Ain University

Jassim Ahmad Al-Gasawneh, Applied Science Private University (ASU)

Qais Hammouri, Applied Science Private University (ASU)

Alaeddin Ahmad, Princess Sumaya University for Technology (PSUT)

Hadeel Abdellatif, Applied Science Private University (ASU)


This research paper investigates the effect of user-generated content (UGC) on consumer intentions to buy cosmetic products in Jordan taking into consideration the mediating role of brand image on this effect. A conceptual research model was developed based on an in-depth review of the extant literature and in accordance with the stimulus-organism-response (SOR) theory. To test the model, an online questionnaire survey was distributed to a convenient sample of cosmetic products buyers in Jordan. A dataset of 316 usable questionnaires were subjected to data analysis using Smart PLS 3.0. The main findings showed that consumer buying intentions are positively affected by both UGC and brand image. Brand image was also found to be significantly predicted by UGC and to mediate its effect on consumer buying intentions. The results demonstrate the significance of UGC as a powerful marketing tool in crafting brand image and driving consumer buying intentions in the context of cosmetics. Findings' discussion, conclusion, research limitations and areas for future research are also provided.


User-generated Content, Brand Image, Buying Intentions, SOR Model.


The wide spread of internet along with the extensive use social media platforms have transformed markets; nowadays power is shifting from businesses towards consumers. Today, consumers have much power and a greater influence. They are exposed to vast amount of data, products and services being buffered generally on the internet and more specifically on social media platforms. Thus, they are becoming more knowledgeable and informative in making buying decisions more than ever before (Bahtar et al., 2018).

Social media as web-based applications formed the bases for UGC, which refers to any content created and posted by users known as fans. It may include pictures, tweets, videos, blog posts and testimonials. It is a form of unpaid marketing where users promote the brand instead of the brand itself (Ngah et al., 2021).

With the constant peer feedback provided, social media is revolutionizing the purchasing process; it plays an important role in forming an attitude or a desire and willingness for a specific product, to making the buying decisions and even after the purchase is completed (Kunja & Acharyulu, 2018; Hossain et al., 2019b; Hawamleh et al., 2020). Any content generated by the user on social media is referred to as “electronic Word of Mouth” (eWOM) (Burgess et al., 2011; Ayeh et al., 2013). Nowadays and in light of the development of social media, consumer all around the world started to share their experiences with different brands (Al-Jabri & Sohail, 2012). Consumers are increasingly expressing their opinions and providing a feedback through a post, picture, video, or even a review of products as well as sharing it with their families and friends. This signifies the increasing importance of the content generated by users, which in fact represents an imperative source of information for companies and other users/customers at the same time.

Empirical evidence suggest that companies cannot ignore the impact of diverse forms of UGC on their reputation, brand image and the buying intentions of customers (Torlak et al., 2014; Confente et al., 2019; Oliveira & Casais, 2019; Mayrhofer et al., 2020). Although the relationship between UGC forms such as eWOM and consumer buying intentions is well established within the extent literature, the focus has been noticeably on new technologies and related industries (Torlak et al., 2014; Kudeshia & Kumar, 2017; Kunja & Acharyulu, 2018) and tourism (Marine-Roig et al., 2017; Narangajavana Kaosiri et al., 2019; Ray & Bala, 2021). Prior research illustrates a direct link between UGC and different marketing outcomes such as behavior (Cox, et al., 2009; Ye et al., 2011); service quality (Duan et al., 2013); and satisfaction (Narangajavana Kaosiri et al., 2019); and product development (Ho-Dac, 2020). However, the mechanism of the effect (e.g., mediation) remains insufficiently explored. Moreover, the majority of such studies have been carried out in Western cultures and developed economies.

The significance of UGC in driving customers’ responses may vary across nations as well as the divers’ types of products been sought. Typically, UGC posted on common Websites enjoys high levels of source credibility (Ma & Atkin, 2017) and is found to be more influential in affecting customer behavior compared to marketing generated content (Tsiakali, 2018). This becomes very significant when buying a risky and high involvement product such as cosmetics, in a highly social context such as Jordan. That is, the product may have a high potential of negative impact on users both mentally and physically if a wrong brand is chosen. The cosmetics sector is among the important sectors in Jordan in terms of size of trade. For instance, the industry reached 340 Million worth of imports in 2017 only (Roya, 2018). However, no research study has been devoted to examine the driving factors of the buying behavior and psychology of consumers in such vital sector. With this knowledge gap in mind, this study aims to examine the effect that a UGC has on consumer intentions to buy cosmetics in Jordan taking into consideration the mediating role of brand image on this effect.

Literature Review

Underpinning Theory: The S-O-R Model

The Mehrabian & Russel's (1974) model, also known as Stimulus-Organism-Response (S-O-R) theory was brought into the marketing literature to describe how customer responses (R) can be explained by internal emotional and cognitive states (O), which are initially triggered by external marketing stimuli (S). According to Chang et al. (2011) the term stimulus in the model is conceptualized as any factor that have a changing impact on individuals’ internal states. Organism comprises individual internal processes including perceptual, physiological feeling and thinking activities, intervening between the external stimuli and the final response of individuals (Bagozzi, 1986). Finally, response is the final outcome in the causality chain of the model, describing how people initially respond to a setting simply as approach or avoidance behaviors (Nusairat et al., 2020a).

Although the S-O-R model has been originated in the scope of environmental psychology, it has inspired much of marketing research over last decades (Donovan & Rossiter, 1982; Chebat & Michon, 2003; Kumar & Kim, 2014; Nusairat et al., 2017; Wu & Li, 2018; Konuk, 2019; Laato et al., 2020; Nusairat et al., 2020b) Aslam & de Luna, 2021). Such stream of literature describes stimulus (S) as any extraneous marketing-related factor, having a changing influence on customer internal states (O), which in turn drive customer response (R). In line with this, the present study introduces UGC as a stimulus, affecting what people think about brand (organism), which in turn fosters their behavioral intentions (response). Thus, based on Mehrabian & Russel's (1974) and Bagozzi (1986) model, which was stated that the Stimulus-Organism-Response (S-O-R) theory was brought into the marketing literature to describe how human behavior (R) can be explained by internal emotional states (O), which are initially triggered by external stimuli (S), the current study applies S-O-R to motivate the consumer buying intention alongside with inserting the brand image and improving it through user-generated content.

UGC and Consumer Buying Intention

As a type of social media content, User Generated Content (UGC) can be described as “media content created by members of the general public and includes any form of online content created, initiated, circulated and consumed by users” (Kim & Johnson, 2016). Prior research highlights the significance of UGC in predicting consumers' responses and in driving their decision making processes (Liu et al., 2011; Sin et al., 2012; Tsiakali, 2018; Khalil et al., 2020). Studies addressing UGC as a holistic concept have mainly focused on examining the particular characteristics of the content (Grover et al., 2019; Assaker, 2020; Daradkeh, 2021; Al-Adamat et al., 2020). In this extent, Owusu et al. (2016) for example examined how UGC features are linked to Web-purchase decision, and found that the web purchase decision can be affected mostly by relevance, credibility and up-to-dateness of the UGC information. Other studies investigated specific forms of UGC such as eWOM and measure its effect on the consumer buying and behavioral intentions (Torlak et al., 2014; Wang, 2015; Al-Gasawneh & Al-Adamat, 2020). Further, Torlak et al. (2014) in their study on the cell phone industry in turkey found that the eWOM has significant positive effect on consumer buying intention. Similarly, Wang (2015) studied the influence of eWOM on the tourists visit intention and revealed a significant impact of eWOM on tourist visit intention to select destinations around the globe. Accordingly, we hypothesize that:

H1 UGC has a positive effect on consumer intentions to buy cosmetic products.

UGC and Brand Image

Brand image is vital to consumers and firms alike. It is a cue, aiding consumers in their purchasing decisions by reducing pre-purchase consumer risk and post-purchase cognitive dissonance, associated with high involvement products (Keller, 2009). Brand image can also affect company-related aspects such as overall company's image and future, competitive advantage, prices of its brands and future marketing strategies (Brakus et al., 2009; Chen, 2010). Several empirical studies indicated that UGC has a significant impact on brand awareness (Elseidi & El-Baz, 2016; Tariq et al., 2017; Kala & Chaubey, 2018; Hossain et al., 2019a; Hossain et al., 2020; Boateng, 2021). In this venue, Torlak et al. (2014) reported a significant effect of eWOM on brand image and consumer buying intention. In the same vein, Elseidi & El-Baz (2016) claimed a significant positive impact of eWOM on brand image, consumer attitude and purchase intention. Moreover, Bambauer-Sachse & Mongold (2011) recommended eWOM as an important element in any online marketing strategy to enhance brand image and consumer buying intention. Therefore, it is hypothesized that:

H2 UGC has a positive effect on cosmetic product brand image.

The Mediating Role of Brand Image on Consumer Buying Intention

Brand image is defined as “the set of beliefs, ideas, and impression that a person holds regarding an object” (Kotler, 2001). It essentially evolves when consumers hold certain feelings, ideas or expectations towards a certain brand as they get to know and remember (Keller, 1993). Empirical evidence suggests that what images consumers form, is crucial to their behavioral intentions (Diamantopoulos et al., 2011; Jalilv & Samiei, 2012; Hutter et al., 2013; Yu et al., 2013; Kazmi & Mehmood, 2016). The findings of Yu & Chen (2013) for instance have asserted the significance of brand image in association with other perceptual states including country-of-origin image COI and self-congruity in fostering consumers buying intentions, with this effect become more powerful, when such states are consistent. Moreover, brand image have a critical role on the progress of consumer in buying decision making process. Consumers typically draw an impression based on product reviews, (Chakraborty & Biswal, 2020; Jahan et al., 2020), leading them to decide whether to proceed with further stages of the buying process or to terminate it (Hutter et al., 2013).

Brand image can also contribute to bridging an association between perceptual variables and behavioral intentions. In this instance, Diamantopoulos et al. (2011) investigated the role of COI and brand image in affecting consumer buying intention and found that COI impacts purchase intention indirectly through brand image. Similarly, brand image was also found to play a mediating role on the effect of country-of-origin image and buying intentions. More recently, Chakraborty & Biswal (2020) reported a mediating effect of brand image on the relationship between online hotel reviews and clients' hotel booking intentions. Accordingly, the following hypotheses are suggested:

H3 Brand image of cosmetic products positively affects consumer buying intentions.

H4 The relationship between UGC and consumer buying intentions is mediated by brand image.


This research is a cross-sectional descriptive study in nature that aims to examine the relationships among a set of interrelated variables. It follows a deductive approach, whereby, a thorough analysis of relevant literature has led to the formulation of several research hypotheses, to be empirically tested.

As an internet-related subject, the most appropriate and convenient place to recruit participants is the internet. Therefore an online questionnaire survey was distributed to a convenient sample of cosmetic products buyers in Jordan over six weeks, from February to mid of March 2021. A total number of 361 questionnaires were returned, 45 of these included frivolous responses, thereby were discarded. As a result, 316 usable questionnaires were considered for data analysis. This meets the minimum sample size of 74 based on G-power calculations as recommended by Green's (1991) and Gefen et al. (2011) considering that our research conceptual model comprises two predicting variables.

The research questionnaire was divided into four parts as follows: the first section encompasses questions relating to personal and demographic information of the respondents such as; age group, gender, marital status and educational level. The second section consists of multi-item scales adapted from previously established measures exploring UGC based on (McKinney et al., 2002; and Negash et al., 2003; Owusu et al., 2016). UGC was particularly investigated in terms of six dimensions including UGC relevance (four items), UCG usefulness (four items), UCG indicativeness (four items), UCG credibility (four items), UCG ease-of-use (four items) and UCG up-to-dateness (four items). Brand image in the third section was explored using six items adapted from (Al-Dmour et al., 2013). The fourth section addressed buying intentions using five items borrowed from (Huy et al., 2019). All of the measurement items used was assessed on a 5-point Likert scale.

Modifications to the questionnaire items were made where needed to ensure that each item meets the study's unique objectives. External academics were also involved in assessing measures' validity. The measurements' internal consistency was also assessed using Cronbach's Alpha values, which were as follows: UCG relevance = 0.71, UCG usefulness = 0.79, UCG indicativeness = 0.72, UCG credibility = 0.89, UCG ease-of-use = 0.82, UCG up-to-dateness = 0.76, brand image=0.86 and buying intentions=0.77, suggesting that the measurements are sufficiently reliable. The main data analysis and hypothesis testing was then carried out using Smart PLS 3.0.


In this study, there is one-second order construct named User- Generated Content and two first-order constructs named buying intention and brand image. The second-order construct (User- Generated Content) comprised of six first-order constructs as follows: UGC relevance, UGC usefulness, UGC indicativeness, UGC credibility, UGC ease-of-use and UGC up-to-dateness.

This will enrich the recognition of its conceptual and consensus elements. Likewise, the LOC was utilized in limiting the number of connections and also the number of hypotheses to be discussed in the structural model. According to Hair et al. (2019), such use facilitates the interpretation of the PLS route model. The two-stage method was used in this research. As a result, the repeated indicator technique was used in the first stage of this research. The LOC for first-order constructs were obtained here, and the weighting of the first-order variables was used in the calculation of the HOC contract's C.R and AVE in the second level. The measurement model was evaluated using convergent validity and discriminant validity in this analysis. The composite reliability, average variance extracts (AVE), and factor loading are all examined as part of the convergent validity evaluation. As a consequence, the results are shown in Figure 1 and Table 1. According to Hair et al. (2019) each item's loading was greater than 0.5, AVE figures were greater than 0.5, and CR figures were greater than 0.7.

Figure 1 Measurement Model

Table 1 Measurement Model
First order Construct/LOC Items Factor loading CR AVE
user generated content relevance (UGC) UGC1 0.889 0.934 0.780
  UGC2 0.880    
  UGC3 0.884    
  UGC4 0.870    
user generated content usefulness (UGCU) UGCU 1 0.906 0.961 0.860
  UGCU 2 0.919    
  UGCU 3 0.946    
  UGCU 4 0.939    
user generated content indicativeness (UGCI) UGCI 1 0.863 0.913 0.725
  UGCI 2 0.868    
  UGCI 3 0.856    
  UGCI 4 0. 818    
user generated content credibility (UGCC) UGCC 1 0.812 0.910 0.718
  UGCC 2 0.855    
  UGCC 3 0.871    
  UGCC 4 0.849    
user generated content ease-of-use (UGCE) UGCE 1 0.840 0.904 0.701
  UGCE 2 0.871    
  UGCE 3 0.847    
  UGCE 4 0.790    
user generated content up-to-dateness (UGCUT) UGCUT 1 0.847 0.909 0.714
  UGCUT 2 0.866    
  UGCUT 3 0.869    
  UGCUT 4 0.795    
brand image (BI) BI 1 0.744 0.884 0.561
  BI 2 0.791    
  BI 3 0.751    
  BI 4 0.675    
  BI 5 0.752    
  BI 6 0.776    
buying intentions (BIN) BIN 1 0.845 0.932 0.733
  BIN 2 0.857    
  BIN 3 0.850    
  BIN 4 0.875    
  BIN 5 0.852    
Second order constructs/HOC        
user generated content (UGC) UCGR 0.882 0.929 0.684
  UGCU 0.840    
  UGCI 0.832    
  UGCC 0.803    
  UGCE 0.813    
  UGCUT 0.790    

HTMT tests the model's discriminant validity (Henseler et al., 2015), and the obtained HTMT build values in this analysis were less than 0.90, with values ranging from 0.065 to 0.881. Table 2 summarizes the findings. As a result, each latent construct calculation in this study was exclusively discriminant against each other, according to Henseler et al. (2015).

Table 2 Discriminant Validity (HTMT)
BIN 0.634                
UGC 0.819 0.718              
UGCC 0.630 0.669 0.862            
UGCE 0.868 0.620 0.878 0.609          
UGCI 0.688 0.648 0.881 0.673 0.639        
UGCR 0.710 0.692 0.847 0.779 0.677 0.832      
UGCU 0.064 0.065 0.394 0.099 0.078 0.080 0.240    
UGCUT 0.754 0.551 0.851 0.582 0.821 0.607 0.611 0.086  

The convergent validity and discriminant validity analysis findings for the measurement model demonstrate the appropriateness and accuracy of the measurement scale in the evaluation of the constructs and their relative objects in the CFA model. As a consequence, Table 1 and Table 2 show the research findings for convergent validity and discriminant validity, respectively.

The R2 values for BI and BIN are 0.580 and 0.479, respectively, as shown in Table 3. This means that 58.0 percent of BI variations are explained by the predictor (UGC), while 47.9% of BIN variations are explained by their predictors (UGC, BI). The R2 values are within the 0.19 cutoff value suggested in the work of (Chin, 1998). The Q2 value of 0.232 for BIN was significantly higher than zero, indicating that the model has predictive value, similar to the suggestions of (Chin, 2010). The model has a good degree of fit and has a lot of predictive power. The VIF values were also 2.692, 2.112, and 3.613, which were all less than 5 (Hair et al., 2019). UGC and BI had p-values of 0.000 and 0.011, respectively, for predicting BIN. The p-value of UGC for the BI predictive factor was 0.000, indicating that the probability of achieving prediction using absolute p-values is between 0.01 and 0.05. In addition, the path coefficient (S, B) values for (UGC>BIN), (UGC>BI), and (BI>BIN) were 0.630, 0.761, and 0.318, respectively, for (UGC>BIN), (UGC>BI), and (BI>BIN). This demonstrates that the relationships are positive, implying that hypotheses H1, H2, and H3 are right (Figure 2).

Table 3 Direct Effects of Structural Model
  Path St, β St. d R2 Q2 F2 VIF T-value P-value Decision
H1 UGC > BIN 0.630 0.122 0.479 0.232 0.051 2.692 5.163 0.000 Supported
H2 UGC> BI 0.761 0.138 0.580   0.057 2.112 5.514 0.000 Supported
H3 BI > BIN 0.318 0.088     0.042 3.613 2.473 0.011 Supported

Figure 2 Hypotheses Testing

Table 4 shows that the indirect effect of UGC on BIN by BI was positive and statistically relevant at the 0.05 level; =0.240, T-value=2.376, P-value = 0.002. Preacher and Hayes (2004) found that the indirect effect of Boot CI Bias Corrected did not straddle a 0 in the middle, indicating that a mediation effect was present (LL=0.055, UL=0.148). According to the findings, the mediation effect is statistically important. As a result, hypothesis H4 is verified.

Table 4 Indirect Effect of Structural Model
  PATH SHAPE St. β St. d T values 2.50% 97.50% p-values Decision
H4 UGC > BI> BIN 0.240 0.101 2.376 0.0 55 0.148 0.002 Supported


The main objective of this research was to investigate the influence of UGC on consumer intentions to buy cosmetic products through examining the mediating role of brand image in Jordan. PLS was utilized to examine the four study hypotheses proposed in this research. Results indicated that all hypotheses were supported. The first hypothesis (H1) relating UGC to consumer buying intentions was supported; UGC in cosmetics market was found to be a significant predictor of customer buying intentions. This conforms to the findings of previous empirical studies reported in the literature (Grover et al., 2019; Assaker, 2020; Daradkeh, 2021). Moreover, Al-Gasawneh & Al-Adamat (2020) claimed that eWOM significantly affect customer buying intentions. In the same vein, Wang (2015) pointed that customers show greater repurchase and revisit intentions for websites with eWOM. Moreover, within the extant literature many studies suggest that UGC can improve the level of brand image (Tariq et al., 2017; Kala & Chaubey, 2018; Boateng, 2021). This result supports the second hypothesis (H2). Such findings revealed that Jordanian customers affected by eWOM on brand image of cosmetic products. This result is in line with previous research findings; for instance, Elseidi & El-Baz (2016) proclaimed that eWOM is one of the best marketing strategies that could be implemented by firms in order to enhance their brand image. Other empirical studies reveal a significant influence between brand image and eWOM on consumer purchasing intention. Such findings imply that online communication among online communities can improve the purchasing decisions of cosmetic products especially when these brands are well known and easily memorable (Hutter et al., 2013; Yu et al., 2013; Kazmi & Mehmood, 2016). Therefore, such findings support hypothesis (H3). Finally, the mediating impact for brand image on customer buying intention was also supported. Thus, the hypothesis (H4) was supported. Such result supports the findings of Chakraborty & Biswal (2020) who reported a mediating role of brand image in the relationship between UGC and consumer buying intention.


The results of this research show the importance of UGC as a strong construct in forming online users' perceptions and driving their behavioral intentions toward utilizing such contents to support their purchasing decisions for cosmetic products. Moreover, UGC plays a vital role on brand image, suggesting that a good brand image push the customer to hold more favorable attitude towards this brand through creating a positive eWOM among peers. The research's contribution can be represented by a set of highlighted points. Theoretically, the research findings boost the existing literature on the influence of UGC on customer buying intentions developing countries such as Jordan. Moreover, on the contrary of prior studies, this research explores the UGC to recognize the impact of proposed elements on customer buying intentions, rather than concentrating on business generated content. Thus, the study's findings provide a comprehensive understanding about the area of UGC in general. Empirically, due to the limited empirical evidence on the impact of UGC on customer buying intentions towards high involvement personal products, this research provides valuable insights into how the UGC can be utilized to enhance brand image and to positively influence customer buying intentions. Finally, the current study provides a clearer understanding of the applicability of the traditional S-O-R model through testing the research model which addresses brand image as a mediating variable. This research suffered from some limitations especially sampling producers, which may not reflect an appropriate representation of cosmetic buyers in Jordan. However, such limitation could be avoided in the future by applying probability sampling technique to recruit respondents, so that generating more reliable and generalizable findings. Further research can be applied through considering the moderating role of demographic factors to understand how the cosmetic buyers' respond to UGC based on their demographics like age, gender and education.


  1. Al-Adamat, A., Al-Gasawneh, J., & Al-Adamat, O. (2020). The impact of moral intelligence on green purchase intention. Management Science Letters , 10(9), 2063-2070.
  2. Al-Gasawneh, J., & Al-Adamat, A. (2020). The mediating role of e-word of mouth on the relationship between content marketing and green purchase intention. Management Science Letters , 10(8), 1701-1708.
  3. Al-Jabri, I.M., & Sohail, M.S. (2012). Mobile banking adoption: application of diffusion of innovation theory. Journal of Electronic Commerce Research , 13(4) 379-391.
  4. Assaker, G. (2020). Age and gender differences in online travel reviews and user-generated-content (UGC) adoption: extending the technology acceptance model (TAM) with credibility theory. Journal of Hospitality Marketing & Management , 29(4), 428-449.
  5. Ayeh, J.K., Au, N., & Law, R. (2013). Do we believe in TripAdvisor?, Examining credibility perceptions and online travelers’ attitude toward using user-generated content. Journal of Travel Research , 52(4), 437-452.
  6. Bagozzi, R.P. (1986). Principles of marketing management (2nd ed.). Chicago: Science Research Associates
  7. Bahtar, A.Z., Muda, M., & Razak, N.A. (2018). The relationships between mobile environment cues towards mobile purchasing intention and spreading positive user–generated content: A pilot study. International Journal of Supply Chain Management, 7(4), 386
  8. Bambauer-Sachse, S., & Mangold, S. (2011). Brand equity dilution through negative online word of- mouth communication. Journal of Retailing and Consumer Services , 18(1), 38-45.
  9. Boateng, S.L. (2021). Electronic word of mouth (eWOM) and makeup purchase intention among gen-z females: The serial mediating effect of brand image and brand integrity. International Journal of Customer Relationship Marketing and Management, 12(2), 17-35. ‏
  10. Brakus, J.J., Schmitt, B.H., & Zarantonello, L. (2009). Brand experience: what is it? How is it measured? Does it affect loyalty?. Journal of Marketing , 73 (3), 52-68
  11. Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2011). Trust perceptions of online travel information by different content creators: some social and legal implications. Information Systems Frontiers , 13(2), 221-235.
  12. Chakraborty, U., & Biswal, S.K. (2020). Impact of online reviews on consumer’s hotel booking intentions: Does brand image mediate, Journal of Promotion Management , 26(7), 943-963
  13. Chebat, J.C., & Michon, R. (2003). Impact of ambient odours on mall shoppers' emotions, cognition, and spending: A test of competitive causal theories. Journal of Business Research , 56(7), 529-539.
  14. Chen, Y.S. (2010). The drivers of green brand equity: Green brand image, green satisfaction, and green trust. Journal of Business Ethics , 93(2), 307-319.
  15. Chin, W.W. (1998). Commentary: Issues and opinion on structural equation modeling.
  16. Chin, W.W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-690). Springer, Berlin, Heidelberg
  17. Confente, I., Siciliano, G.G., Gaudenzi, B., & Eickhoff, M. (2019). Effects of data breaches from user-generated content: A corporate reputation analysis. European Management Journal , 37(4), 492-504.
  18. Daradkeh, M.K. (2021). Exploring the usefulness of user-generated content for business intelligence in innovation: Empirical evidence from an online open innovation community. International Journal of Enterprise Information Systems, 17(2), 44-70.
  19. Diamantopoulos, A., Schlegelmilch, B., & Palihawadana, D. (2011). The relationship between country of origin image and brand image as drivers of purchase intentions: A test of alternative perspectives. International Marketing Review.
  20. Donovan, R.J., & Rossiter, J.R. (1982). Store atmosphere: an environmental psychology approach. Journal of Retailing , 58(1), 34-57.
  21. Duan, W., Cao, Q., Yu, Y., & Levy, S. (2013). Mining online user-generated content: using sentiment analysis technique to study hotel service quality. In 2013 46th Hawaii International Conference on System Sciences (pp. 3119-3128). IEEE.
  22. Elseidi, R.I., & El-Baz, D. (2016). Electronic word of mouth effects on consumers' brand attitudes, brand image and purchase intention: an empirical study in Egypt. The Business & Management Review , 7(5), 268.
  23. Gefen, D., Rigdon, E.E., & Straub, D. (2011). Editor's comments: an update and extension to SEM guidelines for administrative and social science research. Mis Quarterly, iii-xiv.
  24. Green, S.B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3), 499-510.
  25. Grover, P., Kar, A.K., Janssen, M., & Ilavarasan, P.V. (2019). Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions–insights from user-generated content on Twitter. Enterprise Information Systems , 13(6), 771-800.
  26. Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
  27. Hawamleh, A.M.A., Alorfi, A.S.M., Al-Gasawneh, J.A., & Al-Rawashdeh, G. (2020). Cyber security and ethical hacking: The importance of protecting user data. Solid State Technology , 63(5), 7894-7899.
  28. Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variancebased structural equation modeling. Journal of the Academy of Marketing Science , 43(1), 115-135.
  29. Ho-Dac, N.N. (2020). The value of online user generated content in product development. Journal of Business Research , 112, 136-146.
  30. Hossain, M.S., Anthony, J.F., Beg, M.N.A., & Zayed, N.M. (2019a). The consequence of corporate social responsibility on brand equity: A distinctive empirical substantiation. Academy of Strategic Management Journal , 18(5), 1-7.
  31. Hossain, M.S., Hasan, R., Kabir, S.B., Mahbub, N., & Zayed, N.M. (2019b). Customer participation, value, satisfaction, trust and loyalty: an interactive and collaborative strategic action. Academy of Strategic Management Journal , 18(3), 1-7.
  32. Hossain, S., Anthony, J.F., Beg, N.A., Hasan, K.R., & Zayed, N.M. (2020). Affirmative strategic association of brand image, brand loyalty and brand equity: a conclusive perceptual confirmation of the top management. Academy of Strategic Management Journal , 19(2), 1-7.
  33. Hutter, K., Hautz, J., Dennhardt, S., & Fuller, J. (2013). The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook. Journal of Product & Brand Management , 22(5/6), 342-351
  34. Jahan, I., Bhuiyan, K.H., Rahman, S., Bipasha, M.S., & Zayed, N.M. (2020). Factors influencing consumers’ attitude toward techno-marketing: An empirical analysis on restaurant businesses in Bangladesh. International Journal of Management , 11(8).
  35. Jalilv, M.R., & Samiei, N. (2012). The effect of electronic word of mouth on brand image and purchase intention: An empirical study in the automobile industry in Iran. Marketing Intelligence & Planning.
  36. Kala, D., & Chaubey, D.S. (2018). The effect of eWOM communication on brand image and purchase intention towards lifestyle products in India. International Journal of Services, Economics and Management , 9(2), 143-157.
  37. Kazmi, A., & Mehmood, Q. (2016). The effect of electronic word of mouth communication and brand image on purchase intention: A case of consumer electronics in Haripur, Pakistan. Management Science Letters , 6(7), 499-508.
  38. Keller, K. (2009). Managing the growth tradeoff: challenges and opportunities in luxury branding. Journal of Brand Management , 16 (5/6), 290-301.
  39. Keller, K.L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing , 57(1), 1-22.
  40. Khalil, I., Rasel, K.A., Kobra, K., Noor, F., & Zayed, N.M. (2020). Customers' attitude toward sms advertising: A strategic analysis on mobile phone operators in Bangladesh. Academy of Strategic Management Journal , 19(2), 1-7.
  41. Kim, A.J., & Johnson, K.K. (2016). Power of consumers using social media: Examining the influences of brand-related user-generated content on Facebook. Computers in Human Behavior, 58, 98-108.
  42. Konuk, F.A. (2019). The influence of perceived food quality, price fairness, perceived value and satisfaction on customers’ revisit and word-of-mouth intentions towards organic food restaurants. Journal of Retailing and Consumer Services, 50, 103-110.
  43. Kotler, M., Iancu, I., Efroni, R., & Amir, M. (2001). Anger, impulsivity, social support, and suicide risk in patients with posttraumatic stress disorder. The Journal of Nervous and Mental Disease , 189(3), 162-167.
  44. Kudeshia, C., & Kumar, A. (2017). Social eWOM: Does it affect the brand attitude and purchase intention of brands?. Management Research Review.
  45. Kumar, A., & Kim, Y.K. (2014). The store-as-a-brand strategy: The effect of store environment on customer responses. Journal of Retailing and Consumer Services , 21 (5), 685-695.
  46. Kunja, S.R., & Acharyulu, G.V.R.K. (2018). Examining the effect of eWOM on the customer purchase intention through value co-creation (VCC) in social networking sites (SNSs). Management Research Review.
  47. Laato, S., Islam, A.N., Farooq, A., & Dhir, A. (2020). Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-response approach. Journal of Retailing and Consumer Services, 57, 102224.
  48. Liu, Q.B., Karahanna, E., & Watson, R.T. (2011). Unveiling user-generated content: Designing websites to best present customer reviews. Business Horizons , 54, 231-240.
  49. Ma, T.J., & Atkin, D. (2017). User generated content and credibility evaluation of online health information: A meta analytic study. Telematics and Informatics , 34(5), 472-486.
  50. Marine-Roig, E., Martin-Fuentes, E., & Daries-Ramon, N. (2017). User-generated social media events in tourism. Sustainability , 9(12), 2250.
  51. Mayrhofer, M., Matthes, J., Einwiller, S., & Naderer, B. (2020). User generated content presenting brands on social media increases young adults’ purchase intention. International Journal of Advertising , 39(1), 166-186.
  52. Mehrabian, A., & Russell, J. A. (1974). An Approach to Environmental Psychology. Cambridge: MIT Press.
  53. Narangajavana Kaosiri, Y., Callarisa Fiol, L.J., Moliner Tena, M.A., Rodriguez Artola, R.M., & Sanchez Garcia, J. (2019). User-generated content sources in social media: A new approach to explore tourist satisfaction. Journal of Travel Research , 58(2), 253-265.
  54. Ngah, A.H., Gabarre, S., Han, H., Rahi, S., Al-Gasawneh, J.A., & Park, S.H. (2021). Intention to purchase halal cosmetics: Do males and females differ? A multigroup analysis. Cosmetics , 8(1), 19.
  55. Nusairat, N., Hammouri, Q., Al-Ghadir, H., Ahmad, A., & Eid, M. (2020a). The effect of design of restaurant on customer behavioral intentions. Management Science Letters , 10(9), 1929-1938.
  56. Nusairat, N.M., Akhorshaideh, A.H.O., Rashid, T., Sahadev, S., & Rembielak, G. (2017). Social cues-customer behavior relationship: The mediating role of emotions and cognition. International Journal of Marketing Studies , 9(1).
  57. Nusairat, N.M., Hammouri, Q., Al-Ghadir, H., Ahmad, A.M.K., & Abuhashesh, M. (2020b). Fitness centers ambience-customer behavioral intentions relationship: The mediating role of customer emotional states. International Journal of Business and Management , 15(9).
  58. Oliveira, B., & Casais, B. (2019). The importance of user-generated photos in restaurant selection. Journal of Hospitality and Tourism Technology.
  59. Owusu, R.A., Mutshinda, C.M., Antai, I., Dadzie, K.Q., & Winston, E.M. (2016). Which UGC features drive web purchase intent? A spike-and-slab Bayesian Variable Selection Approach.
  60. Roya. (2018). Retrieved from
  61. Sin, S.S., Nor, K.M., & Al-Agaga, A.M. (2012). Factors Affecting Malaysian young consumers’ online purchase intention in social media websites. Procedia-Social and Behavioral Sciences , 40, 326-333.
  62. Tariq, M., Abbas, T., Abrar, M., & Iqbal, A. (2017). EWOM and brand awareness impact on consumer purchase intention: mediating role of brand image. Pakistan Administrative Review , 1(1), 84-102. ‏
  63. Torlak, O., Ozkara, B.Y., Tiltay, M.A., Cengiz, H., &Dulger, M.F. (2014). The effect of electronic word of mouth on brand image and purchase intention: An application concerning cell phone brands for youth consumers in Turkey. Journal of Marketing Development and Competitiveness , 8(2), 61.
  64. Tsiakali, K. (2018). User-generated-content versus marketing-generated-content: Personality and content influence on traveler’s behavior. Journal of Hospitality Marketing & Management , 27(8), 946-972.
  65. Wang, P. (2015). Exploring the influence of electronic word-of-mouth on tourists’ visit intention. Journal of Systems and Information Technology
  66. Wu, Y.L., & Li, E.Y. (2018). Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective. Internet Research.
  67. Yu, C.C., Lin, P.J., & Chen, C.S. (2013). How brand image, country of origin, and self-congruity influence internet users' purchase intention. Social Behavior and Personality: An International Journal , 41(4), 599-611.
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