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Article

Examining Drivers of Brand Community Engagement: The Moderation of Product, Brand and Consumer Characteristics

by
Jintang Wang
1,
Junyun Liao
2,*,
Shiyong Zheng
3 and
Biqing Li
4
1
Economics and Management School, Wuhan University, Wuhan 430072, China
2
Department of Marketing, School of Management, Jinan University, Guangzhou 510632, China
3
Economics and Management School, Wuhan University, Wuhan 430072, China
4
School of Information and Communication Engineering, Hezhou University, Hezhou 542800, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(17), 4672; https://doi.org/10.3390/su11174672
Submission received: 2 August 2019 / Revised: 22 August 2019 / Accepted: 26 August 2019 / Published: 28 August 2019
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Browse Figure
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Abstract

:
Numerous firms operate online brand communities (OBCs) in order to build a close consumer–brand relationship. To succeed in realizing this aim, firms must first sustain members’ brand community engagement. While prior studies have examined a series of drivers of brand community engagement, most of them focused on psychological and social motivations. Limited information is available about the role of product, brand and consumer characteristics in driving brand community engagement. Building on the uses and gratifications (UG) theory, the authors investigate the moderation of product complexity, brand symbolism, and extraversion in the relationship between brand community gratification and brand community engagement. With the collaboration of an online shopping site, 462 validated survey responses were collected to test our hypotheses. The results indicate that product complexity positively moderates the impact of information value on brand community engagement and brand symbolism positively moderates the effect of social value on brand community engagement. Finally, the results show that extraversion positively interacts with social value in enhancing brand community engagement. This study advances the understanding of brand community engagement.

1. Introduction

Brand communities are social groups based on common brand interest [1]. Brand communities enable consumers to exchange product knowledge, share brand stories and build social relationships. The proliferation of social media has enabled the shift of brand communities onto the internet, resulting in online brand communities (OBCs) [2]. Through exchanging and sharing information, knowledge and consumption experience related to the brand and product [2,3], consumers develop an emotional attachment to the community and commitment to the brand as well [4,5,6]. Accordingly, previous research has consistently demonstrated brand community increases brand performance [7,8,9]. With the value of OBC in mind, a growing number of firms have begun to proactively build their own OBCs [10,11,12]. For example, a popular mobile phone brand, Xiaomi, built its online brand community (www.xiaomi.cn) in 2011 and succeeded in attracting millions of consumers in a few years. However, it is not an easy task [13,14].
The viability and sustainability of an online community are conditional on members’ community engagement [12,15,16]. To this end, managers must know what drives consumers’ participation in OBCs first. Prior studies have extensively examined the psychological motivations for consumers’ participation in an online community [4,17,18,19,20]. However, they provide little knowledge about the link between product, brand and consumer characteristics and brand community engagement, which is surprising given the central role of product, brand, and consumer for an OBC. Actually, despite the knowledge of psychological motivations of brand community engagement, managers know less about how their product/brand characteristics and consumer characteristics may influence consumers’ engagement. Considering this research gap, Wirtz et al. [21] and Kaur et al. [22] called for empirical studies on the relationships between brand/product, personal factors and OBC engagement. Unfortunately, little empirical studies arise to date, as Thompson et al. [23] stated that “to date, the impact of product characteristics has gone largely unexamined within the brand community literature” (p. 292). Therefore, as a response to this research gap, the paper aims to examine the role of product, brand and consumer characteristics in motivating consumers’ brand community engagement [3].
We utilize the use and gratification theory to explore this research question. The use and gratification theory maintains that consumers’ media use is motivated by the gratification from media use. Based on this idea, we argue that product, brand and consumer characteristics may affect community engagement because they could influence consumers’ gratification and consumers attach unequal importance to various dimensions of gratification in terms of different products and brands when engaging with the brand community [24]. Drawing from the framework of Wirtz et al. [21] and Kaur et al. [22], we examine three important product, brand, and consumer characteristics, i.e., product complexity, brand symbolism, and extraversion. The authors collected 462 questionnaires from a wide range of brand communities to test hypotheses. The results indicate that informational value and social value are positively associated with brand community engagement. Meanwhile, product complexity positively moderates the relationship between information value and brand community engagement while brand symbolism positively moderates the relationship between social value and brand community engagement. Extraversion strengthens the effect of social value and brand community engagement but a similar moderation effect does not exist in the relationship between informational value and brand community engagement. To the best of our knowledge, this is the first empirical study to investigate the impacts of product, brand and consumer characteristics on consumers’ community engagement. It advances our understanding of the drivers of brand community engagement [16,22,25,26].
The remainder of the paper is organized as follows. First, we introduce the theoretical background of this study. Next, we present the research model and hypotheses. Subsequently, the research methodology and the results of the hypotheses are presented. Finally, we conclude with the theoretical and managerial implications of this study.

2. Theoretical Background

2.1. Brand Community Engagement

Consumers’ engagement in OBCs is important for two reasons. First, the survival of OBCs relies heavily on members’ participation. Since OBCs are voluntary groups, they usually suffer from insufficient participation member base and high turnover [12,27]. Without enough members, an OBC will fail [12,22,28,29]. Second, community engagement may alter consumers’ behaviors in favor of brands. Extensive research has demonstrated that community engagement is highly positively associated with consumer’ brand loyalty [6,30,31], commitment [2,32,33], and actual purchase behaviors [34,35].
Thus, understanding the drivers of brand community engagement is important for both practitioners and academia as well. Prior studies have examined a series of antecedents [16,36]. Among them, Wirtz et al. [21] and Kaur et al. [22] provided a cogent conceptual framework that summarizes the antecedents of OBC engagement. From their work, the drivers can be divided into several parts: (1) brand or product-related drivers (e.g., brand’s symbolic function and self-brand image congruity) [26]; (2) social drivers (e.g., social benefits); and (3) functional drivers (e.g., information quality). They also propose that customer factors (e.g., customer expertise) play their roles in driving OBC engagement. For example, Snyder and Newman [37] indicated that loneliness motivates consumers’ brand community participation. In light of their framework, we reviewed the literature on the antecedents of OBC engagement in Table 1. Wirtz et al. [21] called for empirical research that investigates the relationship between product, brand and consumer characteristics and brand community engagement. This paper is a response to their call for research.

2.2. Uses and Gratifications Theory

Uses and gratifications (UG) theory is a theory that arose originally in the field of mass communication. It was widely used to explain the adoption and consumption of mass communication media, such as TV [50]. This theory has two primary assumptions. First, media users are goal-directed and intentional actors [51]. Second, people select and use various types of media because these media help consumers in satisfying their specific various needs [52]. These needs are determined by social and psychological situations, and in turn, motivate media use when they derive gratifications [53]. Katz et al. [54] summarized the literature on media uses and gratifications, and proposed that expectation is an important construct in the uses and gratification paradigm because the expected benefits shape individuals’ media usage.
In recent decades, its application of uses and gratifications theory has been extended to a large of media services, such as social networking sites [55,56,57,58], mobile shopping [59], and online games [53]. Particularly, the UG approach is also used to understand the motivations of user participation in virtual community environments [22,26,60]. Bagozzi and Dholakia [61] maintained that brand community participation is goal-directed and found that consumers anticipated positive emotions increase their intention to participate in a brand community. In the following studies, Sicilia and Palazón [38] agreed with the view that consumers’ online community participation is goal-oriented, and they adopted the uses and gratification approach to explain consumer participation in OBCs. Kaur et al. [22] found that expected benefits were positively related to community participation behaviors. Accordingly, this theory is a good lens through which to understand consumers’ brand community engagement.

3. Hypotheses Development

In this paper, we draw from the theoretical framework of Wirtz et al. [21] and Kaur et al. [22] to focus on the effect of product complexity, brand symbolism, and extraversion. In their framework, the drivers of OBC engagement are broadly divided into functional drivers and social drivers, which closely relate to product complexity and brand symbolism, respectively. Product complexity enhances a consumer’s need for an OBC as it serves as an important information source. OBCs bring substantial social value for consumers because consumers tend to enjoy sharing symbolic meanings with other like-minded brand admirers. Finally, extraversion may influence the extent to which brand community gratification influences brand community engagement. In what follows, we formally develop our hypotheses. The framework is depicted in Figure 1.

3.1. The Effect of Brand Community Gratification

Many prior studies found that seeking information is one of the main reasons that motivate users to participate in online communities [38,58]. For example, Zhou et al. [41] found that consumers decide to participate in an OBC or not by observing whether the content in the community is helpful for product usage or problem-solving. Wirtz et al. [21] also stated that the primary purpose of many members’ joining in an OBC is to seek functional benefits (e.g., search for information to tackle problems in product use). Taken together, when a consumer buys a product, especially a brand they are not familiar with, they may want to search information related to the brand or the product from its online community given its important role as a source of brand knowledge in the social media era. Butler [28] believed that virtual communities offer a range of benefits, such as product discussion and knowledge sharing. Thus, informational benefits may drive individuals to engage with the brand community [22].
H1:
Informational value has a positive influence on members’ brand community engagement.
An OBC is a channel where a group of consumers with shared interests in the brand get together and socialize. Bagozzi and Dholakia [44] found that the identification of the group of consumers of a brand leads people to participate in the brand community. Within OBCs, consumers usually experience a great deal of meaningful social interaction and build close social relationship such as trust and intimacy [4,37,62]. Therefore, people usually tend to participate in OBCs for social purposes [37,41]. For example, consumers may find friends with similar interests. From participation in OBCs, members are able to get social support, build a social network and social capital status in an online community [63], all of which fall into specific types of social benefits [12,38]. When a consumer is able to derive social benefits from an OBC, their engagement intention will increase.
H2:
Social value has a positive influence on members’ brand community engagement.

3.2. The Moderation of Product Complexity

Product complexity can be defined as the level of difficulty that the use of a product is perceived to entail [64]. Usually, if a product has a variety of attributes, it will be difficult for consumers to fully exploit its functions, thereby the product may be recognized as a complex product [65]. Product complexity can raise a feeling of uncertainty and perceived risk in the mind of the consumer. When people perceive the complexity of a product is high, they always need to spend much time and effort to learn the product before purchasing, such as searching for information about the product [66]. That is to say, the more complex a product is, the more learning is required. In the era of social media, people usually seek useful information about a product from various online communities. OBCs, which center on a brand in many aspects, provide consumers with an ideal place where consumers seek brand knowledge and technical solutions. Apparently, it is relatively convenient and effective for a consumer to find information, solutions and skills for product use from peers of OBCs because other members may have met the same problems and may have provided useful experience in addressing the problems [67]. Thus, when consumers perceive that a product has a high level of complexity, they are more likely to expect information benefits from the brand’s online communities [22,68]. As a result, their brand community engagement will increase.
H3:
Product complexity positively moderates the relationship between informational value and brand community engagement.

3.3. The Moderation of Brand Symbolism

Brand symbolism refers to a brand’s capability of communicating something about the person who owns it [69]. In the seminal work of consumption symbolism by Belk [70], he proposed that one’s possessions reflect their identities. Particularly, brands are especially able to express specific symbolic meanings about the owners. Thus, people purchase brands that match their self-concepts and communicate their social identities [71]. Now it is widely acknowledged that other than functional values and hedonic values, symbolic values are also a significant component of the value system of brands [72]. A symbolic brand has a strong capability of communicating something about its consumers and separate its brand users with nonusers [73]. In other words, for a group of people who are consumers of a brand with high brand symbolism, they are very likely to share some characteristics. People always like to socialize with others who share something with themselves and talk about their shared interests. OBCs are a place for like-minded consumers, where consumers look for a sense of belonging and social affiliation [74]. Users of a symbolic brand who get together in OBCs are more likely to share similar consumption attitudes, lifestyles and values, thereby creating strong social value. Thus,
H4:
Brand symbolism positively moderates the relationship between social value and brand community engagement.

3.4. The Moderating Role of Extraversion

While product/brand characteristics are hypothesized to positively impact expected benefits from community engagement, the impact would be contingent on the consumer’s socialization tendency, i.e., extraversion. Extraversion is a human personality trait that refers to individuals who are energetic, cheerful and sociable [45]. Extraversion influences one’s behaviors in social media. An extraverted person usually presents positive affect [75]. For example, research shows that extraverted people are happier [76]. They are inclined to expect positive outcomes even confronted with difficulties and challenges.
Furthermore, extraverted people are more sociable [77,78]. They find it enjoyable to communicate and interact with others in diverse activities. They also believe they could benefit from the interaction with others whether the benefits are social or functional. While introverted people are shyer and like staying alone. Thus, different expectations would manifest for introverted people and extraverted people regarding the intention to participate in OBCs [45]. Lucas and Fujita [79] showed that extraverted people prefer to join certain groups to interact with other people. By contrast, less extraverted people tend to search for information by themselves independently and reduce the possibility of seeking help from others because they are more bashful [80]. In the specific context of OBCs, we predict that people of high extraversion will expect more benefits through interaction with others, which increases members’ brand community engagement.
H5:
A higher level of extraversion strengthens the impact of informational value on brand community engagement.
H6:
A higher level of extraversion strengthens the impact of social value on brand community engagement.

4. Research Methodology

4.1. Measures

The measurement items of constructs were all adapted from validated instruments used in the prior literature. The measurement items of product complexity were based on items proposed by [81]. Brand symbolism was measured using items adapted from [73]. The measurement items of expected informational value and expected social value were adapted from [41]. We adopted measurement items of brand community engagement from [30]. Finally, we adopted four measurement items from [46] to measure extraversion. We conducted a backward translation process to ensure consistency between the Chinese and English versions of the instrument. All items are rated using a seven-point Likert scale, with 1 indicating “strongly disagree” and 7 indicating “strongly agree.”

4.2. Data Collection

As our study focuses on how product, brand and consumer characteristics influence brand community engagement, the best target population for us is consumers who have been a member of a brand community. For example, when a consumer buys a mobile phone, he or she may intend to participate in its online community to exchange consumption experience and seek information about product use. To this end, we collaborated with a company that operates an online shopping mall to collect data in April 2019. We first designed an online questionnaire, and then with the help of the company we distributed the questionnaire to consumers who completed a purchase in the following product categories: mobile phone, mouse, cosmetics, and jeans. We focused on these four categories because they have varying product complexity and brand symbolism. For example, mobile phones are usually complex, but jeans are less complex. On the other hand, a mouse is usually utilitarian and low in symbolism, but consumers use mobile phone brands to communicate who they are. Such variance enabled us to test the hypotheses regarding the role of product complexity and brand symbolism. We used the screening condition that respondents had once participated (such as initiating a thread or making a reply to others) in any kind of online brand communities in the last three months. In the survey, participants were asked to evaluate their consumption experience, and respond to our research items. We offered participants a 20 RMB coupon that could be used in the next purchase in the online shopping mall to encourage participation.
The data collection continued for two weeks, after which the records showed that 512 members completed the survey. Of these, 50 respondents went below the time baseline (i.e., they viewed the online questionnaire less than 5 min) or did not meaningfully answer the questionnaire, resulting in 462 usable questionnaires for the final empirical test. Table 2 provides the demographic characteristics of the usable sample.

4.3. Measurement Model

We used the maximum likelihood estimation method to test confirmatory factor analysis (CFA), and obtained satisfactory fit indices: χ2 (120) = 373.381 (p < 0.001), the χ2 /df ratio was 3.111, comparative fit index (CFI) = 0.971, Tucker–Lewis index (TLI) = 0.962, normed fit index (NFI) = 0.960, and root mean square error of approximation (RMSEA) = 0.073 [82]. We further estimated construct reliability for each construct. The construct reliability values ranged from 0.857 to 0.950, which were statistically acceptable (product complexity = 0.907, brand symbolism= 0.857, informational value = 0.920, social value = 0.950, extraversion = 0.946, brand community engagement = 0.899) (see Table 3).
We found that all standardized factor loadings for individual indicators, ranging from 0.714 to 0.933, were statistically significant (p < 0.001) [83], and the average variance extracted (AVE) values ranged from 0.670 to 0.865 [84], both indicating satisfactory convergent validity. Discriminant validity was tested by comparing the square roots of AVE to the correlation coefficients among the constructs [84]. As shown in Table 4, all of the square roots of AVE exceeded the correlations in the measurement model, showing good discriminant validity.

4.4. Common Method Variance

Common method variance (CMV) bias may exist when same-source survey data are used. Therefore, following Podsakoff et al. [85], we designed procedures to reduce the threat of bias in the surveying process. We also tested the existence of CMV in two ways in the ex-post analysis. First, principal component factor analysis showed that the largest explained variance before rotation was 38.54%, which indicated that no serious CMV exists [86]. Second, we conducted a confirmatory factor analysis with only one factor. The results showed that the fitness of the one-factor model was worse than that of the multi-factor models. Therefore, CMV should not be a serious problem in our results.

4.5. Hypothesis Test

A hierarchical regression model was used to test our hypotheses. In Model 1, we include control variables; In Model 2, we add our main variables to test our hypotheses H1 to H2; In Model 3, we added interaction items to test our moderation effect from H3 to H6 (see Table 5).
The results indicate that informational value had a positive effect on brand community engagement (β = 0.352, t = 6.661, p < 0.001; supporting H1). Social value was also found to affect brand community engagement significantly (β = 0.158, t = 4.311, p < 0.001; supporting H2). The interaction item regarding the informational value and product complexity was positive and significant (β = 0.352, t = 7.832, p < 0.001; supporting H2). Thus, H3 was supported. That is to say, when product complexity was high, the effect of information value on brand community engagement was stronger. The interaction item regarding the social value and brand symbolism was positive and significant (β = 0.266, t = 4.431, p < 0.001; supporting H4), which indicates that brand symbolism strengthened the effect of social value on brand community engagement. The moderation effect of extraversion in the relationship between informational value and brand community engagement was not significant (β = 0.137, t = 1.053, p > 0.05). Accordingly, H5 was not supported. Finally, the moderation effect of extraversion in the relationship between social value and brand community engagement was significant (β = 0.236 t = 2.804, p < 0.01). Thus, H6 was supported.

5. Discussion and Conclusions

Many firms are making great efforts to sustain members’ online brand engagement. Despite their efforts, a lot of them fail while some succeed. Understanding the drivers of brand community engagement is a question of theoretical and managerial importance. Previous studies have dominantly investigated the motivational drivers of consumers’ participation in OBC. We move beyond this stream of research by examining the role of product, brand and consumer characteristics. In line with Bagozzi and Dholakia [61] who conceptualized consumers’ brand community participation as intentional social action, we propose that consumers’ brand community engagement is driven by members’ gratification from the brand community which is closely associated with product, brand and consumer characteristics. In other words, we demonstrate that certain product, brand, and consumer characteristics are more likely to trigger consumers’ desire for online communities. For example, with a symbolic brand, consumers would feel a strong desire to present their unique symbolic image of the brand, which motivates consumers to socialize with others sharing the same symbolic value. Building on the uses and gratifications theory, we validate that product, brand and consumer characteristics influence consumers’ OBC participation through moderating the derived gratification from the communities, which is consistent with the findings of Adjei et al. [68] and Wirtz et al. [21] who demonstrated that members’ need for brand community is differential for different products and brands.

5.1. Theoretical Implications

Our study can be seen as a response to Thompson et al. [23] and Wirtz et al. [21] who called for empirical studies that examine how product/brand characteristics influence consumers’ community participation. Prior research has already revealed a wide series of factors that would influence consumers’ intention to participate in online brand communities such as seeking useful information, entertainment, social relationships, etc. Although these studies contribute to the literature by uncovering the psychological motivations of community participation, they provide little insight into the relationship between product, brand, consumer characteristics, and brand community engagement. By linking product, brand and consumer characteristics and brand community engagement, our study extends the knowledge of the drivers of brand community engagement.
We also advance the understanding of the origins of expected benefits by applying the uses and gratifications theory into the context of OBCs. Past research has indicated perceived values from OBCs lead to members’ future participation behaviors [22,41], although little is known how the effect of gratifications from brand community can be altered by product and brand characteristics. The uses and gratifications theory proposes that consumers’ behaviors are largely driven by their expectation of media usage and expectations can arise from societal situations and media characteristics [54]. Drawing from this view, we find that product complexity and brand symbolism are associated with the expected value from OBC participation, which further encourages their engagement intention. Therefore, this paper adds to the literature on OBC by demonstrating the role of product and brand characteristics in the relationship between gratification from brand community and future brand community engagement.
Finally, we find that individual-level factors (i.e., extraversion) may affect community participation, which is consistent with the findings of [46,80]. The result of our study indicates that not all consumers attach equal importance to the social value of brand community. Thus, individual differences should be considered an important factor that may influence brand community engagement.

5.2. Managerial Implications

This study holds several important managerial implications. First, our study shows that expected functional and social benefits lead to high community participation intention. Thus, for firms that operate an OBC, they should communicate potential benefits to consumers in attracting a great number of members to join in their communities. It can be done by introducing their OBCs when a consumer buys a product. As OBCs can help members solve problems and cultivate relationships among peers, firms should generate more revenue by decreasing service costs (e.g., operating a large call center) and expanding their loyal customer base. Brands could send a message that emphasizes the functional and social value of their OBCs to attract consumers.
Second, as we show that product complexity will influence one’s intention to participate in a brand community, we propose that it may be very valuable for brands in industries with huge technological details to build communities to meet consumers’ information needs. For users of these brands, they usually find that it is difficult to capture full usage skills and sometimes may even encounter technical problems they cannot solve by themselves. Traditionally, to alleviate those problems, firms must spend considerable marketing resources to educate consumers, such as after-sale personnel and customer education campaigns. However, with OBCs, customers can search product knowledge by themselves and help others to solve product use problems without the help of firms. As such, a great part of marketing resources can be saved for firms by building online brand communities. Firms could emphasize the informational value of their OBCs when soliciting consumers who recently purchased a product for brand communities, especially when their products are complex for consumers [46]. Social value is also important in fostering brand community engagement. Prior research has indicated that consumers see brands as intentional agents and they relate to brands as they relate to people to some extent [87]. In other words, consumers may interact with brands like social relationships. Accordingly, firms should present their good intentions (i.e., warmth) and abilities (e.g., quality information) in brand communities to attract consumers’ engagement. Further, we suggest that brand symbolism may help firms attract brand users to their online communities. It is well acknowledged that people buy brands not only for their utilitarian values but also for symbolic values. Sharing the same values make consumers get together. Thus, when hosting and managing an OBC, firms should not constrain all their managerial efforts within the communities, instead, they could create and communicate a unique brand image to build OBCs. For example, firms could launch communication that presents their warmth in the brand community because the perception of warmth is usually associated with more word-of-mouth [88]. Taken together, our results suggest that firms should take their product and brand characteristics into consideration when managing OBCs because of the different product and brand characteristics create consumers’ distinct expectation on OBCs.
Finally, brand managers should investigate which brand users are more likely to engage with their OBCs and give priority to attracting these consumers to their brand communities accordingly. By doing these, firms can allocate marketing resources more effectively. Our results suggest that extraverts are more likely to participate in online brand communities. Firms can identify such consumers in a series of ways, such as observing consumer behaviors in social media because extraverts tend to be more active in social media and post more content online.

5.3. Limitations and Future Research

This paper has several limitations that call for further research. First, we measure members’ subjective brand community engagement in this paper rather than their actual participation behaviors. Given the importance of users’ actual participation for the survival of an OBC, future studies could collect data on buyers’ actual OBC participation behavior to validate our findings. By doing this, our understanding of the relationship between brand/ product characteristics with brand community engagement would be deepened. Second, the paper discusses only three important product, brand and consumer characteristics that influence consumers’ engagement in the OBCs by evoking expected informational value and social value. Obviously, the effect of other important product, brand and consumer characteristics (e.g., brand personality) deserves further studies.

Author Contributions

J.W. designed the study and processed the data. J.L. performed the review & editing of the manuscript. S.Z. performed data collection. B.L. performed the conceptualization for the first manuscript.

Funding

This project was supported by Jinan University Management School Funding Program, No. GY18004 and transition development Project of Guangdong-Hongkong-Macao Greater, Institute of Enterprise Development, Jinan University (No. 2019GBAZD06). The authors also gratefully acknowledge financial support from the National Natural Science Foundation of China (NSFC) (No. 71802097, No.71572136, No. 71562027, No. 71372169, No. 71772077, No. 71472074 & No. 91746206), Natural Science Foundation of Guangdong Province in China (No. 2014A030311022) and the Fundamental Research Funds for the Central Universities (No. 15JNLH005).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Muniz, A.M.; O’Guinn, T.C. Brand Community. J. Consum. Res. 2001, 27, 412–432. [Google Scholar] [CrossRef]
  2. Essamri, A.; McKechnie, S.; Winklhofer, H. Co-creating corporate brand identity with online brand communities: A managerial perspective. J. Bus. Res. 2019, 96, 366–375. [Google Scholar] [CrossRef]
  3. Shen, X.-L.; Li, Y.-J.; Sun, Y.; Zhou, Y. Person-environment fit, commitment, and customer contribution in online brand community: A nonlinear model. J. Bus. Res. 2018, 85, 117–126. [Google Scholar] [CrossRef]
  4. Swimberghe, K.; Darrat, M.A.; Beal, B.D.; Astakhova, M. Examining a psychological sense of brand community in elderly consumers. J. Bus. Res. 2018, 82, 171–178. [Google Scholar] [CrossRef]
  5. Liu, L.; Lee, M.K.; Liu, R.; Chen, J. Trust transfer in social media brand communities: The role of consumer engagement. Int. J. Inf. Manag. 2018, 41, 1–13. [Google Scholar] [CrossRef]
  6. Coelho, P.S.; Rita, P.; Santos, Z.R. On the relationship between consumer-brand identification, brand community, and brand loyalty. J. Retail. Consum. Serv. 2018, 43, 101–110. [Google Scholar] [CrossRef]
  7. Thompson, S.A.; Loveland, J.M.; Loveland, K.E. The impact of switching costs and brand communities on new product adoption: Served-market tyranny or friendship with benefits. J. Prod. Brand Manag. 2019, 28, 140–153. [Google Scholar] [CrossRef]
  8. Gruner, R.L.; Homburg, C.; Lukas, B.A. Firm-hosted online brand communities and new product success. J. Acad. Mark. Sci. 2014, 42, 29–48. [Google Scholar] [CrossRef]
  9. Park, E.; Rishika, R.; Janakiraman, R.; Houston, M.B.; Yoo, B. Social Dollars in Online Communities: The Effect of Product, User, and Network Characteristics. J. Mark. 2018, 82, 93–114. [Google Scholar] [CrossRef] [Green Version]
  10. Zhang, M.; Hu, M.; Guo, L.; Liu, W. Understanding relationships among customer experience, engagement, and word-of-mouth intention on online brand communities: The perspective of service ecosystem. Internet Res. 2017, 27, 839–857. [Google Scholar] [CrossRef]
  11. Herhausen, D.; Ludwig, S.; Grewal, D.; Wulf, J.; Schoegel, M. Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities. J. Mark. 2019, 83, 1–21. [Google Scholar] [CrossRef]
  12. Hanson, S.; Jiang, L.; Dahl, D. Enhancing consumer engagement in an online brand community via user reputation signals: A multi-method analysis. J. Acad. Mark. Sci. 2019, 47, 349–367. [Google Scholar] [CrossRef]
  13. Liao, J.; Huang, M.; Xiao, B. Promoting continual member participation in firm-hosted online brand communities: An organizational socialization approach. J. Bus. Res. 2017, 71, 92–101. [Google Scholar] [CrossRef]
  14. Coussement, K.; Debaere, S.; De Ruyck, T. Inferior Member Participation Identification in Innovation Communities: The Signaling Role of Linguistic Style Use. J. Prod. Innov. Manag. 2017, 34, 565–579. [Google Scholar] [CrossRef]
  15. Kang, M.; Shin, D.-H. The effect of customers’ perceived benefits on virtual brand community loyalty. Online Inf. Rev. 2016, 40, 298–315. [Google Scholar] [CrossRef]
  16. Gómez, M.; Lopez, C.; Molina, A. An integrated model of social media brand engagement. Comput. Hum. Behav. 2019, 96, 196–206. [Google Scholar] [CrossRef]
  17. Teichmann, K.; Plank, A.; Strobl, A.; Stokburger-Sauer, N.E. Motivational Drivers of Content Contribution to Company- Versus Consumer-Hosted Online Communities. Psychol. Mark. 2015, 32, 341–355. [Google Scholar] [CrossRef]
  18. Ridings, C.M.; Gefen, D. Virtual Community Attraction: Why People Hang Out Online. J. Comput.-Mediat. Commun. 2004, 10. [Google Scholar] [CrossRef]
  19. Ling, K.; Beenen, G.; Ludford, P.; Wang, X.; Chang, K.; Li, X.; Cosley, D.; Frankowski, D.; Terveen, L.; Rashid, A.M.; et al. Using Social Psychology to Motivate Contributions to Online Communities. J. Comput. Commun. 2005, 10. [Google Scholar] [CrossRef]
  20. Kumar, J.; Nayak, J.K. Consumer psychological motivations to customer brand engagement: A case of brand community. J. Consum. Mark. 2019, 36, 168–177. [Google Scholar] [CrossRef]
  21. Wirtz, J.; Ambtman, A.D.; Bloemer, J.; Horváth, C.; Ramaseshan, B.; Van De Klundert, J.; Canli, Z.G.; Kandampully, J. Managing brands and customer engagement in online brand communities. J. Serv. Manag. 2013, 24, 223–244. [Google Scholar] [CrossRef] [Green Version]
  22. Kaur, P.; Dhir, A.; Rajala, R.; Dwivedi, Y. Why people use online social media brand communities. Online Inf. Rev. 2018, 42, 205–221. [Google Scholar] [CrossRef]
  23. Thompson, S.A.; Kim, M.; Smith, K.M. Community Participation and Consumer-to-Consumer Helping: Does Participation in Third Party-Hosted Communities Reduce One’s Likelihood of Helping? J. Mark. Res. JMR 2016, 53, 280–295. [Google Scholar] [CrossRef]
  24. Wu, J.; Fan, S.; Zhao, J.L. Community engagement and online word of mouth: An empirical investigation. Inf. Manag. 2018, 55, 258–270. [Google Scholar] [CrossRef]
  25. Gong, T. Customer brand engagement behavior in online brand communities. J. Serv. Mark. 2018, 32, 286–299. [Google Scholar] [CrossRef]
  26. Islam, J.U.; Rahman, Z.; Hollebeek, L.D.; Cheung, C. Consumer engagement in online brand communities: A solicitation of congruity theory. Internet Res. 2018, 28, 23–45. [Google Scholar] [CrossRef]
  27. Ren, Y.; Harper, F.M.; Drenner, S.; Terveen, L.; Kiesler, S.; Riedl, J.; Kraut, R.E. Building Member Attachment in Online Communities: Applying Theories of Group Identity and Interpersonal Bonds. MIS Q. 2012, 36, 841–864. [Google Scholar] [CrossRef]
  28. Butler, B.S. Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures. Inf. Syst. Res. 2001, 12, 346–362. [Google Scholar] [CrossRef]
  29. Butler, B.S.; Bateman, P.J.; Gray, P.H.; Diamant, E.I. An Attraction-Selection-Attrition Theory of Online Community Size and Resilience. MIS Q. 2014, 38, 699–728. [Google Scholar] [CrossRef]
  30. Algesheimer, R.; Dholakia, U.M.; Herrmann, A. The Social Influence of Brand Community: Evidence from European Car Clubs. J. Mark. 2005, 69, 19–34. [Google Scholar] [CrossRef]
  31. Akrout, H.; Nagy, G. Trust and commitment within a virtual brand community: The mediating role of brand relationship quality. Inf. Manag. 2018, 55, 939–955. [Google Scholar] [CrossRef]
  32. Kim, J.W.; Choi, J.; Qualls, W.; Han, K. It takes a marketplace community to raise brand commitment: The role of online communities. J. Mark. Manag. 2008, 24, 409–431. [Google Scholar] [CrossRef]
  33. Stokburger-Sauer, N.; Stokburger-Sauer, N. Brand community: Drivers and outcomes. Psychol. Mark. 2010, 27, 347–368. [Google Scholar] [CrossRef]
  34. Goh, K.-Y.; Heng, C.-S.; Lin, Z. Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content. Inf. Syst. Res. 2013, 24, 88–107. [Google Scholar] [CrossRef]
  35. Manchanda, P.; Packard, G.; Pattabhiramaiah, A. Social Dollars: The Economic Impact of Customer Participation in a Firm-Sponsored Online Customer Community. Mark. Sci. 2015, 34, 367–387. [Google Scholar] [CrossRef]
  36. Veloutsou, C.; Black, I. Creating and managing participative brand communities: The roles members perform. J. Bus. Res. 2019. [Google Scholar] [CrossRef]
  37. Snyder, D.G.; Newman, K.P. Reducing consumer loneliness through brand communities. J. Consum. Mark. 2019, 36, 337–347. [Google Scholar] [CrossRef]
  38. Sicilia, M.; Palazón, M. Brand communities on the internet: A case study of Coca-Cola’s Spanish virtual community. Corp. Commun. Int. J. 2008, 13, 255–270. [Google Scholar] [CrossRef]
  39. Sung, Y.; Kim, Y.; Kwon, O.; Moon, J. An Explorative Study of Korean Consumer Participation in Virtual Brand Communities in Social Network Sites. J. Glob. Mark. 2010, 23, 430–445. [Google Scholar] [CrossRef]
  40. Jin, X.-L.; Lee, M.K.; Cheung, C.M.; Lee, M.K.O. Predicting continuance in online communities: Model development and empirical test. Behav. Inf. Technol. 2010, 29, 383–394. [Google Scholar] [CrossRef]
  41. Zhou, Z.; Wu, J.P.; Zhang, Q.; Xu, S. Transforming visitors into members in online brand communities: Evidence from China. J. Bus. Res. 2013, 66, 2438–2443. [Google Scholar] [CrossRef]
  42. Wiertz, C.; Ruyter, K. de Beyond the Call of Duty: Why Customers Contribute to Firm-hosted Commercial Online Communities. Organ. Stud. 2007, 28, 347–376. [Google Scholar] [CrossRef]
  43. Casaló, L.V.; Flavián, C.; Guinalíu, M. Promoting Consumer’s Participation in Virtual Brand Communities: A New Paradigm in Branding Strategy. J. Mark. Commun. 2008, 14, 19–36. [Google Scholar] [CrossRef]
  44. Bagozzi, R.P.; Dholakia, U.M. Antecedents and purchase consequences of customer participation in small group brand communities. Int. J. Res. Mark. 2006, 23, 45–61. [Google Scholar] [CrossRef]
  45. Hwang, J.; Han, H.; Hyun, S.S. The antecedents and consequences of visitors’ participation in a private country club community: The moderating role of extraversion. J. Destin. Mark. Manag. 2018, 7, 89–100. [Google Scholar] [CrossRef]
  46. Tsai, H.-T.; Huang, H.-C.; Chiu, Y.-L. Brand community participation in Taiwan: Examining the roles of individual-, group-, and relationship-level antecedents. J. Bus. Res. 2012, 65, 676–684. [Google Scholar] [CrossRef]
  47. Tsai, H.-T.; Bagozzi, R.P. Contribution Behavior in Virtual Communities: Cogntiive, Emotional, and Social Influences. MIS Q. 2014, 38, 143–163. [Google Scholar] [CrossRef]
  48. Simon, F.; Tossan, V. Does brand-consumer social sharing matter? A relational framework of customer engagement to brand-hosted social media. J. Bus. Res. 2018, 85, 175–184. [Google Scholar] [CrossRef]
  49. Wang, Y.; Ma, S.; Li, D. Customer participation in virtual brand communities: The self-construal perspective. Inf. Manag. 2015, 52, 577–587. [Google Scholar] [CrossRef]
  50. Palmgreen, P.; Rayburn, J.D. Uses and Gratifications and Exposure to Public Television: A Discrepancy Approach. Commun. Res. 1979, 6, 155–179. [Google Scholar] [CrossRef]
  51. Ko, H.; Cho, C.-H.; Roberts, M.S. INTERNET USES AND GRATIFICATIONS: A Structural Equation Model of Interactive Advertising. J. Advert. 2005, 34, 57–70. [Google Scholar] [CrossRef]
  52. Palmgreen, P. Uses and Gratifications: A Theoretical Perspective. Ann. Int. Commun. Assoc. 2016, 8, 20–55. [Google Scholar] [CrossRef]
  53. Wu, J.-H.; Wang, S.-C.; Tsai, H.-H. Falling in love with online games: The uses and gratifications perspective. Comput. Hum. Behav. 2010, 26, 1862–1871. [Google Scholar] [CrossRef]
  54. Katz, E.; Blumler, J.G.; Gurevitch, M. Uses and Gratifications Research. Public Opin. Q. 1973, 37, 509–523. [Google Scholar] [CrossRef]
  55. Cheung, C.M.K.; Chiu, P.-Y.; Lee, M.K.O. Online social networks: Why do students use facebook? Comput. Hum. Behav. 2011, 27, 1337–1343. [Google Scholar] [CrossRef]
  56. Raacke, J.; Bonds-Raacke, J. MySpace and Facebook: Applying the Uses and Gratifications Theory to Exploring Friend-Networking Sites. Cyberpsychol. Behav. 2008, 11, 169–174. [Google Scholar] [CrossRef] [PubMed]
  57. Gan, C.; Li, H. Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications. Comput. Hum. Behav. 2018, 78, 306–315. [Google Scholar] [CrossRef]
  58. Kim, M.J.; Lee, C.-K.; Contractor, N.S. Seniors’ usage of mobile social network sites: Applying theories of innovation diffusion and uses and gratifications. Comput. Hum. Behav. 2019, 90, 60–73. [Google Scholar] [CrossRef]
  59. Huang, J.; Zhou, L. Timing of web personalization in mobile shopping: A perspective from Uses and Gratifications Theory. Comput. Hum. Behav. 2018, 88, 103–113. [Google Scholar] [CrossRef]
  60. Dolan, R.; Conduit, J.; Fahy, J.; Goodman, S. Social media engagement behaviour: A uses and gratifications perspective. J. Strateg. Mark. 2016, 24, 261–277. [Google Scholar] [CrossRef]
  61. Bagozzi, R.P.; Dholakia, U.M. Intentional social action in virtual communities. J. Interact. Mark. 2002, 16, 2–21. [Google Scholar] [CrossRef]
  62. Chen, C.-J.; Hung, S.-W. To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Inf. Manag. 2010, 47, 226–236. [Google Scholar] [CrossRef]
  63. De Valck, K.; Van Bruggen, G.H.; Wierenga, B. Virtual communities: A marketing perspective. Decis. Support Syst. 2009, 47, 185–203. [Google Scholar] [CrossRef]
  64. Rogers, E.M. Diffusion of Innovations; Free Press: New York, NY, USA, 1995. [Google Scholar]
  65. Burnham, T.A.; Frels, J.K.; Mahajan, V. Consumer Switching Costs: A Typology, Antecedents, and Consequences. J. Acad. Mark. Sci. 2003, 31, 109–126. [Google Scholar] [CrossRef]
  66. Lakshmanan, A.; Krishnan, H.S. The Aha! Experience: Insight and Discontinuous Learning in Product Usage. J. Mark. 2011, 75, 105–123. [Google Scholar] [CrossRef]
  67. Zaglia, M.E. Brand communities embedded in social networks. J. Bus. Res. 2013, 66, 216–223. [Google Scholar] [CrossRef] [PubMed]
  68. Adjei, M.; Noble, S.; Noble, C. The influence of C2C communications in online brand communities on customer purchase behavior. J. Acad. Mark. Sci. 2010, 38, 634–653. [Google Scholar] [CrossRef]
  69. Escalas, J.E.; Bettman, J.R. Self-Construal, Reference Groups, and Brand Meaning. J. Consum. Res. 2005, 32, 378–389. [Google Scholar] [CrossRef]
  70. Belk, R.W. Possessions and the Extended Self. J. Consum. Res. 1988, 15, 139–168. [Google Scholar] [CrossRef] [Green Version]
  71. Hirschman, E.C.; Holbrook, M.B. Hedonic Consumption: Emerging Concepts, Methods and Propositions. J. Mark. 1982, 46, 92–101. [Google Scholar] [CrossRef] [Green Version]
  72. Tan, T.; Ming, M. Leveraging on symbolic values and meanings in branding. J. Brand Manag. 2003, 10, 208–218. [Google Scholar] [CrossRef]
  73. White, K.; Argo, J.J. When Imitation Doesn’t Flatter: The Role of Consumer Distinctiveness in Responses to Mimicry. J. Consum. Res. 2011, 38, 667–680. [Google Scholar] [CrossRef]
  74. Baldus, B.J.; Voorhees, C.; Calantone, R. Online brand community engagement: Scale development and validation. J. Bus. Res. 2015, 68, 978–985. [Google Scholar] [CrossRef]
  75. Watson, D.; Clark, L.A. Extraversion and Its Positive Emotional Core. In Handbook of Personality Psychology; Elsevier: Amsterdam, The Netherlands, 1997; pp. 767–793. ISBN 978-0-12-134645-4. [Google Scholar]
  76. Pavot, W.; Diener, E.; Fujita, F. Extraversion and happiness. Personal. Individ. Differ. 1990, 11, 1299–1306. [Google Scholar] [CrossRef]
  77. Paul, J.C.; Terracciano, A.; McCrae, R.R. Gender differences in personality traits across cultures: Robust and surprising findings. J. Pers. Soc. Psychol. 2001, 81, 322–331. [Google Scholar]
  78. Mooradian, T.A.; Swan, K.S. Personality-and-culture: The case of national extraversion and word-of-mouth. J. Bus. Res. 2006, 59, 778–785. [Google Scholar] [CrossRef]
  79. Lucas, R.E.; Fujita, F. Factors influencing the relation between extraversion and pleasant affect. J. Pers. Soc. Psychol. 2000, 79, 1039–1056. [Google Scholar] [CrossRef] [PubMed]
  80. Füller, J.; Matzler, K.; Hoppe, M. Brand Community Members as a Source of Innovation. J. Prod. Innov. Manag. 2008, 25, 608–619. [Google Scholar] [CrossRef]
  81. Lovett, M.J.; Peres, R.; Shachar, R. On Brands and Word of Mouth. J. Mark. Res. 2013, 50, 427–444. [Google Scholar] [CrossRef] [Green Version]
  82. Hu, L.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  83. Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  84. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  85. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef] [PubMed]
  86. Podsakoff, P.M.; Organ, D.W. Self-Reports in Organizational Research: Problems and Prospects. J. Manag. 1986, 12, 531–544. [Google Scholar] [CrossRef]
  87. Kervyn, N.; Fiske, S.T.; Malone, C. Brands as Intentional Agents Framework: How Perceived Intentions and Ability Can Map Brand Perception. J. Consum. Psychol. 2012, 22, 166–176. [Google Scholar] [CrossRef] [PubMed]
  88. Andrei, A.G.; Zait, A.; Vătămănescu, E.-M.; Pînzaru, F. Word-of-mouth generation and brand communication strategy. Ind. Manag. Data Syst. 2017, 117, 478–495. [Google Scholar] [CrossRef]
Sustainability 11 04672 g001 550
Figure 1. Research framework.
Figure 1. Research framework.
Sustainability 11 04672 g001
Table
Table 1. Empirical research on antecedents of brand community engagement.
Table 1. Empirical research on antecedents of brand community engagement.
Related StudiesAntecedents of Community EngagementTheoretical FoundationStudies
Functional drivers Functional, entertainment valuesUses and gratifications theory[38]
Information seeking, incentive seeking, and convenience seekingNo explicit theory[39]
Purposive and entertainment valuesExtended information systems (IS) continuance model[40]
Informational valueObservational learning theory[41]
Epistemic valueUses and gratifications theory[22]
Informational valueSocial capital theory[42]
Social drivers Trust, satisfaction, communicationRelationship quality perspective[43]
Social identity, subjective normsThe theory of planned behavior[44]
Similarity, physical appearance; suitable behaviorNo explicit theory[45]
Social valuesUses and gratifications theory[38]
Trust, identification, satisfactionNo explicit theory[46]
Interpersonal utilityNo explicit theory[39]
Perceived social valueObservational learning theory[41]
Subjective norms, group norms, social identityA combination of the theory of planned behavior and the model of goal-directed behavior[47]
Social valuesUses and gratifications theory[22]
Brand-consumer social sharingtheory of close relationships[48]
Relational social capitalSocial capital theory[42]
Customer factorsOnline interaction propensitySocial capital theory[42]
ExtraversionNo explicit theory[45]
Self-construalSelf-construal perspective[49]
Extraversion; need for affiliationNo explicit theory[46]
Product, brand and consumer factorsProduct complexity, brand symbolism, and extraversionUses and gratifications theoryCurrent study
Table
Table 2. Demographic statistics.
Table 2. Demographic statistics.
VariablesCategoryNumbersPercentage
GenderMale 25254.5%
Female21045.5%
Age 21–3021346.1%
31–4016736.1%
>40 8217.8%
Income (RMB)<300010823.3%
3001–500016034.6%
5001–800011124.0%
>80008318.1%
EducationHigh school or below12727.5%
Undergraduate24553.0%
Post-graduate or above9019.5%
Product categoriesMobile phone 12226.4%
Mouse9921.4%
Cosmetics13128.4%
Jeans11023.8%
Table
Table 3. Measures and Items.
Table 3. Measures and Items.
ConstructsItemsSFLCronbach α
Product complexity Getting used to the product requires considerable learning effort0.9040.906
Getting used to the product takes a long time before one can fully understand its advantages0.923
Using the product requires a lot of mental effort0.795
Brand symbolismThe brand expresses who you are0.8210.888
The brand is able to communicate something about the person who uses it0.714
The brand symbolizes what kind of person uses it0.909
Informational value I can obtain useful information from the brand’ online community 0.8840.920
I think the brand’s online community is an important source for related information about the brand.0.862
I think I can find answers to my questions about the brand from the brand’ online community0.927
Social value I feel that participating in the brand’s online community provides an important source of camaraderie for members.0.9190.972
I feel that other members will offer me personal support when I join the brand’s online community0.940
I think joining in the brand’s online community helps me find a friend with a similar brand interest0.932
ExtraversionI see myself as someone who is talkative0.8960.894
I see myself as someone who is outgoing 0.897
I see myself as somebody who is enthusiastic0.888
I think I have an assertive personality0.933
Brand community engagementI am motivated to participate in the brand community’s activities because I feel better afterward.0.9260.899
I am motivated to participate in the brand community’s activities because I am able to support other members.0.850
I am motivated to participate in the brand community’s activities because I am able to reach personal goals.0.817
Note: SFL = standardized factor loadings.
Table
Table 4. Descriptive statistics of construct measures.
Table 4. Descriptive statistics of construct measures.
VariablesMeanStandard DeviationComposite ReliabilityAVE123456
1. Product complexity4.0831.4130.9070.7670.875
2. Brand symbolism 4.6060.9870.8570.6700.198 **0.818
3. Informational value 5.3011.0060.9200.7940.165 **0.2090.891
4. Social value4.6891.6230.9500.8650.331 **0.253 **0.153 **0.930
5. Extraversion4.6971.3530.9460.8170.3750.1990.1710.4860.904
6.Brand community engagement5.0011.1250.8990.7490.230 **0.494 **0.499 **0.311 **0.506 **0.865
Note: * p < 0.05, ** p < 0.01; Diagonal numbers in boldface refer to the square root of AVE (average variance extracted) values; Off-diagonal numbers are the correlation coefficient between latent constructs.
Table
Table 5. Regression models.
Table 5. Regression models.
PredictorsBrand Community Engagement
Model 1Model 2Model 3
Informational value 0.352 ***(6.661)0.321 ***(6.781)
Social value 0.158 ***(4.311)0.104 ***(4.446)
Product complexity 0.118 **(3.436)0.118 ***(4.436)
Brand symbolism 0.157 **(2.736)0.104 (0.736)
Extraversion 0.201 **(2.804)0.201 **(2.804)
Informational value × Product complexity 0.352 ***(7.832)
Social value × Brand symbolism 0.266 ***(4.431)
Informational value × Extraversion 0.137(1.053)
Social value × Extraversion 0.236 **(2.816)
Membership duration0.114 *(1.971)0.001 (0.026)0.201 **(2.804)
Gender−0.056 *(−2.081)−0.062 **(−2.873)−0.055 *(−1.985)
Age−0.018 (−0.411)−0.034 (−0.517)−0.021 (−0.388)
Income−0.004 (−0.050)−0.017 (−0.085)−0.108 (−0.187)
Education0.024 (0.027)0.132 (0.505)0.093 (0.487)
Adjusted R20.0950.1740.216
F18.336 ***15.176 ***17.392 ***
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, t value in the parentheses.

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Wang, J.; Liao, J.; Zheng, S.; Li, B. Examining Drivers of Brand Community Engagement: The Moderation of Product, Brand and Consumer Characteristics. Sustainability 2019, 11, 4672. https://doi.org/10.3390/su11174672

AMA Style

Wang J, Liao J, Zheng S, Li B. Examining Drivers of Brand Community Engagement: The Moderation of Product, Brand and Consumer Characteristics. Sustainability. 2019; 11(17):4672. https://doi.org/10.3390/su11174672

Chicago/Turabian Style

Wang, Jintang, Junyun Liao, Shiyong Zheng, and Biqing Li. 2019. "Examining Drivers of Brand Community Engagement: The Moderation of Product, Brand and Consumer Characteristics" Sustainability 11, no. 17: 4672. https://doi.org/10.3390/su11174672

APA Style

Wang, J., Liao, J., Zheng, S., & Li, B. (2019). Examining Drivers of Brand Community Engagement: The Moderation of Product, Brand and Consumer Characteristics. Sustainability, 11(17), 4672. https://doi.org/10.3390/su11174672

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