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Does Social Media Marketing and Brand Community Play the Role in Building a Sustainable Digital Business Strategy?

Department of Marketing, Feng Chia University, Taichung 40724, Taiwan
Author to whom correspondence should be addressed.
Sustainability 2020, 12(16), 6417;
Submission received: 14 July 2020 / Revised: 5 August 2020 / Accepted: 6 August 2020 / Published: 10 August 2020
(This article belongs to the Special Issue Information Systems and Digital Business Strategy)


This study investigates the effects of customer relationships on a brand’s social network website (BSN) for virtual and physical retail channels in the digital business environment. The authors also further explore the sustainable customer relationships with virtual and physical retail channels (i.e., consumer–community identification, CCI; and consumer–retailer love, C-R Love) and customer attitudinal/behavioral loyalty toward a retailer (re-purchase and word of mouth, WOM). The authors develop a framework to describe and examine the connections among customer relationships for BSN, CCI, C-R Love, and user loyalty for a retailer. Furthermore, it tests the mediating effects of virtual (i.e., CCI) and physical (i.e., C-R Love) channels on the correlation between BSN relationships and customer loyalty. The model and hypotheses in this study employ structural equation modeling with survey data. The study shows that partial customer relationships in BSNs directly or indirectly influence CCI and C-R Love, and both CCI and C-R Love positively influence re-purchase intentions and WOM communications. This study contributes a unique model for a process by which the customer relationships in BSNs can affect a sustainable retail loyalty through the virtual/physical channels. This finding can be viewed as pioneering and as setting a benchmark for future research.

1. Introduction

Consumers who share an interest in a brand often form brand communities, which influence the group’s perceptions and behaviors, including loyalty [1]. Companies can utilize the role that brand communities play in building a consumer–brand relationship [2]. With the prevalence of social network websites (SNs), people often depend on SNs to communicate; and companies have set up brand communities on various social network websites (BSNs). In fact, of Fortune 100 companies, 74% use brand pages on Facebook with nearly 94% with weekly page updates [3]. Further, in Asia, almost 90% use SNs as a platform for ecommerce, with 75% using SN strategies for longer than one year [4]. Therefore, BSNs allow companies/brands to map possible connections on SNs to disseminate information and to expand their relationships [5,6].
Previous studies have proven that BSNs are a tool for companies to build relationships as well as network connections for the promotion of the brand and marketing [7], but what kind of relationship would be provided in a brand community? The authors of [8] discussed a customer-centered model for brand communities that includes the following relationships: (1) customer–product; (2) customer–brand; (3) customer–company and (4) customer–other customers. These four customer groups serve as the essential factors when discussing or managing a brand community on a social network website.
Without a doubt, the topic of BSNs (or online brand communities) is increasingly popular in recent years as more and more retail brands with physical channels have created their own online brand communities; for example, in 2011 Walmart started a brand community on Facebook, which launched the cooperation between retailing and the social network community [9]. For retail businesses, it is essential to keep their fans in the online brand community, but also to transform fans of BSNs (online) into real customers in the physical stores (offline). Most previous studies have focused on identification with the brand community, but no researchers have looked into the role played by retailers in establishing these relationships and the influence of consumer–retailer love. Moreover, previous academic research has been mainly focused on the customer–brand relationship with an online platform, few mention the relationship between online and offline channels. To fill this research gap, this study will investigate the effect of customer relationships in BSNs on virtual and physical retail channels and will explore the influence of BSNs on customer loyalty toward the retailer. According to [10], the relationship with the brand is a concept, i.e., an intangible idea, which requires physical activities for community participation to better develop the community’s identification. For this reason, this research will consider consumer–community identification (CCI) as the customer relationship with the virtual platform (i.e., brand community on a social network website), and consumer–retailer love (C-R Love) as the customer relationship with the physical merchant.
The paper will discuss and find out the relationship between the constructs of community-based social marketing and sustainable consumer behavior, and we will begin by developing a framework that examines the connections among customer relationships in BSNs, CCI, C-R Love, and consumer loyalty. Additionally, our framework will test the mediating effects of both virtual (i.e., CCI) and physical (i.e., C-R Love) channels on the correlation between BSN relationships and customer loyalty. We employ structural equation modeling using questionnaire data. We conclude with its significance in marketing along with its implications and limitations, as well as offering suggestions for future study. The operational definition of this research was shown below in Appendix Section.

2. Literature and Hypotheses Development

2.1. Customer Relationships in BSNs

It is important for a company to generate sustainable marketing assets and demonstrate the value of this practice that can influence short-term results [11]. Social response theory shows that users can perceive media (BSNs) as if it were real. As such, the rules apply to relationships that regulate user behavioral responses to various media [12]. Social response theory suggests that users develop relationships with brand communities via SN.
According to [8], the customer–brand relationship model (C/B) comprises only the customer and the brand (Figure 1a). Ref. [13], who envisioned brand communities as part of the customer–customer–brand (C/C) triad (Figure 1b), initiated the first model of brand community. The authors defined brand communities as “a specialized, non-geographically bound community, based on a structured set of social relationships among users of a brand.” Actually, in this study as well as other related studies regarding consumer collectives [14], [15] indicated that inter-customer relationships are an important element in the loyalty equation. However, [8] emphasized social relationships among customers but they felt the relationship was incomplete. The authors used other things that were related to brand communities to depict a customer-centric model (Figure 1c).
According to [8], “a community is made up of its entities and the relationships among them” (p. 38). Thus, a BSN is made up of several entities, including brand (C/B), products (C/P), other customers (C/F), company (C/C), and SN website, which also forms the platform for the community. When a user uses a BSN to become a member and then participates in community activities through comments, shared experiences, interaction with marketers, and asking or answering questions about the brand or the product, the invisible community becomes visible. Ties are strengthened during these community interactions, as members share any experiences, useful information, or other resources [16]. As a result, it is believed that the BSNs can provide high-context interactions with these four relationships, i.e., relationships between customers and the brand [17,18], products [16,17,19], the company [17,20], and other customers [8,17,21]. Therefore, this study will consider these four customer relationships as measurements for BSNs and examine whether these customer relationships in a retailer’s BSN have an influence on virtual and/or physical retail channels.

2.2. Consumer–Community Identification (CCI)

Ref. [22] showed that social identification is defined as individual perceptions of actual or symbolic belonging to a group. Ref. [23] also showed that identification occurs when they feel, think, and behave as a member of a group, i.e., they distinguish themselves as part of the group’s identity. Using social identification, users perceive themselves with idiosyncratic characteristics that differentiate themselves and the characteristics shared with other members of the group [24].
Current studies first considered the consumer’s relationship with a brand community, i.e., consumer–community identification [25]. Self-categorization happens via consumer comparisons of their characteristics with the characteristics that define the community [26]. A positive commitment moves the process along by giving users a feeling of attachment and belonging [8,27].
Along with previous studies, this study reflects on a customer/brand relationship that is closer and is associated with increased levels of identification with the community. Ref. [8,22] indicated that a proximate customer/company relationship increases levels of identification. Lastly, similar to [8], the more proximate customer/other customer relationships are related to increased levels of identification. Thus, strengthening these relationships within a retailer’s BSN impacts customer identification. Our hypothesis based on this is as follows:
Hypothesis 1 (H1). 
(a) Customer–brand relationship, (b) customer–product relationship, (c) customer–company relationship, and (d) customer–other customers relationships positively influence consumer–community identification for a retailer’s BSNs.

2.3. Consumer–Retailer Love (C-R Love)

Retail managers tend to focus on a relationship marketing paradigm to develop consumer relationships, which was the basis for previous research [28]. The theory of goal-directed behavior proposes that desire is regarded as the psychological state of someone’s mind and leads to his/her behavioral intentions, as well as the feeling of “consumer–retailer love” in the context of online communities [28].
The term “consumer–retail love” is derived from the concept of “brand love,” which has been used in the literature [29,30,31,32]. The literature describes a phenomenon characterized by consumers forming close and affectionate relationships with a brand [33]. Early studies in this area that recognized the antecedents of consumer–firm love are [29,30]. More recently, [34] examined customer–firm love in the apparel and grocery industries. Therefore, this study utilizes this [30] concept in the services setting, which involves a retailer defining it as the degrees of emotional attachment a user has for a particular retailer.
A retailer’s BSN can help to create a consumer’s passionate emotional attachment to this retailer. When a consumer logs on to a retail BSN like Facebook and explores a brand page, shares experiences, interacts with marketers, asks questions, or answers comments, they have a growing involvement in this relationship. This relationship is characterized by meaningful experiences, valuable resources, and emotional attachments between users and brands. In other words, this relationship is strengthened by BSNs. See our next hypothesis below:
Hypothesis 2 (H2). 
(a) Customer–brand relationship, (b) customer–product relationship, (c) customer–company relationship, and (d) customer–other customers relationships positively influence consumer–retailer love.
Most previous studies have focused on identification with the brand community (e.g., [27]), but no researchers have looked into the role played by retailers in establishing these relationships and the influence of consumer–retailer love [35]. However, the subject of brand love is growing in academia [36]. Moreover, almost no one simultaneously considered identification with the virtual retail platform (i.e., BSNs) and the emotional attachment with the physical retailer, or looked at behavioral implications from an online–offline perspective. Thus, we close this gap with our study. Our proposal is that when a BSN member identifies with the brand community, they also have a passionate emotional bond to this merchant. In other words, the consumer–community identification and consumer–retailer love relationships are positive. Our next hypothesis is as follows:
Hypothesis 3 (H3). 
Consumer–community identification positively influences consumer–retailer love.

2.4. Re-Patronage and Word of Mouth (WOM)

Customer loyalty can be inferred from the attitude and behavioral intentions of the customer toward the product offered [37,38]. We used a composite approach for the measurement of loyalty, which also integrates attitudinal and behavioral dimensions [39,40]. Measuring loyalty by using a combination of behavioral dimensions (e.g., repeat patronage, repeat purchase, etc.) and attitudinal measures (e.g., commitment, positive word of mouth, etc.) was found in several studies [41,42].
Sustainable consumer behavior may be approached from different perspectives, including—among others—the policy maker’s view, the marketing view, the consumer interest focus, and the ethical focus [43]. Regarding the behavioral dimension, we adopted consumer repeat patronage intentions as our measurement of loyalty, as it is one of the important ways to examine consumer behavioral intentions for retailing. Repeat patronage intention looks at the customer’s intentions to continue with a product and/or an intention to increase the scale and scope of this relationship [42]. Meanwhile, regarding attitudinal dimension, we adopted word-of-mouth (WOM) communication as it has been confirmed by other scholars that WOM is a significant factor that influences consumer choices [44,45]. Word of mouth is when consumers’ interest in a product or service is reflected in their daily dialogues. It is free advertising triggered by customer experiences Word-of-mouth marketing can be encouraged through different publicity activities (physical or on the BSNs) set up by brands, or by having opportunities to encourage consumer-to-consumer and consumer-to-marketer communications (within communities).
A study about the online brand community [46] showed that high usage intensity along with regular contact will affect the brand relationship and increase the likelihood of WOM. If customers identify themselves with a BSN’s vision and value, then they become more interested in the growth and success of the organization. Further, consumers show positive attitudes, such as participating in word-of-mouth activities [47,48] and behaviors, e.g., re-patronage. Our next hypothesis is as follows:
Hypothesis 4 (H4). 
Consumer–community identification positively influences (a) the repeat patronage intention and (b) WOM communications.
In addition, some literature, [28,49,50] promulgated consumer–retailer love as a direct influence on consumer re-patronage intention. As such, when consumers become emotionally attached to a retailer, this results in loyal intention for the consumer towards the brand and some level of immunity from negative publicity about the brand or product [51]. Therefore, the following hypothesis is proposed:
Hypothesis 5 (H5). 
Consumer–retailer love positively influences (a) the repeat patronage intention and (b) WOM communications.
Figure 2 shows our conceptual model, which integrates hypothesized relationships.

3. Research Methods

3.1. Sample and Data Collection

The data were collected from BSN members. For BSNs, Facebook (FB) is popular and has attracted over 2 billion monthly active users (as of the first quarter of 2018) since February 2004 [52,53]. There were over 18 million members in Taiwan (as of the third quarter of 2017) [9,54], which makes Facebook the top social networking site in terms of number of members [55]. Therefore, this study used Facebook brand pages for the empirical investigation. Taiwan FB communities have four forms: public celebrity, individual sharing, online game/app, and company/brand communities. This study investigates the effect of customer relationships within BSNs on virtual and physical retail channels; thus, only brand communities for physical retail companies were considered in this study.
According to Taiwan Facebook Statistics (2018) [56], the ten leading retail brand communities as of 2014 were S3beauty, 7-Eleven, PChome Personal store, Starbucks Taiwan, FamilyMart, 86shop, Lativ Taiwan, Momo online store, McDonald’s, and MUJI. Using these brands, we looked for clear and complete retail branding development, based on Dawson’s three-level hierarchy model of retail: company brand, store brand, and product brand [57]. Market Intelligence & Consulting Institute (MIC) [58] stated that Taiwanese online shopping habits continued to deepen, and the proportion of online shopping consumption increased to 16.5% in 2018. In addition, the monthly online shopping amount has also increased from CNY 1807 in 2014 to CNY 2207 in 2018, a growth rate of 22.6%.
According to the MIC survey [58] in 2019, the top three online shopping platforms that netizens loved to use were PChome 24-h online shop (45.3%), Shopee 24-h (38.7%), and Momo shopping network (37.1%); the top three favorite shop platforms were Shopee Mall/Auction (49.4%), PChome Mall (40.6%), and Yahoo Super Mall (32.4%). The overall ranking has not changed much compared with the previous year, so the authors of this research chose PChome 24-h as one BSN case.
Furthermore, according to [59], Uni-President Enterprises Corporation (No. 844) engages in manufacturing, processing, and sales in the food business. It operates through the following business segments: Food Group, Instant Food Group, Dairy and Beverage Group, General Food Group, Baking Business Group, Technology Group, and others. 7-Eleven, Starbucks Taiwan, and MUJI were chosen by the authors in the enterprises; [60] indicated Starbucks Taiwan and 7-Eleven were No. 1 and No. 2 in the top 10 most competitive cross-industry companies in 2017, and MUJI was No. 8 on this list. FamilyMart convenience store was a competitor of 7-Eleven and in second place in Taiwan’s convenience store market. Reports [61,62] mentioned that FamilyMart competed with 7-Eleven for the first place, and even stated that during the coronavirus (COVID-19) pandemic, which was 7-Eleven’s profitability recession, FamilyMart’s revenue, profit, and EPS (Earnings Per Share) hit record highs. For this reason, the authors also chose FamilyMart be the case BSN. Finally, McDonald’s was the consumer favorite fast-food restaurant worldwide. McDonald’s has pecked its way to the top of the fast-food industry, hitting nearly $40 billion in sales in 2020, according to a list of the top-selling fast-food chains in America.
Finally, six well-known brand retail communities were chosen: PChome 24-h (No. 17) online shop, 7-Eleven (No. 5), Starbucks Taiwan (No. 20), MUJI (No. 35), FamilyMart (No. 42), and McDonald (No. 26), which were also on the list of the most influential brands in the Brand Asia Asian Influential Brand Survey [63]. Since those are well-known brands, the authors adopted convenience sampling for the data collection method. One of the most important aspects of convenience sampling is its cost-effectiveness [64]. Since most convenience sampling is collected from the populations on hand, the data are readily available for the researcher to collect [65].
For each community, we created a special link to an online survey, which recorded the source of each response. In addition, the questionnaire was modified to display community titles that matched to target respondents. These questionnaire links were then posted for each online community. We asked those who completed the questionnaire to share it with friends in that community. Participants were asked to think about the specific brand community while responding to the questionnaire. Confidentiality and anonymity for all participants was guaranteed. For a good response rate and to reduce non-sampling bias, according to [66], we used self-administered questionnaires that came with a cover letter explaining how to complete the questionnaire. Ref. [67] indicated that a sample size of 150–200 is necessary to get reliable coefficient values with partial least squares (PLS) analysis. A total of 18 variables were to be used in Structural Equation Modelling (SEM), which required a minimum sample size of at least 180. When data collection ended, 350 questionnaires were collected with 330 as valid for the hypotheses analysis.

3.2. Measurement of Variables

The questionnaire was created by adapting measurements from various studies. The customer–brand relationship used three items adapted from [16,18] and the customer–product relationship used three items adapted from [16,19]. To measure the customer–company relationship, we used a three-item scale proposed by [16,20]; and to measure customer–other fans relationships, we used a three-item scale proposed by [16]. To measure consumer–community identification, we used a three-item scale proposed by [26,37]; and to measure consumer–retailer love, we used a three-item scale proposed by [35]. The construct of repeat patronage intention used three items adapted from [42]. Word-of-mouth construct measures used three items adapted from [46,48]. All items that were used to assess the constructs used specific five-point Likert scales, which indicated the level of agreement or disagreement with an item. Table 1 presents the items for each construct and measurement scales.

3.3. Data Analysis

We used PLS to test our hypotheses and to analyze our data. The PLS algorithm lets indicators vary by how much they contributed to a composite score for a latent variable, rather than assuming equal weights for all indicators on a scale [47,67]. Ref. [68] indicated that PLS path modeling is used in marketing and business studies, which necessitates estimation for factor loadings for the measurement model and the path coefficients. We used PLS rather than other SEM methods (i.e., LISREL or AMOS) because the PLS approach does not restrict sample size [67].

4. Results

4.1. Demographic Profile of Respondents

A total of the 330 respondents were as follows: 44% were male and 56% were female; 72% were 20–29 and 18% were under 20 years old, followed by those aged 30–39 years (5%), 40–49 (3%), and 50 and above (2%). Most respondents (30%) were members of the 7-Eleven brand community, followed by Starbucks Taiwan (21%), McDonald’s (19%), PChome Personal store (18%), MUJI (6%), and FamilyMart (6%).

4.2. Measurement Model

This study utilized a two-step approach described by [69]. First, reliability and convergent validity were assessed, and second, discriminant validity (Table 1). For reliability, Cronbach’s alpha showed all constructs had a value greater than 0.6 (adapted by [70]). We tested for convergent validity with composite reliability, factor loadings, and average variance extracted (AVE). Acceptable measures for individual item loadings were greater than 0.7, composite reliability greater than 0.7, and AVE greater than 0.5.
To better examine discriminant validity, we used the [71] criterion, which holds that the average variance shared between each construct and its measures needs to be greater than the variance shared between the constructs. Table 2 shows the correlations for each construct are less than the square root of AVE, which indicates an adequate discriminant validity for all constructs.

4.3. Structural Model

Our structural model was evaluated by looking at the R2 values. Figure 3 shows that the R2 values ranged from 0.370–0.540, suggesting that the modeled variables explained 37.0–54.0% of the variance for the dependent variables.
Figure 3 shows the relationship of customer–retail product (H1b, β = 0.290, p < 0.01) and customer–other fans (H1d, β = 0.429, p < 0.01), which had a significant and positive association with consumer–community identification, whereas the customer–retail brand (H1a) and customer–retail company (H1c) relationship indicated no significant association with consumer–community identification. Thus, we partially support H1. Further, the customer–retail brand relationship had a significant and positive association with consumer–retailer love (H2a, β = 0.284, p < 0.05), while the relationship of customer–retail product (H2b), customer–retail company (H2c), and customer–other fans (H2d) all had no significant association with consumer–retailer love. Thus, we partially support H2. Moreover, consumer–community identification had a positive significant association with consumer–retailer love (H3, β = 0.312, p < 0.01). The path coefficients from consumer–community identification was a significant and positive association with re-patronage intention (H4a, β = 0.354, p < 0.01) and WOM communication (H4b, β = 0.463, p < 0.01). In addition, the path coefficients from consumer–retailer love were a significant and positive association with re-patronage intention (H5a, β = 0.337, p < 0.01) and WOM communication (H5b, β = 0.237, p < 0.05). Therefore, we fully support H4 and H5.
Regarding the effects of mediation, there is a hypothesized chain of relations between C/P and C-R Love such that C/P first causes a change in variable CCI that then causes a change in C-R Love; and also the relations between C/F and C-R Love such that C/F first causes a change in CCI that then causes a change in C-R Love. These paths explain how C/P, C/F affect C-R Love by elaborating the relation to be C/P; C/F to CCI to C-R Love. The mediator CCI is selected for change based on theory and prior empirical research [72]. The literatures [73,74] on how including additional variables in statistical analyses can be beneficial is extensive. This idea can be traced back to the foundations of path analysis when it was suggested that researchers should analyze complex or indirect relations among variables, rather than analyzing only direct influences. In the results of empirical analysis shown in Figure 3, in this model the path C/P to CCI to C-R Love; C/F to CCI to C-R Love represent the mediation effects, the multiplication of the parameter estimates gives estimates of the mediation effect [75]. However, only C/B to C-R Love is the direct effect.
Table 3 summarizes 13 paths that were examined in our structural model.

5. Discussion and Conclusions

We investigated the effect of customer relationships for BSNs on virtual and physical retail channels, and further explored the customer relationships with the virtual and physical retail channels (i.e., CCI and C-R Love) and the customer attitudinal/behavioral loyalty toward the retailer (i.e., re-purchase and WOM). The study shows that partial customer relationships in BSNs directly or indirectly associate with CCI and C-R Love, and both CCI and C-R Love positively associate with re-purchase intentions and WOM communications. Our results make an important contribution with implications for academics and marketers.

5.1. Discussion of the Results

There were few studies on this issue that considered the application of [8] four relationships of brand community. This study proposed a unique model by which customer relationships on BSNs were associated with retail loyalty through virtual/physical channels. These results are consistent in certain ways with other studies that have found that the relationships with online brand communities positively influenced loyalty [15,16,49]. Ref. [8] showed that some scholars (e.g., [76]) believe that, by proactively developing content for customer interactions, marketers would be cultivating customer relationships to strengthen the brand community. Four customer relationships within BSNs were observed in this model and two of these relationships (with product(s) and with other fans) were positive and significant for consumer–community identification (H1b and H1d are supported).
This study shows that the provision of product information and community member interactions are effective in maintaining the consumer–community relationship and create consumers’ identifications with the brand community. However, the relationship of C/B toward CCI (H1a) and C/C (H1c) toward CCI are not supported; this is also known as a brand relationship, which is the relationship that consumers, think, feel, and have with a product or company brand [77,78]. The result shows that the customer–brand and customer–company relationships were less associated with CCI in the context of the customer–community relationship. However, the customer– brand (C/B) directly and positively affects the consumer–retailer love (C-R Love) (H2a is supported). In relation to the non-supported results of H2b, H2c, and H2d, they could be more effective if the model can add a mediator other than CCI, such as the “experience marketing events.” A consumer relationship (C/P, C/C and C/F) such as an experience of the product, a visit to the company, and a physical interaction with the other fans may directly affect the consumer–retailer love. This result is pioneering and, in some ways, demonstrates that the primary use for BSNs is user-generated content. Furthermore, we have confirmed that the main reason for the online brand community is to bring users together and facilitate interactions among them, which is similar to [15,79].
This study reveals the connection between customer relationships in BSNs and customer relationships with the physical retail channels (i.e., consumer–retailer love). Studies that explore the customer relationship with both an online community and offline retailer are not common in the literature, but it is currently a very important issue for retailing to know the online-to-offline relationship. The finding indicates that (1) the customer–brand relationship in BSNs has a direct association with positive effect on C-R Love (H2a is supported); (2) there is a significantly positive effect on the online–offline relationship (H3 is supported); (3) CCI plays a mediating role between customer relationships in BSNs and the C-R Love. This is particularly true for the relationships of customer–product and customer–other fans; therefore, those two customer relationships have an indirect association with positive effect on C-R Love. Our study is consistent with [80,81], because it investigated both behavioral intentions. We found that the positive perception of corporate social responsibility and environmental efforts gives consumers a positive attitude and emotional attachment toward the retailer, and engendered positive behavioral intentions for the product and brand.
Finally, very few studies have addressed the role of CCI (as the customer relationship with online) and C-R Love (as the customer relationship with offline) simultaneously as antecedents of both consumer attitudinal and behavioral loyalty toward retail brands and branding, shown by WOM communications and re-patronage intentions. As the path analysis result from H2a (C/B→C-R Love) to H5a (C-R Love→ ReP), and H2a (C/B→C-R Love) to H5b (C-R Love→ WOM), consumer–retail love is a motivational state of mind leading to behavior [82], which is a mediator that is influenced by the customer–brand relationship and directly affects re-patronage intention and WOM communication. The finding points out that whether customer relationships are with the online (CCI) or offline (C-R Love) retail channels, they both positively affect consumers’ attitudinal and behavioral loyalty (C-R Love and WOM) toward retail brands (H4 and H5 are both supported). Our findings have some consistencies in some ways with other studies, which found that relationships for CCI and C-R Love [35,82] positively influenced loyalty.

5.2. Conclusions

Our findings have practical implications for retail and consumer behavioral practices. It is apparent that two of the customer relationships for BSNs have the potential to exert a significant positive effect on consumer–community identifications. The relationships of customer–brand and customer–company have no significant association with CCI in this study, which does not mean these two BSN relationships are useless. It probably shows that retail brand managers in Taiwan try to create customer relationships in BSNs, e.g., Facebook brand community, mainly through providing retail product information and member interactions, but offer less information and fewer interactions for retail branding and their companies. Therefore, the suggestion to retail brand players is that they should try to build customer–brand and customer–company relationships to strengthen BSN relationships.
Second, people all know that online-to-offline (O2O) operation is a developing trend in the retail industry, but how does it work? Our study offers new insights for retail practitioners to enhance online-to-offline relationships as follows: (1) retail managers should improve a BSN customer relationship with the brand, e.g., strengthening and communicating to customers the information, value, and image of this retail brand, which will lead to positive customer relationships with the physical retail store (C-R Love); (2) retail managers can enhance customers’ emotional attachments toward the physical retailer by building clear and consistent brand community identification with their consumers; (3) improving BSN customer relationships with retail products and other fans can help retail managers to build the consumer–retailer love, but it should be through a clear and consistent brand community identification for consumers.
Finally, this study proves the consumers’ attitudinal and behavioral intentions toward online-to-offline for retail O2O operators. This suggests to retail operators that well-established customer relationships with online (CCI) or offline (C-R Love) retail channels help to increase consumers’ WOM communications and repeat patronage intentions toward the retail store. Therefore, in conclusion, if you, as a physical retail manager, want to join this online-to-offline trend in your retail business, this study provides the following approaches: (1) to maintain a positive BSN relationship through developing the customer relationship with the retail brand, product, and other fans; (2) to create a strong community identification and retailer relationship with consumers; and (3) to develop positive BSN relationships with solid CCI and C-R Love, leading to supportive attitudes and behavior toward the retailer.

5.3. Limitations and Future Research

The limitations of this study are as follows. By addressing our study’s limitations, we can suggest a path for future research. We studied only the Taiwan market, which restricted our findings. Recent research suggests that cognitive, emotional, and behavioral responses will be different across different cultures [83]. Ref. [84] indicated that the cultural dimensions, i.e., Taiwan’s culture, are characterized as collective rather than individualistic. Future research should explore the role of perception and behavior of consumer responses to BSNs across various cultures.
Next, we discussed the online–offline relationship by identifying consumer–community identification (CCI) as the customer relationship with the virtual platform (i.e., brand community on a social network website), and consumer–retailer love (C-R Love) as the customer relationship with the physical retailer. Other multichannel relational variables can also be considered in the O2O relationships. In addition, this study adopted CCI and C-R Love as antecedents of retail loyalty. Other variables, e.g., consumer commitment and satisfaction, can be looked at as antecedents.
Finally, because most of the respondents were young adults (under 30 years old) in the study, the sample might have been biased towards “youth-oriented” customers with a modern view of BSN value. Hence, it may not be possible to generalize their responses to the population at large. However, there was evidence from previous research, e.g., [9,52,53,54,85], supporting that most members on Facebook in Taiwan were 18–34 years old (78.1%). Finally, we examined a specific form of BSN, i.e., Facebook, so our results cannot be used for another online platform. Future research needs explore retail branding and online communities in terms of different types of social media.

Author Contributions

Data curation, C.-W.H.; Investigation, C.-W.H. and Y.-B.W.; Methodology, C.-W.H. and Y.-B.W.; Project administration, Y.-B.W. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.


In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Operational definitions.
Table A1. Operational definitions.
VariablesOperational Definition
BSN customer relationships
BSNs can provide high-context interactions with these four relationships, i.e., relationships such as
customer–brand relationship
customer–product relationship
customer–company relationship
customer–other customers relationships
Consumer–retailer love
(C-R Love)
The term “consumer–retail love” is derived from the concept of “brand love,” which has been used in the literature. The literature describes a phenomenon characterized by consumers forming close and affectionate relationships with a brand. Early studies in this area that recognized the antecedents of consumer–firm love are. More recently, [34] examined consumer–firm love in the apparel and grocery industries. Therefore, this study utilizes this concept in the services setting, which involves a retailer defining it as degrees of emotional attachment a user has for a particular retailer.
Consumer–community identification
(CCI) [84,85]
The concept of CCI is built on social identity theory, where consumers are viewed to be motivated to enhance their self-identity by identifying with specific social groups, including virtual brand communities.
Brand social network website
Social networking websites allow individuals, businesses and other organizations to interact with one another and build relationships and communities online. When companies join these social channels, consumers can interact with them directly. That interaction can be more personal to users than traditional methods of outbound marketing and advertising.
The competitive retailing market compels brands to strive for customer satisfaction and retention. Customer satisfaction increases the likelihood of strengthening customer loyalty and re-patronage intentions. Re-patronage intention is a consumer’s desire to make repeat purchases, positive shopping intentions, and repeat patronage.
Word of mouth (WOM)[92]Word of mouth is when consumers’ interest in a company’s product or service is reflected in their daily dialogues. Essentially, it is free advertising triggered by customer experiences—and usually, something that goes beyond what they expected.
Note. Organized by this research.


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Figure 1. (ac): Key relationships of brand community.
Figure 1. (ac): Key relationships of brand community.
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Figure 2. The conceptual model. Note. (CR=C-R Love)
Figure 2. The conceptual model. Note. (CR=C-R Love)
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Figure 3. Structural model analysis results. Note: ** p < 0.05; *** p < 0.01. (CR=C-R Love)
Figure 3. Structural model analysis results. Note: ** p < 0.05; *** p < 0.01. (CR=C-R Love)
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Table 1. Constructs and their measurement items.
Table 1. Constructs and their measurement items.
ConstructMeasurement ItemsLoading αComposite Reliability Average Ariance Extracted
Customer–Retail Brand Relationship
X is accounted an important position in my mind.0.8580.750.860.67
The message delivered from X is consistent with my value.0.810
I have certain degree of knowledge about X.0.779
Customer–Retail Product Relationship
I am proud of X’s product.0.8270.780.870.69
X’s product is important to me.0.877
I would gather the information related to X’s product.0.788
Customer–Retail Company Relationship
I believe X’s company.0.8950.880.920.80
X’s company understands my needs.0.880
X’s company cares about my opinions.0.909
Customer–Other Fans Relationship
I would share the relevant information with other fans in X’s community.0.8630.900.940.84
I have met wonderful people because of X’s community.0.942
I have a feeling of kinship with other fans in X’s community.0.935
Consumer–Community Identification
If X’s fans planned something, I’d think it as something “we” would do rather than something “they” would do.0.8860.880.920.80
I am very attached to X’s community.0.907
I am proud of being a fan at X’s community. 0.896
Consumer–Retailer Love
(C-R Love)
I always enjoy shopping at X’s store. 0.8360.680.820.61
X is my favorite retail store that I can count on.0.742
X is “my” store.0.759
Re-Patronage Intention
I will definitely keep shopping at X’s store.0.7640.710.830.63
If there is another retailer as good as X, I prefer to shop at X’s store next time.0.777
I shall continue considering X as my main retail store in the next few years.0.835
WOM Communication
I will recommend X to my relatives and friends.0.8640.820.890.74
I will give positive comments about X if someone asks me information.0.866
I often tell others about X.0.844
Table 2. Correlation matrix.
Table 2. Correlation matrix.
C-R Love3.6791.020.530.480.440.380.550.78
Note: Diagonals represent the square root of the average variance extracted while the other entries represent the correlations.
Table 3. Results.
Table 3. Results.
Hypothesized RelationshipCoefficientT-ValueSupported
H1bC/P→CCI0.290 ***2.743Yes
H1dC/F→CCI0.429 ***5.863Yes
H2aC/B→C-R Love0.284 **2.148Yes
H2bC/P→C-R Love0.0180.126No
H2cC/C→C-R Love0.0810.654No
H2dC/F→C-R Love0.0730.680No
H3CCI→C-R Love0.312 ***2.601Yes
H4aCCI→ReP0.354 ***3.457Yes
H4bCCI→WOM0.463 ***5.245Yes
H5aC-R Love→ReP0.337 ***3.176Yes
H5bC-R Love→WOM0.237 **2.215Yes
Note: ** p < 0.05; *** p < 0.01.

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Ho, C.-W.; Wang, Y.-B. Does Social Media Marketing and Brand Community Play the Role in Building a Sustainable Digital Business Strategy? Sustainability 2020, 12, 6417.

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