Home / Peer Reviewed Scholarship / Virtual Brand Communities: Engagement Profiles and Typology
Virtual Brand Communities, Source: Piqsels

Virtual Brand Communities: Engagement Profiles and Typology

Abstract

Within the prevalent digital communication era, users have utilized digital connectivity to form social aggregations and virtual communities (VC) with unique features.  Recently, millions of internet users have joined one or more brand virtual communities to serve their communication, knowledge-seeking, entertainment, and self-fulfillment needs. Virtual communities represent different contexts, objectives, and technical configurations catering to diverse consumption patterns. This article is the first to present virtual communities’ typology focusing on passion, professional, social, and commercial brand communities. This article also explores a comparative framework for brand communities’ engagement profiles in the four types of brand communities. Moreover, it outlines recommendations for future research relating to VC’s engagement in behavioral studies.

Introduction

The internet has revolutionized communication across the globe. The technological advances in the present internet-enhanced economy have unlocked unlimited potential for organizations to develop themselves. Accordingly, they face enormous pressure to integrate such technologies to maintain an adequate market space. In a consumer-oriented culture, brands have become an essential theme for social interaction. With the help of technology, consumers have developed utilities, knowledge, and proficiencies in consuming brands within a virtual social context. Brand managers nurture brand-centered virtual relationships with customers by harnessing the power of integrated and interactive communication across digital channels. Marketers are increasingly exploiting brands to create lasting relationships with customers. Contemporary studies have confirmed brand communities’ efficacy in accomplishing such a purpose (Carlson et al. 2008).

Information and Communication Technologies (ICT) have made it easier for people to create communities and reach out to others with similar interests. Virtual Communities (VC), also known as online communities, provide a forum for sharing knowledge, emotions, and thoughts with other members. Consequently, they became indispensable to their users (Varik and Oostendorp 2013). Virtual communities–particularly communities of practice–pioneer methods to conjoin functional knowledge and innovation (Meret et al. 2020). These communities provide the framework for a more comfortable transfer of information and experiences while empowering prompt responses to customers’ requests (Nguyen et al. 2020). Despite virtual communities’ success and wide usage, there is still a lack of reliability as communities differ in context, objective, and technical configuration for consumption (Gallagher and Savage 2013).

VC research is currently at an exploratory, mounting, and vibrant stage (Malinen 2015). While virtual communities have been reviewed in several contexts, a cohesive review to synthesize the results is absent. In their seminal work, Baldus et al. (2015) delineated the domain of engagement in VC, developed 11 dimensions, and validated a comprehensive scale to profile users’ motivations of different VC.  In the future directions for research, Baldus et al. (2015, 985) propose that “future research should work to categorize the diverse sub-types of brand communities […] to understand better how stable these motivational profiles are across different brands and types of communities.”

This article attempts to comprehend the collective framework of four main types of brand communities: passion-driven, professional, social, and commercial brand communities. The article also emphasizes motivational stimuli associated with user engagement as a criterion for virtual brand community participation. The article starts with a brief review of pertinent research, discusses the adaptation of existing theories in the context of virtual communities, and presents theoretical implications associated with VC’s consumption profiles. The authors present their interpretation and statistical analysis of eleven proposed consumer engagement dimensions. Finally, they propose future directions for research and implications for marketing professionals relating to VC’s engagement profiles.

Literature Review

Virtual Communities

Howard Rheingold presented the first understanding of what constitutes a virtual community. He posited that online communities are “social aggregations that appear from the internet, when enough people carry on public discussions for a sufficient amount of time, with enough human feeling” (Rheingold 1993, 6). The definition adopted in this article is Leimeister, Sidiras, and Krcmar’s (2006, 279): “a VC consists of people who interact together socially on [an online] platform. The community is built on a common interest, a common problem, or a common task of its members that is pursued [based on] implicit and explicit codes of behavior.” This definition combines more than one aspect of analysis, the most important of which are social and technological traits. Operating via an electronic intermediate allows for effortless interactivity and trust accumulation among community members. As for the social aspect, this definition tackles the reasons behind users’ engagement and motivational factors. According to this definition, VC are considered disparate from static websites, where the administrator controls most of the content without contribution or challenge from the users. VC’s collaborative features allow users to impact the content and utilize such power to achieve objectives or seek answers (Arguello et al. 2006).

Most researchers report that virtual communities offer environments and utilities less comparable to face-to-face communities (Francis et al. 2019). However, Wellman et al. (1996) argued that VC deliver better functions than those offered by face-to-face communities, particularly in creating social relationships. Also, Fox and Roberts (1999) suggested that VCs should be the extension of natural communities instead of a complete replacement. The engagement stimuli behind VC behaviors are of significant interest to researchers; Bagozzi and Dholakia (2002) showed that motivations to contribute to a virtual community are determined conjointly by individual determinants and social identities.

Contrary to other online websites, virtual communities are critical because they are self-sustaining social systems in which members engage and connect (Fachrunnisa and Hussain 2020). Their members’ sense of belonging is one of the unique elements of VC. In an attempt to investigate users’ association with a social group, Blanchard and Markus (2002, 2004) outlined collective emotional bonds and a sense of belonging to a social group as a distinctive feature of virtual communities. In 1986, Chavis, McMillan, and Wandersman coined the term Sense of Virtual Community (SOVC). They originally outlined SOVC as the feeling members have of belonging to a community, with collective confidence that loyalty to the community satisfies their needs.

A user’s virtual participation encompasses all activities conducted on a given platform or for the community to acquire and distribute knowledge and experiences (Zhang, Zhu, and Wang 2019).  SOVC reflects on the emotion stemming from a VC consumption experience. Roberts et al. (2002) conducted a qualitative study to examine SOVC. Results showed that despite the variation in VC attachment compared to face-to-face communities, online users experienced a sense of community. The Social Information Processing (SIP) model offered an interpretation of such analysis. SIP Model claims that ICT platforms allow for fewer personal cues when compared to face-to-face communication. The expected relationship expansion process is equal in offline and online communication (Walther 1996). Using an online medium will require more time to reach the proper accumulation of emotional cues (Blanchard 2007).

Reflecting on behavioral cues, Social Network Theory claims that human behavior is implanted in a system of interpersonal associations (Granovetter 1985). The existing literature assures that social networking sites (SNS) significantly impact their users’ behavior. Such comprehension becomes imperative as the number of SNS users and their time on SNS continue to escalate. In other words, the world as we know it today is reshaping itself to develop as a society of overlapping networks; the robust progression of SNS in developing countries is proof of the trend’s magnitude.

Virtual Communities Typology

Existing literature is rich with VC categories, including discussion forums, bulletin boards, communities of practice, enterprise communities, communities of transaction, Social Networks Sites (SNS), wikis, creative communities including open-source software development, and question-answer websites (Malinen 2015). VC’s scopes vary from widespread sites with millions of user capacities to small-numbered communities with as few as ten members.  To explore how different VC utilize technical platforms, Muller et al. (2012) studied more than 180 online enterprise communities, focusing on their participation percentages, relationships, and knowledge allocation. Results concluded that even when offering similar tools, administrative teams and users innovate existing technologies to attain distinct objectives. Studies have also established that the most significant role of a virtual community’s triviality has nothing to do with the technological tools offered, affirming that users may reach satisfaction with minimum usability criteria (Malinen 2015).

In 2004, Li offered an integrative review tackling VC starting the year 1996 to 2004.  Li postulated that most researchers used two main categories to define VC: one focusing on the metaphysical properties and one that concentrates on VC’s practical forms. Metaphysical and practical categories offer another understanding for practitioner and academic fields of study. Kozinets (2002, 62) introduced a novel ethnographic approach to studying online culture and social aggregation known as the “Netnography”. He posited a different viewpoint of virtual communities. The primary group focus and social structure are two central dimensions. The highest point in group engagement/focus is social communication, while the lowest point of group engagement/focus is information exchange.

Computer-Mediated Communication and Social Networking Sites

With the advent of web blogs (weblogs) in the late 1990s, an innovative generation emerged under social media (also known as new media) offering a new framework for constructing a community. A community’s concept is related to perception rather than grounded in location and metaphors of the surrounding municipal (Elshahed 2014).  Social media and the evolving ICT act as pioneering forums for collaboration. They represent a platform where users may find a common place to share ideas, knowledge, and experiences. As users engage in the interface, a shared understanding can develop, creating a personal connotation and strategic relevance (Charlotte et al. 2009).

Social media comes in various forms: Text, used mainly in weblogs, also known as blogs; Microblogging, where the content is delivered in short bursts of information, for example, Twitter; Audio forms, Wikis, which allow you to create, edit, and share information about a subject or topic. Social networking sites (SNS) are all websites that allow you to create a personal profile, chat, discuss, and share information with others. For example, Facebook and WhatsApp allow photo and video sharing, news aggregation, social bookmarking, and free messaging and calling via cell phones, with more than 1 billion users worldwide (WhatsApp 2017).

Boyd and Ellison (2007, 211) defined SNS as “web-based services that allow individuals to construct a public or semi-public profile within a bounded system, articulate a list of other users with whom they share a connection, and navigate their list of connections and those made by others within the system.” Since their introduction, social network sites have attracted millions of users who integrated these sites into their daily practices. Most sites support the maintenance of pre-existing social networks. In contrast, others help strangers connect based on shared interests, political views, activities, beliefs, and consumption habits (Mackenzie 2008 as cited in Elshahed 2014).

SNS are usually used to send messages, photos, and videos to “friends” with access to someone’s SNS profile page. These friends may then interact with each other, creating and expanding existing social ties. SNS share similarities like offering news, various viewpoints, promoting discussion, and a sense of community. However, they differ in structure, purposes, and levels of interactivity (Kaye 2010). VC provide consumer-to-consumer support for consumption, featuring high consumer knowledge, thus influencing users’ behavior; consumers may interact and share information in various forms (Zaglia 2013).

Social media is a significant turning point in consumer–brand relationship building (Morgan-Thomas and Veloutsou 2013). It has become more commonly used by brands as an Integrated Marketing Communication (IMC) tool that connects and establishes strong brand relationships with customers (Ramadan 2017). As social media technologies develop, brand management can create brand communities with less time and financial resources. Nevertheless, choosing the proper brand community type, cultivating consumers’ interaction, and remaining up-to-date with this collective commitment are critical factors to yield effective brand outcomes. With various interactive channels, marketers endeavor to accommodate such evolution. For example, Dell created ‘IdeaStorm’, a website that enables customers to share ideas with the company. During the first years, IdeaStorm passed the 10,000-idea mark and executed nearly 400 ideas. Similarly, Lego Mindstorms and MyStarbuckidea.com utilize consumer feedback to inform organizations of possible consumer trends (Hudson et al., 2012).

Brand Communities

In 1996, Hoffman and Novak constructed the primary conceptual fundamentals for marketing practice associated with information technology and computer-mediated environs. They introduced marketing professionals to revolutionary alterations that might take place within the organization. Furthermore, they offered pioneering means of interacting with customers through internet deployment (as cited in de Valck et al. 2009).  The advent of brand communities has overlapped with the rise of consumer empowerment. Currently, brand communities are effective as platforms for brand trust and loyalty cultivation.

Social scientists use a brand community to label “like-minded consumers who identify with a particular brand and share significant traits” (Kalman 2009). Scholars O’Guinn and Muniz depict brand communities as “shared consciousness, rituals, traditions, and obligation to society” (Habibi et al 2014, 124). Moreover, Muniz and O’Guinn (2001, 412) offered another interpretation for the brand community, explaining that it entails a “specialized, non-geographically bound community, based on a structured set of social relations among admirers of a brand.”  Examples of a brand community include Harley-Davidson riders, Apple Computer enthusiasts, and Starbucks customers.

Several essential factors are required to achieve a successful level of brand community engagement. The most crucial factor of all is the existence of a brand that differentiates its consumers and offers a way through which they may engage in a brand-related shared experience. Moreover, many international brands (like Nike) foster higher-level connections and cultivate loyalty by building situations and sharing experiences that add to their self-esteem traits. Successful global brands tackle customer’s rational and functional needs. Also, they present additional tools for a learning experience or an opportunity to support other members of the brand community (Hudson et al. 2012). Businesses as big as Starbucks and Amazon have exhibited the foundation of a successful brand with minimum mass advertising by focusing on offering a unique value proposition. Although brand communities differ in purpose, they all represent a direct marketing investment on behalf of the organization to maintain and improve long-term relationships with current and potential consumers (Baldus et al. 2014).

To construct a brand identity system, brands provide self-expressive rewards by offering a channel for people to deliver their self-image (Aaker 1996). For example, on Facebook specifically, when a member “likes” a brand, that brand appears on the member’s profile page and becomes part of the consumer’s identity (Wallace et al. 2012). Brand liking is considered an essential enhancer of consumer-brand relationships and a vital measurement tool based on consumers’ perceptions of the brand (Aaker 1991). It is also a demonstration of a brand’s strength against competing brands (Ramadan 2017). Brand liking is a marketing tool that seeks to develop positive consumer attitudes based on the belief that the brand cares about them as individuals (Boutie 1994). Brand liking focuses on the members’ characteristics and aids to construct a brand personality responsible for emotion exchange similar to friendship roles in physical communities, such as trust.

Trust is essential to any relationship’s quality in a given social context, with exceptional significance in a business-to-business setting (Palmatier et al. 2006, 2008). Rousseau et al. (1998, 395) define trust as “a psychological state comprising the intention to accept vulnerability based on positive expectations of the intentions or behaviors of another.” It is a powerful marketing tool available for companies to harness. In an online platform, the engagement process builds on trust, including feelings of confidence and passion towards the brand.

In 2004, Kevin Roberts introduced his book ‘Lovemarks’, which expands on emotional attachment to services and products. Roberts reasons that a successful and effective marketing strategy will propagate a sincere, emotive bond, a lovemark, from the consumers towards the product or services at some point in time. Examples of lovemarks vary from Nike, Starbucks, Lego, and McDonald’s, to Cambridge University. Building on lovemark development, we can evaluate any product or service using an axis of love/respect dimensions where goods and services with low love and low respect are commodities. Goods and services with low respect and high love ratings are trends, and those with high respect and low love rating are brands, but a product that is high in both love and respect ratings is a Lovemark (Roberts 2004).

Brand Communities and Social Networking Conversion

Brands relentlessly pursue means to utilize social media to leverage the largest possible number of consumers. Approximately 83 percent of Fortune 500 companies use social media to create a bond with their consumers in the marketplace (Naylor et al. 2012). Also, the practice of consumer engagement has emerged as a subject of significance to executive personnel, having the objective of company performance’s enhancement at sight.

Facebook is currently the most prominent social networking site. Activated in February 2004, the Facebook community is now estimated at around 2 billion users worldwide, with a mission to “Give people the power to build community and bring the world closer together” (Facebook 2017). Virtual communities found on Facebook have become the most prominent channels for companies among social media tools. According to Facebook, in 2012, more than four million companies have their brand pages on the social network (recent figures indicate above 15 million) (Simon et al. 2016). A recent study has found that active brand fans on Facebook spend around 43 percent more money on the focal brand than non-fans (Syncapse 2013).

Facebook active brand community pages can involve consumers in interactive doings in various methods. The Facebook engagement force enables organizations to create their brand-related fan page, where members can post, comment, share information, and interact with other members worldwide. When consumers execute such activities, a bond develops, and ordinary members with sufficient time turn into engaged fans. When members like a brand-related post or share a story related to such a brand, the post will appear on the consumer’s profile page guaranteeing exposure to the consumers’ friend list. Consequently, their friends’ list creates a snowball effect serving brand exposure (Wallace et al. 2012 in Kabadayi and Price 2014).

Barnard and Knapp (2011) proposed that “likes” on Facebook pages help escalate brand awareness and engagement levels, contributing to sales and investment. Engaged users will search for products related to the brand itself and are more likely to maintain their satisfaction and continue using brand-related products (Cheng et al. 2020).

Commenting behavior grants users the opportunity to convey positive or negative opinions concerning the content found on the brand’s Facebook page, regardless of whether this page is created by the brand’s administration body or regular Facebook members. Behaviors like “liking” and “commenting” allow Facebook users to show affection and empathy towards their brand on their personal profiles (Wallace et al. 2012). “Through this functionality, users can lend their support to a brand and influence their peers solely by liking and commenting on posts, without any purposeful influencing activity” (Naylor et al. 2012). Underwood et al. (2011) offered two modes of collaboration that Facebook users may use. The first mode is “broadcasting,” indicating a “one-to-many” type of communication. The second is the “communicating” mode, depicted by a “one-to-one” or “one-to-few” modes of interaction (Kabadayi and Price 2014).

Connectivity Culture

Consumers’ interests in online brands’ existence started in the 1990s with bulletin boards to express their opinions or present feedback (Kozinets 2002). According to Brown et al. (2006), a brand is the “totality of all stakeholders’ mental associations about the organization” (Stern 2006 as cited in Hollebeek et al. 2014). Users’ commitment enhances the quality of human-to-computer interaction into a rich and positive experience (O’Brien and Toms 2013). Hollebeek et al. (2014) introduce Consumer Brand Engagement (CBE) to elucidate users’ relationship with online brand existence. CBE resembles the generic cognitive, emotional, and behavioral nature of the engagement process. Starting with the ‘cognitive processing,’ it is the consumer-level understanding of any brand-related ideas in a mutual medium involving both the brand and the consumer. ‘Affection’ refers to the level of positive brand-related influence in a medium involving both the brand and the consumer. The last dimension is ‘activation,’ explained as “a consumer’s level of energy, effort and time spent on a brand in a specific medium involving brand and consumer” (Hollebeek et al. 2014, 154).

Reciprocity among users and media presents a mutually gratifying arrangement of diverse resources’ transmission (Chan and Li 2010). As Foa (1971) proposes, “social systems facilitate the exchange of various types of resources by matching available resources with needs” (as cited in Chan and Li 2010, 1034). Consequently, virtual communities achieve the initial step in the resource exchange process.  Brand consumers can have various exchange assets via the transfer of information or social bonds and enjoyment. As consumers reach the resource saturation level, they are more likely to reciprocate (Chan and Li 2010). This article adopts a holistic perception of a brand’s gratification, including acquiring utilitarian or hedonic benefits.

Virtual communities’ mechanisms of user participation cultivate brand loyalty. Participation is a central factor in any successful VC experience (Lee et al. 2020). When studying participation in a face-to-face community context, results showed that collaboration in civic society activities proliferates social capital (Cullen and Sommer 2010).  Similarly, VC collaborations show comparable outcomes, as those who actively participate are connected (Laine et al. 2011). Hence, the more users are involved in a particular organization’s virtual community, the more they are expected to perform these activities offline (Wellman et al. 2001). With social media and social networking sites’ connectivity culture, social capital has increased, and a community’s members’ psychological well-being has been promoted (Ellison, Steinfield, and Lampe 2007; Wellman et al. 2001 as cited in Malinen 2015).

Numerous factors may account for VC’s engagement level, among which is VC’s design. VC design refers to both technical and social decisions taken by the VC’s management team (administrators and moderators) to influence members’ interactions (Ren et al. 2007). The administrative team may design activities to sustain user engagement on VC, for example: through advertisement or unique system design and guiding community interactions to align with proposed themes. Such institutionalized practices and binding community commitments signify VC identities as institutional claims (Whetten and Mackey 2002). Despite the differences in virtual community identities, members join VC to fulfill and develop behavioral characteristics during social interaction (Dholakia et al. 2004).

Theoretical Framework for Virtual Engagement

The social constructionist theory explains social media’s technology usage and its role in social capital construction. Social constructionists consider the public sphere tailored by the interchange and dialogue among users (Charlotte et al. 2009). On the other hand, the social cognitive theory focuses on why virtual communities’ members may spend time and effort on knowledge allocation, proposing that the answer lies in addressing personal cognition and social network context.

The Social Capital Theory supplements the Social Cognitive Theory in addressing personal cognition benefits. The theory posits that users with the knowledge-sharing objective on VC not only seek information sharing or answer to a question, but they use VC as platforms to encounter other people, seek support, and pursue a sense of belonging. Concurring with the Business Week Harris Poll, 35% of those involved in a virtual community say their community is a social group, not a virtual place (Chiu et al. 2006).

Developed to understand the audience’s involvement with mass communication tools, The Uses and Gratifications Theory (U&G) studies the means through which individuals use media and possible reasons behind such usage. U&G theory has previously been useful for comprehending various “old” mass communication media like radio and television. More recently, with the introduction of new media and the consequent impacts of mass communication media, U&G has been revised to include more recent mass communication forms, mainly social media and SNS (Raacke and Bonds-Raacke 2008). Shao and Ross (2015) argue that the effectiveness of U&G theory lies in its capability to propose means of analysis to mediated communication situations via single or multiple sets of psychological motives and several communication channels. Nambisan and Baron (2007) have found dissimilar types of commitment that drive individuals to interact with VCs. The most common motives may fall under social status enhancement, social communication, educating one-self concerning usage of a service or a product, and finally, entertainment.

While U&G theory emphasizes audiences’ needs as motivational factors behind media consumption, the media dependency theory focuses on the audience’s media consumption goals (Hollebeek et al. 2014). Media systems dependency theory views the media as an information system, where this system’s resources impact the users’ capacity to reach their gratification goals (Grant et al. 1991). This theory implies a quasi-addictive relationship in which an individual becomes increasingly dependent on media for gratification purposes. The media becomes increasingly important to that individual. Considering both needs (uses and gratifications) and goals (media systems dependency), developing a more profound understanding of consumer-brand relationships in a social networking context is possible.

Social exchange theory is another fundamental theory aiming to comprehend the behavior of social groups. The theory scrutinizes individuals’ behavioral conduct when interacting with a group. It rationalizes why people tend to show support and help each other and share and exchange information resources (Blanchard 2007). Wellman and Guilia (1999) claimed that the public interchange of support might increase members’ perceptions of being part of a support group when a limited number of users are part of such a supportive process. Nonetheless, because everyone has access to the information, all members benefit from the exchange process despite being absent from the initial creation step.

Scope of the Study

Among the various types of virtual communities, this article focuses on social and commercial virtual brand communities. A social brand community is a distinctive form of brand community that entails integrating community members who may have lacked physical conversation and interactivity before establishing the VC. Nonetheless, they understand the significance and rules of membership and engagement in a given form of social communication (Carlson et al. 2007). There are many types of social brand communities, depending on format and purpose. Consequently, different types will engage in various means, forming unique engagement profiles.

The notion of engagement embodies a multi-dimensional conception comprising behavioral dimensions, along-side cognitive and emotional ones. However, the specific expression of engagement may vary across frameworks. Calder et al. (2009) identified eight online engagement dimensions around stimulation and inspiration. As opposed to satisfaction, engagement is concerned with consumers’ cognitive, emotional, and behavioral facets during the brand consumption process. On the other hand, satisfaction may fall in the aftermath of the consumption process (Hollebeek et al. 2014).

Commercial Brand Communities focus on a particular brand while engaging users to purchase products online or offline, which may build brand equity, promptly or in the long term.  The content provider will try to move the brands away from being a commodity toward being a ‘Lovemark’, respected and loved by its users. Nevertheless, passion brand communities do not promote a commercial brand but rather a social cause, a hobby, or an idea. In some cases, it may promote donating for the cause financially or in-kind, in random or regular patterns, and in large or small amounts.  The passion brand managers also seek Lovemark status and compete with other social causes to maintain their membership momentum.  They offer gratification and lots of information to keep the momentum of support and engagement going. Professional brand communities offer self and professional development opportunities, including expert networks, training, knowledge resources, and recruitment services. Our research presents a typology of virtual brand communities and quantifies each type’s engagement profile along the online communities’ dimensions of engagement, and gleans related managerial implications and future directions for research.

Typology of Virtual Brand Communities

The literature is replete with research and discussions of social and commercial brand communities. We add two more, which are professional and passion/experience virtual brand communities. Here, the ‘brand’ refers to the community’s name by the virtual brand community’s founder. Social brand communities include Facebook, Twitter, Instagram, TikTok, and many others.  Each social community provides a set of options that appeal to a particular segment of users. The brand owners manage commercial brand communities and provide continuous engagement with the brand and its followers. The objectives include building brand awareness, knowledge, affinity, and loyalty, leading to higher brand equity.  Users are aware of the community’s commercial nature, and their affection for the brand keeps them attached to exchange views and share experiences with the brand.

Professional brand communities come in two flavors, with and without a declared brand manager.  Either way, the focus is on the profession as the participants would define it: general (management or medicine) or specialized (marketing research or orthopedic surgery), academic or applied, exclusive to members or open, and geographically bound or global.  The most generic form of the professional community is LinkedIn, aiming to maximize the number of participants, offering them networking opportunities and a set of related premium services.   The declared brand manager could be a professional association or a commercial brand that promotes services and products to the professionals and the knowledge and benefits that are sometimes sold.  In other cases, the community is managed by a group of dedicated professionals without commercial gain.

Passion communities sometimes focus on a social brand or cause. Still, others focus on sharing experiences and emotional attachment to an activity (cycling or cooking) or a social relationship (motherhood or dating). Passion/Experience communities are typically sponsored (through advertising) or managed by a commercial brand with subtle and implied messages supporting the sponsor. Participants resent commercializing their passion but accept it as a survival tool for their community and minimize its impact. In some cases, the community may refuse any advertising relevant to the group’s subject matter to ensure a bias-free environment that members can trust. ‘Patientslikeme.com’ is a good example of a space where patients share their experiences with diseases without commercial influences.

Research Methods

Baldus et al. (2014) developed VC engagement dimensions, the compelling intrinsic motivations to continue interacting with an online brand community. They identified and operationalized eleven independent motivations and found different engagement types that propel people to interact with an online community. These factors are social interaction, social status enhancement, learning more about using the product, and having fun. In this research, we applied the eleven engagement dimensions to our four types of virtual brand communities to portray an engagement profile for each virtual brand community type.

We randomly selected a sample of 455 respondents from the white pages of Greater Cairo and tele-surveyed them after passing a set of screening questions. The screening eliminated respondents below 18 years of age, non-users of the internet, and those who refused to give consent to answer. Table 1 provides a crosstab depicting the gender and age breakdowns of the sample.

Table1: Age and Gender breakdown of the sample

Age Females Males Total
0 - 20 Yrs 20 8 28
21 - 29 Yrs 123 105 228
30 - 45 Yrs 91 108 199
Grand Total 234 221 455

We asked the 455 respondents who passed the screening to identify the three sites they spent the most time on and instructed them to focus on those particular sites. Most of them identified three or two sites. The respondents agreed to take the survey for one, two, or three of their chosen sites, yielding 890 valid responses. Few did not wish to complete the questionnaire for the second or third sites, and the interview was then ended. Table 2 summarizes the choices of our sample after classifying the sites of choice.

Table 2: Sites of choice and their classification and frequencies

Site Type Passion, Hobby or Cause sites Professional or Development Sites Social Media Sites Commercial Brand Sites
Total Choices (N=529) (N=141) (N=1762) (N=235)
Top 5 choices (n) Egybest (123)

Google (109)

Netflix (83)

Shahed (40)

Yallakora (31)

e-Courses (n=73)

College/school(n=12)

Engineers Syndicatetable 1 (n=11)

Lawyers BAR (n=8)

Recruitment (n=8)

Facebook (445)

WhatsApp (402)

Instagram (282)

YouTube (243)

Twitter (119)

Snapchat (118)

Jumia (50)

Souq (47)

OLX (41)

B-Tech (38)

Fashion (26)

Rest (n) 20 Sites (143) 11 sites (n=29) 11 sites (n=153) 8 sites (n=33)
Valid Reponses n=228 n=112 n=448 n=102

With that site in mind, they answered the battery of questions about the eleven dimensions of Baldus et al. (2014), followed by further demographic questions. Based on our stratified random sampling design, the tele-surveys continued until we attained at least 100 respondents and a 10% buffer in each of the four types of virtual brand communities. Then we tabulated and cleaned the data and calculated the mean scores and standard deviations for each of the eleven dimensions for each of the four types.  Moreover, we applied the least significant difference (LSD) multiple comparisons to test the statistical differences between the four brand communities along each of the eleven engagement dimensions, as reported in Table 3.

Findings

Virtual Brand Communities Profiles.

Table 4 summarizes the engagement profiles for each of the four types of virtual brand communities. The social brand communities are the most engaging, with high engagement scores on ten out of eleven engagement dimensions.  Fifty-five percent of the random sample spent more time on a social brand community than any other type of virtual community, reflecting their high engagement profile. The only dimension where the social communities scored low was the utilitarian rewards (e.g., monetary rewards, timesaving, deals or incentives, merchandise, and prizes).

Professional communities such as LinkedIn and self-development sites showed high engagement scores on four dimensions. Two of those dimensions are obvious; seeking assistance and support for decision-making. One would expect that joining a professional community would promise such benefits. The other two engagement dimensions are related and surprising: brand passion and hedonic rewards (e.g., fun, enjoyment, entertainment


Table 3.  The four types of Virtual Brand Communities, their engagement dimension mean scores and standard deviations, and the p-value for their LSD against other VBC engagement scores. (1=very low engagement, 10=very high engagement). 
  1 2 3 4
Type of VBC (Virtual Brand Community)

N=890

Passion VBC

n=228

Professional

n=112

Social VBC

n=448

Commercial

n=102

Engagement Dimension Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Brand influence: the degree to which a community influences its member’s decisions 7.303 3.371 8.589 2.819 8.429 2.343 8.734 1.881
vs 2 .000 vs 3 .538 vs 4 .247 vs 1 .000
vs 3 .000 vs4 .663        
Brand passion: The ardent affection a community promotes for the brand 8.329 3.089 9.080 2.469 9.058 1.995 2.523 2.840
vs 2 .003 vs 3 .922 vs 4 .000 vs 1 .000
vs 3 .000 vs4 .000        
Connecting: The extent to which a member feels affiliated to the brand community   2.421 2.294 3.929 3.226 9.248 1.797 1.798 1.329
vs 2 .000 vs 3 .000 vs 4 .000 vs 1 .024
vs 3 .000 vs4 .000        
Helping: The degree to which a community member wants to help fellow community members by sharing knowledge, experience, or time 2.478 2.435 3.375 3.059 9.299 1.665 1.982 1.533
vs 2 .000 vs 3 .000 vs 4 .000 vs 1 .053
vs 3 .000 vs4 .000        
Like-minded discussion: The extent to which a community member is interested in talking with people similar to themselves about the brand 2.382 2.195 3.250 3.333 9.281 1.773 1.844 1.487
vs 2 .001 vs 3 .000 vs 4 .000 vs 1 .034
vs 3 .000 vs4 .000        

Table 3. (continued)  
  1 2 3 4
Type of VBC (Virtual Brand Community)

N=890

Passion

n=228

Professional

n=112

Social VBC

n=448

Commercial

n=102

Engagement Dimension Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Rewards (hedonic): The degree to which the community member wants to gain hedonic rewards (e.g., fun, enjoyment, entertainment, friendly environment, and social status) through their participation in the community. 2.399 2.176 6.884 3.355 9.163 1.974 1.899 1.507
vs 2 .000 vs 3 .000 vs 4 .000 vs 1 .062
vs 3 .000 vs4 .000        
Rewards (utilitarian): The degree to which the community member wants to gain utilitarian rewards (e.g., monetary rewards, time savings, deals or incentives, merchandise, and prizes) through their participation in the community. 1.539 1.088 2.580 2.789 2.038 1.560 8.688 2.611
vs 2 .000 vs 3 .029 vs 4 .000 vs 1 .000
vs 3 .009 vs4 .000        
Seeking assistance: The degree to which a community member wants to receive help from fellow community members who share their knowledge, experience, or time with them. 2.320 2.259 7.223 3.846 9.181 1.715 2.083 2.266
vs 2 .000 vs 3 .000 vs 4 .000 vs 1 .371
vs 3 .000 vs4 .000        
Self-expression: The degree to which a community member feels that the community provides them with a forum where they can express their true interests and opinions. 2.382 2.363 3.152 3.502 9.288 1.724 1.789 1.439
vs 2 .004 vs 3 .000 vs 4 .000 vs 1 .027
vs 3 .000 vs4 .000        
Up-to-date information: the degree to which a community member feels that the brand community helps them to stay informed or keeps them up-to-date with the brand and product-related information 7.816 3.637 3.991 3.624 9.496 1.488 2.101 1.533
vs 2 .000 vs 3 .000 vs 4 .000 vs 1 .000
vs 3 .000 vs4 .000        
Validation: A community member’s feeling of the extent to which other community members affirm the importance of their opinions, ideas, and interests. 2.338 2.362 3.152 3.345 8.951 2.204 1.917 1.479
vs 2 .004 vs 3 .000 vs 4 .000 vs 1 .144
vs 3 .000 vs4 .000        

 

Table 4.  Virtual Brand Communities’ Profiles, (1=very low engagement, 10=very high engagement).
  1 2 3 4
Type of VBC Passion VBC Professional Social VBC Commercial
N=890 n=228 n=112 n=448 n=102
Engagement Dimension Mean Mean Mean Mean
Brand influence 7.303 8.589 8.429 8.734
Brand passion 8.329 9.08 9.058 2.523
Connecting 2.421 3.929 9.248 1.798
Helping 2.478 3.375 9.299 1.982
Like-minded discussion 2.382 3.25 9.281 1.844
Rewards (hedonic) 2.399 6.884 9.163 1.899
Rewards (utilitarian) 1.539 2.58 2.038 8.688
Seeking assistance 2.32 7.223 9.181 2.083
Self-expression 2.382 3.152 9.288 1.789
Up-to-date information 7.816 3.991 9.496 2.101
Validation 2.338 3.152 8.951 1.917

friendly environment, and social status).  It is a clear reflection that professional communities address the passion, social status, and needs of professionals alongside their development needs.

Commercial brand communities are a straightforward case, with high engagement scores in only two dimensions: brand influence (support in decision-making) and utilitarian rewards (e.g., monetary rewards, timesaving, deals or incentives, merchandise, and prizes).  All other engagement scores are within the first quartile.

Passion/experience brand communities profile included three dimensions with high engagement scores. These communities are passionate about a particular cause, hobby, or lifestyle. They naturally subscribe to communities where they can exchange information, nurture their passion, and enrich their resolutions concerning the focal theme.

Generally speaking, everyone in the sample expected the virtual communities to enhance their decision-making regardless of community type. Except for the commercial brand communities, all the other communities’ members were motivated to share their passion and affection toward the community’s focus. Only in commercial brand communities did their members’ undivided attention go to utilitarian rewards.

Differences along the Engagement Dimensions

More specifically, we report the results of the statistical analysis of the data for each of the eleven dimensions.  For the brand influence, the degree to which a community influences its members’ decisions, passion communities had significantly lower (p=.000) engagement scores than the other three types. However, it was still on the high side (7.303/10).  For the brand passion dimension, professional and social communities showed similar (p=.922) and very high engagement scores, while passion communities showed high (8.329/10) but significantly less (p=.000 and .003) engagement scores. Commercial communities showed very low brand passion, significantly less (p=.000) than all the rest.

Utilitarian rewards are monetary rewards, time savings, deals or incentives, merchandise, and prizes members hope to get through their community participation.  Utilitarian rewards were significantly higher (p=.000) in the commercial brand communities than the rest.  Social communities were significantly higher (p=.000) than the three other types of virtual brand communities in the other eight of the eleven dimensions.

An Agenda for Research and Practice

This research showed four distinct types of virtual brand communities: social, commercial, professional, and passion communities. It showed that each of the four has a unique engagement profile reflecting each community type’s expectations. Significant and apparent differences exist in online surfers’ motivations to join and remain in each type of community. Our findings are an open invitation to explore and exploit opportunities for future research and managerial implications.

Segmenting markets has always been an art and a science that marketing professionals must master and harness. Social media administrators rely on artificial intelligence and big data analytics to understand their customers’ needs. Powerful indeed, but it tends to focus on the trees and forget about the forest. We still need to determine the high-level segmentation before we deep-dive into the individual data. Practitioners need to look at the big picture before losing their insight to details only supercomputers can decipher.

The eleven dimensions of engagement are valid and reliable, but there is seldom one truth about reality. Scholars should be exploring beyond engagement and motivations into other dimensions, such as attitudes and even Lovemarks. The time and money consumers spend on virtual communities would easily qualify virtual communities as a lifestyle. This revelation alone opens the door for modifying a version of values and lifestyle (VALS) segmentation for users of online communities.

The relationship between the eleven dimensions of engagement and demographics or psychographics is another promising field for further research. Insights about gender differences, ethnic backgrounds, socioeconomic strata, and education level and their impact on engagement profiles and specific dimensions hold excellent potential.

Greater insight exists in further segmenting each type of virtual brand communities into subtypes. Future research should consider exploring the four types of virtual brand communities now that we have established their significantly different profiles.  It would be unacceptable to bag them into one category of social network sites or social media platforms.  Academics approached product marketing for decades before realizing that products include goods and services, and that services provide a fertile field for research. Passion, professional, commercial, and social VC are quite general categorizations.  They are a good start, waiting for scholars to explore them further.

References

Aaker, David A. 1991. Managing Brand Equity. New York: Free Press.

Aaker, D. 1996. “Measuring Brand Equity Across Products and Markets.” California Management Review, Vol. 38 No. 3, 102-120.

Arguello, J., B. S. Butler, E. Joyce, R. Kraut, K.S. Ling, C. Rosé, and X. Wang. 2006. “Talk To Me: Foundations For Successful Individual-Group Interactions in Online Communities.” In Proceedings of the SIGCHI conference on Human Factors in computing systems, April, 959-968.

Bagozzi, R. P. and U.M. Dholakia. 2002. “Intentional Social Action in Virtual Communities.” Journal of Interactive Marketing, 16(2), 2-21.

Baldus, Brian J., Clay Voorhees, and Roger Calantone.  2014. “Online Brand Community Engagement: Scale Development and Validation.” Journal of Business Research 68 (2015) 978–985

Barnard, C. and S. Knapp. 2011. “Do Facebook Likes Indicate Marketer ROI?” DM News, Vol. 33 No. 7, 25.

Blanchard, A. L. 2007. “Developing a Sense of Virtual Community Measure.” CyberPsychology & Behavior, 10, 827–830.

Blanchard, A., and M.L. Markus. 2002. “Sense of Virtual Community – Maintaining the Experience of Belonging.” In Proceedings of the 35th annual Hawaii international conference on system sciences (HICSS) 3566–3575. DOI: http://dx.doi.org/10. 1109/HICSS.2002.994449.

Blanchard, A., and M.L. Markus. 2004. “The Experienced Sense of Virtual Community: Characteristics and Processes.” The Data Base for Advances in Information Systems, 35(1), 65–79. http://dx.doi.org/10.1145/968464.968470.

Boyd, D. M., and N.B. Ellison. 2007. “Social Network Sites: Definition, History, and Scholarship.” Journal of computer-mediated communication13(1), 210-230.

Brown, Tom J., Peter A. Dacin, Michael G. Pratt, and David A. Whetten. 2006. “Identity, Intended Image, Construed Image, and Reputation: An Interdisciplinary Framework and Suggested Terminology.” Journal of the Academy of Marketing Science, 34, 2, 99–106.

Calder, Bobby J., Edward M. Malthouse, and Ute Schaedel. 2009. “An Experimental Study of the Relationship between Online Engagement and Advertising Effectiveness,” Journal of Interactive Marketing, 23, 4, 321–31.

Carlson, Brad D., Suter, Tracy A., and Tom J. Brown. 2008. “Social Versus Psychological Brand Community: The Role of Psychological Sense of Brand Community.” Journal of Business Research, 61(4), 284–291.

Chan W. K. and Y.S. Li. 2010. “Understanding Consumer-To-Consumer Interactions in Virtual Communities: The Salience of Reciprocity.” Journal of Business Research Volume 63, Issues 9–10, September–October 2010, Pages 1033-1040

Charlotte, N.G., Hermans, M.B., Sanchez, D., Richmond, C., Bohley, M., and R. Tuttle. 2009. “A Theoretical Framework for Building Online Communities of Practice with Social Networking Tools.” Educational Media International, 46:1, 3-16. dx.doi.org/10.1080/09523980802588626

Chavis, D., Hogge, J., McMillan, D., and Wandersman, A. 1986. “Sense of Community Through Brunswick’s Lens: A First Look.” Journal of Community Psychology, 14, 24–40.

Cheng, F. F., Wu, C. S., and Chen, Y. C. 2020. “Creating Customer Loyalty in Online Brand Communities.” Computers in Human Behavior107, 105752.

Chiu, C. M., Hsu, M. H., and Wang, E. T. G. 2006. “Understanding Knowledge Sharing in Virtual Communities: An Integration of Social Capital and Social Cognitive Theories.” Decision Support Systems, 42, 1872–1888.

Cullen, R., and Sommer, L. 2010. “Participatory Democracy and the Value of Online Community Networks: An Exploration of Online and Offline Communities Engaged in Civil Society and Political Activity.” In Proceedings of the 43rd Hawaii international conference on system sciences (HICSS), 1–10. DOI: http://dx.doi. org/10.1109/HICSS.2010.301.

de Valck, Kristine, van Bruggen, Gerrit H., and Berend Wierenga. 2009. “Virtual Communities: A Marketing Perspective.” Decision Support Systems, 47(3), 185–203.

Dholakia, U.M., Bagozzi, R.P., and L.K. Pearo. 2004. “A Social Influence Model of Consumer Participation in Network-and Small-Group-Based Virtual Communities.” International Journal of Research in Marketing, 21 (3), 241–263.

Elshahed, S. Heba. 2014. “Revisiting the Spiral of Silence in a Social Media Environment.” Dissertation. The American University in Cairo.

Etzioni, A. and O. Etzioni. 1999. “Face-to-Face and Computer-Mediated Communities, A Comparative Analysis.” The Information Society, 15, 241-248.

Facebook, (2017), https://www.Facebook.com/pg/Facebook/about/?ref=page_internal

Fachrunnisa, O., and F. K Hussain. 2020. “A Methodology for Creating Sustainable Communities Based on Dynamic Factors in Virtual Environments.” International Journal of Electronic Business, 15(2), 133-159. Foa UG. Interpersonal and economic resources. Sci 1971; 29:345–51.

Fox, N., and C. Roberts. 1999. “GPS In Cyberspace: The Sociology of A ‘Virtual Community’.” The Sociological Review47(4), 643-671.

Francis, M. K., Wormington, S. V., and C. Hulleman. 2019. “The Costs of Online Learning: Examining Differences in Motivation and Academic Outcomes in Online and Face-To-Face Community College Developmental Mathematics Courses.” Frontiers in psychology, 10, 2054.

Gallagher, S. E., and T. Savage. 2013. “Cross-Cultural Analysis in Online Community Research: A Literature Review.” Computers in Human Behavior, 29(3), 1028–1038. http://dx.doi.org/10.1016/j.chb.2012.09.011.

Granovetter, M. 1985. “Economic Action and Social Structure: The Problem of Embeddedness.” The American Journal of Sociology, 91(3), 481–510.

Grant, A.E., Guthrie, K. and S. J. Ball-Rokeach. 1991. “Television Shopping: A Media System Dependency Perspective.” Communication Research, Vol. 18 No. 6, 773-798.

Habibi, Mohammad Reza, Michel Laroche, and Marie-Odile Richard. 2014. “Brand Communities Based in Social Media: How Unique Are They? Evidence from Two Exemplary Brand Communities.” International Journal of Information Management 34, 123–132.

Hoffman, D.L. and T.P. Novak. 1996. “Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations.” Journal of Marketing, 60, July, 50–68.

Hollebeek, L.D., Glynn, M.S. and R.J. Brodie. 2014. “Consumer Brand Engagement in Social Media: Conceptualization, Scale Development, and Validation.” Journal of Interactive Marketing, Vol. 28 No. 2, 149-165.

Hudson, Simon, Roth., S. Martin., and Thomas Madden. 2012. “Customer Communications Management in the New Digital Era.” Center for Marketing Studies Darla Moore School of Business University of South Carolina

Kabadayi, S. and K. Price. 2014. “Consumer – Brand Engagement on Facebook: Liking and Commenting Behaviors.” Journal of Research in Interactive Marketing, Vol. 8 Issue: 3, pp.203-223, https:// doi.org/10.1108/JRIM-12-2013-0081

Kalman, David. 2009. Brand Communities, Marketing, and Media. Terella Media. (nd)

Kozinets, R. V. 2002. “The Field Behind the Screen: Using the Method of Netnography to research Market-oriented Virtual Communities.” Journal of Consumer Research, 39, 1, 61-72.

Laine, M. S. S., Ercal, G., and L. Bo. 2011. “User Groups in Social Networks: An Experimental Study on YouTube.” In Proceedings of the 44th Hawaii international conference on system sciences, (HICSS), 1–10. DOI: http://dx.doi.org/10.1109/ HICSS.2011.472.

Lee, Y. H., Hsiao, C., Weng, J., and Y.H. Chen. 2020. “The Impacts of Relational Capital on Self-Disclosure in Virtual Communities.” Information Technology & People.

Leimeister, J. M., Sidiras, P., and H. Krcmar. 2006. “Exploring Success Factors of Virtual Communities: The Perspectives of Members and Operators.” Journal of Organizational Computing and Electronic Commerce16(3-4), 279-300.

Li, H. 2004. “Virtual Community Studies: A Literature Review, Synthesis and Research Agenda.” Proceedings of the Americas Conference on Information Systems, New York, New York, August 2004. The Chinese University of Hong Kong.

Malinen, S. 2015. “Understanding User Participation in Online Communities: A Systematic Literature Review of Empirical Studies.” Computers in Human Behavior 46, 228–238. School of Information Sciences, University of Tampere, 33014 University of Tampere, Finland

Meret, C., Iannotta, M., Giacomelli, D., Gatti, M., and I. Sirolli. 2020. “Managing Intellectual Capital Inside Online Communities of Practice: An Integrated Multi-Step Approach.” In Exploring Digital Ecosystems, Springer: Cham, 121-134.

Morgan-Thomas, A., and C. Veloutsou. 2013. “Beyond Technology Acceptance: Brand Relationships and Online Brand Experience.” Journal of Business Research66(1), 21-27.

Muller, M., Ehrlich, K., Matthews, T., Perer, A., Ronen, I., and I. Guy. 2012. “Diversity Among Enterprise Online Communities: Collaborating, Teaming, And Innovating Through Social Media.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

Muniz, A. M., and T.C. O’Guinn. 2001. “Brand Community.” Journal of Consumer Research, 27, 412–432.

Nambisan, S. and R.A. Baron. 2007. “Interactions in Virtual Customer

Environments: Implications for Product Support and Customer Relationship

Management.” Journal of Interactive Marketing, 21, 2, 42–62.

 

Naylor, R.W., Lamberton, C.P. and P.M. West. 2012. “Beyond the Like Button: The Impact of Mere Virtual Presence on Brand Evaluations and Purchase Intentions in Social Media Settings.” Journal of Marketing, 76: 6, 105-120.

Nguyen, L. T. V., Conduit, J., Lu, V. N., and S. Rao Hill. 2020. “Collective Empowerment in Online Communities: Conceptualization, Scale Refinement, and Validation.” Journal of Marketing Theory and Practice, 1-17.

Palmatier, R., Dant, R., Grewal, D. and K. Evans. 2006. “Factors Influencing the Effectiveness of Relationship Marketing: A Meta-Analysis.” Journal of Marketing, 70: 2, 136-153.

Palmatier, R.W., Scheer, L.K., Evans, K.R. and T.J. Arnold. 2008. “Achieving Relationship Marketing Effectiveness in Business-To-Business Exchanges.” Journal of the Academy of Marketing Science, Vol. 36 No. 2, 174-190.

Raacke, J., and J. Bonds-Raacke. 2008. “MySpace and Facebook: Applying the Uses and Gratifications Theory to Exploring Friend-Networking Sites.” Cyberpsychology & Behavior, 11(2), 169–174.

Ramadan, Z. 2017. “Examining the dilution of the consumer-brand relationship on Facebook: the saturation issue.” Qualitative Market Research: An International Journal, Vol. 20 Issue: 3, 335-353. https://doi.org/10.1108/QMR-07-2016-0064

Ren, Y., Kraut, R., and S. Kiesler. 2007. “Applying Common Identity and Bond Theory to Design of Online Communities.” Organization Studies, 28 (3), 377–408.

Rheingold, H. 1993. The Virtual Community. Homesteading on The Electronic Frontier. London: MIT Press.

Roberts, K. 2005. Lovemarks: The future beyond brands. Powerhouse books.

Roberts, L. D., Smith, L. M., and Pollock, C. 2002. “MOOing Till the Cows Come Home: The Search for Sense of Community in Virtual Environments.” In A. Fisher, C. Sonn, & B. Bishop (Eds.), Psychological sense of community: Research, applications, and implications. New York: Kluvert.

Rousseau, D.M., Sitkin, S.B., Burt, R.S. and C. Camerer. 1998. “Not So Different After All: A Cross-Discipline View of Trust.” Academy of Management Review, Vol. 23 No. 3, 393-404.

Shao, W., and M. Ross. 2015. “Testing a Conceptual Model of Facebook Brand Page Communities.” Journal of Research in Interactive Marketing, Vol. 9 Issue: 3, 239-258. https://doi.org/10.1108/ JRIM-05-2014-0027

Smith, C. 2013. “Retailers Say Social Media is Having an Impact on Their Bottom Lines.” Business Insider, 21, June.

Simon, C., Brexendorf., T.O., and M. Fassnacht. 2016. “The Impact of External Social and Internal Personal Forces on Consumers’ Brand Community Engagement on Facebook.” Journal of Product & Brand Management, 25:5, 409-423, https://doi.org/10.1108/JPBM-03-2015-0843

Syncapse. 2013. “The Value of a Facebook Fan 2013: Revisiting Consumer Brand Currency in Social Media.”

Varik, F.J.M. van, and H. Van. Oostendorp. 2013. “Enhancing Online Community Activity: Development and Validation of The CA Framework.” Journal of Computer-Mediated Communication 18, 454–475.

Wallace, E., Buil, I., and L. de Chernatony. 2012. “Facebook “Friendship” and Brand Advocacy.” Journal of Brand Management, Vol. 20 No. 2, 128-146.

Walther, J. B. 1996. “Computer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction.” Communication Research, 22, 33–43.

Whetten, D.A. and A. Mackey. 2002. “A social actor conception of organizational identity and its implications for the study of organizational reputation.” Business & Society, 41 (4), 393–414.

WhatsApp., (2017), About Section.  https://www.whatsapp.com/about/

Wellman, B., Salaff, J., Dimitrova, D., Graton, L., Gulia, M. and C. Haythornthwaite. 1996. “Computer Networks as Social Networks: Collaborative Work, Telework and Virtual Community.” Annual Review of Sociology, 22, 213-238.

Wellman, B., and M. Guilia. 1999. “Net Surfers Don’t Ride Alone: Virtual Communities as Communities.” In B. Wellman (Ed.), Networks in the global village: Life in contemporary communities. Westview.

 

Wellman, B., Haase, A. Q., Witte, J., and K. Hampton. 2001. “Does the Internet Increase, Decrease, or Supplement Social Capital? Social Networks, Participation, And Community Commitment.” American Behavioral Scientist, 45(3), 436–455.

Zaglia, M.E. 2013. “Brand communities embedded in social networks.” Journal of Business Research, Vol. 66 No. 2, 216-223.

Zhang, J., Zhu, Q., and Y. Wang. 2019. “Social Capital on Consumer Knowledge-Sharing in Virtual Brand Communities: The Mediating Effect of Pan-Family Consciousness.” Sustainability, 11(2), 339.

 

 

About Ahmed Taher and Heba El Shahed

Ahmed Taher, PhD is Assistant Professor in the Journalism and Mass Communication Department at the American University in Cairo

Heba Elshahed, PhD is Adjunct Faculty in the Journalism and Mass Communication Department at the American University in Cairo

Check Also

Arab Women self-presentation in feminist podcasts: An Analytical Study (Arabic)

Scroll down for Arabic abstract. In contrast to the negative stereotypical portrayals of Arab women …