Understanding Customer Brand Engagement with Virtual Social Communities: A Comprehensive Model of Drivers, Outcomes and Moderators

Journal of Marketing Theory and Practice ISSN: 1069-6679 (Print) 1944-7175 (Online) Journal homepage: http://www.tandfonline.com/loi/mmtp20 UNDERSTANDING CUSTOMER BRAND ENGAGEMENT WITH VIRTUAL SOCIAL COMMUNITIES: A COMPREHENSIVE MODEL OF DRIVERS, OUTCOMES AND MODERATORS Amélia Carvalho & Teresa Fernandes To cite this article: Amélia Carvalho & Teresa Fernandes (2018) UNDERSTANDING CUSTOMER BRAND ENGAGEMENT WITH VIRTUAL SOCIAL COMMUNITIES: A COMPREHENSIVE MODEL OF DRIVERS, OUTCOMES AND MODERATORS, Journal of Marketing Theory and Practice, 26:1-2, 23-37, DOI: 10.1080/10696679.2017.1389241 To link to this article: https://doi.org/10.1080/10696679.2017.1389241 Published online: 02 Mar 2018. Submit your article to this journal Article views: 8 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=mmtp20 UNDERSTANDING CUSTOMER BRAND ENGAGEMENT WITH VIRTUAL SOCIAL COMMUNITIES: A COMPREHENSIVE MODEL OF DRIVERS, OUTCOMES AND MODERATORS Amélia Carvalho and Teresa Fernandes Social media has established a new dynamic in marketing, enabling customers to engage with brands in a variety of ways. This article aims to develop a comprehensive model of drivers, outcomes, and moderators of customer brand engagement (CBE) on virtual brand communities in social media. Based on a survey of 799 customers, findings identify involvement, interactivity, and flow experience as key drivers of CBE, and satisfaction, trust, word-of-mouth referrals, and commitment as associated outcomes, with identification and trust in the brand community acting as moderators. This study provides new insights about the CBE process, offering valuable suggestions to brand managers. With the advent of social media, the traditional roles of seller and customer have changed: customers can now easily connect, share, and exchange information with other customers (Gambetti et al. 2015; Sashi 2012) and communicate directly with brands (Mangold and Faulds 2009; Tsai and Men 2013) on their own terms and through their chosen channels (Stone and Woodcock 2013). As a result, new forms of customerbrand interactions appeared, and customers became a major factor in influencing various marketing outcomes, such as brand awareness, purchase behavior, and post-purchase evaluation (Brodie et al. 2013; Gambetti et al. 2015; Sashi 2012; Tsai and Men 2013). However, although customers interact with thousands of brands in their lives, they develop an intense connection to only a small subset of these objects. In this sense, customer brand engagement (CBE) plays a key role in a new customer-centric marketing approach (Hollebeek 2011). CBE has emerged as a prominent construct that is capable of affecting customer relationships with brands (Dwivedi 2015), surpassing satisfaction and loyalty, and thus providing a real competitive advantage (Kumar et al. 2010). In this sense, managers are increasingly concerned with how to best engage customers in order to develop favorable customer Amélia Carvalho (Ph.D., Faculty of Economics - University of Porto), Faculty of Economics— University of Porto, Porto, Portugal, 200706318@fep.up.pt. Teresa Fernandes (Ph.D., Faculty of Economics - University of Porto), Assistant Senior Professor, Faculty of Economics— University of Porto, Porto, Portugal, tfernandes@fep.up.pt. experiences (Marbach, Lages and Nunan 2016), namely on virtual social communities such as Facebook, which has become one of the preferred social media engagement platforms (Brodie et al. 2013; Dessart, Veloutsou and Morgan-Thomas 2015, 2016; Malhotra, Malhotra and See 2013). Nevertheless, despite the growing interest on how to manage brand presence and engage customers on social media, much of the existing CBE research is limited in scope and design, and the nomological network of the construct is embryonic and largely conceptual. To date, there has been no single study that has developed a comprehensive examination of the key drivers, outcomes, and moderators of the CBE process. Therefore, our understanding of CBE is incomplete and systematic research is still lacking (France, Merrilees and Miller 2016; Leckie, Nyadzayo and Johnson 2016). Hence, the purpose of this article is to identify and examine the key drivers of the CBE process and integrate them into a comprehensive model, together with main outcomes and moderators. The study addresses the customer (an existing user of the brand) as the focal “engagement subject,” the brand as the “engagement object” and the social networking site Facebook as the “engagement context.” The article analyses CBE as a three-dimensional concept, outlining direct and indirect effects of its key antecedents (customer involvement, participation, interactivity, and flow experience), and testing whether CBE impacts satisfaction, trust, commitment, and word-of-mouth referrals. Further, the study tests the moderating effects of customer’s identification with and trust toward the virtual Journal of Marketing Theory and Practice, vol. 26, nos. 1–2 (Winter–Spring 2018), pp. 23–37. Copyright Ó Taylor & Francis Group, LLC ISSN: 1069–6679 (print) / ISSN 1944–7175 (online) DOI: https://doi.org/10.1080/10696679.2017.1389241 24 Journal of Marketing Theory and Practice brand community in the CBE process. Theoretically and managerially, this research aims to improve our understanding about CBE on social media, which can be used to redefine focused strategies and tactics. The article is organized as follows. First, we present the theoretical background and the research hypotheses. Second, the research methodology is explained, including the survey context, data collection, questionnaire design, and measures used. Finally, the last four sections present the findings and conclusions of the study, as well as the theoretical and managerial implications, limitations, and future research. THEORETICAL BACKGROUND AND HYPOTHESES Customers, Brands and Social Media Social media has established a new dynamic in marketing and has become one of the more prevalent channels through which customers engage with a brand or a firm. Nowadays, through social media, it is possible for brands to interact with and among customers in a dynamic, ubiquitous, and often real-time way (Brodie et al. 2013). Empowered by the rise of the internet, customers are no longer a “passive audience” but “active coproducers,” enabled to build their identities, express themselves creatively, socialize with other customers, and enjoy unique and memorable experiences (Gambetti et al. 2015; Fernandes and Remelhe 2016). The virtual world not only has changed the way customers generate and obtain consumption-related information (Yang et al. 2016), but also allowed brands to communicate with their different segments through online channels and social media platforms (Murdough 2009). According to Ashley and Tuten (2015), marketers have several options within social media for branding. Not only can they use paid display advertising, participate in social networks, and publish brand-related content in social channels, but also marketers can develop branded engagement opportunities for customer participation within social media networks. Social media can be defined as the “group of internet based applications that builds on the ideological and technological foundations of Web 2.0, and it allows the creation and exchange of user-generated content” (Kaplan and Haenlein 2010, p. 61). The last decade has witnessed a proliferation of virtual social platforms, resulting from the massive adoption of advances in the internet: blogs and micro blogging (e.g., Twitter), bookmarking sites (e.g., del.icio.us), video sites (e.g. YouTube), virtual worlds (e.g., Second Life), social networking sites (e.g., Facebook) and mobile technologies (Laroche et al. 2012; Sashi 2012; Wirtz et al. 2013). From a customer engagement perspective, among the various types of platforms, virtual social networking brand communities, such as Facebook brand pages, are considered to be a key driving force of CBE (Brodie et al. 2013; Dessart, Veloutsou and MorganThomas 2015, 2016; Marbach, Lages and Nunan 2016; Malhotra, Malhotra and See 2013), because they are relationship centric and inherently participatory (Dessart, Veloutsou and Morgan-Thomas 2016; Lee et al. 2011; Tsai and Men 2013; Vale and Fernandes 2018). A virtual social network brand community is a specialized, geographically dispersed community based on a structured and dynamic network of relationships among participants sharing a common focus (Dholakia, Bagozzi and Pearo 2004). Peer-to-peer interactions occurring in these social networks are considered beneficial to the firm, but also important to the customer: community members can create and cocreate value for themselves, other members, visitors and/ or organizations (Brodie et al. 2013). Additionally, they give companies the chance to engage their current and potential customers (Greve 2014). However, despite the growing interest in how to manage brand presence and engage customers on social media, the understanding about the CBE process in this context is partial and systematic research on its drivers, outcomes, and moderators is still lacking. The Nature of Customer Brand Engagement First conceptualized by Kahn (1990, p. 694), engagement was defined as “behaviors by which people bring in or leave out their personal selves during work role performances.” Since then, the term began to emerge as a psychological state (e.g., involvement, commitment, attachment, mood), performance construct (effort or observable behavior), disposition or some combination of the above, and a large amount of consensus on its definition emerged in the literature. In one of the first definitions presented, Bowden (2009, p. 65) describes engagement as a “psychological process that models the underlying mechanisms by which customer loyalty forms for new customers of a Winter–Spring 2018 25 service brand as well as the mechanisms by which loyalty may be maintained for repeat purchase customers of a service brand.” Later on, other authors focused on the behavioral dimension of engagement. For instance, van Doorn et al. (2010, p. 254) define customer engagement behaviors as the “customer’s behavioral manifestations that have a brand or company focus, beyond purchase, resulting from motivational drivers,” while Pham and Avnet (2009, p. 2) defined engagement as a “motivational state related to involvement and absorption of attention (. . .) to be inferred from a pattern of action or withdrawal with respect to the target object.” In a context of a virtual brand community, Brodie et al. (2013, p. 107) emphasize that customer engagement involves “specific interactive experiences between customers and the brand, and/or other members of the community.” In the same vein, Vivek, Beatty and Morgan (2012, p. 133) define customer engagement as the “intensity of an individual’s participation in and connection with an organization’s offerings or organizational activities, which either the customer or the organization initiates.” In this conceptualization, the cognitive and affective elements of customer engagement incorporate the experiences and feelings of customers, while the behavioral and social elements capture the participation by current and potential customers. From then on, several authors (Dessart, Veloutsou and Morgan-Thomas 2015; Dwivedi 2015; Glynn and Brodie 2014; Greve 2014; Hollebeek, Leckie, Nyadzayo and Johnson 2016; Vivek et al. 2014; Yang et al. 2016), stressed the importance of a definition of engagement that comprises the cognitive, emotional, and behavioral dimensions. In particular, Hollebeek, Glynn and Brodie (2014, p. 154) conceptualized CBE as “a consumer’s positively valence brand-related cognitive, emotional and behavioral activity during or related to focal consumer/brand interactions.” In this sense, three CBE dimensions were validated: cognitive processing (cognitive CBE dimension), affection (emotional CBE dimension), and activation (behavioral CBE dimension). Cognitive processing is defined as “a consumer’s level of brand-related thought processing and elaboration in a particular consumer/brand interaction” (Hollebeek, Glynn and Brodie 2014, p. 154). Affection refers to “a consumer’s degree of positive brand-related affect in a particular consumer/brand interaction” (p. 154). And, finally, activation states the “consumer’s level of energy, effort and time spent on a brand in a particular consumer/brand interaction” (p. 154). Similarly, Dwivedi (2015, p. 100) derives a conceptualization of CBE from the domain of organizational psychology and defines CBE as “consumers’ positive, fulfilling, brand-use- related state of mind that is characterized by vigor, dedication and absorption.” In this context, vigor symbolizes the high levels of energy and mental resilience of the customer when he/she is interacting with a brand, as well as the customer willingness and ability to invest effort in such interactions. Dedication denotes a sense of significance, enthusiasm, inspiration, pride and challenge, while absorption corresponds to the sense of being fully concentrated and happily engrossed in brand interactions. Each of these dimensions corresponds to the behavioral, emotional, and cognitive aspects of CBE already identify by Hollebeek, Glynn and Brodie (2014). As such, CBE is currently conceptualized as a multidimensional concept (Vivek et al. 2014). Drivers, Outcomes and Moderators of Brand Engagement on Social Media The customer engagement process does not follow an orderly sequential progression of phases over time (Brodie et al. 2013). In reality, it is an interaction of relevant subprocesses, that is, a series of aggregated engagement states. Prior studies have identified some constructs that may act as antecedents of CBE: customer involvement, customer participation, and customer interactivity (Brodie et al. 2011; France, Merrilees and Miller 2016; Hollebeek, Glynn and Brodie 2014; Leckie, Nyadzayo and Johnson 2016; Mangold and Faulds 2009; Mollen and Wilson 2010; Sashi 2012). Yet, in specific contexts like virtual social communities, customer flow experience, customer’s identification with virtual social community, and customer’s trust toward it can also be understood as important factors. Nonetheless, these constructs remain empirically unexplored in a context of CBE. Therefore, this study considers customer involvement, participation, interactivity, and flow experience as potential drivers of CBE on social media, and analyses the moderating impact of customer’s identification and trust in the brand community. Additionally, the study ponders customer cumulative satisfaction, word-of-mouth (WOM) referrals, trust and commitment as outcomes of CBE (Bowden 2009; Brodie et al. 2013; Hollebeek 2011; van Doorn et al. 2010). Accordingly, the following research framework is proposed (Figure 1). 26 Journal of Marketing Theory and Practice Figure 1 Theoretical model Customer Involvement Broadly, involvement can be described as the customer’s personal relevance and interest in relation to a focal object. In this sense, customers with high levels of involvement are more likely to exhibit engagement (Vivek, Beatty and Morgan 2012) regarding thoughts, feelings, and behavioral to a particular object (Gordon, McKeage and Fox 1998). Therefore, Brodie et al. (2011 2013), Hollebeek, Glynn and Brodie (2014), Leckie, Nyadzayo and Johnson (2016) and Wirtz et al. (2013) argue that customer involvement is an antecedent required prior to the expression of CBE. Additionally, when customers are involved, they are open to invest time and energy in the coproduction of contents for the brand (Goodman et al. 1995). Thus, customers are unlikely to participate when they are not involved (Gronroos 1995), because they do not see the future benefit of the relationship. Therefore, the following hypotheses are posited: Hypothesis 1: Customer involvement has a positive effect on CBE. Hypothesis 2: Customer involvement has a positive effect on customer participation. Customer Participation Through participation (either through consumption, contribution, or creation), customers will be able to better know the brand, to adjust their expectations, and to have a stronger perception of customization and cost reductions. As a result, customers who participate tend to take part of an interactive process that conducts to higher customer engagement (Vivek, Beatty and Morgan 2012). Hollebeek (2011), Leckie, Nyadzayo and Johnson (2016) and Vivek, Beatty and Morgan (2012) argue that customer participation is an antecedent required prior to the expression of CBE. Additionally, customer interactivity may be enhanced by customer participation, since a constant dialogue may help to develop and enhance the relationship between the customer and the brand (Cheung and To 2011). Hence, the following hypotheses are proposed: Winter–Spring 2018 27 Hypothesis 3: Customer participation has a positive effect on CBE. Hypothesis 4: Customer participation has a positive effect on customer interactivity. Customer Interactivity Customer interactivity is a psychological state of mind experienced by an individual during interaction (Wu 2006). More frequent, faster, and richer interactions among customers and between the customer and the company enhance the feeling of being engaged with the brand (Sashi 2012). Consequently, Hollebeek (2011) considers interactivity as a required antecedent of CBE. Also, interactivity increases the feeling of “having control” over the interaction, stimulates user’s curiosity, and makes navigation intrinsically interesting (Novak, Hoffman and Yung 2000), all key dimensions of flow experience. Thus, we hypothesize that: Hypothesis 5: Customer interactivity has a positive effect on CBE. Hypothesis 6: Customer interactivity has a positive effect on customer flow experience. Customer Flow Experience Flow experience has become a key element to measure the extent and intensity of the pleasure, and the concentration of customers during their online experience (Novak, Hoffman and Yung 2000). Therefore, flow is an unconscious experience in which the individual is completely focused and enjoying the activity that is developing (Liu et al. 2016). In this sense, customers who experience flow are more likely to perceive their experience as compelling and become engaged with the brand (Novak, Hoffman and Yung 2000). Thus, customer flow experience may act as an antecedent of CBE (Brodie et al. 2011; Mollen and Wilson 2010). So, it is assumed that: Hypothesis 7: Customer flow experience has a positive effect on CBE. Customer Cumulative Satisfaction According to Brunner, Stöcklin and Opwis (2008), cumulative satisfaction comprises “all encounters of the customer-provider relationship.” In this sense, cumulative satisfaction captures the customer’s psychological reactions resulting from their entire experience with the brand (Olsen and Johnson 2003). As a result, it is conceivable to assume that engaged customers are customers satisfied with the brand and its experience. Hence, cumulative satisfaction can be a potential CBE consequence for experienced and/or existing customers (Brodie et al. 2011; Hollebeek 2011). Therefore, the following hypothesis is postulated: Hypothesis 8: CBE has a positive effect on customer cumulative satisfaction. Customer WOM Referrals WOM referrals are “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-Thurau et al. 2004, p. 39). The act of recommending something to others is the key element of a strong relationship. For Fullerton (2011, p. 93), WOM “is a key artifact of a situation where consumers are loyal to their relational partner.” Likewise, it is a behavior undertaken by customers who are actively, emotionally, and attitudinally connected with the brand (Hollebeek and Chen 2014). In addition, customer dissemination of focal positive brand related WOM is viewed as a particular reflection of the customer’s brand attitude (Mazzarol, Sweeney and Soutar 2007) and an expression of CBE (Hollebeek and Chen 2014). Therefore, it is expected that: Hypothesis 9: CBE has a positive effect on customer WOM referrals. Customer Trust Trust is also the most important variable in a relational exchange (Hunt and Lambe 2000), since it reduces uncertainty in an environment in which customers feel vulnerable (Lacey 2007). Customer trust influences 28 Journal of Marketing Theory and Practice choices and behaviors, because it is a psychological state interpreted in terms of “perceived probabilities,” “confidence” or “expectations” in relation to the other party (Delgado-Ballester, Munuera-Aleman and YagiieGuillent 2003). In particular, brand trust is ‘‘the willingness of the average consumer to rely on the ability of the brand to perform its stated function’’ (Chaudhuri and Holbrook 2001, p. 82). In a customerbrand relationship context, customer trust reflects assumptions about reliability, honesty, and altruism that customers attribute to brands (Story and Hess 2006). This construct encompasses both cognitive and affective elements (Delgado-Ballester, Munuera-Aleman and Yagiie-Guillent 2003) that are also present in CBE. Consequently, trust can be a potential consequence for existing customers of the brand (Hollebeek 2011). Therefore, the following hypothesis is formulated: Hypothesis 10: CBE has a positive effect on customer trust. Customer Commitment Commitment is an implicit or explicit pledge to the continuity of a relationship (Wetzels et al. 1998). Morgan and Hunt (1994, p. 23) defined it as “an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it; that is, the committed party believes the relationship is worth working on to ensure that it endures indefinitely.” Brand commitment is a psychological disposition that implies a positive attitude toward the brand and a willingness to maintain a valued relationship with it (Chaudhuri and Holbrook 2001). In this sense, commitment is often expressed in a context of entrenched psychological attachment (Bowden 2009). Hence, commitment can be a potential consequence for existing customers (Hollebeek 2011). So, we posit that: Hypothesis 11: CBE has a positive effect on customer commitment. Customer’s Identification with Virtual Social Community Identification with the brand community is the “strength of the customer’s relationship with the brand community” (Algesheimer, Dholakia and Herrmann 2005, p. 20) and is one of the most relevant characteristics of a brand community (Füller, Matzler and Hoppe 2008). Bhattacharya, Rao and Glynn (1995, p. 47) define identification with a brand community as “the perception of belonging to a group with the result that a person identifies with that group.” Therefore, community membership and identification contributes to higher levels of individual customer intentions and behaviors toward the brand (Bhattacharya, Rao and Glynn 1995; Dholakia, Bagozzi and Pearo 2004; Füller, Matzler and Hoppe 2008). So: Hypothesis 12: Customer’s identification with the social networking site moderates the effect of the drivers on CBE. Customer’s Trust in the Virtual Social Community Another key factor for developing and facilitating relationships exchange within the brand community is trust (Bruhn, Schnebelen and Schäfer 2014). Brand community trust refers to the sense of safety and security arising from the honesty, reliability, and trustworthiness of a brand community (Casaló, Flavián and Guinalíu 2008). Additionally, trust contributes to the cooperative behavior of brand community members (Casaló, Flavián and Guinalíu 2008). Furthermore, trust toward the virtual social network brand communities can also be an important element in influencing the customers’ behavior, in terms of the member’s intentions to maintain the tie, to recommend, and to participate in virtual social network brand communities (Tsai and Men 2013). So: Hypothesis 13: Customer’s trust toward the social networking site moderates the effect of the drivers on CBE. RESEARCH METHODOLOGY AND FINDINGS The main goal of this article is to identify and integrate the key brand constructs related to the CBE process into a comprehensive model. In order to test the theoretical model, a cross-sectional, online survey was used. The questionnaire was designed with the objective of potentially minimizing response biases. Scales were systematically examined to reduce ambiguity and Winter–Spring 2018 29 imprecision. Additionally, a pilot questionnaire was conducted in order to establish the content validity and to improve question format and scales. The structured questionnaire intended to provide data to test the hypotheses and was sent via email along with an introductory text and a link to a webpage with the online survey. In the first question of the questionnaire, customers were asked to name a brand with which they felt strongly engaged and followed. They could refer to a service, a product or an organization, because the main aim was to ensure that the sample was composed of customers who exhibit some degree (although variable) of CBE. Besides respondents’ characteristics, the questionnaire comprised thirtythree questions regarding CBE and its antecedents, using multiple indicators and a seven-point Likert scale. Respondents were also asked about their identification with and trust toward the virtual social community. The questions were derived from existing scales in the literature (see Appendix), adapted in order to suit the context of this study. In particular, the concept of CBE was measured as a multidimensional concept, comprised of cognitive, affective, and behavioral dimensions (Dwivedi 2015). To measure the main drivers of CBE and its impact, a causal model using SEM was developed. Data analysis was performed using the IBM SPSS and SPSS AMOS, version 22. Main results are shown in the following section. Confirmatory factor analysis (CFA) and reliability tests were performed on the items in order to evaluate the psychometric properties of the study’s constructs. As a result, three items were removed: “Consuming” (from the “Customer Participation” construct), “Vigor2” (from the “CBE” construct), and “Affective1” (from the “Customer Commitment” construct). Internal reliability tests of the identified factors showed strong Cronbach’s alpha, ranging from 0.719 to 0.912, Composite Reliability (CR), and Average Variances Extracted (AVE), with all estimates above recommended minimums of 0.70 and 0.50, respectively (Bagozzi and Yi 2012; Fornell and Lacker 2012; Hair et al. 2010). In addition, evidence of the measures’ validity is provided by the fact that all factor loadings are significant and above 0.5, suggesting high levels of internal consistency and adequate item reliability (Hair et al. 2010). Moreover, all latent variables show convergent and discriminant validity, demonstrating the validity of the constructs (Bagozzi and Yi 2012; Fornell and Lacker 2012; Hair et al. 2010). The scales are reported in Table 1 along with reliability, validity, and dimensionality statistics. The measurement model elicits a significant Chi-square (i.e., χ2 (389) = 1253.928, p < 0.05). Other indices suggest an acceptable fit to data: X2/df = 3.223; GFI = 0.905; CFI = 0.956; TLI = 0.947; PGFI = 0.71; PCFI = 0.799; RMSEA = 0.053; P[rmsea ≤ 0.05] < 0.079. Preliminary Analysis and Measurement Models Direct Effects Testing A total of 799 respondents completed the survey. The sample comprises 78.3 percent customers with ages between 18 and 30 years, 71.5 percent female customers and 43.8 percent customers with bachelor’s degrees. Previous studies indicated that women spend more time on Facebook than men (e.g., Hoy and Milne 2010; McAndrew and Jeong 2012; Shepherd 2016) and statistics show that women between the ages of 18 and 29 years are the most active Facebook users (Pew Research Center 2016). In this sense, the sample is in agreement with the population of interest. In total, 283 different brands were represented. Among the brands selected by the customers, the most referred (more than ten times) were Zara (8.83%), Nike (8.08%), Apple (4.98%), Adidas (3.11%), Mango (2.74%), Coca-Cola (2.11%), Springfield (1.37%), Nivea (1.24%), Pandora (1.24%), and Salsa (1.24%). The fit indices of the causal model suggest an acceptable fit to data: X2/df = 3.182; GFI = 0.913; CFI = 0.958; TLI = 0.948; PGFI = 0.687; PCFI = 0.769; RMSEA = 0.052; P[rmsea ≤ 0.05] < 0.127. The model explains 96 percent of the variability of CBE levels observed in the analyzed sample (R2 = 0.96). Table 2 provides an overview of the hypothesis testing results. Overall, customer involvement has the greatest impact on CBE (βCBE.CI = 0.782; p-value < 0.001). In the same way, customer interactivity (βCBE.CInt = 0.184; p-value = 0.001) and customers flow experience (βCBE.CFE = 0.174; p-value < 0.001) exert a positive and direct effect on CBE, supporting hypotheses H5 and H7. Hypotheses H8, H9, H10, and H11 were also supported, settling customer commitment as the main outcome of CBE (βCC.CBE = 1.004; p-value < 0.001). 30 Journal of Marketing Theory and Practice Table 1 Measurement Scales, Reliability, Validity and Dimensionality Statistics Variable Cronbach’s alpha CR AVE Item Standardized factor loadings Customer Brand Engagement (CBE) 0.889 0.843 0.53 Customer involvement (CI) 0.910 0.901 0.696 Customer Participation (CP) 0.863 0.865 0.762 Customer interactivity (CInt) 0.782 0.781 0.549 Customer flow experience (CFE) 0.719 0.743 0.699 Customer word-of-mouth (WOM) referrals(Cwom) 0.856 0.863 0.612 Vigor1 Dedication1 Dedication2 Absorption1 Absorption2 Interest1 Interest2 PersonalRelevance1 PersonalRelevance2 Contributing Creating Control Responsiveness Personalization Escapism IntrinsicEnjoyment1 IntrinsicEnjoyment2 Activity1 0.78 0.74 0.53 0.73 0.84 0.88 0.75 0.89 0.78 0.86 0.88 0.67 0.73 0.79 0.57 0.53 0.62 0.76 Activity2 Praise1 Praise2 Satisfaction1 0.82 0.64 0.89 0.62 Satisfaction2 Reliability1 Reliability2 Reliability3 Intentions1 Intentions2 Affective2 Calculative1 Calculative2 0.63 0.85 0.88 0.84 0.82 0.65 0.83 0.8 0.76 Customer cumulative satisfaction (CCS) 0.898 0.809 0.679 Customer trust (CT) 0.912 0.905 0.659 Customer commitment (CC) 0.883 0.846 0.647 Note: All significant at p < 0.05. Moreover, customer involvement exerts a direct and significant impact on customer participation (βCP.CI = 0.301; p < 0.001), which in turn influences customer interactivity (βCInt.CP = 0.161; p < 0.001). In addition, customer interactivity positively influences customer flow experience. Hence, hypotheses H2, H4, and H6 were supported. Indirect Effects Testing Table 3 provides an overview of all the indirect effects. Despite not being a direct driver of CBE, customer participation indirectly influences CBE. In this sense, the influence of the customers’ participation on brand engagement is mediated by customer’s perceived interactivity (βCBE.CP|CInt = 0.054; p-value = 0.007). Also, customer participation indirectly influences one of the drivers and has a mediating role effect between two of the main drivers of CBE. Customers’ participation indirectly influences customers’ flow experience (βCFE.CP|CInt = 0.139; p-value = 0.013). No less important is the mediating effect of customer participation between customer involvement with the brand and perceived interactivity with the virtual social community (βCInt.CI|CP = 0.048; p-value = 0.013). Winter–Spring 2018 31 Table 2 Overview of Hypothesis Testing Results Hypotheses 1 2 3 4 5 6 7 8 9 10 11 12 13 Hypotheses supported B SE β p-value Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes No Yes 0.643 0.202 0.031 0.027 0.782 0.301 < 0.001 < 0.001 0.211 0.172 0.678 0.207 0.630 0.967 0.670 1.232 –0.098 0.133 0.050 0.045 0.057 0.058 0.036 0.051 0.036 0.056 0.019 0.03 0.161 0.184 0.863 0.174 0.695 0.873 0.778 1.004 –0.202 0.170 < < < < < < < < < < –0.113 0.193 0.019 0.034 –0.213 0.219 < 0.001 < 0.001 0.180 0.088 0.190 0.04 β Pvalue 0.048 0.139 0.054 0.150 0.551 0.617 0.796 0.692 0.232 0.260 0.336 0.292 0.121 0.135 0.174 0.152 0.013 0.013 0.007 0.013 0.013 0.010 0.011 0.012 0.012 0.012 0.012 0.004 0.013 0.012 0.015 0.016 Customer involvement -» CBE Customer Customer involvement -» participation Customer participation -» CBE Customer Customer participation -» interactivity Customer interactivity -» CBE Customer interactivity -» Customer flow experience Customer flow experience -» CBE CBE -» Customer cumulative satisfaction CBE -» Customer WOM referrals CBE -» Customer trust CBE -» Customer commitment CI x ComIde -» CBE CP x ComIde -» CBE CInt x ComIde -» CBE CFE x ComIde -» CBE CI x ComTrust -» CBE CP x ComTrust -» CBE CInt x ComTrust -» CBE CFE x ComTrust -» CBE 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 Notes: B, path coefficients (estimate); SE, standard error; β, standardized path coefficients. Table 3 Overview of Indirect Effects Testing Indirect Effects Customer involvement Customer participation -» -» Customer interactivity -» Customer cumulative satisfaction Customer -» CBE -» involvement Customer participation Customer interactivity -» -» Customer flow experience -» Customer -» CBE -» interactivity Customer flow experience -» Note: β, standardized path coefficients. CBE -» Customer interactivity Customer flow experience CBE CBE Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer trust commitment WOM referrals cumulative satisfaction trust commitment WOM referrals cumulative satisfaction trust commitment WOM referrals 32 Journal of Marketing Theory and Practice Likewise, when customers’ perceived that interactivity results in a flow state for them, this positively contributes to engage the customer with the brand (βCBE. CInt|CFE = 0.150; p-value = 0.013). Moderating Effects Testing The research further tested the moderating effects of customers’ identification with the social networking site (ComIde) and trust toward the social networking site (ComTrust) in the CBE process. Results are shown on Table 2. Customers’ identification with the virtual social community moderates the effect of customer involvement and customer participation on CBE. In fact, customer participation only becomes a driver of CBE in the presence of one of these moderates. In this sense, the higher the customer’s identification with the virtual social community, the higher the impact of customer participation on CBE (βCBE.CP*ComIde = 0.17; p < 0.001). Conversely, this moderating factor reduces the causal effect of customer involvement on CBE. Thus, the higher the customer’s identification with the virtual social community, the lower is the impact of the customer involvement with the brand on CBE (βCBE. CI*ComIde = –0.202; p < 0.001). Trust toward the virtual social community moderates all drivers of CBE, except customer interactivity. Trust toward the virtual social community improves the causal effects of customer participation (βCBE. CP*ComTrust = 0.219; p < 0.001) and customer flow experience on CBE (βCBE.CFE*ComTrust = 0.190; p = 0.04). Conversely, it reduces the effect of customer involvement on CBE (βCBE.CI*ComTrust = –0.213; p < 0.001). Hence, hypotheses H12 and H13 were partially supported. DISCUSSION AND CONCLUSIONS The purpose of this article is to identify and examine the key drivers of the CBE process and integrate them into a comprehensive model, together with main outcomes and moderators. The article shows that customer involvement, customer interactivity, and customer flow experience are the main drivers of CBE, when the outcomes of CBE are customer commitment, positive WOM referrals, and customer trust and cumulative satisfaction. The driver with the highest impact on CBE is customer involvement. However, its role in engaging the customer with the brand is reduced, when moderators such as customer’s identification with the virtual social community and trust toward it are considered in the analysis. Customer interactivity and customer flow experience are indirectly influenced by customer involvement and by customer participation, respectively. In this sense, involved customers who coproduce brandrelated content perceive higher interactivity with the brand community. Similarly, a customer who actively participates in the community is more likely to perceive a better interactivity with the virtual social community and, consequently, experience flow, that is, be completely immersed in the virtual social community and enjoying it. Additionally, when customers are completely immersed in the social networking site and enjoy it, their interactivity experience is better perceived, contributing to engage the customer with the brand. In the same way, when customers perceived interactivity as a pleasant experience, their participation with brand-related content contribute to engage the customer. Customer participation per se is not capable of generating CBE. However, in the presence of the moderators considered in this study, the results show that customer participation is a driver of CBE. Therefore, customers who have a strong and trusting connection with the social community are more likely to engage with the brand when they actively participate in it. THEORETICAL AND MANAGERIAL IMPLICATIONS Unlike previous conceptualizations (e.g., Hollebeek, Glynn and Brodie 2014; Leckie, Nyadzayo and Johnson 2016; Vivek, Beatty and Morgan 2012), this article takes a consolidated and empirical approach to the study of the CBE process. Additionally, the majority of the researchers have offered only conceptual guidelines and, as a result, the nomological network of CBE is still in its embryonic stage of development. Therefore, theoretically, this article improves the understanding about CBE and contributes with new insights. So far, this article is the first to incorporate, examine, and empirically validate the effect of flow experience on CBE, along with the moderating effects of customers’ identification and trust toward the virtual Winter–Spring 2018 33 social community. Another contribution of this research regards the methodological approach. Most of the existing studies on CBE are conceptual or qualitative, while this research was based on a large sample of customers, leading to more generalizable findings. Regarding the managerial implications of this article, the findings of this research enable the development of practical insights that may lead to stronger brands. Currently, managers are constantly trying to improve CBE and assess key performance indicators that contribute to generate high CBE levels. Additionally, little is known about how to approach virtual social communities in a way that maximizes engagement. Given that flow experience was found as a relevant driver of CBE, managers should design effective communication tools that allow and stimulate the immersion and sensation of escapism of the customer during navigation on the virtual social community, as well as pure enjoyment. For example, companies can include in their Facebook pages tabs dedicated to their events, open job opportunities, tips (e.g., fashion trends, latest fashion ideas), videos to spread awareness, compelling photos and GIFs, positive messages about current events, and visitor posts about the brand. Moreover, giving the key role of customer involvement in the CBE process, companies should seek to drive active involvement of existing customers. In order to do so, the branded content of the Facebook page should be designed according to the customers’ demands, and not with those of the company which promotes. Online contents and experiences that stimulate customer interest and personal relevance may create an enjoyable environment favorable to motivating customers to engage with the brand. Simultaneously, given the impact of customer interactivity on CBE, companies should promote and provide an environment that stimulates active exchange of information among members and between members and the brand in the social networking site. For example, in order to show customers and followers that the brand is listening to their needs and is committed to responding to comments, companies can add to the tab “About” their typically frequency of response to customers’ inquiries. Customer service is essential on Facebook. By developing processes to support these specific customer interactions, companies can increase customers’ trust and identification with the brand’s Facebook page, which in turn may help to get engaged with customers. Moreover, by asking customers for suggestions or promoting communication among members, companies can also manage the customer involvement with the brand and the customer flow experience with the brand’s Facebook page. Instead of dealing with customers as recipients of brand initiatives, companies should focus on a process receptive to the insights coming from the daily customer-brand encounters on the Facebook brand page. By enhancing the drivers previously mentioned, companies can not only directly stimulate CBE, but also leverage the CBE process. This can be important, when, for example, companies are not able to track the constant evolution of their customers’ needs, interests or expectations. By promoting customers’ trust and cohesion between the active members of the social community, brands can improve customers’ motivation to engage through interactivity and participation. Furthermore, taking into consideration the indirect effect of customer participation on CBE and CBE drivers, companies should seek ways to encourage coproduction of brand-related content (e.g., post product reviews, produce and upload branded videos, music, and pictures). By getting customers to comment and to share their opinions about the content on the brand’s Facebook page, created either by the brand itself or other visitors, companies can better know their customers, which in turn give them more tools to enhance CBE. Finally, this study also recognizes that CBE is a potential driver of what relationship marketing literature suggests to be the three predictors of customer retention: customer satisfaction, customer commitment, and customer trust (e.g., Morgan and Hunt 1994). In this sense, by identifying which customers are prone to engage with the brand and likely to respond to satisfaction improvement efforts, managers can improve their return on marketing investment. LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH One limitation of this research is that it relied upon data of only one social networking site (Facebook) to test the hypotheses. Although the concepts presumably apply to other platforms, the generalization of the findings should be performed with care. Therefore, future research can test the same nomological network of CBE in other social media contexts, such as Twitter or Instagram, and can also develop a comparative analysis between millennials customers versus “older” customers. 34 Journal of Marketing Theory and Practice Second, this research only considers the positive side of CBE, leaving aside negatively valenced CBE. This would contribute to existing literature and would help companies understand the drivers of customer negative engagement (e.g., Hollebeek and Chen 2014). Third, this article considers as outcomes of CBE customer cumulative satisfaction, customer trust, customer commitment, and customer WOM referrals. Future investigation can also investigate other outcomes, such as the emotional brand attachment, self-brand connection, or cocreation of value as outcomes of CBE. Fourth, data collection was limited to a snapshot of customers at a specific point in time. Giving the dynamic nature of CBE, future researchers may adopt a longitudinal approach that will be able to contribute with new insights about the different CBE stages, dynamics, and triggers. ORCID Amélia Carvalho 1793 Teresa Fernandes 2188 http://orcid.org/0000-0002-9075http://orcid.org/0000-0001-5753- REFERENCES Algesheimer, René, Utpal M. 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Customer participation Muntinga, Moorman and Smit (consuming, contributing, A) view brand-related content and/or comments of (2011, p.16) and creating) others customers B) contribute with brand-related content C) produce and publish brand-related content Customer interactivity Wu (2006, p. 98) Control I was in control of my navigation through the website and virtual social platforms of the brand. Responsiveness I could communicate with the company directly for further questions about the brand if I wanted to. Personalization I perceived the brand virtual social platforms to be sensitive to my needs for information. Searching the brand virtual social platforms “gets Customer flow Mathwick and Rigdon (2004, p. Escapism 330) me away from it all.” experience Intrinsic enjoyment I enjoyed the internet search for its own sake, aside from any products or services I may eventually purchase. I searched for the pure enjoyment of it. Customer word-of Harrison-Walker (2001, p. 72) WOM activity I’ve told more people about this brand than I’ve told mouth (WOM) about most others brands. communication When I tell others about this brand, I tend to talk about it in great detail. WOM praise I have only good things to say about this brand. I am proud to tell others that I use this brand. Customer cumulative Olsen and Johnson (2003, p.189) Affective How far from or close to do you think brand is to satisfaction the ideal brand? How satisfied or dissatisfied are you overall with the brand? Reliability items description This brand name meets my expectations. Customer Trust Delgado- Ballester, MunueraAleman and Yagiie-Guillent It is a brand name that never disappoints me. (2003, p. 41) Intentions items description This brand would be honest and sincere in addressing my concerns. This brand would make any effort to satisfy me. Affective commitment I like brand X. Customer commitment Harrison-Walker (2001, p. 72) I have a special relationship with this brand. Fullerton (2011, p. 97) Calculative or continuance It would be very hard for me to switch away from commitment this brand. It would be too costly for me to switch from this brand to another. Customer Engagement Dwivedi (2015, p. 105) Vigor I am passionate about using brand X. I can continue using brand X for very long periods. Dedication I feel enthusiastic when interacting with brand X. I am proud of brand X. Absorption I get carried away when I interact with brand X. I feel happy when I am interacting with brand X. Customer involvement Mittal (1995, p. 670) Components