Online brand community and consumer brand trust: Analysis from Czech millennials

Abstract

To ensure effective and efficient relationship marketing, brand management has become an asset (pre-requisite) for quality service marketing to thrive. Brands have gone viral to develop communities to interact with existing and novice consumers. However, the relationships between these online communities and brand trust are less explored in the literature, given the fact that consumers and users of information are core beneficiaries of these communities. The study, hence, adopted a referral (snowball) method of sampling technique to identify and gather data from social media users who are mainly millennials from the Czech Republic. Five hypotheses were tested using PLS-SEM with 534 valid respondents. The results show that online brand community has a direct significant effect on consumer’s brand trust, and indirect significant via the mediating role of peer-to-peer interaction and consumer brand engagement. Hence, the research provides managers (brand practitioners) with new insights regarding the motivations (brand promise and trust) as consequence of interacting in online brand communities. Again, this study enhances social media marketing and branding literature for researchers and practitioners to leverage on the relevance of online brand community for a firm’s competitive edge. Limitation and future research directions are considered.

Keywords:

• online brand community
• consumer brand engagement
• consumer brand promise & trust
• peer-to-peer interaction
• millennials
• Czech Republic

PUBLIC INTEREST STATEMENT

Today, brand communities have spawned considerable attention among scholars and marketing practitioners. Previous researches have shown that, the increasingly high interactive nature of Webpages and other social networks have enabled quick and easy exchanges of information among users, hence, contributing to the rapid development of online brand communities. In this light, the present study conceptualises a framework that could enhance brand (or company) managers’ desire to establish the presence of online brand communities and attracting participation of members. Again, it is imperative to note that customers in recent times consider the path that brands take on their way to success. This is based on the belief that most successful businesses have many customers who may or may not like them based on the brand concept that communicate to them. Hence, this study investigates the direct and indirect impact of online brand community toward consumer brand promise and trust among millennials in the Czech Republic. Managerial implications are offered to business organisations.

1. Introduction

Customers nowadays consider the path that brands take on their way to success. This is based on the fact that most successful businesses have a large number of customers who may or may not like them (Haenlein et al., 2020). Extant research postulates that a brand’s success does not inspire these customers to rally around them (Cheng et al., 2020; Kaur et al., 2020). Others, however, argue that it is the combination of passionate customers and the brand itself that makes the best businesses successful (Coelho et al., 2018; Habibi et al., 2014). As a result, brands and their loyal customers form a brand community. According to Thompson and Sinha (2008), a brand community is a specialized, non-geographically bound community based on a structured set of social relations among brand fans, enabling an organization to establish a series of connections with its consumers. Consumers in brand communities play a significant role in this regard.

An online brand community (OBC) is required for businesses to improve their differentiation and competitive position; it enables them to strengthen customer relationships. A growing number of businesses are investing time and resources in designing strategies and managing online brand communities on social media to attract and engage consumers (Ibrahim et al., 2017; Kaur et al., 2020). Consequent to this, businesses now have access to a wealth of new data about their target audience, including information about their habits, needs, wants, and future purchasing intentions (Elia et al., 2020; Kwarteng et al., 2021). They can also be used for one-on-one marketing, allowing companies to connect with customers on a more personal level (Bélisle & Bodur, 2010; Kumar et al., 2019; Steinhoff et al., 2019).

Many academics and practitioners have been focusing on this area of study in recent years because of the new challenges that brands face (Bogomolova & Millburn, 2012; İpek & Yılmaz, 2021). An important question is whether the interactions between members of a brand’s social media community lead to a personal relationship with the brand itself or not? In this light, the brand community dynamics such as consistency in service delivery and consumer trust have been a major concern for patrons in the online brand community, hence, customer trust, for instance, has been the subject of numerous studies (Anaya-Sánchez et al., 2020; Jung et al., 2014). Nonetheless, literature on how millennials happen to be the promoters of the online brand community appears is still a subject under discussion. Jung et al. (2014) and that of Javed et al. (2020), for instance, explored the influence of consumers on online brand community and revisit intentions, and found a significant relationship between attitude and brand trust. Similarly, Anaya-Sánchez et al. (2020) found that trust positively affects repurchase intentions. From the ongoing, it is apparent that extant study on the online brand community has focused on the purchase and repurchase intentions with limited understanding of how trust plays a critical role in the OBC ecosystem.

Today, brands from all product categories, including convenience goods, interact with consumers on social media (Habibi et al., 2014). Additionally, a growing variety of consumer types are accessible to these interactions (Martínez-López et al., 2017). Yet, the question of how brand trust play engenders the outcomes from brand communities for millennials remains unanswered. The mass market is highly competitive, with many products designed for the same purpose and lacking consumer differentiation. Up until now, brands operating in the online community market lacked a direct communication channel with consumers due to technical and operational challenges such as reliability and expertise (Bankuoru Egala et al., 2021). Given the above, this paper seeks to investigate how OBCs influence consumers’ trust. Specifically, the study seeks to; (1) determine the relationship between the online brand community and brand promise and trust and, (2) assess the mediating role of peer-to-peer interaction and consumer brand engagement on the link between the online brand community and consumer brand promise and trust.

This paper explores the significance of OBC to consumer-brand trust specifically millennials, thereby addressing significant gaps in the current literature. This research aims to develop a conceptual model based on a review of the relevant literature and empirical evidence. Although conceptual, this study makes three contributions to the literature on online brand communities and consumer brand trust. The first two aspects are associated with the creation of a conceptual model for the OBC consumer trust as shown in Figure 1. The study contributes to the empirical discussion about the significance of online community and internet-based brand marketing to comprehend consumers’ engagement in an online brand community and investigate the relationship between brand community engagement and consumers’ expectations. The final aspect focuses on assisting practitioners in understanding the role of various brand types within the same product category. The literature review and conceptual framework of the study are described in the following section, followed by the hypothesis, research methods, and results. This paper concludes with a discussion of the findings, limitations of the study, and suggestions for future research directions.

Figure 1. The conceptual framework for the study

2. Conceptual framework and hypotheses development

2.1. Online brand community (OBC) and brand promise & trust (BPT)

OBCs have become increasingly important in brand communication and customer relationship strategies (Casaló et al., 2013; Lin, 2008). As a result, marketing professionals and academics are interested in understanding the mechanisms that contribute to their success. A brand community is a group of customers who have a vested interest in a brand that extends beyond what is sold. A brand community is described as a distinct type of non-geographically constrained community that is based on a standardized set of social ties connected to a specific brand of preference (Muniz & O’guinn, 2001), enabling a company to establish a series of connections with its customers (Thompson & Sinha, 2008). Again, a customer-experiential brand community is a web of relationships in which the customer is embedded (McAlexander et al., 2002). The brand community establishes the relationships between customers and brands, businesses, the products they use, and other customers are all crucial. According to Brogi (2014), online brand communities are highly specialized and usually focus on branded goods and services. Due to their commercial orientation and members’ shared interest in, admiration for, sympathy with, and sometimes even love for a brand, these consumer communities are different from traditional communities (Albert et al., 2008). The ability of the Online Brand Communities members to communicate with one another is its primary uniqueness. Recent research has shown that consumers who engage in an OBC communicate their passion for a certain brand by exchanging information and knowledge or by merely expressing their feelings, and these social interactions influence customers’ relationships with the brand (Brogi, 2014; Jibril et al., 2019; McAlexander et al., 2002). Firms are getting more and more interested in developing online brand communities (OBCs) to manage customers since OBCs have a greater impact on brands and provide a more expansive definition of brand connections (Baldus et al., 2015; Cova & Cova, 2002; Fournier et al., 2009; Porter & Donthu, 2008). Moreover, J. Kumar and Kumar (2020) established that customers who distinguish themselves through online brand communities do so by firmly upholding the common values, beliefs, norms, and trust of one community, which boosts their self-esteem.

Admittedly, an extensive study on the brand has exposed the challenges in identifying it (Hobbs & Goddard, 2015). A brand promise is a value or experience that customer can count on from a business each time they deal with it (Portal et al., 2019; Punjaisri et al., 2008). The greater a company’s brand value is in the eyes of customers and employees, the more it can deliver on that promise (Kim, 2020). Brand trust connects brand promise, customized experience, and corporate culture. The level of customer confidence in a brand is measured by brand trust (Delgado-Ballester & Li et al., 2008). It reveals whether a particular branding consistently fulfils its commitments and upholds its principles or not. Brand Trust is an essential component of any worthwhile social interaction. According to Chaudhuri and Holbrook (2001), brand trust is the consumer’s readiness to depend completely on the brand’s capacity to deliver on its promise. According to Portal et al. (2019), a brand must possess both good intentions and reliable skills to earn consumer confidence. Research works by (Morhart et al., 2015; Schallehn et al., 2014) revealed that brand trust is similar to that of genuine brands, which show their intentions by upstanding organizational principles and their capacity to fulfill their brand promise. According to Kimpakorn and Tocquer (2010), brand trust is “a psychological condition resulting when one party has confidence in an exchange partner’s reliability and integrity. It is more particularly the customers’ perception of a brand’s competence, consistency, reliability, and honesty. Considering this, if a brand successfully fulfils its promise to its customers, trust strengthens the relationship between the brand and its customers (Han et al., 2015; Hyun, 2010; Kim et al., 2006; Wang et al., 2014). Keller (2008) claims that building a brand in customers’ minds through brand promise and brand trust results in the development of a brand relationship. Brand promise and trust are important factors in consumer-business interactions because they represent the extent to which consumers believe the functions specified by the brand can be properly carried out (Kwon et al., 2020). In this light, the authors hypothesize that;

H1: Online brand community positively predicts brand promise and trust perceived by customers.

2.2. Peer-to-peer interaction (PPI)

Peer-to-Peer interaction is a network of interconnected content repositories that is regarded as one of the sources of readily available explicit knowledge (Yang & Chen, 2008). Peer-to-peer interaction is a method of interaction and collaboration between participants in a shared project or activity that is distinguished by network-based organizational structures, a shared common resource base, and the presumption that every participant has the potential to contribute positively (Moon et al., 2019; Van der Linde et al., 2017). In typical digital marketplaces, peer-to-peer exchange of money socializes around the brand and shares information about their value propositions (V. Kumar & Reinartz, 2016; Vargo & Lusch, 2016). For a few decades, the importance of peer-to-peer interactions among customers has been recognized (Verhoef et al., 2009). Again, Choi and Kim (2013) revealed that peer-to-peer interactions influence the satisfaction of other customers through modern technological platforms like social media. With the development of mobile technology and networks, several brands have gained popularity (Owyang, 2015). Furthermore, Hamari et al. (2016) and Ozbal et al. (2020) admonished that with the rise of smartphones over the past ten years, more people are showing interest in P2P digital platforms and businesses (brands), which has altered the consumption patterns of younger generations. P2P interconnections are digital architectures that enable exchanges between large numbers of dispersed buyers and sellers (Bardhi & Eckhardt, 2012; Milojicic et al., 2002; Tussyadiah & Pesonen, 2016). Peer-to-peer interactions are the most common mode of exchange (Lamberton & Rose, 2012), which allows individuals to create, produce, distribute, and consume products and services. Additionally, Ravi (2016) affirmed that youngsters of today’s generation use technological platforms like social media in the 21^st century to interact with their peers on the consumption of a particular brand. This phenomenon of P2P encourages entrepreneurship, lowers barriers to entry for small businesses, enables individuals to compete with established businesses, and offers opportunities for better resource management. Again, demand is met to a greater extent when a platform has a larger number of users, which raises the platform’s value (Ravi, 2016). Hence, the study hypothesizes the following:

H2: Online brand community would positively influence peer-to-peer interactions.

H3: Peer-to-peer interaction would have a positive mediation between online brand community and consumer brand promise & trust.

2.3. Consumer brand engagement (CBE)

The term “brand engagement” describes the development of bonds between customers and brands (Adhikari & Panda, 2019; Di Benedetto, 2021). These relationships, which could be intellectual or emotional, eventually lead to brand promise and trust. This strengthens the brand and enhances the user experience. Hollebeek et al. (2014) define consumer brand engagement (CBE) as a consumer’s cognitive, emotional, behavioural, and co-creative brand-related activities related to specific interactions. It is anticipated that CBE will play a significant role in developing increasingly experiential relationships with consumers, namely brand-consumer relationships. Furthermore, studies by Adhikari and Panda (2019) and Machado et al. (2019) revealed that customer involvement raises close rates while fulfilling contemporary B2B customer expectations, which helps both buyers and suppliers. It, therefore, keeps clients interested throughout the buying process to foster loyalty and get useful data about them. Again, Cheung et al. (2021) emphasized that more customer contacts increase brand value for consumers and give customer insights. These consumer insights help sales operations such as message and outreach techniques, marketing choices, retargeting, and content generation. Adhikari and Panda (2019) assert that the only method to improve brand loyalty, promise, and trust and, thus, the best indicator of present and future performance, is to stimulate a consumer’s involvement with a brand. Engagement acts as a catalyst that converts prospects to consumers, consumers to loyal consumers, and loyal consumers to brand advocates (Duffy Agency, 2015). The concept of consumer brand engagement is not new; however, its use in branding literature has grown in recent years. Consumer-brand engagement research (Hollebeek et al., 2014; Vivek et al., 2012; Gambetti et al., 2012; France et al., 2016; Dessart et al., 2016; Fernandes & Moreira, 2019; Singh & Srivastava, 2019) provides both theoretical and empirical support for its significant and positive relationship with the brand promise and trust.

In line with the discussion, the study postulates the following hypotheses:

H4: Online brand community would positively influence toward consumer brand engagement.

H5: Consumer brand engagement would have a positive mediation between online brand community and consumer brand promise & trust.

Based on the discussions above we synthesize literature on online brand community engagement and derive a conceptual framework (see, Figure 1).

3. Methodology

To comprehend consumer participation in the brand promise and trust arena from the perspective of the online-based-brand community, the article utilized a quantitative research approach. A non-probability sampling technique, mainly the snowball sampling approach, was used for this survey’s sampling methodology. Participants in a study or testing recruit other participants, which is a sort of non-random sampling. This type of sampling technique is used or adopted where it is challenging to locate the necessary participants. The adoption of the snowball technique as revealed by Etikan et al. (2016) is considered by the researchers based on the geographic closeness, willingness to engage, accessibility of participants to the researcher, and cost-effectiveness. The data collection was done in the Zlin region among university students. Again, using the snowball sampling technique, the questionnaire was distributed to students at Tomas Bata University across all the faculties including management and economics, applied informatics, technology, humanities, multimedia communication, and logistics and crisis management who then shared it with their colleagues to participate in the research. Section A of the questionnaire contains the demographics of the respondents whiles the second part contains questions on the study constructs. To accomplish the general objective of this study, a structured self-administered questionnaire design and an online survey were used as the data collection and analysis technique. In all, 534 respondents participated in the questionnaire answering out of the 600 questionnaires distributed. The final data set’s characteristics of the respondents are shown in (Table 1). In terms of data analytics, the conceptual framework and accompanying hypotheses were tested using the PLS-SEM (Partial least squares and structural equation modeling) technique. This was made possible by utilizing the ADANCO software version 2.2.1 (Henseler, 2017). The researchers advised the respondents not to provide any specifics. As revealed by Amoah et al. (2021), this is to ensure a high ethical standard of research. Again, some methodological researchers have criticized PLS. When it is unclear whether the data are a common factor- or composite-based, PLS-SEM is still favored (above CB-SEM). Contrary to covariance-based SEM, PLS-SEM in this approach focuses on maximizing the explained variance of the endogenous components (CB-SEM).

Table 1. Demographic profile of the respondents

Details		Frequency		Percent (%)
Sex	Female Male	343 191		64.3 35.7
Age	18–22 yrs 23–26 yrs Above 27 yrs	239 150 145		44.9 28.0 27.1
Educational level	First degree/undergraduate Master’s/Postgraduate Others	370		69.1
		118 46		22.2 8.7
SM Platforms	Instagram Facebook Twitter Linkedln	245 55 222 12		45.9 10.4 41.5 2.2
Hours spent	Below 1 hour Above 1 hour a day Above 2–5 hours per a day Above 6 hours	420 88 26		78.7 16.4 4.9
Faculties	Mgt & Economics Humanities Applied Informatics Technology Multimedia comm. Logistics & crisis Mgt.	220 78 70 66 54 46		41.20 14.61 13.11 12.36 10.11 8.61
Sample size (n)		534		100

Source: Authors’ field data from Ghana (January to July 2022).

4. Study results

4.1. Measurement of the constructs and test of common method variance (CMV)

In determining the constructs’ validity, the researchers drew inspiration from earlier investigations. Therefore, the study’s constructions were drawn from Online Brand Community (OBC)-(Baldus et al., 2015; Madupu & Cooley, 2010; Muniz & O’guinn, 2001), Peer-to-Peer interactions-PPI (Eysenbach et al., 2004; Van der Linde et al., 2017), Consumer Brand Engagement CBE (Baldus et al., 2015; Hollebeek et al., 2014), Brand Promise, and Trust BPT (Jibril et al., 2019; Özkanal, 2019). The five-point Likert scale (Completely Disagree (1), Disagree (2), Neutral (3), Agree (4), and Completely Agree (5) was used to measure the study constructs (Leung, 2011). Since the data for the current investigation were collected independently, the likelihood of common method variance is relatively high. Additionally, the study’s participants were told that their information would be kept private and advised that there was no right or incorrect response to any of the survey’s questions. The existence of Common Method Bias (CMB) was determined by the study of (Bagozzi & Yi, 1988), which led the researchers to create the questionnaire with the title page description and to treat respondents or participants with the utmost confidence. To be more specific, the questionnaire was designed so that respondents or participants could opt out whenever they wanted. Above all, the researchers ran a multicollinearity test on VIF (variance inflation factor) to determine the presence of Common Method Bias (CMV). Based on VIFs (see, Table 2) where the thresholds are fewer than ten (10) as shown by (see, Kock & Hadaya, 2018; Podsakoff et al., 2003; Salmerón et al., 2020), the post-hoc evaluation results show that CMV has a minimal existence. Finally, the CMB concerns in this poll are considered to be minor, therefore they are not as important.

Table 2. Construct reliability and validity, variance inflation factors (VIFs), and factor loadings

Constructs/Indicators	Dijkstra-Henseler’s rho (ρA)	Jöreskog’s rho (ρc	Cronbach’s alpha(α)	The Average Variance Extracted (AVE)	VIF	Factor Loadings
Brand Promise Trust	0.7200	0.8241	0.7137	0.5414
BPT1					1.6986	0.7931
BPT2					1.3010	0.6934
BPT3					1.6245	0.7973
BPT4					1.1884	0.6481
Online Brand Community	0.6057	0.7875	0.5937	05548
OBC1					1.2443	0.7566
OBC2					1.3662	0.8191
OBC3					1.1419	0.6490
Consumer Brand Engagement	0.5703	0.8224	0.5686	0.6984
CBE1					1.1873	0.8231
CBE2					1.1873	0.8481
Peer-to-Peer Interactions	0.7051	0.8294	0.6948	0.6188
PPI1					1.2966	0.7987
PPI2					1.3671	0.7449
PPI3					1.4198	0.8146

Source: Authors’ processing from ADANCO 2.2.1 version

4.2. Model measurement

As the researchers were inspired by the PLS-SEM application literature of scholarly works (Hair et al., 2017; Hair et al., 2019), they rigorously tested the constructs’ reliability and validity using Dijkstra-rho Henseler’s with Cronbach alpha coefficients. Since all of the coefficient values are more than the threshold value of 0.5 (see, Table 2 below), it shows the constructs with the strongest coefficients (Bagozzi & Yi, 1988; Hair et al., 2017). The psychometric qualities of the underlying items of the research constructs were evaluated using the ADANCO 2.2.1 version. Again, Jöreskog’s rho (pc) and Dijkstra-rho Henseler’s (pA) regarding the composite reliability of constructs as presented in (Table 2) recorded 0.5 and 0.8 minimum and maximum thresholds, which satisfies the prerequisites. Finally, a minimum threshold of 0.5 was recorded for the average variance extracted (AVE), which stands for convergent validity, as shown in (see, Table 2). For Dijkstra-Henseler rho (pA), 0.5703 and 0.7200 were recorded as coefficients construct reliability, respectively. According to (Bagozzi & Yi, 1988), every factor loading for each of the constructs was carefully evaluated and loaded to the appropriate locations, meeting the threshold of 0.6 and demonstrating how effective the indicators are. The coefficients of the corresponding constructs were all over 0.6 in (Table 2 below), with 0.6981 serving as the minimum loading and 0.8481 serving as the maximum loading, respectively. The details of the research constructs with their corresponding loadings are all shown in (Table 2 below). Additionally, the researchers were very concerned about the issue of multicollinearity and used the common method variance (CMV) to discover it using scale measurements of the variance inflation factor (VIF). According to the works of (Amoah et al., 2021; Hair et al., 2017; J. F. Hair et al., 2019), since the VIF is less than five as opposed to a maximum threshold of ten, CMV is not a problem. Table 2 below displays the factor loadings for the research constructs.

Nevertheless, (Henseler et al., 2015; Zaiţ & Bertea, 2011) inspirations led the researchers to use Fornell-Larcker’s to assess the latent variables’ discriminant validity. All the values in the diagonal form (bold) exceed the minimum threshold of more than 0.5, as determined by experts like (Hair et al., 2017; J. F. Hair et al., 2019), which displays the average variance extracted (AVE) of the tested constructs (see, Table 3 below). The basic and strict assumptions of the study constructs were defined once each AVE had to have higher coefficients (both column and row position) than the other constructs, according to Fornell-criterion Larcker’s of discriminant validity.

Table 3. Test of discriminant validity—HTMT

1 = Brand Promise Trust (BPT)	0.5414
2 = Online Brand Community (OBC)	0.2075	0.5548
3 = Consumer Brand Engagement (CBE)	0.3589	0.1936	0.6984
4 = Peer to Peer Interactions (PPI)	0.3083	0.3978	0.1735	0.6188

Note: The diagonal (in bold) is the average variance extracted (AVE) Sources: Author’s processing from ADANCO 2.2.1 version.

4.3. Path analysis for structural modeling

In this current study, concerning the model fit, the researchers observed the essence of path analysis, also known as structural modeling. Figure 2 presents the structural model for this study. The purpose of this analysis is to demonstrate the causal relationship between the research constructs. As a result, the study’s findings strongly suggest that the online brand community (OBC) has the potential to have an effect or impact on current research constructs such as Peer-to-Peer interaction (PPI), Brand Promise, and Trust (BPT), and Consumer Brand Engagement (CBE). The regression coefficients of Beta (β), significant values; T-values >1.96 (or P-values 0.05) concerning the study model are thus shown in Table 4 below. Additionally, the accuracy of the research model for determining values for the regression model was assessed. As a result, the R² of the dependent variables is 49% for Brand Promise and Trust (BPT), whiles the independent variables are: Consumer Brand Engagement (CBE) at 19%, 39% for Peer-to-Peer Interactions, (PPI), as shown in the table and figure below.

Figure 2. Estimated model from ADANCO 2.2.1 version.

Table 4. Hypothetical path coefficient

Relationship	Beta (β)	Standard bootstrap results					Empirical remarks
		Mean value	SD error	t-value	Effect size (Cohen’s f^2)	P-value
H1: OBC->BPT	0.4527	0.4553	0.0585	7.7416	0.2217	0.0000	Agreed
H2:OBC-> CBE	0.4399	0.4436	0.0470	9.3587	0.0000	0.0000	Agreed
H3: OBC> PPI	0.6307	0.6322	0.0331	19.0503	0.1227	0.0000	Agreed
H4: CBE->BPT	0.4487	0.4471	0.0364	12.3215	0.2100	0.0000	Agreed
H5: PPI->BPT	0.3270	0.3256	0.0482	6.7767	0.0068	0.0000	Agreed
Independent. variables:	Coefficient of determination (R^2)				Adjusted R^2
CBE	0.1936				0.1920
PPI	0.3978				0.3966
Dependent Variable:
BPT	0.4971				0.4971

Note: OBC = Online Brand Community, CBE = Consumer Brand Engagement, PPI = Peer-to-Peer Interactions, BPT = Brand Promise and Trust

5. Discussion and theoretical implications

This study’s purpose is predicated on the two underlying cardinal objectives guided by five hypotheses. Based on the data collected, it emerged from the psychometric analysis that female was more vibrant with 64.3%. Also, 18–22 years were found to be more relevant with responses at a percent of 44.9%. This study implies that the OBC offers a behemoth of capabilities to businesses to effectively build cohesive customer relationships. Given the complexity of customers and their constantly changing needs, OBC help builds stronger ties toward customer intimacy, retention, and encouraging repurchase intentions (Anaya-Sánchez et al., 2020). A leading strand of research has been conducted on online brand communities and consumer behavior. Yet, the relationship between OBC and customer brand trust relative to customer engagement and peer-to-peer interaction has been nascent. In essence, we broaden existing empirical literature on OBC and customer trust among millennials. We provide empirical insight into how consumers interact with OBCs. First, we provide insight into how OBC positively predicts brand promise and trust. As affirmed by Mao et al. (2020), trust is key in brand marketing since it promotes customer loyalty. Second, the study provides evidence that OBC positively predicts consumer brand engagement. This essentially, means that continuous engagement by businesses with the consumers in the online community tends to promote customer intimacy since trust is assured (Özkanal, 2019). Third, the study finds that OBC positively influences peer-to-peer interactions. Given that, millennials are often addicted to the healthy consumption of the internet and online activities, they are susceptible to peer pressure (Van Deursen et al., 2015; Goh et al. 2013). It therefore not surprising the study finding affirmed that OBC significantly influences peer interaction. Furthermore, the study provides that, consumer brand engagement will significantly influence brand promise trust (Coelho et al. 2018). Finally, this paper demonstrates that peer-to-peer interaction positively influences brand promise trust.

In literature, the study contributes to providing an understanding of how OBC influences consumer brand trust and in particular the mechanism by which millennials promote the process of brand trust. Thus, we show that OBC has a positive relationship with brand promise trust, consumer brand engagement, and peer-to-peer interaction. These findings are consistent with extant studies (Hernandez-Fernandez & Lewis, 2019; Jibril et al., 2019; Özkanal, 2019) affirming our expectations in line with prior studies on OBC. While Jibril et al. (2019) for instance, acknowledged most OBC cues being generated on social media, the authors add that, brand promise and trust highly stimulated brand loyalty. Hernandez-Fernandez and Lewis (2019) opined that brand trust and perceived value for the brand tend to promote the authenticity of the brand. Even more importantly, marketers may be empowered by the ability to measure and assess the authenticity of their brands to identify new opportunities for brand positioning and value creation. Again, OBC had a significant relationship with consumer brand engagement consistent with these studies (Coelho et al., 2018; Hollebeek et al., 2014; Obilo et al., 2021). Additionally, Cheung et al. (2020) found that, interacting with customers online significantly influences consumer brand engagement. This according to the authors, strengthens brand awareness and brand knowledge. Aside, the study finds that OBC positively influences peer-to-peer interactions affirming prior studies (Elia et al., 2020). Conversely, Liao et al. (2017) found that interactions among peers in online communities exert a stronger positive effect on short-term members than those with long-term usage intention. Furthermore, consumer brand engagement will significantly influence brand promise trust consistent with these studies (Agyei et al., 2020; Jibril et al., 2019; Kosiba et al., 2020). Agyei et al. (2020) aver that firm’s continuous engagement in a bid to promote their brand trust, influences their loyalty. Furthermore, the findings showed that customer engagement significantly enhances customer loyalty and mediates the relationships between trust dimensions and loyalty to a company’s customers. The findings emphasize the importance of establishing trust with customers to increase customer engagement and customer loyalty. Finally, the study finds that peer-to-peer interaction positively influences brand promise trust consistent with (Matzler et al., 2008; Özkanal, 2019; Rueger et al., 2021; Tussyadiah & Park, 2018). Tussyadiah and Park (2018) conclude that it is imperative to investigate the unique processes of peer exchanges and online communities given the provenance role they play in brand promotion. The growing importance of social networking platforms in brand marketing stems from the need to improve brand popularity and competitiveness. As a result, the online brand community models have been heavily relied on by firms to build trust between businesses and consumers.

5.1. Managerial implications

Adding to the foregoing discussion, the study offers useful practical implications and insight to brand managers and social media strategies. The study shows that OBC significantly influences brand promise-trust. This is significant in the wake of stiffer competition among firms, particularly those leveraging online tools to drive their brand campaigns. While OBCs have the tendencies to reach millions of customers and potential customers, leveraging OBCs will help foster a bond between businesses and their brand lovers. Ultimately, brand community practices will generate brand loyalty. This explains how managers must pay attention to members of brand community practices. Given that, members in the OBC are of different needs and levels of anticipation, the platform will offer brand managers the opportunity to identify the latent needs of customers. It is also instructive to know that this affordance emanating from OBC comes at a cheaper cost compared to orthodox brand promotion channels.

Studies have shown that the cost involved in using OBC is far less than traditional advertising media with substantial reach and impact on sales (Akrout & Nagy, 2018). It is partly due to this that brand managers must rethink the means through which they reach out to customers in a bid to promote their brand or products and services. Since brand community fosters brand engagement, this ostensibly means that customers are better served through their constant engagement with the firms’ online managers. Often, brand champions emerged from OBC which advertently enhances their brand promise trust. Thus, this study supplements prior research seeking to promote brand managers’ efforts at building a lasting relationship with customers. As the study finds, consumer brand engagement influences brand promise to trust. Therefore, managers should facilitate sustained interactions among members of the community to foster customer intimacy. This is to say, managers of the community are to reign superiority over the community members which eventually will derail the purpose of the community. Hence, understanding the heterogeneity of the community members as a manager will help guide effective communication among each segment and foster good brand relationships.

6. Conclusion and limitations

The study sought to investigate the significance of OBC to consumer-brand trust specifically millennials to address existing gaps in the current brand community literature. Specifically, the study sought to determine the relationship between the online brand community and brand promise and trust and assess the mediating role of peer-to-peer interaction and consumer brand engagement on the link between the online brand community and consumer brand promise and trust. Using a structural equation meddling and data from 534 university students in Zlin in the Czech Republic, the study found that: a significant relationship exists between OBC and consumer engagement. Again, the study found that there is a link between OBCs and consumer brand trust mediated by the peer-to-peer interaction. Based on the set objectives, five hypotheses were proposed. First, the study result showed a significant relationship among OBCs, brand promise trust, consumer brand engagement, and peer-to-peer interaction. Second, a similar pattern was observed with consumer brand engagement, brand promise trust, and peer-to-peer interactions. In effect, a significant relationship exists between online community practices and brand trust which eventually fosters brand loyalty. This study comes with several implications for theory and management.

This study comes with some limitations. First, the study only investigated the influence of OBC on brand promise trust, consumer brand engagement, and peer-to-peer interactions relative to university students. Given how frequently students engage in online activities, the study results may not be the same for other categories of respondents. Again, the study did not focus on specific media through which OBC is mediated, in other words, we focused on several social media OBCs. Perhaps a study centred on specific OBC on social media could be explored. Furthermore, we did not focus on a particular industry or economic sector. This was done to give a broader view of OBC in varied economic sectors. Hence, future studies could relook at the stated issues and extend this study. Nonetheless, the study findings offer significant contributions to theory and practice.

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Acknowledgements

The authors are grateful to the anonymous reviewers as well as the Editor-in-Chief of this journal for their valued critics and suggestions that helped in shaping up the paper. Again, we are also thankful to the participants for their time spent during the data gathering.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by Tomas Bata University in Zlin through; IGA/FAME/2021/005. Significant factors in the sustainability of economic growth with a focus on the SME segment.