Abstract
Purpose
The purpose of this paper is to analyse the effects of brand trust and online brand community (OBC) trust on consumer repurchase intention and the positive electronic word-of-mouth (eWOM) intention of OBC members.
Design/methodology/approach
The research model was assessed using data from a sample of 628 OBC users using partial least squares structural equation modelling (PLS-SEM).
Findings
The results show that brand trust positively affects repurchase intention and positive eWOM intention, both directly and indirectly through OBC trust. The total effect of brand trust on the results is greater than that of OBC trust.
Originality/value
This research contributes to marketing theory and practice by analysing the combined effect of OBC trust and brand trust on the consumer–brand relationship in the context of OBCs.
Propósito
El objetivo de esta investigación es analizar los efectos de la confianza en la marca y la confianza en la comunidad online de marca en la intención de los miembros de la comunidad de recomprar dicha marca y de hablar bien electrónicamente (eWOM) sobre la misma.
Diseño/metodología/enfoque
El modelo de investigación fue evaluado usando datos de una muestra de 628 usuarios de comunidades online de marca, mediante un modelo de ecuaciones estructurales por el método de mínimos cuadrados parciales (PLS-SEM).
Hallazgos
Los resultados muestran que la confianza en la marca afecta positivamente a la intención de recompra y al eWOM positivo, directa e indirectamente a través de la confianza en la comunidad. El efecto total de la confianza en la marca es mayor que el de la confianza en la comunidad.
Originalidad/valor
Esta investigación contribuye a la teoría y práctica del marketing al analizar el efecto combinado de la confianza en la marca y en la comunidad online de marca, en las relaciones consumidor-marca.
Keywords
Citation
Anaya-Sánchez, R., Aguilar-Illescas, R., Molinillo, S. and Martínez-López, F.J. (2020), "Trust and loyalty in online brand communities", Spanish Journal of Marketing - ESIC, Vol. 24 No. 2, pp. 177-191. https://doi.org/10.1108/SJME-01-2020-0004
Publisher
:Emerald Publishing Limited
Copyright © 2020, Rafael Anaya-Sánchez, Rocío Aguilar-Illescas, Sebastián Molinillo and Francisco J. Martínez-López.
License
Published in Spanish Journal of Marketing - ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
A brand community is a non-geographical, specialised community, with a structure established around the social relationships of brand enthusiasts (Muñiz and O’Guinn, 2001). The increase in online brand communities (OBC) and their possible influence on brand success have led researchers and marketing professionals to strive to understand how companies might create and maintain lasting relationships with them (Kang and Shin, 2016).
Marketing theory has developed solid support for the metaphorical paradigm of consumer–brand relationships at an anthropomorphic level (Aggarwal, 2004; Fournier, 1998; McAlexander et al., 2002). Brand anthropomorphism empowers the brand to play a more central role in the consumers’ life (Delgado-Ballester et al., 2017). According to this research stream, consumers relate to brands in the same way they do with each other in a social context (Aggarwal, 2004). Taking this approach, researchers have shown that, in the online environment, as among people, trust is a crucial variable in the customer–brand relationship (Casaló et al., 2011; Hajli et al., 2017; Jensen and Wagner, 2018; Laroche et al., 2012; Liu et al., 2018). Consumers trust others like themselves and learn from their experiences through online consumption communities, so they can obtain more precise insights of a possible purchase (Belanche et al., 2019). Previous studies have highlighted the importance of consumer trust in both the brand (Akrout and Nagy, 2018; Jain et al., 2018; Kamboj et al., 2018), and trust in the community itself (Bruhn et al., 2014; Kang et al., 2016), as key antecedents of the benefits that OBCs can bring to organisations.
Of these benefits, the literature has highlighted the influence of OBCs on brand loyalty (Islam and Rahman, 2017; Jain et al., 2018; Martínez-López et al., 2016) and on positive electronic word-of-mouth (eWOM) (Akrout and Nagy, 2018; Brodie et al., 2013; Popp and Woratschek, 2016). However, although the effects of brand trust and OBC trust on consumer brand loyalty have been reported in the literature (Appendix), so far few empirical studies have analysed the joint effect of both types of trust on the outcomes provided by OBCs. The joint analysis of both types of trust would help to better understand their interrelationships and effects on consumer behaviour.
The objective of this study is to analyse the direct and indirect effects of brand trust and OBC trust on consumer repurchase and positive eWOM intentions. Data were collected from a sample of 628 consumers through an online survey, and the research model was evaluated using partial least squares structural equation modelling (PLS-SEM). The results show that brand trust and, to a lesser extent, OBC trust, directly and indirectly influence repurchase intention and positive eWOM intention.
2. Hypotheses development
2.1 Brand trust and online brand community trust
Trust is a psychological state that involves the willingness to accept one’s vulnerability but with the expectation that the other party will act honourably (Corritore et al., 2003). Brand trust has been defined as the willingness of the consumer to think that a brand is dependable and has the capability to help him/her achieve his/her desired objectives (Chaudhuri and Holbrook, 2001; Ha and Perks, 2005; Marzocchi et al., 2013). To improve relations with consumers and how their consumers perceive them, brands create OBCs (Muñiz and O’Guinn, 2001; Sung et al., 2010). The benefits that OBCs can bring to an organisation are influenced by trust in the community itself (Corritore et al., 2003; Kang et al., 2016). Trust in the OBC can be defined as the general trust that the members have in each other and in the sponsoring brand; that is, the users consider that neither the other community members nor the community sponsor will act opportunistically or against their interests (Martínez-López et al., 2017; Molinillo et al., 2019). Trust in the OBC decreases perceived risk (Kang et al., 2016) and facilitates interactions among members and stabilises relationships (Bruhn et al., 2014).
To build trust in the OBC, the sponsoring brand must properly manage the community and the relationships among its members (Porter and Donthu, 2008). However, administering the website does not give the brand licence to interfere in the community in its own interests, as that would negatively affect members’ trust in the OBC (Fournier and Lee, 2009; Popp and Woratschek, 2016). Consumers expect the brand to act as another community member, provide information and share control with them, without taking advantage of their privileged position as site administrator (Hennig-Thurau et al., 2010). Consumers who join an OBC are likely to trust the brand (Porter and Donthu, 2008), so they will tend to think that its behaviour in the community is honest, to the extent that they even accept that the brand might carry out certain commercial activities within the community (Luo and Zhang, 2016; Sung et al., 2010). Therefore, the following hypothesis is proposed:
Brand trust positively influences OBC trust.
2.2 Trust and repurchase intention
Trust in the brand is a vital element in establishing and maintaining lasting customer – brand relationships (Chaudhuri and Holbrook, 2001; Porter and Donthu, 2008). High levels of trust in the brand will produce favourable attitudes towards it (He et al., 2012; Kamboj et al., 2018; Kim and Kim, 2017). Previous research has shown that brand trust is one of the most important antecedents of consumer loyalty and repurchase intention (Gibreel et al., 2018; Hajli et al., 2017; Kim and Kim, 2017; Laroche et al., 2012; Papista et al., 2018; Urueña and Hidalgo, 2016). Consumers tend to repurchase brands they trust because they cognitively and affectively value the brand’s reliability (Chaudhuri and Holbrook, 2001; Matzler et al., 2008).
OBCs create a setting for the relationships between consumers and brands that help to increase members’ loyalty towards the brand (Marzocchi et al., 2013; McAlexander et al., 2002). It has been shown that if members feel connected to the community, they will be more inclined to display positive behaviours towards other community members, including the brand (Jain et al., 2018; Kim and Kim, 2017). OBC trust provides a sense of security and protection (Casaló et al., 2008) which leads to constructive behaviours towards the brand itself, encouraging, among other things, repurchase intention (Hur et al., 2011). Similarly, consumer trust is associated with the process of creating a positive brand reputation, which favours loyalty to the brand (Serra-Cantallops et al., 2018). Previous studies have shown that trust in the OBC is a direct (Casaló et al., 2007; Kim and Kim, 2017) and indirect antecedent (Bruhn et al., 2014) of brand loyalty. Consequently, the following hypotheses are proposed:
Brand trust positively influences repurchase intention.
OBC trust positively influences repurchase intention.
2.3 Trust and positive electronic word-of-mouth intention
WOM is defined as all the information about products, services, establishments, businesses, etc., that can be shared between consumers (Brown et al., 2005). Online communities have gained importance in recent years thanks to the information generated in them, which has effects on the states and behaviours of consumers. This importance began in the field of free software (Casaló et al., 2009), and then extended to the most diverse sectors of activity. WOM influences key issues for brands, such as consumer satisfaction (De Matos and Rossi, 2008), purchase decision-making and brand awareness (Hung and Li, 2007). The tools available in Web 2.0 (e.g. social networks, blogs, microblogs, etc.) have increased the impact of WOM by allowing consumer comments to be easily shared (Popp and Woratschek, 2016; Woisetschläger et al., 2008). OBCs are appropriate channels through which to encourage consumers to disseminate positive eWOM about brands (McAlexander et al., 2002; Wang et al., 2013).
Trust is one of the most important antecedents of eWOM (De Matos and Rossi, 2008; Sijoria et al., 2018). Trust influences the individual’s willingness to exchange information and content with others (Yeh and Choi, 2011). Trust felt towards the brand and the OBC itself predisposes community members to feel more integrated and to carry out activities that benefit both the brand and the OBC (Porter and Donthu, 2008). Previous research has found a positive relationship between brand trust and OBC trust, and the tendency towards positive eWOM of consumers participating in the OBC (Akrout and Nagy, 2018; Jain et al., 2018; Kim et al., 2014). Therefore, the following hypotheses are proposed:
Brand trust positively influences positive eWOM intention.
OBC trust positively influences positive eWOM intention.
2.4 Repurchase intention and positive electronic word-of-mouth intention
Brand loyalty also positively affects the dissemination of favourable comments about the brand (Zeithaml et al., 1996). It has been shown that when OBC members are loyal to a brand, they are more likely to make positive comments about the brand (Casaló et al., 2008; Coelho et al., 2019; Sijoria et al., 2018). OBC members tend to display brand-friendly behaviours, which in part explains their participation in these communities and increases the likelihood of them engaging in positive eWOM (Yeh and Choi, 2011). A strong consumer–brand relationship increases the consumer’s intention to spread positive eWOM (Shan and King, 2015). Previous research has shown that loyalty to the brand around which the community revolves positively affects intention to produce positive eWOM (Munnukka et al., 2015; Wang et al., 2011; Zhang et al., 2017). The loyal consumer who repurchases a product is more likely to share his/her positive consumption experience with others (Yeh and Choi, 2011). Consequently, the following hypothesis is proposed:
Repurchase intention positively influences intention to engage in positive eWOM.
Figure 1 shows the theoretical model of the study.
3. Methodology
3.1 Sample
The data for the evaluation of the theoretical model were collected in Spain during March 2015 through a self-administered online survey directed at millennial OBC users who accessed it during the week prior to the study and distributed through social networks (Facebook and Twitter). Millennials are known as digital natives as they are the first generation born in the digital age; they are characterised by their tendency to actively contribute, share, search and consume content on social networks, which influences their consumption behaviour and their relationships with brands (Bolton et al., 2013). This market segment spends the most time on social networks and represents more than a third of all Internet users in Spain (IABSpain, 2018).
Through a convenience sampling procedure, a total of 650 questionnaires were obtained, which was reduced to 628 after invalid returns were discarded, due to inconsistency in the answers or failure to respond to some questions. The sample was divided equally between men (49.4%) and women (50.6%); 47.5% were between 17 and 20 years and 50.9% between 21 and 30 years.
3.2 Measures
The variables were measured using scales validated in previous studies on a five-point Likert scales, moving from 1 “completely disagree” to 5 “completely agree”. Brand trust was measured using three items adopted from Laroche et al. (2012) and OBC trust by three items from Hur et al. (2011). Repurchase intention was also measured with three items adopted from Algesheimer et al. (2005) and eWOM intention by three items adopted from Casaló et al. (2008).
The participants had to answer the questionnaire based on a specific OBC, so if they belonged to several communities, they had to choose the one in which they had participated to the greatest extent in the previous weeks. The OBCs that the respondents referenced belong to different sectors and sizes of companies. The main brand communities were in sports goods (e.g. Adidas, Nike), fashion (e.g. Mango, Zara) and technology (e.g. Apple, Samsung).
4. Results
The research model was evaluated using partial least squares structural equation modelling (PLS-SEM) with SmartPLS3 software (Ringle et al., 2015). The variance-based PLS algorithm is more convergent in its simplicity and facilitates the analysis of data subject to fewer normality restrictions than structural equation models based on covariance (CB-SEM) (Tenenhaus et al., 2005). It is a suitable technique for analysing a complex theoretical model combining direct and mediating relationships (Roldán et al., 2017). The stability of the estimates was verified using the bootstrapping procedure (5,000 subsamples) (Roldán and Sánchez-Franco, 2012).
4.1 Assessment of the measurement model
The assessment of the measurement model was performed by analysing the saturated model (Henseler et al., 2016). The values obtained by bootstrapping for the three exact fit tests (SRMR saturated, D_ULS and D_G2) show levels significantly lower than the recommended maximum value of 0.08 (Dijkstra and Henseler, 2015a, 2015b). Therefore, it was appropriate to go on to the next steps in the analysis.
Then, to evaluate the measurement model, examinations of the reliability of the items, the variables, and the convergent and discriminant validity were carried out. Regarding the reliability of each item, Table 1 shows that the value of the correlations between each indicator and its corresponding variable are above the recommended minimum (0.7) (Barclay et al., 1995).
Cronbach’s alpha coefficient (Cronbach, 1951) and the composite reliability factor (CR) (Nunnally and Bernstein, 1994) were used to check the reliability of the measurement scales, with a minimum recommended value of 0.7. Convergent validity was evaluated by the value of the average variance extracted (AVE) (Fornell and Larcker, 1981), exceeding the 0.5 cut-off recommended value. Finally, to evaluate discriminant validity, two criteria were followed:
the inter-construct correlations must be lower than the square root of the AVE values (Fornell and Larcker, 1981) (Table 2); and
the heterotrait–monotrait relationship (HTMT) of the correlations between two constructs must be less than 0.9 (Henseler et al., 2015) (Table 2). In this study, all the values were below the recommended maximum limits.
4.2 Assessment of the structural model
To evaluate the significance of the path coefficients and the loads of the complete structural model, the bootstrapping method (5,000 subsamples) was used (Hair et al., 2014). Before testing the structural model, there should be no multicollinearity problems between the related variables of the model. In this study, all variance inflation factor values are lower than the recommended maximum (0.5). Then, the R2 values of each of the endogenous constructs were obtained; the results were greater than 0.1 (Table 3), exceeding the minimum recommended value (Falk and Miller, 1992). Assessing the significance of the direct relationships, the loads of the standardised regression paths were studied. All the values were greater than the recommended minimum value of 0.2 (Chin, 1998), except the relationship between OBC trust and repurchase intention.
The ability of the model to predict the dependent constructs and endogenous variables was estimated using the Q2 test and the blindfolding procedure (Distance of omission = 7). All the values obtained were higher than the recommended minimum (0.4). The effect size (f2) shows the impact of an independent construct on a dependent construct. There is a small effect of OBC trust on repurchase intention, and both brand trust and OBC trust on positive eWOM (values between 0.02 and 0.15); a medium effect of repurchase intention on positive eWOM (value between 0.15 and 0.35); and a large effect of brand trust on OBC trust, and brand trust on repurchase intention (values over 0.35) (Table 3) (Chin, 1998). Finally, SRMR is lower than the recommended maximum (0.08); therefore, the model has adequate fit (Henseler et al., 2015). Bootstrap-based exact fit tests of the assessed model were performed (SRMR, D_ULS and D_G2) (Dijkstra and Henseler, 2015a, 2015b). All of them show significant results (p < 0.05), so the model fit is adequate.
All the model relationships are significant, thus all the hypotheses are confirmed, although the path coefficient value of H3 is very small. Regarding the evaluation of the model, its good fit (SRMR = 0.050) and the high R2 values of the variables (Table 3) are noteworthy.
To complete the study, the indirect relationships of the model and the moderating effects of demographics (i.e. gender and age) were also analysed. First, percentile and bias-corrected confidence intervals were used via bootstrapping (Cepeda et al., 2017; Nitzl et al., 2016; Roldán et al., 2017). The results obtained demonstrate the existence of a partial mediation effect (Nitzl et al., 2016) between brand trust and positive eWOM, which indicates that part of the effect is produced by OBC trust and brand loyalty (Table 4). Therefore, the indirect effect adds to the already significant direct effect between brand trust and eWOM. Specifically, it is confirmed that OBC trust mediates the relationship between brand trust and positive eWOM (a1b1). It is also shown that repurchase intention mediates the effect between brand trust and eWOM (a2b2). Finally, it is confirmed that OBC trust and repurchase intention jointly mediate, albeit with less weight, the relationship between brand trust and eWOM (a1a3b2). The explained variance index (Variance Accounted For, VAF) (Hair et al., 2017) showed that the mediation effect of OBC trust between brand trust and positive eWOM had the greatest indirect effect (49.66%).
The moderating effects of gender and age were tested using multi-group analysis (MGA) (Hair et al., 2017) through the PLS-MGA technique (Henseler et al., 2009; Sarstedt et al., 2011). The database was divided into two groups based on gender (male and female) and two groups based on age, using the median value (up to 20 years and more than 20 years). Before carrying out the analysis, the invariance of the measuring instrument (MICOM) was checked to ensure that differences in the model derived from the moderating effect of the variables were not caused by differences in the measurement models of each group. Table 5 shows the results of the MGA.
According to Sarstedt et al. (2011), the difference between two groups is significant at 5% error level probability if the p-value of the difference between the path coefficients of the groups is less than 0.05, or greater than 0.95. In the present study the results showed that at the 5% level there were only significant differences based on gender. Specifically, the path value between brand trust and positive eWOM is higher in men (p < 0.05), and the path value between repurchase intention and positive eWOM is higher in women (p > 0.95). If the margin of error rises to 10%, and interpreting the results very carefully, the differences between path values based on gender will be significant also in the relationship between OBC trust and positive eWOM (higher among women) (p > 0.90), and in the path values based on age in the relationships between brand trust and positive eWOM (higher in the older group) (p > 0.90), and between repurchase intention and positive eWOM (higher in the younger group) (p < 0.10).
5. Discussion and conclusions
OBCs play an important role in the management of customer relations. The literature highlights that trust is a key factor in the success of OBCs, especially in that it helps to increase consumer loyalty towards brands and intention to produce positive eWOM. However, few empirical studies have analysed in the same model the influence of brand trust and OBC trust on two of the main outcomes of the consumer-brand relationship, repurchase intention and positive eWOM intention.
The present study, moreover, makes several further contributions to marketing theory and practice. First, the results show that it is easier for the consumer to trust the OBC if they previously trusted the brand that sponsors the community. This result is consistent with previous studies (Porter and Donthu, 2008). Similarly, it was shown that trust in the brand has a positive influence on repurchase intention, which is in line with the literature (Kamboj et al., 2018; Matzler et al., 2008; Urueña and Hidalgo, 2016). In addition, brand trust has a positive effect on intention to produce positive eWOM, which is also consistent with the literature (Akrout and Nagy, 2018; Jain et al., 2018; Kim et al., 2014). Therefore, this study makes an important contribution by demonstrating in one model the direct relationship between brand trust, OBC trust and the consumer’s loyal behaviour towards the brand.
The model also confirms that OBC trust plays an important role in intention to produce positive eWOM. The consumers who trust the community the most are more likely to recommend the brand. This result is certainly novel, as the previous research has normally focused on brand trust as an antecedent of eWOM and not so much on trust in the OBC itself. OBC trust also has a positive influence on repurchase intention, although this effect is very limited.
The results have also shown that repurchase intention plays a key role among the antecedents of positive eWOM intention. The making of repeat purchases over time increases the consumer’s predisposition to recommend the brand. This result is in line with the literature (Munnukka et al., 2015; Wang et al., 2011; Zhang et al., 2017).
Furthermore, the present study shows that the relationship between brand trust and positive eWOM intention is mediated by OBC trust and repurchase intention. If the user trusts the community and intends to make further purchases of the sponsoring brand, the effect of brand trust on positive eWOM intention will be greater. This is an important contribution of this study as hitherto no known empirical studies have supported these indirect relationships.
All in all, the evaluated research model is parsimonious and is able, to a large extent, to explain repurchase intention through only two variables (i.e. brand trust and OBC trust), and positive eWOM intention through only three variables. In addition, the present study shows the moderating capacity of gender and, to a lesser extent, of age on the effect of brand trust, OBC trust and repurchase intention on positive eWOM. Although no hypothesis about this moderation was formulated in this study, the results contribute to the enhancement of knowledge about the model’s relationships as no previous work has analysed this aspect. This study, therefore, contributes to the marketing literature by broadening the knowledge of the effects of trust on consumer-brand relationships in the context of OBCs.
The results also have important implications for marketing practice. First, to obtain a better relationship with OBC members, and thus achieve higher levels of loyalty (i.e. repurchase and positive eWOM intention), OBC managers should promote both brand trust and trust in the community itself. To achieve these objectives, the sponsoring brand must act honestly, credibly and dependably in its relationship with consumers. In addition, to strengthen trust in the brand, managers might extend product warranties and facilitate returns and claims procedures. Regarding OBC trust, community managers should publish authentic and useful content, avoid manipulation and suppression of negative content and create an atmosphere of freedom that encourages interaction between members. OBC managers might also improve the protection of consumer privacy, for example by offering choices between various levels of self-disclosure and by committing not to use OBC information for commercial purposes. This will help to encourage behaviours beneficial for the brand, such as repurchase and eWOM. Moreover, the results showed that repurchase positively influences eWOM. Thus, brand managers can use loyalty programmes and promotions to increase sales, because their effect will be twofold, on sales and on eWOM.
This study has some limitations. First, the sample is large and proportional with respect to gender but was obtained by a convenience sampling procedure in a single country, which reduces the ability to generalise the results to other contexts. Future studies should use probability sampling for data collection in different countries. Second, although millennials are characterised by their greater use of the internet, it would be advisable to extend the research to other generations so that the model can be validated across the whole OBC user population. Finally, although brand trust and OBC trust have been confirmed as key variables for increasing repurchase intention and positive eWOM intention, to increase the explanatory capacity of the model future studies might consider other relational variables, such as brand engagement, brand attachment and community identification.
Figures
Assessment of the measurement model and convergent validity
| Constructs and items | Mean | SD | CA | CR | AVE | Outer loadings |
|---|---|---|---|---|---|---|
| Brand trust (BT) | 0.840 | 0.904 | 0.758 | 0.852–0.905*** | ||
| This brand gives me everything that I expect out of the product | 5.06 | 1.51 | ||||
| I rely on this brand | 5.34 | 1.43 | ||||
| This brand never disappoints me | 5.12 | 1.49 | ||||
| OBC trust (OBCT) | 0.872 | 0.922 | 0.797 | 0.864–0.912*** | ||
| I trust this brand community | 5.03 | 1.63 | ||||
| I rely on this brand community | 5.41 | 1.40 | ||||
| This is an honest brand community | 4.82 | 1.66 | ||||
| Positive eWOM intention | 0.903 | 0.939 | 0.837 | 0.889–0.928*** | ||
| I am going to spread positive WOM about this brand | 5.33 | 1.55 | ||||
| I will recommend this brand to other customers | 5.52 | 1.43 | ||||
| I will point out the positive aspects of this brand if anybody criticise it | 5.19 | 1.64 | ||||
| Repurchase intention (RI) | 0.893 | 0.933 | 0.824 | 0.887–0.919*** | ||
| I will intent to buy this brand in the near future | 4.92 | 1.83 | ||||
| I would actively search for this brand to buy it | 4.84 | 1.82 | ||||
| I will intent to buy other products of this brand | 5.34 | 1.72 |
***p < 0.001. SD: standard deviation; CA: Cronbach’s alpha; CR: Composite reliability; AVE: Average variance extracted
Discriminant validity
| Construct | BT | OBCT | eWOM | RI |
|---|---|---|---|---|
| Brand trust (BT) | 0.871 | 0.750 | 0.760 | 0.762 |
| OBC trust (OBCT) | 0.647 | 0.893 | 0.645 | 0.539 |
| eWOM | 0.665 | 0.574 | 0.915 | 0.759 |
| Repurchase intention (RI) | 0.662 | 0.479 | 0.683 | 0.907 |
Main diagonal values (in italic) represent the square root of AVE; values below the main diagonal reflect latent variable correlations; above the main diagonal are HTMT ratio values
Evaluation of the structural model
| Hypotheses | Path | Supported | f2 | Q2 | R2 | |
|---|---|---|---|---|---|---|
| H1 | Brand Trust → OBC Trust | 0.647*** | Yes | 0.718 | ||
| H2 | Brand Trust → Repurchase int. | 0.605*** | Yes | 0.382 | ||
| H3 | OBC Trust → Repurchase int. | 0.087** | Yes | 0.008 | ||
| H4 | Brand Trust → Positive eWOM | 0.254*** | Yes | 0.064 | ||
| H5 | OBC Trust → Positive eWOM | 0.213*** | Yes | 0.061 | ||
| H6 | Repurchase int. → Positive eWOM | 0.413*** | Yes | 0.222 | ||
| Brand trust | 0.472 | |||||
| OBC trust | 0.530 | 0.418 | ||||
| Repurchase intention | 0.591 | 0.442 | ||||
| Positive eWOM intention | 0.570 | 0.573 | ||||
Bootstrapping = 5,000; blindfolding = omission distance 7; ***p < 0.001, **p < 0.05. SRMR = 0.050
Summary of mediating effect test
| Direct effect of BT on eWOM (c’) | Indirect effects of BT on eWOM | ||||||
|---|---|---|---|---|---|---|---|
| Coefficient t value | Point estimate | Bootstrap 95% CI | VAF(%) | ||||
| Percentile | BC | ||||||
| 0.254*** | Total | 0.406 | 0.343 | 0.468 | 0.342 | 0.468 | 93.56 |
| a1b1 (via OBCT) | 0.137 | 0.092 | 0.185 | 0.091 | 0.185 | 35.41 | |
| a2b2 (via RI) | 0.246 | 0.199 | 0.297 | 0.199 | 0.297 | 49.66 | |
| a1a3b2 (via OBCT+RI) | 0.023 | 0.005 | 0.042 | 0.005 | 0.042 | 8.49 | |
BT: Brand trust; OBCT: OBC trust; eWOM: positive electronic word-of-mouth intention; RI: Repurchase intention; BC: Bias corrected; CI: Confidence interval; VAF: Variance accounted for; ***p < 0.001
Evaluation of multi-group analysis (MGA)
| Hypotheses | Gender | Age | ||
|---|---|---|---|---|
| Path diff. | p-value | Path diff. | p-value | |
| H1. Brand trust → OBC trust | 0.032 | 0.729 | 0.037 | 0.763 |
| H2. Brand trust → Repurchase int. | 0.016 | 0.580 | 0.021 | 0.398 |
| H3. OBC trust → Repurchase int. | 0.018 | 0.580 | 0.025 | 0.609 |
| H4. Brand trust → Positive eWOM | 0.361 | 0.000 | 0.170 | 0.948 |
| H5. OBC trust → Positive eWOM | 0.154 | 0.945 | 0.072 | 0.201 |
| H6. Repurchase int. → Positive eWOM | 0.186 | 0.989 | 0.104 | 0.096 |
Path diff: Path difference between two groups
Originality of this study
| Authors | Relationships | Differences between the studies |
|---|---|---|
| Akrout and Nagy (2018) | Firm-initiated virtual brand community (FIVBC) trust → brand relationship quality → WOM | Brand trust is considered a dimension of the second-order construct brand relationship quality. This research focuses on Facebook. Other measurement scales are used |
| Bruhn et al. (2014) | Brand trust → brand community trust | This focuses on B2B brand communities. Other measurement scales are used |
| Casaló et al. (2007) | Brand trust → brand loyalty | This focuses on one brand community in particular. Other measurement scales are used |
| Gibreel et al. (2018) | Brand trust → intention to buy | This focuses on social commerce on Instagram |
| Hajli et al. (2017) | Trust → purchase intention | This focuses on social commerce on Instagram. Other scales are used |
| Hur et al. (2011) | Brand community trust → brand community commitment → WOM | This focuses on female brand communities. The relationship between brand trust and WOM is mediated by brand community commitment |
| Jain et al. (2018) | Brand trust → brand loyalty; Brand trust → WOM | Other measurement scales are used |
| Kamboj et al. (2018) | Brand trust → brand loyalty | The measurement scale used for brand loyalty is different |
| Kang et al. (2016) | Brand community trust → brand community engagement | The relationship is different. In addition, brand community trust is measured by a different scale |
| Kim and Kim (2017) | Trust → loyalty | This focuses on online fan communities. Other scales are used |
| Kim et al. (2014) | Brand trust → number of retweets | This focuses on Twitter. It does not relate brand trust with eWOM, but with the number of retweets |
| Laroche et al. (2012) | Brand trust → brand loyalty | Other measurement scales are used |
| Martínez-López et al. (2017) | OBC trust → OBC identification; OBC trust → OBC engagement | The relationships studied are different |
| Marzocchi et al. (2013) | Brand trust → attitudinal loyalty | This focuses on a specific brand community. Other measurement scales are used |
| Molinillo et al. (2019) | Community trust → customer engagement → positive eWOM intention; customer engagement → repurchase intention | This focuses on Facebook and social commerce. The relationships are different. Other measurement scales are used |
| Porter and Donthu (2008) | Trust in a community sponsor → loyalty intentions | The relationship is different. Other measurement scales are used |
Appendix
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Acknowledgements
This research was supported by the Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI) de la Junta de Andalucía, Grupo SEJ-567, and the Universidad de Málaga, Andalucía Tech (Plan Propio de Investigación y Transferencia) (Spain).