Despite growing academic interest in social identification (e.g. customer brand identification) and social exchange (e.g. commitment/loyalty), little remains known regarding the theoretical interface of these concepts in hospitality sector. Building on this research gap, the purpose of this study is to develop and test a model that explores the effects of brand identification, satisfaction, commitment and trust on customer loyalty toward four and five-star hotels. The authors also explore the mediating role of commitment, satisfaction and trust in the association of brand identification and loyalty.
To investigate the objectives of this study, the authors deployed a convenience sample of 345 consumers from four- and five-star hotels in the emerging markets context. Data analysis consisted of confirmatory factor analysis as well as structural equation modeling.
The findings of this study indicate that customer brand identification, trust, commitment and satisfaction exert a positive impact on loyalty. Brand identification also exerts a favorable impact on customer trust, commitment and satisfaction. Specifically, satisfaction was found to exert the largest effect on commitment, trust and loyalty.
The findings may have limited applicability in contexts other than four- and five-star hotels in the emerging market context. Theoretically, this study adds insight into the dynamics characterizing focal social identification and social exchange-based theoretical relationships as observed in the hospitality sector.
The authors adopt an under-explored hybrid social identity/social exchange theoretical perspective to identify the nature and strength of associations among a set of relational, social identity/exchange-based constructs and discuss their key implications for academicians and hospitality managers.
Rather, R.A. and Hollebeek, L.D. (2019), "Exploring and validating social identification and social exchange-based drivers of hospitality customer loyalty", International Journal of Contemporary Hospitality Management, Vol. 31 No. 3, pp. 1432-1451. https://doi.org/10.1108/IJCHM-10-2017-0627Download as .RIS
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Customer loyalty development is one of the most desired marketing objectives and has, therefore, received extensive attention in the literature (Huang et al., 2017; Kandampully et al., 2015). However, despite its significance, there is no consensus on loyalty’s key driving or optimizing factors, which have been found to vary widely across contexts (Rather and Sharma, 2016a). For example, while some studies focus on customer trust (So et al., 2013), satisfaction (Rather, 2018) or perceived quality (Liat et al., 2017) as key loyalty antecedents, others highlight the importance of customer commitment (Su et al., 2016), engagement (Hollebeek et al., 2014, 2016a; Rather and Sharma, 2016b, 2017a) or perceived value (So et al., 2013). Furthering understanding of loyalty’s key drivers – while debated – is crucial for contemporary organizations given its direct effect on firm competitiveness and profitability, including in the hospitality sector (Nunkoo et al., 2017; Pizam et al., 2016). In the present research, we therefore respond to Kandampully et al.’s (2015) call for the undertaking of further empirical research that explores the role of different customer loyalty antecedents in the hospitality sector.
While scholars including Harris and Goode (2004) and Martinez and Rodriguez del Bosque (2014) have shed light on the key role of social exchange-related factors (e.g. customer trust, commitment) to better understand service, the literature to date has paid only limited attention to social identification-related factors (e.g. customer brand identification) on customer loyalty (Huang et al., 2017; Hollebeek, 2011a, 2011b). To address this gap, the present study develops a model that integrates social exchange and social identity-related concepts, which exhibit a significant theoretical fit based on their common interactive, social nature. However, the influence of brand identification and satisfaction on customer loyalty remains under-explored in hospitality sector, including the hotel sub-sector, as therefore explored here (Huang et al., 2017; Yang et al., 2017). We construct a framework that permits the simultaneous investigation of these theoretical perspectives that collectively, yield enhanced understanding of hospitality customers’ underlying relational processes (Brodie et al., 2011; So et al., 2013). As many contemporary hotels are faced with trends, including growing customer demand, severe competition, the emergence of new (e.g. social media, sharing economy) technologies and increasingly empowered customers, the importance of customer-centric strategies is of rising importance. Relatedly, understanding customers’ relational social exchange- and social identity-based processes is increasingly pivotal (Tuskej and Podnar, 2018).
To study our objectives, we develop a conceptual model that explores the theoretical linkages between brand identification, trust, commitment, satisfaction and loyalty (Balaji et al., 2016). While prior brand identification research has explored the concept’s link to loyalty, we add a social exchange theoretical angle that not only acknowledges the role of customer-related social dynamics in use, but also the customer’s anticipated benefit or value from these interactions (Hollebeek, 2011a, 2011b). We explore these dynamics in the Indian luxury (four- and five-star) hotel context, thereby validating previous emerging market-based findings and further strengthening this research stream that is important given the significant growth observed in these markets (Sheth, 2011). For example, Marriott International Hotels plans to build another 80 new hotels in India in the next few years (Forbes, 2017), thereby rendering the Indian market an appropriate study context.
This work offers two main contributions. First, by developing an integrative social exchange/social identity-based model, we contribute further understanding to the role of relational concepts (e.g. customer trust, commitment, loyalty), thereby fitting with broader relationship marketing’s scope. To foster this understanding, we develop and test a conceptual model that is first subjected to a series of confirmatory factor analyses (CFA), followed by structural equation modeling (SEM) analyses by drawing on a sample of 345 customers of Indian four/five-star hotels. Second, we offer managerial recommendations centered on customer retention and loyalty development that are expected to be useful to (luxury) hotel managers and other hospitality stakeholders (Hollebeek and Andreassen, 2018; Hollebeek et al., 2017, 2018).
Theoretical framework and hypothesis development
In the following subsections, we review important literature that addresses our identified concepts of customer brand identification, customer loyalty, brand trust, satisfaction and commitment, followed by the development of an associated set of research hypotheses (Figure 1).
Customer brand identification
Social identity theory (SIT) is an important theoretical foundation for marketing-based customer brand identification (Elbedweihy et al., 2016; Lam et al., 2013; Rather and Sharma, 2017a, 2017b). SIT advocates that people will expend significant effort to develop their own social identity, in addition to their more private identity (Bhattacharya and Sen, 2003; Islam et al., 2017; Tajfel and Turner, 1986). These arguments also fit with social exchange theory (SET), which centers on people’s expected rewards from their social efforts (Blau, 1964; Homans, 1958), thereby exhibiting a suitable link between these perspectives as adopted in this paper.
Under SIT, identification helps explain actors’ motivation to engage with brands or firms (Hollebeek et al., 2016a; Sprott et al., 2009; Turner et al., 1987), which often involves particular cognitive categorization processes where they position themselves as being somehow linked to or members of a collective (e.g. a firm, brand). Here, actors emphasize their similarities to other members and their differences from non-members (Hollebeek, 2018). Then, through the development of a perceived connection and belonging to an object (e.g. a brand), actors are expected to positively identify with the object (Escalas, 2004; Martinez and Rodriguez del Bosque, 2013). Correspondingly, brand identification denotes a consumer’s “psychological state of perceiving, feeling, and valuing his or her belongingness to a brand” (Lam et al., 2013, p. 235).
Bhattacharya and Sen (2003) apply identification to customer/firm relationships. They argue that customers have personal and social identities that collectively, contribute to their sense of self. The authors also suggest that robust customer–firm relationships tend to see a high level of fit of customer/firm values and activities, which in turn help develop customer self-definition, self-continuity and self-enhancement. Consequently, customers can self-express their identity through brand/firm relationships (Escalas, 2004) in which value-receiving individuals can also reciprocate back to the firm (e.g. by helping other customers), thereby reflecting SET’s theoretical foundations. Over time, these behaviors are conducive to the development of customer trust, commitment and loyalty (Morgan and Hunt, 1994).
While customer loyalty’s scope and dimensionality are debated, Oliver’s (1997, p. 392) influential view denotes the concept as “a deeply held [intent] to rebuy or patronize a preferred product or service consistently in the future, despite situational influences and marketing efforts”. Customer loyalty incorporates consumers’ revisit/repurchase intention (i.e. behavioral loyalty) and their brand/firm-related perceptions and willingness to recommend the company to salient others (i.e. attitudinal loyalty; Liat et al., 2017; Naidoo and Hollebeek et al., 2016a, 2016b). In hospitality, there is immense managerial interest to better understand loyalty drivers, which remain nebulous to date (Martinez and Rodriguez del Bosque, 2013; Nunkoo et al., 2017; Rather and Sharma, 2017a, 2017b). To foster customer loyalty, most hotels offer loyalty or reward schemes and routinely monitor their guest likes/dislikes (Martinez and Rodriguez del Bosque, 2013), thereby aiming to enhance customer identification and exchange with the service and fitting with our hybrid SIT/SET-informed perspective.
Customer brand identification’s effect on loyalty
Given brands’ capacity to facilitate social identity expression and development (Elbedweihy et al., 2016), customers may identify with brands that match their (desired) self-concept (Bhattacharya and Sen, 2003). Through this process, customer brand attitudes will develop (Stokburger-Sauer et al., 2012). Social identity can affect customer cognitions, perceptions and evaluations and stimulate the development of customer brand attachment, thereby sparking heightened loyalty (Huang et al., 2017; Yang et al., 2017). Through its SIT-based roots, customer brand identification can be used to explain a range of customer-based outcomes, including loyalty (He et al., 2012). Hence, customers who identify with a hotel brand are expected to purchase that brand as a means of self-expression, particularly for luxury hotels. We thus propose:
Hotel customer brand identification positively influences customer loyalty.
Customer brand identification’s effect on customer satisfaction
Few studies to date have combined customer brand identification and satisfaction into a single model (Suet al., 2016), thereby rendering the importance of empirically testing this association. Rising brand identification is expected to heighten customer satisfaction by virtue of customers’ greater attachment to the brand/firm (Bhattacharya and Sen, 2003). In the hospitality context, customers who identify more with (e.g. a hotel) brand will tend to be more satisfied with the hotel through their psychological brand attachment (Martinez and Rodriguez del Bosque, 2013). We therefore suggest:
Hotel customer brand identification positively influences customer satisfaction.
Customer brand identification’s effect on brand trust and commitment
The literature proposes that brand trust develops predominantly from a customer’s past experience with an object (Escalas, 2004). Further, research has argued that brand/self-image congruence is likely to be higher for customers who regularly use the brand to express or enhance themselves (Sprott et al., 2009). Luxury hotel customers who perceive a high congruence of the hotel’s image with their own (desired) identity are also more likely to trust the hotel (So et al., 2013). Correspondingly, brand identification is conducive to the development of commitment (Rather and Sharma, 2016a; Tuskej et al., 2013). Through its SIT-based roots, Suet al. (2016) further establish that shared values between hotel service providers and their customers will tend to increase customers’ commitment to an ongoing relationship with the hotel. We propose:
Hotel customer brand identification positively influences trust.
Hotel customer brand identification positively influences commitment.
Customer brand trust’s effect on customer loyalty and commitment
Brand trust is defined as “confidence in an exchange partner’s reliability and integrity” (Morgan and Hunt, 1994, p. 23) and is recognized to link to other positive qualities including brand competence, consistency, honesty, responsibility and fairness (Morgan and Hunt, 1994). According to Martinez and Rodriguez del Bosque (2013), it reflects the customer’s belief that the company and its offerings are reliable and will strive to serve the customer’s best long-term interest, thereby illustrating its positive contribution to loyalty. Trust has also been shown conducive to the development of firm efficiency, effectiveness and productivity (Narteh et al., 2013) and, thus, represents a strategic imperative for hospitality service providers (Rather, 2018; So et al., 2013).
Trust is generally held to have two components: Benevolence and credibility. First, benevolence is the customer’s reliance on the firm’s good intentions, honesty and care for its customers. Second, credibility reflects the customer’s belief in the firm’s competence (e.g. the customer’s sense of the firm properly handling their booking; Martinez and Rodriguez del Bosque, 2013). Previous research has established that customer trust affects their ensuing loyalty and commitment, as it reflects the actor’s desire to continue the relationship, thereby stimulating loyalty (He et al., 2012; Morgan and Hunt, 1994). We hence develop the following hypotheses:
Hotel customer brand trust positively affects customer loyalty.
Hotel customer brand trust positively affects customer commitment.
Customer satisfaction’s effect on customer loyalty, commitment and trust
Customer satisfaction reflects a “customer’s overall evaluation of a company’s or brand’s performance” (Anderson et al., 1994, p. 4). According to Oliver (1997), (un)met customer expectations will generate customer (dis)satisfaction, respectively. When customer expectations are exceeded, customer delight is anticipated to result (Su et al., 2016), thereby rendering SET-based customer rewards. Customer satisfaction is an essential in marketing metric as it directly reflects the customer’s evaluation of hihe/sher purchase, consumption and post-purchase processes(Liat et al., 2017; Nunkoo et al., 2017). Satisfaction levels may change because of intrinsic (e.g. changing needs) or extrinsic (e.g. emergence of new competition) changes (Pizam et al., 2016).
When customers are satisfied with their brands, repurchase rates tend to remain stable or increase (Liat et al., 2017; Su et al., 2016). Research also shows satisfaction to have a significant positive effect on service usage, positive word-of-mouth and share-of-wallet. Therefore, customer satisfaction represents a major driver of loyalty, commitment and trust (So et al., 2013, 2014; Su et al., 2016). Accordingly, we propose:
Hotel customer satisfaction positively affects customer loyalty.
Hotel customer satisfaction positively affects customer commitment.
Hotel customer satisfaction positively affects brand trust.
Customer commitment’s effect on customer loyalty
Customer commitment has been defined “as an enduring desire to maintain a valued relationship” (Morgan and Hunt, 1994, p. 23). The concept has been viewed to comprise calculative, affective and moral dimensions (Geyskens et al., 1996). Calculative commitment is the extent to which partners perceive the need to maintain a relationship because of a lack of alternatives or anticipated switching costs. Affective commitment denotes the customer’s feeling of wanting to stay in a relationship with the firm. Normative commitment reflects partners staying in a relationship because they feel they ought to (e.g. for social/cultural reasons; Geyskens et al., 1996), rendering a good fit with our SIT/SET-informed perspective (e.g. commitment generated through social norms or a sense of mutual responsibility). We hypothesize:
Hotel customer commitment positively affects customer loyalty.
Based on SIT, customer responses toward their hotel are predicted to entail the individual’s identification with the hotel’s services that in turn contribute to developing their trust, commitment and loyalty (He et al., 2012). Relatedly, the association between customer trust and loyalty has been reported to be either fully or partially mediated by customer commitment, though the specific extent of this mediation has been debated (Hennig-Thurau et al., 2002; Morgan and Hunt, 1994). Moreover, various hospitality studies suggest that SET-based variables (e.g. commitment) act as key mediating variables in the path to customer loyalty (So et al., 2013). Based on this rationale, we expect customer commitment, trust and satisfaction to exert mediating effects in the association of brand identification and loyalty. We hypothesize:
Hotel customer commitment mediates the effect of customer brand identification on loyalty.
Hotel customer satisfaction mediates the effect of customer brand identification on loyalty.
Hotel customer trust mediates the effect of customer brand identification on loyalty.
Hotel customer trust mediates the effect of customer satisfaction on commitment.
Hotel customer satisfaction mediates the effect of customer brand identification on brand trust.
Hotel customer satisfaction mediates the effect of brand identification on commitment.
An overview of our research hypotheses is provided in Figure 1.
The research model incorporated five latent constructs, detailed below, each of which was measured by using multiple item-scales. A questionnaire was developed where each item was measured on seven-point Likert scales ranging from strongly disagree to strongly agree. For customer brand identification, items were developed based on previous measures (Stokburger-Sauer et al., 2012; Su et al., 2016). The resulting four-item scale, a sample item of which reads I identify strongly with this hotel (BI3), was found to have sound reliability (Cronbach’s alpha = 0.92).
For customer loyalty, items were modified from earlier used attitudinal and behavioral loyalty scales (Huang et al., 2017; Nunkoo et al., 2017; So et al., 2013). The resulting scale, a sample item of which reads I would recommend this hotel to someone who seeks my advice (CL1), also showed good reliability (Cronbach’s alpha = 0.95). For customer trust, we used four-items measuring benevolence and credibility adapted from Martinez and Rodriguez del Bosque (2013, 2014 and So et al. (2013). This scale indicated adequate reliability (Cronbach’s alpha = 0.93). Customer commitment was gauged by using a four-item measure comprising affective, continuance and normative components that was informed by Stokburger-Sauer et al. (2012) and Su et al. (2016). This scale also showed adequate reliability (Cronbach’s alpha = 0.92). For customer satisfaction, we used a four-item measure adapted from Suet al.(2016) and Yang et al.(2017) that similarly demonstrated good reliability (Cronbach’s alpha = 0.94). An overview of our full sets of deployed scales is presented in Table I.
Before the full questionnaire, the main researcher undertook a pre-test by handing out the survey to a random convenience sample of 15 hospitality/marketing students who had previously stayed at a luxury hotel, plus 20 other luxury hotel customers to ensure their appropriate understanding of the questionnaire wording, which revealed no issues. Respondents were also given the opportunity to request clarification about the questionnaire, thereby further contributing to the study’s content validity.
Next, a convenience sample-based pilot study was conducted with 110 randomly chosen hospitality customers to validate our questionnaire. The sample comprised 62 (56 per cent) males and 48 (44 per cent) females. Of these respondents, 5 per cent had stayed at a four- or five-star hotel for one night only, 40 per cent had stayed two to three nights, 35 per cent had stayed three to six nights and 20 per cent had stayed for over seven nights. Based on the findings, we refined the questionnaire items as needed (e.g. by removing item duplication or clarifying focal items as needed). Next, the correlation of each item with the total score for its associated construct was calculated, and items with low correlations were removed (Hair et al., 2010). Further, items with corrected item-to-total correlations of less than 0.35 were removed (Nunnally and Bernstein, 1994). Moreover, internal item consistency was corroborated by using Cronbach’s alpha and exploratory factor analysis (EFA) at the subscale level (Hair et al., 2010). EFA with principal component analysis revealed that the items in each subscale loaded onto a single factor (Table II), thereby evidencing construct unidimensionality. In addition, all eigen-values exceeded the critical threshold of 1.0 (Hair et al., 2010; see Table II). Each of the scales’ coefficient alphas exceeded 0.60, thereby indicating acceptable fit (Hair et al., 2010).
Sampling design and final data collection
Data was collected via a survey administered to four and five-star hotel (e.g. Hyatt, Radison Blue) customers in six Indian cities, including Srinagar, Gulmarg, Phalgam, Jammu, Katra and Amritsar. The hotel sub-sector was selected given hotels’ ability to evoke customer identification with their brand (e.g. by selecting a hotel one feels matches their needs/values; So et al., 2013; Stokburger-Sauer et al., 2012). Using convenience sampling, questionnaires were distributed (in English) over a five-week period in May-June 2017, at different times of day. After selecting 20 four- and five-star hotels, we collected a minimum of 10 completed questionnaires from each of the hotels, thereby ensuring each hotel’s reasonable representation in the sample. To boost participation, the researcher explained the survey purpose to the respondents. Questionnaires were distributed to 400 respondents and 345 fully completed questionnaires were returned, indicating a response rate of 86 per cent. Demographically, 54 per cent of the respondents were male and 46 per cent female, 37 per cent were aged 31-40, 27 per cent 41-50, 21 per cent 20-30 and 15 per cent over 51. Regarding customers’ hotel stay duration, 7 per cent of customers stayed a single night, 42 per cent stayed two to three nights, 37 per cent of customers stayed three to six nights and 14 per cent of customers stayed for over seven nights.
Data analysis and results
Based on Churchill (1979), measurement scale validation starts by undertaking confirmatory factor analysis or CFA. Second, structural equation modeling or SEM was conducted to test the measurement model (Figure 1 and Table III). We used AMOS to conduct the CFA with maximum likelihood estimation, while adopting Anderson and Gerbing’s (1988) two-step approach. The overall CFA goodness-of-fit indices indicated the measurement model’s adequate fit to the data: CFI, NFI, GFI and TLI>0.90; RMSEA < 0.08 (Hair et al., 2010; see Table III). The overall CFA measurement model attained satisfactory fit: χ2 = 577.474, df = 197, χ2/df = 2.931, NFI = 0.95; TLI = 0.95; CFI = 0.96; RMSEA = 0.075; GFI = 0.87 and SRMR = 0.48.
Reliability and convergent validity testing
We also assessed scale reliability and validity, each of which was above the critical value of 0.70, showing good reliability (see Discriminant validity). Applying Fornell and Larcker’s (1981) criteria, we assessed whether each construct’s independent measures converged or were highly correlated to assess convergent validity (Netemeyer et al., 2003). Initially, all standardized factor loadings did exceed 0.50 (p < 0.001), as indicated in Table III. Second, average variances extracted (AVE) exceeded the 0.50 critical value, representing extra support for convergent validity.
Off diagonal variables are correlations while as bold diagonal variables are square root of the variance shared among the constructs.
Discriminant validity testing
We deployed Fornell and Larcker’s (1981) criteria to test for discriminant validity by examining the construct correlations with the AVE’s square root for each of the constructs. As indicated in Table IV, the AVE values exceed those of the squared correlations of any of the construct pairs, thereby supporting discriminant validity. For example, for the construct pair identification-commitment, the results are: χ2 = 120.473, df = 21, χ2/df = 5.73 and p < 0.000, thereby substantiating discriminant validity.
Common method bias
Common method bias (CMB) or common method factor (CMF) issues can take place with self-reported data (Podsakoff et al., 2003). To avoid CMB in the data, we incorporated a common method factor in the structural model to evaluate the degree of potential CMB. Following Podsakoff et al. (2003), we construed a factor to load on selected items used to measure customer loyalty and its drivers. We specified the CMB loadings to be of a similar magnitude to account for common method variance, thereby equally affecting the items and attaining model convergence. Our analyses suggested that hypotheses remained unaffected by the additional factor’s inclusion; therefore, we conclude that common method variance did not significantly affect our results (Islam et al., 2017). The correlation matrix also showed that CMB is not a problem here, because of the lack of very high correlations (Table IV).
Our hypotheses (presented in Table V) were tested by AMOS with maximum likelihood estimation. The structural model attains: χ2 = 607.208, df = 197, χ2/df = 3.82, TLI = 0.94, CFI = 0.96, NFI = 0.94, GFI =0.87, RMSEA = 0.078 and SRMR = 0.062, thereby reflecting excellent model fit. The proposed model explains 80 per cent of the observed variance (R2) in Table IV.
We identified significant, large effects of brand identification on (a) customer satisfaction (β = 0.78, p < 0.0001), (b) customer commitment (β = 0.41, p < 0.0001), (c) customer loyalty (β= 0.30, p < 0.0001), and (d) brand trust (β= 0.21, p < 0.01), thereby supporting H2, H4, H1 and H3, respectively. Brand trust was found to act as a significant antecedent of customer loyalty (β= 0.35, p < 0.0001), thereby supporting H5. In addition, brand trust exerted a significant impact on commitment (β = 0.15, p < 0.0001), thereby supporting H6.
Customer satisfaction showed a significant impact on loyalty (β = 0.21, p < 0.0001), thus supports H7. Customer satisfaction exerted a positive, significant effect on commitment (β = 0.44, p < 0.0001), thereby supporting H8. In addition, customer satisfaction exerted a significant impact on trust (β = 0.29, p < 0.0001), thus supporting H9. We also observed a greater effect of customer satisfaction (relative to commitment, trust or loyalty) on customer brand identification, leading us to conclude satisfaction as a critical brand identification consequence. Moreover, customer commitment was established as an important driver of customer loyalty (β = 0.17, p < 0.05), thereby confirming H10 (Table V). Furthermore, customer brand trust’s direct effect (β = 0.36) on customer loyalty was established to be highest, followed by brand identification (β = 0.30), customer satisfaction (β = 0.21) and customer commitment (β = 0.17). Consequently, brand trust acts as a vital loyalty antecedent for luxury hotel customers (Table V).
To test for mediation, we analyzed the covariance structural model by using the bootstrap method (Zhao et al., 2010). In addition, we adopted Brown’s (1997) procedure to determine direct effects, indirect effects and total mediation effects. Mediation occurs when an independent variable affects a dependent variable at the same time that it influences the mediator, which also affects the dependent variable (Hair et al., 2010). Customer commitment exerted the strongest mediating effect in the association of brand identification and loyalty (H11a, β = 0.52). While satisfaction shows a mediating effect between brand identification and loyalty (H11b, β = 0.28), trust was found to exert a smallest mediation effect in the association of brand identification and loyalty (H11c, β = 0.024). In addition, trust showed a minimal mediating effect in the association of satisfaction and commitment (H11d, β = 0.081). Finally, satisfaction showed a greater mediating effect in the association between brand identification and trust (H11e, β = 0.44) and commitment (H11f, β = 0.43; Table VI).
The findings shown in Figure 2 revealed the endogenous variables significant effects (Cohen, 1988). Particularly, the model captured 81 per cent of the variance (R2) as explained by customer commitment, 80 per cent of variance explained by loyalty, 62 per cent of the variance explained by satisfaction and 53 per cent of the variance explained by brand trust. The variance explained for each of the endogenous variables exceeded 0.25, suggesting that the model largely captured the effects of exogenous variables on the endogenous variables. Therefore, the structural model was found to offer good predictive power (Hair et al., 2010).
Alternate model testing
A non-mediated alternate model was tested in addition to the main model to verify the latter’s validity and robustness (Kelloway, 1998; Stokburger-Sauer et al., 2012). The non-mediated model’s fit indices were inferior to those attained for the proposed model: GFI = 0.77, CMIN/df= 6.766, CFI = 0.87, RMSEA = 0.12 and SRMR = 0.47. In addition, the variances explained were smaller than for the proposed model (e.g.73 per cent vs 80 per cent for customer loyalty). Third, the Browne–Cudeck Criterion and the Akaike Information Criterion (Browne and Cudeck, 1989) also suggested the proposed model’s superiority over the alternate model: Proposed model AIC = 684.54/BCC = 692.56; Non-mediated model AIC = 1,473.42/BCC = 1,480.58. Further, Parsimonious Normed Fit Index (proposed model = 0.803; non-mediated model = 0.756) and the Parsimonious Goodness of Fit Index (proposed model = 0.691; non-mediated model = 0.621) consistently supported the adoption of proposed model over alternate model (Kelloway, 1998), thereby adding to the validity of our findings.
Discussion, contributions and implications
This paper has sought to investigate the nature and strength of associations among a set of relational, social identity/exchange-based constructs. Our findings indicate that customer brand identification, commitment, trust and satisfaction exert a positive impact on loyalty. Further, brand identification also exerts a favorable impact on trust, commitment and satisfaction. Theoretically, our analyses contribute to the development of SIT/SET-based insight by uncovering effects of particular relational constructs on ensuing customer loyalty for four- and five-star hotels, thereby directly responding to Kandampully et al.’s (2015) call for further research on customer loyalty dynamics in the hospitality sector.
While SIT and SET-based findings are typically proposed in isolation, our integrative analyses offer insight into their combined effect. Our results therefore indicate that the development of customer brand identification, trust and commitment is conducive to fostering their behavioral brand loyalty, as exemplified by their undertaking of a broader range of or more in-depth brand-related activities, thereby reflecting higher engagement (Hollebeek et al., 2014, 2016a, 2017). In addition, these concepts are conducive to attitudinal brand loyalty development in the hospitality sector (e.g. customers posting favorable hotel-related reviews on TripAdvisor).
Based on our analyses, motivating customers to engage in brand-related activities represents a strategic priority for marketers. For luxury hotels, these activities may include stimulating customers to disseminate positive word-of-mouth regarding the hotel, to like or follow the hotel on social media, to participate in brand-related self-service (e.g. by booking a room online via the hotel’s website), or to encourage customers to work towards receiving a room upgrade (e.g. as a reward after repeated stays at the hotel; Baumöl et al., 2016; Hollebeek, 2013; Sasser et al., 2014). Our findings thus suggest a positive effect of customer brand identification, commitment, satisfaction and trust on the development of customer loyalty for luxury hotels in the emerging market context. Further, based on SIT, customers tend to be interested to identify with those brands which enhance or maintain their self-esteem (Groeger et al., 2016). Hence, it is important for brands to invest in offering excellent product quality that aligns with customer needs and values, thereby driving heightened customer brand identification, which in turn is conducive to fostering loyalty.
Attaining and maintaining customer loyalty is crucial in the hospitality (including hotel) sector. Given that the cost of attracting new customers tends to be significantly higher than that of retaining current ones (Huang et al., 2017; Reichheld and Sasser, 1990), managers are forever looking for ways to stimulate customer retention and increase their lifetime value (Rust et al., 2004). To do so, many hotels have successfully implemented customer satisfaction surveys, reward or loyalty programs and other customer incentives to help develop customer identification and bonding with their brand and increase their willingness to make brand-related investments through social exchange (Hollebeek, 2011a, 2011b; Hollebeek and Chen, 2014, 2016). The importance of proactive customer contributions to brands is, therefore, increasingly prevalent, as also evidenced by the rise of concepts including customer participation, prosumption, etc. (Xie et al., 2008).
Here, we explored and tested the association between customer loyalty and its key drivers in the luxury hotel context. Our findings underline the importance of social identity (e.g. customer brand identification) and social exchange-based (e.g. customer satisfaction, trust and commitment) drivers of customer loyalty, thereby warranting our hybrid SIT/SET perspective. Fostering customers’ socially-minded brand-related contributions is not only conducive to lowering companies’ resource requirements or cost (e.g. where customers bring/use their own resources (e.g. shampoo) in service delivery; Hollebeek et al., 2016a, 2016b), but also in fostering customers’ perceived brand-related trust and commitment, as shown. That is, by integrating their own resources with those of the hotel, customers are likely to develop a sense of self/brand integration where they start seeing the brand as part of themselves (Escalas, 2004; Sprott et al., 2009). Once this process is triggered, their propensity to make further brand-related investments (e.g. of time, effort) are likely to increase, thereby contributing to their brand loyalty (Hollebeek et al., 2016a).
To stimulate these customer processes, hotel managers have various online and/or offline tools at their disposal. In a competitive hospitality environment characterized by a proliferation of service, high customer demands and customer skepticism towards traditional advertising and brands (Tuškej et al., 2013), online brand communities, social media platforms and mobile apps are suitable platforms for promoting and forging psychological connections and brand identification with customers (Bowden et al., 2017; Hollebeek and Solem, 2018; Sigala, 2018; Viswanathan et al., 2017). Given digital marketing’s typical opt-in approach, consumers are actively choosing to receive particular brand-related content, as opposed to traditional advertising that pushes content upon them (Hollebeek and Macky, 2018). For these opting-in customers, we recommend offering hotel-related content that aligns with their values and (desired) identity, which can be uncovered through (prior) marketing research (Tuskej and Podnar, 2018). This process offers one important way for managers to develop consumers’ brand identification, which we revealed as an important driver of customer loyalty.
Relatedly, non-firm owned or controlled social media (e.g. review sites such as TripAdvisor, Travel 2.0) need to be carefully monitored, as content on these platforms can influence other (prospective) customers’ view of the hotel brand (Hollebeek et al., 2016b). Companies are therefore advised to use social media tracking software, such as HootSuite to monitor content posted about their brand and promptly respond to and resolve any issues. Moreover, public activities such as sponsorship, charity events, social campaigns and so on can be used to enhance consumers’ hotel brand identification (Bhattacharya and Sen, 2003). However, the development and maintenance of high service quality remains a cornerstone underpinning these processes (Yang et al., 2017).
Limitations and future research
Despite these contributions, this research is subject to several limitations. First, our analyses were conducted within a single (luxury hotel) context, thereby limiting the generalizability of our findings in other (e.g. non-luxury, tangible goods) contexts. Second, our cross-sectional analyses restricted the applicability of our findings to a single snapshot of observations in time, which may differ throughout the customer journey. Therefore, future longitudinal study may wish to adopt our model in time series-based analyses.
Third, our analyses focused on the Indian hotel context, thereby rendering the attained insight relevant to this particular (e.g. cultural, market) setting, but not others per se (Hollebeek, 2018). Therefore, further research may incorporate different settings into its research design, thereby permitting cross-context comparisons and contrasts. In addition, we made no distinction between bookings made by leisure (vs corporate) travelers, thereby foregoing insight into any specific effects characterizing these different customer segments (Hollebeek, 2017). Consequently, future study may wish to replicate our analyses in other or a broader range of (e.g. cultural, B2C vs B2B) settings to refine the insight attained here. In addition, researchers could broaden their analyses by incorporating additional relational constructs into their model, including customer engagement, co-creation, customer experience, image or others (Brodie et al., 2011; Hollebeek et al., 2016b; Parrey et al., 2018; Rather, 2018; Sharma and Rather, 2015, 2016). Scholars may also wish to consider the impact of customers’ length of hotel stay and its effect on ensuing customer loyalty.
Constructs and their items adopted
|Brand trust (BT)|
|(BT1) I trust this hotel||Martinez and Rodriguez del Bosque (2014)|
|(BT2) This hotel is safe||Martinez and Rodriguez del Bosque (2013)|
|(BT3) This is an honest hotel||So et al. (2013)|
|(BT4) This hotel is very responsive|
|(SAT1) As a whole, I am satisfied with the service provided||Yang et al. (2017)|
|(SAT2) I feel that my experience with this hotel has been enjoyable||Su et al. (2016)|
|(SAT3) The service of this hotel meets my expectations||Yang et al. (2017)|
|(SAT4) This hotel is a good company to do business with|
|(CM1) I feel committed to this hotel||Su et al. (2016)|
|(CM2) I feel emotionally attached to this hotel||Stokburger-Sauer et al. (2012)|
|(CM3) It would be hard for me to not choose this hotel, even if I wanted to|
|(CM4) I would choose this hotel, because I have sense of obligation to it|
|Brand identification (BI)|
|(BI1) When someone criticizes this hotel, it feels like a personal insult||Su et al. (2016),|
|(BI2) When I talk about this hotel, I usually say “we” rather than “they”||Su et al. (2016)|
|(BI3) I identify strongly with this hotel||Stokburger-Sauer et al. (2012)|
|(BI4) When someone praises this hotel, it feels like a personal compliment||Su et al. (2016)|
|Customer loyalty (CL)|
|(CL1) I would recommend this hotel to someone who seeks my advice||Martinez and Rodriguez del Bosque (2013),|
|(CL2) I would encourage friends and relatives to do business with this hotel||Nunkoo et al. (2017)|
|(CL3) I would say positive things about this hotel to other people||Huang et al. (2017)|
|(CL4) I would do more business with this hotel in next few years||Stokburger-Sauer et al. (2012)|
|(CL5) I am a loyal customer of this hotel||So et al. (2013)|
|(CL6) I intend to keep staying with this hotel||Liat et al. (2017)|
Exploratory factor analysis
|Item label variables||FL||EV||VE||α|
Factor loading (FL); Eigen value (EV); Variance explained (VE) and α = Cronbach Alpha; KMO = 0.843; Barlett’s test chi-square = 1,054.758; df = 66, p = 0.000; total variance explained = 71.66%
Confirmatory factor analysis
SL = standard loadings; SMC = squared multiple correlation; M = mean; SD = standard deviation
AVE = average variance extracted; α = Cronbach’s alpha; CR = construct reliability; BI = brand identification; CL = customer loyalty; BT = brand trust; CM = customer commitment and SAT = customer satisfaction.
Off diagonal variables are correlations while as bold diagonal variables are square root of the variance shared among the constructs
Hypothesized model results
|H1: BI → CL||0.30***||0.81||5.75||Significant|
|H2: BI → SAT||0.78***||0.63||15.42||Significant|
|H3: BI → BT||0.21*||0.54||3.61||Significant|
|H4: BI → CM||0.41***||0.82||8.39||Significant|
|H5: BT → CL||0.36***||0.81||8.77||Significant|
|H6: BT → CM||0.15***||0.82||3.19||Significant|
|H7: SAT → CL||0.21***||0.81||3.21||Significant|
|H8: SAT → CM||0.44***||0.82||7.71||Significant|
|H9: SAT → BT||0.29***||0.54||8.03||Significant|
|H10: CM → CL||0.17**||0.81||3.62||Significant|
|Indirect direct||Direct effects||Total effects|
|H11a: BI → CC → CL||0.526||0.285***||0.811***|
|H11b: B1 → SAT→ CL||0.286||0.216***||0.502***|
|H11c: B1 → BT→ CL||0.024||0.357***||0.381***|
|H11d: SAT → BT → CM||0.081||0.437***||0.519***|
|H11e: BI → SAT→ BT||0.441||0.200*||0.641*|
|H11f: BI → SAT→ CM||0.437||0.396***||0.833***|
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About the authors
Raouf Ahmad Rather, MBA (Marketing) is a Full-time-sponsored Research Scholar, working on research in the area of co-creation, service innovation, customer engagement and customer loyalty, whilst pursuing his PhD in Marketing Management at the Business School, University of Jammu, India. His work to-date is published in journals, including Journal of Global Marketing, International Journal of Tourism Cities, European Journal of Tourism, Hospitality and Recreation, International Journal of Hospitality and Tourism Systems, South Asian Journal of Tourism and Heritage, International Journal on Customer Relations and Pacific Business Review International, among others.
Linda D. Hollebeek, PhD is an Associate Professor at Montpellier Business School and NHH Norwegian School of Economics. Her research interest centers on customer/consumer engagement and interactive consumer/brand relationships, and her work to date has published in Journal of the Academy of Marketing Science, Journal of Service Research, Journal of Business Research, Industrial Marketing Management, European Journal of Marketing and Journal of Interactive Marketing, among others. She was awarded the 2014 ANZMAC Emerging Researcher Award, currently serves as Associate Editor of the European Journal of Marketing and Guest Editor of several Special Issues (e.g. in Journal of Service Research).