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How does the Perceived Ethicality of Corporate Services Brands Influence Loyalty and Positive Word-of-Mouth? Analyzing the Roles of Empathy, Affective Commitment, and Perceived Quality

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Abstract

In the past few decades, a growth in ethical consumerism has led brands to increasingly develop conscientiousness and depict ethical image at a corporate level. However, most of the research studying business ethics in the field of corporate brand management is either conceptual or has been empirically conducted in relation to goods/products contexts. This is surprising because corporate brands are more relevant in services contexts, because of the distinct nature of services (i.e., intangible, heterogeneous, and inseparable) and the key role that employees have in the services sector (i.e., they can build or break the brand when interacting with customers). Accordingly, this article aims at empirically examining the effects of customer perceived ethicality in the context of corporate services brands. Based on data collected for eight service categories using a panel of 2179 customers, the hypothesized structural model is tested using path analysis. The results show that, in addition to a direct effect, customer perceived ethicality has a positive and indirect effect on customer loyalty, through the mediators of customer affective commitment and customer perceived quality. Further, employee empathy positively influences the impact of customer perceived ethicality on customer affective commitment, and customer loyalty positively impacts customer positive word-of-mouth. The first implication of these results is that corporate brand strategy needs to be aligned with human resources policies and practices if brands want to turn ethical strategies into employee behavior. Second, corporate brands should build more authentic communications grounded in their ethical beliefs and supported by evidence from actual employees.

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Abbreviations

AVE:

Average variance extracted

CAC:

Customer affective commitment

CFA:

Confirmatory factor analysis

CL:

Customer loyalty

CMV:

Common method variance

CPE:

Customer perceived ethicality

CPQ:

Customer perceived quality

CPWOM:

Customer positive word-of-mouth

CR:

Composite reliability

CSR:

Corporate social responsibility

EE:

Employee empathy

GC:

Generalizability coefficient

G-theory:

Generalizability theory

PLS:

Partial least squares

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Authors and Affiliations

  1. Department of Marketing, ESADE - Universitat Ramon Llull, Av. Torre Blanca, 59, 08172, Sant Cugat del Vallès (Barcelona), Spain

    Stefan Markovic & Oriol Iglesias

  2. Department of Marketing, Operations and Supply, EADA Business School, C. Aragó, 204, 08011, Barcelona, Spain

    Jatinder Jit Singh

  3. Department of Operations, Innovation and Data Sciences, ESADE - Universitat Ramon Llull, Av. Torre Blanca, 59, 08172, Sant Cugat del Vallès (Barcelona), Spain

    Vicenta Sierra

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  1. Stefan Markovic
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  2. Oriol Iglesias
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  3. Jatinder Jit Singh
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  4. Vicenta Sierra
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Corresponding author

Correspondence to Stefan Markovic.

Appendix 1: Measurement Assessment, Measurement Equivalence, and Common Method Variance

Appendix 1: Measurement Assessment, Measurement Equivalence, and Common Method Variance

Measurement Assessment

In order to assess the adequacy of the measures, we estimated both the convergent (see Table 5) and the discriminant validity (see Table 6). On one hand, we evaluated convergent validity using the following three measures: item reliability, construct reliability, and average variance extracted (AVE hereafter). First, we evaluated item reliability based on the factor loadings of the measures on their respective constructs. All the factor loadings were higher than the threshold value of .6, thereby supporting convergent validity. Second, we evaluated construct reliability using both Cronbach’s alpha coefficients and composite reliability (CR hereafter) values. All the Cronbach’s alpha coefficients and CR values were higher than both the threshold value of .7 and the strictest threshold value of .8, thus supporting convergent validity. Third, we evaluated the AVE, which is a summary indicator of convergence. All the AVE values were higher than the threshold value of .5, thereby supporting convergent validity.

Table 5 Item descriptive and measurement assessment
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On the other hand, we evaluated discriminant validity comparing the square root of the AVE of each construct with all the correlations among constructs (Anderson and Gerbing 1988; Morgan et al. 2007). As all the square root values of the AVE were higher than all the correlations among constructs, discriminant validity was supported.

Table 6 Discriminant validity
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Measurement Equivalence

As data were collected for multiple service categories, the measurement equivalence test needs to be conducted in order to evaluate if the constructs via their related scale items do not vary across service categories (Malhotra and Sharma 2008). Two widely recognized techniques to conduct the measurement equivalence test are generalizability theory (G-theory hereafter) (Cronbach et al. 1972) and confirmatory factor analysis (CFA hereafter) (Steenkamp and Baumgartner 1998).

Because two service categories did not include enough cases to conduct CFA (i.e., sub-sample size lower than 75), the structure invariance of the constructs across service categories was evaluated using G-theory, as suggested by Malhotra and Sharma (2008). G-theory is useful for evaluating the generalizability of the scales that measure constructs across different groups of interest (i.e., service categories). In fact, G-theory enables us to estimate the precision of the measurements in contexts where these measurements are exposed to various sources of variation. The potential sources of variation in our study are the following: service categories (i.e., a high variation would indicate that brands differ when compared to the means of the constructs), customers within service categories (i.e., high values would suggest that there is a variation among customers pertaining to each service category), items within each scale (i.e., a low variation would indicate that there is redundancy among items), interaction among items and service categories (i.e., a low variation would suggest that the pattern of responses is homogeneous among service categories and would enhance generalizability), and finally, the error and other confounding sources (i.e., a low variation would increase generalizability).

We implemented a mixed ANOVA for variance decomposition in SPSS to calculate these five sources of variation, and the generalizability coefficient (GC hereafter) to assess the equivalence across the eight service categories present in our study. Although all the sub-samples did not have the same size, Malhotra and Sharma (2008) argued that the results from G-theory are comparable in both cases, when using equal or unequal sub-sample sizes. The results presented in Table 7 indicate that all the sources of variation follow the above-described patterns, thereby enhancing generalizability. Moreover, all the GCs ranged from .84 to .97, which are quite high values according to Rentz (1987). This further supported the generalizability of the scales across the eight service categories.

Table 7 Measurement equivalence using G-theory
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Common Method Variance

Because all the data in our study were collected from the same respondents (i.e., customers), a potential issue of common method variance (CMV hereafter) may arise. In order to address this potential issue, we applied the marker variable technique suggested by Lindell and Whitney (2001). This technique uses a theoretically unrelated construct (i.e., the marker variable) first to determine the estimate of CMV, and then to adjust the correlations among all the constructs present in the model. We considered the marker variable of customer psychological risk, which contained three items introduced by Keh and Pang (2010). A high correlation between the marker variable and any construct would indicate the existence of CMV. Lindell and Whitney (2001) argued that the lowest of the absolute correlations between the marker variable and all the constructs (r s) is the estimate of CMV. Because an unadjusted correlation is not only influenced by the true covariance but also by CMV, the r s is a conservative estimate (Lindell and Whitney 2001). For our sample, r s is .055 (see Table 8), which corresponds to an R 2 of .003, indicating low common source effect shared between constructs.

Table 8 Correlation coefficients and R 2 between marker and constructs
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In order to control for CMV, we adjusted all the correlations among the constructs, using the r s = .055 previously estimated. If the significant unadjusted correlation coefficients remained significant after adjusting for CMV, the results are not seriously affected by CMV. Table 9 shows that all the correlation coefficients remained significant after adjusting for CMV. This is, after correcting the correlation coefficient regarding the level of common variance shared between the marker variable and the constructs, the adjusted correlation—removing some different degrees of common variance computed with the unrelated marker variable—remained significant. Moreover, we conducted a test of differences between the adjusted and unadjusted correlations, in order to check for possible statistical differences (Steiger 1980). All coefficients were not significant, providing further support to the results obtained by applying the marker variable technique. All in all, we can conclude that the estimations of the parameters of the hypothesized model are not biased by CMV.

Table 9 CMV-adjusted estimates and test of differences between correlation coefficients
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Markovic, S., Iglesias, O., Singh, J.J. et al. How does the Perceived Ethicality of Corporate Services Brands Influence Loyalty and Positive Word-of-Mouth? Analyzing the Roles of Empathy, Affective Commitment, and Perceived Quality. J Bus Ethics 148, 721–740 (2018). https://doi.org/10.1007/s10551-015-2985-6

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