Do Customer Perceptions of Corporate Services Brand Ethicality Improve Brand Equity? Considering the Roles of Brand Heritage, Brand Image, and Recognition Benefits
TL;DR: In this paper, the authors empirically examined the effects of customer perceived ethicality of corporate brands that operate in the services sector, based on data collected for eight service categories using a panel of 2179 customers, the hypothesized structural model is tested using path analysis.
Abstract: In order to be competitive in an era of ethical consumerism, brands are facing an ever-increasing pressure to integrate ethical values into their identities and to display their ethical commitment at a corporate level. Nevertheless, studies that relate business ethics to corporate brands are either theoretical or have predominantly been developed empirically in goods contexts. This is surprising, because corporate brands are more relevant in services settings, given the nature of services (i.e., intangible, heterogeneous, inseparable and perishable), and the fact that services settings comprise a greater number of customer–brand interactions and touch points than goods contexts. Accordingly, the purpose of this article is to empirically examine the effects of customer perceived ethicality of corporate brands that operate in the services sector. Based on data collected for eight service categories using a panel of 2179 customers, the hypothesized structural model is tested using path analysis. The generalizability theory is applied to test for measurement equivalence between these categories. The results of the hypothesized model show that, in addition to a direct impact, customer perceived ethicality has a positive and indirect impact on brand equity, through the mediators of recognition benefits and brand image. Moreover, brand heritage negatively influences the impact of customer perceived ethicality on brand image. The main implication is that managers need to be aware of the need to reinforce brand image and recognition benefits, as this can facilitate the translation of customer perceived ethicality into brand equity.
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TL;DR: In this paper, the authors investigated the effect of sensory brand experience on brand equity in the banking industry, through customer satisfaction and customer affective commitment, and examined whether employee empathy moderates the impacts of sensory-brand experience on customer satisfaction.
229 citations
TL;DR: In this paper, the authors examined the influence of CSR on customer loyalty, considering the mediating roles of co-creation and customer trust, and investigated the influence on customer trust.
Abstract: In an ever more transparent, digitalized, and connected environment, customers are increasingly pressuring brands to embrace genuine corporate social responsibility (CSR) practices and co-creation activities While both CSR and co-creation are social and collaborative processes, there is still little research examining whether CSR can boost co-creation In addition, while previous research has mainly related co-creation to emotional outcomes (eg, customer affective commitment), limited empirical research has related it to rational (eg, customer trust) and behavioral outcomes (eg, customer loyalty) To address these shortcomings in the literature, this paper examines the influence of CSR on customer loyalty, considering the mediating roles of co-creation and customer trust It also investigates the influence of co-creation on customer trust The data were collected in Spain in late 2017 using an online survey, and the sample contained 1101 customers of health insurance services brands Structural equation modeling was used to test the hypothesized relationships simultaneously The results show that CSR influences customer loyalty both directly and indirectly through co-creation and customer trust However, the indirect impact is the stronger of the two, implying that embracing co-creation activities and developing customer trust can make it easier for CSR practices to enhance customer loyalty In addition, co-creation has a direct effect on customer trust
191 citations
TL;DR: In this article, the authors investigated the impact of breadth of external stakeholder co-creation on innovation performance, considering the mediating roles of knowledge sharing and product innovation, using a set of ordinary-least-squares regression models.
128 citations
TL;DR: In this article, a conceptual framework was tested using structural equation modeling with responses from 273 apparel shoppers collected by using a structured questionnaire, and they found evidence of mediating-moderation effect in which the moderating power of perceived brand ethicality is eliminated in the presence of full mediator, brand passion.
112 citations
TL;DR: In this article, the authors examined the role of customer perceptions of CSR in improving customer loyalty by exploring its direct and mediated effects through service quality, customer satisfaction, corporate image and corporate reputation in a cross-country setting.
80 citations
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Book•
01 Dec 1969TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Abstract: Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That a Proportion is .50 and the Sign Test. Differences Between Proportions. Chi-Square Tests for Goodness of Fit and Contingency Tables. The Analysis of Variance and Covariance. Multiple Regression and Correlation Analysis. Set Correlation and Multivariate Methods. Some Issues in Power Analysis. Computational Procedures.
115,069 citations
TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...
56,555 citations
TL;DR: In this paper, the authors provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development, and present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests.
Abstract: In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.
34,720 citations
Book•
06 May 2013
TL;DR: In this paper, the authors present a discussion of whether, if, how, and when a moderate mediator can be used to moderate another variable's effect in a conditional process analysis.
Abstract: I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1. A Scientist in Training 1.2. Questions of Whether, If, How, and When 1.3. Conditional Process Analysis 1.4. Correlation, Causality, and Statistical Modeling 1.5. Statistical Software 1.6. Overview of this Book 1.7. Chapter Summary 2. Simple Linear Regression 2.1. Correlation and Prediction 2.2. The Simple Linear Regression Equation 2.3. Statistical Inference 2.4. Assumptions for Interpretation and Statistical Inference 2.5. Chapter Summary 3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation 3.2. Partial Association and Statistical Control 3.3. Statistical Inference in Multiple Regression 3.4. Statistical and Conceptual Diagrams 3.5. Chapter Summary II. MEDIATION ANALYSIS 4. The Simple Mediation Model 4.1. The Simple Mediation Model 4.2. Estimation of the Direct, Indirect, and Total Effects of X 4.3. Example with Dichotomous X: The Influence of Presumed Media Influence 4.4. Statistical Inference 4.5. An Example with Continuous X: Economic Stress among Small Business Owners 4.6. Chapter Summary 5. Multiple Mediator Models 5.1. The Parallel Multiple Mediator Model 5.2. Example Using the Presumed Media Influence Study 5.3. Statistical Inference 5.4. The Serial Multiple Mediator Model 5.5. Complementarity and Competition among Mediators 5.6. OLS Regression versus Structural Equation Modeling 5.7. Chapter Summary III. MODERATION ANALYSIS 6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny? 6.2. Confounding and Causal Order 6.3. Effect Size 6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously? 6.5. Reporting a Mediation Analysis 6.6. Chapter Summary 7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects 7.2. An Example: Sex Discrimination in the Workplace 7.3. Visualizing Moderation 7.4. Probing an Interaction 7.5. Chapter Summary 8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator 8.2. Interaction between Two Quantitative Variables 8.3. Hierarchical versus Simultaneous Variable Entry 8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance 8.5. Chapter Summary 9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering 9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis 9.3. Artificial Categorization and Subgroups Analysis 9.4. More Than One Moderator 9.5. Reporting a Moderation Analysis 9.6. Chapter Summary IV. CONDITIONAL PROCESS ANALYSIS 10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature 10.2. Conditional Direct and Indirect Effects 10.3. Example: Hiding Your Feelings from Your Work Team 10.4. Statistical Inference 10.5. Conditional Process Analysis in PROCESS 10.6. Chapter Summary 11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study 11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model 11.3. Visualizing the Direct and Indirect Effects 11.4. Mediated Moderation 11.5. Chapter Summary 12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis 12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect? 12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation 12.4. The Pitfalls of Subgroups Analysis 12.5. Writing about Conditional Process Modeling 12.6. Chapter Summary Appendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS
26,144 citations
TL;DR: It is argued the importance of directly testing the significance of indirect effects and provided SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals to enhance the frequency of formal mediation tests in the psychology literature.
Abstract: Researchers often conduct mediation analysis in order to indirectly assess the effect of a proposed cause on some outcome through a proposed mediator. The utility of mediation analysis stems from its ability to go beyond the merely descriptive to a more functional understanding of the relationships among variables. A necessary component of mediation is a statistically and practically significant indirect effect. Although mediation hypotheses are frequently explored in psychological research, formal significance tests of indirect effects are rarely conducted. After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approach advocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature. Electronic copies of these macros may be downloaded from the Psychonomic Society's Web archive at www.psychonomic.org/archive/.
15,041 citations