The Process Model of Corporate Social Responsibility (CSR) Communication: CSR Communication and its Relationship with Consumers’ CSR Knowledge, Trust, and Corporate Reputation Perception
TL;DR: In this paper, the positive effects of corporate social responsibility (CSR) communication factors on consumers' CSR knowledge, trust, and perceptions of corporate reputation were analyzed using a national survey of US consumers.
Abstract: Using a national survey of US consumers, this study demonstrates the positive effects of corporate social responsibility (CSR) communication factors on consumers’ CSR knowledge, trust, and perceptions of corporate reputation. The study also examines the role of a stakeholder-specific factor of consumer–company identification in the process of CSR communication. The findings suggest that the positive effects of CSR informativeness are enduring and independent of consumers’ identification levels with a company, whereas the positive consequences of the personal relevance, transparency, and factual tone of CSR communication intensify as the identification levels increase. Although CSR communication in which a self-promotional tone is adopted has a negative relationship with consumer trust and corporate reputation, such negative effects are not evident among consumers with very high identification levels with a company. Such CSR communication in fact improves consumers’ CSR knowledge and, in turn, has a positive effect on corporate reputation.
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30 Jun 2018
TL;DR: In this paper, the authors investigated the actual and perceived barriers for private small forest owners to join FSC and PEFC and found that the most prevalent obstacle to joining FSC is the fear that the standard develops too far away from the small owners' interests.
Abstract: The awareness of sustainable development challenges is growing. Companies are important players in society's development and in order to follow that development, action towards more sustainable business practises is required. Forests are important to the environment, and the forestry industry is largely influenced by society's demands for social, environmental and economic sustainability. In order to strengthen the reliability of sustainability efforts, many forest owners join environmental certification organizations to get their forests certified. The purpose of forest certification is to create a reliable link between consumer and forest product, that proves the product is produced responsibly in terms of social and environmental aspects.
The most commonly used certifications in Sweden are Forest Stewardship Council (FSC) and the Program for Endorcement of Forest Certification (PEFC).
Forest Stewardship Council (FSC) and Program for Endorsement of Forest Certification (PEFC) are the most common forest certification in Sweden.
Three of the four major forest owners' associations, Norra skogsagarna, Norrskog and Mellanskog do not promote FSC. Many of the small forest owners are instead attracted to the other certification scheme of PEFC. This study aims at investigating the actual and perceived barriers for private small forest owners to join FSC.
This is a case study which is a suitable method used to gain a greater understanding of this specific phenomenon. In order to identify potential barriers, umbrella organizations that offer PEFC were selected as the unit of analyzis, the certification organizations were also interviewed.
The result shows that there is great awareness of social, environmental and economic sustainability. On the other hand, there is a certain difference between the views held by forest owners and representatives of certification organizations, which suggests that the perspectives differ depending on which organization one represents. The FSC and PEFC standards differ slightly, which may be a reason why small forest owners are looking for PEFC to a greater extent. The differences however are actually not that great any more, the standards of FSC and PEFC have become very similar. The differences are rather how the organizations are built up where the FSC is a top-down organization while the PEFC has more of a bottom-up structure. Regardless of certification schemes, forest owners find it costly and time consuming to be certified. The most prevalent obstacle to joining FSC is the fear that the standard develops too far away from the small forest owners' interests. The discussion focuses on what this means for the purpose of certification.
144 citations
Cites background from "The Process Model of Corporate Soci..."
...Sustainability communication has not been studied enough and according to Kim (2017) and Keskitalo & Liljenfeldt (2014) its necessary to do so in order for companies to spread the desired message about sustainability work....
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...Belz & Peattie (2013); Kim (2017);Shannon & Weaver (1949); Nowak & Wärneryd (2001); Nitch (1991). Communication is crucial in every relationship, business or other....
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...Belz & Peattie (2013); Kim (2017);Shannon & Weaver (1949); Nowak & Wärneryd (2001); Nitch (1991)....
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TL;DR: In this paper, the authors investigated the impact of corporate social responsibility pillars on the financial performance of banks and found that banks that are more sensitive to environmental issues also exhibit less risk, by using the Heckman's two-stage model for the treatment of sample selection bias.
Abstract: This paper responds to the need for a deeper empirical investigation of the impact of corporate social responsibility pillars on the financial performance of banks. To address this question, this study first analyzes the factors that encourage banks to be more environmentally friendly and then investigates the relationship between a bank's environmental engagement and its risk. Using a sample of 142 banks from 35 countries covering the period from 2011 to 2015, we document the positive impact of effective corporate governance mechanisms on banks' environmental engagement. Moreover, by using the Heckman's two‐stage model for the treatment of sample selection bias, we find that banks that are more sensitive to environmental issues also exhibit less risk. Stakeholder theory and the conflict resolution hypothesis are useful frameworks to overcome the trade‐off between economy and ecology in the banking industry.
111 citations
TL;DR: This study shows how CSR engagement in external initiatives can improve a bank’s competitiveness because of the relationship between citizenship performance and the positive reputation of a bank.
Abstract: Assuming that corporate social responsibility (CSR) is “a process of accumulating knowledge and experience” (Tang et al., 2012, p. 1298), this paper aims to investigate whether and how CSR knowledge (Asif et al., 2013; Kim, 2017) affects financial performance in the European banking industry.,The empirical research analyses a panel of 72 banks from 20 European countries over seven years (2009-2015). The hypotheses were tested using fixed effects regression analysis and the two-stage Heckman model (1976) to address endogeneity bias.,The findings of this work are twofold. First, consistent with the concept of knowledge absorptive capacity (Cohen and Levinthal, 1990), the internal CSR of banks (Kim et al., 2010) positively affects citizenship performance (Peterson, 2004a). Second, in line with the reputational effect of CSR (Margolis et al., 2009; Bushman and Wittenberg-Moerman, 2012), citizenship performance is a positive predictor of a bank’s financial performance.,From a knowledge-based perspective, the analysis shows that accrued internal CSR knowledge plays a key role in implementing effective CSR programs for external stakeholders. Moreover, this study shows how CSR engagement in external initiatives can improve a bank’s competitiveness because of the relationship between citizenship performance and the positive reputation of a bank.,The management of CSR initiatives may favor the sharing of knowledge and creation of trust relationships among banks and internal and external stakeholders. CSR knowledge contributes to expanded value creation for both society and banks.,The knowledge management perspective of CSR provides new insights into the sustainability of banks’ business models and contributes to advancing the debate on the governance modes and effects of CSR. Moreover, the CSR perspective offers additional opportunities for addressing the challenges associated with sharing tacit knowledge within and outside of organizations.
109 citations
TL;DR: In this article, the impact of corporate social responsibility (CSR) environmental and supplier sustainability practices on firm performance within the manufacturing industry in India was investigated, and the authors also investigated the moderating effect of an important internal factor (plant capability) that is likely to affect sustainability practices of a firm.
104 citations
TL;DR: Corporate social responsibility (CSR) is a vital construct in the banking industry due to its influence on brand credibility, positive word of mouth, and repeat purchases as discussed by the authors. But, it is difficult to quantify the impact of CSR on bank performance.
Abstract: Corporate social responsibility (CSR) is a vital construct in the banking industry due to its influence on brand credibility, positive word of mouth, and repeat purchases. The purpose of this resea...
91 citations
Cites background or result from "The Process Model of Corporate Soci..."
...It is hard to evaluate how the customer perceives CSR in the banking sector because the nature of this sector means that the customer often relates any activity to its financial value (Bhattacharya, Korschun, and Sen 2009; Ramlugun and Raboute 2015; Kim 2017)....
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...The third hypothesis (H3), Social CSR positively influences the brand credibility in the banking sector and this relationship supports the findings of a study by Kim (2017), which confirms the relationship between a firm’s social activities and its image, which will lead to a more credible brand....
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...Moreover, Kim (2017) found the relationship between a firm’s social activities and its image leads to a more credible brand that, in turn, leads to loyalty intention, repeat purchase and positive word of mouth....
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References
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TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
80,095 citations
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 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: An overview of simple and multiple mediation is provided and three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model are explored.
Abstract: Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.
25,799 citations
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats
23,353 citations