A Meta‐Analysis of Employees' Corporate Social Responsibility Attributions: Examining Demographic Antecedents, Outcomes, and Theoretical Correlates

ABSTRACT Corporate social responsibility (CSR) attribution is the process by which individuals infer the reasons behind CSR initiatives. We present a comprehensive meta‐analysis of employees' CSR attributions. Based on attribution theory, we categorized CSR attributions into two types—CSR intrinsic attribution (CSR‐IA) and CSR extrinsic attribution (CSR‐EA). First, we investigated demographic antecedents and found that age, education, gender, and tenure do not significantly correlate with either CSR‐IA or CSR‐EA. Second, in terms of theoretical correlates, we found both leader‐member exchange (LMX) and perceived organizational support (POS) were positively correlated with CSR‐IA, but not with CSR‐EA. Third, in examining the outcomes of CSR attributions, we found CSR‐IA positively related to organizational identification, organizational trust, and work meaningfulness, whereas CSR‐EA showed no significant associations with these variables. Finally, we found that organizational identification mediated the positive relationship between CSR‐IA and several important outcomes, including task performance, organizational citizenship behavior (OCB), job satisfaction, and organizational commitment. Our findings contribute to a deeper understanding of the distinct roles of CSR attributions. Theoretical and practical implications are discussed.

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Publication Details

Journal
Business Ethics the Environment & Responsibility
Published
2025-11-12
DOI
https://doi.org/10.1111/beer.70042
Primary Topic
Corporate Social Responsibility Reporting
Type
article
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article

A Meta‐Analysis of Employees' Corporate Social Responsibility Attributions: Examining Demographic Antecedents, Outcomes, and Theoretical Correlates

Yuxiang Luan, Zheyuan Wang
Business Ethics the Environment & Responsibility
Corporate Social Responsibility Reporting
article

A Meta‐Analysis of Employees' Corporate Social Responsibility Attributions: Examining Demographic Antecedents, Outcomes, and Theoretical Correlates

Yuxiang Luan, Zheyuan Wang
article en

Abstract

ABSTRACT Corporate social responsibility (CSR) attribution is the process by which individuals infer the reasons behind CSR initiatives. We present a comprehensive meta‐analysis of employees' CSR attributions. Based on attribution theory, we categorized CSR attributions into two types—CSR intrinsic attribution (CSR‐IA) and CSR extrinsic attribution (CSR‐EA). First, we investigated demographic antecedents and found that age, education, gender, and tenure do not significantly correlate with either CSR‐IA or CSR‐EA. Second, in terms of theoretical correlates, we found both leader‐member exchange (LMX) and perceived organizational support (POS) were positively correlated with CSR‐IA, but not with CSR‐EA. Third, in examining the outcomes of CSR attributions, we found CSR‐IA positively related to organizational identification, organizational trust, and work meaningfulness, whereas CSR‐EA showed no significant associations with these variables. Finally, we found that organizational identification mediated the positive relationship between CSR‐IA and several important outcomes, including task performance, organizational citizenship behavior (OCB), job satisfaction, and organizational commitment. Our findings contribute to a deeper understanding of the distinct roles of CSR attributions. Theoretical and practical implications are discussed.

Business Ethics the Environment & Responsibility
Sichuan University (CN), Science and Technology Department of Sichuan Province (CN), Sichuan University of Science and Engineering (CN), Renmin University of China (CN)
Openalex Percentile: Top 51%
Corporate Social Responsibility Reporting
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A Meta‐Analysis of Employees' Corporate Social Responsibility Attributions: Examining Demographic Antecedents, Outcomes, and Theoretical Correlates — Yuxiang Luan, Zheyuan Wang · Business Ethics the Environment & Responsibility (2025) | TGRS Research Map | TGRS