Quantitative research on corporate social responsibility: a quest for relevance and rigor in a quickly evolving, turbulent world
In this article, the co-editors of the corporate responsibility: quantitative issues section of the journal provide an overview of the quantitative CSR field and offer some new perspectives on where the field is going. They highlight key issues in developing impactful, theory-driven, and ethically g...
| Authors: | ; ; |
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| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Springer Science + Business Media B. V
2023
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| In: |
Journal of business ethics
Year: 2023, Volume: 187, Issue: 1, Pages: 1-15 |
| Further subjects: | B
Corporate social responsibility
B Methodology B Theory-driven B Social Impact B Aufsatz in Zeitschrift B Quantitative |
| Online Access: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Summary: | In this article, the co-editors of the corporate responsibility: quantitative issues section of the journal provide an overview of the quantitative CSR field and offer some new perspectives on where the field is going. They highlight key issues in developing impactful, theory-driven, and ethically grounded research and call for research that examines complex problems facing businesses and the society (e.g., big data and artificial intelligence, political polarization, and the role of CSR in generating social impact). By examining topics that are under-researched, forward-looking, and socially oriented, scholars can expand the boundary of CSR’s substantive domain and produce research that helps businesses act in a long-term, socially responsible way in this quickly evolving, turbulent environment. They also discuss ways to enhance the methodological rigor of quantitative CSR research and encourage scholars to employ cutting-edge, innovative methods to shed light on the micro-level mechanisms of CSR and reveal patterns and relationships hidden in unstructured big data. |
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| ISSN: | 1573-0697 |
| Contains: | Enthalten in: Journal of business ethics
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| Persistent identifiers: | DOI: 10.1007/s10551-022-05297-6 |