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Cited by 215

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Corporate Greening beyond Formal Programs, Initiatives, and Systems: A Conceptual Model for Voluntary Pro‐environmental Behavior of Employees

Open access
Lülfs, Regina, Hahn, Rüdiger·2013·European Management Review

Abstract

This paper draws on insights from environmental psychology to develop a conceptual model for explaining pro‐environmental behavior within companies. We delineate the voluntary pro‐environmental behavior of employees (VPBE) from other forms of pro‐environmental behavior (e.g., organizationally prescribed behavior) and identify determinants of VPBE. Specifically, we advance hitherto existing concepts by connecting different psychological models for the explanation of private sphere pro‐environmental behavior and then adapting them to the corporate sphere. We conclude by assessing some implications for corporate interventions and providing recommendations for further research.

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Crossref · 10.1111/emre.12008

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Metadata

Authors
Lülfs, R., & Hahn, R.
Venue
European Management Review
Publisher
Wiley
Published
2013-04
Volume
10
Issue
2
Pages
83-98
ISSN
  • 1740-4754 (ISSN-L)
  • 1740-4762
OpenAlex
W1546717724
Cited by
215
References
18
FWCI
22.47
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Corporate Greening beyond Formal Programs, Initiatives, and Systems: A Conceptual Model for Voluntary Pro‐environmental Behavior of Employees. https://app.cochat.ai/papers/10.1111/emre.12008
The paper
Lülfs, R., & Hahn, R. (2013). Corporate Greening beyond Formal Programs, Initiatives, and Systems: A Conceptual Model for Voluntary Pro‐environmental Behavior of Employees. European Management Review. https://doi.org/10.1111/emre.12008

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.