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Validating and Applying the Mathematical Models for Predicting Corporate Social Responsibility Behavior in Construction Firms: A Roadmap

Autor(en): ORCID


Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 10, v. 12
Seite(n): 1666
DOI: 10.3390/buildings12101666
Abstrakt:

The prevalence of the sophisticated doctrine of corporate social responsibility (CSR) is increasing, given the perennial environmental concerns and social demands in the construction industry worldwide. Firms’ CSR implementation has been influenced by a broad spectrum of external impetuses and internal motives, yet fragmented assessments of such influences make the prediction and implementation of CSR in construction problematic. This study aimed to validate and apply mathematical models for predicting CSR practices in construction firms. Mobilizing integrated institutional theory, stakeholder theory, and self-determination theory, a questionnaire survey within the top-tier construction contractors was undertaken. Eight mathematical models were developed to predict the key dimensions of CSR practices, such as “government commitment” and “environmental preservation”, and validated by five subjective matter expert interviews. The results demonstrated the comprehensiveness, practicality, and robustness of the CSR prediction models in the construction industry. The results also highlighted the perceived importance of CSR practices; external coercive and normative forces, together with internal organizational culture, were the most influential factors directly enhancing construction firms’ CSR implementation. Conceptually, the findings refined CSR practice prediction in a construction management context. The proposed CSR assessment checklists can help practitioners improve the often-tenuous overall CSR performance and spur competitiveness in the construction market.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10699759
  • Veröffentlicht am:
    11.12.2022
  • Geändert am:
    15.02.2023
 
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