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Journal Article

Citation

Singh A, Misra SC. Safety Sci. 2020; 127: e104702.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ssci.2020.104702

PMID

unavailable

Abstract

Managing safety in the workplace is a pressing need for organizations and firms especially in the highly competitive world where the success of firms hinges on the overall productivity of organizations. Ensuring high productivity clearly relies on the efficiency of its workforce. Safety of employees is, therefore, an immediate concern that needs to be addressed to avoid negative consequences in terms of cost to the organizations and the subsequent reduction in productivity. Past literature is indicative of the fact that the major reason responsible for accidents occurring at workplace is unsafe behavior. To assess the behavior of the employees in safety context, perception analysis was conducted through a survey-based study of 98 employees of a construction industry where the Workplace Safety Scale (WSS) was administered and the analysis was performed using Dominance based Rough Set Analysis (DRSA). DRSA proved instrumental in dealing with the ambiguities in the preference ordered scenario and decision rules that were derived were a better and more intuitive display of the real-world situation with exceptionally high accuracy. Analysis revealed that the safety of the Supervisor and the co-workers amounted to be the better predictors of safety compliance of the employees. This primarily signifies the role that management has to play in ensuring the safety at workplace and increasing awareness amongst the employees about the importance of following safety rules while at work. Interestingly, the results also showed that all the constructs in the WSS contributed positively in the prediction of the safety compliance of the employees which was found to be consistent with the past studies done in the field of occupational hazards and safety.


Language: en

Keywords

Data mining; Dominance based rough sets; Occupational safety; Rough sets; Safety compliance

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