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Journal Article

Citation

Maalouf MM, Hoque I. Safety Sci. 2022; 156: e105903.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2022.105903

PMID

unavailable

Abstract

Under pressure from international buyers and local government, garment suppliers in Bangladesh have intensified their efforts to improve work conditions in their factories. Available studies have identified a range of interdependent organizational factors leading to improved OHS outcomes using large-N statistical studies and estimating average effect sizes. Yet, with such interdependence among organizational factors, no study has yet identified and compared combinations of organizational factors or pathways across a set of cases examining OHS outcomes. In this paper, we use small-N fuzzy set qualitative comparative analysis to identify combinations of organizational factors leading to improved OHS performance in the garment industry of Bangladesh. Fourteen cases were selected through purposeful sampling aiming to maximize the variations in causal conditions and output, which facilitates the identification of pathways among the selected garment suppliers. Based on the available studies, we identified and coded five organizational factors (causal conditions) to examine their influence on OHS performance.

RESULTS reveal two distinct pathways leading to improved OHS performance. Common to the two pathways are three causal conditions: Accountability, Auditing, and Training. In addition, to the three common causal conditions, pathway 1 and pathway 2 include respectively Leadership and Buyers, revealing two distinct approaches to improve OHS performance among garment suppliers. Contrary to the expectations in the literature, our study indicates that long-term relationships and partnerships with international buyers is the main driver for improved OHS performance in pathway 2, thus compensating for the absence of active leadership and management commitment.


Language: en

Keywords

Developing country; Fuzzy set qualitative comparative analysis; Garment industry; Occupational health and safety

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