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Erschienen in: Soft Computing 20/2023

02.03.2023 | Application of soft computing

A study protocol in modeling the factors affecting unsafe behaviors with a case study on Iranian workers

verfasst von: Mahdi Malakoutikhah, Moslem Alimohammadlou, Mehdi Jahangiri, Hadiseh Rabiei, Seyed Aliakbar Faghihi, Mojtaba Kamalinia

Erschienen in: Soft Computing | Ausgabe 20/2023

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Abstract

Identifying and modeling the factors affecting unsafe behaviors (UBs) have been conducted by various methods such as qualitative studies, game theory, and modeling using dynamic system. Therefore, the present study aimed to provide a study protocol in the field of modeling the factors affecting UBs by conducting a case study in Iranian workers. This protocol consists of four phases including identifying the factors affecting UBs using grounded theory, weighting the factors extracted by fuzzy best–worst and mapping relationships with fuzzy cognitive map, modeling effective factors with system dynamic, and examining possible scenarios to reduce UBs; all its stages are described in detail and finally the results of a case study is reported. Since the implementation of policies and adoption of the best method to reduce UBs is based on trial and error and always requires a lot of time, cost, and equipment, using modeling study can help reduce the cost of choosing an intervention method. Also, by using the protocol present in each industry, the best scenarios and models can be identified in accordance with the specified factors and specific to the same industry; we can also select and implement the most appropriate strategy to reduce UBs appropriate for preventing unsafe behavior and reduce industrial accidents.

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Metadaten
Titel
A study protocol in modeling the factors affecting unsafe behaviors with a case study on Iranian workers
verfasst von
Mahdi Malakoutikhah
Moslem Alimohammadlou
Mehdi Jahangiri
Hadiseh Rabiei
Seyed Aliakbar Faghihi
Mojtaba Kamalinia
Publikationsdatum
02.03.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 20/2023
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-023-07941-3

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