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Uğurlu, Ö, Kartal, ŞE, Gundogan, O, Aydin, M and Wang, J (2022) A statistical analysis-based Bayesian Network model for assessment of mobbing acts on ships. Maritime Policy and Management. ISSN 0308-8839
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Abstract
Mobbing is a fundamental problem that disrupts the organization’s structure and negatively affects its employees’ safe work environment. The most critical issue in combating mobbing is increasing the awareness of victims, businesses and society about this problem. The importance of identifying this problem, which will adversely affect the professional life in the maritime profession, as in every professional group, is obvious. This study offers a statistical analysis-based dynamic Bayesian network to model seafarers’ mobbing acts in merchant ships. In this research, measures against mobbing in the maritime industry are also recommended after determining the most frequent mobbing elements in ships. It is observed that the seafarers who have just stepped into onboard are more exposed to mobbing; in contrast, mobbing attacks experienced by seafarers decrease with an increase in age. The most frequent mobbing behaviours are listed as: “I am continually given new tasks“, “My superiors restrict the opportunity for me to express myself” and ”Unfounded rumours about me is circulated in the ship”. The study reveals that while the maritime authorities such as PSC and the ITF have limited capabilities for solving mobbing related problems, the companies may have a crucial role to play in the process.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Science & Technology; Technology; Transportation; Mobbing; seafarer; occupational safety; safe work environment; bullying; WORK-ENVIRONMENT; WORKPLACE; Logistics & Transportation; 14 Economics; 15 Commerce, Management, Tourism and Services; 16 Studies in Human Society |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HE Transportation and Communications V Naval Science > V Naval Science (General) |
| Divisions: | Engineering |
| Publisher: | Taylor & Francis |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 11 Aug 2022 11:04 |
| Last Modified: | 27 Jul 2023 00:50 |
| DOI or ID number: | 10.1080/03088839.2022.2029606 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/17375 |
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