Integrated risk management model implementation's contextual dependence on company size

Novel integrated risk management model for standardized management systems, such as ISO 9001:2015 for quality management systems, ISO 14001:2015 for environmental management systems, ISO/IEC 27001:2013 for information security management systems, ISO 45001:2018 for occupational health and safety man...

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Bibliographic Details
Main Authors: Alherian Nuri Mohamed Saad, Spasojević-Brkić Vesna K., Perišić Martina B., Alsharif Abdulghder Mohahmed
Format: Article
Language:English
Published: Savez inženjera i tehničara Srbije 2021-01-01
Series:Tehnika
Subjects:
Online Access:https://scindeks-clanci.ceon.rs/data/pdf/0040-2176/2021/0040-21762103361A.pdf
Description
Summary:Novel integrated risk management model for standardized management systems, such as ISO 9001:2015 for quality management systems, ISO 14001:2015 for environmental management systems, ISO/IEC 27001:2013 for information security management systems, ISO 45001:2018 for occupational health and safety management systems, and ISO 22000:2018 for food safety management systems, has been proposed in order to enable that organizations can manage their processes and associated risks versus requirements of each internal and external stakeholder, due to the fact that those models rarely exist in literature. Proposed model consists of three levels - correspondence, coordination and integration and put in place a clear and structured approach to controlling organizational risks. Using sample of 30 Serbian companies the proposed model has been checked empirically to contextual independence of proposed model using Mann-Whitney U*test and it has been proved that model is context free and applicable to companies different in size since there were no differences between micro & small vs. medium & large companies. Limitation of this research for sure is the sample size, so its extension is recommended. Further recommendation for future research is also a more detailed analysis on collected data done by using more sophisticated statistical analysis tools, such as regression analysis, structural equations modeling and similar to see interrelations between variables in the proposed model.
ISSN:0040-2176
2560-3086