Risk perception and decision making in the supply chain: theory and practice

Tse, Y; Chung, C; Pawar, K

Abstract

For over sixty years, academics and practitioners from different backgrounds, including psychology, sociology, and management, have studied the perception of risk and how different decision making affects daily life and business activities. Although it is almost six hundred years since Machiavelli stressed the importance of calculation of risk and effective response to it, approaches to risk measurement and assessment, and to decision making in risky situations, continue to develop and evolve. In the business world, managers strive to find ways to understand how different internal and external factors influence risk, how to judge and interpret the available evidence on the possibility of loss, and how to take individual actions to manage the risk (Slovic 2000). In this decade, a number of risk management frameworks (e.g. IS031000) have been proposed and employed in different areas. These frameworks provide foundations and building blocks for managers to collect available data to analyse risk. Most importantly, such frameworks allow managers to gather knowledge intellectually, to properly judge their experience and to assess the current situation, so as to enter into the most appropriate decision.

Citation

Tse, Y., Chung, C., & Pawar, K. (2018). Risk perception and decision making in the supply chain: theory and practice. Industrial Management and Data Systems, 118(7), 1322-1326. https://doi.org/10.1108/IMDS-08-2018-605

• Journal Article Type: Editorial
• Acceptance Date: Aug 13, 2018
• Online Publication Date: Sep 28, 2018
• Publication Date: Sep 28, 2018
• Deposit Date: Mar 3, 2020
• Publicly Available Date: Mar 23, 2020
• Journal: Industrial Management and Data Systems
• Print ISSN: 0263-5577
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• Volume: 118
• Issue: 7
• Pages: 1322-1326
• DOI: https://doi.org/10.1108/IMDS-08-2018-605
• Keywords: Industrial and Manufacturing Engineering; Strategy and Management; Computer Science Applications; Industrial relations; Management Information Systems
• Public URL: https://nottingham-repository.worktribe.com/output/4083373
• Publisher URL: https://www.emerald.com/insight/content/doi/10.1108/IMDS-08-2018-605/full/html