A Fully Completed Spherical Fuzzy Data-Driven Model for Analyzing Employee Satisfaction in Logistics Service Industry

Abstract

This study proposes a two-stage MCDM model that combines Delphi and decision-making trial and evaluation laboratory methods based on spherical fuzzy sets (SF-Delphi and SF-DEMATEL) to analyze the motivation and demotivation factors affecting employee satisfaction in the Vietnamese logistics service industry. In the first stage, the SF-Delphi approach is used to gather expert opinions and develop consensus on the significance of criteria. In the second stage, the SF-DEMATEL technique explores causal linkages between the criteria and identifies root causes of the issues. Based on a comprehensive literature review and feedback from 40 experts, this study identified crucial factors affecting employee satisfaction related to both motivation and demotivation aspects. The findings of this study provide recommendations for managers to improve employee satisfaction, such as establishing clear and detailed wage and bonus rules, offering training courses, developing a positive work culture, recognizing employee efforts, and addressing poor treatment by supervisors and inadequate leadership support. Furthermore, the proposed model accurately identifies essential elements, represents uncertainty, adapts to various contexts, has resilience and accuracy, and has practical implications for mitigating demotivating factors and enhancing motivation, thereby positively influencing employee satisfaction in the logistics service industry.