An Adaptive Network-based Fuzzy Inference System for predicting organizational commitment according to different levels of job satisfaction in growing economies. (April 2018)

• Record Type: Journal Article
• Authors: Rabiei, Peyman; Arias-Aranda, Daniel
• Other Names: Mustafee Navonil guest-editor.; Mittal Saurabh guest-editor.; Diallo Saikou guest-editor.; Zacharewicz Gregory guest-editor.
• Is Part Of: Simulation. Volume 94:Number 4(2018)
• Page Start: 341
• Page End: 358
• Publication Date: 2018-04
• Subjects: Job satisfaction -- organizational commitment -- Adaptive Network-based Fuzzy Inference System (ANFIS) -- growing economies -- food industrial companies; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549717712037
• Languages: English
• ISSNs: 0037-5497

Abstract

The relationship between organizational commitment and job satisfaction has received plenty of attention in the literature. However, similar studies in growing economies are scarce. The objective of this study is to cover such a gap by introducing an intelligent algorithm for predicting organizational commitment considering job satisfaction as well as comparing its performance to conventional Multiple Linear Regression (MLR). In doing so, data was collected by distributing questionnaires among 200 employees from the food industry in Shiraz (Iran), which represents one of the most dynamic economies of the country. A 73% response rate was achieved. The respondents completed the questionnaire, which assessed six dimensions of job satisfaction (satisfaction with supervision, overall job, company policy and support, promotion and advancement, pay, and coworkers) and organizational commitment. Using MLR, the results indicated that workers' had higher satisfaction with overall job, company policy and support, and coworkers, bringing about significantly higher employees' organizational commitment level. An Adaptive Network-based Fuzzy Inference System (ANFIS) is also developed and tested for the purpose of this study to predict organizational commitment level based on different levels of job satisfaction. Comparing the results obtained from ANFIS and MLR shows that the proposed intelligent algorithm has better performance than conventional MLR and predicts organizational commitment more accurately, based on their root mean square error values (RMSE). A simulation model based on the rules learned by the ANFIS algorithm is also presented to simulate the organizational commitment level of employees by establishing their position on various indexes of job satisfaction. This model can help managers to achieve higher levels of employees' organizational commitment, since the main aspects of job satisfaction that need more focus are simulated. Different scenarios and situations could be simulated by this system, which is a main contribution of the current work. In terms of presenting an intelligent algorithm in order to predict organizational commitment level based on job satisfaction in food industrial companies, this study is pioneering among other studies.