Quantitative measurable model of employee satisfaction index

Abstract

Organizations pay a great deal of attention to human resources (HR) motivation because it has a direct impact on the organization's growth. It has been observed that there is a correlation between the organization's motivation policy, the employment satisfaction of its employees, and the organization's success. Therefore, there is an imperative need to define and evaluate job satisfaction in human resources. Despite the fact that motivation theories are well-defined in management literature, there are no quantifiable mathematical methods for measuring job satisfaction. This study centers on a quantitative evaluation of job satisfaction in human resources. The ‘Quantitative Measure of Employee Satisfaction Index’ (QMESI) is a newly developed model. The QMESI model employs job satisfaction criteria found in the majority of motivation theories, specifically (i) up-skilling training, (ii) career development, (iii) performance enhancement, (iv) merit increase, (v) bonus structure, (vi) promotion criteria, (vii) team spirit enhancement, (viii) core hours, (ix) innovation initiatives, (x) dispute resolution procedures, and (xi) shared decision-making. The QMESI model then employs Multi-Criteria Decision Making (MCDM) techniques, specifically the Multi-Attribute Utility Theory (MAUT), to evaluate the utility functions and scores of criteria. In addition, the QMESI model employs the swaying weight method to evaluate criteria weights and the point allocation method to evaluate sub-criteria weights. The ‘Work Satisfaction – Index’ (WS-Index) is then evaluated. The model is applied to sixty-four (64) employees of a manufacturer in Lebanon, and the relevant data are collected. The model indicates that the ‘personal accomplishment’ criterion is the most essential with a weight of 34%, followed by the ‘company recognition’ criterion with a weight of 28%. In contrast, the ‘performance enhancement’ sub-criteria is the most crucial, followed by the ‘merit increase’ sub-criteria. It is determined that the WS-Index follows a normal distribution, with a mean of 67% and a standard deviation of 3%. Thus, it can be demonstrated that there is a 95% chance that local employees are satisfied with their employment to a degree between 64% and 70%. Analysis of Sensitivity indicates that changes in criterion weights between -30% and +30% of the original values affect the WS-Index by no more than 5%. This research is applicable to both enterprise administrators, particularly human resource managers, and academics, as it develops a quantitative model of employee job satisfaction.