مقاله انگلیسی رایگان در مورد برآورد بی طرفانه عملکرد کارکنان با استفاده از یادگیری ماشین – الزویر 2024

Abstract

Most of the companies’ sustainability and growth depend on how well its employees perform. However, the measurement of employees’ performance until now is inconclusive and inexhaustive. To accurately assess and predict an employee’s performance, numerous external factors (physical/environmental, social, and economic) related to an employee’s life have been taken into account in this work. The purpose of this research is to explore an unbiased AI algorithmic solution to predict future employee performance considering physical, social, and economic environmental factors that affect employee performance. We collected data of 1109 employees from the ‘For-Profit Organization’ in Bangladesh from both employers and employees to cover all the factors that justified the unbiased outcome. We utilized a few machine learning tools in this study including the Logistic Regression classifier, the Gaussian Naive Bayes, the Decision Tree classifier, the K-Nearest Neighbors (K-NN), the SVM classification, etc., in order to predict the employee performance evaluation. Then, we compared the effectiveness of those machine learning models by analyzing their precisions, recall, F1-score, and accuracy. This work can be utilized to obtain bias-free employee performance reviews. This fair employee performance assessment can aid decision-makers in making moral choices regarding employee promotions, career advancement, and training needs, among other things. The study also describes notes for future researchers.

Introduction

Performance evaluation, which includes assessing current performance, identifying good and poor performers, and providing feedback to staff, is one of the most challenging components of Human Resource Management (HRM) (Cherian et al., 2021, Fogoroș et al., 2020, Stone et al., 2015). Employee performance evaluations are not practiced systematically by numerous organizations. As a result, the evaluation method becomes erratic and ineffective. A systematic approach should be adopted in order to evaluate employees at the planning stage on a regular basis (Ahmed et al., 2013). Employees with these attributes—skills, dedication, attitudes, and knowledge—are valued as assets by the company (Al-Tit et al., 2022, Li et al., 2008, Yang and Lin, 2009). By creating new knowledge at firms’ level, an organization’s human resources can the firms to innovate (Terán-Bustamante et al., 2021). The accurate assessment of the employees’ performance contributes to the mission of the company with the maximum satisfaction of the employees (Pap et al., 2022). Since company progress depends on employee advancement, numerous executives search for efficient ways to improve performance drastically (Abbas and Yaqoob, 2009, Salam, 2021). To boost performance, employers first need to know the performance condition of the employee in any organization. An article in the Harvard Business Review (Antonio, 2018) claims that essential functions of the organizations, such as prediction, upselling, cross-selling, and performance management can be remarkably influenced by AI technologies (Ledro et al., 2023). In the future, businesses, communities, and nations will be significantly impacted by big data, automation, and machine learning (Lada et al., 2023, Tao et al., 2023). In this regard, AI has started to play a role in business, particularly in HRM, concerning the prediction and decision-making (Nilashi et al., 2023, Qureshi et al., 2023).

Conclusion

The performance of the company’s personnel determines a large portion of its sustainability and growth. Many external aspects (physical/environmental, social, and economic) relevant to an employee’s life have been included in this work in order to measure and anticipate an employee’s performance effectively. The goal of this project is to create an AI algorithmic-based ethical decision-making framework that takes into account the various environmental factors—physical, social, and economic – that have an impact on worker performance. Our results were impartial since we gathered information from a few “For-Profit Organizations” in Bangladesh, both objectively and subjectively. In this study, we used a variety of machine learning methods, and we were able to acquire a reasonable accuracy score. The Random Forest model obtained the highest accuracy score, and the Gaussian naïve Bayes model had the lowest. An impartial employee performance review can be obtained by using this work. Decision-makers can use this equitable employee performance evaluation to help them make moral decisions about training requirements, career advancement, and employee promotions, among other things.