Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry
Main Article Content
Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues.
M. Arora, A. Prakash, A. Mittal, and S. Singh, “Transforming Human Resource Management,” pp. 288–293, 2022.
S. R. Basariya and Ramyarrzgarahmed, “A study on attrition – Turnover intentions of employees,” Int. J. Civ. Eng. Technol., vol. 10, no. 1, pp. 2594–2601, 2019.
R. D. Johnson, D. L. Stone, and K. M. Lukaszewski, “The benefits of eHRM and AI for talent acquisition,” J. Tour. Futur., vol. 7, no. 1, pp. 40–52, 2020, doi: 10.1108/JTF-02-2020-0013.
G. Bhardwaj, S. V. Singh, and V. Kumar, “An empirical study of artificial intelligence and its impact on human resource functions,” Proc. Int. Conf. Comput. Autom. Knowl. Manag. ICCAKM 2020, pp. 47–51, 2020, doi: 10.1109/ICCAKM46823.2020.9051544.
R. Chakraborty, K. Mridha, R. N. Shaw, and A. Ghosh, “Study and Prediction Analysis of the Employee Turnover using Machine Learning Approaches,” 2021 IEEE 4th Int. Conf. Comput. Power Commun. Technol. GUCON 2021, pp. 1–6, 2021, doi: 10.1109/GUCON50781.2021.9573759.
A. Hughes, C.; Robert, L.; Frady, K.; Arroyos, “Artificial intelligence, employee engagement, fairness, and job outcomes. In Managing technology and middle-and low-skilled employees,” Manag. Technol. middle-and low-skilled employees, vol. 21, no. 3, pp. 1–12, 2018.
K. K. Ramachandran, A. Apsara Saleth Mary, S. Hawladar, D. Asokk, B. Bhaskar, and J. R. Pitroda, “Machine learning and role of artificial intelligence in optimizing work performance and employee behavior,” Mater. Today Proc., vol. 51, pp. 2327–2331, 2022, doi: 10.1016/j.matpr.2021.11.544.
M. Subramony and B. C. Holtom, “The Long-Term Influence of Service Employee Attrition on Customer Outcomes and Profits,” J. Serv. Res., vol. 15, no. 4, pp. 460–473, 2012, doi: 10.1177/1094670512452792.
A. . A. D.Alao, “Analyzing Employee Attrition using Decision Tree Algorithms,” Inf. Syst. Dev. Informatics, vol. 4, no. 1, pp. 17–28, 2013, [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1012.2947&rep=rep1&type=pdf.
C. Prentice, S. Dominique Lopes, and X. Wang, “Emotional intelligence or artificial intelligence– an employee perspective,” J. Hosp. Mark. Manag., vol. 29, no. 4, pp. 377–403, 2020, doi: 10.1080/19368623.2019.1647124.
A. Qutub, A. Al-Mehmadi, M. Al-Hssan, R. Aljohani, and H. S. Alghamdi, “Prediction of Employee Attrition Using Machine Learning and Ensemble Methods,” Int. J. Mach. Learn. Comput., vol. 11, no. 2, pp. 110–114, 2021, doi: 10.18178/ijmlc.2021.11.2.1022.
S. Yadav, A. Jain, and D. Singh, “Early Prediction of Employee Attrition using Data Mining Techniques,” Proc. 8th Int. Adv. Comput. Conf. IACC 2018, pp. 349–354, 2018, doi: 10.1109/IADCC.2018.8692137.
F. Fallucchi, M. Coladangelo, R. Giuliano, and E. W. De Luca, “Predicting employee attrition using machine learning techniques,” Computers, vol. 9, no. 4, pp. 1–17, 2020, doi: 10.3390/computers9040086.
S. C. Eickemeyer, J. Busch, C. Te Liu, and S. Lippke, “Acting instead of reacting—ensuring employee retention during successful introduction of i4.0,” Appl. Syst. Innov., vol. 4, no. 4, pp. 1–18, 2021, doi: 10.3390/asi4040097.
G. Marvin, M. Jackson, and M. G. R. Alam, “A Machine Learning Approach for Employee Retention Prediction,” in 2021 IEEE Region 10 Symposium (TENSYMP), Aug. 2021, vol. 4, no. 1, pp. 1–8, doi: 10.1109/TENSYMP52854.2021.9550921.
R. Jain and A. Nayyar, “Predicting employee attrition using xgboost machine learning approach,” Proc. 2018 Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2018, pp. 113–120, 2018, doi: 10.1109/SYSMART.2018.8746940.
R. Punnoose and P. Ajit, “Prediction of Employee Turnover in Organizations using Machine Learning Algorithms,” Int. J. Adv. Res. Artif. Intell., vol. 5, no. 9, pp. 22–26, 2016, doi: 10.14569/ijarai.2016.050904.
R. Garg, A. W. Kiwelekar, L. D. Netak, and A. Ghodake, “i-Pulse: A NLP based novel approach for employee engagement in logistics organization,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100011, 2021, doi: 10.1016/j.jjimei.2021.100011.
A. M. Votto, R. Valecha, P. Najafirad, and H. R. Rao, “Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100047, 2021, doi: 10.1016/j.jjimei.2021.100047.
A. Ikram, M. Fiaz, A. Mahmood, A. Ahmad, and R. Ashfaq, “Internal corporate responsibility as a legitimacy strategy for branding and employee retention: A perspective of higher education institutions,” J. Open Innov. Technol. Mark. Complex., vol. 7, no. 1, pp. 1–12, 2021, doi: 10.3390/joitmc7010052.
E. Meddeb, “The Human Resource Management challenge of predicting employee turnover using machine learning and system dynamics,” CEUR Workshop Proc., vol. 2991, pp. 184–196, 2021.
N. Kaushal, R. P. S. Kaurav, B. Sivathanu, and N. Kaushik, Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis, no. 0123456789. Springer International Publishing, 2021.