Ethical HR Analytics in the Digital Age: Using Exit Interview Mining to Support Purpose-Driven Retention Strategies

Sharda Singh and Anirban Nandi

Associate Professor, Department of HRM, Xavier Institute of Social Service, Ranchi, India, Email: [email protected]

Human Resource Business Partner at Tata Consultancy Services – Kolkata, India, Email: [email protected]

ABSTRACT:

In the present digital age, Human Resources (HR) needs to adopt data-driven tools and techniques to solve persistent challenge such as employee attrition. To analyze relatively less explored sources of valuable employee data, i.e., exit interviews, to understand the reasons for attrition among IT/ITES employees through data mining techniques. Thoughtful exit interview data can be an important source to conduct attrition and retention research. As the data is in textual form, data mining tools like text and sentiment analysis are effective in analyzing it. Data mining techniques, namely, text analytics and sentiment analysis using RStudio, have been used to uncover the underlying meaning of the contents provided in exit interviews regarding attrition by Indian IT/ITES employees. There are three important findings in the present study. First, the more experienced employees are facing more pressure in their work environment. Second, supervisors have a lot of roles in the retention of their employees. Third, employees consider working in the IT/ITES sector as prestigious, which leads to further satisfaction. HR managers were reluctant to provide exit interview data. The present study focuses only on the IT/ITES industry. Hence, generalizability is questionable. This research includes the development of targeted retention strategies tailored to the unique needs of millennial employees, informed by insights received from exit interviews. Organizations can use the retention model to enhance engagement, satisfaction, and loyalty, thereby mitigating turnover and preserving talent capital. Text mining and sentiment analysis is a new area entering behavioral research and business analytics. This study may encourage HR practitioners to use such tools for analyzing available data and using the results for strategic decision-making.

Keywords: Ethical HR Analytics, Exit Interviews, Artificial Intelligence, Text Mining, Sentiment Analysis, Employee Attrition, IT/ITES Sector, Sustainable HRM, Ethical Leadership.

DOI: https://doi.org/10.70994/jjdms.11103.11116

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REFERENCES:

Business Today Desk. (2025, February 24). Indian tech industry to reach $300 billion revenue milestone in FY26: Nasscom. Business Today. Retrieved from https://www.businesstoday.in/technology/news/story/indian-tech-industry-to-reach-300-billion-revenue-milestone-in-fy26-nasscom-465747-2025-02-24

Chen, G., Ployhart, R. E., Thomas, H. C., Anderson, N., & Bliese, P. D. (2011). The power of momentum: A new model of dynamic relationships between job satisfaction change and turnover intentions. Academy of Management Journal, 54(1), 159–181.

Cotton, J. L., & Tuttle, J. M. (1986). Employee turnover: A meta-analysis and review with implications for research. Academy of management Review, 11(1), 55-70.

Economic Times of India. (2018, August 30). Over 93,000 candidates, including 3,700 PhD holders apply for peon job in UP. The Economic Times. Retrieved from https://economictimes.indiatimes.com/news/politics-and-nation/over-93000-candidates-including-3700-phd-holders-apply-for-peon-job-in-up/articleshow/65604396.cms

Economic Times. (2023). Attrition levels drop as executives stay put in uncertain business environment. The Economic Times. Retrieved from https://economictimes.indiatimes.com/jobs/hr-policies-trends/india-inc-sees-a-dip-in-attrition-to-17-in-2023-amidst-global-tech-downturn-deloitte-survey/articleshow/106329014.cms

Freedman, C. D. (2000). The protection of computer software in copyright and the law of confidence: Improper decompilation and employee-poaching. International Journal of Law and Information Technology, 8(1), 25–45.​

Georgellis, Y., & Lange, T. (2012). Traditional versus secular values and the job–life satisfaction relationship across Europe. British Journal of Management, 23(4), 437–454.

Global Talent Monitor (2022): EVP preferences. Gartner. Retrieved from https://www.gartner.com/en/human-resources/trends/global-talent-monitor.

Hackett, R. D. (1989). Work attitudes and employee absenteeism: A synthesis of the literature. Journal of Occupational Psychology, 62(3), 235–248.

Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature. Journal of Applied Psychology, 92(5), 1332–1356.

Humphrey, W. S. (1989). Managing the software process. Addison-Wesley.

India Brand Equity Foundation. (2024). IBEF annual report 2023–24. India Brand Equity Foundation. Retrieved from https://www.ibef.org/uploads/IBEF-Annual-Report-2023-24.pdf 

Jantan, H., Yusoff, N. M., & Noh, M. R. (2012). Intelligent DSS for talent management: A proposed architecture using knowledge discovery approach. In Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication (Article 85). doi:10.1145/2184751.2184857

Judge, T. A., Scott, B. A., & Ilies, R. (2006). Hostility, job attitudes, and workplace deviance: Test of a multilevel model. Journal of Applied Psychology, 91(1), 126–138.

Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. (2001). The job satisfaction–job performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127(3), 376–407. doi:10.1037/0033-2909.127.3.376

Kim, J.-H. (2007). Employee poaching, predatory hiring and covenants not to compete. Munich Personal RePEc Archive. Retrieved from https://mpra.ub.uni-muenchen.de/83254/

Lahoti, A. A., & Ramteke, P. L. (2014). Data mining technique: Its needs and using its applications. International Journal of Computer Science and Mobile Computing, 3(4), 572–579.​

NASSCOM. (2019). Strategic review: IT-BPM sector in India 2019: Decoding digital (First print February 2019). NASSCOM. Retrieved from https://nasscom.in/sites/default/files/uploads/temp/NASSCOM_Strategic_Review_2019_Decoding_Digital_Secured_15032019.pdf

Netemeyer, R. G., Maxham III, J. G., & Lichtenstein, D. R. (2010). Store manager performance and satisfaction: Effects on store employee performance and satisfaction, store customer satisfaction, and store customer spending growth. Journal of Applied Psychology, 95(3), 530–545. doi:10.1037/a0017631

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.

Podsakoff, P. M., MacKenzie, S. B., Paine, J. B., & Bachrach, D. G. (2000). Organizational citizenship behaviors: A critical review of the theoretical and empirical literature and suggestions for future research. Journal of Management, 26(3), 513–563.

Pugno, M., & Depedri, S. (2009). Job performance and job satisfaction: An integrated survey. Department of Economics Working Papers. University of Trento.

Punnoose, R., & Ajit, P. (2016). Prediction of employee turnover in organizations using machine learning algorithms. International Journal of Advanced Research in Artificial Intelligence, 5(9), 22–26. doi:10.14569/IJARAI.2016.050904

Raman, R., Bhattacharya, S., & Pramod, D. (2019). Predict employee attrition by using predictive analytics. Benchmarking: An International Journal, 26(1), 2–18.

Sacco, J. M., & Schmitt, N. (2005). A dynamic multilevel model of demographic diversity and misfit effects. Journal of Applied Psychology, 90(2), 203–231.

Sheldon, P., & Li, Y. (2013). Localized poaching and skills shortages of manufacturing employees among MNEs in China. Journal of World Business, 48(2), 186–195.

Spain, E., & Groysberg, B. (2016). Making exit interviews count. Harvard Business Review, 94(4), 20.

Stovel, M., & Bontis, N. (2002). Voluntary turnover: Knowledge management–friend or foe? Journal of Intellectual Capital, 3(3), 303–322. doi:10.1108/14691930210435501

Stumpf, S. A., & Dawley, P. K. (1981). Predicting voluntary and involuntary turnover using absenteeism and performance indices. Academy of Management Journal, 24(1), 148–163.

Von Hippel, C., Kalokerinos, E. K., & Henry, J. D. (2013). Stereotype threat among older employees: Relationship with job attitudes and turnover intentions. Psychology and Aging, 28(1), 17–27. doi:10.1037/a0031326