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2020 | OriginalPaper | Chapter

Role of Data Analytics in Human Resource Management for Prediction of Attrition Using Job Satisfaction

Authors : Neerja Aswale, Kavya Mukul

Published in: Data Management, Analytics and Innovation

Publisher: Springer Singapore

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Abstract

The reputed management publications like Harvard Business Review (HBR) have started stressing upon the emergence of data-driven management decisions. The enhancing investments in data and analytics are underlining the aforementioned emergence. According to International Data Corporation, this investment is expected to grow up to $200 billion by 2020. In such a data lead management world collecting, managing, and analysing the human resources-related data becomes a key for any rather every organization. Human resource analytics is changing into necessary as strategic personnel designing is the need of the hour and helps organizations to investigate each side of HR metrics. HR analytics could be a holist approach. According to KPMG—India’s Annual Compensation Trends Survey 2018–19 the average annual voluntary attrition across sectors is 13.1%. This is a considerably high percentage. Hence, antecedents leading to attrition are needed to be explored in order to propose appropriate HR policies, strategies, and practices. In relevance to these facts, this study focused on proposing a data-driven predictive approach that examines the relationship between the attrition (dependent variable) and other demographic and psychographic independent variables (Antecedents). The present study found that there is a strong relationship between job satisfaction and attrition. Further, there is a higher probability that the employees having work experience between 0–5 years may leave the organizations. Such data-based outcomes may offer help to HR managers in addressing the problems like attrition which intern may increase ROI. Thus, this paper underlines the emergence and relevance of analytics with special reference to human resource management domain.

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Literature
1.
go back to reference Muscalu E, Serban A (2014) HR analytics for strategic human resource management. In: Proceeding of 8th international management conference Muscalu E, Serban A (2014) HR analytics for strategic human resource management. In: Proceeding of 8th international management conference
2.
go back to reference Momin, W.Y.M., Taruna, : HR analytics transforming human resource management. Int J Appl Res 9(1), 2349–5869 (2015) Momin, W.Y.M., Taruna, : HR analytics transforming human resource management. Int J Appl Res 9(1), 2349–5869 (2015)
3.
go back to reference Sujeet, Mishra, Dev, Lama, Yogesh, Pal: Human resources predictive analytics (HRPA) For HR management in organizations. Int J Sci Technol Res 5(5), 2277–8616 (2016) Sujeet, Mishra, Dev, Lama, Yogesh, Pal: Human resources predictive analytics (HRPA) For HR management in organizations. Int J Sci Technol Res 5(5), 2277–8616 (2016)
4.
go back to reference Bindu, K.: A review of poor analytical skills. Int J Innov Res Technol 3(1), 2349–6002 (2016) Bindu, K.: A review of poor analytical skills. Int J Innov Res Technol 3(1), 2349–6002 (2016)
5.
go back to reference Madhavi Lakshmi, P., Siva Pratap, P.: HR analytics- a strategic approach to HR effectiveness. Int J Hum Resour Manag Res 6(3), 2249–7986 (2016) Madhavi Lakshmi, P., Siva Pratap, P.: HR analytics- a strategic approach to HR effectiveness. Int J Hum Resour Manag Res 6(3), 2249–7986 (2016)
7.
go back to reference Rajbhar, A.K., Khan, T., Puskar, S.: A study on HR analytics transforming human resource management. J Invest Manag 6(4), 2328–7721 (2017) Rajbhar, A.K., Khan, T., Puskar, S.: A study on HR analytics transforming human resource management. J Invest Manag 6(4), 2328–7721 (2017)
8.
go back to reference Shweta, T.: Analytics application in human resource context. Int J Sci Eng Res 6(1), 2347–3878 (2018) Shweta, T.: Analytics application in human resource context. Int J Sci Eng Res 6(1), 2347–3878 (2018)
Metadata
Title
Role of Data Analytics in Human Resource Management for Prediction of Attrition Using Job Satisfaction
Authors
Neerja Aswale
Kavya Mukul
Copyright Year
2020
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-32-9949-8_5