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Human Resource Predictive Analytics (HRPA) For HR Management In Organizations
[Full Text]
AUTHOR(S)
Sujeet N. Mishra, Dev Raghvendra Lama, Yogesh Pal
KEYWORDS
Predictive Analytics, Talent Analytics, HR Analytics, Human Resource Management, Modelling, Return on Investment (ROI), Decision Making,
ABSTRACT
Human resource predictive analytics is an evolving application field of analytics for HRM purposes. The purpose of HRM is measuring employee performance and engagement, studying workforce collaboration patterns, analyzing employee churn and turnover and modelling employee lifetime value. The motive of applying HRPA is to optimize performances and produce better return on investment for organizations through decision making based on data collection, HR metrics and predictive models. The paper is divided into three sections to understand the emergence of HR predictive analytics for HRM. Firstly, the paper introduces the concept of HRPA. Secondly, the paper discusses three aspects of HRPA: (a) Need (b) Approach & Application (c) Impact. Lastly, the paper leads to the conclusion on HRPA.
REFERENCES
[1] L. Bassi, “Raging Debates in HR Analytics”, People & Strategy, Vol. 34, Issue 2, 2011
[2] M. Molefe, “From Data to Insights : HR Analytics in Organizations,” Gordon Institute of Business Science, University of Pretoria, 11 Nov. 2013
[3] L. Bassi and D. McMurrer, “A Quick Overview of HR Analytics: Why, What, How, and When?” Association for talent development, March 04, 2015
[4] D. Handa and Garima, “Human Resource (HR) Analytics: Emerging Trend In HRM (HRM)”, IJRCM, Vol. No. 5, Issue No. 06, June 2014, ISSN 0976-2183
[5] K. Ladimeji, “5 Things that HR Predictive Analytics will Actually Predict.” Recruiter (Jan. 23, 2013), sec. I p.1.
[6] J Fitz-enz and J. R. Mattrox II, “Predictive Analytics for Human Resource.” Wiley Publication, SAS Institute Inc., Cary, North America, USA, 2014 pp. 2-3
[7] D. Ulrich, B. Schiemann and L. Sartain, “The Rise of HR: Wisdom from 73 Thought Leaders,” HR Certification Institute, Alexandria, VA, Ed. 2015 pp. 19-23
[8] C. Waxer, “HR Executives: Analytics Role Needs Higher Profile,” Data Informed, 13 March, 2013
[9] “Predictive Talent Analytics the Future of HR,” Press Trust of India, Aug 28, 2015, http://www.business-standard.com/article/pti-stories/predictive-talent-analytics-the-future-of-hr-in-india-115082500643_1.html
[10] T. S. Dey, & P. De, “Predictive Analytics in HR: A Primer,” TCS White Paper, 2015, http://www.tcs.com/SiteCollectionDocuments/White-Papers/Predictive-Analytics-HR-0115-1.pdf
[11] “Applying Advanced Analytics to HR Management Decisions,” James C. Sesil, Pearson Publication, New Jersey, March 2014, pp. 13-25
[12] B. Khatri, “Talent Analytics: Toolkit for Managing HR Issues,” Sai Om Journal of Commerce & Management, Vol. 1, Issue 5 , May 2014, Online ISSN-2347-7571
[13] J. Miller-Merrell, “13 Best HR & Workforce Metrics Formula Examples,” Blogging4Jobs (3 April 2012)
[14] E. Muscalu and A. Şerban, “HR Analytics for Strategic Human Resource Management”, in Proc. 8th International Management Conference on Management Challenges for Sustainable Development, Bucharest, Romania, November 6th -7th, 2014, pp. 939.
[15] M. Blankenship, “Managing and Measuring Talent Risk,” SHRM Foundation Thought Leaders Conference, Oct 2011.
[16] L. Smeyers, “7 Benefits of Predictive Retention Modeling (HR analytics).” iNostix (May 6, 2013)
[17] K. Mølgaard-Pedersen, “The Power of HR Analytics in Strategic Planning,” SVP Global HR Competence Centre, Vestas, 2010
[18] “Cisco Success Story” (Available online: ibm.com/business-analytics)
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