Integration of Information Systems for Predictive Workforce Analytics: Models, Synergy, Security of Entrepreneurship
Open accessAbstract
The era of information economy leads to redesigning not only business models of organizations but also to rethinking the human resources paradigm to harness the power of state-of-the-art technology for Human Capital Management (HCM) optimization. Predictive analytics and computational intelligence will bring transformative change to HCM. This paper deals with issues of HCM optimization based on the models of predictive workforce analytics (WFA) and Business Intelligence (BI). The main trends in the implementation of predictive WFA in the world and in Ukraine, as well as the need to protect business data for security of entrepreneurship and the tasks of predictive analysis in the context of proactive HCM were examined. Some models of effective integration of information systems for predictive WFA were proposed, their advantages and disadvantages were analyzed. These models combine ERP, HCM, BI, Predictive Analytics, and security systems. As an example, integration of HCM system, the analytics platform (IBM SPSS Modeler), BI system (IBM Planning Analytics), and security platform (IBM QRadar Security Intelligence Platform) for predicting the employee attrition was shown. This integration provides a cycle ‘prediction – planning – performance review – causal analysis’ to support protected data-driven decision making in proactive HCM The results of the research support ensuring the effective management of all spectrum of risks associated with the collection, storage and use of data. Keywords: Workforce Analytics (WFA), Human Capital Management (HCM), Predictive Analytics, Proactive Management, BI, Information Systems (IS), Integration, Security of Entrepreneurship
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AI-driven HR analytics allows organisations to develop workforce capability, improve teamwork, support flexible working, and improve performance measurement.
“This will allow organisations to develop the capability of workforce, improve teamwork, support flexible working and improve performance measurement (Chornous and Gura, 2020).”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as Chornous and Gura, 2020
The Materials and Methodology section describes using workforce analytics for training and development and evaluating employees’ potential productivity. The Results section discusses HR metrics and reporting, which supports the performance-measurement part. Neither section establishes that these tools improve teamwork or support flexible working. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI is valuable in HR decision-making because of the potential to avoid subjectivity, with more objective decisions guided by information extracted through employee data mining.
“AI is valuable in HR decision-making (case in point employees) because of the potential to avoid subjectivity, with more objective decisions guided by the information extracted through employee data mining (Chornous and Gura, 2020).”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as Chornous and Gura, 2020
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Cites
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms
Punnoose, R., & Ajit, P. (2016) · International Journal of Advanced Research in Artificial Intelligence · doi:10.14569/ijarai.2016.050904 · Cited by 244 · cited 1× · see citing works
“There are plenty of examples of using predictive analytics models to solve this problem (Saravakos & Sirakoulis, 2014; Rohit & Ajit, 2016; Zhu, 2016).”
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Research works printed but not linked: 9
- Cascio, W., & Boudreau, J. (2015). Investing in People: Financial Impact of Human Resource Initiatives. New Jersey: FT Press.
- Isson, J.P., & Harriott, J.S. (2016). People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent. New Jersey: John Wiley & Sons, Inc.
- Kavanagh, M. J., Thite M., & Johnson, R.D. (2017). Human Resource Information Systems: Basics, Applications, and Future Directions. Los Angeles: SAGE Publications, Inc.
- Merriman, K.K. (2017). Valuation of Human Capital: Quantifying the Importance of an Assembled Workforce. Basingstoke: Palgrave Macmillan.
- Popkova, E.G., Khmeleva, G.A., & Ostrovskiy, V.I. (2015). Innovative approach to providing economic security. Actual Problems of Economics, 169(7), 99-105.
- Soundararajan, R., & Singh, K. (2019). Winning on HR Analytics: Leveraging Data for Competitive Advantage. Los Angeles: SAGE Publications Pvt. Ltd.
- Saravakos, P., & Sirakoulis, G.Ch. (2014). Modeling Employees Behavior in Workplace Dynamics. Journal of Computational Science, 5(5), 821-833. doi: 10.1016/j.jocs.2014.05.001 · DOI
- Silva, M. S. A. e, & Lima, C. G. da S. (2018). "The Role of Information Systems in Human Resource Management", Maria Pomffyova M. (Ed.) Management of Information Systems, IntechOpen, pp. 111- 126. doi: 10.5772/intechopen.79294. · DOI
- Morgan Kaufmann Workforce Analytics. (n.d.). Retrieved from https://www.cornerstoneondemand.com/glossary/workforce- analytics World Economic Forum. (2017). Preparing people for the future of work The Global Human Capital Report. Cologny/Geneva Switzerland.
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Sorted by citations
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100899 · Cited by 927 · see citing works
- Authors
- Chornous, G. O., & Gura, V. L.
- Affiliations
- Taras Shevchenko National University of Kyiv (UA)
- Venue
- European Journal of Sustainable Development
- Published
- 2020-02-01
- Volume
- 9
- Issue
- 1
- Pages
- 83
- ISSN
- 2239-5938 (ISSN-L)
- 2239-6101
- OpenAlex
- W3003384833
- Cited by
- 23
- References
- 1
- Citations by year
- 2025: 7 · 2024: 8 · 2023: 3 · 2022: 4 · 2021: 1
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc
- Locations
- European Journal of Sustainable Development (publishedVersion, cc-by-nc)
- Funders
- Science and Technology Center in Ukraine
- Taras Shevchenko National University of Kyiv
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Keywords
- workforce analytics, human capital management, predictive analytics, business intelligence, information systems integration, employee attrition prediction, data security, proactive management, enterprise resource planning, data-driven decision making, human resource management
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