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Purpose: The purpose of this publication is to present the applications of usage of business analytics in human resource analytics. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: This paper explores the transformative potential of business analytics within human resource (HR) analytics, particularly in the context of Industry 4.0. It highlights how the integration of advanced analytics tools and methodologies enables organizations to gain deep insights into workforce behaviors, trends, and patterns, ultimately facilitating more informed decision-making and strategic workforce management. Through applications such as talent acquisition and retention, performance management, workforce planning, and employee well-being, business analytics empowers HR professionals to optimize HR processes, enhance employee satisfaction, and drive organizational success. However, challenges such as data quality issues, privacy concerns, and skills gaps among HR professionals underscore the need for a strategic approach and investment in technology and talent to fully realize the benefits of business analytics in HR analytics. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of human resource analytics.
Rocznik
Tom
Strony
629--640
Opis fizyczny
Bibliogr. 20 poz.
Twórcy
autor
- Silesian University of Technology, Organization and Management Department, Economics and Informatics Institute
autor
- Economic University in Poznań
autor
- Economic University in Poznań
autor
- The War Studies University
Bibliografia
- 1. Adel, A. (2022). Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. Journal of Cloud Computing, 11(1), 40.
- 2. Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., Edinbarough, I. (2022). State of Industry 5.0-Analysis and Identification of Current Research Trends. Applied System Innovation, 5(1), DOI: 10.3390/asi5010027.
- 3. Aslam, F., Wang, A.M., Li, M.Z., Rehman, K.U. (2020). Innovation in the Era of IoT and Industry 5.0: Absolute Innovation Management (AIM) Framework. Information, 11(2), doi:10.3390/info11020124
- 4. Bakir, A., Dahlan, M. (2022). Higher education leadership and curricular design in industry 5.0 environment: a cursory glance. Development and Learning in Organizations.
- 5. Cam, J.D. Cochran, J.J., Ohlmann, M.J.F. (2021). Business analytics: descriptive, predictive, prescriptive. Boston: Cengage.
- 6. Charles, V., Garg, P., Gupta, N., Agrawal, M. (2023). Data Analytics and Business Intelligence: Computational Frameworks, Practices, and Applications. New York: CRS Press.
- 7. Cillo, V., Gregori, G.L., Daniele, L.M., Caputo, F., Bitbol-Saba, N. (2022). Rethinking companies’ culture through knowledge management lens during Industry 5.0 transition. Journal of Knowledge Management, 26(10), 2485-2498.
- 8. Dameri, R.P. (2016). Smart City and ICT. Shaping Urban Space for Better Quality of Life. In: Information and Communication Technologies in Organizations and Society. Cham, Switzerland: Springer International Publishing.
- 9. Di Marino, C., Rega, A., Vitolo, F., Patalano, S. (2023). Enhancing Human-Robot Collaboration in the Industry 5.0 Context: Workplace Layout Prototyping. Lecture Notes in Mechanical Engineering, 454-465.
- 10. Dutta, J., Roy, S., Chowdhury, C. (2019). Unified framework for IoT and smartphone based different smart city related applications. Microsystem Technologies, 25(1), 83-96.
- 11. Ghibakholl, M., Iranmanesh, M, Mubarak, M.F., Mubarik, M. Rejeb, A. Nilashi, M. (2022). Identifying industry 5.0 contributions to sustainable development: A strategy roadmap for delivering sustainability values. Sustainable Production and Consumption, 33, 716-737.
- 12. Greasley, A. (2019). Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics. Boston: deGruyter.
- 13. Herdiansyah, H. (2023). Smart city based on community empowerment, social capital, and public trust in urban areas. Glob. J. Environ. Sci. Manag., 9, 113-128.
- 14. Hurwitz, J., Kaufman, M., Bowles, A. (2015). Cognitive Computing and Big Data Analytics. New York: Wiley.
- 15. Javaid, M., Haleem, A. (2020). Critical Components of Industry 5.0 Towards a Successful Adoption in the Field of Manufacturing. Journal of Industrial Integration and Management-Innovation and Entrepreneurship, 5(2), 327-348, doi: 10.1142/ S2424862220500141.
- 16. Javaid, M., Haleem, A., Singh, R.P., Haq, M.I.U., Raina, A., Suman, R. (2020). Industry 5.0: Potential Applications in COVID-19. Journal of Industrial Integration and Management-Innovation and Entrepreneurship, 5(4), 507-530, doi: 10.1142/ S2424862220500220.
- 17. Nourani, C.F. (2021). Artificial Intelligence and Computing Logic: Cognitive Technology for AI Business Analytics (Innovation Management and Computing). New York: CRC Press.
- 18. Olsen, C. (2023). Toward a Digital Sustainability Reporting Framework in Organizations in the Industry 5.0 Era: An Accounting Perspective. Lecture Notes in Networks and Systems, 557, 463-473.
- 19. Peter, G.S., Amit, C.B., Deokar, V., Patel, N.R. (2023). Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner. New York: Wiley.
- 20. Scappini, A. (2016). 80 Fundamental Models for Business Analysts: Descriptive, Predictive, and Prescriptive Analytics Models with Ready-to-Use Excel Templates. New York: Create Space.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dae1c190-9f5f-439b-bed2-f8d69d9d7441
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