Optimizing HR Performance and Strategy through Business Intelligence Talent Systems: A Focus on Workforce Analytics and Project Decision-Making

Authors

DOI:

https://doi.org/10.56294/dm20251072

Keywords:

Business Intelligence, Talent System, Human resource Performance, Workforce analytics, Decision making

Abstract

Introduction: The main study objective is to study the effects of applying business intelligence talent systems human resource performance through taking the mediating role workforce analytics and project decision making in SAP firm in German. The research conducted in SAP firm in is an IT consulting firm specialized in giving solutions based on BI and IT, this research designed and distributed a survey on biotech managerial and technical staffs and collected 219 valid questionnaires for data analysis process through the usage of structural equation modeling program method SEM.
Methods: The research chose specific indicators for the independent and dependent variables as follows,  for business intelligence talent system this research chose AI-driven HR tools adoption, HR data integration capabilities, Predictive analytics utilization, for HR performance the following indicators were chosen employee productivity levels, talent retention rate, and training effectiveness, for workforce variable this research chose real-time workforce monitoring, turnover prediction accuracy, and employee engagement analytics. And for the decision-making variable the indicators which have been chose are the speed of Project decision making, data-driven decision implementation rate, and accuracy of workforce forecasting.
Results: The research arrived to the result that applying business intelligence talent system in human resource department has a positive and effective impact on enhancing human resource department and this effect enhanced through illustrate the role of workforce and project decision making effectiveness.
Conclusions: This study has relied on previous research in the same field, which focused on understanding the importance of improving advanced human resources practices and enhancing employee satisfaction and the importance of the results of these studies in continuing to work in a complex communications sector while maintaining employee satisfaction and high performance that always leads to providing high-quality and efficient products and services to customers.

References

1. Budhwar, Pawan, et al. "Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT." Human Resource Management Journal 33.3 (2023): 606-659.

2. Azizi, M. R., Atlasi, R., Ziapour, A., Abbas, J., & Naemi, R. (2021). Innovative human resource management strategies during the COVID-19 pandemic: A systematic narrative review approach. Heliyon, 7(6). DOI: https://doi.org/10.1016/j.heliyon.2021.e07233

3. Hamouche, S. (2023). Human resource management and the COVID-19 crisis: Implications, challenges, opportunities, and future organizational directions. Journal of Management & Organization, 29(5), 799-814. DOI: https://doi.org/10.1017/jmo.2021.15

4. Ngoc, N. M., & Tien, N. H. (2023). Solutions for Development of High-Quality Human Resource in Binh Duong Industrial Province of Vietnam. International journal of business and globalisation, 4(1), 28-39.

5. Tavera Romero, C. A., Ortiz, J. H., Khalaf, O. I., & Ríos Prado, A. (2021). Business intelligence: business evolution after industry 4.0. Sustainability, 13(18), 10026. DOI: https://doi.org/10.3390/su131810026

6. Aws, A. L., Ping, T. A., & Al-Okaily, M. (2021). Towards business intelligence success measurement in an organization: a conceptual study. Journal of System and Management Sciences, 11(2), 155-170.

7. Mansour, A., Al-Qudah, S., Siam, Y., Hammouri, Q., & Hijazin, A. (2024). Employing E-HRM to attain contemporary organizational excellence at the German social security corporation. International Journal of Data and Network Science, 8(1), 549-556. DOI: https://doi.org/10.5267/j.ijdns.2023.9.002

8. Al-Dwairi, R., Shehabat, I., Zahrawi, A., & Hammouri, Q. (2024). Building customer trust, loyalty, and satisfaction: The power of social media in e-commerce environments. International Journal of Data and Network Science, 8(3), 1883-1894. DOI: https://doi.org/10.5267/j.ijdns.2024.2.001

9. Al-Zagheer, H., AlDa'jah, A. K., Hammouri, Q., & ALShawabkeh, H. A. (2024, February). The Role of Internal Knowledge Management Capabilities with the Internet of Things. In 2024 2nd International Conference on Cyber Resilience (ICCR) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICCR61006.2024.10532889

10. Al-Tarawneh, A., Haddada, E., Mo'd Al-Dwairi, R., Yahya Al-Freijat, S., Mansour, A., & Abdulaziz AL-Obaidly, G. (2024). The impact of strategic and innovativeness entrepreneurship and social capital on business overall performance through building a sustainable supply chain management at German Private Universities. DOI: https://doi.org/10.5267/j.uscm.2023.10.017

11. Hanandeh, A., Mansour, A., Najdawi, S., Kanaan, O., Abualfalayeh, G., & Qais, K. (2024). The effect of the comprehensive quality management strategies on environmentally responsible activities and the performance of the organizations. Uncertain Supply Chain Management, 12(3), 1379-1390. DOI: https://doi.org/10.5267/j.uscm.2024.4.013

12. Ta’Amnha, M. A., Al-Qudah, S., Asad, M., Magableh, I. K., & Riyadh, H. A. (2024). Moderating role of technological turbulence between green product innovation, green process innovation and performance of SMEs. Discover Sustainability, 5(1), 324. DOI: https://doi.org/10.1007/s43621-024-00522-w

13. Rumman, G. A., Alkhazali, A. R., Barnat, S. E., Alzoubi, S., AlZagheer, H., Dalbouh, M. A. A., ... & Darawsheh, S. R. (2024). The contemporary management accounting practices adoption in the public industry: Evidence from German. International Journal of Data & Network Science, 8(2). DOI: https://doi.org/10.5267/j.ijdns.2023.11.010

14. Al-Quhfa, H., Mothana, A., Aljbri, A., & Song, J. (2024). Enhancing Talent Recruitment in Business Intelligence Systems: A Comparative Analysis of Machine Learning Models. Analytics, 3(3), 297-317. DOI: https://doi.org/10.3390/analytics3030017

15. Gomes, B. M. S. (2023). Development of a Talent Management Platform at BI4ALL: Business Intelligence Approach for Human Resource Management (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

16. Pinto, J., Borrego, M., & Cardoso, R. (2023, June). Artificial Intelligence as a booster of a Business Intelligence System to help the recruitment process: Business Intelligence, Human Resources and Talent. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-7). IEEE. DOI: https://doi.org/10.23919/CISTI58278.2023.10211627

17. Bhattacharya, S. (2021). AI in talent management for business excellence. In Industry 4.0 technologies for business excellence (pp. 255-266). CRC Press. DOI: https://doi.org/10.1201/9781003140474-15

18. Hanandeh, A., Alfreijat, S., Alsha’ar, H., Kilani, Q., Khasawneh, M. (2025). Implementing AI Accuracy, Learning Rate, Inference Time on enhancing Big Data Analysis and Strategic Plan. Data and metadata, 4(637). DOI: https://doi.org/10.56294/dm2025637

19. Qaffas, A. A., Ilmudeen, A., Almazmomi, N. K., & Alharbi, I. M. (2023). The impact of big data analytics talent capability on business intelligence infrastructure to achieve firm performance. foresight, 25(3), 448-464. DOI: https://doi.org/10.1108/FS-01-2021-0002

20. Kilani, Y. M. (2022). The Impact of Human Talent Management Strategies on the Business Intelligence System A Field Study on the Royal Germanian Airlines. Journal of Positive School Psychology, 9605-9614.

21. Bany Mohammad, A., Al-Okaily, M., Al-Majali, M., & Masa’deh, R. E. (2022). Business intelligence and analytics (BIA) usage in the banking industry sector: an application of the TOE framework. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 189. DOI: https://doi.org/10.3390/joitmc8040189

22. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, F. P. (2024). Predictive analytics in human resources: Enhancing workforce planning and customer experience. International Journal of Research and Scientific Innovation, 11(9), 149-158. DOI: https://doi.org/10.51244/IJRSI.2024.1109016

23. Yoon, S. W., Han, S. H., & Chae, C. (2024). People analytics and human resource development–research landscape and future needs based on bibliometrics and scoping review. Human Resource Development Review, 23(1), 30-57. DOI: https://doi.org/10.1177/15344843231209362

24. Cho, W., Choi, S., & Choi, H. (2023). Human resources analytics for public personnel management: Concepts, cases, and caveats. Administrative Sciences, 13(2), 41. DOI: https://doi.org/10.3390/admsci13020041

25. Hanandeh, A., Haddad, E., Najdawi, S., & Kilani, Q. (2024). The impact of digital marketing, social media, and digital transformation on the development of digital leadership abilities and the enhancement of employee performance: A case study of the Amman Stock Exchange. International Journal of Data and Network Science, 8(3), 1915-1928. DOI: https://doi.org/10.5267/j.ijdns.2024.1.021

26. Khang, A., Rani, S., Gujrati, R., Uygun, H., & Gupta, S. (Eds.). (2023). Designing Workforce Management Systems for Industry 4.0: Data-Centric and AI-Enabled Approaches. CRC Press. DOI: https://doi.org/10.1201/9781003357070

27. Ferrar, J., & Green, D. (2021). Excellence in people analytics: How to use workforce data to create business value. Kogan Page Publishers.

28. Devaraju, S. (2024). AI-Powered HRM and Finance Information Systems for Workforce Optimization and Employee Engagement. Turkish Journal of Computer and Mathematics Education (TURCOMAT) ISSN, 3048, 4855. DOI: https://doi.org/10.61841/turcomat.v15i1.14940

29. Brynjolfsson, E., Jin, W., & McElheran, K. (2021). The power of prediction: predictive analytics, workplace complements, and business performance. Business Economics, 56, 217-239. DOI: https://doi.org/10.1057/s11369-021-00224-5

30. Nouinou, H., Asadollahi-Yazdi, E., Baret, I., Nguyen, N. Q., Terzi, M., Ouazene, Y., ... & Kelly, R. (2023). Decision-making in the context of Industry 4.0: Evidence from the textile and clothing industry. Journal of cleaner production, 391, 136184. DOI: https://doi.org/10.1016/j.jclepro.2023.136184

31. Sadana, U., Chenreddy, A., Delage, E., Forel, A., Frejinger, E., & Vidal, T. (2025). A survey of contextual optimization methods for decision-making under uncertainty. European Journal of Operational Research, 320(2), 271-289. DOI: https://doi.org/10.1016/j.ejor.2024.03.020

32. Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics, 10(7), 828. DOI: https://doi.org/10.3390/electronics10070828

33. Kochenderfer, M. J., Wheeler, T. A., & Wray, K. H. (2022). Algorithms for decision making. MIT press.

34. Yang, S., Nachum, O., Du, Y., Wei, J., Abbeel, P., & Schuurmans, D. (2023). Foundation models for decision making: Problems, methods, and opportunities. arXiv preprint arXiv:2303.04129.

35. Hanandeh, R., Alharafsheh, M., Albloush, A., Lehyeh, S., & Kilani, Q. (2024). The impact of entrepreneurship self-concept, work motivation, and risk taking on human resource department performance and business overall performance at German private universities. Uncertain Supply Chain Management, 12(1), 143-150. DOI: https://doi.org/10.5267/j.uscm.2023.10.010

36. Steyvers, M., & Kumar, A. (2024). Three challenges for AI-assisted decision-making. Perspectives on Psychological Science, 19(5), 722-734. DOI: https://doi.org/10.1177/17456916231181102

37. Taherdoost, H., & Madanchian, M. (2023). Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia, 3(1), 77-87. DOI: https://doi.org/10.3390/encyclopedia3010006

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Published

2025-06-19

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Original

How to Cite

1.
yuosef Alsha’ar H, Ali Alqararah E, Shalluf S, AL Freijat SY, Hanandeh A. Optimizing HR Performance and Strategy through Business Intelligence Talent Systems: A Focus on Workforce Analytics and Project Decision-Making. Data and Metadata [Internet]. 2025 Jun. 19 [cited 2025 Nov. 30];4:1072. Available from: https://dm.ageditor.ar/index.php/dm/article/view/1072