DECODING THE FUTURE OF HUMAN RESOURCE: HOW HUMAN RESOURCE ANALYTICS REVOLUTIONISE THE ORGANISATIONAL LANDSCAPE
DOI:
https://doi.org/10.15282/ijim.18.4.2024.10330Keywords:
Big Data Analysis, Human Resource Analytics, Bibliographic Coupling, Co-Word AnalysisAbstract
Technological advances and digitalisation have revolutionised human resource management (HRM) by increasing the quantity of workforce data and widening its access to facilitate decision-making in businesses. This study aims to provide an in-depth understanding of big data analysis (BDA) by evaluating the current and future trends in human resource (HR) analytics through bibliometric analysis. The findings revealed significant research clusters on the knowledge structure and mapping of research streams in HR analytics. Several challenges in BDA application and firm performances were also identified, indicating its current and future trends in HR analytics. Implications for the new HRM landscape include the benefits and risks of using HR analytics tools that organisations must carefully monitor. Moreover, HR practitioners must understand the organisation's business needs and goals, analyse high-quality data that are relevant to the specific problem or question being addressed, and possess the technical skills and resources to implement and use HR analytics effectively.
References
Aljohani, M. A., & Alqahtani, S. S. (2023). A Unified Framework for Automating Software Security Analysis in DevSecOps. In 2023 International Conference on Smart Computing and Application (ICSCA), 1-6.
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11.
Arunprasad, P., Dey, C., Jebli, F., Manimuthu, A., & El Hathat, Z. (2022). Exploring the remote work challenges in the era of COVID-19 pandemic: review and application model. Benchmarking: An International Journal, 29(10), 3333-3355.
Atmaja, D. S., Fachrurazi, Abdullah, Fauziah, Zaroni, A. N., & Yusuf, M. (2023). Actualization Of Performance Management Models For The Development Of Human Resources Quality, Economic Potential, And Financial Governance Policy In Indonesia's Ministry Of Education. Multicultural Education, 9(01), 1–15.
Aziz, F. (2023). Data analytics impacts the field of accounting. World Journal of Advanced Research and Review, 18(02), 946-951.
Biabanifard, M., Asgari, S., Biabanifard, S., & Abrishamian, M. S. (2019). Analytical design of tunable multi-band terahertz absorber composed of graphene disks. Optik, 182, 433–442.
Bondarouk, T., & Brewster, C. (2016). Conceptualising the future of HRM and technology research. International Journal of Human Resource Management, 27(21), 2652–2671.
Bonilla-Chaves, E. F., & Palos-Sánchez, P. R. (2023). Exploring the evolution of human resource analytics: a bibliometric study. Behavioral Sciences, 13(3), 244.
Boudreau, J., & Cascio, W. (2017). Human capital analytics: why are we not there? Journal of Organizational Effectiveness, 4(2), 119–126.
Bulsari, S., & Pandya, K. (2023). Future of HR Analytics: Applications to Recruitment, Employee Engagement, and Retention. In Managing Technology Integration for Human Resources in Industry 5.0, 140-162.
Cho, W., Choi, S., & Choi, H. (2023). Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats. Administrative Sciences, 13(2), 41.
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 1-21.
Dahlbom, P., Siikanen, N., Sajasalo, P., & Jarvenpää, M. (2020). Big data and HR analytics in the digital era. Baltic Journal of Management, 15(1), 120–138.
Das, D. K. (2022). Appraisal of the linkage among urban infrastructure and human resources and the growth of the Information Technology (IT) industry in Indian cities. Cogent Engineering, 9(1), 1-20.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture. British Journal of Management, 30(2), 341–361.
Edwards, M. R., Charlwood, A., Guenole, N., & Marler, J. (2022). HR analytics: An emerging field finding its place in the world alongside simmering ethical challenges. Human Resource Management Journal, 34(2), 326-336.
Engler, S. (2014). Bibliometrics and the study of religions1. Religion, 44(2), 193-219.
Fernandez, V., & Gallardo-Gallardo, E. (2021). Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review, 31(1), 162–187.
Goel, P., Mehta, S., Kumar, R., & Castaño, F. (2022). Sustainable Green Human Resource Management practices in educational institutions: An interpretive structural modelling and analytic hierarchy process approach. Sustainability, 14(19), 1-21.
Gunay, H. B., Shen, W., & Newsham, G. (2019). Data analytics to improve building performance: A critical review. Automation in Construction, 97, 96-109.
Gupta, B. (2013). Human resource information system (HRIS): important element of current scenario. IOSR Journal of Business and Management, 13(6), 41-46.
Haenlein, M., Kaplan, A., Tan, C. W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4), 341–343.
Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95.
Han, G., Liu, T., & Kang, P. (2023). Bibliometric analysis of Ewing sarcoma from 1993 to 2022. BMC Cancer, 23(1), 1-13.
Harney, B., & Collings, D. G. (2021). Navigating the shifting landscapes of HRM. Human Resource Management Review, 31(4), 1-10.
Hennekam, S., Follmer, K., & Beatty, J. (2021). Exploring mental illness in the workplace: the role of HR professionals and processes. The International Journal of Human Resource Management, 32(15), 3135-3156.
Horani, O. M., Khatibi, A., AL-Soud, A. R., Tham, J., & Al-Adwan, A. S. (2023). Determining the factors influencing business analytics adoption at organizational level: a systematic literature review. Big Data and Cognitive Computing, 7(3), 1-18.
Huang, X., Yang, F., Zheng, J., Feng, C., & Zhang, L. (2023). Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review, 28(4), 598-610.
Jabir, B., Falih, N., & Rahmani, K. (2019). HR analytics a roadmap for decision making: Case study. Indonesian Journal of Electrical Engineering and Computer Science, 15(2), 979-990.
Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345.
Jaouadi, M. H. O. (2022). Investigating the influence of big data analytics capabilities and human resource factors in achieving supply chain innovativeness. Computers and Industrial Engineering, 168, 1-10.
Kale, H., Aher, D., & Anute, N. (2022). HR Analytics and its Impact on Organizations Performance. International Journal of Research and Analytical Reviews, 9(3), 619-630.
Karwehl, L. (2021). Traditional and new ways in competence management: Application of HR analytics in competence management, 52(7), 1-24.
Kashive, N., & Khanna, V. T. (2023). Emerging HR analytics role in a crisis: an analysis of LinkedIn data. Competitiveness Review: An International Business Journal, 33(6), 1179-1204.
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70.
Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Defining analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114–124.
Madhani, P. M. (2023). Human Resources Analytics: Leveraging Human Resources for Enhancing Business Performance. Compensation & Benefits Review, 55(1), 31-45.
Mahmood, Q. U. A., Ahmed, R., & Philbin, S. P. (2022). The moderating effect of big data analytics on green human resource management and organizational performance. International Journal of Management Science and Engineering Management, 18(3), 177-189.
Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. International Journal of Human Resource Management, 28(1), 3–26.
McCartney, S., & Fu, N. (2022). Bridging the gap: why, how and when HR analytics can impact organizational performance. Management Decision, 60(13), 25-47.
McIver, D., Lengnick-Hall, M. L., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics: Integrating science and agility. Business Horizons, 61(3), 397-407.
Medici, G., Grote, G., Igic, I., & Hirschi, A. (2023). Technological self-efficacy and occupational mobility intentions in the face of technological advancement: a moderated mediation model. European Journal of Work and Organizational Psychology, 32(4), 538-548.
Mohamad, N. I., Mokhtar, A., Rahman, I. A., & Othman, A. S. (2023). Development of a Structural Model for Sustainable Environment Training and Knowledge Transfer. Sustainability, 15(3), 2322, 1-19.
Nocker, M., & Sena, V. (2019). Big data and human resources management: The rise of talent analytics. Social Sciences, 8(10), 273, 1-19.
Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences, 31(4), 415-425.
Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: developing a framework. Journal of Organizational Effectiveness, 7(2), 203–219.
Penpokai, S., Vuthisopon, S., & Saengnoree, A. (2023). The relationships between technology adoption, HR competencies, and HR analytics of large-size enterprises. International Journal of Professional Business Review, 8(3), 1-13.
Perez-Sanagustin, M., Hilliger, I., Maldonado-Mahauad, J., & Perez-Alvarez, R. (2022). Building Institutional Capacity for Learning Analytics: Top-Down & Bottom-Up Initiatives. Revista Iberoamericana de Tecnologias Del Aprendizaje, 17(3), 281–289.
Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 1-18.
Peterson, J., Tahssain-Gay, L., Salvetat, D., Perez, F., & Hennekam, S. (2023). How managers approach data analytics: a typology through a Resource Orchestration perspective. Management Decision, 61(5), 1225–1243.
Polzer, J. T. (2022). The rise of people analytics and the future of organizational research. Research in Organizational Behavior, 42, 1-13.
Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242.
Reddy, P. R., & Lakshmikeerthi, P. (2017). HR analytics–an effective evidence based HRM tool. International Journal of Business and Management Invention, 6(7), 23-34.
Rosen, A. F., Auger, E., Woodruff, N., Proverbio, A. M., Song, H., Ethridge, L. E., & Bard, D. (2022). The multiple indicator multiple cause model for cognitive neuroscience: An analytic tool which emphasizes the behavior in brain–behavior relationships. Frontiers in Psychology, 13, 1-15.
Salovaara, A., Lyytinen, K., & Penttinen, E. (2019). High reliability in digital organizing: Mindlessness, the frame problem, and digital operations. MIS Quarterly: Management Information Systems, 43(2), 555–578.
Samson, K., & Bhanugopan, R. (2022). Strategic human capital analytics and organisation performance: The mediating effects of managerial decision-making. Journal of Business Research, 144, 637–649.
Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2022). Analysing the impact of sustainable human resource management practices and industry 4.0 technologies adoption on employability skills. International Journal of Manpower, 43(2), 463–485.
Shet, S. V., Poddar, T., Wamba Samuel, F., & Dwivedi, Y. K. (2021). Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications. Journal of Business Research, 131(August 2020), 311–326.
Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Á. (2019). Decision-making based on big data analytics for people management in healthcare organizations. Journal of medical systems, 43, 1-10.
Talaoui, Y., Kohtamäki, M., Ranta, M., & Paroutis, S. (2023). Recovering the divide: A review of the big data analytics—strategy relationship. Long Range Planning, 56(2), 1-40.
Thite, M. (2022). Digital human resource development: where are we? Where should we go and how do we go there? Human Resource Development International, 25(1), 87–103.
Ulrich, D. (2016). HR at a crossroads. Asia Pacific Journal of Human Resources, 54(2), 148–164.
Van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. Journal of Organizational Effectiveness, 4(2), 157–178.
Van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness, 4(2), 127–132.
Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: a fine-grained view of the early stages leading to adoption. International Journal of Human Resource Management, 29(22), 3046–3067.
Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 62, 738–752.
Wirges, F., & Neyer, A. K. (2022). Towards a process-oriented understanding of HR analytics: implementation and application. Review of Managerial Science, 17(6), 2077-2108.
Zel, S., & Kongar, E. (2020). Transforming digital employee experience with artificial intelligence. In 2020 IEEE/ITU International Conference on Artificial Intelligence for Good, 176-179. IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
