Comprehending the Aspects, Antecedents, and Consequences of the Data-Driven Human Resources Management Based on Concept Analysis Methodology

Ebrahimi, Bahar; Danaeefard, Hasan; Kazemi, Seyyed Hossein; Hosseini, Seyyed Yagoub

doi:10.30473/ipom.2026.73794.5142

Document Type : Exploratory

Authors

¹ Ph.D. Candidate, Department of Public Administration, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

² Professor, Department of Public Administration, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

³ Assistant Professor, Department of Public Administration, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

⁴ Associate Professor, Department of Business Administration, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.

https://doi.org/10.30473/ipom.2026.73794.5142

Abstract

Introduction
The rapid development of information technology and digital transformation has transformed the role of human resource management (HRM) from a primarily administrative function into a more strategic component of organizational performance and value creation. In this context, data-driven human resource management (DDHRM) has emerged as an approach that improves HR decision-making through the systematic collection, analysis, interpretation, and use of employee and organizational data. Unlike traditional approaches that rely heavily on managerial intuition and experience, DDHRM emphasizes evidence-based decision-making. The growing use of data in HR has also generated overlapping concepts, including HR analytics, people analytics, human capital analytics, workforce analytics, and data-driven HR decision-making. Although these concepts share common elements, their boundaries and relationships remain unclear. This conceptual ambiguity has limited theoretical development and created challenges for organizations seeking to implement data-driven HR practices. Therefore, this study aimed to clarify the concept of DDHRM by identifying its defining dimensions, antecedents, and consequences and by establishing a more coherent conceptual foundation for future research and organizational practice.

Methodology
This qualitative study employed Rodgers’ evolutionary concept analysis methodology, which is particularly appropriate for emerging concepts whose meanings evolve across time and contexts. The approach examines the defining characteristics of a concept, its antecedents and consequences, related concepts, and, where appropriate, empirical referents. The literature search was conducted in the Web of Science (WoS) and Scopus databases using a range of keywords associated with data-driven HRM and HR analytics. The search covered titles, abstracts, and keywords without imposing a publication-year restriction. The initial search yielded 480 publications, including 285 records from Scopus and 195 from Web of Science. After merging the databases, 95 duplicate records were removed. Screening of titles and abstracts resulted in the exclusion of 313 publications because of insufficient relevance. Consequently, 72 articles were selected for full-text analysis. The inclusion criteria consisted of English-language journal articles containing relevant keywords in the title, abstract, or keywords and providing access to the full text. Conference papers and books were excluded. The selected studies were systematically reviewed, and information concerning the characteristics, antecedents, and consequences of DDHRM was extracted and coded. Thematic analysis was conducted through iterative comparison and refinement of emerging themes, following the components of Rodgers’ evolutionary concept analysis. The resulting concepts, dimensions, and operational definitions were also reviewed by two experts involved in the analytical process to enhance the credibility of the findings.

Findings
The analysis identified five central dimensions of data-driven human resource management: data collection and analysis, data governance, data technology, data storytelling, and data-based decision-making.
First, data collection and analysis constitutes the foundation of DDHRM. It involves systematically obtaining data from internal sources, such as HR management systems, employee surveys, and performance evaluations, as well as external labor-market and industry sources. Descriptive, predictive, and prescriptive analytics can then be applied to identify patterns and predict future developments.
Second, data governance encompasses the policies, processes, responsibilities, standards, and structures required to ensure data quality, security, privacy, appropriate access, and regulatory compliance. Effective DDHRM therefore requires clear ownership and accountability for employee data and explicit rules governing its collection, storage, analysis, and use.
Third, data technology refers to the technological infrastructure and analytical tools that enable organizations to store, integrate, process, visualize, and analyze HR data. Technology connects data resources with HR processes and provides the infrastructure necessary for transforming raw data into actionable information.
Fourth, data storytelling represents the ability to translate analytical results into meaningful and understandable narratives for managers and organizational stakeholders. It serves as a bridge between technical analysis and managerial action, because analytical capabilities alone do not guarantee that insights will be incorporated into organizational decisions.
Fifth, data-based decision-making represents the practical realization of DDHRM. It occurs when decisions concerning recruitment, performance management, employee development, and other HR functions are systematically informed by evidence and analytical results rather than relying solely on intuition, precedent, or personal experience. Together, these dimensions demonstrate that DDHRM is a socio-organizational process rather than an analytical activity.
The study also identified several antecedents necessary for successful implementation, including information technology capability and infrastructure, skilled data analysts, data governance, a data-oriented organizational culture, organizational size and resources, managerial capabilities and support, and behavioral factors such as performance expectancy, social influence, and facilitating conditions. These findings indicate that successful adoption requires the simultaneous development of human, organizational, managerial, and behavioral capabilities.
The identified consequences include improved HR decision-making, greater efficiency and effectiveness, stronger alignment between HR activities and organizational objectives, improved talent management, and enhanced organizational performance and competitiveness. Improved HR processes may also contribute to employee well-being. However, DDHRM can create challenges concerning employee privacy, data security, and the responsible use of personal information, which depend substantially on effective governance of employee data.

Discussion and Conclusion
The findings provide a comprehensive conceptualization of DDHRM by integrating technological, analytical, organizational, and human dimensions. The five identified dimensions demonstrate that data-driven HRM extends beyond the technical application of HR analytics. In particular, data storytelling highlights the importance of transforming analytical outputs into managerial understanding and actionable knowledge. The findings further indicate that DDHRM should be understood as an integrated system in which technology, human expertise, organizational culture, governance, and managerial support interact. Consequently, investment in analytical technologies alone is unlikely to ensure successful adoption unless organizations simultaneously develop analytical capabilities, governance mechanisms, and an evidence-oriented culture. Conceptually, the study helps clarify the boundaries of DDHRM in relation to related constructs such as HR analytics, people analytics, and human capital analytics. Although these concepts overlap, DDHRM represents a broader process integrating data collection and analysis, governance, technology, interpretation, and evidence-based decision-making within HR practices. From a practical perspective, organizations should approach DDHRM as an organizational transformation rather than simply as the acquisition of analytical software. Developing technological infrastructure and storytelling competencies, data governance, a data-oriented culture, and managerial support are complementary requirements for effective implementation. At the same time, organizations must establish appropriate safeguards for data security and responsible data use. Overall, DDHRM is a multidimensional and evolving concept that can strengthen the strategic role of HRM when technological and analytical capabilities are combined with effective governance, human expertise, organizational readiness, and responsible decision-making.

Keywords

Main Subjects

Conceptualization

References

Alamelu, R., Nalini, R., Motha, L. C. S., Amudha, R., & Bowiya, S. (2017). Adoption factors impacting human resource analytics among employees. International Journal of Economic Research, 14(6), 417–423.

Albrecht, S. L., Bakker, A. B., Gruman, J. A., Macey, W. H., & Saks, A. M. (2015). Employee engagement, human resource management practices and competitive advantage: An integrated approach. Journal of Organizational Effectiveness: People and Performance, 2(1), 7–35. https://doi.org/10.1108/JOEPP-08-2014-0042

Alfes, K., Truss, C., Soane, E. C., Rees, C., & Gatenby, M. (2013). The relationship between line manager behavior, perceived HRM practices, and individual performance: Examining the mediating role of engagement. Human Resource Management, 52(6), 839–859. DOI: 10.1002/hrm.21512

Al-Shammari, M., Al Bin Ali, F. A., AlRashidi, M. A., & Albuainain, M. S. (2024). Big data and predictive analytics for strategic human resource management: A systematic literature review. International Journal of Computing and Digital Systems, 17(1), 1–9. DOI:10.12785/ijcds/1571015706

Ameer, R., & Garg, P. (2022). Factors impacting adoption of human resource analytics among HR professionals in India. Transnational Marketing Journal, 10(3), 623–635.

Anam, N. A., & Haque, M. I. (2023). Behavioural intention of HR professionals to use HR analytics in the Indian context: An analysis using the UTAUT model. International Journal of Indian Culture and Business Management, 28(1), 101–123. https://doi.org/10.1504/IJICBM.2023.128486

Angrave, D., Charlwood, A., Kirkpatrick, I., & Lawrence, M. (2016). HR and performance: The role of the human resource function in the performance of organizations. International Journal of Human Resource Management, 27(10), 1072–1094.

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.

https://doi.org/10.1111/1748-8583.12090

Anitha, J. (2014). Determinants of employee engagement and their impact on employee performance. International Journal of Productivity and Performance Management, 63(3), 308–323. https://doi.org/10.1108/IJPPM-01-2013-0008

Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. L. (2000). Manufacturing advantage: Why high-performance work systems pay off. Cornell University Press.

Arora, M., Prakash, A., Mittal, A., & Singh, S. (2022). HR analytics: What’s holding users back? Global Knowledge, Memory and Communication. Advance online publication. Memory and Communication, 73(3), 432–452. https://doi.org/10.1108/GKMC-03-2022-0067

Asghari Sarem, A., Danaei Fard, H., Fani, A. A., & Gholipour, A. (2016). Exploring the role of organizational storytelling in human resource development: A survey of research literature. Public Management, 8(1), 181–218. (In Persian) DOI: 10.22059/jipa.2016.57513

Ashton, C., & Morton, L. (2005). Managing talent for competitive advantage: Taking a systemic approach to talent management. Strategic HR Review, 4(5), 28–31. DOI: 10.1108/14754390580000819

Bahuguna, P. C., Srivastava, R., & Tiwari, S. (2024). Human resources analytics: Where do we go from here? Benchmarking: An International Journal, 31(2), 640–668. https://doi.org/10.1108/BIJ-06-2022-0401

Bakker, A. B., & Demerouti, E. (2008). Towards a model of work engagement. Career Development International, 13(3), 209–223. DOI: 10.1108/13620430810870476

Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD–R approach. Annual Review of Organizational Psychology and Organizational Behavior, 1(1), 389–411. DOI: https://doi.org/10.1146/annurev-orgpsych-031413-091235

Bakker, A. B., Tims, M., & Derks, D. (2012). Proactive personality and job performance: The role of job crafting and work engagement. Human Relations, 65(10), 1359–1378. DOI: 10.1177/0018726712453471

Bates, S. (2004). Getting engaged. HR Magazine, 49(2), 44–51.

Bechter, B., Brandl, B., & Lehr, A. (2022). The role of the capability, opportunity, and motivation of firms for using human resource analytics to monitor employee performance: A multi-level analysis of the organisational, market, and country context. New Technology, Work and Employment, 37(3), 398–424. https://doi.org/10.1111/ntwe.12239

Becker, B. E., Huselid, M. A., & Ulrich, D. (2001). Making HR a strategic asset. Future Trends, 1, 57–63.

Bedarkar, M., & Pandita, D. (2014). A study on the drivers of employee engagement impacting employee performance. Procedia - Social and Behavioral Sciences, 133, 106–115. DOI: 10.1016/j.sbspro.2014.04.174

Belizón, M. J., & Kieran, S. (2022). Human resources analytics: A legitimacy process. Human Resource Management Journal, 32(3), 603–630. DOI: 10.1111/1748-8583.12417

Biswas, S., & Bhatnagar, J. (2013). Mediator analysis of employee engagement: Role of perceived organizational support, P-O fit, organizational commitment and job satisfaction. Vikalpa, 38(1), 27–40. DOI: 10.1177/0256090920130103

Bonilla-Chaves, E. F., & Palos-Sánchez, P. R. (2023). Exploring the evolution of human resource analytics: A bibliometric study. Behavioral Sciences, 13(3), 244. DOI: 10.3390/bs13030244

Boudreau, J. W., & Ramstad, P. M. (2004). Talentship and human resource measurement and analysis: From ROI to strategic organizational change. University of Southern California.

Boudreau, J. W., & Ramstad, P. M. (2005). Talentship, talent segmentation, and sustainability: A new HR decision science paradigm for a new strategy definition. Human Resource Management, 44(2), 129–136. DOI: 10.1002/hrm.20054

Briner, R. B., Denyer, D., & Rousseau, D. M. (2009). Evidence-Based Management: Concept cleanup time? Academy of Management Perspectives, 23(4), 19–32. DOI: 10.5465/AMP.2009.45590138

Buller, P. F., & McEvoy, G. M. (2012). Strategy, human resource management and performance: Sharpening line of sight. Human Resource Management Review, 22(1), 43–56. DOI: 10.1016/j.hrmr.2011.11.002

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

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), 100899.
DOI: 10.1016/j.hrmr.2022.100899

Cohen, D. J. (2015). HR past, present and future: A call for consistent practices and a focus on competencies. Human Resource Management Review, 25(2), 205–215. DOI: 10.1016/j.hrmr.2015.01.006

Conboy, K., Dennehy, D., & O’Connor, M. (2020). ‘Big time’: An examination of temporal complexity and business value in analytics. Information & Management, 57(1), 103077. DOI: 10.1016/j.im.2018.05.010

Coolen, P., van den Heuvel, S., Van De Voorde, K., & Paauwe, J. (2023). Understanding the adoption and institutionalization of workforce analytics: A systematic literature review and research agenda. Human Resource Management Review, 33, 100985. DOI: 10.1016/j.hrmr.2023.100985

Chandani, A., Mehta, M., Mall, A., & Khokhar, V. (2016). Employee engagement: A review paper on factors affecting employee engagement. Indian Journal of Science and Technology, 9(15), 1–8. DOI: 10.17485/ijst/2016/v9i15/92145

Cook, S. (2008). The essential guide to employee engagement: Better business performance through staff satisfaction. Kogan Page.

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874–900. DOI: 10.1177/0149206305279602

Crane, B., & Hartwell, C. J. (2019). Global talent management: A life cycle view of the interaction between human and social capital. Journal of World Business, 54(2), 82–92.
DOI: 10.1016/j.jwb.2018.11.002

Dahlbom, P., Siikanen, N., Sajasalo, P., & Järvenpää, M. (2020). Big data and HR analytics in the digital era. Baltic Journal of Management, 15(1), 120–138. DOI: 10.1108/BJM-11-2018-0393

Danaeifard, H., Rahnavard, F., Rajabzadeh-Qatri, A., & Salarieh, N. (2022). A closer look at the role of human resource management quality dimensions in organizational commitment, organizational identity and the tendency to leave the job of employees of Iranian government organizations. Public Management Research, 15(56), 59–73.

Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88(10), 52–58.

de Souza Moraes, S., & Damian, I. P. M. (2021). People analytics: Far beyond the data. AtoZ: Novas Práticas em Informação e Conhecimento, 10(3), 1–11. DOI: 10.5380/atoz.v10i3.79536

Dey, T., & De, P. (2015). Predictive analytics in HR: A primer. Tata Consultancy Services.
Dhankhar, K., & Singh, A. (2023). Employees’ adoption of HR analytics: A theoretical framework based on career construction theory. Evidence-based HRM, 11(3), 395–411.
DOI: 10.1108/EBHRM-02-2022-0053

Din Mohammadi, M., Rafii, F., Peyravi, H., & Mehrdad, N. (2010). Professional socialization in nursing: An evolutionary concept analysis. Journal of Hayat, 16(2), 15–28.

Douthitt, S., & Mondore, S. (2014). Creating a business-focused HR function with analytics and integrated talent management. People & Strategy, 36(4), 16–21.

Dutot, V., & Mondor, E. (2017). Talent management in the digital era: How to attract, develop, and retain the best talents. Human Resource Management International Digest, 25(5), 19–22. DOI: 10.1108/HRMID-03-2017-0051

Ekka, S., & Singh, P. (2022). Predicting HR professionals' adoption of HR analytics: An extension of UTAUT model. Organizacija, 55(1), 77–93. DOI: 10.2478/orga-2022-0006

Espegren, Y., & Hugosson, M. (2023). HR analytics-as-practice: A systematic literature review. Journal of Organizational Effectiveness: People and Performance. Advance online publication. DOI: 10.1108/JOEPP-11-2022-0345

Falletta, S. V., & Combs, W. L. (2021). The HR analytics cycle: A seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management, 13(1), 51–68. DOI: 10.1108/JWAM-03-2020-0020

Fawcett, J. (2012). Thoughts on concept analysis: Multiple approaches, one result. Nursing Science Quarterly, 25(3), 285–287. DOI: 10.1177/0894318412447545

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. DOI: 10.1108/CR-12-2019-0163

Fu, N., Keegan, A., & McCartney, S. (2023). The duality of HR analysts' storytelling: Showcasing and curbing. Human Resource Management Journal, 33(2), 261–286. DOI: 10.1111/1748-8583.12466

Gerber, M., Krause, A., Probst, J., & Heimann, M. (2024). HR analytics between ambition and reality. Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO). Advance online publication.

Ghadirian, F., Salsali, M., & Cheraghi, M. A. (2014). Nursing professionalism: An evolutionary concept analysis. Iranian Journal of Nursing and Midwifery Research, 19(1), 1–10. DOI: 10.4103/1735-9066.137770

Ghanbari-Vanani, R., Danaeifard, H., Kazemi, S. H., & Delhada, J. (2022). Understanding strategies for promoting human resource well-being at work in Iranian government organizations: A qualitative study. Public Management, 14(4), 554–579. (In Persian) DOI: 10.22059/jipa.2022.343684.3163

Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. DOI: 10.1080/0960085X.2021.1927213

Gulia, R., & Rastogi, V. (2024). The impact of information technology on data-driven decision making in human resource management. International Journal for Multidisciplinary Research, 6(6). DOI: 10.36948/ijfmr.2024.v06i06.30314

Gurusinghe, R. N., Arachchige, B. J. H., & Dayarathna, D. (2021). Predictive HR analytics and talent management: A conceptual framework. Journal of Management Analytics, 8(2), 195–221. DOI: 10.1080/23270012.2021.1899857

Haines, V. Y., & Lafleur, G. (2008). Information technology usage and human resource roles and effectiveness. Human Resource Management, 47(3), 525–540. DOI: 10.1002/hrm.20230

Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue. Human Resource Management, 57(3), 679–684. DOI: 10.1002/hrm.21915

Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology, 87(2), 268–279. DOI: 10.1037/0021-9010.87.2.268

Johnson, B. A., Coggburn, J. D., & Llorens, J. J. (2022). Artificial intelligence and public human resource management: Questions for research and practice. Public Personnel Management, 51(4), 538–562. DOI: 10.1177/00910260221126498

Kalvakolanu, S., Chendragiri, M., & Shaik, K. (2023). Exploring the adoption of human resource analytics among human resource professionals. International Journal of Organizational Leadership, 12(4), 425–441. DOI: 10.33844/ijol.2023.60387

Katfi, A., El Mnouer, O., Ghallal, S., Katfi, H., Boudriche, F., & Thierry, C. (2025). Use of big data in human resource management to strengthen human capital management? Review of Economics, Management and Social Sciences, 2(1), e202505. DOI: 10.23882/emss25115

Kharde, Y., Seranmadevi, R., Susendiran, S., Senthilkumar, T., Babu, M. N., & Pawar, V. (2025). Analyzing the model performance in human resources predictive algorithms through machine learning. In Challenges in information, communication and computing technology (pp. 759–764). CRC Press.

King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487–495. DOI: 10.1177/1534484316675818

Kroon, B., Freese, C., & Schalk, R. (2015). A strategic HRM perspective on i-deals. In Idiosyncratic deals between employees and organizations (pp. 73–91). Routledge. DOI: 10.4324/9780429468339-22

Kurikala, V., & Parvathi, V. (2023). Identifying the characteristics that lead to effective data analytics deployment in human resource management—A paradigm for consequences. Journal of Chinese Human Resource Management, 14(1), 60–89. DOI: 10.47297/wspchrmWSP2040-800503.20231401

Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692–724. DOI: 10.5465/256287

Karatepe, O. M. (2013). High-performance work practices and hotel employee performance: The mediation of work engagement. International Journal of Hospitality Management, 32, 132–140. DOI: 10.1016/j.ijhm.2012.05.003

Kataria, A., Garg, P., & Rastogi, R. (2013). Psychological climate and organizational effectiveness: Role of work engagement. The IUP Journal of Organizational Behavior, 12(3), 33–46.

Lawler, E. E., III, Levenson, A., & Boudreau, J. W. (2004). HR metrics and analytics: Uses and impacts. Human Resource Planning Journal, 27(4), 27–35.

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2022). The challenges of algorithm-based HR decision-making for personal integrity. In Business and the ethical implications of technology (pp. 71–86). Springer. DOI: 10.1007/s10551-019-04204-w

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Scherer, A., & Wildhaber, I. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. DOI: 10.1007/s10551-019-04204-w

Lockwood, N. R. (2007). Leveraging employee engagement for competitive advantage. SHRM Research Quarterly, 52(3), 1–12.

Madhani, P. M. (2023). Human resources analytics: Leveraging human resources for enhancing business performance. Compensation and Benefits Review, 55(1), 31–45. DOI: 10.1177/08863687221131730

Majam, T., & Jarbandhan, D. B. (2022). Data driven human resource management in the Fourth Industrial Revolution (4IR). Public Sector Review, 10(1), 12.

Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. DOI: 10.1016/j.hrmr.2021.100795

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. DOI: 10.1080/09585192.2016.1244699

Martin-Rios, C., Pougnet, S., & Nogareda, A. M. (2017). Teaching HRM in contemporary hospitality management: A case study drawing on HR analytics and big data analysis. Journal of Tourism, 17(1), 34–54. DOI: 10.1080/15313220.2016.1276874

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

McCartney, S., & Fu, N. (2022). Promise versus reality: A systematic review of the ongoing debates in people analytics. Journal of Organizational Effectiveness, 9(2), 281–311. DOI: 10.1108/JOEPP-01-2021-0013

McCartney, S., Murphy, C., & McCarthy, J. (2021). 21st century HR: A competency model for the emerging role of HR Analysts. Personnel Review, 50(6), 1495–1513. https://doi.org/10.1108/PR-12-2019-0670

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. DOI: 10.1016/j.bushor.2018.01.005

Melo, P. N., & Machado, C. (2021). Digital HRM transformation through analytics: A review and bibliometric analysis. Journal of Enterprise Effectiveness, 24(2), 1–7.

Minbaeva, D. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701–713. DOI: 10.1002/hrm.21848

Paauwe, J., & Boselie, P. (2003). Challenging 'strategic HRM' and the relevance of the institutional setting. Human Resource Management Journal, 13(3), 56–70. https://doi.org/10.1111/j.1748-8583.2003.tb00098.x

Padhi, A. (2023). The role of human resource analytics: Analyzing the factors influencing it in organizations using SEM and ANOVA. Journal of Uncertain Systems. DOI: 10.1142/S175289092350006X

Parry, E., & Tyson, S. (2011). Desired goals and actual outcomes of e-HRM. Human Resource Management Journal, 21(3), 335–354. DOI: 10.1111/j.1748-8583.2010.00149.x

Patre, S. (2016). Six thinking hats approach to HR analytics. South Asian Journal of Human Resources Management, 3(2), 191–199. https://doi.org/10.1177/232209371667831

Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: Developing a framework. Journal of Organizational Effectiveness, 7(2), 203–219. DOI: 10.1108/JOEPP-04-2020-0071

Rahman, M. H., Mutsuddi, P., Roy, S. K., Al-Amin, M., & Jannat, F. (2020). Performance efficiency evaluation of information and communication technology (ICT) application in human resource management during COVID-19 pandemic: A study on banking industry of Bangladesh. Social Science and Economics, 8(4), 46–56. DOI: 10.9734/sajsse/2020/v8i430218

Ramachandran, R., Babu, V., & Murugesan, V. P. (2023). Human resource analytics revisited: A systematic literature review of its adoption, global acceptance and implementation. Benchmarking.

DOI: 10.1108/BIJ-09-2022-0552

Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. DOI: 10.1016/j.orgdyn.2015.05.008

Rodgers, B. L. (1989). Concepts, analysis and the development of nursing knowledge: The evolutionary cycle. Journal of Advanced Nursing, 14(4), 330–335. DOI: 10.1111/j.1365-2648.1989.tb03420.x

Rodriguez, J. M., & Walters, K. (2017). The importance of concept analysis in the development of theory. Nursing Science Quarterly, 30(2), 125–130.

Rodgers, B. L. (2000). Concept analysis: An evolutionary view. In B. L. Rodgers & K. A. Knafl (Eds.), Concept development in nursing (pp. 77–102). W.B. Saunders.

Rich, B. L., Lepine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents and effects on job performance. Academy of Management Journal, 53(3), 617–635. https://doi.org/10.5465/amj.2010.51468988

Robinson, D., Perryman, S., & Hayday, S. (2004). The drivers of employee engagement. Institute for Employment Studies.

Saxena, M., Bagga, T., Gupta, S., & Mittal, A. (2022). Employees' experiences of accepting and adopting HR analytics: A phenomenology study. Open Psychology Journal, 15(1), e18743501. https://doi.org/10.2174/18743501-v15-e2208040

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Shaikh, M. Z., Diana, P. D., Raina, S., Sharma, N., & Alam, S. (2023). Relationship between performance expectancy, effort expectancy and decision making through HR analytics. Pacific Business Review International, 16(6), 109–119.

Shet, S. V., Poddar, T., Samuel, F. W., & 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, 311–326. DOI: 10.1016/j.jbusres.2021.03.021

Stephenson, E. H., Jr. (2015). Using data-driven human capital decisions to improve basic personnel functions. In Innovations in human resource management (pp. 279–286). Routledge.

Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2), 216–231. DOI:10.1016/j.hrmr.2015.01.002

Strohmeier, S. (2007). Research in e-HRM: Review and implications. Human Resource Management Review, 17(1), 19–37. https://doi.org/10.1016/j.hrmr.2006.11.002

Suri, N., & Lakhanpal, P. (2024). People analytics enabling HR strategic partnership: A review. South Asian Journal of Human Resource Management, 11(1), 130–164. https://doi.org/10.1177/232209372211195

Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600–619. https://doi.org/10.1108/02683940610690169

Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. https://doi.org/10.1023/A:1015630930326

Shuck, B., & Wollard, K. (2010). Employee engagement and HRD: A seminal review of the foundations. Human Resource Development Review, 9(1), 89–110. DOI: 10.1177/1534484309353560

Tavana, M., Nazari-Shirkouhi, S., Mashayekhi, A., & Mousakhani, S. (2022). An integrated data mining framework for organizational resilience assessment and quality management optimization in trauma centers. Operations Research Forum.

Townsend, A. M., & Bennett, J. T. (2003). Human resources and information technology. Journal of Labor Research, 24(3), 361–364. DOI: 10.1007/s12122-003-1007-0

Tunsi, W., Tayyoun, R. A., Othman, M., Saleh, Y., Assaf, R., Bakir, A., & Al-Sartawi, A. (2023). Factors influencing adoption of HR analytics by individuals and organizations. Information Sciences Letters, 12(7), 3193–3204. https://doi.org/10.18576/isl/120744

Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224–247. DOI: 10.1016/j.ijinfomgt.2018.07.004

Tursunbayeva, A., Di Loreto, A., & Puglieri, M. (2018). Big data in HRM: A literature review. European Journal of International Management, 12(5), 550–576.

Ulrich, D., & Brockbank, W. (2016). Creating a winning culture: Next step for leading HR professionals. Strategic HR Review, 15(2), 51–56. https://doi.org/10.1108/SHR-02-2016-0009

Ulrich, D., Dulebohn, J. H., & Meisler, G. (2015). The rise of HR: Wisdom from 73 thought leaders. HR Certification Institute.

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: People and Performance, 4(2), 157–178. DOI:10.1108/JOEPP-03-2017-0022

Van der Laken, P. (2018). Data-driven human resource management: The rise of people analytics and its application to expatriate management [Doctoral dissertation, Tilburg University].

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. The International Journal of Human Resource Management, 29(22), 3046–3067. DOI: 10.1080/09585192.2018.1446181

Vance, R. J. (2006). Employee engagement and commitment: A guide to understanding, measuring and increasing engagement in your organization. SHRM Foundation.

Walker, L. O., & Avant, K. C. (2005). Strategies for theory construction in nursing (4th ed.). Pearson/Prentice Hall.

Walker, L. O., & Avant, K. C. (2011). Strategies for theory construction in nursing (5th ed.). Pearson.

Walsh, K., Sturman, M. C., & Longstreet, J. (2010). Key issues in strategic human resources. Cornell HR Review, 1–15.

Wandhe, D. P. (2020). A role of effectiveness of human resource information system (HRIS) in 21st century. Advances in Applied Science Research, 11(3), 1–8. http://dx.doi.org/10.2139/ssrn.3718247

Wei, D., Varshney, K. R., & Wagman, M. (2015, June 27–July 2). Optigrow: People analytics for job transfers [Paper presentation]. 2015 IEEE International Congress on Big Data, New York, NY, United States. https://doi.org/10.1109/BigDataCongress.2015.

Welbourne, T. M. (2015). Data-driven storytelling: The missing link in HR data analytics. Employment Relations Today, 41(4), 27–33.

Wirges, F., & Neyer, A. K. (2023). Towards a process-oriented understanding of HR analytics: Implementation and application. Review of Managerial Science, 17(6), 2077–2108. DOI:10.1007/s11846-022-00574-0

Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Work engagement and financial returns: A diary study on the role of job and personal resources. Journal of Occupational and Organizational Psychology, 82(1), 183–200. DOI: 10.1348/096317908X285633

Zeidan, S., & Itani, N. (2020). HR analytics and organizational effectiveness. International Journal on Emerging Technologies, 11(2), 683–688.

Zins, C. (2007). Conceptual approaches for defining data, information, and knowledge. Journal of the American Society for Information Science and Technology, 58(4), 479–493. DOI: 10.1002/asi.20508

Public Organizations Management

Comprehending the Aspects, Antecedents, and Consequences of the Data-Driven Human Resources Management Based on Concept Analysis Methodology

References

References

Volume 14, Issue 3 - Serial Number 55
June 2026
Pages 1-24

Comprehending the Aspects, Antecedents, and Consequences of the Data-Driven Human Resources Management Based on Concept Analysis Methodology

References

References

Volume 14, Issue 3 - Serial Number 55June 2026Pages 1-24

Volume 14, Issue 3 - Serial Number 55
June 2026
Pages 1-24