Management Implications for HR Analytics in Light of Systems Theory
Keywords:HR Analytics, Information Technology, Systems Theory, Bibliometric Analysis, Human Resources Management, Workforce Analytics, People Analytics
AbstractSince the 2000s, the HR Analytics field has experienced a steady growth in published works, with a variety of focus, but aiming the value added to HR Management. Recently, the literature has been increasingly focused on factors to be considered in HR Analytics frameworks, suggesting the question of “how” HR Analytics should be put into practice and drove by objectives; unlike initial (but still abundant) approaches of “what” should be done. This paper aims to address gaps that could help setting management resources, researching if there are relevant nuances in HR Analytics objectives that may imply in distinct ways to manage the activity. Two main approaches were combined: (i) a quantitative and qualitative analysis of recent publications and (ii) an approach under the Systems Theory constructs. HR Analytics definitions, approaches, underlying themes, related areas of study and academic gaps were analyzed from 231 publications in the Scopus database until 2021. The analysis highlighted main features of interest, which were clustered and drove to the drawing of distinct (but related) objectives and ways of manage HR Analytics. Moreover, comparisons with knowledge creation of correlated activities led to the proposition of a taxonomy as a driver to objectives and a research agenda.
Abbott, K. (2014). Why labor economics is inadequate for theorizing industrial relations. Journal of Interdisciplinary Economics, 26(1-2), 61-90.
Alamelu, R., Nalini, R., Cresenta Shakila Motha, L., Amudha, R., & Bowiya, S. (2017). Adoption factors impacting human resource analytics among employees. International Journal of Economic Research, 14(6), 417-423.
Ameer, M., Rahul, S. P., & Manne, S. (2020, May). Human Resource Analytics using Power Bi Visualization Tool. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1184-1189). IEEE.
Andersen, MK (2017). Human capital analytics: the winding road. Journal of Organizational Effectiveness: People and Performance.
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.
Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5), 913-931.
Aviv, I., Barger, A., & Pyatigorsky, S. (2021, October). Novel Machine Learning Approach for Automatic Employees' Soft Skills Assessment: Group Collaboration Analysis Case Study. In 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) (pp. 1-7). IEEE.
Bassi, L. (2011). Raging debates in HR Analytics. People and Strategy, 34(2), 14.
Beatty, RW, Huselid, MA, & Schneier, CE (2003). New HR Metrics: Scoring on the Business Scorecard. Organizational Dynamics, 32(2), 107-121.
Becker, BE, Huselid, MA, Huselid, MA, & Ulrich, D. (2001). The HR scorecard: Linking people, strategy, and performance. Harvard Business Press.
Berhil, S., Benlahmar, H., & Labani, N. (2020). A review paper on artificial intelligence at the service of human resources management. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 32-40.
Boulding, KE (1956). General systems theory—the skeleton of science. Management science, 2(3), 197-208.
Brynjolfsson, E., & McAfee, A. (2015). Will humans go the way of horses. Foreign Aff., 94, 8.
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534.
Cascio, W., & Boudreau, J. (2010). Investing in people: Financial impact of human resource initiatives. Ft Press.
Caron, E., & Batistic, S. (2019, July). Knowledge Hubs in Competence Analytics: With a Case Study in Recruitment and Selection. In ICSOFT (pp. 585-594).
Chahtalkhi, N. (2016). What Challenges does HR face when implementing HR Analytics and What actions have been taken to solve these challenges (Doctoral dissertation, Thesis).
Chatterjee, S., Chaudhuri, R., Vrontis, D., & Siachou, E. (2021). Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach. International Journal of Manpower.
Cheng, MM, & Hackett, RD (2019). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 100698.
Churchman, C. (1972). Introduction to systems theory. 2nd Ed., Petrópolis, Voices.
Coron, C. (2021). Quantifying human resource management: a literature review. Personnel Review.
Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. Academy of management review, 24(3), 522-537.
Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Press.
Davenport, TH, Harris, J., & Shapiro, J. (2010). Competing on Talent Analytics. Harvard business review, 88(10), 52-58.
Deng, H., Duan, S. X., & Wibowo, S. (2022). Digital technology driven knowledge sharing for job performance. Journal of Knowledge Management.
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app‐work in the Gig Economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114-132.
Dulebohn, JH, & Johnson, RD (2013). Human resource metrics and decision support: A classification framework. Human Resource Management Review, 23(1), 71-83.
Durai, D. S., Rudhramoorthy, K., & Sarkar, S. (2019). HR metrics and workforce analytics: it is a journey, not a destination. Human Resource Management International Digest, 27(1), 4-6.
Ellmer, M., & Reichel, A. (2021). Staying close to business: the role of epistemic alignment in rendering HR analytics outputs relevant to decision-makers. The International Journal of Human Resource Management, 32(12), 2622-2642.
Enwereuzor, I. K. (2021). Diversity climate and workplace belongingness as organizational facilitators of tacit knowledge sharing. Journal of Knowledge Management.
Fernandez, V., & Gallardo-Gallardo, E. (2021). Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal, 31(1), 162-187.
Frank, MR, Author, D., Bessen, JE, Brynjolfsson, E., Cebrian, M., Deming, DJ, ... & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, 116(14), 6531-6539.
Friedman, G. (2014). Workers without employers: shadow corporations and the rise of the gig economy. Review of Keynesian Economics, 2(2), 171-188.
Gal, U., Jensen, TB, & Stein, MK (2017). People analytics in the age of big data: An agenda for IS research. In ICIS 2017: Transforming Society with Digital Innovation. Association for IS. AIS Electronic Library (AISeL).
Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301.
Gallen, TS (2018). Is the labor wedge due to rigid wages? Evidence from the self-employed. Journal of Macroeconomics, 55, 184-198.
Gandini, A. (2019). Labor process theory and the gig economy. Human Relations, 72(6), 1039-1056.
Gao, F., Li, M., & Clarke, S. (2008). Knowledge, management, and knowledge management in business operations. Journal of knowledge management.
Garcia-Arroyo, J., & Osca, A. (2019). Big Data contributions to human resource management: a systematic review. The International Journal of Human Resource Management, 1-26.
Gaur, B., Shukla, VK, & Verma, A. (2019, April). Strengthening People Analytics through wearable IOT device for real-time data collection. In 2019 international conference on automation, computational and technology management (ICACTM) (pp. 555-560). IEEE.
Giermindl, LM, Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2021). The dark sides of people analytics: reviewing the profiles for organizations and employees. European Journal of IS, 1-26.
Gurusinghe, RN, Arachchige, BJ, & Dayarathna, D. (2021). Predictive HR analytics and talent management: a conceptual framework. Journal of Management Analytics, 8(2), 195-221.
Hamilton, RH, & Sodeman, WA (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.
Hansen, N. K., Güttel, W. H., & Swart, J. (2019). HRM in dynamic environments: Exploitative, exploratory, and ambidextrous HR architectures. The International Journal of Human Resource Management, 30(4), 648-679.
Hasija, S., Padmanabhan, VP, & Rampal, P. (2020). Will the pandemic push knowledge paper into the gig economy. Harvard Business Review Digital Articles, June, 1, 2-8.
Hevner, AR, & March, ST (2003). The IS research cycle. Computer, 36(11), 111-113.
Hota, J. (2021). Framework of Challenges Affecting Adoption of People Analytics in India Using ISM and MICMAC Analysis. Vision, 09722629211029007.
Huselid, MA (2018). The science and practice of Workforce Analytics: Introduction to the HRM special issue. Human Resource Management, 57(3), 679-684.
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.
Jantan, H., Hamdan, AR, & Othman, ZA (2009). Knowledge discovery techniques for Talent forecasting in human resource application. World Academy of Science, Engineering and Technology, 50, 775-783.
Jensen-Eriksen, K. (2016). The role of HR Analytics in creating data-driven HRM: Textual network analysis of online blogs of HR professionals.
Johnson, G., & Stafford, F. (1999). The labor market implications of international trade. Handbook of labor economics, 3, 2215-2288.
Jörden, NM, Sage, D., & Trusson, C. (2021). 'It's so fake': Identity performances and cynicism within a people analytics team. Human Resource Management Journal.
Karwehl, LJ, & Kauffeld, S. (2021). Traditional and new ways in competence management: Application of HR analytics in competence management. group. Interaction. organization. Zeitschrift für Angewandte Organizationspsychologie (GIO), 52(1), 7-24.
Khan, SA, & Tang, J. (2016). The paradox of human resource analytics: being mindful of employees. Journal of General Management, 42(2), 57-66.
Konovalova, VG, Aghgashyan, RV, & Galazova, SS (2021). Perspectives and Restraining Factors of HR Analytics in the Conditions of Digitization of Human Resources Management. In Socio-economic Systems: Paradigms for the Future (pp. 1015-1024). Springer, Cham.
Larsson, AS, & Edwards, MR (2021). Insider econometrics meets people analytics and strategic human resource management. The International Journal of Human Resource Management, 1-47.
Lazear, EP (1999). Personnel Economics: Past lessons and future directions presidential address to the society of labor economists, San Francisco, May 1, 1998. Journal of Labor Economics, 17(2), 199-236.
Legge, K. (1995). What is human resource management?. In: Human resource management (pp. 62-95). Palgrave, London.
Leonardi, P., & Contractor, N. (2018). Better People Analytics. Harvard Business Review, 96(6), 70-81.
Levenson, A., Stevenson, M., & Fink, A. (2021). Are OD and Analytics Twins Separated at Birth? Toward an Integrated Framework. In Research in Organizational Change and Development. Emerald Publishing Limited.
Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685-700.
Levenson, A., & Fink, A. (2017). Human Capital Analytics: too much data and analysis, not enough models and business insights. Journal of Organizational Effectiveness: People and Performance.
Lim, S., Saldanha, T., Malladi, S., & Melville, NP (2009). Theories used in IS research: identifying theory networks in leading IS journals. ICIS 2009 Proceedings, 91.
Liu, L., Akkineni, S., Story, P., & Davis, C. (2020, April). Using HR analytics to support managerial decisions: a case study. In Proceedings of the 2020 ACM Southeast Conference (pp. 168-175).
Lunsford, DL (2019). An Output Model for Human Resource Development Analytics. Performance Improvement Quarterly, 32(1), 13-35.
Lydgate, XKM (2018). Human Resource Analytics: Implications for Strategy Realization and Organizational Performance.
Madhvapaty, H., & Rajesh, A. (2018). HR tech startups in India. Human Resource Management International Digest, 26(3), 11-13.
Manokha, I. (2020). The Implications of Digital Employee Monitoring and People Analytics for Power Relations in the Workplace. Surveillance & Society, 18(4), 540-554.
Margherita, A. (2021). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 100795.
Marler, JH, & Boudreau, JW (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
Martín-Alcazar, F., Romero-Fernandez, PM, & Sánchez-Gardey, G. (2005). Strategic human resource management: integrating the universalistic, contingent, configurational and contextual perspectives. The International Journal of Human Resource Management, 16(5), 633-659.
Massingham, P. (2014). An evaluation of knowledge management tools: Part 2–managing knowledge flows and enablers. Journal of Knowledge Management.
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.
Minbaeva, D. (2017). Human Capital Analytics: why aren't we there? Introduction to the special issue. Journal of Organizational Effectiveness: People and Performance.
Minbaeva, DB (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713.
Mintzberg, HA, & Ahlstrand, B. (1998). B. & Lampel, J. (1998). Strategy safari.
Mishra, SN, Lama, DR, & Pal, Y. (2016). Human Resource Predictive Analytics (HRPA) for HR management in organizations. International Journal of Scientific & Technology Research, 5(5), 33-35.
Mitchell, T., & Brynjolfsson, E. (2017). Track how technology is transforming paper. Nature, 544(7650), 290-292.
Nocker, M., & Sena, V. (2019). Big data and human resources management: The rise of talent analytics. Social Sciences, 8(10), 273.
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization science, 5(1), 14-37.
Nonaka, I., Toyama, R., & Nagata, A. (2000). A firm as a knowledge-creating entity: a new perspective on the theory of the firm. Industrial and corporate change, 9(1), 1-20.
Oentaryo, RJ, Lim, EP, Ashok, XJS, Prasetyo, PK, Ong, KH, & Lau, ZQ (2018). Talent flow analytics in online professional network. Data Science and Engineering, 3(3), 199-220.
Oltra, V. (2005). Knowledge management effectiveness factors: the role of HRM. Journal of knowledge management.
Österle, H., Becker, J., Frank, U., Hess, T., Karagiannis, D., Krcmar, H., ... & Sinz, EJ (2011). Memorandum on design-oriented IS research. European Journal of IS, 20(1), 7-10.
Pape, T. (2016). Prioritizing data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2), 687-698.
Papoutsoglou, M., Mittas, N., & Angelis, L. (2017, August). Mining people analytics from stack overflow job advertisements. In 2017 43rd euromicro conference on software engineering and advanced applications (seaa) (pp. 108-115). IEEE.
Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: developing a framework. Journal of organizational effectiveness: people and performance.
Peres, Alexandre. R.; Riccio, Edson L., Laurindo, Fernando J. B. (2021) Definir para Gerenciar: Implicações Gerenciais para HR Analytics à Luz da Teoria dos Sistemas. In: 18º CONTECSI-International Conference on Information Systems and Technology Management.
Peres, Alexandre. R. & Laurindo, Fernando J. B. (2020) Uma Proposta de Estrutura para a Problemática de Human Resources Analytics. In: 17º CONTECSI-International Conference on Information Systems and Technology Management.
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, 113290.
Pfau, BN, & Cohen, SA (2003). Aligning Human Capital practices and employee behavior with shareholder value. Consulting Psychology Journal: Practice and Research, 55(3), 169.
Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for Talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. "O'Reilly Media, Inc.".
Qamar, Y., & Samad, T. A. (2021). Human resource analytics: A review and bibliometric analysis. Personnel Review.
Qureshi, TM (2020). HR analytics, fad or fashion for organizational sustainability. In Sustainable Development and Social Responsibility—Volume 1 (pp. 103-107). Springer, Cham.
Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR Analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242.
Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of information science, 33(2), 163-180.
Schalk, R., Timmerman, V., & Van den Heuvel, S. (2013). How strategic considerations influence decision making on e-HRM applications. Human Resource Management Review, 23(1), 84-92.
Schwartz, H., & Davis, SM (1981). Matching corporate culture and business strategy. Organizational dynamics, 10(1), 30-48.
Sharma, A., & Sharma, T. (2017). HR analytics and performance appraisal system: A conceptual framework for employee performance improvement. Management Research Review, 40(6), 684-697.
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.
Shrivastava, S., Nagdev, K., & Rajesh, A. (2018). Redefining HR using people analytics: the case of Google. Human Resource Management International Digest, 26(2), 3-6.
Simón, C., & Ferreiro, E. (2018). Workforce analytics: A case study of scholar–practitioner collaboration. Human Resource Management, 57(3), 781-793.
Singh, M., Ramamurthy, K. N., & Vasudevan, S. (2017). Propensity modeling for employee Re-skilling. In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 893-897). IEEE.
Singh, S., & Muduli, A. (2021). Factors Influencing Information Sharing Intention for Human Resource Analytics. Economic Studies, 30(3).
Sivathanu, B., & Pillai, R. (2018). Smart HR 4.0–how industry 4.0 is disrupting HR. Human Resource Management International Digest.
Speer, AB (2021). Empirical attrition modeling and discrimination: Balancing validity and group differences. Human Resource Management Journal.
Taylor, M., Marsh, G., Nicol, D., & Broadbent, P. (2017). Good paper: The Taylor review of modern papering practices.
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.
Vaidya, A., Munde, V., & Shirwaikar, S. (2020). Analytics on Talent search examination data. International Journal of Business Intelligence and Data Mining, 16(1), 20-32.
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.
Van der Laken, P., Bakk, Z., Giagkoulas, V., van Leeuwen, L., & Bongenaar, E. (2018). Expanding the methodological toolbox of HRM researchers: The added value of latent bathtub models and optimal matching analysis. Human Resource Management, 57(3), 751-760.
Varshney, KR, Chenthamarakshan, V., Fancher, SW, Wang, J., Fang, D., & Mojsilović, A. (2014, August). Predicting employee expertise for talent management in the enterprise. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1729-1738).
Walton, RE (1989). Up and running: Integrating information technology and the organization. Harvard Business SchoolPress.
Welbourne, T. M. (2015). Data‐Driven Storytelling: The Missing Link in HR Data Analytics. Employment Relations Today, 41(4), 27-33.
Werkhoven, J. (2017). Conceptualizing business value creation through human resource analytics. America’s Conference on Information Systems: A Tradition of Innovation, AMCIS, Volume 2017.
Witte, L. (2016). We have HR Analytics! So what?: an exploratory study into the impact of HR Analytics on strategic HRM (Master's thesis, University of Twente).
Yasodha, S., & Prakash, PS (2012, March). Data mining classification technique for Talent management using SVM. In 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) (pp. 959-963). IEEE.
Yeh, Y. J., Lai, S. Q., & Ho, C. T. (2006). Knowledge management enablers: a case study. Industrial Management & Data Systems.
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