From Analytics To Experience: How Organisational Communication and Work Environment Link HR Analytics Capability with Perceived Organisational Performance
DOI:
https://doi.org/10.64882/ijrt.v14.i4.2041Keywords:
HR analytics, HRM system strength, organisational performance, work environment, organisational communication, people analyticsAbstract
Purpose – This study examines how human resource analytics capability (HRAC) is associated with organisational performance through employee-facing processes, focusing on organisational communication and work environment.
Design/methodology/approach – A cross-sectional employee survey was analysed after data-integrity and response-quality screening, yielding 234 usable cases. A maximum-likelihood confirmatory factor analysis (CFA) evaluated the reflective measurement model before standardised composite-based path models with controls were estimated using HC3 robust standard errors. Indirect associations were tested with 10,000 bootstrap resamples.
Findings – HRAC was positively associated with organisational communication (β = 0.557, p < 0.001). Communication was positively associated with work environment (β = 0.733, p < 0.001), which was positively associated with perceived organisational performance (β = 0.215, p = 0.019). The serial indirect association was positive (β = 0.088, 95% CI [0.022, 0.172]), while HRAC retained a direct association with performance. A reverse-order specification was also statistically viable, so temporal ordering cannot be established from the cross-sectional data.
Practical implications – Organisations should assess analytics value not only through tools and dashboards but also through whether analytics-informed insights are communicated clearly and reflected in employees’ working conditions.
Originality – The study advances an employee-facing value-realisation perspective grounded in HRM system-strength theory. Its contribution is to distinguish analytics capability from the communication of organisational signals and the working conditions employees experience, extending established HR analytics–performance relationships. The proposed and reverse-order serial indirect associations are examined without claiming a uniquely supported temporal sequence.
References
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M. and Stuart, M. (2016), “HR and analytics: why HR is set to fail the big data challenge”, Human Resource Management Journal, Vol. 26 No. 1, pp. 1–11, doi: 10.1111/1748-8583.12090.
Arora, M. and Mittal, A. (2024), “Enhancing organizational performance through HR analytics capabilities: mediating influence of innovative capability and moderating role of technological turbulence”, The International Journal of Human Resource Management, Vol. 35 No. 19, pp. 3271–3304, doi: 10.1080/09585192.2024.2403541.
Arora, S., Chaudhary, P. and Singh, R.K. (2025), “Adoption of HR analytics for future-proof decision making: role of attitude toward artificial intelligence as a moderator”, International Journal of Organizational Analysis, Vol. 33 No. 9, pp. 3047–3063
Bakker, A.B. and Demerouti, E. (2017), “Job demands–resources theory: taking stock and looking forward”, Journal of Occupational Health Psychology, Vol. 22 No. 3
Bowen, D.E. and Ostroff, C. (2004), “Understanding HRM–firm performance linkages: the role of the ‘strength’ of the HRM system”, Academy of Management Review
Espegren, Y. and Hugosson, M. (2025), “HR analytics-as-practice: a systematic literature review”, Journal of Organizational Effectiveness: People and Performance, Vol. 12 No. 5, pp. 83–111, doi: 10.1108/JOEPP-11-2022-0345.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, Vol. 43 No. 1, pp. 115–135, doi: 10.1007/s11747-014-0403-8.
Kock, N. (2015), “Common method bias in PLS-SEM: a full collinearity assessment approach”, International Journal of e-Collaboration, Vol. 11 No. 4, pp. 1–10, doi: 10.4018/IJEC.2015100101.
Marler, J.H. and Boudreau, J.W. (2017), “An evidence-based review of HR Analytics”, The International Journal of Human Resource Management, Vol. 28 No. 1, pp. 3–26, doi: 10.1080/09585192.2016.1244699.
McCartney, S. and Fu, N. (2022), “Bridging the gap: why, how and when HR analytics can impact organizational performance”, Management Decision, Vol. 60 No. 13, pp. 25–47, doi: 10.1108/MD-12-2020-1581.
McIver, D., Lengnick-Hall, M.L. and Lengnick-Hall, C.A. (2018), “A strategic approach to workforce analytics: integrating science and agility”, Business Horizons, Vol. 61 No. 3, pp. 397–407, doi: 10.1016/j.bushor.2018.01.005.
Minbaeva, D.B. (2018), “Building credible human capital analytics for organizational competitive advantage”, Human Resource Management, Vol. 57 No. 3, pp. 701–713, doi: 10.1002/hrm.21848.
Ostroff, C. and Bowen, D.E. (2016), “Reflections on the 2014 Decade Award: is there strength in the construct of HR system strength?”, Academy of Management Review, Vol. 41 No. 2, pp. 196–214, doi: 10.5465/AMR.2015.0323.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879–903, doi: 10.1037/0021-9010.88.5.879.
Presbitero, A., Teng-Calleja, M. and Farndale, E. (2022), “Employee perceptions of HRM system strength: examining outcome and boundary conditions among HR and non-HR employees”, Personnel Review, Vol. 51 No. 9, pp. 2142–2161
Raj, G. and Krishnan, T.N. (2025), “The story beyond the stats: decoding the psychological impact of human resource analytics on employees”, Human Resource Management Review, Vol. 35 No. 4, 101102, doi: 10.1016/j.hrmr.2025.101102.
Rasmussen, T. and Ulrich, D. (2015), “Learning from practice: how HR analytics avoids being a management fad”, Organizational Dynamics, Vol. 44 No. 3, pp. 236–242, doi: 10.1016/j.orgdyn.2015.05.008.
Rigamonti, E., Colaiacovo, B., Gastaldi, L. and Corso, M. (2025), “HR analytics and the data collection process: the role of attributions and perceived legitimacy in explaining employees’ fear of datafication”, Journal of Organizational Effectiveness: People and Performance, Vol. 12 No. 5, pp. 1–23, doi: 10.1108/JOEPP-06-2023-0246.
Sivathanu, B. and Pillai, R. (2020), “Technology and talent analytics for talent management – a game changer for organizational performance”, International Journal of Organizational Analysis, Vol. 28 No. 2, pp. 457–473, doi: 10.1108/IJOA-01-2019-1634.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
