From Readiness to Performance: Modelling AI-Enabled HR Analytics Adoption Capability as a Second-Order Formative Construct in Nigerian SMEs
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Abstract
Objective: This study examines how AI-enabled HR Analytics Adoption Capability (AIHRA), a second-order formative construct, is associated with operational performance among Nigerian SMEs.
Design: Drawing on UTAUT2, the Resource-Based View, and Socio-Technical Systems Theory, a survey was administered to 296 respondents across SMEs in Lagos, Abuja, Port Harcourt, and Kano. PLS-SEM via SmartPLS 4.0.9.9 was used for analysis.
Findings: Technological Readiness (weight = 0.584, p<0.001) and Organisational Support (weight = 0.532, p<0.001) significantly form AIHRA. Perceived Value and Use is non-significant (0.119, p = 0.219) but shows absolute importance. AIHRA is positively associated with operational performance (β = 0.626, R2 = 0.392).
Policy Implications: Managers and policymakers should prioritise digital infrastructure and organisational support before AI procurement.
Originality: This study contributes an empirically tested higher-order formative model linking AI-HR analytics adoption capability to operational performance in Nigerian SMEs, extending prior technology-organisation-environment evidence by showing structural determinants dominate perceptual constructs within a multi-theory, capability-based specification.
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Copyright (c) 2026 Michael Raphael Onenyi, Peter Ugbedeojo Nelson, Muhammed Idris Ogbike
This work is licensed under a Creative Commons Attribution 4.0 International License.
Michael Raphael Onenyi, Department of Business Administration, Prince Abubakar Audu University, Anyigba, Nigeria
The data used in this study are not publicly available due to confidentiality agreements with participating organizations but are available from the corresponding author upon reasonable request.
Alvarez-Gutierrez, F. J., Stone, D. L., Castano, A. M., & Garcia-Izquierdo, A. L. (2022). Human resources analytics: A systematic review from a sustainable management approach. Journal of Work and Organizational Psychology, 38(3), 129-147. https://doi.org/10.5093/jwop2022a18 CrossrefGoogle Scholar
Arroyabe, M. F., Arranz, C. F., De Arroyabe, I. F., & De Arroyabe, J. C. F. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, 102733. https://doi.org/10.1016/j.techsoc.2024.102733 CrossrefGoogle Scholar
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108 CrossrefGoogle Scholar
Becker, J., Klein, K., & Wetzels, M. (2012). Hierarchical Latent Variable models in PLS-SEM: Guidelines for using Reflective-Formative Type Models. Long Range Planning, 45(5–6), 359–394. https://doi.org/10.1016/j.lrp.2012.10.001 CrossrefGoogle Scholar
Bonilla-Chaves, E. F., Palos-Sanchez, P. R., Folgado-Fernandez, J. A., & Marino-Romero, J. A. (2024). The effect of innovation performance on the adoption of HR analytics. Electronic Research Archive, 32(2), 1126-1144. https://doi.org/10.3934/era.2024054 CrossrefGoogle Scholar
Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models. Journal of Business Research, 61(12), 1250-1262. https://doi.org/10.1016/j.jbusres.2008.01.013 CrossrefGoogle Scholar
Creswell, J. W., & Creswell, J. D. (2018). Research design (5th ed.). SAGE Publications.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators. Journal of Marketing Research, 38(2), 269-277. https://doi.org/10.1509/jmkr.38.2.269.18845 CrossrefGoogle Scholar
Ebuka, A. A., Emmanuel, D., & Idigo, P. (2023). Artificial intelligence as a catalyst for the sustainability of SMEs in Nigeria. Annals of Management and Organization Research, 5(1), 1-11. https://doi.org/10.35912/amor.v5i1.1719 CrossrefGoogle Scholar
Ekka, S., & Singh, P. (2022). Predicting HR professionals adoption of HR analytics: An extension of UTAUT model. Organizacija, 55(1), 77-93. https://doi.org/10.2478/orga-2022-0006 CrossrefGoogle Scholar
Faiz, F., Le, V., & Masli, E. K. (2024). Determinants of digital technology adoption in innovative SMEs. Journal of Innovation and Knowledge, 9(4), 100610. https://doi.org/10.1016/j.jik.2024.100610 CrossrefGoogle Scholar
Faruk, O. M. (2025). Artificial intelligence in business intelligence. 1st Global Research and Innovation Conference, 01(01), 589-617.
Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2021). The dark sides of people analytics. European Journal of Information Systems, 31(3), 410-435. https://doi.org/10.1080/0960085X.2021.1927213 CrossrefGoogle Scholar
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on PLS-SEM (2nd ed.). SAGE Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on PLS-SEM (3rd ed.). SAGE Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8 CrossrefGoogle Scholar
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431. https://doi.org/10.1108/IMR-09-2014-0304 CrossrefGoogle Scholar
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-319. CrossrefGoogle Scholar
Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1–21. https://doi.org/10.1509/jim.17.0151 CrossrefGoogle Scholar
Hussain, S., Khan, A., & Ahmed, M. (2024). Strategic AI adoption in SMEs: A prescriptive framework. arXiv preprint. https://doi.org/10.48550/arxiv.2408.11825
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101 CrossrefGoogle Scholar
Marler, J. H., & Boudreau, J. W. (2016). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699 CrossrefGoogle Scholar
National Bureau of Statistics. (2023). SMEs in Nigeria: A report on the small and medium enterprise sector. Federal Republic of Nigeria.
Pasmore, W. A. (1988). Designing effective organisations. Wiley.
Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 CrossrefGoogle Scholar
Raza, H., Arif, S., Luhana, K. K., & Mushtaq, A. (2025). The impact of AI-BA opacity on operational inefficiency. Social Science Review Archives, 3(3), 624-637. https://doi.org/10.70670/sra.v3i3.893 CrossrefGoogle Scholar
Razali, N. and Wah, Y. (2011) Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(10, 21-33.
Rigdon, E. E. (2016). Choosing PLS path modelling as analytical method. European Management Journal, 34, 598-605. https://doi.org/10.1016/j.emj.2016.05.006 CrossrefGoogle Scholar
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS GmbH.
Sadiq, O., Hack-Polay, D., Fuller, T., & Rahman, M. (2022). Barriers to the Effective Integration of Developed ICT for SMEs in Rural NIGERIA. Businesses, 2(4), 501-526. https://doi.org/10.3390/businesses2040032 CrossrefGoogle Scholar
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. https://doi.org/10.1016/j.jbusres.2022.01.044 CrossrefGoogle Scholar
Sarstedt, M., Hair, J. F., Cheah, J., Becker, J., & Ringle, C. M. (2019). How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003 CrossrefGoogle Scholar
Sekaran, U., & Bougie, R. (2016). Research methods for business (7th ed.). Wiley.
Shadrach, M., & Umemezia, E. (2025). Adoption of HRIS among SMEs in Nigeria: A review. Nigerian Journal of Management Sciences, 26(1a).
Shahadat, M. M. H., Nekmahmud, M., Ebrahimi, P., & Fekete-Farkas, M. (2023). Digital technology adoption in SMES: What technological, environmental and organizational factors influence in emerging countries? Global Business Review. https://doi.org/10.1177/09721509221137199 CrossrefGoogle Scholar
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/ejm-02-2019-0189 CrossrefGoogle Scholar
SMEDAN. (2013). SMEDAN and National Bureau of Statistics collaborative survey.
Tanantong, T., & Wongras, P. (2024). A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand. Systems, 12(1), 28. https://doi.org/10.3390/systems12010028 CrossrefGoogle Scholar
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the Longwall method. Human Relations, 4(1), 3-38. https://doi.org/10.1177/001872675100400101 CrossrefGoogle Scholar
Venkatesh, V. (2021). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1-2), 641-652. https://doi.org/10.1007/s10479-020-03918-9 CrossrefGoogle Scholar
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540 CrossrefGoogle Scholar
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412 CrossrefGoogle Scholar
Wang, L., Zhou, Y., Sanders, K., Marler, J. H., & Zou, Y. (2023). Determinants of effective HR analytics implementation. Journal of Business Research, 170, 114312. https://doi.org/10.1016/j.jbusres.2023.114312 CrossrefGoogle Scholar
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171-180. https://doi.org/10.1002/smj.4250050207 CrossrefGoogle Scholar
Xue, L., Rashid, A. M., & Ouyang, S. (2024). The UTAUT in higher education: A systematic review. SAGE Open, 14(1). https://doi.org/10.1177/21582440241229570 CrossrefGoogle Scholar
Zhang, P., Dillard, N., & Cavallo, T. (2025). Navigating the limitations of algorithmic management: STS Theory and Strategic HRD. Human Resource Development Review, 24(3), 279-303. https://doi.org/10.1177/15344843251320252 CrossrefGoogle Scholar