Human capital analytics adoption in the SMEs in the city of Gweru: Opportunities and challenges

Authors

  • Ndabezinhle Mfandaidza Midlands State University, Zimbabwe

Abstract

This paper examines the extent of Human Capital Analytics (HCA) adoption in Zimbabwean Small to Medium Entreprises, alongside the opportunities and barriers shaping its integration. A qualitative research design was employed, relying on unstructured face-to-face interviews with SME representatives selected through convenience sampling. Findings reveal that HCA adoption in Zimbabwean SMEs is still embryonic. The primary constraints include inadequate technological infrastructure, resistance to organisational change, limited financial investment, insufficient statistical expertise among Human Resources (HR) professionals, and resource scarcity. These barriers collectively restrict the ability of SMEs to embed analytics into HR decision-making processes. Nonetheless, the study highlights considerable opportunities for Zimbabwean SMEs to benefit from HCA adoption. Data-driven decision-making could optimise talent acquisition, identify and address skill gaps, and inform the development of targeted training initiatives. Furthermore, embedding HCA has the potential to enhance workforce productivity, support eff ective resource allocation, and improve overall operational efficiency. The study concludes that while structural, financial, and technological barriers continue to impede HCA uptake in Zimbabwean SMEs, strategic investment and capacity-building could enable these enterprises to harness its benefits. Addressing these challenges would not only strengthen HR practices but also contribute to sustainable organisational growth in a competitive and data-driven global economy.

References

Amoako, R., Jiang, Y., Adu-Yeboah, S. S., Frempong, M. F., & Tetteh, S. (2023). Factors influencing electronic human resource management implementation in public organisations in an emerging economy: An empirical study. South African Journal of Business Management, 54(1), a2937. https://doi.org/10.4102/sajbm.v54i1.2937.

Boakye, A., & Lamptey, Y. A. (2020). The rise of HR analytics: Exploring its implications from a developing country perspective. Journal of Human Resource Management, 8(3), 181–189. https://doi.org/10.11648/j.jhrm.20200803.19.

Chalutz Ben-Gal, H. (2019). An ROI-based review of HR analytics: Practical implementation tools. Personnel Review, 48(6), 1429–1448. https://doi.org/10.1108/PR-11-2017-0362.

Decramer, A., Smolders, C., Vanderstraeten, A., & Christiaens, J. (2012). The impact of institutional pressures on employee performance management systems in higher education in the low countries. British Journal of Management, 23(S1), S88–S103. https://doi.org/10.1111/j.1467-8551.2012.00820.x.

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.

Etukudo, R. (2019). Strategies for using analytics to improve Human Resource Management (Doctoral dissertation, Walden University).

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. https://doi.org/10.1108/CR-12-2019-0163.

Financial Tribune (2018, February) SMEs key to lifting Zimbabwe’s economy. Availabe at https://financialtribune.com/articles/world-economy/81471/smes-key-to-lifting-zimbabwe-economy.

Harney, B., & Alkhalaf, H. (2021). A quarter-century review of HRM in small and medium-sized enterprises: Capturing what we know, exploring where we need to go. Human Resource Management, 60(1), 5–29. https://doi.org/10.1002/hrm.22010.

Kanaiyalal, T. G., & Sinha, K. (2023). Human resource management, accounting and artificial intelligence: A promising integration. Chartered Secretary, 81-87.

Keerthi, L., & Reddy, P. R. (2018). Adoption issues of HR analytics. In A book on HR analytics (pp. 10).

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854. https://doi.org/10.1080/0142159X.2020.1755030.

Kinange, U., Bhasme, S., Fernandes, A., & Shaikh, A. (2023). The willingness and challenges to adopt HR analytics among small and medium–sized enterprises. GBS Impact: Journal of Multi-Disciplinary Research, 9(1), 26–36. https://doi.org/10.58419/gbs.v9i1.912303.

Maphosa, V. (2021). The adoption of e-HRM: A view of a telecommunications company in Zimbabwe. Advances in Research, 22(6), 1–13. https://doi.org/10.9734/air/2021/v22i630315.

Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. https://doi.org/10.1016/j.hrmr.2020.100795.

Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278–301. https://doi.org/10.1108/IMDS-11-2021-0695.

Muhammad, G., Siddiqui, M. S., Rasheed, R., Shabbir, H., & Sher, R. F. (2024). Role of external factors in adoption of HR analytics: Does statistical background, gender and age matter? Journal of Business Analytics, 7(1), 1–14. https://doi.org/10.1080/2573234X.2023.2231966.

Mukuze, K., Chavhunduka, D., Manuere, F., & Ndhlovu, P. (2022). The nexus between human capital analytics and human resource information systems (HRIS) in Zimbabwean state universities. International Journal of Social Science Research and Review, 5(12), 141–156. https://doi.org/10.47814/ijssrr.v5i12.664.

Mukuze, K., Chavhunduka, D., Manuere, F., Ndhlovu, P., & Department of Human Resource Management. (2023). An integrative review of HR analytics implementation. Middle East Research Journal. https://doi.org/10.18639/MERJ.2023.9900076

Noutsa, F. A., Kala Kamdjoug, J. R., & Fosso Wamba, S. (2017). Acceptance and use of HRIS and influence on organizational performance of SMEs in a developing economy: The case of Cameroon. In Recent advances in information systems and technologies (pp. 563–580). Springer International Publishing. https://doi.org/10.1007/978-3-319-56535-4_57.

Paauwe, J. (2024). The context4ually-based Human Resource Theory. In Progressing performance and well-being at work (pp. 19–27). Edward Elgar Publishing.

Park, J. H., & Kim, Y. B. (2021). Factors activating big data adoption by Korean firms. Journal of Computer Information Systems, 61(3), 285–293. https://doi.org/10.1080/08874417.2019.1631133.

Shah, N., Michael, F., & Chalu, H. (2020). Conceptualizing challenges to electronic human resource management (e-HRM) adoption: A case of small and medium enterprises (SMEs) in Tanzania. Asian Journal of Business and Management, 8(4). https://doi.org/10.24203/ajbm.v8i4.6066.

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. https://doi.org/10.1016/j.jbusres.2021.03.054.

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.

Ushara, S. A. I., & Chandrika, K. A. C. (2022). Individual level adaptation of human resource analytics among human resource professionals in large-scale apparel companies of Sri Lanka. In Proceedings of the 6th CIPM International Research Symposium – Sri Lanka (p. 126).

Downloads

Published

2025-09-14

How to Cite

Mfandaidza, N. (2025). Human capital analytics adoption in the SMEs in the city of Gweru: Opportunities and challenges. The Dyke, 18(3), pp. 448–470. Retrieved from https://thedyke.msu.ac.zw/index.php/thedyke/article/view/549

Issue

Vol. 18 No. 3 (2024): The Dyke: Leadership & Management

Section

Special Issue

License

Copyright (c) 2025 Upon acceptance and publication, authors grant The Dyke a non-exclusive licence to publish and distribute their article in all formats and media. This means: Authors may freely share, deposit, and republish their work (e.g., in institutional repositories, websites, or future publications), provided proper citation and acknowledgment of The Dyke as the original publisher.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles in The Dyke are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Under this licence:

  • Others may copy, redistribute, remix, transform, and build upon the work for any purpose, even commercially.
  • Appropriate credit must be given to the original author(s) and source (The Dyke), along with a link to the license.
  • Any changes made must be indicated.

Full licence details: https://creativecommons.org/licenses/by/4.0/

Archiving and Preservation

The Dyke supports long-term preservation of scholarly work through partnerships with digital repositories and indexing services, including Sabinet African Journals. Authors are also encouraged to deposit a copy of their published article in institutional or subject-specific repositories.