Developing scalable HR analytics platforms for SMEs with data-driven strategies to empower smaller businesses
Human resource management in small and medium-sized enterprises (SMEs) faces significant technological challenges in today's data-driven business environment. This study examines HR analytics adoption in U.S. SMEs, by identifying barriers such as limited capital, skills gaps, and inconsistent data practices. Despite these challenges, 50% of U.S. SMEs, approximately 3.2 million businesses, have adopted HR management software, which highlights a growing shift toward digital HR solutions. Through a systematic literature review and market analysis, the study evaluates scalable, cloud-based HR analytics solutions, which include performance tracking systems, employee engagement platforms, and predictive retention tools that enhance workforce management. The research analyzes various HR analytics tools, evaluates their effectiveness for SMEs, and explores how modular, cloud-based solutions enhance workforce management. Utilizing systematic literature review and market analysis, the study explores the impact of data-driven approaches on SME performance. Findings indicate that SMEs that utilize HR analytics experience up to 75% increase in employee engagement and 31% reduction in voluntary turnover. The study emphasizes the need for cost-effective analytics tools, robust information systems, effective change management initiatives, targeted upskilling programs, and strategic data governance frameworks to drive adoption. This research contributes valuable insights to help SMEs harness HR analytics and data-driven approaches, to enhance human resource management and organizational competitiveness in an increasingly technology-driven marketplace.
Institutions
- University of Arkansas at Little Rock (US)
- Northern Illinois University (US)
- McDaniel College (US)
- Georgia State University (US)
- University of the Potomac (US)
- Arkansas Department of Health (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-08-31
- DOI
- https://doi.org/10.5281/zenodo.17695605
- Primary Topic
- AI and HR Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00