Abstract
In the era of data-driven human resource management, transforming payroll systems from simple administrative tools into strategic sources of workforce intelligence is essential for aligning talent management with organizational goals. This study examines how small and medium-sized enterprises (SMEs) can leverage payroll data to generate predictive insights that inform strategic HR decisions and enhance workforce planning. Specifically, it explores the links between payroll data and people analytics, investigates how payroll-generated data can provide actionable insights for talent retention, turnover prediction, and workforce forecasting, and develops a conceptual model linking payroll data usage to strategic HR outcomes.
Organizations increasingly recognize the strategic importance of workforce planning for competitive advantage. People analytics enables data-driven decision-making (CIPD, 2021), and large organizations have leveraged it to strengthen strategic outcomes. However, limited expertise and financial constraints often prevent SMEs from developing the analytical literacy needed for adoption (Willetts et al., 2020; Kinage et al., 2023). Payroll systems already generate data on attendance, performance, compensation, and turnover (Shukla & Bhandari, 2019), which may be considered small data in comparison with data generated in other departments. Harnessing these variables allows SMEs to shift from reactive reporting to predictive forecasting, enhancing workforce planning and decision-making.
The theoretical framework, using RBV, proposes that organizational data can be deployed as a valuable internal resource to enhance competitive advantage if effectively deployed. The research question is: To what extent can routine payroll data influence strategic workforce planning outcomes in SMEs?
This study will employ a sequential explanatory mixed-methods design to examine how payroll data informs strategic workforce planning in small and medium-sized enterprises (SMEs) (Ivankova et al., 2006). A quantitative survey and qualitative case studies will be combined to provide both breadth and depth of insight