Balancing the Load: How Workload Management Shapes Employee Performance at Igara Tea Factory — A Mixed-Methods Approach

Abstract

This study addressed a knowledge gap on how workload management influences employee performance in tea factory settings, where existing literature had largely focused on burnout, occupational stress, or general HRM practices rather than structured workload allocation. The study aimed to examine the effect of workload management on employee performance at Igara Tea Factory. It was anchored on the Job Demands–Resources theory and Goal-Setting theory, which explain how balanced job demands and clear task goals enhance employee outcomes. A descriptive research design supported by a mixed-methods approach was adopted to capture both numerical trends and contextual experiences. The target population comprised 489 employees, including supervisors, clerical staff, casual workers, and support staff. A sample size of 220 respondents was determined using Yamane’s formula, with proportionate stratified random sampling and simple random sampling applied for the quantitative component, while purposive sampling was used to select 19 supervisors for qualitative interviews. Quantitative findings revealed a strong and significant positive relationship between workload management and employee performance (r = 0.688, p < 0.05), while regression analysis confirmed workload management as a significant predictor (B = 0.467, p = 0.000). Qualitative findings further indicated that poor workload distribution reduced efficiency and morale, whereas fair task allocation and clear instructions enhanced performance. The study concluded that effective workload management significantly improves employee performance. It is significant to policy and practice by informing structured workload allocation systems and improved HR planning in tea factories. The study contributes to literature by integrating workload management within performance frameworks in agro-industrial contexts and extending application of JD-R and Goal-Setting theories to workload-performance relationships.