The Impact of Strategic Leadership, Human Resource Capacity Building and Stakeholder Engagement on Kenya Smallholder Tea Industries Performance

Abstract

This study examines the relationships among strategic leadership, human resource capacity building, stakeholder involvement, government policy, and the performance of smallholder tea factories in Kenya. The study is motivated by the need to understand why performance varies across factories operating within a common industry and regulatory environment. Drawing on Transformational Leadership Theory, Human Capital Theory, Stakeholder Theory, and Contingency Theory, the study adopted a quantitative cross-sectional design. A target population of 213 key informants was identified across 71 KTDA-managed factories, and 198 completed responses were retained for analysis, representing a 92.9% response rate. Data were collected using a structured five-point Likert questionnaire and analysed using descriptive statistics, Pearson correlation, multiple regression, and PROCESS-based mediation and moderation analysis. The results show a positive and significant association between strategic leadership and human resource capacity building (r = 0.60, p < 0.01), and between strategic leadership and factory performance (r = 0.46, p < 0.01). Human resource capacity building was the strongest predictor in the full regression model (β = 0.484, p < 0.001), while stakeholder involvement and the government policy context were also significant positive predictors. The indirect effect of strategic leadership on factory performance through human resource capacity building was 0.309, with a 95% bootstrap confidence interval of [0.222, 0.406]. The interaction between stakeholder involvement and the focal predictor was significant (B = 0.185, p = 0.0004). The study concludes that leadership is most useful when translated into employee capability and supported by constructive stakeholder relationships within the prevailing regulatory environment. The findings provide practical implications for factory management and policy, while the cross-sectional design requires that the reported relationships be interpreted as associations rather than definitive causal effects.