Algorithmic performance control and knowledge hiding in public organizations: the roles of evaluation anxiety and public service motivation

Purpose As public organizations become increasingly data-driven, employees’ work is monitored and evaluated through algorithmic performance systems. Although these systems are intended to improve performance management, their implications for employees’ knowledge-related behaviors remain underexplored. This study aims to examine the association between algorithmic performance control and knowledge hiding in public organizations. Design/methodology/approach Drawing on conservation of resources (COR) theory, the authors analyze survey data from 420 employees in Chinese government agencies and public institutions. The authors test the hypothesized associations using hierarchical regression and bootstrapped tests of mediation and moderated mediation. Findings The results indicate that algorithmic performance control is positively associated with knowledge hiding. Evaluation anxiety statistically mediated this association: stronger perceived algorithmic performance control was associated with greater evaluation anxiety, which, in turn, was associated with more knowledge hiding. Public service motivation (PSM) weakened the positive association between evaluation anxiety and knowledge hiding. Accordingly, the indirect association between algorithmic performance control and knowledge hiding through evaluation anxiety was weaker at higher levels of PSM. Originality/value This study extends knowledge-hiding research by identifying algorithmic performance control as a digitally mediated evaluative context in public organizations. It identifies evaluation anxiety as a psychological pathway statistically linking algorithmic performance control and knowledge hiding and PSM as a value-based boundary condition that weakens this indirect association. The study thereby advances understanding of how employees respond to digitally mediated performance evaluation in public organizations.