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Drawing on conservation of resources theory, this study aims to examine how employees’ experiences of excessive workloads may direct them away from efforts to share knowledge with other organizational members, as well as the circumstances in which this process is more or less likely. To untangle the process, the authors predict a mediating role of job dissatisfaction and moderating roles of two complementary resources that help employees cope with failure: resilience as a personal resource and organizational forgiveness as an organizational resource.

Survey data were gathered from employees of an organization that operates in the construction retail sector. The Process macro provides an empirical test of the moderated mediation dynamic that underpins the proposed conceptual framework.

The statistical findings affirm that an important channel through which employees’ perceptions that their work demands are unreasonable escalate into a diminished propensity to share knowledge is their lack of enthusiasm about their jobs. Their ability to recover from challenging work situations and their beliefs that the organization does not hold grudges against people who commit mistakes both mitigate this harmful effect.

For organizational practitioners, this research shows that when employees feel frustrated about extreme work pressures, the resource-draining situation may escalate into diminished knowledge sharing, which might inadvertently undermine their ability to receive valuable feedback for dealing with the challenges. From a positive perspective, individual resilience and organizational forgiveness represent resources that can protect employees against this negative spiral.

This study explicates an unexplored harmful effect of strenuous workloads on knowledge sharing, which is explained by employees’ beliefs that their organization fails to provide satisfactory job experiences. This effect also is mitigated to the extent that employees can draw from valuable personal and organizational resources.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.