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Grounded on the conservation of resources (COR) theory, this study examines the relationship between tyrannical leadership and knowledge hiding. Additionally, this study aims to investigate the mediating role of psychological distress and the moderating role of psychological safety.

Data was gathered from 435 employees in the corporate sector in China. The study used the partial least squares structural equation modelling approach to assess the proposed connections and analysed the data collected with the help of SmartPLS 4 software.

In the study, it was found that there is a positive relationship between tyrannical leadership and knowledge hiding, and this association is mediated by psychological distress. Additionally, the results asserted that the positive effect of tyrannical leadership on knowledge hiding through psychological distress is less pronounced when there is a greater degree of psychological safety.

Leaders should avoid being tyrannical and adopt a supportive leadership style. They should be aware of the effects of their behaviour on employee well-being, provide resources to help employees cope with distress and foster a culture of psychological safety. This approach promotes knowledge sharing, innovation and employee well-being within the organisation.

This study contributes to the existing literature by investigating a new factor that influences knowledge hiding: tyrannical leadership. Furthermore, it explains that employees who experience tyrannical leadership are more prone to psychological distress, such as anxiety and fear, and are likelier to engage in knowledge-hiding behaviours. Finally, the study identifies psychological safety as a factor that can mitigate the negative effects of tyrannical leadership on knowledge hiding.

The purpose of this study is to address the low localization accuracy and frequent tracking failures of traditional visual SLAM methods in low-light and weak-texture situations, and we propose a mobile robot visual-inertial localization method based on the improved point-line features VINS-mono algorithm.

First, the line feature information is introduced into VINS-mono. Subsequently, the EDlines line feature extraction algorithm is optimized with a short line merging strategy and a dynamic length suppression strategy to reduce redundant short lines and fragmented segments. In the back-end sliding window optimization, line feature reprojection errors are incorporated, and Huber kernel functions are added to the inertial measurement unit residuals, point-line feature residuals and loop closure constraints to reduce the impact of outliers on the optimization results.

Comparison and verification experiments are carried out on the EuRoC MAV Data set and real weakly textured environment. In the real low-light and weak-texture scenarios, the improved mobile robot localization system achieves over 40% higher accuracy compared to VINS-mono.

The main contribution of this study is to propose a new visual-inertial SLAM method combining point-line features, which can achieve good localization effect in low-light and weak-texture scenes, with higher accuracy and robustness.