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Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable.

The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques.

The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one.

This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the “supervisor BLM–knowledge hiding” relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees.

It was verified that the micro-texture in the front and back of the tool at the same time had a positive effect on improving the milling behavior and surface quality of the tool. The purpose of this study is to explore the rationality of simultaneous placement of micro-textures on the front and rear surfaces of ball-end milling cutters, analyze the influence of micro-texture parameters on tool milling behavior and workpiece surface quality, reveal its internal mechanism, and obtain the best micro-texture parameters by optimization.

First, the mechanism of micro-texture is studied based on the energy loss model. Second, the orthogonal experiment is designed to analyze the influence of micro-texture parameters on tool milling behavior and reveal its mechanism by combining simulation technology and cutting experiment. Finally, the parameters are optimized based on the artificial bee colony algorithm.

The results show that the simultaneous placement of micro-texture on the rake face and flank face of the tool has a positive effect on improving the milling behavior and surface quality of the tool. Taking milling force, tool wear and surface roughness as the evaluation criteria, the optimal parameter combination is obtained: the rake face micro-texture diameter is 50 µm, the distance from the micro-texture is 200 µm and the distance from the cutting edge is 110 µm; the diameter of the micro-textured flank is 40 µm, the distance from the micro-texture is 170 µm and the distance from the cutting edge is 130 µm.

Taking milling force, tool wear and surface roughness as the evaluation criteria, the optimal parameter combination is obtained: the rake face micro-texture diameter is 50 µm, the distance from the micro-texture is 200 µm and the distance from the cutting edge is 110 µm; the diameter of the micro-textured flank is 40 µm, the distance from the micro-texture is 170 µm and the distance from the cutting edge is 130 µm, which provides theoretical support for the further study of the micro-textured tool.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2023-0022/