Greening human resource management and employee commitment toward the environment: An interaction model

In response to a greater environmental awareness, organizations are concerned more and more about the “greening” human resource management (GHRM). Although the literature on GHRM has been extending, published studies have paid little attention to the research of GHRM and its contribution to employee commitment toward the environment, especially the interactions of GHRM practices, so far. Thus, to bridge this research gap, this study extends the Ability-Motivation-Opportunity and the Social exchange theories in the green context by investigating a new conceptual framework, which explores the indirect and interactive effects of GHRM practices (training, reward, and organizational culture) on employee environmental commitment. A quantitative study is conducted through a survey involving 209 respondents. Findings suggest that: (1) three GHRM practices are important tools in stimulating directly employees to commit to the environmental activities, (2) a two-way interaction of green training and green organizational culture can unlock employee commitment for the environment, especially at the high and average levels of green organizational culture, (3) the commitment is also increased significantly through a three-way interaction, the two strongest effects are recognized with the conditions of high-green organizational culture and the average- and high-green reward, whereas (4) the interacting between green training and green reward is an unimportant factor in encouraging employee environmental attachment

Structure and Properties of Double Perovskite System La2_2Co1−x_{1-x}Fex_{x}MnO6_{6}

Low Fe-doped insulating ferromagnets La$_2$CoMnO$_6$ were prepared and studied. The compounds crystallized in orthorhombic space group Pnma with slight changes in the lattice constants. We have observed a significant reduction of resistivity due to doping, together with an increase of magnetization and saturated magnetization as doping level increased. The doping also reduced $T_C$ for both transitions at around 220 and 140 K which attribute for the different spin orderings of the magnetic ions. The small features were also seen at around 40 K and should correspond to the cluster glassy region with spin disorders

Indicators for TQM 4.0 model: Delphi Method and Analytic Hierarchy Process (AHP) analysis

Anchoring on Socio-technical system (STS) theory, this study applied Delphi and analytic hierarchy process (AHP) techniques to explore the key factors and specific indicators of the TQM 4.0 model implementation in manufacturing enterprises. An analysis of two Delphi rounds through experts who are academia, consultants, and production/quality supervisors/managers found ten factors and 41 indicators. In the third round, the study weighted the importance of each factor and indicator through an analysis of the AHP technique. The research suggested that social factors were more important than technical factors. Importantly, the findings indicated three key factors of the TQM 4.0 model, including top management, quality culture 4.0, and integrating sustainable development. Furthermore, the study revealed that top management commitment, quality-driven mindfulness, and employee empowerment were specified as the most critical indicators of the TQM 4.0 model. Results could be valuable for both researchers and practitioners in assessing TQM 4.0 implementation in the manufacturing sector in the future. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Tomas Bata University in Zlin [VaV-IP-RO/2020/01

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model