Greening human resource management and employee commitment towards 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 towards 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. © 2019 The Author(s).Internal Grant Agency of FaME TBU [IGA/FaME/2018/009]; La Trobe Business School, Australi

Leader's envy and knowledge hiding in universities in Pakistan

The present study examines the role of the leader's envy in knowledge hiding. Based on 28 semi-structured interviews from the faculty members of different Universities in Pakistan, we explain that how leader's perception of relative power as compare to their followers lead them to get envious of their followers and results in negative behaviours, such as knowledge hiding. Furthermore, this paper attempts to explain when such phenomenon exists in a work setting, and what kind of knowledge-hiding behaviours - rationalized hiding, evasive hiding, or playing dumb - leaders demonstrate. Results show that as a result of social comparisons with competent subordinates, leaders engage in the feeling of envy. As a behavioural response of envy, leaders engage in different knowledge-hiding behaviours

How to drive brand engagement and EWOM intention in social commerce: A competitive strategy for the emerging market

Brand engagement and eWOM intention have been found to be critical factors driving competitive advantage for companies, as the evolution of social networking sites has changed the perspective of how companies engage with customers. Based on social exchange theory, the current research proposes an empirical model that emphasizes (1) the unique role of social commerce characteristics, including personalization, socialization, and information availability, in enhancing consumer-brand engagement, (2) the connection between consumer-brand engagement and eWOM intention, and (3) the moderating influence of trust towards such connection. A survey of 248 Facebook users with online shopping experience was employed. By using PLS-graph 3.0, structural equation modelling, the findings demonstrate that personalization and socialization positively influence brand engagement, which in turn leads to eWOM intention. Furthermore, trust moderates the brand engagement-eWOM intention relationship. Unexpectedly, information availability has shown no significant effect on brand engagement. The study encompasses the knowledge of social exchange theory into the social commerce environment by investigating the linkage between the social commerce environment and brand engagement. It contributes value to marketing theories by describing the moderating role of trust from the viewpoint of Gen Y. In addition, the study's findings may shed light on how firms in emerging markets can increase competitiveness by stimulating brand engagement and eWOM intention, as well as enhancing consumer trust in the comments regarding the products/services within the social commerce environment. © 2020 Tomas Bata University in Zlín. All rights reserved.Internal Grant Agency of the Faculty of Management and Economics, Tomas Bata University in Zlin [IGA/FaME/2018/015

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

Extraction of Polyphenols from Mentha aquatica Linn. var. crispa

Mentha aquatica Linn. var. crispa is commonly used as a spice in many Asian countries. Although its biological activities, such as its applications, antimicrobial properties, have been studied, its antioxidation properties have not been investigated. This study establishes the most suitable extraction conditions concerning the independent variables affecting the total polyphenol content (TPC) and antioxidant activity (AA) of M. aquatica extract (stem and leaf). Investigated factors include the type of solvent used; solvent concentration, the ratio of raw material to solvent, extraction time and extraction temperature. The efficiency of polyphenol extraction was evaluated by TPC and AA through the ability to neutralize the free radicals 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2\u27-azinobis (3-ethylbenzothiazoline-6- sulfonic acid) (ABTS), and the ferric reducing antioxidant power (FRAP) was used as the evaluation indicator. The results have shown that acetone at a concentration of 50%, at a ratio of 1:20 (w/v), extraction time of 2 h and a temperature of 40 °C give the highest values of TPC and AA, with values of 120.92 mg GAE g-1 dw for TPC, 169.36 μmol TE g-1 dw by DPPH assay, 264.03 μmol by ABTS assay, and 425.35 μmol Fe2+ g-1 dw by FRAP assay. This study demonstrates that extracts of M. aquatica can be used for research as food antioxidant

Extraction of Polyphenols from Mentha aquatica Linn. var. crispa

Mentha aquatica Linn. var. crispa is commonly used as a spice in many Asian countries. Although its biological activities, such as its applications, antimicrobial properties, have been studied, its antioxidation properties have not been investigated. This study establishes the most suitable extraction conditions concerning the independent variables affecting the total polyphenol content (TPC) and antioxidant activity (AA) of M. aquatica extract (stem and leaf). Investigated factors include the type of solvent used; solvent concentration, the ratio of raw material to solvent, extraction time and extraction temperature. The efficiency of polyphenol extraction was evaluated by TPC and AA through the ability to neutralize the free radicals 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2\u27-azinobis (3-ethylbenzothiazoline-6- sulfonic acid) (ABTS), and the ferric reducing antioxidant power (FRAP) was used as the evaluation indicator. The results have shown that acetone at a concentration of 50%, at a ratio of 1:20 (w/v), extraction time of 2 h and a temperature of 40 °C give the highest values of TPC and AA, with values of 120.92 mg GAE g-1 dw for TPC, 169.36 μmol TE g-1 dw by DPPH assay, 264.03 μmol by ABTS assay, and 425.35 μmol Fe2+ g-1 dw by FRAP assay. This study demonstrates that extracts of M. aquatica can be used for research as food antioxidant

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

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph-matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7% while using only a ResNet-50.Comment: Update Appendi