Drawing on the stress and coping theory, conservation of resources (COR) theory and social role theory, this study aims to investigate the impact of social media overload on…
Abstract
Purpose
Drawing on the stress and coping theory, conservation of resources (COR) theory and social role theory, this study aims to investigate the impact of social media overload on knowledge withholding behavior and examine the gender differences in social media overload, engendering knowledge withholding.
Design/methodology/approach
By hiring a professional online survey company, this study collected valid responses from 325 general social media users. The structural equation modeling (SEM) technique, bootstrapping method and multi-group analysis were used to test the proposed theoretical model.
Findings
The empirical results reveal that three types of social media overload positively affect users' knowledge withholding behavior and that emotional exhaustion significantly mediates the above relationships. The multi-group analysis demonstrates that gender differences do exist in the decision-making process of knowledge withholding; for example, females are more likely than males to become emotionally exhausted from social media overload, while males are more likely than females to engage in knowledge withholding behavior in the case of emotional exhaustion.
Originality/value
This study contributes to the existing body of knowledge by examining the relationship between social media overload and knowledge withholding, verifying the mediating role of emotional exhaustion as the key mechanism linking them, and narrowing the research gap of lacking gender differences research in knowledge withholding literature.
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Dongqing Yu, Junjun Chen and Masoumeh Kouhsari
This study aims to examine the roles of principal resilience (psychological, social and spiritual) and trust in colleagues in predicting the flourishing of school principals…
Abstract
Purpose
This study aims to examine the roles of principal resilience (psychological, social and spiritual) and trust in colleagues in predicting the flourishing of school principals, considering different career stages.
Design/methodology/approach
Utilising multigroup structural equation modelling and mediation analysis, data were collected from a sample of 1,274 school principals in China. The study investigates the direct impact of resilience on flourishing and the mediating role of trust in colleagues, with a focus on variations across different career stages.
Findings
The findings reveal that all aspects of a principal’s resilience significantly impact their flourishing, with trust serving as a partial mediator. Notably, the influence of spiritual resilience on flourishing is significant only for principals in the later stages of their careers.
Originality/value
This research contributes to the expanding body of literature on educational leadership by highlighting the importance of resilience and trust in fostering principal flourishing. The insights gained offer valuable knowledge for developing support strategies to enhance the flourishing experiences of school principals throughout their careers.
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Junjun Liu, Yunting Feng, Qinghua Zhu and Joseph Sarkis
Green supply chain management (GSCM) and the circular economy (CE) overlap but also differ. The purpose of this paper is to clarify linkages between these two concepts. It…
Abstract
Purpose
Green supply chain management (GSCM) and the circular economy (CE) overlap but also differ. The purpose of this paper is to clarify linkages between these two concepts. It identifies mutual theory applications used to study GSCM and CE.
Design/methodology/approach
A systematic literature review is conducted to identify theories from GSCM and CE studies. A critical analysis explores the theories that can provide mutual applications between GSCM and CE fields. Propositions are developed.
Findings
In all, 12 theories are applied in both GSCM and CE studies. Several theories are only applied in GSCM studies, but can help to advance CE study. These theories include complexity, transaction cost economics, agency, and information theories. Each of the eight theories only applied to CE can potentially advance GSCM study.
Research limitations/implications
The findings contribute to further theory development for both GSCM and CE study. A methodological review can advance theoretical development and cross-pollination in both fields.
Originality/value
This work is the first study to explicitly explore linkages of GSCM and CE from a theoretical perspective.
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Fashion marketers are adopting attractive virtual personalities to replace human influencers on social media, but the impact of consumer bias against virtual influencer acceptance…
Abstract
Purpose
Fashion marketers are adopting attractive virtual personalities to replace human influencers on social media, but the impact of consumer bias against virtual influencer acceptance is not fully understood. Drawing upon match-up hypothesis, attribution theory and speciesism against artificial intelligence (AI), this research investigates how speciesism shapes the influencer-product attractiveness transference in AI-powered influencer marketing for fashion products.
Design/methodology/approach
Three studies were conducted (N = 1,385) to test the influencer-product attractiveness transference, the moderating role of influencer type and the moderated moderating role of speciesism against AI.
Findings
Our studies validated the attractiveness transference and revealed that influencers’ attractiveness promotes purchase intention via perceived product attractiveness. The adoption of virtual (vs human) influencers weakens the attractiveness transference and attenuates the mediating effect. Low speciesism boosts the effectiveness of virtual influencers, such that attractiveness transference disappears only when high-speciesism consumers react to virtual influencers.
Originality/value
Our findings clarify how influencers’ physical appearance, AI application and speciesism together impact interactive fashion marketing, offering practical insights into successful influencer strategies on social media.
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Greg G. Wang, David Lamond, Verner Worm, Wenshu Gao and Shengbin Yang
The purpose of this paper is to examine the indigenous Chinese concept of suzhi (素质) with the aim of furthering the development of Chinese human resource management (HRM) research…
Abstract
Purpose
The purpose of this paper is to examine the indigenous Chinese concept of suzhi (素质) with the aim of furthering the development of Chinese human resource management (HRM) research and practice.
Design/methodology/approach
An extensive review of the literature on suzhi, published in the West, as well as in China, is the basis for proffering an organizational-level conceptualization of suzhi in the Chinese context.
Findings
Instead of understanding it as a free-floating signifier, we argue that suzhi can be considered as a criterion-based framework for HRM research and practice. Suzhi research is classified into two major sources – indigenous Chinese and indigenized Western constructs. We further make a distinction between intrinsic and extrinsic suzhi, and analyze a popular set of suzhi criteria, considering de (morality) and cai (talent), while focusing on de in HRM selection (德才兼备, 以德为先). As multilevel and multidimensional framework, suzhi criteria may form different gestalts in different organizations and industries.
Research limitations/implications
From a social cultural and historical perspective, HRM research that incorporates a combination of indigenous and indigenized suzhi characteristics may receive better acceptance by individuals, organizations and the society in the Chinese context. Accordingly, the reconstruction of suzhi into manageable and measurable dimensions can be undertaken for more effective HRM practice in the Chinese context.
Originality/value
The HRM literature is advanced by linking the indigenous suzhi discourse to Chinese indigenous HRM research and practice.
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Jidong Han, Chun Qiu and Peter Popkowski Leszczyc
This paper aims to investigate how competition among online auction sellers influences the setting of both open and secret reserve prices, thereby affecting auction outcome.
Abstract
Purpose
This paper aims to investigate how competition among online auction sellers influences the setting of both open and secret reserve prices, thereby affecting auction outcome.
Design/methodology/approach
Using a data set collected from eBay consisting of 787 identical product auctions, three empirical models have been proposed. Model 1 simultaneously estimates the effects of auction competition on a seller’s own open and secret reserve price strategies; Model 2 estimates the effects of auction competition on bidder participation; and Model 3 estimates the direct and indirect effects of auction competition on selling price.
Findings
Competition among sellers is central to shaping sellers’ reserve price strategies. When there are more concurrent auctions for identical items, sellers tend to specify a low open reserve and are less likely to set a secret reserve. Sellers are strongly influenced by competitors’ reserve price strategies, and tend to follow competition. Finally, auction competition and competitive reserve price strategies influence both bidder entry and selling prices.
Practical implications
This study has important implications for both sellers and bidders. It highlights the importance for sellers to adapt their reserve price strategies in light of their competitors’ reserve price strategies and offers implications for bidders regarding auction selection. An auction with low starting bid does not necessarily lead to a lower selling price as it attracts more bidders.
Originality/value
This paper focuses on competition among auction sellers, whereas previous literature has focused on competition among bidders. This paper is the first to study the impact of competing reserve prices in auctions.
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Weixin Zhang, Zhao Liu, Yu Song, Yixuan Lu and Zhenping Feng
To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most…
Abstract
Purpose
To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.
Design/methodology/approach
The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.
Findings
The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.
Research limitations/implications
The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.
Originality/value
This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.