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In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.

Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.

The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.

This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.

This paper aims to develop an innovative 3D printer based on material extrusion to expand applied material field and shorten the production cycle. The developed 3D printer can fabricate products directly using various powders, including polymers and fillers. In addition, the influence of extrusion on the orientation of thermal conductive filler is also investigated.

To ensure the plasticizing effect and the mixing ability, the printing head is a conical twin-screw extruder, which have a smaller volume. PA12 and h-BN powders were selected for printing as matrix and filler, respectively. The properties of printing products were characterized.

The results show that the new printer can fabricate products directly using polymer powders because of the mixing ability of the twin-screw. The h-BN filler orient in the PA12 matrix and form thermal conduction paths due to the extrusion process, which make the printed samples have an anisotropic thermal conductivity.

The innovative 3D printer provides a method of printing products directly using powders, which can expand material field and shorten the production cycle. For composites, the extrusion process can make fillers orient in the matrix to fabricate products with anisotropic characteristics.

The carbon complementary supply chain (CCSC) is a collaborative framework that facilitates internal carbon credit trading agreements among supply chain agents in compliance with prevailing carbon regulations. Such agreements are highly beneficial, prompting agents to consider joint investment in emission reduction initiatives. However, capital investments come with inevitable opportunity costs, compelling agents to weigh the potential revenue from collaborative investments against these costs. Thus, this paper mainly explores carbon abatement strategies and operational decisions of the CCSC members and the influence of opportunity costs on the strategic choice of cooperative and noncooperative investment.

The authors propose a novel biform game-based theoretical framework that captures the interplay of pricing competition and investment cooperation among CCSC agents and assesses the impact of opportunity costs on CCSC profits and social welfare. Besides, the authors also compare the biform game-based collaborative scenario (Model B) to the noncooperative investment scenario (Model N) to investigate the conditions under which collaborative investment is most effective.

The biform game-based collaborative investment strategy enhances the economic performance of the traditional energy manufacturer, who bears the risk of opportunity costs, as well as the retailer. Additionally, it incentivizes the renewable energy manufacturer to improve environmental performance through renewable projects.

This research contributes significantly by establishing a theoretical framework that integrates the concepts of opportunity costs and biform game theory, offering new insights into the strategic management of carbon emissions within supply chains.

Global business practices show that during periods of demand volatility, manufacturing firms often engage in asymmetric cost management (ACM), a behavior in which costs increase faster than they decrease when demand decreases by the same amount. However, whether managing asymmetric costs will enhance or impede firm resilience remains an open question. We aim to investigate the impact of ACM on firm resilience and its boundary conditions.

Using unbalanced panel data of 2,273 Chinese manufacturing listed companies from 2002 to 2021, we conduct an empirical analysis using a double fixed effects model.

Our findings reveal that ACM has a negative effect on firm resilience. This suggests that in coping with external environmental fluctuations, ACM fails to fulfill its expected role effectively. Instead, it manifests as a severe agency problem affecting firm resilience. Further, we find that managerial myopia and digitalization diminish the negative effect, while customer instability exacerbates it.

This study contributes to the literature on the organizational resilience of manufacturing firms by providing an in-depth understanding of cost management and emphasizing the need to consider agency issues carefully when managing asymmetric costs.

Purpose: In this study, monolith analysis methods, microservice identification, and decomposition methods proposed for the transition to microservice architectures that enable the development of appropriate solutions by adapting to the complex demands that will shape the technological infrastructure of the future are evaluated.

Need for the study: Decomposition from monolithic architectures to microservices has become a popular approach in organizations and companies with Industry 5.0. This transformation of Industry 5.0 enables businesses to gain a competitive advantage and can provide a quick solution to personalized problems such as personal service systems.

Methodology: The study, decomposition from monolith to microservice, initially includes monolith analysis, followed by microservice decomposition review. Various classification methods have been proposed for microservice identification and decomposition and are aligned with Industry 5.0 principles, focusing on artificial intelligence (AI)-based approaches, especially human-centered AI.

Findings: Four analysis methods (domain, static, dynamic, and version) are identified for monolith analysis, with static and dynamic being the most common. Version analysis is not typically used alone. In the decomposition phase, clustering-based methods are prevalent due to the uncertain dimensions of microservices. Rule-based and unsupervised methods are identified for decomposition, with AI algorithms like affinity propagation, Kmeans clustering, hierarchical clustering, Hungarian algorithm, genetic algorithm, latent Dirichlet allocation (LDA), and minimum spanning tree (MST) being employed.

Practical implications: Microservice architecture enables flexibility, scalability, and resilience compared to monolithic structures. Decomposing large-scale monolith projects into microservices is challenging, requiring selection of appropriate monolith analysis methods based on project details (e.g., domain analysis for detailed Unified Modelling Language (UML) diagrams) before proceeding with decomposition. This transformation improves deployment, maintenance, fault isolation, and scalability, while allowing for diverse service-specific databases and programming languages.

This paper aims to empirically examine the effects of waste management (WM) practices on financial distress (FD) in a heavily regulated environmental context and investigates the moderating role of green initiatives (GINVs) on the WM−FD relationship.

This study uses a sample of 1,667 firm years of UK-based companies from 2002 to 2021 and applies a panel regression analysis controlling for industry- and year-fixed effects. Data on WM, GINVs and governance are sourced from LSEG (formerly known as Refinitiv Asset4 ESG), whereas financial data are collected from WorldScope. The study also adopts alternative measures for FD and WM practices and uses a two-stage least squares analysis and the Heckman selection model as robustness checks.

The findings reveal that FD levels decrease significantly when waste generation declines and waste recycling increases, suggesting that firms with better WM practices have lower FD levels. The results further show the moderating effect of GINVs on the link between waste generation and FD and suggest that increased GINVs are effective in reducing FD by mitigating waste levels. However, waste recycling and GINVs are found to have a substitutive effect on FD. The findings remain robust to the use of alternative measures and endogeneity issues.

This work is among the first to investigate the WM-FD nexus and highlights the importance of effective WM practices in improving the financial sustainability of UK firms. The study also extends prior research by testing the moderating impact of GINVs and suggests that firms need to carefully balance their GINVs with waste recycling efforts to achieve optimal financial sustainability in a heavily regulated environmental context, such as the UK.

This paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication resources.

The authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication constraints.

The proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization accuracy.

This method requires the low communication resources, has a high control accuracy and is thus suitable for engineering applications.

A novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.

Despite the growing emphasis on sustainability and the need to manage environmental, social, and governance (ESG) risks, the direct relationship between enterprise risk management (ERM) and green growth (GG) has not been investigated. This study seeks to fill this gap by examining the effect of ERM on the GG of oil and gas (O&G) companies in Malaysia.

The study used panel data regression models to analyze panel data from 2012 to 2021. For computing GG, we adapted the Organization for Economic Cooperation and Development’s (OECD) GG framework. ERM is computed using COSO and WBCSD guidelines for ESG-related risks. Weighted content analysis is used to measure ERM and GG

The findings derived from the content and descriptive statistics analyses indicate a consistent and ongoing rise in the adoption of ERM practices over time. However, some companies are still in the initial stages of incorporating ERM to address ESG risks. The study’s findings unequivocally establish a substantial and positive relationship between ERM and GG. ERM drives GG by significantly influencing its environmental and resource productivity dimensions. The study further reveals that the impact of ERM on economic opportunities and policy responses, as well as the natural asset base, is statistically significant, albeit with relatively lower coefficient values.

To enhance the legitimacy of organizations and foster positive stakeholder relationships, regulators, governments, and policymakers should actively promote the adoption of ERM standards that specifically address ESG risks, as outlined by COSO and WBCSD. This strategic alignment with risk management practices will ultimately contribute to improving green growth for organizations.

To the best of the authors' knowledge, this is the first study examining ERM’s effect on GG. The study adds to the existing literature by focusing on ERM’s role in a company’s GG. It clarifies ERM’s significant effect on diminishing emerging ESG risks and advancing GG

Despite the increasing interest in the role of supply chain concentration (SCC) in improving performance, its influence on firms' sustainability performance remains unexplored, as do the underlying mechanisms of this relationship. Drawing on resource dependence theory, the authors investigate the relationship between SCC and manufacturing firms' sustainability performance and the moderating roles of operational slack and information transparency.

The authors use secondary data from 3,581 manufacturing firms listed on the Shanghai and Shenzhen A-share stock markets from 2006 to 2020 to conduct an empirical analysis using panel data regression models.

Manufacturing firms' SCC is negatively related to sustainability performance until it reaches a certain point, where SCC positively affects sustainability performance, presenting a U-shaped relationship. In addition, operational slack represented by a quick ratio moderates the relationship between SCC and sustainability performance by flattening the curve. Operational slack represented by receivable turnover ratio moderates the relationship between SCC and sustainability performance by steepening the curve and shifting the turning point left. Information transparency strengthens the effect of SCC on the sustainability performance by steepening the curve.

This investigation provides a comprehensive view of the SCC– sustainability performance relationship.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.