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Green technology, characterized by its environmentally friendly attributes and sustainable practices, has emerged as a crucial tool in harmonizing the economic and ecological benefits. However, the challenge lies in selecting the most effective strategies for acquiring green technology. This paper aims to explore how chemical enterprises choose green technology acquisition strategies across diverse scenarios.

Considering the influence of competition effects, spillover effects and their interactions on selecting green technology acquisition strategies, this paper develops three decision models (independent R&D, cooperative R&D and technology introduction). Drawing on the duopoly game theory as its theoretical framework, this paper delves into the examination of the economic and environmental benefits within distinct scenarios.

Cooperative R&D excels in promoting green technology R&D when spillover effects are strong, while independent R&D demonstrates superiority when spillover effects are weak. The threshold for the strength of spillover effects is related to competition effects. Additionally, cooperative R&D typically yields greater financial advantages than independent R&D and technology introduction. Moreover, the economic and environmental benefits may not be optimized simultaneously. Only enterprises that satisfy low competition and spillover effects as well as high competition and spillover effects, can achieve win-win economic and environmental benefits.

Although green technology R&D and introduction are alternative strategies, they have typically been considered separately in prior literature. This study attempts to incorporate green technology R&D and introduction into a strategic system to investigate the selection of green technology acquisition strategies, taking into account competition effects, spillover effects and their interactions.

Drawing upon social comparison theory, this paper explores the (in)congruence effect of leader and follower overqualification on leader's downward envy, which in turn leads to leader undermining behavior.

Using two-wave, multi-sourced data gathered in China, a polynomial regression was conducted on 301 leader-follower dyads.

Results show asymmetrical incongruence effects, indicating greater leader's downward envy when leader overqualification was lower than follower overqualification. In addition, by increasing downward envy, leader-follower (in)congruence in overqualification has an indirect positive effect on leader undermining behavior.

This study highlights the importance of being conscious of both parties’ overqualification levels to avoid unfavorable outcomes. Meanwhile, training for both parties is crucial, offering a holistic understanding of leader-follower overqualification differences and downward envy, as well as skills to manage “triggers” of leader undermining.

Our study is among the first to examine the effects of overqualification from a leader-follower dyadic congruence perspective. It suggests that leader's downward envy and undermining behaviors toward followers are influenced by both follower and leader overqualification.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

In the context of Industry 4.0, intelligent construction technologies (ICT) represented by information technology and networking will undoubtedly provide new impetus to the development of the prefabricated building supply chain (PBSC), but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies. Therefore, this paper aims to reveal the interactions between stakeholders and clarify the critical risk nodes and interactions in information sharing of PBSC (IS-PBSC), and propose targeted risk mitigation strategies.

Firstly, this paper creatively delineates the risks and critical stakeholders of IS-PBSC. Secondly, Data is collected through questionnaires to understand the degree of risks impact. Thirdly, with the help of NetMiner 4 software, social network analysis is conducted and IS-PBSC risk network is established to reveal critical risk nodes and interactions. Finally, further targeted discussion of critical risk nodes, the effectiveness and reasonableness of the risk mitigation strategies are proposed and verified through NetMiner 4 software simulation.

The results show that the critical risks cover the entire process of information sharing, with the lack of information management norms and other information assurance-related risks accounting for the largest proportion. In addition, the government dominates in risk control, followed by other stakeholders. The implementation of risk mitigation strategies is effective, with the overall network density reduced by 41.15% and network cohesion reduced by 24%.

In the context of Industry 4.0, ICT represented by information technology and networking will undoubtedly provide new impetus to the development of the PBSC, but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies.

Based on the results of risk network visualization analysis, this paper proposes an ICT-based IS-PBSC mechanism that promotes the development of the integration of ICT and PBSC while safeguarding the benefits of various stakeholders.

Prior studies have shown various mediating and moderating mechanisms regarding the effect of employees' perceived overqualification on intention to leave (ITL). Nonetheless, only a few empirical studies have shed light on the negative underlying processes that explain this relationship. Furthermore, less is known about the role of high-performance work systems (HPWSs) in the overqualification literature. Drawing upon relative deprivation theory (RDT), this research attempts to fill these gaps by examining the mediating role of work-related boredom and the moderating role of perceived HPWSs in the association between perceived overqualification and ITL.

Data from a sample of 188 employees working in a Greek manufacturing company were analyzed using the PROCESS macros for SPSS.

The results indicated that work-related boredom mediates the association between perceived overqualification and ITL. Moreover, HPWSs attenuated the relationship of perceived overqualification with both work-related boredom and ITL, such that their association was positive only when employees' perceptions of HPWSs were low.

This study adds to the existing literature regarding why and how perceived overqualification affects ITL. Even more, this is one of the first studies that examine the role of HPWSs in the literature of overqualification. Theoretical and practical implications were also considered.

Though the circular economy (CE) is a current buzzword, this still lacks a precise definition. In the absence of a clear notion of what that term includes, actions taken by the government and companies may not be well informed. In particular, those actions need to consider what people mean when people talk about the CE, either to refocus people's decisions or to undertake a more effective communications strategy.

Since people voice people's opinions mainly through social media nowadays, special attention has to be paid to discussions on those media. In this paper, the authors focus on Twitter as a popular social platform to deliver one's thoughts quickly and fast. The authors' research aim is to get the perceptions of people about the CE. After collecting more than 100,000 tweets over 16 weeks, the authors analyse those tweets to understand the public discussion about the CE. The authors conduct a frequency analysis of the most recurring words, including the words' association with other words in the same context and categorise them into a set of topics.

The authors show that the discussion focuses on the usage of resources and materials that heavily endanger sustainability, i.e. carbon and plastic and the harmful habit of wasting. On the other hand, the two most common good practices associated with the CE and sustainability emerge as recycling and reuse (the latter being mentioned far less). Also, the business side of the CE appears to be relevant.

The outcome of this analysis can drive suitable communication strategies by which companies and governments interested in the development of the CE can understand what is actually discussed on social media and call for the attention.

This paper addresses the lack of a standard definition the authors highlighted in the Introduction. The results confirm that people understand CE by looking both at CE's constituent activities and CE's expected consequences, namely the reduction of waste, the transition to a green economy free of plastic and other pollutants and the improvement of the world climate.

The purpose of this paper is to develop a model considering synergy effect for prefabricated construction service combination selection.

This research defines prefabricated construction service as a service-led construction method that meets the specific requirements of clients. Based on network theory, the multi-dimensional collaborative relationships of the prefabricated construction inter-services are formulated. The synergy effect is quantitatively calculated through the linear weighting of the strengths of collaborative relationships. Further, a weighted synergy network (WSN) is developed, from which a service composition selection model considering the synergy effect is established. Then, a genetic algorithm is employed to implement the model.

The results showed that (1) when the number of prefabricated construction services is increased, the synergy effect of combination options is enhanced; (2) The finer-grained prefabricated construction services, the stronger the synergy effect of service combination; (3) Clients have heterogeneous preferences for collaborative relationships, and there are differences in the synergy effect of service combination.

The contribution of this research includes proposed a method to quantify the synergy effect from the perspective of collaborative relationships, explored the specific procedure for the prefabricated construction service combination selection under the service-led construction, and provided a reference for promoting the development in construction. Besides, the model proposed could be applied to prefabricated construction service composition selection with diverse research boundaries or client preferences by executing the same procedure.

Tracking physical resources at the construction site can generate information to support effective decision-making and building production control. However, the methods for conventional tracking usually offer low reliability. This study aims to propose the integrated Smart Twins 4.0 to track and manage metallic formworks used in cast-in-place concrete wall systems using internet of things (IoT) (operationalized by radio frequency identification [RFID]) and building information modeling (BIM), focusing on increasing quality and productivity.

Design science research is the research approach, including an exploratory study to map the constructive system, the integrated system development, an on-site pilot implementation in a residential project and a performance evaluation based on acquired data and the perception of the project’s production team.

In all rounds of requests, Smart Twins 4.0 registered and presented the status from the formworks and the work progress of buildings in complete correspondence with the physical progress providing information to support decision-making during operation. Moreover, analyses of the system infrastructure and implementation details can drive researchers regarding future IoT and BIM implementation in real construction sites.

The primary contribution is the system proposal, centralized into a mobile app that contains a Web-based virtual model to receive data in real time during construction phases and solve a real problem. The paper describes Smart Twins 4.0 development and its requirements for tracking physical resources considering theoretical and practical previous research regarding RFID, IoT and BIM.

The development of prefabrication into full-scale offsite manufacturing processes in the construction industry is paradigm-shifting. Moreover, Building Information Modelling (BIM) is becoming the primary mode of communication and integration in construction projects to facilitate the flow of information. Although research has been performed on BIM and Offsite Construction (OSC), integrating these two concepts remains ambiguous and complex and lacks documentation and structure, especially in New Zealand. Therefore, this paper develops a robust framework for OSC and BIM integration. The study focusses on identifying integration challenges and proposes strategies for overcoming these challenges.

This study applied scientometric analysis, a systematic literature review (SLR) and semi-structured expert interviews to investigate OSC and BIM integration challenges. Multiple themes were investigated and triangulation conducted in this research supports the creation of applicable knowledge in this field.

Multiple gaps, research trends and the pioneer countries in the paper's scope have been identified through scientometric analysis. Then, a classified cluster of challenges for OSC and BIM implementation and integration strategies of OSC and BIM were demonstrated from the findings. The interviews provided comprehensive and complementary data sets and analyses. The findings from the Systematic Literature Review and interview structured the integration framework.

The contribution of this paper to existing knowledge is a developed framework that serves as a guideline for the OSC stakeholders. This framework can assess OSC's alignment with BIM and consolidate strategies for incorporating OSC into a BIM-based project delivery process. The framework consists of 23 strategies categorised into 8 clusters: a policy document, training and professional development, documentation, technology management, governmental development, contract development, accurate definition and detailing and communication. The proposed strategies will streamline integration by reducing potential challenges, thus enhancing project productivity.

The widespread use of washing machines in textile dyeing and finishing boosts product quality while leading to significant wastewater production. This wastewater poses environmental risks due to the textile industry's high pollution levels and water consumption. Sustainability hinges on minimizing water usage and treating wastewater for reuse. This study employs Matlab R2020a and Python 2023 to model experimental designs for treating textile production wastewater using the Fenton oxidation method, aiming to address sustainability concerns in the industry.

The Fenton oxidation process's efficacy and optimal operating conditions were determined through experimental sets employing the Box–Behnken design. Assessing machine learning algorithms on the data, Matlab R2020a utilized an artificial neural network (ANN), while Python 2023 employed support vector regression (SVR), decision trees (DT), and random forest (RF) models. Evaluation of model performance relied on regression coefficient (R2) and mean square error (MSE) outcomes. This methodology aimed to refine the Fenton oxidation process and identify the most efficient parameters, leveraging a combination of experimental design and advanced computational techniques across different programming platforms.

The study identified optimal conditions: pH 3, Fe+2 concentration of 0.75 g/L, and H2O2 concentration of 5 mM, yielding 87% COD removal. The Box–Behnken design achieved a high R2 of 0.9372, indicating precise predictions. Artificial neural networks (ANN) and support vector regression (SVR) exhibited successful applications, notably achieving an R2 of 0.99936 and low MSE of 0.00416 in the ANN (LOGSIG) model. However, decision trees (DT) and random forests (RF) proved less effective with limited datasets. The findings underscore technology integration in treatment modeling and the environmental imperative of wastewater purification and reuse.

This study, in which water use and wastewater treatment are evaluated with technological integration such as machine learning and data management, reveals how to contribute to targets 6, 9, 12, and 14 within the scope of UNEP 2030 sustainable development goals.