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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.

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.

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.

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.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

The purpose of this study was to investigate the impact of artificial intelligence (AI) adoption on knowledge workers' innovative work behaviors (IWB), as well as the mediating role of stress appraisal and the moderating role of individual learning abilities.

This study analyzed the questionnaire results of 313 knowledge workers, and data analysis was conducted by using SPSS 25.0, SPSS 25.0 macro-PROCESS and AMOS 28.0.

This study found that AI adoption has a double-edged sword effect on knowledge workers' IWB. Specifically, AI adoption can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal. Moreover, individual learning ability significantly moderated the relationship between AI adoption and stress appraisal, which further influenced IWB.

This study integrates the conflicting findings of previous studies and proposes a comprehensive theoretical model based on the theory of cognitive appraisal of stress. This study enriches the research on AI in the field of knowledge management, especially extending the understanding of the relationship between AI adoption and knowledge workers’ IWB by unraveling the psychological mechanisms and behavior outcomes of users' technology usage. Additionally, we provide new insights and suggestions for organizations to seek the cooperation and support of employees in introducing new technologies or driving intelligent transformation.

With the wide usage of digital technologies, employees’ digital creativity serves as a stepping stone in driving the process of organizational digital innovation. However, scant attention has been devoted to understanding the relationship between leadership and employees’ digital creativity within the digital technology usage context. Drawing upon social cognitive theory, our study aims to explore the relationship between transformational leadership and employees’ digital creativity through the mediating roles of creative self-efficacy and ambidextrous learning.

A field survey was conducted in China, garnering survey data from 223 employees actively engaged with digital technologies in their daily work. We empirically test the structural equation model to verify the hypotheses.

The results reveal a positive association between transformational leadership and employees’ digital creativity, with a consequential cascade mediation facilitated through creative self-efficacy and exploitation and exploration.

The empirical research not only enriches comprehension of individual-level digital creativity but also provides valuable practical insights for managers seeking to effectively drive digital innovation within their organizations.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.