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Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

Carbon emissions trading is an effective instrument in reducing greenhouse gas emissions. There is a notable scarcity of comprehensive reviews on the modelling techniques within carbon trading research in construction.

This paper reviews 68 relevant articles published in 19 peer-reviewed journals using systematic search. Scientometric analysis and content analysis are undertaken.

Generally, China was the largest contributor to carbon trading research using quantitative models (representing 36% of the total articles). From the results, the modelling techniques identified were multi-objective grasshopper optimisation algorithm; system dynamics; interpretive structural modelling; multi-agent-based model; decision-support model; multi-objective chaotic sine cosine algorithm; optimised backpropagation neural network; sequential panel selection method; Granger causality test; and impulse response analysis. Moreover, the advantages and disadvantages of these techniques were identified. System dynamics was recommended as the most suitable modelling technique for carbon trading in construction.

This study is significant, and through this review paper, practitioners can easily be more familiar with the significant modelling techniques, and this will motivate them to better understand their uses.

This conceptual paper aims to develop a theoretical framework for carbon trading in the built environment through theories to expand current knowledge on components of carbon trading systems.

This theoretical framework was developed and supported with existing theories and past empirical literature from built environment, economics and finance. Underlying theories used in the framework were selected due to their significance and applicability to carbon trading projects. Hypotheses set in the study summarise the propositions developed from the theories and past empirical literature.

The framework reveals four major components of carbon trading for the built environment. Six hypotheses were further proposed to unravel the resultant influence of their interactions on each component in the trading system.

This paper sought to undertake a theoretical review of classical theories and past studies on carbon trading. Even though a systematic review was undertaken, the constructs in the theoretical framework may not be exhaustive.

This study contributes and advances the body of knowledge on the components that comprise the mechanism of how carbon trading operates in the built environment. Theoretically, the framework developed serves as a multi-dimensional guide on the operations of carbon trading in the built environment.

The theoretical framework developed endeavours to consolidate multi-faceted theories from varying disciplines on the components that comprise carbon trading in the built environment.

The research objective is to establish a smart system for building operation and maintenance using self-organizing map-based cluster merging (SOMCM) algorithm.

The process begins with a thorough literature review to establish the interface framework, followed by its design. An empirical study in Taoyuan City’s industrial park, involving 46 buildings and 3,526 maintenance records, informed development. By integrating the “Shared Facility Management System Equipment Repair Module” and the “Maintenance Management System for Existing Facilities,” 21 enhanced interface components were created. All work orders are stored in a database for aggregation, statistical analysis and clustering using the algorithm SOMCM, aiding repair decision-making.

The outcomes stemming from the proposed methodology culminate in the identification of seven patterns that can significantly enhance the efficiency of maintenance operations: (1) simplify current self-repair to outsourcing; (2) modify the current traditional contract type to open contract type; (3) adopt massive procurement for major facilities (e.g. air conditioning); (4) schedule power supply systems in a systematic and efficient way; (5) establish maintenance patterns as suggested to eliminate warehouse for spares; (6) reallocate maintenance resources in a seasonal cycle; (7) set up a standby team to resolve emergency repairs. The findings can reduce a significant amount of time and cost for the investigated industrial park.

Maintenance work has faced delays, aging equipment has caused component damage, and park structures no longer meet operational needs. Addressing these challenges, the study introduces a novel SOMCM approach for smart building operation and maintenance. This approach emphasizes creating a user-friendly, practical system pivotal to platform success. By integrating demand-driven strategies, it enhances traditional maintenance processes and offers innovative solutions to operational and management issues, ensuring alignment with modern requirements and improved efficiency.

The post-pandemic era has shifted most industries, businesses and consumers online, increasing the demand for electronic devices, mainly laptops. Additionally, most non-Western countries inhabit highly religious but cash-strapped individuals, making them a potential market for second-hand laptops. With this in mind, this study aims to explore the effects of lenient return policy (LRP) and religiosity on consumer confidence in retailer (CCR), consumer well-being and purchase intention.

This paper conducted a between-subjects field experiment comparing two return policy conditions (cash return vs. other return) with a sample of 222 participants. Data were analysed using partial least squares structural equation modelling (PLS-SEM) to test the hypothesised relationships, and multigroup analysis (MGA) was employed to assess the experimental effects based on the return policy conditions. The moderating effects of religiosity were also examined. All analyses were conducted using SmartPLS software.

The results confirm that an LRP positively predicts consumer confidence in retailer, well-being and purchase intention. Religiosity had a moderating effect on LRP outcomes. Additionally, the experiment confirmed that consumers experienced better well-being and were more likely to purchase if offered full cashback.

Retailers of second-hand shopping products should offer LRP (full cashback) to foster consumer confidence, well-being and purchase intention. Additionally, for highly religious consumers, aligning return policies with religious principles should further enhance consumer well-being and purchase intention.

This study is among the earliest to investigate the impact of LRP on CCR and well-being. Moreover, a novel attempt is made to explore the moderating effects of religiosity on LRP outcomes. Likewise, a field experiment to validate the greater effects of cashback on consumer well-being and purchase intention adds to the novelty of this study.

This study aims to analyse the relationship between a patent’s network position in a knowledge search network and the likelihood and speed of patent transactions. Additionally, it explores whether patent scope moderates these relationships.

In this empirical study, the authors collected a sample of patents in the artificial intelligence industry over the period of 1985–2018. Then, the authors examined the direct roles of degree centrality, betweenness centrality and closeness centrality on the likelihood and speed of patent transactions and the moderating role of patent scope in the knowledge search network using the logit and accelerated failure time models.

The findings reveal that degree centrality positively affects both the likelihood and speed of patent transactions, while betweenness centrality enhances the likelihood, and closeness centrality significantly boosts both. However, regarding the speed of patent transactions, closeness centrality is the most impactful, followed by degree centrality, with no significant influence of betweenness centrality. Additionally, the patent scope moderates how betweenness centrality affects the likelihood of transactions.

This study has limitations owing to its exclusive use of data from the Chinese Intellectual Property Office, lack of visibility of the confidential terms of most patent transactions, omission of transaction directionality and focus on a single industry, potentially restricting the breadth and applicability of the findings. In the future, expanding the data set and industries and combining qualitative research methods may be considered to further explore the content of this study.

This study has practical implications for developing a better understanding of how network structure in the knowledge search network affects the likelihood and speed of patent transactions as well as the identification of high-value patents. These findings suggest future directions for patent holders and policymakers to manage and optimise patent portfolios.

This study expands the application boundaries of social network theory and the knowledge-based view by conducting an in-depth analysis of how the position characteristics of patents within the knowledge search network influence their potential and speed of transactions in the technology market. Moreover, it provides a theoretical reference for evaluating patent value and identifying high-quality patents by quantifying network positions. Furthermore, the authors construct three centrality measures and explore the development of patent transactions, particularly within the context of the developing country.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

Experiential learning is considered to be a crucial factor in students' perceived learning outcomes. This study aims to explore the development of a mechanism in hospitality education and to analyze the learning outcomes (i.e. perceived quality, perceived value and learning satisfaction) using experiential learning (i.e. previous learning experience and personalized learning environment).

The course design consisted of two activities (instructing and learning activities) and three phases (before, during and after class) using the Moodle version 3.5 online platform as the educational and training site to sustain e-learning archives and activities. A longitudinal survey using a sample of 207 hospitality students in blended e-learning environment indicates positive relationships among the previously mentioned factors.

Results indicated that both previous learning experience and personalized learning environment have positive direct effects on perceived quality and perceived value, while both perceived quality and perceived value have positive direct effects on learning satisfaction. Furthermore, both perceived quality and perceived value mediate the relationship between previous learning experience and learning satisfaction, as well as the relationship between personalized learning environment and learning satisfaction.

These findings sustain the value of experiential learning, particularly the curriculum, student and faculty development in sustainable development education for hospitality.

While the phenomenon of technostress has received significant attention from researchers in recent years, empirical findings concerning the consequences of specific forms of techno-stressors have remained scattered and contradictory. The authors aim to integrate the conclusions of previous studies to understand the effects of specific techno-stressors on strain and job performance.

This study employs meta-analytic techniques to calibrate the findings of 67 studies investigating more than 63,100 employees.

In general, not all techno-stressors have adverse effects. In particular, techno-uncertainty does not impact job performance. In addition, relative weight analyses reveal the relative importance of techno-complexity and techno-insecurity as predictors of both strain and job performance. Finally, this study finds that the effects of specific techno-stressors on job performance vary depending on research participants' gender, educational attainment and employment status.

First, this study provides a more nuanced view of the effects of specific techno-stressors. Second, this research clarifies the relative importance of specific techno-stressors as predictors of strain and job performance. Finally, this study reveals the moderating effects of demographic variables on the relationships between specific techno-stressors and job performance.

The study aims to investigate the mediating impact of supplier quality integration on the operational performance of the pharmaceutical supply chain (PSCs) by comparing mature and evolving PSCs.

The study adopted a quantitative method where data were gathered through a survey instrument to identify the differentiators of dynamic capabilities and establish the extent of quality integration in PSCs. Thus, 310 questionnaires were collected from mature and evolving PSCs, where the PROCESS technique was used to analyse the data.

The results demonstrate the significant paths that enable companies to create, extend and modify the resources to develop their dynamic capabilities. The results reveal significant differences in internal and supplier quality implementation and their impact on operational performance between mature and evolving PSCs.

To the best of our knowledge, this is the first study to examine dynamic capabilities aspects of the pharmaceutical supply chain quality integration in mature and evolving PSCs, which extends the body of knowledge and makes a practical contribution.