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The purpose of the study is to examine the impact of augmented reality (AR) on users' tour experience and psychological well-being in museums. The study utilizes the Information System (IS) Success Model and the Perceived Value Theory to investigate the relationships between AR quality, content quality, system quality, vividness, utilitarian value, hedonic value, tour experience, and psychological well-being.

The study adopts a quantitative research approach and surveys 286 visitors at the Wuhan Natural History Museum. The collected data is analyzed using PLS-SEM to test the proposed model.

The study finds that content quality, system quality, and vividness significantly influence utilitarian and hedonic value. Utilitarian and hedonic values mediate the relationship between AR quality and tour experience and psychological well-being, with hedonic value having a greater impact. Utilitarian value positively influences customers' hedonic value, while tour experience positively influences customers' psychological well-being.

This study contributes to the existing literature by applying the Information System Success Model and Perceived Value Theory to explore the impact of AR on users' tour experience and psychological well-being in museums. It provides empirical evidence supporting the importance of content quality and perceived value in promoting AR adoption and use. The findings add value to the field of augmented reality research and offer practical implications for AR service providers.

The present work highlights the outstanding properties of Cannabis sativa that can be harnessed for various utilitarian functions and its climate friendly properties.

In this paper, the authors reviewed current research on all possible utilities from household work to manufacturing of various products that are environmentally sustainable. The authors have presented some of their research on this materials and also exploration of hemp as an archaeological material based on the findings from wall paintings of Ellora caves.

There are references of hemp use in mixing with earthen/lime plaster of western Indian monuments. Around 1,500 years of Ellora’s earthen plaster, despite harsh climatic conditions, survived due to the presence of hemp in the plaster that adds durability, fibrosity and its capacity to ward off insects and control humidity. Furthermore, the outstanding quality of Cannabis as carbon sequestrant was harnessed by Indians of ancient times in Ellora mural paintings.

This work discusses some relevant literature on the potential use of hempcrete aligned with Agenda 2030 of sustainable development goals.

There are several research going on in producing sustainable materials using hemp that have the least environmental impact and can provide eco-friendly solutions.

The authors impress upon the readers about multifarious utility of the hemp and advices for exploration of this material to address many environmental issues.

This paper presents both review of the existing papers and some components coming directly from their laboratory investigations.

This study aims to assess the competency gap among construction industry professionals concerning the competencies required for identifying construction activities that produce recyclable materials. Accordingly, the study objectives are threefold: to identify key competencies, analyze the disparity between the level of importance and level of competence for each competency, and quantify the criticality of the competency gaps.

A systematic literature review was conducted to identify and categorize 20 competencies into knowledge, skills, and abilities. The competencies formed the basis for a questionnaire survey distributed to construction industry professionals. 120 valid responses were collected and analyzed using mean and normalized values, Wilcoxon signed-ranks test, and competency gap analysis.

The findings highlight eight key competencies: ability to prepare sustainable designs, skills in trading construction waste materials, skills in lean construction techniques, skills in designing out waste in construction activities, knowledge of diagnosing construction waste generation, knowledge of construction-related waste design, knowledge of the practice of sustainable facilities management, and knowledge on the sustainable characteristics of construction activities. Despite their importance, critical competency gaps exist among these key competencies and must be addressed.

This study equips decision-makers with insights to manage natural resources efficiently, offers practitioners a list of key competencies for sustainable project management, and provides researchers with a solid foundation for further investigations to advance sustainability in construction.

Construction activities generate overwhelming waste that is typically disposed of in landfills, which has significant environmental consequences and hinders national progress. However, with the appropriate competencies, there is an opportunity to identify construction activities that produce recyclable materials, offering a path to a sustainable future. This study aims to assess the competencies for identifying construction activities that produce recyclable materials. To attain that aim, the study seeks to identify the key competencies and assess the index level of the competencies.

A systematic literature review was conducted, and 20 competencies were identified and categorized into knowledge, skills, and abilities. A questionnaire survey was developed based on the competencies and completed by 101 individuals. The collected data were analyzed using normalized mean analysis, confirmatory factor analysis, and fuzzy synthetic evaluation (FSE).

The results revealed that the key competencies are problem-solving skills, communication skills, skills in providing vocational training, and knowledge of the environmental impacts of construction activities. The FSE ranks the constructs in order of skills, knowledge, and abilities. Also, the FSE illustrated that the overall index level is inclined to be important.

This study leads to saving natural resources, using raw materials efficiently, protecting from environmental pollution, and mitigating resource depletion by providing the index level of the competencies.

The findings can guide professionals in effective waste management, policymakers in creating new policies and regulations, and researchers in compiling a list of competencies for identifying construction activities that produce recyclable materials.

The present research aims to investigate how lean project management (LPM) and agile project management (APM) affect project performance outcomes in the construction sector in Jordan. This study focuses on six key project performance outcomes, namely cost, time, quality, client satisfaction, innovation and responsiveness.

The present study employed a quantitative approach to achieve the research objectives. Accordingly, a multi-item survey questionnaire was prepared to gather data from 392 project managers from construction companies in Jordan. The study’s model showed acceptable levels regarding reliability, validity, fit indices and discriminant validity. In order to test the hypotheses of this study, path analysis was employed using Amos 24.0 software.

LPM demonstrated a remarkably high positive impact on cost performance. It also positively affected quality performance and client satisfaction. However, LPM insignificantly affected time, innovation and responsiveness performance measures. On the other hand, APM showed a notably high positive impact on innovation and responsiveness. The findings also revealed that APM positively impacted quality performance and client satisfaction. In addition, APM negatively impacted cost performance and insignificantly impacted time performance.

This study is one of the first comprehensive studies to empirically examine the impact of both LPM and APM on various project performance outcomes in the construction industry in the context of a developing country. It reveals some similarities and differences between LPM and APM with regard to their impacts on project management outcomes. The findings are expected to guide managers in selecting the appropriate project management approach based on the desired performance outcomes. Accordingly, it offers important implications for project managers in construction companies.

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

In public firms, the largest shareholder can make decisions on cash dividends in favor of its own interests at the expense of other investors. While the second largest shareholder can actively participate in corporate governance and protect the interests of investors, its impact has not been fully understood. This research investigates how shareholding ratio and ownership type of the second largest shareholder moderate the relationship between controlling shareholder's shareholding ratio and cash dividends.

The authors conducted econometrics analysis based on a panel data of China's A-share listed companies from 2007 to 2017.

The authors find that the controlling shareholder's shareholding ratio has a significant negative impact on cash dividends. However, this influence is conditional on the shareholding ratio of the second largest shareholder. The negative impact is weakened when the second largest shareholder holds a large proportion of shares or when the shareholding gap between the second largest and the controlling shareholder is small.

This research extends the existing literature by highlighting the nuanced moderating effect of the second largest shareholder on the relationship between the controlling shareholder and cash dividends, thus making a unique contribution to the understanding of corporate governances in the emerging financial market in China.

Supervisory abuse is a form of destructive leadership. Research has continued to document many deleterious consequences associated with such improper behavior at work. This study further extends its consequences by testing a model linking abusive supervision to job apathy through a climate of fear.

This study drew on affective events theory in investigating the role of the climate of fear in instances where casual workers perceive abuse in commercial banks. A three-wave and three-week time lag approach was adopted for data collection from 245 casual workers in southeastern Nigeria.

Results from partial least squares structural equation modeling supported the model by showing that abusive supervision had a direct positive relationship with the climate of fear and job apathy, while the climate of fear related positively to job apathy. Results also revealed that the climate of fear partially mediated the relationship between abusive supervision and job apathy.

Managers can be trained to become more supportive and less abusive to address the problem of abusive supervision. Furthermore, casual workers are encouraged to report any abuse from their manager to higher authorities inside or outside their workplace.

This study sheds new insights and advances the abusive supervision literature by investigating the climate of fear as the underlying mechanism.

The elevator installation in old communities (EIOC) can effectively improve the public infrastructure of urban communities. However, differences in the decision-making behaviours of stakeholders lead to frequent conflicts, thereby hindering the implementation of EIOC. The purpose of this study is to explore the decision-making behavior of core stakeholders which are the government, community owners and elevator enterprises at different stages in the EIOC using the evolutionary game method.

A tripartite evolutionary game model involving the government, community owners and elevator enterprises was developed, and their evolutionary stabilisation strategies were explored in different stages. The dynamic change of the stakeholders' decision-making behaviours at different stages of the project and the influencing mechanism of the key factors on the decision-making behaviours of the three stakeholders were analysed through numerical simulation.

The results of this study showed that: Divergent interests led the government, community owners and elevator enterprises to adopt distinct decision-making behaviours at different stages, resulting in diverse attitudes and actions among stakeholders. A dynamic reward and penalty mechanism effectively motivated community owners and elevator enterprises to engage actively, fostering broad participation. However, the high regulatory cost diminished the government's regulatory effectiveness. This imbalance between penalties and incentives posed a challenge, impacting the overall effectiveness and efficiency of implementing the EIOC.

Existing research lacks exploration of the decision-making behaviours of stakeholders in community public infrastructure. This study developed a dynamic tripartite evolutionary game model in the EIOC from the gaming perspective. The results of this study provide a reference for dealing with the stakeholders' interests in the community public infrastructure and contribute to the theoretical basis for establishing an effective supervision mechanism.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.