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This study aims to explore and conceptualize metaverse adoption using a systematic literature review (SLR). It also aims to propose a conceptual model that identifies significant factors affecting metaverse adoption in the entertainment, education, tourism and health sectors.

A SLR was conducted using the “preferred reporting items for systematic reviews and meta-analyses” report protocol and the “theory, context, characteristics, methods” framework to include all relevant articles published up to March 2023, which were sourced from the Scopus and Web of Science databases.

The reviewed literature revealed that the countries with the highest publications in the field of metaverse were China and the USA. It was also found that the technology acceptance model was the most used theoretical framework. Survey-based research using purposive and convenience sampling techniques emerged as the predominant method for data collection, and partial least square-structural equation modeling was the most used analytical technique. The review also identified the top six journals and the variables that help to develop a proposed model.

This review presents a novel contribution to the literature on metaverse adoption by forming a conceptual model that incorporates the most used variables in the entertainment, education, tourism and health sectors. The possible directions for future research with identified research gaps were also discussed.

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

The purpose of this paper is to analyse the current state of research on the integration of blockchain and building information modelling (BIM) in the Architecture, Engineering, Construction and Operations (AECO) industry as a means of identifying gaps between the existing paradigm and practical applications for determining future research directions and improving the industry. The study aims to provide clear guidance on areas that need attention for further research and funding and to draw academic attention to factors beyond the technical dimension.

A mixed-method systematic review is used, considering multiple literature types and using a sociotechnical perspective-based framework that covers three dimensions (technic, process and context) and three research elements (why, what and how). Data are retrieved and analysed from the Web of Science and Scopus databases for the 2017–2023 period.

While blockchain has the potential to address security, traceability and transparency and complement the system by integrating supporting applications, significant gaps still exist between these potentials and widespread industry adoption. Current limitations and further research needs are identified, including designing fully integrated prototypes, empirical research to identify operational processes, testing and analysing operational-level models or applications and developing and applying a technology acceptance model for the integration paradigm. Previous research lacks contextual settings, real-world tests or empirical investigations and is primarily conceptual.

This paper provides a comprehensive, critical systematic review of the integration of blockchain with BIM in the construction industry, using a sociotechnical perspective-based framework which can be applied in future reviews. The study provides insight into the current state and future opportunities for policymakers and practitioners in the AECO industry to prepare for the transition in this disruptive paradigm. It also provides a phased plan along with a clear direction for the transition to more advanced applications.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

Although, numerous optimization algorithms have been devoted to construct an electrical ladder network model (ELNM), they suffer from some frail points such as insufficient accuracy as well as the majority of them are unconstrained, which result in optimal solutions that violate certain security operational constraints. For this purpose, this paper aims to propose a flexible-constraint coyote optimization algorithm; the novelty lies in these points: penalty function is introduced in the objective function to discard any unfeasible solution, an advanced constraint handling technique and empirical relationship between the physical estimated parameters and their natural frequencies.

Frequency response analysis (FRA) is very significant for transformer winding diagnosis. Interpreting results of a transformer winding FRA is quite challenging. This paper proposes a new methodology to synthesize a nearly unique ELNM physically and electrically coupled for power transformer winding, basing on K-means and metaheuristic algorithm. To this end, the K-means method is used to cluster the setting of control variables, including the self-mutual inductances/capacitances, and the resistances parameters. Afterward, metaheuristic algorithm is applied to determine the cluster centers with high precision and efficiency.

FRA is performed on a power transformer winding model. Basing on the proposed methodology, the prior knowledge in selecting the initial guess and search space is avoided and the global solution is ensured. The performance of the abovementioned methodology is compared using evaluation expressions to verify its feasibility and accuracy.

The proposed method could be generalized for diagnosis of faults in power transformer winding.

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

The existing literature indicates that the ultimate purpose of green human resource management (GHRM) practices is to enhance sustainable corporate performance by shaping employees’ green behaviors. In this vein, we argue that green organizational culture and employee existing pro-environmental behaviors are the important factors or channels through which GHRM practices shape green employee behaviors for sustainable corporate performance. Consequently, we draw on the ability, motivation, and opportunity (AMO) framework to examine how firms’ GHRM practices indirectly shape employee green behavior for sustainable corporate performance by cultivating and reinforcing green organizational culture under the boundary condition of high employee pro-environmental behavior.

This study uses multi-source, dyadic, and time-lagged data collected from green HR managers and employees in 242 ISO-14001-certified green firms in the Kingdom of Saudi Arabia. The study applies structural equation modeling through LISREL 12 software for testing of hypotheses.

The findings support the postulation that GHRM practices, directly and indirectly, shape employee green behaviors for sustainable performance. GHRM practices indirectly enhance employee green behaviors for sustainable performance by cultivating and fostering the green organizational culture in the presence of high pro-environmental behavior.

This study outlines theoretical and practical implications on how HRM managers require an established green organizational culture and employee pro-environmental behaviors to effectively direct GHRM for enhanced sustainable corporate performance. HRM managers should make use of appropriate interventions, including but not limited to GHRM practices, to foster a green organizational culture and employee pro-environmental behaviors.

This is an original study that outlines the importance of alignment between Green HRM practices and employee pro-environmental behaviors towards shaping green organizational culture and employee behaviors for corporate sustainability. The study demonstrates how GHRM practices enhance sustainable corporate performance through sequential mediations of green organizational culture and employee green behaviors, and under the boundary condition of pro-environmental behavior.

This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market.

This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic.

EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened.

This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market.

This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

The purpose of this study is to discuss how organizations can drive organizational performance through human capital (HC) investment through systematic thinking.

This study analyzes three companies from various industries, adopts systems thinking and uses three leading indicators from the balanced scorecard framework to explore the effects of strategic orientations for HC on innovation ecosystems and organizational performance.

In terms of academic contributions, this study broadly verifies the innovation ecosystem model for organizations and reveals that customer-oriented, internal process-oriented and innovation learning-oriented HC strategies reinforce the pathways in organizational innovation ecosystems, thereby enriching the literature on innovation ecosystems.

In terms of practical contributions, this study provides a novel HC-based perspective on developmental dynamics and details the relationships among each aspect of the innovation ecosystem and HC strategies.

The proposed architecture and strategic frameworks provide a reference for corporations to implement strategic orientations of HC, drive operations in organizational innovation ecosystems and improve organizational performance.

The purpose of this study is to use the grounded theory to summarize the types of celebrity persona and to construct a theoretical model for celebrity persona on consumer purchase intention. Based on the study results, it provides better suggestions for merchants and live streamers and is an expansion of previous research on live-streaming e-commerce.

The grounded theory is recognized as the most scientific qualitative research method and is the ideal explorative method for generating theory. First, the participants were interviewed, and interview data were collected. Then the interview data were organized and analyzed. Finally, this paper summarizes the types of celebrity persona and constructes a theoretical model framework of celebrity persona on consumers' purchase intention.

The results show that the celebrity live streamer persona can be divided into two types: personalized persona and professional persona. Through emotional attachment, the celebrity's persona affects the consumer's purchase intentions. As well as, product type plays a moderating role between celebrity persona and consumer purchase intentions.

The contribution of this research is to start from the celebrity persona, link the celebrity persona with the consumer purchase intentions and expand the research scope of the celebrity persona. It opens the “black box” of the heterogeneity of celebrity live streamers' characteristics on consumer purchase intentions.