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This chapter explores the multifaceted relationship between quantum computing (QC) and sustainability, with a focus on the Quadratic Unconstrained Binary Optimisation (QUBO) framework. The manuscript delves into the theoretical underpinnings of QUBO and its formulation as a quantum annealing problem, identifying the quantum principles that facilitate the resolution of such optimisation challenges. It offers a critical analysis of the suitability of QUBO for unconstrained problems and its efficacy in consistently locating the global minimum – a pivotal concern in optimisation tasks. Further, this study provides a nuanced discussion on the intersection of QC and sustainability. It delineates the types of optimisation problems within sustainability initiatives that are amenable to formulation as QUBO problems, while also highlighting sustainability challenges that elude the QUBO framework. It argues for the integration of quantum solutions into business operations, highlighting the potential for QC to play a transformative role in achieving sustainability objectives. The critique of the current hype surrounding QC provides a balanced viewpoint, ensuring a grounded approach to the adoption of quantum technologies in tackling pressing global issues.

This paper studies the critical factors from the perspectives of technological quality, personal compatibility and organisational commitment using the technological adoption framework (TAF). The proposed TAF studies the critical factors that influence the intention to use building information modelling (BIM) taking into consideration of the “Perceived Ease of Use (PEU)” and “Perceive Usefulness (PU).”

The proposed study is a quantitative research study using the TAF model and the statistical analysis using “Partial Least Squares-Structural Equation Modelling (PLS-SEM).” The questionnaires are developed based on the literature review study and disseminated to the stakeholders in the Malaysian construction industry, including consultants, contractors, and clients. The data collected are analysed using PLS-SEM to identify the correlation between the critical factors influencing BIM adoption and the moderation influence of the PEU and PU towards the “Intention to Use (IU)” BIM.

The data collected from 185 construction industry stakeholders in Malaysia was utilised to develop the structural equation model. The measurement model was analysed in terms of composite reliability, discriminant validity, and collinearity issues. Subsequently, the SEM is analysed, and the findings on the hypothesis on the correlation between the critical factors and the intention to use BIM are examined. The study also examines the mediation effects of the PEU and PU towards the BIM adoption in the Malaysian construction industry.

This research conceptual framework, TAF, is derived from the integration of the existing underpinning theories of the technological adoption model and the technology–organisation–environment framework. This new TAF can be used for the study of new technology adoption. This cross-sectional research study is in line with the “Construction 4.0 Strategic Plan” in Malaysia to establish the current BIM adoption scenario and formulate the framework to promote incentives to promote BIM adoption.

This study aims to assess the situation of a nascent field of research dealing with the impact of nationalism on foreign direct investment (FDI) by providing a general overview of the field, critically reviewing the literature to identify the gaps and making suggestions for future research directions.

This study uses a combination of a bibliometric analysis and a critical literature review. A total of 154 papers were included in the sample. They were selected from the Web of Science database based on specific criteria.

The study concluded with the presentation of the key descriptive information – most prolific authors, journals, affiliations, countries and disciplines; number of publications per year; and most cited articles – from the bibliometric analysis to illustrate the structure of the literature. Moreover, the critical discussion focused on six main categories: constituents of nationalism, types of nationalism, relevant ideologies, related actors, outcomes of nationalism and the contexts investigated. Furthermore, the need for further empirical research to shed light on the relationship between nationalism and FDI was emphasized and suggestions for future research directions were made.

This study represents one of the pioneering attempts to comprehensively examine the relationship between nationalism and FDI. Moreover, the systematic review of the methodical sections of the articles examined and the suggestions made have the potential to contribute to improving the quality of future research in this area.

This study aims to investigate the relationship between tax losses and the likelihood of emerging from bankruptcy procedures as reorganized entities, exploiting the exogenous shock provided by the 1997 Taxpayer Relief Act (TRA) in the USA.

The empirical analysis uses a difference-in-differences approach and investigates a sample of US public firms filing under Chapter 11 bankruptcy procedures during the period 1984–2016. The analysis includes several robustness and endogeneity tests, including heterogeneous treatment effect analysis, entropy balancing and propensity score matching.

The findings show that filing firms with higher tax losses are more likely to emerge from the procedure as reorganized firms. Further, this paper shows that firms with high tax losses reduced their likelihood of reorganization after the enactment of the 1997 TRA, as the new regulation reduced tax benefits related to losses. Additional analyses show that tax losses contribute to the reorganization of filing firms requiring more cash resources and restructuring efforts and they increase post-bankruptcy creditors’ recovery rates.

This study informs policymakers, shareholders, creditors and other stakeholders that tax policy can facilitate business continuity within restructuring frameworks and preserve economic and social value.

This study contributes to the literature on taxation and corporate restructuring with evidence that tax-loss policies incentivize reorganization during bankruptcy procedures. To the best of the authors’ knowledge, this is the first study to empirically demonstrate that tax losses influence the form of exit from bankruptcy procedures, adding to the set of firms’ features affecting bankruptcy emergence.

Networked learning facilitates collaboration and learning interactions. This study aims to explore networked learning in the workplace (knowledge sharing and connection building) and gain insights into the contextual factors (learner and environmental) of learning interactions.

Thematic analysis was conducted to explore how learning interactions were facilitated among healthcare workers in a large hospital in Singapore. This study examined both the learner and environmental factors of learning interactions. Interview and focus group discussions qualitative data were included in the analysis.

The findings of this study reveal that more emphasis can be placed on the environmental factors, and targeting these factors would provide a good foundation for networked learning in the workplace, whereas learner factors could be promoted to enhance additional learning interactions. This study also found that workers learn most frequently from experienced seniors, indicating the value of mentorship programmes in fostering high-quality learning interactions.

This paper sheds light on the comprehensive set of factors that encourage networked learning among healthcare professionals and provides empirical findings that might direct future studies in similar domains.

To maximize health synergistic benefits and provide a scientific foundation for enhancing air pollution control policies, this research proposed a causal inference framework with a decomposition ensemble prediction model for comprehensive policy assessment.

We introduced the CD-OASVR-Ensemble-CC method, a policy assessment approach that combined machine learning-based decomposition ensemble prediction (CD-OASVR-Ensemble) with counterfactual contrast (CC) analysis. The CD-OASVR-Ensemble model incorporated support vector regression (SVR) optimized through swarm intelligent optimization algorithms (OA), complete ensemble empirical model decomposition of adaptive noise (CD) and a linear ensemble model (Ensemble). This approach was applied to analyze patterns in historical PM2.5 data and predict the future trend. The predicted PM2.5 served as a counterfactual trend, enabling an evaluation of the policy’s environmental and health effects.

Analysis of prediction errors confirmed the proposed CD-OASVR-Ensemble model’s accuracy and robustness in predicting PM2.5 levels. The findings indicated that the implementation of “Regulation 2019” led to a significant reduction in PM2.5 concentration, preventing an estimated 7,960 premature deaths and saving approximately 2,602.95 m yuan in health-related economic costs.

This study introduces an innovative framework for evaluating the environmental effect of policies, with a specific emphasis on health synergistic benefits.

By harnessing technology developments such as Internet-of-Things (IoT)-enabled intelligent sensors and immersive virtual reality (VR) experiences, facility managers can access real-time, precise information on thermal comfort-related indicators through virtual facility models. While prior research studies have developed key technologies for improving the understanding of thermal comfort and its impact on the occupants’ well-being and productivity, there remain areas yet to be explored, especially in relation to integrating both real-time data from multimodal IoT-enabled smart sensors and VR technologies. Hence, this study demonstrates the potential of integrating IoT and VR technologies for real-time thermal comfort assessment and visualization as well as user interaction with HVAC systems to enhance thermal comfort.

To develop the proposed integrated analytical framework in this paper, various steps were implemented. First, four multimodal IoT-enabled sensing stations were created and installed to collect real-time thermal comfort-related data (i.e. temperature and relative humidity). Second, a VR environment was developed using the Unity engine to offer an immersive experience. Third, the real-time data from the IoT-enabled sensing stations was integrated into the VR environment by transmitting it to the cloud via the MQTT protocol server, and various programming scripts were developed to provide multiple functionalities to the users, including visualizing the thermal comfort along the entire indoor space as well as interacting with and controlling the cooling and heating HVAC systems. Fourth, the applicability and effectiveness of the developed framework was validated and evaluated by 92 participants using a survey questionnaire.

The obtained survey results validated the importance and effectiveness of the developed framework on various aspects including graphical satisfaction, spatial presence, involvement, experienced realism, low-to-no cybersickness and overall application satisfaction, among others. More specifically, the findings reflected that the participants’ average scores for graphical satisfaction, sense of spatial presence, involvement and experienced realism were 4.69, 4.61, 4.71 and 4.53 out of 5, respectively. Hence, the results showed that the visualization capabilities of the developed framework serve as a powerful feature that enables a comprehensive visualization of thermal comfort variations across the entire room/office space. Also, the results showed that there were no statistically significant differences between the responses of participants with prior VR experience with those from participants with limited-to-no prior VR experience, thus further highlighting the usefulness of the proposed technology not only for experienced users but also for users from different skills and background.

This research has the potential to revolutionize the way built environments are managed and interacted with, where facility managers can monitor, assess and visualize thermal comfort in real-time as well as interact with the HVAC systems and control multimodal IoT devices in the real-world from a distance through virtual facility models. The proposed framework’s ability to provide dynamic and continuously updated assessments of thermal conditions in real-time positions it as a valuable tool for prompt adjustments to optimize occupants’ comfort levels. Ultimately, the proposed framework provides an intuitive and immersive platform to manage thermal comfort, thus promoting healthier, more productive and eco-friendly indoor environments.

The purpose of the present study is to reveal the antecedents of the new green product success (NGPS) of small and medium-sized enterprises (SMEs) that manufacture in Türkiye with a middle-income economy. To this end, a research model for the serial mediating role of green human capital (GHC) and green adaptive ability (GAA) in the relationship between green mindfulness (GM) and NGPS was developed and tested. Determining the driving forces of NGPS is essential due to the contributions of SMEs manufacturing, especially in middle-income economies, such as Türkiye, to the national economy.

The present study used partial least squares structural equation modeling (PLS-SEM) to test the hypothesized relationships. The study hypotheses were tested in a sample of 396 manufacturing enterprises, qualified as SMEs in Türkiye with ISO 14001 certificates.

Empirical evidence indicates that GM, GHC and GAA act as significant antecedents of NGPS. Furthermore, empirical evidence demonstrates that GHC and GAA, in turn, mediate the relationship between GM and NGPS.

Managers, especially in manufacturing SMEs, should be aware that they need to develop intangible resources to improve NGPS. Increased investment in GM results in higher levels of GHC and GAA, which collectively leads to improved NGPS.

The current literature has not previously explained the mechanism of the relationship between GM and NGPS. The originality of the present study, which discusses the relevant research gap, is to develop an integrated framework to investigate how GM impacts NGPS, specifically for SMEs, and to test it empirically.

This study aimed to investigate higher education students’ perceived behaviour towards artificial intelligence (AI) acceptance in their learning.

Theory of technology acceptance model (TAM) and its critical constructs were used to develop research model and hypotheses. The research model was validated using structural equation modelling (SEM) based on 405 useable survey responses.

The findings report that the critical constructs of TAM theory are helpful to explain higher education students’ perceived behaviour towards their AI acceptance in learning. The findings also confirm the reliability and strength of TAM theory to assess higher education students’ behavioural intention to accept AI.

The findings will help authorities of higher education to understand the students’ behavioural intention to accept AI. The study will assist authorities, policymakers, designers, developers and decision-makers of higher education to facilitate the effective integration of AI in higher education.

The study contributes to enhance new knowledge in literature regarding AI acceptance in higher education. It fills the knowledge gap by confirming the influence of higher education students’ perceived behaviour on their AI acceptance. Additionally, it uses SEM approach, which is very new in quantitative analysis, to test and establish the research model.