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Purpose: This study quantitatively assesses the impact of ESG profile on equity value and risk, as well as identifies potential differences occurring in different sectors, based on the data of the Nasdaq Nordic market.

Methodology: To reach this purpose, (i) the stock return and volatility analysis is being conducted (using the methods of paired sample t-test, correlation, etc.), and (ii) panel data models with constant, fixed and random effects are being constructed. The analysis is based not only on the company’s ESG performance but also on a cross-sectoral approach.

Findings: The results revealed that although ESG factors appeared to have a significant impact in most of the constructed models, the impact of these factors varies depending on the sector.

Implications: This research provides a comprehensive and comparative approach to the importance of the ESG profile for investment performance and therefore can be useful both for impact investors making investment decisions in dynamic global financial markets and for companies developing or reforming their ESG strategies.

Limitations: Due to the problem of data availability, the cross-sectoral comparison was performed based on the limited number of sectors. In addition, the limited availability of ESG data in the analysed market did not allow the use of additional methods to assess the impact of ESG.

Future Research: Expanding the data sample and assessing the impact of a company’s ESG profile not only in different sectors but also in different phases of the economic cycle might be the direction for future research.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

Advancements in responsive manufacturing have been supporting companies over the last few decades. However, manufacturers now operate in a context of continuous uncertainty. This research paper explores a mechanism where companies can “elastically” provision and deprovision their production capacity, to enable them in coping with repeated disruptions. Such a mechanism is facilitated by the imitability and substitutability of production resources.

An inductive study was conducted using Gioia methodology for this theory generation research. Respondents from 20 UK manufacturing companies across multiple industrial sectors reflected on their experience during COVID-19. Resource-based view and resource dependence theory were employed to analyse the manufacturers' use of internal and external production resources.

The study identifies elastic responses at four operational levels: production-line, factory, company and supply chain. Elastic responses that imposed variable-costs were particularly well-suited for coping with unforeseen disruptions. Further, the imitability and substitutability of manufacturers helped others produce alternate goods during the crisis.

While uniqueness of production capability helps manufacturers sustain competitive advantage against competitors during stable operations, imitability and substitutability are beneficial during a crisis. Successful manufacturing companies need to combine these two approaches to respond effectively to repeated disruptions in a context of ongoing uncertainties. The theoretical contribution is in characterising responsive manufacturing in terms of resource heterogeneity and resource homogeneity, with elastic resourcing as the underlying mechanism.

This study investigates the impact and role of digital twin technology in building automation (DTBA) from a sustainability viewpoint. It aims to enhance the understanding of how DTBA can boost efficiency, optimize quality and support sustainable practices in contemporary construction. By exploring the integration of DTBA with sustainable practices, the study seeks to demonstrate how DT can revolutionize building management and operations, leading to significant improvements in resource efficiency, environmental impact and overall operational excellence.

This research employs a bibliographic analysis and systematic review of 176 publications from the past five years (January 1, 2019 to December 31, 2023), focusing on the application and development of DTBA. The study methodically analyzes current trends, identifies research gaps and suggests future directions by synthesizing data from various studies, offering a comprehensive overview of the current state of DTBA research. The approach combines quantitative and qualitative analyses to provide robust insights into the advancements and challenges in the field.

The review identifies key development areas in DTBA, such as energy and environmental management, resource utilization within a circular economy and technology integration and interoperability. It highlights the necessity for further research to maximize DTBA’s potential in sustainable building automation. The findings suggest that while significant progress has been made, there is a critical need for innovations in data interoperability, predictive analytics and the integration of renewable energy sources to fully realize the benefits of DTBA in enhancing building sustainability.

This paper provides a thorough review of DTBA from a sustainability perspective, offering valuable insights into its current applications and future development potential. It serves as a crucial resource for researchers and practitioners looking to advance sustainable practices in the construction sector using DT technology. By bridging the gap between theoretical research and practical applications, the paper underscores the transformative potential of DTBA in driving sustainable development and provides a roadmap for future research and innovation in the field.

Effectively predicting research and development project portfolio benefit (R&D PPB) could assist organizations in monitoring the execution of research and development project portfolio (R&D PP). However, due to the uncertainty and complexity of R&D PPB, current research remains lacking a valid R&D PPB prediction tool. Therefore, an R&D PPB prediction model is proposed via a backpropagation neural network (BPNN).

The R&D PPB prediction model is constructed via a refined immune genetic algorithm coupling backpropagation neural network (RIGA-BPNN). Firstly, considering the characteristics of R&D PP, benefit evaluation criteria are identified. Secondly, the benefit criteria values are derived as input variables to the model via trapezoidal fuzzy numbers, and then the R&D PPB value is determined as the output variable through the CRITIC method. Thirdly, a refined immune genetic algorithm (RIGA) is designed to optimize BPNN by enhancing polyfitness, crossover and mutation probabilities. Lastly, the R&D PPB prediction model is constructed via the RIGA-BPNN, followed by training and testing.

The accuracy of the R&D PPB prediction model stands at 99.26%. In addition, the comparative experiment results indicate that the proposed model surpasses BPNN and the immune genetic algorithm coupling backpropagation neural network (IGA-BPNN) in both convergence speed and accuracy, showcasing superior performance in R&D PPB prediction. This study enriches the R&D PPB predicting methodology by providing managers with an effective benefits management tool.

The research implications of this study encompass three aspects. First, this study provides a profound insight into R&D PPB prediction and enriches the research in PP fields. Secondly, during the construction of the R&D PPB prediction model, the utilization of the composite system synergy model for quantifying synergy contributes to a comprehensive understanding of intricate interactions among benefits. Lastly, in this research, a RIGA is proposed for optimizing the BPNN to efficiently predict R&D PPB.

This study carries threefold implications for the practice of R&D PPM. To begin with, the approach proposed serves as an effective tool for managers to predict R&D PPB. Then, the model excels in efficiency and flexibility. Furthermore, the proposed model could be used to tackle additional challenges in R&D PPM, such as gauging the potential risk level of R&D PP.

Effective predicting of R&D PPB enables organizations to allocate their limited resources more strategically, ensuring optimal use of capital, manpower and time. By accurately predicting benefit, an organization can prioritize high-potential initiatives, thereby improving innovation efficiency and reducing the risk of failed investments. This approach not only strengthens market competitiveness but also positions organizations to adapt more effectively to changing market conditions, fostering long-term growth and sustainability in a competitive business environment.

Incorporating the characteristics of R&D PP and quantifying the synergy between benefits, this study facilitates a more insightful R&D PPB prediction. Additionally, improvements to the polyfitness, crossover and mutation probabilities of IGA are made, and the aforementioned RIGA is applied to optimize the BPNN. It significantly enhances the prediction accuracy and convergence speed of the neural network, improving the effectiveness of the R&D PPB prediction model.

Provisions for the minimisation of human error are essential through governance structures such as recruitment, human resource allocation and education/training. As predictors of safety attitudes/behaviours, employees’ personality traits (e.g. conscientiousness, sensation-seeking, agreeableness, etc.) have been examined in relation to human error and safety education.

This review aimed to explore research activity on the safety attitudes of healthcare staff and their relationship with the different types of personalities, compared to other complex and highly regulated industries. A scoping review was conducted on five electronic databases on all industrial/work areas from 2001 to July 2023. A total of 60 studies were included in this review.

Studies were categorised as driving/traffic and industrial to draw useful comparisons between healthcare. Certain employees’ personality traits were matched to positive and negative relationships with safety attitudes/behaviours. Results are proposed to be used as a baseline when conducting further relevant research in healthcare.

Only two studies were identified in the healthcare sector.

The necessity for additional research in healthcare and for comparisons to other complex and highly regulated industries has been established. Safety will be enhanced through healthcare governance through personality-based recruitment, human resource allocation and education/training.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

Safety climate plays an important role in the high-risk construction industry. Advances have been made in the understanding of construction safety climate in terms of four interrelated themes, specifically, its definition, measurement, antecedents and consequences. However, knowledge remains fragmented as the studies are scattered, and a systematic review covering these four themes is lacking. To address this research gap, this study aims to perform a systematic literature review of construction safety climate literature regarding the four themes.

Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol guidelines, 178 eligible articles were obtained. This study provided thematic analysis of the 178 papers to identify what is known and what is not yet fully known regarding the four themes of construction safety climate. This study also conducted a descriptive analysis to identify the influential scholars, keywords, theories and research methods used by the literature, and finally presented an integrative framework directing future research.

The literature has not reached a consensus on the definition and measurement of construction safety climate. While it has identified the impact of safety climate on both behavioral and accident consequences, it has paid less attention to the antecedents and their underlying mechanisms regarding safety climate. Fang D. and Lingard H. are identified as the most influential authors in this field. “Questionnaire” and “safety behavior” are the keywords most closely related to safety climate. Unfortunately, the existing evidence for the causal relationships between safety climate and its antecedents and consequences is weak, as many studies lack clear theoretical substance, use a concurrent research design and focus only on individual-level climate perceptions. Finally, to support the development of construction safety climate around the four themes, potential research directions and research methods supporting them are illustrated.

This review makes contributions by integrating existing construction studies covering its definition, measurement, antecedents and consequences. This review also makes contributions to specific themes: no review exists on the antecedents of construction safety climate, and this review fills that gap; with regard to consequences, the existing reviews focus either on safety outcomes or safety behavior, but this review included both of them and further elaborated the different theories underpinning the relationships between safety climate and them. It is hoped that this systematic review will be helpful to the research community toward developing a nomologic network and promoting knowledge integration with respect to construction safety climate.

Artificial Intelligence (AI) has revolutionized teaching and learning methods in higher education, especially in English language teaching and learning. This chapter contributes to the existing knowledge by exploring how AI has developed within the framework of teaching and learning of English, highlighting the challenges, dangers, and moral issues associated with its application. The typical classroom environment has significantly changed because of the integration of AI-powered tools and platforms in English instruction. Chatbots, automated grading systems, and language learning apps driven by AI have streamlined language education, increasing its effectiveness and accessibility. But these benefits accompany a variety of challenges and worries. Ethical concerns about data privacy, algorithmic biases, and the depersonalization of education arise as AI becomes more deeply ingrained in educational methods. Reliance on AI may inadvertently exacerbate educational disparities as long as learners' access to technology and its advantages remain unequal. In addition, significant thought must be given to the ethical ramifications of AI-generated content as well as the possible loss of human connection in language learning settings. This chapter examines these dangers and challenges and makes the case for a well-rounded strategy that maximizes AI's benefits while minimizing any potential downsides. Together, educators and legislators need to create moral guidelines that balance the potential of AI with human-centered learning experiences. To ensure responsible and fair AI integration and promote an inclusive learning environment that prioritizes students' holistic development while exploiting technology breakthroughs, comprehensive assessment of the associated obstacles, risks, and ethical issues is necessary.

Digital transformation provides a new impetus for the development of declining firms. However, there is currently a lack of sufficient research on whether digital transformation is beneficial for the turnaround of declining firms. This paper aims to explore the relationship between digital transformation and the turnaround of declining firms.

Drawing on the theoretical foundations of the resource-based view and dynamic capabilities theory, this research uses a comprehensive dataset of Chinese A-share listed companies from 2010–2021 to explore the influence of digital transformation on the turnaround of declining firms.

The research findings show that digital transformation contributes to the turnaround of declining firms. Mechanism analyses demonstrate that digital transformation enhances dynamic capabilities and attracts more analysts, thereby facilitating the turnaround process. Moreover, the moderation analysis reveals that CEO equity incentives strengthen the positive correlation between digital transformation and the turnaround of declining firms. Heterogeneity analysis indicates that the association between digital transformation and the turnaround of declining firms is particularly significant for firms with low financing constraints and high-tech firms. Moreover, this research reveals that digital transformation can facilitate the turnaround of firms in deep and long-term decline.

This research contributes to the literature on the digital transformation of enterprises and provides important insights for the turnaround of declining firms.