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This paper aims to examine the relationship between the readability of annual reports and corporate performance in Chinese listed firms.

This research examined the annual report readability factors of Chinese listed companies by using a textual analysis method using Python to extract the text from the annual reports, convert it into numerical form to facilitate statistical analysis and then merge the results with data from the Chinese stock market to explain the impact on corporate performance and predict future earnings in the Chinese financial markets from 2008 to 2021.

Study findings indicate that firms with better financial reporting readability are more profitable, incur lower agency costs and have low earnings in the Chinese stock markets when readability is low (i.e. more complexity and length of annual reports). It was also found that when a listed company has a good performance, it prefers to use a short space to explain its operating and financial status. More generally, the means of the report length are short, and accounting terms are used less frequently; in the case of a poor company, the annual report is particularly long and accounting terms are more frequently used. In the context of the COVID-19 crisis, this study served as a proxy measure of returns prior to the announcement of the COVID-19 pandemic. In addition, an instrumental variable approach is used, which helps results to remain robust and control for fixed effects and potential endogeneity problems.

Although this study’s results cannot be generalised globally because of their limited scope, they can still be generalised across non-English speaking countries. Thus, future cross-country research is encouraged to examine the textual analysis of financial reports across those countries.

This study conveys two messages to investors and policymakers within the Chinese market. First, investors ought to pay greater attention to the nonfinancial information contained in annual reports to improve the accuracy of their predictions regarding future firm performance. Second, Chinese policymakers are encouraged to instate a policy for the use of plain English in annual reports to make them more readable by international investors.

This study contributes to the paucity of research that examines English-written annual reports in non-English speaking countries by examining the readability of annual reports in the Chinese market.

This study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust.

The authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses.

The results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits.

This study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.

Sharing project information is critical for the success of medical crowdfunding campaigns. However, few users share medical crowdfunding projects on their social networks, and the sharing behavior of medical crowdfunding projects on social networking sites has not been well studied. Therefore, this study explored the factors and potential mechanisms influencing users’ sharing behaviors on networking sites.

A research model was developed based on the attribution-affect model of helping and social capital theory. Data were collected using a longitudinal survey. Partial least squares structural equation modeling was used to analyze the collected data. We conducted post hoc analyses to validate the results of the quantitative analysis.

The analysis results verified the effects of perceived external attribution, perceived uncontrollable attributions, and perceived unstable attributions on sympathy and identified the effect of sympathy and social characteristics of medical crowdfunding users on sharing behavior.

This research provides a comprehensive theoretical understanding of users’ sharing behavior characteristics and provides implications for enhancing the efficiency of medical crowdfunding activities.

The purpose of this study is to examine the relationship between the corporate social responsibility (CSR) performance of job-hopping executives at their former and subsequent firms.

We conduct regression analyses using a sample of firms listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2020 to examine whether CSR performance is similar from one firm to the next as executives switch jobs.

We find a positive relationship between the CSR performance of former and subsequent firms under job-hopping executives. This relationship is the strongest in the year of the job switch; it weakens in the second year and eventually disappears in the third year. In addition, we show that this relationship benefits different CSR stakeholder groups and is contingent on executive and subsequent firm attributes and job-hopping characteristics. Furthermore, we demonstrate that firms that hire a new chief executive officer from a firm with a strong track record in CSR, the new firm experiences a significant surge in CSR performance compared with firms that do not experience such a shock.

This study has implications for executive hiring decisions.

This study extends the understanding of CSR determinants through the lens of inter-organisational ties associated with job-hopping executives.

The objective of this study is to explore the level of understanding and actions taken by the people of Gurugram (erstwhile Gurgaon) to mitigate the impact of climate change, given its critical importance as a global issue.

Using a qualitative approach, primary data were collected through in-depth interviews by means of semi-structured interview methods.

The findings indicate that while people are aware of climate change, the information is deficient for them to translate their knowledge into effective action. Some of the major challenges identified are lack of appropriate understanding, resources, education, motivation and government initiatives, as well as the old habits, peer influence, feeling of incapability and limited media exposure. To bridge the intention-action gap, it is recommended that people should be empowered to act desirably. There is a change need for awareness and education on ways to mitigate the effects of climate change. The study has implications for researchers, environmentalists, policymakers, non-government organizations and local residents of Gurugram.

This study provides unique insights into the understanding of climate change by the general public and challenges faced in taking pro-environment actions. It emphasizes the urgent need to create awareness and educate individuals about ways to mitigate the impact of climate change.

A comprehensive understanding of the determining factors and implications of the frameworks for appreciating the relationships between climate risks and cultural heritage remains deficient. To address the gap, the review analysed literature on the management of climate risk in cultural heritage. The review examines the strengths and weaknesses of climate risk management (CRM) frameworks and attendant implications for the conservation of cultural heritage.

The study adopted a two-phased systematic review procedure. In the first phase, the authors reviewed related publications published between 2017 and 2021 in Scopus and Google Scholar. Key reports published by organisations such as the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and International Council on Monuments and Sites (ICOMOS) were identified and included in Phase Two to further understand approaches to CRM in cultural heritage.

Results established the changes in trend and interactions between factors influencing the adoption of CRM frameworks, including methods and tools for CRM. There is also increasing interest in adopting quantitative and qualitative methods using highly technical equipment and software to assess climate risks to cultural heritage assets. However, climate risk information is largely collected at the national and regional levels rather than at the cultural heritage asset.

The review establishes increasing implementation of CRM frameworks across national boundaries at place level using high-level technical skills and knowledge, which are rare amongst local organisations and professionals involved in cultural heritage management.

The review established the need for multi-sectoral, bottom-up and place-based approaches to improve the identification of climate risks and decision-making processes for climate change adaptation.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

With the great changes brought by information technology, there is also a challenge for the elderly's acceptance. This study aimed to determine the antecedents of elderly people's usage intention of financial artificial intelligent customer service (FAICS) and to examine the relationships between various factors and thus to help them better adapt to the digital age.

A mixed method, including the qualitative and quantitative study, was utilized to explore answers of the research questions. As the qualitative study, the authors used semi-structured interviews and data coding to uncover the influencing factors. As the quantitative study, the authors collected data through questionnaires and tested hypotheses using structural equation modeling.

The results of data analysis from interviews and questionnaires suggested that perceived anthropomorphism and virtual identity of elderly users have a positive impact on their perceived ease of use, and the perceived intelligence of elderly users positively influences their perceived ease of use, satisfaction and perceived usefulness. Additionally, the elderly's cognition age can moderate the effects of perceived usefulness and satisfaction on their usage intention of FAICS.

This study contributes to the literature by taking the elderly group as the research participants and combining those influencing factors with technology acceptance model and information systems success model. The findings provide a basis for accelerating the promotion of FAICS and help address the problem that the elderly have difficulty adapting to a new technology.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

This paper aims to analyse the bibliometric characteristics of the ISO 50001 publication, map the state of the art of the research topic and identify future research issues.

This research is a bibliometric study. The data were collected from Scopus. Both performance and science mapping analysis were performed.

The research results showed the top author, paper and country of ISO 50001 publications. There are four author collaboration clusters and five country collaboration clusters. Eight research themes were mapped into four quadrants based on the density and centrality. The bibliometric coupling analysis showed six research clusters. Finally, the research issues were mapped. The implications were discussed.

This research gave several implications for researchers, practitioners and public policymakers. For researchers, the bibliometric analysis provides several research issues that can be followed up by future research. For practitioners, the bibliometric analysis showed that applied tools and methods that can assist the implementation of ISO 50001-based energy management have been developed. For public policymakers, the bibliometric analysis offered the knowledge structure on ISO 50001 that can be used in public policymaking development. The author collaboration cluster and the bibliometric coupling cluster can be used to trace the scientific information that is needed as the foundation of public policy.

Many ISO 50001 studies have been performed. However, based on the search in several main academic scientific paper databases, there is no bibliometric study on the research topic. This is the first bibliometric study on ISO 50001 publication. This study takes a holistic approach combining performance analysis and science mapping analysis that includes elaborated thematic mapping and evolution analysis.