Search results

This study aims to evaluate patterns, trends and knowledge networks within social security research. By using bibliometric analysis, the research seeks to provide a comprehensive perspective on the evolution of global social security research. The purpose extends to identifying significant contributors, collaborative clusters and multifaceted issues addressed in the field.

This study uses bibliometric analysis to assess social security research trends and knowledge networks from 2015 to 2023. Using the Web of Science database, 6,152 relevant articles are analyzed. Quantitative techniques such as coauthorship network analysis, institutional productivity rankings and keyword clustering are applied for a comprehensive understanding.

The findings indicate a rising trajectory of publications in social security research, with the USA, China and Europe emerging as top contributors. Coauthorship patterns reveal collaborative clusters with focused research interests. Prominent authors emphasize key aspects like public policy, economics, health and labor dynamics related to social security. Keyword clustering identifies nine thematic clusters, ranging from inequality and poverty to retirement and disability reforms. A thematic map visualizes overarching categories, including motor themes, basic themes, niche themes and emerging themes.

This bibliometric study offers original insights into global social security research, providing a comprehensive understanding of its evolution, significant contributors and diverse thematic issues addressed. The originality lies in the application of quantitative techniques, including coauthorship network analysis and keyword clustering, to reveal collaborative patterns and thematic clusters. The study’s value extends to facilitating evidence-based decision-making for advancing the critical domain of social security through international collaboration and impactful research aligned with societal needs.

This study aims to investigate the influence of corporate governance (CG) on environmental disclosure (ED) practices within UK and US firms, addressing the contemporary challenges confronting firms in both contexts.

Using the dynamic panel regression framework of system generalised method of moment (GMM), this study analyses a sample comprising 121 FTSE and 200 S&P firms from 2010 to 2020.

The findings emphasise the dynamic nature of ED practices among UK and US firms, demonstrating their propensity to swiftly adjust to desired levels whenever deviations occur. Besides, this study identifies board independence and the frequency of board meetings as significant determinants of ED for UK firms. In contrast, for US firms, board independence and audit committee independence are found to be significant determinants of ED.

The research highlights the fundamental role played by CG in shaping how firms in the UK and the US navigate agency problems and respond to diverse stakeholder demands through ED in their annual reports. This study advocates for the promotion of robust governance systems that concurrently serve the purposes of accountability and monitoring to bridge the information expectation gap between firms and stakeholders. The findings reinforce the necessity for regulatory initiatives involving policy formulation and corporate oversight to enhance private sector awareness regarding environmental reporting practices.

This study contributes to the scarce literature on the impact of board and audit committee characteristics on ED practices in the UK and US contexts. In addition, by using the system GMM estimation technique, this study provides robust and updated evidence that addresses the weaknesses inherent in previous studies.

This study aims to investigate the impact of various board characteristics on environmental innovation (EI) among companies listed on the STOXX Europe 600. It also examines the moderating role of CSR committees on the board–EI nexus.

The sample consists of companies listed on the STOXX Europe 600 index over 12 years (2011–2022). This study uses the Refinitiv Eikon database to evaluate the extent of EI. Panel data regression analysis is used, with two-stage least squares and lagged models used as robustness tests to control for endogeneity.

The results indicate that board independence and gender diversity significantly increase EI, whereas CEO duality negatively impacts it. Other board attributes, such as board size, show no impact on EI. In addition, the presence of CSR committees moderates these relationships, enhancing the positive effects of gender diversity and board independence and mitigating the negative impact of CEO duality.

This study provides valuable insights for policymakers and corporate strategists aiming to advance environmental responsiveness through strategic board composition and establishing CSR committees. Emphasizing the importance of board independence, gender diversity and CSR committees, the findings suggest practical pathways for enhancing the adoption of EI by creating governance structures that support sustainable practices.

To the best of the authors’ knowledge, this is the first study to examine the moderating role of CSR committees on the associations between board characteristics and EI. This research addresses a crucial gap in the current literature, enriching the understanding of corporate governance and sustainability. It provides critical insights for developing policies and strategies that promote EI through effective board composition and the implementation of CSR committees.

This study aims to explore the influence of critical mass of female directors on corporate sustainability disclosure in emerging market firms with two-tier boards system. Critical mass of female director presented by three types of female director proportion, there are women on board of director (BOD) that are less than 20%, between 20% and 40% and more than 40%.

The observation comprises 456 firm-year firms listed on the Indonesia Stock Exchange for the period from 2017 to 2022. This study used the static panel data model and dynamic panel data model based on generalize method of moments.

The research discovered that the proportion of female director has positive effect on corporate sustainability disclosure in emerging market firms with two-tier boards system. The mass of female director with at least 20% but less than 40% has positive relationship on corporate sustainability disclosure. Moreover, the mass of female with balance proportion on BOD has stronger positive impact on corporate sustainability disclosure. However, there is insignificant impact between the mass of female directors and corporate sustainability disclosure with less than 20% proportion of female director.

The companies must facilitate the involvement of women on the board of directors to promote pro-sustainability disclosure initiatives. The findings indicate that corporations should deliberately examine the ratio of female directors to enhance corporate sustainability disclosure.

The research studies will add value to the limited literature and addressed the dynamic nature of the relationship and mitigated the endogeneity bias.

The quest for improved facilities management (FM) delivery is receiving immense focus through the incorporation of innovative technologies such as cyber-physical systems (CPS). The system’s high computational capabilities can aid in the abatement of some of the challenges plaguing FM functions. However, the requisite ingredients for the uptake of the system for FM have still not gained scholarly attention. Because performance measurement is a vital index in determining the outcome of FM methods, this study aims to investigate the influence of performance measurement indicators that are influential to the uptake of CPS for delivering FM functions.

A qualitative technique was adopted using the Delphi technique. The panel of experts for the study was selected through a well-defined process based on stipulated criteria. The experts gave their opinions in two rounds before consensus was attained on the identified performance measurement indicators, whereas methods of data analysis were measures of central tendency, inter-quartile deviation and Mann–Whitney U test.

Results from this study showed that 11 of the performance indicators were of very high significance in the determination of the uptake of CPS for FM functions, whereas 5 of the indicators were proven to be of high significance. Furthermore, there was no statistical difference in the opinions of the experts based on their affiliation with academic institutions and professional practice.

The findings of this study contribute practically by aiding policymakers, facility managers and relevant stakeholders with the vital knowledge of delivery mandates for efficient FM services that can spur the uptake of digital technologies such as CPS.

This study contributes to the body of knowledge as it unveils a roadmap of the expected performance output and its accompanying evaluation that would drive the adoption of a promising technology such as CPS in the delivery of FM tasks.

The purpose of this study is to perform bibliometric analysis to systematically and comprehensively examine the current landscape of digital knowledge, integration and performance in the transformation of sustainability accounting, reporting and assurance.

This research uses a systematic literature review, following the Scientific Procedures and Rationales for Systematic Literature Review protocol and uses various bibliometric and performance analytical methods. These include annual scientific production analysis, journal analysis, keyword cooccurrence analysis, keyword clustering, knowledge gap analysis and future research direction identification to evaluate the existing literature thoroughly.

The analysis reveals significant insights into the transformative impact of digital technologies on sustainability practices. Annual scientific production and journal analyses highlight key contributors to the adoption of digital technologies in sustainability accounting, reporting and assurance. Keyword cooccurrence analyses have identified key themes in sustainability accounting, reporting and assurance, highlighting the transformative role of digital technologies such as artificial intelligence (AI), blockchain, Internet of Things (IoT) and big data. These technologies enhance corporate accountability, transparency and sustainability by automating processes and improving data accuracy. The integration of these technologies supports environmental, social and governance (ESG) reporting, circular economy initiatives and strategic decision-making, fostering economic, social and environmental sustainability. Cluster-by-coupling analyses delve into nine broader revealing that IoT improves ESG report accuracy, eXtensible Business Reporting Language structures ESG data and AI enhances life cycle assessments and reporting authenticity. In addition, digital transformation impacts environmental performance, big data optimizes resource use and edge computing improves eco-efficiency. Furthermore, this study identifies avenues for future research to advance the understanding and implementation of digital technology in sustainability accounting, reporting and assurance practices.

Academically, this research enriches the understanding of how digital technologies shape sustainability practices and identifies gaps in digital knowledge and integration. Practically, it provides actionable insights for organizations to improve sustainability reporting and performance by effectively leveraging these technologies. Policy-wise, the findings advocate for frameworks supporting the effective implementation of these technologies, ensuring alignment with global sustainability goals.

This study offers a detailed analysis of the performance and intellectual framework of research on implementing digital technology in sustainability accounting, reporting and assurance. It highlights the evolving research landscape and emphasizes the need for further investigation into how emerging technologies can be leveraged to achieve sustainability goals.

Building information modeling (BIM) is a popular technology in modern construction, but its implementation faces numerous difficulties, which are known as problematic issues of BIM implementation (PIBIs). This study aims to identify and analyze PIBIs based on the perceptions of practitioners in Vietnamese BIM-implemented construction contractors and similar BIM adoption-level countries.

The research is conducted in three stages. After a comprehensive literature review, PIBIs are identified and ranked based on the Delphi method and questionnaire survey. Next, interpretive structural modeling (ISM) is used to provide a hierarchy of system PIBIs. Finally, the driving and dependence power of PIBIs are determined using Fuzzy-Matrice d'Impacts Croisés-Multiplication Appliquée à un Classement (MICMAC) analysis.

This study evaluates 32 PIBIs for small and medium contractors (SMCs) and 28 PIBIs for large contractors (LCs) in Vietnam. This study also develops a systematic framework for contextual relationships and analyzes the relationships among PIBIs using ISM-Fuzzy MICMAC analysis. The results show that different standards or techniques, modeling software limitations and information management significantly impacted LCs, while a lack of senior leadership support and incompatible software affected SMCs, causing other PIBIs.

This study is among the first attempts to investigate the problematic issues emerging during the BIM implementation process at the organizational level in Vietnam as well as other developing countries. The study results could be considered valuable references for BIM practitioners. Furthermore, this knowledge can enable contractors to avoid problems while adopting BIM, thereby increasing their ability to implement BIM successfully and using critical resources more efficiently.

Construction cost management is one of the important processes that should be achieved effectively and accurately for successful project delivery. Modern-day construction cost management demands a high level of spatial skills. Augmented reality (AR) can potentially increase the stakeholders’ spatial skills as a supportive technology to traditional cost management tools and techniques. AR is a breakthrough technology that could considerably ease execution in various industries, but AR applicability in cost management has not been studied extensively. Thus, this study aims to explore the use of AR in construction cost management tools and techniques.

Data were collected using a qualitative approach consisting of two rounds of the Delphi technique. A total of 22 experts in the construction and information technology fields were interviewed using a purposive sampling technique. The manual content analysis helped analyse data.

The study identified AR features with the potential to increase the usage of cost management tools and techniques. AR can enable spatial skills (abilities, thinking and tasks) in most cost management tools and techniques. However, technical, cultural and technical and cultural barriers obstruct the use of AR in the construction industry.

The usage of AR in construction cost management tools and techniques has not been examined in detail until now. Thus, the study was developed to meet the industry needs and fill the literature gap to investigate the potential use of AR in construction cost management tools and techniques.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.