Fu-Ling Chung, Hsin-Hsuan Chung and Shu-Min Lin
This study aims to help scholars comprehend the major research themes on sustainable development goals (SDGs) in higher education which researchers from various fields have…
Abstract
Purpose
This study aims to help scholars comprehend the major research themes on sustainable development goals (SDGs) in higher education which researchers from various fields have explored and to propose several potential future research directions of the least researched SDG in higher education to support scholars in making up the gap in the field.
Design/methodology/approach
The authors adopted a bibliometric analysis method to review the extant literature from the Web of Science on SDGs in higher education from 2015 to 2023 and took a closer examination of the most researched SDGs discussed by scholars. This study specifically concentrated on studies that explicitly mentioned the term “Sustainable Development Goal” (or “SDG”) and applied VOSviewer to cluster common keywords of the most researched SDGs and explored related themes. Also, this study provided several potential future research directions of least researched SDG in higher education.
Findings
SDGs 3 and 4 were the most researched, and SDG 15 was the least researched. The three major themes of SDG 3 were Adult Issues of Sustainability, South Africa Issues of Sustainability, and Relationship between SDG 3 and SDG 4. The three major themes of SDG 4 were the Role of Universities in Sustainability, Sustainability during Covid-19, and Challenges of Implementation.
Originality/value
This study provided several potential future research directions of the least researched SDG in higher education to support scholars to make up the gap in the field. Also, this study pointed out some pedagogical strategies and competencies needed to aid higher education institutions in achieving the 17 SDGs.
Details
Keywords
Hsin-Hsuan Chung and Jiangping Chen
This paper aims to understand the characteristics of current misinformation detection studies, including the datasets used by researchers, the computational models or algorithms…
Abstract
Purpose
This paper aims to understand the characteristics of current misinformation detection studies, including the datasets used by researchers, the computational models or algorithms being developed or applied, and the performance of misinformation detection models or algorithms.
Design/methodology/approach
We first identified articles from the Scopus database with inclusion and exclusion criteria. Then a coding scheme was derived from the articles based on research questions. Next, datasets, models, and performance were coded. The paper concluded with answers to research questions and future research directions.
Findings
From 115 relevant articles published during 2019–2023 on misinformation detection. We found that most studies used previously existing datasets. Twitter (now X) has been the most widely used source for collecting social media misinformation data. The ten most frequently used datasets are identified. Most studies (96.1%) developed or applied machine learning, especially deep learning models. The most advanced current misinformation detection models could achieve pretty high performance. For example, among 104 studies reporting performance with accuracy, 44.2% achieved an accuracy of 0.95 or higher, and 24.0% achieved 0.90–0.94 on accuracy.
Research limitations/implications
Our study only reviewed English articles from 2019–2023 that are included in the Scopus database. Articles that are not included in the Scopus database are not reviewed.
Practical implications
The high performance of misinformation detection indicates that social media should be able to detect most misinformation if they are willing to do it. However, no system or algorithm could achieve 100% misinformation on performance. Due to the complexity of misinformation, users of social media still need to improve their capabilities of evaluating information on the Internet.
Social implications
This study provides evidence to policymakers that social media platforms have the capability of detecting most misinformation posted. These platforms are responsible for alerting to suspicious postings with misinformation.
Originality/value
This study identifies datasets, computer models, and performance of models from current misinformation detection research. The findings will help social media companies, computer scientists, and information system designers improve their misinformation detection systems. It will also help students in information science and computer science to study the latest models and algorithms. Information professionals may work with computer scientists to improve datasets used for misinformation detection.
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Keywords
Jiyang Yu, Hua Zhong and Marzia Bolpagni
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.