• Economic development and construction safety research: a bibliometrics approach

      Luo, Fansong; Li, Rita Yi Man; Crabbe, M. James C.; Pu, Ruihui; Hong Kong Shue Yan University; Oxford University; University of Bedfordshire; Shanxi University; Srinakharinwirot University (Elsevier, 2021-10-14)
      The construction industry contributes significantly to economic development worldwide, yet it is one of the most hazardous industries where numerous accidents and fatalities happen every year. Little research to date has shed light on the impact of economic development on construction safety research. In this paper, we conduct an analysis of construction safety articles published in the 21st century via a bibliometrics approach. We have analysed: (1) construction safety in developed and developing countries; (2) the major organisations that have conducted construction safety research; (3) authors and territories of the research and (4) topics in construction safety and future research directions. The largest number of published construction safety documents were published by scholars from the US and China; the total number of published articles by these two countries was 1,125, at 56% of the 2000 articles that were published. Both countries showed high levels of research collaboration. While our results suggest that economic development may drive academic construction safety research, there has been an increase in construction safety research conducted by developing countries in recent years, probably due to an improvement in their economic development. While authors’ keywords evidenced the popularity of research on safety management and climate, the network analysis on all keywords, i.e. keywords given by Web of Science and authors, suggest that construction safety research focused on three areas: construction safety management, the relationship between people and construction safety, and the protection and health of workers’ impact on construction safety. We found that there is a new interdisciplinary research trend where construction safety combines with digital technologies, with the largest number involving deep learning. Other trends focus on machine learning, Building Information Modelling, machine learning and visualisation.
    • Safety knowledge sharing on Twitter: a social network analysis

      Yao, Qi; Li, Rita Yi Man; Song, Lingxi; Crabbe, M. James C.; Chongqing Technology and Business University; Hong Kong Shue Yan University; Rajamangala University of Technology Tawan-Ok; Oxford University; University of Bedfordshire (Elsevier, 2021-07-28)
      Many studies show that unsafe behavior is the main cause of construction accidents. Safety education and training are effective means to minimise people’s unsafe behaviors. Apart from traditional face-to-face construction knowledge sharing, social media is a good tool because it is convenient, efficient, and widely used. We applied both social network analysis and sentiment analysis to investigate knowledge sharing on Twitter. Our study is a novel attempt to understand social structure of “construction safety”- related twitter networks and the opinion leaders. We selected and analyzed 6561 tweets of three users’ networks on Twitter – “construction safety”, “construction health” and “construction accident”. We found that three networks had low density and many isolated vertices, which showed that users did not actively interact with each other. The opinion leaders in this study were mostly organizations or government agencies. The top one is “cif_ireland”, the Irish construction industry’s representative body, the Construction Industry Federation. 3200 Tweets of the top opinion leader were analyzed through graph metrics calculation, cluster analysis, sentiment analysis, and correlation analysis. The opinion leader used Twitter as a medium to disseminate the latest safety news. Thus, we may use Twitter to stimulate people’s interest on construction safety topics, share construction safety knowledge, opinions and ideas. Besides, our results showed that sentiment valence had no correlation with number of favorites or retweets. Nevertheless, there was a positive correlation between favorites and retweets.