Workers in the U.S. construction industry experience workplace hazards that can lead to work-related injuries that sometimes are fatal. Reported in this paper is a case-control study of risks factors associated with 4,845 injured workers and their work environments that led to fatal rather than nonfatal injuries during 2015-2017. These injury data originally were assembled from information collected by the U.S. Bureau of Labor Statistics that were used in a machine learning competition, but were repurposed for this secondary analysis of injury risks. Sixty-one percent of workers recorded in this dataset were injured fatally. Multiple logistic regression was applied to model the probability of a fatal injury as a function of the nature of the injury, part of body injured, human factors involved, whether the injured worker was carrying out a regularly assigned task at the time of the injury, and the manner in which the injury was inflicted. Related positively, relative to benchmarks, to the probability of a fatality injury were: falls and strikes; electrocution; asphyxiation and drowning; injury to the head and neck; and working at a task not regularly assigned. Negatively related to the probability of a fatal injury were: chemical/temperature burns; amputation and crushing; fractures and dislocations; injuries to fingers, hands, wrists, and other extremities; and falls from an elevation or to the same level, although this last negative relationship is anomalous in the light of independent research findings. Findings of this study do not necessarily culpable causes of work-related death. Rather these findings identify risk factors that might prove fruitful for further analysis of the incidence, severity, and costs of construction injuries.

Although previous studies on human resource management (HRM) research trends in the global context provided very useful information about the structures of HRM research trends, they have critical limitations. Despite the growing contribution and significance of HRM practices in Korea, a dominant group of scholars has rarely focused on what is going on in the research community in the country. To overcome the limitations and fill the gaps found in studies on the global HRM trends, the purpose of this study is to conduct the keyword network analysis investigating the semantic network structure composed of Korean HRM studies. A total of 1158 research papers published by three top peer-reviewed HRM journals in Korea that were published from 2007 to 2018 were analyzed. The result shows that the whole network structure of Korean HRM has a complex semantic structure that is socially constructed. Additionally, this study identified the top 10 prominent keywords and its ego-centric networks, and nine thematic clusters. By adopting keywords network analysis in bibliometric methods, this study provides an accurate structural interpretation of Korean HRM research practice to facilitate the sustainable development of the studies on the global HRM trends.