Assessing the impact of digital education and the role of the big data analytics course to enhance the skills and employability of engineering students.

This study aims to explore the role of digital education in the development of skills and employability for engineering students through researching the role of big data analytics courses. The empirical study proposes the hypothesis that both soft and hard skills have positive effects on human capital, individual attributes, and the career development dimensions of engineering students. This is achieved through constructing a framework of three dimensions of engineering students' employability and two competency development dimensions of big data analytics courses. A questionnaire survey was conducted with 155 college engineering students and a structural equation model (SEM) was used to test the hypotheses. The results found that courses on big data analytics have a positive impact on engineering students' abilities in both hard skills (p < 0.01) and soft skills (p < 0.001) dimensions, while soft skills have a more significant impact on engineering students' employability. The study has practical and theoretical implications that further enriches the knowledge base on engineering education and broadens our understanding of the role of digitalization in enhancing the skills and employability of engineering students.

Testing the Effects of the Digital Linguistic Landscape on Engineering Education for Smart Construction.

This study investigates the mechanism of digital linguistic landscapes in enabling engineering education for smart construction according to the educational dimensions of A (ability), S (skill), and K (knowledge). A questionnaire survey was conducted based on the core concepts of the informative dimension and symbolic dimension in digital language landscape as well as the ability dimension, knowledge dimension, and skill dimension in engineering education. Structural equation modeling (SEM) was used as the test method. The results of the research demonstrate that the informative dimension and symbolic dimension are two main aspects of DLL in education of engineering students for smart construction. Additionally, DLL has a significant positive impact on the ability, knowledge, and skill education of engineering students for smart construction. The research has theoretical and practical significance, as it not only enriches research on the relationship between DLL and engineering education for smart construction but also expands the theoretical understanding of engineering education from the perspective of linguistics. Furthermore, the study explores the path of the practical application of digital language landscape to engineering education for smart construction.

Factors influencing unsafe behaviors: A supervised learning approach.

Despite its potential, the use of machine learning in safety studies had been limited. Considering machine learning's advantage in predictive accuracy, this study used a supervised learning approach to evaluate the relative importance of different cognitive factors within the Theory of Reasoned Action (TRA) in influencing safety behavior. Data were collected from 80 workers in a tunnel construction project using a TRA-based questionnaire. At the same time, behavior-based safety (BBS) observation data, % unsafe behavior, was collected. Subsequently, with the TRA cognitive factors as the input attributes, six widely-used machine learning algorithms and logistic regression were used to develop models to predict % unsafe behavior. The receiver operating characteristic (ROC) curves show that decision tree provides the best prediction. It was found that intention and social norms have the biggest influence on whether a worker was observed to work safely or not. Thus, managers aiming to improve safety behaviors need to pay specific attention to social norms in the worksite. The study also showed that a TRA survey can be used to extend a BBS to facilitate more effective interventions. Lastly, the study showed that machine learning algorithms provide an alternative approach for analyzing the relationship between the cognitive factors and behavioral data.

Does company size matter? Validation of an integrative model of safety behavior across small and large construction companies.

INTRODUCTION: Previous safety climate studies primarily focused on either large construction companies or the construction industry as a whole, while little is known about whether company size has significant effects on workers' understanding of safety climate measures and relationships between safety climate factors and safety behavior. Thus, this study aims to: (a) test the measurement equivalence (ME) of a safety climate measure across workers from small and large companies; (b) investigate if company size alters the causal structure of the integrative model developed by Guo, Yiu, and González (2016). METHOD: Data were collected from 253 construction workers in New Zealand using a safety climate measure. This study used multi-group confirmatory factor analyses (MCFA) to test the measurement equivalence of the safety climate measure and structure invariance of the integrative model. RESULTS: Results indicate that workers from small and large companies understood the safety climate measure in a similar manner. In addition, it was suggested that company size does not change the causal structure and mediational processes of the integrative model. CONCLUSIONS: Both measurement equivalence of the safety climate measure and structural invariance of the integrative model were supported by this study. Practical applications: Findings of this study provided strong support for a meaningful use of the safety climate measure across construction companies in different sizes. Safety behavior promotion strategies designed based on the integrative model may be well suited for both large and small companies.

Identifying behaviour patterns of construction safety using system archetypes.

Construction safety management involves complex issues (e.g., different trades, multi-organizational project structure, constantly changing work environment, and transient workforce). Systems thinking is widely considered as an effective approach to understanding and managing the complexity. This paper aims to better understand dynamic complexity of construction safety management by exploring archetypes of construction safety. To achieve this, this paper adopted the ground theory method (GTM) and 22 interviews were conducted with participants in various positions (government safety inspector, client, health and safety manager, safety consultant, safety auditor, and safety researcher). Eight archetypes were emerged from the collected data: (1) safety regulations, (2) incentive programs, (3) procurement and safety, (4) safety management in small businesses (5) production and safety, (6) workers' conflicting goals, (7) blame on workers, and (8) reactive and proactive learning. These archetypes capture the interactions between a wide range of factors within various hierarchical levels and subsystems. As a free-standing tool, they advance the understanding of dynamic complexity of construction safety management and provide systemic insights into dealing with the complexity. They also can facilitate system dynamics modelling of construction safety process.