Investigating the Barriers to Applying the Internet-of-Things-Based Technologies to Construction Site Safety Management.

The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to "productivity reduction due to wearable sensors", "the need for technical training", and "the need for continuous monitoring" were the most significant, while "limitations on hardware and software and lack of standardization in efforts," "the need for proper light for smooth functionality", and "safety hazards" were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM.

What Makes People Hide Knowledge? Influence of Passive Leadership and Creative Self-Efficacy.

Although numerous studies have been conducted in the field of knowledge sharing with a focus given to its importance, very little attention has been given to knowledge hiding practices. A very few studies have been found to make an attempt to figure out its impact and antecedents. Likewise, the negative role of passive leadership in the project management literature has not been evidenced enough despite its existence in project-based organizations. Both knowledge hiding and passive leadership are the highly neglected areas in the project management literature. Therefore, this study not only attempts to investigate the influence of passive leadership on knowledge hiding but also aims to explore the role of creative self-efficacy between them. IT project organizations were chosen to collect data because of their high failure rate due to an insufficient knowledge transfer. The findings of this study revealed that the neglected passive leadership greatly influences the knowledge hiding practices among individuals. However, according to the results, knowledge hiding practices are found to reduce the presence of creative self-efficacy. Thus, the antecedents of knowledge hiding should be considered to create an innovative and successful business environment. The results are highly significant not only for the field of project management but also for other practitioners.

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects.

Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.