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.
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.