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According to McGrath (2013), the innovation of new business models enabled by information technology is disrupting existing competition and giving new companies transient competitive advantage. In the face of volatile and uncertain business environments, both dominant and emerging companies need to be able to quickly propose innovative projects in order to cope with intensifying competition. However, a quick introduction to new projects can result in poor requirement management, which is considered the root cause of many dynamic risks, including scheduling and budgeting, as well as operational, technical, and programmatic risks (Angelow, 2015). These dynamic risks are highly complex and unpredictable in today’s creative economy, and the inability to manage and mitigate these risks can wreak havoc on IT projects (Cooper & Chapman, 1987).
An effective assessment of project risks needs to consider structural (e.g. hierarchy of authority, centralization) and contextual characteristics (e.g. corporate culture and size) (F. Warren McFarlan, 1981). PM tools and methodologies have been commonly employed to minimize project risks because they can be adapted to different project characteristics. For instance, qualitative methods can be used to rate the magnitude of potential risks resulting from tangible and intangible threats on a low, medium or high scale (Lanfranchi, Giannetto, De Pascale, & Hornoiu, 2015). Other researchers favor the use of quantitative risk management tools (e.g. Monte Carlo simulation) because of their strength in managing projects that have uncertain project schedules and low budgets (Purnus & Bodea, 2014). One study has shown that software tools that comply with capability maturity model integration (CMMI) or project management body of knowledge (PMBOK) can effectively manage project management processes and achieve project success (Pereira, Gonçalves, Von Wangenheim, & Buglione, 2013). Although the literature has emphasized the importance of using the right tools and methods, it is not clear the extent to which their use can contribute to containing project risks and helping with project success (H. Thamhain, 2013). The lack of formal risk management tools could become a barrier to the execution of a risk management program (Parker & Mobey, 2004).
In addition, organizational support consists of the cognitive and emotional involvement of the management and is indispensable for the success of projects (Liu., Wang, & Chua, 2015). This study provides insight into the relative influence of organizational support on project success in comparison with project risks, tools, and methodologies.
The remaining sections are organized as follows: first is a literature review, followed by the development of a theoretical model. Hypotheses are proposed against the research model, and the research methods are presented with a detailed discussion of the data collection and analysis methods. Analysis of the results is reported with their theoretical and practical implications. The paper concludes with a discussion of its limitations and suggestions for future research.