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Top1. Introduction
The technological landscape in Information Technology is very dynamic. In the last decade, newer IT projects have increased in several IT areas. Ironically, the IT project management literature does not clarify what helps project managers succeed in IT projects, especially in this dynamic, ever-changing, complex technological milieu.
We addressed IT projects in relatively new areas such as Data Analytics, Big Data, Data Science, Blockchain, Cloud Computing, IT Security, IoT, and Enterprise Systems. Enterprise Systems include Customer Relationship Management (CRM), Supply Chain Management (SCM), Enterprise Application Integration (EAI), and Enterprise Resource Planning (ERP). We did not differentiate between IT projects and software development projects because many IT projects involve software development (Nasir and Sahibuddin, 2011).
Many of these projects exceed their budgets, run late, or fail to meet functionality objectives (Joslin and Müller, 2016). In the United States, 83.9% partially or entirely fail (The Standish Group Report, 2019). Understanding critical success factors (CSFs) could help managers achieve better project outcomes. A CSF refers to “any circumstance, fact, or influence which contributes to a result” (Lim and Mohamed, 1999, p.243). The IT project management literature does not adequately clarify these factors.
Most research into CSFs has addressed industries such as engineering, manufacturing, construction, defense, and healthcare (Tukel and Rom, 2001; Hyvari, 2006; Radhakrishnan et al., 2016). No set of factors applies to all industries (Dvir et al., 1998). Because IT projects have characteristics different from others, such as employment of changing, sophisticated technologies, and they use different management practices and processes, we cannot expect CSFs developed for non-IT projects to apply to them (Ibbs and Kwak, 2000; Cooke-Davies and Arzymanow, 2002; Zwikael and Globerson, 2006; Nasir and Sahibuddin, 2011).
Schwalbe (2018) delineated two types of development approaches in IT Project Management – predictive life cycle and adaptive life cycle approaches. If a project’s scope is unambiguous and clear, we can accurately predict the schedule and the budget, so a predictive life cycle approach is appropriate. The adaptive approach is more suitable if the requirements are not precise early in the life cycle but instead evolve as the project progresses. Popular predictive models include the waterfall, spiral, incremental build, prototyping, and rapid application development models. Agile methodology is a commonly used adaptive life cycle model (Schwalbe, 2018). The term “agility” refers to “the project’s team ability to quickly change the project plan as a response to customer and stakeholders’ needs, market, or technology demands in order to achieve better project performance in an innovative and dynamic environment” (Conforto et al., 2016, p.667).
Prior studies used inconsistent definitions of project success. Some proposed that projects are successful if we meet the “iron triangle” criteria: delivered on time, within budget, and meeting the preset specification measures (Atkinson, 1999; Lim and Mohamed, 1999; Mantel and Meredith, 2009; Morris et al., 2010). However, some researchers employed more success criteria for large and complex projects (Bryde and Robinson, 2005), like safety, quality, user needs satisfaction, stakeholders’ satisfaction, appreciation from contracting partners, appreciation from project personnel, project impact, and future potential (Wateridge, 1998; Lim and Mohamed, 1999; Shenhar et al., 2001; Westerveld, 2003; Jugdev and Müller, 2005; Lee and Xia, 2010; Müller and Jugdev, 2012; Khan et al., 2013; Serrador and Turner, 2015). We measured project success more comprehensively along five dimensions: on-time project completion, on-budget project completion, attainment of project specifications, organizational benefits, and stakeholder satisfaction (adapted from Lee and Xia, 2010; Khan et al., 2013; Serrador and Turner, 2015).