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To date, most IT/IS Project Portfolio Management (PPM) research has been focused on how to identify, evaluate and prioritize projects that can contribute to organizational objectives. This, in turn, gave rise to various approaches, such as those proposed in (Anantatmula & Webb, 2014; Bardhan & Sougstad, 2004; Eilat, Golany, & Shtub, 2006; Fliedner & Liesiö, 2016; Killen & Hunt, 2013; Killen, 2017; Medaglia, Graves, & Ringuest, 2007; Verma & Sinha, 2002), to help making portfolio choices more efficient. At the heart of this quest has been an emphasis on how to design project interdependencies to secure their synergistic effects on portfolio performance. For example, a project portfolio aimed at delivering new banking services may benefit more from sharing knowledge that could be later incorporated to develop more innovative services. As this example implies, the interdependencies between projects can play a vital role in bringing about increased benefits, and thus contribute to portfolio success in one way or another. Despite the widely recognized importance of project interdependencies (PIs) in the context of IT/IS project portfolios, little is known about how PIs can be managed efficiently when they get more and more complex. This can seriously disrupt work in the portfolio. Thompson (1967) was a pioneer in drawing attention to interdependencies in organizational systems. Thompson categorized interdependencies into three forms representing different degrees of contingency: pooled, sequential, and reciprocal. In pooled interdependency, each organizational unit contributes independently to and is supported by the whole organization. To the contrary, in sequential interdependency an output of one organizational unit is an input for another unit, while in reciprocal interdependency each unit’s output is input for the other units. Pooled interdependencies can be handled using standardized routines while sequential and reciprocal interdependencies can be handled using schedules and plans, and mutual adjustments respectively (Thompson, 1967). In this sense, managing a multitude of interproject dependencies with varying degrees of complexity can become overwhelming and very difficult to handle (Fliedner & Liesiö, 2016). Several other authors have noted that the complexity of the project portfolio increases by a number of factors including: number of projects, the degree of interdependency between the projects, and the magnitude and frequency of change in projects and interdependencies (Blecic, Cecchini, & Pusceddu, 2008; Danilovic & Browning, 2007; Teller, Unger, Kock, & Gemünden, 2012; Voss & Kock, 2013). However, only a few authors have offered to examine the complexity aspects of managing PIs. For example, Bardhan, Bagchi, & Sougstad (2004) introduced a model that accounts for the complexity in valuing project portfolios while considering the impact of PIs on portfolio value. Another author (Killen, 2013) found that graphical representations of projects and their interdependencies can help in identifying and reducing the complexity of managing these interdependencies. Despite the efforts to incorporate PIs in many approaches it remains unclear how to overcome the complexity of managing these interdependencies along the PPM lifecycle and further handle them during unexpected events.