Value-driven IT Project Portfolio Management: Process Model, Evaluation Framework, and Decision Support
2022 | International Conference on Information Systems | Citations: 0
Authors: Karrenbauer, Christin; Breitner, Michael H.
Abstract: Companies must optimize their information technology (IT) project portfolio to ...
Expand
Abstract: Companies must optimize their information technology (IT) project portfolio to achieve goals. However, IT projects often exceed resources and do not create their promised value, for example, because of missing structured processes and evaluation methods. Continuous IT portfolio management is thus of importance and a critical business activity to reach value-driven goals. Guided by Design Science Research with literature reviews and expert interviews, we develop, evaluate, and adjust an IT project portfolio management process model, a holistic IT project evaluation framework, and implement a decision support system prototype. Our results and findings synthesize and extend previous research and expert opinions and guide decision-makers to make more informed and objective IT project portfolio management decisions aligned with optimal value creation. Furthermore, we deduce new research opportunities for IT project portfolio management process models, decision tools, and evaluation frameworks. Introduction and MotivationInformation technology (IT) impacts a company's long-term performance and competitiveness and forms a critical success factor (Bezdrob et al. 2020). Thereby, IT projects are characterized by complexity, crossfunctionality, dynamics, non-routine, temporality, and uncertainty. These make IT project portfolio management (ITPPM) a challenging task (Chiang and Nunez 2013;Kester et al. 2011). Selecting the "right" IT projects is essential to create optimal value. Nevertheless, ITPPM is often unstructured and decisions are made ad hoc instead of long-term planning. Thus, many IT projects deviate from their defined objectives, are not completed, or completely fail (Varajão and Trigo 2016). According to the Project Management Institute (PMI 2017a), roughly $97 million US Dollars per $ 1 billion investments in IT projects are wasted. Similarly, Lee et al. (2021) refer that on average 66% of implemented IT projects are more expensive and 33% require longer as planned. Failed IT projects due to weak ITPPM processes lead to resource losses and exceedances (e.g., Hershey: $150 in lost sales, 19% drop in earnings), project abandonment (e.g., Dell: $200 million), or bankruptcies (e.g., FoxMeyer) (Fadlalla and Amani 2015; Hughes et al. 2017). Thus, companies need adequate and resilient methods for the critical business activity of ITPPM. Considering existing interdependencies and constraints, these methods ensure that selected IT projects fit the company's strategy and create value (Chiang and Nunez 2013;Kester et al. 2011). If departments and functions are aligned to strategy, IT projects are more likely to be completed successfully ITPPM: Process Model, Evaluation Framework, and DSS Forty-Third International Conference on Information Systems, Copenhagen 2022 2 (PMI 2017a). Additionally, uniform IT guidelines stabilize the performance of IT projects (Martin 2006). However, many decisions are motivated by subjectivity, personal experiences and perceptions. Often uniform ITPPM methods, evaluation and selection criteria, and their consistent usage are missing (Varajão and Trigo 2016). An ITPPM tool can further support decision-makers (DM) to ensure efficient, transparent, and consistent decisions more rapidly (Caniëls and Bakens 2012;Killen et al. 2020).Even though ITPPM is considered to be a structured process, besides best practices for PPM (e.g., PMI 2017b) only few ITPPM process models exist in literature which are rather perfunctory. Mostly, existing models encompass three (Ajjan et al. 2016) to five main phases (Archer and Ghasemzadeh 1999). Typically, they include activities such as the definition of baseline conditions (Alaeddini and Mir-Amini 2020), IT project identification (Montgomery 2007), evaluation (Chiang and Nunez 2013), selection (Ajjan et al. 2016), and monitoring with adaptions (Miller 2002). Other than that, existing models vary in their phases and activities. Moreover, the procedure is mostly sequential with no or few re-cycles between and within phases. To address this and better meet IT project characteristics, we propose an integrated ITPPM process model that allows an objective and value-driven ITPPM. To do this, we synthesize existing process models and expand them with expert interviews. It comprises activities from different models combined within an integrated one. Unlike many existing models, it also allows re-cycles between and within individual phases and allows to address peculiarities of IT projects. It enables, e.g., a flexible reaction to changes and uncertainties and a discussion between stakeholders. In addition, IT project evaluation, prioritization, and selection are critical activities (Varajão and Trigo 2016). Several methods have been researched on this topic ranging from rather simple financial project selections (Rosacker and Olson 2009), and balance scorecard approaches (Asosheh et al. 2010), to more advanced optimization models (Cho and Shaw 2013) and fuzzy programming (Heidary et al. 2020), among others (Mohagheghi et al. 2019). In rather simple methods, IT project decisions are often based only on the evaluations. In more advanced methods, several constraints are considered, however, the project evaluation is often already available and not described in more detail (Archer and Ghasemzadeh 1999). We combine both approaches and develop a holistic scoring framework that enables a quantification of subjective estimations and thus an objective evaluation of IT projects of different types and sizes. The scoring results then serve as an input for our decision support system (DSS) prototype. It allows to select from a large number of IT project proposals those that maximize the value proposition, considering interdependencies and limitations. In doing so, we mitigate disadvantages resulting from the sole use of scoring approaches (Mohagheghi et al. 2019) and decisions are not made on an individual project level but consider other factors such as resource constraints and dependencies. Our main contributions are our three artifacts, an integrated ITPPM process model, a holistic scoring framework, and our DSS prototype. These Level 1 and Level 2 artifacts (Gregor and Hevner 2013) support ITPPM and enhance and contribute to the knowledge base. We provide knowledge to increase transparency and objectivity to make more informed IT portfolio decisions and thus decrease IT project failure rates, minimize subject manipulations, increase value creation, and goal achievement. We follow a Design Science Research (DSR) process (Hevner et al. 2004) to address the following research questions (RQ):RQ 1: What activities constitute a value-driven ITPPM process? RQ 2: How can IT projects (and proposals) be uniformly evaluated to generate a value-driven IT portfolio? RQ 3: How can a DSS support IT project selection and scheduling?First, we describe the theoretical background of ITPPM. Then, we motivate our DSR-oriented research design. Afterwards, we present our results and findings of our literature reviews and expert interviews, including an ITPPM process model and a scoring framework. Then, we introduce our DSS prototype and provide an applicability check. Finally, we discuss our results and findings, their implications, deduce recommendations for theory and practice and a research agenda, present limitations, and conclusions.
Collapse
Semantic filters:
IS project selectionchange management
Topics:
IT project project management decision support system value creation knowledge base
Methods:
qualitative interview business process modeling design artifact literature study design science
The Organizational Context of Process Reengineering Project Initiatives
1997 | Americas Conference on Information Systems | Citations: 1
Authors: Teng, James T C
Abstract: In this study, three potentially facilitating sources of influence on BPR initia ...
Expand
Abstract: In this study, three potentially facilitating sources of influence on BPR initiatives -- innovative capacity of the organization, IS maturity and Strategy-IS interface -- were examined. It was found that while factors related to IT factors such as experience in mainframe and client/server computing may facilitate the decision to reengineer, they are not critical to project success. On the other hand, factors having significant relationships beyond the initial decision include variables pertaining to innovative capacity of the organization and Strategy-IS interface.
Collapse
Semantic filters:
IS project selectionchange management
Topics:
IS maturity change management innovation management project management IS project selection
