Developing a Scoring Model for Optimal Selection of IT Programs to Reduce Wasted Investments Open Access
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As early as 1984, researchers viewed IT program selection as a significant issue. Melone and Wharton (1984) were prescient when they expressed the necessity of having an “agreed-upon set of factors used to evaluate IS [Information Systems] projects…[that] ...allows for an objective comparison of the strengths and weaknesses of alternative projects…” More recent academic research indicates a link between decision-making models and program success. Furthermore, the popular view of the IT department as simply a cost center adds to business leaders paying close attention to the spending within IT and, by extension, a skeptical eye when looking at IT programs. It is unlikely that programs are considered in terms of providing any revenue, directly or indirectly, to the organization within the decision-making process. Some researchers suggest moving beyond that cost center mentality and instead regard the IT department as a revenue-producing area within the organization. Towards the end of marrying both the decision-making model’s ability to bring about program success and viewing IT programs from a return on investment perspective, this research proposes a multi-criteria model for the selection of IT programs using an objective scoring method to commit investment to those programs offering the highest potential return on investment. This research identifies Business Capabilities, Resources, Program Dependencies, and Business Values as significant criteria in selecting for program success and the highest potential return on investment. In order to validate the proposed decision-making model and the criteria, the model is implemented for use in the IT Program Management Office of a mid-size, Software as a Service corporation. Statistical analysis is then completed on the proposed model’s accuracy and significance of the criteria against the existing legacy decision-making model and its criteria with both historical, pre-implementation program data and experimental, post-implementation program data. The research identifies several proposed criteria as being statistically significant towards predicting program success in terms of return on investment as well as identifying two that are particularly significant. As a novel approach to IT program decision-making modeling, this research is able to confirm its feasibility and opens up avenues of future academic research towards other uses and forms of the approach.