Toward an Economically Optimal Team Design in IT-Related Innovation Projects

Driven by the increased relevance of digitalised and hypercompetitive business environments, companies need to focus on IT-related innovation projects (ITIPs) to guarantee long-term success. Although prior research has illustrated that an appropriate team design can increase project performance, an approach for determining the economically optimal team design from an ex ante perspective is missing. Against this backdrop, we follow analytical modelling research and develop a model that determines the optimal team design for an ITIP by transferring central findings of previous research regarding relevant influencing factors, e.g., team size and academic background diversity, into an ex ante economic evaluation. Thereby, our model allows the comparison of different team designs (i.e., team compositions) with regard to the prospective monetary project performance. Generally, the results show that only about a fifth of the random team designs resulted in a positive profit. In contrast, the well-founded, optimal team designs proposed by our model led to a positive profit in almost 90% of all cases. Regarding the influencing parameters, we observe that team size is the most important factor since a deviation from the optimum has a much more significant effect on the expected profit than do other factors such as work experience. To ensure the real-world fidelity and applicability of our model, we discuss the underlying assumptions with two practitioners. Our contribution is manifold: Inter alia, from an academic perspective, we enhance existing research on team design by converting well-accepted qualitative findings from a frequently investigated field outside business administration (i.e., [social] psychology) into a quantitative model that allows for the ex ante economic evaluation of team design parameters. For practitioners, we provide a model that assists managers in designing ITIP teams that are more likely to deliver desired results. This model enables managers to avoid relying only on gut feeling when designing ITIP teams, as is currently often the case due to a lack of alternative approaches.