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In spite of substantial interest in open innovation (OI), both research and practice lack methods that support companies in managing their IT innovation projects (ITIPs) relative to OI. We contribute to the closure of this gap by providing an approach for an ex ante financial evaluation of OI application, which involves developing a theoretical model that determines the optimal degree of openness in ITIPs. Based on our model, we examine relevant causal relationships by analyzing the influence of a company's ability to manage OI and the probability of success in OI application on the theoretical optimum. We find that the optimal openness level is linked with the company's ability to manage OI, which can incorporate organizational, cultural, and technological maturity. To increase the value contribution of OI, companies should focus on a steady improvement in managing OI. The results provide both an indicator for practical decision‐making and a starting point for future research.
IT and IT innovations are now considered as one of the most relevant value drivers from an economic and social perspective and an important factor for business success. Thereby, creation and adoption of IT innovations can lead to manifold benefits for a company, such as higher expected cash flows due to the first mover advantage. However, they are also associated with various challenges, such as a high uncertainty about their future development. This doctoral thesis contributes to the field of (IT) innovation management by providing new approaches that support the management of the creation and adoption of IT innovations following the principles of a value-based management (VBM). Related to creation of IT innovations, research papers P1 and P2 focus on improvement of IT innovation projects’ value contribution and investigate how companies can evaluate their projects from an ex ante perspective to optimize the degree of openness (P1), as well as to optimize the team design (P2). Related to adoption of IT innovations, research paper P3 focuses on improvement of the investment strategy and investigates how to allocate a strategic IT innovation budget to IT innovations with different maturity to balance their risk and return potentials. Based on the example of Big Data Analytics, P4 addresses the need for carefully planning and structuring the adoption of IT innovations to ensure the realization of their value contribution in the sense of VBM, whereas P5 points out the importance of evaluating the changes arising through this adoption, for example by assessing the associated IT security risks.
