Purpose: The purpose of this thesis is to explore the components of the innovation value chain, in particular the relationship between the main influencing factors and how inbound innovations are evaluated and selected in practice within an open innovation framework.

Problem formulation: The innovation value chain involves a number of phases. A common description is that of a sequence consisting of idea generation, idea selection and idea diffusion. The literature on idea generation is quite vast, and multiple methodologies exist to allow companies to implement frameworks for idea generation and sourcing. But how does one select which ideas to invest in and progress to development and implementation? Specifically, when externally sourced inbound ideas are included, there is a need to understand how these ideas are rated and selected. This leads us to our research question; How is the selection process designed for external innovations in an open innovation framework?

Method: A European company was selected to understand how this process is implemented in practice. This is described and analyzed by a single embedded case study. The sources of data consisted of interviews and documentation. The research process was iterative, consisting of a cycle of literature review, semi-structured interviews and documentation review. The data was then analyzed using investor triangulation, to ensure the quality of the data and validity of the case study, and was categorized according to the main influential factors identified.

Findings: It is found that the case company has two initiatives for sourcing of inbound innovations. In both these cases, the selection process is well developed and formalized, and a process similar to the evaluation matrix method is implemented to evaluate and rank inbound ideas. The particular method employed ranks innovations along different dimensions, however there are differences depending on the time scope of the particular initiative. For iGrow, the more short-term scope initiative, business viability is among the highest ranking parameters, while for weFund, the more long term scope initiative, the scientific merits of the innovation and the alignment with the company’s long term strategy is more important. It is also found that there is a strong connection to the company strategy in the selection process, however this connection is more diffuse and not formalized. Based on the data analysis, it is found that the company may benefit from creating a stronger coupling between innovation selection and company strategy by formalizing the connection. It is also found that including sequencing opportunities in the innovation evaluation matrix may better allow for a chain of innovations to be created that steers the company towards the goals set in the company strategy.