In response to uncertainty of prospective technologies and how they might fit market demand, firms tend to establish R&D alliances. In this chapter the effect over time of continuation of underperforming R&D alliances on innovation performance during the pre-market stage is investigated. This stage is characterized by non-linearity, as expected outcomes and market demands are uncertain. Literature suggests that computational modeling in particular agent-based modeling can be used to investigate such non-linear processes. Agent based modeling starts with simple behavioral rules that develop into emergent system-level behaviors, and in that way controlled system level experiments are used to identify in an inductive way causal mechanisms that drive the system development. In this chapter’s simulation model, an agent decides to continue its R&D alliance based on its strategic and cooperation objectives. After evaluating if the strategic goals is met, firms can decide about the extent to which to continue the R&D alliances if the strategic goal is not met. This is called persistency. The model is aimed to explain developmental paths and patterns of the co-evolution of alliances and technology. Despite suggestions to investigate non-linear processes in the pre-market phase by using an agent-based model, agent-based models so far do not focus on the impact of alliance continuation on innovation performance over the path of technology development. In previous research these paths mainly have been investigated in case and cross sectional studies but not in an agent based model. A base-line model is developed and the extent to which it reflects reality is analyzed in order to improve the model’s performance.
|Title of host publication||Modeling and Simulation Techniques for Improved Business Processes|
|Number of pages||23|
|Publication status||Published - 1 Jan 2018|