Arbeitspapiere des Instituts für Betriebswirtschaftslehre, Universität Kiel
Managers entrusted with new product development (NPD) have to seek the optimal balance between prior competencies and learning activities to generate successful products. Yet prior NPD research has largely taken a positivistic view of learning despite an inkling that too much learning can lead to dysfunction. This study attempts to contribute the NPD literature by analyzing whether and, if so, when technological learning engenders shortcomings and affects new product commercialization. We use a multi-informant longitudinal design combining unique survey and patent data on a multi-industry sample of about 163 product-based R&D collaborations in Germany. The results of a Cox regression reveal that prior technological knowledge is pervasively important for NPD and confirm that an inverted U-shaped relationship holds between the likelihood of commercialization and technological learning. The inverted U-shaped relationship supports the characterization of learning as a double-edged sword that helps overcome rigidities but also has downsides if taken too far. Exploitive learning, in particular, is the later of the two to incur downsides. This draws attention to the inherent uncertainty and costs of experimentation when learning is geared to exploration. As expected, technological progress determines NPD, but the influence is negligibly small. We argue that inertia dominates NPD projects, at least in the short-term. Our findings mainly contribute to the NPD literature by demonstrating a differentiated learning-performance relationship. The results reveal whether and, if so, when, learning can become a drag to NPC and traps may occur. Managers are advised to ensure, at least, a fit between project requirements and prior competencies, but also to ensure adequate learning. Irrespective whether learning is geared to exploration or exploitation, it is advisable to maintain it at an appropriate level. This article suggests a patent-based instrument to monitor and manage learning efforts.
new product development technological learning prior technological knowledge technological progress patent data