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We study the role that switching costs, compatibility, and mergers and acquisitions, play in influencing the evolution of a multi-market industry. By looking at the case of the Local Area Networking industry, we propose a 'history friendly model' to replicate its evolution during the 1990s. Our model explains how a firm can start from a dominant position in one of the existing markets and exploit switching costs and compatibility to enter a new market when it opens. Mergers and acquisitions also play an important role as the new market is pioneered by a new start-up, which is soon acquired by the dominant incumbent. As a result of the acquisition, the acquiring firm becomes leader also in the new market.

Recent scholarship on entrepreneurship suggests that the pre-entry know-how of start-ups, embodied in their founders, affects not only entry, but also performance. Although prior work focuses on new entrants from the focal industry (i.e., employee spinouts) and academic organizations (i.e., university spinoffs), this study identifies and examines a hybrid category of start-ups from downstream user-industries, which we call "user-industry spinouts." We draw from the literature on evolutionary theory, user innovation, and industrial dynamics to propose that, given the unique combination of knowledge inherited by these entrants, their choices at entry and their ability to survive in the focal industry will differ with respect to other start-ups. We do this by extending existing work on new firm creation to investigate entry and performance in different product markets. Our findings, based on a dataset of start-ups in the semiconductor industry over a 10-year period (1997–2007), show that user-industry spinouts are more likely both to enter and to survive in market-specific product categories. We suggest that the pre-entry knowledge resources of spinouts may support entry and survival across industry boundaries. However, the specific nature of these resources will influence which product markets they choose to enter. Our results have theoretical implications for the literatures on entrepreneurship, industrial dynamics, and strategy.

AbstractIn the context of product innovation, there is an emerging trend to use Machine Learning (ML) models with the support of Design Of Experiments (DOE). The paper aims firstly to review the most suitable designs and ML models to use jointly in an Active Learning (AL) approach; it then reviews ALPERC, a novel AL approach, and proves the validity of this method through a case study on amorphous metallic alloys, where this algorithm is used in combination with a Random Forest model.