Looking under the hood—How incumbent characteristics reduce the innovation impact of trajectory-changing demand-pull policy mixes for battery electric vehicles

Abstract

Policymakers increasingly implement trajectory-changing demand-pull policies that support the emergence of technologies outside old innovation trajectories. While previous studies provide important insights into how such demand-pull policies drive innovation, we lack a detailed understanding of the role of incumbents' heterogeneity. Understanding the determinants of incumbents' responses to trajectory-changing demand-pull policy mixes is valuable, as these firms might hinder but also enable sustainability transitions. We introduce the idea of demand-pull policy mixes and then investigate how incumbents' characteristics shape the impact of demand-pull policy mixes on incumbents' innovation activity. Building on the literature on incumbent adaptation, we find empirical evidence that incumbents' technological capabilities, firm performance, and commitment to old technology can inhibit the impact of trajectory-changing demand-pull policy mixes on their innovation activity. This highlights that policy impacts depend on incumbents' heterogeneity and, thus, on the incumbent firm population within a country. In addition, the effectiveness of trajectory-changing demand-pull policy mixes may be enhanced by the use of phase-out and technology-push policies. However, some policies that are considered destabilizing for old technologies may be less effective than assumed in changing innovation trajectories. Moreover, our findings suggest that characteristics that inhibit incumbents' adaptation to technological change in general, such as commitments to old technology, also constrain their responsiveness to trajectory-changing demand-pull policy mixes. Thus, incumbents that experience the greatest inertia to adapt—and hence are most in need of external incentives—are also the least responsive to incentives, such that they are at risk of being caught in a double trap.