Market Returns to Digital Innovations: A Group Based Trajectory Approach

In this paper, we examine how stock market valuations and investor expectations shape the response of incumbent firms to technological innovation. We situate our study in the context of the significant and pervasive shift across diverse industries towards digitally biased innovations, and seek to understand which incumbent firms have been able to adopt new digital technologies as well as extract value from them. In doing so, our analyses address the endogenous relationship between technology adoption and performance that often renders it difficult to discern the impact of technological innovation. We use group-based trajectory (GBT) models to first identify data-derived market performance types that are difficult to theoretically predict ex ante. We then examine these types to descriptively derive the underlying strategic position that conditions their response to technological change. Finally, we use propensity score matching to assess the propensity for digital innovations and the subsequent returns to digital innovation by comparing the ex post performance of digital innovators (treatment) with a matched sample of digital laggards (control) for each performance type. Using data at the intersection of COMPUSTAT, CRSP and the Google patent assignment database spanning the years 1981-2010, we find that firms with greater market expectations of future growth versus current period profits are more likely to adopt digital innovation and obtain higher ex post market valuations. Our results advance the rich body of work that highlights the strategic significance of external stakeholders in the face of technological change.