Adopting Dr. Robot: Responses to Competitor Adoption of Innovation

Research Summary: How do an organization's task requirements affect the ways in which it reacts to competitors' strategic investments? This study uses a novel measure of task requirements (Case Mix Index), to test the competitive and spillover effects of prior adoption on a focal organization's timing of adoption, while accounting for the underlying demand-side drivers of adoption. Results of an empirical study of the adoption of robotic surgery systems by U.S. hospitals validate our measure as a predictor of the timing of adoption. Further, this measure moderates the effect of adoption by competitors, delaying investment in the technology for those hospitals most likely to adopt robotic surgery based on their patient population, while accelerating adoption for those with the lowest demand for the technology.Managerial Summary: When a nearby competitor adopts an important innovation, an organization may react by either imitating their strategy in order to stay competitive or deferring the choice to adopt the technology in order to differentiate and focus elsewhere. Which of these paths it decides to follow may be influenced by the type and difficulty of problems it handles for its customers (task requirements). Using the adoption of robotic surgery in the U.S. Hospital industry, we find that organizations that serve more difficult customer needs defer investment in response to a competitor's technology adoption, while those who address less difficult problems on average are likely to respond by accelerating adoption.

Automated summary

Research shows that an organization's task requirements can significantly impact its response to competitors' strategic investments. By using a novel measure of task requirements (Case Mix Index), this study examines the competitive and spillover effects of prior adoption on a focal organization's timing of adoption, while considering the underlying demand-side drivers. Empirical findings from the adoption of robotic surgery systems by U.S. hospitals validate the measure as a predictor of adoption timing. Furthermore, the measure acts as a moderator of the adoption effect, delaying investment for hospitals likely to adopt based on patient population and accelerating adoption for those with low demand for the technology.