A critical component of corporate research and development (R&D) efforts is access to funding. To raise the large amounts of capital required for continued growth, R&D-intensive start-ups must often turn to initial public offerings (IPOs) or takeovers by established firms. Yet, these transactions are not simply short-term events that infuse capital into firms, but delineate distinct stages in the evolution of high-tech ventures. Thus, these transactions may subsequently shape the very innovative activities they are funding. This dissertation consists of two studies that examine the relationships among organizational innovation, learning, and corporate restructuring at different stages in the life cycles of medical device ventures.
The first study focuses on the period before any liquidity event. It develops and tests theory concerning how and when private ventures' capabilities for technological innovation affect their rates of IPO versus acquisition. I show that the strength of a firm's technological knowledge base increases IPO rates, while the strength of a firm's product knowledge base increases acquisition rates. Exploratory search that builds on knowledge developed by technologically distant firms has a positive effect on IPO rates, but a negative effect on acquisition rates. I also show that product-based exploitative search increases acquisition rates, and product-based exploratory search increases both types of liquidity event rates. In addition, equity market conditions, venture capital investment, and alliances moderate the relationships between organizational innovation and liquidity event rates.
The second study investigates whether, how, and when going public affects firms' innovative activities. I propose that an IPO fundamentally reshapes a firm's internal operations, and consider implications for the firm's overall rate of innovation and its innovation search strategies. I find that a firm's overall rate of innovation increases after the firm goes public. Going public also increases the proportion of innovative search that exploits internally developed knowledge and the rate of exploratory innovation building on public-sector knowledge. I use inverse probability of treatment and censoring weights to account for self-selection into IPO and the presence of time-dependent confounders; estimates represent population average treatment effects.