Our project investigates the effects of team commingling on startups’ innovation performance post acquisition. It delves into external causes of team commingling, one of which is the cost of air travel between parent and target companies. In particular, we study the consequences of introducing direct flights between R&D locations on the commingling of inventors and propose that direct flights lower the cost of distant collaboration, and as a result, benefit innovation and discovery. To examine direct flights as an exogenous shock to the cost of team commingling, we first built a tool that takes in pairs of locations as inputs and outputs the number of direct flights for each month between 1994 and 2018. However, data on company R&D locations is imperative to our research. Hence, we built a string matching algorithm that gathers addresses of patent assignees to 5000+ companies and a geolocation our geolocation clustering algorithm that groups these locations to pinpoint the companies’ R&D centers.
Through this research experience, I gained tremendous insights into how researchers think and approach problems. I was amazed at how an obstacle along the way would lead us to learn new coding practices and try out very different solutions. While implementing and analyzing a large amount of data, I learned to slowly dissect problems, optimize algorithms, and even learned to use Wharton’s HPCC. Most importantly, I came to understand the importance of computer science in conducting any sort of research, especially in assimilating data from different databases.
I am incredibly thankful for this opportunity and to my advisor for including me in multiple projects. This glimpse into what academic research would be like is invaluable in helping to shape the course of my education.