Founding team heterogeneity and the performance of new firms: An Empirical Analysis of the AI industry.

The thesis explores the effects of human capital heterogeneities within founding teams on the temporal survival of new firms in the Artificial Intelligence industry. A sample of 484 firms, established between 2000 and 2015, is examined for this study. A survival analysis, the Weibull model, is used to explore the effect of team diversity on the hazard of exit. The findings show that academic field heterogeneity in the founding teams, as measured by edu-cational diversity, negatively affects the hazard rate for all types of market exits. In contrast, no significant impact on the hazard rate is found for functional or demographic heterogenei-ty. Additionally, a secondary duration analysis is performed in this thesis to gain a better understanding of the effects of founding team heterogeneity on the exit modes of the firms. The results reveal that diversity across educational fields has a positive impact on the hazard rate of bankruptcy. Furthermore, functional diversity has a significant negative impact on the dissolution hazard rate of bankruptcy. This suggests that teams with greater diversity in pre-vious professions face lower odds of encountering a dissolution. Conversely, demographic diversity demonstrated no significant effects within this context. The results point to the dif-ferent effects of the dimensions of diversity in founding teams. It highlights the role of sur-face-level heterogeneities and deeper-level heterogeneity in the performance of new firms. With this thesis, I aim to shed light on the literature gap on the characteristics and dynamics in the Artificial Intelligence industry. I aspire to contribute to filling the literature gap on the effects of heterogeneity in founding teams.