Bocconi University
Research
Selling Attention: Ecosystem Composition and Targeting
Strategy in Ad-Funded Platforms
with Claudio Panico (Bocconi) and Carmelo Cennamo (CBS)
Ad-funded platforms monetize users’ attention by brokering it to advertisers through targeted ads, creating an inherent tension between advertiser monetization and user experience. We develop a formal model of a platform that sets advertising prices and attention brokering intensity while serving heterogeneous users, who differ in annoyance from ads, and heterogeneous advertisers, who differ in brand awareness. We show that platform pricing and advertiser targeting reach depend jointly on the composition of both sides: the platform induces broader reach and greater attention brokering when low-annoyance users predominate or when advertisers have lower brand awareness. The platform systematically induces reach beyond what users would prefer, with exploitation most severe when strong direct network effects prevent high-annoyance users from exiting, and its extent varying with ecosystem composition.
Using Machine Learning to Estimate the Effect of General Partners on Venture Capital Performance Persistence
with Corrado Botta (Bocconi) and Mario D. Amore (Bocconi)
Despite extensive research on performance persistence in the venture capital industry, the commonly held assumption that general partners drive persistence has received limited empirical attention. In this paper, we employ machine learning methods to isolate and quantify the persistent effect (if any) of general partners on performance across multiple funds. Analyzing a panel dataset of 29,021 quarterly observations covering 722 funds managed by 811 general partners between 1997 and 2022, we document statistically significant albeit modest effects of general partners on performance persistence. These magnitudes are substantially smaller than those reported in the literature, highlighting the limited external validity of extant methods for the task at hand. Moreover, the general partner effect consistently exceeds that of venture capital firms, suggesting that individual-level analyses provide greater insights than firm-level ones. Our results indicate that most of the variation in venture capital performance is not attributable to the organizational characteristics of venture capital firms and can be explained only partially by the individuals managing them.
Similarity among Startups in Accelerators
Start-up accelerators are key players in the entrepreneurial ecosystem, offering cohort-based programs to accelerate the growth of early-stage start-ups. Despite a growing literature on the benefits of accelerator participation, little is known about whether start-ups gain from exposure to peers within accelerator cohorts. In this paper, I investigate the role of business similarity among start-ups within accelerator cohorts and argue that it may lead to improved post-acceleration performance via greater exposure to relevant knowledge and peers. I test this argument using a rich, unique dataset comprising 2,505 start-ups accelerated in 128 cohorts run by eight U.S.-based accelerators between 2005 and 2018. I find strong evidence that business similarity is positively associated with post-acceleration performance: start-ups accelerated with more similar peers raise funding more quickly and exhibit improved long-term outcomes in terms of survival, employee growth and exit via acquisition. In preliminary analyses, I also show that the effect of similarity appears to exhibit decreasing marginal returns, which may signal the presence of factors that reduce the benefit of similarity at higher levels. These results offer actionable insights for accelerator managers seeking to design effective programs and for start-ups evaluating participation in an accelerator.