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 (VC) industry, evidence that general partners (GP) drive such persistence remains limited. Using an out-of-sample machine learning framework on 29,021 fund-quarter observations across 722 VC funds, we test whether the identity of a fund’s GPs improves the prediction of its future performance. We find that it does, raising predictive accuracy by 2.2 percentage points. This gain is recovered by the fund’s partners’ prior performance and vanishes when partners have no track record, indicating persistence that operates through realized performance rather than observable human capital or demographics. The persistence resides in individuals: firm identity adds nothing once GPs are known. Our findings illustrate where persistent advantage resides in VC and that managerial effects are context dependent.

Similarity among Startups in Accelerators

with Kai Becker (VU Amsterdam)

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.

The Dual Effects of Mentorship in Early-Stage Entrepreneurship:
Evidence from Pre-Accelerators

with Monica Masucci (University of Sussex), Adriana Cesa Bianchi (Bocconi) and Andrea Fosfuri (Bocconi)

Pre-accelerators are emerging entrepreneurial support organizations (ESOs) targeting individuals at the earliest stages of venture creation. Mentorship dynamics within these early phases are extremely important and yet remain understudied. Building on an uncertainty-reduction perspective, we examine how related mentorship, whereby mentors possess domain-specific knowledge and professional experience in the mentee’s industry, shapes two key early-stage outcomes: entrepreneurial intention and entrepreneurial persistence. Using original data from 113 founders across eleven batches (2020-2025) of the pre-acceleration program of a European university, we find a dual pattern of associations. Related mentorship is positively associated with entrepreneurial intention, suggesting it strengthens founders’ motivation to pursue entrepreneurship. Concurrently, it is negatively associated with entrepreneurial persistence, consistent with more informed and efficient decision-making regarding project viability. These findings contribute to the literature on early-stage ESOs by highlighting the heterogeneous and context-specific effects of mentorship in pre-accelerators.