My research explores how Artificial Intelligence (AI) and Bayesian -and Non-Bayesian- learning reshape entrepreneurship and strategy in uncertain contexts.
Neoclassical economics assumes decision-makers navigate an exogenous, probabilistic environment—one that exists independently of their actions. But what if the environment is endogenous, actively shaped by agents' decisions? This perspective challenges traditional models and raises a key question: how can probabilistic (Bayesian) thinking and AI, a prediction machine, drive innovation in uncertain conditions?
Through large-scale lab and field experiments, I examine how AI and strategic decision-making intersect to empower entrepreneurs. My work aims at shedding light on how different levels of Bayesian learning and AI can be tools not just for navigating the unknown but for actively reshaping it, offering scholars fresh perspectives on strategy and innovation in the age of intelligent systems.
(Authors' names are listed in alphabetic order)
PUBLICATIONS
Included as Best Paper, AOM 2022, TIM Division
WORKING PAPERS
with Arnaldo Camuffo and Alfonso Gambardella (Reject & Resubmit at Management Science)
Finalist Best Conference Paper prize, AOM 2023, STR Division
Included as Best Paper, AOM 2023, STR Division
with Annamaria Conti (SSRN working paper)
WORK IN PROGRESS
with Claudia Frosi
A Scientific Approach to Entrepreneurial Decision Making: Large Scale Replication and Extension
(Strategic Management Journal)
This paper runs a large-scale replication of Camuffo et al. (2020), involving 759 firms in four randomized control trials. The larger sample generates novel and more precise insights about the teachability and implications of a scientific approach in entrepreneurship. We observe a positive impact on idea termination and results that are consistent with a non-linear effect on radical pivots, with treated firms running few over no or repeated pivots. We provide a theoretical interpretation of the empirical results: the scientific approach enhances entrepreneurs' efficiency in searching for viable ideas and raises their methodic doubt because, like scientists, they realize that there may be alternative scenarios from the ones that they theorize
Design-Based and Theory-Based Approaches to Strategic Decisions
(Organization Science)
Recent literature highlighted two important dimensions of strategic decisions. On the one hand decision-makers formulate and test theories about future scenarios; on the other hand, they take actions to shape future scenarios. Both dimensions are embedded in what decision makers do. However, this paper develops a unified framework that encompasses these two approaches and disentangles their implications. We provide evidence using a 3-arm randomized control trial conducted in Italy that trained 308 early-stage entrepreneurs randomly allocated to a training that emphasizes the former approach, a training that emphasizes the latter approach, and a control group. We find that both dimensions entail the collection of fewer information to make decisions. However, the design-based dimension implies that entrepreneurs still take actions to change scenarios when they receive unfavorable information, while the theory-based dimension induces entrepreneurs to terminate their projects. The theory-based dimension is associated with greater performance conditional on survival. We conclude that the theory-based dimension is ideal when decision-makers seek high performance, while the design-based dimension ensures that projects earn fair returns and survive in spite of negative information.
Entrepreneurship and Unknown Events: Experimental Evidence
(Rej & Rev Management Science)
Entrepreneurs can either focus on actions to shape events into preferred ones and create value, or develop and test "theories" of value creation to predict future events. While uncertainty, in both cases, is characteristic of many entrepreneurial decisions, there is no empirical evidence about how entrepreneurs react when they anticipate events that they cannot describe (unknown events). What kind of belief updating rules do they adopt, if any? We leverage data from a 3-arm randomized control trial. We find that all entrepreneurs change optimally their belief distribution when they anticipate unknown events. Compared to entrepreneurs trained to focus on actions, and a control group, entrepreneurs trained to develop and test theories exhibit higher expected values and change their distribution of beliefs to a lesser extent. Our interpretation is that testing theories reflects an efficient search of business opportunities. Moreover, we find that they adopt well-known updating rules, consistently with probabilistic reasoning. The paper's unique data and evidence can open interesting avenues for academic research on the details of decision making under uncertainty, and its implications for practice.
The Selective Tailwind Effect of Artificial Intelligence
(available at SSRN)
What role does AI play in entrepreneurial decision making? We explore this question by analyzing the impact of AI predictive tools on the performance of a large sample of US startups. We exploit the random release of Google Analytics 4 (GA4) which introduced AI predictive tools especially useful for mobile app developers. Leveraging this shock in a difference-in-differences model, we find that post-GA4-release there is a boost in customer acquisition. However, the positive premium is driven by the upper tail of the treatment effect distribution, and not by marginal improvements. These effects are largest for innovative startups led by highly skilled founders. Shedding light on the mechanisms, we show that GA4 boosts the productivity of A/B testing tools. Overall, these findings suggest that AI predictive tools are useful for complementing skilled human capital in formulating new testable business hypotheses, especially relevant for the detection of breakthroughs.
The Devil is in The Tails. Entrepreneurial Uncertainty and Strategic Changes: Evidence from a Field Experiment in India
This study explores the role of theory-based experimentation (TbE) in entrepreneurship, focusing on its impact within contexts of fundamental uncertainty. Conducted over 14 months with early-stage entrepreneurs in Hyderabad, India, during the COVID-19 pandemic, the research delves into the relationship between uncertainty, theory-based decision-making, and entrepreneurial actions such as pivoting. The findings indicate that TbE entrepreneurs demonstrate higher willingness to change theory, registering more strategic changes (pivots) compared to a control group. TbE entrepreneurs exhibit a higher tolerance for uncertainty, valuing both uncertainty in their predictions and the potential for higher rewards. The study suggests that TbE entrepreneurs show a greater openness to uncertain value realizations in their projects. Additionally, the research contributes to entrepreneurship studies by empirically capturing entrepreneurs' perceived uncertainty and substantiating the theoretical debate on the entrepreneurship-uncertainty nexus. It also adds to the growing body of research emphasizing the benefits of theorizing in decision-making processes.