My most recent research stream focuses on the impact of adopting different strategies to cope with uncertainty. Although the debate is fierce and vibrant, there is little empirical evidence of how building and testing theories impacts decision outcomes and monetary performance. Theory-based strategies are based on the assumption that entrepreneurs explore an exogenous environment. What happens if we relax this assumption and entrepreneurs conveice the environment as endogenous to their actions? I run large field experiments to investigate these questions.
WORKING PAPERS
with Alfonso Gambardella (Under review)
Included as Best Paper, AOM 2022, TIM Division
with Arnaldo Camuffo and Alfonso Gambardella (Under review)
Finalist Best Conference Paper prize, AOM 2023, STR Division
with Arnaldo Camuffo, Alfonso Gambardella, Elena Novelli, Emilio Paolucci, Chiara Spina (2nd round R&R)
WORK IN PROGRESS
with Claudia Frosi
Theory-Based and Design-Based Approaches to Strategic Decisions
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
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.
A Scientific Approach to Entrepreneurial Decision Making: Large Scale Replication and Extension
This large-scale replication of Camuffo et al. (2020) - 759 firms in 4 randomized control trials - confirms that a scientific approach to entrepreneurial decisions can be taught and leads to superior results. The paper yields novel contributions. First, the adoption of a scientific approach generates fewer pivots, which is associated with higher performance. Second, it develops a theoretical framework that sheds light on the underlying mechanisms: methodic doubt (greater caution) and efficient search (better information). We show that fewer pivots imply that, in our sample, the former mechanism dominates. Third, results are robust to the use of a measure of the adoption of the scientific approach instrumented by the treatment, and to models that account for the joint determination of variables.
Optimal experimentation and behavioral traits: evidence from India
Recent theoretical and empirical studies highlighted the benefits of applying experimentation when making decisions under uncertainty, particularly within highly uncertain contexts such as entrepreneurship. We focus on an explicit experimental strategy, Theory-based Experimentation (TbE) and explore two dimensions, that is the number of pivots and pivot thresholds as consequence of signals collected through experiments. We address two critical questions: 1) when do entrepreneurs decide to pivot? 2) to what extent do entrepreneurs’ behavioral biases impact the pivoting strategy? We leverage data from a randomized controlled trial with 427 early-stage entrepreneurs in India. The resulting story is multifaceted. We find that the TbE approach helps copying with behavioral biases, such as loss and risk aversion. Moreover, we show, consistently with models of experimentation, that TbE entrepreneurs are more likely to pivot when their perceived uncertainty is higher, following an optimal experimentation policy.