Danilo Messinese

Field: Strategy, Entrepreneurship

Research Interests: Strategies under uncertainty, Entrepreneurial strategies, Field        experiments

Graduation: June 22nd 2022

References

• Alfonso Gambardella alfonso.gambardella@unibocconi.it
• Arnaldo Camuffo arnaldo.camuffo@unibocconi.it
• Elena Novelli elena.novelli.1@city.ac.uk
• Luisa Gagliardi luisa.gagliardi@unibocconi.it

Contact
Bocconi University, Department of Management and Technology, Office 4.B1.10, Via G. Roentgen 1, 20136, Milan (Italy)
Email: danilo.messinese@unibocconi.it
Mobile: (+39) 340 6009199

Danilo, during his doctoral studies, has developed a research agenda centered around how managers and entrepreneurs can effectively make strategic commitments in the face of uncertainty by adopting different strategies to acquire information. Specifically, his research tries to help answer two interrelated questions: (1) what is the most efficient way for strategists to acquire information? (2) how does this impact firms’ strategy and performance? Danilo ‘s research adopts formal modelling, large field experiments with entrepreneurs and managers, and simulation games he developed. He teaches courses in Management (Strategy module), Data-driven analysis and decision-making in business, among others.

JOB MARKET PAPER
Information acquisition with predictive and non-predictive strategies

Decision-makers can reduce uncertainty by acquiring information via a predictive or a non-predictive strategy. Predictive strategies focus on estimating unknown states of the world through probabilistic tools, while non-predictive strategies focus on transforming unknown states of the world through design tools. This paper reconciles these two - groups of - strategies with a model of information acquisition. We tested the model with data from a field experiment designed to train a group of entrepreneurs to collect information to predict unknowns, and a second group to gather information to shape the environment. The experiment included 308 entrepreneurs and 3,388 observations. Consistent with the predictions of the model, the paper finds that the two groups acquire less information to make a decision with respect to an untrained "pure" control group. However, only predictive entrepreneurs follow an optimal policy to acquire information because this policy requires a precise estimation of the unknown value of the idea. Conversely, non-predictive entrepreneurs pursue actions that they believe can be better or that depend on their preferences, eschewing prediction. The paper provides evidence of this mechanism and shows that predictive decision-makers perform better in monetary terms.

PUBLICATIONS

Camuffo A., Gambardella A., Messinese D., Novelli E., Paolucci E., Spina C. 2022. A Scientific Approach to Innovation Management: Theory and Evidence from Four Field Experiments. CEPR Discussion Paper No. DP15972;

Messinese D. 2022. Exploring with Predictive and Control Strategies under Uncertainty, Academy of Management Proceedings Volume 2022, Issue 101 Jan 2022; included as “best paper”(AOM 2022, TIM Division)

WORKING PAPERS

Camuffo A., Gambardella A., Messinese D. - Theory-based experimentation and "Reverse Bayesianism"

Messinese D. - A systematic approach to decision-making: evidence from a lab experiment with a simulation game

Messinese D. - Information acquisition to face uncertainty in venture capital firms

Battaglia D., Colombelli A., Messinese D., Panelli A., Paolucci E., Raguseo E. - Training Aspiring Entrepreneurs to Act like Scientist: A Field Experiment with Tech Start-ups in Italy

Messinese D., Spina C. - The Way You See the Problem is the Problem: Fostering Experimentation in New Entrepreneurial Teams