Stefano Benigni

Summary

My research lies at the intersection of strategy and innovation, with an emphasis on how individual and organizational decision processes shape firms’ performance outcomes. These outcomes can relate to firms’ innovation efforts as well as their broader performance. In examining this phenomenon, I study the underlying mechanisms such as learning, biases in evaluation and information aggregation structures, and the effects of mental representation that both shape individual and organization search efforts and their consequences. This work can contribute both theoretically and empirically to the literatures on strategic management, technology innovation management, and organizational theory. This research is also of practical interest to managers seeking to understand the trade-offs associated with their R&D portfolio choices.

In my dissertation, I examine how firms’ R&D evaluation processes influence the outcomes of innovation, as well as how managers' individual cognition evolves as they engage in these processes. The challenge that managers face when evaluating R&D investments is determined by the interplay of two fundamental issues. On the one hand, the commercial and strategic value of R&D opportunities is uncertain and depends on multiple interdependent factors. On the other, complexity and the extended time lags between investments and commercialization make it difficult to learn from past decisions. Hence, managers often rely on combinations of portfolio approaches and collective decision processes to make R&D evaluations.

In theoretically examining this challenge, I first focus on the individual cognition of managers who routinely evaluate R&D opportunities. I build on the managerial cognition and neuro-cognitive literatures to propose a learning mechanism that can explain how individuals may learn even when feedback is highly noisy or unobserved. This mechanism is explained in terms of changes in the structure of individuals’ mental representations. I empirically test my predictions and find that more experienced individuals develop more complex mental representations when feedback is highly noisy or unobserved, which in turn lead to more accurate evaluations.

I then explore further implications of my proposed learning mechanism in an agent-based computational simulation. This study builds on NK models of search over policies and representations and shows performance trade-offs associated with different learning strategies.

Finally, I examine a group decision process in which managers solicit and integrate the knowledge of domain experts. This setting reveals one of the mechanisms that underpins the fundamental trade-off between the costs and accuracy of different information aggregation structures.

The empirical context for my dissertation is patent evaluations and termination decisions. I use proprietary data related to a large corpus of written patent evaluation statements at a high-tech firm between 1995 and 2015.

In my research, I use quantitative methods, formal models, and natural language processing tools. I have developed an efficient algorithm for computing large scale NK models (N>100), web-scraping tools to collect data from online databases such as Google Patents, Scopus, Orbis, and LinkedIn, and NLP tools for the analysis of large text corpora.

Before starting the Doctoral programme at Imperial College, I worked as an Aerospace R&D engineer and gained some entrepreneurial experience by founding and selling a short-term rentals business.

PUBLISHED RESEARCH

• Posen, H., Ross, J., Wu, B., Benigni, S., & Cao, Z. (forthcoming) "Reconceptualizing Imitation: Implications for Dynamic Capabilities, Innovation, and Competitive Advantage", Academy of Management Annals
•  Walker, J., Brewster, C., Fontinha, R., Haak-Saheem, W., Benigni, S., Lamperti, F., & Ribaudo, D. (2022). "The unintended consequences of the pandemic on non-pandemic research activities". Research Policy, 51(1), 104369.

CONFERENCE PRESENTATIONS

• 2022 AOM Annual Meeting, Seattle
• 2022 SMS 42th Annual Conference, London
• 2021 Annual European Strategy, Entrepreneurship and Innovation (SEI) Doctoral Consortium, 01-03 October, ESADE Business School
• 2021 AOM Annual Meeting, presented "Theoretical learning: how individuals learn with delayed and ambiguous feedback" (session 1194)
• 2020 SMS 40th Annual Conference, presented "Theoretical learning: how individuals learn with delayed and ambiguous feedback"

AWARDS

• Outstanding Reviewer for the STR Division for the 2021 annual AOM conference

Publications

Journals

More Publications