Imperial College London


Business School

Research Postgraduate







Business School BuildingSouth Kensington Campus





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 impact the outcomes of their innovation efforts, as well as how the cognitive aspects of these processes evolve at the individual level. The challenge that managers face when evaluating potential or ongoing R&D investments is determined by the interplay of two fundamental issues. On the one hand, the commercial or strategic value of R&D opportunities is uncertain and depends on multiple interdependent factors. On the other, the extended time lags between investments and commercialization make it difficult to learn from past decisions. Hence, managers must rely on combinations of portfolio approaches and group decision processes to make R&D evaluations.

In theoretically examining this challenge, I first focus on the individual cognition of managers. I build on the organizational learning, managerial cognition, and neuro-cognitive literatures to propose a learning mechanism that is mediated by changes in the structure of individuals’ mental representations. I test my theoretical predictions empirically and find that individuals can learn even when feedback is highly noisy or unobserved.

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 both policies and representations and shows performance trade-offs associated with different learning strategies.

Finally, I examine a group decision process in which decision-makers solicit and integrate the knowledge of other domain experts. This setting enables me to unpack one of the mechanisms that underpins the fundamental trade-off between the costs and biases of different information aggregation structures.

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

I use quantitative and natural language processing methods. I have developed web-scraping tools to collect data from online databases such as Google Patents, Scopus, FAME/AMADEUS, LinkedIn, and more for academic publishing purposes.

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.


  • 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.


  • 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"


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



Posen HE, Ross J-M, Wu B, et al., 2022, Reconceptualizing Imitation: Implications for Dynamic Capabilities, Innovation, and Competitive Advantage, Academy of Management Annals, ISSN:1941-6067

Walker J, Brewster C, Fontinha R, et al., 2021, The unintended consequences of the pandemic on non-pandemic research activities, Research Policy, Vol:51, ISSN:0048-7333

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