Knowledge Domains and the Theory-based Approach: three essays on Strategic Decision-Making

Strategic, high-stakes decisions often occur under situations of fundamental uncertainty, cases where decision-makers cannot predict future outcomes due to a lack of historical data (Rindova & Courtney, 2020; Camuffo et al., 2024b). An early-stage founder developing a venture’s innovation trajectory, an individual choosing her next professional role, or a manager selecting the most promising business activities to invest on at a large corporation will likely face a future ripe with occurrences that they may completely ignore at the time of the decision. As such, these decision makers require theories—explicit or implicit—to guide their strategies and search (Felin & Zenger, 2009; 2017; Agarwal et al., 2023; Sorenson, 2024; Valentine et al., 2024), to the point that developing such theories becomes a source of competitive advantage (Felin & Zenger, 2009; Camuffo et al., 2024b). Despite evidence pointing to the benefits of applying theory-based approaches, particularly in entrepreneurship (Camuffo et al., 2020; 2024a; Gambardella & Messinese, 2023; Novelli & Spina, 2024), little is known about 1) the factors that reinforce or diminish the effects of this approach; and 2) the mechanisms driving these effects. This thesis addresses these issues to improve the applicability and understanding of the theory-based approach across diverse contexts. In the first chapter of this thesis, my co-authors and I investigate how awareness of one’s domain, the knowledge coming from one’s background and experiences, interacts with the theory-based approach in determining the direction of search. A field experiment with entrepreneurs reveals that this combination fosters strategic exploration and leads to more distant search. While such exploration has short-term performance costs, it yields significant benefits in terms of sales and funding in the long term, 10 months after the experiment. The second chapter takes a different angle and investigates how individual mindsets influence the absorption and portability of decision-making frameworks. Data from two randomized controlled trials reveal that individuals with theoretical, abstraction-oriented (educational) domains are better at creating transferable theories of value and applying them to varied contexts and over time. Sustained by more general mental models, these individuals are more likely to select roles requiring greater abstraction (inductive reasoning, deductive reasoning, problem sensitivity skills) in the 2 years following the training. In shedding light on mindsets as a source of heterogeneity, and in providing evidence of the impact of theorizing beyond traditional entrepreneurial outcomes, this study extends our understanding of the benefits of rigorous decision-making approaches, highlighting their “general-purpose” nature. The last chapter further builds on the idea that theorizing can give rise to performance benefits. This study shifts to the context of large corporations and, particularly, to diversification decisions, introducing the construct of "statistical spillovers", synergies derived from identifying and sharing signals across activities, as a complementary construct to "managerial spillovers", traditional synergies coming from the sharing of resources across activities. Through the provision of two distinct decision-making “algorithms” to address decisions of both types, my co-authors and I provide insights into the processes to adopt, and the organizational structures that can support, portfolio decisions under these two types of spillovers. In making this distinction, we not only introduce the idea of “prediction as value” - to be coupled to the more consolidated “resource as value” – but we also provide an explanation for the recent trend of decentralized, hyper-growth corporates, leveraging on statistical spillovers, vis-à-vis the long-established centralized managerial corporation, built on managerial spillovers.