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12:00 PM - 1:30 PM
Carolyn Fu: Harvard Business School
Setting the Stage: The Interplay of Firm Boundary and Learning at the Opera
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Setting the Stage: The Interplay of Firm Boundary and Learning at the Opera
Speaker: Carolyn Fu: Harvard Business School
Time: 12:00 PM - 1:30 PM
Location:
12:00 PM - 1:30 PM
Nicolaj Siggelkow: Wharton
Learning about contingencies: The power of initial simplicity
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Learning about contingencies: The power of initial simplicity
Speaker: Nicolaj Siggelkow: Wharton
Time: 12:00 PM - 1:30 PM
Location:
This paper investigates the performance consequences of how managers update their contingency beliefs in response to feedback, emphasizing the interaction between first-order learning (updating performance expectations) and second-order learning (updating contingency beliefs). Managers often begin tasks with inaccurate contingency assumptions—either overly simple or overly complex—which shape their initial decisions. Our research explores whether starting with simple or complex contingency beliefs is more advantageous when learning occurs in environments characterized by varying degrees of true contingencies. Employing a contextual multi-arm bandit simulation, we reveal nuanced learning dynamics: without second-order learning, complex initial beliefs consistently outperform simpler ones. However, when second-order learning is enabled, initially simple beliefs significantly enhance managerial decision-making over time, even potentially surpassing the performance of managers with initially accurate beliefs. Conversely, those with overly complex initial beliefs demonstrate limited improvement through second-order learning alone. Importantly, this pattern changes substantially when managers have considerable prior information about action outcomes across contexts; under these conditions, complex initial beliefs become advantageous. Thus, the benefits of simplicity versus complexity in contingency beliefs critically depend on the interplay between first- and second-order learning and the availability of prior information, offering actionable insights for managerial decision-making in uncertain environments.
8:00 AM - 5:00 PM
Connie Helfat: Tuck School of Business
Junior Faculty Strategy Research Summer Camp
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Junior Faculty Strategy Research Summer Camp
Speaker: Connie Helfat: Tuck School of Business
Time: 8:00 AM - 5:00 PM
Location:
8:00 AM - 5:00 PM
Connie Helfat: Tuck School of Business
Junior Faculty Strategy Research Summer Camp
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Junior Faculty Strategy Research Summer Camp
Speaker: Connie Helfat: Tuck School of Business
Time: 8:00 AM - 5:00 PM
Location:
10:30 AM - 12:00 PM
John de Figueiredo: Duke Univeristy Fuqua School of Business
Does Politics Matter in Entrepreneurship? Political Polarization, Nonpartisans, and Entrepreneurial Team Performance
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Does Politics Matter in Entrepreneurship? Political Polarization, Nonpartisans, and Entrepreneurial Team Performance
Speaker: John de Figueiredo: Duke Univeristy Fuqua School of Business
Time: 10:30 AM - 12:00 PM
Location:
Social homophily has been shown to be a feature of entrepreneurial team formation, but its performance implications remain unclear. This paper examines the recent rise of a new source of social homophily—political homophily—and its relationship to the performance of entrepreneurial founding teams. We construct a dataset linking over 13,000 founders of 3,700 California-based startups to their voter registration records from 2007 to 2019. We first document substantial and rising levels of political homophily. We then analyze how team political composition relates to fundraising outcomes, focusing on the likelihood and speed of raising subsequent funding. We find that teams composed entirely of similar political partisans underperform relative to politically mixed teams. The highest-performing teams include substantial political heterogeneity, encompassing Democrats, Republicans, and Nonpartisan founders. These results remain qualitatively consistent in two-stage estimations using time-varying measures of negative and positive political advertising in each of California’s 14 media markets as instrumental variables. These findings suggest that Nonpartisan founders may serve a cross-cutting or boundary-spanning role, helping teams navigate the coordination challenges of ideological diversity.
11:00 AM - 12:30 PM
Rebecca Karp: Harvard Business School
Incumbent’s Advantage via Expertise Asymmetry: Revitalization of the Chinese Pearl Industry with Livestreaming
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Incumbent’s Advantage via Expertise Asymmetry: Revitalization of the Chinese Pearl Industry with Livestreaming
Speaker: Rebecca Karp: Harvard Business School
Time: 11:00 AM - 12:30 PM
Location:
TBD
12:00 PM - 1:30 PM
Mana Heshmati: University of Washington
LLMs as Belief Amplifiers: How Human Mental Representations Shape AI-Augmented Strategy
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LLMs as Belief Amplifiers: How Human Mental Representations Shape AI-Augmented Strategy
Speaker: Mana Heshmati: University of Washington
Time: 12:00 PM - 1:30 PM
Location:
Artificial Intelligence (AI) systems based on Large Language Models (LLMs) have made rapid progress in general capabilities (e.g., programming, dialogue, and exam performance), but they may struggle with strategy problems marked by high complexity, dynamism, and uncertainty. Strategy research on cognition has long demonstrated that high quality decisions depend not just on general intelligence, but on the mental representations used to simplify, interpret, and act in such complex settings. We therefore ask: how does using LLMs affect human strategic decision-making, and how do human mental representations moderate this effect? Using an abductive approach, we attempt to empirically answer these questions in a classroom experiment of participants (N≈200) using the Back Bay Battery strategy simulation in a 2×2 randomized design: treatment of access to an LLM (OpenAI’s o4-mini), and treatment of prior training in disruptive theory. Among active LLM users, the effect of using the LLM hinged on their mental representation: without disruption training, greater LLM use led to more investment in the simulation’s legacy profitable yet declining business; with disruption training, greater LLM use increased investment in the emerging technology. Text analysis of chat logs indicates a mental representation mechanism: trained students issue technology-focused prompts that pushed back more on the LLM, whereas untrained students were more broadly focused and pushed back less on the LLM’s responses. These results indicate that LLMs function as belief amplifiers. Paired with forward-looking mental representations, the push back on an LLM reinforces and executes forward-looking strategies; absent them, they can entrench backward-looking exploitation. We suggest that a key role of strategists in the age of AI is to supply the most useful representation, framework, or theory for a given situation.
12:15 PM - 1:45 PM
Jeho Lee
N/A
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N/A
Speaker: Jeho Lee
Time: 12:15 PM - 1:45 PM
Location:
12:15 PM - 1:45 PM
Cha Li: The University of Texas at Austin
Borrowed cognition: How AI exposure impacts idea generation and evaluation
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Borrowed cognition: How AI exposure impacts idea generation and evaluation
Speaker: Cha Li: The University of Texas at Austin
Time: 12:15 PM - 1:45 PM
Location:
Generative artificial intelligence (AI), such as large language models (LLMs), is increasingly embedded in organizational innovation processes. By relaxing cognitive constraints in generating alternatives, AI can shift the bottleneck toward screening and selection. Yet we know little about how individuals' decision-making changes once the tool is no longer available. We examine how AI-assisted idea generation affects subsequent unaided idea generation and unaided idea evaluation, and how these aftereffects depend on individuals' modes of cognitive engagement during AI-assisted idea generation. We conduct a multi-stage randomized experiment in which participants receive AI access for an initial idea generation task and then complete an unaided idea generation task and an unaided evaluation task in a new domain. AI access during the initial idea generation task has no detectable effect on subsequent unaided idea generation, but it significantly reduces subsequent unaided evaluation accuracy. Further analyses show that this decline is driven by a shift toward omission errors and is concentrated among participants who contribute less independent content during AI-assisted idea generation. We further use a parsimonious simulation model to examine how these individual-level aftereffects scale to organization-level performance outcomes. The simulation shows that omission-prone screening can reduce value capture when implementation capacity is slack, even when AI increases idea volume and quality. Together, the results shift attention from performance with AI to performance after AI and clarify when delegating ideation to AI tools improves or undermines innovative outcomes.TBD
Speaker: Maciej Workiewicz: ESSEC
Time: 12:15 PM - 1:45 PM
Location:
TBD