AI-Driven Decision-Making: Balancing Effectuation and Causation in Entrepreneurial Decision-Making Strategy
(2025) ENTN19 20251Department of Business Administration
- Abstract
- In the rapidly evolving landscape of entrepreneurship, Artificial Intelligence (AI) has emerged as a pivotal tool reshaping how founders navigate uncertainty and allocate resources. Grounded in Sarasvathy’s dual logics of causation (predictive, goal-oriented planning) and effectuation (adaptive, resource-driven experimentation), this study investigates whether AI reinforces entrepreneurs’ default decision-making style or catalyzes a shift toward the alternate logic. Drawing on a global survey of 103 startup founders and CEOs—predominantly in technology and healthcare sectors—our research integrates validated measures of baseline decision orientation (EffBase, CauBase) with novel scales capturing AI prompt engineering practices (EffPrompt,... (More)
- In the rapidly evolving landscape of entrepreneurship, Artificial Intelligence (AI) has emerged as a pivotal tool reshaping how founders navigate uncertainty and allocate resources. Grounded in Sarasvathy’s dual logics of causation (predictive, goal-oriented planning) and effectuation (adaptive, resource-driven experimentation), this study investigates whether AI reinforces entrepreneurs’ default decision-making style or catalyzes a shift toward the alternate logic. Drawing on a global survey of 103 startup founders and CEOs—predominantly in technology and healthcare sectors—our research integrates validated measures of baseline decision orientation (EffBase, CauBase) with novel scales capturing AI prompt engineering practices (EffPrompt, CauPrompt). Hierarchical moderated regressions reveal a pronounced reinforcement effect: entrepreneurs predisposed to effectual reasoning who engage in adaptive, iterative prompting exhibit a significant amplification of their heuristic, exploratory approach (β = 0.503, p < .001; ΔR² ≈ 12.7%). In contrast, causal entrepreneurs do not experience a comparable enhancement through rigid, goal-focused prompts; if anything, a marginally negative interaction (β = –0.178, p = .058) hints at a nascent “shift” toward more flexible reasoning. These findings underscore that AI’s impact is not neutral—its strategic value hinges on how prompts are crafted. By situating AI as both amplifier and boundary condition of entrepreneurial cognition, this study bridges effectuation theory with contemporary AI strategy research. Practically, our results advocate for tailored prompt-engineering training: adaptive techniques to deepen effectual strengths, and deliberate interface designs that invite exploration even for goal-driven users. We conclude with recommendations for longitudinal and cross-cultural investigations, call for analysis of actual AI‐prompt logs, and propose linking prompting styles to concrete innovation outcomes, thereby charting a research agenda at the intersection of AI and entrepreneurial decision-making. (Less)
- Popular Abstract
- Entrepreneurs routinely choose between two thinking styles: causation, which focuses on setting a clear goal and planning step by step, and effectuation, which emphasizes using available resources to experiment and adapt. As AI tools become more accessible, it is unclear whether they simply support these existing approaches or actually nudge entrepreneurs into the other style. To explore this, we surveyed 103 startup leaders worldwide, measuring both their natural decision-making orientation and the way they craft questions (“prompts”) when using AI. We found that effectual thinkers who ask AI open-ended, exploratory questions become even more flexible and innovative in their strategies. By contrast, those who start with a goal-driven... (More)
- Entrepreneurs routinely choose between two thinking styles: causation, which focuses on setting a clear goal and planning step by step, and effectuation, which emphasizes using available resources to experiment and adapt. As AI tools become more accessible, it is unclear whether they simply support these existing approaches or actually nudge entrepreneurs into the other style. To explore this, we surveyed 103 startup leaders worldwide, measuring both their natural decision-making orientation and the way they craft questions (“prompts”) when using AI. We found that effectual thinkers who ask AI open-ended, exploratory questions become even more flexible and innovative in their strategies. By contrast, those who start with a goal-driven mindset do not gain extra planning power from rigid AI queries; they may even show a slight tendency to embrace more adaptive thinking. In short, AI doesn’t just boost what you already do—it responds to how you interact with it. For founders, this means learning to ask AI the “right” kinds of questions can unlock greater creativity and resilience. Our study recommends specialized workshops on AI prompt design and urges future research to track these effects over time and across different cultures and industries. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9204582
- author
- Cori, Francesco LU and Gilakamsetty, Aniruddh LU
- supervisor
-
- Ziad El-Awad LU
- organization
- course
- ENTN19 20251
- year
- 2025
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Artificial Intelligence (AI) in Entrepreneurship Effectuation Causation Entrepreneurial Decision-Making Prompt Engineering Startup Founders Adaptive, Resource-Driven Experimentation Goal-Oriented Planning
- language
- English
- id
- 9204582
- date added to LUP
- 2025-07-01 08:34:04
- date last changed
- 2025-07-01 08:34:04
@misc{9204582,
abstract = {{In the rapidly evolving landscape of entrepreneurship, Artificial Intelligence (AI) has emerged as a pivotal tool reshaping how founders navigate uncertainty and allocate resources. Grounded in Sarasvathy’s dual logics of causation (predictive, goal-oriented planning) and effectuation (adaptive, resource-driven experimentation), this study investigates whether AI reinforces entrepreneurs’ default decision-making style or catalyzes a shift toward the alternate logic. Drawing on a global survey of 103 startup founders and CEOs—predominantly in technology and healthcare sectors—our research integrates validated measures of baseline decision orientation (EffBase, CauBase) with novel scales capturing AI prompt engineering practices (EffPrompt, CauPrompt). Hierarchical moderated regressions reveal a pronounced reinforcement effect: entrepreneurs predisposed to effectual reasoning who engage in adaptive, iterative prompting exhibit a significant amplification of their heuristic, exploratory approach (β = 0.503, p < .001; ΔR² ≈ 12.7%). In contrast, causal entrepreneurs do not experience a comparable enhancement through rigid, goal-focused prompts; if anything, a marginally negative interaction (β = –0.178, p = .058) hints at a nascent “shift” toward more flexible reasoning. These findings underscore that AI’s impact is not neutral—its strategic value hinges on how prompts are crafted. By situating AI as both amplifier and boundary condition of entrepreneurial cognition, this study bridges effectuation theory with contemporary AI strategy research. Practically, our results advocate for tailored prompt-engineering training: adaptive techniques to deepen effectual strengths, and deliberate interface designs that invite exploration even for goal-driven users. We conclude with recommendations for longitudinal and cross-cultural investigations, call for analysis of actual AI‐prompt logs, and propose linking prompting styles to concrete innovation outcomes, thereby charting a research agenda at the intersection of AI and entrepreneurial decision-making.}},
author = {{Cori, Francesco and Gilakamsetty, Aniruddh}},
language = {{eng}},
note = {{Student Paper}},
title = {{AI-Driven Decision-Making: Balancing Effectuation and Causation in Entrepreneurial Decision-Making Strategy}},
year = {{2025}},
}