Modeling Generative AI and Social Entrepreneurial Searches: A Contextualized Optimal Stopping Approach

Abstract

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit.

1. Introduction

The rapid advancement and proliferation of generative artificial intelligence (generative AI) have begun to reshape the landscape of social entrepreneurship by introducing new approaches to opportunity discovery, ideation, and strategic decision-making in the pursuit of social impact (Shore et al., 2024). Unlike conventional computational tools, generative AI combines the capacity to process vast data sets with the ability to generate novel insights and solutions, making it uniquely suited to tackle ill-defined problems. This duality—analytical strength and creative synthesis—holds particular value in social entrepreneurship, where ventures often operate under normative ambiguity, stakeholder plurality, and institutional fragmentation (Austin et al., 2006). Compared to commercial entrepreneurs, social entrepreneurs frequently face conflicting demands from diverse beneficiaries, unclear market signals, and mission-driven rather than profit-maximizing goals, leading to elevated levels of strategic and ethical uncertainty. In such settings, generative AI offers tools to simulate alternative futures, frame opportunity spaces, and navigate ambiguity with greater precision and speed (Schrepel & Pentland, 2024; Al-Emran et al., 2024).

Crucially, generative AI enables what social entrepreneurs increasingly require: the ability to process incomplete information and generate mission-aligned options in resource-constrained environments. From ideation platforms to social innovation accelerators, AI systems are being embedded in participatory design tools, early-stage validation frameworks, and community-centered solution mapping (K. Kim et al., 2021). Yet, despite this growing integration, scholarship is unable to explain how these technologies alter fundamental cognitive and strategic processes, particularly search behavior, under uncertainty. While some recent efforts have begun to explore the role of generative AI in entrepreneurship and decision-making contexts (Chen et al., 2020; Duong et al., 2025), a cohesive theoretical framework explaining how these technologies reshape opportunity search and timing under uncertainty is still lacking. Understanding this shift—from traditional opportunity discovery methods to AI-enabled entrepreneurial searching—is essential for grasping how entrepreneurs not only find new ideas but also determine the right time to act.

Within the context of social entrepreneurship, generative AI can be understood as operating through two foundational mechanisms: reducing search costs (α) and enhancing the probability of discovering social success (β). These mechanisms are especially critical in mission-driven ventures, where resources are limited, stakeholder demands are heterogeneous, and measurable impact is often delayed or diffuse (Dees, 1998; Mair & Marti, 2006). By reducing the cognitive and computational load required to evaluate multiple opportunities, and by increasing the reliability of idea generation through advanced data synthesis, generative AI supports earlier and more confident decision-making. This recalibration of decision thresholds has implications for responsiveness in time-sensitive fields such as education, health, the environment, and inclusion.

However, the transformative role of generative AI also presents strategic and ethical trade-offs. While it enhances creative exploration and lowers entry barriers for underrepresented founders (Meltzer, 2023), it may simultaneously constrain the diversity of ideation by encouraging convergence on algorithmically derived solutions. In social entrepreneurship—where equity, stakeholder legitimacy, and contextual appropriateness are central—this tension between individual efficiency and collective inclusivity remains under-theorized. Prior studies have not adequately captured how generative AI influences not just the opportunities discovered, but those whose needs are addressed, and which types of values are prioritized (Austin et al., 2006; Salman et al., 2025).

Despite AI’s growing role in entrepreneurship, most existing research remains focused on commercial innovation and cost-based efficiency, with limited attention given to social impact contexts. Particularly absent are formal models that explain how entrepreneurs dynamically update decision thresholds under the influence of intelligent technologies. In this domain, there is a clear gap in understanding how the dual functions of generative AI—reducing search costs (α) and enhancing the success probability (β)—alter entrepreneurial behavior under uncertainty. While some exploratory work has acknowledged AI’s role in entrepreneurial cognition, few studies offer testable, theory-driven frameworks grounded in decision science and tailored to mission-driven environments.

This study addresses that gap by applying optimal stopping theory (Bellman, 1958; Ciocan & Mišić, 2022) to model how generative AI transforms search behavior in social entrepreneurship. Optimal stopping theory offers a rigorous framework for analyzing sequential decision-making under uncertainty, where agents must decide not only what action to take, but when to take it. It formalizes the tension between exploration and exploitation by quantifying the costs and expected benefits of continued searching. This makes it especially relevant in social entrepreneurship, where delays in decision-making can lead to missed impact opportunities or misaligned interventions.

To this end, this study aims to theoretically model how generative AI influences the search dynamics of social entrepreneurs by modifying both cost and probability structures within the opportunity search process. It asks three core questions:

(1)

How does generative AI reshape search behavior under uncertainty in social entrepreneurship?

(2)

What is the relative influence of cost reduction and success probability enhancement on optimal stopping decisions?

(3)

What ethical and strategic trade-offs arise from AI-enabled search acceleration in mission-driven innovation?

By embedding generative AI into the structure of social entrepreneurial decision-making, this study contributes by revealing the nuanced trade-offs between efficiency and effectiveness in contexts defined by social purpose, uncertainty, and complexity. It highlights the dual potential of generative AI to democratize access to innovation while also raising critical questions about equity, inclusion, and originality. Accordingly, this study positions the optimal stopping theory not simply as a computational tool, but as a theoretically grounded lens capable of capturing the temporal and cognitive dynamics of opportunity search under the distinctive constraints of social entrepreneurship. By extending this framework to mission-driven contexts, this study offers a more contextually embedded and value-sensitive understanding of entrepreneurial decision-making under uncertainty, thereby laying the foundation for future empirical work to explore how AI technologies reshape mission-aligned strategies in the social sector.

2. Theoretical Background

Optimal stopping theory offers a powerful framework for modeling decision-making under uncertainty, particularly in settings where individuals must determine the ideal moment to cease searching and act. In entrepreneurial contexts—especially those marked by uncertainty, limited resources, and high stakes—this approach allows scholars to formally analyze the cost–benefit trade-offs entrepreneurs face when navigating opportunity landscapes (Dixit & Pindyck, 1994). While alternative theoretical lenses such as bounded rationality (Simon, 1972) and real options theory (McGrath, 1999) have also been applied to entrepreneurial decision-making, optimal stopping theory provides a unique advantage: it explicitly models dynamic decision thresholds over time, making it especially suitable for capturing the temporal and strategic nature of entrepreneurial searches.

Recently, this framework has gained traction in studies on entrepreneurial cognition and decision theory, enabling researchers to better understand how uncertainty, information asymmetry, and resource constraints influence search behavior (Shiryaev, 2011). Moreover, optimal stopping theory has been successfully applied in managerial and algorithmic settings involving sequential decision-making under constraints (e.g., Ciocan & Mišić, 2022), which supports its theoretical robustness and transferability to entrepreneurial problems. Given the high uncertainty and value-driven nature of social ventures, optimal stopping theory offers a compelling lens for analyzing strategic search processes. This study extends the application of optimal stopping to social entrepreneurship and examines how generative AI—through reducing search costs (α) and enhancing the success probability (β)—alters the strategic logic of opportunity pursuit in mission-driven contexts.

Social entrepreneurship is characterized by a dual commitment to social impact and entrepreneurial opportunity (Dees, 1998; Mair & Marti, 2006). Unlike commercial entrepreneurs, social entrepreneurs often operate in highly complex environments where institutional rules are fragmented, stakeholder needs are often in conflict, and success is difficult to measure using standard financial indicators (Austin et al., 2006). They must make strategic decisions that not only ensure organizational viability but also uphold ethical integrity and generate systemic social value. This dual burden—balancing mission fidelity with entrepreneurial agility—intensifies the challenge of identifying optimal moments to act. Moreover, unlike commercial entrepreneurs who can often rely on market signals and competitive benchmarks, social entrepreneurs frequently face ambiguous or absent feedback loops, making search processes inherently more uncertain and cognitively demanding. Generative AI, with its capacity for creative ideation and information synthesis, provides new tools for addressing this challenge. While traditional AI tools have focused on prediction and classification, generative AI produces novel outputs—such as text, images, models, or conceptual frames—that can support ideation, problem structuring, and exploratory thinking tailored to socially embedded goals.

In social entrepreneurial contexts, generative AI modifies both key variables of the optimal stopping problem. First, it reduces search costs (α) by automating ideation tasks, accelerating hypothesis generation, and enabling the simultaneous evaluation of multiple scenarios. These features are particularly valuable in resource-constrained ventures where manual search processes are often too costly or time-consuming. Second, it increases the probability of opportunity success (β) by enabling social entrepreneurs to identify unmet needs, synthesize stakeholder perspectives, and map systemic interdependencies—especially across traditionally siloed knowledge domains (Schrepel & Pentland, 2024; Bao et al., 2023). The resulting improvements in both efficiency and insight generation fundamentally reshape the entrepreneur’s cost–benefit calculus.

This transformation is particularly pronounced under high uncertainty. Unlike conventional markets with clear demand signals, social entrepreneurs frequently operate in domains where feedback loops are slow, success is multidimensional, and environmental volatility is the norm. In such settings, decision quality depends less on marginal gains from efficiency and more on the entrepreneur’s ability to navigate ambiguity. Generative AI addresses this challenge by combining causal reasoning (goal-driven planning) with effectual reasoning (means-based adaptation), enabling the dynamic recalibration of search strategies as conditions evolve (Dawa & Marks, 2023; Bao et al., 2023). Thus, it enhances both agility and alignment with long-term mission objectives.

In practical terms, the application of generative AI in social entrepreneurship is already underway. Tools such as AI-generated grant writers, stakeholder engagement simulators, and predictive social needs platforms are becoming integrated into the early-stage decision-making processes of social ventures (Agrawal et al., 2021). These technologies not only improve organizational performance but also help democratize access to entrepreneurship by lowering cognitive, linguistic, and technical barriers—particularly for underserved communities and grassroots innovators (Meltzer, 2023; J. Kim & Jin, 2024). Moreover, by embedding intelligence into design, outreach, and impact evaluation processes, generative AI facilitates the creation of distributed innovation ecosystems where problem-solving becomes more collaborative, iterative, and inclusive.

While this body of work offers important operational insights, it has primarily emphasized task-level applications—automating ideation, augmenting proposal writing, or enhancing stakeholder analysis—without addressing how AI fundamentally reshapes the decision-making logic underpinning entrepreneurial searches. In contrast, this study shifts the analytical focus from operational improvements to the structural foundations of decision-making. By embedding generative AI into a formal optimal stopping model, this research theorizes how AI modifies key cognitive thresholds—namely search costs, success probabilities, and stopping conditions—under uncertainty. This approach not only complements the prior AI-enabled entrepreneurship literature but also advances a more integrated understanding of AI’s strategic role in mission-driven opportunity pursuit.

However, these benefits come with ethical considerations. Generative AI systems trained on biased or incomplete datasets risk reinforcing existing structural inequalities or privileging dominant narratives at the expense of community knowledge. In social entrepreneurship, where legitimacy, co-creation, and inclusivity are central to value generation, this can undermine trust and stakeholder engagement. Additionally, over-reliance on algorithmic outputs may overrule experiential wisdom and diminish democratic participation, especially in participatory or community-led initiatives (Salman et al., 2025). Such risks necessitate the careful rethinking of human–AI collaboration in socially sensitive domains.

To address these challenges, scholars have called for “ethics-by-design” and “sustainability-by-design” frameworks for AI development that emphasize transparency, adaptability, and human agency (Dwivedi et al., 2021; Dawa & Marks, 2023; Salman et al., 2025). In the context of optimal stopping theory, this means designing AI systems that support—not replace—human judgment, and that allow users to define, modify, and contextualize decision thresholds based on evolving community needs. The real value of generative AI lies not in its automation potential but in its ability to expand the social entrepreneur’s capacity to explore, experiment, and respond to uncertainty in thoughtful, mission-aligned ways.

From this perspective, generative AI reshapes social entrepreneurial decision-making through three mechanisms. First, it reduces search costs (α), allowing entrepreneurs to identify and act on viable opportunities more quickly and with fewer resources. Second, it disproportionately amplifies the role of success probability (β), suggesting that improved discovery capacity is more influential than mere cost savings. Third, its impact is most significant in complex and uncertain environments—the very contexts in which social entrepreneurs are most active—where its ability to facilitate adaptive threshold-setting becomes a key strategic advantage.

In summary, this study positions generative AI as both a cognitive and strategic complement to human decision-making in social entrepreneurship. By formalizing its influence within an optimal stopping framework, we not only advance the theoretical boundaries of entrepreneurial decision theory but also provide a structured foundation for empirical investigation. This includes examining how generative AI affects the behavior, outcomes, and ethical orientations of social ventures across diverse institutional and geographic settings. As AI systems become more embedded in the entrepreneurial process, ongoing research must grapple with their dual role—as enablers of innovation and actors within socio-technical systems—where power, equity, and purpose remain contested. This study takes a step in that direction by highlighting the contextual, dynamic, and value-laden nature of AI-enabled entrepreneurial research.

3. Analytical Model Development

To formally investigate the influence of generative AI on social entrepreneurial opportunity search, this study develops a dynamic analytical model grounded in optimal stopping theory. Entrepreneurial search is conceptualized as a sequential decision-making process, wherein entrepreneurs evaluate whether to continue exploring for potentially more valuable opportunities or to terminate the search and exploit currently available ones. This framework is particularly well-suited to capturing the inherent trade-offs in opportunity search under uncertainty, which is a defining characteristic of entrepreneurial environments, especially in socially oriented ventures.

This model assumes that entrepreneurs can engage in a finite number of search periods, up to a maximum of N, reflecting real-world constraints such as funding deadlines or limited program durations. At each time period t, the entrepreneur, modeled here as a single, autonomous decision-maker, incurs a search cost c and must decide whether to stop and exploit the current opportunity or continue searching. The expected reward from a successful opportunity is denoted by V, where V represents not only economic gain but also anticipated social impact. The baseline probability of discovering a valuable opportunity in each period is λ, which is treated as exogenous and constrained such that 0 < λ < 1. The time preference is captured through a discount factor δ, where 0 < δ ≤ 1.

Generative AI is introduced into the model as a technological augmentation that alters two critical dimensions of the search process: it reduces the per-period cost of searching and increases the likelihood of identifying viable opportunities. These mechanisms are operationalized through two parameters: α ∈ (0, 1), denoting the percentage reduction in search costs due to generative AI, and β ∈ (0, 1), representing the relative increase in the probability of a successful opportunity discovery. In the absence of generative AI, the optimal stopping rule for the entrepreneur follows the standard recursive formulation:

Ut = max{λV − c, δE[Ut + 1] − c}

This expression captures the trade-off between the immediate expected payoff of exploiting an opportunity in period t and the discounted expected value of continuing the search in period t + 1. It reflects the logic that entrepreneurs will continue to search only if the marginal gain from waiting outweighs the immediate reward of acting.

In contrast, when generative AI is deployed, the per-period search cost is reduced to c′ = c(1 − α) and the success probability is increased to λ′ = λ(1 + β). The entrepreneur’s decision rule is accordingly modified as follows:

U_t = max{λ(1 + β)V − c(1 − α), −c(1 − α) + δE[U′_t+1]}

The corresponding optimal stopping condition under generative AI becomes the following:

λ(1 + β)V − c(1 − α) ≥ −c(1 − α)+ δE[U′_t+1]

This inequality illustrates how generative AI increases the immediate expected value of exploiting an opportunity by improving the quality of discovered opportunities (β) while also reducing the associated search cost (α). Together, these mechanisms restructure the decision environment and lower the opportunity cost of action, encouraging earlier execution. For social entrepreneurs, who must navigate complex stakeholder demands, ambiguous definitions of success, and competing institutional logics, this translates into faster and potentially more mission-aligned decision-making. By mitigating cognitive overload and enabling the rapid synthesis of multidimensional inputs, generative AI can support the timely responses that honor the double bottom line commitments of social ventures.

Importantly, the effect of generative AI on the entrepreneur’s decision is moderated by the level of environmental uncertainty. In conditions of high uncertainty—modeled as lower-base success probabilities (λ)—the marginal benefits of increased success probability (β) become more pronounced. This sensitivity mirrors the realities often faced by social entrepreneurs operating in decentralized, data-scarce, or institutionally weak environments. In such contexts, the probability-enhancing effect of generative AI exerts a disproportionately large influence on the decision to stop, suggesting that its primary contribution lies not merely in reducing costs but in improving the strategic clarity and expected payoff of action.

This model formalizes how generative AI reshapes decision thresholds and behavior by embedding it directly into the opportunity search process in mission-driven entrepreneurship. It contributes to a growing body of research that seeks to integrate algorithmic intelligence into models of entrepreneurial action (Shane & Venkataraman, 2000), particularly in contexts where strategic choices are not driven by profit maximization alone but by ethical alignment, stakeholder legitimacy, and long-term systemic impact.

Moreover, this modeling approach lays a foundation for empirically testable hypotheses regarding how generative AI affects the timing, quality, and structure of opportunity decisions. It provides a basis for future research to examine how AI-enabled searching influences social venture performance, accelerates problem identification, and enhances organizational responsiveness under uncertainty. The model predicts that social entrepreneurs who effectively integrate generative AI into their decision routines will reach the optimal stopping point more quickly, resulting in increased social agility and the greater capacity to respond to pressing societal needs.

4. Model Analysis and Propositions

Building upon the analytical model presented in the previous section, this study showcases a set of propositions that illuminate the mechanisms through which generative AI alters social entrepreneurial searching decisions (Zahra et al., 2009). These propositions offer theoretical insights into the dual functions of generative AI—its ability to reduce search costs and to enhance the probability of successful discovery—and how it formalizes respective influences on the optimal timing of opportunity exploitation (Ciocan & Mišić, 2022). By contextualizing these effects within mission-driven entrepreneurship and incorporating environmental uncertainty, this model contributes to a deeper understanding of how algorithmic tools can support decision-making in socially complex and resource-constrained settings.

Proposition 1:

Generative AI adoption accelerates the social entrepreneur’s optimal stopping time by enabling the earlier pursuit of high-impact, mission-aligned opportunities.

The introduction of generative AI shifts the entrepreneur’s payoff structure by simultaneously reducing the cost of searching and increasing the probability of successful opportunity identification. Formally, the entrepreneur’s decision to stop searching occurs at the earliest time period t when the following inequality holds:

λ(1 + β)V − c(1 − α) ≥ −c(1 − α) + δE[U′_t+1]

This condition demonstrates how generative AI increases the immediate expected payoff from stopping. Due to the increased success probability and the reduced cost shift and trade-off toward earlier action, generative AI incentivizes social entrepreneurs to exploit high-potential opportunities sooner. This dynamic is particularly advantageous in time-sensitive social contexts—such as public health emergencies or environmental crises—where delays in decision-making can significantly diminish impact. This proposition illustrates the broader strategic value of generative AI in enhancing social responsiveness, reducing inertia, and accelerating mission-oriented action.

Proposition 2:

Increasing the success probability (β) has a greater strategic impact than reducing search costs (α), particularly in enhancing the quality and mission alignment of opportunity discovery.

This proposition compares the marginal effects of the two core mechanisms modified by generative AI. By analyzing the partial derivatives of the value function with respect to β and α, we obtained the following inequality:

$${\displaystyle \frac{\mathsf{\partial }{U}_t^{\prime }}{\mathsf{\partial }\beta }}>{\displaystyle \frac{\mathsf{\partial }{U}_t^{\prime }}{\mathsf{\partial }\alpha }},\hspace{1em}\forall t$$

This inequality indicates that the marginal benefit from the increased probability of a successful search outweighs the marginal benefit from reduced search costs, implying that the probability-enhancing effect of AI dominates its cost-reducing effect. Formally, this proposition is supported by comparing marginal effects:

$${\displaystyle \frac{\mathsf{\partial }{U}_t^{\prime }}{\mathsf{\partial }\beta }}=\lambda V,\hspace{1em}{\displaystyle \frac{\mathsf{\partial }{U}_t^{\prime }}{\mathsf{\partial }\alpha }}=C$$

Given that entrepreneurs act when the expected payoff exceeds the cost (λV > c), the following is directly seen:

$$\left|{\displaystyle \frac{\mathsf{\partial }{U}_t^{\prime }}{\mathsf{\partial }\beta }}\right|=\lambda V>c=\left|{\displaystyle \frac{\mathsf{\partial }{U}_t^{\prime }}{\mathsf{\partial }\alpha }}\right|$$

This result confirms that enhancing success probability (β) contributes more to opportunity identification than reducing search costs (α). In social entrepreneurship, where value is often multidimensional and context-specific, improving the likelihood of discovering impactful, stakeholder-relevant opportunities becomes more significant than simply lowering search costs. Thus, this proposition highlights the strategic primacy of insight quality in socially embedded decision-making.

Proposition 3:

The effectiveness of generative AI increases under conditions of greater uncertainty and social complexity, where inclusive, ethically aligned opportunity search is most difficult.

This proposition extends the model by incorporating environmental variance as a moderating factor. Specifically, as the uncertainty of potential rewards—captured by the variance Var[V]—increases, the marginal benefits of both cost reduction and probability enhancement become more pronounced. Mathematically, this can be expressed as follows:

$${\displaystyle \frac{{\mathsf{\partial }}^2{U}_t^{\prime }}{\mathsf{\partial }a\mathsf{\partial }\mathit{Var}\left[V\right]}}>0,\hspace{1em}{\displaystyle \frac{{\mathsf{\partial }}^2{U}_t^{\prime }}{\mathsf{\partial }\beta \mathsf{\partial }\mathit{Var}\left[V\right]}}>0,$$

These expressions imply that in highly uncertain environments—typical of many social ventures—generative AI’s capacity to reduce ambiguity and reveal viable paths forward is significantly amplified. Moreover, in socially complex settings involving diverse stakeholder values, ambiguous legitimacy criteria, and multidimensional success metrics, the ethical design and application of AI becomes essential. Rather than being a mere decision aid, AI emerges as a strategic enabler of inclusive innovation. This highlights the necessity of integrating ethical reasoning into AI-supported decision models in order to avoid exacerbating structural inequities.

Collectively, these propositions advance our theoretical understanding of how generative AI reshapes entrepreneurial search behavior through analytically distinct but interdependent mechanisms. The model not only captures the direct effects of cost (α) and probability (β) on decision thresholds but also explains how these relationships shift in response to contextual uncertainty. By formalizing these dynamics within an optimal stopping framework, this study establishes a conceptual foundation for a new class of theory-driven, testable propositions at the intersection of entrepreneurship, decision science, and artificial intelligence. These findings provide an empirical agenda for future research aimed at understanding AI-enabled entrepreneurial searching in diverse environments, particularly where equity, legitimacy, and social value are central concerns.

5. Conclusions and Future Research

This study offers a robust analytical investigation into the transformative role of generative AI in reshaping social entrepreneurial search strategies, using a formal framework rooted in optimal stopping theory. The theoretical contributions illuminate how generative AI influences two pivotal dimensions of the search process: reducing search costs (α) and enhancing the success probability (β). Through rigorous modeling, the analysis yields three key insights that advance theoretical understanding while opening pathways for future interdisciplinary research.

First, the findings demonstrate that generative AI accelerates the optimal stopping time for social entrepreneurs. By lowering search costs and increasing the expected payoffs from opportunity discovery, generative AI enables the earlier identification and pursuit of high-impact, mission-aligned solutions. This acceleration enhances entrepreneurial responsiveness and strategic agility, which are critical for addressing time-sensitive social problems such as health crises, education inequality, or environmental degradation. Importantly, this dynamic supports the faster mobilization of social impact and strengthens the capacity of social entrepreneurs to act under pressing conditions.

Second, the analysis reveals that the marginal benefit of improved success probability (β) outweighs that of search cost reduction (α) in shaping stopping decisions. While traditional entrepreneurship research often emphasizes cost efficiency, this study reframes the narrative by highlighting the strategic value of cognitive augmentation. In the context of social entrepreneurship—where opportunities are often ambiguous, stakeholder-driven, and impact-oriented—the ability to discover inclusive, feasible, and socially resonant opportunities is more valuable than simple cost reduction. Generative AI thus emerges not just as an economic tool, but as an intelligent partner in mission-driven decision-making.

Third, the value of generative AI is amplified in environments characterized by high uncertainty and social complexity. As variance in the expected value of opportunities increases—including reflecting factors such as weak institutional infrastructure, fragmented information, or shifting social needs—the benefits of enhanced discovery probability become increasingly critical. This positions generative AI as a strategic enabler of inclusive innovation, particularly in underserved or information-scarce environments where conventional heuristics and intuition may fall short.

Collectively, these insights generate three major theoretical contributions. First, this study develops a formal and analytically tractable model that contextualizes optimal stopping theory within the domain of social entrepreneurship. Second, by demonstrating the dominance of probability enhancement over cost reduction, it challenges efficiency-driven models and instead prioritizes impact-driven strategy formation. Third, it introduces a context-sensitive lens to technological adoption in entrepreneurship, highlighting how AI functions differently in settings characterized by equity concerns, stakeholder diversity, and mission complexity.

Beyond its theoretical implications, this study provides a platform for future empirical work. The propositions derived from the model are testable and may be validated through experimental, field-based, or large-scale survey research. In particular, future studies could examine how social entrepreneurs adjust their search intensity, opportunity framing, and timing of decision-making when supported by generative AI. Additionally, while this model focuses on individual decision-makers, future research could extend the framework to multi-agent or organizational contexts using agent-based modeling or game theory. Such approaches could explore how AI adoption affects resource sharing, collaborative search, and stakeholder negotiation among mission-driven actors.

Each of the three propositions developed in this study offers a pathway for empirical investigation. Proposition 1, which posits that generative AI accelerates the optimal stopping time for social entrepreneurs, can be examined through controlled behavioral experiments that measure decision timing, opportunity framing, and choice quality under AI-assisted versus non-assisted conditions. Proposition 2, which highlights the greater strategic value of increasing success probability (β) over reducing search costs (α), invites quantitative modeling such as conjoint analysis or field experiments that elicit entrepreneurial preferences under varying cognitive and resource constraints. Proposition 3, which introduces uncertainty and social complexity as moderators, can be tested through cross-sector comparative studies, simulations, or surveys targeting diverse institutional and stakeholder contexts. These avenues not only validate the analytical model but also enrich our understanding of how generative AI interacts with human decision-making under socially embedded uncertainty.

From a practical standpoint, this study offers several implications for social entrepreneurs, technology developers, and ecosystem enablers. For practitioners in the social sector, the findings suggest that generative AI can serve as a strategic ally in navigating complex problem spaces, helping them identify higher-quality opportunities more efficiently and act with greater confidence under uncertainty. Nonprofit leaders, public innovators, and mission-driven startups can leverage generative AI not only for operational automation but also for inclusive ideation, stakeholder engagement, and scenario modeling. For AI developers and intermediaries, this study highlights the importance of embedding ethical and context-sensitive features—such as bias mitigation, value alignment, and interpretability—into AI tools targeted for use in social innovation. Finally, policymakers and funders can use these insights to support capacity-building programs and infrastructure that lower barriers for socially oriented entrepreneurs to access and adopt generative AI technologies.

Finally, the long-term implications of AI adoption in social entrepreneurship warrant deeper exploration. As generative AI becomes more embedded in social innovation ecosystems, questions arise about its impact on access to opportunity, ethical design, and distributional equity. While this study models success probability (β) as a uniform and exogenous factor, real-world AI deployment may produce unequal effects depending on who has access to data, infrastructure, and interpretive capacity. These disparities may reinforce or alleviate existing inequalities, particularly for marginalized or resource-constrained entrepreneurs. Future research should investigate how generative AI may distort or enhance equitable access to opportunity, especially in environments characterized by digital divides, algorithmic bias, or linguistic exclusion. Such an inquiry would extend this study’s conceptual contribution and deepen our understanding of how AI shapes not only what decisions are made, but also who decides and whose needs are prioritized. In summary, this study provides a theoretically grounded and socially contextualized foundation for understanding how generative AI redefines search, decision-making, and strategic behavior in the evolving landscape of mission-oriented entrepreneurship.