هوش مصنوعی مولد و تشخیص فرصت کارآفرینانه در اقتصادهای نوظهور: ارائه یک چارچوب مفهومی

ABSTRACT




Objective: Entrepreneurial opportunity recognition, as the central core of the entrepreneurial process, plays a fundamental role in the formation, continuity, and growth of new ventures. A substantial body of classical and contemporary entrepreneurship literature considers this process as the result of the interaction among prior knowledge, entrepreneurial alertness, social networks, and individual cognitive processes. However, this stream of research implicitly assumes the existence of a certain level of information transparency, access to reliable data, and the presence of efficient intermediary institutions, such that the entrepreneur’s main challenge is not the lack of information but its interpretation. In contrast, emerging economies face fundamentally different conditions, where chronic shortages of reliable data, dispersed market signals, weak regulatory institutions, and extensive institutional voids impose serious constraints on the process of opportunity recognition. In such environments, formal information is either unavailable or of insufficient quality, resulting in the structural reproduction of information asymmetry. Consequently, a condition of “limited observability” emerges, in which many potential opportunities remain essentially undetectable. This situation reduces entrepreneurial activities to limited, short-term, and arbitrage-based opportunities while constraining the identification of innovative and scalable ventures. Against this backdrop, the emergence of generative artificial intelligence offers a new perspective for rethinking these limitations. Unlike previous technologies that primarily relied on structured datasets, generative AI is capable of producing novel insights from incomplete, fragmented, and heterogeneous information. Nevertheless, an important question remains unanswered: how do these capabilities redefine the cognitive boundaries of entrepreneurial opportunity recognition in data-scarce environments, and under what conditions can they contribute to the discovery of more valuable entrepreneurial opportunities?
Method: This study adopts a conceptual and theory-building approach and, by drawing upon the logic of theory synthesis and abductive reasoning, integrates insights from the literature on entrepreneurial opportunity recognition, institutional voids, digital entrepreneurship, and recent studies on artificial intelligence. Within this framework, key concepts from these domains are recombined to provide an integrated explanation of the role of generative artificial intelligence in reshaping the opportunity recognition process. The central argument of the paper is that generative artificial intelligence can be conceptualized as a distinct form of cognitive augmentation that enhances entrepreneurs’ ability to observe, interpret, and recombine market signals under conditions of information scarcity. More importantly, the proposed framework positions generative AI not merely as an analytical tool but as an active cognitive partner capable of expanding entrepreneurs’ search space, enriching sense-making processes, and reshaping the boundaries of entrepreneurial cognition.
Results: The findings of this study, presented in the form of a conceptual framework, indicate that the impact of generative artificial intelligence on opportunity recognition is realized through three primary mechanisms. The first is data augmentation, referring to the ability to combine and integrate dispersed market signals into actionable representations. The second is market insight generation, achieved through identifying hidden patterns and transforming heterogeneous data into meaningful narratives about emerging market trends. The third is pattern recognition and recombination, which enhances entrepreneurial imagination and enables the transition from limited opportunities toward innovative and scalable ones. Collectively, these mechanisms suggest that generative AI can partially compensate for the absence of reliable market-supporting institutions by expanding entrepreneurs’ ability to identify opportunities that would otherwise remain invisible. At the same time, the findings demonstrate that this process is not free from limitations. In some situations, AI-generated outputs may lead to misinterpretations, excessive reliance on algorithmic recommendations, or even the construction of false entrepreneurial opportunities, thereby emphasizing the continuing importance of human judgment.
This study extends opportunity recognition theory by introducing the concept of limited observability as a fundamental constraint in emerging economies and conceptualizes generative artificial intelligence as a mechanism for reducing this limitation. In addition, two variables—the intensity of institutional voids and entrepreneurs’ digital literacy—are introduced as key moderating factors influencing both the magnitude and nature of AI's impact. The proposed framework further demonstrates that, alongside its cognitive enhancement capabilities, risks such as automation bias, algorithmic hallucinations, and excessive dependence on AI-generated outputs may substantially influence the quality of opportunity recognition. From this perspective, the study contributes to the digital entrepreneurship literature by highlighting the unique role of generative technologies in data-scarce and institutionally weak contexts. More importantly, it argues that the primary value of generative AI in emerging economies lies not merely in improving decision efficiency, but in enabling entrepreneurs to observe, imagine, and formulate opportunities that would otherwise remain hidden under conditions of severe informational and institutional constraints.
Conclusion: Overall, the findings suggest that generative artificial intelligence has the potential to transform entrepreneurial opportunity recognition from a limited, experience-based activity into a more systematic, insight-driven, and data-recombination-oriented process, provided that its application is accompanied by human judgment, critical thinking, and continuous validation of AI-generated outputs. Accordingly, from a policy perspective, investment in digital literacy development, improvement of data infrastructures, broader access to intelligent technologies for entrepreneurs, and the design of flexible regulatory frameworks are expected to play decisive roles in enabling the effective use of generative AI for fostering entrepreneurship and innovation in emerging economies.