Introduction
Behavioral economics, Artificial Intelligence (AI), and entrepreneurship have emerged as critical pillars of modern management, forming a combination of three fields that redefine strategic approaches to business and organizational challenges (Plastino & Purdy, 2018). Over the past decades, advances in these areas have triggered a paradigm shift in understanding and managing individual and collective behaviors in entrepreneurial contexts. However, the integration of these three domains remains a challenge as their evolution has often occurred in parallel rather than through a synergistic framework (Beerbaum & Puaschunder, 2019).
The intersection of behavioral economics (Kahneman & Tversky, 1979) and entrepreneurship has illuminated how cognitive biases (Thomas, 2018), heuristics (Bryant, 2007), and social dynamics (Zhang, 2024) influence decision-making processes. As key decision-makers, entrepreneurs are frequently subject to non-rational factors that shape their choices, including overconfidence (Chen et al., 2018a, b), loss aversion, and bounded rationality (Robinson & Marino, 2015). While these insights have enriched management theory, they remain underutilized in practical applications due to the lack of systematic frameworks that incorporate recent technological advancements linked to AI (Lindebaum et al., 2024).
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Simultaneously, AI has revolutionized the landscape of decision-making, providing unprecedented tools to analyze complex datasets, predict outcomes, and automate processes (Obschonka & Audretsch, 2020). The ability of AI to mimic human decision-making processes, coupled with its capacity for scalability and precision, makes it a powerful ally for addressing the challenges highlighted by behavioral economics (Giuggioli et al., 2024). Yet, its integration into entrepreneurial contexts is often fragmented, as technological solutions are frequently designed without fully understanding human behavioral nuances (Lacárcel, 2025) and, rather, focused on business strategies (Jarrahi, 2018).
In this way, the integration of behavioral economics, AI, and entrepreneurship offers an immense potential for transforming management practices (Secinaro et al., 2025). In particular, combining AI-driven analytics with behavioral insights could help entrepreneurs anticipate market fluctuations more accurately by incorporating real-time feedback loops and heuristics-based indicators, thus moving beyond purely rational assumptions. For example, when combining AI-driven analytics with behavioral insights can enhance decision-making processes, reduce cognitive biases, and create adaptive strategies in dynamic environments (Saura et al., 2024). Entrepreneurs can leverage these integrated frameworks to navigate uncertainty better, optimize resource allocation (Hernández-Tamurejo et al., 2024), and foster innovation (Giuggioli & Pellegrini, 2023). Despite this potential, the existing literature often treats these domains in isolation, resulting in a fragmented understanding of their combined impact on management, economics models, or AI adoption (Choudhary et al., 2025). Indeed, without a holistic perspective, important shared insights such as leveraging behavioral data to calibrate AI-driven market predictions (Caplin et al., 2025), risk remaining overlooked, limiting the practical applicability of these theoretical insights.
In this context, there is a growing need for understanding this gap in the literature and synthesizing insights from behavioral economics and AI to address the unique challenges of entrepreneurship management. While existing studies provide valuable insights into these individual fields, their intersection remains fragmented and underexplored as a combination of intrinsically linked concepts. For instance, although prior research has highlighted how AI can facilitate entrepreneurial creativity (Kang et al., 2020) or mitigate biases in decision-making (Al Halbusi et al., 2024), few have examined how such applications might intersect with core principles of behavioral economics to reshape managerial strategies in entrepreneurship. Specifically, the literature often fails to address how the convergence of behavioral insights and AI technologies can address the unique challenges faced by entrepreneurs in real-world decision-making scenarios. This gap raises the present study’s central research question (RQ): How can the integration of behavioral economics and AI transform management practices in the context of entrepreneurial decision-making?
Therefore, this article aims to propose an updated and original framework that aligns behavioral economics, AI, and entrepreneurship within a unified management perspective. Exploring the connections and tensions between these disciplines, the present study seeks to highlight their combined contributions to solving real-world management problems. The present study emphasizes the importance of context, adaptability, and technological innovation in modern entrepreneurial settings. Thus, in order to complement the RQ, the following secondary objectives are proposed:
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Identify theoretical perspectives on the intersection of behavioral economics and AI in entrepreneurial contexts.
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Explore the implications of using AI technologies to influence entrepreneurial decision-making and behavior.
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Provide guidelines for the ethical and practical application of AI-driven tools informed by behavioral economics to support entrepreneurial innovation and adaptability.
To achieve the proposed objectives, a systematic literature review (SLR) was conducted. Based on the findings, a Latent Dirichlet Allocation (LDA) (Dana et al., 2024) model was implemented in Python to extract key thematic areas. These insights informed the presentation of the research findings and the formulation of future recommendations for the effective application of behavioral economics, AI, and entrepreneurship. The present study identifies eight thematic areas revealing how AI, combined with behavioral economics, can transform entrepreneurial management. Findings emerge in innovation, risk mitigation, and decision-making, showcasing AI’s role in bridging creative ventures with evolving market conditions. AI-driven analytics address cognitive biases, anticipate consumer behavior and enhance adaptive strategies. These data-driven insights refine entrepreneurial knowledge sharing, support network development, and foster social capital. Though some themes remain underexplored, they emphasize the significance of studying AI in strategic management, knowledge accumulation, and resource allocation. Overall, the findings highlight a framework for more resilient, informed, and innovative entrepreneurial ecosystems.
The structure of the manuscript is as follows. After the introduction, the theoretical framework is presented. Next, methodology approaches and analysis or results are shown. Following methods, the discussion and future research questions are presented. Finally, conclusions and theoretical and practical implications are offered.
Theoretical framework
Behavioral economics and entrepreneurship
Behavioral economics provides valuable insights into the psychological (Chegini, 2010) and social factors (Shepherd et al., 2015) influencing decision-making, particularly within entrepreneurial contexts. Entrepreneurs often operate in uncertain and resource-constrained environments where cognitive biases and heuristics significantly shape their decisions. Key concepts such as bounded rationality (Felin et al., 2014), overconfidence (Kraft et al., 2022), and loss aversion (Koudstaal et al., 2016) offer critical lenses to understand how entrepreneurs assess opportunities and risks in dynamic markets (Dobrew et al., 2025).
Likewise, bounded rationality, a concept introduced by Simon (1997), challenges the traditional assumption of fully rational actors in decision-making processes, emphasizing entrepreneurs’ cognitive limitations when processing information and evaluating complex trade-offs. Entrepreneurs often navigate overwhelming information while making quick decisions, leading them to rely on mental shortcuts or heuristics (Kruse et al., 2023). While these heuristics can facilitate efficiency in decision-making, they frequently introduce systematic errors. For instance, overconfidence—a well-documented cognitive bias—can result in entrepreneurs overestimating the success probability of a venture, which often leads to resource misallocation (Banerjee & Moll, 2010), premature scaling (Joseph et al., 2023), or an inability to pivot when faced with challenges (Kirtley & O’Mahony, 2023). On the other hand, loss aversion —a tendency to prioritize avoiding losses over acquiring equivalent gains, may discourage entrepreneurs from pursuing innovative but riskier strategies, even when data suggests a favorable outcome (Lee & Kim, 2024). This duality highlights the importance of recognizing and addressing cognitive biases to optimize decision-making.
Thus, entrepreneurial decision-making is further influenced by social and contextual dynamics as behavioral economics underscores then the role of social comparisons, peer influence, and emotional responses in shaping entrepreneurial behavior (Caliskan et al., 2024). Entrepreneurs are often embedded in networks where social proof and competitive pressures significantly influence decision-making (Åstebro et al., 2014). For instance, observing peers’ success may drive entrepreneurs to emulate similar strategies without fully considering their context or capabilities. Additionally, cultural norms and local market dynamics can amplify specific cognitive biases, such as risk aversion (Cramer et al., 2002) or over-optimism (Coelho, 2010), depending on the entrepreneurial ecosystem. Therefore, understanding these social influences is vital for improving decision-making processes, particularly in environments where collaboration and competition coexist.
In this context, behavioral economics also highlights the importance of feedback and learning mechanisms in entrepreneurial decision-making (Kraus et al., 2016). Entrepreneurs often operate in iterative cycles of trial and error, where learning from past outcomes is critical to refining future strategies. However, cognitive biases can distort the interpretation of feedback, leading to either overreaction or insufficient adjustments (Zhang & Cueto, 2017). For example, confirmation bias, the tendency to seek or interpret evidence that aligns with existing beliefs (Cossette, 2014), can hinder entrepreneurs from recognizing necessary pivots or changes in strategy. To address these challenges, behavioral economics suggests interventions such as structured reflection practices or external feedback mechanisms that encourage more objective evaluations of outcomes (Acciarini et al., 2021). These strategies can enhance entrepreneurs’ ability to learn effectively from both successes and failures.
Likewise, another significant contribution of behavioral economics to entrepreneurship is the concept of nudges (Thaler, 2018), which are small intentional changes in the decision-making environment designed to steer individuals toward better choices without restricting their freedom. Nudges have been successfully applied in various domains to encourage behaviors that align with long-term goals, such as saving money or adopting healthier lifestyles (Sugden, 2017). In entrepreneurial contexts, nudges can be designed to counteract specific cognitive biases. For example, framing options in terms of potential gains rather than losses can help mitigate loss aversion while providing pre-commitment tools can reduce the impact of overconfidence. Such interventions can be particularly effective in high-stakes decisions, where the consequences of biases are magnified (Black, 2024). Finally, the role of emotional regulation in entrepreneurial decision-making deserves attention, as entrepreneurs often face high levels of stress and uncertainty, which can exacerbate cognitive biases and impair rational thinking (Monteiro & Artes, 2024). Therefore, behavioral economics provides insights into how emotional states influence decision-making processes, highlighting the need for strategies that promote emotional resilience. Techniques such as stress management interventions or structured decision-making frameworks can help entrepreneurs maintain clarity and focus under pressure (Al Halbusi et al., 2024).
In order to summarize the behavioral economics and entrepreneurs’ main connections to AI, Table 1 presents the main behavioral economics concepts linked to these research fields.
Table 1
Behavioral economics and entrepreneurs’ main connections
Behavioral Economics | Description | Connection to entrepreneurs | Benefis for entrepreneurs | Authors |
|---|---|---|---|---|
Bounded Rationality | Cognitive limitation in processing information and making optimal decisions. | ▪ Limited capacity to evaluate all potential business scenarios. ▪ Difficulty in assessing complex market data. ▪ Challenges in prioritizing opportunities among constraints. | o Provide structured decision-making frameworks. o Develop tools to handle complex data more efficiently. o Create educational programs for better cognitive management. o Facilitate mentorship programs to broaden perspectives. | Dobrew et al. (2025) Robinson and Marino (2015) Felin et al. (2014) Simon (1997) |
Overconfidence | Excessive confidence in one’s abilities or decisions. | ▪ Overestimation of business success probability. ▪ Premature scaling or risky investments. ▪ Resistance to seeking external advice. | o Offer realistic feedback mechanisms. o Implement tools to evaluate risks and adjust confidence levels. o Promote peer-led workshops to mitigate overconfidence. | Kraft et al. (2022) Robinson and Marino (2015) |
Loss Aversion | Preference for avoiding losses rather than acquiring equivalent gains. | ▪ Avoidance of innovative but high-risk opportunities. ▪ Focus on short-term safety over long-term gain. ▪ Reluctance to experiment with new strategies. | o Frame decisions to highlight potential gains. o Support entrepreneurs in balancing risk and innovation. o Build simulation environments for testing risky decisions. o Train in long-term strategic thinking. | Saeedikiya et al. (2024) Koudstaal et al. (2016) Cramer et al. (2002) |
Heuristics | Mental shortcuts used to simplify decision-making. | ▪ Reliance on rules of thumb for quick decisions. ▪ Prone to systematic errors in volatile markets. ▪ Over-dependence on past experiences as templates. | o Train entrepreneurs to recognize heuristic pitfalls. o Provide analytical tools to complement intuitive decision-making. o Offer case studies demonstrating the limits of heuristics. | Kruse et al. (2023) Cossette (2014) Bryant (2007) |
Social Proof | Influence of others’ behaviors or decisions on an individual’s choices. | ▪ Adoption of strategies based on competitors’ actions. ▪ Peer pressure shaping business choices. ▪ Decisions influenced by perceived social norms. | o Encourage critical evaluation of competitors’ strategies. o Promote unique approaches tailored to individual contexts. o Facilitate learning from diverse entrepreneurial ecosystems. o Provide platforms for collaborative strategy-building. | Caliskan et al. (2024) Åstebro et al. (2014) |
Confirmation Bias | Tendency to favor information that confirms existing beliefs. | ▪ Disregard for contradictory feedback or market signals. ▪ Resistance to pivoting strategies. ▪ Over-reliance on supportive evidence. | o Introduce unbiased evaluation systems. o Foster openness to alternative perspectives. o Develop dynamic feedback loops for real-time adjustments. | Caplin et al. (2025) Acciarini et al. (2021) Thomas (2018) Zhang and Cueto (2017) |
Nudges | Subtle changes in the decision-making environment to guide choices. | ▪ Potential to guide entrepreneurs toward better financial decisions. ▪ Encouragement to focus on sustainable practices. ▪ Help in structuring complex problem-solving approaches. | o Design personalized nudges for financial planning. o Create environmental cues to enhance goal alignment. o Develop AI tools to incorporate nudge principles effectively. o Test behavioral interventions in controlled environments. | Leal and Oliveira (2024) Thaler (2018) Sugden (2017) |
AI in management: enhancing decision-making processes
AI has transformed management practices across industries by equipping entrepreneurs and decision-makers with advanced tools to analyze vast datasets, identify patterns, and predict future trends (Füller et al., 2022). AI enables entrepreneurs to allocate resources efficiently and prioritize strategic decisions when automating routine tasks and providing predictive insights (Obschonka & Audretsch, 2020). Its applications various domains, from streamlining operations to improving customer engagement, offering organizations the opportunity to enhance competitiveness and agility in complex environments (Shepherd & Majchrzak, 2022). However, the true potential of AI in management lies in its ability to complement human judgment, particularly in navigating the nuanced dynamics of organizational behavior and decision-making. AI systems can process structured and unstructured data, uncovering relationships that may not be immediately apparent to human decision-makers (Saura et al., 2023a, b). This capability is particularly valuable for management tasks such as market analysis, performance forecasting, and resource allocation (Saura et al., 2023a). For instance, machine learning algorithms can identify emerging market trends or analyze customer preferences, enabling entrepreneurs to anticipate demand shifts and adjust strategies proactively. Also, predictive models can support critical management functions, such as pricing strategies, inventory optimization, and risk assessment, reducing uncertainty and allowing entrepreneurs to make more informed decisions in volatile environments (Duong, 2024).
Facing this paradigm, predictive analytics also offers AI powerful tools for real-time decision support (Csaszar et al., 2024), which is critical for dynamic management contexts. At the same time, natural language processing (NLP) algorithms (Kang et al., 2020), for example, can analyze customer feedback, employee sentiment (Saura et al., 2022a, b), or external media to provide actionable insights into organizational performance (Olan et al., 2022) or brand perception (Park & Ahn, 2024). Similarly, AI-driven optimization tools can enhance resource management by identifying cost-effective solutions to challenges such as supply chain disruptions or workforce planning (Farrow, 2022). However, integrating AI into entrepreneurship management presents several challenges. For example, a key limitation is the lack of context awareness in many AI systems, particularly regarding human behavior (Benvenuti et al., 2023). While AI models are designed to optimize outcomes based on predefined parameters, they often fail to account for the cognitive biases and emotional factors that influence entrepreneurial decisions. For instance, an AI tool might recommend a strategic investment based on favorable data patterns, yet an entrepreneur influenced by risk aversion might hesitate to act (Bonilla & Vergara, 2021). This disconnect underscores the importance of aligning AI tools with behavioral insights to bridge the gap between data-driven recommendations and human-centric decision-making (Battisti et al., 2022).
Likewise, ethics is a significant consideration in adopting AI within management (Baker-Brunnbauer, 2021; Heyder et al., 2023). The use of AI to guide decisions, whether through personalized recommendations (Barbosa et al., 2024) or predictive analytics, raises important questions about transparency, accountability, and inclusivity. Entrepreneurs must ensure that AI-driven tools adhere to ethical standards while fostering trust among stakeholders (Saura et al., 2022c). For example, opaque or “black-box” models (Hassija et al., 2024), which produce recommendations without explaining the rationale, can erode confidence among users. Implementing explainable AI (XAI) models that provide clear and interpretable reasoning behind decisions is essential for fostering accountability and promoting adoption (Graham & Bonner, 2024). Thus, entrepreneurs are more likely to rely on AI tools when they easily understand and validate the basis for their recommendations (Saura et al., 2024).
In addition to transparency, AI applications in management must address issues of bias and diversity (Matta et al., 2022). Data-driven systems inherently depend on their training datasets’ quality and representativeness. Biased or incomplete data can lead to skewed outcomes, perpetuating inequities or reinforcing flawed assumptions (Zuboff, 2023). Entrepreneurs must critically evaluate the algorithms and data used by AI systems to ensure that outputs reflect diverse perspectives and are free from discriminatory patterns. This is particularly crucial in global organizations, where cultural, economic, and social diversity plays a significant role in shaping strategic decisions (Stone et al., 2020).
Likewise, integrating AI into management requires a synergistic approach that combines technological precision with a nuanced understanding of organizational behavior. AI tools informed by behavioral insights can help entrepreneurs address cognitive biases such as overconfidence or anchoring by providing balanced and data-driven perspectives (Battisti et al., 2022). For example, decision dashboards can present scenarios that challenge assumptions or prompt entrepreneurs to consider alternative strategies, fostering more comprehensive and robust decision-making processes. Moreover, AI holds significant potential for enhancing collaboration and decision-making in teams. AI-driven platforms that connect entrepreneurs with resources, partners, or collaborators based on shared objectives can strengthen organizational ecosystems. Likewise, scenario planning tools, supported by AI, can enable teams to simulate potential outcomes of strategic choices, fostering more informed group decisions and encouraging innovation (Keding, 2021). Finally, AI offers transformative potential for management by enhancing data analysis, streamlining operations, and supporting strategic planning. However, its effectiveness hinges on aligning with human judgment and addressing ethical considerations (Saura et al., 2021). When integrating AI into management practices, entrepreneurs can confidently navigate complexity, leveraging technology to boost innovation and achieve sustainable growth. To summarize the content presented in this section, Table 2 presents the main connections of AI to entrepreneurial management.
Table 2
AI in management
AI oncepts in Management | Description | Connection to Management | Possible improvements for Management | Authors |
|---|---|---|---|---|
Predictive Analytics | Using historical data to predict future outcomes and trends. | ▪ Helps entrepreneurs forecast market trends and demand shifts. ▪ Supports financial planning and risk assessment. ▪ Improves decision-making accuracy. | o Enhance accuracy of predictive models through robust datasets. o Integrate behavioral data to improve decision relevance. o Expand applications to include holistic organizational forecasting. | Saura et al. (2024) Duong (2024) Obschonka and Audretsch (2020) |
Natural Language Processing (NLP) | Analyzing text and speech data to extract insights or automate tasks. | ▪ Automates customer feedback analysis and sentiment tracking. ▪ Enhances employee sentiment analysis for organizational health. ▪ Assists in real-time communication monitoring. | o Develop multilingual NLP tools for global operations. o Increase automation in repetitive communication tasks. o Use NLP insights to shape employee training programs. | Park and Ahn (2024) Olan et al. (2022) Kang et al. (2020) |
Optimization Models | Mathematical models to optimize resource allocation and processes. | ▪ Optimizes supply chain and logistics management. ▪ Reduces operational inefficiencies. ▪ Allocates resources based on data-driven priorities. | o Improve algorithms for complex and large-scale optimizations. o Combine optimization tools with human oversight. o Develop adaptive systems for rapidly changing conditions. | Benvenuti et al. (2023) Farrow (2022) Saura et al. (2022a) |
Explainable AI (XAI) | AI systems designed to provide clear and interpretable decision-making processes. | ▪ Builds trust by providing transparent decision-making frameworks. ▪ Fosters accountability in automated decisions. ▪ Enhances adoption by explaining outcomes. | o Simplify interpretability for non-technical entrepreneurs. o Embed XAI principles in all organizational AI tools. o Conduct regular reviews to ensure accountability. | Graham and Bonner (2024) Meske et al. (2022) |
Real-Time Decision Support | Providing immediate insights and recommendations for dynamic contexts. | ▪ Enables rapid responses to market changes. ▪ Supports crisis management with real-time data. ▪ Aids in dynamic workforce allocation. | o Enhance AI tools with context-aware capabilities. o Train entrepreneurs to use real-time insights effectively. o Develop systems for seamless integration with other tools. | Csaszar et al. (2024) Dahanayake et al. (2011) Burstein et al. (2010) |
Scenario Planning | Simulating potential outcomes of various strategies to support planning. | ▪ Helps entrepreneurs anticipate risks and opportunities. ▪ Encourages collaborative decision-making. ▪ Promotes innovation through iterative strategy testing. | o Create interactive platforms for collaborative scenario planning. o Integrate behavioral and contextual insights into simulations. o Provide intuitive interfaces for non-expert users. | Battisti et al. (2022) Keding (2021) Saura et al. (2021) |
Bias and Inclusivity in AI | Ensuring AI systems produce fair and representative outcomes. | ▪ Identifies and mitigates biases in AI-driven outcomes. ▪ Promotes diversity in decision-making frameworks. ▪ Ensures ethical alignment of AI tools. | o Regularly audit AI models for potential biases. o Encourage diverse datasets during model training. o Implement inclusive AI policies across all organizational levels. o Establish monitoring systems to detect biased outcomes. | Zuboff (2023) Matta et al. (2022) Stone et al. (2020) |
Methodology
Systematic literature review
Firstly, a SLR is a structured and methodical approach to identifying, evaluating, and synthesizing relevant literature on a specific topic (Nightingale, 2009). Unlike traditional reviews, an SLR adheres to a transparent and replicable methodology, ensuring that the process minimizes bias and maximizes coverage (Paul et al., 2024). This method is particularly valuable in emerging fields where the body of knowledge is fragmented, dispersed across multiple disciplines, and rapidly evolving (Singh, 2024). In such cases, an SLR allows researchers to consolidate insights, identify gaps, and establish a clear foundation for advancing theory and practice. For this study, the SLR was designed to address the intersection of behavioral economics, AI, and Entrepreneurship, three fields that have garnered significant attention yet often evolve in parallel without substantial integration.
The review was conducted using a structured search strategy across five major academic databases following Saura et al. (2024): Web of Science (WoS), ScienceDirect, IEEE Xplore, ACM Digital Library, and AIS Electronic Library. The search query employed the following keywords: Behavioral Economics AND Entrepreneur AND Management OR AI. Thus, AI was not included as a mandatory search term because its application within entrepreneurship management is still in an emerging phase, and preliminary searches yielded no relevant results suitable for inclusion in the sample (Hiebl, 2023).
Therefore, the SLR was designed to incorporate AI implications, ensuring that the results and their significance are more comprehensible and directly applicable to the intersection of the study’s key domains. The search was conducted on November 2, 2024, and yielded diverse publications. The systematic approach ensured comprehensive coverage of the literature, enabling the synthesis of existing knowledge and the identification of conceptual and methodological gaps. The primary publication categories selected for this study were Business Economics, Behavioral Sciences, Psychology, and Operations Research Management Sciences. Given the emerging nature of the research field, these categories provided an effective framework for filtering and refining the search results in the academic databases used. Additionally, only original research articles were included in the final sample, while proceedings, books, and theses were excluded to maintain a focused and rigorous dataset. This was particularly important given the interdisciplinary nature of the topic, which required integrating insights from management, technology, and behavioral science.
The SLR conducted in this study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which provide a rigorous framework for identifying, screening, and selecting relevant literature. Finally, using an SLR ensures methodological rigor and establishes a credible, replicable foundation for subsequent analysis and the development of the proposed framework. This approach is essential in emerging fields requiring clear frameworks to advance academic inquiry and practical application (Rethlefsen et al., 2021).
Latent Dirichlet Allocation (LDA)
Secondly, an LDA algorithm in Python is developed by following a structured, multi-step workflow. First, text collection and preprocessing are undertaken to standardize the input. This involves cleaning, tokenizing, removing stopwords, and lemmatizing the textual data from the SLR results (Sawant & Sonawane, 2024). Second, topic determination is carried out by combining exploratory analysis with domain expertise to estimate a suitable number of topics. Third, the model training phase computes probability distributions of topics within documents and words within topics, thereby identifiying latent thematic relationships (Singh et al., 2023).
Following the LDA model training, interpretation of results includes examining the proportion of topics in each document, identifying the most relevant words in each topic, and highlighting dominant themes across the corpus (Moro et al., 2015). Python libraries such as gensim and scikit-learn (Owa, 2021) facilitate this process by providing robust tools for text preprocessing, model training, and results visualization. Additionally, coherence metrics such as topic coherence (Mimno et al., 2011) are employed to assess the semantic consistency among the most frequent words in each topic, aiding in determining whether the chosen number of topics is optimal. Subsequently, each document is assigned a topic distribution that reveals the prevalence of specific themes, which can then be aggregated to identify dominant or emerging patterns across the corpus. Visualization tools (Zini & Awad, 2022) further enhance interpretability by illustrating distances and term relevance within each topic. This iterative approach—encompassing preprocessing, model parameter selection, and post-processing evaluation—ensures that researchers can fine-tune the model for greater accuracy and clarity. Ultimately, the combination of LDA and Python’s capabilities empowers scholars and practitioners to extract nuanced, data-driven insights that support strategic decision-making in areas such as management, behavioral sciences, and AI, as Sawant and Sonawane (2024) stated.
The outcomes of LDA provide significant insights as they reveal the proportion of topics in each document, facilitating the understanding of their thematic connections. They also identify the most relevant words within topics and highlight dominant themes across the corpus. Pythom’s scalability and adaptability allow researchers to handle large datasets and tailor analyses to specific research goals, while its open-source nature fosters collaboration and innovation. LDA is closely linked to NLP, contributing to tasks such as text summarization, sentiment analysis, and trend identification (Sawant & Sonawane, 2024).
Analysis of results
Systematic literature review results
The SLR yielded an initial total of 179 articles across five major academic databases. Specifically, WoS provided 97 results, of which 16 were retained for further analysis. From ScienceDirect, 61 articles were identified, narrowing down to 4 relevant studies. IEEE Xplore initially contributed 6 articles, none of which were included after screening. Similarly, the ACM Digital Library provided 15 results, but none were deemed suitable for the review. The AIS Library did not yield any relevant results. The selection of articles adhered strictly to the PRISMA guidelines, emphasizing alignment with the study’s objectives, a direct connection to the core research themes, and the use of appropriate terminology pertinent to the present study. This rigorous process ensured that only the most relevant and high-quality studies were included in the final analysis.
To enhance the quality of the studies included in the analysis, a Risk of Bias Assessment (RBA) was conducted, as presented in Table 3. The RBA evaluates multiple variables to determine the reliability and validity of the studies incorporated into the results of the systematic literature review (SLR). The RBA considers six key dimensions that assess the methodological rigor and potential biases in each study. Thus, Study Design (SD) examines the overall quality and coherence of the study’s framework. Next, Random Sequence Generation (RSG) evaluates whether appropriate methods were employed to ensure unbiased sampling and to eliminate systematic patterns that could distort the findings. In this way, Blinding of Outcome Assessment (BOA) focuses on techniques or methods implemented to reduce bias in the evaluation of results. Withdraw and Dropout (WDO) assesses whether the study adequately addressed potential issues arising from high rates of participant withdrawal or dropout, which could lead to incomplete datasets. Also, Inclusion-Exclusion Criteria (IEC) reviews whether the variables or indicators used in the study were clearly defined and properly justified. Finally, Reporting Adverse Events (RAE) examines whether the study transparently documented any limitations or challenges encountered during its development.
Table 3
Risk of Bias Assessment of the SLR studies
Authors | SD | RSG | BOA | WDO | IEC | RAE |
|---|---|---|---|---|---|---|
Dew et al. (2009) | + | - | + | - | + | - |
Basukie et al. (2020) | + | - | - | ? | - | - |
Afi et al. (2022) | - | - | + | - | ? | ? |
Nguyen and Nguyen (2024) | + | + | - | ? | - | ? |
Zou et al. (2023) | ? | - | - | ? | - | + |
Cristofaro et al. (2024) | + | ? | - | + | ? | - |
Vaghefi et al. (2024) | + | - | - | - | - | ? |
Harima et al. (2021) | ? | - | + | - | - | + |
? | - | ? | - | ? | ? | |
Ahmad et al. (2021) | ? | - | ? | + | - | + |
Link (2023) | - | ? | ? | - | ? | + |
Brenes et al. (2021) | - | - | + | + | ? | ? |
Dwivedi et al. (2021) | + | - | ? | + | ? | - |
Fairchild (2011) | ? | ? | + | ? | ? | + |
Kim and Sohn (2016) | + | ? | + | ? | - | + |
Levine et al. (2017) | - | ? | - | + | ? | - |
Peralta et al. (2019) | - | - | ? | ? | ? | + |
Wu et al. (2024) | + | ? | ? | - | + | ? |
Vitali et al. (2013) | ? | ? | + | ? | ? | - |
Yazdipour (2009) | ? | - | + | + | + | - |
LDA results
The methodological framework for this study uses several Python libraries to facilitate data preprocessing, thematic modeling, and visualization. Text preprocessing was carried out using NLTK (Hardeniya et al., 2016), which allowed for the tokenization of textual data, the removal of irrelevant characters, and the elimination of stop words. These processes ensured that the corpus was clean and standardized, providing a robust foundation for subsequent analysis. The tokenized and filtered text was further structured to optimize its use in topic modeling. To identify and extract key topics from the corpus, the study employed Gensim (Srinivasa-Desikan, 2018), utilizing the LDA algorithm. This method enabled the detection of latent themes by analyzing word distributions across documents. The output of the LDA model included keyword lists and associated weights for each topic, which were instrumental in understanding the thematic structure of the data. It should be highlighted that Gensim’s capabilities provided a computationally efficient and scalable approach to modeling topics within a complex corpus. Data manipulation and organization were conducted using Pandas (McKinney & Team, 2015), which facilitated the creation of structured tables, the calculation of frequencies and weights, and the refinement of keyword distributions. For mathematical operations, such as the simulation of variations in topic trends, NumPy (Gupta & Bagchi, 2024) was employed.
In this way, Matplotlib (Shreemathi et al., 2024) was used for generating foundational plots, including bar charts, stacked area graphs, and heatmaps. Complementing this, Seaborn (Sial et al., 2021) was employed to enhance the aesthetic quality and interpretability of the visualizations, particularly for advanced heatmaps that depicted the correlation and distribution of topics. The integration of these libraries created a cohesive analytical pipeline that combined text preprocessing, topic modeling, data manipulation, and visualization. Thus, the key insights derived from the computation of the LDA model are presented below. Table 4 outlines the identified topics, numbered from 0 to 7, along with a detailed description, the most relevant keywords, their frequency, and the percentage they represent within the total analyzed corpus.
Table 4
LDA topic modeling results
Topic | Title | Description | Keywords | Frequency | % of Corpus |
|---|---|---|---|---|---|
Topic 0 | Strategic Management | Focuses on skills and strategies in organizational management. | nancier, empathy, nancing, value, skills | 0.02369 | 1.98144 |
Topic 1 | Entrepreneurship Knowledge | Emphasizes entrepreneurial knowledge and practical applications. | social, making, capital, entrepreneurial, style | 0.03673 | 3.07206 |
Topic 2 | Decision Making | Explores processes and strategies for making decisions. | strategic, management, performance, data, decisions | 0.00548 | 4.58863 |
Topic 3 | Social Capital | Examines the role of networks and social capital in management. | venture, risk, academic, entrepreneurs, user | 0.02458 | 2.05615 |
Topic 4 | Behavioral Economics | Discusses decision-making theories and behavioral impacts. | creative, entrepreneurs, behavior | 0.02464 | 2.06112 |
Topic 5 | Innovation | Highlights innovation processes in management and entrepreneurship. | knowledge, entrepreneurs, innovation, | 0.01414 | 1.1830 |
Topic 6 | Risk Management | Covers risk assessment and mitigation strategies. | strategic, loss, affordable, decision, management | 0.03181 | 2.66104 |
Topic 7 | Market Dynamics | Analyzes market behaviors and competitive strategies. | entrepreneurs, makets, competitors, | 0.03347 | 2.79958 |
In order to facilitate the interpretation of topic weights identified in the sample of documents from the SLR, Fig. 1 illustrates the distribution of weights across topics. The horizontal axis (X) represents the topics by their assigned number, while the vertical axis (Y) displays the total weight of each topic. The color gradient, transitioning from blue to red, reflects the absolute weight of each topic relative to the others, with warmer tones indicating higher significance within the thematic distribution.
Fig. 1
Distribution of weights across topics. Source: The authors.
Similarly, to better understand the weight of individual words comprising the identified topics, the heatmap of word weights per topic is presented below (Fig. 2). This visualization highlights the distribution of words and their relative importance within the total analyzed database, providing insights into the contribution of each word to the thematic structure in the corpus analyzed.
Fig. 2
Heatmap of word weights per topic. Source: The authors
Additionally, in Table 5, the Correlation Matrix of Topics is presented in a half-matrix (triangular) layout, providing a concise yet detailed depiction of the relationships between the identified topics based on their shared keyword weights provides a detailed visual representation of the relationships between the identified topics based on the shared weights of their keywords. In the correlation matrix, the values represent the Pearson correlation coefficients (Baak et al., 2020), which measure the linear relationship between two variables—in this case, the identified topics from the LDA model. The matrix quantifies the correlation coefficient between pairs of topics. These coefficients range from − 1 to + 1, representing the strength and direction of the relationships. A correlation coefficient of + 1 indicates a direct relationship where the keywords of two topics exhibit similar weight distributions across the corpus. Conversely, a coefficient of −1 suggests an inverse relationship, where keywords of one topic are weighted more heavily when those of the other topic are less prominent. A coefficient near 0 reflects the absence of a meaningful relationship between the topics. The diagonal cells of the matrix consistently display a correlation of + 1, as each topic is perfectly correlated with itself.
This correlation matrix identifies thematic overlaps, distinct topics, and potential research gaps. Topics with strong positive correlations often share keywords and conceptual elements, suggesting they are closely related within the corpus. For instance, a high correlation between “Behavioral Economics” and “Decision Making” might reflect their shared emphasis on similar terms linked to these topics, indicating their conceptual alignment. On the other hand, low or negative correlations highlight distinct or independent topics. For example, a negative correlation between “Social Capital” and “Risk Management” could suggest that discussions around networks and social capital focus on different aspects than those emphasizing risk assessment and mitigation. Topics with no significant correlation may represent underexplored areas or opportunities for integrating disparate themes. Additional correlations are discussed in the discussion section.
Table 5
Triangular correlation matrix of topics
Topics names | Topic 0 | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | Topic 7 |
|---|---|---|---|---|---|---|---|---|
Strategic Manag. | 1.00 | |||||||
Entrepreneurship Kno. | −0.21 | 1.00 | ||||||
Decision Making | −0.18 | −0.18 | 1.00 | |||||
Social Capital | −0.20 | −0.20 | −0.18 | 1.00 | ||||
Behavioral Economics | −0.11 | −0.11 | −0.097 | 0.089 | 1.00 | |||
Innovation | −0.21 | −0.21 | −0.19 | −0.083 | 0.071 | 1.00 | ||
Risk Management | −0.25 | −0.24 | 0.29 | 0.082 | 0.045 | −0.049 | 1.00 | |
Market Dynamics | −0.11 | −0.11 | −0.10 | 0.16 | 0.34 | 0.37 | 0.31 | 1.00 |
Discussion
The development of the present study developed in two methods has discovered important correlations that reveal how interconnected certain thematic areas are, particularly in relation to decision-making processes, risk management, and innovation in entrepreneurial management, domains where AI integration has transformative potential. In this way, a notable correlation (0.37) between Topic 5 (Innovation) and Topic 7 (Market Dynamics) highlights the relationship between entrepreneurial innovation and market behavior. As indicated by Mikalef et al. (2021) AI plays a pivotal role in this dynamic by enabling real-time market analysis and fostering data-driven innovation. These insights suggest that AI can bridge the gap between creative entrepreneurship and rapidly evolving market landscapes. This is supported by studies that demonstrate how AI-powered tools, such as predictive analytics and machine learning algorithms, are transforming innovation processes and enhancing entrepreneurial decision-making (Obschonka & Audretsch, 2020).
Also, the connection between Topic 4 (Behavioral Economics) and Topic 7 (Market Dynamics) with a positive correlation of 0.34 illustrates the influence of behavioral insights on market behavior. Behavioral economics focuses on understanding decision-making theories and behavioral impacts, and the adoption of AI can further enhance this understanding by processing complex datasets and identifying subtle behavioral patterns. AI’s ability to analyze and predict consumer behavior offers businesses a competitive edge in tailoring strategies to dynamic market trends, as stated by Petrescu et al. (2024). Research in this area has highlighted the role of AI in integrating behavioral insights into dynamic pricing strategies, personalized recommendations, and adaptive digital marketing campaigns, which further reinforce the relevance of this correlation. The correlation of 0.31 between Topic 7 (Market Dynamics) and Topic 6 (Risk Management) underscores the interconnectedness of understanding market behaviors and managing associated risks effectively. AI adoption plays a critical role in this relationship by enabling organizations to anticipate market fluctuations and identify potential risks before they materialize. For instance, AI-driven tools, such as sentiment analysis and real-time monitoring of economic indicators, can help businesses forecast demand trends while simultaneously assessing the risks involved in expanding market strategies. Studies like Teece et al. (2016) have shown that such tools enhance organizational agility by combining risk management with competitive analysis, ensuring businesses maintain resilience in rapidly changing market environments.
Likewise, the positive correlation (0.29) found between Topic 2 (Decision Making) and Topic 6 (Risk Management) within the LDA model underscores a significant link between strategic decision-making and the evaluation of risks, especially within managerial contexts. Thus, AI adoption in these areas can enhance predictive capabilities and optimize decision outcomes, enabling organizations to navigate uncertainty better. Authors such as Ghaffarian et al. (2023) demonstrates that AI can provide decision-makers with advanced risk modeling and scenario planning, reducing biases and improving the reliability of decisions in uncertain contexts.
Although no positive correlations were obtained for Topic 0 (Strategic Management), Topic 1 (Entrepreneurship Knowledge), and Topic 3 (Social Capital), their identification in the results of this study underscores their relevance to entrepreneurial management, behavioral economics, and the influence of AI. These topics represent critical dimensions that are inherently connected to the broader themes explored in this research. In this way, Topic 0 (Strategic Management) focuses on skills and strategies in organizational management, which are foundational to entrepreneurial success. Despite the lack of strong correlations with other topics, strategic management remains a key component of entrepreneurship, as it emphasizes planning, resource allocation, and value creation. The integration of AI in this domain has been highlighted in studies that demonstrate its potential to enhance decision-making, improve resource optimization, and support adaptive strategies in dynamic business environments (Duan et al., 2019). AI-driven tools, such as predictive analytics and scenario modeling, allow managers to respond more effectively to uncertainty and complexity, reinforcing entrepreneurial ventures’ strategic dimension.
In this way, Topic 1 (Entrepreneurship Knowledge) emphasizes practical applications and the accumulation of knowledge in entrepreneurship, making it central to the understanding of entrepreneurial ecosystems. The absence of positive correlations with other topics does not diminish its importance, as entrepreneurship knowledge inherently overlaps with multiple disciplines, including behavioral economics and management. Studies like Qin (2024) have shown that the adoption of AI can significantly enhance entrepreneurial knowledge by providing access to large datasets, uncovering market trends, and fostering innovation through knowledge-sharing platforms. This connection highlights how AI accelerates learning and also enables entrepreneurs to make informed decisions in competitive and uncertain environments. Finally, Topic 3 (Social Capital) examines the role of networks and relationships in entrepreneurial contexts. While no strong correlations were identified, social capital remains a pivotal element in entrepreneurship, as it facilitates resource access, trust-building, and collaboration. The integration of AI in this area has been explored in studies (Lee et al., 2020) that illustrate how AI-powered network analysis applications can optimize social connections and identify key stakeholders in entrepreneurial ecosystems.
Combining behavioral economics, AI and entrepreneurship: an updated framework for management
The integration of behavioral economics, AI, and entrepreneurship offers an opportunity to redefine managerial practices by addressing both human and technological dimensions. The topics and correlations identified in this study underscore the relevance of key intersections across these domains, providing fertile ground for future research. Below, we propose eight research directions based on the findings, each emphasizing how behavioral economics and AI can transform entrepreneurial management.
Proposition 1: AI-based tools informed by behavioral economics enhance strategic management practices in entrepreneurial settings
Building on Topic 0 (Strategic Management), AI has the potential to revolutionize strategic management by addressing inherent cognitive and organizational biases (Acciarini et al., 2021). Behavioral economics highlights that decision-making processes often deviate from rational models due to bounded rationality, emotions, and heuristics (Ahmad et al., 2021). AI-powered tools, such as predictive analytics and scenario modeling, can mitigate these biases by providing data-driven insights and objective evaluations of strategic options. For instance, research has shown that AI can enhance resource allocation and long-term planning by modeling complex organizational behaviors and forecasting market trends with higher accuracy (Füller et al., 2022). Therefore, integrating AI with behavioral economics is key for managers in entrepreneurial contexts as it can better align strategic goals with human dynamics, improving adaptability in uncertain environments. This intersection remains underexplored, especially in understanding how empathy-driven AI systems can support leadership and team dynamics in Small and medium-sized enterprises (SME) and startups (Mashat, 2020).
Proposition 2: AI-driven knowledge-sharing systems enhance entrepreneurial knowledge and behavioral adaptability within entrepreneurial ecosystems
Drawing from Topic 1 (Entrepreneurship Knowledge), AI can significantly enhance entrepreneurial ecosystems by facilitating knowledge dissemination and adaptive learning (Minn, 2022). As previously analyzed, behavioral economics underscores the importance of social capital and collective learning in entrepreneurial success. Thus, AI-driven platforms, such as intelligent recommendation systems and dynamic content curation, enable entrepreneurs to access personalized resources, uncover market trends, and foster innovation. Studies such as De la Peña and Granados (2024) have demonstrated that AI can reduce information asymmetry within ecosystems by democratizing access to critical insights, enabling even resource-constrained entrepreneurs to compete effectively. Additionally, AI technologies that integrate behavioral data can predict entrepreneurial success by identifying behavioral patterns associated with resilience, creativity, and adaptability (Kim & Sohn, 2016). This research direction is pivotal for understanding how AI can drive inclusivity and sustainability in entrepreneurial knowledge networks.
Proposition 3: AI leveraging behavioral economics to improve decision-making processes and risk assessment in entrepreneurial management
Building on Topic 2 (Decision Making), AI has emerged as a key technology for improving decision-making in uncertain and dynamic environments. Behavioral economics has long emphasized the impact of cognitive biases, such as overconfidence and anchoring, on managerial decisions (Kraft et al., 2022). When integrating AI, decision-makers can leverage behavioral data to identify and counteract these biases, resulting in more rational and effective outcomes. Therefore, AI-powered decision-support systems can simulate multiple scenarios, incorporate probabilistic modeling, and generate recommendations that account for both risk and opportunity (Teece et al., 2016). The current research Al Halbusi et al. (2024) suggests that such systems are particularly valuable in entrepreneurial contexts, where uncertainty and resource constraints magnify the consequences of poor decisions. Future studies should explore how AI can combine behavioral insights with real-time data to enhance decision-making frameworks across different entrepreneurial stages.
Proposition 4: AI technologies enhance the development of social capital in entrepreneurial ecosystems by leveraging insights from behavioral economics
From Topic 3 (Social Capital), the role of AI in optimizing social networks is a promising area of research. Behavioral economics highlights that trust, reciprocity, and network strength are fundamental to entrepreneurial success. AI-powered tools and platforms, such as graph analytics and machine learning algorithms, can analyze complex network structures to identify influential actors, predict collaboration potential, and recommend optimal connections (Saura et al., 2023a, b). Likewise, AI can enhance the effectiveness of business partnerships by modeling trust dynamics and detecting early signs of network inefficiencies. AI systems can incorporate behavioral data that foster stronger networks by tailoring interventions to specific cultural or social contexts if these applications are implemented within the entrepreneurial ecosystems (Chegini, 2010). Exploring this intersection would provide insights into how AI can amplify the benefits of social capital, particularly in globalized and digital-first entrepreneurial environments.
Proposition 5: AI-driven behavioral models improve the understanding of market dynamics and consumer behavior in entrepreneurial ventures
Building on Topic 4 (Behavioral Economics) and Topic 7 (Market Dynamics), AI offers the potential to deepen the integration of behavioral insights into understanding and predicting market trends. Behavioral economics has demonstrated that consumer behavior is often driven by non-rational factors, such as biases, emotions, and social influences (Zhang, 2024). AI-powered tools, such as machine learning algorithms, can process vast datasets to detect subtle behavioral patterns that are invisible to traditional models. Thus, AI can be successfully applied in dynamic pricing strategies, where algorithms adapt to consumer willingness to pay based on real-time behavioral data. Furthermore, AI-driven behavioral segmentation allows businesses to personalize digital marketing strategies (Barbosa et al., 2024), enhancing engagement and customer retention. This proposition emphasizes the need for future research to explore how AI can refine behavioral models in competitive entrepreneurial markets, offering new ways to predict demand and adapt to rapidly changing consumer preferences.
Proposition 6: AI-powered tools augment entrepreneurial creativity and foster innovation in management practices
Rooted in Topic 5 (Innovation), this proposition focuses on how AI can augment entrepreneurial creativity and foster innovation in management processes. Behavioral economics has shown that creativity is influenced by cognitive heuristics and decision-making biases, which can both constrain and enable innovation (Bryant, 2007). Therefore, AI can play a dual role by automating routine tasks to free up cognitive resources for creative thinking and by generating novel ideas through advanced generative algorithms. As Kang et al. (2020) stated, AI tools, such as NLP and generative design or generative AI, can help entrepreneurs prototype solutions and develop innovative business models more efficiently. Additionally, AI can simulate market conditions to test the feasibility of innovative ideas before full-scale implementation. Future research should examine the interaction between AI-driven innovation processes and the behavioral drivers of creativity, particularly in resource-limited entrepreneurial contexts.
Proposition 7: AI integrates behavioral insights to enhance entrepreneurial management risk assessment and mitigation strategies
From Topic 6 (Risk Management), research should explore how AI can transform risk assessment and mitigation strategies in entrepreneurial management. As analyzed before, behavioral economics has highlighted the role of risk perception and decision biases, such as loss aversion and overconfidence, in shaping risk-related decisions. AI-driven tools, such as predictive analytics and risk modeling algorithms, can provide objective assessments of potential risks while accounting for behavioral tendencies. For example, research has shown that AI systems that integrate behavioral data can improve risk evaluation by simulating various scenarios and quantifying uncertainty (Farrow, 2022). Moreover, AI can assist entrepreneurs in developing adaptive risk management strategies, particularly in volatile markets where traditional approaches often fail (Cramer et al., 2002). This research direction emphasizes the need to understand how AI and behavioral economics can jointly optimize risk management practices across entrepreneurial ecosystems.
Proposition 8: AI-driven market intelligence tools enhance the integration of behavioral insights into entrepreneurial market strategies
Building on Topic 7 (Market Dynamics), this proposition explores how AI can enhance the strategic alignment of market behaviors with entrepreneurial decision-making. The theory of behavioral economics suggests that market dynamics are often shaped by consumer biases, competitive pressures, and socioeconomic factors. In this way, AI technologies provide entrepreneurs with actionable insights and knowledge discovery strategies into these dynamics. Studies such as Basukie et al. (2020); Saura et al. (2022a, b) have demonstrated that AI-powered market intelligence tools can detect shifts in consumer sentiment, predict competitor behavior, and identify emerging opportunities. Then, future research should investigate how these AI-driven capabilities can be integrated into behavioral models to enable entrepreneurs to make more informed and proactive decisions in competitive markets.
Next, in order to summarize the findings, Fig. 3 illustrates the three variables studied in the present study (Behavioral Economics, AI, and Entrepreneurship), each represented by a separate circle and linked to their connections to future directions for management. The purpose of this depiction is to highlight the foundational elements within each field after analyzing the results and the connections between these areas. Therefore, Fig. 3 underscores each domain’s theoretical underpinnings, research streams, and practical applications. In doing so, it provides a clear overview of how these spheres contribute individually to the broader research framework proposed in this study.
Fig. 3
Summary of the results and the links between Behavioral Economics, AI, and Entrepreneurship related to Management. Source: The authors
In this way, circle 1, labeled Behavioral Economics, enumerates the psychological and social factors that shape human decision-making. This encompasses cognitive biases such as overconfidence or anchoring, as well as bounded rationality and the underlying emotional mechanisms that influence choices. The figure also includes constructs related to social capital (namely trust, reciprocity, and network strength), which are critical for understanding how individuals or groups navigate interdependent contexts. Additionally, key behavioral drivers such as herd behavior and status quo bias are listed, reflecting the importance of collective dynamics. These elements underscore how perception, emotion, and social ties undergird decision processes, particularly in uncertain or high-stakes environments. Circle B, titled AI, captures the range of data-driven and computational techniques that enable advanced analytics, scenario modeling, and automated decision support. AI-powered tools and machine learning algorithms, for instance, provide predictive insights and real-time analytics, while generative AI and other advanced methods (e.g., NLP or generative design) foster novel approaches to innovation and problem-solving. The figure also highlights adaptive platforms and uncertainty quantification methodologies, emphasizing the relevant role AI plays in gathering, processing, and leveraging large-scale data for improved decision-making outcomes. Finally, Circle C, marked Entrepreneurship, details the core processes and challenges associated with launching, managing, and scaling new ventures. Strategic management is emphasized, particularly in resource-constrained or volatile markets, reflecting the need for agile and proactive decision-making. The figure also points to entrepreneurial knowledge creation and dissemination—central to building supportive ecosystems— and emphasizes both innovation processes (e.g., idea generation, prototyping) and social capital development within entrepreneurial networks. Collectively, these elements acknowledge the inherently uncertain and rapidly evolving nature of entrepreneurial activity, as well as the necessity of risk management and market responsiveness. Once the key themes related to Behavioral Economics, AI, and Entrepreneurship are visualized in separate circles, the figure clarifies each domain’s contribution to the study’s overarching framework. This figure aids conceptual clarity and sets the stage for subsequent discussions on how the interplay among these three fields can generate impactful insights into managerial decision-making, strategic planning, innovation, and broader organizational outcomes within entrepreneurial contexts.
Conclusions
The findings of the present study reveal eight key topics central to the integration of behavioral economics, AI, and entrepreneurship for management. These topics include strategic management, decision-making, innovation, social capital, behavioral economics, risk management, market dynamics, and entrepreneurship knowledge. Together, they highlight the relevance of these fields in addressing contemporary managerial challenges and their interconnectedness through AI integration. Positive correlations identified in the analysis, particularly between innovation, market dynamics, and risk management, underscore AI’s potential to enhance entrepreneurial decision-making and foster adaptability to dynamic market conditions. Behavioral insights further analyze these relationships, showcasing how AI can address cognitive biases and optimize management strategies, creating more resilient and data-informed entrepreneurial ecosystems. In particular, these correlations illustrate that when entrepreneurs integrate AI-powered tools with behavioral insights, they can better anticipate market fluctuations and devise strategic approaches that align with actual consumer behaviors rather than relying solely on abstract economic assumptions.
In relation to the RQ (How can the integration of behavioral economics and AI transform management practices in the context of entrepreneurial decision-making?), the findings suggest that the integration of behavioral economics and AI transforms management practices by enabling more adaptive, bias-aware and precise entrepreneurial decision-making. Behavioral economics reveals the cognitive and behavioral tendencies that influence entrepreneurial actions, such as overconfidence, loss aversion, and heuristics. Likewise, AI operationalizes these insights by employing machine learning algorithms, predictive analytics, and real-time monitoring to simulate decision scenarios, evaluate risks, and optimize resource allocation. For example, AI-driven simulation platforms can incorporate risk tolerance indices derived from behavioral data, allowing decision-makers to visualize the impact of various biases on long-term outcomes. This operational synthesis of AI and behavioral economics ensures that decisions are not merely data-based but also grounded in real-world patterns of human judgment. Also, the results proved that AI enhances decision-making by addressing biases and uncertainties, ensuring that entrepreneurial strategies are both data-driven and behaviorally informed. Moreover, the positive correlation between behavioral insights and market dynamics highlights AI’s ability to predict consumer behavior and adapt strategies to market fluctuations.
Such predictive capabilities can be further refined by analyzing user-generated content on social media or customer feedback platforms, revealing micro-level shifts in consumer sentiment that traditional market research might overlook. These findings emphasize how integrating AI and behavioral economics creates a framework for sustainable and innovative management practices in entrepreneurial contexts, aligning decision-making processes with human and technological dimensions. This intersection ultimately fosters a deeper understanding of market dynamics and strengthens strategic decision-making. Finally, the enhanced alignment between human cognition and AI-driven analytics emerges as a relevant step in building more robust, forward-thinking ventures, highlighting the vital role of these interdisciplinary areas in shaping next-generation managerial paradigms.
Theoretical implications
The theoretical framework of this study integrates established theories from behavioral economics, entrepreneurship, and AI, contextualizing them within the findings. Behavioral economics, particularly Prospect Theory (Kahneman & Tversky, 1979), provides insights into how cognitive biases influence decision-making. The identified correlation between Behavioral Economics and Market Dynamics highlights how AI can model these biases and adapt strategies to dynamic market conditions, aligning with the concept of bounded rationality. Also, entrepreneurial theories, such as the Resource-Based View (Barney, 1991) and Effectuation Theory (Sarasvathy, 2001), frame the importance of knowledge and adaptability. In this context, the findings suggest that when using AI capabilities, such as NLP and predictive analytics, can further operationalize these classic theoretical constructs, allowing researchers to quantify entrepreneurial resources and adapt effectual processes in real-time. The study’s findings also suggest that AI boots these aspects by optimizing networks and supporting strategic decision-making, thus amplifying entrepreneurial resources. Similarly, Schumpeter’s Theory of Economic Development (Schumpeter, 1934) updated in 2021 (Schumpeter and Swedberg, 2021) resonates with the role of AI in fostering innovation and creating competitive advantage. Thus, integrating behavioral insights into strategic planning, AI facilitates creativity and sustainable innovation. This study extends these theoretical perspectives, demonstrating how behavioral economics and entrepreneurship converge with AI to create a cohesive framework. The enriched framework can be viewed as a multi-layered model where AI functions not merely as a tool but as a catalyst that operationalizes and scales the mechanisms posited by these foundational theories. Future theoretical work may employ this multi-layered approach to explore how entrepreneurs can better leverage AI to identify emerging market gaps, develop novel product strategies, and sustain competitive advantages. The findings enrich existing theories by highlighting the transformative role of AI in addressing uncertainty, optimizing decision-making, and improving innovation in entrepreneurial management.
Practical implications
The practical implications of the present study highlight the transformative potential of integrating behavioral economics, AI, and entrepreneurship into management practices. The findings demonstrate how AI can address cognitive biases in decision-making, optimize risk management, and foster innovation, offering actionable insights for entrepreneurs and managers. For instance, deploying AI-driven dashboards that incorporate behavioral metrics (e.g., risk preference scores and optimism bias indicators) can enable entrepreneurs to spot early warning signs of suboptimal decisions and realign strategies accordingly. Also, AI-driven tools can complement strategic planning by providing real-time data analysis, improving market adaptability, and enabling more informed resource allocation. In entrepreneurial ecosystems, AI’s ability to optimize networks and facilitate knowledge sharing is particularly valuable. Using AI in businesses can strengthen social capital, identify key stakeholders, and foster collaboration, ultimately improving competitiveness in dynamic markets. Moreover, AI-facilitated mentorship matching platforms can automatically connect novice entrepreneurs with experienced mentors using algorithms that analyze behavioral compatibilities and similar risk profiles. This could significantly streamline community building within entrepreneurial ecosystems.
Additionally, the integration of behavioral insights into AI systems allows organizations to personalize customer engagement and adapt to market trends more effectively, thereby aligning business strategies with consumer behavior. These findings emphasize the need for managers to adopt AI technologies as enablers of behavioral and strategic adaptability. Organizations that incorporate AI into decision-making and innovation processes can gain a competitive edge, better navigate uncertainty, and foster sustainable entrepreneurial growth. Notably, AI-powered sentiment analysis can guide marketing campaigns by pinpointing emotional triggers that influence consumer purchase decisions from behavioral economics strategies while simultaneously assisting product teams in refining offerings that resonate with evolving market demands and changing human behavior.
The framework proposed in this study offers practical guidance for applying these insights to real-world challenges in management and entrepreneurship. When systematically integrating AI-driven insights with behavioral economic principles, firms can not only reduce biases but also identify new opportunities for strategic differentiation by remaining agile in rapidly shifting market landscapes. In essence, the combined application of these tools and theories provides an evidence-based roadmap for organizations seeking to thrive in the complex, data-intensive domains of modern entrepreneurship.
Limitations
This study has several limitations that should be acknowledged. First, the SLR SLR, while comprehensive, is inherently constrained by the scope of the databases and keywords used. Relevant studies may have been excluded due to variations in terminology or the specificity of search parameters. Additionally, the reliance on published academic literature limits the inclusion of insights from industry practices, which could provide a more holistic perspective on the integration of behavioral economics, AI, and entrepreneurship in emerging situations and business activities. Second, the use of LDA for topic modeling, although effective for identifying thematic patterns, has inherent methodological constraints. LDA is sensitive to pre-processing decisions, such as the removal of stop words and the number of topics specified, which may influence the results. Furthermore, the interpretability of the topics relies heavily on the researchers’ subjective analysis, potentially introducing bias. Future research could address these limitations by incorporating alternative machine learning techniques, expanding the scope of the SLR to include non-academic sources, and validating the findings through empirical studies or expert interviews.
Declarations
Competing interests
The authors did not receive support from any organization for the submitted work. The authors declare they have no financial interests. The authors have no relevant financial or nonfinancial interests to disclose.
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