International Journal of Innovation and Economic Development

Volume 11, Issue 1, April 2025, Pages XX-XX

Modelling Entrepreneurial Psyches and Marketing Narratives Using Large Language Models

DOI: 10.18775/ijied.1849-7551-7020.2015.111.2005

URL: https://doi.org/10.18775/ijied.1849-7551-7020.2015.111.2005

¹Suresh Sood, ²Hugh Pattinson,

¹University of Technology Sydney, Australia ²Western Sydney University, Australia

Abstract: This study explores the use of Large Language Models (LLMs), specifically ChatGPT, to analyze entrepreneurial psyches and marketing narratives, drawing insights from Steve Jobs, Elon Musk, and Leonardo da Vinci. Employing a Narrative Inquiry approach, this research structures AI-generated narratives to examine the decision-making processes, adaptability, and storytelling techniques of entrepreneurs. The study builds on the SPARE framework, which integrates Sensemaking, Psychological Capital, Arc of Narrative, Recognition-Primed Decision, and Effectuation to guide LLM-generated responses. Narrative Inquiry allows for the exploration of entrepreneurial experiences as dynamic, context-driven stories, enabling a deeper understanding of the psychological and strategic factors shaping entrepreneurial behavior. Findings indicate that when guided by SPARE, LLMs effectively simulate Entrepreneurial Marketing Narratives (EMNs), capturing the complexity and fluidity of entrepreneurial cognition. The framework serves both as a conceptual model and a practical tool, equipping entrepreneurs with strategic decision-making support, educators with resilience-building methodologies, and business leaders with insights into risk management and branding. This study presents a novel integration of Narrative Inquiry with LLMs, demonstrating AI-driven storytelling’s potential to bridge theory and practice in entrepreneurship research, education, and business strategy. The findings suggest that structured AI-generated narratives function as valuable learning tools, strategic assets, and decision-making aids, contributing to both academic research and real-world entrepreneurial applications. As a hybrid study, this research merges conceptual analysis with practical application, reinforcing the relevance of AI-driven Narrative Inquiry in contemporary entrepreneurship.

Keywords: ChatGPT, Entrepreneurial Psyche, Entrepreneurial Spirit, Large Language Models, Marketing Narratives, Narrative Inquiry, SPARE Framework

1. Introduction

Understanding the psychological underpinnings and narratives driving successful ventures is crucial in entrepreneurial marketing. Traditional approaches to studying entrepreneurial behaviour rely on retrospective accounts (O'Reilly, 2016; Ucbasaran et al., 2013), case studies (Yin, 2018; Cope, 2011; Eisenhardt,1989), and qualitative interviews (Moriano, 2012; Shepherd, 2003). Existing methods, while valuable, can be limited owing to subjective biases and the challenges of capturing real-time decision-making processes. The advent of Large Language Models (LLMs) such as ChatGPT (OpenAI, 2024) presents a novel opportunity to explore Entrepreneurial Marketing Narratives (EMNs) dynamically and more comprehensively than previously considered.

1.1 Defining the Problem

Several key challenges have long hindered the study of entrepreneurial marketing. First, there is the issue of subjective bias in traditional data collection methods. Entrepreneurs may unintentionally skew their accounts of decision-making to align with perceived success narratives owing to recall bias and post-hoc rationalization (Ucbasaran et al., 2013; Cope, 2011; Shepherd, 2003). Second, the static nature of retrospective interviews often fails to capture the fluidity and iterative nature of entrepreneurial decision-making, resulting in temporal disconnect (Yin, 2018) and contextual changes (Cope, 2011). Finally, existing models frequently overlook the complex cognitive processes (Klein, 1998) underpinning entrepreneurial actions, including intuition (ibid), sensemaking and the sociohistorical context of individual entrepreneurs (Weick, 1995).

1.2 Purpose of the Study

The primary purpose of this study is to leverage the capabilities of LLMs to capture the psyche or spirit of entrepreneurs and generate authentic and comprehensive Entrepreneurial Marketing Narratives (EMNs). By doing so, the study aims to:

1. Illuminate the psychological processes driving entrepreneurial decision-making, including intuition, sensemaking, and pattern recognition.
2. Provide a dynamic and iterative methodology for exploring entrepreneurial narratives, capturing the real-time thought processes and contextual influences on entrepreneurs.
3. Enhance the understanding of entrepreneurial marketing strategies through detailed, contextually rich narratives generated from the LLM interactions.
4. Address key challenges in entrepreneurial research, such as subjective bias in traditional data collection methods, the static nature of retrospective interviews, and the oversight of complex cognitive processes like intuition and sensemaking.

The study approach provides a well-rounded representation of different entrepreneurial styles and strategies, offering diverse insights into the psychological and contextual factors influencing entrepreneurial decision-making.

1.3 Scope of the Study

This study employs a structured narrative generation process to produce EMNs, focusing on iconic and fictional entrepreneurs using LMMs. The key steps of the novel method are available in Table 1.

Table 1

Structured Narrative Generation Process for Entrepreneurial Marketing Narratives

Step	Action	Outcome
1. Initial Exploration	Conduct preliminary research on selected entrepreneurs to gather background information and identify key themes.This foundational step ensures that generated narratives are grounded in real-world contexts and relevant themes.	Establish a contextual foundation and insight for developing focused prompts toget inside the entrepreneur's mind and generate EMNs using LLMs.
2. Prompt Development and Refinement	Create and refine prompts to guide LLM interactions to ensure the elicitation of detailed and relevant responses.	The study introduces and utilizes the SPARE prompting methodology to generate entrepreneurial psyches and analyze theory-driven EMNs.
3. Data Collection	Use refined prompts in multiple sessions with ChatGPT to gather comprehensive narrative data.	Achieve data saturation by capturing a wide range of entrepreneurial experiences.
4. Thematic Analysis and Narrative Construction	Analyze the collected data to identify recurring themes and synthesize them into coherent narratives.	SPARE prompting framework integrates specific ChatGPT prompts to produce detailed and contextually rich narratives
5. Human Validation and Feedback	Cross-check narratives against existing literature and case studies to ensure authenticity and refine them based on these sources.	Enhance the credibility and accuracy of the findings by incorporating validation and feedback.
6. Finalization	Prepare the narratives for investigation, presentation, and publication.	Ensure the narratives are well-written, coherent, and ready for distribution, effectively communicating the research findings.

 

Integrating theoretical frameworks of Sensemaking (Weick, 1995), Psychological Capital (Luthans, 2002), Arc of Narrative (Boyd & Pennebaker, 2020), Recognition-Primed Decision-making (Klein, 1998), and Effectual Thinking (Saravathy,2001), this study provides a multifaceted approach to understanding the complexities of entrepreneurial thinking and actions. The generated narratives offer insights into the strategies and actions of entrepreneurs and the psychological and contextual factors influencing decision-making.

1.4 LLMs and Entrepreneurial Narratives

LLMs, such as ChatGPT (OpenAI, 2024), are artificial intelligence systems capable of understanding and generating human-like text, audio, images, and video. LLM models train on vast amounts of data, e.g. ChatGPT requires over a petabyte of diverse multimodal data (text, images, audio, video), allowing the production of coherent and contextually relevant responses based on the input prompts (Table 2).

Table 2

Basic LLM prompts relevant to entrepreneurs and entrepreneurial marketing.

Prompt Type	Description	Example
Role-Playing	Simulating interactions or conversations with specific roles.	"Act as a startup founder pitching to venture capitalists about a new AI-driven marketing tool."
Table Generation	Creating structured tables based on given information.	"Generate a table comparing different social media platforms for marketing startups."
Concise Responses	Providing information briefly and clearly.	"Concisely explain the importance of having a unique value proposition for a startup."
Summarization	Condensing larger texts into shorter summaries.	"Summarize the main points of a successful crowdfunding campaign for a new tech product."
Classify	Categorizing or organizing information into groups.	"Classify these marketing strategies into digital and traditional categories."
Brainstorm	Generating ideas or brainstorming on a given topic.	"Brainstorm some innovative ideas for reducing customer acquisition costs for startups."
Language Translation	Translating text from one language to another.	"Translate this product description from English to Spanish."
Formal/Informal	Rewriting text in a formal or informal tone.	"Rewrite this business proposal in a more formal tone." or "Make this email to a client sound more informal."
Expand Answers	Providing more detailed responses to a given answer.	"Expand on the benefits of social media marketing for new businesses."
1st, 2nd, or 3rd Person Narratives	Writing in first-, second-, or third-person perspective.	"Describe the journey of starting a business from a first-person perspective." or "Explain the customer onboarding process in a second-person narrative."

 

LLMs engage in complex conversations, mimicking the decision-making processes of successful entrepreneurs, making them valuable tools for exploring and generating entrepreneurial narratives.

The relevance of LLMs to entrepreneurial narratives lies in an ability to:

1. Generate rich, detailed narratives encapsulating the intricacies of entrepreneurialexperiences, capturing both the emotional and strategic aspects of decision-making.
2. Facilitate real-time interaction by simulating conversations with entrepreneurs; LLMs can provide insights into the real-time thought processes and adaptive strategies used in entrepreneurial ventures.
3. Overcome subjective bias by properly guiding LLMs through carefully designed prompts. The crafted prompts help mitigate the subjective biases inherent in self-reported data, offering a more objective perspective on entrepreneurial behaviours.
4. Enhance thematic exploration as LLMs can assist in exploring and identifying recurring themes and patterns within entrepreneurial narratives, providing a deeper understanding of the underlying psychological and contextual factors.

This study seeks to address the need for a more detailed yet dynamic understanding of entrepreneurial marketing by utilizing the advanced capabilities of LLMs.The resulting narratives contribute to academic knowledge and provide practical insights for entrepreneurs and marketers seeking to navigate the complexities of marketing entrepreneurship from respective vantage points.

1.5 Integrating Narrative Inquiry and LLMs for Entrepreneurial Research

This study employs a Narrative Inquiry approach, enhanced using LLMs, to explore the entrepreneurial journeys of iconic entrepreneurs within the marketing context. A detailed expansion of the methodology includes:

Narrative Inquiry Approach

Narrative Inquiry is a qualitative research methodology focusing on individuals' stories about their experiences. The method aims to understand how people make sense of their own lives and actions through storytelling. This approach is particularly useful in exploring complex, multifaceted phenomena like entrepreneurship, wherein personal experiences and individual decision-making play crucial roles.

Enhancement with LLMs

The study leverages the advanced capabilities of LLMs to enhance the Narrative Inquiry approach given the design of LLMs like ChatGPT to understand and generate human-like text based on a huge amount of training data. This use of LLMs helps the study develop rich, fine-level narratives that capture the intricacies of entrepreneurial experiences.

Structured Prompts

The study employs structured prompts to guide the LLMs in generating relevant and insightful narratives. Carefully crafting these prompts to elicit detailed responses provides deep insights into entrepreneurial behavior, decision-making processes, and marketing strategies. The structured prompts help mitigate subjective biases and ensure comprehensive and contextually rich narratives.

Exploring Entrepreneurial Journeys

By examining the narratives generated through LLMs, the study aims to uncover the real-time thought processes and contextual influences on entrepreneurs. This dynamic and iterative methodology allows for a thorough understanding of how entrepreneurs navigate the complexities of marketing and make strategic decisions.

Generating Contextualized Insights

The AI-generated narratives produced through this approach are valuable for academic research and provide practical insights for entrepreneurs and marketers. The study offers a well-rounded or holistic perspective on entrepreneurial marketing strategies by capturing emotional and strategic aspects of entrepreneurial decision-making.

 Thematic Exploration

LLMs assist in exploring and identifying recurring themes and patterns within the entrepreneurial narratives. This thematic exploration provides a deeper understanding of the underlying psychological and contextual factors influencing entrepreneurial behaviour, enhancing the ability to draw meaningful conclusions from the narratives and apply them to real-world entrepreneurial contexts.

By integrating Narrative Inquiry with the advanced capabilities of LLMs, this study aims to generate rich, contextualized insights into the entrepreneurial journeys of high-profile entrepreneurs, offering valuable contributions to academic knowledge and practical applications in marketing entrepreneurship.

2. Entrepreneurial Psyche in the Methodology – The SPARE Framework and Prompts

The concept of the psyche (Jung, 1921) is central to the method and study representative of the "entrepreneurial spirit" (Smith, 2023). The psyche provides a comprehensive approach to exploring the entrepreneurial mind and guides the use of psyche-informed prompts in our engagement with LLMs, e.g. ChatGPT-4o (OpenAI, 2024) throughout this study. The concept of the entrepreneurial psyche builds on Jung's work. It considers a 3-level model (Figure 1): the conscious mind (ego), the personal unconscious and the collective unconscious inheriting patterns from cultural narratives, myths and archetypes (Woodside, Megehee& Sood, 2012).

Figure 1

• Thus, understanding the entrepreneurial psycherequires a structured yet holistic approach accounting for rational decision-making and unconscious cognitive influences.

The SPARE frameworkselects theories mappingdirectly onto the different psychological and cognitive dimensions of entrepreneurial behaviour, ensuring a comprehensive exploration of how and why entrepreneurs think and act the way they do (Marchand & Sood, 2014). To model the entrepreneurial psyche as well as guide our interaction with LLMs, established theories and frameworks from psychology, organizationalbehaviour, decision-making, and entrepreneurshipcombine, forming the SPARE framework (Sensemaking, Psychological Capital, Arc of Narrative, Recognition Primed Decision and Effectuation - see Table 3).

Table 3

SPARE Framework of Entrepreneurial Psyche

SPARE components	Jungian Psyche Level	Description and Rationale for Component Selection
Sensemaking (SM; Weick,1995)	Conscious & Personal Unconscious	It helps entrepreneurs interpret and construct meaning from complex, uncertain information, guiding strategic decisions through constantly reframing market signals and making sense of new opportunities.
Psychological Capital(PsyCap; Luthans,2002)	Personal & Collective Unconscious	This component represents the emotional and motivational foundation of entrepreneurship. A positive psychological state characterized by hope, efficacy, resilience, and optimism (HERO) is crucial for entrepreneurial success. Enhances understanding how HERO influences entrepreneurial behaviour, decision-making, and marketing strategies to persist through challenges and failures.
Arc of Narrative(AoN; Boyd & Pennebaker, 2020)	Collective Unconscious & Ego	Grounded in literary theory and adapted for analyzing the structure and content of entrepreneurial stories to understand journeys, key turning points, and identity construction. Above all, entrepreneurs are not just decision-makersbut also storytellers.
Recognition-Primed Decision (RPD; Klein,1998) Model	Personal Unconscious	Under pressure, entrepreneurs make rapid and effective decisions, recognizing patterns and drawing on past experiences.
Effectuation (EF; Sarasvathy, 2001)	Conscious & Collective Unconscious	How entrepreneurs start without clear goals,leveraging available resources, managing risks and turning contingencies into opportunities. Not through predictive planning but through adaption to changing circumstances.

 

The acronym"SPARE"is a metaphor for the established theoretical foundations emphasizing resourcefulness, adaptability, simplicity, focus, and preparedness.These qualities are crucial for entrepreneurial success. To ensure the real-world relevance of SPARE, we use the keywords from the work of Robert Smith relating directly to "entrepreneurial spirit" (Smith, 2023) to map the SPARE components (Figure 1). As a practitioner, Smith is a highly influential entrepreneur and investor managing over $100 billion in equity investments. Thus, his insights validatethe "entrepreneurial spirit" in practice and directly reinforce the selection of SPARE's theoretical constructs

Figure 2

While SPARE integrates established theories, the novelty of the framework lies in generating a structured, AI-compatible methodology for analyzing and generating entrepreneurial narratives.SPARE provides a multi-dimensional view of entrepreneurship, capturing cognitive strategies (rational decision-making, sensemaking, and effectuation) and emotional resilience (psychological capital and storytelling).Such a holistic approach ensures entrepreneurial behaviour is examined in full complexity rather than through isolated constructs during AI prompting sessions.

We now focus on the prompt development and refinement (Figure 2). Prompts can result in hallucination (incorrect or fabricated text) as LLMs do not memorize answers but pattern match or generalize. However, the entrepreneurial psyche is a complication as entrepreneurs exhibit deception, "often bending it [truth]" (Jensen et al., 2021) or engaging in strategic deception (Pech &Stamboulidis, 2010). This behaviour can be difficult for LLMs to capture accurately, especially when the models rely heavily on text patterns that do not fully account for the somewhat deceptive nature of entrepreneurial communication. Due to these issues, trial-and-error experimentation with many prompts is necessary. Researchers must carefully craft and refine prompts to minimize the risk of hallucinations and better capture the complexity of entrepreneurial narratives. This iterative process is time-consuming and requires a deep understanding of LLMs' capabilities and limitations. A structured approach to prompts, the SPARE framework, rather than trial and error alone or freely playing with prompts, saves considerable time and effort and helps generate content both coherent and accurate, reducing the risk of hallucination but does not obfuscate the need for a prompt design process to ensure accuracy.

Figure 3

Prompt Design Process for SPARE

Through the multi-step process commencing with theory mapping (Figure 3), we distil the core principles of the respective SPARE component, translating into questions suitable for eliciting relevant information and insights from entrepreneurs or an LLM (Table 3).This structured approach ensuresprompts align with the theoretical underpinnings of the SPARE framework, providing a comprehensive and multi-dimensional understanding of entrepreneurial behaviour.To ensure accuracy, coherence, and minimal bias, an iterative refinement process helps refine prompts. Pilot testing with multiple LLM iterations (e.g., GPT-4) helps evaluate response coherence and alignment with entrepreneurial theories. If any prompt responses are unrealistic, tuning the wording introduces constraints. If evaluation by human experts flags misleading or biased responses, further prompt modification occurs. Further bias reduction

techniques use neutral and open-ended phrasing to minimize bias towards specific business strategies.

By leveraging these prompts with LLMs, researchers can simulate and analyze entrepreneurial narratives, capturing the richness and complexity of the entrepreneurial psyche. The SPARE prompts (Table 3) help generate rich, realistic, and contextually relevant entrepreneurial narratives when used with LLMs like ChatGPT. The prompts help ensure the AI-generated content from LLMs is grounded in theory, combined with real-world validation, enhancing the overall quality and reliability of the narratives.By applying these prompts, researchers and practitioners can gain deeper insights into the entrepreneurial psyche and behaviour, fostering a better understanding of what drives entrepreneurial success and innovation.

Table 4

PromptExtraction from the SPARE Framework Components

SPARE Component	Core Principles	SPARE Prompts (1-20)
SM	Interpretation of Events IdentityConstruction      Retrospection                       Enactment                                 Social Context	1. Tell me about when you had to navigate a significant market change.How did you make sense of the situation?  2. How do you construct and communicate the identity of your venture?                                                3. Describe how your past experiences have informed your current entrepreneurial decisions.                                                     4. Can you explain how your actions have shaped your market environment?
PsyCap	Hope                       Efficacy                                 Resilience                               Optimism	5. Describe a challenging situation you faced and how you remained hopeful.                                          6. How do you build and maintain your confidence in your entrepreneurial journey?                                                   7. Share an experience where you had to bounce back from a significant setback. 8. What strategies do you use to stay motivated and positive about the future of your venture?
AoN	Plot Structure.                    Character Development        Themes and Motifs	9. Outline your entrepreneurial journey from the beginning to where you are now.What were the key turning points?                                                    10. How have you and your team evolved throughout your venture? 11. What are the central themes and values have driven your entrepreneurial efforts? 12. Can you share a pivotal moment in your journey that significantly influenced your path?
RPD	Experience-basedDecision Making                Mental Simulation            Intuitive Expertise	13. Describe a high-pressure decision you made.What previous experiences influenced your decision?14. How do you simulate potential outcomes when making strategic decisions?                                             15. Can you provide an example of when you relied on your intuition to make a critical decision?                                                         16. How do past successes and failures shape your current decision-making process?
EF	Means-DrivenApproach   Affordable Loss                 Leveraging Contingencies.                   Partnerships	17. How did you leverage your existing resources to start your venture?18. What is your approach to managing risk and uncertainty in your entrepreneurial activities? 19. Can you share an example of how you turned an unexpected event into an opportunity?        20. How do you form and maintain strategic partnerships?

 

Simulating responses from iconic entrepreneursSteve Jobs, Elon Musk, Leonardo da Vinci and hypothetical entrepreneurs, the ChatGPT prompts help to conduct virtual interviews with iconic or fictitious entrepreneurs. General LLM prompts (See Table 1) effectively provide instructions to the LLM with the role prompts explicitly telling the LLM how to behave, e.g., as Steve Jobs, the entrepreneur

This approach helps pilot test the SPARE prompts in a virtual interview with Steve Jobs and other entrepreneurs using ChatGPT. The responses from ChatGPT shown in italics represent the EMNs. Also, the desire is for reactions from the LLM to be concise and accordingly add this command as part of the prompt structure (Table 1).

Our initial examples combine the general prompts for role-playing with a concise response as a preamble to the SPARE component Sensemaking (SM) prompt 1:

ChatGPT Prompt 1

"Act as the entrepreneur and founder of Apple, Steve Jobs and concisely tell me aboutthe time you had to navigate a significant market change.How did you make sense of the situation?"

Response from ChatGPT as Steve Jobs:

One of the most significant market changes I navigated was during the introduction of the iPhone in 2007.At the time, the mobile phone market was dominated by established players like Nokia and Blackberry, and the concept of a smartphone was still in its infancy.We observed that existing smartphones were too complex and lacked a user-friendly interface.

To make sense of the situation, we focused on the convergence of technology and user experience.We saw an opportunity to combine a phone, an iPod, and an internet communicator into one device, but it had to be simple and intuitive for users.We leveraged our strengths in design and software to create a revolutionary multi-touch interface that eliminated the need for physical buttons.

We also understood that to succeed, we needed to build a robust ecosystem, so we developed the App Store, enabling developers to create applications that would enhance the functionality of the iPhone.This strategic move not only differentiated us from competitors but also created a new revenue stream and fostered a loyal community of developers and users.

By focusing on innovation, design, and user experience, we were able to redefine the mobile phone market and establish the iPhone as a groundbreaking product that set new standards for the industry.

ChatGPT Prompt 2

"Act as the entrepreneur Elon Musk and concisely tell me about the time you had to navigate a significant market change.How did you make sense of the situation?"

Response from ChatGPT as Elon Musk:

A significant market change I navigated was during the early days of Tesla when the electric vehicle (EV) market was virtually non-existent.Traditional car manufacturers dominated the industry with gasoline-powered vehicles, and there was widespread scepticism about the viability of EVs.

To make sense of the situation, I focused on the long-term vision of sustainable transportation and the potential for technological innovation to disrupt the status quo.We identified that key barriers to EV adoption were range anxiety, high costs, and limited charging infrastructure.

We addressed these challenges by investing heavily in battery technology to increase range and reduce costs.We built the Gigafactory to scale up production and drive down battery prices.Additionally, we developed the Supercharger network to provide fast, convenient charging options for Tesla owners, addressing the infrastructure gap.

We also realized the importance of creating a desirable product.The Roadster demonstrated that EVs could be high-performance and exciting, while the Model S showcased luxury and practicality.By focusing on innovation, vertical integration, and creating a compelling product, we were able to change public perception and establish Tesla as a leader in the automotive industry, paving the way for the broader adoption of electric vehicles.

ChatGPT Prompt 3

"Act as Leonardo da Vinci and concisely tell me about the time you had to navigate a significant market change.How did you make sense of the situation?"

Response from ChatGPT as Leonardo da Vinci

During my time, a significant market change was the shift in patronage from the Church to private individuals and families, such as the Medici in Florence.This change altered the landscape of art and invention, requiring artists and thinkers to adapt to new sources of funding and support.

To make sense of this situation, I diversified my skills and interests.I realized that to attract patrons, I needed to offer more than just traditional religious art.I expanded my repertoire to include portraits, architectural designs, and innovative machines.I also began to emphasize the scientific and technical aspects of my work, which appealed to the intellectual curiosity of my patrons.

For instance, I produced detailed anatomical drawings and conceived groundbreaking inventions like flying machines and war devices.By blending art with science and engineering, I was able to meet the diverse needs of my patrons and secure their support.This adaptability allowed me to thrive in a changing market and leave a lasting legacy in multiple fields.

The narratives generated by ChatGPT are cross-checked against existing literature and case studies to ensure authenticity and refine them based on these sources.This human validation and verification involveresearchingthe selected iconic entrepreneurs and is a crucial step in qualitative research to enhance the credibility of the findings (Maxwell, 2013). This exploratory research focuses onSteve Jobs, Elon Musk and Leonardo da Vinci using Existing depth biographies (Isaacson 2023; 2017; 2011), contextualizingeach entrepreneur, background and challenges, and ensuring the ChatGPT responses and data collection are relevant and focused.Feedback from these sources (ibid) validates the concise narratives, ensuring they are grounded in the real-world experiences of the entrepreneurs (Yin, 2018; Eisenhardt, 1989).

Employing LLMs such as ChatGPT with the SPARE prompts provides a structured approach to generating theory-driven and practice-validated entrepreneurial narratives. As the pilot testing shows, these promptdesigns elicit detailed narratives with structured and comprehensive responses regarding specific entrepreneurial decisions and strategies.

Based on this initial research, collecting20 SPARE prompts (Table 3) helps guide the future AI interactions for this study and comprise the individual entrepreneurial psyches when taken together. While not presented here, multiple sessions conducted with ChatGPT using the SPARE prompts enable the gathering of comprehensive narrative data beyond this publication.Conducting multiple sessions by behaviour or individual entrepreneurs allows for collecting diverse and extensive data, covering various aspects of entrepreneurial behaviour untildata saturation.

Grounded theory research emphasizes data saturation to ensure no new themes emerge (Glaser & Strauss, 1967).By leveraging LLMs, manually coding data into themes (Braun & Clarke, 2006) such as decision-making processes, marketing strategies, and challenges are eliminated as the responses align directly with the psychological dimensions associated with the prompt. For example, effectual thinking explains how entrepreneurs adapt to changing circumstances using available meansis critical for constructing dynamic and realistic narratives (Sarasvathy, 2001).

Boyd & Pennebaker (2020) emphasize the importance of coherent and engaging storytelling in narrative research. Afinalization step ensuresthe narratives are free of inconsistencies, clear, coherent, and ready for dissemination, which is critical for effectively communicating research findings (Creswell &Poth, 2016).This comprehensive and systematic approach ensures the rigour and validity of the research, providing valuable insights into how entrepreneurs think and behave.

4.Findings – Using the SPARE Method to Generate Psyches and Narratives

This study employs the SPARE methodology prompts to generate entrepreneurial psyches and enables the analysis of theory-driven EMNs. Our findings from invoking the process integrate the steps with the specific ChatGPT prompts used throughout the research. The findings commence with the entrepreneurial psyche of Steve Jobs (including specific examples). Other entrepreneurs, including Elon Musk and Leonardo da Vinci, are added and presented in comparison tables (See Tables 4 and 5).

ChatGPT Prompt:

"Concisely map the SPARE framework to different aspects of Steve Jobs' entrepreneurial psyche, including specific examples and use abbreviations where relevant."

Table 5

Concise mapping of SPARE to different aspects of Steve Jobs's Entrepreneurial Psyche

Entrepreneurial Psyche Component	SPARE Component	Examples of Application to Steve Jobs	Impact on Entrepreneurial Psyche
Thought Processes
Decision-Making	RPD	I returned to Apple in 1997, using past experiences as guidance.	Enhanced intuitive decision-making under pressure.
Problem-Solving	SM	Focused on innovation and design in a saturated PC market	Improved adaptability and strategic thinking.
Innovative Thinking	AoN	Leveraged resources creatively in a garage to build the first Apple computers	Fosters creative thinking and innovation.
Emotions
EmotionalResilience	PsyCap	Remained hopeful after being ousted from Apple, founded NeXT, and acquired Pixar.	Built resilience and optimism.
Passion and Drive	SM	Driven by a passion for creating innovative products like the iPhone and iPad	Sustainable motivation and a commitment to excellence.
Adaptability	EF	Turned ousting from Apple into founding NeXT and acquiring Pixar	Enhanced flexibility and opportunity recognition.
Risk Tolerance	EF	Embraced risk and uncertainty, exemplified by launching the iPhone	Developa calculated risk-taking approach.
Motivations
Intrinsic Motivations	AoN	Motivated by a deep-seated passion for innovation	Strengthened intrinsic motivation.
Extrinsic Motivations	AoN	Validated by external recognition, such as market success and awards	Reinforce the importance of external validation.
Attitudes
Optimism and Confidence	PsyCap	Confidence from past successes like the iPod	Maintain a positive outlook and confidence.
Resourcefulness	EF	Creatively used available resources to build Apple in a garage	Fostering ingenuity and practicality.
Leadership	AoN	Inspired and led teams with a clear vision, exemplified by his leadership at Apple	Enhance leadership skills and ability to motivate and guide teams.
Networking	EF PsyCap	Built strategic partnerships, such as with Pixar and various tech collaborations	Strengthened networking skills and strategic relationship management.
Persistence	PsyCap	Persevered through challenges like being ousted from Apple, founded NeXT, and acquired Pixar.	Developed persistence and determination.

 

ChatGPT Prompt:

"Create a table that maps the SPARE prompts to specific examples from the lives and careers of Steve Jobs, Elon Musk, and Leonardo da Vinci. Provide relevant examples illustrating how these entrepreneurs handled situations that align with the prompts for each framework component."

 

Table 6

SPARE Framework Prompts - Steve Jobs, Elon Musk, and Leonardo da Vinci

SPARE	Prompt	Steve Jobs	Elon Musk	Leonardo da Vinci
SM	"Tell me about when you had to navigate a significant market change.How did you make sense of the situation?"	Navigating the collapse of the personal computer market in the mid-1980s.Focused on innovation and design, leading to the development of the Macintosh.	Navigating the challenges of launching the Falcon 1 rocket. Realized the importance of cost-effective space travel and focused on reusability.	Developing new techniques for painting The Last Supper, adapting to the limitations of fresco painting and environmental conditions.
RPD	"Describe a high-pressure decision you made.What previous experiences influenced your decision?"	The decision to return to Apple in 1997.Previous experience with Apple's early success and failure and lessons learned at NeXT.	The decision to keep Tesla running during the 2008 financial crisis.Influenced by his previous experiences with PayPal and SpaceX.	The decision to move to Milan and offer his services to Ludovico Sforza.Influenced by his experiences in Florence and his desire for larger-scale projects.
AoN	"Outline your entrepreneurial journey from the beginning to where you are now.What were the key turning points?"	From building the Apple I in his garage to the launch of the Apple II and the Macintosh, his ousting from Apple, founding NeXT and Pixar, and his return to Apple, leading to the iPod, iPhone, and iPad.	From starting Zip2 to X.com/PayPal, founding SpaceX, Tesla, and SolarCity, and his vision for Mars colonization and sustainable energy.	From apprenticeship in Florence to independent commissions, he moved to Milan and worked for the Sforza family,as well as his engineering, anatomy, and art innovations.
PsyCap	"Describe a challenging situation and how you remained hopeful and optimistic."	Facing criticism and setbacks with the original Macintosh.Remained optimistic about the potential of personal computing.	Overcoming multiple rocket failures at SpaceX.Maintained resilience and belief in the mission to make space travel affordable.	Struggling with unfinished projects and criticism.Maintained hope and continued to innovate in various fields.
EF	"How did you leverage your existing resources to start your venture?"	He leveraged his technical knowledge and partnerships with Wozniak and others to start Apple in his garage.	He used funds from selling PayPal to invest in SpaceX and Tesla, leveraging his vision and ability to attract top talent.	Leveraged his skills in multiple disciplines to secure patronage and work on diverse projects.

 

The final narrative integrates key dimensions of SPARE into an entrepreneurial psyche for a fictitious entrepreneur. Araki addresses climate change, particularly global warming, through innovative green technologies influencing microclimate weather patterns (See Table 7).

 

ChatGPT Prompt:

"Areki is a fictitious entrepreneur focused on addressing climate change, particularly global warming, through innovative green technologies that influence microclimate weather patterns. Provide sample Areki responses to the SPARE prompts and resulting actions".

 

"Areki is a fictitious entrepreneur focused on addressing climate change, particularly global warming, through innovative green technologies that influence microclimate weather patterns. Provide sample Areki responses to the SPARE prompts and resulting actions".

 

Table 7

SPARE Prompts with Sample Responses and Resulting Actions for Areki

SPARE Framework	Prompt	Sample Response from Areki	Resulting Actions
SM	"Tell me about when you had to navigate a significant market change.How did you make sense of the situation?"	"When international climate agreements started to enforce stricter regulations, I realized we had to adapt quickly.I made sense of the situation by assessing the urgency of the regulatory changes and the growing demand for sustainable solutions."	Aligned our business model with new regulations, increased R&D investment in compliance technologies, and collaborated with policymakers to stay ahead.
RPD	"Describe a high-pressure decision you made.What previous experiences influenced your decision?"	"During the rollout of our first microclimate control system, we encountered unexpected technical challenges.Drawing from my experience with rapid prototyping, I decided to proceed with a limited release to gather real-world data."	Released a beta version to early adopters, used feedback to iterate and improve the product quickly, and built a stronger, more resilient system.
AoN	"Outline your entrepreneurial journey from the beginning to where you are now.What were the key turning points?"	"I started with a passion for renewable energy during my university years.Key turning points included securing initial seed funding, developing our prototype, and establishing partnerships with environmental organizations."	Expanded our team, secured more funding rounds, scaled production, and continually adapted our technology based on partner feedback and environmental impact assessments.
PsyCap	"Describe a challenging situation and how you remained hopeful and optimistic."	"When we faced a major funding shortfall just before our product launch, it was a tough time.I stayed hopeful by focusing on our mission and the potential global impact of our technology."	Launched a successful crowdfunding campaign, engaged with community stakeholders to build support, and restructured our financial strategy to ensure sustainability.
Effectuation	"How did you leverage your existing resources to start your venture?"	"I utilized my network from university, secured initial grants, and partnered with professors to kickstart our research and development."	Formed a diverse team with complementary skills, maximized the use of academic facilities for early testing, and developed a functional prototype with minimal funding.

 

• Benefits of the SPARE Framework

The SPARE framework offers a comprehensive, theory-driven, and practice-validated approach to understanding entrepreneurial decision-making and cognition. By integrating Sensemaking, Psychological Capital, Arc of Narrative, Recognition-Primed Decision, and Effectuation, SPARE is a powerful tool for analyzing, simulating, and enhancing entrepreneurial behaviour, particularly in AI-generated narratives.

1. Theoretical Rigor Combined with Real-World Validation

The framework is grounded in established academic theories (Weick's Sensemaking, Luthans' Psychological Capital, Klein's RPD, and Sarasvathy's Effectuation), ensuring scientific credibility. At the same time, integrating practitioner insights such as Robert F. Smith's reflections on entrepreneurial spirit, resilience, and adaptability validates SPARE's real-world applicability, making the tool relevant for research and practice.

1. Captures Both Rational and Intuitive Decision-Making

Entrepreneurs blend structured reasoning with intuition, relying on pattern recognition and past experiences to make high-stakes decisions. SPARE accounts for analytical approaches (Sensemaking, Effectuation) and experience-driven intuition (RPD, PsyCap), ensuring a realistic yet holistic representation of entrepreneurial cognition rather than through isolated constructs.

2. Enhancing AI-Generated Entrepreneurial Narratives

By structuring LLM prompts around the SPARE model, AI-generated narratives become more relevant, nuanced, and aligned with actual entrepreneurial behaviour. This structure allows for better simulations, scenario analyses, and educational applications, making SPARE a valuable AI-augmented research tool.

3. Practical Applications for Entrepreneurs, Educators, and Business Leaders

For entrepreneurs, SPARE refines decision-making processes and helps navigate uncertainty. When facing market disruptions, a founder can use an LLM to explore different decision-making styles, comparing intuition-driven (RPD), resilience-based (PsyCap), and resource-driven (Effectuation) approaches to determine the best strategy. Similarly, startups exploring new business models can generate SPARE-based entrepreneurial narratives to identify optimal resource allocation and market entry strategies. The Arc of Narrative enables entrepreneurs to craft compelling business stories, tailoring investor pitches and customer messaging by leveraging multiple storytelling styles.

 

1. SPARE offers a structured framework for teaching resilience, risk management, and adaptability in education and coaching. Business schools can integrate LLM-generated SPARE-driven case studies, immersing students in realistic startup challenges such as securing investment, overcoming failure, and navigating strategic pivots. Students strengthen their psychological capital by analyzing AI-generated entrepreneurial stories, developing resilience and a stronger risk-tolerance mindset. Educators can further use the Arc of Narrative to guide students through entrepreneurial storytelling workshops, refining their ability to communicate vision and personal branding strategies.
2. SPARE provides a structured lens for business leaders and investors to assess entrepreneurial behaviour and decision-making under pressure. Investors can use AI-generated narratives to evaluate how founders respond to financial crises, market shifts, or competitive threats, revealing whether they lean toward effectuation (resource-driven adaptability) or predictive planning (Sensemaking-based forecasting). Similarly, corporate executives can apply SPARE to internal innovation projects, assessing whether teams demonstrate RPD-driven rapid decision-making or PsyCap-driven resilience strategies. AI-generated narratives can also simulate leadership scenarios, enhancing executive training programs by preparing leaders for high-stakes decision-making.

5.Discussion, Contributions & Limitations

• Discussion

Using LLMs to generate and analyze entrepreneurial narratives offers a revolutionary approach to understanding the complexities of entrepreneurial behaviour and marketing strategies. By leveraging the SPARE framework, integrating Sensemaking, Psychological Capital, Arc of Narrative, Recognition-Primed Decision, and Effectuation, the study provides a comprehensive methodology for capturing the multifaceted nature of the entrepreneurial psyche in theory and practice. This integration allows for a theory-driven detailed exploration of how entrepreneurs interpret their environments, build psychological resilience, construct their narratives, make high-pressure decisions, and leverage their resources to navigate ventures successfully.

One of the significant advantages of using LLMs is the ability to simulate detailed and contextually rich narratives reflecting iconic entrepreneurs' thought processes and decision-making strategies. For instance, through guided prompts, the model generates narratives illustrating how Steve Jobs navigates market changes by focusing on innovation and design or how Elon Musk leverages his risk-taking propensity and analytical approach to overcome technical challenges in space travel.

These theory-driven narratives reflect strategic decisions and encapsulate the psychological resilience, visionary thinking, and strategic adaptability characteristic of successful entrepreneurs. Furthermore, through capturing these elements, the SPARE framework helps elucidate how entrepreneurs like Jobs, Musk, and Leonardo da Vinci turn challenges into opportunities, maintain optimism and perseverance in the face of setbacks, and innovatively leverage available resources to achieve their goals. This deep dive into the psyche of iconic entrepreneurs provides a richer understanding of the intrinsic and extrinsic motivators driving entrepreneurial success, highlighting the importance of psychological capital and effectuation in overcoming adversities. Each narrative encapsulates a distinct entrepreneurial psyche, offering diverse insights into entrepreneurial behaviour and decision-making. This approach ensures that the study captures a broad spectrum of entrepreneurial experiences, providing a richer and more intricate understanding of the field than previously available. The theory-driven narratives include:

1. a) Innovator (Steve Jobs) emphasizes creativity, simplicity, and brand identity. His narratives demonstrate how a relentless focus on innovation and user experience creates a lasting impact.
2. b) Visionary (Elon Musk) focuses on ambitious goals and public engagement. Musk's story illustrates the power of a bold vision and the ability to inspire and mobilize public and investor support
3. c)Polymath (Leonardo da Vinci) highlights interdisciplinary knowledge and creativity. The Da Vinci narrative shows how diverse expertise can drive innovative solutions and creative breakthroughs.
4. d)Composite Entrepreneur (Areki) combines elements from multiple psyches to represent a holistic entrepreneurial approach. The fictitious entrepreneur Areki's narrative integrates various strategies and characteristics, providing a comprehensive view of entrepreneurial success in the context of green technology.

These narratives offer diverse insights into marketing entrepreneurship, serve as a foundation for further research and help directly address the limitations of LLMs. The study provides a multi-dimensional perspective on entrepreneurial behaviour and strategy by incorporating these varied entrepreneurial psyches.

Moreover, the ability of LLMs to generate real-time responses reduces the biases and limitations associated with self-reported data and retrospective accounts of entrepreneurial behaviours and actions. This dynamic and iterative approach allows researchers to explore entrepreneurial behaviours more detailed and intricately, providing a deeper understanding of the underlying psychological and strategic processes. The flexibility and adaptability of LLMs in generating varied scenarios and responses make them invaluable tools for simulating entrepreneurial environments and decision-making processes, offering a granularity well beyond traditional research methods. However, it is essential to recognize the utility of LLMs in studying entrepreneurs is not without challenges. The quality and relevance of the generated narratives are highly dependent on the input prompts' design and the training data's breadth. Ensuring the prompts effectively capture the essence of the SPARE framework components is crucial for generating meaningful insights. Furthermore, while LLMs can simulate human-like responses, they may not fully capture the emotional depth and complexity of real-world entrepreneurial experiences. The narratives produced by LLMs build on patterns identified in the training data and may not always align with the unique and often unpredictable nature of entrepreneurial journeys.

5.2 Academic and Managerial Contributions

The academic contributions of this study are multifaceted. Firstly, the study pioneers using LLMs with a structured framework, SPARE, demonstrating how AI leverage helps generate rich, contextually accurate entrepreneurial narratives. This innovative approach provides a new methodological tool for researchers, enabling them to explore complex psychological and decision-making processes with unprecedented depth and accuracy. This model is a new lens for exploring many subtle psychological elements in entrepreneurial activities. The study offers a comprehensive lens to examine entrepreneurial behaviour by integrating multiple theoretical frameworks to create SPARE. This holistic approach enriches the existing body of literature and paves the way for future research to build on these foundational insights. Moreover, the study's emphasis on theory-driven narratives with real-world validation adds a valuable dimension to entrepreneurial research. By capturing the detailed and multifaceted nature of entrepreneurial decision-making, the narratives are a robust resource for scholars seeking to understand the cognitive and strategic processes underlying successful entrepreneurship.

From a managerial perspective, the contributions of this study are equally valuable. The practical applications of LLM-generated narratives include equipping entrepreneurs and managers with powerful tools to enhance their decision-making and strategic planning. One of the key managerial contributions is the development of scenario simulation techniques. Managers can gain actionable insights into effective strategies and potential pitfalls by using LLMs to simulate various business scenarios and explore how iconic entrepreneurs like Steve Jobs or Elon Musk might respond. This approach enables managers to anticipate challenges, refine their strategy, and make more informed decisions in real-world contexts. The study also underscores the importance of reflective practice for managers. By regularly reflecting on decisions and actions using frameworks like SPARE, managers can identify strengths, weaknesses, and areas for improvement. This continuous learning process fosters critical thinking and adaptive strategies, essential for navigating the ever-evolving business environments. Furthermore, managers can better align their company mission and values with their strategic goals by developing and refining entrepreneurial narratives, creating a more cohesive and engaging narrative for investors, customers, and stakeholders. The study proposes new theoretical pathways and combinations to evaluate integrating advanced and emerging technologies, such as AI, with current and emerging business practices emphasizing an entrepreneurial marketing perspective.

5.3 Limitations of LLMs for Entrepreneurial Research

While applying LLMs in entrepreneurial research presents numerous advantages, a few limitations require acknowledgement. One of the primary concerns is the risk of generating plausible but inaccurate or biased content, known as confabulations or "fluent bullshit" (Katwala, 2022). Confabulation refers to creating coherent stories or filling in gaps with fabricated or distorted information, often without the intention to deceive. In the context of LLMs, hallucination is a commonly used term. Still, confabulation is a suitable choice for our use owing to an emphasis on narrative coherence and the psychological aspect of filling in gaps.

Due to these issues, we have seen the necessity for moving beyond trial-and-error experimentation, and methodological considerations require a wide range of prompts to ensure accuracy. Another limitation is the dependency on high-quality, domain-specific data. The effectiveness of fine-tuning LLMs for entrepreneurial research is contingent on the availability and quality of annotated datasets accurately representing entrepreneurial experiences. The absence of robust data compromises the LLM's ability to generate insightful and relevant narratives. Additionally, LLMs may struggle with entrepreneurial decision-making's dynamic and context-sensitive nature. Entrepreneurs often make decisions based on complex factors, including real-time market conditions, personal intuition, and social influences. Capturing this level of complexity in LLM-generated narratives requires continuous refinement and validation against real-world scenarios. Moreover, the interpretability of LLM-generated insights poses a challenge. While LLMs can produce coherent and contextually relevant narratives, understanding the underlying reasoning and ensuring the accuracy of the generated content requires careful investigation. Researchers must critically evaluate the generated responses and consider the broader context to ensure the insights are valid and meaningful.

Finally, ethical considerations regarding using LLMs in research require addressing. As discussed, the models can inadvertently perpetuate existing biases present in the training data, leading to skewed or discriminatory narratives (Bolukbasi et al., 2016). Further, mitigating these limitations requires implementing rigorous validation processes. Cross-referencing AI-generated content with established literature and case studies helps ensure the authenticity and reliability of the narratives. Feedback from domain experts further refines the narratives and enhances their validity. Moreover, the ethical implications of using AI-generated content in entrepreneurial research must be carefully considered, particularly regarding transparency and accountability. Ensuring the responsible use of AI, maintaining transparency in the methodology, and safeguarding against potential misuse of the technology is essential for maintaining the integrity of the research.

6. Directions for Future Research &Conclusion

6.1 Directions for Future Research

This study sets a precedent for future research applying narrative analysis through AI. Exploring entrepreneurial narratives using LLMs opens several promising avenues for future research. To fully understand the extent and impact of hallucinations, we must systematically analyze outputs from various LLMs going beyond GPT-4o (OpenAI) to include open-source Mixtral 8x7b and LLaMA3 70b (Meta).

Another significant direction is expanding narrative generation to include a broader range of entrepreneurial psyches and contexts. Incorporating theories from behavioural economics, cognitive psychology, and organizational behaviour can provide a more holistic understanding of entrepreneurial behaviour. This multi-disciplinary approach can enhance the robustness of the insights and offer a more comprehensive perspective on the factors driving entrepreneurial success. However, more is possible, including expanding the scope of the research to include a more diverse range of entrepreneurial contexts and demographics. Researchers can uncover unique patterns and insights otherwise overlooked in a more homogeneous sample by studying entrepreneurs from different industries, cultural backgrounds, genders, and business development or sales stages.

Further, researchers should explore the potential of using LLMs in real-time entrepreneurial decision-making and strategy development. By simulating real-world scenarios and capturing real-time responses, LLMs can provide valuable insights into how entrepreneurs navigate complex and rapidly changing environments. This approach can also inform the development of decision-support systems and AI-driven tools for entrepreneurs. Applying LLMs in educational and training contexts offers another exciting research avenue. Developing simulations of entrepreneurial scenarios, including expanding the concept of the virtual interview, could provide aspiring entrepreneurs with valuable experiential learning opportunities. Researchers could investigate the effectiveness of these tools in improving entrepreneurial skills and decision-making abilities, contributing to more effective entrepreneurship education programs.

Finally, ongoing validation and refinement of the SPARE methodology are essential for ensuring the findings' rigour, reliability and accuracy. Continuous feedback from entrepreneurs, domain experts, and stakeholders helps identify areas for improvement and ensure that the research remains relevant and impactful.

6.2 Conclusion

Integrating sophisticated prompt engineering techniques with LLMs is a major advancement in understanding entrepreneurial psyches and generating marketing narratives.By refining the design of input prompts, researchers can improve the relevance and accuracy of LLM-generated narratives, ensuring they capture the intricacies of entrepreneurial decision-making and psychological processes.Enhanced by the SPARE framework, this approach allows for creating contextually rich EMNs, providing deep insights into successful entrepreneurs' cognitive processes and strategic decision-making.

This study pioneers and underscores the importance of combining multiple theoretical frameworks, including Sensemaking, Psychological Capital, the Arc of Narrative, the RPD Model, and Effectual Thinking with real-world validation (Smith, 2023).Bycapturing the complexities of entrepreneurial behaviour, the research offers valuable academic and practical insights for entrepreneurs and educators.The detailed and contextually rich narratives serve as useful resources for understanding the intricacies of marketing entrepreneurship, offering entrepreneurs signposts and inspiration for future endeavours.

The dynamic capabilities of LLMs, combined with the structured analysis provided by the SPARE framework, can potentiallyrevolutionize the study of entrepreneurship.This integrated approach addresses traditional research methods' limitations and provides a transformative tool for early-stage entrepreneurs.By simulating realistic scenarios and applying comprehensive decision-making frameworks, entrepreneurs can enhance their critical thinking, adaptive strategies, and storytelling abilities. Some immediate practical steps for early-stage entrepreneurs include:

Scenario Simulation

Utilize LLMs to simulate various business scenarios or case studies and explore how successful entrepreneurs like Steve Jobs would react to help in understanding different approaches and strategies, for example, simulating how Steve Jobs might handle a major competitor entering the market and analyzing potential responses built around a case study.

Reflective Practice

Regularly reflect on decisions and actions using the SPARE framework to identify strengths, weaknesses, and areas for improvement. For example, reviewing a failed marketing campaign through the lens of sensemaking and effectuation to learn and adapt future strategies.

Storytelling workshops

Develop and refine the entrepreneurial narratives generated by LLMs through workshops and feedback sessions.Focus on creating a compelling story aligning with a company's mission and values, for example, crafting a pitch communicatingastartup story in a manner that connects emotionally with investors and customers.

Resource Leveraging and Risk Management

Apply effectuation principles to maximize available resources and manage risks effectively. Leveraging resources and risk management involves being proactive and adaptable, for example, forming strategic partnerships to expand reach and mitigate financial risks.

Virtual Interviews

Conduct virtual interviews with LLMs simulating successful entrepreneurs to gain insights into their thought processes and strategies. For example, interview an LLM-simulated version of Elon Musk to understand his approach to innovation and risk-taking.

By integrating these frameworks, early-stage entrepreneurs and learners develop a well-rounded approach to decision-making, problem-solving, and strategy formulation, helping them handle the multifaceted challenges of launching and growing a new business.Using LLMs to simulate various business scenarios allows entrepreneurs to explore different strategies and outcomes, enhancing their ability to anticipate and respond to real-world challenges.Reflecting on decisions and actions using the SPARE framework fosters critical thinking, adaptive strategies, and effective storytelling.

This study highlights the transformative potential of integrating LLMs with structured methodologies to advance our understanding of entrepreneurial behaviour and provide practical tools for future entrepreneurs.

The insights gained pave the way for future research directions and practical applications, ultimately contributing to the success and growth of entrepreneurs at various stages of their journeys.This integrated approach, combining LLMs with the SPARE framework, offers valuable insights for both academic research and practical applications, revolutionizing the study of entrepreneurship and providing deep insights into the minds and strategies of successful entrepreneurs.With a multi-layered approach, SPARE provides a powerful bridge between research and practice, ensuringAI-generated narratives offer genuine, actionable value in entrepreneurship.

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