AI–human data hybridization: a hybrid approach to unveiling global entrepreneurial motivations, challenges and opportunities Open Access

AI

= Artificial intelligence;

CG

= ChatGPT;

CS

= ChatSonic;

GEM

= Global entrepreneurship monitor;

GG

= Google Gemini;

GNI

= Gross national income;

GPT

= Generative pre-trained transformer;

HI

= High income;

LI

= Lower income;

LLM

= Large language models;

LM

= Lower-middle income;

MC

= Microsoft Copilot;

NLP

= Natural language processing;

QP

= Quora Poe;

SDR

= Special drawing rights; and

UM

= Upper-middle income.

Artificial intelligence (AI) is a key catalyst for innovation and change in the contemporary world, as it enables unprecedented levels of technological advancement and disruption in various domains (Haefner et al., 2021). A remarkable example of this phenomenon is the emergence of AI chatbots (Paliwal et al., 2019); these intelligent agents are reshaping various applications such as scientific coding (Merow et al., 2023), medical assistance (Nadarzynski et al., 2019), learning and training (Wu and Yu, 2023) and entrepreneurship (Weber et al., 2021). In support of this increase in applications and dependence on AI chatbots, von Kameke (2023) quotes 62% of respondents across the four Southeast Asian countries expressing strong chances to adopt their future use for the purpose of online search.

In entrepreneurship, Bartoletti et al. (2020) provided an exploration of the applications of AI, deep learning and AI chatbots in the domain of FinTech. The book indeed focuses on these technologies to help the investor and entrepreneur understand the benefits and implications that they may bring about, more specifically in improving financial decision-making and hence fostering innovation within the greater FinTech ecosystem. Hassan and Al Moaraj (2022) examined the intertwined role of entrepreneurship and AI-powered chatbots in driving the creation of new products and shaping future economic growth. Despite the increased prominence of AI chatbots in entrepreneurship, fewer studies are grappling to examine this role of AI chatbots in understanding the challenges and opportunities that entrepreneurs face in this diverse economy. More research is of great importance to unravel the capacity of AI machines, such as chatbots, to provide insights and drive innovation in the entrepreneurial landscapes globally (Giuggioli and Pellegrini, 2022; Lévesque et al., 2020; Shepherd and Majchrzak, 2022; Obschonka and Audretsch, 2019; Soltanifar et al., 2021).

In the context of global entrepreneurship, the World Bank’s classification of economies based on Gross National Income (GNI) per capita provides a pivotal framework for understanding global economic diversity and its potential influence on entrepreneurial landscapes. The GNI per capita data is denominated in US dollars and converted from local currency using the World Bank Atlas method (Fleming, 2020). This measure undergoes annual inflation adjustments using the Special Drawing Rights deflator. The deflator is calculated as a weighted average of the gross domestic product deflators from China, Japan, the UK, the USA and the euro Area (World Bank, 2022a). Updated annually in July, the 2024–2025 classification categorizes 217 out of 218 countries into four income groups: low income (≤$1,145, comprising 26 economies), lower-middle income ($1,146–4,515, encompassing 51 economies), upper-middle income ($4,516–14,005, including 54 economies) and high income (> $14,005, representing 86 economies) (World Bank, 2025). Table 1 presents the last four classifications (2021–2025) with their income thresholds.

In previous literature, there is a continuous call for understanding the motivations, challenges and opportunities faced by entrepreneurs across the four income groups (developed vs developed nations) (Baker et al., 2005; Terjesen et al., 2016). The constant adjustments in the income threshold over the years (most likely increasing trend as shown in Table 1) and the change in countries count and rank each year are motives for using an innovative approach that facilitates data importation and analysis across the four income groups. Therefore, this study is twofold. First, it uses five distinct AI chatbots – ChatGPT, Copilot, Google Gemini, ChatSonic and Quora Poe – as text-generation tools. Each AI chatbot will be prompted with scenarios and questions designed to elicit responses representative of a hypothetical entrepreneur operating within one of the four World Bank income classifications (low, lower-middle, upper-middle and high income). Second, a human validation phase is employed through semi-structured interviews of entrepreneurs from diverse countries covering the four income groups. Due to the limitations of the Generative Pre-trained Transformers (GPT) models in terms of training data, equal coverage of the four nations and potential biases and inaccuracies (Abaddi, 2025a; Abaddi, 2025b), the human contextualization will verify, correct and enrich the initial insights derived from the AI chatbots.

Supported by AI–human hybridization, this study aims to redefine the context of global entrepreneurship by leveraging AI chatbots as sophisticated analytical instruments in conjunction with human verification to examine entrepreneurs’ motivations, challenges and prospects at various income levels. The contributions of this study are as follows:

• positioning AI chatbots as analytical instruments for uncovering global entrepreneurship trends using five AI models;

• bridging the fields of AI and global entrepreneurship studies to generate novel comparative insights across economic strata; and

• comparing historical data of AI models and real-world entrepreneurial conditions and introducing the concept of AI–human data hybridization.

This article continues in Section 2 with an underlying literature review of international entrepreneurship and AI chatbots. In Section 3, the novel AI–human hybridization (AI chatbot and human entrepreneur data) is explained. Later sections include analysis and results in Section 4, discussion of results in Section 5, future implications in Section 6 and, finally, conclusions of findings and limitations in Section 7.

AI chatbots, with enough sophisticated advances and breakthroughs, represent intelligent conversational computer programs that provide human speech in a natural language, hence offering their service of automated online help and support (Zhang et al., 2020). According to Lauriola et al. (2021), Natural Language Processing (NLP) and machine learning are two AI fields that are used by AI chatbots. While machine learning helps AL chatbots to learn from data and gradually improve their performance, NLPs enable chatbots to comprehend and produce natural language (Ayanouz et al., 2020).

AI chatbots have been used in a variety of fields and applications, including e-commerce (Cheng et al., 2021), entertainment (Garcia-Mendez et al., 2021), health care (Nadarzynski et al., 2019) and education (Kooli, 2023). Carvalho and Ivanov (2023) concluded that AI chatbots have the potential to improve customer experience, save operating costs, boost productivity and offer tailored services. Regarding their limitations, Ray (2023) insisted that he brought to light a couple of ethical and safety issues related to data bias, the challenges in human–AI interaction and the digital divide, whereas Deng and Lin (2023) revealed that they can be a source of misinformation spread, as well as the limited capabilities of generating text based on provided input and the risk of biased or offensive language in responses.

A flourishing recent advancement in AI chatbot research is the development of GPTs (Abaddi, 2025b). They are a type of large model of neural network used for human-like outputs that can be in string forms, given a context or some sort of input. GPTs are taught to recognize the syntactic and semantic patterns of natural language using vast volumes of text data from diverse sources, including books, articles, websites, social media and news stories. Additional information and parameters may be used to fine-tune GPT models for particular activities or domains, such as AI chatbot applications (Bubeck et al., 2023). Using GPT models, recent AI chatbots were developed to conduct natural and coherent interactions with users in different fields. Some of the best examples of today’s AI chatbots, which have been progressed on the basis of significant improvements, include Google Gemini/Bard, Copilot/Bing Chat, ChatSonic and Quora Poe (Rudolph et al., 2023).

ChatGPT was created by OpenAI, it can shape and direct a discussion toward the ideal volume, structure, tone and language. The most recent iterations of OpenAI’s exclusive GPT model family, GPT-3 and GPT-4, serve as the foundation for ChatGPT (Wu et al., 2023). Bing Chat is another AI chatbot created by Microsoft to provide Bing searchers with information and support. Google Gemini/Bard is an AI chatbot created by Google that can produce original writing, such as poetry, tales, jokes, songs and conversation, based on user input or preferences (Rudolph et al., 2023). Writesonic company developed ChatSonic, which is an AI chatbot that has many personas, features and real-time data access, however, its free trial edition has a 2,500 monthly word restriction (Chaka, 2023). Finally, Poe was created by Quora, and it allows users to ask questions, get prompt responses and have conversations with various bots (D’Angelo, 2023).

Based on the previous, AI chatbots have shown recent promising results in various domains and applications, however, there is a scarcity of research on using them as interviewees to understand the challenges and opportunities for entrepreneurs across different nations.

Entrepreneurship is a complicated phenomenon (Bruyat and Julien, 2001). It has drawn the interest of academics from a range of fields and viewpoints. Several theories and frameworks have tried to explain the nature, function and influence of entrepreneurs in various circumstances. According to Toma et al. (2014), entrepreneurship and economic development are intimately related, both of which are impacted by post-war experiences. Although economic development can include a number of objectives, including the creation of jobs and the enhancement of the infrastructure supporting production, no widely recognized definition combines all the features.

The Theory of Economic Development by Schumpeter (1934) proposed that business owners invent novel combinations of goods, services, processes, markets, or sources of supply. They stimulate economic change and progress by causing market disequilibrium. The phrase “creative destruction” was invented by Schumpeter to describe the method used by entrepreneurs to demolish outdated structures and build brand-new ones. Mcclelland (1961) argued in the Need for Achievement Theory that business owners have a strong desire to succeed, which drives them to pursue difficult objectives and overcome challenges. Furthermore, they have an underlying desire for independence and self-governing behavior. According to Rotter (1966), the Locus of Control Theory, owners of business or entrepreneurship themselves possess an internal locus of control. They frequently exhibit proactivity, independence and optimism. Additionally, they can tolerate ambiguity and uncertainty quite well. The Opportunity Recognition Theory proposed by Shane and Venkataraman (2000) presents another view of entrepreneurship, describing it as the process of finding, assessing and taking advantage of possibilities to develop new products and services. It analyses the antecedents influencing the discovery and pursuit of entrepreneurial opportunities and goes further to delve deep into studying the influence the various variables have on the individual-opportunity nexus. In the Business Model Innovation Theory, Chesbrough (2010) discussed that entrepreneurs may innovate by designing new ways of delivering, capturing and sharing value with customers and stakeholders. Finally, Ries (2011) in the Lean Startup Theory applied lean manufacturing principles to entrepreneurship, emphasizing validated learning, iterative development and delivering value to customers through minimum viable products.

Cross-sectional studies point out opportunities and challenges that come in the way of entrepreneurs in their entrepreneurial journey due to differences between countries (Bruns et al., 2017; Guerrero et al., 2020; Naudé, 2009; Thomas and Mueller, 2000). Naudé (2009) highlighted the need for more theoretical and empirical studies in this area and discussed the potential and problems faced by entrepreneurs in various countries. Kim et al. (2022) revealed that developing economies with a significant manufacturing sector benefit from opportunity-driven entrepreneurship. This tends to underline the necessity that due consideration must be given to the fact that entrepreneurial activity and the tempo of technological development change drastically in each individual sector. Luthans et al. (2000) found that the environment, encompassing political, economic, legal and cultural factors, shapes entrepreneurial development in transitional economies. Mwobobia (2012) analyzed the obstacles small-scale female entrepreneurs in Kenya confront and suggested some solutions to overcome them. Gender norms, lack of funding, discrimination and restricted access to the legal system were the top challenges. However, a number of public and private sector actors are trying to assist female entrepreneurs through programs like the Women Enterprise Fund. Halkias et al. (2011) went on to stress that these findings point to the possibility of female entrepreneurship development and the influential role that may be played by characteristics of both family and microfinancing. Chinomona and Maziriri (2015) stressed the struggles women business owners in South Africa’s Gauteng region encounter, such as a lack of education, difficulty obtaining financing, gender discrimination and unfavorable attitudes. The study recommended some solutions to policymakers. Isaga (2018) conducted a study on the most serious problems faced by female entrepreneurs in lower-middle-income nations and identified three main challenges: lack of access to finance, gender-related issues and social and cultural obligations.

In developed countries, Ahmed and McQuaid (2005) focused on the scarcity of knowledge that addresses the challenges and opportunities encountered by entrepreneurs in high-income countries in connection to sustainable development. Cetindamar (2005) revealed that underutilization of youth and women’s resources, reliance on the informal market, weak organizational linkages, bureaucratic problems and erratic governmental policies are the main challenges in upper-middle-income nations. Guerrero et al. (2020) argued that entrepreneurs in developed nations face fierce competition, high operating expenses and rigorous regulations. However, they can utilize skilled labor, cutting-edge infrastructure and technology and hospitable entrepreneurial environments to spur innovation and internationalization. Sultan and Sultan (2020) discovered that women entrepreneurs in developed and developing nations have suffered as a result of the Corona crisis, which has decreased their output, turnover and profit. However, they embraced creative techniques, including using digital marketing and adequate cash management.

Despite the increasing literature on entrepreneurship and innovation, there is still a gap in understanding the motivation, challenges and opportunities facing entrepreneurs across different nations. Previous theories have focused on the individual, organizational, institutional and environmental factors that influence entrepreneurial behavior and outcomes but have not adequately addressed the cross-cultural and comparative aspects of entrepreneurship and innovation (Bartoletti et al., 2020; Dabic et al., 2012; Nissan et al., 2012). Therefore, there is a need for more empirical and theoretical research that explores how entrepreneurs from different income contexts perceive and respond to the opportunities and challenges in their respective environments, and how they leverage their cultural and social capital to create value and impact. Such research would contribute to advancing the knowledge and practice of entrepreneurship and innovation in a globalized world.

The methodological framework of this study is underpinned by a pragmatic epistemological stance, which prioritizes the practical application of research methods to address the complex phenomenon of global entrepreneurship across diverse economic contexts (Morgan, 2014). Pragmatism, in this context, justifies the integration of both AI-generated text and human-derived data, valuing the results gained from each approach without adhering strictly to a single paradigm (Tashakkori and Teddlie, 2010).

This design commences with the generation of textual data through AI chatbots, treating them as sophisticated tools capable of rapidly simulating narratives based on their extensive training data sets. The generated textual data then serves as the basis for informing the subsequent qualitative data collection phase, where semi-structured interviews with human entrepreneurs from various countries within the defined income classifications will be conducted. The rationale for this design lies in its potential to leverage the breadth and speed of AI-driven text generation to identify salient themes from all over the globe, which are then examined through the lived experiences offered by human participants.

The selection of five AI chatbots – ChatGPT, Bing Chat, Google Gemini, ChatSonic and Quora Poe – for this study was a deliberate strategy aimed at maximizing the breadth and potential diversity of generated textual data. Powered by large language models (LLMs) such as GPT-3.5 (ChatGPT), PaLM (Google Gemini) and integrative proprietary models (Bing Chat, ChatSonic and Quora Poe), provide a varied spectrum of responses, which helps mitigate the inherent biases and limitations of any single model (Reyhan et al., 2024). ChatGPT by OpenAI is recognized for its sophisticated natural language understanding and generation capabilities (OpenAI, 2024). Copilot (formerly Bing), integrated within Microsoft’s search engine, adds to the GPT 3.5 model access to more recent information compared to its initial training data cutoff. Google Gemini (formerly Bard) provides different perspectives in its generated responses (Rudolph et al., 2023). ChatSonic, from Writesonic, is marketed for its ability to generate factual and engaging content, claiming to integrate real-time data access through Google Search (Chaka, 2023). Finally, Quora Poe serves as an aggregator of various AI models, such as GPT and Anthropic’s Claude, allowing for a comparative analysis of responses generated by different underlying architectures to the same prompts (D’Angelo, 2023). The five AI chatbots were purposively drawn from the top list of AI chatbots in the year 2023, recognized by Rebelo (2023). Their inclusion was driven by their prominent status in the field and the additional advantage of free access and usage (except for ChatSonic– see Table 2).

The study’s prompts were meticulously designed following a structured protocol, directly referencing the World Bank income classifications (World Bank, 2025). Each AI chatbot was asked 12 identical questions (see  Appendix. To ensure the rigor of the AI-generated data, the interview prompts were reviewed and refined by three experts: a professor of entrepreneurship, a computer science professor and a prompt engineering specialist. This verification process led to minor modifications aimed at enhancing clarity and reducing potential biases. For each question, the AI chatbot provided 4 answers based on the entrepreneur’s income. In total, 48 responses are expected from each AI chatbot or 240 from the entire sample. To reduce bias and the risk of priming the AI models toward specific narrative structures, follow-up prompts were used, such as “Can you tell me more about that?” or “Can you give me an example?” (Binns, 2018). Finally, the time and content of the AI chatbots’ responses were recorded using a stopwatch and screenshots to facilitate capabilities comparison and later analysis, as shown in Table 2.

The textual data generated by each of the five AI chatbots, which reflects all four different income levels, was taken in and brought into ATLAS.ti, a qualitative data analysis software. Both initial thematic coding by applying predefined codes derived from the research questions (motivations, challenges, opportunities) and emergent coding derived from textual content were performed and most important quotes were recorded and analyzed. AI-powered ATLAS.ti v23 software is integrated with ChatGPT, allowing for AI-driven assistance in coding, annotation, visual analysis, memo writing and report generation, resulting in faster and more efficient results (ATLAS, 2023). Lopezosa and Codina (2023) unleashed the potential of combining ChatGPT with computer-assisted qualitative data analysis software to assist researchers in coding interviews and compiling results. Furthermore, they provided examples of applications in academic research.

Recognizing the inherent limitations of relying solely on AI-generated text, which lacks genuine lived experiences and dependence on old 2021 training data (Jungwirth and Haluza, 2023), Phase 2 incorporates human data collection through semi-structured interviews with entrepreneurs operating within the four World Bank income classifications (World Bank, 2025).

A purposive sampling strategy (Campbell et al., 2020) will be used to recruit a target sample of (n = 20) entrepreneurs (5 from each nation). This number is deemed sufficient for in-depth qualitative exploration within each income group, allowing for the identification of recurring themes and nuanced perspectives (through saturation) while remaining manageable for rigorous analysis (Guest et al., 2006). The selection criteria will prioritize entrepreneurs who have been operating their ventures for a minimum of two years (µ = 4.9, σ = 1.9 years) to ensure they possess relevant experience regarding motivations, challenges and opportunities within their contexts. Furthermore, efforts were made to ensure maximum variation sampling (Creswell and Poth, 2023) within each income group by recruiting participants from diverse national contexts. Ethical considerations, including informed consent and anonymity, were paramount throughout the recruitment and data collection process.

The semi-structured interview protocol was informed by the initial thematic analysis of the AI-generated text. The AI chatbots themes identified served as a starting point for constructing the interview guide. However, the semi-structured nature allowed for flexibility, meaning new, unexpected and insightful responses (Galletta, 2013). The interview questions are open-ended, encouraging to share their experiences, perspectives and reflections on their entrepreneurial journeys in relation to their motivations, challenges and opportunities within their specific economic environment.

Due to geographical dispersion, the interviews were conducted remotely via Zoom. The duration was (µ = 59, σ = 6.5 min) as shown in Table 3. They were all conducted in English during April 2025 by a specialized team of four individuals after they were trained on the process. Each person was assigned to one region. To ensure accuracy and check the team’s progress, all interviews were audio-recorded with the explicit consent of the participants. They were transcribed verbatim for preserving the nuances of language, including pauses, intonations and specific word choices (Hill et al., 2022). The transcribed data will then be imported into ATLAS.ti, the same software used for the AI-generated text, allowing for a systematic and comparative thematic analysis across both data sets.

The integrated data analysis uses comparative thematic analysis Miles et al. (2020) to juxtapose themes from AI-generated text and human interview transcripts (n = 20). This iterative process will identify points of convergence, divergence and unique insights, facilitated by ATLAS.ti. The analysis explores both explicit and underlying themes to understand the nuances of entrepreneurial motivations, challenges and opportunities across diverse economic contexts.

Validation and triangulation (Denzin, 1970) are pivotal for ensuring the study’s rigor. Human interview data will serve to validate, refine and potentially correct initial AI-driven insights, addressing concerns about bias and hallucinations. Data triangulation, comparing various sources, will enhance the comprehensiveness of the findings. In this study, triangulation will occur across three dimensions:

1. between the AI-generated text and human participant interviews;

2. across different income levels and national contexts; and

3. between interviews and secondary data (literature).

Furthermore, the rich contextual information gleaned from the “Unique Contextual Factor” identified for each human participant will be crucial in interpreting both the convergent and divergent findings, adding a layer of situated understanding that AI models inherently lack. This integration assists in hearing both the voices of humanpreneurs and AIpreneurs.

Ethical considerations for this study prioritize the distinction between AI as a research tool and human participants. Formal informed consents were obtained from all (n = 20) entrepreneurs, detailing the study’s purpose, methodology (including AI use), participation rights and confidentiality measures. Participants were assured of anonymity and the secure handling of their data, adhering to general data protection regulation guidelines. This research complied with all ethical standards for human participant research. Formal approval was granted before data were collected. Participant anonymity and confidentiality were ensured throughout the course of the study.

The study acknowledges the ethical implications of using AI-generated content aligning with the principles of the European Union (2019). Ethical challenges related to AI include addressing bias and hallucinations in AI-generated content. The limitation of training data (cut off at 2021) of various models is another issue. To mitigate these concerns, human validation and triangulation were employed to ensure the accuracy and relevance of AI insights, particularly in overcoming biases such as Western-centric perspectives (Denzin, 1970). Another technique was the employment of multiple (n = 5) AI chatbots to collect various perceptions and remain recent based on their capabilities.

This analysis and results section presents the findings from both the AI-generated text and the human interview data, structured to facilitate a comprehensive comparison. Thematic analysis was adopted following the six-step process by Braun and Clarke (2012) to conduct a thematic qualitative analysis of the interviews. The process involved familiarizing with the data, coding data segments, generating themes from the codes, reviewing the themes for validity and coherence, defining and naming the themes and writing up the findings. This section will first detail the thematic analysis of the AI-generated text (Section 4.2), followed by the thematic analysis of the human interview data (Section 4.3). Finally, Section 4.4, presents a comparative analysis of the themes emerging from both data sources across the four income levels.

To facilitate the analysis and help understand data exported from the AI chatbots, the entrepreneur from low income was referred to as “LI,” from lower middle income as “LM,” from upper middle income as “UM,” and from high income as “HI.” To distinguish the responses provided by each AI chatbot during the analysis and reflect them on the semantic diagrams, the name of the AI chatbots was appended to the answers. “CG”, “MC”, “GG”, “CS” and “QP” stand for ChatGPT, Microsoft Copilot, Google Gemini, ChatSonic and Quora Poe, respectively. For quotes, a combination of the nation and the AI chatbot abbreviations was used. For instance, “HI-GG” indicates that the Google Gemini generated the response and represents the input from the high-income entrepreneur.

Motivation was one of the themes that came out of the conversations with the AI chatbots. The term is defined as the process of turning a regular person into a successful entrepreneur who can seize opportunities and contribute to economic growth and wealth maximization. An entrepreneur needs motivation because it spurs them on to pursue their objectives, overcome obstacles and come up with novel solutions. Personal, social, economic and environmental factors, among others, can all have an impact on motivation. The semantic network in Figure 1 facilitates understanding the motivations due to the extensive data generated by the AI chatbots.

This suggests that there are two common primary motivations for entrepreneurship in different nations: opportunity and survival. The desire to get out of poverty and meet fundamental requirements is the driving force behind survival motivation in low-income and lower-middle-income nations. Quotes discussed by AI chatbots were as follows:

The phenomenon of entrepreneurship is sophisticated. In addition to necessity, there are other reasons for starting a business, such as opportunity or professional aspirations (Mota et al., 2019). The Global Entrepreneurship Monitor (GEM) states that opportunity entrepreneurs pursue a company concept because they see a market need or a potential profit, whereas necessity entrepreneurs start a firm because they have no other options for employment (GEM, 2022). Market gaps and the desire to add value were some motivators that drive entrepreneurs to take advantage of opportunities. The style of motivation also reflects each country’s level of institutional support, technology infrastructure, innovation and economic growth. Some of the shared quotes were:

The previous highlighted the opportunity motivation. Based on the generated text, it is linked to innovation, value-creation, skills, sustainability and vision. Entrepreneurs can spot market opportunities and seize them, provide value for clients and set themselves apart from other competitors (Baron, 2006; Choi and Shepherd, 2004). Furthermore, opportunity entrepreneurs emerge in any situation where there is a potential market for their goods or services; they are not constrained by their financial status or place of origin. However, depending on the institutional and economic framework in which they operate, they may encounter various challenges and opportunities.

The second theme that emerged was the challenges facing entrepreneurs. Challenges in the context of entrepreneurship are the obstacles entrepreneurs face as they develop, launch and expand their new ventures. These challenges varied depending on the income level of the four nations as shown in Figure 2. The most common challenges for low-income and lower-middle-income countries were a lack of access to finance, poor infrastructure and bureaucratic hurdles. The following quotes were the most significant in relation to both nations:

Entrepreneurs in upper-middle and high-income nations are not immune to challenges that may hinder their success. Some of the top challenges they face include high costs of doing business, such as salaries and taxation, market saturation and differentiation, innovation management and adoption and competitive advantage and sustainability. In relation to these challenges, the interviewees shared the following quotes from their experiences:

The analysis revealed that entrepreneurs across income levels face unique challenges. Starting from poor infrastructure and lack of funding in low-income nations and moving to high costs, competitive advantage, innovation management and intense competition in high-income countries.

The concept of opportunities refers to the discovery and exploitation of unmet market demands or gaps that may be filled by providing worthwhile, practical and lucrative goods or services (McMullen et al., 2007). Depending on the market size and needs, competition, regulation and innovation, entrepreneurs from various income-level nations may encounter various opportunities, as shown in Figure 3.

For instance, low-income entrepreneurs could discover greater chances in essential services, enhancing their education and skills, building community trust and support and accessing microfinance. In the same context, the interviewees mentioned:

However, entrepreneurs with lower-middle incomes could have better success adapting to customer preferences, talent management and retention, access to incubators and accelerators and using technological advancements in their projects. The following are the main quotes raised during the interviews:

Entrepreneurs with upper-middle and high income have many shared opportunities that assist their access to international markets, such as using high-tech or green technologies, investing in developing customized products, complying with ethical and legal standards and reducing environmental impact. Based on data from GEM, Szirmai et al. (2011) found that the economic growth of transition and high-income countries is more likely to be affected by entrepreneurship and innovation, as these countries have higher levels of entrepreneurial activity, especially in the opportunity-driven and high-impact segments. The interviewees mentioned the following:

Entrepreneurs differ by virtue of the challenges that come their way and the market and income status in respective nations, meaning they are exposed to opportunities that will enable them to get where they have set as their goals. Market gaps, new trends, development of technologies and changing needs by consumers are all driving forces that push entrepreneurs toward innovation, adaptation and the creation of value within their own particular contexts.

During this section, the analysis from human participants (HPs) is presented. The same criteria in the previous section for the four nations was used but with mentioning the ID of the participant based on Table 3. For example, HP-LI-03 refers to a human participant (#03) from low low-income region.

In analyzing the entrepreneurs’ motivations based on HPs’ data, a distinct narrative emerges that complements and extends the insights drawn from the AI-generated text. HPs shed light on more nuanced, context-specific factors that influence entrepreneurship in addition to the behavior-specific socio-economic context.

In low-income settings, the 'survival’ motivation was indeed prominent, but intertwined with a profound sense of responsibility toward family and community. For example, participants highlighted a motivation extending beyond mere personal survival to encompass collective well-being, social impact and desire to preserve cultural identity:

Across the lower-middle and upper-middle income brackets, the “opportunity” motivation took on more diverse forms than simply filling market gaps. It was frequently linked to a desire for autonomy and self-determination, a yearning for creative expression and a drive to solve locally relevant problems. Also, social entrepreneurship was introduced, where individuals use their entrepreneurial ventures to address both market gaps and social issues. HPs raised the following quotes:

Also, UM income entrepreneurs’ motivations were increasingly tied to personal fulfillment and professional aspirations. The innovation-driven motivations for Gen Z seen in the adoption of AI and GPT models, aligning with Abaddi (2025c), offer a personal and emotional fulfillment angle that wasn’t mentioned by AI chatbots. Entrepreneurs pointed to seeking self-expression, market differentiation and visionary leadership in addition to financial success as motivational factors, as seen in HP-UMI-15 and HP-UMI-11:

At the HI level, entrepreneurial motivation often revolves around impact and legacy. Personal values and ethical considerations are driving some HI entrepreneurs. Environmental and social drivers were also highlighted, with the notice that some were encouraged to launch their startup based on encouragements from venture capitals, angel investors and other investing programs that provide opportunities and grants to solve real-world problems:

The human interview data highlights richer motives than AI analysis. It covered deep motivations like cultural preservation, social entrepreneurship and personal fulfillment. It emphasizes that entrepreneurship is driven by emotional, personal and cultural factors – not just transactional ones – showing the complexity across diverse socio-economic settings.

The HPs’ data corroborated several challenges highlighted in the AI-generated text, such as access to finance and bureaucratic hurdles in lower-income economies, but extended context-specific nuances and interconnected obstacles.

For entrepreneurs in low-income nations, the lack of access to finance was not only about the unavailability of loans but also the absence of alternative funding mechanisms. Poor infrastructure extended beyond just unreliable electricity; it includes internet, e-payment methods (especially for digital entrepreneurs) and transportation networks.

In LM-income economies, the challenges evolve to focus on market saturation, competition and the need for innovation. Additionally, the sense of instability and the hidden costs associated with navigating bureaucracy were less evident in the AI’s portrayal compared to HPs:

For UM-income entrepreneurs, the challenges focus on maintaining competitive advantage, innovation management and adapting to rapid technological changes. This adds emphasis on speed and market timing, which are pivotal in the tech and AI industry, as mentioned by the participants:

Finally, HI entrepreneurs’ challenges include high operational costs, intense market competition and the pressure to meet sustainability goals. Also, some introduced regulatory complexity as a challenge, especially in emerging technologies:

The human data thus offers more nuanced and context-sensitive information into the complex issues confronting the entrepreneur compared to AI chatbots, capturing the complex interaction of economic, infrastructural, regulational and social forces underlying several stages of economic growth.

Entrepreneurs’ opportunities from the HPs data reveal a diverse landscape of opportunities that expand the AI chatbots’ points. For example, HPs emphasize context-specific innovations and strategic insights that entrepreneurs leverage to adapt to local challenges and drive growth. LI entrepreneurs focus on community-driven solutions and resource optimization, while HI entrepreneurs explore international markets, emerging technologies and strategic partnerships.

For LI entrepreneurs, the opportunity often lies in personal determination, community trust and resourcefulness. Some entrepreneurs confirmed that education and skill-building are critical, as highlighted by the AI chatbots. However, the relationship-oriented perspectives and focus on long-term customer loyalty were new points highlighted:

In LM-income economies, the adaptability to market trends and the harnessing of technological advancements were the main opportunities. HPs emphasized opportunities in bridging the gap between traditional industries and the digital economy and in catering to the aspirations of a growing middle class. The focus on addressing the specific primary needs of their communities was highlighted by the LM-income participants:

The opportunities for UM and HI entrepreneurs are in using new technology (AI, Metaverse, blockchain, etc.) in innovative product development and market expansion. The human data revealed other opportunities in fostering collaborative ecosystems, networking and sharing diverse skill sets:

Finally, some HI entrepreneurs added the possibility of benefiting from living in developed countries, as they are pioneers in innovation and digital entrepreneurship in addition to the availability of competitive financing opportunities for businesses of various sizes.

The aim of the comparative analysis between AI-generated text and the human interview data is to create a harmonious synthesis that allows understanding the complexities of global entrepreneurship across different income levels.

Post comparison, clear points of convergence emerged, but equally revealing were the points of divergence. These differences demonstrate the limitations of AI models in grasping the intricacies of human experience while appreciating the synergistic potential of combining both forms. For example, both the AI and human participants acknowledged that opportunity and survival were key drivers. However, human participants revealed that motivations were often multi-layered and interwoven with personal goals, cultural values and a strong desire for social change. AI models captured the economic aspect of motivation, but human entrepreneurs added the emotional, cultural and social dimensions. For example, HP-LI-02 mentioned:

In terms of entrepreneurial challenges, both AI and HPs identified financial constraints, infrastructure issues and competition as primary obstacles. However, AI models gave generic and broad examples, and humans had more customized cases. Additionally, the emotional layer of challenge captured by HPs was a unique element that the AI models could not convey. For example:

When analyzing the entrepreneurial opportunities discussed by both sources, AI highlighted general themes like market gaps, technological advancements and global connectivity. But the human responses added a much-needed layer of contextual richness. For example, AI has missed the importance of cultural preservation in the face of globalization and human-driven, value-based innovation. This represents a missing piece in the AI models, which focus heavily on economic (profit potential) and technological factors. The Venn diagrams in Figure 4 summarize the comparison between AI chatbots and HPs concerning the three themes.

This comparative analysis reveals that while AI can provide a structural framework, human data brings fresh personalized perspectives – such as the role of community trust in LI markets, the balance of tradition and innovation in UM-income countries and the ethics-driven innovation in HI economies. Integrating the two sources has taken the study beyond the limitations of both AI and traditional qualitative research, offering a more comprehensive, nuanced and authentic understanding of entrepreneurship across different income levels.

The analysis of both AI-generated text and human entrepreneur interviews is consistent with previous research that suggests that primary motivations for entrepreneurship in different nations: opportunity and survival, align with the classical motivation theories (Aceytuno et al., 2020; Belda and Cabrer-Borrás, 2018; Civera et al., 2020). However, the human narratives expanded upon these categories, highlighting the crucial role of ‘communal responsibility’ in low-income contexts, where entrepreneurship is often seen as a means to uplift entire communities, echoing the concept of social entrepreneurship. Furthermore, the desire for ‘autonomy’ and ‘self-determination’ emerged as a potent motivator, particularly in middle-income countries, reflecting a drive to escape restrictive employment structures and pursue more fulfilling career paths, a factor less emphasized in the AI’s general depiction of opportunity-seeking. This aligns with research on the importance of autonomy in entrepreneurial motivation. Finally, HPs introduced a value-driven approach in HI-contexts where entrepreneurs are driven by the desire to create sustainable and socially responsible ventures.

The two sources share commonalities in identifying key entrepreneurial challenges. Both sources confirmed that lack of finance, poor infrastructure and bureaucratic hurdles are primary challenges for entrepreneurs across all income levels (GEM, 2022; Isaga, 2018). However, human responses provide deeper insights into the emotional toll of entrepreneurship and the psychological strain entrepreneurs face, an element largely absent in AI-generated data. Emotional strain is especially significant in low and lower-middle-income contexts, where entrepreneurs are often juggling multiple roles without sufficient support systems (Aldrich and Cliff, 2003). Furthermore, while AI identified infrastructure issues as a central challenge, human participants revealed how local cultural barriers and market saturation exacerbate these challenges, especially in crowded local markets where differentiation becomes critical yet tricky. Some of the top challenges for HI countries were the high costs of doing business, such as salaries and taxation, market saturation and differentiation, innovation management and adoption and competitive advantage and sustainability (Guerrero et al., 2020; Sultan and Sultan, 2020).

Both sources’ results stressed that entrepreneurs in different nations have different opportunities depending on their level of economic development, market potential and innovation capacity. From this point of view, LI entrepreneurs might promote better access to improved essential services, better education and skills, build community trust and support and microfinance access (World Bank, 2022b; Yago et al., 2008). These opportunities could help them improve their living standards, create social value and overcome the barriers of poverty and exclusion. Entrepreneurs from LM economies could have better success adapting to customer preferences, talent management and retention, access to incubators and accelerators and using technological advancements in their projects. These opportunities could help them increase their competitiveness, productivity and growth potential in emerging markets. In addition, Bayar et al. (2018), Omri (2018) and Sak et al. (2018) identify several common opportunities for UM and HI entrepreneurs to enter the international market, including the necessity to use high and green technologies, invest in developing tailored products, comply with ethical and legal standards and minimize the environmental impact. However, human data introduced additional opportunities related to local contexts, cultural factors and ethical considerations; they mentioned collaborative and networking opportunities, sustainability-driven markets, ethical innovation and financing options. These opportunities could help them exploit global opportunities, create innovative solutions and achieve sustainable development. Therefore, entrepreneurs in different nations need to recognize and pursue the opportunities that are most suitable for their context and capabilities.

Future work may greatly improve methodological control and increase variance through use of open-source pre-trained LLMs. Hugging Face and other platforms provide access to rich model ecosystems such as GPT-Neo, LLaMA-2, OPT, Falcon and Mistral. The unique benefit of those open-source models is the fact that the models are open and available for researchers to access directly and, in some instances, fine-tune their architectures and training parameters.

The theoretical implications of this study provide new directions for AI and global entrepreneurship research. Using AI to explore entrepreneurial dynamics across diverse income levels, this study extends entrepreneurship theory beyond traditional models focused on resource constraints and market opportunities (Shane, 2003). The study introduces the concept of AI–human data hybridization, proposing that AI can serve as a robust analytical tool to identify overarching patterns while human insights provide the necessary contextual depth and emotional complexity often overlooked in purely quantitative models. The new methodology paves an efficient path for future research, particularly in cross-cultural and large-scale studies. It proves how AI can be responsibly integrated into social science inquiry, paving the way for future research that leverages AI as a tool. Finally, the study assesses the capabilities and limitations of various LLMs and compares their results to human output.

This research offers several practical implications that can benefit entrepreneurs, academics, policymakers and AI developers. For entrepreneurs, it provides a global comparative perspective on entrepreneurial motivations, challenges and opportunities. This can foster a sense of worldwide awareness and identify potential strategies for overcoming challenges or leveraging opportunities. Furthermore, the study highlights the importance of incorporating purpose and ethical considerations into their business models. Finally, both data sources shared the technological advancement opportunities and the need for adaptation with trends (Figure 4) for successful business models. This could include exploiting AI and GPTs for digital entrepreneurs aligning with Abaddi (2023).

For academics, the AI–human hybridization encourages conducting large-scale, cross-cultural studies more efficiently and in less time. The study also offers a rich data set and an analytical framework that can be used for further research contexts. Finally, the evaluation of AI chatbots calls for responsible use and emphasizes the need for human validation and careful interpretation of AI-generated data. For Policymakers, the study findings inform the design of more effective policies and support programs for entrepreneurs at different areas. For example, policymakers in LI and LM-income countries may need to provide more access to finance, infrastructure and education for entrepreneurs, while policymakers in HI countries may need to foster more market competition and environmental sustainability. Policymakers also need to collaborate with other stakeholders, such as universities, research centers, financial institutions and civil society organizations, to create and nurture entrepreneurial ecosystems that enable the creation and growth of entrepreneurial ventures.

For AI developers and users, the study provides feedback on the capabilities and limitations of current large language. The results call for developers to improve AI’s ability to process qualitative data, identify subtle patterns in human language and generate more realistic and culturally sensitive simulations. AI users can utilize the results to facilitate the selection of the AI chatbot that aligns with their work based on output in the three semantic diagrams (Figures 1–3) as an example.

This study used a bold and new approach that integrates AI-generated textual narratives with human entrepreneur interviews to explore global entrepreneurial motivations, challenges and opportunities across diverse income strata. The research uncovers not only the universal challenges and opportunities entrepreneurs face but also the nuanced, emotional, cultural and other external dimensions that drive entrepreneurial behavior. AI–human hybridization challenges traditional models, offering new pathways for theory development in entrepreneurship and policy formulation. While AI can be a good exploratory tool for identifying broad thematic patterns, it has limitations in capturing the rich, customized complexities of human entrepreneurial experiences.

Based on the learning from our own ongoing investigation, next wave of investigations may greatly benefit from the increasingly dynamic state of the large pre-trained foundation models. Although our investigation made use of an eclectic group of common AI-based chatbots, the new generation of exemplars like GPT-4, Claude or LLaMA-3 promises greater scale, reason and contextual appreciation. These newer models have larger parameter sizes and were trained on still larger and more heterogeneous data sets, perhaps resulting in an enhanced, richer initial thematic analysis. Their greater capacity for handling sophisticated queries, producing more cohesive and pertinent textual output and even exhibiting an embryonic type of “reasoning” may sharpen the AI-based exploratory stage, perhaps locating subtler patterns and interrelations in entrepreneurial aspirations, obstacles and opportunities that may less clearly appear in existing tools. This would further enhance the “AI–human data hybridization” strategy, making the AI-based hypotheses generated all the more accurate, allowing human verification procedurally to reach deeper and faster into them, bringing the power of AI analytic exploration in qualitative social science all the closer to the point where it may be integrated into mainstream social science work and query design workflows.

The study has several limitations that suggest directions for future research. In the first place, the reliance on AI-generated text may result in biases due to the training data sets of the models, which may be disproportionately influenced by Western-centric and English-language perspectives, limiting the applicability of findings in non-Western or low-income contexts. While the inclusion of human interviews mitigated this, the sample size (n = 20) limits the generalizability. Future research should explore larger samples to capture the dynamic nature of entrepreneurship and investigate the impact of specific cultural or institutional factors. Furthermore, using AI methodologies that can incorporate real-time data and contextual awareness would enhance the validity and applicability of AI chatbots in future research.

The author would like to thank the editorial board and the anonymous peer reviewers for their valuable feedback. The author would also like to thank the human entrepreneurs and the AIpreneurs, ChatGPT, Copilot/Bing Chat, Google Gemini/Bard, ChatSonic and Quora Poe, for their participation in the study. Finally, the author extends special thanks to the team that helped conduct the interviews.

In this conversation, I will be asking nearly 12 prompts. You are required to answer, pretending you are an entrepreneur living in 4 different nations*. In the first scenario, you are an entrepreneur called “LI” living in a low-income nation, in the second you are an entrepreneur called “LM” who lives in a l lower middle-income nation, in the third, you are an entrepreneur called “UM”, who lives in an upper middle-income nation and in the fourth you are an entrepreneur called “HI” who lives in a high-income nation. I expect you to answer each question from the four perspectives and to mention your name before each answer, based on the character.

*Note that the World Bank (in its latest report) classifies countries into four income groups based on their GNI per capita in 2024–2025. low income (GNI per capita ≤$1,145, comprising 26 economies), lower-middle income ($1,146–4,515, encompassing 51 economies), upper-middle income ($4,516–$14,005, including 54 economies) and high income (> $14,005, representing 86 economies) (World Bank, 2025). These classifications are used to allocate resources and monitor development progress across the world. Can you repeat for me the four scenarios that you will play? And are you prepared for the discussion?

• Can you please introduce yourself as an entrepreneur from a [name of your nation]?

• What are the main industries or sectors in which entrepreneurs prefer to/operate in [name of your nation]?

• What motivated you to become an entrepreneur in [name of your nation]?

• What are some of the main challenges that you face as an entrepreneur in [name of your nation]?

• How do you cope with or overcome these challenges?

• What are some of the opportunities that you see or pursue as an entrepreneur in [name of your nation]?

• What are some of the strategies or skills that you use or develop to take advantage of these opportunities?

• How do you measure your success as an entrepreneur in [name of your nation]?

• How do you balance your personal and professional life as an entrepreneur in [name of your nation]?

• What are some of the benefits or drawbacks of being an entrepreneur in [name of your nation] compared to other nations?

• What are some of the current or future trends or issues that affect entrepreneurship in [name of your nation]?

• 12 What are some of advice or recommendations that you would give to aspiring or existing entrepreneurs in [name of your nation]?