Technology Empowering Innovation: The Impact of Artificial Intelligence on Global Entrepreneurial Activities

Technology Empowering Innovation: The Impact of Artificial Intelligence on Global Entrepreneurial Activities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Technology Empowering Innovation: The Impact of Artificial Intelligence on Global Entrepreneurial Activities Xiaowen Wang, Yuqi Tian, Nanxu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5648468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The emergence of artificial intelligence technology has led to transformative advancements across various industries, particularly in innovative sectors, creating both new challenges and opportunities. This study systematically examines the impact of artificial intelligence technology on global entrepreneurial activities. The basic theoretical framework is constructed using entrepreneurial factor theory and human capital theory, and a two-way fixed effects model along with a mediation effect model are employed to study the impact of artificial intelligence technology on corporate entrepreneurial activities in 36 countries and regions from 2010 to 2023, spanning a total of 14 years. The results revealed that innovation in artificial intelligence technology significantly promotes entrepreneurial activities, particularly opportunity-driven ventures. Notably, the impact is especially significant for entrepreneurs aged 18–34 and those in the tertiary sector. Additionally, artificial intelligence technology positively influences entrepreneurial activities through two paths: entrepreneurship education and artificial intelligence investment. In terms of research contributions, this study first identified two paths through which artificial intelligence technology influences entrepreneurial activities. It revealed the heterogeneous effects of artificial intelligence technology across different entrepreneurial types, age groups, and industries. The research also made academic contributions to the application of artificial intelligence technology in economics and innovation sectors, providing valuable insights and support for relevant stakeholders. Artificial Intelligence Technology Entrepreneurial activities Entrepreneurship education Investment Figures Figure 1 1. Introduction Artificial intelligence (AI) is driving a new wave of scientific and technological revolution and industrial transformation, emerging as a key element in advancing the 'fourth' industrial revolution (Dwivedi et al., 2019). The 2024 Artificial Intelligence Index Report, released by Stanford University's AI Institute (Stanford HAI), highlights that AI has surpassed human performance on several benchmarks, including image classification, visual reasoning, and English comprehension.[1] Furthermore, robots equipped with AI technology can effectively improve the total factor productivity, reduce output prices, and increase labor productivity (Graetz & Michaels, 2018). AI technology has become a crucial tool for enhancing the competitiveness of enterprises and countries, and promoting the transformation of AI technology into tangible results has become a global consensus (Adigwe et al., 2024). Amid intensified global efforts to harness AI for technological advancement, the U.S. National Science Foundation, alongside federal agencies and other stakeholders, announced a $ 140 million investment to establish seven new National Artificial Intelligence Research Institutes (NSF, 2023). With the continued maturation of AI technology and increasing investments from both government and industry, the global AI industry and the economy are entering a new phase of comprehensive integration, expected to experience rapid growth over the next decade. The rapid development of AI technology is unstoppable and has become key to enhancing corporate production, management practices, and innovation efficiency in the new century (Giorgi et al., 2022; Allioui & Mourdi., 2023; Olutimehin et al., 2024). AI technology not only has the characteristics of general information technology, but also has the synergistic characteristics of synergizing with various economic factors to improve economic efficiency, as well as the creative characteristics of replacing several work of the human brain. Therefore, AI technology brings new techno-economic paradigms. The transformation of techno-economic paradigms are accompanied by a large number of new markets and opportunities, which can stimulate upsurge of innovation and entrepreneurial activities. By the first quarter of 2024, the number of AI enterprises worldwide has reached 30,000[2] . Some start-ups are actively using AI technology to launch their entrepreneurial activities. Such as Stitch Fix's, a start-up to change the way people find clothes they love by combining AI technology with the personal touch of seasoned style experts, has achieved good sales results. Entrepreneurial activities serve as an endogenous driving force of economic growth (Van et al., 2005), playing a crucial role in driving employment (Glaeser et al., 2015) and structural change (Noseleit et al., 2013). Entrepreneurs enhance social wealth by transforming resources and production factors into higher-value forms through recombining these factors (Bai et al., 2020). Successful entrepreneurial activities can improve innovation efficiency (Wong et al., 2005), optimize industrial structures and resource allocation, expand domestic consumer demand, and boost regional entrepreneurial activities. Technological advancements are considered to have a significant impact on the entrepreneurial opportunities and processes (Davidsson et al., 2020). The application of AI technology in entrepreneurial activities will change classic entrepreneurial activities paradigm and prompt entrepreneurial activities to be more intelligent. What’s more, AI technology can assist key personnel in entrepreneurial activities by evaluating business needs and goals, building data infrastructure, and reducing operating costs. AI products and services, with their significant market potential and commercial value, may create a great number of entrepreneurial opportunities. However, the researchers of this study observed that few prior studies have focused on how AI technology affects entrepreneurial activities, or on which types of entrepreneurial activities or groups it has the more significant impact. This study aims to address this research gap by conducting a panel data analysis of 36 countries and regions worldwide. The paper is structured as follows: The second section reviews the existing literature; the third section conducts a theoretical analysis and proposes research hypotheses; the fourth section empirically explores the impact and channels of artificial intelligence technology on entrepreneurial activities; and the fifth section presents this study conclusions and further discussion. 2. Literature Review With the deepening of the integration of AI technology with different industries, the role of AI technology has gradually emerged in a larger scope, such as scientific research, manufacturing industry and urban development. Specifically, AI technology improves data analysis and processing efficiency for scientific research, builds complex models, and performs automated experimental tasks (Xu et al.,2021). Industrial intelligence helps manufacturing industry to improve production efficiency, reduce production costs, improve product quality and promote technological innovation. Intelligent transportation systems and smart city management platforms improve the urban environment and the quality of life of residents, contributing to sustainable urban development (Wey & Huang, 2018). The widespread use of artificial intelligence has fostered sustainable economic development. AI technologies, such as industrial robots, have contributed to inclusive growth (Chen et al., 2022), innovation in low-tech sectors (Liu et al., 2020), green innovation (Zhan et al., 2021), and green development (Chen et al., 2024). The rise in AI patents has stimulated industrial innovation (Liu et al., 2020), improved production efficiency (Parteka et al., 2023), and enhanced urban resilience (Liu et al., 2024). As AI technology continues to diffuse and penetrate various economic activities, its impact and mechanisms on the entrepreneurial field have increasingly attracted researchers' attention and discussion. Existing research on the impact of artificial intelligence on entrepreneurial activities covers both theoretical and empirical aspects. Theoretical research primarily examines AI’s influence on entrepreneurial theory and practice. For instance, Obschonka et al. (2019) analyzed the potential effects of AI technology and big data on entrepreneurial activities from the perspectives of external factors, human factors, and entrepreneurship education. Lévesque et al. (2020) investigated the opportunities and challenges that AI may present in entrepreneurial activities. In terms of entrepreneurial practice, AI technology has facilitated cost reduction (Fossen & Sorgner, 2019), opportunity identification (Brown et al., 2017), and business model reconstruction (Garbuio & Lin, 2019). Liu and Zhang (2024) provided a comprehensive analysis of how large AI models impact the entrepreneurial process and introduced the concept of the "big model entrepreneurial paradigm." Empirical research on AI’s impact on entrepreneurial activities can be categorized into two types. One type uses entrepreneurial activities as a mediating variable to explore AI’s effects on other key variables. For example, Liu et al. (2024) investigated how AI technology influences urban economic resilience through entrepreneurial activities as a mediating variable. The other type treats entrepreneurial activities as the explained variable. For instance, Li et al. (2024) employed the Probit model to examine the impact of AI technology on urban entrepreneurial activities among migrant workers, using national dynamic monitoring survey data from 2013 to 2017. In summary, research on the impact of AI technology on entrepreneurial activities has generated substantial theoretical insights and established a relatively comprehensive research framework. This body of work has not only infused traditional entrepreneurial theory with contemporary value and scientific depth but also provided theoretical support for applying AI technology in entrepreneurial practice. However, current studies are often restricted to specific entrepreneurial groups within particular countries, lacking an international perspective and broader applicability. Additionally, there is a scarcity of literature that empirically examines the mechanisms through which AI technology influences entrepreneurial activities. Moreover, no study has yet conducted an in-depth analysis of how AI technology's impact varies across different entrepreneurial groups and types. The potential marginal contributions of this paper are as follows: First, this study offers empirical evidence for understanding how artificial intelligence technology affects global entrepreneurial activities. Utilizing global entrepreneurial activities data from the Global Entrepreneurial activities Monitor (GEM), which covers 36 countries and regions, this research provides the newest results with a global perspective. Second, this paper enriches the theoretical framework regarding the impact of artificial intelligence on entrepreneurial activities. Based on existing theories, it introduces new insights into the channels and mechanisms through which AI technology influences entrepreneurial activities. Third, this paper refines the research samples by categorizing entrepreneurs into different industries and age groups, further exploring the heterogeneity in AI’s impact on various entrepreneurial groups. This analysis offers a practical foundation for developing more targeted policies to support entrepreneurs. 3. Hypothesis Artificial intelligence enabling entrepreneurial activities refers to the process in which entrepreneurs actively utilize or collaborate with AI technology to jointly exploit entrepreneurial opportunities (Liu & Wang, 2020). In the early stages of AI development, AI technology served as a high-tech production tool for entrepreneurs, with functions limited to solving problems in specific fields. However, as AI technology has advanced and become more widespread, its functions in the entrepreneurial process have diversified, and its applications have broadened. AI technology now not only assists entrepreneurs in addressing technical challenges but also offers valuable insights for decision-making through its vast databases and advanced analytical capabilities (Li et al., 2022). Given the growing versatility and intelligence of AI applications, the mechanism by which AI technology empowers entrepreneurial activities has become increasingly complex. By enhancing critical stages in the entrepreneurial process, AI technology reduces the difficulty of entrepreneurial activities and thereby boosts entrepreneurial activities. This article categorizes the role of AI technology in entrepreneurial activities into direct and indirect effects (see Fig. 1 ). 3.1. The mechanism based on Entrepreneurial Factor Theory Timmons et al. (2014) proposed the classic entrepreneurial factor theory, which posits that the entrepreneurial process comprises entrepreneurial opportunities, entrepreneurial resources, and entrepreneurial teams. The alignment and integration of these three components drive the overall entrepreneurial process. AI technology influences each of these elements—entrepreneurial opportunities, resources, and teams—thereby enhancing entrepreneurial activities. The specific mechanisms through which AI acts are as follows. AI technology empowers entrepreneurial opportunities. Identifying entrepreneurial opportunities is the initial step in entrepreneurial activities. According to Baron (2006), key factors in recognizing opportunities include actively seeking them out, staying alert, and possessing prior knowledge of specific industries or markets. The innovation and advancement of artificial intelligence can generate more opportunities across various industries for entrepreneurs (Davidsson et al., 2023). Compared to human entrepreneurs, AI technology is not only more attuned to potential opportunities but also benefits from extensive prior knowledge through machine learning. This allows AI technology to create new combinations of familiar ideas. By leveraging a priori cognitive frameworks, AI technology can discern connections between seemingly unrelated events or trends, leading to the generation of new ideas and the discovery of novel opportunities. AI technology empowers entrepreneurial resources. Entrepreneurial resources include the technical, talent, capital, and policy conditions necessary to support the entrepreneurial process and are essential for developing and utilizing entrepreneurial opportunities. AI technology provides technical support by enabling entrepreneurs to efficiently collect data and information and by collaborating in commercial activities such as production and marketing (Obschonka et al., 2019). For instance, AI-based personalized recommendation systems can enhance marketing accuracy on e-commerce platforms, while AI-driven data analysis helps companies better understand customer needs, leading to optimized product design and services. As AI technology continues to advance, entrepreneurs' efficiency in collecting information and solving problems has improved, resulting in a higher success rate for entrepreneurial ventures. Furthermore, AI-focused development strategies implemented by various countries offer policy and financial support for related entrepreneurial activities within the industry. AI technology empowers entrepreneurial teams. An entrepreneurial team consists of individuals and groups involved in entrepreneurial activities. Given that AI possesses cognitive functions similar to those of humans, it has a significant impact on entrepreneurial decision-making and team collaboration. Townsend and Hunt (2019) argue that entrepreneurial actions, judgments, and decisions in uncertain environments are central to entrepreneurial activities, and AI technology can enhance these processes by reducing uncertainty. Liebregts et al. (2019) highlight that verbal and non-verbal behavioral cues in social interactions significantly affect personal decision-making in an entrepreneurial context. AI technology can assist entrepreneurs in effectively identifying these social signals and influencing corporate decisions. Additionally, AI technology can improve collaboration and communication within entrepreneurial teams. AI systems can analyze team members' work habits and efficiency, provide optimization suggestions, and help team members better understand and share information, thereby enhancing overall team effectiveness. Therefore, this paper proposes the following research hypotheses: H1 AI technology has a positive effect on entrepreneurial activities. 3.2 The mechanism of improving entrepreneurial ability through entrepreneurship education Human capital theory posits that educational investment is a primary means of developing human capital, and an individual's knowledge, skills, and abilities can significantly impact outcomes (Martin et al., 2012; Ployhart et al., 2011). Consequently, entrepreneurship education can assist entrepreneurs in accumulating capital related to entrepreneurial activities (He et al., 2023). AI technology has lowered the educational barriers to entrepreneurial activities. The integration of AI into the field of education has transformed the creation and application of knowledge (Gu & Li, 2022), offering learners diverse methods and formats for knowledge presentation. Intelligent learning scenarios and more precise knowledge analysis enhance learners' understanding and improve learning efficiency (Du & Gu, 2022). AI-driven human-computer interactions enable more efficient knowledge retrieval and data processing (Christian et al., 2019), increasing the accuracy and reliability of knowledge sources. Specifically, deep learning and computer vision technologies analyze inherent patterns and representations within large datasets, generate new knowledge and computational solutions, and expedite the process of knowledge reorganization (Agrawal et al., 2018). Moreover, AI technology has also assisted commercial companies in enhancing their data collection capabilities, accelerating the development of tools for processing large volumes of data, and improving the efficiency of data collection. This, in turn, facilitates entrepreneurs in acquiring new knowledge. The process of gathering, processing, and integrating massive amounts of data is also a process of knowledge re-creation. Entrepreneurs benefit from this integration by absorbing new knowledge, generating new ideas, and acquiring new skills, which in turn enhances their entrepreneurial capabilities. AI technology has provided convenient channels for improving entrepreneurial ability. The continuous diffusion and widespread adoption of AI technology across various economic sectors have made it easier for entrepreneurs to access relevant innovation resources. This development has fostered the generation and application of innovative thinking, expanded the business scope for entrepreneurs, and unlocked the innovation spillover benefits of AI technology. AI’s ability to transfer knowledge and skills, learn rapidly, and apply mastered skills to address new challenges has proven invaluable. With capabilities in reasoning, learning, associating, and problem-solving based on existing knowledge, AI-powered tools have become highly reliable and effective for analytical purposes, enhancing learning and decision-making abilities (Vecchiarini et al., 2023). AI technology is continually optimizing computer programs to simulate human learning behavior and acquire new knowledge and skills. The increasing availability and open-source nature of AI technology have made many AI tools and platforms more accessible. This accessibility allows entrepreneurs to leverage AI tools for acquiring professional knowledge and market information, even without an extensive AI background. Consequently, AI technology has lowered the technical barriers to entrepreneurial activities, providing more individuals with the opportunity to become entrepreneurs. Based on the above, the following research hypotheses are proposed: H2: AI technology has a positive effect on entrepreneurial activities through entrepreneurship education. 3. 3 The mechanism of enhancing entrepreneurial motivation through investment Venture capital, as an effective method of equity financing, focuses on long-term investment in start-ups and can address the issue of insufficient funding for enterprises (Metrick, 2006). The start-ups generally characterized by smallness and newness. Financing constraints are one of the important reasons for the difficulty in survival and short life span of start-ups. Start-ups have the congenital disadvantage of low anti-risk ability due to their small business scale and small market share. When faced with external negative shocks such as economic downturn, rising operating costs, and supply chain disruptions, start-ups may be forced to abandon business projects, miss market opportunities, or even go bankrupt due to financing constraints (Van Praag et al., 2005; Nicolas, 2021). Therefore, adequate venture capital can mitigate the risk of disruptions in the capital chain of entrepreneurial activities and increase the likelihood of entrepreneurial success. The deep integration of AI with the economy and society has led to more systematic, complex, and specialized AI technologies. In sectors with high AI technology density, industrial innovation clusters have begun to emerge. The innovative use of AI technology in fields such as medicine, finance, education, and transportation offers entrepreneurs more opportunities to develop new products and services. The promising development prospects and substantial financial support have encouraged more entrepreneurs to establish new ventures and capitalize on market opportunities. The high added value and profit margins of artificial intelligence products have attracted numerous investors. According to Stanford's “Artificial Intelligence Index Report 2024,” funding for generative AI surged, nearly octupling from 2022 to reach $ 25.2 billion. Factors such as technological advancements, growing market demand, and government policy support have bolstered investor confidence in AI's development prospects, leading to increased financial backing for AI-related entrepreneurial activities and a rise in entrepreneurial activities. Therefore, this paper proposes the following research hypothesis: H3 AI technology positively affects entrepreneurial activities through investment. 4. Study Design 4.1. The model To assess the impact of artificial intelligence on entrepreneurial activities across different countries, this paper constructs a two-way fixed effects model as follows: $$\:{Entr}_{it}=\alpha\:+{\beta\:}_{1}{Patent}_{it}+{\beta\:}_{2}{X}_{it}+{\delta\:}_{i}+{\epsilon\:}_{t}+{\mu\:}_{it}$$ where the subscripts i and t respectively represent a particular nation and year, 𝜶 is a constant term, βis a coefficient of each variable, \(\:{Entr}_{it}\) is the mediating variable, \(\:{Patent}_{it}\) is the explanatory variable, \(\:{X}_{it}\) is the control variable, \(\:{\delta\:}_{i}\) and \(\:{\epsilon\:}_{t}\) represents the country and year fixed effects, and \(\:{\mu\:}_{it}\) is the random interference term. 4.2. Data and variables This study focuses on countries and regions, using artificial intelligence patent data from the Center for Security and Emerging Technologies (CSET) at Georgetown University and entrepreneurial data from the Global Entrepreneurial activities Monitor (GEM) for the years 2010 to 2023. Control variables, including GDP per capita and total population, are sourced from the World Development Indicators (WDI) database. Due to some samples having missing years, the data is matched across the three databases by country, region, and year. Samples with insufficient data are excluded, resulting in a final dataset comprising 36 countries and regions. This sample includes major global economies as well as countries and regions at various stages. Explanatory Variables: The number of AI patents refers to the count of AI-related patents granted by the National Patent Office. The number of AI patents represents the progress of AI technology, which is a typical indicator to measure the development of AI technology. The data is sourced from the Center for Security and Emerging Technologies (CSET) at Georgetown University. CSET's primary research areas include AI-related talent, computing power, and the application of AI in cybersecurity and national security contexts. Currently, more than 40 publicly available survey reports utilize CSET data. Explained Variable: Entrepreneurial activities. This paper adopts the approach of Audretsch (2015) and uses Total Early-Stage Entrepreneurial activities (TEA) as an indicator to measure entrepreneurial activities. TEA represents the percentage of adults aged 18–64 who are either owners or managers of new businesses or startups (established within the last 42 months). The data on entrepreneurial activities is sourced from the Global Entrepreneurial activities Monitor (GEM) database. Established in 1999, GEM has gathered data from a total of 115 economies over various years, collecting over 200,000 adult entrepreneurial activities survey samples annually. GEM’s research data has become a crucial resource for major international organizations, including the World Bank, OECD, and the World Economic Forum. According to official statistics, nearly 1,000 papers have cited the GEM database as of 2023. Control Variables: To account for other regional characteristics that may influence entrepreneurial activities, this paper controls the following variables based on the approaches of Wang et al. (2024) and He et al. (2024) GDP per Capita (ln GDP): Higher levels of economic development generally correspond to greater economic capacity and higher returns on entrepreneurial activities, impacting entrepreneurial activities. Total Population (ln People): The total population reflects market size. Regions with larger populations tend to have more market demand and business opportunities, attracting more entrepreneurs. Entrepreneurial Environment: This includes factors such as the entrepreneurial financing environment, government project support, commercial and professional infrastructure, internal market openness, physical and services infrastructure and the entrepreneurial culture. These external environmental factors are measured using the mean scores from entrepreneur questionnaires compiled by GEM. Table 1 Descriptive statistics of all variables Variable Sample Size Mean Standard Deviation Minimum Maximum TEA 259 9.217 4.446 1.924 25.136 lnpatent 259 3.391 2.213 0.693 11.370 lnGDP 259 9.905 1.037 7.121 11.597 lnpeople 259 17.569 1.376 15.332 21.068 Finance 259 4.395 0.788 2.100 7.130 Government 259 4.648 0.876 2.230 6.600 Commercial 259 5.088 0.701 2.100 6.940 Openness 259 4.391 0.724 2.150 6.930 Infrastructure 259 6.454 0.826 3.500 8.590 Culture 259 4.850 0.916 2.490 7.170 5. Empirical Analysis 5.1 Basic results The regression results presented in Table 2 indicate that the number of artificial intelligence patents has a significant positive effect on overall entrepreneurial activities at the 5% level, supporting hypothesis 1. Additionally, separate regressions were conducted to examine the impact of artificial intelligence on different types of entrepreneurial motivation. Due to missing data for opportunity entrepreneurial activities and survival entrepreneurial activities in 2019 and 2023, only 191 eligible regression samples were available. The regression results show that for every unit increase in the number of artificial intelligence patents. This finding supports the theoretical analysis that the development of AI technology enhances entrepreneurial opportunities. Table 2 Fixed effects estimations. TEA Entrepreneurial motivation Opportunity Necessity lnpatent 0.389 ** 0.471 ** 0.0303 (0.141) (0.163) (0.0641) lnGDP 0.353 1.816 -1.293 * (0.537) (1.380) (0.543) lnpeople 0.437 18.71 ** 0.848 (1.050) (5.926) (2.332) Finance 0.323 -0.251 0.0526 (0.279) (0.311) (0.122) Goverment 1.795 *** 2.162 0.271 (0.500) (0.630) (0.248) Commercial 0.199 -0.423 0.298 (0.562) (0.553) (0.218) Openness 1.516 ** 0.703 -0.000220 (0.531) (0.589) (0.232) Infrastructure -0.101 0.323 -0.373 * (0.386) (0.373) (0.147) Culture 0.000801 0.467 0.0528 (0.393) (0.486) (0.191) Time fixed effects yes yes yes Region fixed effects yes yes yes _cons -3.363 -335.1 ** 1.407 (20.95) (102.9) (40.50) N 259 191 191 R 2 0.099 0.214 0.109 Note: ***, **, * represent respectively 1%, 5%, 10% significance levels. 5.2 Robustness test This paper conducts a robustness test by eliminating certain samples. Given that the sample includes several major global economies, the regression results may be skewed and may not fully reflect the impact of artificial intelligence on entrepreneurial activities in countries with smaller economies. To address this, we used the GDP rankings of countries from 2010 to 2023 to match the top ten global economies with the sample countries. We then excluded overlapping countries (the United States, China, Japan, Russia, Germany, France, and Italy) and performed the regression analysis on the remaining sample observations. The results in Table 3 indicate that the number of AI patents has a significant positive effect on overall entrepreneurial activities at the 1% level, and a significant positive effect on opportunistic entrepreneurial activities at the 10% level. This demonstrates that, even after controlling for sample bias, the positive impact of AI technology on entrepreneurial activities remains robust. Table 3 Robustness test Variable TEA Entrepreneurial motivation Opportunity Necessity lnpatent 0.821 *** 0.567 * 0.008 (0.200) (0.261) (0.102) lnGDP 0.712 2.358 -1.041 (0.563) (2.085) (0.815) lnpeople 0.659 17.56 * 2.124 (1.094) (7.122) (2.785) Finance 0.155 -0.401 0.0953 (0.304) (0.394) (0.154) Goverment -1.537* -2.318 -0.191 (0.614) (0.792) (0.310) Commercial 0.0244 -0.501 0.315 (0.638) (0.673) (0.263) Openness 1.256* 0.715 0.111 (0.600) (0.771) (0.301) Infrastructure -0.603 0.340 -0.437 * (0.417) (0.464) (0.181) Culture 0.253 0.670 -0.112 (0.427) (0.642) (0.251) Time fixed effects yes yes yes Region fixed effects yes yes yes _cons -7.429 -311.2 * -22.38 (21.26) (122.8) (48.03) N 201 144 144 R 2 0.157 0.212 0.101 Note: ***, **, * represent respectively 1%, 5%, 10% significance levels. 5.3 Mechanism test In the theoretical analysis, this paper suggests that artificial intelligence can enhance entrepreneurial activities through two main mechanisms: the improvement of entrepreneurial ability based on entrepreneurship education and the reinforcement of entrepreneurial motivation driven by investment. To test these mechanisms, this paper employs a mediation effect model to examine the channels through which AI technology influences entrepreneurial activities. The mediation effect model involves proposing one or more mediating variables M. The causal relationships between these variables and the outcome variable Y are theoretically intuitive and logically consistent in terms of time and space, which negates the need for formal causal inference methods to explore the relationship between the mediating variable and the dependent variable (Jiang, 2022). Given that existing literature supports the roles of entrepreneurship education and investment-driven factors in promoting entrepreneurial activities, this paper will focus on testing how artificial intelligence technology impacts these two areas. The specific settings for the analysis are as follows: $$\:{M}_{it}=\lambda\:+{\beta\:}_{3}{Patent}_{it}+{\beta\:}_{4}{X}_{it}+{\delta\:}_{i}+{\epsilon\:}_{t}+{\mu\:}_{it}$$ Where the subscripts i and t respectively represent a particular nation and year, 𝜆 is a constant term, β is a coefficient of each variable, \(\:{M}_{it}\) is the mediating variable, \(\:{Patent}_{it}\) is the explanatory variable, \(\:{X}_{it}\) is the control variable, \(\:{\delta\:}_{i}\) and \(\:{\epsilon\:}_{t}\) represents the country and year fixed effects, and \(\:{\mu\:}_{it}\) is the random interference term. In examining the channels through entrepreneurship education, this paper uses "school entrepreneurship education" and "adult entrepreneurship education" from the GEM as mediating variables. AI-enhanced entrepreneurial activities education offers entrepreneurs more diverse methods and forms of knowledge presentation, enhances their understanding of relevant entrepreneurial knowledge, improves learning efficiency, and thereby boosts entrepreneurial abilities and activities. The results of the mechanism test in Table 4 indicate that AI technology significantly promotes the development of adult entrepreneurial activities education, leading to an increase in entrepreneurial activities. Thus, hypothesis 2 is verified. In testing the investment-driven channels, this paper uses statistics on private market investment in AI across various countries, compiled by the Center for Security and Emerging Technology at Georgetown University, excluding investments in listed companies. This data reflects the amount of investment in non-listed companies, including start-ups, in the field of AI. The results of the mechanism test in Table 4 show that an increase in AI technology patents has led to a rise in the total amount of private investment in AI. This influx of investment has attracted more entrepreneurs and provided financial support for AI-related entrepreneurial activities, ultimately increasing overall entrepreneurial activities in the region. Thus, hypothesis 3 is verified. Table 4 Mechanism test Variable Entrepreneurship education Investment School Adult AI investment lnpatent 0.00165 0.0540** 0.328** (0.0199) (0.0205) (0.125) Control variables yes yes yes Time fixed effects yes yes yes Region fixed effects yes yes yes N 259 259 259 R 2 0.591 0.344 0.155 Note: ***, **, * represent respectively 1%, 5%, 10% significance levels. 5.4 Heterogeneity analysis The rapid development of AI technology may have varying impacts on entrepreneurial groups of different ages, education levels, and industries. Therefore, this paper will explore the individual heterogeneity of AI technology's effects on entrepreneurial activities. Given that the database provides comprehensive entrepreneur characteristic data, this study will use ungrouped heterogeneity analysis to examine the relationship between the overall characteristics of the sample and the variables. The results are presented below. Table 5 examines the differences in the impact of AI technology on entrepreneurial groups of different ages. The explained variable is the percentage of entrepreneurs in each age group within each country, with the country as the unit of analysis. The sum of the proportions of entrepreneurs across all age groups is 100%. The regression results show that AI technology has a significant positive effect at the 1% level for the entrepreneurial group aged 18–24, at the 5% level for the entrepreneurial group aged 25–34, and is positively significant for the entrepreneurial group aged 35–64. The effect is not significant for the oldest age group. Therefore, AI technology exhibits notable intergenerational heterogeneous effects on global entrepreneurial groups. This may be because younger people have stronger social adaptability, better learning abilities, and more flexible thinking, making them more receptive to new technologies. As a result, they are more likely to respond quickly to opportunities in the booming field of AI technology and exhibit a higher willingness to start a business. Table 5 Heterogeneity test for distinguishing age Variable 18–24 25–34 35–44 45–54 55–64 lnpatent 1.268 *** 0.970 * 0.228 -0.754 0.183 (0.324) (0.396) (0.396) (0.331) (0.270) Control variables yes yes yes yes yes Time fixed effects yes yes yes yes yes Region fixed effects yes yes yes yes yes N 220 220 220 220 220 R 2 0.102 0.469 0.215 0.167 0.162 Note: ***, **, * represent respectively 1%, 5%, 10% significance levels. Table 6 examines the differences in the impact of AI technology on entrepreneurial groups across various industries. The explained variable is the percentage of entrepreneurs in each country within different industries. According to the GEM, the business service industry includes sectors such as finance, insurance, real estate, etc., while the customer service industry encompasses retail, automotive, health education, social services, and entertainment. The regression results in Table 6 show that AI technology has a significant positive effect on both the business service industry and the customer service industry at the 10% level, while its impact on the primary and secondary industries is not significant. This indicates that artificial intelligence has industry-specific effects on the global entrepreneurial community. This may be because AI applications in the business service and customer service industries offer higher commercial value and broader market prospects, thereby attracting more entrepreneurs to these sectors. Table 6 Heterogeneity test for distinguishing industry Variable extractive sector transformative sector business services consumer services lnpatent 0.304 -0.420 1.239 * 0.856 * (0.201) (0.508) (0.613) (0.405) Control variables yes yes yes yes Time fixed effects yes yes yes yes region fixed effects yes yes yes yes N 220 220 259 220 R 2 0.129 0.192 0.178 0.109 Note: ***, **, * represent respectively 1%, 5%, 10% significance levels. Table 7 examines the differences in the impact of AI technology on entrepreneurial groups with varying educational levels. Given that AI is a high-tech industry with significant technical complexity, entrepreneurs with higher education levels may find it easier to engage with and invest in AI-related ventures. Thus, this study uses the percentage of educational attainment among entrepreneurial groups in each country as the explained variable for regression analysis. The regression results in Table 7 show that the impact of AI technology on entrepreneurial groups with different educational levels is not significant. Therefore, AI technology does not exhibit a heterogeneous impact on entrepreneurial groups based on educational attainment. Table 7 Heterogeneity test for distinguishing education Variable secondary experience secondary degree post-secondary degree graduate experience lnpatent -0.407 -1.536 0.359 0.170 (1.433) (1.003) (0.467) (0.281) Control variables yes yes yes yes Time fixed effects yes yes yes yes region fixed effects yes yes yes yes N 220 220 220 220 R 2 0.194 0.124 0.106 0.065 Note: ***, **, * represent respectively 1%, 5%, 10% significance levels. 6. Conclusion and policy implication AI technology introduces new techno-economic paradigms, with its impact on entrepreneurial activities being one of the most important aspects that cannot be overlooked. AI technology has surpassed the simple tool attributes of previous digital technologies and is now acting as a collaborator and entrepreneur in starting business ventures. AI technology brings specialized expertise, cutting-edge technologies, and a depth of experience that can significantly optimize entrepreneurial decisions (Olutimehin, D. O. etal., 2024). As organizations navigate the complexities of integrating AI technology into their business models, collaboration and partnerships emerge as key strategies for success (Reim etal., 2020). The application of AI technology i n entrepreneurial activities has changed classic entrepreneurial activities paradigm and prompt entrepreneurial activities to be more intelligent. However, despite its significant impact, few studies have specifically explored how AI technology affects entrepreneurial activities, or which types of entrepreneurial activities and groups it will have the more profound impact on. In addressing this research gap, the present study systematically investigates the impact of AI technology on global entrepreneurial activities through empirical analysis. The research adopts the entrepreneurial factor theory as its foundational theoretical framework and utilizes both a two-way fixed effects model and a mediation effect model for its analysis. It examines the influence of AI technology on entrepreneurial activities across enterprises in 36 countries and regions over a 14-year period, from 2010 to 2023. The research results provide practical evidence supporting the theoretical achievements of AI technology in the field of entrepreneurial activities. Additionally, they expand the channels and mechanisms through which AI technology influences entrepreneurial activities and enrich the existing theoretical framework. The study further divides entrepreneurial groups and reveals the heterogeneity in the impact of AI technology based on the characteristics of the entrepreneurs themselves. The findings are as follows: ( 1 ) AI technology significantly promotes entrepreneurial activities. ( 2 ) From the perspective of influence channels, AI technology enhances entrepreneurs’ learning abilities and professional skills through entrepreneurship education, thereby increasing entrepreneurial activities. Additionally, AI technology supports entrepreneurial activities by attracting related investments. ( 3 ) Regarding entrepreneurial types, age groups, and industry divisions, AI technology has a more pronounced impact on opportunistic entrepreneurial activities, entrepreneurial groups aged 18–34, and the tertiary industry. According to the above conclusions, we offer the following policy implications: First, accelerate the construction of an entrepreneurship education system that teaches artificial intelligence skills. While AI technology provides crucial support for entrepreneurial activities, it also poses challenges for entrepreneurs. The era of intelligent technology has raised the bar for the basic skills required of entrepreneurs. The government can establish AI courses to help entrepreneurs master fundamental knowledge. For older entrepreneurial groups, tailored education courses should be developed to improve their acceptance of AI technology. Additionally, special courses with industry-specific characteristics can be created to meet the needs of entrepreneurs across different sectors. Second, increase support for artificial intelligence-related entrepreneurial projects. The government can establish a special fund for AI-related entrepreneurial activities and attract capital from local departments, financial institutions, and investment firms to create AI venture investment sub-funds in the form of equity or debt. Government-led entrepreneurial funds can promote capital inflow and the success of entrepreneurial projects while encouraging more researchers and entrepreneurs to explore AI technology applications in various fields, thereby facilitating its widespread adoption. Additionally, these funds should clarify the focus of AI venture capital investments, guiding capital toward high-growth potential projects within the AI entrepreneurial sector. Last but not least, improve legislative oversight of artificial intelligence and establish standardization policies for data use. AI models often involve large amounts of personal data and sensitive information when acquiring online information, leading to potential legal issues such as data infringement, patent leakage, and trade secret violations. To ensure data security and privacy protection for entrepreneurs, the government should enhance the applicability of regulatory penalties from a legislative perspective. It can regulate the data usage behaviors of entrepreneurs and entrepreneurial organizations while clarifying principles for the use and sharing of sensitive data. 7. Limitations and future research This study has some limitations that future research may address. Firstly, due to limitations in available data, the sample of countries or regions selected for this study includes some of the world's economies but does not cover all countries and regions. As a result, the conclusions may have regional limitations. Additionally, missing years in the sample countries and regions led to the exclusion of some samples, which may affect the sample size. Secondly, the most recent sample selection is from 2023, which introduces a potential time limitation. Generative AI technologies, such as Chat GPT, were released and widely adopted at the end of 2022. Consequently, this study cannot use adequate empirical results to assess the impact of the latest AI technologies on entrepreneurial activities. The future research may continue to discuss how generative AI technologies affect different types of entrepreneurial activities, which will enrich existed theoretical framework. If the relevant data is available, we can obtain empirical results and explore more intriguing facts. Declarations Competing Interests The authors declare that they have no conflict of interest. Data availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Ethical approval Ethical approval was not required as the study did not involve collecting data from human participants. Informed consent This article does not contain any studies with human participants performed by any of the authors. Acknowledgments The Key Research and Development Plan of Gansu Province Science and Technology Plan2020 (20YF3GAO11) and study on the construction of Hexi Corridor Economic belt (2022ZD009). Author Contributions All authors contribute to the study conception and design. Funding acquisition and supervision were performed by Wang Xiaowen. Original draft and data curation were performed by Tian Yuqi. Methodology was performed by Chen Nanxu. All authors read and approved the final manuscript. References Adigwe, C. S., Olaniyi, O. O., Olabanji, S. O., Okunleye, O. J., Mayeke, N. R., & Ajayi, S. A. (2024). Forecasting the Future: The Interplay of Artificial Intelligence, Innovation, and Competitiveness and its Effect on the Global Economy. Asian Journal of Economics Business and Accounting , 24(4), 126–146. https://doi.org/10.9734/ajeba/2024/v24i41269 Agrawal A., Gans J. S., and Goldfarb A. (2017). What to expect from Artificial Intelligence. MIT Sloan Management Review 58 (3):23-26. http://mitsmr.com/2jZdf1Y Allioui, H., & Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses. International Journal of Computer Engineering and Data Science (IJCEDS) , 3(2), 1-12. Retrieved from https://ijceds.com/ijceds/article/view/59 Bai J.H., Zhang Y.X., & Bian Y.C. （2022）. Do innovation-driven policies enhance urban entrepreneurial activity? Empirical evidence from the national innovative city pilot policy. China Industrial Economy , 06:61-78. doi:10.19581/j.cnki.ciejournal.2022.06.016. Baron, R. A. (2006). Opportunity Recognition as Pattern Recognition: How entrepreneurs “Connect the dots” to identify new Business opportunities. Academy of Management Perspectives, 20(1), 104–119. https://doi.org/10.5465/amp.2006.19873412 Brown, T. E. (2017). Sensor-based entrepreneurial activities: A framework for developing new products and services. Business Horizons, 60(6), 819–830. https://doi.org/10.1016/j.bushor.2017.07.008 Chen D., & Qin Z.Y. （2022）. Artificial Intelligence and inclusive growth: Evidence from global industrial robot use. Economic Research, 57 (4): 85-102. http://hfgga60aabc7d15084b00sk9uwqq0xxxwb6059.fhaz.libproxy.ruc.edu.cn/kcms2/article/abstract?v=uagkXMi-j-VEX_x_Kyat5XCu7JFOpv9m3RVNiDPble2x2IU5UZnbcUc8qLyl2AzNpHf2HnaJZV6Ka1RxPuUWvwRaEjtvWzusDSx-wbh8dCnR_lJPjLe1J8D4ssGRWDe1EvBNG9jfTIscDc55XvEFYCETtbrsELVJBtjfm3xLQH2zUFill7QUtFRF4Fp6, 1yDn8mi7wGNO5xM=anduniplatform=NZKPTandlanguage=CHS Chen, M., Wang, S., & Wang, X. (2024). How Does Artificial Intelligence Impact Green Development? Evidence from China. Sustainability , 16(3), 1260. https://doi.org/10.3390/su16031260 Christian, V., Constantinescu, C., and Popescu, D. (2019). Application potentials of Artficial Intelligence for the design of innovation processes. Procedia CIRP 84:810-813. https://doi.org/10.1016/B978-0-444-88864-8.50014-5. Davidsson, P., Recker, J., & Von Briel, F. (2018). External Enablement of New Venture Creation: a framework. Academy of Management Perspectives , 34(3), 311–332. https://doi.org/10.5465/amp.2017.0163 Davidsson, P., & Sufyan, M. (2023). What does AI think of AI as an external enabler (EE) of entrepreneurial activities? An assessment through and of the EE framework. Journal of Business Venturing Insights, 20, e00413. https://doi.org/10.1016/j.jbvi.2023.e00413 Du H., & Gu X.Q. （2022）. Artificial Intelligence promotes knowledge understanding: An empirical study aiming at concept transformation. Journal of East China Normal University, 40 (09):67-77. doi:10.16382/j.cnki.1000-5560.2022.09.007. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management , 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Fossen, F. M., & Sorgner, A. (2019). Digitalization of work and entry into entrepreneurial activities. Journal of Business Research , 125, 548–563. https://doi.org/10.1016/j.jbusres.2019.09.019 Garbuio, M., & Lin, N. (2018). Artificial intelligence as a growth engine for health care startups: Emerging business models. California Management Review , 61(2), 59–83. https://doi.org/10.1177/0008125618811931 Giorgi, G., Ariza-Montes, A., Mucci, N., & Leal-Rodríguez, A. L. (2022). The dark side and the light side of Technology-Related stress and stress related to workplace innovations: from artificial intelligence to business transformations. International Journal of Environmental Research and Public Health , 19(3), 1248. https://doi.org/10.3390/ijerph19031248 Glaeser, E. L., Kerr, S. P., & Kerr, W. R. (2014). Entrepreneurial activities and Urban Growth: An Empirical Assessment with Historical Mines. The Review of Economics and Statistics, 97(2), 498–520. https://doi.org/10.1162/rest_a_00456 Graetz, G., & Michaels, G. (2018). Robots at work. The Review of Economics and Statistics, 100(5), 753–768. https://doi.org/10.1162/rest_a_00754 Gu X.Q., & Li S.J. （2022）.Artificial Intelligence promotes the development of future education: Essential connotations and expected directions. Journal of East China Normal University 40 (09):1-9. doi:10.16382/j.cnki.1000-5560.2022.09.001. He, L., Zheng, L. J., Sharma, P., & Leung, T. (2023). Entrepreneurship education and established business activities: An international perspective. The International Journal of Management Education , 22(1), 100922. https://doi.org/10.1016/j.ijme.2023.100922 Jiang T. （2022）. Mediating Effect and Moderating Effect in Empirical Research on Causal Inference. China Industrial Economy (05):100-120. doi:10.19581/j.cnki.ciejournal.2022.05.005. Lévesque, M., Obschonka, M., & Nambisan, S. (2020). Pursuing impactful entrepreneurial activities research using artificial intelligence. Entrepreneurial activities Theory and Practice , 46(4), 803–832. https://doi.org/10.1177/1042258720927369 Li L.B., & Liu Z.Y. （2024）. Research on the impact of industrial intelligence on urban entrepreneurial activities of migrant workers. Modern Economic Discussion 06:1-15. doi:10.13891/j.cnki.mer.2024.06.011. Li, X., Zhang, X., Liu, Y., Mi, Y., & Chen, Y. (2022). The impact of artificial intelligence on users’ entrepreneurial activities. Systems Research and Behavioral Science , 39(3), 597–608. https://doi.org/10.1002/sres.2854 Liebregts, W., Darnihamedani, P., Postma, E., & Atzmueller, M. (2019). The promise of social signal processing for research on decision-making in entrepreneurial contexts. Small Business Economics, 55(3), 589–605. https://doi.org/10.1007/s11187-019-00205-1 Liu, J., Chang, H., Forrest, J. Y., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of china’s manufacturing sectors. Technological Forecasting and Social Change, 158, 120142. https://doi.org/10.1016/j.techfore.2020.120142 Liu J.Q., Xue F.， & Ru S.F. （2024）. Research on the impact of Artificial Intelligence technology on urban economic resilience. Soft Science 2:1-12. http://hfgga60aabc7d15084b00hk9uwqq0xxxwb6059.fhaz.libproxy.ruc.edu.cn/kcms/detail/51 .1268.G3.20231214.1612.010.html. Liu Z.Y., & Zhang Y.Q. （2024）. Entrepreneurial activities with the Large Language Models: Paradigmatic evolution and theoretical construction, Journal of Renmin University of China 38 (03):87-99. http://hfgga60aabc7d15084b00sk9uwqq0xxxwb6059.fhaz.libproxy.ruc.edu.cn/kcms2/article/abstract?v=uagkXMi-j-U-Hycqh6BhK7W2aoJiD5d03uFvrrDCsJtdRdX1Oi04YYkaGHVq4vyhqxB23FKfpQbUnX0x7r6E6R5DgmvBBI3TuCGm KP0WhS1KTI_WVtAmB11OceuKOTNj9o9RCREMLfTEpwy8P_wsjvQVqV2xSUAbfua7_LAjrKJgpr _r1rCkoLuzAv9rENpuV- -3dQLpQUM=anduniplatform=NZKPTandlanguage=CHS Martin, B. C., McNally, J. J., & Kay, M. J. (2012). Examining the formation of human capital in entrepreneurial activities: A meta-analysis of entrepreneurship education outcomes. Journal of Business Venturing , 28(2), 211–224. https://doi.org/10.1016/j.jbusvent.2012.03.002 Metrick, A. (2006). Venture capital and the finance of innovation. http://depot.som.yale.edu/icf/papers/fileuploads/2689/original/2011_ICF_WPS_The_Best_Venture_Capitalists_-_Metrick.pdf National Science Foundation. (2023, June 5). NSF announces 7 new National Artificial Intelligence Research Institutes. NSF - National Science Foundation. https://new.nsf.gov/news/nsf-announces-7-new-national-artificial Nicolas, T. (2021). Short-term financial constraints and SMEs’ investment decision: evidence from the working capital channel. Small Business Economics , 58 (4), 1885–1914. https://doi.org/10.1007/s11187-021-00488-3 Noseleit, F. (2012). Entrepreneurial activities, structural change, and economic growth. Journal of Evolutionary Economics , 23(4), 735–766. https://doi.org/10.1007/s00191-012-0291-3 Obschonka, M., & Audretsch, D. B. (2019). Artificial intelligence and big data in entrepreneurial activities: a new era has begun. Small Business Economics , 55(3), 529–539. https://doi.org/10.1007/s11187-019-00202-4 Olutimehin, N. D. O., Ofodile, N. O. C., Ejibe, N. I., Odunaiya, N. O. G., & Soyombo, N. O. T. (2024). IMPLEMENTING AI IN BUSINESS MODELS: STRATEGIES FOR EFFICIENCY AND INNOVATION. International Journal of Management & Entrepreneurial activities Research , 6(3), 863–877. https://doi.org/10.51594/ijmer.v6i3.940 Parteka, A., & Kordalska, A. (2023). Artificial intelligence and productivity: global evidence from AI patent and bibliometric data. Technovation , 125, 102764. https://doi.org/10.1016/j.technovation.2023.102764 Ployhart, R. E., & Moliterno, T. P. (2011). Emergence of the Human Capital Resource: a multilevel model. Academy of Management Review , 36(1), 127–150. https://doi.org/10.5465/amr.2009.0318 Reim, W., Åström, J., & Eriksson, O. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business model innovation. AI, 1(2), 180–191. https://doi.org/10.3390/ai1020011 Timmons, J.A., Spinelli, S., & Adams, R. J. (2014). New venture creation entrepreneurial activities for the 21st century. https://ci.nii.ac.jp/ncid/BA91287795 Townsend, D. M., & Hunt, R. A. (2019). Entrepreneurial action, creativity, & judgment in the age of artificial intelligence. Journal of Business Venturing Insights , 11, e00126. https://doi.org/10.1016/j.jbvi.2019.e00126 Van Praag, M., De Wit, G., & Bosma, N. (2005). Initial capital constraints hinder entrepreneurial venture performance. The Journal of Private Equity , 9 (1), 36–44. https://doi.org/10.3905/jpe.2005.605369 Van Stel, A., Carree, M., & Thurik, R. (2005). The effect of entrepreneurial activity on national economic growth. Small Business Economics , 24(3), 311–321. https://doi.org/10.1007/s11187-005-1996-6 Vecchiarini, M., & Somià, T. (2023). Redefining entrepreneurial activities education in the age of artificial intelligence: An explorative analysis. The International Journal of Management Education , 21(3), 100879. https://doi.org/10.1016/j.ijme.2023.100879 Wey, W., & Huang, J. (2018). Urban sustainable transportation planning strategies for livable City’s quality of life. Habitat International , 82, 9–27. https://doi.org/10.1016/j.habitatint.2018.10.002 Wong, P. K., Ho, Y. P., & Autio, E. (2005). Entrepreneurial activities, Innovation and Economic Growth: Evidence from GEM data. Small Business Economics , 24(3), 335–350. https://doi.org/10.1007/s11187-005-2000-1 Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation , 2(4). https://doi.org/10.1016/j.xinn.2021.100179 Zhan, H., Hou, M., & Tan, F. (2022). Influence of intelligentization on enterprise green innovation: Evidence from listed companies of new energy industry in China. Resource Science , 44(5), 984–993. https://doi.org/10.18402/resci.2022.05.09 Zhi yang, L., & Zemin, W. (2020). AI Enabling Entrepreneurial activities: Comparison of theoretical frameworks. Foreign Economies Management , 42(12), 3–16. https://doi.org/10.16538/j.cnki.fem.20201020.101 Footnotes Artificial Intelligence Index Report 2024 Whitepaper on the Global Digital Economy 2024 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5648468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":395909734,"identity":"d06d9e12-a731-487c-8c97-c41b80db64a2","order_by":0,"name":"Xiaowen Wang","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Wang","suffix":""},{"id":395909735,"identity":"5bbd2c6a-3e0f-4bf8-acb1-d8fe3ef31de9","order_by":1,"name":"Yuqi Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACZjBpw8PP33zwAJh9gIAOHoiWNDnJGccSiNQCoQ4bGxzIMSBOiz0787PHPGXMiQ0Hznw4+LONQY7vRgLj5wK8DmMzN+Y5x5bY2Ny74YBkG4Ox5I0EZukZ+P1iJs3bxpPYzHB2wwHDNobEDTcS2Jh58Gph/wbUIpHYxpDz4ACQrCdCCw/IFgNjHoYchgMH2xgSDAhqOcxTJjnnXIKchMQxg4MN5yQMZ5552CyNTwt7//FtEm/K/vPYn29++PBHmY083/Hkg5/xaQEBJh42OFsCiBkbCGgAKvnBRlDNKBgFo2AUjGQAAI1WSu/ko8toAAAAAElFTkSuQmCC","orcid":"","institution":"Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Tian","suffix":""},{"id":395909736,"identity":"1fc0ccd3-531e-4463-91fd-dbfd13c4ce5c","order_by":2,"name":"Nanxu Chen","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Nanxu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-12-15 16:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5648468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5648468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72744440,"identity":"dcc8dbbf-bb47-4cf9-8a33-4272b081d935","added_by":"auto","created_at":"2025-01-01 11:51:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116935,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5648468/v1/f1cd68bca54ee72f1447790f.png"},{"id":81186282,"identity":"57902faa-aabd-4331-903c-f047b2d1e128","added_by":"auto","created_at":"2025-04-23 08:24:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5648468/v1/e4481521-ae01-4b7b-ad26-b1033ec3cbba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Technology Empowering Innovation: The Impact of Artificial Intelligence on Global Entrepreneurial Activities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is driving a new wave of scientific and technological revolution and industrial transformation, emerging as a key element in advancing the 'fourth' industrial revolution (Dwivedi et al., 2019). The 2024 Artificial Intelligence Index Report, released by Stanford University's AI Institute (Stanford HAI), highlights that AI has surpassed human performance on several benchmarks, including image classification, visual reasoning, and English comprehension.[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e Furthermore, robots equipped with AI technology can effectively improve the total factor productivity, reduce output prices, and increase labor productivity (Graetz \u0026amp; Michaels, 2018). AI technology has become a crucial tool for enhancing the competitiveness of enterprises and countries, and promoting the transformation of AI technology into tangible results has become a global consensus (Adigwe et al., 2024). Amid intensified global efforts to harness AI for technological advancement, the U.S. National Science Foundation, alongside federal agencies and other stakeholders, announced a \u003cspan\u003e$\u003c/span\u003e140\u0026nbsp;million investment to establish seven new National Artificial Intelligence Research Institutes (NSF, 2023). With the continued maturation of AI technology and increasing investments from both government and industry, the global AI industry and the economy are entering a new phase of comprehensive integration, expected to experience rapid growth over the next decade.\u003c/p\u003e \u003cp\u003eThe rapid development of AI technology is unstoppable and has become key to enhancing corporate production, management practices, and innovation efficiency in the new century (Giorgi et al., 2022; Allioui \u0026amp; Mourdi., 2023; Olutimehin et al., 2024). AI technology not only has the characteristics of general information technology, but also has the synergistic characteristics of synergizing with various economic factors to improve economic efficiency, as well as the creative characteristics of replacing several work of the human brain. Therefore, AI technology brings new techno-economic paradigms. The transformation of techno-economic paradigms are accompanied by a large number of new markets and opportunities, which can stimulate upsurge of innovation and entrepreneurial activities. By the first quarter of 2024, the number of AI enterprises worldwide has reached 30,000[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. Some start-ups are actively using AI technology to launch their entrepreneurial activities. Such as Stitch Fix's, a start-up to change the way people find clothes they love by combining AI technology with the personal touch of seasoned style experts, has achieved good sales results.\u003c/p\u003e \u003cp\u003eEntrepreneurial activities serve as an endogenous driving force of economic growth (Van et al., 2005), playing a crucial role in driving employment (Glaeser et al., 2015) and structural change (Noseleit et al., 2013). Entrepreneurs enhance social wealth by transforming resources and production factors into higher-value forms through recombining these factors (Bai et al., 2020). Successful entrepreneurial activities can improve innovation efficiency (Wong et al., 2005), optimize industrial structures and resource allocation, expand domestic consumer demand, and boost regional entrepreneurial activities. Technological advancements are considered to have a significant impact on the entrepreneurial opportunities and processes (Davidsson et al., 2020). The application of AI technology in entrepreneurial activities will change classic entrepreneurial activities paradigm and prompt entrepreneurial activities to be more intelligent. What\u0026rsquo;s more, AI technology can assist key personnel in entrepreneurial activities by evaluating business needs and goals, building data infrastructure, and reducing operating costs. AI products and services, with their significant market potential and commercial value, may create a great number of entrepreneurial opportunities. However, the researchers of this study observed that few prior studies have focused on how AI technology affects entrepreneurial activities, or on which types of entrepreneurial activities or groups it has the more significant impact. This study aims to address this research gap by conducting a panel data analysis of 36 countries and regions worldwide.\u003c/p\u003e \u003cp\u003eThe paper is structured as follows: The second section reviews the existing literature; the third section conducts a theoretical analysis and proposes research hypotheses; the fourth section empirically explores the impact and channels of artificial intelligence technology on entrepreneurial activities; and the fifth section presents this study conclusions and further discussion.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eWith the deepening of the integration of AI technology with different industries, the role of AI technology has gradually emerged in a larger scope, such as scientific research, manufacturing industry and urban development. Specifically, AI technology improves data analysis and processing efficiency for scientific research, builds complex models, and performs automated experimental tasks (Xu et al.,2021). Industrial intelligence helps manufacturing industry to improve production efficiency, reduce production costs, improve product quality and promote technological innovation. Intelligent transportation systems and smart city management platforms improve the urban environment and the quality of life of residents, contributing to sustainable urban development (Wey \u0026amp; Huang, 2018). The widespread use of artificial intelligence has fostered sustainable economic development. AI technologies, such as industrial robots, have contributed to inclusive growth (Chen et al., 2022), innovation in low-tech sectors (Liu et al., 2020), green innovation (Zhan et al., 2021), and green development (Chen et al., 2024). The rise in AI patents has stimulated industrial innovation (Liu et al., 2020), improved production efficiency (Parteka et al., 2023), and enhanced urban resilience (Liu et al., 2024). As AI technology continues to diffuse and penetrate various economic activities, its impact and mechanisms on the entrepreneurial field have increasingly attracted researchers' attention and discussion.\u003c/p\u003e \u003cp\u003eExisting research on the impact of artificial intelligence on entrepreneurial activities covers both theoretical and empirical aspects. Theoretical research primarily examines AI\u0026rsquo;s influence on entrepreneurial theory and practice. For instance, Obschonka et al. (2019) analyzed the potential effects of AI technology and big data on entrepreneurial activities from the perspectives of external factors, human factors, and entrepreneurship education. L\u0026eacute;vesque et al. (2020) investigated the opportunities and challenges that AI may present in entrepreneurial activities. In terms of entrepreneurial practice, AI technology has facilitated cost reduction (Fossen \u0026amp; Sorgner, 2019), opportunity identification (Brown et al., 2017), and business model reconstruction (Garbuio \u0026amp; Lin, 2019). Liu and Zhang (2024) provided a comprehensive analysis of how large AI models impact the entrepreneurial process and introduced the concept of the \"big model entrepreneurial paradigm.\" Empirical research on AI\u0026rsquo;s impact on entrepreneurial activities can be categorized into two types. One type uses entrepreneurial activities as a mediating variable to explore AI\u0026rsquo;s effects on other key variables. For example, Liu et al. (2024) investigated how AI technology influences urban economic resilience through entrepreneurial activities as a mediating variable. The other type treats entrepreneurial activities as the explained variable. For instance, Li et al. (2024) employed the Probit model to examine the impact of AI technology on urban entrepreneurial activities among migrant workers, using national dynamic monitoring survey data from 2013 to 2017.\u003c/p\u003e \u003cp\u003eIn summary, research on the impact of AI technology on entrepreneurial activities has generated substantial theoretical insights and established a relatively comprehensive research framework. This body of work has not only infused traditional entrepreneurial theory with contemporary value and scientific depth but also provided theoretical support for applying AI technology in entrepreneurial practice. However, current studies are often restricted to specific entrepreneurial groups within particular countries, lacking an international perspective and broader applicability. Additionally, there is a scarcity of literature that empirically examines the mechanisms through which AI technology influences entrepreneurial activities. Moreover, no study has yet conducted an in-depth analysis of how AI technology's impact varies across different entrepreneurial groups and types.\u003c/p\u003e \u003cp\u003eThe potential marginal contributions of this paper are as follows: First, this study offers empirical evidence for understanding how artificial intelligence technology affects global entrepreneurial activities. Utilizing global entrepreneurial activities data from the Global Entrepreneurial activities Monitor (GEM), which covers 36 countries and regions, this research provides the newest results with a global perspective. Second, this paper enriches the theoretical framework regarding the impact of artificial intelligence on entrepreneurial activities. Based on existing theories, it introduces new insights into the channels and mechanisms through which AI technology influences entrepreneurial activities. Third, this paper refines the research samples by categorizing entrepreneurs into different industries and age groups, further exploring the heterogeneity in AI\u0026rsquo;s impact on various entrepreneurial groups. This analysis offers a practical foundation for developing more targeted policies to support entrepreneurs.\u003c/p\u003e"},{"header":"3. Hypothesis","content":"\u003cp\u003eArtificial intelligence enabling entrepreneurial activities refers to the process in which entrepreneurs actively utilize or collaborate with AI technology to jointly exploit entrepreneurial opportunities (Liu \u0026amp; Wang, 2020). In the early stages of AI development, AI technology served as a high-tech production tool for entrepreneurs, with functions limited to solving problems in specific fields. However, as AI technology has advanced and become more widespread, its functions in the entrepreneurial process have diversified, and its applications have broadened. AI technology now not only assists entrepreneurs in addressing technical challenges but also offers valuable insights for decision-making through its vast databases and advanced analytical capabilities (Li et al., 2022).\u003c/p\u003e \u003cp\u003eGiven the growing versatility and intelligence of AI applications, the mechanism by which AI technology empowers entrepreneurial activities has become increasingly complex. By enhancing critical stages in the entrepreneurial process, AI technology reduces the difficulty of entrepreneurial activities and thereby boosts entrepreneurial activities. This article categorizes the role of AI technology in entrepreneurial activities into direct and indirect effects (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. The mechanism based on Entrepreneurial Factor Theory\u003c/h2\u003e \u003cp\u003eTimmons et al. (2014) proposed the classic entrepreneurial factor theory, which posits that the entrepreneurial process comprises entrepreneurial opportunities, entrepreneurial resources, and entrepreneurial teams. The alignment and integration of these three components drive the overall entrepreneurial process. AI technology influences each of these elements\u0026mdash;entrepreneurial opportunities, resources, and teams\u0026mdash;thereby enhancing entrepreneurial activities. The specific mechanisms through which AI acts are as follows.\u003c/p\u003e \u003cp\u003eAI technology empowers entrepreneurial opportunities. Identifying entrepreneurial opportunities is the initial step in entrepreneurial activities. According to Baron (2006), key factors in recognizing opportunities include actively seeking them out, staying alert, and possessing prior knowledge of specific industries or markets. The innovation and advancement of artificial intelligence can generate more opportunities across various industries for entrepreneurs (Davidsson et al., 2023). Compared to human entrepreneurs, AI technology is not only more attuned to potential opportunities but also benefits from extensive prior knowledge through machine learning. This allows AI technology to create new combinations of familiar ideas. By leveraging a priori cognitive frameworks, AI technology can discern connections between seemingly unrelated events or trends, leading to the generation of new ideas and the discovery of novel opportunities.\u003c/p\u003e \u003cp\u003eAI technology empowers entrepreneurial resources. Entrepreneurial resources include the technical, talent, capital, and policy conditions necessary to support the entrepreneurial process and are essential for developing and utilizing entrepreneurial opportunities. AI technology provides technical support by enabling entrepreneurs to efficiently collect data and information and by collaborating in commercial activities such as production and marketing (Obschonka et al., 2019). For instance, AI-based personalized recommendation systems can enhance marketing accuracy on e-commerce platforms, while AI-driven data analysis helps companies better understand customer needs, leading to optimized product design and services. As AI technology continues to advance, entrepreneurs' efficiency in collecting information and solving problems has improved, resulting in a higher success rate for entrepreneurial ventures. Furthermore, AI-focused development strategies implemented by various countries offer policy and financial support for related entrepreneurial activities within the industry.\u003c/p\u003e \u003cp\u003eAI technology empowers entrepreneurial teams. An entrepreneurial team consists of individuals and groups involved in entrepreneurial activities. Given that AI possesses cognitive functions similar to those of humans, it has a significant impact on entrepreneurial decision-making and team collaboration. Townsend and Hunt (2019) argue that entrepreneurial actions, judgments, and decisions in uncertain environments are central to entrepreneurial activities, and AI technology can enhance these processes by reducing uncertainty. Liebregts et al. (2019) highlight that verbal and non-verbal behavioral cues in social interactions significantly affect personal decision-making in an entrepreneurial context. AI technology can assist entrepreneurs in effectively identifying these social signals and influencing corporate decisions. Additionally, AI technology can improve collaboration and communication within entrepreneurial teams. AI systems can analyze team members' work habits and efficiency, provide optimization suggestions, and help team members better understand and share information, thereby enhancing overall team effectiveness.\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes the following research hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eAI technology has a positive effect on entrepreneurial activities.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The mechanism of improving entrepreneurial ability through entrepreneurship education\u003c/h2\u003e \u003cp\u003eHuman capital theory posits that educational investment is a primary means of developing human capital, and an individual's knowledge, skills, and abilities can significantly impact outcomes (Martin et al., 2012; Ployhart et al., 2011). Consequently, entrepreneurship education can assist entrepreneurs in accumulating capital related to entrepreneurial activities (He et al., 2023).\u003c/p\u003e \u003cp\u003eAI technology has lowered the educational barriers to entrepreneurial activities. The integration of AI into the field of education has transformed the creation and application of knowledge (Gu \u0026amp; Li, 2022), offering learners diverse methods and formats for knowledge presentation. Intelligent learning scenarios and more precise knowledge analysis enhance learners' understanding and improve learning efficiency (Du \u0026amp; Gu, 2022). AI-driven human-computer interactions enable more efficient knowledge retrieval and data processing (Christian et al., 2019), increasing the accuracy and reliability of knowledge sources. Specifically, deep learning and computer vision technologies analyze inherent patterns and representations within large datasets, generate new knowledge and computational solutions, and expedite the process of knowledge reorganization (Agrawal et al., 2018). Moreover, AI technology has also assisted commercial companies in enhancing their data collection capabilities, accelerating the development of tools for processing large volumes of data, and improving the efficiency of data collection. This, in turn, facilitates entrepreneurs in acquiring new knowledge. The process of gathering, processing, and integrating massive amounts of data is also a process of knowledge re-creation. Entrepreneurs benefit from this integration by absorbing new knowledge, generating new ideas, and acquiring new skills, which in turn enhances their entrepreneurial capabilities.\u003c/p\u003e \u003cp\u003eAI technology has provided convenient channels for improving entrepreneurial ability. The continuous diffusion and widespread adoption of AI technology across various economic sectors have made it easier for entrepreneurs to access relevant innovation resources. This development has fostered the generation and application of innovative thinking, expanded the business scope for entrepreneurs, and unlocked the innovation spillover benefits of AI technology. AI\u0026rsquo;s ability to transfer knowledge and skills, learn rapidly, and apply mastered skills to address new challenges has proven invaluable. With capabilities in reasoning, learning, associating, and problem-solving based on existing knowledge, AI-powered tools have become highly reliable and effective for analytical purposes, enhancing learning and decision-making abilities (Vecchiarini et al., 2023). AI technology is continually optimizing computer programs to simulate human learning behavior and acquire new knowledge and skills. The increasing availability and open-source nature of AI technology have made many AI tools and platforms more accessible. This accessibility allows entrepreneurs to leverage AI tools for acquiring professional knowledge and market information, even without an extensive AI background. Consequently, AI technology has lowered the technical barriers to entrepreneurial activities, providing more individuals with the opportunity to become entrepreneurs. Based on the above, the following research hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH2: AI technology has a positive effect on entrepreneurial activities through entrepreneurship education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.\u003cb\u003e3 The mechanism of enhancing\u003c/b\u003e entrepreneurial motivation \u003cb\u003ethrough investment\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eVenture capital, as an effective method of equity financing, focuses on long-term investment in start-ups and can address the issue of insufficient funding for enterprises (Metrick, 2006). The start-ups generally characterized by smallness and newness. Financing constraints are one of the important reasons for the difficulty in survival and short life span of start-ups. Start-ups have the congenital disadvantage of low anti-risk ability due to their small business scale and small market share. When faced with external negative shocks such as economic downturn, rising operating costs, and supply chain disruptions, start-ups may be forced to abandon business projects, miss market opportunities, or even go bankrupt due to financing constraints (Van Praag et al., 2005; Nicolas, 2021). Therefore, adequate venture capital can mitigate the risk of disruptions in the capital chain of entrepreneurial activities and increase the likelihood of entrepreneurial success.\u003c/p\u003e \u003cp\u003eThe deep integration of AI with the economy and society has led to more systematic, complex, and specialized AI technologies. In sectors with high AI technology density, industrial innovation clusters have begun to emerge. The innovative use of AI technology in fields such as medicine, finance, education, and transportation offers entrepreneurs more opportunities to develop new products and services. The promising development prospects and substantial financial support have encouraged more entrepreneurs to establish new ventures and capitalize on market opportunities.\u003c/p\u003e \u003cp\u003eThe high added value and profit margins of artificial intelligence products have attracted numerous investors. According to Stanford's \u0026ldquo;Artificial Intelligence Index Report 2024,\u0026rdquo; funding for generative AI surged, nearly octupling from 2022 to reach \u003cspan\u003e$\u003c/span\u003e25.2\u0026nbsp;billion. Factors such as technological advancements, growing market demand, and government policy support have bolstered investor confidence in AI's development prospects, leading to increased financial backing for AI-related entrepreneurial activities and a rise in entrepreneurial activities.\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes the following research hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eAI technology positively affects entrepreneurial activities through investment.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Study Design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The model\u003c/h2\u003e \u003cp\u003eTo assess the impact of artificial intelligence on entrepreneurial activities across different countries, this paper constructs a two-way fixed effects model as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Entr}_{it}=\\alpha\\:+{\\beta\\:}_{1}{Patent}_{it}+{\\beta\\:}_{2}{X}_{it}+{\\delta\\:}_{i}+{\\epsilon\\:}_{t}+{\\mu\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the subscripts i and t respectively represent a particular nation and year, \u0026#120630; is a constant term, βis a coefficient of each variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Entr}_{it}\\)\u003c/span\u003e\u003c/span\u003eis the mediating variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Patent}_{it}\\)\u003c/span\u003e\u003c/span\u003eis the explanatory variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003eis the control variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the country and year fixed effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the random interference term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Data and variables\u003c/h2\u003e \u003cp\u003eThis study focuses on countries and regions, using artificial intelligence patent data from the Center for Security and Emerging Technologies (CSET) at Georgetown University and entrepreneurial data from the Global Entrepreneurial activities Monitor (GEM) for the years 2010 to 2023. Control variables, including GDP per capita and total population, are sourced from the World Development Indicators (WDI) database. Due to some samples having missing years, the data is matched across the three databases by country, region, and year. Samples with insufficient data are excluded, resulting in a final dataset comprising 36 countries and regions. This sample includes major global economies as well as countries and regions at various stages.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExplanatory Variables: The number of AI patents refers to the count of AI-related patents granted by the National Patent Office. The number of AI patents represents the progress of AI technology, which is a typical indicator to measure the development of AI technology. The data is sourced from the Center for Security and Emerging Technologies (CSET) at Georgetown University. CSET's primary research areas include AI-related talent, computing power, and the application of AI in cybersecurity and national security contexts. Currently, more than 40 publicly available survey reports utilize CSET data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExplained Variable: Entrepreneurial activities. This paper adopts the approach of Audretsch (2015) and uses Total Early-Stage Entrepreneurial activities (TEA) as an indicator to measure entrepreneurial activities. TEA represents the percentage of adults aged 18\u0026ndash;64 who are either owners or managers of new businesses or startups (established within the last 42 months). The data on entrepreneurial activities is sourced from the Global Entrepreneurial activities Monitor (GEM) database. Established in 1999, GEM has gathered data from a total of 115 economies over various years, collecting over 200,000 adult entrepreneurial activities survey samples annually. GEM\u0026rsquo;s research data has become a crucial resource for major international organizations, including the World Bank, OECD, and the World Economic Forum. According to official statistics, nearly 1,000 papers have cited the GEM database as of 2023.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eControl Variables: To account for other regional characteristics that may influence entrepreneurial activities, this paper controls the following variables based on the approaches of Wang et al. (2024) and He et al. (2024) GDP per Capita (ln GDP): Higher levels of economic development generally correspond to greater economic capacity and higher returns on entrepreneurial activities, impacting entrepreneurial activities. Total Population (ln People): The total population reflects market size. Regions with larger populations tend to have more market demand and business opportunities, attracting more entrepreneurs. Entrepreneurial Environment: This includes factors such as the entrepreneurial financing environment, government project support, commercial and professional infrastructure, internal market openness, physical and services infrastructure and the entrepreneurial culture. These external environmental factors are measured using the mean scores from entrepreneur questionnaires compiled by GEM.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of all variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpeople\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical Analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Basic results\u003c/h2\u003e \u003cp\u003eThe regression results presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that the number of artificial intelligence patents has a significant positive effect on overall entrepreneurial activities at the 5% level, supporting hypothesis 1. Additionally, separate regressions were conducted to examine the impact of artificial intelligence on different types of entrepreneurial motivation. Due to missing data for opportunity entrepreneurial activities and survival entrepreneurial activities in 2019 and 2023, only 191 eligible regression samples were available. The regression results show that for every unit increase in the number of artificial intelligence patents. This finding supports the theoretical analysis that the development of AI technology enhances entrepreneurial opportunities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFixed effects estimations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEntrepreneurial motivation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpportunity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNecessity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.389\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0641)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.293\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.543)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpeople\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.71 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.332)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.122)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoverment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.795\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.248)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.218)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.516\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.232)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.373\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.191)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-335.1 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(20.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(102.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(40.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, * represent respectively 1%, 5%, 10% significance levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Robustness test\u003c/h2\u003e \u003cp\u003eThis paper conducts a robustness test by eliminating certain samples. Given that the sample includes several major global economies, the regression results may be skewed and may not fully reflect the impact of artificial intelligence on entrepreneurial activities in countries with smaller economies. To address this, we used the GDP rankings of countries from 2010 to 2023 to match the top ten global economies with the sample countries. We then excluded overlapping countries (the United States, China, Japan, Russia, Germany, France, and Italy) and performed the regression analysis on the remaining sample observations. The results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicate that the number of AI patents has a significant positive effect on overall entrepreneurial activities at the 1% level, and a significant positive effect on opportunistic entrepreneurial activities at the 10% level. This demonstrates that, even after controlling for sample bias, the positive impact of AI technology on entrepreneurial activities remains robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEntrepreneurial motivation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpportunity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNecessity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.821\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.567 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.102)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.815)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpeople\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.56 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.785)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.304)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoverment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.537*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.310)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.263)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.256*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.301)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.437 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.251)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-311.2 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(21.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(122.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(48.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, * represent respectively 1%, 5%, 10% significance levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Mechanism test\u003c/h2\u003e \u003cp\u003eIn the theoretical analysis, this paper suggests that artificial intelligence can enhance entrepreneurial activities through two main mechanisms: the improvement of entrepreneurial ability based on entrepreneurship education and the reinforcement of entrepreneurial motivation driven by investment. To test these mechanisms, this paper employs a mediation effect model to examine the channels through which AI technology influences entrepreneurial activities.\u003c/p\u003e \u003cp\u003eThe mediation effect model involves proposing one or more mediating variables M. The causal relationships between these variables and the outcome variable Y are theoretically intuitive and logically consistent in terms of time and space, which negates the need for formal causal inference methods to explore the relationship between the mediating variable and the dependent variable (Jiang, 2022). Given that existing literature supports the roles of entrepreneurship education and investment-driven factors in promoting entrepreneurial activities, this paper will focus on testing how artificial intelligence technology impacts these two areas. The specific settings for the analysis are as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{M}_{it}=\\lambda\\:+{\\beta\\:}_{3}{Patent}_{it}+{\\beta\\:}_{4}{X}_{it}+{\\delta\\:}_{i}+{\\epsilon\\:}_{t}+{\\mu\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the subscripts i and t respectively represent a particular nation and year, \u003cem\u003e\u0026#120582;\u003c/em\u003e is a constant term, \u003cem\u003eβ\u003c/em\u003e is a coefficient of each variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the mediating variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Patent}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the explanatory variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the control variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the country and year fixed effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the random interference term.\u003c/p\u003e \u003cp\u003eIn examining the channels through entrepreneurship education, this paper uses \"school entrepreneurship education\" and \"adult entrepreneurship education\" from the GEM as mediating variables. AI-enhanced entrepreneurial activities education offers entrepreneurs more diverse methods and forms of knowledge presentation, enhances their understanding of relevant entrepreneurial knowledge, improves learning efficiency, and thereby boosts entrepreneurial abilities and activities. The results of the mechanism test in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that AI technology significantly promotes the development of adult entrepreneurial activities education, leading to an increase in entrepreneurial activities. Thus, hypothesis 2 is verified.\u003c/p\u003e \u003cp\u003eIn testing the investment-driven channels, this paper uses statistics on private market investment in AI across various countries, compiled by the Center for Security and Emerging Technology at Georgetown University, excluding investments in listed companies. This data reflects the amount of investment in non-listed companies, including start-ups, in the field of AI. The results of the mechanism test in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that an increase in AI technology patents has led to a rise in the total amount of private investment in AI. This influx of investment has attracted more entrepreneurs and provided financial support for AI-related entrepreneurial activities, ultimately increasing overall entrepreneurial activities in the region. Thus, hypothesis 3 is verified.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEntrepreneurship education\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInvestment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAI investment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0540**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.125)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, * represent respectively 1%, 5%, 10% significance levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eThe rapid development of AI technology may have varying impacts on entrepreneurial groups of different ages, education levels, and industries. Therefore, this paper will explore the individual heterogeneity of AI technology's effects on entrepreneurial activities. Given that the database provides comprehensive entrepreneur characteristic data, this study will use ungrouped heterogeneity analysis to examine the relationship between the overall characteristics of the sample and the variables. The results are presented below.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e examines the differences in the impact of AI technology on entrepreneurial groups of different ages. The explained variable is the percentage of entrepreneurs in each age group within each country, with the country as the unit of analysis. The sum of the proportions of entrepreneurs across all age groups is 100%. The regression results show that AI technology has a significant positive effect at the 1% level for the entrepreneurial group aged 18\u0026ndash;24, at the 5% level for the entrepreneurial group aged 25\u0026ndash;34, and is positively significant for the entrepreneurial group aged 35\u0026ndash;64. The effect is not significant for the oldest age group. Therefore, AI technology exhibits notable intergenerational heterogeneous effects on global entrepreneurial groups. This may be because younger people have stronger social adaptability, better learning abilities, and more flexible thinking, making them more receptive to new technologies. As a result, they are more likely to respond quickly to opportunities in the booming field of AI technology and exhibit a higher willingness to start a business.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity test for distinguishing age\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.268 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.270)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: ***, **, * represent respectively 1%, 5%, 10% significance levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e examines the differences in the impact of AI technology on entrepreneurial groups across various industries. The explained variable is the percentage of entrepreneurs in each country within different industries. According to the GEM, the business service industry includes sectors such as finance, insurance, real estate, etc., while the customer service industry encompasses retail, automotive, health education, social services, and entertainment. The regression results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show that AI technology has a significant positive effect on both the business service industry and the customer service industry at the 10% level, while its impact on the primary and secondary industries is not significant. This indicates that artificial intelligence has industry-specific effects on the global entrepreneurial community. This may be because AI applications in the business service and customer service industries offer higher commercial value and broader market prospects, thereby attracting more entrepreneurs to these sectors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity test for distinguishing industry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eextractive sector\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etransformative sector\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebusiness services\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003econsumer services\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.239 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.856 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.405)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eregion fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ***, **, * represent respectively 1%, 5%, 10% significance levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e examines the differences in the impact of AI technology on entrepreneurial groups with varying educational levels. Given that AI is a high-tech industry with significant technical complexity, entrepreneurs with higher education levels may find it easier to engage with and invest in AI-related ventures. Thus, this study uses the percentage of educational attainment among entrepreneurial groups in each country as the explained variable for regression analysis. The regression results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the impact of AI technology on entrepreneurial groups with different educational levels is not significant. Therefore, AI technology does not exhibit a heterogeneous impact on entrepreneurial groups based on educational attainment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity test for distinguishing education\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esecondary experience\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esecondary degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epost-secondary degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003egraduate experience\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.281)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eregion fixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ***, **, * represent respectively 1%, 5%, 10% significance levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and policy implication","content":"\u003cp\u003eAI technology introduces new techno-economic paradigms, with its impact on entrepreneurial activities being one of the most important aspects that cannot be overlooked. AI technology has surpassed the simple tool attributes of previous digital technologies and is now acting as a collaborator and entrepreneur in starting business ventures. AI technology brings specialized expertise, cutting-edge technologies, and a depth of experience that can significantly optimize entrepreneurial decisions (Olutimehin, D. O. etal., 2024). As organizations navigate the complexities of integrating AI technology into their business models, collaboration and partnerships emerge as key strategies for success (Reim etal., 2020). The application of AI technology \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ei\u003c/span\u003en entrepreneurial activities has changed classic entrepreneurial activities paradigm and prompt entrepreneurial activities to be more intelligent. However, despite its significant impact, few studies have specifically explored how AI technology affects entrepreneurial activities, or which types of entrepreneurial activities and groups it will have the more profound impact on.\u003c/p\u003e \u003cp\u003eIn addressing this research gap, the present study systematically investigates the impact of AI technology on global entrepreneurial activities through empirical analysis. The research adopts the entrepreneurial factor theory as its foundational theoretical framework and utilizes both a two-way fixed effects model and a mediation effect model for its analysis. It examines the influence of AI technology on entrepreneurial activities across enterprises in 36 countries and regions over a 14-year period, from 2010 to 2023. The research results provide practical evidence supporting the theoretical achievements of AI technology in the field of entrepreneurial activities. Additionally, they expand the channels and mechanisms through which AI technology influences entrepreneurial activities and enrich the existing theoretical framework. The study further divides entrepreneurial groups and reveals the heterogeneity in the impact of AI technology based on the characteristics of the entrepreneurs themselves. The findings are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) AI technology significantly promotes entrepreneurial activities. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) From the perspective of influence channels, AI technology enhances entrepreneurs\u0026rsquo; learning abilities and professional skills through entrepreneurship education, thereby increasing entrepreneurial activities. Additionally, AI technology supports entrepreneurial activities by attracting related investments. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Regarding entrepreneurial types, age groups, and industry divisions, AI technology has a more pronounced impact on opportunistic entrepreneurial activities, entrepreneurial groups aged 18\u0026ndash;34, and the tertiary industry. According to the above conclusions, we offer the following policy implications:\u003c/p\u003e \u003cp\u003eFirst, accelerate the construction of an entrepreneurship education system that teaches artificial intelligence skills. While AI technology provides crucial support for entrepreneurial activities, it also poses challenges for entrepreneurs. The era of intelligent technology has raised the bar for the basic skills required of entrepreneurs. The government can establish AI courses to help entrepreneurs master fundamental knowledge. For older entrepreneurial groups, tailored education courses should be developed to improve their acceptance of AI technology. Additionally, special courses with industry-specific characteristics can be created to meet the needs of entrepreneurs across different sectors.\u003c/p\u003e \u003cp\u003eSecond, increase support for artificial intelligence-related entrepreneurial projects. The government can establish a special fund for AI-related entrepreneurial activities and attract capital from local departments, financial institutions, and investment firms to create AI venture investment sub-funds in the form of equity or debt. Government-led entrepreneurial funds can promote capital inflow and the success of entrepreneurial projects while encouraging more researchers and entrepreneurs to explore AI technology applications in various fields, thereby facilitating its widespread adoption. Additionally, these funds should clarify the focus of AI venture capital investments, guiding capital toward high-growth potential projects within the AI entrepreneurial sector.\u003c/p\u003e \u003cp\u003eLast but not least, improve legislative oversight of artificial intelligence and establish standardization policies for data use. AI models often involve large amounts of personal data and sensitive information when acquiring online information, leading to potential legal issues such as data infringement, patent leakage, and trade secret violations. To ensure data security and privacy protection for entrepreneurs, the government should enhance the applicability of regulatory penalties from a legislative perspective. It can regulate the data usage behaviors of entrepreneurs and entrepreneurial organizations while clarifying principles for the use and sharing of sensitive data.\u003c/p\u003e"},{"header":"7. Limitations and future research","content":"\u003cp\u003eThis study has some limitations that future research may address. Firstly, due to limitations in available data, the sample of countries or regions selected for this study includes some of the world's economies but does not cover all countries and regions. As a result, the conclusions may have regional limitations. Additionally, missing years in the sample countries and regions led to the exclusion of some samples, which may affect the sample size.\u003c/p\u003e \u003cp\u003eSecondly, the most recent sample selection is from 2023, which introduces a potential time limitation. Generative AI technologies, such as Chat GPT, were released and widely adopted at the end of 2022. Consequently, this study cannot use adequate empirical results to assess the impact of the latest AI technologies on entrepreneurial activities. The future research may continue to discuss how generative AI technologies affect different types of entrepreneurial activities, which will enrich existed theoretical framework. If the relevant data is available, we can obtain empirical results and explore more intriguing facts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required as the study did not involve collecting data from human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Key Research and Development Plan of Gansu Province Science and Technology Plan2020 (20YF3GAO11) and study on the construction of Hexi Corridor Economic belt (2022ZD009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAll authors contribute to the study conception and design. Funding acquisition and supervision\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewere performed by Wang Xiaowen. Original draft and data curation were performed by Tian Yuqi. Methodology was performed by Chen Nanxu. All authors read and approved the final manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdigwe, C. S., Olaniyi, O. O., Olabanji, S. O., Okunleye, O. J., Mayeke, N. R., \u0026amp; Ajayi, S. A. (2024). Forecasting the Future: The Interplay of Artificial Intelligence, Innovation, and Competitiveness and its Effect on the Global Economy.\u003cem\u003e Asian Journal of Economics Business and Accounting\u003c/em\u003e, 24(4), 126\u0026ndash;146. https://doi.org/10.9734/ajeba/2024/v24i41269\u003c/li\u003e\n\u003cli\u003eAgrawal A., Gans J. S., and Goldfarb A. (2017). What to expect from Artificial Intelligence. MIT Sloan Management Review 58 (3):23-26. http://mitsmr.com/2jZdf1Y\u003c/li\u003e\n\u003cli\u003eAllioui, H., \u0026amp; Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses. \u003cem\u003eInternational Journal of Computer Engineering and Data Science (IJCEDS)\u003c/em\u003e, 3(2), 1-12. Retrieved from https://ijceds.com/ijceds/article/view/59\u003c/li\u003e\n\u003cli\u003eBai J.H., Zhang Y.X., \u0026amp; Bian Y.C. （2022）. Do innovation-driven policies enhance urban entrepreneurial activity? Empirical evidence from the national innovative city pilot policy. \u003cem\u003eChina Industrial Economy\u003c/em\u003e, 06:61-78. doi:10.19581/j.cnki.ciejournal.2022.06.016.\u003c/li\u003e\n\u003cli\u003eBaron, R. A. (2006). Opportunity Recognition as Pattern Recognition: How entrepreneurs \u0026ldquo;Connect the dots\u0026rdquo; to identify new Business opportunities.\u003cem\u003e Academy of Management Perspectives, \u003c/em\u003e20(1), 104\u0026ndash;119. https://doi.org/10.5465/amp.2006.19873412\u003c/li\u003e\n\u003cli\u003eBrown, T. E. (2017). Sensor-based entrepreneurial activities: A framework for developing new products and services. \u003cem\u003eBusiness Horizons,\u003c/em\u003e 60(6), 819\u0026ndash;830. https://doi.org/10.1016/j.bushor.2017.07.008\u003c/li\u003e\n\u003cli\u003eChen D., \u0026amp; Qin Z.Y. （2022）. Artificial Intelligence and inclusive growth: Evidence from global industrial robot use. \u003cem\u003eEconomic Research,\u003c/em\u003e 57 (4): 85-102. http://hfgga60aabc7d15084b00sk9uwqq0xxxwb6059.fhaz.libproxy.ruc.edu.cn/kcms2/article/abstract?v=uagkXMi-j-VEX_x_Kyat5XCu7JFOpv9m3RVNiDPble2x2IU5UZnbcUc8qLyl2AzNpHf2HnaJZV6Ka1RxPuUWvwRaEjtvWzusDSx-wbh8dCnR_lJPjLe1J8D4ssGRWDe1EvBNG9jfTIscDc55XvEFYCETtbrsELVJBtjfm3xLQH2zUFill7QUtFRF4Fp6,\u003cbr\u003e1yDn8mi7wGNO5xM=anduniplatform=NZKPTandlanguage=CHS\u003c/li\u003e\n\u003cli\u003eChen, M., Wang, S., \u0026amp; Wang, X. (2024). How Does Artificial Intelligence Impact Green Development? Evidence from China. \u003cem\u003eSustainability\u003c/em\u003e, 16(3), 1260. https://doi.org/10.3390/su16031260\u003c/li\u003e\n\u003cli\u003eChristian, V., Constantinescu, C., and Popescu, D. (2019). Application potentials of Artficial Intelligence for the design of innovation processes. \u003cem\u003eProcedia CIRP \u003c/em\u003e84:810-813. https://doi.org/10.1016/B978-0-444-88864-8.50014-5.\u003c/li\u003e\n\u003cli\u003eDavidsson, P., Recker, J., \u0026amp; Von Briel, F. (2018). External Enablement of New Venture Creation: a framework. \u003cem\u003eAcademy of Management Perspectives\u003c/em\u003e, 34(3), 311\u0026ndash;332. https://doi.org/10.5465/amp.2017.0163\u003c/li\u003e\n\u003cli\u003eDavidsson, P., \u0026amp; Sufyan, M. (2023). What does AI think of AI as an external enabler (EE) of entrepreneurial activities? An assessment through and of the EE framework. \u003cem\u003eJournal of Business Venturing Insights,\u003c/em\u003e 20, e00413. https://doi.org/10.1016/j.jbvi.2023.e00413\u003c/li\u003e\n\u003cli\u003eDu H., \u0026amp; Gu X.Q. （2022）. Artificial Intelligence promotes knowledge understanding: An empirical study aiming at concept transformation. \u003cem\u003eJournal of East China Normal University, \u003c/em\u003e40 (09):67-77. doi:10.16382/j.cnki.1000-5560.2022.09.007.\u003c/li\u003e\n\u003cli\u003eDwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. \u003cem\u003eInternational Journal of Information Management\u003c/em\u003e, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002\u003c/li\u003e\n\u003cli\u003eFossen, F. M., \u0026amp; Sorgner, A. (2019). Digitalization of work and entry into entrepreneurial activities. \u003cem\u003eJournal of Business Research\u003c/em\u003e, 125, 548\u0026ndash;563. https://doi.org/10.1016/j.jbusres.2019.09.019\u003c/li\u003e\n\u003cli\u003eGarbuio, M., \u0026amp; Lin, N. (2018). Artificial intelligence as a growth engine for health care startups: Emerging business models.\u003cem\u003e California Management Review\u003c/em\u003e, 61(2), 59\u0026ndash;83. https://doi.org/10.1177/0008125618811931\u003c/li\u003e\n\u003cli\u003eGiorgi, G., Ariza-Montes, A., Mucci, N., \u0026amp; Leal-Rodr\u0026iacute;guez, A. L. (2022). The dark side and the light side of Technology-Related stress and stress related to workplace innovations: from artificial intelligence to business transformations. \u003cem\u003eInternational\u003c/em\u003e \u003cem\u003eJournal of Environmental Research and Public Health\u003c/em\u003e, 19(3), 1248. https://doi.org/10.3390/ijerph19031248\u003c/li\u003e\n\u003cli\u003eGlaeser, E. L., Kerr, S. P., \u0026amp; Kerr, W. R. (2014). Entrepreneurial activities and Urban Growth: An Empirical Assessment with Historical Mines. \u003cem\u003eThe Review of Economics and Statistics,\u003c/em\u003e 97(2), 498\u0026ndash;520. https://doi.org/10.1162/rest_a_00456\u003c/li\u003e\n\u003cli\u003eGraetz, G., \u0026amp; Michaels, G. (2018). Robots at work. \u003cem\u003eThe Review of Economics and Statistics,\u003c/em\u003e 100(5), 753\u0026ndash;768. https://doi.org/10.1162/rest_a_00754\u003c/li\u003e\n\u003cli\u003eGu X.Q., \u0026amp; Li S.J. （2022）.Artificial Intelligence promotes the development of future education: Essential connotations and expected directions.\u003cem\u003e Journal of East China Normal University\u003c/em\u003e 40 (09):1-9. doi:10.16382/j.cnki.1000-5560.2022.09.001.\u003c/li\u003e\n\u003cli\u003eHe, L., Zheng, L. J., Sharma, P., \u0026amp; Leung, T. (2023). Entrepreneurship education and established business activities: An international perspective.\u003cem\u003e The International Journal of Management Education\u003c/em\u003e, 22(1), 100922. https://doi.org/10.1016/j.ijme.2023.100922\u003c/li\u003e\n\u003cli\u003eJiang T. （2022）. Mediating Effect and Moderating Effect in Empirical Research on Causal Inference. \u003cem\u003eChina Industrial Economy\u003c/em\u003e (05):100-120. doi:10.19581/j.cnki.ciejournal.2022.05.005.\u003c/li\u003e\n\u003cli\u003eL\u0026eacute;vesque, M., Obschonka, M., \u0026amp; Nambisan, S. (2020). Pursuing impactful entrepreneurial activities research using artificial intelligence. \u003cem\u003eEntrepreneurial activities Theory and Practice\u003c/em\u003e, 46(4), 803\u0026ndash;832. https://doi.org/10.1177/1042258720927369\u003c/li\u003e\n\u003cli\u003eLi L.B., \u0026amp; Liu Z.Y. （2024）. Research on the impact of industrial intelligence on urban entrepreneurial activities of migrant workers. \u003cem\u003eModern Economic Discussion\u003c/em\u003e 06:1-15. doi:10.13891/j.cnki.mer.2024.06.011.\u003c/li\u003e\n\u003cli\u003eLi, X., Zhang, X., Liu, Y., Mi, Y., \u0026amp; Chen, Y. (2022). The impact of artificial intelligence on users\u0026rsquo; entrepreneurial activities. \u003cem\u003eSystems Research and Behavioral Science\u003c/em\u003e, 39(3), 597\u0026ndash;608. https://doi.org/10.1002/sres.2854\u003c/li\u003e\n\u003cli\u003eLiebregts, W., Darnihamedani, P., Postma, E., \u0026amp; Atzmueller, M. (2019). The promise of social signal processing for research on decision-making in entrepreneurial contexts. \u003cem\u003eSmall Business Economics,\u003c/em\u003e 55(3), 589\u0026ndash;605. https://doi.org/10.1007/s11187-019-00205-1\u003c/li\u003e\n\u003cli\u003eLiu, J., Chang, H., Forrest, J. Y., \u0026amp; Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of china\u0026rsquo;s manufacturing sectors. \u003cem\u003eTechnological Forecasting and Social Change,\u003c/em\u003e 158, 120142. https://doi.org/10.1016/j.techfore.2020.120142\u003c/li\u003e\n\u003cli\u003eLiu J.Q., Xue F.， \u0026amp; Ru S.F. （2024）. Research on the impact of Artificial Intelligence technology on urban economic resilience.\u003cem\u003e Soft Science\u003c/em\u003e 2:1-12. http://hfgga60aabc7d15084b00hk9uwqq0xxxwb6059.fhaz.libproxy.ruc.edu.cn/kcms/detail/51\u003cbr\u003e.1268.G3.20231214.1612.010.html.\u003c/li\u003e\n\u003cli\u003eLiu Z.Y., \u0026amp; Zhang Y.Q. （2024）. Entrepreneurial activities with the Large Language Models: Paradigmatic evolution and theoretical construction, \u003cem\u003eJournal of Renmin University of China \u003c/em\u003e38 (03):87-99. http://hfgga60aabc7d15084b00sk9uwqq0xxxwb6059.fhaz.libproxy.ruc.edu.cn/kcms2/article/abstract?v=uagkXMi-j-U-Hycqh6BhK7W2aoJiD5d03uFvrrDCsJtdRdX1Oi04YYkaGHVq4vyhqxB23FKfpQbUnX0x7r6E6R5DgmvBBI3TuCGm\u003cbr\u003eKP0WhS1KTI_WVtAmB11OceuKOTNj9o9RCREMLfTEpwy8P_wsjvQVqV2xSUAbfua7_LAjrKJgpr\u003cbr\u003e_r1rCkoLuzAv9rENpuV-\u003cbr\u003e-3dQLpQUM=anduniplatform=NZKPTandlanguage=CHS\u003c/li\u003e\n\u003cli\u003eMartin, B. C., McNally, J. J., \u0026amp; Kay, M. J. (2012). Examining the formation of human capital in entrepreneurial activities: A meta-analysis of entrepreneurship education outcomes. \u003cem\u003eJournal of Business Venturing\u003c/em\u003e, 28(2), 211\u0026ndash;224. https://doi.org/10.1016/j.jbusvent.2012.03.002\u003c/li\u003e\n\u003cli\u003eMetrick, A. (2006). Venture capital and the finance of innovation. http://depot.som.yale.edu/icf/papers/fileuploads/2689/original/2011_ICF_WPS_The_Best_Venture_Capitalists_-_Metrick.pdf\u003c/li\u003e\n\u003cli\u003eNational Science Foundation. (2023, June 5). NSF announces 7 new National Artificial Intelligence Research Institutes. NSF - National Science Foundation. https://new.nsf.gov/news/nsf-announces-7-new-national-artificial\u003c/li\u003e\n\u003cli\u003eNicolas, T. (2021). Short-term financial constraints and SMEs\u0026rsquo; investment decision: evidence from the working capital channel. \u003cem\u003eSmall Business Economics\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(4), 1885\u0026ndash;1914. https://doi.org/10.1007/s11187-021-00488-3\u003c/li\u003e\n\u003cli\u003eNoseleit, F. (2012). Entrepreneurial activities, structural change, and economic growth. \u003cem\u003eJournal of Evolutionary Economics\u003c/em\u003e, 23(4), 735\u0026ndash;766. https://doi.org/10.1007/s00191-012-0291-3\u003c/li\u003e\n\u003cli\u003eObschonka, M., \u0026amp; Audretsch, D. B. (2019). Artificial intelligence and big data in entrepreneurial activities: a new era has begun. \u003cem\u003eSmall Business Economics\u003c/em\u003e, 55(3), 529\u0026ndash;539. https://doi.org/10.1007/s11187-019-00202-4\u003c/li\u003e\n\u003cli\u003eOlutimehin, N. D. O., Ofodile, N. O. C., Ejibe, N. I., Odunaiya, N. O. G., \u0026amp; Soyombo, N. O. T. (2024). IMPLEMENTING AI IN BUSINESS MODELS: STRATEGIES FOR EFFICIENCY AND INNOVATION. \u003cem\u003eInternational Journal of Management \u0026amp; Entrepreneurial activities Research\u003c/em\u003e, 6(3), 863\u0026ndash;877. https://doi.org/10.51594/ijmer.v6i3.940\u003c/li\u003e\n\u003cli\u003eParteka, A., \u0026amp; Kordalska, A. (2023). Artificial intelligence and productivity: global evidence from AI patent and bibliometric data. \u003cem\u003eTechnovation\u003c/em\u003e, 125, 102764. https://doi.org/10.1016/j.technovation.2023.102764\u003c/li\u003e\n\u003cli\u003ePloyhart, R. E., \u0026amp; Moliterno, T. P. (2011). Emergence of the Human Capital Resource: a multilevel model. \u003cem\u003eAcademy of Management Review\u003c/em\u003e, 36(1), 127\u0026ndash;150. https://doi.org/10.5465/amr.2009.0318\u003c/li\u003e\n\u003cli\u003eReim, W., \u0026Aring;str\u0026ouml;m, J., \u0026amp; Eriksson, O. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business model innovation. AI, 1(2), 180\u0026ndash;191. https://doi.org/10.3390/ai1020011\u003c/li\u003e\n\u003cli\u003eTimmons, J.A., Spinelli, S., \u0026amp; Adams, R. J. (2014). \u003cem\u003eNew venture creation entrepreneurial activities for the 21st century.\u003c/em\u003e https://ci.nii.ac.jp/ncid/BA91287795\u003c/li\u003e\n\u003cli\u003eTownsend, D. M., \u0026amp; Hunt, R. A. (2019). Entrepreneurial action, creativity, \u0026amp; judgment in the age of artificial intelligence.\u003cem\u003e Journal of Business Venturing Insights\u003c/em\u003e, 11, e00126. https://doi.org/10.1016/j.jbvi.2019.e00126\u003c/li\u003e\n\u003cli\u003eVan Praag, M., De Wit, G., \u0026amp; Bosma, N. (2005). Initial capital constraints hinder entrepreneurial venture performance. \u003cem\u003eThe Journal of Private Equity\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 36\u0026ndash;44. https://doi.org/10.3905/jpe.2005.605369\u003c/li\u003e\n\u003cli\u003eVan Stel, A., Carree, M., \u0026amp; Thurik, R. (2005). The effect of entrepreneurial activity on national economic growth. \u003cem\u003eSmall Business Economics\u003c/em\u003e, 24(3), 311\u0026ndash;321. https://doi.org/10.1007/s11187-005-1996-6\u003c/li\u003e\n\u003cli\u003eVecchiarini, M., \u0026amp; Somi\u0026agrave;, T. (2023). Redefining entrepreneurial activities education in the age of artificial intelligence: An explorative analysis. \u003cem\u003eThe International Journal of Management Education\u003c/em\u003e, 21(3), 100879. https://doi.org/10.1016/j.ijme.2023.100879\u003c/li\u003e\n\u003cli\u003eWey, W., \u0026amp; Huang, J. (2018). Urban sustainable transportation planning strategies for livable City\u0026rsquo;s quality of life. \u003cem\u003eHabitat International\u003c/em\u003e, 82, 9\u0026ndash;27. https://doi.org/10.1016/j.habitatint.2018.10.002\u003c/li\u003e\n\u003cli\u003eWong, P. K., Ho, Y. P., \u0026amp; Autio, E. (2005). Entrepreneurial activities, Innovation and Economic Growth: Evidence from GEM data. \u003cem\u003eSmall Business Economics\u003c/em\u003e, 24(3), 335\u0026ndash;350. https://doi.org/10.1007/s11187-005-2000-1\u003c/li\u003e\n\u003cli\u003eXu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., \u0026amp; Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. \u003cem\u003eThe Innovation\u003c/em\u003e, 2(4). https://doi.org/10.1016/j.xinn.2021.100179\u003c/li\u003e\n\u003cli\u003eZhan, H., Hou, M., \u0026amp; Tan, F. (2022). Influence of intelligentization on enterprise green innovation: Evidence from listed companies of new energy industry in China. \u003cem\u003eResource Science\u003c/em\u003e, 44(5), 984\u0026ndash;993. https://doi.org/10.18402/resci.2022.05.09\u003c/li\u003e\n\u003cli\u003eZhi yang, L., \u0026amp; Zemin, W. (2020). AI Enabling Entrepreneurial activities: Comparison of theoretical frameworks. \u003cem\u003eForeign Economies Management\u003c/em\u003e, 42(12), 3\u0026ndash;16. https://doi.org/10.16538/j.cnki.fem.20201020.101\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes ","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003e Artificial Intelligence Index Report 2024\u003c/span\u003e \u003c/li\u003e\u003cli\u003e\u003cspan\u003e Whitepaper on the Global Digital Economy 2024\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence Technology, Entrepreneurial activities, Entrepreneurship education, Investment","lastPublishedDoi":"10.21203/rs.3.rs-5648468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5648468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe emergence of artificial intelligence technology has led to transformative advancements across various industries, particularly in innovative sectors, creating both new challenges and opportunities. This study systematically examines the impact of artificial intelligence technology on global entrepreneurial activities. The basic theoretical framework is constructed using entrepreneurial factor theory and human capital theory, and a two-way fixed effects model along with a mediation effect model are employed to study the impact of artificial intelligence technology on corporate entrepreneurial activities in 36 countries and regions from 2010 to 2023, spanning a total of 14 years. The results revealed that innovation in artificial intelligence technology significantly promotes entrepreneurial activities, particularly opportunity-driven ventures. Notably, the impact is especially significant for entrepreneurs aged 18\u0026ndash;34 and those in the tertiary sector. Additionally, artificial intelligence technology positively influences entrepreneurial activities through two paths: entrepreneurship education and artificial intelligence investment. In terms of research contributions, this study first identified two paths through which artificial intelligence technology influences entrepreneurial activities. It revealed the heterogeneous effects of artificial intelligence technology across different entrepreneurial types, age groups, and industries. The research also made academic contributions to the application of artificial intelligence technology in economics and innovation sectors, providing valuable insights and support for relevant stakeholders.\u003c/p\u003e","manuscriptTitle":"Technology Empowering Innovation: The Impact of Artificial Intelligence on Global Entrepreneurial Activities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 11:51:41","doi":"10.21203/rs.3.rs-5648468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14305b75-876a-45d2-bf51-76fa004bb8a3","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T08:24:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 11:51:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5648468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5648468","identity":"rs-5648468","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}