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date: 01 October 2022

Artificial Intelligence and Entrepreneurship Researchfree

Artificial Intelligence and Entrepreneurship Researchfree

  • Martin ObschonkaMartin ObschonkaFaculty of Economics and Business, University of Amsterdam
  •  and Christian FischChristian FischEntrepreneurship, Innovation, and New Technology, University of Luxembourg

Summary

Advances in Artificial Intelligence (AI) are intensively shaping businesses and the economy as a whole, and AI-related research is exploding in many domains of business and management research. In contrast, AI has received relatively little attention within the domain of entrepreneurship research, while many entrepreneurship scholars agree that AI will likely shape entrepreneurship research in deep, disruptive ways.

When summarizing both the existing entrepreneurship literature on AI and potential avenues for future research, the growing relevance of AI for entrepreneurship research manifests itself along two dimensions. First, AI applications in the real world establish a distinct research topic (e.g., whether and how entrepreneurs and entrepreneurial ventures use and develop AI-based technologies, or how AI can function as an external enabler that generates and enhances entrepreneurial outcomes). In other words, AI is changing the research object in entrepreneurship research.

The second dimension refers to drawing on AI-based research methods, such as big data techniques or AI-based forecasting methods. Such AI-based methods open several avenues for researchers to gain new, influential insights into entrepreneurs and entrepreneurial ventures that are more difficult to assess using traditional methods. In other words, AI is changing the research methods. Given that, so far, human intelligence could not fully uncover and comprehend the secrets behind the entrepreneurial process that is so deeply embedded in uncertainty and opportunity, AI-supported research methods might achieve new breakthrough discoveries.

We conclude that the field needs to embrace AI as a topic and research method more enthusiastically while maintaining the essential research standards and scientific rigor that guarantee the field’s well-being, reputation, and impact.

Subjects

  • Entrepreneurship
  • Research Methods
  • Technology and Innovation Management

Introduction

Because of its disruptive nature, artificial intelligence (AI) is a popular and often controversial topic. McCarthy (2007, p. 2), a pioneering figure in the advancement of AI, defines it as “the science and engineering of making intelligent machines, especially intelligent computer programs.” One of the leading textbooks on AI (Russel & Norvig, 2021, p. 19) describes AI as the field “concerned with not just understanding but also building intelligent entities—machines that can compute how to act effectively and safely in a wide variety of novel situations.” Lévesque et al. (2020, p. 2) add that AI “comprises machine-based, intelligent learning and problem-solving techniques” and that AI “encompasses the new, widely accessible, advanced research techniques facilitated by recent major advances in computer power, big data, and computer science.” Hence, this article uses an understanding of AI that includes advanced technologies such as machine learning and deep learning (e.g., deep neural networks).

AI-related research is increasing in many, if not all, domains of business and management research. This rapid rise is driven by the increasing implementation of AI in the world of practice, where advances in AI are intensively shaping businesses and the economy (Zhang et al., 2021). Examples of AI-related research in business and management research are works from management (e.g., Phan et al., 2017), marketing (e.g., Huang & Rust, 2021; Mustak et al., 2021), operations management (e.g., Grover et al., 2020), strategy and leadership (e.g., Iansiti and Lakhani, 2020), business model innovation (Burström et al., 2021), human resources (e.g., Tambe et al., 2019), accounting (Moll & Yigitbasioglu, 2019), or career research (Gati & Kulcsár, 2021).

In contrast, AI as a research topic has received relatively little attention within the domain of entrepreneurship research to date (e.g., Townsend & Hunt, 2019; Williamson et al., 2022). It seems safe to assume, however, that AI will likely shape entrepreneurship research, including theory-building and -testing, in deep, disruptive ways (e.g., Lévesque et al., 2020; Obschonka & Audretsch, 2020; Shepherd & Majchrzak, 2022), just like other business and management fields. In a recent survey of entrepreneurship scholars, consensus was relatively broad that AI will have a dramatic impact on entrepreneurship practice, imposing novel research questions such as “How, why, and under what conditions might AI . . . substitute for, rather than support entrepreneurial activity?” (Van Gelderen et al., 2021, p. 1270).

To add to this debate, this work aims to provide an overview of existing AI-related entrepreneurship research and to develop a comprehensive agenda for future research. The article considers the intersection of entrepreneurship research and AI along two distinct pathways. The first path refers to studying AI as a research topic. Studying AI as a research topic mainly refers to understanding whether and how entrepreneurs and entrepreneurial ventures use and develop AI-based technologies. For example, entrepreneurial ventures often act as critical catalysts in the development and commercialization of new technologies such as AI. Hence, understanding their strategies, success factors, and obstacles is essential. Moreover, AI can function as an external enabler that generates and enhances entrepreneurial outcomes. The second pathway for future entrepreneurship research refers to drawing on AI-based research methods, such as big data techniques or AI-based forecasting methods. Such AI-based methods open several avenues for researchers to gain new, influential insights into entrepreneurs and entrepreneurial ventures that are more difficult to assess using traditional methods. Thus, a better understanding of AI-based research methods advances the methodological toolkit available for entrepreneurship researchers and can help craft impactful, future-oriented research.

The remainder of the article proceeds as follows. The next section briefly describes the field of entrepreneurship research. The following section reviews existing research on AI and entrepreneurship and briefly outlines the potential impact of AI along this process. The penultimate section explicates several promising research frontiers at the intersection of AI and entrepreneurship. This section also describes challenges and barriers for the research field. The final section provides a brief conclusion.

The Domain of Entrepreneurship Research

Entrepreneurship is a relatively young research field in the business research domain, and competing views on what entrepreneurship research studies are still coexist (Davidsson, 2021).

The most prominent definition of entrepreneurship research was put forward by Shane and Venkataraman (2000), who define entrepreneurship research as “the scholarly examination of how, by whom, and with what effects opportunities to create future goods and services are discovered, evaluated, and exploited” (Shane & Venkataraman, 2000, p. 218). The term “opportunity” is at the core of this Shanian view of entrepreneurship research (see Davidsson, 2021), according to which entrepreneurial opportunities are “situations in which new goods, services, raw materials, and organizing methods can be introduced and sold at greater than their cost of production” (Shane & Venkataraman, 2000, p. 220). This definition highlights three distinct real-world targets of entrepreneurship research that are connected to entrepreneurial opportunities: (a) the study of the sources of opportunities; (b) the process of discovering, evaluating, and exploiting opportunities; and (c) the study of the individuals who engage with entrepreneurial opportunities. Importantly, a special focus in this Shanian view lies in the fit between the entrepreneurial individual and the opportunity, coined the individual–opportunity nexus (e.g., Eckhardt & Shane, 2010; Shane, 2003; Shane & Venkataraman, 2000). The entrepreneur is the single most important agent in the entrepreneurial process. The entrepreneur is the individual who recognizes, evaluates, and exploits entrepreneurial opportunities. Also, the entrepreneur–opportunity (E–O) nexus framework is linked to the fundamental structure–agency duality (Giddens, 1979) and to a fundamental understanding of the characteristic interplay between individual and entrepreneurial opportunity (see Davidsson, 2021).

The Shanian view of entrepreneurship has received criticism, and a major subject of debate is the opportunity concept itself (e.g., Foss & Klein, 2020; McMullen et al., 2007; Wright & Phan, 2020). Alvarez and Barney (2007), for example, highlight the distinction between this discovery view and the creation view. According to the creation view, opportunities are not objectively preexisting phenomena waiting to be discovered by the entrepreneur (discovery view) but are cocreated by entrepreneurs and other stakeholders (Sarasvathy, 2001). An alternative paradigm is the venture creation view (Davidsson, 2021). This view suggests replacing the abstract concept of entrepreneurial opportunity with the more concrete and better quantifiable term venture creation, which denotes the “journey from non-existence to existence of new ventures” (Davidsson, 2021, p. 8).

Notwithstanding these essential debates, it is likely that the field of entrepreneurship research will remain focused on opportunity-related concepts in the near future (Van Gelderen et al., 2021) as it has been over the past two decades (Busenitz et al., 2014). Moreover, there is a broad consensus in the field that a process perspective to entrepreneurship is the leading general paradigm (Shepherd et al., 2019). It is thus useful to start conceptualizing the impact of AI on the entrepreneurial process for each stage of this process separately (Chalmers et al., 2020), for example, with reference to opportunity-related concepts and a venture creation focus.

How Can AI Influence Entrepreneurship Research? A Review of Existing Research

Research on AI and entrepreneurship is still in its infancy. This section highlights several recent articles that constitute initial attempts to conceptualize the impact of AI on entrepreneurship research. As noted in the Introduction, we make a broad distinction between studies that focus on AI as a research topic and studies that draw on AI-based methods as part of their research design.

Studies on AI as a Research Topic

Chalmers et al. (2020) provide a comprehensive review of the potential impact of AI on entrepreneurship practice. Most of the areas highlighted by Chalmers et al. (2020) open promising avenues for studying AI as a research topic. Specifically, the authors develop a conceptual framework for how AI affects entrepreneurial ventures’ processes, practices, and outcomes. Their conceptual model considers the impact of AI on (a) the antecedents of venture creation; (b) firm-level activities that involve the prospecting and production of new ideas; and (c) the outcomes of venture creation. Chalmers et al. (2020) outline how AI could influence each of these areas in practice, while also deriving opportunities for entrepreneurship research based on their observations. Regarding (a) the antecedents of venture creation, the authors consider how AI could affect the likelihood of engaging in entrepreneurship, as well as the types of ventures that are founded. For example, Chalmers et al. (2020) highlight an increasing dominance of AI-driven technology platforms that might lead to a heightened mediation of entrepreneurial opportunities by private firms, which could lead to stagnating entrepreneurial activity. Part (b) of their framework refers to the impact of AI on the processes of entrepreneurial ventures involved in the identification and exploitation of entrepreneurial ideas. In this area, the authors highlight the importance of leveraging large data sets and learning algorithms in future entrepreneurial activities, for example in the search for and prediction of new ideas and innovations. This includes potential changes to the organizational design of ventures, which will reform around AI systems and create new job roles in entrepreneurial ventures, while other jobs will be increasingly outsourced. Finally, (c) the outcomes of venture creation, refers to how the rewards of entrepreneurship can be captured. For example, the authors speculate that AI will enable entrepreneurs with great technological literacy to capture higher rewards with less effort due to an AI-based automation of selling activities and superior market insights.

Obschonka and Audretsch (2020), in their editorial of a special issue on AI and entrepreneurship research in the field journal Small Business Economics, consider the implications of AI for both research and practice, and the reciprocal relationship between research and practice. Regarding entrepreneurship research, the authors name various avenues at the intersection of AI and entrepreneurship that warrant more conceptual and empirical studies. These avenues include the domains of entrepreneurship education and entrepreneurship policy, and business model processes. Regarding the impact of AI on real-world entrepreneurship, Obschonka and Audretsch (2020) first focus on AI’s role as an external enabler for new entrepreneurial activity, which can generate strategically actionable knowledge (Kimjeon & Davidsson, 2021). Second, they consider AI’s potential to alter how individuals participate in entrepreneurship. For example, in an extreme case, AI might partially replace entrepreneurs, whereas, in a less extreme and probably more realistic scenario, AI-supported entrepreneurial behavior and decision-making might not replace entrepreneurs but, rather, help them to be more effective and efficient in the entrepreneurial process. Third, the authors describe how AI could change how entrepreneurship is taught and trained, especially considering that AI might make several human tasks obsolete, while simultaneously demanding higher technological expertise from entrepreneurs.

Shepherd and Majchrzak (2022) provide a comprehensive description of the nexus of entrepreneurship and AI, and focus on AI-enabled opportunities for entrepreneurship. For example, they highlight opportunities for entrepreneurs that arise from AI redistributing occupational skills in the economy and opportunities that expand the role of humans in developing AI. The authors then describe several avenues for future entrepreneurship research that are critical for understanding the intersection of AI and entrepreneurship. Specifically, the authors discuss how AI may augment an interactive perspective of entrepreneurship (e.g., when entrepreneurs interact with stakeholders or when noticing opportunities) and that AI can augment activitiy-based entrepreneurship (e.g., the process of new venture creation).

Studies on AI as a Research Method

The second pathway for how AI might be able to help advance entrepreneurship research is concerned with methodological aspects, and refers to the promise of using AI-based research techniques in effective and efficient ways to make new scientific discoveries in the field. Entrepreneurship researchers have thus begun reflecting on AI from a method perspective (e.g., Schwab & Zhang, 2019). While some scholars have discussed scenarios where AI might even replace human researchers (e.g., in the context of literature reviews, Robledo et al., 2021; see also Obschonka & Audretsch, 2020), AI might be able to support (human) scholars in fundamental ways, just as it could support (human) entrepreneurs in fundamental ways. This should be particularly the case when the individual scholar is able to comprehend AI results, where AI and human understanding go hand in hand, also termed “explainable AI” (see, for example, Rai, 2020).

The article by Lévesque et al. (2020) provides a comprehensive overview of how AI-based methods can support entrepreneurship research in rigorous and impactful ways. The authors describe AI as a potential means of increasing the relevance and impact of entrepreneurship research, which is currently one of the field’s grand challenges (Wiklund et al., 2019). However, this potential increase in impact is at odds with the traditional theory-based research process that is currently prevalent in entrepreneurship research (the focus on theory and theory testing, development, and building). To overcome this dilemma and to keep a traditional theory focus while embracing the new AI methods in research, Lévesque et al. (2020) explore a variety of opportunities and challenges that come with the use of AI in entrepreneurship research and offer suggestions for leveraging these opportunities and for overcoming challenges. Specifically, Lévesque et al. (2020) develop two scenarios that they consider likely for how AI will affect entrepreneurship research: the “disruption” scenario, according to which AI will radically change established research methods and practices (e.g., a radical transformation of the research process with an integration of AI methods into theory building and testing); and the “it’s all hype” scenario, according to which entrepreneurship research will stick to its established research methods and practices. While the “disruption” scenario marks a radical change to how entrepreneurship research is conducted, the “it’s all hype” scenario still offers some opportunities for advancing entrepreneurship research. Lévesque et al. (2020) also provide a comprehensive view of AI-enabled opportunities (e.g., increase in construct validity, more effective testing using machine learning algorithms) and challenges (e.g., evaluating AI bias potential, unfamiliarity with the AI context) for entrepreneurship research. Finally, while focusing on the effect of AI on the research dimensions of relevance and rigor, the authors also consider how AI could affect entrepreneurship research’s application contexts, methodologies, and institutional forces.

Townsend and Hunt (2019) focus on how AI affects entrepreneurial uncertainty and action. The authors review the leading theories of entrepreneurial action, such as effectuation theory, and outline AI research opportunities. For example, Townsend and Hunt (2019) highlight a research opportunity regarding effectuation theory in the role of creative AI. Creative AI could augment humans in the design of artifacts, such as products, which would benefit from AI-based customer insights.

Other entrepreneurship studies already draw on AI-based research methods as part of their research design. For example, several studies use AI algorithms to generate novel entrepreneurship data on personalities and emotions. Obschonka et al. (2017) and Obschonka and Fisch (2018) use an AI-based algorithm to infer the Big Five personality traits based on entrepreneurs’ Twitter messages. The authors then compare the personality of these entrepreneurs to the personalities of managers. Block et al. (2019) use the same AI-based algorithm to infer the personality traits of business angels, wealthy individual investors who provide early-stage financing to young startups. The authors empirically investigate how business angels’ personality traits influence their investment behavior and investment success. Obschonka et al. (2020) use a similar approach of Twitter-based personality estimates aggregated at the regional level to explain geographical differences in the prevalence of entrepreneurship across US counties. While Williamson et al. (2022) use AI to recognize passion in the context of social ventures, Momtaz (2021) uses an AI algorithm to infer positive and negative affect from photos of CEOs pictures for a sample of entrepreneurial ventures seeking funding. The results show that investors discount entrepreneurial ventures more heavily if the CEO displays negative affect. Assessing whether AI processes can increase investors’ returns, Blohm et al. (2021) show that their AI algorithm does outperform the average business angel in their sample in terms of realized investment returns. The algorithm’s outperformance is highest in comparison to novice investors, while a group of very experienced investors outperforms their algorithm. Focusing on venture success, Van Witteloostuijn and Kolkman (2019) apply machine learning to explore venture growth and find that machine learning techniques may explain firm growth better than traditional regression approaches. Focusing on firm survival, Antretter et al. (2019) use a machine learning model to predict venture success and failure based on ventures’ Twitter accounts. The algorithm can predict five-year survival with an accuracy of up to 76%.

Apart from these initial studies that apply AI-based methods to entrepreneurship research that mostly uses AI to generate data and measures, more comprehensive implementations of AI (e.g., deep learning) are still rare in entrepreneurship research (Lévesque et al., 2020).

New Frontiers in Entrepreneurship Research

With AI a topic or research method still widely under the radar in contemporary entrepreneurship research, several impactful research questions deserve urgent attention. This section lists a few of them. The potential and promise of AI for entrepreneurship research should be greater, but also depends on the creativity and risk-taking of entrepreneurship scholars (Lévesque et al., 2020). Again, this article distinguishes between frontiers that refer to AI as a research topic and frontiers that arise from using AI-based research methods (and they may sometimes overlap).

Future Research Opportunities for Studying AI as a Research Topic

AI Replacing Entrepreneurs Versus AI Supporting Entrepreneurs

One of the most fascinating and potentially impactful research questions in the context of AI and entrepreneurship is whether (and to what extent) AI might replace human entrepreneurs, as well as human entrepreneurial activity and decision-making (e.g., Obschonka & Audretsch, 2020; see also Van Gelderen et al., 2021). Alan Turing, one of the fathers of AI, once famously asked, “Can machines think?” (Turing, 1950, p. 433). This has been followed by progress over the past 70 years in actually “building machines that learn and think like people” (Lake et al., 2017).

While this discussion is still largely absent in the entrepreneurship literature, a closer look at the broader literature on AI and work reveals that, historically, the question of how AI will impact employment is often assessed in terms of AI either replacing or augmenting existing jobs and human work tasks. According to the replacement view, AI has a vast potential to eliminate jobs across a range of categories (e.g., Frey & Osborne, 2017). In contrast, the augmentation view argues that AI will complement and enhance human work, so that humans have to upgrade their skills to master AI-based work tasks (Daugherty & Wilson, 2018)—which might also apply to entrepreneurs (e.g., Shepherd & Majchrzak, 2022). Tschang and Almirall (2021) seek to integrate both views and suggest that the era of AI-based automation is likely going to lead to a highly technical workforce, in which most of the returns accrue for a small number of very technical individuals, while others might be replaced by AI. Again it needs to be discussed and explored whether this might also apply to entrepreneurs. We can already witness ongoing, sometimes heated discussions about the actual impact of AI on single professions. For example, psychiatrists are currently engaged in a lively debate whether or not AI will replace their profession, with good arguments for the pros and cons (see Brown et al., 2021). We call on entrepreneurship researchers to engage in similar discussions and related research projects.

To illustrate, entrepreneurship research should prioritize the fundamental research questions that explore whether machines can also think in entrepreneurial ways, despite our still very underdeveloped understanding of entrepreneurial cognitions and the link between human intelligence and entrepreneurship (Obschonka & Audretsch, 2020). Envisioning and researching how AI can augment and support human entrepreneurs along the entrepreneurial process in concrete ways seems to be particularly important and potentially impactful (e.g., Shepherd & Majchrzak, 2022), and it has been highlighted that this could also involve perspectives from design science in pragmatic ways (e.g., Berglund et al., 2020; Nambisan, 2017; Zhang & Van Burg, 2020). For example, how can AI contribute to processes of entrepreneurial design (e.g., via artifacts such as pitches, business models, or prototypes) that help developing opportunities-as-artifacts iteratively, at the interface between intelligent machines (or AI-supported humans) and their environments. Entrepreneurship research should also draw from the progress and discussions in fundamental AI research and the cognitive sciences addressing the question how AI can emulate human cognitions (e.g., memory, attention, planning, and decision-making). Experts highlight that despite major progress in these fields, many challenges still exist. It seems, at least so far, it is fair to conclude that AI cannot completely replace human cognition (Jiménez et al., 2021; Kotseruba & Tsotsos, 2020), which might also apply to entrepreneurial cognition. Future entrepreneurship research could study AI and its application from a perspective of entrepreneurial cognitive architectures—toward a cognitive design for artificial entrepreneurial minds, so to speak (Lieto, 2021). This might also involve entrepreneurial decision-making utilizing intuition and heuristics that go beyond mere rational human decision-making processes (Frantz, 2003; Gigerenzer & Todd, 1999; Simon, 2019).

Digital Entrepreneurship

The topic of AI has a natural link to the broader trend of digitization, which has transformed the global economy in deep ways. The intersection of digitization and entrepreneurship is at the core of digital entrepreneurship, a soaring field at the cutting edge of entrepreneurship research (e.g., Bertoni et al., 2021; Nambisan, 2017; Sahut et al., 2021). Digital entrepreneurship includes the impact of digital technologies on the outcomes (e.g., products and services) and processes of entrepreneurship.

AI is one of the drivers of digital transformation (e.g., Brynjolfsson & McAfee, 2014). Thus, AI is also critical for the rise of digital entrepreneurship, and the intersection of digital entrepreneurship and AI opens several grand research avenues. For example, future entrepreneurship research could assess how AI can help ventures to capitalize on the opportunities enabled by digital transformation. This refers, for example, to new digital business ideas and business models that build on AI, and to how AI can be used to improve processes in existing entrepreneurial ventures. Another research avenue is the effect of using AI on the success of entrepreneurial ventures. For example, does AI help entrepreneurial ventures to outcompete their peers? Does AI help ventures to scale faster and create more jobs? Finally, policy-makers are interested in stimulating digital transformation. Future research could assess how best to develop and implement policies that encourage the development and utilization of AI technology by entrepreneurial ventures. The nurturing of AI ventures is going to be a critical factor for the future competitiveness of countries.

Entrepreneurial Finance

The impact of AI on the domain of entrepreneurial finance is another research frontier. Entrepreneurial ventures typically need to secure external financing to scale up their business and to succeed. This funding typically comes from entrepreneurial finance investors such as venture capitalists or business angels. These financial intermediaries specialize in financing young, innovative ventures, which are often highly uncertain but can lead to huge payoffs. Venture capitalists typically seek to invest in these high-risk, high-reward projects. Because entrepreneurial ventures with an AI-based business model are often highly uncertain but potentially revolutionary, they attract venture capital investment. Indeed, the recent decade has seen a huge increase in the funding of AI ventures, which are considered a hot market among venture capital investors that is projected to become even hotter in the near future (e.g., Schmelzer, 2020; Wilhelm and Heim, 2021).

Focusing on the intersection of AI and entrepreneurial finance as a research topic, future research could test, for example, to what extent and in what ways AI could help entrepreneurs to acquire essential financial resources (e.g., by supporting entrepreneurial pitches of their proposed venture or even current new venture). Another promising research area is assessing how investors evaluate AI ventures. A better understanding of how investment decisions about AI ventures are made would help these ventures to better understand how to position themselves to acquire funding, which is essential for their prospects of success. Finally, future research could focus on AI ventures that successfully acquired venture capital (VC) funding and assess how VC funding affects the course of these ventures. This research is related to the vast literature in entrepreneurial finance that assesses the “treatment effect” of receiving VC financing (e.g., Bertoni et al., 2011).

Formalizing Entrepreneurial Uncertainty

The problem of formalizing entrepreneurship, which involves operationalizing entrepreneurial uncertainty and opportunity, is one of the greatest challenges in entrepreneurship research as a scientific domain, yet it is rarely discussed explicitly. Applied AI often relies on rule-driven approaches to real-world problems and challenges. This raises the question whether AI will ever be able to truly operationalize entrepreneurial uncertainty and opportunity in better ways than human intelligence can (Davidsson, 2021). If AI can indeed solve these fundamental issues that pose a significant challenge to scientific progress in contemporary entrepreneurship research, one has to go one step further and ask what that means for the entrepreneurship in practice. For example, it refers to how the nature of competition and market dynamics might change when algorithms, instead of humans, make entrepreneurial decisions; when AI has truly outperformed entrepreneurial humans not only in research but also in the capacity to employ intelligent solutions in entrepreneurial tasks; and when, simply put, AI algorithms compete with each other (and not against humans any more), based on superior (unbiased) data and technology.

Research on Entrepreneurship and Democracy

Another topical research opportunity concerns the broader discussion on entrepreneurship and democracy. In a recent influential paper, Audretsch and Moog (2020) highlight the decline of entrepreneurship in many countries (see also Haltiwanger, 2022), and outline historical and present-day links to (declining) democracy. The authors conclude that “an important policy mandate for entrepreneurship may be to ensure the independent, decentralized and autonomous decision-making serving as a cornerstone of democracy” (Audretsch & Moog, 2020, p. 1). Such dynamics might be particularly prevalent, or likely, in the context of AI-supported entrepreneurship (e.g., startups based on business models exploiting AI technologies). Future entrepreneurs’ access to the latest software, technology (e.g., powerful computers), and unbiased data might be particularly impeded by such societal and political trends threatening independent, decentralized, and autonomous decision-making, where incumbents and other large institutions might enjoy and protect a certain competitive advantage due to better access to AI technology and relevant data (see, for example, discussions of digital entrepreneurship restricted by digital platforms as “entrepreneurial ecosystems,” owned and dominated by incumbents; Nambisan et al., 2018, 2019).

Future research could therefore address in greater detail whether AI and its increasing application might indeed contribute to a declining democratizing of entrepreneurship, or whether we might see an opposite effect, where AI technology could help foster and secure the independent, decentralized, and autonomous decision-making that is at the heart of democracy (and entrepreneurship).

Ethical Challenges Associated with AI Applications

Finally, research should also devote special attention to the ethical and fair use of AI in entrepreneurial practice. Ethical challenges and respective guidelines for practice have become a very important topic in the general AI literature (e.g., Jobin et al., 2019). For example, scholars have been discussing ethical and trustworthy applications of AI that are fair and do not introduce, maintain, or amplify social bias (e.g., Floridi, 2019; Zou & Schiebinger, 2018). Many (future) entrepreneurs might not be fully aware of such potential ethical issues and dilemmas associated with AI, and entrepreneurship research has a particular mandate to deliver and disseminate impactful, actionable insights in this regard. Ethical issues and dilemmas play an important role for many entrepreneurs but can also differ substantially across cultures (Bucar et al., 2003; Robinson et al., 2007). With the rise of AI and its application in entrepreneurial practice come new, but so-far underresearched and not fully understood ethical challenges and dilemmas for entrepreneurs and their business ventures.

For example, as highlighted in Chalmers et al. (2020), AI could be used for unethical business practices such as manipulating potential and existing customers and creating dependencies or even addiction to certain products. This also involves important privacy issues when AI is used to exploit private data (e.g., from social media) for business purposes. Some entrepreneurs might face the dilemma that they want to exploit “fascinating” new business opportunities arising from AI applications while obviously operating in ethical gray zones, where productive entrepreneurial rule-breaking can quite easily turn into unproductive or destructive entrepreneurial rule-breaking. Business scholars have repeatedly highlighted the urgent need to define what “ethical AI” for businesses exactly entails, that it might require, for example, both a responsible (e.g., human-centric, fair, harmless) and accountable approach (Thomaz et al., 2021). From this perspective, entrepreneurs should be encouraged to apply AI ethically in responsible and accountable manners. Future entrepreneurship research could study mechanisms how different applications of AI for entrepreneurship (e.g., ethical vs. unethical applications) actually lead to productive, unproductive, and destructive forms of entrepreneurship (Baumol, 1993), and how entrepreneurs can be empowered to be aware of, and effectively deal with ethical challenges so that AI is used to promote productive (instead of unproductive or destructive) entrepreneurship.

Method-Related Opportunities and Challenges for Future AI-Based Entrepreneurship Research

Scholars have reflected on the application of AI-based research methods in entrepreneurship research (e.g., Lévesque et al., 2020; Obschonka & Audretsch, 2020; Robledo et al., 2021; Schwab & Zhang, 2019) but more methods-focused work is needed to guide scholars in the field. Future research should devote much more attention to methodological implications of the current AI revolution, to inform and empower the great diversity of entrepreneurship scholars (and not only those with a preexisting affinity for advanced statistical methods). This could also help to educate scholars in the field about how to access, use, and interpret such research methods and the results they deliver (e.g., when reviewing and reading the works of others). In other words, it will be important that AI advances become part of mainstream entrepreneurship research, instead of a niche in the field.

Data Versus Theory?

With the new availability of big data (e.g., from social media, Williamson et al., 2020) and AI-based research methods (e.g., machine learning/deep learning), scholars might be tempted to follow a strictly exploratory, data-driven research process—to let the data speak, in contrast to a hypothetico-deductive model that starts with preexisting knowledge and frameworks (e.g., theories). This raises the question of to what extent and in what way scholars and other stakeholders (e.g., practitioners, educators, policy-makers) should still rely on theory in this new data-driven world, in particular because entrepreneurship research has been criticized by some as being relatively “theory-free” with respect to entrepreneurship-specific theories (Suddaby et al., 2015). Starting from the premise that theory (and thus theory-building and -testing) is indeed essential for entrepreneurship research and its various stakeholders (e.g., Baker & Welter, 2020; Baumol, 1993; Phan, 2004; Shepherd & Suddaby, 2017), it seems advisable to intensify the use of AI-based research methods in theory-building and -testing in strategic ways (Lévesque et al., 2020). This would imply that data (and AI-based research methods) do not compete with a theory-driven approach but, rather, they are complementary. Thus, the analysis of data sets can be informed and guided by existing theories, and theories can be built, revised, and extended as a consequence of new research insights from data. Nonetheless, we will probably see much more exploratory, data-driven entrepreneurship research using big data and AI-based research methods, because of the increased availability of big data, but also because such data might allow scholars to analyze and identify patterns that have not been formalized in theories so far (e.g., related to entrepreneurial uncertainty and opportunity).

Moreover, future research should ensure the credibility of the research field by preventing questionable research practices (Brinkerink, 2021), such as HARKing, from becoming more prevalent (e.g., because scholars use big data and AI-based research methods in exploratory ways but then communicate a different research process in their research articles, Murphy & Aguinis, 2019). It is well documented that issues like HARKing can be very harmful to the progress of a research field (e.g., via threats to reproducible science; Munafò et al., 2017).

Rigor and Relevance

In addition to a focus on theory, two critical elements essential for progress in an empirical research field such as entrepreneurship research are rigor and relevance. Scientific rigor (i.e., the strict application of scientific methods to ensure an unbiased research process) is an absolute hygiene factor for an empirical research field. With new complex data and comprehensive AI-based research methods may come new challenges for scholars to ensure a high level of such scientific rigor. It will be important to ensure awareness of such challenges while maintaining a strong focus on achieving rigor. For example, AI-based research results can entail several types of bias (Abadi et al., 2016; Lapuschkin et al., 2019; Norori et al., 2021; Ntoutsi et al., 2020), jeopardizing scientific rigor with various unintended consequences (Suresh & Guttag, 2019), particularly if there is a “blind trust” in such AI results (Logg et al., 2019). Hence, a critical and informed perspective is essential when working with, and interpreting, such complex data as well as AI-based research methods (Rai, 2020). On the other hand, AI-based research methods can enable scholars to increase rigor, for example, when examining multifaceted multilevel phenomena that require complex data and analytic methods, when scholars utilize big data closely related to a phenomenon to address the tensions between construct validity and external validity (Lévesque et al., 2020), or when employing machine learning and deep learning to identify entrepreneurs in historical data sets more accurately than with traditional methods (Montebruno et al., 2020).

While a great deal of attention should be invested in scientific rigor when planning, executing, and communicating such new research, a sole focus on rigor without considering the real-world relevance of the research has been highlighted as potentially dangerous for an applied research field (e.g., Bennis & O’Toole, 2005; Schultz, 2010). As noted earlier, increasing the relevance and impact of entrepreneurship research is currently seen as an important overarching priority in the field (Wiklund et al., 2019), as in many other fields. One of the significant promises of AI-related research is that it should help to generate (more) impactful research insights, with manifold relevance for the real world (Lévesque et al., 2020). Future research that is able to combine rigor and relevance might thus deliver the most influential research insights, for the field but also for the world of practice.

AI Literacy

AI literacy (here defined as the human ability to master AI-based research methods and tools), together with access to technology and unbiased data, will be critical for future, ambitious entrepreneurship research and its scholars aiming to benefit from new AI-based methods not only in a research niche but also in the broader mainstream field. To this end, it will be critical for scholars to invest in their own AI literacy or in strategic partnerships with AI specialists (e.g., data scientists) for interdisciplinary projects that combine expertise in AI-based research methods with expertise in entrepreneurship research. The research field may need to develop more “AI-friendly” research infrastructures, which concern not only tools that help in producing, evaluating, and communicating such research but also specific training programs for both the current research community and, particularly, the next generation of entrepreneurship researchers. Given that AI-based research methods are becoming more mainstream in many research fields, it is important for entrepreneurship research to also make AI literacy more mainstream within the field.

Conclusion

So far, research on the intersection of AI and entrepreneurship is still relatively scarce. To address this shortcoming, this article provides an overview of existing and potential AI-related research in the field of entrepreneurship, and reviews several papers and studies that comprehensively illustrate the virtues of addressing and leveraging AI in entrepreneurship research. A broad distinction can be made between research that views AI as a promising research topic and research that draws on AI-based research methods. Despite some recent advances in the field, it can be concluded that entrepreneurship research is apparently somewhat slower than other domains of business and management research in adopting and exploiting AI as a research topic and method. Whether this signals a certain resistance to innovation or other structural conditions in the field goes beyond the scope of the present work.

We do think, however, that the field needs to embrace AI as a topic and research method more enthusiastically while maintaining the essential research standards and scientific rigor that guarantee the well-being, reputation, and impact of a research field. To stimulate the exploration and adoption of AI as a research topic or research method in entrepreneurship research, this article provides a comprehensive account of influential avenues that future research could pursue. It will be fascinating to see what insights can be generated from future entrepreneurship research that addresses and leverages machine intelligence superseding human intelligence. So far, it seems that human intelligence could not fully uncover and comprehend the secrets behind the entrepreneurial process that is deeply embedded in uncertainty and opportunity. We look forward to what AI can achieve in this regard, be it as part of the research process or as part of the phenomenon of entrepreneurship in practice.

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