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Artificial Intelligence and Big Data in Entrepreneurship: A New Era Has Begun
Martin Obschonka, David B. Audretsch
TL;DR
AI and Big Data are rapidly changing entrepreneurship research and practice, raising concerns about researchers being overwhelmed and human agency being diminished. The paper emphasizes reciprocal interaction between research and practice and proposes entrepreneurial handling of the emerging augmented era.
Problem
Rapid changes in AI and Big Data may overwhelm entrepreneurship research, while reliance on these systems might erase human agency.
Method
The paper presents Big Data techniques and AI-based language analyses of social-media data as approaches used in the special-issue research.
Results
AI and Big Data have clearly and inevitably begun transforming both entrepreneurship research and practice, including how research insights are applied and entrepreneurial activity informs future research.
Takeaways & Limitations
An entrepreneurial handling of the AI- and Big-Data-augmented era is proposed as one way to advance new entrepreneurship research and its application.
Takeaways & Limitations
Scenarios may quickly become outdated because AI and Big Data are progressing rapidly, and uncritical reliance on AI results may create problems.
Abstract
from arXiv · showhide
While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., 'smart entrepreneurship')? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.
1 Introduction
AI and Big Data have received growing attention across research and application fields but comparatively little in entrepreneurship. This editorial reflects on their emerging effects on entrepreneurship research and practice while emphasizing uncertainty, near-term priorities, and reciprocal transformation.
- Motivation: AI and Big Data are increasingly important across foundational research and application fields, yet entrepreneurship has received comparatively little attention.The editorial situates entrepreneurship within broader changes in economics, policy, innovation, management, psychology, industry, and business management.
- Orientation: The editorial focuses on near-future research priorities, infrastructure changes, and collaboration while offering concrete suggestions and reflections for research and practice.It treats entrepreneurship research and entrepreneurship as a real-world phenomenon as interlinked and co-evolving.
- Limitations: Long-term predictions may quickly become outdated because AI and Big Data are dynamic fields, so the authors limit their scope to reflections and near-future orientation.They explicitly acknowledge that their scenarios may become incomplete or obsolete as circumstances change.
- Conceptual framing: The authors define AI broadly as machine-demonstrated intelligence and Big Data as large, varied datasets processed and analyzed through non-traditional methods.They describe machine learning as a subset of AI and deep learning as a subset of machine learning, while noting that AI can also use smaller datasets.
- Emergence: The editorial argues that the era of AI and Big Data in entrepreneurship has begun for both research and practice.It presents this development as potentially disruptive to entrepreneurship research, entrepreneurial activity, and their interaction.
- Implications: AI and Big Data may shape opportunity development, business ideas, smart strategies, and interactions between entrepreneurial people and machine intelligence.The authors connect these possibilities to changes in both the real-world phenomenon of entrepreneurship and research about it.
2 Research priorities
The paper calls for expanded conceptual and empirical entrepreneurship research on AI and Big Data, spanning methods, applications, ethics, and long-term societal effects.
- Overall research agenda: The authors argue that AI and Big Data create unprecedented opportunities while demanding new methods, datasets, study designs, ethical safeguards, and statistical approaches.
- Conceptual priorities: Future conceptual work should examine productive versus destructive entrepreneurship, intelligence and prediction, expert performance, and tensions between prediction and explanation.
- Conceptual priorities: Researchers need practical guidance on Big Data and AI methods, including computerized methods, smartphone data, data mining, and machine learning.
- Conceptual priorities: Key ethical concerns include data protection, privacy, generalizability, and the intrusiveness of data collection.
- Empirical priorities: Empirical priorities include new entrepreneurial metrics, digital footprints, prediction of entrepreneurial outcomes, and analyses of regions, policy, education, networks, and finance.
- Empirical priorities: Further empirical opportunities cover underrepresented populations, business models, well-being, biological factors, ecological costs, and long-term effects on society.
3 Potential impact of AI and Big Data on entrepreneurship as a real-world phenomenon
AI and Big Data may transform both entrepreneurship research and entrepreneurial practice, while near-term development is framed more as human–machine collaboration than replacement.
- AI and Big Data may influence not only research methods but also the entrepreneurial phenomena those methods study, causing the research domain and practice to co-evolve.
- The technologies could close boundaries between entrepreneurship research and practice through reciprocal knowledge spillovers.
- Large firms may hold incumbent advantages because they possess greater infrastructure, economic resources, and technological knowhow.
- Complete replacement of human entrepreneurial agents is considered unlikely in the near future, although autonomous machine entrepreneurship is discussed as an extreme scenario.
- A more likely near-term scenario is AI supporting entrepreneurs with tasks and organizational goals, producing collaboration between humans and machines.
- Entrepreneurial uncertainty creates a tension with AI systems that rely on existing data and patterns, despite research on decision-making under uncertainty.
- Entrepreneurs should critically evaluate AI outputs because uncritical reliance may produce biased decisions and negative consequences.
- Education and training should prepare entrepreneurs to interpret AI results and address technological, ethical, social, and ecological issues.
4 A preliminary conclusion and outlook
The paper urges entrepreneurship research to engage proactively with AI and Big Data through interdisciplinary collaboration, new infrastructures, and focused study of machine-based opportunities.
- Entrepreneurship scholars are encouraged to initiate interdisciplinary collaborations and revise research structures for the AI and Big Data era.
- The authors position entrepreneurship research as part of broader interdisciplinary efforts linking intelligent machines, human intelligence, and societal transformation.
- Proposed infrastructure includes research centers, access to powerful technologies and knowhow, new conferences, workshops, repositories, and sharing channels.
- A concrete next step is studying the sources, processes, and individuals associated with opportunities around machine-based intelligence.
5 The articles in this Special Issue
The Special Issue presents seven reviewed articles illustrating how AI and Big Data support established and new entrepreneurship research questions while exposing continuing challenges.
- The Special Issue provides an interdisciplinary platform for conceptual and empirical papers addressing AI and Big Data opportunities and challenges in entrepreneurship.
- The collection demonstrates innovative methods while leaving open challenges for applying AI and Big Data in entrepreneurship research.
- Coad and Srhoj use Big Data to identify predictors of high-growth firms, but prediction remains challenging with 10% explanation power.
- Obschonka and colleagues use social-media language analysis to measure regional entrepreneurial personality, which predicts regional activity similarly to traditional self-reports.
- Liebregts and colleagues examine behavioral and non-behavioral cues in entrepreneurial decision-making, while Zhang and Van Burg connect genetic algorithms with design science and effectuation.
- Other articles analyze crowdfunding data with neural networks and language processing, entrepreneurial skill demand using 7.7 million job-vacancy data points, and media–regional activity links.