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Artificial Intelligence as an Enabler for Entrepreneurs

Special issue call for papers from International Journal of Entrepreneurial Behavior & Research

The submission portal for this special issue will open October 1, 2020.

Guest Editors:
Yann Truong, Burgundy School of Business, France
Dirk Schneckenberg, Rennes School of Business, France
Rachid Jabbouri, Burgundy School of Business, France

Aims and Scope
Artificial Intelligence (AI), broadly defined as an overarching science that is concerned with intelligent algorithms (Agrawal, Gans, & Goldfarb, 2018), is about to disrupt businesses and societies at large in greater magnitude than any previous technological revolutions (Makridakis, 2017). As a general-purpose technology (Cockburn, Henderson, & Stern, 2018), AI and its associated sub-category technologies will affect virtually all businesses across industries by disrupting their current practices (von Krogh, 2018). Unsurprisingly, AI has been gaining momentum among management scholars from all disciplines (Obschonka & Audretsch, 2019), as evidenced by the number of calls for papers in 2019 in top-tier management journals including MISQ, Journal of Business Research, Small Business Economics, European Journal of Marketing, and the Journal of the Association for Information Systems. Similarly, research investigating the impact of AI in the field of entrepreneurship is in a nascent stage (Obschonka & Audretsch, 2019) but ought to rapidly become a major area of focus as the immense potential of AI technologies provides a considerable leverage for the pursuit of entrepreneurial activities (Nambisan, 2017; von Briel, Davidsson, & Recker, 2018).

Specifically, the automation and predictive capabilities of AI can be leveraged throughout all stages of the entrepreneurial process, that is, the identification, development, and exploitation of entrepreneurial opportunities (Shane, 2000). For instance, AI can facilitate the process of venture creation by expediting market studies and product testing in the exploration phase of venturing with automated data collection on social media, and improving market targeting and positioning in the exploitation phase of venturing with the help of predictive models. A concrete example is that of biotechnology ventures which have improved their R&D performance by using predictive models to find candidate molecules with optimal properties rather than going through the traditionally long journey of testing large samples of molecules. In this case, not only has AI significantly reduced the firms’ R&D costs but it also accelerated the time to market of their new products. Another example is machine learning capabilities that allow resource-constrained new ventures to automate routine tasks related to organizing such as accounting, financial control, document sorting and classification, and customer relationship management. Related to AI is the increasing availability of data that are collected through online platforms (social media and online markets) and connected objects (Internet of Things, IoT). Big data facilitate the identification and exploitation of business opportunities, and when combined with powerful AI algorithms, they can considerably reduce the uncertainty that is inherent to the entrepreneurial process (McMullen & Shepherd, 2006).

At a strategic level, AI technologies may disrupt the strategy and business model of firms (Agrawal et al., 2018). Amazon’s current strategy is driven by “order and ship”, but as its business model is highly dependent upon always shorter shipping time, the predictive power of AI technologies can enable the company to anticipate what its customers might need and ship the item even before they express such a need. The company’s model would then shift from “order and ship” to “ship and return”. For entrepreneurs, AI can unlock more variations of business models in venture creation (Lee et al., 2019; Loebbecke & Picot, 2015, Gomez-Uribe & Hunt, 2015). Consider the case of Rushmix which provides post-production video services to users online: Its current business model is locked-up by considerable human work on raw video footage, but the founders are now developing AI to assist humans by automating some time-consuming post-production work such as the pre-identification of key moments in the footage. This improvement would enable the startup to reduce delivery time, lower costs, and process higher volumes of video footage, thus reordering the basic elements of its business model.

Altogether, AI and big data can lower entry barriers for nascent entrepreneurs, facilitate the process of venture creation, accelerate growth, and increase the chances of survival.

This special issue focuses on the conditions under which AI can serve as an enabler for entrepreneurs in the process of creation and growth. We encourage submissions of papers that examine the ways AI and its capabilities (e.g. visual recognition, predictive models, classification, machine learning algorithms, natural language processing, clustering, etc.) can be leveraged throughout the entrepreneurial process from opportunity recognition, organization creation, resource acquisition, product development and commercialization, and growth (Baron, 2008; Bhave, 1994; Shane, 2003). Potential authors should note that this special issue is not fit for papers that study AI-based new ventures (new ventures that incorporate AI capabilities in its offerings). For example, we are interested in how entrepreneurs can use AI capabilities to develop new products and services (by automating market studies or product testing) but not in how entrepreneurs develop AI capabilities in their products and services. The focus is on the entrepreneurial process rather than on products or services.

Possible topics
The Guest Editors encourage submissions of theoretical and empirical contributions that address the following list of possible topics. The list is non-exhaustive so we welcome submissions that broaden our understanding of the enabling role of AI for entrepreneurs.

AI in the venture creation process

  • How can particular AI technologies (e.g. visual recognition, predictive models, machine learning algorithms, classification, natural language processing, clustering, etc.) be leveraged at each stage of the entrepreneurial process from opportunity recognition, organization creation, resource acquisition, product development and commercialization, and growth?

AI and business model design in the nascent phase

  • How do AI technologies affect the current business model design of nascent entrepreneurs in terms of value proposition, strategic implementation, and maintenance?
  • How can AI unlock the current business model of established new ventures?

AI and new venture growth and survival

  • What roles can AI technologies play in unlocking growth for established new ventures?
  • How can AI improve the survival of new ventures beyond the notorious 3-year threshold?

Submissions: Papers should be submitted via the journal’s online submission system available through the journal homepage. When submitting please choose the special issue: “Artificial intelligence as an enabler for entrepreneurs” as the article type from the drop-down menu. All papers must follow the guidelines outlined by the journal for submission, available at:

For any questions, interested authors can contact the corresponding guest editor: Yann TRUONG ([email protected])

Submission deadline: 30th of November 2020

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence: Harvard Business Press.

Baron, R. A. (2008). The role of affect in the entrepreneurial process. Academy of Management Review, 33(2), 328-340.

Bhave, M. P. (1994). A process model of entrepreneurial venture creation. Journal of business venturing, 9(3), 223-242.

Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation: National Bureau of Economic Research.

Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 13.

Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149-157.

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.

McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and the role of uncertainty in the theory of the entrepreneur. Academy of Management Review, 31(1), 132-152.

Nambisan, S. (2017). Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrepreneurship Theory and Practice, 41(6), 1029-1055.

Obschonka, M., & Audretsch, D. B. (2019). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics, 1-11.

Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organization science, 11(4), 448-469.

Shane, S. A. (2003). A general theory of entrepreneurship: The individual-opportunity nexus: Edward Elgar Publishing.

von Briel, F., Davidsson, P., & Recker, J. (2018). Digital technologies as external enablers of new venture creation in the IT hardware sector. Entrepreneurship Theory and Practice, 42(1), 47-69.

von Krogh, G. (2018). Artificial Intelligence in Organizations: New Opportunities for Phenomenon-Based Theorizing. Academy of Management Discoveries, 4(4), 404-409.