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2022-04-07 - Article/Dans un journal avec peer-review - Anglais - 27 page(s)

Raneri Santo, Lecron Fabian , Hermans Julie, Fouss François, "Predictions through Lean startup? Harnessing AI-based predictions under uncertainty" in International Journal of Entrepreneurial Behaviour and Research

  • Edition : Emerald, Bingley (United Kingdom)
  • Codes CREF : Intelligence artificielle (DI1180), Technologies de l'information et de la communication (TIC) (DI4730), Management (DI4360)
  • Unités de recherche UMONS : Management de l'Innovation Technologique (F113)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) Purpose – Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected. Design/methodology/approach – Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study. Findings – The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices. Originality/value – Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.


Mots-clés :
  • (Anglais) Causation
  • (Anglais) Artificial intelligence
  • (Anglais) Effectuation
  • (Anglais) Uncertainty
  • (Anglais) Digital entrepreneurship
  • (Anglais) Lean startup