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Towards a general framework for innovation shaped with AI to create and transform market offerings
[Vers un cadre général d’innovation façonné par l’IA pour créer et transformer les offres du marché]

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  • Gilbert Giacomoni

    (UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, AgroParisTech)

Abstract

Our research focuses on early-stages of innovation, especially in the Biotech and Pharmaceutical industry, wherein the business research is still hit-and-miss, mainly based on trial and error and experimentation. We leverage the story of a high-growth startup, building and expanding on behavioral economics that examines the relationships between science and innovation, especially dynamic capabilities and related problemistic search and strategic options generation. A general framework for innovation shaped with both narrow and general AI (advanced data analytics, intelligent algorithms, etc.) is proposed to create and transform market offerings, hybridize domains heretofore dissociated and build organizational fit with prior and novel core elements. For firms, it can be exploitable as a competitive advantage, making it possible to efficiently anticipate, hence adapt to the most general types of change in representation or taking place in the environment.

Suggested Citation

  • Gilbert Giacomoni, 2022. "Towards a general framework for innovation shaped with AI to create and transform market offerings [Vers un cadre général d’innovation façonné par l’IA pour créer et transformer les offres du march," Post-Print hal-04083376, HAL.
    • Handle: RePEc:hal:journl:hal-04083376
    DOI: 10.1111/emre.12492
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-04083376
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    1. Wang, Zongrun & Zhang, Taiyu & Ren, Xiaohang & Shi, Yukun, 2024. "AI adoption rate and corporate green innovation efficiency: Evidence from Chinese energy companies," Energy Economics, Elsevier, vol. 132(C).

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