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Beliefs, Controversies, and Innovation Diffusion: The case of Beliefs, Controversies, and Innovation Diffusion: The case of Generative AI in a Large Technological Firm Generative AI in a Large Technological Firm

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  • Frantz Rowe

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université)

  • Raphaël Suire

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université, Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université)

  • Myriam Raymond
  • Florence Jacob

    (Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université, LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université)

Abstract

The reality of how generative artificial intelligences (GenAIs) spread and are used in business is largely unknown. This research aims to describe this in the context of a large technology company, exploring and questioning the ways in which diffusion is accelerated or, on the contrary, the reasons for resistance due to divergent beliefs. Based on diffusionist approaches to innovation and an original questionnaire administered to 1,665 employees, we propose a typology of user profiles. We show that the spread of GenAI is not simply a matter of percolating GenAI systems selected by strategists and spreading them from peer to peer in experiments organized by top management (so-called sandbox experiments). We partition the population into pure experimenters, early and natural adopters, strong early adopters and non-users. Overall, across the different profiles of users and non-users, the initial level of education seems to play an important role in commitment to experimentation, but also in non-use when the level of qualification is low. Those who speak up very often (spreaders) find that GenAI should be generalized are found more often among strong early adopters, while inhibitors are more likely to be found among pure experimenters. Spreaders and inhibitors coexist in professions, creating a fertile ground for controversy. This paper enables us to analyze the diffusion and specific features of GenAI innovation within a large seemingly tech-savvy company. It highlights the need for close support in addressing perceptions and competencies if the goal is to scale up usage within the company.

Suggested Citation

  • Frantz Rowe & Raphaël Suire & Myriam Raymond & Florence Jacob, 2024. "Beliefs, Controversies, and Innovation Diffusion: The case of Beliefs, Controversies, and Innovation Diffusion: The case of Generative AI in a Large Technological Firm Generative AI in a Large Technol," Post-Print hal-04973613, HAL.
  • Handle: RePEc:hal:journl:hal-04973613
    Note: View the original document on HAL open archive server: https://cnrs.hal.science/hal-04973613v1
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    References listed on IDEAS

    as
    1. Frantz Rowe & François-Charles Wolff & Carole Daniel, 2023. "Does Addictive Pleasure at Work and Building a Personal IS on One's Smartphone Lead to Problematic Smartphone Dependency?," Post-Print hal-04820972, HAL.
    2. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2023. "Generative AI at Work," Papers 2304.11771, arXiv.org, revised Nov 2024.
    3. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    4. Ojelanki Ngwenyama & Frantz Rowe, 2024. "Should We Collaborate with AI to Conduct Literature Reviews? Changing Epistemic Values in a Flattening World," Post-Print hal-04820922, HAL.
    5. Rao, Balkrishna C., 2013. "How disruptive is frugal?," Technology in Society, Elsevier, vol. 35(1), pages 65-73.
    6. Ojelanki Ngwenyama & Frantz Rowe & Stefan Klein & Helle Zinner Henriksen, 2023. "The Open Prison of the Big Data Revolution: False Consciousness, Faustian Bargains, and Digital Entrapment," Post-Print hal-04820943, HAL.
    7. Daron Acemoglu, 2024. "The Simple Macroeconomics of AI," NBER Working Papers 32487, National Bureau of Economic Research, Inc.
    8. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
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