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

Frantz Rowe (), Raphaël Suire (), Myriam Raymond and Florence Jacob ()
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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
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

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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.

Keywords: Generative AI Innovation Diffusion Case study Beliefs Risks Controversies; Generative AI; Innovation Diffusion; Case study; Beliefs; Risks; Controversies (search for similar items in EconPapers)
Date: 2024-12
Note: View the original document on HAL open archive server: https://cnrs.hal.science/hal-04973613v1
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Published in DIGIT 2024, Dec 2024, Bangkok, Thailand

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