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« AI for Social Good » Adoption Through Divergent Perceptions: Exploring A Social Landlord’s Strategizing Process

Author

Listed:
  • Magalie Duarte

    (BSB - Burgundy School of Business (BSB) - ESC Dijon-Bourgogne, CEREN - Centre de Recherche sur les Entreprises - BSB - Burgundy School of Business (BSB) - ESC Dijon-Bourgogne)

  • Guillaume Biot Paquerot

    (BSB - Burgundy School of Business (BSB) - ESC Dijon-Bourgogne, CEREN - Centre de Recherche sur les Entreprises - BSB - Burgundy School of Business (BSB) - ESC Dijon-Bourgogne)

  • Carine Dominguez-Pery
  • Kenza Arab

Abstract

The promises of artificial intelligence (AI) are tremendous but controversial. Developing a "good AI society" is becoming an imperative for researchers in information systems (IS), practitioners, and policymakers. This challenge involves weighing up the benefits and risks of AI for society. Social purpose organizations are particularly struggling to adopt and implement AI. IS research provides an understanding of individual and organizational drivers of AI adoption. However, a lack of awareness on how divergent positions towards AI lead to adoption decisions remains within organizations. Our paper focuses on the day-to-day strategic decision-making process discourse on AI adoption rather than the adoption results through a case study of a social landlord, contributing towards the understanding of social influence in AI adoption processes in the context of AI for social good. It also recommends a future research agenda focusing on collective action in AI adoption theories.

Suggested Citation

  • Magalie Duarte & Guillaume Biot Paquerot & Carine Dominguez-Pery & Kenza Arab, 2024. "« AI for Social Good » Adoption Through Divergent Perceptions: Exploring A Social Landlord’s Strategizing Process," Post-Print hal-05842680, HAL.
    • Handle: RePEc:hal:journl:hal-05842680
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