Practice co-evolution: Collaboratively embedding artificial intelligence in retail practices

Francesca Bonetti, Matteo Montecchi, Kirk Plangger, Hope Jensen Schau

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Many retailers invest in artificial intelligence (AI) to improve operational efficiency or enhance customer experience. However, AI often disrupts employees’ ways of working causing them to resist change, thus threatening the successful embedding and sustained usage of the technology. Using a longitudinal, multi-site ethnographic approach combining 74 stakeholder interviews and 14 on-site retail observations over a 5-year period, this article examines how employees’ practices change when retailers invest in AI. Practice co-evolution is identified as the process that undergirds successful AI integration and enables retail employees’ sustained usage of AI. Unlike product or practice diffusion, which may be organic or fortuitous, practice co-evolution is an orchestrated, collaborative process in which a practice is co-envisioned, co-adapted, and co-(re)aligned. To be sustained, practice co-evolution must be recursive and enabled via intentional knowledge transfers. This empirically-derived recursive phasic model provides a roadmap for successful retail AI embedding, and fruitful future research avenues.

Original languageEnglish (US)
JournalJournal of the Academy of Marketing Science
DOIs
StateAccepted/In press - 2022

Keywords

  • Artificial intelligence (AI)
  • Knowledge transfer
  • Practice co-evolution
  • Practice enablement
  • Practice theories
  • Retail

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics
  • Marketing

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