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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
31 Downloads (Pure)

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
JournalJournal of the academy of marketing science
DOIs
Publication statusAccepted/In press - 22 Jul 2022

Keywords

  • practice co-evolution
  • practice enablement
  • artificial intelligence (AI)
  • retail
  • practice theories
  • knowledge transfer

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