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

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

doi:10.1007/s11747-022-00896-1

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

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

^*Corresponding author for this work

Marketing : King's Business School

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

133 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 language	English
Journal	Journal of the academy of marketing science
DOIs	https://doi.org/10.1007/s11747-022-00896-1
Publication status	Accepted/In press - 22 Jul 2022

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s11747-022-00896-1

Bonetti et al. (Accepted Version) Practice co-evolution - JAMSAccepted author manuscript, 896 KB

Cite this

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title = "Practice co-evolution: Collaboratively embedding artificial intelligence in retail practices",

abstract = "Many retailers invest in artificial intelligence (AI) to improve operational efficiency or enhance customer experience. However, AI often disrupts employees{\textquoteright} 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{\textquoteright} practices change when retailers invest in AI. Practice co-evolution is identified as the process that undergirds successful AI integration and enables retail employees{\textquoteright} 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.",

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N2 - 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.

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

Abstract

Keywords

UN SDGs

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