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Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention

Author

Listed:
  • Nika Meyer (née Mozafari)

    (University of Goettingen, Smart Retail Group)

  • Melanie Schwede

    (University of Goettingen, Smart Retail Group)

  • Maik Hammerschmidt

    (University of Goettingen, Smart Retail Group)

  • Welf Hermann Weiger

    (Alfaisal University)

Abstract

Firms use robots to deliver an ever-expanding range of services. However, as service failures are common, service recovery actions are necessary to prevent user churn. This research further suggests that firms need to know how to design service robots that avoid alienating users in case of service failures. Robust evidence across two experiments demonstrates that users attribute successful service outcomes internally, while robot-induced service failures are blamed on the firm (and not the robot), confirming the well-known self-serving bias. While this external attributional shift occurs regardless of the robot design (i.e., it is the same for warm vs. competent robots), the findings imply that service recovery minimizes the undesirable external shift and that this effect is particularly pronounced for warm robots. For practitioners, this implies prioritizing service robots with a warm design for maximizing user retention for either type of service outcome (i.e., success, failure, and failure with recovery). For theory, this work demonstrates that attribution represents a meaningful mechanism to explain the proposed relationships.

Suggested Citation

  • Nika Meyer (née Mozafari) & Melanie Schwede & Maik Hammerschmidt & Welf Hermann Weiger, 2022. "Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2491-2505, December.
  • Handle: RePEc:spr:elmark:v:32:y:2022:i:4:d:10.1007_s12525-022-00613-4
    DOI: 10.1007/s12525-022-00613-4
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    References listed on IDEAS

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    Cited by:

    1. Rainer Alt, 2022. "Electronic Markets on AI and standardization," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1795-1805, December.

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    More about this item

    Keywords

    Human–robot interaction; Service failure; Service recovery; Social cognition; Responsibility attribution; User retention;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • I10 - Health, Education, and Welfare - - Health - - - General

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