Using Online Customer Reviews to Classify, Predict, and Learn About Domestic Robot Failures

Shanee Honig, Alon Bartal, Yisrael Parmet, Tal Oron-Gilad

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


There is a knowledge gap regarding which types of failures robots undergo in domestic settings and how these failures influence customer experience. We classified 10,072 customer reviews of small utilitarian domestic robots on Amazon by the robotic failures described in them, grouping failures into twelve types and three categories (Technical, Interaction, and Service). We identified sources and types of failures previously overlooked in the literature, combining them into an updated failure taxonomy. We analyzed their frequencies and relations to customer star ratings. Results indicate that for utilitarian domestic robots, Technical failures were more detrimental to customer experience than Interaction or Service failures. Issues with Task Completion and Robustness and Resilience were commonly reported and had the most significant negative impact. Future failure-prevention and response strategies for utilitarian domestic robots should address the robot’s technical ability to meet functional goals, operate and maintain structural integrity over time. Usability and interaction design were less detrimental to customer experience, indicating that customers may be more forgiving of failures that impact these aspects for the robots and practical uses examined. Further, we developed a Natural Language Processing model capable of predicting whether a customer review contains content that describes a failure and the type of failure it describes. With this knowledge, designers and researchers of robotic systems can prioritize design and development efforts towards essential issues.

Original languageEnglish
JournalInternational Journal of Social Robotics
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.


The first author is supported by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative and by the Marcus Endowment Fund both at Ben-Gurion University of the Negev.

FundersFunder number
Marcus Endowment Fund
Leona M. and Harry B. Helmsley Charitable Trust


    • Data-driven design
    • Domestic robots
    • Failure perception
    • Failure types
    • Home robots
    • Robot failures


    Dive into the research topics of 'Using Online Customer Reviews to Classify, Predict, and Learn About Domestic Robot Failures'. Together they form a unique fingerprint.

    Cite this