TY - UNPB
T1 - Using Online Customer Reviews to Classify, Predict, and Learn about Domestic Robot Failures
AU - Honig, Shanee
AU - Bartal, Alon
AU - Parmet, Yisrael
AU - Oron-Gilad, Tal
PY - 2022/1/10
Y1 - 2022/1/10
N2 - 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 & Resilience were commonly reported and had the most
significant negative impact. Future failure-prevention and response
strategies should address the technical ability of the robot 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.
AB - 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 & Resilience were commonly reported and had the most
significant negative impact. Future failure-prevention and response
strategies should address the technical ability of the robot 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.
KW - Computer Science - Robotics
KW - Computer Science - Human-Computer Interaction
U2 - https://doi.org/10.48550/arXiv.2201.03287
DO - https://doi.org/10.48550/arXiv.2201.03287
M3 - Preprint
BT - Using Online Customer Reviews to Classify, Predict, and Learn about Domestic Robot Failures
ER -