What Makes An Apology More Effective? Exploring Anthropomorphism, Individual Differences, And Emotion In Human-Automation Trust Repair
Peggy Pei-Ying Lu; Makoto Konishi; Shin Sano; Sho Hiruta; Francis Ken Nakagawa · 2022 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Recent advances in technology have allowed an automation system to recognize its errors and repair trust more actively than ever. While previous research has called for further studies of different human factors and design features, their effect on human-automation trust repair scenarios remains unknown, especially concerning emotions. This paper seeks to fill such gaps by investigating the impact of anthropomorphism, users' individual differences, and emotional responses on human-automation trust repair. Our experiment manipulated various types of trust violations and apology messages with di
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Metadata source: arXiv