# A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures

## TL;DR

This study investigates how different voice assistant failures impact user trust and future reliance, highlighting the importance of failure type and task stakes in trust recovery.

## Contribution

It introduces a new dataset of 199 voice assistant failures and analyzes their effects on user trust through mixed-methods research.

## Key findings

- Failures due to overcapturing input significantly reduce trust
- Users temporarily stop using voice assistants after failures
- Low stakes tasks help rebuild trust after failures

## Abstract

Despite huge gains in performance in natural language understanding via large language models in recent years, voice assistants still often fail to meet user expectations. In this study, we conducted a mixed-methods analysis of how voice assistant failures affect users' trust in their voice assistants. To illustrate how users have experienced these failures, we contribute a crowdsourced dataset of 199 voice assistant failures, categorized across 12 failure sources. Relying on interview and survey data, we find that certain failures, such as those due to overcapturing users' input, derail user trust more than others. We additionally examine how failures impact users' willingness to rely on voice assistants for future tasks. Users often stop using their voice assistants for specific tasks that result in failures for a short period of time before resuming similar usage. We demonstrate the importance of low stakes tasks, such as playing music, towards building trust after failures.