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Immunizing with information - Inoculation messages against conversational agents' response failures.

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Inoculation messages can reduce user resistance to conversational agents (CAs) after response failures. Preparing users for potential low performance, especially with qualitative information, improves CA adoption.

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Area of Science:

  • Human-Computer Interaction
  • Communication Studies
  • Psychology

Background:

  • Conversational agents (CAs) often fail to meet user expectations, leading to resistance and hindering adoption.
  • Existing research primarily focuses on improving CA performance, neglecting strategies for managing user resistance after repeated failures.
  • Inoculation theory and the elaboration likelihood model offer frameworks for preparing users for potential negative experiences.

Purpose of the Study:

  • To investigate the effectiveness of inoculation messages in mitigating user resistance following CA response failures.
  • To examine how performance level (high/low) and information form (qualitative/quantitative) of inoculation messages influence user discontinuance intentions.
  • To understand the processing pathways (central vs. peripheral) through which different inoculation message types affect user perceptions.

Main Methods:

  • A randomized experiment involving 558 users was conducted.
  • Participants were exposed to CAs with varying performance levels (high/low) and inoculation message types (qualitative/quantitative).
  • User decisions to discontinue CA usage after a response failure were measured.

Main Results:

  • Inoculation messages highlighting low performance significantly alleviated the negative impact of CA response failures on user discontinuance.
  • Quantitative performance information moderated effects via central processing, while qualitative information influenced peripheral processing.
  • The study identified specific conditions under which inoculation messages are most effective.

Conclusions:

  • Inoculation messages serve as a viable ex-ante strategy to manage user resistance to conversational agents.
  • Tailoring the linguistic form of performance information in inoculation messages can optimize their impact based on user processing.
  • Findings provide practical insights for designing more resilient and user-accepted conversational agent systems.