Toward Metaphor-Fluid Conversation Design for Voice User Interfaces

TL;DR

This paper proposes a dynamic Metaphor-Fluid Design for Voice User Interfaces that adapts metaphors to different conversational contexts, improving user engagement and satisfaction over static, traditional designs.

Contribution

It introduces a novel adaptive metaphorical approach for VUI design, demonstrating its effectiveness through empirical studies and highlighting the importance of context-specific metaphors.

Findings

Metaphor preferences vary across different use-contexts.

Metaphor-Fluid VUI improves perceived intention to adopt, enjoyment, and likability.

Personalization is necessary due to individual differences in metaphor preferences.

Abstract

Metaphors play a critical role in shaping user experiences with Voice User Interfaces (VUIs), yet existing designs often rely on static, human-centric metaphors that fail to adapt to diverse contexts and user needs. This paper introduces Metaphor-Fluid Design, a novel approach that dynamically adjusts metaphorical representations based on conversational use-contexts. We compare this approach to a Default VUI, which characterizes the present implementation of commercial VUIs commonly designed around the persona of an assistant, offering a uniform interaction style across contexts. In Study 1 (N=130), metaphors were mapped to four key use-contexts-commands, information seeking, sociality, and error recovery-along the dimensions of formality and hierarchy, revealing distinct preferences for task-specific metaphorical designs. Study 2 (N=91) evaluates a Metaphor-Fluid VUI against a Default VUI, showing that the Metaphor-Fluid VUI enhances perceived intention to adopt, enjoyment, and likability by aligning better with user expectations for different contexts. However, individual differences in metaphor preferences highlight the need for personalization. These findings challenge the one-size-fits-all paradigm of VUI design and demonstrate the potential of Metaphor-Fluid Design to create more adaptive and engaging human-AI interactions.