Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions
Chang Li, Hideyoshi Yanagisawa

TL;DR
This study explores how intrinsic motivation influences user interactions with virtual assistants, focusing on how expectation and uncertainty affect spontaneous engagement, and proposes a new model validated through experiments.
Contribution
It introduces a novel motivation model emphasizing intrinsic factors and empirically demonstrates how expectation and uncertainty impact user motivation in human-AI interaction.
Findings
High expectation increases intrinsic motivation.
Uncertainty suppresses intrinsic motivation.
Reducing uncertainty shifts motivation from extrinsic to intrinsic.
Abstract
With the growing utility of today's conversational virtual assistants, the importance of user motivation in human-AI interaction is becoming more obvious. However, previous studies in this and related fields, such as human-computer interaction and human-robot interaction, scarcely discussed intrinsic motivation and its affecting factors. Those studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation. The current study aims to cover intrinsic motivation by taking an affective-engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments are conducted where these two factors are manipulated by making participants believe they are interacting with the smart speaker "Amazon Echo".…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
