  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". Intrinsic
motivation is measured both by using questionnaires and by covertly monitoring
a five-minute free-choice period in the experimenter's absence, during which
the participants could decide for themselves whether to interact with the
virtual assistants. Results of the first experiment showed that high
expectation engenders more intrinsically motivated interaction compared with
low expectation. The results also suggested suppressive effects by uncertainty
on intrinsic motivation, though we had not hypothesized before experiments. We
then revised our hypothetical model of action selection accordingly and
conducted a verification experiment of uncertainty's effects. Results of the
verification experiment showed that reducing uncertainty encourages more
interactions and causes the motivation behind these interactions to shift from
non-intrinsic to intrinsic.
