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Weston L. Baxter

dblp:287/7476 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 50% Usability and user experience research · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
conversational agents
0.412019
Understanding Affective Experiences with Conversational Agents · CHI 2019
Usability and user experience research › user affect
emotional experience
0.412019
Understanding Affective Experiences with Conversational Agents · CHI 2019

Methods — techniques the papers use, named apart from their topics

survey study · 0.4
YearPublicationVenuePosition
2019 Understanding Affective Experiences with Conversational Agents
abstract
While previous studies of Conversational Agents (e.g. Siri, Google Assistant, Alexa and Cortana) have focused on evaluating usability and exploring capabilities of these systems, little work has examined users' affective experiences. In this paper we present a survey study with 171 participants to examine CA users' affective experiences. Specifically, we present four major usage scenarios, users' affective responses in these scenarios, and the factors which influenced the affective responses. We found that users' overall experience was positive with interest being the most salient positive emotion. Affective responses differed depending on the scenarios. Both pragmatic and hedonic qualities influenced affect. The factors underlying pragmatic quality are: helpfulness, proactivity, fluidity, seamlessness and responsiveness. The factors underlying hedonic quality are: comfort in human-machine conversation, pride of using cutting-edge technology, fun during use, perception of having a human-like assistant, concern about privacy and fear of causing distraction.
Xi Yang 0010, Marco Aurisicchio, Weston L. Baxter
CHI3