Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Sofía Álvarez Arias

dblp:393/4785 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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-robot interaction · 70% Usability and user experience research · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › robot communication
robot persuasion
1.012026
The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities · IEEE Trans. Robotics 2026
Human-robot interaction
social robot
1.012026
The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities · IEEE Trans. Robotics 2026
Usability and user experience research
user study
1.012026
The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities · IEEE Trans. Robotics 2026
Human-robot interaction
human-robot collaboration
0.312026
The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities · IEEE Trans. Robotics 2026

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

user study · 1.0social judgement theory · 1.0
YearPublicationVenuePosition
2026 The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities
abstract
Social robots are increasingly used in healthcare and education, but technological gaps, fears of human replacement, and moral or social beliefs can limit their acceptance. In collaborative settings, the activities to complete may influence users' willingness to participate, raising the question of how moral and social attitudes shape human-robot interaction. This paper studies the effect of the Social Judgement Theory on social robotics to analyse which factors affect the users' willingness to complete the robot's requests. The methodology classifies the activities requested by the robot into assimilation (activities people typically accept), non-commitment (activities people usually reject) and the contrast (activities some people accept) groups. We conducted a user study with 63 participants interacting with the Mini social robot in a collaborative session where it requested some actions from the user. We analyse whether the kind of activity requested by the robot, its expressiveness, and demographic, moral, social, and robot factors influence the user behaviour. Results show that the Social Judgement Theory can be extended to social robotics since the kind of activity affects the user's willingness to complete it. Besides, the results indicate that an expressive robot convinced users more than a non-expressive robot and that participants who lied about their completed activities were more easily persuaded. We also found that participants with moderate knowledge of robotics completed more activities than those with low knowledge, and individuals with previous experience interacting with Mini were more likely to comply with its requests. However, demographic factors such as age or gender do not seem to influence robot persuasion despite previous studies suggesting they are important in human-robot collaboration.
Marcos Maroto-Gómez, Sara Carrasco-Martínez, Sofía Álvarez Arias, Enrique Fernández-Rodicio, María Malfaz
IEEE Trans. Robotics3