Evidence map›Paper›PMID 39056845›Full record

ArticleBiomimetics (Basel, Switzerland)2024

Perceptive Recommendation Robot: Enhancing Receptivity of Product Suggestions Based on Customers' Nonverbal Cues.

Masaya Iwasaki, Akiko Yamazaki, Keiichi Yamazaki, Yuji Miyazaki, Tatsuyuki Kawamura, Hideyuki Nakanishi

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Masaya IwasakiGraduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan.ORCID 0000-0002-2285-3632
Akiko YamazakiSchool of Media Science, Tokyo University of Technology, Tokyo 192-0982, Japan.ORCID 0000-0003-2654-755X
Keiichi YamazakiGraduate School of Humanities and Social Sciences, Saitama University, Saitama 338-8570, Japan.
Yuji MiyazakiGraduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo 113-0033, Japan.ORCID 0000-0003-0721-7109
Tatsuyuki KawamuraIndependent Researcher, Kyoto, Japan.
Hideyuki NakanishiFaculty of Informatics, Kindai University, Osaka 577-0813, Japan.ORCID 0000-0001-9712-5114

Funding

Japan Society for the Promotion of Science JP18KK0053Japan Society for the Promotion of Science JP19H00605Japan Society for the Promotion of Science JP19K21718Japan Society for the Promotion of Science JP20H01585Japan Society for the Promotion of Science JP22K18548
6 · The paper itself

Abstract

Service robots that coexist with humans in everyday life have become more common, and they have provided customer service in physical shops around the world in recent years. However, their potential in effective sales strategies has not been fully realized due to their low social presence. This study aims to clarify what kind of robot behavior enhances the social presence of service robots and how it affects human-robot interaction and purchasing behavior. We conducted two experiments with a sales robot, Pepper, at a retail shop in Kyoto. In Experiment 1, we showed that the robot's social presence increased and that customers looked at the robot longer when the robot understood human gaze information and was capable of shared attention. In Experiment 2, we showed that the probability of customers picking up products increased when the robot suggested products based on the humans' degree of attention from gaze and posture information. These results indicate that the robot's ability to understand and make utterances about a customer's orientation and attention effectively enhances human-robot communication and purchasing motivation.

Indexed as

field experimentHRIsales robotservice robot

Identifiers

PMID39056845
PMCPMC11274967

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.