Evidence map›Paper›PMID 42329994›Full record

ArticlePloS one2026

The occurrence of a particular state is a predictor of successful travel consultation.

Hidehito Honda, Takatomi Kubo, Ryosuke Hisamatsu, Yoshimasa Ohmoto, Kazushi Ikeda, Kazuhiro Ueda

Abstract read
In one paragraph

Article in PloS one, 2026. 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.

Hidehito HondaGraduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan.ORCID https://orcid.org/0000-0002-6384-7837
Takatomi KuboGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma-shi, Nara, Japan.
Ryosuke HisamatsuGraduate School of Interdisciplinary Information Studies, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Yoshimasa OhmotoFaculty of Informatics, Shizuoka University, Hamamatsu-shi, Shizuoka, Japan.
Kazushi IkedaGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma-shi, Nara, Japan.ORCID https://orcid.org/0000-0003-3330-6121
Kazuhiro UedaGraduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan.ORCID https://orcid.org/0000-0003-1244-7945

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dyadic interactions, such as consultations between clerks (i.e., travel agent sales staff) and customers, involve multiple behaviors that reflect people's internal states, such as interests and preferences. Thus, the time-series patterns of multiple behaviors between two people reveal the characteristics of the interaction. In this study, we conducted an experiment on travel consultations between clerks and customers, which was as close as possible to a real situation, and analyzed the time-series shifts of multiple behaviors in a 30-minute dyadic interaction using a hidden Markov model. We found differences in interactions between successful and unsuccessful consultations. In particular, in the successful interaction, a customer entered an interactional state associated with interest in the content of the consultation in the first 10 minutes, and such a state was likely to persist during the consultation. These findings suggest that early interactional engagement may serve as a potential behavioral marker associated with consultation success and offer a preliminary process-level perspective on interaction dynamics in naturalistic dyadic consultations.

Indexed as

Interpersonal RelationsTravelHidden Markov ModelsHumans

Identifiers

PMID42329994
PMCPMC13286169

What OpenQuestion holds

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Registered trials

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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.