Evidence map›Paper›PMID 38743686›Full record

ArticlePLOS digital health2024

Acceptance of voice assistant technology in dental practice: A cross sectional study with dentists and validation using structural equation modeling.

Spencer Warren, Daniel Claman, Beau Meyer, Jin Peng, Emre Sezgin

Abstract read
In one paragraph

Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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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

5 authors.

Spencer WarrenDepartment of Pediatric Dentistry, Nationwide Children's Hospital, Columbus, Ohio, United States of America.
Daniel ClamanDivision of Pediatric Dentistry, The Ohio State University College of Dentistry, Columbus, Ohio, United States of America.ORCID https://orcid.org/0000-0003-4286-3333
Beau MeyerDivision of Pediatric Dentistry, The Ohio State University College of Dentistry, Columbus, Ohio, United States of America.
Jin PengInformation Technology Research & Innovation, Nationwide Children's Hospital, Columbus, Ohio, United States of America.
Emre SezginCenter for Biobehavioral Health, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Voice assistant technologies (VAT) has been part of our daily lives, as a virtual assistant to complete requested tasks. The integration of VAT in dental offices has the potential to augment productivity and hygiene practices. Prior to the adoption of such innovations in dental settings, it is crucial to evaluate their applicability. This study aims to assess dentists' perceptions and the factors influencing their intention to use VAT in a clinical setting. A survey and research model were designed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was sent to 7,544 Ohio-licensed dentists through email. The data was analyzed and reported using descriptive statistics, model reliability testing, and partial least squares regression (PLSR) to explain dentists' behavioral intention (BI) to use VAT. In total, 257 participants completed the survey. The model accounted for 74.2% of the variance in BI to use VAT. Performance expectancy and perceived enjoyment had significant positive influence on BI to use VAT. Perceived risk had significant negative influence on BI to use VAT. Self-efficacy had significantly influenced perceived enjoyment, accounting for 35.5% of the variance of perceived enjoyment. This investigation reveals that performance efficiency and user enjoyment are key determinants in dentists' decision to adopt VAT. Concerns regarding the privacy of VAT also play a crucial role in its acceptance. This study represents the first documented inquiry into dentists' reception of VAT, laying groundwork for future research and implementation strategies.

Identifiers

PMID38743686
PMCPMC11093337

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LicenceCC BY
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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.