Psychometric analysis of a measure of acceptance of new technologies (UTAUT), applied to the use of haptic virtual simulators in dental students

Bravo, Luís González; Sagredo, Marcelo Fernández; Martínez, Pilar Torres; Penna, Carolina Barrios; Molina, Juan Fonseca; Stanciu, Dorin; Nistor, Nicolae

doi:10.1111/eje.12559

Cited by 22 publications

(18 citation statements)

References 55 publications

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“…Finally, 36 articles were included for final analysis and 11 articles were excluded after full text read, for the following reasons: Description of haptic simulator development process 9–12 ; haptic exercise/tooth simulation design 13–17 ; haptics in other specialties or indirectly related psychometrics 18,19 . The PRISMA flow diagram 20 is presented in (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

The utility of haptic simulation in early restorative dental training: A scoping review

Al‐Saud

2020

Journal of Dental Education

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Background Haptic dental simulators are becoming increasingly available in dental schools around the world; however, there is a paucity of evidence on their pedagogical effectiveness particularly in early dental training for the acquisition of the highly specific fine motor dental skills. Methods A scoping review was performed to broadly map the available evidence and to detect knowledge gaps on the utility of haptic dental simulation in early dental training. The review is reported using the PRISMA‐ScR guidelines. Eight bibliographic databases were searched: Web of Science, Scopus, MEDLINE via PubMed, Cochrane library, CENTRAL, ERIC, IEEE Xplore, and TRIP. Charted data were reported by clustering results according to study characteristics, research themes, research purpose, and type of validity evidence identified. Results The review process resulted in the inclusion of 36 studies published between 2009 and 2020. The majority of the studies were cross‐sectional in design with short‐term evaluation data. Of the studies included, 64% investigated commercially available haptic simulators, while 36% investigated experimental haptic simulators. The research themes identified were skill acquisition and transfer, task‐specific haptic training, trainee level discrimination, feedback, subjective user evaluation, performance prediction, and human factors in haptic training. Conclusion Short‐term evaluation evidence from reviewed studies indicates the usefulness of the haptic simulators in early dental training. They complement the existing phantom head simulators by offering qualitatively different features. Further empirical research is needed to investigate the long‐term impact of training with haptic dental simulators, to improve the availability of validation evidence and to enhance the results generalizability.

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Section: Methodsmentioning

confidence: 99%

The utility of haptic simulation in early restorative dental training: A scoping review

Al‐Saud

2020

Journal of Dental Education

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“…The main questions in such questionnaires are whether the students think that the simulator assisted their study, improving their understanding of the related curriculum, and whether they are willing to use the simulator in their future study [70]. In addition, Venkatesh et al made the Unified Theory of Acceptance and Use of Technology questionnaire proposal to measure the acceptance of a new technology [71], and Bravo et al applied it to the use of dental simulators and concluded that it can assess the acceptance of dental simulators in dental education [72].…”

Section: Questionnairesmentioning

confidence: 99%

The Current Situation and Future Prospects of Simulators in Dental Education

Fan

et al. 2021

J Med Internet Res

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The application of virtual reality has become increasingly extensive as this technology has developed. In dental education, virtual reality is mainly used to assist or replace traditional methods of teaching clinical skills in preclinical training for several subjects, such as endodontics, prosthodontics, periodontics, implantology, and dental surgery. The application of dental simulators in teaching can make up for the deficiency of traditional teaching methods and reduce the teaching burden, improving convenience for both teachers and students. However, because of the technology limitations of virtual reality and force feedback, dental simulators still have many hardware and software disadvantages that have prevented them from being an alternative to traditional dental simulators as a primary skill training method. In the future, when combined with big data, cloud computing, 5G, and deep learning technology, dental simulators will be able to give students individualized learning assistance, and their functions will be more diverse and suitable for preclinical training. The purpose of this review is to provide an overview of current dental simulators on related technologies, advantages and disadvantages, methods of evaluating effectiveness, and future directions for development.

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“…A number of models have been used to predict factors associated with the behavioral intention of m-Health [23][24][25][26][27]. Recently, UTAUT is considered as the most valid technology acceptance model used to assess behavioral intention and actual use behavior of m-Health [28][29][30][31][32]. Previous studies have shown that users' behavioral intention of information technology is the basic premise of users' use behavior [33,34].…”

Section: Introductionmentioning

confidence: 99%

Determinants of the behavioral intention to use a mobile nursing application by nurses in China

Pan

Gao

2021

BMC Health Serv Res

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Background Although a mobile nursing application has began to adopt in nursing, few studies have focused on nurses’ behavioral intention of it. The objective of this study is to gain insight into the behavioral intention of nurses, i.e. chinese nurses of the future, to use a mobile nursing application. This study adopted an extension of the Unified Theory of Acceptance and Use of Technology to examine Chinese nurses’ acceptance of a mobile nursing application. Methods A total of 1207 nurses participated in the cross-sectional survey. The majority of nurses were female (96.2%). The mean age of the participants was 34.18 (SD 7.39). The hypothesized relationships were tested using AMOS structural equation model. Results All constructs exhibited an acceptable level of reliability and validity with Cα and CR > 0.7 and AVE > 0.5. An extension of the Unified Theory of Acceptance and Use of Technology Model had good explanatory power for nurses’ behavioral intention of a mobile nursing application. Although effort expectancy and perceived risks had a surprisingly insignificant effect on nurses’ behavioral intention to use a mobile nursing application, performance expectancy, social influence, facilitating conditions, self-efficacy, and perceived incentives demonstrated significant influence with β = .259, p < .001, β = .296, p < .001, β = .063, p = .037, β = .344, p < .001, β = .091, p = .001, respectively. Conclusion With 70.2% of the variance in behavioral intention to use a mobile nursing app explained by this model, it could be helpful for potential adopters, and further investigation should test the actual usage behavior for a mobile nursing app and investigate the related factors.

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Psychometric analysis of a measure of acceptance of new technologies (UTAUT), applied to the use of haptic virtual simulators in dental students

Cited by 22 publications

References 55 publications

The utility of haptic simulation in early restorative dental training: A scoping review

The utility of haptic simulation in early restorative dental training: A scoping review

The Current Situation and Future Prospects of Simulators in Dental Education

Determinants of the behavioral intention to use a mobile nursing application by nurses in China

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