Cargando…

Continuous usage intention of mobile health services: model construction and validation

BACKGROUND: Mobile health (mHealth) services can not give full play to their value if only it is used in the short term, and their continuous usage can achieve better effects in health management. This study aims to explore the factors that affect continuous usage intentions of mHealth services and...

Descripción completa

Detalles Bibliográficos
Autores principales: Nie, Li, Oldenburg, Brian, Cao, Yingting, Ren, Wenjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159674/
https://www.ncbi.nlm.nih.gov/pubmed/37143005
http://dx.doi.org/10.1186/s12913-023-09393-9
_version_ 1785037151771033600
author Nie, Li
Oldenburg, Brian
Cao, Yingting
Ren, Wenjie
author_facet Nie, Li
Oldenburg, Brian
Cao, Yingting
Ren, Wenjie
author_sort Nie, Li
collection PubMed
description BACKGROUND: Mobile health (mHealth) services can not give full play to their value if only it is used in the short term, and their continuous usage can achieve better effects in health management. This study aims to explore the factors that affect continuous usage intentions of mHealth services and their mechanism of action. METHODS: First, considering the uniqueness of health services and social environmental factors, this study constructed an extended Expectation Confirmation Model of Information System Continuance (ECM-ISC) to investigate factors that may influence the intention of continuous usage of mHealth services based on three dimensions, namely individual characteristics, technology and environment. Second, the survey method was used to validate the research model. The questionnaire items were derived from validated instruments and discussed by experts and data were collected both online and offline. The structural equation model was used for data analysis. RESULTS: There were 334 avidity questionnaires through cross-sectional data and these participants had used mHealth services ever. The reliability and validity of the test model were good, in which Cronbach’s Alpha values of 9 variables exceeded 0.9, composite reliability 0.8, the average variance extracted value 0.5, and the factor loading 0.8. The modified model had a good fitting effect and strong explanatory power. It accounted for 89% of the variance in expectation confirmation, 74% of the variance in perceived usefulness, 92% of the variance in customer satisfaction, and 84% of the variance in continuous usage intention. Compared with the initial model hypotheses, perceived system quality was deleted according to the heterotrait-monotrait ratio, so paths related to it were deleted; perceived usefulness wasn’t positively associated with customer satisfaction, and its path was also deleted. Other paths were consistent with the initial hypothesis. The two new added paths were that subjective norm was positively associated with perceived service quality (β = 0.704, P < 0.001), and perceived information quality (β = 0.606, P < 0.001). Electronic health literacy (E-health literacy) was positively associated with perceived usefulness (β = 0.379, P < 0.001), perceived service quality (β = 0.200, P < 0.001), and perceived information quality (β = 0.320, P < 0.001). Continuous usage intention was influenced by perceived usefulness (β = 0.191, P < 0.001), customer satisfaction (β = 0.453, P < 0.001), and subjective norm (β = 0.372, P < 0.001). CONCLUSIONS: The study constructed a new theoretical model including E-health literacy, subjective norm and technology qualities to clarify continuous usage intention of mHealth services, and empirically validated the model. Attention should be paid to E-health literacy, subjective norm, perceived information quality, and perceived service quality to improve continuous usage intention of users and self–management by mHealth Apps managers and governments. This research provides solid evidence for the validity of the expanded model of ECM-ISC in the mHealth field, which can be a theoretical and practical basis for mHealth operators’ product research and development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09393-9.
format Online
Article
Text
id pubmed-10159674
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-101596742023-05-06 Continuous usage intention of mobile health services: model construction and validation Nie, Li Oldenburg, Brian Cao, Yingting Ren, Wenjie BMC Health Serv Res Research BACKGROUND: Mobile health (mHealth) services can not give full play to their value if only it is used in the short term, and their continuous usage can achieve better effects in health management. This study aims to explore the factors that affect continuous usage intentions of mHealth services and their mechanism of action. METHODS: First, considering the uniqueness of health services and social environmental factors, this study constructed an extended Expectation Confirmation Model of Information System Continuance (ECM-ISC) to investigate factors that may influence the intention of continuous usage of mHealth services based on three dimensions, namely individual characteristics, technology and environment. Second, the survey method was used to validate the research model. The questionnaire items were derived from validated instruments and discussed by experts and data were collected both online and offline. The structural equation model was used for data analysis. RESULTS: There were 334 avidity questionnaires through cross-sectional data and these participants had used mHealth services ever. The reliability and validity of the test model were good, in which Cronbach’s Alpha values of 9 variables exceeded 0.9, composite reliability 0.8, the average variance extracted value 0.5, and the factor loading 0.8. The modified model had a good fitting effect and strong explanatory power. It accounted for 89% of the variance in expectation confirmation, 74% of the variance in perceived usefulness, 92% of the variance in customer satisfaction, and 84% of the variance in continuous usage intention. Compared with the initial model hypotheses, perceived system quality was deleted according to the heterotrait-monotrait ratio, so paths related to it were deleted; perceived usefulness wasn’t positively associated with customer satisfaction, and its path was also deleted. Other paths were consistent with the initial hypothesis. The two new added paths were that subjective norm was positively associated with perceived service quality (β = 0.704, P < 0.001), and perceived information quality (β = 0.606, P < 0.001). Electronic health literacy (E-health literacy) was positively associated with perceived usefulness (β = 0.379, P < 0.001), perceived service quality (β = 0.200, P < 0.001), and perceived information quality (β = 0.320, P < 0.001). Continuous usage intention was influenced by perceived usefulness (β = 0.191, P < 0.001), customer satisfaction (β = 0.453, P < 0.001), and subjective norm (β = 0.372, P < 0.001). CONCLUSIONS: The study constructed a new theoretical model including E-health literacy, subjective norm and technology qualities to clarify continuous usage intention of mHealth services, and empirically validated the model. Attention should be paid to E-health literacy, subjective norm, perceived information quality, and perceived service quality to improve continuous usage intention of users and self–management by mHealth Apps managers and governments. This research provides solid evidence for the validity of the expanded model of ECM-ISC in the mHealth field, which can be a theoretical and practical basis for mHealth operators’ product research and development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09393-9. BioMed Central 2023-05-05 /pmc/articles/PMC10159674/ /pubmed/37143005 http://dx.doi.org/10.1186/s12913-023-09393-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Nie, Li
Oldenburg, Brian
Cao, Yingting
Ren, Wenjie
Continuous usage intention of mobile health services: model construction and validation
title Continuous usage intention of mobile health services: model construction and validation
title_full Continuous usage intention of mobile health services: model construction and validation
title_fullStr Continuous usage intention of mobile health services: model construction and validation
title_full_unstemmed Continuous usage intention of mobile health services: model construction and validation
title_short Continuous usage intention of mobile health services: model construction and validation
title_sort continuous usage intention of mobile health services: model construction and validation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159674/
https://www.ncbi.nlm.nih.gov/pubmed/37143005
http://dx.doi.org/10.1186/s12913-023-09393-9
work_keys_str_mv AT nieli continuoususageintentionofmobilehealthservicesmodelconstructionandvalidation
AT oldenburgbrian continuoususageintentionofmobilehealthservicesmodelconstructionandvalidation
AT caoyingting continuoususageintentionofmobilehealthservicesmodelconstructionandvalidation
AT renwenjie continuoususageintentionofmobilehealthservicesmodelconstructionandvalidation