{"title":"移动医疗采用意向的内在前因:SEM-ANN方法","authors":"Vaidik Bhatt, Samyadip Chakraborty","doi":"10.4018/ijegr.298139","DOIUrl":null,"url":null,"abstract":"Healthcare is not left behind in the technological era, where almost every industry uses technological advances to serve customers' needs and wants. Increasingly, patients and doctors are using modern technological infrastructure to deliver care services. This study focuses on the intrinsic factors that lead to provider adoption of mHealth. The study uses PLS-SEM and neural networks to build on UTAUT theory. Study collects data from 316 care providers practicing in government and private health canters, hospitals and clinic found that intrinsic factors like self-efficacy, personal innovativeness, and performance expectancy positively related to mHealth adoption by physicians, whereas technology anxiety negatively related to adoption behaviour. Effort expectancy is not significant, indicating that m-Health adoption is driven by usefulness and result rather than convenience. If the expertise is not easily available, the physician's best interest for the patient may often drive them to adopt m-Health.","PeriodicalId":170341,"journal":{"name":"Int. J. Electron. Gov. Res.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Intrinsic Antecedents to mHealth Adoption Intention: An SEM-ANN Approach\",\"authors\":\"Vaidik Bhatt, Samyadip Chakraborty\",\"doi\":\"10.4018/ijegr.298139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Healthcare is not left behind in the technological era, where almost every industry uses technological advances to serve customers' needs and wants. Increasingly, patients and doctors are using modern technological infrastructure to deliver care services. This study focuses on the intrinsic factors that lead to provider adoption of mHealth. The study uses PLS-SEM and neural networks to build on UTAUT theory. Study collects data from 316 care providers practicing in government and private health canters, hospitals and clinic found that intrinsic factors like self-efficacy, personal innovativeness, and performance expectancy positively related to mHealth adoption by physicians, whereas technology anxiety negatively related to adoption behaviour. Effort expectancy is not significant, indicating that m-Health adoption is driven by usefulness and result rather than convenience. If the expertise is not easily available, the physician's best interest for the patient may often drive them to adopt m-Health.\",\"PeriodicalId\":170341,\"journal\":{\"name\":\"Int. J. Electron. Gov. Res.\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Electron. Gov. Res.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijegr.298139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Electron. Gov. Res.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijegr.298139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intrinsic Antecedents to mHealth Adoption Intention: An SEM-ANN Approach
Healthcare is not left behind in the technological era, where almost every industry uses technological advances to serve customers' needs and wants. Increasingly, patients and doctors are using modern technological infrastructure to deliver care services. This study focuses on the intrinsic factors that lead to provider adoption of mHealth. The study uses PLS-SEM and neural networks to build on UTAUT theory. Study collects data from 316 care providers practicing in government and private health canters, hospitals and clinic found that intrinsic factors like self-efficacy, personal innovativeness, and performance expectancy positively related to mHealth adoption by physicians, whereas technology anxiety negatively related to adoption behaviour. Effort expectancy is not significant, indicating that m-Health adoption is driven by usefulness and result rather than convenience. If the expertise is not easily available, the physician's best interest for the patient may often drive them to adopt m-Health.