Huda Hussein Mohammad Jawad, Zainuddin Bin Hassan, B. Zaidan
{"title":"Factors Influencing the Behavioural Intention of Patients with Chronic Diseases to Adopt IoT-Healthcare Services in Malaysia","authors":"Huda Hussein Mohammad Jawad, Zainuddin Bin Hassan, B. Zaidan","doi":"10.55463/issn.1674-2974.50.1.4","DOIUrl":null,"url":null,"abstract":"The Internet of Things (IoT) in healthcare is the newest trend in the healthcare market. IoT allows healthcare providers to expand their reach beyond the usual clinical environment. They are driven to maximize the possibilities of digitally linked healthcare services to improve the user experience, diagnostic accuracy, and communication among healthcare professionals. Sensors, wearables, and health monitors have made healthcare cheaper, faster, and more effective. Despite the privileges of the IoT in healthcare services, the adoption rate of these services is still in the early stages. The aim of this research was to examine the adoption of IoT-enabled healthcare services among Malaysian chronic patients. To achieve this purpose, the study offered an integrated framework to investigate the influence of the identified factors on Behavioural Intention (BI) to adopt IoT healthcare services. The novelty lies in combining the Unified Theory of Acceptance and Use of Technology (UTAUT), the Theory of Organizational Environments for Technology Adoption (TOE), and the Social Exchange Theory (SE). Patients in Malaysia dealing with chronic illnesses were the subjects of an online survey. Eleven predicted predictive constructs' impacts were investigated using partial least square structural equation modeling. The findings revealed that individual and technological factors and their dimensions, significantly affected chronic disease patients’ BI toward IoT-healthcare services adoption. Similar results were observed for the effect of BI on Use Behaviour (UB). Meanwhile, trust partially mediated the effect of individual and technological-related factors on BI.","PeriodicalId":15926,"journal":{"name":"湖南大学学报(自然科学版)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"湖南大学学报(自然科学版)","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.55463/issn.1674-2974.50.1.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Internet of Things (IoT) in healthcare is the newest trend in the healthcare market. IoT allows healthcare providers to expand their reach beyond the usual clinical environment. They are driven to maximize the possibilities of digitally linked healthcare services to improve the user experience, diagnostic accuracy, and communication among healthcare professionals. Sensors, wearables, and health monitors have made healthcare cheaper, faster, and more effective. Despite the privileges of the IoT in healthcare services, the adoption rate of these services is still in the early stages. The aim of this research was to examine the adoption of IoT-enabled healthcare services among Malaysian chronic patients. To achieve this purpose, the study offered an integrated framework to investigate the influence of the identified factors on Behavioural Intention (BI) to adopt IoT healthcare services. The novelty lies in combining the Unified Theory of Acceptance and Use of Technology (UTAUT), the Theory of Organizational Environments for Technology Adoption (TOE), and the Social Exchange Theory (SE). Patients in Malaysia dealing with chronic illnesses were the subjects of an online survey. Eleven predicted predictive constructs' impacts were investigated using partial least square structural equation modeling. The findings revealed that individual and technological factors and their dimensions, significantly affected chronic disease patients’ BI toward IoT-healthcare services adoption. Similar results were observed for the effect of BI on Use Behaviour (UB). Meanwhile, trust partially mediated the effect of individual and technological-related factors on BI.