Literature DB >> 27564428

User acceptance of mobile health services from users' perspectives: The role of self-efficacy and response-efficacy in technology acceptance.

Xiaofei Zhang1,2, Xiaocui Han1, Yuanyuan Dang1, Fanbo Meng1,2, Xitong Guo1, Jiayue Lin1.   

Abstract

With the swift emergence of electronic medical information, the global popularity of mobile health (mHealth) services continues to increase steadily. This study aims to investigate the efficacy factors that directly or indirectly influence individuals' acceptance of mHealth services. Based on the technology acceptance model, this research incorporates efficacy factors into the acceptance decision process. A research model was proposed involving the direct and indirect effects of self-efficacy and response-efficacy on acceptance intention, along with their moderating effects. The model and hypotheses were validated using data collected from a field survey of 650 potential service users. The results reveal that: (1) self-efficacy and response-efficacy are both positively associated with perceived ease of use; and (2) self-efficacy and response-efficacy moderate the impact of perceived usefulness toward adoption intention. Self-efficacy and response-efficacy both play an important role in individuals' acceptance of mHealth services, which not only affect their perceived ease of use of mHealth services, but also positively moderate the effects of perceived usefulness on adoption intention. Our findings serve to provide recommendations that are specifically customized for mHealth service providers and their marketers.

Keywords:  Adoption intention; mHealth services; perceived ease of use; perceived usefulness; response-efficacy; self-efficacy

Mesh:

Year:  2016        PMID: 27564428     DOI: 10.1080/17538157.2016.1200053

Source DB:  PubMed          Journal:  Inform Health Soc Care        ISSN: 1753-8157            Impact factor:   2.439


  22 in total

1.  Evaluating clinicians' user experience and acceptability of LearnTB, a smartphone application for tuberculosis in India.

Authors:  Tripti Pande; Kavitha Saravu; Zelalem Temesgen; Al Seyoum; Shipra Rai; Raghavendra Rao; Deekshith Mahadev; Madhukar Pai; Marie-Pierre Gagnon
Journal:  Mhealth       Date:  2017-07-27

2.  Exposure Detection Applications Acceptance: The Case of COVID-19.

Authors:  Adi Alsyouf; Abdalwali Lutfi; Mohammad Al-Bsheish; Mu'taman Jarrar; Khalid Al-Mugheed; Mohammed Amin Almaiah; Fahad Nasser Alhazmi; Ra'ed Masa'deh; Rami J Anshasi; Abdallah Ashour
Journal:  Int J Environ Res Public Health       Date:  2022-06-14       Impact factor: 4.614

3.  Ethical, Legal, and Social Implications of Symptom Checker Apps in Primary Health Care (CHECK.APP): Protocol for an Interdisciplinary Mixed Methods Study.

Authors:  Anna-Jasmin Wetzel; Roland Koch; Christine Preiser; Regina Müller; Malte Klemmt; Robert Ranisch; Hans-Jörg Ehni; Urban Wiesing; Monika A Rieger; Tanja Henking; Stefanie Joos
Journal:  JMIR Res Protoc       Date:  2022-05-16

4.  Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study.

Authors:  Martien J P van Bussel; Gaby J Odekerken-Schröder; Carol Ou; Rachelle R Swart; Maria J G Jacobs
Journal:  BMC Health Serv Res       Date:  2022-07-09       Impact factor: 2.908

5.  The Determinants of M-Health Adoption in Developing Countries: An Empirical Investigation.

Authors:  Ahmad Alaiad; Mohammad Alsharo; Yazan Alnsour
Journal:  Appl Clin Inform       Date:  2019-10-30       Impact factor: 2.342

6.  Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar.

Authors:  Luis Fernandez-Luque; Meghna Singh; Ferda Ofli; Yelena A Mejova; Ingmar Weber; Michael Aupetit; Sahar Karim Jreige; Ahmed Elmagarmid; Jaideep Srivastava; Mohamed Ahmedna
Journal:  BMC Med Inform Decis Mak       Date:  2017-04-13       Impact factor: 2.796

7.  What Predicts Patients' Adoption Intention Toward mHealth Services in China: Empirical Study.

Authors:  Zhaohua Deng; Ziying Hong; Cong Ren; Wei Zhang; Fei Xiang
Journal:  JMIR Mhealth Uhealth       Date:  2018-08-29       Impact factor: 4.773

8.  Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review.

Authors:  Lorenz Harst; Hendrikje Lantzsch; Madlen Scheibe
Journal:  J Med Internet Res       Date:  2019-05-21       Impact factor: 5.428

9.  Impact of Training and Integration of Apps Into Dietetic Practice on Dietitians' Self-Efficacy With Using Mobile Health Apps and Patient Satisfaction.

Authors:  Juliana Chen; Margaret Allman-Farinelli
Journal:  JMIR Mhealth Uhealth       Date:  2019-03-04       Impact factor: 4.773

10.  Characterizing Consumer Behavior in Leveraging Social Media for E-Patient and Health-Related Activities.

Authors:  Ira Puspitasari; Alia Firdauzy
Journal:  Int J Environ Res Public Health       Date:  2019-09-11       Impact factor: 3.390

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