Cited by 241Technology Adoption and User BehaviourHealth, Technology, Consumer BehaviorMobile Health and mHealth Applications

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

Zhang, Xiaofei, Han, Xiaocui, Dang, Yuanyuan, Meng, Fanbo, Guo, Xitong, Lin, Jiayue·2017·Informatics for Health and Social Care

DOI 10.1080/17538157.2016.1200053

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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.

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Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664
Modern Methods for Business Research
(1998) · doi:10.4324/9781410604385 · Cited by 6,534
Putting the fear back into fear appeals: the extended parallel process model
Witte, K. (1992) · Communication Monographs · doi:10.1080/03637759209376276 · Cited by 3,880
Information technology acceptance by individual professionals: a model comparison approach
Chau, P. Y. K., & Hu, P. J. (2001) · Decision Sciences · doi:10.1111/j.1540-5915.2001.tb00978.x · Cited by 1,409
Mobile computing acceptance factors in the healthcare industry: a structural equation model
Wu, J.-H., Wang, S.-C., & Lin, L.-M. (2007) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.06.006 · Cited by 603
A brief intervention changing oral self-care, self-efficacy, and self-monitoring
Schwarzer, R., Antoniuk, A., & Gholami, M. (2015) · British Journal of Health Psychology · doi:10.1111/bjhp.12091 · Cited by 159

Show all 8 references

Research works printed but not linked: 41

Istepanian RSH, 2003, Some imperative notes on m-health
10.1089/tmj.2014.0166 · DOI
10.1016/j.im.2013.03.001 · DOI
10.1089/tmj.2013.0092 · DOI
10.1057/palgrave.ejis.3000717 · DOI
Adams DA, Nelson RR, Todd PA. Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly 1992:227–47:0276–7783. · DOI · Paper page
Venkatesh V, 2003, MIS Quarterly, 27, 0276, 10.2307/30036540 · DOI · Paper page
Yu C-S., 2012, Journal of Electronic Commerce Research, 13, 104
10.1016/j.im.2003.12.013 · DOI
Park I, 2015, Journal of Consumer Research, 12, 382
10.1089/dia.2014.0341 · DOI
Nour MM, 2015, JMIR Research Protocols, 4, e92, 10.2196/resprot.4665 · DOI
10.1089/tmj.2013.0080 · DOI
10.1007/s12525-010-0043-x · DOI
10.1007/s10916-010-9600-2 · DOI
Johnston AC, 2010, MIS Quarterly, 34, 0276, 10.2307/25750691 · DOI
10.1016/j.elerap.2015.11.001 · DOI
Kapoor K, 2014, Piercy N, Lal B, Weerakkody V. RFID integrated systems in libraries: Extending TAM model for empirically examining the use. Journal of Enterprise Information Management, 27, 731
10.1002/mar.10045 · DOI
Fishbein M, 1975, Belief, attitude
10.1037/0003-066X.37.2.122 · DOI · Paper page
Bandura A., 1986, Social foundations of thought and action
10.2307/249688 · DOI · Paper page
Huang Y-C, Tsay W-D, Huang C-H, Lin Y-H, Lai M-C, editors. The influence factors of electronic bill presentment and payment—A case study of mobile phone bill2011: IEEE.
10.1287/isre.11.4.418.11876 · DOI
10.1080/00223980.1975.9915803 · DOI · Paper page
10.2307/25750694 · DOI
10.1108/ITP-12-2012-0147 · DOI
10.2307/249008 · DOI · Paper page
10.1287/mnsc.35.8.982 · DOI · Paper page
10.1197/jamia.M1475 · DOI
10.1016/j.ijmedinf.2008.07.006 · DOI
10.2307/25750691 · DOI
Gefen D, Straub DW, Boudreau MC. Structural equation modeling and regression: Guidelines for research practice. Communications of AIS 2000;7(7):1–78. · DOI · Paper page
10.1177/002224378101800104 · DOI · Paper page
10.1177/002224378101800104 · DOI · Paper page
Okazaki S, 2015, Journal of Electronic Commerce Research, 16, 194
Ng S-N, 2015, Electronic Journal of Health Informatics, 9, e7
Hanus B, 2016, Wu YA. Impact of users’
10.1016/j.im.2012.04.002 · DOI
10.1016/j.appet.2014.10.013 · DOI

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“…through an adapted TAM questionnaire containing 29 items distributed across 6 dimensions—perceived usefulness (6 items) [17], perceived ease of use (6 items) [18], behavioral intention to use (4 items) [16], social influence (5 items) [17], trust (3 items) [14], and perceived risk (5 items) [16].”
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Ding, X., Marchand, K., Holsti, L., Schmidt, J., Parde, N., Sakakibara, B. M., & Barbic, S. P. (2025) · Digital Health · doi:10.1177/20552076251365073
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Jairam, D., Swathi, K. S., & Nayak, S. (2025) · F1000Research · doi:10.12688/f1000research.160126.3
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Zeng, K., Dong, L., Xu, Y., & Zheng, X. (2024) · Digital Health · doi:10.1177/20552076241281214 · Cited by 1
Perception and Usage of Telemedicine Among National Health Insurance Participants in Padang City
Roza, S. H., & Sari, A. F. (2023) · Jurnal Ilmu Kesehatan Masyarakat · doi:10.26553/jikm.2023.14.2.281-292 · Cited by 3
“…services for patients and users are expected to periodically update information on healthcare services, which should improve their telemedicine services, such as consultation and information, by using mobile health services; however, it is easy for patients or users to access and understand 21 .”
Background
Unveiling Consumer Preferences and Intentions for Cocreated Features of a Combined Diet and Physical Activity App: Cross-Sectional Study in 4 European Countries
Kahriz, B. M., Snuggs, S. J., Sah, A., Clot, S., Lamport, D. J., Forrest, J., Helme-Guizon, A., Wilhelm, M.-C., Caldara, C., Anin, C. V., et al. (2023) · JMIR Human Factors · doi:10.2196/44993 · Cited by 1
“Higher self-efficacy is also related to a higher intention to use mobile health services [48].”
Background
Understanding English as a Foreign Language (EFL) Teachers’ Acceptance to Teach Online During Covid-19: A Chinese Case
Gao, Y.-J., Wong, S.-L., Khambari, M. N., & Noordin, N. (2021) · International Journal of Academic Research in Business and Social Sciences · doi:10.6007/ijarbss/v11-i9/10856 · Cited by 1
“SE has a direct influence on the user's behavioral intention to use technology (Banas & York, 2014; Zhang et al., 2017) and PEU (Mei, Brown, & Teo, 2018).”
Background
M-health Services Equipped with Public-Sector Community Health Centers in China: Investigating Adoption using UTAUT and Channel Expansion TheoryPreprint
Chudhery, M. A. Z., Safdar, S., Li, F.-G., Rehman, H.-U., Rafique, R., Huo, J.-Z., & Gao, L. (2020) · doi:10.21203/rs.3.rs-109637/v1 · Cited by 1
Exploring the Future Research Agenda for Health Applications Adoption: A Systematic Literature Review
Fauzi, R., Permanasari, A., & Fauziati, S. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160810 · Cited by 1
“[12], [14], [18], [20], [26], [27], [30], [32], [34], [35], [36], [37], [38], [41], [45], [46], [51], [54], [59], [60], [62], [64], [66], [68], [70], [72], [73], [80], [85], [91] Health & IT Literation The user's difficulty in using m-health devices due to low health literacy or education suggests…”
Wireless Mobile Communication and Healthcare: 8th EAI International Conference, MobiHealth 2019, Dublin, Ireland, November 14-15, 2019, Proceedings
O'Hare, G., O'Grady, M., Akan, O., O'Donoghue, J., & Henn, P. (2020) · International ICST Conference on Wireless Mobile Communication and Healthcare · doi:10.1007/978-3-030-49289-2 · Cited by 1
Citizen science for malaria control in Rwanda : Engagement, motivation, and behaviour change
Asingizwe, D. (2020) · doi:10.18174/526218 · Cited by 2
“The association between response efficacy and perceived ease of use of mobile health services was revealed by Zhang et al. (2016 ).”
Background
Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs.
Zhou, Z., Zhou, Y., Ouyang, B., Man, S. S., & Chan, A. H. S. (2026) · Healthcare · doi:10.3390/healthcare14131978
“These constructs were measured using 21 items adapted from the work of Zhang et al. [75] and Chung and Dong [76], with each factor represented by three specific indicators.”
Testing Restorative Narratives in a College Student Resilience Project
Ray, E. C., Arpan, L. M., Oehme, K., Perko, A., & Clark, J. J. (2019) · Innovative Higher Education · doi:10.1007/s10755-019-9464-4 · Cited by 10
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Predictors of Mobile Health App Acceptance Among Nurses in Kashan, Iran: Cross-Sectional Study
Mirabootalebi, N., Holl, F., Swoboda, W., Tagharrobi, Z., Jeddi, F. R., & Akbari, H. (2026) · JMIR mHealth and uHealth · doi:10.2196/93888
“…roles for health care professionals, including task mobility, individual performance, technological maturity [19], self-efficacy, responsiveness [41], organizational support, environmental and organizational influences, adaptability [17], and acceptance of MHA; however, these factors were not…”
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“All statistical analyses were completed using IBM SPSS Statistics for Windows (IBM Corp., Armonk, NY, USA), Version 29.”
Factors predicting the intentions to use mHealth applications for mental wellness
Padaliya, D., Arya, B., Bisht, V., Sharma, M. R., & Kumar, H. (2026) · Journal of Human Behavior in the Social Environment · doi:10.1080/10911359.2026.2703853
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Kumar, N., Murthy, K. K. S. V., Moorthy, V., Upreti, K., & Singh, S. (2026) · Journal of Health Organization and Management · doi:10.1108/jhom-05-2025-0283 · Cited by 1
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“Our observation that self-efficacy was positively associated with acceptance of LLMs in healthcare aligns with mHealth research [25], while links to conscientiousness and rest intolerance imply that LLM-based tools may be perceived as efficient channels for health information processing [26].”
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Zhao, Y., Wang, Z., Ke, F., & Ma, X. (2026) · Healthcare · doi:10.3390/healthcare14131892
“…such as technological facilitators (e.g., perceived usefulness, ease of use, and convenience) [8,9], personal attributes (e.g., self-efficacy and age) [10,11], and external influences (e.g., social support) [12,13], thereby contributing to our understanding of MHIS adoption among older adults.”
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Mouloudj, K., Njoku, A., Bouarar, A., Asanza, D. M., Evans, M. A., & Baviskar, S. (2026) · Geriatrics · doi:10.3390/geriatrics11040076
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“(Bashir et al., 2022; Zhang et al., 2017).”
Exploring mobile health service adoption among diabetic patients using an integrated UTAUT-PMT model
Saputra, R., Adhy, S., & Yasin, H. (2026) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v15i3.9998
“Unlike prior hybrid frameworks such as UTAUT2-HBM [17] or PMT-TAM [12], this study uniquely integrates UTAUT’s cognitive determinants with PMT’s coping-appraisal components to capture both technological and psychological readiness among diabetic patients.”
Influential
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Kim, M. J., & Chong, H. J. (2026) · Digital Health · doi:10.1177/20552076261466801
“25,34 Therefore, in this study, AI competency was adopted as a theoretically aligned proxy for perceived ease of use, an extended construct encompassing the essence of ease of use.”
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Theile, F., Schaal, T., Mäder, M., & Schoenfelder, T. (2025) · BMC Public Health · doi:10.1186/s12889-025-25472-4
“To enhance DSCI adoption, interventions could incorporate elements that strengthen self-efficacy, such as clear guidance, positive feedback, and gradual goal setting [47].”
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Self-citation
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Lau, B. H. P., Shum, E. N.-Y., & Yoon, J. (2025) · Journal of Applied Gerontology · doi:10.1177/07334648251349907
“Higher household income, education, and technological pro ﬁ ciency are associated with more intention to use mobile and wearable health devices (Chandrasekaran et al., 2020, 2021; Fowe & Boot, 2022; Zhang et al., 2017).”
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“This includes the efficacy of deepfake detection methods and education and awareness initiatives [8].”
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Sabara, E., Mahendra, & Riana, D. (2024) · Journal Medical Informatics Technology · doi:10.37034/medinftech.v2i3.45
“Additionally, individual characteristics such as age, gender, and prior smartphone experience play a role in moderating these relationships [2].”
Background
Gamification in personal health record systems: a regulatory fit perspective
Taşbaş, E. H. O., Yildirim, B. F., & Attila, M. R. (2024) · Psychology and Health · doi:10.1080/08870446.2024.2370312
“The intention of participants to adopt was assessed using the three-item scale which was originally developed by Zhang et al. (2017).”
Public Perception of the COVID-19 Pandemic and Protective Measures in Nigeria
Mbah, P., & Kim, D. K. D. (2024) · Communicatio · doi:10.1080/02500167.2024.2405900
“It also refers to the degree of belief in whether the recommended responses ensures avoidance of the threat (Brooks and Bubela 2020; of mobile health services in China, Zhang et al. (2016) response efficacy as a cognitive process that focuses on the formation of thoughts regarding an individual’s appraisal of his or h…”
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[Role of emotions and representations in the use of m-health among members of an online cancer-related community, the Seintinelles].
Pannard, M., Leroy, T., Escriva-Boulley, G., Bernetière, C., Desfriches, O., Paquienséguy, F., & Préau, M. (2022) · Bulletin du Cancer · doi:10.1016/j.bulcan.2022.04.016
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Positive Technology
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Authors: Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin, J.
Affiliations: Hong Kong Polytechnic University (HK)
Harbin Institute of Technology (CN)
Corresponding author: Zhang, Xiaofei
ORCID: Zhang, Xiaofei
Dang, Yuanyuan
Meng, Fanbo
Guo, Xitong
Lin, Jiayue
Venue: Informatics for Health and Social Care
Publisher: Informa UK Limited
Published: 2016-08-26
Volume: 42
Issue: 2
Pages: 194-206
ISSN: 1753-8157 (ISSN-L)
1753-8165
DOI: 10.1080/17538157.2016.1200053
OpenAlex: W2508383655
Cited by: 241
References: 49
Citations by year: 2026: 18 · 2025: 43 · 2024: 31 · 2023: 34 · 2022: 39 · 2021: 34 · 2020: 17 · 2019: 11 · 2018: 12 · 2017: 2
FWCI: 15.78
Type: journal article
Language: en
Open access: closed
Locations: Informatics for Health and Social Care (publishedVersion)
PubMed (publishedVersion)
PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
Funders: National Natural Science Foundation of China (71471048, 71531007, 71471049)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords: mobile health, mHealth adoption, user acceptance, self-efficacy, response efficacy, technology acceptance model, perceived ease of use, perceived usefulness, adoption intention, moderating effect
MeSH terms: Adult, Attitude to Computers, Female, Humans, Male, Middle Aged, Patient Acceptance of Health Care, Perception, Telemedicine, Self Efficacy, Intention

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

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