User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The constructs SE, FC, and SI were measured using 21 items adapted from Zhang et al. [75] and Chung and Dong [76], with each factor represented by three indicators.
“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.”
From Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs. (2026) · cites this paper as Zhang et al. [75]
Cites
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 · see citing works
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 · see citing works
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664 · see citing works
Modern Methods for Business Research
(1998) · doi:10.4324/9781410604385 · Cited by 6,534 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Technology Acceptance in Healthcare: A Systematic Review
AlQudah, A. A., Al‐Emran, M., & Shaalan, K. F. (2021) · Applied Sciences · doi:10.3390/app112210537 · Cited by 404 · see citing works
“[191] 2017 Journal Article Mobile Health Services 650 Service Users China Extended TAM”
BackgroundTheories Predicting End-User Acceptance of Telemedicine Use: Systematic Review
Harst, L., Lantzsch, H., & Scheibe, M. (2019) · Journal of Medical Internet Research · doi:10.2196/13117 · Cited by 320 · see citing works
“From a patient perspective, usefulness is achieved when, for example, telemedicine use improves quality of life or makes the care process more convenient for the patient [48].”
BackgroundMethodWhat Predicts Patients’ Adoption Intention Toward mHealth Services in China: Empirical Study
Deng, Z., Hong, Z., Ren, C., Zhang, W., & Xiang, F. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9316 · Cited by 224 · see citing works
“Corroborating the results of a study by Zhang et al [60], we also validated that although perceived usefulness and perceived ease of use are still influential in explaining the adoption of mHealth services, their significance seems to decline.”
ResultTao, D., Wang, T., Wang, T.-S., Zhang, T., Zhang, X., & Qu, X.-D. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.09.023 · Cited by 203 · see citing works
Self-citationBalapour, A., Reychav, I., Sabherwal, R., & Azuri, J. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.03.005 · Cited by 220 · see citing works
Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach
Alam, M. Z., Hu, W., Kaium, M. A., Hoque, M. R., & Alam, M. M. D. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101255 · Cited by 204 · see citing works
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lutfi, A., Alrawad, M., Mulhem, A. A. A., Alkhdour, T., et al. (2022) · Electronics · doi:10.3390/electronics11223662 · Cited by 255 · see citing works
Review of the Factors Affecting Acceptance of AI-Infused Systems
Ismatullaev, U. V. U., & Kim, S. (2022) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211064707 · Cited by 198 · see citing works
Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy
He, Y., Chen, Q., & Kitkuakul, S. (2018) · Cogent Business & Management · doi:10.1080/23311975.2018.1459006 · Cited by 176 · see citing works
“To illustrate, Zhang et al. (2017) explored user acceptance of mobile health services from both self-efficacy and response efficacy.”
BackgroundA Framework of Mobile Banking Adoption in India
Kumar, A. R., Dhingra, S., Batra, V., & Purohit, H. K. S. (2020) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc6020040 · Cited by 138 · see citing works
Vu, H. T., & Lim, J. (2021) · Behaviour and Information Technology · doi:10.1080/0144929x.2021.1884288 · Cited by 135 · see citing works
“Research using TAM has frequently used two major ‘ traditional ’ variables, PU and PEOU, and more recently technology e ffi cacy (Yi and Hwang 2003; Zhang et al. 2017), this study suggests the inclusion of anxiety or technophobia in the model, especially when investigating public perception of…”
BackgroundMethodThe Determinants of M-Health Adoption in Developing Countries: An Empirical Investigation
Alaiad, A., Alsharo, M., & Alnsour, Y. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-1697906 · Cited by 115 · see citing works
Liu, Y., Lu, X., Zhao, G., Li, C., & Shi, J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.944976 · Cited by 102 · see citing works
Reychav, I., Beeri, R., Balapour, A., Raban, D. R., Sabherwal, R., & Azuri, J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.09.024 · Cited by 99 · see citing works
Factors Influencing Continued Usage Behavior on Mobile Health Applications
Wu, P., Zhang, R., Zhu, X., & Liu, M. (2022) · Healthcare · doi:10.3390/healthcare10020208 · Cited by 100 · see citing works
“In addition, previous studies have conducted and proposed key factors affecting users’ adoption intentions in using mHealth, such as performance expectancy, effort expectancy, social influence, and facilitating conditions [10,11]; self-efficacy and response-efficacy [12].”
BackgroundInfluentialBinyamin, S. S., & Zafar, B. A. (2021) · Health Informatics Journal · doi:10.1177/1460458220976737 · Cited by 89 · see citing works
“Therefore, the objectives of this paper are to (1) systematically review related articles within the domain of mHealth acceptance, (2) identify the most commonly investigated significant relationships in these articles, (3) assess the strength of the identified relationships, and (4) propose a general model with laten…”
BackgroundA multi-method approach to examining consumer intentions to use smart retail technology
Fazal-e-Hasan, S. M., Amrollahi, A., Mortimer, G., Adapa, S., & Balaji, M. S. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106622 · Cited by 76 · see citing works
Mobile application user behavior in the developing countries: A survey in Iran
Hajiheydari, N., & Ashkani, M. (2018) · Information Systems · doi:10.1016/j.is.2018.05.004 · Cited by 73 · see citing works
“[115] are on of the belief that the self-efficacy of mobile application is associated with the perceived ease of use, attitude and adoption intention of mobile applications.”
BackgroundInfluentialDeterminants of the behavioral intention to use a mobile nursing application by nurses in China
Pan, M., & Gao, W. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-06244-3 · Cited by 90 · see citing works
Embracing the Smart-Home Revolution in Asia by the Elderly: An End-User Negative Perception Modeling
Pal, D., Papasratorn, B., Chutimaskul, W., & Funilkul, S. (2019) · IEEE Access · doi:10.1109/access.2019.2906346 · Cited by 80 · see citing works
“Several researchers have identified a positive relationship between self-efficacy and the intention to adopt technology [71]– [73].”
BackgroundResultBaltrusch, S., Houdijk, H., van Dieen, J. V., & de Kruif, J. D. (2020) · Journal of occupational rehabilitation · doi:10.1007/s10926-020-09891-1 · Cited by 67 · see citing works
“When users perceive high self-efficacy regarding assistive technology, they will have a high adoption intention [33].”
Backgroundvan Bussel, M. J. P., Odekerken–Schröder, G. J., Ou, C., Swart, R. R., & Jacobs, M. J. G. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08189-7 · Cited by 60 · see citing works
“The first was self-efficacy, which refers to the perceived ability to acquire expected information and conduct a specific behavior [102].”
BackgroundMethodResultInfluentialExposure Detection Applications Acceptance: The Case of COVID-19
Alsyouf, A., Lutfi, A., Al‐Bsheish, M., Jarrar, M., Al‐Mugheed, K., Almaiah, M. A., Alhazmi, F. N., Masa’deh, R. M. T., Anshasi, R. J., & Ashour, A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19127307 · Cited by 78 · see citing works
“[72] reported a positiv PU/i tention to use mHealth relati nship, and Binyamin and Zafar [73] showed th significant i fluenc of PU on the intention to use an mHealth app.”
BackgroundMethodInfluentialEzeudoka, B. C., & Fan, M. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03457-9 · Cited by 54 · see citing works
Alsyouf, A., Masa’deh, R. M. T., Albugami, M. A., Al‐Bsheish, M., Lutfi, A., & Alsubahi, N. (2021) · Risks · doi:10.3390/risks9100179 · Cited by 56 · see citing works
Social Media Communication about HPV Vaccine in China: A Study Using Topic Modeling and Survey
Jiang, S.-H., Wang, P.-P., Liu, P. L., Ngien, A., & Wu, X.-T. (2021) · Health Communication · doi:10.1080/10410236.2021.1983338 · Cited by 47 · see citing works
“…effect can be particularly salient in the social media environment, where negative news is sometimes exaggerated without rigorous editorial review, and users are often alerted of real-time scandals and spontaneously voice strong negative emotions such as anger or disgust (L. Zhang et al., 2016).”
BackgroundYe, Q., Deng, Z., Chen, Y., Liao, J., Li, G., & Lu, Y. (2019) · JMIR mhealth and uhealth · doi:10.2196/13491 · Cited by 52 · see citing works
“Extant studies mainly focus on the impact of individual characteristics or technological factors on adoption of mHealth [44-46].”
Predictors of contact tracing app adoption: Integrating the UTAUT, HBM and contextual factors
van der Waal, N. E., de Wit, J., Bol, N., Ebbers, W. E., Hooft, L., Metting, E., & van der Laan, L. N. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102101 · Cited by 47 · see citing works
Expectations of telemedicine health service adoption in rural Bangladesh.
Zobair, K. M., Sanzogni, L., & Sandhu, K. (2019) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2019.112485 · Cited by 45 · see citing works
Mensah, I. K., Zeng, G., & Mwakapesa, D. S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1020474 · Cited by 44 · see citing works
“For instance, it was demonstrated that self-efficacy is positively related to the perceived ease of use of mobile health services and also moderates the impact of perceived usefulness on the adoption of m-health services (34).”
BackgroundZhou, X., Krishnan, A., & Dincelli, E. (2021) · Behaviour and Information Technology · doi:10.1080/0144929x.2021.1915383 · Cited by 56 · see citing works
Puspitasari, I., & Firdauzy, A. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16183348 · Cited by 52 · see citing works
“This finding is consistent with previous studies in health technology acceptance, suggesting that users would adopt health technologies when they considered that the use of the technology could reduce health threats [26,37,69].”
ResultHow habit affects continuous use: evidence from Jordan’s national health information system
Alsharo, M., Alnsour, Y., & Alabdallah, M. (2018) · Informatics for Health and Social Care · doi:10.1080/17538157.2018.1540423 · Cited by 49 · see citing works
Ramírez-Correa, P., Ramírez-Rivas, C., Alfaro-Peréz, J., & Mariano, A. M. (2020) · Symmetry · doi:10.3390/sym12101593 · Cited by 48 · see citing works
Hsu, W.-L., & Silalahi, A. D. K. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100329 · Cited by 38 · see citing works
Effective and efficient usage of big data analytics in public sector
Merhi, M. I., & Bregu, K. (2020) · Transforming Government People Process and Policy · doi:10.1108/tg-08-2019-0083 · Cited by 38 · see citing works
Fernández-Luque, L., Singh, M., Ofli, F., Mejova, Y., Weber, I. G., Aupetit, M. J. M., Jreige, S. K., Elmagarmid, A. K., Srivastava, J., & Ahmedna, M. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0432-6 · Cited by 34 · see citing works
Understanding the Drivers of Ghanaian Citizens' Adoption Intentions of Mobile Health Services
Mensah, I. K. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.906106 · Cited by 45 · see citing works
“The findings on the significant impact of self-efficacy on both perceived usefulness and perceived ease of use of mobile health services corroborate findings that suggested self-efficacy is positively related to perceived usefulness and perceived ease of use ( 96).”
BackgroundSeow, A., Choong, C., Chen, I.-C., & Choong, Y. (2021) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-10-2020-0189 · Cited by 36 · see citing works
Predicting subjective well-being among mHealth users: a readiness – value model
Aboelmaged, M. G. S., Hashem, G., & Mouakket, S. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102247 · Cited by 34 · see citing works
Understanding affiliation to cashback programs: An emerging technique in an emerging country
Christino, J., Silva, T., Cardozo, É. A. A., Carrieri, A. D. P., & Nunes, P. D. P. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2018.10.009 · Cited by 36 · see citing works
Chen, J., & Allman-Farinelli, M. (2019) · JMIR mHealth and uHealth · doi:10.2196/12349 · Cited by 35 · see citing works
“Computer self-efficacy research models propose that antecedents of computer self-efficacy include prior performance experiences with using computers, computer knowledge, behavioral modeling in computer training, social support, and encouragement provided by similar others, such as colleagues [34-38], which coincide wi…”
Backgroundvan Velsen, L., Evers, M., Bara, C.-D., den Akker, H. O., Boerema, S., & Hermens, H. J. (2018) · JMIR Formative Research · doi:10.2196/10474 · Cited by 37 · see citing works
“For example, Zhang and colleagues [41] studied the role of self-efficacy and end-users’ belief in mHealth’s capability to avert negative threats to one’s health.”
BackgroundPark, H. K., Chung, J., & Ha, J. (2022) · Technology and Society · doi:10.1016/j.techsoc.2022.102182 · Cited by 33 · see citing works
Kim, H., & Chakraborty, S. (2023) · Internet Research · doi:10.1108/intr-09-2022-0727 · Cited by 32 · see citing works
Kisubi, M., Bonuke, R., & Korir, M. (2021) · Cogent Business & Management · doi:10.1080/23311975.2021.1938348 · Cited by 32 · see citing works
“For instance, according to Zhang et al. (2017) self-efficacy has a significantly positive moderating effect on the relationship between perceived usefulness and adoption intention of mobile health services.”
BackgroundExploring individuals’ adoption of COVID-19 contact-tracing apps: a mixed-methods approach
Nguyen, T. T., Nguyen, T. C. A. H., & Tran, C. D. (2021) · Library Hi Tech · doi:10.1108/lht-06-2021-0180 · Cited by 32 · see citing works
Marhefka, S., Lockhart, E. A., Turner, D., Wang, W., Dolcini, M. M., Baldwin, J., Roig-Romero, R., Lescano, C., & Glueckauf, R. (2019) · Aids and Behavior · doi:10.1007/s10461-019-02723-1 · Cited by 31 · see citing works
Ahikiriza, E., Wesana, J., Huylenbroeck, G. V., Kabbiri, R., de Steur, H., Lauwers, L., & Gellynck, X. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107413 · Cited by 30 · see citing works
Paganin, G., & Simbula, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18179366 · Cited by 37 · see citing works
Chen, T., Stewart, M., Bai, Z., Chen, E., Dabbish, L., & Hammer, J. (2020) · Designing Interactive Systems Conference · doi:10.1145/3357236.3395522 · Cited by 34 · see citing works
“Without this strengthened belief in the effectiveness of the tools, the increased self-efficacy could have a less pronounced impact on the participant’s actual behavior and belief [58] due to inaccurate perceptions about tool effectiveness and difficulty of implementation [52].”
BackgroundInfluentialPsychological influences and implications for household disaster preparedness: a systematic review
Ni, M., Xia, L., Wang, X., Wei, Y., Han, X., Liu, Y., & Pan, S. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1457406 · Cited by 26 · see citing works
Self-citationTao, D., Chen, Z.-X., Qin, M., & Cheng, M.-T. (2023) · Healthcare · doi:10.3390/healthcare11111550 · Cited by 29 · see citing works
“, 2017 [17] China M-health services 650 TAM PEOU, PU, self-efficacy, and response-efficacy Cilliers et al.”
Xie, H., Fang, Y.-R., Wang, M., Liu, J., & Lv, A.-Q. (2023) · Work, Aging and Retirement · doi:10.1093/workar/waad004 · Cited by 31 · see citing works
Skalkos, A., Stylios, I. C., Karyda, M., & Kokolakis, S. (2021) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp1040036 · Cited by 29 · see citing works
“[108] PRE3 I am confident that the biometric authentication system will not use my personal data for other purposes.”
BackgroundSun, Y., Feng, Y., Shen, X., & Guo, X. (2022) · Information Technology and People · doi:10.1108/itp-07-2021-0519 · Cited by 36 · see citing works
Self-citationZhong, L., Cao, J., & Xue, F. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1408998 · Cited by 29 · see citing works
Maisha, K., & Shetu, S. N. (2023) · Future Business Journal · doi:10.1186/s43093-023-00214-3 · Cited by 31 · see citing works
“The degree to which users perceive their propensity to engage in continuous usage behaviors is referred to as continued usage intentions [107].”
BackgroundAmoako, R., Jiang, Y.-C., Frempong, M. F., Tetteh, S., & Adu-Yeboah, S. S. (2022) · SAGE Open · doi:10.1177/21582440221088852 · Cited by 25 · see citing works
“In addition, this finding contributes to studies such as Zhang et al. (2017) and Tsai et al. (2019) in the area of technology adoption that argues the essence of individual or employee self-efficacy and technology oriented knowledge on the success of technology adoption and utilization.”
BackgroundJilani, M. M. A. K., Moniruzzaman, M., Dey, M., Alam, E., & Uddin, M. A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19052752 · Cited by 30 · see citing works
“Studies show that individuals having a strong selfdiscipline motivation and a positive attitude use more health-related services on mHealth platforms [38,39].”
BackgroundInfluentialTurcotte, S., Bouchard, C., Rousseau, J., Leduc, R. D., Bier, N., Kairy, D., Dang-Vu, T., Sarimanukoglu, K., Dubé, F., Racine, C. B., et al. (2023) · Australasian Journal on Ageing · doi:10.1111/ajag.13244 · Cited by 24 · see citing works
“61,62 Ensuring the development of the older adults' self-efficacy in using technologies can be done by providing them with early opportunities to practice and have a positive experience (mastery experience) with the telehealth technology.”
BackgroundConsumer Acceptance of Sports Wearables: The Role of Products Attributes
Wang, Z., Fang, D., Liu, X., Zhang, L., Duan, H., Wang, C., & Guo, K. (2023) · SAGE Open · doi:10.1177/21582440231182653 · Cited by 32 · see citing works
“Third, the boundary of TAM theory is explored by identifying the moderator in the influence of perceived ease of use and perceived usefulness (X. F. Zhang et al., 2017).”
BackgroundOrganic Food Preferences: A Comparison of American and Indian Consumers
Boobalan, K., Sivakumaran, B., & Susairaj, M. (2022) · Food Quality and Preference · doi:10.1016/j.foodqual.2022.104627 · Cited by 24 · see citing works
Bian, D., Xiao, Y., Song, K., Dong, M., Li, L., Millar, R. W., Shi, C., & Li, G. (2023) · Journal of Medical Internet Research · doi:10.2196/37671 · Cited by 29 · see citing works
“Although prior research has examined the factors that can inhibit individuals’IHT adoption, most research focuses on the factors of privacy risk, performance risk, and legal concerns [33,90,91], ignoring workload and doctor-patient dispute problems.”
Understanding Determinants of End-User’s Continuance Intention on Fitness Wearable Technology
Sun, H., & Gu, C. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2124360 · Cited by 24 · see citing works
“…hand, Cho et al. (2014) found that eHealth literacy has a positive impact on health-app self-efficacy, while individual ’ s self-efficacy was previously found to be strongly associated with perceived usability in the context of information technology (Ariff et al., 2012; X. Zhang et al., 2017).”
BackgroundResultSaigí-Rubió, F., Vidal‐Alaball, J., Torrent‐Sellens, J., Jiménez‐Zarco, A. I., Seguí, F. L., Hernández, M. C., Reig, X. A., Bonet, J. M., González, M. A., Piera-Jiménez, J., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/28944 · Cited by 33 · see citing works
“A professional’s intention to use the tool will also be affected by professional and demographic variables [42,43].”
Applying citizen science for malaria prevention in Rwanda: An integrated conceptual framework
Asingizwe, D., Poortvliet, P. M., Koenraadt, C. J. M., van Vliet, A. J. H., Murindahabi, M. M., Ingabire, M. C., Mutesa, L., & Feindt, P. H. (2018) · NJAS - Wageningen Journal of Life Sciences · doi:10.1016/j.njas.2018.06.002 · Cited by 33 · see citing works
Mobile mindfulness: Predictors of mobile screen time tracking
Oeldorf-Hirsch, A., & Chen, Y. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107170 · Cited by 22 · see citing works
Bao, H., & Lee, E. W. J. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2183062 · Cited by 33 · see citing works
“In recent years, there has been a proliferation of studies that have applied TAM to explain the use of health apps and wearables (e.g. Ahmad et al. 2020; Bel-dad and Hegner 2018; Zhang et al. 2017).”
BackgroundMethodInfluentialHealth Seekers’ Acceptance and Adoption Determinants of Telemedicine in Emerging Economies
Zobair, K. M., Sanzogni, L., Houghton, L., Sandhu, K., & Islam, M. J. (2021) · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · doi:10.3127/ajis.v25i0.3071 · Cited by 23 · see citing works
“Perceived self-efficacy in telemedicine combines the patients’ assessment of the effectiveness of services performance and the ability or competency to conduct the behaviour (Zhang et al., 2017).”
BackgroundDo ride-hailing drivers' psychological behaviors influence operational performance?
Idug, Y., Niranjan, S., Manuj, I., Gligor, D., & Ogden, J. A. (2023) · International Journal of Operations & Production Management · doi:10.1108/ijopm-06-2022-0362 · Cited by 20 · see citing works
Getting Connected to M-Health Technologies through a Meta-Analysis
Calegari, L. P., Tortorella, G., & Fettermann, D. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054369 · Cited by 20 · see citing works
Zhang, X., Wang, Y., & Leung, S.-O. (2022) · Technology, Pedagogy and Education · doi:10.1080/1475939x.2022.2152861 · Cited by 19 · see citing works
“Empirical evidence suggested that for users who had strong SE regarding their use of a technology, the positive impact of PU on their adoption intention could be strengthened (G. Huang & Ren, 2020; Zhang et al., 2016).”
Self-citationSarı, H., Othman, M., & Al-Ghaili, A. (2018) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-99007-1_68 · Cited by 19 · see citing works
Liu, F.-J., Makady, H., Nah, S., & McNealy, J. E. (2023) · Journal of Information Technology & Politics · doi:10.1080/19331681.2023.2294363 · Cited by 18 · see citing works
“…corroborate the significant moderating role of AI self-efficacy on literacy, resonating with earlier studies that higher levels of technological self-efficacy invariably lead to enhanced literacy levels (Hatlevik, Throndsen, Loi, & Gudmundsdottir, 2018; Shu, Tu, & Wang, 2011; Zhang et al., 2017).”
How do ChatGPT's benefit-risk-coping paradoxes impact higher education in Taiwan and Indonesia?
Panggabean, E. M., & Silalahi, A. D. K. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100412 · Cited by 17 · see citing works
Pande, T., Saravu, K., Temesgen, Z., Seyoum, A., Rai, S., Rao, R. K., Mahadev, D., Pai, M., & Gagnon, M. (2017) · mHealth · doi:10.21037/mhealth.2017.07.01 · Cited by 17 · see citing works
“(48,51,52), our results did not report a correlation between PEU and IU, which rejects our second hypothesis.”
ResultThe Adoption of Mobile Health Applications by Patients in Developing Countries: A Systematic Review
Aljohani, N. F., & Chandran, D. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120403 · Cited by 21 · see citing works
“TAM 10 [6] [7] [13] [17] [20] [21] [33] [15] [23] [36]”
Recent Developments in Individual Difference Research to Inform the Adoption of AI Technology
Symasek, L. W., Yeazitzis, T., Weger, K., & Mesmer, B. L. (2025) · Systems · doi:10.3390/systems13030156 · Cited by 18 · see citing works
Youn, S., Jaso, B. A., Eyllon, M., Sah, P., Hoyler, G. H., Barnes, J. B., Jarama, K., Murillo, L., O'Dea, H., Orth, L., et al. (2023) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-023-01292-9 · Cited by 16 · see citing works
“The goals of these train - ings and experiential learning opportunities were to increase integrated clinicians’ knowledge and self-efficacy with the digital therapeutics as they serve as key implementation change drivers (Zhang et al., 2017 ).”
Wetzel, A.-J., Koch, R., Preiser, C., Mueller, R., Klemmt, M., Ranisch, R., Ehni, H.-J., Wiesing, U., Rieger, M. A., Henking, T., et al. (2022) · JMIR Research Protocols · doi:10.2196/34026 · Cited by 15 · see citing works
“Simultaneously, concepts will be derived from existing literature that is connected to the use of health apps and could reveal the potential characteristics of the user group such as eHealth Literacy [36], personality [37,38], hypochondria [39], self-efficacy [40,41], and need for cognition [42].”
BackgroundTsai, W.-H., Wu, Y., Cheng, C.-S., Kuo, M.-H., Chang, Y.-T., Hu, F.-K., Sun, C., Chang, C., Chan, T., Chen, C., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/27069 · Cited by 22 · see citing works
Ramírez-Rivas, C., Alfaro-Perez, J. L., Ramírez-Correa, P., & Mariano-Melo, A. (2021) · Journal of Information Systems Engineering & Management · doi:10.29333/jisem/9618 · Cited by 14 · see citing works
“A reactive telemedicine system is one where an individual can contact a doctor through their mobile rather than having to attend an in-person consultation (Zhang et al., 2017).”
BackgroundSun, M.-R., & Jiang, L. C. (2020) · Health and Social Care in the community · doi:10.1111/hsc.13107 · Cited by 14 · see citing works
Understanding the insight of factors affecting mHealth adoption
Kaium, M. A., Bao, Y.-K., Alam, M., Hasan, N., & Hoque, M. R. (2019) · International Journal of Research In Business and Social Science · doi:10.20525/ijrbs.v8i6.522 · Cited by 14 · see citing works
Empowerment of Women Self Help Groups: Human Centered Design of a Participatory IoT solution
Sreeraj, S., Unnikrishnan, A., Vishnu, K., Kennith, N. E., Anand, S., & Ramesh, M. (2020) · IEEE Global Humanitarian Technology Conference · doi:10.1109/ghtc46280.2020.9342858 · Cited by 13 · see citing works
“Self-efficacy is an important factor when it comes to the acceptance of technology by people who were not exposed to such technologies previously [20].”
BackgroundKopka, M., Slagman, A. C., Schorr, C., Krampe, H., Altendorf, M. B., Balzer, F., Bolanaki, M., Kuschick, D., Möckel, M., Napierala, H., et al. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100517 · Cited by 18 · see citing works
Investigating M-Payment Intention across Consumer Cohorts
Thoumrungroje, A., & Suprawan, L. (2024) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer19010023 · Cited by 12 · see citing works
A scale to measure the perceived quality of mHealth by elderly patients with hypertension in China
Guo, M., & Lyu, L. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-09357-z · Cited by 11 · see citing works
“Existing studies have shown that long-term relationships between physicians and patients have an impact on chronic disease management [55] and evaluation of mHealth apps [36, 56].”
BackgroundLu, F., Huang, X.-G., & Wang, X. (2022) · Sustainability · doi:10.3390/su142315932 · Cited by 12 · see citing works
“The self-efficacy perceived by online patients in ICT-based remote health promotion technologies consists of two main components: the efficacy expectancy of physician-patient matching and the outcome expectancy of medical guidance [26,55].”
BackgroundZhang, W.-J., & Li, D. (2022) · Industrial management & data systems · doi:10.1108/imds-04-2022-0209 · Cited by 12 · see citing works
Fernández-Luque, L., Singh, M., Ofli, F., Mejova, Y., Weber, I., Aupetit, M., Jreige, S. K., Elmagarmid, A., Srivastava, J., & Ahmedna, M. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0457-x · Cited by 12
Atinafu, W. T., Tilahun, K., Yilma, T., Mekonnen, Z., Walle, A. D., & Adem, J. B. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-10392-z · Cited by 11 · see citing works
“A study done in China [39], indicates that PEOU has a significant effect on perceived usefulness.”
BackgroundMethodAlam, M. Z., & Khanam, L. (2023) · International Journal of Pharmaceutical and Healthcare Marketing · doi:10.1108/ijphm-05-2021-0055 · Cited by 17 · see citing works
Lin‐Hi, N., Haensse, L., Hollands, L., & Blumberg, I. (2023) · Journal of Decision System · doi:10.1080/12460125.2023.2171390 · Cited by 14 · see citing works
“For example, studies have found that technology-related attitudes (Yang & Yoo, 2004), age (Shishah & Alhelaly, 2021), education (Porter & Donthu, 2006), well-being (Lu et al., 2019), trust (Pavlou, 2003), and self-efficacy (Zhang et al., 2017) affect technology acceptance.”
Altawaiha, I., Atan, R., Yaakob, R. B., & Abdullah, R. B. H. (2024) · International journal of information technology · doi:10.1007/s41870-024-01742-z · Cited by 10 · see citing works
“To tackle this complexity, we use the Performance expectancy [23–25] It reflects the anticipated benefits users perceive from technology in terms of its functionality, efficiency, and overall impact on their tasks.”
Zahed, K., Markert, C., Dunn, P. J., & Sasangohar, F. (2023) · Digital Health · doi:10.1177/20552076231215904 · Cited by 12 · see citing works
“10,15 To our knowledge, Dou et al. 16 is the only study that investigated the health beliefs as predictors of intention to use a Self-Measured Blood Pressure (SMBP) monitoring intervention that included a blood pressure (BP) cuff device and an associated mHealth tool for daily self-measurements and…”
MethodLee, H., Lee, S., Nan, D., & Kim, J.-H. (2022) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.300766 · Cited by 10 · see citing works
“Existing studies (e.g., Zhang et al., 2017; Keikhosrokiani et al., 2020) on user experiences of healthcare services used a limited number of samples and traditional approaches (e.g., SEM).”
BackgroundLin, S.-C., Chuang, M.-C., Huang, C.-Y., & Liu, C.-E. (2023) · SAGE Open · doi:10.1177/21582440231208483 · Cited by 13 · see citing works
“Self-efficacy also directly influences users’ perceived ease-of-use (Asimakopoulos et al., 2017; Balapour et al., 2019; Jaradat et al., 2018; Zhang et al., 2017); the higher the level of self-efficacy of healthcare workers in using mobile technology, the more positive their attitude toward using it.”
BackgroundDeterminants of Chatbot adoption among older adults: An extended TAM approach using PLS-SEM
Wu, C., Chen, C.-T., Huang, K., & Chou, Y.-Y. (2025) · Information Development · doi:10.1177/02666669251315839 · Cited by 7 · see citing works
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
