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The evolution of e-health technologies presents promising alternatives for health-care excellence. Despite the benefits arising from mobile e-health (m-health) and wearables technologies, the literature stands many contradictories signs regarding how users accept and engage in using these technologies. This study aims to synthesize the estimations about m-health user acceptance technologies.

A meta-analytic structural equation modeling was carried out using the 778 relationships estimated by 100 previous research. The estimations follow the relations and constructs proposed in the UTAUT2 technological acceptance model.

The results indicate the performance expectancy, effort expectancy, social influence and habit constructs are most important for predicting the behavioral intention of use of m-health technologies. The Latin American users of e-health technologies are still underestimated in the literature.

The study presents a guide to understanding the acceptance process of m-health technologies and delivers a general orientation for developing new m-health devices considering their acceptance by users.

While attempting to persuade surgeons to accept their health technology, sales representatives for medical devices face daily challenges in the operating room. Surgeons exhibit cognitive complexity (abstractness vs. concreteness) when accepting any form of health technology. Surgeons choose technologies on behalf of their patients, taking patient priorities and expectations into account. Prior research has focused on cognitive complexity in the context of health technology adoption, but the issue of technology acceptance has not been addressed. The purpose of this study to use the construal level (CL) theory to determine the role of behavioural abstraction levels in the acceptance of surgical health technology.

On the basis of 556 min of seminar-based data and semi-directive interviews, the surgeons’ experiences regarding the acceptance of health technology were analysed. A non-directive observational method was used to permit the spontaneous emergence of CL dimensions in a natural environment. A categorization model was used for data coding, and MAXQDA, in addition to traditional multidimensional scaling and hierarchical cluster analysis, was used to generate results with joint displays.

Effort expectancy, learning curve, performance risk, habit, patient clinical condition, clinical outcome expectancy, technology setting and social influence were construed at a low construal level (LCL). On the other hand, patient paying capacity, technology cost, price value, financial risk and patient performance expectation were construed at a high construal level (HCL). The study also reveals duality-based factors which showed proximity to HCL but intersected at LCL, and vice versa. Duality-based factors such as effort expectancy, surgical technique, trust and perceived risk intersected at HCL, whereas performance expectancy, relative advantage, time expectancy, perceived value, physical risk and peer group influence intersected at LCL.

This is one of the early studies that presented the impact of behavioural abstraction on behavioural intention to accept health technology for surgeries.

Today, e-government (electronic government) applications have extended to the frontiers of health-care delivery. E-Nabız contains personal health records of health services received, whether public or private. The use of the application by patients and physicians has provided efficiency and cost advantages. The success of e-Nabız depends on the level of technology acceptance of health-care service providers and recipients. While there is a large research literature on the technology acceptance of service recipients in health-care services, there is a limited number of studies on physicians providing services. This study aims to determine the level of influence of trust and privacy variables in addition to performance expectancy, effort expectancy, social influence and facilitating factors in the unified theory of acceptance and use of technology (UTAUT) model on the intention and behavior of using e-Nabız application.

The population of the study consisted of general practitioners and specialist physicians actively working in any health facility in Turkey. Data were collected cross-sectionally from 236 physicians on a voluntary basis through a questionnaire. The response rate of data collection was calculated as 47.20%. Data were collected cross-sectionally from 236 physicians through a questionnaire. Descriptive statistics, correlation analysis and structural equation modeling were used to analyze the data.

The study found that performance expectancy, effort expectancy, trust and perceived privacy had a significant effect on physicians’ behavioral intentions to adopt the e-Nabız system. In addition, facilitating conditions and behavioral intention were determinants of usage behavior (p < 0.05). However, no significant relationship was found between social influence and behavioral intention (p > 0.05).

This study confirms that the UTAUT model provides an appropriate framework for predicting factors influencing physicians’ behaviors and intention to use e-Nabız. In addition, the empirical findings show that trust and perceived privacy, which are additionally considered in the model, are also influential.

This empirical study examines the drivers of consumer intention to adopt wearable technology for healthcare in a South American country. Specifically, it proposes and tests a model of nonuser consumer intention to adopt fitness trackers for healthcare purpose in Chile.

This study draws on the technology acceptance framework UTAUT2 to develop and test a conceptual model of wearable technology adoption. Data were collected through an online survey applied to 470 nonusers of wearable technology in Santiago, Chile.

Findings indicate that hedonic motivation, social influence and perceived usefulness have the strongest influence on intention to adopt fitness trackers in Chile for healthcare. In addition, health motivation is an indirect predictor of consumer's intention to adopt wearable technology through its effect on perceived usefulness.

The study contributes to a better understanding of consumer intention to adopt wearable technology for healthcare objectives in a less developed country in South America. Findings are useful for wearable technology managers, retail business and public policymakers.

Despite the vast growth and importance of wearable technology for healthcare purposes, academic research considering less developed countries is scarce, especially the South American region. The proposed model and findings can extend this research gap. Theoretical and managerial implications are discussed as well as implications for public policy.

The increasing importance of electronic health (e-health) has raised the significance of exploring the factors influencing the users’ acceptance of e-health applications. There has been an extensive usage of the technology acceptance model (TAM) in e-health applications acceptance research. However, not all TAM relationships are borne out in all the studies. There is a variation in predicted effects in several studies with different user type and application type. The purpose of this paper is to bridge a research gap by providing a holistic view of the e-health applications acceptance research by integrating the findings of existing relevant literature.

A statistical meta-analysis of the effect size of causal relationships between common TAM constructs was conducted on 111 peer-reviewed academic studies published in various journals.

The results confirm the validity and robustness of the TAM in e-health applications acceptance research. Further, a moderator analysis based on user type and e-health application type demonstrated that the effect size of causal relationships between TAM constructs majorly depends on the user type, but not on e-health application type.

This research provides a ready reference of the existing studies on e-health applications acceptance to the researchers. Further, if researchers or practitioners want to learn more about the particular user or application type, they may find the results valuable.

This research suggests that the general public can be used as the surrogates for patients in e-health applications acceptance research. The implementation strategy found successful for medical staff may not work for general public and patients. This research also suggests ways to enhance acceptance of e-health applications among different user groups.

The research is original and is based on the existing literature and its interpretation.

This paper aims to investigate the influence of health beliefs and trust by senior adults as associated with the perceived ease of use and perceived usefulness, for the acceptance of smart technology with a focus on smartwatch technology.

Structural equation modeling is used to conceptualize the model using survey data collected from 243 randomly selected senior adults 60+ years of age.

This paper presents that perceived usefulness, perceived ease of use, trust and health belief are direct and indirect predictors of senior adults’ technology acceptance and intention to use smartwatch technology.

The study reveals the moderator effect of social influence on relation between perceived usefulness, perceived ease of use and intention to use. The authors highlight the effect of health belief and trust on perceived usefulness and perceived ease of use and the role of intention to use smartwatch technology.

The authors contribute bridging developers of health technologists and senior adults as end-user perspectives. For marketing of health-care technology products, specifically smartwatch, to seniors, a focus on health beliefs and trust is essential to build, maintain and improve perceived usefulness and perceived ease of use.

The present study contributes empirical evidence to the literature on factors affecting the acceptance of the smartwatch technology by senior adults.

This study aims to examine the determinants of mobile health technology and applications use intention. The factors are delineated from prior literature and theories of individual traits and adoption characteristics, technology acceptance and health belief.

Data from 409 respondents were collected from Indian participants through a questionnaire survey. The construct “use intention” was measured using individual traits to mobile services, subjective norm, health consciousness, awareness and perceived usefulness, and the model was tested.

The study found that mobile health technology and the applications awareness and personal innovativeness influence intention to use.

Previous studies have often looked at technology adoption and acceptance models separately and are less adequately discussed in the Indian context. The components that determine mobile health technology and the applications’ acceptance by literature and theories of individual traits and adoption characteristics, technology acceptance and health beliefs were also inadequately discussed. The significant contribution of this research also includes policy recommendations for improving mobile health acceptance in India.

The purpose of this paper is to investigate the factors associated with consumer’s intention to adopt wearable technology in healthcare, and to examine the moderating effects of product type on consumer’s adoption intention.

An integrated acceptance model was developed based on unified theory of acceptance and use of technology 2 (UTAUT2), protection motivation theory (PMT), and privacy calculus theory. The model was tested with 462 respondents using a survey.

Consumer’s decision to adopt healthcare wearable technology is affected by factors from technology, health, and privacy perspectives. Specially, fitness device users care more about hedonic motivation, functional congruence, social influence, perceived privacy risk, and perceived vulnerability, but medical device users pay more attention to perceived expectancy, self-efficacy, effort expectancy, and perceived severity.

This study is among the first to investigate healthcare wearable device from behavioral perspective. It also helps to comprehensively understand emerging health information technology (HIT) acceptance from technology, health, and privacy perspectives.

With the function to store individual’s data input, personal health record (PHR) enhanced the accessibility to personal health information. This study aims to assess the factors that impact the intention of Malaysian internet users to use PHR and create a modified technology acceptance model (TAM) for eHealth.

Multivariate statistical analysis was performed on a total of 216 responses using the partial least square technique based on the cross-sectional survey among Malaysian internet users.

Behavioral intention was positively associated to PHR. Subjective norm significantly influenced both attitude and intention to use, whereas trust and perceived usefulness significantly influenced attitude. There was no significant positive impact in the relationships between compatibility and perceived ease of use and intention to use; nevertheless, they positively influenced perceived usefulness. Attitude exhibited mediating influence between trust, perceived usefulness and subjective norm and intention to use. Nonetheless, perceived risk did not affect behavioral intention. Thus, PHR acceptance was well-justified by the modified TAM in evaluating eHealth acceptance.

The eHealth vendors can enhance their marketing and development strategies on related products.

Literatures and empirical evidence on eHealth are still scarce, especially in emerging markets. The role of attitude may not be well-researched in health-care context, therefore was included in this study’s modified TAM. Critical determinants, namely, trust and risk, were added to the model.

Blockchain systems have been proposed as a solution for exchanging electronic health records (EHR) because they enable data sharing in decentralised networks. This paper aims to analyse the user acceptability of blockchain technology in enabling EHR exchange and to formulate practical implications for increasing user acceptability.

A technology acceptance model [extended Unified Theory of Acceptance and Use of Technology (UTAUT) model] was used as a framework to measure the effects of 13 factors. The authors conducted a survey and analysed data from 214 participants using partial least square path modelling.

The acceptance of blockchain for EHR sharing is positively influenced by performance expectancy, social influence and perceived trust. Effort expectancy and facilitating conditions do not influence acceptance. The UTAUT model explains the variance in acceptance at 58.4%. Self-efficacy influences effort expectancy, incentives influence facilitating conditions and security predicts perceived trust.

Three implications are drawn: (1) Users need to clearly understand system’s purpose, functions, security mechanism and environmental impacts. (2) Users are incentivised to share health data via a blockchain solution if the technology offers personalising options and health information. (3) Health personnel can socially impact patients to use blockchain-based solutions.

Studies have shown that blockchain technology is a valuable solution for exchanging EHR. The novelty of this work is to identify how and why patients may accept this emerging technology for EHR exchange.