Determinants of the behavioral intention to use a mobile nursing application by nurses in China

Mental health practitioners' perceptions and adoption intentions of AI-enabled technologies: an international mixed-methods study

BACKGROUND

As mental health disorders continue to surge, exceeding the capacity of available therapeutic resources, the emergence of technologies enabled by artificial intelligence (AI) offers promising solutions for supporting and delivering patient care. However, there is limited research on mental health practitioners' understanding, familiarity, and adoption intentions regarding these AI technologies. We, therefore, examined to what extent practitioners' characteristics are associated with their learning and use intentions of AI technologies in four application domains (diagnostics, treatment, feedback, and practice management). These characteristics include medical AI readiness with its subdimensions, AI anxiety with its subdimensions, technology self-efficacy, affinity for technology interaction, and professional identification.

METHODS

Mixed-methods data from N = 392 German and US practitioners, encompassing psychotherapists (in training), psychiatrists, and clinical psychologists, was analyzed. A deductive thematic approach was employed to evaluate mental health practitioners' understanding and familiarity with AI technologies. Additionally, structural equation modeling (SEM) was used to examine the relationship between practitioners' characteristics and their adoption intentions for different technologies.

RESULTS

Qualitative analysis unveiled a substantial gap in familiarity with AI applications in mental healthcare among practitioners. While some practitioner characteristics were only associated with specific AI application areas (e.g., cognitive readiness with learning intentions for feedback tools), we found that learning intention, ethical knowledge, and affinity for technology interaction were relevant across all four application areas, underscoring their relevance in the adoption of AI technologies in mental healthcare.

CONCLUSION

In conclusion, this pre-registered study underscores the importance of recognizing the interplay between diverse factors for training opportunities and consequently, a streamlined implementation of AI-enabled technologies in mental healthcare.

Enhancing digital readiness and capability in healthcare: a systematic review of interventions, barriers, and facilitators

INTRODUCTION

The rapid integration of digital technologies in healthcare requires healthcare professionals to be digitally ready and capable. This systematic review aims to identify interventions that improve digital readiness and capability among health professionals and to understand the barriers and facilitators they encounter during this digital transformation.

METHODOLOGY

A mixed-methods systematic review was conducted following the Joanna Briggs Institute (JBI) guidelines. We searched five databases CINAHL Plus, MEDLINE, EMBASE, PsychINFO, and Web of Science. The review used the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate factors influencing technology adoption. Studies were selected based on predefined inclusion and exclusion criteria, focusing on health professionals' digital capability in healthcare settings. Quality assessment was performed using the MMAT checklist, and data were analysed and synthesized to extract relevant themes and sub-themes.

RESULTS

Initially, 1140 studies were identified, with 21 meeting the inclusion criteria after screening. These studies, published between 2017 and 2023.The results were categorized into four main themes: Performance Expectancy, Effort Expectancy, Facilitating Conditions, and Social Influence, with two sub-themes. The studies indicated that technology positively impacts job performance, facilitating acceptance among healthcare professionals. Ease of use was crucial for technology acceptance, while complexity and multiple logins were significant barriers. The importance of sufficient training and organizational support was highlighted to enhance digital competency and address technical issues, with inadequate training and infrastructure being major barriers. Social influence, including motivation of healthcare workers and shared decision-making, played a significant role in technology acceptance.

CONCLUSION

This review highlights critical factors influencing the digital readiness and capability of healthcare professionals. Interventions enhancing performance expectancy, addressing effort expectancy, improving facilitating conditions, and leveraging social influence are essential for successful digital health adoption. Future research should develop comprehensive frameworks to overcome barriers and promote digital health readiness. Integrating specialized training into educational programs is crucial for preparing healthcare professionals to navigate the evolving digital landscape.

Intention to use mobile health in antenatal care service: Among primary health care unit workers, Bench-Sheko zone, southwest Ethiopia

The use of mobile health (mHealth) technology has the potential to enhance maternal and child health care, particularly in low-income countries. However, evidence regarding its practicality and effectiveness remains limited. In Ethiopia, research on health workers' intention to adopt mHealth technology for maternal health services is notably scarce, emphasizing the need for thoughtful evaluation and further studies to explore its real-world application. This study aims to assess the intention of primary health care unit workers towards using mobile health technology in antenatal care services in Bench-Sheko Zone, Southwest Ethiopia. A cross-sectional study was conducted from June 27, 2023, to July 27, 2023. Data were collected from 316 primary health care unit workers using a simple random sampling technique. SPSS version 25 was used for data analysis, including multivariable linear regression modeling. The response rate was 98.7%. The mean age of participants was 29.2 years, and the mean score for behavioral intention to use mobile technology in ANC services was 65%. Predictors included perceived mobile self-efficacy (β=0.318, p<0.001), perceived compatibility (β=0.601, p<0.001), mobile ownership (β=1.173, p=0.041), eHealth training (β=0.768, p=0.008), and mobile use experience (β=0.176, p<0.001). Local health managers should facilitate training to boost health workers' mobile self-efficacy, and policymakers should consider the compatibility of mHealth technology with existing practices.

Intention to use telemonitoring for chronic illness management and its associated factors among nurses and physicians at public hospitals in Bahir Dar, northwest Ethiopia: using a modified UTAUT2 model

Background

Patients with chronic illnesses need to take care of themselves and seek ongoing medical attention. By using technology, telemonitoring can minimize hospitalization and care costs, while increasing professional productivity, providing constant medical attention and enhancing patient self-care management. Despite all these advantages, nothing is known regarding the intentions of Ethiopian professionals and nurses to adopt telemonitoring technologies. Therefore, the purpose of this study is to evaluate the telemonitoring intention of Ethiopian professionals and nurses, as well as the factors related to it.

Methods

A total of 781 randomly chosen nurses and physicians who worked at public hospitals in Bahir Dar City, northwest Ethiopia, participated in a cross-sectional survey. To give everyone an opportunity, the sample size was distributed equitably among the hospitals and the profession according to staffing numbers. The sample was obtained using a simple random sampling technique. Data were gathered by skilled data collectors utilizing a self-administered questionnaire. For additional cleaning and descriptive statistical analysis, the data were imported into EpiData version 4.6 and exported to Statistical Package for Social Science version 25. Analysis of Moment Structure version 23 structural equation modeling was used to ascertain the degree of the association between the variables.

Result

The response rate was 732/781 (93.7%), with 55.7% (408/732) of the participants being men and two-thirds (67.6%, 495/732) being nurses. About 55.9% [95% confidence interval (CI): 52.3-59.6] of respondents intended to use telemonitoring. The desire to employ telemonitoring is positively impacted by performance expectancy (β = 0.375, 95% CI: 0.258-0.494), effort expectancy (β = 0.158, 95% CI: 0.058-0.252), facilitating condition (β = 0.255, 95% CI: 0.144-0.368), and habit (β = 0.147, 95% CI: 0.059-0.233). Age and gender positively affected the link between effort expectancy and intention to employ telemonitoring. It was discovered that being young and male has a beneficial relationship impact. Age positively moderated the association between the intention to use telemonitoring and the facilitating conditions, and adults were strongly linked with the relationship.

Conclusion

In Bahir Dar City public hospitals, over half of the doctors and nurses have the intention to use telemonitoring. Predictive indicators of intention to utilize telemonitoring that were statistically significant were performance expectancy, effort expectancy, facilitating condition, and habit.

Health workers' adoption of digital health technology in low- and middle-income countries: a systematic review and meta-analysis

Objective

To conduct a systematic review and meta-analysis of the facilitators of and barriers to the acceptance and use of digital health technology by health workers in low- and middle-income countries.

Methods

We searched several databases for relevant articles published until 25 April 2024. We extracted data on four unified theories of acceptance and use of technology factors (performance expectancy, effort expectancy, social influence and facilitating conditions) and six additional factors (attitude, habit, incentive, risk, trust and self-efficacy); how these affected the outcomes of behavioural intention and actual use; and the strength of association if reported. We conducted a meta-analysis of the quantitative studies.

Findings

We reviewed 36 publications, 20 of which were included in our meta-analysis. We observed that performance expectancy was the most frequently reported facilitator (in 21 studies; 58.3%) and that lack of facilitating conditions was the most cited barrier (10; 27.8%). From our meta-analysis, trust (r = 0.53; 95% confidence interval, CI: 0.18 to 0.76) and facilitating conditions (r = 0.42; 95% CI: 0.27 to 0.55) were the leading facilitators of behavioural intention and actual use, respectively. We identified concerns with performance expectancy (r = -0.14, 95% CI: -0.29 to 0.01) as the primary barrier to both outcomes.

Conclusion

Our approach of clustering the facilitators of and barriers to the acceptance and use of digital health technology from the perspective of health workers highlighted the importance of creating an enabling ecosystem. Supportive infrastructure, tailored training programmes and incentive policies should be incorporated in the implementation of digital health programmes in low- and middle-income countries.

Acceptance of Digital Health Care Technology and the Role of Nursing Education

BACKGROUND

As digital health care technology develops, the use of technology in the nursing field has become an essential part of nursing education. However, few studies have linked nurses' awareness of digital health care with nursing education. This study examines the direct effects and mediating effects of factors in technology acceptance on nurses' intention to accept digital health care technology.

METHOD

To empirically investigate these relationships, a survey was conducted among nurses in South Korea.

RESULTS

This study emphasizes the importance of a multifaceted approach that considers personal, organizational, and innovation-related factors in understanding nurses' intentions toward acceptance of digital health care technologies.

CONCLUSION

The findings confirm that performance expectancy and facilitating conditions play crucial roles in nurses' acceptance of digital health care technologies. The mediating effects of performance expectancy and facilitating conditions on intentions to accept technologies suggest that these factors can also play vital indirect roles. [J Contin Educ Nurs. 2024;55(12):575-587.].

National waiting time monitoring in oral healthcare - The role of triage dental nurses

OBJECTIVES

Our study analyzed dental nurses' use and use behavior determinants of electronic patient IS modules in telephone triage. The modules were implemented in public oral healthcare organizations' patient ISs to digitalize the national waiting time monitoring system.

METHODS

For the cross-sectional survey, we collected data from dental nurses using convenience sampling and applied a modified UTAUT as the theoretical framework.

RESULTS

The results indicate that using the module for different purposes varied, and the nurses used it sparsely in recording data for monitoring national waiting times. Using the module was laborious, and triage work was busy.

CONCLUSION

Dental nurses' low system usage resulted in poor-quality data for waiting time monitoring. As healthcare data is increasingly used for purposes other than clinical decision making, we must ensure that healthcare professionals performing clinical tasks perceive data recording for non-clinical purposes as meaningful and have time for proper data entry.

Analyzing health service employees' intention to use e-health systems in southwest Ethiopia: using UTAUT-2 model

BACKGROUND

E-health systems have the potential to improve healthcare delivery and access to medical services in resource-constrained settings. Despite its impact, the system exhibits a low level of consumer acceptance and intention to use it. This research paper aims to analyze the intention of health service employees to use e-health systems in southwest Ethiopia using the UTAUT-2 model.

METHOD

Institutional-based cross-sectional studies were conducted at four referral hospitals (two private and two public) to examine the acceptance of e-health among consumers. Employees who had previous experience with diagnostic information systems and the health logistic information system were given structured questionnaires based on the UTAUT-2 model. The data were analyzed using the PLS-SEM method to identify the key factors that influence the intention to use e-health systems. The data were analyzed using SPSS version 20 and SmartPLS 3 software.

RESULT

Out of the 400 surveyed employees, 225 (56.25%) valid questionnaires were collected. The findings indicate that three factors-effort expectancy (β = 0.276, t = 3.015, p = 0.001), habit (β = 0.309, t = 3.754, p = 0), and performance expectancy (β = 0.179, t = 1.905, p = 0.028)-had a significant positive impact on employees' intention to use e-health systems. On the other hand, factors such as social influence, facilitating conditions, hedonic motivation, and price values did not appear as significant predictors of intention to use e-health. The study model was able to predict 63% of employees' intentions to use e-health systems.

CONCLUSION

Effort expectancy, habit, and performance expectancy were significant predictors of employees' intention to use e-health systems among health service employees in southwest Ethiopia. The study supports the ideas that ease of use, experience with information systems, and the role of the systems in improving job performance contribute to employees' intention to use e-health. Policymakers and healthcare organizations in the region can use these findings to develop strategies for successful implementation and adoption of e-health systems, ultimately improving healthcare services and outcomes for the population.

Nursing home caregivers' acceptance of, and experiences with, a new digital intervention for oral healthcare: A qualitative feasibility study

OBJECTIVES

The objectives of the present study were to qualitatively investigate nursing home caregivers' acceptance of, and experiences with, a new digital intervention for oral healthcare after a period of real-life testing in three nursing homes located in Rogaland county, Norway.

BACKGROUND

Nursing home caregivers report numerous barriers when it comes to provision of adequate oral care for care-dependent older adults. Among the reported barriers are lack of knowledge and skills, care-resistant behaviours, lack of adequate routines and documentation systems for oral health, a high workload, and unclear responsibilities. To overcome these barriers, a digital tool named SmartJournal was developed to assist caregivers in preserving nursing home residents' oral health.

MATERIALS AND METHODS

Semistructured interviews were performed with selected caregivers (n = 12) taking part in SmartJournal testing. A theory-driven thematic analysis based on technology acceptance model was conducted.

RESULTS

SmartJournal was generally described as a userfriendly and useful tool. The initial reaction of the participants was mixed; some were positive, some had concerns, but many seemed to have a neutral stance to the intervention. Both barriers and facilitating factors for SmartJournal usage were revealed. Interestingly, the usage gradually changed from norm-based to routine-based behaviour during the test period. Although the tool was well-accepted, as demonstrated with study participants' willingness to use it in the future, they had several suggestions for improvements to further adapt it to a nursing home setting.

CONCLUSION

Results from the present study provide valuable information on questions about SmartJournal acceptance and intervention delivery, thereby preparing the ground for a larger scale evaluation study assessing measurable effects of SmartJournal usage in nursing homes.

Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool

During the pandemic, artificial intelligence was employed and utilized by students around the globe. Students' conduct changed in a variety of ways when schooling returned to regular instruction. This study aimed to analyze the student's behavioral intention and actual academic use of communicational AI (CAI) as an educational tool. This study identified the variables by utilizing an integrated framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and self-determination theory. Through the use of an online survey and Structural Equation Modeling, data from 533 respondents were analyzed. The results showed that perceived relatedness has the most significant effect on the behavioral intention of students in using CAI as an educational tool, followed by perceived autonomy. It showed that students use CAI based on the objective and the possibility of increasing their productivity, rather than any other purpose in the education setting. Among the UTAUT2 domains, only facilitating conditions, habit, and performance expectancy provided a significant direct effect on behavioral intention and an indirect effect on actual academic use. Further implications were presented. Moreover, the methodology and framework of this study could be extended and applied to educational technology-related studies. Lastly, the outcome of this study may be considered in analyzing the behavioral intention of the students as the teaching-learning environment is still continuously expanding and developing.

Factors affecting medical students' intention to use Rain Classroom: a cross-sectional survey

BACKGROUND

Rain Classroom was one of the most popular online learning platforms in Chinese higher education during the pandemic. However, there is little research on user intention under the guidance of technology acceptance and unified theory (UTAUT).

OBJECTIVE

This research aims to determine factors influencing students' behavioural intention to use Rain Classroom.

METHODS

In this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modelling technique was chosen to confirm the hypotheses.

RESULTS

Except for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioural intentions to use Rain Classroom.

CONCLUSIONS

The research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioural intentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms.

Intention to use patient portal and its predictors among patient with diabetes in Amhara region referral hospitals, Northwest Ethiopia, 2023: UTAUT-2 model

Background

Patient portal technology is increasingly utilized in the health care system for diabetes management as a means of communication and information-sharing tools, and it has the potential to improve access, quality, and outcomes for patients with diabetes. However, its adaptation is very low, and patients' intention toward the patient portal is unknown. This study aims to fill this gap by determining the intention to use the patient portal and its predictors among patients with diabetes in Ethiopia.

Method

An institution-based cross-sectional study was conducted on patients with diabetes from April 3 to May 8 in eight referral hospitals in the Amhara region, Ethiopia 2023. Samples were proportionally allocated for each hospital, and participants were selected by using a systematic random sampling method. The data were collected by using an interviewer-administered questionnaire using the Kobo collection mobile app. Descriptive statistics were performed using SPSS version 26. The degree of association between exogenous and endogenous variables was assessed and validated using structural equation modeling using AMOS version 21.

Result

A total of 1037 (96.2% response rate) patients with diabetes participated in the study. Of them, 407 (39.25%), 95% CI: [36.4-42.2] were found to have an intention to use the patient portal. Digital literacy (β = 0.312, 95% CI: [0.154-0.465], p < .01), performance expectancy (β = 0.303, 95% CI: [0.185-0.420], p < .01), effort expectancy (β = 0.25, 95% CI: [0.131-0.392], p < 0.01) facilitating condition (β = 0.22, 95% CI: [0.081-0.36], p < .01) and habit (β = 0.111, 95% CI: [-0.009 to 0.227], p < .05) were significantly associated with the intention to use patient portals. Effort expectancy and facilitating conditions were positively moderated by gender.

Conclusion

This study found that patient with diabetes' intention to use patient portals was low. To increase patients with diabetes' intention to use the patient portal, interventions in digital literacy, performance expectations, effort expectations, facilitating conditions, and habits are required.

How to promote telemedicine in underdeveloped cities in central China? Qualitative interviews with medical personnel in tertiary A-level hospital of Xinyang City

Background

Telemedicine has emerged as a novel healthcare service model that plays a vital role in addressing the unequal distribution of medical resources. Telemedicine has recently gained significant traction in economically prosperous cities such as Beijing, Shanghai, and Guangzhou in China. However, Xinyang City in Henan Province is an economically less developed city, and telemedicine is still in its early stages. By exploring the views of medical staff and administrators at Xinyang City's tertiary A-level hospital on telemedicine, this study aims to determine the development status of telemedicine services in Xinyang City. The objective was to identify the challenges in the construction process and formulate corresponding strategies for advancing telemedicine in Xinyang City.

Methods

Face-to-face personal interviews were conducted with 16 medical staff and administrators from a tertiary A-level hospital in Xinyang City, Henan Province, and the data were collected and analyzed using grounded theory. In the process of data analysis, NVivo12 software was used to encode and organize the data line by line.

Results

The development of telemedicine in Xinyang City has the potential to benefit residents, hospitals, and healthcare personnel despite several challenges, including the absence of laws and regulations, inadequate policy support, limited hospital cooperation, and low resident awareness, which must be addressed to unlock the full potential of telemedicine.

Conclusion

The telemedicine system in Xinyang City is currently in a coordinated developmental phase, and several areas require further improvement. The development of standardized telemedicine in Xinyang City requires government support, better training for general practitioners, public awareness campaigns, and improved technology while ensuring reasonable work schedules and motivating medical personnel.

Acceptance of mobile application-based clinical guidelines among health professionals in Northwestern Ethiopia: A mixed-methods study

Background

Globally, healthcare providers have faced significant difficulties in adhering to clinical guidelines. Applying mobile health systems is a crucial strategy for enhancing the dissemination and accessibility of clinical guidelines. This study aimed to assess the acceptance of mobile app-based primary healthcare clinical guidelines and associated factors among health professionals in central Gondar health centers.

Methods

A cross-sectional study supplemented with qualitative data was conducted on 403 health workers. Data were collected using a pre-test structured printed questionnaire and entered into EpiData version 4.6. Analysis was conducted using Stata version 14, which included bivariable and multivariable logistic regression analyses. For qualitative data, thematic analysis was conducted using Open Code v.4.2.

Results

Approximately 28% (95% confidence interval (CI): 23%-32%) of health professionals had utilized mobile app-based clinical guidelines. The availability of IT support (adjusted odds ratio (AOR)  = 3.51, 95% CI: 1.82-6.78), good knowledge (AOR = 3.46, 95% CI: 1.5-6.78), perceived usefulness (AOR = 2.21, 95% CI: 1.00-4.99), m-Health app exposure (AOR = 2.34, 95% CI: 1.2-4.50), and ease of use (AOR = 5.77, 95% CI: 2.50-13.32) were significantly associated with the acceptance of the mobile app-based clinical guideline. In qualitative data, lack of training and supervision and access to smartphones were barriers to acceptance of the mobile app-based clinical guideline.

Conclusion

In summary, acceptance of the app is currently low. However, it can be increased by improving the availability of IT support in the workplace, offering training and supervision, and enhancing access to smartphones.

A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines

The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt online due to the strict quarantine implementations in the country. This study intended to predict and evaluate factors influencing the intention and usage behavior towards online groceries incorporating the integrated Protection Motivation Theory and an extended Unified Theory of Acceptance and Use of Technology applying machine learning ensemble. A total of 373 Filipino consumers of online groceries responded to the survey and evaluated factors under the integrated framework. Artificial Neural Network that is 96.63 % accurate with aligned with the result of the Random Forest Classifier (96 % accuracy with 0.00 standard deviation) having Perceived Benefits as the most significant factor followed by Perceived Vulnerability, Behavioral Intention, Performance Expectancy, and Perceived. These factors will lead to very high usage of online grocery applications. It was established that machine learning algorithms can be used in predicting consumer behavior. These findings may be applied and extended to serve as a framework for government agencies and grocers to market convenient and safe grocery shopping globally.

Adopting e-government to monitor public infrastructure projects execution in Nigeria: The public perspective

Public infrastructure projects (PIPs) are critical to the socioeconomic development of any country and similar to most public activities, their governance requires effective public participation to be successful. Information and communication technology adoption in government-public engagements (i.e., e-government) has improved public participation in governance in developed countries. This study utilizes the unified theory of acceptance and use of technology to investigate determining factors for the Nigerian public to adopt e-government tools to promote public participation in monitoring PIPs execution. It adopts questionnaire survey and structural equation modelling techniques to show that performance expectancy, facilitating conditions, social influence, and effort expectancy significantly and positively affect behavioral intention to adopt e-government tools to monitor PIPs execution in Nigeria. This study provides scholars with an exploratory baseline for extension of e-government adoption to public infrastructure project management. This study also provides recommendations to policy makers, government technocrats, and project engineers on the need for policy changes, creation of interactive and up-to-date project websites for PIPs in Nigeria.

Investigating Factors Influencing Medical Practitioners' Resistance to and Adoption of Internet Hospitals in China: Mixed Methods Study

BACKGROUND

The swift shift toward internet hospitals has relied on the willingness of medical practitioners to embrace new systems and workflows. Low engagement or acceptance by medical practitioners leads to difficulties in patient access. However, few investigations have focused on barriers and facilitators of adoption of internet hospitals from the perspective of medical practitioners.

OBJECTIVE

This study aims to identify both enabling and inhibiting predictors associated with resistance and behavioral intentions of medical practitioners to use internet hospitals by combining the conservation of resources theory with the Unified Theory of Acceptance and Use of Technology and technostress framework.

METHODS

A mixed methods research design was conducted to qualitatively identify the factors that enable and inhibit resistance and behavioral intention to use internet hospitals, followed by a quantitative survey-based study that empirically tested the effects of the identified factors. The qualitative phase involved conducting in-depth interviews with 16 experts in China from June to August 2022. Thematic analysis was performed using the qualitative data analysis software NVivo version 10 (QSR International). On the basis of the findings and conceptual framework gained from the qualitative interviews, a cross-sectional, anonymous, web-based survey of 593 medical practitioners in 28 provincial administrative regions of China was conducted. The data collected were analyzed using the partial least squares method, with the assistance of SPSS 27.0 (IBM Corp) and Mplus 7.0 (Muthen and Muthen), to measure and validate the proposed model.

RESULTS

On the basis of qualitative results, this study identified 4 facilitators and inhibitors, namely performance expectancy, social influence, work overload, and role ambiguity. Of the 593 medical practitioners surveyed in the quantitative research, most were female (n=364, 61.4%), had a middle title (n=211, 35.6%) or primary title (n=212, 35.8%), and had an average use experience of 6 months every year. By conducting structural equation modeling, we found that performance expectancy (β=-.55; P<.001) and work overload (β=.16; P=.005) had the most significant impact on resistance to change. Resistance to change fully mediated the influence of performance expectancy and partially mediated the influences of social influence (variance accounted for [VAF]=43.3%; P=.002), work overload (VAF=37.2%; P=.03), and role ambiguity (VAF=12.2%; P<.001) on behavioral intentions to use internet hospitals. In addition, this study found that the sex, age, professional title, and use experience of medical practitioners significantly moderated the aforementioned influencing mechanisms.

CONCLUSIONS

This study investigated the factors that facilitate or hinder medical practitioners' resistance to change and their behavioral intentions to use internet hospitals. The findings suggest that policy makers avoid the resistance and further promote the adoption of internet hospitals by ensuring performance expectancy and social influence and eliminating work overload and role ambiguity.

What drives the adoption of online health communities? An empirical study from patient-centric perspective

BACKGROUND

Online health communities (OHCs) provide platforms for patients to seek advice from physicians and receive professional suggestions online. It can improve the efficiency of patients' diagnosis of simple diseases and alleviate hospital congestion. However, few empirical studies have comprehensively explored the factors influencing patients' intention to use OHCs through objective data. This study aims to fill this gap by identifying key factors that influence patients' acceptance of OHCs and proposing effective ways to promote the applications of OHCs in China.

METHODS

Based on the Unified Theory of Acceptance and Usage of Technology (UTAUT), extended with additional constructs identified with patients' information demands in OHCs, this study developed a research model and proposed nine hypotheses. An online survey involving 783 valid responses was conducted in China to collect data to validate the proposed model. Confirmatory factor analysis and partial least squares (PLS) path model were conducted for instrument validation and hypothesis testing.

RESULTS

Price value, eHealth literacy, and performance expectancy are the most prominent constructs in the study context. Interestingly, relation quality was also found to have a significant positive relationship with behavioral intention.

CONCLUSIONS

Based on these findings, OHC operators need to create a user-friendly platform, improve information quality, set reasonable prices, and establish consummate security systems. Physicians and related organizations can raise awareness and assist patients in developing the skills to appropriately comprehend and utilize information in OHCs. This study contributes to both technology adoption theory and practice.

Social Acceptance of Mobile Health among Young Adults in Japan: An Extension of the UTAUT Model

The unprecedented development of information and communication technologies has opened up immense possibilities in the field of health care. Mobile health (mHealth) is gaining increasing attention as an important technology for solving health-related problems. Although a high rate of smartphone usage among young people in Japan has been identified, smartphone usage for health management is not high. As Japanese youth are important potential users of mHealth, it is necessary to explore theories that influence the behavioral intention of Japanese youth to adopt mHealth. This study conducted a questionnaire survey in a Japanese university and collected 233 valuable responses. This study was adapted and extended from the unified theory of acceptance and use of technology (UTAUT) model to measure eight constructs: health consciousness, social influence, facilitation conditions, perceived risk, trust, performance expectancy, effort expectancy, and behavioral intention. Structural equation modeling was used for hypothesis testing. We found that trust, performance expectancy, and effort expectancy directly influenced the behavioral intention to use mHealth. Health consciousness and social influence indirectly influence behavioral intention through trust and performance expectancy. Facilitation conditions indirectly influenced behavioral intention through effort expectancy. This study makes a vital theoretical contribution to policymakers and product developers for the further diffusion of mHealth among young people in Japan.

Google Scholar or University Digital Libraries: A comparison of student perceptions and intention to use

Google Scholar has become an important search platform for students in higher education, and, as such, can be regarded as a competitor to university libraries. Previous research has explored students’ intention to use Google Scholar (GS) and University Digital Libraries (UDLs), but there is a lack of comparative studies that explore students’ preferences between these two platforms. Therefore, this study seeks to explore the search behaviour of a select group of users, international postgraduate students and more specifically compares the factors that influence their use of Google Scholar and University Digital Libraries (UDLs). A questionnaire-based survey, based on the factors in the UTAUT model (unified theory of acceptance and use of technology) was conducted to collect data on acceptance and use of technology of GS and UDL’s respectively. Data was collected from 400 international postgraduate students studying in the United Kingdom. Confirmatory factor analysis was used to establish the contextual influencing factors, whilst structural equation modelling examined the predicted model. The results suggest some differences between the influence of various factors between the UDL dataset and the GS datasets. They suggest that social influence (SI) did not affect behavioural intention (BI) for either data set, but that for the UDL dataset, effort expectancy did not affect BI, whereas for the GS dataset facilitating conditions did not influence BI. The approach taken in this study further facilitates research into the use of search tools to progress beyond ease of use as a main driver and to explore the relationship between internal and external influences of use. Recommendations for further research are suggested and the value of the insights gained for UDLs and their provision and support for all students is discussed.

Understanding the Drivers of Ghanaian Citizens' Adoption Intentions of Mobile Health Services

Mobile health (m-health) application development and diffusion in developing countries have always been a challenge; therefore, research that seeks to provide an elucidation of the drivers of m-Health adoption is vital. Mobile health information systems and applications can contribute to the delivery of a good healthcare system. This study examined the factors influencing citizens' adoption of mobile health services. The Technology Acceptance Model (TAM) was used as the research underpinning for this study, while the data gathered were analyzed with SmartPLS through the use of the structural equation modeling technique. The results showed that perceived usefulness and ease of use were both significant predictors of the behavioral intention to use and recommend the adoption of mobile health services. Also, perceived risk was negative but significant in predicting the intention to use and recommend adoption. Mobile self-efficacy was found to significantly determine the behavioral intention to use, intention to recommend, perceived usefulness, and perceived ease of use of mobile health services. Besides, word-of-mouth showed a positive impact on both the intention to use and recommend. Contrary to expectations, the intention to use had no significant impact on the recommendation intention. The theoretical and practical implications of these findings are thoroughly examined.

Factors affecting physicians using mobile health applications: an empirical study

Background

Mobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians’ participation in mHealth apps.

Objective

This study aims to empirically explore factors affecting physicians’ usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings.

Methods

We collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians’ usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias.

Results

Our results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians’ cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention.

Conclusions

This study contributes to the existing literature on UTAUT2 extension of physicians’ acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians’ usage behaviors on mHealth apps in China and provide such apps’ managers with an insight into the promotion of physicians’ active acceptance and usage behaviors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07339-7.

Does age matter? The influence of age on citizen acceptance of a proximity tracing application in France

Previous literature has suggested that age indirectly influences the intention to adopt an information technology, and notably a m-health application. However, few studies have investigated this link. Voluntarily proximity tracing applications (PTA) are the first mobile applications to be implemented nationwide for population health issues. Our paper investigates the effect of age on the antecedents (perceived ease of use and usefulness, trust, and privacy control) of the intention to adopt a PTA. Our model is tested on a representative sample of 1000 French citizens. All variables were measured using scales drawn from the extant literature and adapted to suit the context. Age was measured as a continuous variable. . We found that age directly influences privacy control, but it has no direct effect on trust nor on the perceived ease of use or the perceived usefulness of a PTA. The results show that age is not a direct determinant of the antecedents of behavioral intention except of privacy control.

Barriers to eHealth adoption in routine antenatal care practices: Perspectives of expectant mothers in Uganda - A qualitative study using the unified theory of acceptance and use of technology model

Current empirical evidence suggests that successful adoption of eHealth systems improves maternal health outcomes, yet there are still existing gaps in adopting such systems in Uganda. Service delivery in maternal health is operating in a spectrum of inadequacy, hence eHealth adoption cannot ensue. This study set out to explore the challenges that impede eHealth adoption in women's routine antenatal care practices in Uganda. A qualitative approach using semi-structured interviews was employed to document challenges. These challenges were classified based on a unified theory of acceptance and use of technology constructs. One hundred and fifteen expectant mothers, aged between 18 and 49 years, who spoke either English or Luganda were included in the study that took place between January to May 2019. Thematic analysis using template analysis was adopted to analyse qualitative responses. Challenges were categorised based on five principal unified theories of acceptance and use of technology constructs namely: performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention. Facilitating conditions had more influence on technology acceptance and adoption than the other four constructs. Specifically, the lack of training prior to using the system, technical support, computers and smart phones had a downhill effect on adoption. Subsequently, the cost of data services, internet intermittency, and the lack of systems that bridge the gap between mothers and health providers further hindered technology uptake. In conclusion, strategies such as co-development, training end-users, garnering support at the national and hospital levels should be advocated to improve user acceptance of technology.