scholarly journals Integrating Health Belief Model and Technology Acceptance Model: An Investigation of Health-Related Internet Use

2015 ◽  
Vol 17 (2) ◽  
pp. e45 ◽  
Author(s):  
Ashraf Sadat Ahadzadeh ◽  
Saeed Pahlevan Sharif ◽  
Fon Sim Ong ◽  
Kok Wei Khong
Keyword(s):  
Technology Acceptance ◽  
Health Belief Model ◽  
Technology Acceptance Model ◽  
Internet Use ◽  
Health Belief ◽  
Health Related ◽  
Acceptance Model ◽  
Belief Model

scholarly journals Health Advertising on Short-Video Social Media: A Study on User Attitudes Based on the Extended Technology Acceptance Model

2020 ◽  
Vol 17 (5) ◽  
pp. 1501 ◽  
Author(s):  
Jie Zhao ◽  
Jianfei Wang
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Purchase Intention ◽  
User Acceptance ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Perceived Ease Of Use ◽  
Health Related ◽  
Short Video ◽  
Acceptance Model

The rapid development of short-video social network platforms provides us with an opportunity to conduct health-related advertising and recommendation. However, so far, there are no empirical evidence on whether users are willing to accept health-related short-video advertisements. Here, acceptance refers to purchase intention, meaning that users will read short-video ads, share ads with others, or even open the product link embedded in ads to purchase the product. In this paper, we make the first attempt to model and quantify user acceptance of health-related short-video advertisements. Particularly, we propose a new research model that enhances the Technology Acceptance Model (TAM) with two new designs. First, we propose four new antecedents including social interaction, intrusiveness, informativeness, and relevance into the original TAM to reflect the features of short-video social networks. Second, we introduce two mediator variables including perceived usefulness and attitude so that we can better study how different factors affect user acceptance of health-related short-video ads. We perform a survey on the Internet and conduct an empirical analysis of the surveyed data. The results show that the four antecedents as well as the perceived ease of use have significant influences on perceived usefulness, attitude, and purchase intention. Further, perceived usefulness plays a valid mediating role in attitude and purchase intention. We also found that users’ perceived ease of use on health-related short-video ads cannot significantly predict users’ attitudes toward ads. This is a new finding in social media-oriented ads. Finally, we integrate the empirical findings and present reasonable suggestions for advertisers and marketers to promote health-related short-video ads.

scholarly journals Perception of Ophthalmologists to COVID-19 Using Health Belief Model

2020 ◽  
Author(s):  
Enmar Almazyad ◽  
Abeer Ahmad ◽  
Deema Jomar ◽  
Rajiv Khandekar ◽  
Samar Al-Swailem
Keyword(s):  
Risk Perception ◽  
Health Behavior ◽  
Health Belief Model ◽  
Health Belief ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Preventive Health Behavior ◽  
Health Related ◽  
Belief Model ◽  
The Health Belief Model

Abstract Purpose:To assess ophthalmologists preparedness in such a critical period in the history of pandemics, a logical socio-psychological framework assessment using the health belief model (HBM) is essential to evaluate their risk perception, their willingness to actively participate in engaging in protective health behavior and acknowledge its benefits and their capability to perform adequate successful methods in limiting the spread of COVID-19 and overcome the barriers they might encounter while implementing such precautions.Methods:A cross-sectional study conducted in King Khaled Eye Specialist Hospital using a questionnaire-based (HBM) was distributed to 135 ophthalmologists in the institute to evaluate their risk perception on COVID-19, and determine which components of the HBM contribute to preventive health behavior related to the COVID-19 infection.Results:The questionnaire had a reasonable response rate ( 79.3%, 107 ophthalmologists including; 48 consultants, 51 fellows, and 36 residents). The study demonstrated that this model is useful and mapped how several components were significantly correlated to actions. Most significantly, perceived susceptibility was the most important predictor of action. The second most important determinant of action was the perceived benefit.Conclusion:Pandemics such as COVID-19 are more likely to happen again in the future. Explicit attention to factors influencing motivation, such as threat perception to adopt appropriate health-related behavior to limit the spread of communicable diseases, is necessary. This study has successfully represented preparedness and risk behavior perception of ophthalmologists to the novel COVID-19 pandemic in one of the largest tertiary eye hospitals in the middle east using the health belief model.

scholarly journals Integrating Health Belief Model, Technological Self-efficacy, and the PEN-3 cultural model: Conceptualizing the impact of culture and technology on health for advancing Sustainable Smart Health

2020 ◽  
Vol 218 ◽  
pp. 02019
Author(s):  
Xue Wu ◽  
Man Zhao ◽  
Han-Teng Liao
Keyword(s):  
Health Belief Model ◽  
Self Efficacy ◽  
Health Belief ◽  
Integrated Model ◽  
Cultural Model ◽  
Technological Factors ◽  
Health Related Behaviors ◽  
Health Related ◽  
Belief Model ◽  
The Impact

As people record, visualize, analyze, share, reflect on, etc. their everyday life using digital and network technologies, how can researchers and designers empower them to engage both the technologies and health about themselves? Though the Health Belief Model (HBM) has been used to explain and predict healthrelated behaviors, and the Technological Self-efficacy (TSE), and the PEN-3 cultural model has been used as constructs of technological and cultural self-efficacy, it remains a challenging task to tease out the impact of cultural and technological factors for people to improve their health conditions and well-being by taking direct and indirect actions. With the aim to develop a conceptual framework to overcome such a challenge, this study examined and selected a few constructs from the TSE and PEN-3 cultural models, respectively, and then use them to enrich the HBM so that the impact of cultural and technological factors can be better integrated and examined. The integrated model can be used as an analysis tool for both researchers and designers to identify first the relevant cultural and technological factors (using selected constructs), and then formulate and then test hypotheses regarding how these factors shape their health and technology actions (using the causal modeling of the enriched HBM).The integrated model proposed and illustrated in this study shows the ways in which both cultural and technological factors can be conceptualized to explain and predict health-related behaviors via perceived beliefs (often related to technology and health). For example, self-tracking visualization involves both cultural and technological factors that may facilitate or impede health-related behaviors.

scholarly journals Using the Technology Acceptance Model to Identify Factors That Predict Likelihood to Adopt Tele-Neurorehabilitation

2020 ◽  
Vol 11 ◽  
Author(s):  
Marlena Klaic ◽  
Mary P. Galea
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Computer Anxiety ◽  
New Technology ◽  
Health Sector ◽  
Theoretical Frameworks ◽  
Related Quality ◽  
Health Related ◽  
Human Decision ◽  
Acceptance Model

Tele-neurorehabilitation has the potential to reduce accessibility barriers and enhance patient outcomes through a more seamless continuum of care. A growing number of studies have found that tele-neurorehabilitation produces equivalent results to usual care for a variety of outcomes including activities of daily living and health related quality of life. Despite the potential of tele-neurorehabilitation, this model of care has failed to achieve mainstream adoption. Little is known about feasibility and acceptability of tele-neurorehabilitation and most published studies do not use a validated model to guide and evaluate implementation. The technology acceptance model (TAM) was developed 20 years ago and is one of the most widely used theoretical frameworks for predicting an individual's likelihood to adopt and use new technology. The TAM3 further built on the original model by incorporating additional elements from human decision making such as computer anxiety. In this perspective, we utilize the TAM3 to systematically map the findings from existing published studies, in order to explore the determinants of adoption of tele-neurorehabilitation by both stroke survivors and prescribing clinicians. We present evidence suggesting that computer self-efficacy and computer anxiety are significant predictors of an individual's likelihood to use tele-neurorehabilitation. Understanding what factors support or hinder uptake of tele-neurorehabilitation can assist in translatability and sustainable adoption of this technology. If we are to shift tele-neurorehabilitation from the research domain to become a mainstream health sector activity, key stakeholders must address the barriers that have consistently hindered adoption.

scholarly journals Predicting intention to receive COVID-19 vaccine among the general population using the Health Belief Model and the Theory of Planned Behavior Model

2020 ◽  
Author(s):  
Liora Shmueli
Keyword(s):  
General Population ◽  
Influenza Vaccine ◽  
Health Belief Model ◽  
Health Belief ◽  
Related Factors ◽  
The Public ◽  
Health Related ◽  
Belief Model ◽  
To Receive ◽  
The Health Belief Model

AbstractBackgroundA novel coronavirus (COVID-19) was declared a global pandemic by the World Health Organization (WHO) in March, 2020. Until such time as a vaccine becomes available, it is important to identify the determining factors that influence the intention of the general public to accept a future COVID-19 vaccine. Consequently, we aim to explore behavioral-related factors predicting intention to receive COVID-19 vaccine among the general population using the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB) model.MethodsAn online survey was conducted among adults aged 18 years and older from May 24 to June 24, 2020. The survey included socio-demographic and health-related questions, questions related to the HBM and TPB dimensions, and intention to receive COVID-19 vaccine. Associations between questionnaire variables and COVID-19 vaccination intention were assessed by univariate and multivariate analyses.ResultsEighty percent of 398 eligible respondents stated their willingness to receive COVID-19 vaccine. A unified model including HBM and TPB covariates as well as demographic and health-related factors, proved to be a powerful predictor of intention to receive COVID-19 vaccine, explaining 78% of the variance (adjusted R2 = 0.78). Men (OR=4.35, 95% CI 1.58–11.93), educated respondents (OR=3.54, 95% CI 1.44–8.67) and respondents who had received the seasonal influenza vaccine in the previous year (OR=3.31, 95% CI 1.22–9.00) stated higher intention to receive COVID-19 vaccine. Participants were more likely to be willing to get vaccinated if they reported higher levels of perceived benefits of COVID-19 vaccine (OR=4.49, 95% CI 2.79–7.22), of perceived severity of COVID-19 infection (OR=2.36, 95% CI 1.58–3.51) and of cues to action (OR=1.99, 95% CI 1.38–2.87), according to HBM, and if they reported higher levels of subjective norms (OR=3.04, 95% CI 2.15–4.30) and self-efficacy (OR=2.05, 95% CI 1.54–2.72) according to TPB. Although half of the respondents reported they had not received influenza vaccine last year, 40% of them intended to receive influenza vaccine in the coming winter and 66% of them intended to receive COVID-19 vaccine.ConclusionsProviding data on the public perspective and predicting intention for COVID-19 vaccination using HBM and TPB is important for health policy makers and healthcare providers and can help better guide compliance as the COVID-19 vaccine becomes available to the public.

E-commerce user behavior model: an empirical study

2000 ◽  
Vol 19 (4) ◽  
pp. 265-276 ◽  
Author(s):  
James J. Jiang ◽  
Maxwell K. Hsu ◽  
Gary Klein ◽  
Binshan Lin
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Internet Use ◽  
User Behavior ◽  
The Internet ◽  
Long Term Consequences ◽  
Acceptance Model ◽  
Facilitating Conditions ◽  
Impact Data

As the business importance of the Internet continues to rise, understanding of the factors that encourage internet use becomes critical. This report describes a modification of a Technology Acceptance Model to describe usage behavior. Utilization was hypothesized to be a result of anticipated near and long-term consequences, with experience and facilitating conditions also having an impact. Data from an international sample of 335 college students served to confirm the model. Promoters must concern themselves with making Internet use as easy as possible while actively promoting the benefits identified.

scholarly journals Predicting intention to receive COVID-19 vaccine among the general population using the health belief model and the theory of planned behavior model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liora Shmueli
Keyword(s):  
Theory Of Planned Behavior ◽  
Influenza Vaccine ◽  
Health Belief Model ◽  
Planned Behavior ◽  
Health Belief ◽  
The Public ◽  
Health Related ◽  
Belief Model ◽  
To Receive ◽  
The Health Belief Model

Abstract Background This study aim to explore the intentions, motivators and barriers of the general public to vaccinate against COVID-19, using both the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB) model. Methods An online survey was conducted among Israeli adults aged 18 years and older from May 24 to June 24, 2020. The survey included socio-demographic and health-related questions, questions related to HBM and TPB dimensions, and intention to receive a COVID-19 vaccine. Associations between questionnaire variables and COVID-19 vaccination intention were assessed by univariate and multivariate analyses. Results Eighty percent of 398 eligible respondents stated their willingness to receive COVID-19 vaccine. A unified model including HBM and TPB predictor variables as well as demographic and health-related factors, proved to be a powerful predictor of intention to receive COVID-19 vaccine, explaining 78% of the variance (adjusted R squared = 0.78). Men (OR = 4.35, 95% CI 1.58–11.93), educated respondents (OR = 3.54, 95% CI 1.44–8.67) and respondents who had received the seasonal influenza vaccine in the previous year (OR = 3.31, 95% CI 1.22–9.00) stated higher intention to receive COVID-19 vaccine. Participants were more likely to be willing to get vaccinated if they reported higher levels of perceived benefits of COVID-19 vaccine (OR = 4.49, 95% CI 2.79–7.22), of perceived severity of COVID-19 infection (OR = 2.36, 95% CI 1.58–3.51) and of cues to action (OR = 1.99, 95% CI 1.38–2.87), according to HBM, and if they reported higher levels of subjective norms (OR = 3.04, 95% CI 2.15–4.30) and self-efficacy (OR = 2.05, 95% CI 1.54–2.72) according to TPB. Although half of the respondents reported they had not received influenza vaccine last year, 40% of them intended to receive influenza vaccine in the coming winter and 66% of them intended to receive COVID-19 vaccine. Conclusions Providing data on the public perspective and predicting intention for COVID-19 vaccination using HBM and TPB is important for health policy makers and healthcare providers and can help better guide compliance as the COVID-19 vaccine becomes available to the public.

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