Examining the Role of Technology Anxiety and Health Anxiety on Elderly Users’ Continuance Intention for Mobile Health Services Use

Meng, Fanbo; Guo, Xitong; Zhang, Xiaofei; Peng, Zeyu; Lai, Kee‐hung

doi:10.24251/hicss.2020.403

Cited by 26 publications

(24 citation statements)

References 45 publications

(49 reference statements)

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“…Within the health sector, patients main concern is related to health anxiety which refers to the patients' apprehension or fear from any results that indicate severe illness. Studies in the medical sector, therefore, put more emphasis on the negative effects of anxiety and perceived risk on using technology (Kamal et al, 2020;Meng et al, 2020). Within the banking sector, rather different types of fear are found that stem from customers' attitude and perception towards technology itself.…”

Section: Literature Review and Knowledge Gapmentioning

confidence: 99%

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Al-Maroof

Salloum

Hassanien

et al. 2020

Interactive Learning Environments

164

128

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This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases.

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Section: Literature Review and Knowledge Gapmentioning

confidence: 99%

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Al-Maroof

Salloum

Hassanien

et al. 2020

Interactive Learning Environments

164

128

View full text Add to dashboard Cite

show abstract

“…In the health sector, patients are mainly worried about health and this signifies the apprehension or fear of patients regarding any results that suggest serious conditions. Hence, studies carried out in the field of medicine place greater emphasis on how anxiety and perceived risk negatively influence the use of technology [4], [5]. Various kinds of fear are also present in the banking sector and these emerge from the attitude of customers towards technology.…”

Section: Introductionmentioning

confidence: 99%

“…Fear and technology acceptance have been examined in the past few studies. The TAM model [2], [3], [5]- [8], as well as other models [1], [4], [9], [10], are used in the majority of these studies. The focus of the studies is on how the fear of technology itself influences technology acceptance.…”

Section: Introductionmentioning

confidence: 99%

Examining the Factors Influencing the Mobile Learning Usage During COVID-19 Pandemic: An Integrated SEM-ANN Method

2021

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The way in which the emotion of fear affects the technology adoption of students and teachers amid the COVID-19 pandemic is examined in this study. Mobile Learning (ML) has been used in the study as an educational social platform at both public and private higher-education institutes. The key hypotheses of this study are based on how COVID-19 has influenced the incorporation of mobile learning (ML) as the pandemic brings about an increase in different kinds of fear. The major kinds of fear that students and teachers/instructors are facing at this time include: fear because of complete lockdown, fear of experiencing education collapse and fear of having to give up social relationships. The proposed model was evaluated by developing a questionnaire survey which was distributed among 280 students at Zayed University, on the Abu Dhabi Campus, in the United Arab Emirates (UAE) with the purpose of collecting data from them. This study uses a new hybrid analysis approach that combines SEM and deep learning-based artificial neural networks (ANN). The importance-performance map analysis is also used in this study to determine the significance and performance of every factor. Both ANN and IPMA research showed that Attitude (ATD) are the most important predictor of intention to use mobile learning. According to the empirical findings, perceived ease of use, perceived usefulness, satisfaction, attitude, perceived behavioral control, and subjective norm played a strongly significant role justified the continuous Mobile Learning usage. It was found that perceived fear and expectation confirmation were significant factors in predicting intention to use mobile learning. Our study showed that the use of mobile learning (ML) in the field of education, amid the coronavirus pandemic, offered a potential outcome for teaching and learning; however, this impact may be reduced by the fear of losing friends, a stressful family environment and fear of future results in school. Therefore, during the pandemic, it is important to examine students appropriately so as to enable them to handle the situation emotionally. The proposed model has theoretically given enough details as to what influences the intention to use ML from the viewpoint of internet service variables on an individual basis. In practice, the findings would allow higher education decision formers and experts to decide which factors should be prioritized over others and plan their policies appropriately. This study examines the competence of the deep ANN model in deciding non-linear relationships among the variables in the theoretical model, methodologically.

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“…Various research studies have assessed the issues that relate to technological acceptance and fear. These research studies are based on the TAM [29,30,[32][33][34][35] and several other models [28,31,36,37], and most of these research studies have assessed how the fear of technology can influence technology acceptance. Various technology users have provided justifications for their fear of technology use.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The education sector is not the only sector that has exhibited a fear of technology adoption. Medical sector students usually perceive risks and exhibit negative anxiety when technology is used [ 31 , 32 ]. In addition, health anxiety is one of the top concerns of the health care sector.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

Akour¹,

Alshurideh²,

Kurdi³

et al. 2021

JMIR Med Educ

114

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Background Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. Objective This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. Methods An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students’ adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. Results Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. Conclusions Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic.

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Examining the Role of Technology Anxiety and Health Anxiety on Elderly Users’ Continuance Intention for Mobile Health Services Use

Cited by 26 publications

References 45 publications

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Examining the Factors Influencing the Mobile Learning Usage During COVID-19 Pandemic: An Integrated SEM-ANN Method

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

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