Examining consumer use of mobile health applications by the extended UTAUT model

Semiz, Buket Bora; SEMİZ, Tarık

doi:10.15295/bmij.v9i1.1773

Cited by 24 publications

(40 citation statements)

References 40 publications

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“…The results indicate that consumers give a lot of importance to the hedonic aspect. Similar results were seen by Chau et al [67], Nikolopoulou et al [68], and Semiz and Semiz [69]. On the other hand, an empirical study by Handoko [70], and Alfansi and Daulay [71] shows hedonic motivation does not affect behavioral intention.…”

supporting

confidence: 81%

Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach

Linge

Chaudhari

Kakde

et al. 2023

Human Behavior and Emerging Technologies

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The main aim of this research study is to examine the impact of five independent variables viz. performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation on behavioral intention to use mobile payment apps. This study is further aimed at investigating the influence of behavioral intention on the use behavior of mobile payment app users. This study strives to investigate the use behavior of people who have already used mobile payment apps like Google Pay, PhonePe, and PayTM previously for making payments for the products and services they have purchased from various sellers. The researchers used the UTAUT2 theory to examine the relationship between the independent and dependent variables mentioned above. The data was collected from 618 mobile payment app users from Vidarbha, M.S., India. Availability sampling and purposive sampling techniques were adopted for the final selection of the respondents. A structured questionnaire was designed by the researchers for collecting the required primary data. The six proposed hypotheses were tested by using SMART-PLS 3.3.5 software. The results indicated support for all six proposed hypotheses. The proposed model explained a substantial amount of variance in behavioral intention ( R 2 = 71.8 % and R 2 = 76.5 %) in use behavior towards mobile payment apps explained by independent variables. Facilitating conditions exhibited the strongest effect on behavioral intention.

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supporting

confidence: 81%

Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach

Linge

Chaudhari

Kakde

et al. 2023

Human Behavior and Emerging Technologies

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“…In the UTAUT model, Venkatesh et al (2003) proposed and confirmed that performance expectancy had a significant positive effect on individuals' behavioral intentions. Semiz and Semiz (2021) also reported that performance expectancy had an important impact on users' intention to use mHealth Apps in their survey. Mobile healthcare can provide users with timely and valuable information resources, which can significantly reduce the time for users to queue for registration, saving much time, energy and physical costs.…”

Section: Performance Expectancymentioning

confidence: 94%

“…In fact, the particularity of mHealth services makes subjective norm play a very important role in users’ intention of adoption. When important persons (relatives, friends, experts) recommend them to use mHealth services, their trust in products or services will be significantly improved, and the intention to use mHealth will be stronger ( Semiz and Semiz, 2021 ). In summary, this study proposes the following hypothesis:…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Adoption of mobile health services using the unified theory of acceptance and use of technology model: Self-efficacy and privacy concerns

Liu

Lu²,

Zhao³

et al. 2022

Front. Psychol.

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Mobile health (mHealth) services have been widely used in medical services and health management through mobile devices and multiple channels, such as smartphones, wearable equipment, healthcare applications (Apps), and medical platforms. However, the number of the users who are currently receiving the mHealth services is small. In China, more than 70% of internet users have never used mHealth services. Such imbalanced situation could be attributed to users’ traditional concept of medical treatment, psychological factors (such as low self-efficacy) and privacy concerns. The purpose of this study is to explore the direct and indirect effects of mHealth users’ self-efficacy and privacy concerns on their intention to adopt mHealth services, providing guidelines for mHealth service providers to enhance users’ intention of adoption. A questionnaire was designed by the research team and 386 valid responses were collected from domestic participants in China. Based on the unified theory of acceptance and use of technology (UTAUT) model, a research model integrated self-efficacy and privacy concerns was constructed to investigate their effects on users’ intention to adopt mobile mHealth services. The results show that self-efficacy could facilitate users’ intention to adopt mHealth services, and had a significantly positive effect on perceived ubiquity, effort expectancy, performance expectancy and subjective norm. This study verifies the direct and indirect effects of self-efficacy and privacy concerns on users’ intention to adopt mHealth services, providing a different perspective for studying mHealth adoption behavior. The findings could provide guidelines for mHealth service providers to improve their service quality and enhance users’ intention of adoption.

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“…The advocacy of this theoretical model can add to the existing literature review as it diversifies the aspects that influence the adoption of mHealth applications along with the mediating effect of intention to use such services. The UTAUT model that has been chosen for conducting the current study is extensively used by numerous scholars in performing studies related to the customers’ acceptance of new and emerging technologies (Petersen et al, 2020; Semiz & Semiz, 2021). Ndayizigamiye et al (2020) found in their research that social influence, facilitating conditions, awareness, performance expectancy, effort expectancy, behavioral intention, and usage behavior variables might drive the acceptability of mHealth applications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Performance expectancy is the intensity of confidence of a smartphone user regarding enhancing performance in following healthier eating habits and diet patterns through mHealth applications (Alam et al, 2020; Semiz & Semiz, 2021). It may be simply characterized as to how much one feels that using a particular approach may help increase work performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Is Youth Ready for the Looming Technology Frontier in Healthcare? Examining Intentions and Adoption of Mobile Health (mHealth)

Kaur¹,

Ahuja²,

Jain³

et al. 2022

Business Perspectives and Research

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Owing to radical advances in the area of technology, the health sector has gained access to technologies to meet patient requirements; for example, mobile health (mHealth) care. Despite the possible benefits of mHealth, the effectiveness of these interventions ultimately depends on whether citizens use those initiatives. The current study have thus sought to explore mHealth applications’ adoption behavior. A cross-sectional investigation process has been employed on 440 potential consumers from North India to enumerate the consumers’ intention and their adoption. In addition to the core constituents highlighted in previous studies, this research focuses more on the relationship of several other factors affecting behavioral intentions and their eventual impact on the acceptance of mHealth apps. The key considerations in the current study are the inclusion of trust in the existing UTAUT model and the mediating role of behavioral intentions in determining mHealth services adoption.

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Examining consumer use of mobile health applications by the extended UTAUT model

Cited by 24 publications

References 40 publications

Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach

Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach

Adoption of mobile health services using the unified theory of acceptance and use of technology model: Self-efficacy and privacy concerns

Is Youth Ready for the Looming Technology Frontier in Healthcare? Examining Intentions and Adoption of Mobile Health (mHealth)

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