Consumer Acceptance of mHealth Services: A Comparison of Behavioral Intention Models

Schuster, Lisa; Tossan, Vessélina; Drennan, Judy

doi:10.1080/15332969.2017.1289791

Cited by 20 publications

(25 citation statements)

References 38 publications

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“…Over the last decade, considerable health services have been partially or wholly shifted to mobile phones. Since the concept of mobile health (mHealth) was first coined in 2005 [ 2 , 3 ], mHealth has incorporated innovative medical services, such as mobile dietetics services. Most mHealth services consist of innovative solutions such as web-based consultation systems with physicians, web-based health conferences, health-relevant data, medical inspection results via portable and wearable gadgets, and smartphone-based apps [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik

Akdur¹,

Aydın²,

Akdur³

2020

JMIR Mhealth Uhealth

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Background Dietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey’s most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users. Objective The aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users. Methods We conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling. Results Statistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app. Conclusions This research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform’s dietitians to ensure that users’ interactions with the dietitians are based on trust for the platform and the brand.

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Section: Introductionmentioning

confidence: 99%

Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik

Akdur¹,

Aydın²,

Akdur³

2020

JMIR Mhealth Uhealth

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“…A notable exception is the study by Piçarra and Giger (2018), which applies the model of goal-directed behaviour (MGB) (Perugini and Bagozzi, 2001) to understand consumer acceptance of social robots, showing that the MGB better explains acceptance compared to the TPB, a behavioural model. Similarly, Schuster et al (2017) show that the MGB explains consumers' acceptance of mHealth services for mental health more fully than the TPB. Drawing from this past research examining consumer goals in technology acceptance, this study utilises the EMGB (Perugini and Conner, 2000), which extends the MGB, as its conceptual foundation.…”

Section: Literature Review 21 Technology-based Service Acceptancementioning

confidence: 89%

“…Parkinson et al, 2018;Song et al, 2011). For example, while only negative anticipated emotions predicted behavioural desire to interact with a social robot (Piçarra and Giger, 2018), neither positive nor negative anticipated Consumers' adoption of mHealth services emotions predicted behavioural desire to use an mHealth service for mental health (Schuster et al, 2017) in contrast to an earlier study where only positive anticipated emotions predicted behavioural desire (Schuster et al, 2015). To date, there has been no investigation of the reason for this inconsistency, which is the second aim of this research and addressed through Study 2.…”

Section: Integrating Goal and Construal-level Theory To Improve Understanding Of Mhealth Adoptionmentioning

confidence: 98%

“…It is also noteworthy that the effect of positive and negative anticipated emotions on the desire to perform a behaviour and subsequent behavioural intention has varied significantly across empirical studies of technology-based service adoption (e.g. Piçarra and Giger, 2018;Schuster et al, 2015Schuster et al, , 2017 and in other contexts (e.g. Parkinson et al, 2018;Song et al, 2011).…”

Section: Integrating Goal and Construal-level Theory To Improve Understanding Of Mhealth Adoptionmentioning

confidence: 99%

“…Of note, little research accounts for the influence of consumers' goals on their decision to adopt mHealth services. Previous research shows that accounting for the influence of goals improves explanation of consumer adoption of technology-based services (Piçarra and Giger, 2018;Schuster et al, 2017), although there is some inconsistency in empirical findings that suggests further investigation is warranted. On this basis, while a focus on consumers' goal(s) in using mHealth services is likely to provide additional insight into why consumers choose to adopt (or not) these services, there is also a need to improve understanding in this domain.…”

Section: Introductionmentioning

confidence: 99%

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Personal goals in consumers' adoption of mHealth services

Schuster

Parkinson

2021

JSTP

Self Cite

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PurposemHealth services are effective and cost efficient, yet wide-scale adoption of these services by consumers has yet to be achieved, constraining their public health benefit. Further investigation of non-technological determinants of mHealth adoption is needed; specifically, the role of consumers' goals has received scant attention and forms the research focus.Design/methodology/approachStudy 1 comprised 20 interviews with participants who possess a health goal, with the data analysed using an abductive reasoning approach. Study 2 was a 15-min online survey (n = 653), with the data analysed using multi-group structural equation modelling.FindingsStudy 1 identified several antecedents to the desirability and feasibility of consumers' health goals, which influence their desire to use mHealth services. Study 2 shows significant differences in the determinants of mHealth service acceptance depending on whether consumers set concrete as opposed to abstract goals, but social acceptance of mHealth services of these services is important for both groups.Practical implicationsThe findings suggest emphasising the importance of health goals to achieving other consumer goals (e.g. work or travel goals), the efficacy of mHealth services relative to other service alternatives for achieving those health goals, and the social acceptance of mHealth services to increase their uptake.Originality/valueThis study is the first to use construal-level theory to improve understanding of the role of consumers' goals in the adoption of mHealth services. By identifying the antecedents to goal desirability and feasibility, it also broadens the model of goal-directed behaviour.

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Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions

Piçarra

Giger

2018

Computers in Human Behavior

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Consumer Acceptance of mHealth Services: A Comparison of Behavioral Intention Models

Cited by 20 publications

References 38 publications

Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik

Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik

Personal goals in consumers' adoption of mHealth services

Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions

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