The privacy–personalization paradox in mHealth services acceptance of different age groups

Guo, Xitong; Zhang, Xiaofei; Sun, Yongqiang

doi:10.1016/j.elerap.2015.11.001

Cited by 266 publications

(253 citation statements)

References 78 publications

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“…This result is similar with other studies. For example, Guo et al concluded that privacy concerns significantly influence user's trust toward mhealth (Guo et al, 2016). Zhang et al also found that privacy concern can affect adoption intention via attitude (Zhang, Guo, Guo, & Lai, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…This coincides with several studies. For example, Guo et al found that the influence of personalization and privacy concerns on trust toward mhealth were different between the old and young generations (Guo et al, 2016). Morris and Venkatesh also concluded that young people's attitudes had a greater influence on their decisions on technology, while the older people were more easily influenced by subjective norm and perceived adoption control (Morris & Venkatesh, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…Enabled by the Internet, users could communicate their personal sensitive information, such as private diseases or emotions, with others easily. But the risk of information leakage could hinder users to utilize mobile health services (Guo, Zhang, & Sun, 2016;Silva et al, 2015). Given the fact that no specific law enforcement to regulate the mobile health service in China, risks incurred by legal concerns may also emerge.…”

mentioning

confidence: 99%

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What Predicts Patients� Adoption Intention Toward mHealth Services in China: Empirical Study (Preprint)

Deng¹,

Hong²,

Ren³

et al. 2017

Preprint

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Background:With the increasing concern of Healthy China and the development of information technology, mobile health enables patients access health information and interacting with doctors anytime and anywhere. Since the success of mobile health would depend on the adoption of patients, examining patients' willingness to use it is considered critical. Objective:This study aims to explore the determinants of mobile health services adoption among Chinese patients by an extended TAM with trust and perceived risk. Method:We conducted a questionnaire-based survey in three large Chinese hospitals, and analyzed the data with structural equation model. Results:The results reveal that the proposed model fits well. Specifically, trust, perceived usefulness and perceived ease of use positively correlate with mobile health services adoption. While, privacy risk and performance risk negatively correlate with the patients' trust and adoption intention toward mobile health. In addition, age and chronic condition of patients could predict their trust level and adoption intention towards mobile health respectively. Conclusion:We conclude that TAM works for the context of mobile health adoption in general, though the significance has declined. Supplementary to technical factors, trust and perceived risk are critical in explaining mobile health services adoption among Chinese patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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What Predicts Patients� Adoption Intention Toward mHealth Services in China: Empirical Study (Preprint)

Deng¹,

Hong²,

Ren³

et al. 2017

Preprint

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“…As a revolutionary technology, mobile health care has received considerable attention in recent years [54]. TAMs, as proposed by Davis [55], are usually used as basic theoretical models when studying the adoption of information technology.…”

Section: Analysis Of the Characteristics And Adoption Of Mobile Healtmentioning

confidence: 99%

“…This study contributes by analyzing the influence factors of consumers' adoption intention, which enables mobile health care products to be promoted. The promotion process presents various obstacles, such as users' concerns regarding the safety and privacy of personal information [11] and the effect of e-payments on medical reimbursement [12]. Previous studies on mobile health care have mostly focused on the technological development and clinical expansion [13,14,15], and few studies have been conducted on consumer adoption.…”

mentioning

confidence: 99%

Key Influence Factors of Consumers� Intentions Towards Mobile Health Care Promotion: An Empirical Study Based on the DDANPMV Model

Liu¹,

Yang²,

Liu³

et al. 2017

Preprint

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IntroductionMobile health care is an important aspect of electronic medicine that involves using mobile devices (e.g., mobile phones, tablet computers, and personal digital assistants) to provide medical services and health information to patients [1]. Health care faces numerous problems, including high medical costs, restricted coverage, complex medical procedures, and uneven distribution of resources [2]. Mobile health care can help to reduce medical costs and waiting times and improve the efficiency of medical resources use [3]. Mobile health care involves the following categories [4]: (1) Mobile phone applications such as Chun Yu Yi Sheng, which acts as an information exchange platform linking doctors and patients, and comprehensive mobile health care platforms that hospital service providers can apply for appointment registrations, payments, and medical alerts. (2) The collection of users' medical data through wearable devices or mobile intelligent terminals for health monitoring. For example, Kangkang launched a portable intelligent sphygmomanometer that can dynamically monitor blood pressure over a 24-hour period and automatically send data to a corresponding server. Users can view their 24-hour blood pressure analysis report through WeChat. (3) Diagnosis systems that connect with the Internet of Things (IoT). For example, an integrated diagnosis system developed by German researchers can transmit medical data to smart phones through Bluetooth technology. Smartphones applications can then process these data for reference and diagnosis.With the development of mobile networks, many countries have entered the 4G era and benefit from continuously improving mobile communication networks and innovation in software and hardware technology. Abstract: Identifying the key influence factors of mobile health care adoption is a key issue. We established an evaluation index system for consumer adoption and verified the effectiveness of our model by using a DDANPMV model. This model comprises three parts: (1) the decision making trial and evaluation laboratory (DEMATEL) technique, which is used to establish an influence network relationship map at both the "dimensions" and "criteria" levels; (2) a DEMATEL-based analytic network process (ANP; DANP), which determines the interrelationships and influential weights among the criteria; and (3) a modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, which applies the influence weights obtained through DANP to performance gaps regarding consumer perception. VIKOR thereby evaluates and improves performance gaps with the aims of satisfying consumer needs, achieving continuous improvement, and enabling sustainable product development. The main innovation of this method is the construction of the DDANPMV model, which integrates the DEMATEL, DANP, and modified VIKOR approaches to examine consumers' adoption of mobile health care. This method was developed to not only help decision-makers evaluate alternative mobile health care and determine the best option b...

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Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

Shi,

Lu,

Zhou

2023

J of Consumer Behaviour

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The role of artificial intelligence (AI)‐based DoctorBots in improving healthcare and the medical industry is expected to increase significantly in the coming decades. However, contrary to the general view that AI can gradually replace human work, user acceptance of DoctorBots has become an obstacle to the development of AI medical diagnosis. Building on the service robot acceptance model (sRAM), this study investigates the potential of the functional, socioemotional, and relational elements of DoctorBots to reconcile the personalization–privacy paradox, thus enhancing user acceptance. Via two scenario‐based experiments with 398 participants, this study reveals that the negative influence of the personalization–privacy paradox on user acceptance is exacerbated when users' technology anxiety is high. In addition, an online survey of 400 DoctorBot users indicates that ease of use, subjective social norms, social presence, and rapport are effective in addressing both nonpersonalization (NPC) and privacy concerns (PVC). These findings suggest that the healthcare industry can leverage DoctorBots to implement self‐diagnosis. Specifically, DoctorBots' functional elements are effective in mitigating users' NPC, and their relational elements are effective in extenuating users' PVC.

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The privacy–personalization paradox in mHealth services acceptance of different age groups

Cited by 266 publications

References 78 publications

What Predicts Patients� Adoption Intention Toward mHealth Services in China: Empirical Study (Preprint)

What Predicts Patients� Adoption Intention Toward mHealth Services in China: Empirical Study (Preprint)

Key Influence Factors of Consumers� Intentions Towards Mobile Health Care Promotion: An Empirical Study Based on the DDANPMV Model

Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

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