Privacy paradox in mHealth applications: An integrated elaboration likelihood model incorporating privacy calculus and privacy fatigue

Zhu, Mengxi; Wu, Chuanhui; Huang, Shijing; Zheng, Kai; Young, Sean D.; Yan, Xi; Qinjian, Yuan

doi:10.1016/j.tele.2021.101601

Cited by 61 publications

(73 citation statements)

References 81 publications

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“…More generally, these findings suggest that a privacy calculus that emphasizes the relationship between the concerns and benefits is a more suitable model to explain self-disclosure than the privacy paradox, which overlooks the anticipated benefits of self-disclosure. This is also consistent with the findings of a recent study that used an elaboration likelihood model to investigate privacy calculus and privacy fatigue within the context of a health application (Zhu et al, 2021). The authors found that while perceived benefits of sharing personal information was positively associated with disclosure intentions, this relationship was reversed for privacy concerns.…”

Section: Discussionsupporting

confidence: 87%

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Getting the privacy calculus right: Analyzing the relations between privacy concerns, expected benefits, and self-disclosure using response surface analysis

2022

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Rational models of privacy self-management such as privacy calculus assume that sharing personal information online can be explained by individuals’ perceptions of risks and benefits. Previous research tested this assumption by conducting conventional multivariate procedures, including path analysis or structural equation modeling. However, these analytical approaches cannot account for the potential conjoint effects of risk and benefit perceptions. In this paper, we use a novel analytical approach called polynomial regressions with response surface analysis (RSA) to investigate potential non-linear and conjoint effects based on three data sets (N1 = 344, N2 = 561, N3 = 1.131). In all three datasets, we find that people self-disclose more when gratifications exceed concerns. In two datasets, we also find that self-disclosure increases when both risk and benefit perceptions are on higher rather than lower levels, suggesting that gratifications play an important role in determining whether and how risk considerations will factor into the decision to disclose information.

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Section: Discussionsupporting

confidence: 87%

“…The authors found that while perceived benefits of sharing personal information was positively associated with disclosure intentions, this relationship was reversed for privacy concerns. More importantly, perceived benefits were a stronger predictor of disclosure intentions than privacy concerns (Zhu et al, 2021).…”

Section: Discussionmentioning

confidence: 94%

Getting the privacy calculus right: Analyzing the relations between privacy concerns, expected benefits, and self-disclosure using response surface analysis

2022

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“…In previous studies, the ELM has been applied to online marketing ( Cyr et al, 2018 ), customers’ initial trust in mobile banking ( Gu et al, 2017 ), crowdfunding intentions ( Wang and Yang, 2019 ), and other contexts, including mHealth user acceptance, and privacy context in mHealth ( Zhu et al, 2021 ). However, the research described the information quality and source credibility as factors that enable individuals to acknowledge the information on SNSs effectively ( Tseng and Wang, 2016 ; Kang and Namkung, 2019 ).…”

Section: Theoretical Background and Hypothesis Buildingmentioning

confidence: 99%

Source Credibility and the Information Quality Matter in Public Engagement on Social Networking Sites During the COVID-19 Crisis

Shah

Lü

2022

Front. Psychol.

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During the coronavirus disease 2019 (COVID-19) pandemic, people use social networking sites (SNSs) to seek social support, ease the move toward the social distance, and communicate and engage with one another. However, there is growing evidence that trustworthiness and quality of information can affect individuals’ online engagement behaviors. This study proposes a theoretical model to test people’s online engagement during the COVID-19 pandemic by applying the elaboration likelihood model (ELM). Through a questionnaire survey of 630 SNS users, the study examines whether and how source credibility and information quality affect people’s online engagement during the COVID-19 pandemic. The model was tested using structural equation modeling. The findings show that source credibility and information quality have a significantly positive relationship with perceived benefit, while negative and significantly associated with perceived risk. Furthermore, perceived benefit is a stronger predictor of online public engagement than the perceived risk. To improve online public engagement as a crisis response strategy, careful source selection and careful generation of online crisis information should not be overlooked.

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“… Duan and Deng, H. (2021) [ 110 ] Contact tracing apps Performance expectancy Perceived privacy risk Perceived value of information disclosure The analysis result confirmed that performance expectancy and perceived privacy risks are indirectly significant on the adoption through the influence of perceived value of information disclosure. Zhu et al (2021) [ 111 ] mHealth apps Perceived benefits Privacy concern Disclosure intention When determining information disclosure, the users’ benefits perception for using mHealth applications is two or three times more influential than their privacy concerns. Zhang et al (2018) [ 9 ] Online health communities Perceived informational support Perceived emotional support Privacy concern Disclosure intention Results indicate that health information privacy concerns, together with informational and emotional support, significantly influence personal health information (PHI) disclosure intention.…”

Section: Table A1mentioning

confidence: 99%

Examining the Intention of Authorization via Apps: Personality Traits and Expanded Privacy Calculus Perspectives

Tang

Zhang

Xiao

2022

Behavioral Sciences

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By integrating the extended privacy calculus theory with the Big Five personality theory, this research proposes and validates a conceptual model in the context of mobile application (App) information authorization. It investigates the implications of each component of privacy costs, privacy advantages, and trust on users’ willingness to authorize their information, and explores how the five personality traits affect App users’ perceived benefits, privacy concern, and trust. Simultaneously, the links between prior negative experience and privacy concern as well as the final authorizing willingness were uncovered. We employed a questionnaire to collect 455 users’ data, and the partial least squares structural equation model (PLS-SEM) was used to test the hypotheses. The findings demonstrate that App users’ perceived benefits and trust have a positive impact on their privacy authorization intention, whereas privacy concerns negatively affect their disclosure willingness. Just as Extraversion and Agreeableness would make someone pay a heightened attention to the benefits, agreeable, neurotic, and conscientious users are more easily stimulated by privacy concern. Respectively, Agreeableness and Neuroticism affect users’ trust positively and negatively. Additionally, prior negative experience will trigger an individual’s privacy concern, which in turn hinders their willingness to authorize his/her information. All of the aforementioned can serve as a guide for App providers as they optimize the features of their products and services, implement the necessary privacy protections to alleviate users’ privacy concern, and boost users’ trust belief. More importantly, these results effectively demonstrate the significance of personal traits in the formation of users’ privacy perceptions.

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Privacy paradox in mHealth applications: An integrated elaboration likelihood model incorporating privacy calculus and privacy fatigue

Cited by 61 publications

References 81 publications

Getting the privacy calculus right: Analyzing the relations between privacy concerns, expected benefits, and self-disclosure using response surface analysis

Getting the privacy calculus right: Analyzing the relations between privacy concerns, expected benefits, and self-disclosure using response surface analysis

Source Credibility and the Information Quality Matter in Public Engagement on Social Networking Sites During the COVID-19 Crisis

Examining the Intention of Authorization via Apps: Personality Traits and Expanded Privacy Calculus Perspectives

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