The impact of privacy enhancing technologies in online mental health platforms on users' disclosure intention.
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| Title: | The impact of privacy enhancing technologies in online mental health platforms on users' disclosure intention. |
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| Authors: | Hu, Xiayu1 (AUTHOR) 3120225854@bit.edu.cn, Jia, Lin1,2 (AUTHOR) jialin87@bit.edu.cn, Zhu, Yijin3 (AUTHOR) 3315436980@qq.com, Chang, Younghoon4 (AUTHOR) Younghoon.Chang@nottingham.edu.cn, Takahashi, Kenichi5 (AUTHOR) kenichitakahashi@foxmail.com, Zhu, Yuer1 (AUTHOR) zzzyexmhy@sina.com |
| Source: | Industrial Management & Data Systems. 2026, Vol. 126 Issue 7, p2255-2293. 39p. |
| Subjects: | Anonymity, Disclosure, Data privacy |
| Abstract: | Purpose: This study aims to examine and compare the effects of four types of privacy enhancing technologies (i.e. ephemerality, anonymity, voice modulation and image tampering) in online mental health platforms and their combinations on users' disclosure intention of private information. Design/methodology/approach: This research conducts two scenario-based experiments to demonstrate the role of single factor and multiple factors of privacy enhancing technologies, further building on the privacy calculus model to explore the underlying behavioral mechanism through perceived costs (i.e. privacy risk and privacy concerns) and perceived benefits (i.e. privacy control, trust and psychological distance). Findings: The single-factor analysis results show the different effects of these four technologies on privacy disclosure intention. Specifically, anonymity and image tampering have greater influences on privacy disclosure intention than ephemerality and voice modulation. The multi-factor analysis results demonstrate that users in the scenario of combining ephemerality, anonymity, voice modulation and image tampering are more willing to disclose their personal information. Practical implications: This research advances the theoretical understanding of privacy enhancing technologies (PETs) in online mental health services, further explaining users' behavioral mechanisms through the privacy calculus model and provides actionable insights for privacy protection settings design on the platforms. Originality/value: Limited studies investigated the effects of different privacy enhancing technologies and their combinations on users' privacy disclosure intentions. This study provides a new perspective to deepen the understanding of PETs' effects through the underlying behavioral mechanisms. [ABSTRACT FROM AUTHOR] |
| Copyright of Industrial Management & Data Systems is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194705741 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The impact of privacy enhancing technologies in online mental health platforms on users' disclosure intention. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hu%2C+Xiayu%22">Hu, Xiayu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 3120225854@bit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jia%2C+Lin%22">Jia, Lin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jialin87@bit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yijin%22">Zhu, Yijin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> 3315436980@qq.com</i><br /><searchLink fieldCode="AR" term="%22Chang%2C+Younghoon%22">Chang, Younghoon</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> Younghoon.Chang@nottingham.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Takahashi%2C+Kenichi%22">Takahashi, Kenichi</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> kenichitakahashi@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yuer%22">Zhu, Yuer</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zzzyexmhy@sina.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Industrial+Management+%26+Data+Systems%22">Industrial Management & Data Systems</searchLink>. 2026, Vol. 126 Issue 7, p2255-2293. 39p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Anonymity%22">Anonymity</searchLink><br /><searchLink fieldCode="DE" term="%22Disclosure%22">Disclosure</searchLink><br /><searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: This study aims to examine and compare the effects of four types of privacy enhancing technologies (i.e. ephemerality, anonymity, voice modulation and image tampering) in online mental health platforms and their combinations on users' disclosure intention of private information. Design/methodology/approach: This research conducts two scenario-based experiments to demonstrate the role of single factor and multiple factors of privacy enhancing technologies, further building on the privacy calculus model to explore the underlying behavioral mechanism through perceived costs (i.e. privacy risk and privacy concerns) and perceived benefits (i.e. privacy control, trust and psychological distance). Findings: The single-factor analysis results show the different effects of these four technologies on privacy disclosure intention. Specifically, anonymity and image tampering have greater influences on privacy disclosure intention than ephemerality and voice modulation. The multi-factor analysis results demonstrate that users in the scenario of combining ephemerality, anonymity, voice modulation and image tampering are more willing to disclose their personal information. Practical implications: This research advances the theoretical understanding of privacy enhancing technologies (PETs) in online mental health services, further explaining users' behavioral mechanisms through the privacy calculus model and provides actionable insights for privacy protection settings design on the platforms. Originality/value: Limited studies investigated the effects of different privacy enhancing technologies and their combinations on users' privacy disclosure intentions. This study provides a new perspective to deepen the understanding of PETs' effects through the underlying behavioral mechanisms. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Industrial Management & Data Systems is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 2255 Subjects: – SubjectFull: Anonymity Type: general – SubjectFull: Disclosure Type: general – SubjectFull: Data privacy Type: general Titles: – TitleFull: The impact of privacy enhancing technologies in online mental health platforms on users' disclosure intention. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hu, Xiayu – PersonEntity: Name: NameFull: Jia, Lin – PersonEntity: Name: NameFull: Zhu, Yijin – PersonEntity: Name: NameFull: Chang, Younghoon – PersonEntity: Name: NameFull: Takahashi, Kenichi – PersonEntity: Name: NameFull: Zhu, Yuer IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02635577 Numbering: – Type: volume Value: 126 – Type: issue Value: 7 Titles: – TitleFull: Industrial Management & Data Systems Type: main |
| ResultId | 1 |
