Objective: The aim is to investigate the determinants and mechanisms that influence user's highly sensitive privacy disclosure intention (HSPDI) in home intelligent health service system (HIHSS).
Methods: This study improves the privacy calculus theory by considering the influence of service providers' trust enhancement mechanism besides benefit and risk factors and investigates their impact on users' HSPDIs. This study takes perceived valence and perceived security as the trade-off result among perceived benefits, perceived risks, financial trust enhancement mechanism, and the technical trust enhancement mechanism and suggests that perceived valence and perceived security further affect users' HSPDI in HIHSS. Moreover, the common and differential effects of the perceived justice of privacy violation compensation (PJOPVC) and the perceived effectiveness of privacy protection technologies (PEOPPTs) are studied. The structural equation model is used to analyze 204 valid samples to test the proposed model.
Results: The results show that perceived benefits and perceived risks are important predictors of perceived valence and perceived security, and further affect users' HSPDI. We find PJOPVC has a greater impact on perceived valence while PEOPPT has a greater impact on perceived security.
Conclusions: We recommend that the HSPDI of users with low perceived valence can be improved by providing privacy violation compensation while the HSPDI of users with low perceived security can be enhanced by popularizing relevant knowledge of privacy protection technologies.
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http://dx.doi.org/10.1177/20552076231219444 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
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January 2025
Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience, Linköping University, Linköping, Sweden.
Social relationships are central to well-being. A subgroup of afferent nerve fibers, C-tactile (CT) afferents, are primed to respond to affective, socially relevant touch and may mitigate the effects of stress. The endocannabinoid ligand anandamide (AEA) modulates both social reward and stress.
View Article and Find Full Text PDFInfancy
January 2025
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.
The ability to recognize and act on others' emotions is crucial for navigating social interactions successfully and learning about the world. One way in which others' emotions are observable is through their movement kinematics. Movement information is available even at a distance or when an individual's face is not visible.
View Article and Find Full Text PDFBehav Res Methods
January 2025
College of Psychology, Liaoning Normal University, No. 850 Huanghe Road, Dalian, 116029, Liaoning, China.
Nonverbal emotional vocalizations play a crucial role in conveying emotions during human interactions. Validated corpora of these vocalizations have facilitated emotion-related research and found wide-ranging applications. However, existing corpora have lacked representation from diverse cultural backgrounds, which may limit the generalizability of the resulting theories.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Leiden University Centre for Linguistics, Leiden University, Leiden, The Netherlands.
Previous studies suggested that pitch characteristics of lexical tones in Standard Chinese influence various sensory perceptions, but whether they iconically bias emotional experience remained unclear. We analyzed the arousal and valence ratings of bi-syllabic words in two corpora (Study 1) and conducted an affect rating experiment using a carefully designed corpus of bi-syllabic words (Study 2). Two-alternative forced-choice tasks further tested the robustness of lexical tones' affective iconicity in an auditory nonce word context (Study 3).
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