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This paper aimed to explain why individuals still tend to disclose their privacy information even when privacy risks are high and whether individuals disclose or withhold information following the same logic.

This study develops a configurational decision tree model (CDTM) for precisely understanding individuals' decision-making process of privacy disclosure. A survey of location-based social network service (LBSNS) users was conducted to collect data, and fuzzy-set qualitative comparative analysis (fsQCA) was adopted to validate the hypotheses.

This paper identified two configurations for high and low disclosure, respectively, and found that the benefits and the risks did not function independently but interdependently, and the justice would play a crucial role when both the benefits and the risks were high. Furthermore, the authors found that there were asymmetric mechanisms for high disclosure and low disclosure, and males focused more on perceived usefulness, while females concerned more about perceived enjoyment, privacy risks and perceived justice.

This paper further extends privacy calculus model (PCM) and deepens the understanding of the privacy calculus process from a configurational perspective. In addition, this study also provides guidance for future research on how to adopt the configurational approach with qualitative comparative analysis (QCA) to revise and improve relevant theories for information systems (IS) behavioral research.

This study aims to identify the roles that privacy experiences and social media use play in influencing privacy-protection behaviors. As social media use expands in terms of the number of users and functionality; it is important to understand social media user privacy-protection behaviors and the users’ psychological underpinnings driving those behaviors. Among these, perceptions are the users’ evaluation of their privacy concerns and data sharing benefits inherent in social media use which influence the users’ behaviors to protect their privacy.

To research these issues, a theoretical model and hypotheses were developed, based on self-efficacy theory. The theoretical model was empirically tested using 193 questionnaire responses collected from students enrolled in business courses at a medium-sized university in the western USA. All the respondents reported that they routinely use social media. The empirical analysis was performed using structural equations modeling in PC SAS version 9.4, procedure Calis.

The estimation of the paths in the structural model indicates that privacy concerns positively influence social media users’ protection behaviors while the perceived benefits of data sharing negatively influence protection behaviors. Privacy experience positively influences privacy concerns. Alternatively, social media use positively influences social media self-efficacy and perceived usefulness, which, in turn, have meaningful influences on data sharing benefits.

Previous findings about the effect of self-efficacy on protection behaviors has been inconclusive. This study adds some clarity. Specifically, the findings suggest that the effect depends upon the foci of self-efficacy. While higher self-efficacy with respect to using privacy-related features of a specific technology tends to lead to greater privacy concerns, higher self-efficacy with respect to the more general technology (e.g. social media, computer) seems to affect protection behaviors through perceived benefits. Further, the results of this study offer conclusions about the roles that privacy experiences, social media use and perceived social media benefits play in affecting protection behaviors.

The COVID-19 pandemic represents a unique challenge for public health worldwide. In this context, smartphone-based tracking apps play an important role in controlling transmission. However, privacy concerns may compromise the population’s willingness to adopt this mobile health (mHealth) technology. Based on the privacy calculus theory, this study aims to examine what factors drive or hinder adoption and disclosure, considering the moderating role of age and health status.

A cross-sectional survey was conducted in a European country hit by the pandemic that has recently launched a COVID-19 contact-tracing app. Data from 504 potential users was analyzed through partial least squares structural equation modeling.

Results indicate that perceived benefits and privacy concerns impact adoption and disclosure and confirm the existence of a privacy paradox. However, for young and healthy users, only benefits have a significant effect. Moreover, older people value more personal than societal benefits while for respondents with a chronical disease privacy concerns outweigh personal benefits.

The study contributes to consumer privacy research and to the mHealth literature, where privacy issues have been rarely explored, particularly regarding COVID-19 contact-tracing apps. The study re-examines the privacy calculus by incorporating societal benefits and moving from a traditional “self-focus” approach to an “other-focus” perspective. This study further adds to prior research by examining the moderating role of age and health condition, two COVID-19 risk factors. This study thus offers critical insights for governments and health organizations aiming to use these tools to reduce COVID-19 transmission rates.

With the popularity of mobile applications and increasing consumer awareness of application privacy, this paper aims to introduce a new construct of service-privacy fit (i.e. the perceived degree of match between the service of a mobile application and a privacy permission request) to predict consumers’ mobile application adoption.

Four experiments were carried out to test the hypothesized relationships. The first study investigated the direct impact of service-privacy fit on application adoption and the mediators of benefit expectancy and privacy concerns. The second, third and fourth studies further applied the moderated mediation model to analyze the moderating roles of benefit message type, regulatory focus type and privacy assurance.

The results show that service-privacy fit influences application adoption not only directly but also indirectly via the mediators of benefit expectancy and privacy concerns. Furthermore, the findings confirm the moderators of benefit message type, regulatory focus type and privacy assurance.

Drawn from the perspectives of task-technology fit and information boundary theory, this paper introduces a new construct of service-privacy fit as a determinant of application adoption. Grounded in privacy calculus theory, it further explains this relationship through mediating effects of benefit expectancy and privacy concerns. Furthermore, this paper proposes that benefit messages and privacy assurance are effective coping strategies to increase the benefit expectancy and reduce the privacy concerns of applications. Based on the perspective of regulatory fit theory, this study further shows that the effects of coping strategies rely on personal traits. The findings enrich the existing knowledge of mobile application adoption and application privacy, suggesting that practitioners should consider mobile consumers’ perception of service-privacy fit when developing applications.

Understanding the privacy concerns of individuals in the adoption of contact tracing apps is critical for the successful control of pandemics like COVID-19. This paper explores the privacy paradox in the adoption of contact tracing apps in Australia.

A comprehensive review of the related literature has been conducted, leading to the development of a conceptual model based on the privacy calculus theory and the antecedent-privacy concern-outcome framework. Such a model is then tested and validated using structural equation modelling on the survey data collected in Australia.

The study shows that perceived benefit, perceived privacy risk and trust have significant influences on the adoption of contact tracing apps. It reveals that personal innovativeness and trust have significant and negative influences on perceived privacy risk. The study further finds out that personal innovativeness is insignificant to perceived benefit. It states that perceived ease of use has an insignificant influence on perceived privacy risk in the adoption of contact tracing apps.

This study is the first attempt to use the privacy calculus theory and the antecedent–privacy concern–outcome framework for exploring the privacy paradox in adopting contact tracing apps. This leads to a better understanding of the privacy concerns of individuals in the adoption of contact tracing apps. Such an understanding can help formulate targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control through effective contact tracing for better emergency management.

Community health is placed under the limelight during the COVID-19 crisis, providing a unique context for investigating citizens' health-privacy tradeoff in accepting social surveillance technology. To elucidate this tradeoff dilemma, an extended privacy calculus framework integrated with the Health Belief Model, legislative protection, and individual collectivism was examined using the case of national contact-tracing apps.

The hypotheses were tested through PLS-SEM analysis with data collected from a survey on Bluezone – a national app in Vietnam.

The results indicated the negative impact of privacy concerns, which was offset by the positive effect of perceived benefits in using contact-tracing apps. The effect size of perceived benefits on usage frequency was twice as large as that of privacy concerns. Individual collectivism was revealed as a mitigator of the tradeoff dilemma, as it was positively associated with perceived benefits, whereas legislative protection had no such role. Citizens may perceive legislation protection as invalid when the technologies are developed, implemented, and monitored by the authorities.

The theoretical contributions lie in the extension of the privacy calculus model as well as its application in the context of mobile health apps and surveillance technology. The study empirically corroborated that the privacy calculus theory holds when technologies move along the pervasiveness spectrum. This study also provided actionable insights for policymakers and developers who advocate the mass acceptance of national contact-tracing apps.

Online social networks (OSNs) provide users with mechanisms such as social circles and individual selection to define the audiences (i.e., privacy policy) of the shared information. This privacy decision-making process is a hard and tedious task for users because they have to assess the cost-benefit in a complex environment. Moreover, little is known about how users assess the cost-benefit of matching the elements of online communication and their interests. Therefore, the purpose of this paper is to develop and test a research model to understand the impact that the types of receivers and the sensitivity of messages have on privacy decisions.

A study was conducted to understand how users evaluate the cost-benefit of the disclosure action in online social networks for the different types of receivers identified and the sensitivity of the message. Data from 400 respondents was collected and analyzed using partial least squares modeling.

The findings of this study demonstrated a trade-off variance between the perceived cost-benefit and the disclosure of sensitive information with different receiver types. Disclosing personal information with trusted receivers, influencer receivers and receivers from the circle of coworkers had a positive significant effect on social capital building. Conversely, disclosing personal information with receivers from the circle of family or unknown receivers had a significant negative effect on social capital building and even a significant positive effect on privacy concerns.

Recent literature has documented the increasing interest of the research community in understanding users' concerns and interests in making the most suitable privacy decisions. However, most researchers have worked on understanding the disclosure action from a user-centered perspective and have not considered all of the elements of online communication. This study puts the focus on all of the elements of communication during disclosure actions, taking into account the properties of the message and receivers and the impact on users' cost benefit value.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

Based on communication privacy management (CPM) theory, this study aimed to explore how gender and age criteria, motivational criterion (locatability), benefit-risk ratio criteria (perceived benefits of privacy information disclosure and privacy concerns), contextual criteria (privacy experience) and boundary coordination and turbulence (perceived effectiveness of privacy policy) interact and combine in leading to voice assistant (VA) users' intention to reveal or conceal their privacy information.

A questionnaire was designed and distributed to car drivers who have intelligent VA use experience while driving. A total of 230 valid responses were collected. The fuzzy-set qualitative comparative analysis (fsQCA) approach was used to analyse the data.

Via configuration analysis with fsQCA, five equivalent configurations leading to the high intention of disclosing privacy information, and one configuration leading to the intention of concealing privacy information were identified. Four theoretical configurational propositions were proposed. Moreover, the theoretical and practical implications of the findings are discussed.

This study offers some insightful implications in the field of private information communication in artificial intelligence applications as well as practical implications for intelligent VA application design. The findings provide empirical evidence of privacy communication management in the context of smart VA usage.

This work empirically evaluates the effectiveness of the novel ontology-based access-control mechanism and the common password-protected access-control mechanism for social blogs. The paper aims to discuss these issues.

The ontology-based access-control scheme is designed to fit two characteristics of blog activities: social relationships and tags. A laboratory experiment is conducted to assess the perceived privacy benefit and perceived ease of use of the two mechanisms.

Analytical results indicate that, with the ontology-based access-control scheme, users perceive more privacy benefit than with the password-protected access-control scheme. The perceived ease of use with the ontology-based and password-protected access-control systems did not differ significantly.

Cross-boundary collaborations need an appropriate approach to control communication access. Further study is required to evaluate the ontology-based access-control scheme applied in cross-organizational and cross-departmental collaborations.

From a knowledge management perspective, blogs can store personal and organizational knowledge and experiences. The ontology-based access-control scheme encourages knowledge sharing for appropriate persons.

The new ontology-based access-control mechanism can help online users keep secrets from selected people to gain more privacy benefits than the existing password-protected access-control mechanism.