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Privacy Calculus
Abstract
The Privacy Calculus theory states that individuals always rationally weigh the potential benefits and potential risks of data disclosure decisions. The rational assumption, however, often turns out to be wrong. The scientific literature has identified some influencing factors. These can be, for example, emotions or the current mood. In addition, there are thinking styles such as the need for cognition or faith into intuition. Also, the framing of a message, status quo bias, anchoring effect, positivity bias, or peer pressure (herding effects and affective commitment) can lead to irrational data disclosure decisions. The trustworthiness and reputation (in particular the possibly resulting halo effect) can additionally lead to decisions with little cognitive effort.
To identify contextual adjustments of the Privacy Calculus theory, expert interviews were conducted with ten Internet users. Respondents feel well informed about the potential benefits, but not about risks. However, information is often difficult to read, too long or not clear. Rational data disclosure decisions are more likely to be made in sensitive contexts (e.g. finance or health care), and irrational decisions tend to be made in less-sensitive contexts (e.g. social networks or e-commerce), with respondents focusing primarily on emotions, peer pressure, or trust and reputation.
To investigate the impact of Privacy Calculus decisions on firm performance, a survey was conducted with 12 internet firms. Most companies explicitly consider privacy-specific characteristics when designing their products, but more likely to show the benefits than the risks of data disclosure. The Privacy Calculus theory is almost completely unknown in practice. However, if it is known, it will also be used to develop the privacy policy, business strategy and business model. However, most companies do not see an impact of Privacy Calculus decisions on firm performance.
... Privacy Calculus Theory. Privacy calculus (PC) theory posits that individuals, groups, or institutions determine for themselves when, how, and to what extent information about them is communicated to others [27]. PC theory assumes that individuals consider the future impact (costs and benefits) of taking actions [28]. ...
... Human Behavior and Emerging Technologies privacy, costs are related to the risks of disclosing information. When individuals provide personal information, the individual is involved in analyzing benefits and risks [27]. According to PC Theory, self-disclosure occurs when the benefits of communicating the information outweigh the costs [10]. ...
The prevalence of social networking sites (SNS) raises questions about what information is private and what is not. Some users willingly share information on the site expecting some benefits, but others may be reluctant to do so due to fear of losing control of the shared information. To better understand the delicate relationship between privacy, perceived benefits, and self-disclosure, this study examines the antecedents of self-disclosure behavior on professional SNS (i.e., LinkedIn). A model contextualizing privacy calculus theory combined with the trust factor was developed and evaluated using 661 quantitative data collected through a questionnaire. Then, the data was analyzed using covariance-based structural equation modeling method. The results show that perceived benefit (e.g., self-presentation, career advancement, professional network development, learning, and information exchange), privacy concerns, and perceived control are the factors that directly influence LinkedIn users to disclose personal information. These factors become significant predictors of self-disclosure behavior. Meanwhile, trust in LinkedIn members, perceived severity, and perceived likelihood indirectly influence self-disclosure through privacy concerns. Finally, perceived control directly influences trust in LinkedIn members and trust in the LinkedIn provider. The findings of this study help to understand SNS users' behavior, particularly self-disclosure behavior. SNS users can become more aware of the benefits and risks of their disclosure behavior, allowing them to make more informed decisions. These findings can also be helpful for SNS providers to improve product experience and strategy by effectively encouraging and facilitating self-disclosure practices.
Researchers from different fields of study applied numerous theories to study and understand users’ online information privacy concerns, behaviours, attitudes, preferences, as well as the outcomes of their behaviours on the website. Up to date, there are only a few studies that reviewed and integrated theories in the literature to produce an integrated theoretical framework for online information privacy. This study reviewed twelve theoretical frameworks used in online information privacy. It followed the common approach of the literature review. The initial search resulted in over 102 studies from ScienceDirect, EBSCO and Scopus databases. The findings of the study revealed that reviewed theoretical frameworks have limitations recognized in the literature which include conflicting norms/duties, as well as overlapping spaces, minimization of emotional response, ignorance of biological differences and hormonal responses, missing of constructs, such as trust and explicit focus on the individuals. There were also criticisms of reviewed theoretical frameworks noted in the literature such as oversimplified views of social influence on individuals, theories not falsifiable and the use of surveys as a form of cultural inquiry. Finally, the findings of the study revealed open issues in online information privacy such as hidden cookies, unsecured location sharing, scanning cyber security, as well as data never forgetting individuals. The study recognizes that there are various theories or frameworks in this field, all of which deal with privacy behaviour, implying that studies in the future should combine theories or frameworks to better understand users’ online information privacy perceptions.Keywordsonline information privacytheoretical frameworkinformation disclosure
Monetizing their users’ personal information instead of charging a fee has become an established revenue
model for platform operators—a new form of media companies specialized in aggregating, managing,
and distributing user-generated online content. However, the commodification of privacy leads
to privacy concerns that might be a risk for such businesses. Thus, a new approach is to focus on consumers’
willingness to pay for privacy, assuming that monetizing privacy protection might be an alternative
revenue model. Following the freemium idea, we developed an innovative research design,
offering 553 online survey participants the opportunity to subscribe to a fictional premium version of
Facebook with additional privacy control features in return for a monthly fee. Based on the theory of
planned behavior, we developed and tested a research model to explain actual willingness to pay for
privacy behavior. Our findings show that perceived usefulness and trust significantly affect willingness
to pay. In contrast, perceived internet privacy risk was not found to have a significant influence.
We thus conclude that consumers are willing to pay for privacy in the form of a privacy-freemium
model, provided they perceive the premium version as offering added value and as trustworthy.
The advent of e-commerce puts traditional retail companies under a lot of pressure. A way retailers try to attract more customers to their physical stores is by offering online services on the retail sales floor. Such services are enabled through pervasive retail systems. These systems, however, do not only offer new opportunities but also bear risks for retailers because they heavily depend on privacy-related data, which customers could perceive as a potential privacy threat. In the present paper, we thus investigate the antecedents of customers’ usage intention towards such systems and the trade-off between the perceived benefits and the perceived privacy costs that are associated with their use. To this end, we propose a model based on the most recent version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Extended Privacy Calculus Theory. We validate our model considering a smart fitting room application and show that the model is able to explain 67.1% of the variance in the behavioral intention to use the system and 43.1% of the variance in a person’s willingness to disclose private information. Our results can be leveraged to design pervasive systems that are perceived as valuable instead of privacy threatening.
Little is known about the influence of motivators that drive consumers to grant permission to be contacted via personalized communication. In this study, a framework is developed to investigate the effect of select drivers of consumers granting permission to receive personalized messages. The authors distinguish between drivers related to benefit and cost to the consumers. They identify the influence of perceived personal relevance, entertainment, and consumer information control as well as monetary incentives and lottery participation as benefit-related factors. Cost-related factors entail the registration process, privacy concerns, and perceived intrusiveness. The authors find that, except for monetary incentives and lottery participation, the identified drivers significantly influence consumers’ decision to grant permission. The strong negative influence of privacy concerns on the probability of granting permission can be lessened by two benefit-related factors, namely message content with entertainment value or personal relevance for the consumer. The study helps to improve firm measures aimed at getting more permissions – granted by customers for interactive campaigns.
With the increased popularity of mobile devices, hotels and online travel agencies have started focusing on mobile hotel booking (MHB) in recent years. However, there has been limited research on users” loyalty intentions toward MHB technology. This research attempted to provide an integrated theoretical model that examines the determinants of MHB loyalty. The proposed model, which incorporates personalization, privacy concern, trust, perceived risk, and loyalty, was tested via structural equation modeling (SEM) by using data collected from 396 MHB users. The study results demonstrated that personalization is a strong predictor of MHB users” privacy concerns, trust, and risk perceptions. In addition, privacy concern had a significant impact on trust, and trust significantly influenced perceived risk. Finally, the results revealed that trust and perceived risk were associated with loyalty. This study provides valuable theoretical contributions for researchers and practical implications for online travel agencies, hotel operators and hospitality technology vendors.
Health consumers such as patients and caregivers often join virtual health communities (VHCs) to seek and provide health-related information and emotional support. To do so, they converse with other individuals in platforms such as public discussion boards and blogs. During these online conversations, people may communicate their personal health information (PHI) to others. A potential driver for this form of revealing PHI is the immediate positive outcomes that it can provide for contributors and the community. PHI disclosure, however, can entail privacy risks and concerns for community members, which may ultimately hamper their participation in those communities. Moreover, one's emotional attachment to a VHC (namely, affective commitment) may influence one's PHI sharing behaviors in that community. Thus, to understand how various factors impact communicating PHI in public VHC discussions, we drew on the privacy calculus model and the notion of affective commitment, developed a theoretical model, and empirically tested the model. To do so, we administered a survey to individuals from three different populations including students, faculty, and staff at a large university and visitors to clinics. We performed a set of hierarchical moderated multiple regressions on the dataset. The results revealed that privacy concerns along with expected personal and community-related outcomes of communicating PHI affected willingness to communicate PHI in public VHC discussions. The results, however, refuted the hypothesized direct and moderating effects of affective commitment on willingness to share PHI in these virtual platforms. The findings of this study provide contributions to research and practice.
Over the last decades, personal data has become a crucial asset
for digital services.
The exploit
a
tion
of this asset, however, entai
ls severe threats to privacy.
Recently, so
-
called Privacy Dashboards have
been presented, which are tools that allow users to gain insight and exercise control over data that a
digital service provider has accumulated about them. This innovation enables no
t only privacy prote
c-
tion but also new ways of collaboration of users and providers of digital services. Privacy Das
h
boards
have the potential to allow users to participate in the generation of user profiles for personalized se
r-
vices, thereby contributing
to improved services. However, w
hile a v
ariety of Privacy Dashboards has
been presented, factors leading to their actual adoption by users are largely unexplored. To fill this
research gap, this paper provides an empirical analysis of antecedents of users’
adoption of Pr
i
vacy
Dashboards, in that focusing in particular on the currently most
-
prominent Privacy Dashboard
“Google My Account”. Integrating the Technology Acceptance Model and the Privacy Calculus, our
analysis shows that trust is the crucial factor
in users’ adoption of the examined Privacy Dashboard
and that Privacy Dashboards can both support users in protec
t
ing their privacy but also induce them
to disclose personal data
and thereby contribute to more precise user profiles
Despite individuals’ and organizations’ best efforts, many significant information security threats exist. To alleviate these threats, researchers and policy makers have proposed new digital environments called identity ecosystems. These ecosystems would provide protection against attackers in that a third party intermediary would need to authenticate users of the ecosystem. While the additional security may help alleviate security threats, significant concern exists regarding ecosystem users’ privacy. For example, the possibility of targeted attacks against the centralized identity repository, potential mismanagement of the verified credentials of millions of users, and the threat of activity monitoring and surveillance become serious privacy considerations. Thus, individuals must be willing to surrender personal privacy to a known intermediary to obtain the additional levels of protection that the proposed ecosystems suggest. We investigate the reasons why individuals would use a future identity ecosystem that exhibits such a privacy-security tradeoff. Specifically, we adopted a mixed-methods approach to elicit and assess the major factors associated with such decisions. We show that 1) intrapersonal characteristics, 2) perceptions of the controlling agent, and 3) perceptions of the system are key categories for driving intentions to use ecosystems. We found that trustworthiness of the controlling agent, perceived inconvenience, system efficacy, behavioral-based inertia, censorship attitude, and previous similar experience significantly explained variance in intentions. Interestingly, general privacy concerns failed to exhibit significant relationships with intentions in any of our use contexts. We discuss what these findings mean for research and practice and provide guidance for future research that investigates identity ecosystems and the AIS Bright ICT Initiative.
Location-based social network sites (LBSNS) are the integrations of social network service and location-based service. In this study, we examine two opposite factors, the benefits and privacy concerns, which affect the disclosure intention of users on LBSNS in a mobile environment. We utilise the privacy calculus perspective framework on the disclosure intention of location information. Also, the Big Five model is used to examine the impacts of dispositional factors, such as personality traits, on the concern for privacy. The privacy control, legislation awareness and privacy invasion are control variables for both disclosure intention and privacy concern. Data were collected from 298 respondents residing in Ho Chi Minh City, Vietname. The results indicate that the legislation awareness does not seem to exert a significant influence on both privacy concerns and disclosure intention; it can be explained by the poor effectiveness of legislation in a developing country like Vietnam. However, we clearly found that the benefits and privacy concerns have the opposite influence on behavioural decisions of social network users, with the former positively affecting disclosure intention and latter negatively. Moreover, findings in the study show that perceived benefits such as connectedness, locatability and personalisation have stronger effects than privacy concerns have stronger effects than privacy concerns do.
Consumer-generated self-disclosure is better than firm-generated advertising and sales reports in increasing contact opportunities and also more credible for firms to foster alignment with future market expectations. Previous research mostly assesses online self-disclosure from the rational approach of anticipated benefits and privacy risks without considering the “privacy paradox” phenomenon (users behave contrarily to privacy concern) in social networking sites (SNSs). We develop a theoretical model, grounded in constraint-based (lock-in) and dedication-based (trust-building) mechanisms and social identity theory, to predict online self-disclosure. We test the proposed theoretical model by surveying 395 consumers with participation experience in an online SNS. Different from the rational approach behind personalization, we advance knowledge on how to apply social identity, as well as constraint-based and dedication-based mechanisms, to motivate online self-disclosure induced by consumers. We provide theoretical and practical insights based on our research findings for managing the motivational mechanisms of online self-disclosure.