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Upon reading this chapter, the reader will understand

• How consumers perceive a privacy loss when exposed to retailers' big data analytics

• The role played by the social environment in terms of the opinions of relatives and friends largely influence how youth perceive the risk of privacy loss

• What makes the information about retailers' usage of data not entirely accessible by consumers

• Consumers perception of retailers' usage of their data

How consumers perceive a privacy loss when exposed to retailers' big data analytics

The role played by the social environment in terms of the opinions of relatives and friends largely influence how youth perceive the risk of privacy loss

What makes the information about retailers' usage of data not entirely accessible by consumers

Consumers perception of retailers' usage of their data

Many online transactions and digital services depend on consumers’ willingness to take privacy risks, such as when shopping online, joining social networks, using online banking or interacting with e-health platforms. Their decisions depend on not only how much they would suffer if their data were revealed but also how uncomfortable they feel about taking such a risk. Such an aversion to risk is a neglected factor when evaluating the value of privacy. The aim of this paper is to propose an empirical method to measure both privacy risk aversion and privacy worth and how those affect privacy decisions.

The authors let individuals play privacy lotteries and derive a measure of the value of privacy under risk (VPR) and empirically test the validity of this measure in a laboratory experiment with 148 participants. Individuals were asked to make a series of incentivized decisions on whether to incur the risk of revealing private information to other participants.

The results confirm that the willingness to incur a privacy risk is driven by a complex array of factors, including risk aversion, self-reported value for private information and general attitudes to privacy (derived from surveys). The VPR does not depend on whether there is a preexisting threat to privacy. The authors find qualified support for the existence of an order effect, whereby presenting financial choices prior to privacy ones leads to less concern for privacy.

Attitude to risk in the domain of privacy decisions is largely understudied. In this paper, the authors take a first step toward closing this empirical and methodological gap by offering (and validating) a method for the incentivized elicitation of the implicit VPR and proposing a robust and meaningful monetary measure of the level of aversion to privacy risks. This measure is a crucial step in designing and implementing the practical strategies for evaluating privacy as a competitive advantage and designing markets for privacy risk regulations (e.g. through cyber insurances).

The present study advances research on the economics of consumer privacy – one of the most controversial topics in the digital age. In light of the proliferation of privacy regulations, the mentioned method for measuring the VPR provides an important instrument for policymakers’ informed decisions regarding what tradeoffs consumers consider beneficial and fair and where to draw the line for violations of consumers’ expectations, preferences and welfare.

The authors present a novel method to measure the VPR that takes account of both the value of private information to consumers and their tolerance for privacy risks. The authors explain how this method can be used more generally to elicit attitudes to a wide range of privacy risks involving exposure of various types of private information.

The paper aims to examine ways to reduce privacy risk and its effects so that adoption of e‐services can be enhanced.

Consumers that form a viable target market for an e‐service are presented with the task of experiencing the e‐service and expressing their attitudes and intentions toward it. Structural equation modeling is used to analyze the responses.

The paper finds that consumer beliefs that the e‐service will be easy to use and that the e‐service provider is credible and capable reduce privacy risk and its effects, thus enhancing adoption likelihood.

The focus on a financial services product (online bill paying) suggests that similar research should be conducted with other high‐risk e‐services (such as those dealing with healthcare) and lower‐risk e‐services (such as subscription services and social networks).

In addition to addressing consumers' privacy risk directly, e‐service providers can also reduce privacy risk and its effects by enhancing corporate credibility and perceived ease of use of the service. Increased assessments of privacy risk perceptions and efforts to reduce those perceptions will likely yield higher usage rates for e‐services.

The use of the technology acceptance model from information systems research, combined with a multi‐faceted conceptualization of privacy risk, moves the examination of privacy risk to a higher level, particularly in light of the examination of the additional factors of perceived ease of use and corporate credibility.

The study aims to empirically understand individuals' tendency to disclose private information online following different forms of data breach (i.e. reversible and irreversible victimization).

Survey methodology is applied to measure the perception of victims of data breaches on key indicators of information disclosure.

Analysis of responses from 309 victims of data breaches show that while victims' irreversible data breach victimization experience influences both dimensions of privacy concerns, reversible data breach victimization experiences influenced only peer privacy concerns (PPCs). Furthermore, only institutional privacy concerns impacted online disclosure and fully mediate the relationship between victimization experience and online disclosure.

The findings contribute to the privacy literature by expanding the dimension of victimization and considering their differential effect on privacy concerns. Additionally, the study uncovers the efficacy of privacy dimension on privacy recalibration following a data breach announcement.

For practice, the results provide insights for managers on how to manage customer restitution after a data breach. Management of the process of privacy recalibration should not be homogenous but be based on degree of consequence.

This research provides deeper understanding of how the ascendancy of privacy breaches affect privacy management. The findings illuminate why the increasing trend in online activities is observed.

The study is the first to identify two dimensions of data breach victimization experience based on the breach level index (BLI). The two dimensions of victimization (i.e. reversible and irreversible privacy victimizations) were used to understand individuals' tendency to disclose private information online.

The purpose of this research is to examine the effect of information privacy concern on users' social shopping intention.

Based on the 340 valid responses collected from a survey, structural equation modeling (SEM) was employed to examine the research model.

The results indicated that while disposition to privacy positively affects privacy concern, both reputation and laws negatively affect privacy concern, which in turn decreases social shopping intention. In addition, trust partially mediates the effect of privacy concern on social shopping intention.

The results imply that social commerce companies need to mitigate users' privacy concern in order to facilitate their shopping behavior.

This research disclosed that privacy concern receives a tripartite influence from users (disposition to privacy), platforms (reputation) and governments (laws). The results help us gain a complete understanding of information privacy concern mitigation in social shopping.

Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users' stated preferences. This article investigates the level of modeling that contemporary approaches rely on to explain said inconsistencies and whether drawn conclusions are justified by the applied modeling methodology. Additionally, it provides resources for researchers interested in using computational modeling.

The article uses data from a pre-existing literature review on the privacy paradox (N = 179 articles) to identify three characteristics of prior research: (1) the frequency of references to computational-level theories of human decision-making and perception in the literature, (2) the frequency of interpretations of human decision-making based on computational-level theories, and (3) the frequency of actual computational-level modeling implementations.

After excluding unrelated articles, 44.1 percent of investigated articles reference at least one theory that has been traditionally interpreted on a computational level. 33.1 percent of all relevant articles make statements regarding computational properties of human cognition in online privacy scenarios. Meanwhile, 5.1 percent of all relevant articles apply formalized computational-level modeling to substantiate their claims.

The findings highlight the importance of formal, computational-level modeling in online privacy research, which has so far drawn computational-level conclusions without utilizing appropriate modeling techniques. Furthermore, this article provides an overview of said modeling techniques and their benefits to researchers, as well as references for model theories and resources for practical implementation.

During 2020, governments around the world introduced contact-tracing apps to fight the COVID-19 pandemic. In order for contact-tracing apps to be efficient tools in combatting pandemics, a significant proportion of the population has to install it. However, in many countries, the success of apps introduced during the COVID-19 pandemic has been limited due to lack of public support. This paper aims to better understand why consumers seem unwilling to install and use a contact-tracing app.

In this study, the authors test a number of determinants hypothesized to influence acceptance of contact-tracing apps based on the theory of privacy calculus (Dinev and Hart, 2006). Both perceived privacy concerns, as well as perceived hedonic, utilitarian and pro-social benefits are included. The hypotheses are tested through SEM analysis on a representative sample of 1,007 Swedish citizens.

The results indicate significant privacy concerns with using contact-tracing apps. However, this is to some extent offset by perceived hedonic and pro-social positive consequences of using the app. This study further shows that a general positive attitude towards innovation increases acceptance of the app.

The study contributes to research on consumer privacy, both in general in its application of the calculus model but also specifically in the context of contact-tracing apps. Moreover, as the results highlight which aspects that are important for consumers to accept and install an app of this kind, they also represent an important contribution to policymakers in countries around the world.

The question over who “owns” and controls consumer data on the internet is emerging as an important issue as individuals increasingly share more of their personal information with marketers in return for services and benefits. This paper aims to examine how compensating consumers for their personal information affects their expectations for data ownership and privacy control.

The authors conduct two online scenario-based experiments with a sample of adult consumers. The results were analyzed using multivariate and univariate analysis of variance.

The findings show that receiving compensation, especially when it is a monetary reward, reduces consumer expectations for privacy protection. These effects depend on whether the information provided to marketers is perceived to be sensitive in nature.

While a number of privacy studies have investigated the effects of compensation on encouraging self-disclosure on the internet, there is a lack of research that examines the effect of compensation on privacy expectations. To the authors' knowledge, this is the first paper to test empirically the construct of information ownership in the context of privacy exchanges.

Drawing on the control agency theory and the network effect theory, the purpose of this paper is to examine the effect of privacy assurance approaches, network externality and technology complementarity on consumers’ self-disclosure in mobile payment (MP) applications. The authors identify four types of privacy assurance approaches: perceived effectiveness of privacy setting, perceived effectiveness of privacy policy, perceived effectiveness of industry self-regulation and perceived effectiveness of government legislation. The research model considers how these privacy assurance approaches influence privacy concerns and consumers’ self-disclosure in MP applications under boundary conditions of network externality and technology complementarity.

An online survey with 647 sample users was conducted to empirically validate the model. The target respondents were current consumers of a popular MP application. The empirical data were analyzed by a structural equation modeling approach.

The empirical results reveal several major findings. First, privacy assurance approaches can effectively decrease privacy concerns, which ultimately formulates consumers’ self-disclosure in MP applications. Second, network externality and technology complementarity weaken the effect of perceived effectiveness of privacy setting on privacy concerns. Third, network externality and technology complementarity strengthen the relationship between perceived effectiveness of government legislation and privacy concerns, while they have non-significant interaction effect with perceived effectiveness of privacy policy and industry self-regulation on privacy concerns.

MP providers and stakeholders can harness the efficacy of privacy assurance approaches in alleviating privacy concerns and promoting consumers’ self-disclosure in MP applications.

The authors’ work contributes to the information privacy literature by identifying effective privacy assurance approaches in promoting consumers’ self-disclosure in MP applications, and by highlighting boundary conditions of these privacy assurance approaches.

This essay examines ethical aspects of the use of facial recognition technology for surveillance purposes in public and semipublic areas, focusing particularly on the balance between security and privacy and civil liberties. As a case study, the FaceIt facial recognition engine of Identix Corporation will be analyzed, as well as its use in “Smart” video surveillance (CCTV) systems in city centers and airports. The ethical analysis will be based on a careful analysis of current facial recognition technology, of its use in Smart CCTV systems, and of the arguments used by proponents and opponents of such systems. It will be argued that Smart CCTV, which integrates video surveillance technology and biometric technology, faces ethical problems of error, function creep and privacy. In a concluding section on policy, it will be discussed whether such problems outweigh the security value of Smart CCTV in public places.