Valuation of Personal Data in the Age of Data Ownership

In order to tackle uncertainties about data ownership and data misuse, more accessible and competitive data markets are proposed, especially concerning the use and access rights of data generated by the Internet of Things (IoT) devices. Legal proposals suggest that companies and individuals become owners of their self-generated data, enabling new ways of data monetization. Still, individuals are often uncertain about the value and price of their own generated data. This research builds on construal level theory to propose influencing factors fostering an understanding of intraindividual data value. The results of a pilot study survey (n = 104), conducted during the ICIS 2022, show that data proximity and data sensitivity factors significantly influence intraindividual data value. Our research extends the knowledge on data value from individual perspectives and builds the foundation for future work on data valuation and pricing in intraindividual data trading

On the value of information - what Facebook users are willing to pay

In the age of Web 2.0 users contribute to platforms success by providing personal information by actively uploading information (e.g. messages, preferences, biographies) and also by leaving traces of their online behavoiour as can be derived from their clicks, navigation paths, etc. While there is a market for trading such information among companies, there is little knowledge about how users actually value their personal information. In an online survey-based experiment we have asked 1.045 Facebook users how much they would be willing to pay for keeping their personal information. Surprisingly, 48.1 percent of participants are not willing to pay a single Euro, - thus, valuing their information at zero. Results indicate that people that show 'spamming' behaviour and users that use Facebook for 'diary keeping' are significantly more willing to pay a certain amount higher than zero to be able to keep their personal Facebook information. Interestingly, having analysed various kinds of user behaviour, the regression model still explains no more than 14.2 percent of variance. Additionally, this article discusses four different method manipulations for eliciting people's willingness to pay for personal information and provides methodical guidance for future research in the field

Privacy as a Part of the Preference Structure of Users App Buying Decision

Information privacy and personal data in information systems are referred to as the ‘new oil’ of the 21st century. The mass adoption of smart mobile devices, sensor-enabled smart IoT-devices, and mobile applications provide virtually endless possibilities of gathering users’ personal information. Previous research suggests that users attribute very little monetary value to their information privacy. The current paper assumes that users are not able to monetize their value of privacy due to its abstract nature and non-transparent context. By defining privacy as a crucial product attribute of mobile applications the authors provide an approach to measure the importance of privacy as part of users’ preference structure. The results of the conducted choice-based conjoint Analysis emphasize the high relevance of privacy in users’ preference structure when downloading an app and provide an interesting contribution for theory and practice

Putting a Price Tag on Personal Information - A Literature Review

In the digital age, personal information is claimed to be the new commodity with a rising market demand and profitability for businesses. Simultaneously, people are becoming aware of the value of their personal information while being concerned about their privacy. This increases the demand of direct compensation or protection. In response to the commodification of privacy and the increased demand for compensation, a number of scholars have shed light on the value people assign to their personal information. However, these findings remain controversial as their results differ tremendously due to different research methods and contexts. To address this gap, we conducted a systematic literature review to gain insights into the current research state and to identify further research avenues. By synthesizing and analyzing 37 publications, we provide an integrative framework along with seven contextual factors affecting individuals’ valuation of privacy

Social Network Services: Competition and Privacy

Social Network Services (SNS) business models highly depend on the gathering and analyzation of user data to obtain an advantage in competition for advertising clients. Nevertheless, an extensive collection and analysis of this data poses a threat to users’ privacy. Based on an economic perspective it seems rational for Social Network Operators (SNO) to ignore the users’ desire for privacy. However, privacy-friendly services might have the potential to earn users’ trust, leading to an increased revelation of personal data. Addressing these issues, we examine the existing privacy problem in SNS in the context of competition between SNO to investigate whether competition tend to enhance user privacy or whether it is the root of its violation. Therefore, this paper investigates the interconnectedness of the market structure and privacy problems in SNS. After analyzing the users’ and the advertisers’ side of SNS, their competitiveness and its influence on user privacy are examined

What is Your Selfie Worth? A Field Study on Individuals’ Valuation of Personal Data

Referred to as the new oil, undoubtedly personal data is a valuable resource for organizations. Contrary, it is still blurred, to what extent individuals value their data even though, in a digitized world, users are requested to exchange their data for adequate services. Former research on individuals’ valuation of personal data result in scattered, partly contradictious values, depending on the data type, context, and the measurement method. In this study, we aimed to facilitate the valuation for individuals by applying a new and promising measurement methodology: the participants of our field experiment had the chance to sell their selfies in a name-your-own-price auction with repeated bidding and feedback loops. As a result, 39% of our participants were willing to donate or sell their selfies with a median of 5€. Additionally, bidding clusters were identified. Implications for research on the valuation of personal data in terms of privacy are discussed

A Game-Theoretic Study on Non-Monetary Incentives in Data Analytics Projects with Privacy Implications

The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoff scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibria that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal

PERCEIVED RISKS AND BENEFITS OF ONLINE SELF-DISCLOSURE: AFFECTED BY CULTURE? A META-ANALYSIS OF CULTURAL DIFFERENCES AS MODERATORS OF PRIVACY CALCULUS IN PERSON-TO-CROWD SETTINGS

Disclosing personal information to a crowd, with all its risks and benefits, is almost ubiquitous in Web 2.0. Drawing on privacy calculus (PC) theory, we investigate whether cultural differences moderate the effect of risk and benefit assessment on online self-disclosure (OSD) in person-to-crowd settings. Empirically, our study relies on a (statistical) meta-analysis of 38 studies. Our findings support the assumptions regarding the effect of privacy calculus on OSD: benefits and trust beliefs increase OSD, privacy concerns and risk beliefs reduce it. Furthermore, the positive effect of the former PC aspects on OSD is larger than the negative effect of the latter. The effects of benefits and risk beliefs on OSD are moderated by cultural differences, unlike those of privacy concerns and trust beliefs. Uncertainty avoidance and indulgence reduce the positive effect of benefits on OSD, masculinity and power distance enhance it. The negative effect of risk beliefs is reduced by uncertainty avoidance and long-term orientation, but aggravated by indulgence. In addition to advocating increased cultural awareness for online service providers, our findings support PC as a useful concept in OSD research, but suggest that the most prominent cultural dimensions might not be the most relevant ones in intercultural OSD research