Personal Data in Exchange for Free Services: An experiment on Zero-price Offers and Privacy Decisions
with Caroline Goukens
In this project, we employ online experiments to to investigate how offering digital content at a zero price but in exchange for personal data influence consumers’ decisions about the use of digital content and sharing of personal data.
Free offers are prevalent in nowadays online markets. We can communicate with our friends through social networks, store our files in clouds, navigate the city, manage our finances or even find a life partner using online products and services without paying a single penny. This, however, does not yet mean that we give nothing in exchange. We do provide our private information that might be profitably used by suppliers of free digital content. Since these transactions affect consumers and their personal data, they fall within the scope of two fields of the European Union (EU) law – data protection and consumer law. The overall objectives of these two areas of law are to protect the privacy of consumers and to balance their position against more powerful transaction partners, i.e., businesses. The question which interests us most is whether these objectives are indeed achieved with the current design of the rules, given people’s heuristics and biases in decision making about free products. Behavioral research has demonstrated that consumers tend to overestimate the benefits and underestimate nonmonetary costs of free digital content in the form of exposure to advertisements. Yet, it is still unknown how free offers influence consumer decisions that are relevant from a legal perspective, i.e., decisions that involve consumer rights and privacy.
Non-monetary costs of free and paid digital transactions. An empirical analysis.
In this project, I compare transactions for free and paid digital goods and services to find out whether they differ with respect to potential non-monetary and deferred costs imposed on the users.
Specifically, I investigate samples of digital goods and services in four categories: web-based music streaming, web-based dating services, navigation and personal finance mobile applications. For each product or service in the sample I collect, code and analyze the following information: (1) types of personal data gathered, (2) privacy-relevant permissions requested, (3) presence of ads and newsletters, (4) presence and type of a license to user-generated content, (5) the content of provisions on data protection and sharing, (6) the content of warranty and liability disclaimers, (7) the content of provisions on modification of terms and services, dispute resolution and termination.
Future research could investigate whether the terms and conditions that potentially impose non-monetary and/or deferred costs on consumers change over time depending on the business model of a supplier as well as on the popularity of a product or service.
Not in my House? A Big Data Approach to the Impact of Non-discrimination Policy on a Home Sharing Platform
with Malka Guillot and Andrew Thompson
Discrimination happens everywhere – also in the sharing economy. Research has revealed that users with African American names are more likely than those with distinctively white names to have their booking requests rejected (Edelman et al. 2017) or their ride cancelled (Ge et al. 2016). Sharing platforms do react to this by issuing non-discrimination policies or by changing platform design in a way that would prevent discrimination. Although such initiatives are very welcome, their effectiveness in reducing discrimination has not yet been established. Therefore, in our project we focus on studying the effects of non-discrimination policies and designs on the behavior of platform users. This is particularly important to study given the increasing role these platforms play in the current markets and the number of people that could potentially be affected by discriminatory practices.
To this end, we rely on two changes targeted at discriminatory behavior introduced by the home sharing platform Airbnb. First, by November 1, 2016 all hosts were required to consent to the Community Commitment and the new non-discrimination policy. Second, on October 22, 2018 Airbnb introduced a change in the platform design. According to this new measure, hosts are not able to see photos of potential guests requesting bookings until after the booking is confirmed. If the host requests to see a picture of a guest after the booking is confirmed, the only way not to host someone is to cancel the booking. We use these two events – soft and hard non-discrimination tool – as natural experiments to study how they affect various measures, such as a cancellation rate, serving as a proxy for discriminatory behavior.
Women’s perceived incompetence in group discussions
with Angela Dorrough, Sandra Werner, Anna-Sophie Galley, Enis Akin, Jaqueline Bachmann, Marius Bruske, Ulla Burghardt, Franziska Simandi
Women are still underrepresented in many male-associated areas. One reason for this imbalance is that men and women are evaluated differently in many different contexts. The present research investigates how men and women are evaluated in group discussions. In four studies (N = 668), using a variant of a Hidden Profile Task, we find that, when experimentally and/or statistically controlling for actual gender differences in behavior, the female contribution to a group discussion is devalued in comparison to the male contribution. This effect was observed for fellow group members (Study 1) or outside observers (Studies 2-4), for student only samples (Study 1) and mixed samples (Studies 2-4), for different measures of perceived competence as well as across different discussion formats (Studies 1 and 2: pre-formulated chat messages; Studies 3 and 4: open chat). In contrast to our hypothesis, we did not find a moderating effect of selection procedure in that women were devalued to a similar degree in situations with a women’s quota and without.