The use of smartphone apps has numerous advantages for app providers and users. However, the users of many smartphone apps are confronted with a trade-off between usage benefits and preferences for personal data protection. We investigate the acceptability of data sharing in different hypothetical scenarios describing five types of these apps from key industries of the digital transformation. In a representative survey for the German population (𝑁 = 1,013), we examine to what extent the acceptance of data sharing is influenced by potential recipients, collected information attributes, and the promoted benefits of data sharing. We differentiate the promoted benefits in two treatments according to monetary (or personal) and environmental (or public) benefits. Our results show no treatment effects but significant differences in acceptance values for different recipients and information attributes. We further observe that participants with stronger green consumption values, participants with a stronger risk propensity, men, and younger participants show a higher acceptance towards data sharing in the described scenarios.

While recycling helps to limit the use of primary resources, it also requires considerable technological investments in regional circular flow systems. The effectiveness of recycling systems, however, also depends on household behavior. Therefore, current research increasingly focuses on behavioral and psychological theories of altruism, moral behavior, and social preferences. From an economic perspective, recycling systems can be understood as public goods with contributions resulting in positive externalities. In this context, the literature shows that recycling behavior highly depends on the perception of how others behave. In neutrally framed public good experiments, contributions tend to increase when alternative public goods are offered and group identity is generated. We aim to contribute to this discussion by observing household behavior concerning recycling opportunities in controlled settings. For this purpose, we study a laboratory experiment in which individuals contribute to recycling systems: At first, only one public recycling system (public good) is offered. After dividing societies into two clubs, “high” and “low” according to their environmental attitudes, excludable club systems (club goods) are added as alternative recycling options for each club. The results of our pilot experiment show that adding a more exclusive recycling club option increases individual contributions to recycling compared with a pure public good framework. However, this increase in cooperation is only significant for those clubs where members with higher environmental attitudes are pooled. Graphic abstract

We investigate whether individuals’ self-stated privacy behavior is correlated with their reservation price for the disclosure of personal and potentially sensitive information. Our incentivized experiment has a unique setting: Information about choices with real implications could be immediately disclosed to an audience of fellow first semester students. Although we find a positive correlation between respondents’ willingness to accept (WTA) disclosure of their private information and their stated privacy behavior for some models, this correlation disappears when we change the specification of the privacy index. Independent of the privacy index chosen we find that the WTA is significantly influenced by individual responses to personal questions, as well as by different decisions to donate actual money, indicating that the willingness to protect private information depends on the delicacy of the information at stake.

Online employer review platforms (ERPs) enable employees to evaluate their current and former companies anonymously online. Job-seekers can use the aggregated reviews to obtain information about potentially attractive companies and thus limit the number of suitable companies. However, the matching process between job-seekers and companies can only be effective if the information provided on ERPs is representative and can be trusted. This paper investigates specific characteristics of ERPs using the two large ERPs Kununu and Glassdoor as examples. It is argued that the ERP environment is very different from the well-known and -studied reputation system environment of online marketplaces, and that specific factors can potentially bias reviews on ERPs. Based on a new data set containing the Kununu and Glassdoor reviews of 114 major German employers, it is analyzed if and how design aspects of ERPs and other specific factors affect reviews. Results show that overall (and industry-specific), average review scores on Kununu and Glassdoor differ significantly from each other. Further results indicate that factors such as employees’ awareness of their impact on a company’s reputation also affect reviews. Suggestions are made on how ERPs could reduce the influence of these factors in order to present the aggregated information more effectively.

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