Quantifying different psychological costs of user behavioral info for overcoming the 'take-it-or-leave-it' condition - Sangjun Nam; Youngsun Kwon (International Telecommunications Society (ITS), 2022)
Take-it-or-leave-it, in which users have to provide personal information as required by service providers, has been a dominant form of agreement between online service providers and users. The regulators recently began to prohibit dominant online platforms from collecting personal data based on the 'take-it-or-leave-it' basis because this clause is likely to harm consumer welfare without giving users choices for using the service. In order to improve regulatory efficiency, we need to devise more flexible alternative service provisions balancing privacy concerns and enhanced service based on personal preference. To accomplish this goal, we need to understand the users' attitudes related to personal behavioral data collection for both regulators and online platforms. In this context, we aim to estimate the psychological costs that users bear when they need to exchange personal data for service use. Quantifying the perceived cost of personal data collection with monetary reward was common. However, it is not easy to determine whether the perceived cost is high or not because the monetized value of personal data is not self-evident. To address this issue, we consider attention cost, one of the representative inconvenience costs of using free online services in the analysis. This study collects the data using a conjoint survey and estimates the psychological costs of personal data collection using the mixed logit model and latent-class logit model. Our results show that the respondents' perceived cost for overcoming the 'take-it-or-leave-it' condition is heterogeneous, and only one of four respondent segments (around 30% of respondents) perceived it as significant. Moreover, the results suggest that the perceived risks and benefits of personal data collection affect the psychological cost. It implies that privacy calculus theory can be a meaningful framework for understanding users' attitudes toward behavioral data collection on online platforms.
Keywords: 330; Personal data; Online platform; Privacy calculus theory; Information disclosure; Attention cost