The Unintended Consequences of Labeling Complimentary Offers as Free
with Caroline Goukens and Vicki G. Morwitz
Prior literature has traditionally emphasized the attractiveness of zero-price offers. This research, however, demonstrates that explicitly labeling complimentary offers as “free” can increase their likelihood of being rejected. Across a series of incentivized online panel experiments involving 3,960 participants, we observed that individuals are more inclined to reject beneficial offers when described as “free” compared to when their complimentary nature is not highlighted—even when accepting them would result in financial gains.
This negative impact of the word “free” may have particularly undesirable implications for public and private initiatives—e.g., free health screening, apps, or legal advice—where organizations may want to highlight the no-cost aspect. We therefore tested a series of interventions that could reduce the negative impact of the word “free.” Our findings show that adding a trust signal, like using a logo from a trustworthy institution (e.g., a university), effectively conveyed the offer’s reliability and non-profit intent, mitigating the adverse effect. Conversely, strategies such as fostering familiarity with participants through repeated interactions, assuring them that the offer comes with “no strings attached,” or encouraging careful consideration of the offer did not alleviate the negative effect.
Our findings emphasize the complexity of consumer perceptions. Companies and policy makers should be mindful of consumers’ tendency to reject offers labeled as “free” when designing promotions, public initiatives, or any programs where participation does not require any payments.
Non-monetary costs of free and paid digital products. An empirical analysis.
In this project, I compare 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 three categories — online news services, and 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.
Judges and machines: how do ‘predictive justice’ tools affect judicial decision-making?
with Matthias van der Haegen
In this project, we study the influence of predictive justice on judicial decision-making in a highly realistic experimental setting. Predictive justice has risen to prominence over the past years, as the legal industry discovers the potential of machine learning within the legal domain. Predictive justice holds many advantages: it may increase efficiency, accuracy and consistency of judicial decisions. In order to make sure that these advantages are indeed achieved an no negative side effects occur that would compromise the requirements of a fair trial, we need to ascertain how these tools affect judges’ decision making.
In the experiment, judges will decide upon a fictitious landlord-tenant dispute under the assumption they are judges in a fictitious country. Relevant material such as legal provisions, precedents and doctrine will be provided, but some participants will also find an A.I-generated prediction on the probability of a landlord being liable in a given case. In our experiment, we will measure the influence of this prediction on the final decision and the decision-making process. We are currently piloting our experimental design with law students. As the next step, we plan to run the study with judges of one of the Dutch District Courts.
The impact of preferential selection on group decision-making
with Angela Dorrough, Andreas Gloeckner, and Madeline Heilman
In addition to securing gender equality, legislators claim that preferential selection will also have positive effects on corporate performance by, for example, increasing the quality of group decision-making. Previous research on preferential selection has primarily focused on individual task performance whereas the evidence on the effects on group behaviors is scarce. We plan to investigate whether preferential selection of women has an effect on the quality of group decision-making in male-dominated domains and shed light on the underlying mechanisms. Specifically, we will focus on the evaluation of men’s and women’s performance in a group task and the group members’ willingness to interact with each other after the introduction of preferential treatment.
Monika Leszczyńska 