The goal of the study was to determine which aspects of interpersonal touch interactions lead to a positive or negative experience. Previous research has focused primarily on physical characteristics. We suggest that this may not be sufficient to fully capture the complexity of the experience. Specifically, we examined how fulfilment of psychological needs influences touch experiences and how this relates to physical touch characteristics and situational factors.In two mixed-method studies, participants described their most positive and most negative interpersonal touch experience (...) within a specific time frame. They reported fulfilment of nine needs, affect, intention, and reason for positivity/negativity, as well as the body part(s) touched, location, type of touch, interaction partner, and particular touch characteristics (e.g. humidity).Positive and negative touch experiences shared similar touch types, locations, and body parts touched, but differed in intended purpose and reasons. Overall, the valence of a touch experience could be predicted from fulfilment of relatedness, the interaction partner and initiator, and physical touch characteristics. Positive affect increased with need fulfilment, and negative affect decreased.The results highlight the importance of relatedness and reciprocity for the valence of touch, and emphasise the need to incorporate psychological needs in touch research. (shrink)

While algorithmic decision-making (ADM) is projected to increase exponentially in the coming decades, the academic debate on whether people are ready to accept, trust, and use ADM as opposed to human decision-making is ongoing. The current research aims at reconciling conflicting findings on ‘algorithmic aversion’ in the literature. It does so by investigating algorithmic aversion while controlling for two important characteristics that are often associated with ADM: increased benefits (monetary and accuracy) and decreased user control. Across three high-powered (Ntotal = (...) 1192), preregistered 2 (agent: algorithm/human) × 2 (benefits: high/low) × 2 (control: user control/no control) between-subjects experiments, and two domains (finance and dating), the results were quite consistent: there is little evidence for a default aversion against algorithms and in favor of human decision makers. Instead, users accept or reject decisions and decisional agents based on their predicted benefits and the ability to exercise control over the decision. (shrink)