Trait motivational reactivity as a predictor of online dating app behavior

Online dating apps have become an increasingly common way to meet prospective partners and form relationships. Though previous research lends insight into who uses these platforms and why, relatively little is known about the nature of in-app behavior during that usage. The current study directly monitors sessions of dating app usage, observing how users cycle through and then make decisions about assortments of potential partners. Adopting a motivated cognition perspective (Lang, 2006), trait levels of appetitive (ASA) and defensive motivational reactivity (DSA) were found to significantly influence a variety of in-app behaviors. Specifically, individuals with relatively lower trait levels of DSA and higher levels of trait ASA were found to browse prospective partner profiles at the fastest rate. Further, trait DSA was positively associated with the amount of information sought before a decision was made to “like” or reject a prospective partner, with this effect moderated by trait ASA. Finally, trait DSA of the app user was negatively associated with the likelihood of a given profile being liked. These results provide new empirical insight into patterns of behavior occurring within sessions of dating app usage. Additionally, they highlight a special consideration for applying a motivated cognition perspective to predict media choices with motivationally-relevant real-world implications: namely, the possibility that trait motivation tendencies in external offline behavior may influence the usage of media with related consequences. •Trait ASA and DSA significantly predict a variety of in-app behaviors.•Individuals with lower DSA and higher ASA browsed prospective partner profiles at the fastest rate.•Trait DSA was positively associated with information seeking before a decision. This effect was moderated by ASA.•Trait DSA of the app user was negatively associated with the likelihood of a given profile being liked.•DSA, ASA, and level of information all interacted to influence the likelihood of a given profile being liked.