In this app-based event sampling study, we observed the intentional formation of new study habits. A sample of 91 university students defined individual study habits and logged data over 6 weeks on motivational conflict, motivational interference (MI) and automaticity of behavior after each habit repetition using an app on their phone. The app was specifically created for this study and gave feedback on habit automaticity. A total of N = 2,574 habit repetitions have been generated and were analyzed using multilevel modeling. The results suggest that (1) app-based intentional habit building works, as automaticity of behavior could be predicted by habit repetition, (2) motivational impairments during studying can be reduced by building habits, as want conflicts and MI decreased with automaticity, and (3) trait self-control supports studying indirectly by fostering habit building rather than directly by suppressing impulses during the activity, as self-control predicted automaticity, but not motivational impairments during the habit execution. The effect of self-control on automaticity of the new study habit was fully mediated by the general automaticity of the students' other study habits (general study habit strength). This study showcases an app-guided genesis of new study habits and its beneficial motivational effects for learning behavior.

In this article, we investigate the role of self-efficacy (SE) in intentional habit building. We analyzed event sampling data from a habit building app we created that helps define and track habit data. We used hierarchical growth curve modeling and multilevel mediation to test our hypotheses. In a first study, N = 91 university students built new study habits over a period of 6 weeks in a controlled study. We found that the trait-like (Level 2) general self-efficacy predicted automaticity (i.e., habit strength) but not the experience of motivational interference (MI). In a second study with real user data, N = 265 idiographic habits have been analyzed. The specific SE associated with these habits – habit-specific self-efficacy (Level 1, HSE) – was measured during habit formation. We found that lagged HSE predicted automaticity and that lagged automaticity predicted HSE, indicating a positive feedback mechanism in habit building. Furthermore, we found that lagged HSE predicted less MI during habit performance. A multilevel mediation analysis showed significant effects of lagged HSE (Level 1) and aggregated HSE (Level 2) on MI, which were both partially mediated by automaticity. These results show the importance of defining the specificity of SE beliefs and how they interact with automaticity in the habit building process.

In this paper, we investigate the effects of context stability on automaticity and goal attainment in intentional habit building. We used hierarchical growth curve modeling and multilevel mediation to test our hypotheses on two datasets. In Study 1, N = 95 university students (N = 2,482 habit repetitions) built new study habits over a period of 6 weeks with manipulated context stability. One group was instructed to constantly vary the context of their habit repetitions by changing rooms and times and the other group was instructed to keep the context of habit performance stable. In Study 2, N = 308 habits (N = 2,368 habit repetitions) from N = 218 users of a published habit building app were analyzed without manipulating but measuring context stability. We found the same pattern in both datasets: Context stability predicted more automaticity and higher habit repetition goal attainment. We also found that the effect of context stability on habit repetition goal attainment was partially mediated by automaticity in both datasets. These results show that context does not only act as a trigger for habit instigation but also has an ongoing effect on habit execution.