DOI: 10.1002/jcal.70284 ISSN: 0266-4909

A Day Begins With Morning: How Morning Goal Motivation Profiles Shape Daily Emotions From a Complex Dynamic Systems Perspective

Meng‐Ting Lo, Yen‐Yu Chen, Shao‐Hsuan Lin

ABSTRACT

Background

Individuals pursue goals for autonomous or controlled reasons, and distinct combinations of these motivations form daily motivational profiles. Although such profiles have been studied, little is known about their day‐to‐day stability and variability, which is critical for understanding how motivation evolves over time. How motivational and emotional dynamics emerge in the daily lives of university students remains unclear, and person‐oriented temporal analyses can reveal maladaptive patterns that mean‐level analyses often obscure.

Objectives

This study aims to identify daily autonomous and controlled goal‐pursuit motivation profiles, examine their day‐to‐day stability and variability and explore how these profiles relate to university students' emotional transitions throughout the day.

Methods

Using the VaSSTra method, we identified profiles that summarise students' motivational states for each day, examined sequences of these profiles across days, and clustered students who exhibited similar patterns of transitions. Multilevel modelling and transition network analysis were employed to explore the relationship between these profiles and students' emotional experiences and transitions.

Results and Conclusions

Three profiles emerged: high controlled goals, moderate intrinsic goals, and autonomous and moderate controlled goals. Transition network analysis revealed that students with moderate intrinsic or autonomous and moderate controlled profiles tended to maintain more positive emotional states, exhibiting fewer systematic emotional transitions. In contrast, on days marked by a high controlled goal‐pursuit profile, students were more likely to shift towards heightened negative emotions. This study extends theoretical perspectives on motivation and demonstrates the value of novel learning analytics for untangling the complex, interactive relationships between motivation and emotions.

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