The flow experience refers to a state characterized by complete involvement in a task. According to flow theory, the flow state is preceded by three antecedents, skill-task match, goals, and feedback. These antecedents lead to a flow state, which is exemplified by six components, merging of action and awareness, centering of attention, loss of self-consciousness, temporal distortion, sense of control, and autotelic nature. In a flow state, individuals persist on a task without regard or awareness of themselves or their surroundings. Currently, flow is a two-stage model in which the three antecedents lead to a flow state. Flow theory is severely limited as no mediating processes have been specified between flow antecedents and the flow state. The missing mediating processes in flow theory do not allow for empirically examining testable a priori predictions. Further, failure to specify a mediator brings into question the current flow antecedents and components. The aim of this study was to recast flow theory within a self-regulation framework to ameliorate these issues.

I borrow from the self-regulation literature and propose that “feeling right” mediates the relationship between flow antecedents and components. Feeling right is a positive cognitive experience that arises from successful regulatory fit. I further posit that the antecedents of flow are the antecedents of feeling right, motivational orientation and goal pursuit strategies. Finally, I propose that the flow state only be characterized by four components, merging of action and awareness, centering of attention, loss of self-consciousness, and temporal distortion. Thus, in my revised model of flow, alignment between motivational orientation and goal pursuit will lead to feeling right, which will then lead to a flow state, characterized by the four aforementioned components. A secondary goal of this study was to examine the relationship between flow and task performance. I hypothesized that individuals in a state of regulatory fit would experience flow, operationalized by intense concentration, time distortion, and loss of self-consciousness. I further hypothesized that flow would mediate the relationship between regulatory fit and performance and that type of fit would influence performance quality or quantity. I utilized an experiment design to test this revised flow model in the context of a computer game. A path model was conducted to test these predictions.

Results revealed that individuals in a state of regulatory fit exhibited greater time distortion and loss of self-consciousness. However, flow did not mediate the relationship between fit and performance. Based on these results, flow can successfully be applied to a self-regulatory framework. There is initial evidence that motivational orientation and goal pursuit, i.e., regulatory fit, are causal antecedents to a flow state. There was stronger evidence for the relationship between regulatory fit and flow when behavioral flow indicators were used. Future research should focus on identifying behavioral flow indicators and continue to explore the flow construct within a self-regulatory framework.