Reciprocal Path Model of Autonomous Motivation and Motivational Regulation: Socially Shared Regulation in Intellectual Group Activities
Bibliographic record
Abstract
Self- and social regulation are widely expected to increase autonomous motivation; however, few empirical studies have examined the reciprocal influences of autonomous motivation and motivational regulation. This study examined the reciprocal path model between autonomous motivation and three modes of motivational regulation (self-, co-, and socially shared regulation) in intellectual group activities by comparing university students with working adults. The participants were 181 university students and 295 working adults who completed an online questionnaire consisting of psychological measurements. With respect to autonomous motivation and the three modes of motivational regulation, a bidirectional model of university students and working adults was established and statistically analyzed on the basis of two time points of data, one month apart (T1 and T2). The hypothesized path model had a good fit through a multi-group structural equation modeling analysis. Autonomous motivation at T1 positively predicted socially shared regulation, co-regulation, and self-regulation at T2, one month later, for both groups. However, the three modes of regulation did not positively or significantly predict autonomous motivation in either group. Considering the reciprocal influences of autonomous motivation and motivational regulation, we discuss the necessity of implementing these practices in universities and workplaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".