From thriving to task focus: the role of needs-supplies fit and task complexity
Bibliographic record
Abstract
Can thriving at work be a self-sustaining phenomenon? In our study we incorporated the person-environment fit perspective into socially embedded model of thriving by Spreitzer’s et al. ( Organization Science , 16 (5), 537–549, 2005) to explore how and when thriving affects individuals’ task focus, an agentic behavior often considered an antecedent of thriving. We proposed that individuals who thrive at work tend to perceive that the rewards supplied by the job meet their needs of growth and development, which leads to more focus on their tasks. We also proposed that task complexity interacts with thriving to influence the mechanism of needs-supplies (N-S) fit. Based on two-wave data from 170 product engineers, the results of our study showed that thriving indirectly affects task focus through perceived N-S fit. When faced with high-complexity tasks, high-thriving employees generated higher N-S fit perceptions. When thriving was low, N-S fit was highest in in the low-complexity task context, suggesting that matching thriving to task complexity could be an important strategy by which managers might maintain higher levels of task focus, which we speculate could in turn promote thriving. Our study advances research on thriving, fit perceptions and work behavior by revealing these relationships.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".