A Network Model of Goals Boosts Convergent Creativity Performance
Bibliographic record
Abstract
To increase employee creativity is critical for organizational success, and yet we still know very little about what organizational contexts promote creative performance. Our research proposes that goal regulation in the workplace may have consequences for creativity. While there is an increasing trend for organizations and workers to visualize the structure of their goals (e.g., management hierarchy, concept-map, flowchart), prior research suggests the visualization approaches differ as one of the three types: hierarchical, network, sequential models. Because a network model (vs. hierarchical and sequential models) highlights multiple connections between goals and reveals unobvious connections between them, we hypothesized that the use of a network goal model might increase people’s ability to integrate seemingly unrelated ideas, even on subsequent unrelated tasks, leading to higher (convergent) creative performance. To test the hypothesis, we conducted an experiment in 2017 manipulating participants’ goal models (N = 191; Median age = 19; hierarchical, network, sequential) and measured their creativity. Results suggests that those in the network model condition performed better in the kind of creativity task that requires meaningful integration of unrelated ideas (i.e., convergent creativity); in contrast, there was no difference between goal model conditions on divergent creative performance. These findings thus illuminate how goal models may influence creativity, providing new insights into situational inductions that can boost creative performance. Theoretical and practical implications, limitations, and future directions of the work are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".