Nonwork-Related Creativity: Toward A New Perspective About Creativity via Enterprise Social Media
Bibliographic record
Abstract
Organizations are increasingly relying on enterprise social media (ESM) platforms to develop their employee capacity to generate new and useful ideas. In this article, we argue that ESM allows employees to generate creative ideas, not only in the work domain but also in the nonwork domain. However, the combination of nonwork-related creativity and work-related creativity has not been examined despite prior qualitative research pointing to the capacity of employees to discover work-related ideas when they are exposed to nonwork-related ideas on ESM. We contribute to the IS literature on ESM by examining an increasingly common, yet understudied form of creativity embedded in the nonwork domain of ESM. Through the lens of the spillover theory (ST), this study develops a research model to examine the role of nonwork-related creativity in fostering employee idea generation. We use two field studies to develop a theoretically informed and empirically tested model. We show that nonwork-related creativity is negatively associated with employee idea generation. Still, such association becomes positive when mediated with work-related creativity. Our study extends research in two main ways. First, it proposes the new concept of nonwork-related creativity to embrace a specific category of useful and new nonwork-related ideas that emerge on ESM. Second, our results extend prior qualitative work by theorizing and empirically supporting the idea that, while nonwork-related and work-related ESM uses are semantically independent, nonwork-related creativity and work-related creativity are dependent on each other notably in regard to their influence on employee idea generation.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".