Heedful proactivity: How individual tactical considerations contribute to pre‐screening of innovative ideas in the hierarchy
Bibliographic record
Abstract
Purposefully fostering creativity and innovation through stimulating proactivity requires grappling with an apparent trade‐off. On the one hand, organization members need some autonomy to initiate change. On the other hand, managers might want to steer initiatives and retain control over outcomes. The current paper advances recent work on how proactivity is enacted as a compromise between autonomy and control by studying the process through which bottom‐up ideas are shared in highly hierarchical organizations. Based on an abductive analysis of data from informants in 42 organizations, we develop the concept of pre‐screening, which denotes collective action patterns geared towards qualifying individuals' innovative ideas before they are made subject to formal decision making. We explain how proactive individuals' tactical considerations—informed by their holistic prospective thinking, risk hedging, temporal splitting, and a both/and approach to proactivity and hierarchy—influence the actions through which ideas are shared and who are approached first (e.g., supervisors vs. peers). We also exemplify how action patterns accomplishing idea sharing and pre‐screening are entangled with more mundane workplace routines. Overall, the paper sheds new light on ideas' journeys in the context of hierarchy and opens up multiple avenues for future research.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".