Boxing-In and Box-Breaking of Attention: a Process Model of Innovation Measurement
Bibliographic record
Abstract
In this paper, we present a process model on how the measurement of innovation performance within firm shapes the attention of organizational members. Most firms measure innovation performance on a regular basis. Yet, this practice is double-edged. On the one hand, innovation measurement may help audit innovation performance thus enabling the improvement of innovative activities. On the other, measurement may hinder creativity and make managers focus their attention too narrowly. Addressing this dilemma, existing studies has taken a strong normative stance, providing lists of best- practice metrics. At the same times, these lists are often overlapping and the advice given sometimes contradictive and we lack an over-all theoretical framework to understand how the measurement of innovation performance within firms influences behavior. Drawing on attention-based theory, we propose that innovation metrics have a boxing-in effect on attention by allowing organizational members to sort issues and prioritize answers. Equally important, however, we also argue that the activity of innovation measurement provides a forum and language for communication across hierarchical, functional and temporal domains. As such, we propose that the activity of measuring has a box-breaking effect on attention, and we identify issue and answer coordination and issue and answer translation as important theoretical mechanisms underlying this process.
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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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".