Between sharing and hiding: How consumers shade knowledge
Bibliographic record
Abstract
This paper advances research on consumer knowledge management by introducing the concept of knowledge shading, a middle ground between fully sharing and completely hiding knowledge. The analysis of 47 interviews with fishers and netnographic data identifies how consumers strategically navigate complex tensions between individual and collective interests. It uncovers four scenarios where knowledge takes different roles (scarce, vested, sanctioned, and essential) and how these scenarios shape consumers’ knowledge management decisions. When these scenarios intersect, consumers engage in knowledge shading through six approaches: generalizing, misdirecting, withholding, coding, managing, and bartering. The findings contribute theoretically by challenging the binary view of knowledge sharing versus hiding, explaining why and how consumers shade knowledge, and discussing the effect of knowledge sharing, hiding, and shading for the evolution of consumption practices. The research also provides managerial recommendations for brands to manage how consumers share, hide, and shade knowledge.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".