My knowledge: The negative impact of territorial feelings on employee's own innovation through knowledge hiding
Bibliographic record
Abstract
Summary Innovation is critical for organizational success but innovation often depends on employee's willingness to share, which they surprisingly are not always willing to do despite potential negative costs to the individuals who hide information. Drawing on psychological ownership theory, we explain how knowledge engenders territorial feelings and leads employees to hide knowledge. Using social exchange theory, we explain how certain types of knowledge hiding behavior negatively impact the hider by reducing their own innovation. To explore potential mitigating factors, we proposed that affect‐based trust alleviates the relationship between territorial feelings and knowledge hiding and buffers the harmful effect of territorial feelings on innovative behavior. We tested the model in two studies: a pilot study of 133 full‐time employees (Study 1) and a two‐wave investigation of 30 supervisors and 240 employees (Study 2). Results revealed that (a) territorial feelings positively influenced evasive hiding and playing dumb but not rationalized hiding, (b) evasive hiding and playing dumb mediated the link between territorial feelings and innovative behavior, (c) affect‐based trust moderated the relationship between territorial feelings and evasive hiding as well as the indirect effects of territorial feelings on employee innovative behavior via evasive hiding. The theoretical and practical implications 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".