Psychological Ownership, Territorial Behavior, and Being Perceived as a Team Contributor: The Critical Role of Trust in the Work Environment
Bibliographic record
Abstract
In this field study, we develop and test a theory regarding the role of trust in the work environment as a critical condition that determines the relationship between psychological ownership, territoriality, and being perceived as a team contributor. We argue that, dependent upon the context of trust in the work environment, psychological ownership may lead to territorial behaviors of claiming and anticipatory defending and that, dependent upon the context of trust, territorial behavior may lead coworkers to negatively judge the territorial employee as less of a team contributor. A sample of working adults reported on their psychological ownership and territorial behavior toward an important object at work, and a coworker of each provided evaluations on the level of trust in the work environment and rated the focal individual's contributions to the team. Findings suggest that a work environment of trust is a “double‐edged sword”: On the one hand, a high trust environment reduces the territorial behavior associated with psychological ownership; on the other hand, when territorial behavior does occur in high trust environments, coworkers rate the territorial employee's contributions to the team significantly lower. We discuss the nature and management of territorial behavior in light of these findings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".