The Effects of Creating Psychological Ownership on Physicians' Acceptance of Clinical Information Systems
Bibliographic record
Abstract
OBJECTIVE: Motivated by the need to push further our understanding of physicians' acceptance of clinical information systems, we propose a relatively new construct, namely, psychological ownership. We situated the construct within a nomological net using a prevailing and dominant information technology adoption behavior model as a logical starting point. DESIGN: A mail survey was sent to the population of users of a regional physician order entry (POE) system aimed at speeding up the transmission of clinical data, mainly laboratory tests and radiology examinations, within a community health network. MEASUREMENTS: All scales, but one, were measured using previously validated instruments. For its part, the psychological ownership scale was developed using a multistage iterative procedure. RESULTS: Ninety-one questionnaires were returned to the researchers, for a response rate of 72.8%. Our findings reveal that, in order to foster physicians' adoption of a clinical information system, it is important to encourage and cultivate a positive attitude toward using the new system. In this connection, positive perception of the technology's usefulness is crucial. Second, results demonstrate that psychological ownership of a POE system is positively associated with physicians' perceptions of system utility and system user friendliness. Last, through their active involvement and participation, physicians feel they have greater influence on the development process, thereby developing feelings of ownership toward the clinical system. CONCLUSION: Psychological ownership's highly significant associations with user participation and crucial beliefs driving technology acceptance behaviors among physicians affirm the value of this construct in extending our understanding of POE adoption.
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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 | 0.050 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".