The Impact of Organizational Information Culture on Information Use Outcomes in Policing: An Exploratory Study
Bibliographic record
Abstract
Introduction. The information management practices of an organization along with the information behaviour and values of its personnel impact on organizational performance and the achievement of specific information use outcomes, positively and/or negatively. The aim of this study was to determine whether a theoretical model previously used in other fields to study the information management and information culture of organizations was applicable to policing, and examine which factors had the greatest impact on the achievement of the outcomes of problem solving, creating beneficial work, and information sharing within three Canadian police organizations.Method. A total of 134 sworn officers from various ranks across three Canadian police organizations completed an online survey.Analysis. Factor analysis and regression analysis were conducted using statistical analysis SPSS software.Results. Considering six information factors, regression analysis revealed that information pro-activeness and information management played significant roles in the achievement of the three information use outcomes. Factor analysis, using information management and five information behaviours, uncovered two new factors (information quality control and pro-active collaboration) that accounted for 71% of variance in the achievement of information use outcomes within this policing context.Conclusion. A conceptual framework for future police organization analysis is presented and the need for information use outcome scales is explored.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".