Implementing virtual reality training in policing: A case study using the technology acceptance model
Bibliographic record
Abstract
The purpose of this article is to explore the acceptance of virtual reality (VR) training in a single police service that implemented the technology as a key part of its training procedures. We examined satisfaction data from surveys of police officers and civilian staff collected over three years, complemented by interviews with staff involved in the development and use of VR. The technology acceptance model (TAM) provides the theoretical framework for exploring six hypotheses based on previous research, enabling the study to assess the perceived ease of use, usefulness, enjoyment, immersion, interaction, and future intention to use VR technology. Insights were derived from a combination of descriptive and inferential statistics, along with thematic analysis. Results show a consistent upward trend in officer satisfaction with VR, along with strong evidence of perceived usefulness, immersion, and interactivity. Significant findings indicate a link between satisfaction with VR and education, with PhD holders reporting the highest levels of satisfaction. Gender differences were also evident, with female participants expressing higher satisfaction than males. In addition, participants with more than 10 years of service reported significantly lower satisfaction than mid-service officers, suggesting that age may be a contributory factor. These findings are discussed in the context of the interplay between demographic factors and technology acceptance in policing. We emphasize the need for the development of tailored training and communication strategies to support the effective implementation of VR technology as a medium for instruction for employees of all ages and genders.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".