Virtual reality: A review and a new framework for integrated adoption
Bibliographic record
Abstract
Abstract Scholarly research on virtual reality (VR) is characterized by a dynamic tension between VR's potential and the challenges impeding its adoption. Grounded in a mixed‐methods systematic review, this research examines the drivers influencing consumer VR adoption by rigorously combining qualitative and quantitative analyses of 158 scholarly articles ranging from 1996 to 2023. Based on an extensive analysis of VR adoption literature, we introduce the Virtual Reality Integrated Adoption Framework (VRIAF), which is the first mixed‐methods systematic review focusing exclusively on VR adoption. This empirically substantiated model integrates key determinants of VR adoption such as consumer attitudes, perceived enjoyment, ease of use, social influences, and previous user experiences. The research identifies four pivotal themes through qualitative exploration, further elucidated by quantitative meta‐analyses and weight analyses. These themes encompass the user experience in VR environments, the role of VR in construction and design, the immersive attributes of VR technologies, and the ongoing technological advancements influencing adoption patterns. This research contributes significantly to the theoretical understanding of VR adoption and provides practical insights for VR professionals. By delineating future research directions, the study bridges the gap between theoretical exploration and practical application, offering a valuable resource for both scholars and practitioners in the field of VR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".