A holistic analysis towards understanding consumer perceptions of virtual reality devices in the post-adoption phase
Bibliographic record
Abstract
Despite gaining consumer momentum and interest of Virtual Reality (VR) in the consumer marketplace, the literature has lagged in exploring the continuance usage behaviour and factors associated with the post-adoption. To build on this, the current research seeks to identify factors that support the continuance usage of current VR users. To examine this, we employ a mixed-method approach. In Study 1, we initially gathered a total of 3,205 actual purchasers (Amazon verified purchase) from the top 10 VR brands listed in Amazon.com, Through a nethnographic content analysis, the key determinants of post-adoption of VR devices emerged (i.e. perceived functional benefit, perceived discomfort, perceived focused immersion, temporal dissociation, perceived health risk, and task quality). In Study 2, hypotheses were tested using structural equation modelling from 119 current VR users. The results demonstrate temporal dissociation and task quality were found to be the most significant antecedents affecting continuance usage. Theoretical and managerial implications are debated, as well as suggestions for future research.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".