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Record W3123270788 · doi:10.1080/0144929x.2021.1876767

A holistic analysis towards understanding consumer perceptions of virtual reality devices in the post-adoption phase

2021· article· en· W3123270788 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehaviour and Information Technology · 2021
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsContinuanceStructural equation modelingPerceptionPsychologyVirtual realityImmersion (mathematics)Applied psychologyMarketingSocial psychologyBusinessComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.339
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it