Towards a framework for successful metaverse adoption in Small and Medium-sized Enterprises: An exploratory study
Bibliographic record
Abstract
The digital transformation of our physical lives, driven by cutting-edge technologies like AI, 5G, and Extended Reality, is rapidly unfolding through the metaverse. This study investigates the factors influencing the adoption intention of the metaverse and its impact on small and medium-sized enterprises (SMEs) performance, employing the technology-organization-environment (TOE) framework. A cross-sectional analysis involving 173 owners/senior managers of SMEs in Canada was conducted to develop a conceptual model. Exploratory factor analysis and Cronbach’s alpha were employed to assess the questionnaire’s validity and reliability. Correlation, regression analysis, and Baron and Kenny’s method were used to evaluate relationships and mediation effects. The findings revealed seven significant factors influencing SMEs’ metaverse adoption intention. Notably, anxiety, security, and privacy did not impact adoption intention. Conversely, a positive correlation emerged between metaverse adoption intention and SME performance. The study demonstrated that metaverse adoption intention fully mediated the relationship between predictors and performance, with the indirect effect moderated by relationship qualities (RQs) like service experience and customer engagement. These insights provide decision-makers with valuable guidance for prioritizing essential factors crucial for successful metaverse technology implementation in SMEs.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".