Retail Metaverse acceptance: A meta-analysis with Hofstede’s cultural moderation
Bibliographic record
Abstract
• A meta-analysis of 140 studies ( N = 46,547) maps the drivers of Metaverse acceptance. • The SHIFT framework integrates five levers of consumer adoption. • Individual self and affective enjoyment strongly predict user attitudes. • Cultural values moderate multiple acceptance mechanisms. • Eight insights extend Metaverse research and retail application. As the utility of Metaverse gains momentum among Retailers, there is growing interest in exploring how this platform can enhance customer engagement and connection. Although previous studies have identified various drivers of adoption, the findings remain fragmented and seldom consider cross-cultural differences. This paper synthesizes 140 independent studies, involving 46,547 participants, through a meta-analysis guided by the SHIFT framework (Social Influence, Habit, Individual Self, Feelings and Cognition, and Tangibility) to develop an integrated understanding of the psychological levers shaping Metaverse acceptance. Findings indicate that individual self-elements and affective enjoyment are among the strongest predictors of attitudes, consumer intention and satisfaction. Additionally, national cultural values, operationalized through Hofstede’s six dimensions, moderate many of these relationships. For instance, power distance and long-term orientation amplify the effects of usability and affective engagement, respectively. The results provide a practical roadmap for retailers by connecting psychological theory with cultural insights, outlining how Metaverse strategies can be localized for different markets. Implications for retail design, platform development, and future research are also discussed, along with eight key insights.
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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 | 0.057 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.048 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".