How the Metaverse reshapes new venture emergence
Bibliographic record
Abstract
The Metaverse, an emerging virtual universe powered by advancements in virtual reality (VR), augmented reality (AR), and blockchain technologies, presents new avenues for reshaping the entrepreneurial landscape. This study explores the Metaverse's multifaceted roles as an enabler, a market offering (output), and a contextual environment, emphasizing its influence across the prospecting, development, and exploitation stages of new venture emergence. Utilizing a Theory Synthesis approach grounded in a systematic search and critical review (SSCR), the research integrates fragmented insights from entrepreneurial process theory and digital technology frameworks to develop a conceptual understanding of the Metaverse's transformative impact. The findings show that the Metaverse facilitates immersive prototyping, market validation, and global collaboration, while reshaping entrepreneurial contexts through mechanisms that reduce uncertainty, accelerate development, and enable new forms of value creation. By highlighting mechanisms, the study moves beyond descriptive accounts and establishes the basis for stage-specific propositions. The study contributes theoretically by clarifying how and when the Metaverse influences different stages of venture creation. By linking its multifaceted roles to a staged model of entrepreneurship, the paper refines existing theories of venture emergence. It extends digital entrepreneurship research and offers insights for entrepreneurs and policymakers seeking Metaverse-driven strategies for innovation and sustainable growth.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".