Overcoming Challenges to Enable the Potential of Metaverse Platforms: A Qualitative Approach to Understand Value Creation
Bibliographic record
Abstract
Metaverse is the buzzword of modern society. Practitioners and researchers have discussed metaverse platforms extensively, but the potential and meaning of the metaverse remain controversial. In this paper, we investigate and identify challenges that enable the potential of metaverse platforms. If these challenges are overcome, there will be value creation for practitioners, organizations, and society. We used a qualitative approach whereby we interviewed 34 metaverse experts to identify the challenges, potential, and value associated with the metaverse. Our results demonstrate that technical and societal challenges obstruct the ability to handle user-related and organizational challenges. If these challenges can be overcome, we can use the opportunities that our participants identified to create functional, social, and emotional value. Our work theoretically contributes to current knowledge on metaverse platforms by elaborating on handling metaverse platform ecosystems and determining instrumental challenges in their realization. With our qualitative approach, we provide room and directions for future research to develop a better understanding of the role and meaning of value creation in the metaverse. Our findings are useful to practitioners by presenting challenges organizations must overcome to create metaverse platforms or participate in a metaverse ecosystem. Furthermore, we present opportunities for vendors of metaverse platforms and organizations by identifying relevant processes that can be transferred into the metaverse.
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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 | 0.058 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".