Enablers for Growth of Cryptocurrencies: A Fuzzy–ISM Benchmarking
Bibliographic record
Abstract
Cryptocurrencies and their market capitalisation have experienced vibrant growth in the last few years. Their total market cap is more than USD 858 billion as of the date of writing and is growing, with nearly 21,984 tradeable cryptos in 530 exchanges. It is emerging as one of the biggest threats to the traditional fundraising market. The issue of the industry’s long-term viability and steady expansion is of paramount importance. Even though unsustainable and uneven growth could help boost economic activity in the short term, it would be detrimental in the long run because of the risk of extinction. This paper is one of the first attempts to identify the factors contributing to the growth of the cryptocurrency market and their effects. This paper is based on the hybrid MCDM methodology of research and uses fuzzy–ISM (interpretive structural modelling). This method is divided into three phases: identification, expert opinion, and interpretation. Sixteen factors were chosen from the previous literature and suggestions from industry professionals. Seven barriers have been framed based on the fuzzy–ISM analysis to better understand the impacts of and interrelationships among the identified barriers. The factors are further classified using fuzzy MICMAC into four major categories based on the drive power and dependence power extracted from the fuzzy matrix. This paper explains the importance of all identified factors as enablers of the acceptance of cryptocurrencies for investment and fundraising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".