Profiling Turkish Cryptocurrency Owners: Payment Users, Crypto Investors and Crypto Traders
Bibliographic record
Abstract
With ownership estimates of up to 25%, Turkey is at the forefront of cryptocurrency adoption, rendering it an interesting example to study the proclaimed use cases of cryptocurrencies. Using exploratory factor analysis based on a sample of 715 Turkish cryptocurrency owners, we identified 3 different owner groups and their underlying motives. The first group (payment users) looks at cryptocurrency as an option for payments, thereby disregarding its speculative element, while the second group (crypto investors) can best be described as experienced investors holding cryptocurrency as part of their investment strategy. The third group (crypto traders) consists of risk-tolerant traders. Further analyses show that groups not only differentiate by demographics, income and education, but also by factors such as ideology, purchase intention and the use of domestic or foreign exchanges. The results contribute to the understanding of Turkish cryptocurrency owners, their intrinsic and extrinsic motivations and can be incorporated into the pending regulatory processes in the country. The findings suggest that cryptocurrencies have outgrown the use case of mere speculation in Turkey. Those in the group of Turkish payment users are identified as potential lead users whose current needs may represent common needs for crypto users in similar markets in the future. These findings motivate further research on the diffusion and usage patterns of cryptocurrency in emerging markets and innovation in general in the context of lead markets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".