What is driving consumer resistance to crypto‐payment? A multianalytical investigation
Bibliographic record
Abstract
Abstract Despite the extensive interest in cryptocurrencies over the past years, their application as a means of payment in e‐commerce and retail purchases continues to be much slower than anticipated. This paper investigates the underlying mechanisms and elements that drive consumer resistance in this space. Drawing upon the stimulus‐organism‐response paradigm and the innovation resistance theory, the paper explores how the characteristics of the current cryptocurrency landscape contribute to different factors associated with crypto‐payment rejection. Our findings from empirical and experimental studies reveal how ecosystem volatility and the lack of structural assurances for cryptocurrencies foster negative consumer perceptions, leading to resistance against crypto‐payment use. The paper develops new insights into the main predictors of consumer resistance to crypto‐payment, which is a precursor to the mainstream use of cryptocurrencies. Moreover, it sheds light on the interactions among context‐specific, psychological, and functional determinants of behavioral consumer response.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".