Bibliographic record
Abstract
Consumer behaviour and the blockchain are particularly interesting at the moment for several reasons.Cryptocurrencies like Bitcoin, Ethereum, and many others have soared in popularity not only as alternatives to fiat currencies, but as highly volatile instruments of investment and speculation.There have even been instances where nations like El Salvador and Central African Republic have adopted Bitcoin as their official currency or have issued their own national cryptocurrencies like China.Ethereum is the cryptocurrency and blockchain underwriting many gaming communities and would-be metaverses.It is also the currency in which the majority of NFTs (non-fungible tokens) are purchased.With the volatility of Bitcoin is multiplied by the volatility of NFTs, it is understandable that investors, collectors, and speculators in NFTs were in for a wild ride before the cryptocurrency value collapse and the bursting of the NFT bubble economy in early 2022 (Belk et al., 2022;Dwivedi et al., 2023).One comprehensive study of the NFT market (Rosen, 2023) concluded that more than 95% of all NFTs are now worth nothing.Twitter-founder Jack Dorsey's NFT of his first tweet sold for $2.9 million in 2021 but received only one bid of $280 in 2022.It later sold for $10,000 -0.34% of its original value (Handagama, 2022;Kauflin, 2022).This is not to say that either cryptocurrencies or NFTs are done.In fact, by mid-May of 2024 Bitcoin had more than quadrupled its value since its lows in 2022.So, all of this makes for an extremely interesting set of consumer behaviours to attempt to make sense of.The current volume makes a bold attempt to take on this interesting challenge.These chapters address topics ranging from central bank cryptocurrencies, the environmental impact of Bitcoin, and cryptocurrencies as a form of gambling to consumer investment preferences, cryptocurrency and NFT adoption behaviours, and brand hive minds as a stabilising influence for decentralised entities like Bitcoin.The chapters also employ a broad range of methods in pursuing these questions, including bibliometric analysis and systematic literature reviews, conceptual analysis, experimentation, survey research, technical analysis, historical analysis, conjoint analysis, graphic analysis, and ethnographic and netnographic research.They also introduce or invoke a wide array of concepts, including token-pegged reserve, problem gambling severity index, metamorphic moments, speculative bubbles, branded NFTs, stakeholder theory, and cancellable biometrics.This is a wide-ranging and imaginative treatment diagnosing the current situation and outlining a critical agenda for future research.There is much here to devour, ponder, and utilise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.021 | 0.078 |
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; both teacher heads agree on what is shown here.
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".