Table 1 Comparable studies on the field of cryptocurrency acceptance
Source | Research question | Existing or hypothetical technology | Data collection and analysis | Theoretic context | Source, sample size |
|---|---|---|---|---|---|
Thelwall (2018) | Factors influencing the value of SteemIt posts | Existing (STEEM cryptocurrency) | Quantitative; descriptive; bivariate, sentiment analysis | – | Online community; 925,092 posts in English |
Arias-Oliva et al. (2019) | Factors influencing cryptocurrency use | Existing (cryptocurrencies) | Quantitative; PLSc-SEM | Extended UTAUT (Risk, Financial literacy) | Country specific (Spain); 402 |
Jung et al. (2019) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; MCFA, MSEM* | Extended UTAUT (Risk, Economic benefit, Payment convenience, Government regulation) | Country specific (China, South-Korea, Vietnam); 208 |
Sohaib et al. (2020) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; PLS-SEM, ANN, IPMA** | TRAM (TRI*** & TAM) | University of Technology Sydney; 140 |
Treiblmaier et al. (2020) | travellers’ intention to use cryptocurrencies for payment purposes | Existing (cryptocurrencies) | Quantitative and Qualitative | Cryptocurrency adoption model | Asia–Pacific region; 161 travellers |
Alharbi and Sohaib (2021) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; PLS-SEM, ANN, IPMA | TRI*** | University of Technology Sydney; 160 |
Palos-Sanchez et al. (2021) | Factors influencing Bitcoin adoption in businesses | Existing (Bitcoin cryptocurrency) | Quantitative; PLS-SEM | Extended TAM (Risks, Trust, Privacy) | Businesses; 248 executives |
Ter Ji-Xi et al. (2021) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; PLS-SEM | Extended UTAUT (Risk) | Country specific (Malaysia); 233 |
Jariyapan et al. (2022) | Factors influencing intention to use cryptocurrencies during pandemic | Existing (cryptocurrencies) | Quantitative; PLS-SEM | Extended TAM 3 (Risk, Financial literacy) | Country specific (Pakistan); 357 |
Lansiaux et al. (2022) | Cryptocurrency prices affected by tweets, prediction of future price | Existing (Dogecoin; Litecoin cryptocurrencies) | Quantitative; causality and correlation analysis | - | Online community; Twitter content |
Koroma et al. (2022) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; PLS-SEM | Trust, Ethical issues, Blockchain transparency, Technology attachment | Country specific (Mano River Union States); 421 |
Mashatan et al. (2022) | Factors influencing intention to use crypto-payment | Existing (crypto-payment) | Quantitative; PLS-SEM | Trust, risk, anonymity, traceability | Toronto Metropolitan University; 327 |
Miraz et al. (2022) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; PLS-SEM | Modified UTAUT (Trust, Transaction transparency, Volatility) | Country specific (Malaysia); 263 |
Sukumaran et al. (2022) | Factors influencing intention to use cryptocurrencies | Existing (cryptocurrencies) | Quantitative; PLS-SEM | Perceived risk and value | Country specific (Malaysia); 211 |
Quan et al. (2023) | Factors influencing intention to visit a destination | Existing (cryptocurrencies, traditional and mobile payment) | Quantitative; SEM | Extended TAM (Perceived security) | Country specific (South Korea & China); 378 & 407 |