Submitted:
01 April 2024
Posted:
02 April 2024
Read the latest preprint version here
Abstract
Keywords:
1. Introduction
2. Literature Review
| Year | Number of mentions of the word “Digital Skills” |
|---|---|
| 2023 | 14,166 |
| 2022 | 11,887 |
| 2021 | 10,602 |
| 2020 | 8,481 |
| 2019 | 6,965 |
| 2018 | 6,015 |
| 2017 | 5,568 |
| 2016 | 5,132 |
| 2015 | 5,167 |
| 2014 | 4,406 |
| 2013 | 3,986 |
| 2012 | 3,940 |
| 2011 | 3,433 |
| 2010 | 3,145 |
- Cryptocurrency as a tool for risk management
- Decentralization of crypto transactions using blockchain and fintech
- Regulatory and digital framework of cryptocurrencies
- Market efficiency of cryptocurrencies
- Pricing efficiency of cryptocurrencies
- Price clustering and liquidity in crypto transactions
- Cryptocurrency as an investment asset
- Portfolio diversification using cryptocurrency
- Trading volume, return, and volatility of cryptocurrencies
- The role of information in the volatility of cryptocurrency prices
3. DigComp and Digital Skills in the Use of Cryptocurrencies
3.1. General Information
3.2. DigComp’s Axes for the Use of Cryptocurrencies
3.2.1. Information and Communication Technology (ICT)
3.2.2. Communication Skills
3.2.3. Digital Cultural Understanding
3.3. Usefulness of DigComp for the Use of Cryptocurrencies
3.3.1. Skills Assessment
3.3.2. Education and Training
3.3.3. Technical Understanding
3.3.4. Professional Use
4. Research Questions
4.1. The Concept of Security
4.2. The Concept of Problem Solving
4.3. The Concept of Information and Data Knowledge
5. Methodology
6. Results
6.1. Descriptive Analysis and Results
6.2. Technical Analysis and SEM Results
- Define the theoretical constructs: Identify the latent variables that represent your theoretical constructs. These are unobserved variables that cannot be directly measured but can be inferred from observed indicators.
- Select indicators: Determine the observed indicators or measurements for each latent variable. These are observable variables that provide information about the underlying construct.
- Specify measurement models: Specify how the observed indicators relate to their corresponding latent variables through measurement models. This involves assigning factor loadings that indicate the strength of the relationship between each indicator and its corresponding latent variable.
- Connect latent variables: Define structural models by specifying relationships between different latent variables in your model. This involves identifying paths or connections between the latent variables and assigning regression coefficients to indicate their strength and direction.
- Assess model fit: Evaluate how well your CSEM model fits the data using various fit indices such as chi-square, Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), etc.
- Refine and modify: If necessary, refine your model by modifying paths, adding or removing indicators, or adjusting factor loadings based on statistical indices and theoretical considerations.
- Estimate parameters: Use statistical software specifically designed for CSEM (e.g., Mplus, Lavaan in R) to estimate parameters in your model based on maximum likelihood estimation or other appropriate methods.
- Interpret results: Examine estimates of factor loadings and regression coefficients to understand how each indicator contributes to its respective construct and how different constructs relate to each other within your model.
7. Discussion
- They will be able to use digital currency management software more effectively.
- They will be able to identify more easily the risks, threats, and fraud that will be presented online.
- They will evaluate any form of information as valid or fake wherever it comes from.
- They will be able to solve problems encountered in digital currency management applications, such as e-wallets through communication.
- They will contribute to optimal communication through the use of terminology between experts (computer application technicians, economists, and other professionals).
- They will participate in the better dissemination of essential information on the use of digital currencies, making them active citizens.
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| Year | Number of mentions of the word “Cryptocurrencies” | Number of mentions of the word “Digital Currencies” |
|---|---|---|
| 2023 | 1.805 | 2.049 |
| 2022 | 1.489 | 1.669 |
| 2021 | 1166 | 1.443 |
| 2020 | 779 | 1.129 |
| 2019 | 499 | 804 |
| 2018 | 267 | 705 |
| 2017 | 62 | 524 |
| 2016 | 46 | 471 |
| 2015 | 40 | 505 |
| 2014 | 11 | 346 |
| 2013 | 1 | 361 |
| 2012 | - | 291 |
| 2011 | - | 280 |
| 2010 | - | 284 |
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