A Holistic Approach to Define Important Digital Skills for the Digital Society
Abstract
:1. Introduction
2. Literature Review
- 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
Competence | Examples in the Use of Cryptocurrencies |
---|---|
Information and data knowledge | Evaluation of the reliability, transparency, and validity of transactions by using cryptocurrencies. |
Communication and collaboration | Improving communication with bots on e-commerce sites that accept this kind of payment and collaboration between all the users of the network to prevent possible frauds. |
Digital content creation | Participating in the creation of digital content to make cryptocurrencies easier to be used by senior citizens. |
Security | Securing the use of cryptocurrencies on e-commerce sites and reducing the risk of e-wallet hacking. |
Problem solving | Solving any problem related to cryptocurrency transaction in everyday transactions, such as more direct and efficient communication with banking institutes, digital security certificate providers, etc. |
4. Hypotheses
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
- LV1: Information and data knowledge;
- LV2: Communication and collaboration;
- LV3: Digital content creation;
- LV4: Security;
- LV5: Problem resolution.
- Define the theoretical constructs: identify the latent variables that represent the 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 the 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 the CSEM model fits the data using various fit indices, such as chi-square, the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), etc.
- Refine and modify: if necessary, refine the 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 v.8.3 and Lavaan v. 0.6-12 in R) to estimate parameters in the 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 the model.
- LV5: Problem resolution;
- LV2: Communication and collaboration;
- LV1: Information and data knowledge;
- LV3: Digital content creation;
- LV4: Security.
7. Discussion
- They will be able to use digital currency management software more effectively.
- They will be able to more easily identify 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.
- Citizens will be able to understand and deal with risks by making the tools and software of banking institutions more effective.
- In the event of a cyber-attack by unknown persons, citizens will be able to effectively protect themselves by reducing the risk of destabilizing the system of digital currency transactions.
- The state will reduce losses from any cyber-attack as citizens will be significantly informed about not responding to messages that compromise cybersecurity.
- The state will be able to extend all its traditional financial activities more effectively to digital systems as citizens will be adequately educated and knowledgeable, thus increasing the degree of efficiency and effectiveness of the public tax collection system.
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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User Model Versus Baseline Model | ||
---|---|---|
Comparative Fit Index (CFI) | 0.705 | 0.705 |
Tucker–Lewis Index (TLI) | 0.661 | 0.661 |
Robust Comparative Fit Index (CFI) | 0.708 | |
Robust Tucker–Lewis Index (TLI) | 0.665 | |
Root Mean Square Error of Approximation: | ||
RMSEA | 0.143 | 0.136 |
Regressions | ||||
---|---|---|---|---|
Estimate | Std.Err | z-Value | p(>|z|) | |
LV1~LV2 | 0.237 | 0.065 | 3.618 | 0.000 |
LV1~LV5 | 0.738 | 0.262 | 2.814 | 0.005 |
LV2~LV3 | 0.573 | 0.218 | 2.621 | 0.009 |
LV3~LV5 | 0.982 | 0.207 | 4.741 | 0.000 |
LV4~LV5 | 1.191 | 0.142 | 8.373 | 0.000 |
LV5~LV1 | 0.576 | 0.204 | 2.817 | 0.005 |
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Zervas, I.; Stiakakis, E.; Athanasiadis, I.; Tsekouropoulos, G. A Holistic Approach to Define Important Digital Skills for the Digital Society. Societies 2024, 14, 127. https://doi.org/10.3390/soc14070127
Zervas I, Stiakakis E, Athanasiadis I, Tsekouropoulos G. A Holistic Approach to Define Important Digital Skills for the Digital Society. Societies. 2024; 14(7):127. https://doi.org/10.3390/soc14070127
Chicago/Turabian StyleZervas, Ioannis, Emmanouil Stiakakis, Ioannis Athanasiadis, and Georgios Tsekouropoulos. 2024. "A Holistic Approach to Define Important Digital Skills for the Digital Society" Societies 14, no. 7: 127. https://doi.org/10.3390/soc14070127
APA StyleZervas, I., Stiakakis, E., Athanasiadis, I., & Tsekouropoulos, G. (2024). A Holistic Approach to Define Important Digital Skills for the Digital Society. Societies, 14(7), 127. https://doi.org/10.3390/soc14070127