This study seeks to investigate the investment persistence among individuals who have maintained cryptocurrency holdings in the previous year. Financial naivety, online compulsive buying, and excessive gaming behaviours influence continuance intention, either through overconfidence or risk tolerance. The sample comprised 1,097 participants selected from the five provinces of Indonesia's most significant internet user bases. Structural equation modeling is utilised in this study. This study demonstrates that excessive gaming behaviour and financial naivety are associated with continuance intention, either directly or indirectly, via overconfidence or risk tolerance. Investors should be cautious regarding their financial naivety and excessive gaming behaviour, as these elements increase the tendency to be overly optimistic. The findings are consistent with the Dunning-Kruger effect. Conversely, this study fail to substantiate the correlation between compulsive buying and continuance intention. The implication is to provide investors with valuable insights into the psychological effects of specific behaviors on investment decisions.
Ivan Ivanovich Kiryushin, Igor' Petrovich Ivanov, Viktor Vladimirovich Timofeev, D Yu Zhmurko
This article explores the possibilities of using blockchain technology in police work. Examples of the use of blockchain in various areas of police activity, such as personal data management, control of drug trafficking and other prohibited substances, traffic monitoring and the fight against cybercrime, are considered. The authors note that thanks to the storage of data in the blockchain, it becomes possible to increase the protection of the confidentiality of personal information, ensure transparency and efficiency of police work, as well as prevent fraud and corruption. The conclusion of the article emphasizes that the use of blockchain can improve the work of the police and ensure greater security of citizens. Distributed ledger technology, or blockchain as a service (BaaS) is indeed a relatively new product on the market that allows you to provide blockchain services for corporate clients. This solution allows you to use more reliable and secure methods of data processing and transaction management within the organization. All these economic effects can lead to a reduction in costs and an increase in the efficiency of the police. In general, the use of blockchain technology in the police can have a number of advantages, such as increasing transparency and accountability, reducing data processing time and combating data falsification. However, it is necessary to take into account some risks, such as the possibility of data privacy violations, as well as difficulties in integrating with existing systems and training personnel. In general, the introduction of blockchain technology into the police requires careful analysis and an approach that takes into account all aspects of the use of technology and its impact on the organization.
Open access
Advanced Technologies in Various Fields
Legal, Health, Environmental and COVID-19 Challenges
The main goal of the research is to predict the future monthly returns of cryptocurrencies using the Vector Error Correction Model (VECM). Time series for the period 2018-2021 consists o f data on monthly returns for the cryptocurrencies Bitcoin, Ethereum and Ripple, as well as monthly returns on gold and the S&P500 stock index. Within the VECM, using the Johansen and Granger tests, short-term cointegration and causality among variables were determined, without the existence o f long-term equilibrium. The resulting model for short-term prediction o f the monthly returns o f the cryptocurrency Bitcoin was evaluated as unbiased and stable with a realistic forecast error o f 0.168 (16.8%).
F Farnuod Ahmadi, Abbas Toloie Eshlaghy, Reza Radfar
Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of cryptocurrency price prediction using deep learning algorithms were satisfactory; and the comparison of predictions across all cryptocurrencies indicated minimal differences between the algorithms studied, suggesting that they were efficient and had low error rates. Based on the obtained results regarding Bitcoin price prediction, the best algorithm was SimpleRNN; for Ethereum price prediction, the best algorithm was MLP; for Binance Coin price prediction, the best algorithm was SimpleRNN; for Cardano price prediction, the best algorithm was MLP; and for Ripple price prediction, the best algorithm was MLP.
M. Lawanyashri, K. Santhi, Saurya Raj Pandey, Balamurugan Balusamy
This chapter explores the transformative potential of blockchain technology in revolutionizing supply chain finance (SCF). Through a framework integrating service-dominant logic and social exchange theory, we analyze the roles of key participants, their motivations, and the resources and practices employed to create value for all stakeholders. We delve deeper into the specific mechanisms through which buyers, suppliers, financial institutions, and platform providers benefit from blockchain-driven SCF solutions. By analyzing value creation across financial, operational, risk management, sustainability, and social impact dimensions, we demonstrate the tangible benefits of this technology. Furthermore, we showcase real-world examples through case studies and explore emerging applications such as decentralized trade finance, data-driven insights, and tokenization of assets. These advancements unlock new possibilities and underscore the transformative potential of blockchain in shaping a more efficient, transparent, and sustainable future for global supply chains.
Objective: The objective of the article is to comprehensively examine the application and adoption of blockchain technology in SMEs. Recently, blockchain technology has garnered substantial attention owing to its transformative potential across diverse industries. Blockchain represents a decentralized and distributed ledger system that ensures data transparency, security, and immutability. This unique set of attributes has garnered attention from various sectors, ranging from finance and healthcare to supply chain and beyond. While predominant attention has been directed towards its impact on large corporations and financial institutions, the application and adoption of blockchain technology in small and medium-sized enterprises (SMEs) remains a relatively unexplored area. Research Design & Methods: This research utilized a narrative and critical literature review of the existing literature on blockchain technology and SMEs. Findings: We identified the key areas of application and drivers and barriers to SMEs’ adoption of blockchain technology. Supply chain and finance have emerged as primary domains witnessing heightened blockchain implementation. The intricate nature of supply chain operations involving a multitude of stakeholders and the centralized nature of financing with inherent information asymmetry have propelled blockchain adoption within these sectors. However, the complex nature of technology, regulatory uncertainty, and lack of technological capabilities of SMEs have been the barriers inhibiting the widespread adoption of blockchain technology in SMEs. Implications & Recommendations: The insights derived from this study can facilitate the successful design and implementation of blockchain-based solutions for SMEs. Blockchain solution providers must understand and tailor the solutions to SMEs. Blockchain-as-a-service (BaaS) can accelerate flexible application development, expediting blockchain integration in SMEs. Government, regulatory bodies, and SME groups are urged to collaborate in enhancing technological literacy among SMEs, facilitating their capacity to harness the advantages offered by blockchain technology. Contribution & Value Added: This research contributes to the field by shedding light on the underexplored realm of blockchain technology in SMEs. The created taxonomy, examination of adoption drivers and barriers, and the formulated opportunities-challenges framework provide valuable tools for understanding and navigating blockchain technology’s application and adoption-related challenges in SMEs. The identified gaps and proposed areas for future research further contribute to the ongoing discourse in this evolving field.
Against the backdrop of increasing integration of the global economy, supply chain finance faces challenges such as information silos, high cost of paper document transmission, and difficulty in risk control. This study focuses on the application of blockchain technology in enhancing the transparency of sustainable procurement for enterprises. Through an indepth analysis of successful cases such as the FILO platform and Walmart, the study reveals how blockchain technology can effectively solve the problem of information asymmetry in the traditional procurement process and improve the transparency and efficiency of procurement through its decentralization, data inerrancy, and traceability throughout the process. The study explores the potential value of blockchain technology in the field of supply chain finance using case study and model construction. The results show that blockchain technology can significantly optimize the procurement management process, reduce the risk of human intervention, promote collaborative supply chain operations, and enhance consumer trust. In addition, the study also looks forward to the future trend of the integration and application of blockchain with artificial intelligence, big data and other technologies, and puts forward relevant policy recommendations and practical insights, which provides a reference path for enterprises to implement blockchain technology to enhance procurement transparency.
Seaam Bin Masud, Md. Masud Rana, Hossain Jaman Sohag, Fisan Shikder · 6 authors
This study examines the integration of blockchain technology and machine learning (ML) to enhance financial transaction security, with a focus on fraud detection, data privacy, and operational transparency.The study explores the combined capabilities of blockchain's decentralized ledger and ML's predictive analytics in securing financial transactions.A systematic review was conducted, sourcing relevant studies from academic databases where literature resources are stored, such as IEEE Xplore, Google Scholar, Scopus, Web of Science, DOAJ, and SCImago.3037 study papers were collected from those academic databases.After screening and testing eligibility, 137 papers were selected to conduct this study.Studies covering blockchain, ML, and their collaborative impact on financial security were selected, classified, and analyzed.Comparative analysis methods highlighted both the strengths and limitations of this dual-technology approach.Results indicate that blockchain's immutability and transparency, alongside ML's data-driven fraud detection capabilities, create a robust framework for transaction security.Blockchain effectively ensures data integrity and transparency, while ML algorithms improve fraud detection and decision-making through real-time data analysis.However, challenges such as scalability, high energy consumption, and high implementation costs persist, limiting adoption in small and medium-sized institutions.The combined application of blockchain and ML presents a transformative potential for financial sectors, particularly in enhancing transaction integrity, regulatory compliance, and risk management.This framework can serve as a model across various industries beyond finance, including government and non-financial organizations, to foster a secure transaction environment.This study primarily relies on qualitative data and lacks empirical validation through quantitative measures.Further, blockchain's energy-intensive nature and ML's data dependency pose obstacles to widespread implementation, especially in resource-constrained settings.Future research should aim at developing costeffective and energy-efficient blockchain and ML solutions to support broader adoption.Additionally, advancements in quantum computing and AI-driven blockchain could address existing security vulnerabilities, making the technology more accessible and scalable.
Université du Dokuz Eylul d’Izmir, Turquie, Kâmil Tüğen, Ramazan GÖKBUNAR, Université du Manisa Celal Bayar d’Manisa, Turquie · 6 authors
The effects of today's popular technology concepts in different disciplines are extensively discussed and examined in the literature. In terms of economics and administrative sciences, the effects of related technologies are evaluated and a roadmap is tried to be revealed for the policies and procedures to be put forward in the future. In other words, technology and the rapid development of technology force the social and human fields to change and adapt. Since technology develops for people with people, it should keep up with this change in human life and social areas. Technologies leading the development in the recent period are Internet of Things, Industry 4.0, 5G and emerging future mobile communication technologies, artificial intelligence, augmented reality, virtual reality, blockchain technologies and its applications, cloud computing, cyber security/cryptology and electric vehicles etc. Many of these technologies have been extensively studied in the literature and still continue to be studied. Recently, the concepts of cryptocurrencies, Non-fungible token (NFT) and Metaverse have come to the fore. While searching for the answer of these concepts whether they will be a popular bubble or permanent technologies that will actively exist in the future life, on the other hand these technologies have also reached a serious use and demand level. While the Metaverse has the infrastructure and technological constraints to reach its anticipated usage and potential, cryptocurrencies and NFT have settled into life in today's world. While it is obvious that these technologies have and will affect every field, it can be predicted that they have led the public sphere and public finances to a radical change and transformation. Considering all these important developments, in this study, first of all, technical information about cryptocurrencies, NFT and Metaverse, and information on what these technologies are and their effects on human life will be shared. Later, various evaluations will be made about what kind of transformations and changes the mentioned technologies can cause in imminent taxation in the near future or how public finance should deal with it. The aim of the study is to present a vision for the measures that the taxing system should take against these rapidly developing or widespread technologies and the studies that should be done in order to adapt to these systems.
Digital assets have become widely adopted by young investors mostly aged under 34; however; there are various questions and concerns about the underlying socioeconomic fundamentals of these digital assets, especially bitcoin, because bitcoin constituted 47 percent of the total market capitalization of cryptocurrencies in 2021.The numbers of crypto ownership are expected to reach over 320 million users worldwide in 2022.The data reveal that there was a rapid growth in digital assets investment during 2021 and 2022, and bitcoin reached an annual growth rate of 60 percent in 2021.The objective of this study is to examine the underlying socioeconomic fundamentals of global bitcoin market.Our newly developed theoretical model and empirical findings reveal the possibility of digital asset to align and integrate within the portfolio asset allocation.The model and empirical evidences indicate explicit coherence between bitcoin and some socioeconomics fundamentals that reflect the cost of living.The trend component in bitcoin data is observed.The results are consistent with financial portfolio model.
The growing dependence on digital financial and banking transactions has brought about a significant focus on implementing strong security protocols. Blockchain technology has proved itself throughout the years to be a reliable solution upon which transactions can safely take place. This study explores the use of blockchain technology, specifically Ethereum Classic (ETC), to enhance the security of digital financial and banking transactions. The aim is to develop a system using an LSTM model to predict and detect anomalies in transaction data. The proposed LSTM model was trained before being tested and the results prove that the proposed model can effectively enhance the security, especially when compared to other studies in the same domain. The proposed model achieved a prediction accuracy of 99.5%, demonstrating its effectiveness in enhancing security by preventing overfitting and identifying potential threats in network activities. The results suggest significant improvements in digital transaction security, enhancing both the traceability and transparency of blockchain transactions while reducing fraud rates. Future work will extend this model's applicability to larger-scale decentralized finance systems.