Social media has become an indispensable part of modern life, promoting information dissemination, interpersonal communication, brand promotion, and profoundly influencing people’s social habits and ways of obtaining information. In the rapidly developing technology field, social network platforms have gradually evolved into an important part of the social structure. On the other hand, with the rise and expansion of-blockchain technology from decentralized and distributed ledger technology to many fields, its inherent advantages have also begun to be valued. This study deeply analyzes the current situation and problems encountered in the integration process of social network and block chain and puts forward corresponding solutions. Through detailed case investigation, this paper shows the specific obstacles of the integration link and discusses the potential solutions. The research aims to promote the perfect integration of social media and blockchain technology and aims to provide valuable references for these two rapidly growing areas to support their future development.
Especially new generation investors may prefer to use stocks of popular companies that use advanced technologies and cryptocurrencies as investment instruments. Gold, one of the classical investment instruments, still maintains its place among the commodity assets in the portfolios of investors around the world. These asset groups were evaluated in this study. As the first group investment tool, decacorn and hectocorn technology companies called the new generation the magnificent five; Company stock returns of Apple, Microsoft, Amazon, Alphabet, Nvidia Corporation and Tesla were analyzed. In addition, as the second financial asset, cryptocurrencies, which are used as investment instruments as well as being used in daily life with the evolution of technology, and Bitcoin (BTC), which remains popular among these cryptocurrencies, were the subject of the study. Finally, the study evaluated gold mines, one of the world's oldest valuable investment instruments, compared with other financial assets. The study examined the magnificent five stocks, BTC and gold ounce prices between the periods of 2020:01 and 2023:12, using mutual cointegration, vector error correction (VEC) and Granger causality analyses. Findings of the study; Short-term shocks caused by variables in BTC stabilise after about a month. In this process, as NVDA shares increase, BTC value decreases, and as gold value increases, BTC value increases.
D. Mythili, M. Ganeshwari, Suleiman Ibrahim Shelash Mohammad, B. Anitha · 6 authors
Crypto-tech is a technology secured by cryptography, which enables the exchange of data and facilitates duplicated and distributed transactions across the entire network of computer systems on the blockchain. This paper aims to study cryptocurrency’s Awareness, perception, and impact among investors with special reference to Coimbatore city. This study will help provide baseline information on the factors influencing investors in cryptocurrency investing. The study found that cryptocurrency is likely to become the next financial platform due to the large amount of cryptocurrency flows in different systems, the huge increase and growth in cryptocurrency consumption and production, and the opportunities that cryptocurrency systems offer. And the level of awareness and preference in the use of cryptocurrency is moderate. However, investors are not yet fully aware of the dangers of using cryptocurrencies. Many cryptocurrency forms do not yet qualify for that level of trust. Investors should take extra precautionary measures when using cryptocurrency until it is well maintained. The future of the cryptocurrency concept is bright as there are many opportunities for positive change and progress in the e-business and e-payment sectors. As technology advances rapidly, cryptocurrency development continues.
Amir Hasan Hamadeh, Rasha Mohammad Nouraldeen, Rasha Mahboub, Mohamed Hashem
The purpose of this study is to examine the effect of the two determinants of the technology acceptance model (TAM3), perceived ease of use (PEOU), and perceived usefulness (PU) on auditors’ intention to adopt and use blockchain technology (BT) in Lebanon. This study also aims to investigate the moderating role of age on these associations to determine the antecedents of PU and PEOU. A sample of 332 auditors working in Lebanon was used to collect data and the analysis was conducted using the third version of partial least squares structural equation modeling (PLS3-SEM). Results show that perception of external control and computer self-efficacy significantly affect the PEOU. Job relevance and output quality are PU antecedents and positively influence the variable. In addition, PEOU and PU have a significant positive impact on auditors’ intention to adopt BT. This shows that auditors in Lebanon are more inclined to adopt BT if they feel that BT does not require substantial effort and that BT provides tangible benefits to their work. According to the researchers’ knowledge, this study is the first to examine auditors’ perception of using BT in one of the Middle Eastern countries, Lebanon, and the first to investigate the moderating role of age on the relationship between TAM3 determinants and auditors’ intention to adopt BT. In addition, this study highlights the practical implications of adopting BT in auditing in Lebanon by pinpointing the need for training programs, collaboration between auditors and other departments, developing regulatory frameworks to enhance efficiency, and organizing awareness and educational campaigns. Additionally, investments in infrastructure are critical to facilitate the smooth implementation and adoption of BT. Furthermore, audit firms should organize workshops to educate auditors on the application and the benefits of BT, invest in upgrading the IT systems to be compatible with BT platforms, and provide case studies and pilot projects to promote confidence in BT adoption.
This paper provides a comprehensive analysis of cryptocurrency as a financial instrument, examining its underlying mechanisms, market structure, and risk characteristics. The study begins with an overview of cryptocurrency fundamentals, including blockchain technology and the evolution of the cryptocurrency market. Through quantitative analysis of daily returns for six cryptocurrencies and six traditional assets between January 2018 and December 2021, the research demonstrates cryptocurrency's distinctive risk-return profile. Principal Component Analysis reveals three major risk factors driving cryptocurrency returns, while clustering analysis identifies meaningful groupings among cryptocurrencies. The findings indicate that cryptocurrencies exhibit significantly higher volatility and tail risk compared to traditional assets but provide substantial diversification benefits when incorporated into conventional portfolios. Tangency portfolio analysis shows that adding cryptocurrencies to traditional assets substantially improves risk-adjusted returns, with the combined portfolio achieving a Sharpe ratio of 2.72, compared to 0.39 for traditional assets alone. The study further examines regulatory challenges, tax implications, and emerging frameworks, particularly within the European Union. This research contributes to understanding cryptocurrency's role in modern investment portfolios while highlighting the unique risks and regulatory considerations that accompany this emerging asset class.
Purpose: The tourism industry is more and more turning to cutting-edge technologies to tackle issues concerning security, operational effectiveness, and confidence. The decentralized, transparent, and unchangeable characteristics of blockchain technology provide a revolutionary opportunity to improve these areas. This study investigates how blockchain can protect operational processes, secure data, and support service providers and regulators. This research explores the relationship between trust, technology adoption, and regulation by suggesting ways to incorporate blockchain in the tourism industry with a focus on both innovation and responsibility. Design/methodology/approach: The study used quantitative methods to survey 326 professionals in the tourism industry and frequent travelers to explore their interactions with tourism services based on blockchain technology. Data underwent analysis through the utilization of SPSS and SmartPLS 4. This method sought to comprehend the various viewpoints on the advantages and difficulties of incorporating BCT into their activities. Findings: This study Outcomes show the blockchain adoption is perceived. Although some believe that BCT has the potential to improve security and operational efficiency, there are also positive opinions on the matter. Both customers and providers agree on BCT's trustworthiness, however, they have differing opinions on privacy and security improvements. Moreover, the research highlights the crucial importance of regulatory enforcement in promoting blockchain adoption, as blockchain presents valuable chances for ensuring compliance and reducing risks. Implication: This study adds to the current knowledge by offering perspectives from service providers and customers on blockchain technology in the travel sector. It highlights the challenges of integrating new blockchain technologies and urges further research to tackle the raised issues, providing a fresh perspective on utilizing distributed ledger technologies to improve security and operational effectiveness
This study investigates whether cryptocurrency investments have a distinct impact on corporate liquidity depending on when they are held and the stage of a firm’s life cycle at the time of holding, using a sample of Korean companies. The empirical findings first show that cryptocurrency investments affect a company’s liquidity differently depending on when they are held. The findings demonstrate that, three years prior, labeled as t-1, t-2, and t-3, the two-year-old cryptocurrency investments appear to have greatly increased the company’s financial liquidity. Second, this study discovers that cryptocurrency investments have a different effect on a company’s liquidity based on the four stages of its life cycle, which comprise Introduction, Growth, Maturity, and Decline, at the time of holding. According to the findings, cryptocurrency investments at the Mature stage appear to contribute significantly and positively to the company’s financial liquidity. When the coefficients of interaction terms between each year and each life cycle are examined, it is observed that the holding of cryptocurrencies at the Mature stage in year t-2 has the most favorable influence on the company’s financial liquidity in year t. Although the findings do not conclude that the company’s cryptocurrencies held in year t-2 and at the Mature life cycle stage are the only ones that improve financial liquidity, they do suggest that a corporation may profit if it makes astute cryptocurrency investments at the appropriate time to suit its specific set of circumstances.
Mark Ng, Monica Law, Brian Wong Chi Bo, Michael Liang
Purpose This study explores key factors influencing individuals' intentions to invest in NFTs, focusing on personal innovativeness, reward sensitivity, knowledge, subjective norms, perceived value and perceived risk. The aim is to provide insights into what motivates investors within this emerging market, addressing a gap in the understanding of NFT adoption from an investor perspective. Design/methodology/approach An online survey collected data from 272 participants in China and Hong Kong. The research employs partial least squares-structural equation modeling (PLS-SEM) to assess the relationships between various individual, social and market factors and NFT investment intentions. Findings The results suggest that personal innovativeness, reward sensitivity, NFT knowledge, subjective norms and perceived value positively impact NFT investment intentions. Additionally, age and income moderate the effects of subjective norms and perceived value on investment intentions, highlighting demographic influences. Practical implications For practitioners, insights into investor motivators can inform strategies to promote NFT investments, such as promoting the high reward potential, enhancing investor knowledge, leveraging social proof and emphasizing NFTs' perceived value. For academics, the findings open pathways for further research into investor psychology and the evolving dynamics of NFT and traditional investment markets. Originality/value This study advances NFT literature by identifying determinants of NFT investment behavior, a relatively uncharted area. By incorporating theories from investment behavior and technology adoption, it provides a new framework to understand the psychological and social drivers specific to NFT investments.
Andreia de Castro Costa Xavier, Cláudio Gottschalg Duque, Tomás Roberto Cotta Orlandi
This study proposes an archival management method for Electronic Health Records (EHRs) based on architectural techniques and Blockchain and Smart Contracts technologies to ensure governance, security, and privacy in Health 4.0 contexts. Given the increasing relevance of EHRs as sources of information, evidence, and research in digital healthcare ecosystems, the research highlights challenges related to data governance, interoperability, and cybersecurity. Through a qualitative, exploratory approach, the authors present a method structured in seven macro-processes, covering the EHR lifecycle from capture to permanent archival or disposal. The implementation of private Blockchain networks and Smart Contracts automates processes, guarantees data integrity, and strengthens patients' control over their personal data, aligned with legal frameworks for data protection. The findings reinforce the need for innovative archival practices and technological strategies to enhance efficiency, security, and transparency in healthcare information management.
This research aims at examining how blockchain and smart contract technologies can enhance the circular economy in construction. In this quantitative research, data was collected from 134 construction industry professionals from different countries with majority from the UK and Australia by an online questionnaire. The research targeted respondents who possessed certain levels of experience in the field of engineering, construction, project management, and consultation and, therefore, used purposive sampling. An analysis of the survey data indicated that there was a level of support for the application of blockchain (BC) and smart contract (SC) technologies in enhancing circular economy practises. Industry professionals provided higher consensus regarding the benefits of these technologies for promoting circular economy in construction, with mean scores in all the statements above 4.6 on a 5-point scale. The pre to posttest findings were statistically significant t (58.00) p < .000 indicating that the participants’ views shifted from being neutral about the technologies’ advantages. From these findings, the research suggests awareness creation and training, implementation partnership models, policy and incentives support, and more research on the application of BC and SC technologies in the construction sector circular economy.
The new trading through cryptocurrency in India commenced an important role in inspiring individuals to make investments and earn profits.This study aims to dissect the influence of behaviour on investment decisions by futuristic generation Cryptocurrency investors.This type of research is descriptive research with a quantitative approach.This research aims to test and explain the influence of the direct association between the behavioural variables on investment decisions by cryptocurrency investors through hypothesis testing.This research has a sample size of 48 investors, achieved through circulating a G-Form questionnaire using a purposive sampling technique.This research uses the F-test with the SPSS 23 application.This research shows that investor behaviour on investment decisions is duly influenced by the Herding factors, Intrinsic Motivation factors, Macroeconomic factors and Perceived assumptions like over/under confidence in their investment choices.
Yongsheng Guo, Ezaddin Yousef, Mirza Muhammad Naseer
This study investigates the key drivers and the economic and social impacts of cryptocurrency adoption. Based on panel data across 37 countries from 2020 to 2023, this research examines the interplay between cryptocurrency adoption and technology development, monetary policies, and economic and social development. Employing a mixed-methods approach, the research incorporates panel data analysis across multiple countries to explore correlations and causal relationships between these variables. The study found that technology development, measured by the Network Readiness Index (NRI) enables cryptocurrency adoption. Economic conditions measured by higher national inflation rates and monetary policy indicators, including lower interest and exchange rates are the key drivers for cryptocurrency adoption. The empirical findings reveal that cryptocurrency adoption has negative relationships with economic development measured by the GDP growth rate, unemployment rate, and social development represented by the governance quality corruption index. It implies that cryptocurrency is used as a virtual anchor (digital gold) for national inflation. Findings reveal how network readiness, economic conditions, and monetary policies contribute to fostering cryptocurrency adoption, while resulting in impacts on economic growth, labour markets, and governance. The research contributes to the literature by integrating technological, economic, and governance perspectives to elucidate the role of cryptocurrency in reshaping the global economic and social systems.
Smart contracts are essential to decentralized finance (DeFi) and blockchain ecosystems but are increasingly vulnerable to exploits due to coding errors and complex attack vectors. Traditional static analysis tools and existing vulnerability detection methods often fail to address these challenges comprehensively, leading to high false-positive rates and an inability to detect dynamic vulnerabilities. This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG) to enhance the accuracy and efficiency of smart contract auditing. By integrating domain-specific knowledge from ERC standards and employing advanced techniques such as QLoRA for efficient fine-tuning, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither, as well as zero-shot large language model (LLM) prompting methods such as GPT-3.5 and GPT-4. Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities, including reentrancy and access control issues. This research advances smart contract security by offering a scalable and effective auditing solution, supporting the secure adoption of decentralized applications.
Senior Software Engineering Manager in Payments, The Huntington National Bank, Columbus, Ohio, USA, Pushpalika Chatterjee
Background: The rapid evolution of technology in the financial technology (fintech) sector has necessitated the adoption of innovative solutions to address increasing demands for higher data rates, lower latency, and enhanced security. Traditional centralized systems are susceptible to data tampering, service disruptions, and man-in-the-middle attacks, compromising the integrity of sensitive financial transactions. Objectives: This paper aims to explore the convergence of blockchain technology, smart contracts, and machine learning to address security, transparency, and operational efficiency challenges in the fintech sector. It investigates the implications of these technologies for compliance, regulatory frameworks, and ethical governance. Method: The study reviews existing literature and data sources spanning the last decade, with a focus on blockchain applications in fintech. Inclusion criteria include studies on decentralized ledgers, smart contract automation, and machine learning algorithms for predictive analytics. Comparative analyses are presented through flowcharts, graphs, and tables to highlight operational improvements, security enhancements, and cost reductions. Findings: Blockchain technology provides a decentralized and immutable ledger that enhances transparency and security in financial transactions. Smart contracts automate processes, reducing operational costs and improving accessibility to underserved populations. Machine learning enhances blockchain applications by enabling predictive analytics and data-driven decision-making. Despite significant advancements, challenges remain, including the need for robust governance structures to ensure ethical implementation and compliance with regulatory standards. Significance: This review offers new insights by integrating blockchain and machine learning in the context of fintech, addressing critical aspects such as operational efficiency, security, and regulatory compliance that were underexplored in prior studies. It highlights the transformative potential of these technologies in fostering innovation while providing a roadmap for overcoming bottlenecks and paving the way for a secure, inclusive, and efficient financial ecosystem. Keywords: Fintech, AI in Finance, Smart Contracts, Distributed Ledgers, Blockchain
This paper proposes a supply chain transparency improvement and smart contract optimization method based on blockchain, artificial intelligence and communication network. First, combining the decentralized characteristics of blockchain, a transparent and tamper-proof data management platform is designed to automatically execute supply chain transactions through smart contracts, and at the same time, the efficient data transmission characteristics of the communication network are used to achieve rapid synchronization of information between multiple nodes. Secondly, machine learning and deep learning algorithms are used to optimize demand forecasting, resource scheduling and smart contract terms in the supply chain, and real-time data collection and transmission are achieved through the communication network, further improving the overall operation efficiency of the supply chain. Experimental simulation results show that the supply chain system based on this method has significant advantages in improving transparency, the execution efficiency of smart contracts is improved by 30%, and the overall response time is shortened by 20%.
This study compares the effectiveness of the ARIMA and GRU models in predicting Bitcoin price movements, addressing the need for reliable predictive tools amidst the high volatility of the cryptocurrency market. Previous research has highlighted the strengths of each model in financial forecasting: ARIMA for short-term, stationary data and GRU for capturing complex temporal patterns. The purpose of this study is to evaluate which model performs better in the context of Bitcoin price prediction, offering insights for investors to minimize risks and enhance decision-making in this unpredictable market. The research methodology involves applying both models to Bitcoin price data and comparing their accuracy using the Mean Absolute Percentage Error (MAPE) across various forecasting intervals. Results indicate that GRU achieves higher accuracy in long-term forecasts, while ARIMA performs optimally for shorter time frames. However, both models demonstrate limitations, especially as the prediction horizon extends, underscoring the inherent challenges of cryptocurrency price forecasting. These findings suggest that GRU may be better suited for longer investment horizons, while ARIMA remains effective for short-term predictions. The conclusions affirm the potential of using these models selectively to align with specific investment strategies in cryptocurrency markets, although further research is recommended to improve predictive accuracy under evolving market conditions.
This paper discusses the use of Artificial Intelligence (AI) to enhance workplace productivity and employee well-being. By integrating machine learning (ML) techniques with neurobiological data, the proposed approaches ensure alignment with human ethical standards through value alignment models and Hierarchical Reinforcement Learning (HRL) for autonomous task management. The system utilizes biometric feedback from employees to generate personalized health prompts, fostering a supportive work environment that encourages physical activity. Additionally, we explore decentralized multi-agent systems for improved collaboration and decision-making frameworks that enhance transparency. Various approaches using ML techniques in conjunction with AI implementations are discussed. Together, these innovations aim to create a more productive and health-conscious workplace. These outcomes assist HR management and organizations in launching more rational career progression streams for employees and facilitating organizational transformation.
Samer Mohammed Fakhri Darar, Giasuddin Ahmed, Hanan Mohammed Ibrahim
The research aims to identify the influencing macroeconomic factors in Bitcoin and an attempt to explain the reasons for the sharp fluctuations in this currency. The research used the inductive approach as well as the standard method through comparison between Standard methods Approved and including (OLS, ARDL) which means trying to detect N Some spurious relationships between the studied variables. The research reached to There is a direct relationship between changes in the global gold price and the Bitcoin price, and an inverse relationship between the federal interest rate and the Bitcoin price, as well as a direct relationship between Economic shocks and Price changes a job And Bitcoin, and the research presents some proposals to address the sharp fluctuations in Bitcoin prices, including holding an international conference focusing on examining the causes of these fluctuations and ways to reduce their serious effects on investment portfolios. For individuals and financial institutions.
In today's digital world, online transactions are an everyday occurrence.From shopping and sending money to sharing information, these digital exchanges provide convenience but also come with significant risks.Ensuring the safety of these transactions is crucial, as a single security breach can lead to substantial financial losses and erode trust in the system.Blockchain technology presents a promising solutionby decentralizing control, making it much harder for hackers to manipulate the data.This paper explores how blockchain protects digital transactions, the challenges it faces, and its potential to transform various industries, such as finance and healthcare.Understanding blockchain security helps us appreciate its role in fostering trust and transparency in our online activities, ultimately making digital interactions safer for everyone involved.
In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.
As crypto exchanges and decentralized exchanges have proven untrustworthy, this paper seeks to create a rigorous Blockchain Project Evaluation Model (BPEM) for assessing the trustworthiness of a blockchain project. This BPEM will collate the components of technical audit, on-chain and off-chain analytics, liquidity indicators, and behavioral indicators into a single score of the project. The relevance of this work is driven by widespread instances of trade volume manipulation, opaque tokenomics, and tightening regulatory requirements (including in the context of MiCA). The scientific novelty lies in the creation of a hybrid MCDM architecture with an automated Incongruity Detection System (IDS) module that cross-checks tokenomics, liquidity, on-chain activity, and public statements and introduces a penalty coefficient into the final rating. The results of BPEM validation across case studies of wash trading and hidden centralization demonstrate that the key indicators of project resilience are not nominal volume but market depth, liquidity quality, and the integrity of on-chain data, and that identified inconsistencies act as early markers of scam projects and systemic risks. It is shown that a comprehensive multifactor analysis significantly outperforms the use of isolated metrics and can serve as a backbone for listing and compliance procedures. The article is of practical value for researchers of decentralized finance, risk managers, crypto exchange analysts, and digital asset regulators.