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All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Maturi, Mohan Harish
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Evaluating Behavioral Intention and Financial Stability in Cryptocurrency Exchange App: Analyzing System Quality, Perceived Trust, and Digital Currency Yadulla, Akhila Reddy; Nadella, Geeta Sandeep; Maturi, Mohan Harish; Gonaygunta, Hari
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.12

Abstract

This study evaluates the factors influencing financial stability (FS) and behavioral intention (BI) in a cryptocurrency exchange app, explicitly focusing on system quality (SQ), perceived trust (PT), and digital currency (DC) within the Indonesian context. Utilizing structural equation modeling (SEM) with SmartPLS, the research analyzed data from 345 respondents who are active users of the cryptocurrency exchange app. The results confirmed that SQ significantly enhances PT (β = 0.832, t = 27.216, p < 0.001) and BI (β = 0.718, t = 12.675, p < 0.001). Additionally, DC positively impacts FS (β = 0.578, t = 8.177, p < 0.001), while PT influences both FS (β = 0.391, t = 5.478, p < 0.001) and BI (β = 0.198, t = 3.490, p = 0.001). These findings validate all five proposed hypotheses, highlighting the critical role of SQ and PT in driving FS and user engagement in cryptocurrency exchange apps. The study's measurement model demonstrated good reliability and validity, with Cronbach's alpha values exceeding 0.7 for all constructs: SQ (0.891), PT (0.812), DC (0.767), FS (0.819), and BI (0.745). Composite reliability values were also high, ranging from 0.855 to 0.933. Average Variance Extracted (AVE) values indicated good convergent validity, with SQ (0.822), PT (0.727), DC (0.689), FS (0.743), and BI (0.663). Discriminant validity was confirmed using the Fornell-Larcker criterion. The structural model's fit indices, including an SRMR of 0.045 and an NFI of 0.914, demonstrated a good model fit. The R² values for BI (0.791), FS (0.873), and PT (0.693) indicated substantial explanatory power. Despite its contributions, this study has limitations, including its focus on a single cryptocurrency exchange app in Indonesia, which may affect the generalizability of the findings. Future research should expand the sample to include multiple apps and geographical contexts. Additionally, incorporating other relevant factors, such as user experience and regulatory compliance, could provide a more comprehensive understanding of FS in digital financial services. This research underscores the importance of SQ and PT in achieving long-term success and sustainability in the rapidly evolving digital finance landscape.
Volatility Comparison of Dogecoin and Solana Using Historical Price Data Analysis for Enhanced Investment Strategies Yadulla, Akhila Reddy; Maturi, Mohan Harish; Nadella, Geeta Sandeep; Satish, Snehal
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.13

Abstract

This study compares the volatility of two prominent cryptocurrencies, Dogecoin (DOGE) and Solana (SOL), using historical price data spanning five years from June 3, 2019, to June 3, 2024. By leveraging detailed daily trading information, the analysis provides a comprehensive understanding of the risk profiles associated with each cryptocurrency. The methodology involves data preprocessing, exploratory data analysis (EDA), volatility calculation using 30-day rolling windows, and statistical testing, including two-sample t-tests and variance ratio tests. The findings indicate that both DOGE and SOL exhibit significant price variability, with SOL showing higher average prices and greater standard deviation compared to DOGE. For instance, the mean closing price for DOGE was $0.0875 with a standard deviation of $0.0941, while SOL had a mean closing price of $54.6754 and a standard deviation of $59.3020. Historical volatility trends reveal distinct patterns: DOGE’s volatility is primarily influenced by social media trends and speculative trading, whereas SOL’s volatility is driven more by technological advancements and market developments. The two-sample t-test results show no significant difference in the mean volatilities of DOGE and SOL (t-statistic: -0.8674, p-value: 0.3858), but the variance ratio test highlights that SOL’s volatility is significantly more variable than DOGE’s, with a variance ratio of 10.7028. These results suggest that while the average risk levels of DOGE and SOL are similar, their volatility behaviors differ significantly. For investors, understanding these distinct volatility characteristics is crucial for making informed decisions regarding asset allocation and risk management. The study's insights also provide valuable guidance for financial analysts and portfolio managers, emphasizing the importance of considering both average volatility and its variability when assessing the risk profiles of cryptocurrencies. Future research should explore the impact of external factors such as regulatory changes and macroeconomic events on cryptocurrency volatility and expand the analysis to include other digital assets and longer time periods. Incorporating high-frequency trading data and advanced econometric models could further enhance the accuracy of volatility predictions, offering deeper insights into the behavior of digital currencies under various market conditions
Sales Trends and Price Determinants in the Virtual Property Market: Insights from Blockchain-Based Platforms Yadulla, Akhila Reddy; Maturi, Mohan Harish; Meduri, Karthik; Nadella, Geeta Sandeep
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i2.9

Abstract

The virtual property market, driven by blockchain-based platforms like Decentraland, Cryptovoxels, and The Sandbox, parallels the physical real estate market. This study analyzes sales trends and identifies key factors influencing property prices in Decentraland, covering over 10,000 transactions from January 2020 to December 2023. Objectives include examining daily, weekly, and monthly sales trends, analyzing price distributions by property type, and exploring correlations between property prices, Mana cryptocurrency, and land prices. Daily sales fluctuated significantly, with peak days reaching up to 150 transactions and off-peak days as low as 10. Weekly sales trends indicated cyclical patterns, with notable peaks every four to six weeks, while monthly trends showed a 5% average growth rate. Price distribution analysis revealed parcels ranged from 1,000 to 50,000 Mana (mean: 15,000 Mana), and roads ranged from 500 to 20,000 Mana (mean: 8,000 Mana). A very strong positive correlation (r = 0.99) was found between property prices and land prices, indicating land prices are a significant determinant of property values. Conversely, the correlation between property prices and Mana prices was weak (r = -0.05), suggesting limited direct influence of cryptocurrency volatility on property values. Traditional real estate markets are influenced by factors like location and property characteristics, while virtual property markets are significantly affected by digital factors such as cryptocurrency prices and virtual locations. The integration of virtual reality (VR) and augmented reality (AR) technologies in real estate has transformed property presentation and buyer engagement, enhancing decision-making. Digital tools like Google Trends have proven useful in predicting market trends. This study addresses the gap in understanding digital influences on virtual property values, providing insights for investors, developers, and policymakers. The methodology includes data collection, preprocessing, and analysis using advanced statistical and machine learning tools, offering a comprehensive understanding of Decentraland's virtual property market to aid informed decision-making.
Co-Authors Gonaygunta, Hari Meduri, Karthik Nadella, Geeta Sandeep Satish, Snehal Yadulla, Akhila Reddy