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INDONESIA
Journal of Digital Market and Digital Currency
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30480981     DOI : https://doi.org/10.47738/jdmdc
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
Journal of Digital Market and Digital Currency publishes high-quality research on: Digital Marketing Digital Currencies Cryptocurrency Trends Blockchain Applications Fintech Innovations Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by the Journal of Digital Market and Digital Currency.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 35 Documents
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Examining User Satisfaction and Continuous Usage Intention of Digital Financial Advisory Platforms in Indonesia: An Integrated Model Approach Emary, Ibrahiem M. M. El; Sanyour, Rawan; Abdullah, Manal
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.30

Abstract

This study evaluates user satisfaction (US) and continuous intention (CI) to use digital financial advisory platforms in Indonesia. Utilizing structural equation modeling (SEM) with SmartPLS, we examined the relationships between perceived ease of use (PEU), perceived enjoyment (PE), service quality (SQ), US, and CI. Data were collected from 413 respondents via an online survey conducted between February and March 2024. The descriptive statistics for the main variables indicated that the mean scores ranged from 5.3 to 5.9 on a 7-point Likert scale, with standard deviations between 1.1 and 1.3. Our results show that PEU significantly influences PE (β = 0.923, t-value = 88.677, p < 0.001) and CI (β = 0.471, t-value = 13.950, p < 0.001). PE positively affects the US (β = 0.211, t-value = 7.248, p < 0.001), while SQ is a strong predictor of the US (β = 0.773, t-value = 29.423, p < 0.001). Furthermore, the US significantly impacts CI (β = 0.518, t-value = 15.117, p < 0.001). The R-squared values for the key constructs were 0.851 for PE, 0.876 for US, and 0.878 for CI, indicating substantial explanatory power. These findings underscore the importance of usability, enjoyment, and SQ in enhancing US and retention. The study contributes to the literature by providing an integrated model that combines these key variables, offering a comprehensive framework for understanding user behavior in digital financial advisory platforms. Theoretical contributions include extending the Technology Acceptance Model (TAM) by incorporating enjoyment and SQ. Practical implications suggest that platform providers prioritize user-friendly design, engaging features, and high service standards to improve the US and foster long-term engagement. Future research should explore additional factors, such as perceived security and trust, to further enrich the understanding of user behavior in digital financial services.
Factors Influencing User Adoption of Mobile Payment System: An Integrated Model of Perceived Usefulness, Ease of Use, Financial Literacy, and Trust Utomo, Fandy Setyo; Suryana, Nanna; Azmi, Mohd Sanusi
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.31

Abstract

In the digital age, mobile payment systems have revolutionized financial transactions by offering convenience, efficiency, and security. This study aims to explore the factors influencing the adoption of the mobile payment system in Indonesia, focusing on perceived usefulness (PU), perceived ease of use (PEU), financial literacy (FL), and perceived trust (PT). Data was collected from 400 respondents using an online survey and analyzed using SmartPLS 3 software. The results indicate that PU and PEU significantly impact users' intention to use (BI) the mobile payment system, with path coefficients of 0.928 (t-value = 28.570) and 0.955 (t-value = 154.251) respectively. PEU also positively influences PU (β = 0.955, p < 0.001). FL was found to affect PT significantly (β = 0.222, p = 0.006), which in turn influences BI (β = 0.068, p = 0.059), although the direct effect of PT on BI was marginally non-significant. The R^2 values for BI, PT, and PU were 0.977, 0.814, and 0.912 respectively, indicating a high explanatory power of the model. This study extends the Technology Acceptance Model (TAM) by integrating FL and PT, providing a comprehensive understanding of the factors driving mobile payment adoption. The findings offer valuable insights for developers, service providers, and policymakers to enhance user experience, build trust, and improve FL, ultimately promoting higher adoption rates of mobile payment systems. Future research should consider a more diverse population and explore additional factors such as social influence and facilitating conditions to validate and extend these findings further.
Analyzing the Evolution of AIGenerated Art Styles Using Time Series Analysis: A Trend Study on NFT Artworks Maidin, Siti Sarah; Yang, Qingxue; Samson, A Sunil
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.32

Abstract

This study investigates the development of AI-generated art styles within the growing non-fungible token (NFT) market. Using time series analysis, the research identifies key trends and shifts in art styles from 2022 to 2024, revealing how various art forms, algorithms, and mediums evolved in response to technological advancements and market forces. Data was collected from a sample of 10,000 NFT artworks, categorized by creation date, style, and algorithm usage. Exploratory Data Analysis (EDA) techniques, including line graphs and heatmaps, were employed to visualize and interpret trends across different art styles and AI tools. Results indicate a significant increase in the popularity of styles like surrealism and realism, with deepdream and GANpaint algorithms being frequently associated with these styles. Stacked area charts further highlighted the proportional growth of art styles over time, providing insights into both short-term popularity spikes and long-term trends. The findings suggest that the integration of AI algorithms significantly influenced the rise of specific art genres, with certain algorithms correlating strongly with particular styles. Practical implications for artists and collectors include the potential for data-driven insights to guide creative choices and investment strategies. The study's limitations, such as the lack of broader market data, provide a foundation for future research to explore the intersection of AI-generated art, NFT marketplaces, and cultural influences. The paper concludes that AI and NFTs are reshaping the traditional art market, presenting new opportunities for creativity, ownership, and artistic value in a digital age.
Market Analysis of NFT Integration in Video Games Hasibuan, Muhammad Said; Putra, Arie Setya; Syarif, Admi; Mahfut; Sulistiyanti, Sri Ratna
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.33

Abstract

The integration of Non-Fungible Tokens (NFTs) into the gaming industry has introduced a novel economic model, reshaping monetization strategies and player engagement. This paper analyzes the market potential of NFT integration in video games through a comprehensive approach combining market segmentation, trend analysis, and predictive modeling. Using historical sales data from various genres and platforms, the research identified key segments that show high potential for NFT adoption, particularly action, role-playing, and sports games on mainstream platforms such as PlayStation and Xbox. The market segmentation, achieved through K-Means clustering, revealed distinct groups of video games based on genre, platform, and regional sales performance. Trend analysis using time series models like ARIMA and Prophet highlighted emerging and declining popularity across different genres and platforms. The study also applied predictive modeling techniques, including Random Forest and Gradient Boosting, to forecast the potential success of NFTs in specific game genres. The models demonstrated strong performance, with low mean absolute error (MAE) and root mean squared error (RMSE), confirming that high-engagement genres are likely to benefit most from NFT integration. The findings suggest that NFTs can enhance player experiences by offering unique, tradable in-game assets, thus creating new revenue streams for developers. The paper concludes by recommending strategies for NFT implementation, targeting high-potential genres and platforms, and addressing regional market preferences. Limitations related to data constraints and emerging trends are discussed, and future research directions are proposed, focusing on consumer sentiment analysis and real-world case studies of NFT integration in video games.
Predicting Customer Conversion in Digital Marketing: Analyzing the Impact of Engagement Metrics Using Logistic Regression, Decision Trees, and Random Forests Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.34

Abstract

This research explores the impact of engagement metrics on predicting customer conversion rates within digital marketing, employing three advanced predictive modeling techniques: Logistic Regression, Decision Trees, and Random Forests. Using a comprehensive dataset of 8,000 customer interactions, the study evaluates critical engagement metrics such as PagesPerVisit, TimeOnSite, and EmailClicks to determine their influence on conversion outcomes. The results indicate that PagesPerVisit and TimeOnSite are the most significant predictors of customer conversion, with the Random Forest model outperforming others, achieving an accuracy of 87.1% and an ROC-AUC score of 0.6979. The Logistic Regression model demonstrated the highest recall for the conversion class at 99.8%, but its performance in predicting non-conversions was less robust, highlighting the challenges of imbalanced datasets. Decision Trees, while offering valuable interpretability, showed a lower accuracy of 79.6% and struggled with precision in identifying non-conversions. These findings suggest that enhancing on-site customer engagement and refining email marketing strategies are pivotal for improving conversion rates. The study contributes to the field of digital marketing analytics by providing empirical evidence on the relative importance of various engagement metrics and offering practical insights for optimizing digital marketing strategies. Additionally, it highlights the benefits of using ensemble methods like Random Forests to achieve more balanced and accurate predictions in customer conversion scenarios.
Assessing the Impact of Laptop Condition on Pricing Using Statistical Analysis: Insights for Digital Marketing Strategies on eBay Evelyn, Evelyn; Suryodiningrat, Satrio Pradono
Journal of Digital Market and Digital Currency Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i3.35

Abstract

This study investigates the influence of laptop condition on pricing in the eBay marketplace, using statistical analysis to provide actionable insights for digital marketing strategies. The analysis is based on a dataset containing 2,952 laptop listings, categorized by condition into New, Open box, Excellent - Refurbished, Very Good - Refurbished, and Good - Refurbished. An ANOVA test revealed a significant difference in mean prices across these conditions (F-value = 76.69, p < 0.0001), indicating that condition is a critical factor in pricing. Post-hoc analysis using Tukey's HSD test further highlighted specific pairwise differences. For instance, the price difference between New and Good - Refurbished laptops was found to be approximately $192.66 (p < 0.0001), confirming that even minor wear significantly impacts consumer perception and pricing. Additionally, Excellent - Refurbished laptops were priced, on average, $62.79 higher than their New counterparts (p = 0.0008), suggesting a premium for well-maintained refurbished models. A multiple linear regression model was employed to quantify the impact of various factors on pricing, including condition, brand, RAM, and processor type. The model, with an R-squared value of 0.429, indicated that these variables collectively explain 42.9% of the variation in laptop prices. Despite the model's moderate fit, the coefficients provided insights into the relative importance of each factor, with condition emerging as the most influential determinant. The findings suggest that eBay sellers should prioritize accurate and detailed descriptions of product condition to optimize pricing strategies. These results underscore the importance of condition-based pricing in digital marketing, offering a data-driven approach to maximizing profitability in online marketplaces.
Investigating the Correlation Between Tesla Stock Prices and Cryptocurrency Prices Using Pearson and Spearman Correlation Analysis (2010-2024) Iqbal, Muhammad; Efendi, Syahril
Journal of Digital Market and Digital Currency Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i3.36

Abstract

This study investigates the correlation between Tesla's stock prices and the prices of the two leading cryptocurrencies, Bitcoin and Ethereum, from 2010 to 2024. By employing both Pearson and Spearman correlation analyses, the research identifies strong positive correlations between these assets, with Tesla-Bitcoin showing a Pearson coefficient of 0.76 and Tesla-Ethereum at 0.82. The Spearman correlations further validate these findings, indicating that Tesla's stock prices are significantly aligned with the movements of Bitcoin and Ethereum. The results suggest that similar market forces and investor sentiments influence both Tesla and these cryptocurrencies. This strong correlation implies that investors should be cautious when seeking diversification, as these assets tend to move together, potentially amplifying portfolio risk. The study's findings underscore the importance of considering cross-market dynamics in investment strategies and highlight the interconnectedness of traditional and digital financial markets. Future research could explore the relationships between other technology stocks and digital assets, as well as the evolving correlations in response to major market events.
Analyzing Historical Trends and Predicting Market Sentiment in Digital Currency Using Time Series Decomposition and ARIMA Models on Crypto Fear and Greed Index Data Mendoza, Christian Paul T.; Tubice, Noel G.
Journal of Digital Market and Digital Currency Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i3.37

Abstract

This study analyzes historical trends and predicts market sentiment in digital currencies using time series decomposition and ARIMA models, focusing on the Crypto Fear and Greed Index. The volatile nature of cryptocurrency markets, driven largely by investor sentiment, necessitates a thorough understanding of market mood to anticipate price movements and market dynamics. The research utilized time series decomposition to uncover significant trends and seasonal patterns within the sentiment data. The ARIMA model was applied to predict future sentiment, achieving a Mean Absolute Error (MAE) of 11.15 and a Root Mean Square Error (RMSE) of 13.30, indicating strong alignment with actual market behavior. Additionally, the study employed the Prophet model, which, although less precise with an MAE of 22.56 and RMSE of 24.98, provided valuable insights into the seasonal components of market sentiment. These results underscore the importance of sentiment analysis in digital currency markets, offering actionable insights for traders and investors. Limitations of the models are acknowledged, with suggestions for future research including the integration of additional data sources and more sophisticated modeling techniques to further refine sentiment predictions. This research contributes to the expanding body of knowledge on the role of sentiment analysis in financial markets, particularly within the dynamic field of digital currencies.
Predicting Customer Conversion in Digital Marketing: Analyzing the Impact of Engagement Metrics Using Logistic Regression, Decision Trees, and Random Forests Li, Shuang; Pigultong, Matee
Journal of Digital Market and Digital Currency Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i3.38

Abstract

This research explores the impact of engagement metrics on predicting customer conversion rates within digital marketing, employing three advanced predictive modeling techniques: Logistic Regression, Decision Trees, and Random Forests. Using a comprehensive dataset of 8,000 customer interactions, the study evaluates critical engagement metrics such as PagesPerVisit, TimeOnSite, and EmailClicks to determine their influence on conversion outcomes. The results indicate that PagesPerVisit and TimeOnSite are the most significant predictors of customer conversion, with the Random Forest model outperforming others, achieving an accuracy of 87.1% and an ROC-AUC score of 0.6979. The Logistic Regression model demonstrated the highest recall for the conversion class at 99.8%, but its performance in predicting non-conversions was less robust, highlighting the challenges of imbalanced datasets. Decision Trees, while offering valuable interpretability, showed a lower accuracy of 79.6% and struggled with precision in identifying non-conversions. These findings suggest that enhancing on-site customer engagement and refining email marketing strategies are pivotal for improving conversion rates. The study contributes to the field of digital marketing analytics by providing empirical evidence on the relative importance of various engagement metrics and offering practical insights for optimizing digital marketing strategies. Additionally, it highlights the benefits of using ensemble methods like Random Forests to achieve more balanced and accurate predictions in customer conversion scenarios.
Segmentation and Profiling of Electric Vehicle Market Using Clustering Analysis: A Case Study with Implications for Digital Marketing in the EV Sector Henderi, Henderi; Zailani, Achmad Udin; Tuah, Nooralisa Mohd; Abas, Ashardi bin
Journal of Digital Market and Digital Currency Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i3.39

Abstract

The electric vehicle (EV) market has experienced unprecedented growth in recent years, largely fueled by technological advancements, increasing environmental concerns, and supportive government policies. However, this growth presents a challenge: effectively reaching and engaging diverse consumer segments within the EV market. To address this, our study employs advanced clustering analysis to segment the EV market using a comprehensive dataset from Washington state, which includes vehicle characteristics such as model year, electric range, and geographic distribution. Through this analysis, we identified four distinct clusters within the EV market. Clusters 2 and 3 are characterized by high-end, newer EV models with extended electric ranges, predominantly located in affluent urban and suburban areas. These clusters are likely composed of environmentally conscious consumers who are early adopters of advanced technologies. In contrast, Clusters 0 and 1 consist of older EV models with shorter electric ranges, appealing to a more budget-conscious demographic spread across various geographic regions, including rural areas. The insights gained from these clusters have significant implications for digital marketing strategies in the EV sector. For Clusters 2 and 3, digital marketing campaigns should focus on highlighting the luxury, cutting-edge technology, and sustainability features of premium EV models, using targeted online advertising and social media engagement. For Clusters 0 and 1, marketing efforts should emphasize the practicality, cost-effectiveness, and everyday usability of EVs, addressing potential concerns such as charging infrastructure and range anxiety. The study demonstrates the critical importance of market segmentation in crafting effective digital marketing strategies that resonate with specific consumer groups. By leveraging data-driven insights, marketers can enhance engagement, drive higher conversion rates, and ultimately accelerate the adoption of electric vehicles. Future research should explore broader datasets and additional variables to further refine these segmentation strategies and support the evolving needs of the global EV market.

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