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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 5 Documents
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Search results for , issue "Vol. 2 No. 3 (2025): Regular Issue September 2025" : 5 Documents clear
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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