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International Journal Research on Metaverse
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30626927     DOI : https://doi.org/10.47738/ijrm
Core Subject : Science,
Computer Science & IT
Virtual and augmented reality technologies Network infrastructure and architecture for the metaverse Digital economy and transactions in the metaverse Social and cultural aspects of virtual environments Development and design of content in the metaverse Impact of the metaverse on industries such as education, healthcare, entertainment, and business Regulation, policy, and ethics in the metaverse IJRM aims to foster interdisciplinary dialogue and collaboration, contributing to the body of knowledge that drives the adoption and evolution of metaverse technologies. Papers published in IJRM are grounded in rigorous research methods and are expected to articulate their implications for theory and practice clearly. Authors are encouraged to state their contributions to the state-of-the-art in the field explicitly. Subject Area and Category: The International Journal Research on Metaverse focuses on virtual and augmented reality, network infrastructure, digital economy, social and cultural impacts, content development, industry-specific applications, regulation and ethics, and practical case studies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 35 Documents
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Comprehensive Analysis of Twitter Conversations Provides Insights into Dynamic Metaverse Discourse Trends Kumar, Aayush; Hananto, Andhika Rafi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.2

Abstract

The metaverse, a concept originating from science fiction, has gained substantial traction in recent years as advancements in technology have brought it closer to reality. This virtual shared space, accessed through immersive technologies like virtual reality (VR) and augmented reality (AR), has captivated the imagination of both tech enthusiasts and the general public. This study aims to explore the dynamics of the metaverse discourse by analyzing online discussions across various platforms. We employed a combination of data collection methods, including Twitter API access and web scraping, to gather a diverse dataset of tweets related to the metaverse. Subsequently, the collected data underwent extensive preprocessing to ensure consistency and prepare it for analysis. Our analysis encompassed user statistics, word analysis in tweets, hashtag analysis, and tweet distribution patterns. The findings reveal intriguing insights into user behavior, content trends, and temporal patterns within the metaverse discourse. We observed prominent usernames, geographic distributions of users, prevalent words and hashtags, as well as temporal fluctuations in tweet activity. For instance, the most common username is "Fatemeh ashoobian" with 800 users, indicating a significant presence in the metaverse community. Furthermore, the number of tweets about the metaverse per day over a certain period shows daily fluctuations with the highest peak on November 14, 2023. These insights contribute to a deeper understanding of the metaverse ecosystem and its implications for society, technology, and culture. Through our research, we aim to provide valuable insights to stakeholders across various sectors, including technology developers, policymakers, content creators, and end-users. By understanding the emergent trends and themes within the metaverse discourse, stakeholders can navigate this rapidly evolving landscape more effectively and harness its transformative potential for the benefit of humanity.
Exploring User Experience and Immersion Levels in Virtual Reality: A Comprehensive Analysis of Factors and Trends Putawa, Rilliandi Arindra; Sugianto, Dwi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.3

Abstract

Virtual Reality technology has advanced rapidly in recent years, opening up new opportunities in various fields from entertainment to education. This research aims to investigate the factors influencing users' level of immersion in VR environments. Data were collected from 500 different VR users regarding their age, gender, play duration, VR headset used, and perceived motion sickness level. Analysis was conducted to evaluate the demographic distribution of users, immersion levels, play duration, and motion sickness levels. The research findings indicate that the majority of VR users are aged between 30-40 years old, with 42% of users aged 30 to 36 and 38% aged 37 to 44. Immersion levels are predominantly moderate to high, with 48% of users reporting level 3 immersion and 28% reporting level 4 immersion. Longer play durations tend to correlate with higher immersion levels, with the average play duration being 27 minutes for users with level 4 immersion compared to 18 minutes for users with level 2 immersion. Higher motion sickness levels are associated with lower immersion levels. The average motion sickness level is 2.5 for users with level 1 immersion and 1.8 for users with level 4 immersion. Additionally, the Oculus Rift VR headset proves to be the top choice for users, with 45% of the total sample using this headset and reporting an average immersion level of 3.8. This is followed by PlayStation VR with 30% of users and an average immersion level of 3.5, and HTC Vive with 25% of users and an average immersion level of 3.6. These findings provide valuable insights into users' preferences and experiences in VR environments, as well as highlighting the importance of considering factors such as age, play duration, and VR headset type in content development and interaction design. By gaining a deeper understanding of human-computer interaction dynamics in virtual environments, this research is expected to make a meaningful contribution to the future development of VR technology.
In-Depth Analysis of Web3 Job Market: Insights from Blockchain and Cryptocurrency Employment Landscape Izumi, Calvina; Hariguna, Taqwa
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.4

Abstract

The emergence of Web3, underpinned by blockchain technology, has reshaped the digital realm, ushering in a decentralized and trustless internet paradigm. In this paper, we conduct an extensive analysis of the Web3 job market, leveraging data from 2000 job postings to delineate prevalent keywords, sought-after skills, prevalent job titles, and salary determinants. Our examination reveals compelling insights into the job landscape, showcasing the dominance of technical competencies such as Ethereum proficiency and software development expertise. Among the top skills sought by employers, Ethereum (371 occurrences), React (213 occurrences), NFT (213 occurrences), Java (205 occurrences), and Rust (102 occurrences) prominently feature. Moreover, our analysis uncovers the ascendancy of specialized roles in cybersecurity, technical leadership, and project management, which command premium compensation levels. Notably, security positions emerged as the highest paying roles (average salary: $153,295.86), followed by tech lead (average salary: $121,526.32) and operations (average salary: $120,396.55). These findings offer valuable insights for job seekers, employers, educators, and policymakers navigating the evolving Web3 job landscape. By delineating key trends and challenges, our study contributes to a nuanced understanding of the transformative potential of Web3 and its implications for the future of work.
Navigating Financial Transactions in the Metaverse: Risk Analysis, Anomaly Detection, and Regulatory Implications Srinivasan, Bhavana; Wahyuningsih, Tri
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.5

Abstract

Blockchain technology has emerged as a disruptive force in the realm of finance, offering decentralized and transparent mechanisms for conducting financial transactions. This paper explores the landscape of blockchain-based financial transactions, focusing on risk analysis, anomaly detection, regulatory frameworks, and ethical considerations. Drawing on interdisciplinary insights from finance, computer science, economics, law, and ethics, the study investigates the opportunities and challenges presented by blockchain finance. Leveraging quantitative analysis, machine learning algorithms, case studies, and regulatory reviews, the research sheds light on the complexities of blockchain ecosystems. Key findings include the importance of robust risk management strategies, the role of anomaly detection in safeguarding financial integrity, and the evolving regulatory landscape surrounding blockchain transactions. The study identifies gaps in current research and proposes avenues for future investigation, emphasizing the need for interdisciplinary approaches to address the multifaceted challenges of blockchain-based finance. Ultimately, this research aims to inform stakeholders about the implications of blockchain technology in financial transactions and foster responsible innovation and sustainable development in digital finance ecosystems.
Metaverse Dynamics: Predictive Modeling of Roblox Stock Prices using Time Series Analysis and Machine Learning Abdul Ghaffar, Soeltan; Setiawan, Wilbert Clarence
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.6

Abstract

Stock price prediction is a critical task in finance and investment, enabling investors to make informed decisions and capitalize on market opportunities. This paper explores the application of predictive modeling techniques to forecast the stock prices of Roblox Corporation, a prominent player in the gaming industry. Despite the growing interest in predictive analytics, there remains a research gap concerning the application of these techniques to specific companies, particularly within the gaming sector. To address this gap, we employ a comprehensive dataset spanning from March 2021 to June 2023, obtained from Yahoo Finance, to develop predictive models using both time series analysis and machine learning algorithms. Our analysis encompasses exploratory data analysis, model development, and evaluation, culminating in insights into Roblox's stock price dynamics and model performance. The evaluation of our predictive models reveals promising results, with a Mean Squared Error (MSE) of 1.22, Root Mean Squared Error (RMSE) of 1.10, and a high R-squared (R2) score of 0.998. These metrics indicate relatively low prediction errors and a strong explanatory power of the models in capturing the variance in Roblox's closing prices. The findings shed light on the unique challenges and opportunities in predicting stock prices within the gaming industry and contribute to the growing body of knowledge in finance and investment. Through our research endeavors, we aim to empower investors and stakeholders with actionable insights to navigate the complexities of financial markets and make informed decisions with confidence and agility.
Exploring the Impact of Virtual Reality Experiences on Tourist Behavior and Perceptions Sukmana, Husni Teja; Kim, Jong Il
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.v1i2.8

Abstract

This study explores the impact of virtual reality (VR) experiences on tourist behavior and perceptions, utilizing logistic regression and analysis of variance (ANOVA) to understand these relationships. The logistic regression analysis revealed that VR experience (coefficient = 0.432, p = 0.020) significantly enhances the likelihood of being a tourist. Demographic factors such as gender (coefficient = -0.512, p = 0.018), income (coefficient = -0.301, p = 0.001), and age (coefficient = 0.298, p = 0.003) also play crucial roles: females and higher-income individuals are less likely to be tourists, while older individuals are more likely to travel. ANOVA results indicated significant differences in emotional responses (EMO1: F = 6.40, p = 0.012; EMO2: F = 4.63, p = 0.032; EMO3: F = 7.77, p = 0.006; EMO4: F = 5.77, p = 0.017), flow states (FLOW1: F = 12.21, p = 0.001; FLOW2: F = 20.39, p < 0.001; FLOW3: F = 17.38, p < 0.001; FLOW4: F = 14.52, p < 0.001), and intentions to visit (INT2: F = 7.79, p = 0.006; INT4: F = 4.61, p = 0.032) based on VR experience. These findings suggest that VR significantly influences emotional and cognitive states, fostering engagement, satisfaction, and increased intentions to visit real-world destinations. The results underscore the potential of VR as a powerful tool in tourism marketing, capable of driving tourism interest and behavior. Future research should investigate the long-term effects of VR on tourist behavior and consider cultural and technological advancements to further optimize VR's application in tourism. This study offers actionable insights for tourism marketers to develop targeted, effective, and immersive VR promotional strategies.
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.
Geospatial Analysis of Virtual Property Prices Distributions and Clustering Sugianto, Dwi; Hananto, Andhika Rafi
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.v1i2.10

Abstract

This paper presents an analysis of property prices in the virtual world, focusing on geographical distribution and district comparisons. Utilizing a dataset of virtual properties, we applied scatter plot analysis, cluster analysis using DBSCAN, and box plot comparison to identify key patterns and opportunities within this market. The scatter plot analysis revealed that property prices are unevenly distributed, with higher prices clustering in specific regions, indicating areas of higher desirability and value. The DBSCAN clustering identified distinct high-value clusters, each containing 10 to 67 properties, and highlighted 1,067 properties as noise, suggesting a dispersed distribution of lower-value properties. Box plot comparisons across districts showed significant variations in property values. Some districts exhibited higher median prices, with the highest at 35,452.60 MANA, while others had lower medians. Variability within districts varied, with some showing a wide range of prices and others more uniform values. Outliers suggested unique investment opportunities in both premium and undervalued properties. For virtual real estate investors, the findings emphasize the importance of location and strategic investment. High-value districts and emerging areas offer potential for significant returns. Developers and urban planners can use these insights to focus on high-demand areas, enhancing project value through strategic investments in infrastructure and amenities. This study highlights the dynamic nature of the virtual real estate market and the importance of ongoing research to understand factors influencing property values. Stakeholders can make informed decisions and capitalize on opportunities in this evolving market.
The Impact of Market Activity on Property Valuations in Digital Real Estate Through a Quantitative Analysis of Bidding and Sales Dynamics Saputra, Jeffri Prayitno Bangkit; Putri, Nadya Awalia
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.v1i2.11

Abstract

This study investigates the impact of market activities, specifically the number of bids and sales, on property prices in digital real estate markets. With the rise of virtual environments and digital assets, understanding the factors that drive property valuations in these markets has become increasingly important. Utilizing a dataset of 2,000 property transactions, this research employs correlation and regression analyses to explore how competitive bidding and sales frequency influence prices. The results indicate a significant positive correlation (r=0.38r = 0.38r=0.38) between the number of bids a property receives and its final sales price, suggesting that properties attracting more bids are perceived as more valuable, leading to higher prices. The regression analysis further supports this, showing that each additional bid is associated with an increase of 6.63×10216.63 \times 10^{21}6.63×1021 in the sales price (p
Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence Wahyuningsih, Tri; Chen, Shih Chih
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.v1i2.12

Abstract

This study explores the emerging virtual property market within the digital world, with a focus on identifying the key factors influencing property prices, market activity, and sales volume. Using a dataset of 2,000 virtual property transactions, the research provides a comprehensive analysis of market dynamics in this new frontier of digital real estate. The findings reveal significant volatility in transaction activity, with a peak of 1,222 transactions in January 2022 followed by a sharp decline to 539 in February 2022 and just 24 in March 2022, indicative of a nascent and speculative market. The analysis identifies land price as the most significant determinant of virtual property values, showing a near-perfect correlation of 0.992 with sales prices. This highlights the critical role of location and land value, similar to traditional real estate markets. Additionally, the study finds that properties attracting more bids tend to sell at higher prices, with a moderate correlation of 0.380 between bids count and sales price, reflecting the impact of competitive bidding in driving up values. However, the market is relatively illiquid, with a mean sales count of just 1.79, indicating that most properties are held as long-term investments rather than frequently traded assets. Interestingly, the research also uncovers a weak negative correlation of -0.051 between sales price and the underlying cryptocurrency, MANA, suggesting that the value of virtual properties may be increasingly decoupled from cryptocurrency volatility as the market matures. These insights provide valuable guidance for investors, developers, and policymakers navigating the evolving landscape of virtual real estate. The study concludes with a discussion of the implications for future market stability and potential areas for further research.

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