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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2024): Regular Issue December" : 5 Documents clear
Exploring the Adoption of Metaverse Platforms in Corporations Irfan, Muhamad
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.13

Abstract

This study explored the adoption of metaverse platforms in Indonesian corporations through a moderated model integrating Self-Determination Theory and the Technology Acceptance Framework. Data were collected from 355 respondents through a structured questionnaire, of which 344 were deemed valid after a validation step confirming prior use of metaverse platforms. The research focused on understanding the factors influencing Usage Intention (UI) by examining the roles of Customization Capability (CUS), Immersive Experience Features (IMF), Social Influence (SOC), and Technology Reliability (TR) on Perceived Usefulness (PU). The results indicated that CUS, IMF, and TR significantly enhanced PU, which was a strong predictor of UI, underscoring the critical mediating role of perceived usefulness in driving adoption. The findings revealed that customization and reliability were pivotal in enhancing perceived utility, while the impact of immersive features, though positive, was less pronounced. SOC had a modest effect on UI, suggesting that direct functional benefits of the platform were prioritized by users over peer validation. The study contributed to the literature by providing an integrated model that highlights the importance of both individual and contextual factors in technology adoption within corporate environments. Practical implications suggest that corporations should focus on developing customizable, reliable, and functionally beneficial metaverse platforms to foster sustained adoption.
Assessing the Adoption of Metaverse Platforms: A Structural Equation Modeling Approach with Mediating Effects of Switching Costs El Emary, Ibrahiem M. M.
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.14

Abstract

The adoption of Metaverse platforms, a burgeoning technological innovation, holds significant potential for transforming various sectors, yet its uptake in emerging markets like Indonesia remains underexplored. This study addresses this gap by investigating the key factors influencing the Intention to Use (IU) Metaverse platforms in Indonesia, focusing on the roles of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Relative Advantage (RA), and the mediating effect of Switching Costs (SC). The primary objective of this research was to develop and validate a model that explains the relationships between these factors and how they collectively impact user adoption decisions. Specifically, the study aimed to understand how PE, EE, SI, and RA influence the intention to use Metaverse platforms, with SC acting as a mediator. A quantitative research design was employed, utilizing Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to analyze data collected from 380 distributed questionnaires. Of these, 361 were valid and used in the analysis, providing a robust sample to examine the study’s hypotheses. Participants were surveyed on their perceptions and intentions regarding Metaverse platforms. The analysis focused on examining the direct effects of PE, EE, SI, and RA on the intention to use, as well as the indirect effects mediated by SC. The findings revealed that PE, EE, SI, and RA significantly influence the intention to adopt Metaverse platforms, with SC playing a crucial mediating role. The study underscores the importance of reducing perceived switching barriers to enhance adoption, especially in a culturally diverse market like Indonesia. These results contribute to the broader understanding of technology adoption in emerging markets and offer practical implications for developers and marketers aiming to promote Metaverse platforms. Future research should explore additional factors such as technological anxiety or perceived risk and consider longitudinal designs to capture changes in user perceptions over time. This study provides a foundational model that can guide further exploration and application of Metaverse technologies in similar contexts.
Analyzing Genre Patterns in Virtual-Themed Animated Films Using Association Rule Mining Pratama, Satrya Fajri; Priyanto, Eko
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.15

Abstract

This study investigates patterns in virtual-themed animated films using association rule mining to explore the relationships between genre combinations, production companies, and their impact on both popularity and revenue. The dataset consists of films from various genres, with a focus on those exploring virtual worlds, alternate realities, and futuristic settings, aligning with metaverse concepts. The analysis revealed several significant findings. The association rule mining results identified that films combining Fantasy and Science Fiction genres are 1.8 times more likely to achieve high box office revenue, with a confidence level of 80%. Additionally, Pixar adventure films were found to have a 2.1 times higher likelihood of attaining high popularity. Films blending Fantasy and Adventure genres showed a strong correlation with high revenue, with a 70% confidence level and a lift value of 1.9. These patterns suggest that imaginative storytelling and virtual world elements are key drivers of success in animated films. Revenue analysis demonstrated that 30% of the virtual-themed films in the dataset generated more than 1 billion USD, while 50% earned between 0.5 and 1 billion USD. The popularity analysis further highlighted that Fantasy, Science Fiction, and Adventure genres consistently rank highest in audience engagement. These findings underscore the significant commercial potential of films exploring virtual and digital environments, particularly as audience demand for immersive experiences continues to grow. This study concludes that films featuring virtual world themes, particularly those combining Fantasy, Science Fiction, and Adventure genres, are well-positioned to succeed both financially and in terms of audience engagement. As AR, VR, and metaverse technologies advance, the demand for immersive cinematic experiences is likely to increase, offering filmmakers new opportunities to innovate and expand this genre.
Predicting the Success of Virtual-Themed Animated Movies Using Random Forest Regression Doan, Minh Luan
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.16

Abstract

This paper presents a study using Random Forest Regression to predict the success of virtual-themed animated movies, with a focus on revenue and popularity. The dataset included 100 animated films, featuring attributes such as runtime, vote average, and genres. The objective was to identify the key factors influencing movie success. The model achieved an R² of 0.85 for predicting popularity, with vote average being the most significant predictor (importance score = 0.50), followed by runtime (importance score = 0.25). However, predicting revenue was more challenging, with the model achieving an R² of 0.65 and RMSE of 100, indicating that external factors like marketing and competition play a significant role. The findings reveal that audience reception, as captured by vote average, is crucial for predicting both popularity and revenue. The novelty of this research lies in its focus on virtual-themed animated movies and the use of machine learning to identify success factors in this niche genre. The study contributes to understanding the dynamics of movie success, offering valuable insights for filmmakers and production companies. Future research should explore the inclusion of external factors and advanced techniques to improve revenue prediction accuracy.
Predicting Consumer Perceptions of Metaverse Shopping Through Insights from Machine Learning Models Lenus, Latasha
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.17

Abstract

This study investigates consumer perceptions of Metaverse shopping and the factors that influence these perceptions, using machine learning models to classify and analyze the data. Four models—Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)—were employed to predict whether consumers view Metaverse shopping favorably or unfavorably. Among these, the SVM model achieved the highest performance, with an accuracy of 94.17%, precision of 97.14%, and an AUC-ROC score of 98.13%. These results indicate that machine learning can reliably classify consumer perceptions based on demographic and experience-related data. Furthermore, the Random Forest model was used to analyze the importance of features influencing consumer attitudes. The findings revealed that experience-related factors—such as interactivity, personalization, and consumer engagement—were more significant in shaping perceptions than product-specific attributes. The most important feature, MC2 (interactivity), contributed 23.6% to the model’s predictive power, highlighting the importance of user experience in driving positive sentiment. These insights suggest that businesses aiming to enter the Metaverse retail space should focus on enhancing the overall shopping experience to foster positive consumer perceptions. Machine learning models provide valuable tools for understanding consumer behavior and tailoring virtual shopping environments accordingly. This research offers a data-driven approach to predicting and understanding consumer perceptions of the Metaverse, providing actionable insights for businesses in this emerging market.

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