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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal ULTIMA InfoSys Prosiding National Conference for Community Service Project Cyber Security dan Forensik Digital (CSFD) Journal Informatic, Education and Management (JIEM) Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen INOVTEK Polbeng - Seri Informatika Journal of Computer Science and Technology Application
Eryc
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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An Exploratory Study of Video Game Pricing in the Southeast Asian Market Wibowo, Tony; Sandriawan, Elfan; Eryc
ULTIMA InfoSys Vol 16 No 2 (2025): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v16i2.4456

Abstract

This exploratory study examines optimal video game pricing within the Southeast Asian market from the perspective of gamers. Employing a two-phase quantitative analysis of 405 respondents across six nations—Indonesia, Malaysia, Singapore, Thailand, the Philippines, and Vietnam—the study identifies a consistent psychological pricing framework. Key findings reveal two stable price thresholds: a normative expectation of approximately USD 20 for indie titles and an upper limit of USD 40–60 for premium AAA games. The research confirms a significant market transition from price sensitivity to value sensitivity, where developer reputation and production quality directly influence price acceptance. Furthermore, the study substantiates the emergence of a "premium indie" category, indicating consumers are willing to invest more in independent games that demonstrate high levels of trust, quality, and artistry. These insights offer crucial guidance for developers and publishers, highlighting the need for localized pricing strategies that respect established value perceptions while adjusting for regional purchasing power. This study addresses a notable gap in the literature concerning consumer pricing behavior in the rapidly growing Southeast Asian gaming market.
Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques Eryc; Nasib; Muh. Fahrurrozi; Ramzi Zainum Ikhsan; Parker, Jonathan
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/p8sbs746

Abstract

This study, titled Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques, explores how artificial intelligence (AI) particularly machine learning (ML) can enhance the accuracy and strategic impact of business forecasting in dynamic markets. Traditional statistical forecasting methods often fail to accommodate complex, nonlinear, and high-dimensional data. To address this gap, the research develops and validates a machine learning–based forecasting model designed to integrate predictive analytics into strategic decision-making. The study adopts a quantitative approach and employs Structural Equation Modeling (SEM) using SmartPLS 3 to examine the interrelationships among four latent variables: Market Trends (MT), Forecasting Accuracy (FA), Strategic Planning Efficiency (SPE), and Business Performance (BP). Each construct is measured using three indicators, forming a structural model that tests six hypothesized relationships. The results indicate that understanding market trends significantly improves forecasting accuracy and strategic planning efficiency, which in turn positively influences business performance. Furthermore, forecasting accuracy directly enhances both planning efficiency and overall performance, emphasizing the strategic value of data-driven insights. The findings validate the reliability and predictive power of the proposed model, offering a robust framework for organizations aiming to leverage machine learning in strategic forecasting. By bridging the gap between algorithmic prediction and managerial application, this study contributes to the growing field of AI-driven business analytics and supports the development of more agile, informed, and resilient business strategies in a data-centric economy.
Co-Authors Adrian Leonardus Joviyanto Saragi Alvin Siswanto Angeline Ardiansyah Ardiansyah Muhammad Calvin Decholas Valentino Wang Erved Indasari Indasari Deu Liegestu, Suryo Pramono Muh. Fahrurrozi Muhammad Ardiansyah Nasib Parker, Jonathan Ramzi Zainum Ikhsan Sandriawan, Elfan Santoso, Didi Santoso, Vincent Stanley Tjahyadi, Surya Tony Wibowo, Tony Wenky Windi