The digital gaming community appreciates the visual style classification system to search for information about a game product. However, scholars have discovered that the applied visual style classification system frequently leads to frustration and dissatisfaction among users owing to inaccurate search results. Moreover, there are yet any research studies that accurately measure user satisfaction during game searching activity in the digital game library based on the current visual style classification system. Therefore, this study was performed to investigate the influence of information quality components (Accuracy, Content, Ease of Use, Format, and Timeliness) on User Satisfaction during game searching activity in the digital game library. A cross-sectional study was conducted by distributing self-administered digital questionnaires to 239 game players in Malaysia. The 12-item instrument survey questionnaire, which consists of four main sections, adopted the End-user Computing Satisfaction (EUCS) model to examine the relationships between user satisfaction and information quality components. The results were then analysed using descriptive statistics, preliminary data analysis, and Confirmatory Factor Analysis (CFA). Based on the findings, more than half of the respondents were male (n = 139, 58.2%) with an age range between 19 and 24 years old, and 79.1% (n = 189) have experience in searching digital games based on visual styles. The EUCS model revealed that user satisfaction during game searching activity using the visual style information highly correlated with Content, Accuracy, and Ease of Use. Users were satisfied when they receive accurate, precise, and sufficient information. In addition, a user-friendly and simplified navigation interface improved the searching experience and stimulate users to further their searching activity. In contrast, the Format and Timeliness showed a weak correlation. Providing visual classification and appeal format has less impact on user satisfaction. In addition, the fast speed of information retrieval and up-to-date information showed an insignificant contribution to user satisfaction. Overall, this research demonstrated that information quality components, namely Content, Accuracy, and Ease of Use, influenced User Satisfaction when searching for games based on the visual style. Future studies should explore and evaluate the effectiveness of visual style information systems for digital game distribution platforms in Malaysia.
The digital gaming community appreciates visual style information in digital games as it facilitates information seeking. Nevertheless, learned scholars have discovered that the digital game visual style classification is inconsistent and easily modified, potentially limitingthe information and leading to inaccurate visual terminologies during information discovery. Therefore, this cross-sectional study wasperformed to assess multiple visual style classification terms and their definitions among Malaysian game developers using the closedcard sorting exercise. A total of seven professional game developers participated in an online survey that comprised thirty-five digital game case studies using a card sorting technique. They were asked to classify nineteen visual style classification terms, including psychedelic, text, illusionism, photorealism, televisualism, handicraft, caricature, celshaded, comic book (anime), watercolour, Lego, minimalism, pixel art, silhouette, bright, dark, maplike, colourful, and black and white. The Fleiss’ kappa intercoder reliability assessment was performed to measure the coders’ agreement on visual style classification, followed by the think-aloud protocol descriptive analysis to gather assessment insights into the visual style descriptions. The intercoder reliability test achieved a significantly moderate agreement based on the results. The professional game developers agreed on eighteen visual stylesand rejected the bright visual style classification due to its overlapping description with the colourful visual style. The definition of ten visual style classifications was improved from the existing Video Game Metadata Schema (VGMS) description, contributing to the digital game’s coherence and consistency. This improvement will enhance visual style classification information for machine-learning-based recommendation systems for digital game distribution platforms and digital archiving.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.