Digital game visual style classification terms reference model

The digital gaming community appreciates visual style information in digital games as it facilitates information seeking. The rationale why it is critical to preserve digital game information entities such as genre, visual style, point of view, theme, and mood, are to ensure the information remains accessible and valuable over time. However, scholars have discovered that the digital game visual style terms is inconsistent and easily modified, which is debatable. Interpretation of visual style was occasionally confused by terminology and descriptions. Thus, searching for specific visual styles using user information frequently leads to frustration and dissatisfaction owing to inaccurate search results. A research framework was developed to assess visual style classification terms and correlate them to user satisfaction in information seeking. This research was divided into two distinct phases. The first phase of the research is an assessment of visual style classification terms from the perspective of professional game developers. A total of seven professional game developers were participated in the online survey. Thirty-five digital game case studies using cardsorting technique to classify nineteen visual style classifications. The Fleiss’ kappa intercoder reliability analysis was performed to assess coders’ classification terms agreement. The analysis included subject matter expert demographics, think aloud transcription, and kappa magnitude of agreement results. Based on the results of interrater reliability agreement of visual style terms was moderate, with statistically significant reliability and validity. The professional game developers agreed on eighteen visual style classification terms and rejected bright visual style classification due to misclassified term overlap with the colourful visual style. Moreover, 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. The first phase’s outcomes indicated that the assessment visual style classification terms would be used as an input for a web-based visual style classification system. The second phase focuses on how users seek visual style information to gain knowledge and satisfy user curiosity. The problem arose when inaccurate searching for game information on visual style classifications often dissatisfied users. So far, no studies have accurately measured user satisfaction during game searching in the existing game library. Information exploration and development can directly influence user satisfaction, and in computing studies, it correlates to the information system quality. 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. A cross-sectional study was conducted involving Malaysian digital game players using the 12-item survey questionnaires based on End-user Computing Satisfaction (EUCS) model to measure user satisfaction and information quality components. Descriptive statistic, preliminary data analysis, and Confirmatory Factor Analysis (CFA) were used to analyse the results that was performed using SmartPLS 3 software. Five hypothesis were constructed to examine the correlations between information quality components and user satisfaction. Results of this survey of 239 respondents indicates 79.1% of 19 to 24 years old have experience exploring digital games based on visual style information. This research revealed that Content, Accuracy, and Ease of Use influenced User Satisfaction when searching for game information based on the visual style. In contrast, the Format and Timeliness correlated weakly. Providing visual classification and appeal format has less impact on user satisfaction, while fast information retrieval and up-to-date information contributed insignificantly to user satisfaction. The motivation of this study is to assess terms used to classify visual styles and their application to information seeking, as these terms have an influence on user satisfaction. This study addressed the research objectives and aimed in a scholarly manner. Thus, the study contributes significant findings to future research on the visual styles classification information to be consistent and accurately presented for machine learning-based recommendation systems for digital game distribution platforms and digital game information archiving