South African Generation Y students’ behavioral intentions to use university websites

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University websites are increasingly crucial in meeting the evolving digital demands of students. To effectively manage university websites, it is necessary to first determine students’ behavioral usage intentions of university websites and the factors that influence their intentions, which forms the purpose of this study. Data were collected at a single point in time and described the characteristics of the sample. This study, involving 319 Generation Y students registered at two South African university campuses (one traditional and one university of technology campus), utilizes structural equation modeling to explore the predictive relationships among information quality, system quality, playfulness, ease of use, trust, attitude, satisfaction, and behavioral intentions related to university website use. The study underscores the pivotal role of the university’s website in shaping student satisfaction, with information quality standing out as a significant positive influence. Additionally, playfulness significantly impacts both satisfaction and overall attitudes toward university websites. The system quality of the university website is also noteworthy, showing a statistically significant positive effect on ease of use and fostering trust among students. Furthermore, satisfaction is anticipated by ease of use, creating a cascade effect where satisfaction predicts trust and trust predicts attitudes. Ultimately, students’ attitudes emerge as a critical predictor for their behavioral intentions to use university websites. The model exhibits acceptable fit indices, demonstrating substantial explanatory power (SRMR = 0.1, RMSEA = 0.06, IFI = 0.94, TLI = 0.93, CFI = 0.94). These findings offer insights for university management and web designers to enhance online platforms, fostering student satisfaction, trust, and usage.

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    • Figure 1. Structural paths
    • Table 1. Sample’s demographic breakdown
    • Table 2. Summary statistics
    • Table 3. Measurement model statistics
    • Table 4. Path analysis
    • Conceptualization
      Marko van Deventer, Heleneze-Tianè Lues
    • Data curation
      Marko van Deventer, Heleneze-Tianè Lues
    • Formal Analysis
      Marko van Deventer
    • Methodology
      Marko van Deventer
    • Project administration
      Marko van Deventer, Heleneze-Tianè Lues
    • Software
      Marko van Deventer
    • Validation
      Marko van Deventer, Heleneze-Tianè Lues
    • Writing – original draft
      Marko van Deventer, Heleneze-Tianè Lues
    • Writing – review & editing
      Marko van Deventer, Heleneze-Tianè Lues
    • Investigation
      Heleneze-Tianè Lues