User-centered evaluation of an IntuNav in multi-browser virtual reality across diverse cognitive user profiles
Mochammad Hannats Hanafi Ichsan, Cecília Sik‐Lányi, Tibor Guzsvinecz, Aisshah Roesiana Dewi · Computers in Human Behavior Reports · 2025
This study examines the usability, user experience, and cognitive performance of an IntuNav that incorporates a Multi-Browser Virtual Environment (VE), a user-friendly desktop virtual reality (VR) system. The evaluation included three user groups that varied in characteristics: mainstream users, older adults, and students with neurodivergent conditions (Autistic Spectrum Disorders and Attention Deficit Hyperactivity Disorder). Fourteen hypotheses were developed to investigate differences in objective (5Q score, task time, error count, and perplexity) and subjective (SUS, IPQ, and NASA-TLX) metrics using a between-subjects experimental design. Statistical analyses indicated no significant differences in core performance metrics (5Q scores, error count) among groups, implying the system's overall usability. Significant variations in task time and perplexity were observed between older adults and neurodivergent users compared to mainstream users, highlighting the impact of cognitive and generational factors on navigational complexity. Older adults exhibited the highest subjective usability and presence scores, whereas cognitive load levels were elevated among older and neurodiverse users. The results indicate that the IntuNav navigation model and Multi-Browser VE provide inclusive and accessible desktop VR interaction for a diverse user base. This demonstrates the system's practical applicability in contexts necessitating multi-window VR interaction, including education, research, and digital productivity. Design recommendations are presented to enhance inclusivity, minimize cognitive demands, and improve adaptive navigation in future VR systems. An anonymized dataset and complete evaluation scripts are publicly accessible (OSF: 10.17605/OSF.IO/HU478), along with implementation resources (GitHub), which allows for reproducibility.