The Structure of Risky Riding: A Factor Analysis of a Modified MRBQ to Predict Crash Risk in a Low-Income Setting - Audinda Virsa Leinia; Sham Sidhiq (Politeknik Negeri Semarang, 2026)
Road traffic injuries are a serious global health issue, especially in nations with low and moderate incomes. In 2021, for example, the Asia-Pacific region accounted for approximately 60% of all worldwide traffic fatalities, a figure that highlights the urgency of the problem. In Indonesia, a developing country, motorcycles are the most popular mode of transport, but they are also the deadliest, with riders accounting for over 70% of traffic deaths between 2013 and 2018. This issue is particularly pronounced among young adults (aged 18–25), who are responsible for 51% of all traffic accidents in the country.To address this, a comprehensive study was conducted using a modified Motorcycle Rider Behaviour Questionnaire (MRBQ) engaged to the Indonesian context. The research aimed to identify factors associated with unsafe riding behaviors, which are primarily driven by human factors. Of the 96 riders approached, 92 participated (96% response rate); most were male (61.25%), aged 19–20 years, students (97.80%), and used automatic motorcycles (89.70%), with 72% having less than 10 years of riding experience and 58% reporting a near-crash in the past year. Traffic violations (M = 3.65 ± 0.46) and errors (M = 2.12 ± 0.32) were the most prevalent behaviors and were significantly associated with negative attitudes, social norms, and crash involvement (r = 0.15–0.45, p < 0.01), whereas stunt-related behaviors were rare (M ≈ 1.02) and formed a distinct factor.These findings highlight the need for targeted interventions that focus on psychological factors like negative attitudes and social norms, which were found to be strongly linked to risky riding and crash history. This study demonstrates that the MRBQ exhibits a context-specific four-factor structure (errors, violations, safety gear violations, and stunts) among Indonesian young adult motorcyclists with strong psychometric support, while also highlighting the need for further item refinement and alternative data collection methods to address contextual behaviors.