Using Structural Equation Modeling and the Behavioral Sciences Theories in Predicting Helmet Use

Abstract

In Malaysia, according to road accidents data statistics motorcycle users contributes more than 50% of fatalities in traffic accidents, and the major cause due to head injuries. One strategy that can be used to reduce the severity of head injuries is by proper usage of helmet. Although the safety helmet is the best protective equipment to prevents head injury, majority motorcycle user did not use or did not fasten properly. In understanding this problem, the behavioral sciences theory and engineering aspect are needed to provide better explanation and comprehensive insights into solutions. The Theory Planned Behavior (TPB) and Health Belief Model (HBM) were used in predicting the behavioral intention toward proper helmet usage among motorcyclist. While, a new intervention approach were used in Technology Acceptance Model (TAM) that based on the perception of a conceptual system called Safety Helmet Reminder System (SHR). Results show that the constructs variables are reliable and statistically significant with the exogenous and endogenous variables. The full structured models were proposed and tested, thus the significant predictors were identified. A multivariate analysis technique, known as Structural Equation Model (SEM) was used in modeling exercise.  Finally, the good-of-fit models were used in interpreting the implication of intervention strategy toward motorcyclist injury prevention program.