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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.12.2023
DOI
https://doi.org/10.7307/ptt.v35i6.393
ISSUE
2023 (Vol 35), Issue 6
LICENSE
Copyright (c) 2024 Fei YE, Wen CHENG
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Exploring Factors That Influence Instant Delivery Service Riders’ Red Light Running Behaviour – Development and Validation of a Questionnaire Based on the Theoretical Domains Framework

Authors:Fei YE, Wen CHENG

Abstract

To develop effective interventions to transit the instant delivery service riders towards avoiding red light running behaviour, a valid and reliable questionnaire is needed to identify the potential theoretical factors that influence the intention. This study describes the development and validation of the red light running behaviour causes questionnaire based on the theoretical domains framework. First, the exploratory factor analysis was used to identify the initial questionnaire’s underlying structure, including a set of 67 items in 13 domains. Next, confirmatory factor analysis was undertaken to assess the questionnaire’s reliability, discriminant validity and goodness of fit. CFA produced a proper fit with adequate discriminant validity and internal consistency. CFA and Cronbach’s alpha results in the final version of the RLRBCQ consisted of 39 items assessing 13 domains, explaining 69.799% of the variance, and internal consistency reliability values ranging from 0.710 to 0.825. These results suggest that the RLRBCQ demonstrates reliable, stable and valid properties, which can be used to assess potential determinants of avoiding red light running behaviour following the domains of the TDF. It can be utilised by safety managers and practitioners to guide the design of interventions for various traffic safety behaviours.

Keywords:instant delivery service riders, red light running behaviour, quantitative, theoretical domains framework, questionnaire development and validation

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