Abstract
Recent studies adopted models of user acceptance of information technology to predict and explain drivers’ acceptance of traffic information. Among these frameworks, the most commonly used is the Technology Acceptance Model (TAM). However, TAM is too general and does not consider drivers’ response in specific traffic conditions or choice scenarios. This study combines an extended TAM with different choice scenarios displayed by Variable Message Signs (VMS) into a Hybrid Choice Model (HCM). Two models are proposed. The first model takes into account the causal relationships among latent variables based on the following hypotheses: Information Quality (IQ) has a positive effect on Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) which, in turn, have a positive effect on the Behavioural Intention (BI) to use traffic information. In the second model, the four latent variables PU, PEOU, IQ, and BI are directly added to the utility function without any causal relationships. 339 drivers with valid licence were interviewed via Stated Preference (SP) survey and the results show that TAM can explain travellers’ response to VMS if the causal relationships among latent variables are taken into account. In addition, all hypothesized relationships are strongly supported. Practical and academic implications are also discussed.
Highlights
The Chinese economy has experienced an unprecedented boom in the last 30 years
In order to demonstrate the importance of accounting for the causal relationships among latent variables, we developed a model (HCM_ALL) in which all four latent variables are simultaneously incorporated into the utility function
Information Quality (IQ) has a direct and positive effect on Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), which have a direct positive effect on Behavioural Intention (BI), which, in turn, has a direct and positive effect on route diversion even though there is no improvement in terms of model fit, the model accounting for the hierarchical relationships has better explanatory power and is more conform with Technology Acceptance Model (TAM) than the model that directly adds the latent variables into the utility function
Summary
The Chinese economy has experienced an unprecedented boom in the last 30 years. The economic rise took place after China’s adoption of free-market strategies, which allowed the creation of new job opportunities and a fast development of Chinese cities. Several research efforts in the transportation arena have adopted psychometric methods of user acceptance of information technology to better understand travellers’ response to road guidance systems – see Isa et al (2015) for review. These methods focused on the useroriented design of ATIS and have demonstrated that attitudes and perceptions are major factors affecting drivers’ acceptance of traffic information. Previous research has demonstrated that travellers’ response to ATIS is affected by their attitudes and perceptions and by current traffic conditions such as occurrence of congestion, travel time, delay, number of signalized intersections, etc. The last section concludes this study with implications, limitations and directions for future research
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