In neuroscience, eye-tracking technology offers a novel approach to analyze the processes of the human brain by collecting participants’ eye movement data. This study employs eye-tracking equipment to delve deeply into the impact of brand preference on students’ cognitive behavior within the context of automobile marketing practices. Drawing upon prior research, the study’s framework is formulated. The hypothesis posits that preference intensity significantly influences the cognitive decision-making process, which is further subdivided into effects on information gathering behavior and the subsequent cognitive processing of that information. To facilitate this research, a questionnaire—developed by synthesizing, adapting, and refining previous studies—was administered. Its reliability and validity were evaluated through preliminary research. The study’s experiment was designed to combine eye-tracking data with questionnaire responses, aiming to capture and statistically analyze the cognitive behavior of relevant college students. The data was then used to validate the proposed hypothesis. Leveraging the eye tracker for experimental design, as opposed to conventional subjective evaluation methods, promises a more objective analysis of college students’ cognitive behaviors.
This paper established a theory framework of the correlation between consumers’ web information search and related products’ sales. Taking the Chinese customers’ search behavior using Baidu search engine and the search data left during the decision-making process, this paper built up and filtered a search keyword thesaurus with high correlation of automobile sales and leading time difference. Then a brand automobile monthly sales prediction model was set up based on BP Neutral Network. On this basis, this paper took a specific automobile model for example and predicted its monthly sales for a month. The predicted results showed that the absolute average percentage error was 5.6%, which was 0.5% lower than the MAPE model by improved principal component analysis, and the prediction accuracy was improved. The validity and rationality of the model were verified. This paper provides a new idea for product sales forecasting of automobile enterprises, and also can be used as a reference for other industries.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.