A Comparative Study Assessing the Effectiveness of Machine Learning Technology Versus the Questionnaire Method in Product Aesthetics Surveys

In the current market landscape, it is imperative for products not only to meet consumer needs but also to possess an appealing appearance. Consequently, comprehending how consumers perceive the aesthetics of a product becomes crucial. This study aims to fill existing gaps by (1) critically evaluating the effectiveness of current survey tools for assessing consumer product aesthetics, and (2) exploring the application of machine learning (ML) technologies in consumer surveys. Additionally, we seek to (3) compare the questionnaire method and the method employed by ML technologies with conventional survey tools to gauge the practicality of assessing consumer product aesthetics. Our research uncovered two key findings: Firstly, ML technology exhibits remarkable accuracy in predicting consumer perceptions across five representative aesthetic descriptors. Secondly, when comparing the accuracy of the questionnaire method and ML technology in predicting consumer perceptions for these descriptors, ML consistently outperforms the traditional approach. These results indicate that AI, specifically ML technology, not only adeptly forecasts consumer perceptions of product aesthetics but also demonstrates superior performance compared to traditional methods. This study makes theoretical contributions by proposing a survey tool that utilizes artificial intelligence technology to investigate consumer product aesthetics, incorporating practical guidelines for the assessment. Furthermore, it offers valuable insights into consumer product aesthetics and explores the potential impact of technology on this field.