Analyzing AI-Generated Packaging's Impact on Consumer Satisfaction With Three Types of Datasets

Abstract

The study quantitatively examines how AI-generated cosmetic packaging design impact consumer satisfaction, offering strategies for database-driven development and design based on this evaluation. A comprehensive evaluation system consisting of 18 indicators in five dimensions was constructed by combining literature review and user interviews with expert opinions. On this basis, a questionnaire survey on AI-generated packaging design was conducted based on three types of datasets. In addition, importance-performance analysis was used to analyze the satisfaction of AI-generated packaging design indicators. The study found that while consumers are highly satisfied with the information transmission and creative attraction of AI-generated packaging design, the design's functional availability and user experience still have to be improved. It is suggested that the public model be combined into the data warehouse to build an AI packaging service platform. Focusing on the interpretability and controllability of the design process will also help increase consumer satisfaction and trust.

SSVEP-Enhanced Threat Detection and Its Impact on Image Segmentation

“…This approach could open new avenues for creating visually compelling content that resonates with human emotions and possesses a high degree of realism. For instance, recent studies have quantitatively examined how AI-generated cosmetic packaging designs impact consumer satisfaction and emotions (Chen, Luh, & Wang, 2023). By extending these approaches to emotional images, we could uncover new insights into creating more accurate and emotionally resonant images.…”