基于IPA分析人工智能生成的包装设计满意度及策略研究

Analysis on Design Satisfaction and Strategies of Artificial Intelligence Generated Packaging Based on IPA

中文关键词: 包装设计  AI生成设计  IPA分析法  消费者满意度

英文关键词: packaging design  AI generated design  IPA analysis method  user satisfaction

中文摘要:

      目的 从消费者感知评价角度出发，对人工智能(AI)生成化妆品包装设计的关键因素进行定量研究，分析其与消费者满意度之间的关系，提供基于AI的包装设计相关开发策略。方法 通过文献研究及用户访谈，综合专家意见构建出包含5个维度共18项因子指标的评价体系;依据评价指标对AI生成的包装设计进行受访者问卷调查;采用IPA模型分析AI生成化妆品包装设计的各项因子指标的重要程度与满意度。结果 研究发现消费者对AI生成包装设计的信息传达度和创意吸引力维度等方面表现出较高的认可度和满意度，而对包装的功能可用性和用户体验性方面则普遍倾向于不满意。结论 增强AI生成包装的实用性和用户体验是提升消费者满意度的关键。可通过现有成熟的包装公模训练AI模型、强化多模态学习丰富数据集、优化AI设计生成过程的可解释性及可控性、构建多元利益相关者参与的创生设计平台等策略提升AI设计的有效性及消费者满意度。

英文摘要:

      The work aims to quantitatively study the key factors of artificial intelligence (AI)-generated cosmetic packaging design from the perspective of user perception evaluation to analyze the relationship between them and user satisfaction, and provide AI-based packaging design-related development strategies. Through literature research and user interviews, an evaluation system containing 5 dimensions and 18 factor indicators was constructed in combination with experts' opinions; A respondent questionnaire survey was conducted on AI-generated packaging designs based on the evaluation indicators; And an IPA model was used to analyze the degree of importance and satisfaction of each factor indicator of AI-generated cosmetic packaging designs. The research found that users showed high recognition and satisfaction with AI-generated packaging design in terms of information communication and creative appeal dimensions, while they generally tended to be dissatisfied with the functional usability and user experience aspects of the packaging. Therefore, enhancing the utility and user experience of AI-generated packaging is a key to improve user satisfaction. The effectiveness and user satisfaction of AI design can be enhanced through strategies such as training AI models with existing mature packaging public models, enriching data sets by enhancing multi-modal learning, optimizing the interpretability and controllability of the AI design generation process, and building a multi-stakeholder participatory platform for generative design.