With the development of smart technology and the increasing variety of everyday products, factors influencing product service touchpoint design have become more diverse and complex. Existing service touchpoint design methods and models often focus narrowly on user research, co-design, and risk analyses, lacking a systematic approach. Consequently, they struggle to deliver solutions that align with user needs. This misalignment may result in issues such as increased cognitive load during product use, a diminished user experience, and lower evaluations of the product. In response, this paper proposes a service touchpoint design model, the “BEDFITA” model. It starts with user behavior and follows a structured, systematic process that includes understanding user behavior, recording user emotions, matching user needs, designing product functions, planning interaction experiences, designing service touchpoints, and analyzing failure risks. The Kano model is employed in the user requirement identification phase to provide more precise user requirement parameters, while FMEA is employed in the failure risk analysis phase to generate more accurate failure risk assessments. This ensures that the final service touchpoint design meets user needs and offers reliability and robustness. Finally, the feasibility and effectiveness of the proposed model are validated through a case study on the service touchpoint design of a smart desk.

In the traditional decision-making process for product form design, designers and experts often prioritize schemes based on their own knowledge and experience. This approach can lead to an oversight of user preferences, ultimately affecting decision outcomes. In contrast, crowd-intelligence-driven, multi-attribute decision-making for product form design in the cloud environment builds upon traditional approaches by leveraging the vast and diverse expertise of individuals on cloud platforms, engaging participants from various fields and roles in the decision-making process to enhance comprehensiveness and accuracy. To address the issue of a single decision-maker and limited user participation in the decision-making process for product form design schemes in the cloud environment, a multi-attribute decision-making method integrating expert knowledge and user preferences is proposed. This method aims to select a product form design scheme that optimally balances expert and user satisfaction. Initially, the Pythagorean Hesitant Fuzzy Set (PHFS) is used to quantify qualitative product attributes and to establish a comprehensive multi-attribute evaluation system. In the aspect of expert decision-making, a gray correlation coefficient decision matrix based on expert knowledge is established and the overall score of the base alternative is calculated by the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Improved Osculating Value method. In terms of user decision-making, weights are determined by calculating the similarity between user evaluation matrices, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to calculate scores for product form designs based on user preferences. Ultimately, optimal selection is achieved by aggregating the aforementioned expert evaluation values and user preference values. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design schemes.