# Big Data and AI-Driven Product Design: A Survey
## 3 Traditional Product Design Methods
### KANO Model  
The KANO model is utilized in user research tasks to classify user requirements by revealing the nonlinear relationship between user satisfaction and product performance. For a long time, it was believed that user satisfaction is directly proportional to product performance. However, it has been shown that fulfilling individual product performance to a great extent does not necessarily lead to high user satisfaction, and not all performance factors are equally important. In the 1980s, [PERSON] conducted a detailed study on user satisfaction and proposed the KANO model [56], which divides user requirements into five types (Figure 6). Must-be quality refers to requirements that users take for granted. When fulfilled, users just feel normal; otherwise, they will be incredibly dissatisfied, such as the call function for mobile phones. One-dimensional quality is proportional to user satisfaction, such as the battery life for mobile phones. Attractive quality increases satisfaction when fulfilled but does not cause dissatisfaction when unfulfilled, such as the temperature display for cups. Indifferent quality has no impact on user satisfaction, such as the built-in pocket for coats. Reverse quality results in dissatisfaction when fulfilled, such as pearls on men's wear. Researchers have utilized the KANO model for various applications, including [PERSON] et al. [19], who categorized twelve features of mobile security applications through a structured KANO questionnaire, and [PERSON] et al. 's [13] examination of customer satisfaction based on aesthetic sentiments by integrating the KANO model with QFD. The KANO model is an essential tool for understanding user satisfaction and prioritizing product development efforts [57; 16]. According to the priority assigned by the KANO model, enterprises must ensure the must-be quality reach the threshold, and any additional investment is a waste; invest in one-dimensional quality as much as possible; prioritize attractive quality when the budget permits; avoid reverse quality and never waste resources on indifferent quality. However, not all customer requirements are equal, even within the same category. As the KANO model cannot distinguish the differences among requirements within the same category [58], [PERSON] et al. [59] proposed the IF-KANO model. This model adopts logical KANO classification criteria to categorize requirements. They  
Figure 6: The KANO model for user requirements classification [56]. The horizontal axis indicates how fulfilled the requirement is and the vertical axis indicates how satisfied the user is. take elevators as an example; results showed that both load capacity and operation stability belong to a one-dimensional quality but the priority of operation stability is slightly higher than load capacity. The KANO model relies on the data acquisition of the KANO questionnaire, which traditionally provides a single option or options within a given range, failing to fully capture the ambiguity and complexity of customers' preferences. As a result, numerous scholars have attempted to enhance the KANO questionnaire's options [60, 61]. For instance, [PERSON] et al. [62] proposed the fuzzy KANO questionnaire, which uses percentages instead of fixed options and allows participants to make multiple choices. [PERSON] and [PERSON] [63] introduced the Likert scale to gauge the degree of satisfaction or dissatisfaction. [PERSON] and [PERSON] [64] combined the Servqual scale and the KANO model in a complementary way. While some researchers have recognized that text can better express opinions than scale, they also note that text is challenging for traditional statistical methods [65]. In addition to improving questionnaire options, some studies have focused on questioning skills to enhance the KANO questionnaire. For example, [PERSON] et al. [66] suggested that questions should avoid polar wording. While improvements have been made to the KANO questionnaire, as mentioned in Section 3.1, it is important to acknowledge its limitations.