Research on Innovative Design of Two-in-One Portable Electric Scooter Based on Integrated Industrial Design Method

Abstract

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain.

1. Introduction

In recent years, with the advancement of new energy technologies and the rise of personalized consumerism, electric scooters have gained increasing popularity worldwide [1]. Electric scooters function based on a combination of hardware and software, including footboard, wheels, motors, batteries, and control systems. They are lightweight and convenient, as well as trendy and individualistic. These attributes make them not only a convenient means of transportation but also a leisure product for daily entertainment, which has won them the favor of a broad consumer base, especially among the youth [2]. However, due to the rapid development of the electric scooter industry and excessive market competition, the quality of products in the industry is uneven, and the degree of product similarity is relatively high. Some electric scooter products excessively pursue a gorgeous and individualistic appearance while neglecting the actual needs of users. As a result, electric scooters are often left idle after use, with low sustainable usability. Therefore, it is necessary to conduct in-depth research on the innovative design of electric scooters to increase user stickiness and promote the sustainable development of electric scooter products [3].

Electric scooters, as an emerging trendy product, have garnered widespread attention globally, prompting scholars worldwide to conduct extensive research. For instance, Triviño-Cabrera et al. made advancements in wireless charging technology for electric scooters by designing and developing a magnetic resonance charger [4]. Tappa et al. proposed a micro smart electric scooter aimed at improving urban transportation [5], while Moosavi et al. explored the attitudes and perceptions of university students and staff toward using shared free-floating electric scooters (SFFES) on campus along with influencing factors through machine learning models [6].

As the attention towards this emerging product increases, research on the design of electric scooters is gradually becoming more in-depth. Wu et al., taking the product aesthetic design of electric vehicles as an example, proposed a preference-based evaluation-fuzzy-quantification method to determine the priority of developing the attractiveness factors of electric vehicles. Research shows that the quality of attractive perceptual images is reliable and can be used as the first choice to meet consumers’ perceptual needs [7]. Kwon et al. proposed a novel structure for electric scooters that is convenient for portable folding. After determining the specifications of the electric scooter to be manufactured, such as maximum speed, riding capability, tire diameter, and gradient angle, a prototype was fabricated and tested, leading to relevant conclusions on structural design [8]. Liu and Wang embarked on their research from the perspective of innovative service design for electric scooters. They utilized focus group interviews and user journey mapping to analyze user scenarios and requirements. Subsequently, they employed the theory of quality function deployment (QFD) to conduct an in-depth analysis of user needs and technologies. Based on these analyses, they developed design prototypes and service blueprints. Finally, they carried out a preliminary evaluation using the Kano model [9]. Hsiao and Chang proposed a design process based on the concept of concurrent design. In this method, objective tree analysis was first used to identify functions and establish design criteria. Then, the morphological chart method was applied to generate visual design solutions. The analytic hierarchy process and concept selection methods were employed to analyze and evaluate design details, resulting in the optimal design solution and thus creating an innovative electric scooter with higher competitiveness in the current market. The results show that the electric scooter is closer to consumer needs [10]. Ye et al. constructed an optimization model from two levels of functional experience and perceptual vision and applied it to the optimization design of two-wheeled electric scooters for campus sharing [11].

The current design research of electric scooter products mainly focuses on aspects such as product styling, structural design, and information services, while lacking exploration of user needs and product functions. This research applies the objective analysis algorithm of industrial design to the research of electric scooter design. On one hand, it carries out design work primarily through quantitative research to enhance the scientific nature and objectivity of design research. On the other hand, it targets the main user needs, integrates functional design with styling design organically, and achieves a harmonious unity between the two, resulting in an innovative electric scooter design that is not only functionally practical but also aesthetically pleasing to users.

2. Research Methods

2.1. Theoretical Model

The affinity diagram method, also referred to as the KJ method, finds extensive application across various disciplines, including management, statistics, and design. By continuously indulging in induction and summarization, it identifies connections and distinctions among various items and categorizes them, thereby visually representing complex and ambiguous issues with remarkable operability. The KJ method encourages diverse personnel to make bold hypotheses, spark inspiration, transcend traditional thinking boundaries, and engage in collective collaboration to make decisions, fostering new ideas and approaches to tackle thorny problems [12]. Liang and Luo applied the KJ method to analyze user demands for pet toys, establish an analytic hierarchy process model, and carry out design innovation for pet toys [13]. In the research on functional optimization of electric vehicle charging stations, Liu used the KJ method to analyze and organize the collected data, condensing the main needs of users [14].

The analytic hierarchy process (AHP) is a decision-making analysis method that integrates qualitative and quantitative analysis. It can systematize and clarify problems involving multiple objectives and criteria [15]. AHP is based on a systematic approach. It constructs a multi-level analytical structure model that includes the goal level, criterion level (sub-criterion level), and alternative level by considering the interconnections and distinctions among the elements in the system. Then, it performs pairwise comparisons of the elements within each level to form judgment matrices, calculates the weights for each level, and determines the priority rankings to identify the final preference requirements [16]. Kotecki and Pasławski applied the AHP method to the research of passenger rail concentration issues in the city of Poznań, highlighting the significant problems that need improvement [17]. De Regge et al. used AHP to investigate the importance of micro, meso, and macro levels of the palliative care ecosystem in Flanders, Belgium, guiding the effective implementation of palliative care [18]. Anjamrooz et al. employed AHP to evaluate the weights of sustainability selection criteria for construction projects, identifying key portfolio selection criteria [19].

The application of AHP first requires the identification of the problem system under research. The specific issue is rationally decomposed to form a multi-level analytical structural model, where the elements of the decision-making problem are transformed into a hierarchical model. This model consists of the top level (overall goal layer), intermediate level (criterion layer), and bottom level (alternative layer), representing a process from disorder to order [20].

Next, based on the AHP evaluation scale, a pairwise comparison method is applied to the elements at each level to construct a judgment matrix, as shown in

Table 1. AHP evaluation scale.

Scale	Definition
1	Indicates that two elements are of equal importance.
3	Indicates that the former is slightly more important than the latter.
5	Indicates that the former is significantly more important than the latter.
7	Indicates that the former is strongly more important than the latter.
9	Indicates that the former is extremely more important than the latter.
2, 4, 6, 8	Intermediate values representing compromises between adjacent judgments.
Reciprocals (1–9)	Indicates the inverse importance when comparing the elements in reverse order.

. The judgment matrix adopts the 1–9 scale method, where experts in the relevant field or representative users assign specific numerical values to assess the relative importance between pairs of elements [21].

The judgment matrix constructed using the scale method is denoted as $E$, and its solution formula is as follows:

$$E={(e_{ij})}_{n\times m}=\left[\begin{array}{cccc}{e}_{11}& e_{12}& \cdots & e_{1m}\\ e_{21}& e_{22}& \cdots & e_{2m}\\ ⋮& ⋮& \ddots & ⋮\\ e_{n1}& e_{n2}& \cdots & e_{nm}\end{array}\right]$$

(1)

In the equation, $e_{ij}$ represents the relative importance degree of the $i$-th element compared to the $j$-th element within the current hierarchy when evaluating a specific element from the upper level. The judgment matrix $E$ satisfies the condition $e_{ij}\times e_{ji}=1$, where $e_{ij}>0$ and $e_{ii}=1$.

The maximum eigenvalue of judgment matrix $E$ is denoted as ${\lambda }_{\max }$, and its corresponding eigenvector is represented by $\theta ={({\theta }_1,{\theta }_2,\cdots ,{\theta }_n)}^T$, which yields the following relationship:

$$E_{\theta }={\lambda }_{\max }\theta$$

(2)

The judgment matrix $E$ is normalized by column to obtain matrix $H={(h_{ij})}_{n\times m}$, which can be expressed as:

$$h_{ij}=e_{ij}/{\displaystyle \sum _{k=1}^n{e}_{kj}}(i,j=1,2,\cdots ,n)$$

(3)

To further process matrix $H$, the sum of each row (or column) is computed, expressed as:

$$s_i={\displaystyle \sum _{j=1}^n{h}_{ij}}(i=1,2,\cdots ,n)$$

(4)

The weight vector can be obtained by dividing the computed vector sum by $n$, that is:

$$\theta =s_i/n$$

(5)

The maximum eigenvalue of the judgment matrix can thus be calculated as:

$${\lambda }_{\max }={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n\frac{{(E_{\theta })}_i}{{\theta }_i}}$$

(6)

where ${(E_{\theta })}_i$ represents the $i$-th element in vector $E_{\theta }$, $i=1,2,\cdots ,n$.

The process of hierarchical single-level ranking is performed. Hierarchical single-level ranking refers to determining the weight vector that represents the importance ranking of elements at a given hierarchy level relative to a specific element at the immediately superior level, using the judgment matrix.

To ensure the rationality of element weights, a consistency check must be performed on the normalized judgment matrix. $CR$ (consistency ratio) is calculated as a consistency indicator to determine whether the consistency test is passed. The formula for the consistency test is as follows:

$$CR={\displaystyle \frac{CI}{RI}}={\displaystyle \frac{{\lambda }_{\max }-n}{n-1}}\times {\displaystyle \frac{1}{RI}}$$

(7)

$$CI={\displaystyle \sum _{j=1}^m{e}_j{CI}_j}$$

(8)

$$RI={\displaystyle \sum _{j=1}^m{e}_j{RI}_j}$$

(9)

Here, ${\lambda }_{\max }$ represents the maximum eigenvalue of the judgment matrix; $n$ denotes the order of the judgment matrix; $CI$ (consistency index) is the consistency measure of the judgment matrix; and $RI$ (random index) refers to the average random consistency index value for the corresponding order, with $RI$ values as specified in

Table 2. RI values.

Order	1	2	3	4	5	6	7	8	9
RI	0.00	0.00	0.58	0.89	1.12	1.24	1.32	1.41	1.45

.

When the random consistency ratio $CR<0.1$, the judgment matrix is considered to have passed the consistency test; otherwise, it fails and requires revision [22].

The core concept of quality function deployment (QFD) is to establish a strong linkage between customer requirements and manufacturing processes, effectively translating them into critical component and technical characteristic requirements in product design and manufacturing [23]. The house of quality (HoQ) serves as the pivotal tool for QFD implementation, where various elements including customer requirements, technical characteristics, and engineering parameters are systematically deployed through the HoQ framework [24]. A key advantage of QFD theory lies in its ability to quantitatively transform customer needs into technical requirements. By taking customer demands as the starting point, it effectively captures market trends and enhances product design competitiveness. However, this approach necessitates standardized and rigorous acquisition of customer requirements at the preliminary stage; otherwise, it may lead to deviations in subsequent product development and design. Li et al. applied QFD theory to map students’ differentiated needs for massive open online courses (MOOCs) into quality characteristics, proposing a student-need-driven MOOC quality improvement framework [25]. Tsang and Au employed the QFD method to construct an HoQ for the smart development of theme parks. Their research of 14 smart tourism technologies revealed varying correlation patterns with four smart tourism experience attributes (accessibility, informativeness, interactivity, and personalization) in theme parks [26]. Li and Kim investigated the relationship between user needs and design requirements for intangible cultural heritage craft applications using QFD theory, developing a user experience (UX) app design framework [27].

TRIZ theory refers to the theory of inventive problem solving developed by Soviet inventor Genrich S. Altshuller, which constitutes a systematic methodology for innovation and invention [28]. Derived from comprehensive analysis of 2.5 million global patents, this theory distills fundamental principles and universal patterns of innovation embedded within these patents. It provides a structured approach to guide innovative design and invention for technical problems, thereby reducing development costs while enhancing invention quality [29]. As a knowledge-based and systems-thinking-oriented problem-solving theory, TRIZ offers innovators a toolkit of knowledge-driven methodologies tailored to diverse problem types. Different problem models correspond to distinct toolkits, enabling innovators to systematically identify appropriate solutions [30]. Shie et al. integrated TRIZ theory with Kansei Engineering to effectively optimize healthcare and elderly care service processes [31]. Yao et al. employed the TRIZ methodology to develop an innovative drainage cover design addressing descaling, deodorization, and pest control [32].

2.2. Rationale for Methodological Selection

First, compared to alternative approaches, the KJ-AHP-QFD-TRIZ integrated method proposed in this research demonstrates distinct advantages in product innovation research. This integrated approach enables more systematic organization of users’ core requirements, effectively connects with inventive principles to drive innovation, and mitigates arbitrary design decisions. In contrast, other research methods exhibit notable limitations. For instance, while Yu et al. employed the AHP-TOPSIS integrated method for emotional design of children’s furniture [33], their design practice remained overly reliant on designers’ subjective judgments, which tends to generate ineffective solutions. Similarly, Desai et al. utilized TRIZ for conceptual innovation of wheelchairs and applied the AHP method for solution screening [34], yet their approach revealed evident deficiencies in addressing users’ core requirements. Consequently, the KJ-AHP-QFD-TRIZ integrated method proves more suitable for addressing the user needs and functional innovation of electric scooters.

Second, the KJ-AHP-QFD-TRIZ integrated method effectively bridges key research gaps in electric scooter product design, as illustrated in Figure 1: the KJ method resolves “ambiguity in user requirements” (Gap 1), the AHP method addresses “subjectivity in weight allocation” (Gap 2), and the QFD-TRIZ integration tackles “discontinuity in requirements–attributes transformation” (Gap 3).

Finally, the KJ-AHP-QFD-TRIZ integrated method exhibits strong complementarity and logical coherence. The process begins with the KJ method for extracting unstructured requirements, followed by the AHP method to quantify requirement priorities, then the QFD method to translate requirements into technical parameters, and concludes with the TRIZ method to resolve technical contradictions, thereby forming a comprehensive research framework. For example, Su and Wei applied the AHP method and QFD theory, subsequently leveraging TRIZ to propose solution strategies for critical components of a rosebud harvester, ultimately achieving favorable practical outcomes through optimized design [35].

In summary, the KJ-AHP-QFD-TRIZ integrated method demonstrates significant practical value in the design of electric scooter products. This research conducts electric scooter product innovation precisely under the guidance of this objective industrial design analysis framework.

2.3. Research Framework

Over the past years, integrated theoretical approaches have been extensively applied in product design with remarkable outcomes. The KJ method, as a qualitative research approach, when combined with AHP, enhances the rationality and scientific rigor of design processes. Wei et al. integrated the KJ method with AHP to develop a bottom-up exogenous innovation integration approach, which was successfully implemented in the design of community-based new energy vehicle charging services [36]. Liu et al. employed an integrated AHP-QFD methodology to identify core requirements of elderly drivers, thereby improving the age-friendly experience in vehicle–human interaction systems [37]. In the design of parent–child interactive literacy toys, Miao et al. utilized AHP to accurately prioritize user needs and applied QFD theory to transform these requirements into innovative design elements [38]. However, it should be noted that QFD theory itself does not provide engineering solutions. Practical applications often require integration with other methodologies to resolve technical contradictions and optimize product design and development. For instance, Hameed et al. proposed improvement strategies for pressure release valves (PRVs) through a QFD-TRIZ integrated approach, achieving sustainable innovative designs [39].

In existing research on electric scooter design, integrated theories have not been effectively utilized. This research focuses on user needs for electric scooters and incorporates industrial design objective analysis algorithms for innovative design. First, questionnaire surveys and user interviews are conducted to gather basic user requirements. The KJ method is then employed to analyze and categorize primary user needs, followed by the application of AHP to establish a hierarchical model of user requirements and determine their priorities. Subsequently, QFD method is integrated to construct an HoQ for electric scooter design, translating user needs into specific design requirements. Finally, TRIZ theory is applied to resolve technical contradictions and drive innovation in key components, while ensuring coherence with the overall product design. The research framework is illustrated in Figure 2.

3. Results

3.1. User Requirements Analysis of Electric Scooters Based on the KJ Method

Quantitative analysis of electric scooter user needs was conducted through questionnaire surveys, while qualitative analysis was performed via user interviews. The KJ method was employed to aggregate users’ core needs, which were then classified into primary, secondary, and tertiary levels of demand.

3.1.1. User Requirements Collection

Questionnaire surveys serve as a critical method for investigating user needs. To explore people’s preferences and intentions regarding electric scooters and preliminarily understand their usage demands, 249 online questionnaires were distributed. Within one week, 242 valid responses were collected, along with 7 invalid ones, resulting in a valid response rate of 97%. The questionnaire covered demographic information such as gender, age, and educational background, as well as factors influencing electric scooter purchases, usage purposes, frequency, and expected price ranges.

The survey results showed that 73.14% of respondents were male and 26.86% were female, while the age distribution indicated that 54.96% were aged 18–25, 36.36% were 26–35, with smaller proportions being under 18 (4.55%) or over 36 (4.13%), as illustrated in Figure 3.

User interviews can directly and effectively capture user needs, categorized into three types: structured, semi-structured, and open-ended interviews. Structured interviews focus on specific questions, offering low cost and rapid execution. However, the questions and responses tend to be limited, resulting in relatively superficial insights. Open-ended interviews typically explore in-depth perspectives on a particular issue, allowing interviewees to elaborate freely. This approach helps designers uncover users’ underlying thoughts, but requires careful moderation to prevent deviation from the core topic. Semi-structured interviews, positioned between structured and open-ended interviews, follow an outline that guides the discussion from general to specific questions. This method effectively assists designers in understanding user needs and uncovering deeper underlying issues. In this research, semi-structured interviews were conducted with 15 representative electric scooter users to obtain more intuitive user demands. An interview outline was designed based on users’ behavioral characteristics and usage needs regarding electric scooter products, and the interviews were conducted accordingly. See

Table 3. Interview outline for user research.

No.	Interview Questions
Q1	What is your age?
Q2	What is your occupation?
Q3	What is your highest level of education attained?
Q4	How frequently do you use electric scooters?
Q5	In what scenarios do you typically use electric scooters?
Q6	What inconveniences have you encountered during usage or storage?
Q7	What factors matter most to you when using electric scooters?
Q8	What are the main shortcomings of current electric scooters in your opinion?
Q9	What additional features would you like electric scooters to have?

for details.

3.1.2. KJ Analysis and Demand Categorization

This research employed the KJ method to systematically categorize and synthesize data collected from questionnaire surveys and user interviews, supplemented by expert recommendations. The primary user requirements for electric scooters were identified as first-level demands. These were further classified into second-level demand categories, including functional requirements, aesthetic requirements, human-machine interaction requirements, and safety requirements. Through iterative refinement, third-level specific demands were subsequently screened and extracted, as detailed in

Table 4. Summary of electric scooter user requirements.

First-Level Requirements	Second-Level Requirement	Third-Level Requirements
Electric Scooter User Requirements	Functional requirements	Load-bearing capacity; Foldable; Portable; Multifunctional; Long battery life; Fast and convenient charging
	Aesthetic requirements	Sleek and streamlined; Fashionable and distinctive; Harmonious color scheme; Aesthetically pleasing form; Exquisite detailing; Well proportioned
	Human–machine interaction requirements	App-connectivity; Equipped with display interface; Intuitive control interface; Voice prompts; Smart unlocking mechanism; Ergonomically optimized
	Safety requirements	Sustainable material composition; Robust structure; Real-time location tracking; Smart auto-lock; Night illumination; High-efficiency braking

.

3.2. AHP Model Construction and Weighting

The AHP method was employed to establish a hierarchical analysis model of user requirements for electric scooter products, constructing judgment matrices for user needs and performing consistency verification; through aggregation and prioritization of weight calculations across all hierarchical levels, the priority ranking of each user requirement was determined.

3.2.1. AHP Model Construction

According to the user demand indicator system, the secondary-level user demands identified through inductive analysis were converted into first-level AHP indicators, while the tertiary-level demands were transformed into second-level indicators, thereby establishing an AHP model. This hierarchical model consists of three distinct layers: the goal layer, criterion layer, and sub-criterion layer. See Figure 4 for details.

3.2.2. Judgment Matrix Formation and Weight Calculation

Four experts in the field of industrial design were invited to score pairwise comparisons of demand elements at the same level under the primary and secondary indicators of electric scooters according to AHP evaluation criteria.

Table 5. The four experts’ qualifications.

Expert ID	Academic/ Professional Title	Years of Experience	Affiliation	Conflict of Interest Statement
E1	Professor	20	[Anonymized] School of Design, China	No conflict of interest
E2	Professor	14	[Anonymized] School of Design, China	No conflict of interest
E3	Senior Industrial Designer	8	[Anonymized] Industrial Design Company, China	No conflict of interest
E4	Senior Engineer	12	[Anonymized] Electric scooter Manufacturer, China	No conflict of interest

summarizes the four experts’ qualifications.

First, the scores of the primary indicators in the criterion layer were compiled, and four judgment matrices for the primary indicators were constructed accordingly, as shown in

Table 6. Judgment matrix of primary indicators by expert 1.

	B1	B2	B3	B4
B1	1	7	3	1/2
B2	1/7	1	1/3	1/8
B3	1/3	3	1	1/4
B4	2	8	4	1

,

Table 7. Judgment matrix of primary indicators by expert 2.

	B1	B2	B3	B4
B1	1	6	3	1
B2	1/6	1	1/3	1/4
B3	1/3	3	1	1/4
B4	1	4	4	1

,

Table 8. Judgment matrix of primary indicators by expert 3.

	B1	B2	B3	B4
B1	1	7	1	1/3
B2	1/7	1	1/5	1/9
B3	1	5	1	1/3
B4	3	9	3	1

and

Table 9. Judgment matrix of primary indicators by expert 4.

	B1	B2	B3	B4
B1	1	2	4	1/6
B2	1/2	1	2	1/9
B3	1/4	1/2	1	1/9
B4	6	9	9	1

.

The geometric mean method was employed to calculate the average scores assigned by the four experts to each element under the primary indicators of electric scooter requirements. The calculation formula is as follows:

$$\mathrm{G}=\sqrt[n]{a_1\times a_2\times \cdots a_n}$$

(10)

The primary indicator judgment matrix integrating evaluations from all four experts was calculated using Formula (10), as presented in

Table 10. The synthesized judgment matrix for primary indicators.

	B1	B2	B3	B4
B1	1	4.924298	2.449490	0.408248
B2	0.203075	1	0.459150	0.140149
B3	0.408248	1/2	1	0.219346
B4	2.449490	7.135243	4.559014	1

.

A consistency test was conducted on the judgment matrix of primary indicators for comprehensive scoring. Based on Equation (6), the maximum eigenvalue (${\lambda }_{\max 0}$) of the judgment matrix was calculated as 4.029. Using Equation (8), the consistency index (${CI}_0$) was determined to be 0.01, while the random index (${RI}_0$) was 0.89. According to Equation (7), the consistency ratio (${CR}_0$) of the judgment matrix was computed as 0.011. Since ${CR}_0<0.1$, the judgment matrix satisfied the consistency check, confirming its rationality and validity.

Similarly, judgment matrices were constructed for the demand elements within the sub-criteria layer, as shown in

Table 11. The synthesized judgment matrix for the secondary indicator “Functional requirements (B1)”.

	C1	C2	C3	C4	C5	C6
C1	1	0.795271	0.422107	0.577350	1.203801	1.565085
C2	1.257433	1	0.789690	0.562341	0.930605	1.106682
C3	2.369096	1.266320	1	0.840896	2.300327	2.702400
C4	1.732051	1.778279	1.189207	1	2.783158	3.201086
C5	0.830702	1.074570	0.434721	0.359304	1	2.059761
C6	0.638943	0.903602	0.370041	0.312394	0.485492	1

,

Table 12. The synthesized judgment matrix for the secondary indicator “Aesthetic requirements (B2)”.

	C7	C8	C9	C10	C11	C12
C7	1	1.967990	3.130169	1.446254	1.000000	0.945742
C8	0.508133	1	2.645751	1.800103	0.945742	1.124683
C9	0.319472	0.377964	1	0.500000	0.250986	0.359304
C10	0.691442	0.555524	2.000000	1	0.508133	0.537285
C11	1.000000	1.057371	3.984283	1.967990	1	0.622333
C12	1.057371	0.889140	2.783158	1.861210	1.606857	1

,

Table 13. The synthesized judgment matrix for the secondary indicator “Human–machine interaction requirements (B3)”.

	C13	C14	C15	C16	C17	C18
C13	1	2.407603	0.440056	2.149140	1.106682	0.260847
C14	0.415351	1	0.241498	1.812825	0.795271	0.193049
C15	2.272439	4.140825	1	5.634626	2.912951	0.594604
C16	0.465302	0.551625	0.177474	1	0.438691	0.219346
C17	0.903602	1.257433	0.343295	2.279507	1	0.268642
C18	3.833659	5.180040	1.681793	4.559014	3.722419	1

and

Table 14. The synthesized judgment matrix for the secondary indicator “Safety requirements (B4)”.

	C19	C20	C21	C22	C23	C24
C19	1	0.260847	0.866025	1.057371	0.268642	0.379918
C20	3.833659	1	4.486046	4.486046	2.059767	1.235931
C21	1.154701	0.222913	1	1.861210	0.594604	0.290715
C22	0.945702	0.222913	0.537285	1	0.386097	0.343295
C23	3.722419	0.485492	1.681793	2.590020	1	0.903602
C24	2.632148	0.809107	3.439791	2.912951	1.106682	1

, and consistency tests were subsequently performed for each matrix.

A consistency test was conducted on the judgment matrix of the secondary indicator “Functional requirements (B1)” for comprehensive scoring. Based on Equation (6), the maximum eigenvalue (${\lambda }_{\max 1}$) of the judgment matrix was calculated as 6.108. Using Equation (8), the consistency index (${CI}_1$) was determined to be 0.022, while the random index (${RI}_1$) was 1.24. According to Equation (7), the consistency ratio (${CR}_1$) of the judgment matrix was computed as 0.017. Since ${CR}_1<0.1$, the judgment matrix satisfied the consistency check, confirming its rationality and validity.

A consistency test was conducted on the judgment matrix of the secondary indicator “Aesthetic requirements (B2)” for comprehensive scoring. Based on Equation (6), the maximum eigenvalue (${\lambda }_{\max 2}$) of the judgment matrix was calculated as 6.122. Using Equation (8), the consistency index (${CI}_2$) was determined to be 0.024, while the random index (${RI}_2$) was 1.24. According to Equation (7), the consistency ratio (${CR}_2$) of the judgment matrix was computed as 0.019. Since ${CR}_2<0.1$, the judgment matrix satisfied the consistency check, confirming its rationality and validity.

A consistency test was conducted on the judgment matrix of the secondary indicator “Human–machine interaction requirements (B3)” for comprehensive scoring. Based on Equation (6), the maximum eigenvalue (${\lambda }_{\max 3}$) of the judgment matrix was calculated as 6.096. Using Equation (8), the consistency index (${CI}_3$) was determined to be 0.019, while the random index (${RI}_3$) was 1.24. According to Equation (7), the consistency ratio (${CR}_3$) of the judgment matrix was computed as 0.015. Since ${CR}_3<0.1$, the judgment matrix satisfied the consistency check, confirming its rationality and validity.

A consistency test was conducted on the judgment matrix of the secondary indicator “Safety requirements (B4)” for comprehensive scoring. Based on Equation (6), the maximum eigenvalue (${\lambda }_{\max 4}$) of the judgment matrix was calculated as 6.098. Using Equation (8), the consistency index (${CI}_4$) was determined to be 0.020, while the random index (${RI}_4$) was 1.24. According to Equation (7), the consistency ratio (${CR}_4$) of the judgment matrix was computed as 0.016. Since ${CR}_4<0.1$, the judgment matrix satisfied the consistency check, confirming its rationality and validity.

For the judgment matrices that passed the consistency test, further calculations were performed. The eigenvector corresponding to the maximum eigenvalue of each judgment matrix represents the weights of the respective indicators. The sum–product method was employed to normalize the eigenvectors, thereby obtaining the local weights of each indicator. These local weights were then multiplied by the corresponding weights of their parent-level indicators to derive the global weights, as presented in

Table 15. Summary of weighting coefficients for hierarchical indicators in electric scooter user requirements.

Primary Indicators	Weight	Secondary Indicator	Relative Weight	Comprehensive Weight	Rank
B1	0.2724	C1	0.1293	0.0352	12
		C2	0.1419	0.0387	10
		C3	0.2422	0.0660	5
		C4	0.2723	0.0724	4
		C5	0.1262	0.0344	13
		C6	0.0882	0.0240	15
B2	0.0623	C7	0.2799	0.0174	16
		C8	0.1809	0.0113	21
		C9	0.0638	0.0638	6
		C10	0.1174	0.0073	23
		C11	0.2015	0.0126	19
		C12	0.2166	0.0135	18
B3	0.1214	C13	0.1214	0.0147	17
		C14	0.0729	0.0089	22
		C15	0.2746	0.0333	14
		C16	0.0540	0.0066	24
		C17	0.1022	0.0124	20
		C18	0.3749	0.0455	8
B4	0.5439	C19	0.0752	0.0409	9
		C20	0.3326	0.1809	1
		C21	0.0930	0.0506	7
		C22	0.0693	0.0377	11
		C23	0.1951	0.1061	3
		C24	0.2348	0.1277	2

.

3.3. Electric Scooter Design Strategy Based on the Integrated Theory of QFD and TRIZ

3.3.1. Construction of HoQ Model for Electric Scooter Products

According to the QFD theory, an HoQ model for electric scooters was established. First, the 24 user requirement indicators and their comprehensive weight values obtained from the previous research were filled into the left wall of the HoQ. After reviewing relevant literature [40] and consulting expert opinions, 13 technical characteristic indicators were summarized and categorized into the ceiling of the HoQ: functional zoning, compact size, lightweight design, styling and color, material and structure, smart interaction, usability, safety devices, frame, footboard, handlebars, motor and control system, and battery pack. The roof of the HoQ was constructed by analyzing the pairwise positive or negative correlations among these technical characteristics. Next, the user requirement indicators and technical characteristic indicators were compared pairwise, and their relationships were quantified using an assignment method to determine the degree of correlation. The resulting scores were filled into the room section of the HoQ, forming a correlation matrix between user requirements and technical characteristics. Finally, the absolute and relative weights of the technical characteristics were calculated, as illustrated in Figure 5.

Through the construction of HoQ, user requirements for electric scooters were systematically translated into technical characteristic requirements. Based on the computational results, the relative weights of the technical characteristics were prioritized as follows for innovative product development (with percentage weights indicated): frame (11.23%), footboard (10.72%), handlebars (10.62%), usability (10.08%), safety devices (10.04%), material/structural integrity (9.17%), functional zoning (7.6%), compact size (6.65%), lightweight design (5.49%), smart interaction (5.14%), battery pack (4.71%), styling and color (4.64%), and motor and control system (3.91%). This indicates that compared to components such as batteries and motors, parts like the frame, footboard, and handlebars require more focused design. At the same time, electric scooter products need to be designed for ease of use, portability, and safety assurance. Furthermore, functional diversification, along with material and structural considerations, constitutes another critical focus in the design process. In terms of functionality, the product’s basic features and extended capabilities should be organically integrated; in terms of material and structure, a reasonable design is essential to meet ergonomic requirements while controlling product weight and complexity.

Through the analysis of positive and negative correlation degrees among technical characteristic indicators in the HoQ, it is evident that there are three pairs of negatively correlated technical characteristic indicators. This indicates the existence of technical contradictions between functional zoning and compact size, lightweight design and material/structural integrity, as well as smart interaction and usability, which require further analysis and resolution.

3.3.2. Contradiction Analysis Based on TRIZ Theory

The TRIZ theory is applied to address these three pairs of contradictions derived from the HoQ. It is necessary to determine the type of contradiction to select the corresponding toolset for problem-solving.

(1)

Functional zoning versus compact size

The user’s paramount expectation for electric scooters lies in functional diversification, which translates into the technical characteristic of functional zoning. Typically, a product’s fixed spatial domain can accommodate only one primary function, and diversified functional zoning necessitates increased volumetric dimensions of functional components to accommodate additional functional areas. However, this contradicts the essential requirement for lightweight and portability in electric scooters, where the spatial footprint of functional components must be minimized. Consequently, this creates a physical contradiction within the scooter’s internal components—the simultaneous demand for both extensive functional partitioning and minimal spatial dimensions.

(2)

Lightweight design versus material/structural integrity

According to the TRIZ unified engineering parameters, the lightweight characteristic of electric scooters corresponds to the parameter “weight of moving object,” while the material/structural integrity characteristic aligns with “stability of the structure.” Consequently, the contradiction between lightweight design and material/structural integrity is identified as a technical conflict.

(3)

Smart interaction versus usability

The primary consumer demographic for electric scooters consists of young adults, for whom smart connectivity with IoT ecosystems (particularly smart homes) is an essential requirement. As the software-level UI design and hardware-level human–machine interfaces become increasingly sophisticated, user experience has been significantly enhanced. However, this evolution has concurrently increased operational complexity in terms of both procedural steps and interdependencies among system components, thereby introducing contradictions within the scooter’s subsystems.

When analyzed through the TRIZ unified engineering parameters framework: The smart interaction characteristics are defined as “complexity of the device”. The usability characteristics are defined as “ease of operation”. This constitutes a verified technical contradiction between smart interaction and usability.

Based on the comprehensive analysis above, three types of conflicts in electric scooter products have been identified, as presented in

Table 16. Identification of contradictions.

Contradiction	Conflict Type	Improving Parameter	Worsening Parameter
Functional zoning vs. Compact size	Physical conflict	——	——
Lightweight design vs. Material/structural integrity	Technical conflict	Weight of moving object	Stability of structure
Smart interaction vs. Usability	Technical conflict	Ease of operation	Complexity of device

.

3.3.3. Conflict Resolution (TRIZ-Based Problem Solving)

(1)

Applying the Physical Contradiction Resolution Theory to Address the Tension Between Functional Zoning and Compact Size.

The theory of physical contradictions cannot resolve contradictory conflicts between components through conventional inventive methods as technical contradictions do. TRIZ theory specifically addresses physical contradictions by proposing the principle of separation. Its fundamental premise is that a single component cannot simultaneously possess contradictory characteristics in the same space, time, or under the same conditions. Therefore, physical contradictions can be resolved by separating conflicting requirements according to different spaces, times, or conditions. The four separation principles of physical contradiction resolution theory are explained in detail in

Table 17. The four separation principles and their definitions.

Separation Principle	Definition
Spatial Separation	Separating contradictory properties into distinct spatial domains through distance or intermediate media.
Temporal Separation	Applied when contradictory properties appear exclusively in different time periods.
Whole-Part Separation	Resolving conflicting requirements by distinguishing between macro (system-level) and micro (component-level) characteristics.
Condition-based Separation	Separation achieved by altering environmental parameters (e.g., temperature, pressure) to satisfy opposing requirements under different conditions.

.

The electric scooter product can be broadly divided into functional components such as the handlebars, footboard, frame, battery module, motor system, and decorative parts. Considering the functional requirement of cargo-carrying capacity, an analysis of each component’s functional attributes reveals that the spatial separation principle can be applied to innovatively redesign the footboard component. According to TRIZ theory, the spatial separation principle encompasses 10 inventive principles, as detailed in

Table 18. Inventive principles corresponding to spatial separation and their definitions.

No.	Inventive Principle	Definition
1	Segmentation	Divide an object into independent parts or make it modular.
2	Extraction	Remove an interfering part or property from an object, or extract only the necessary part.
3	Local Quality	Transition from a uniform structure to non-uniform composition or optimize different zones for different functions.
4	Asymmetry	Replace symmetrical forms with asymmetrical ones to enhance functionality.
7	Nesting	Designing a system or object to be embedded within another system or object.
13	Inversion	Applying reverse thinking to flip or reposition objects, systems, or operational processes.
17	Dimensionality Change	Altering an object’s motion or configuration across 1D, 2D, or 3D spaces to achieve varied functional effects.
24	Intermediary	Introducing a mediator (temporary or permanent) between interacting objects or systems.
26	Copying	Substituting unsuitable materials with low-cost, readily available, or redesigned alternatives.
30	Flexible Shells	Replacing rigid structures with flexible enclosures or membranes to isolate or protect systems.

.

Through systematic analysis, Principles 2 (Extraction), Principles 3 (Local Quality), and Principles 13 (Inversion) were employed to resolve the physical contradiction between functional zoning and compact size in electric scooters. Regarding the footboard component, its primary functions are distributed as follows: upper surface for rider standing, internal space for battery assembly and related components, and side surfaces for decorative elements or light strips. The introduction of cargo-carrying functionality on the upper surface would create interference with the standing function, necessitating increased footboard dimensions to maintain riding comfort. To address this, we implemented spatial separation by relocating the cargo function to the underutilized lower surface through reverse thinking. This innovative approach maintains compact overall dimensions while adding functionality. During normal riding mode, the underside cargo system employs a foldable concealed design; when transporting goods, it transforms into hand truck mode by deploying the reverse-side functionality. This dual-mode solution achieves multifunctional zoning without spatial expansion, thereby eliminating the physical contradiction.

As illustrated in Figure 6, the retractable truck cover in stowed position attaches compactly to the battery module, preserving the scooter’s streamlined profile. When activated (Figure 7), the cover pivots on its rear hinge to deploy folding wing, with locking mechanisms forming a four-sided cargo enclosure. This foldable container design provides operational convenience through intuitive transformation, versatile functional zoning, and seamless integration with the scooter’s aesthetic design.

(2)

Application of Technical Contradiction Resolution Theory to Address the Dilemma Between Lightweight Design and Material/Structural Integrity.

A contradiction matrix was constructed to address the conflict between lightweight design and material/structural integrity in electric scooters, with the improving parameter being “weight of moving object” and the worsening parameter being “structural stability.” According to the TRIZ contradiction matrix analysis, this specific conflict corresponds to four inventive principles: 1 (Segmentation), 35 (Parameter Changes), 19 (Periodic Action), and 39 (Inert Environment), as detailed in

Table 19. Resolution principles for the conflict of lightweight design and material/structural integrity.

No.	Principle	Definition
1	Segmentation	Applying decomposition methodology to divide an integrated system into multiple distinct subsystems for redesign purposes.
35	Parameter Changes	Modifying physical states (solid/liquid/gas) or adjusting parameters such as concentration, temperature, and pressure.
19	Periodic Action	Converting continuous actions into rhythmic periodic operations, or altering existing periodic frequencies.
39	Inert Environment	Substituting normal environments with inert ones, or introducing inert elements into object systems.

.

In the product design idea, the principle 1 (Segmentation) is used for reference, and the parts of the electric scooter are divided according to the functional needs. In the actual use of the electric scooter, different parts bear different functional divisions, that is, the structural strength required by different parts is different, so the structure of the parts that the owner of the electric scooter needs to be stressed can be strengthened in the design. The technical conflict between lightweight and material/structural integrity can be solved by reasonably reducing the component counterweight while strengthening the stability and strength of key components in terms of material selection to meet the needs of different functional components.

Departing from the traditional integrated design connecting the frame and footboard in conventional electric scooters, this research employs the combined decomposition method based on Principle 1: Segmentation to propose a novel structure. This structure features dual left-right frames, a fixed-axis connection to the footboard, and integrated dual DC brushless motors controlling the front wheel. This architectural optimization significantly reduces overall vehicle weight, as illustrated in Figure 8. Furthermore, handlebar-controlled rotation and folding of the dual-frame assembly achieve enhanced portability, fulfilling the demand for a lighter and more compact user experience.

(3)

Employing Technical Contradiction Resolution Theory to Reconcile the Tension Between Smart Interaction and Usability.

To resolve the tension between smart interaction and usability in electric scooter design, a TRIZ contradiction matrix was constructed with operability (TRIZ parameter #33) as the improving parameter and device complexity (TRIZ parameter #36) as the worsening parameter. Analysis of the TRIZ contradiction matrix indicates four corresponding inventive principles: 32 (color changes), 26 (copying), 12 (equipotentiality), and 17 (dimensionality change), as systematically detailed in

Table 20. Resolution principles for the conflict of smart interaction and usability.

No.	Principle	Definition
32	Color Changes	Altering the color, transparency, or other visual attributes of an object or environment to enhance visibility.
26	Copying	Replacing an unsuitable material system with a low-cost, readily available, or redeveloped material system.
12	Equipotentiality	Modifying working conditions or redesigning the object/environment to counteract inherent system stresses.
17	Transition to Another Dimension	Changing the spatial dimensions (1D, 2D, or 3D) of an object or its motion to achieve different application effects.

.

Referring to principle 32 (change color), it is necessary to clearly design the color hierarchy in the intelligent interactive interface of electric scooter. This ensures distinct visual layers among elements, enhancing usability and avoiding misinterpretation of operations. Synthesizing Principle 26 (copying) and Principle 12 (equipotentiality), the design balances overall complexity between the hardware system and software interaction. Specifically, the smart lock/unlock function is replicated beyond the app interface onto devices utilizing near-field communication (NFC) technology. This includes dedicated NFC unlockers or NFC capability integrated into common devices like smartphones and smartwatches. This implementation enables tap-to-unlock functionality. By optimizing this frequent user operation according to user needs, the design reduces systemic complexity, enhances the intelligent user experience and effectively resolves the technical conflict between sophisticated smart interaction and usability.

4. Design Practice

4.1. Design Development

Based on user needs research, the functional positioning of the innovative electric scooter product is defined for riding and cargo transport. The electric scooter mode caters to commuting and recreational needs, while the truck mode meets cargo-carrying demands. The two modes can be switched according to different scenario requirements, resulting in a portable 2-in-1 electric scooter design.

4.1.1. Conceptual Sketch Design

The design is further developed based on the key components of the electric scooter, incorporating the priority of user needs to refine the overall vehicle, as illustrated in the conceptual sketch design (Figure 9). To enhance safety, a full-width taillight is adopted at the rear for better visibility. The rounded edges of the footboard align with the scooter’s overall smooth and curved aesthetic. The handlebars and U-shaped frame feature a hollow design while maintaining a continuous connection, ensuring structural stability. Additionally, the proportions and styling of the scooter are adjusted to optimize its appearance, achieving a harmonious balance between functionality and structural integrity.

4.1.2. Ergonomic Analysis of Electric Scooter

Ergonomics is a comprehensive discipline that studies the dimensional relationship between humans, products, and environments. Through ergonomic analysis of the innovative electric scooter, reasonable product dimensions can be determined to enhance user-product compatibility, forming an effective human–machine system and improving usability. This innovative electric scooter product is designed for the Chinese market, and therefore its ergonomic analysis is based on the National Standard of Human Dimensions for Chinese Adults (GB/T 10000-2023) implemented by the Standardization Administration of China [41].

In the electric scooter mode, the user stands on the footboard while riding, where the human–machine contact points in this interactive system are the handlebars and footboard. Therefore, key design considerations include the height from the footboard to the handlebars, as well as the width and length dimensions of the footboard, to ensure comfortable riding. In the truck mode’s human–machine system, the sole contact point is the handlebars, making the vertical height from the handlebars to the ground after folding the primary design focus. Based on this analysis, the handlebar-to- footboard height must exceed the standing functional hand height; the electric scooter’s footboard width should be greater than the single-foot width; and the footboard length must be longer than the double-foot length. The standing functional hand height, foot length, and foot width measurements for Chinese adults (male and female) from GB/T 10000-2023 are referenced in

Table 21. Anthropometric dimensions of functional hand height, foot length and foot width for Chinese adults (unit: mm).

Gender	Age Range	Percentile	Functional Hand Heigh	Foot Length	Foot Width
Male	18–70	1st	649	224	85
Male	18–70	5th	681	232	89
Male	18–70	10th	696	236	91
Male	18–70	50th	750	250	98
Male	18–70	90th	806	264	104
Male	18–70	95th	823	269	106
Male	18–70	99th	854	278	110
Female	18–70	1st	617	208	77
Female	18–70	5th	644	215	82
Female	18–70	10th	658	218	83
Female	18–70	50th	705	230	90
Female	18–70	90th	753	243	96
Female	18–70	95th	767	247	98
Female	18–70	99th	797	256	102

.

The current Chinese national standard GB/T 12985-1991 (General Principles for Applying Body Dimensions Percentiles in Product Design) [42] specifically stipulates the selection criteria of body dimension percentiles for industrial product design involving human body dimensions. According to product functional characteristics, it categorizes product dimension design into four types, as shown in

Table 22. General principles for selecting body dimension percentiles in product design.

Category	Definition	Body Dimension Percentiles	Examples
Type I	Dual-limit design: Adjustable products to accommodate users of different body sizes	Upper limit: $P_{99}$/Lower limit: $P_1$ or $P_5$	Car seats, rearview mirrors
Type IIA	Large-size design: Products only need to accommodate users with maximum body dimensions	Upper limit: $P_{99}$, $P_{95}$ or $P_{90}$	Doors, cup handles
Type IIB	Small-size design: Products only need to accommodate users with minimum body dimensions	Lower limit: $P_1$, $P_5$ or $P_{10}$	Bookshelves, fan grilles
Type III	Average-size design: Products where user size is irrelevant or limit-based design is not applicable	($P_{50}$ male + $P_{50}$ female)/2	Door handles, public seating

.

The 2-in-1 portable electric scooter, being designed as a seatless product with non-adjustable handlebar height, is classified as a Type III product for ergonomic dimension design according to the principles of GB/T 12985-1991. Based on data from Table 20 and the calculation formula in Table 21, the determined height from handlebar to footboard is 727.5 mm, where appropriate dimensional adjustments according to human body percentile measurements are both reasonable and necessary in product design. To ensure optimal comfort, the actual dimensional design should incorporate functional corrections (additional dimensions to guarantee product functionality) and psychological corrections (dimensions considered to mitigate or eliminate spatial constraints, discomfort, and to enhance visual aesthetics), consequently requiring the optimal handlebar-to-footboard height to exceed 727.5 mm. Similarly, the optimal footboard length should exceed 480 mm and the optimal width should exceed 94 mm. All other dimensions of the 2-in-1 portable electric scooter are likewise determined through comprehensive consideration in accordance with national standards GB/T 10000-2023 and GB/T 12985-1991, ensuring overall human–machine system coordination and improved user satisfaction. The resulting dimensional design is illustrated in Figure 10 and Figure 11.

4.1.3. Digital Modeling of 2-in-1 Electric Scooter

The 3D digital model of the 2-in-1 portable electric scooter was constructed using Rhino 6 software, incorporating complete assemblies of main components (handlebars, frame, footboard, tires) and other detailed accessories. The Rhino interface features four interconnected viewports: the top-right viewport displays the perspective view, the top-left shows the Top view, the bottom-left presents the Front view, and the bottom-right exhibits the right view. The 3D digital model in electric scooter mode is illustrated in Figure 12, while the truck mode and folded storage mode are shown in Figure 13 and Figure 14 respectively.

4.2. Final Design

4.2.1. Design Outcomes

The 2-in-1 portable electric scooter features smart positioning and integrates with smart home IoT systems via app. The functional structure emphasizes robust yet rational design, particularly focusing on the frame, handlebars, and footboard to highlight lightweight portability. The conversion mechanism between scooter and truck modes is designed to be simple and logical, significantly improving product usability. Product renderings are shown in Figure 15, Figure 16, Figure 17 and Figure 18.

In terms of material design, the frame and handlebars of the product are primarily constructed from aluminum alloy. Aluminum alloy is characterized by its lightweight nature, oxidation resistance, and robust texture, enabling it to withstand a certain degree of impact and compression. The footboard is mainly made of aluminum alloy and ABS plastic. ABS plastic exhibits heat resistance, chemical corrosion resistance, a certain level of surface strength, and good stability. The synergy between different materials meets the requirements for both balance, stability, and portability. The tires are fabricated from PU (polyurethane) material, which boasts high wear resistance, aging resistance, and excellent elasticity. This enables the tires to provide superior shock absorption, ensuring a smooth and stable riding experience.

The product’s exterior design is positioned as youthful and fashionable, with a well-considered color scheme that offers a variety of color options to cater to the diverse preferences of users. In terms of product form, the design adheres to a minimalist and fluid aesthetic, where form follows function. The product’s shape and color scheme achieve an aesthetically pleasing and harmonious integration while aligning with its functional structure.

4.2.2. Mode Transition Diagram

The 2-in-1 portable electric scooter can be converted between electric scooter mode and truck mode according to user needs, achieving effortless transformation and multi-functional use. The electric scooter mode is designed for daily commuting and leisure activities. As illustrated in Figure 19, to convert to electric scooter mode, first, position the folded electric scooter upright with its front side facing upward. Second, press the release button on the frame to unlock and open the locking plate, thereby releasing the frame. Third, disengage the frame lock and rotate the frame to its fixed position for normal riding. For storage, simply reverse the above steps.

The trick mode serves multiple scenarios for shopping and goods transportation, and can function as a storage basket when idle. As illustrated in Figure 20, to convert to truck mode, first, position the folded electric scooter horizontally with its reverse side facing upward. Second, unlock the fixing rod, rotate it to the footboard slot for fixation, while simultaneously deploying the folding wings to form the truck compartment. Third, optionally attach the compatible elastic net as needed to complete the functional conversion. For storage, simply reverse the operation steps. The operational diagram of the truck mode is shown in Figure 21.

5. Discussion

This research applies objective algorithms in industrial design to the innovative development of electric scooter products. The integrated use of the KJ method, AHP, QFD, and TRIZ theory can support industrial design throughout the entire process, forming a closed-loop development system that significantly enhances the objectivity and rationality of the design.

Industrial design is an engineering science centered on user needs, and product development ultimately reflects user requirements. In product design, some scholars have analyzed design elements from the perspective of user needs by employing the KANO-AHP combined method to guide design practice [43]. However, this approach lacks consideration of technical attributes. Conversely, other scholars have used TRIZ algorithms to seek optimal solutions for engineering problems [44], but their product development relies heavily on external inputs, lacking an objective demand transmission mechanism. Additionally, many scholars have combined AHP and QFD to address the transformation of requirements into technical attributes [45], yet this approach still lacks a systematic method for resolving contradictions.

The KJ-AHP-QFD-TRIZ framework proposed in this research helps overcome the limitations of existing research methods and addresses the issue of disconnection in current methodological chains. By clustering unstructured demands using the KJ method, the problem of ambiguous requirements is optimized. The combined AHP-QFD method maps user needs to product technical attributes, while TRIZ theory is integrated to achieve innovation in product engineering technology.

6. Conclusions

This research project was conducted based on the needs of electric scooter users, guiding design practice through qualitative and quantitative research methods. Initially, questionnaire surveys and user interviews were carried out separately. The KJ method was employed to organize and summarize various user needs according to their affinity degrees. Subsequently, an AHP-based hierarchical analysis model for electric scooter user needs was established. Experts in the field of industrial design were invited to assign scores, thereby constructing a user needs judgment matrix. The weights of needs at different levels were calculated and ranked. Furthermore, the design research of electric scooters was conducted by integrating the QFD theory and the TRIZ theory. An HoQ model for electric scooters was constructed to transform user needs into technical characteristics. The TRIZ theory was applied to analyze conflict types and generate solutions, forming design strategies. Finally, innovative design practice for electric scooter products was carried out.

Specifically, the research work of this project has achieved the following outcomes:

(1)

The application of objective analysis methods in industrial design to the research on electric scooter design has pioneered a new approach to product design research. It provides a scientific and effective methodological basis and theoretical guidance, from identifying problems, analyzing them, to solving them. It offers relatively objective research data for design practice, reducing subjectivity and blind adherence in design innovation, and enhancing the objectivity and accuracy of design work.

(2)

By reviewing the literature in the field of electric scooter design, this research comprehensively discusses the research progress trends and opportunities, and proposes innovative content for the dual-function integration of electric scooters.

(3)

Through quantitative data analysis, user needs for electric scooters are studied, resulting in user need indicators with different priorities. This enriches the data on user research in the electric scooter field and provides guidance and a basis for industry product positioning and design decision-making.

(4)

Innovative design practice for electric scooters is completed. The design positioning of the product is established, with design efforts focused on functional innovation, structural design, color schemes, and so on.

Although this research has achieved preliminary results, there are still some limitations. The algorithms employed in this paper can guide design decision-making through quantitative data to a certain extent. However, this method still relies on expert subjective experience for scoring, which may affect the objectivity of the design research. Therefore, further optimization is necessary in subsequent research.