A Sustainability-Oriented Evaluation Framework for Growth-Adaptive Modular Children’s Cabinets: A GSOWCELM-Based Study

Abstract

The growing demand for child-friendly, growth-adaptive furniture necessitates the establishment of an evaluation framework that integrates user perception and modular design. This study has proposed a model framework that encompasses eight dimensions, including Growth adaptability, Safety, Organization, Warmth, Convenience, Emotionality, Learning support, and Modularity (GSOWCELM)—aimed at evaluating modular children’s cabinets from a user-perception-oriented, sustainability-focused perspective. The study uses a hybrid weighting method combining the Analytic Hierarchy Process (AHP) and Entropy Weighting Method (EWM) for evaluation based on expert judgment and Likert scale feedback from 20 parents of children aged 1 to 12. The results show that Emotionality, Learning support, Safety, and Growth adaptability are the issues of greatest concern to users. This marks a shift in design focus from traditional practical functionality to emotional resonance and developmental support. Based on priority indicators, the study further proposes a modular configuration strategy tailored to children’s ages. This research provides a replicable, perception-centered framework for evaluating and optimizing children’s furniture systems, contributing to the development of sustainable home environments and offering insights for designers, educators, and policymakers.

1. Introduction

The evolution of the concept of “environmental sustainability” highlights the importance of the ecological performance of industrial products, with consumers increasingly concerned about environmental issues [1]. The global trend toward sustainable product consumption has led to a greater emphasis on products that are efficient throughout their life cycle [2,3]. In the field of sustainability-related products, scholars have developed a multidimensional measurement tool—Sustainability-Related Product Satisfaction (SPS)—aimed at enhancing overall product satisfaction by integrating multiple sustainability dimensions [4]. However, in the specific field of children’s furniture, the lack of comprehensive assessment indicators for sustainability-related dimensions has led to a critical research gap between cross-industry sustainability progress and industry-specific practices.

In children’s furniture, demand is growing for growth-adaptive designs, driven by the rapid physical and cognitive development of children aged 1–12 [5]. Fixed-size, single-function furniture requires frequent replacement to keep pace with this growth, generating material waste and carbon emissions. Traditional children’s cabinets, with an average lifespan of only 2–3 years, contradict circular economy principles and sustainable development goals—highlighting the need for innovative designs that adapt to children’s growth, extend product lifespans, and meet evolving needs [6,7].

Existing research on children’s furniture spans three domains: modular design studies, which emphasize structural flexibility and demonstrate how interchangeable components enhance adaptability to spatial changes [8,9,10]; ergonomic research, focused on safety, advocating age-appropriate sizing and non-toxic materials to prevent harm [11,12,13,14]; and user perception studies, emphasizing emotional and educational value, showing that furniture supporting play and learning reduces premature discard [15,16]. These studies, however, often focus on isolated dimensions, lacking an integrated framework linking user perceptual needs to modular adaptability through a sustainability lens. Three critical gaps persist:

• Lack of a unified assessment model integrating growth adaptability, user perception (emotional and educational dimensions), and modular sustainability;
• Discrepancies between expert evaluations and parental preferences, resulting in misaligned design outcomes;
• Insufficient research on specific age-based module configurations for children aged 1 to 12, limiting applicability across different developmental stages.

To address these gaps, this study proposes the GSOWCELM model—an 8-dimensional evaluation framework comprising Growth adaptability, Safety, Organization, Warmth, Convenience, Emotionality, Learning support, and Modularity, operationalized through 24 specific indicators. Using a hybrid weighting approach—analytic hierarchy process (AHP) to capture expert insights and entropy weight method (EWM) to collect parental feedback—we prioritize indicators and propose age-based modular strategies.

This study aims to answer the following research questions:

• What are the key user-perception dimensions for evaluating growth-adaptive children’s cabinets?
• What is the relative priority of these dimensions from both expert and user perspectives?
• How can these priorities inform actionable, age-specific modular design strategies?

The contributions of this work are threefold:

• Developed a replicable, perception-oriented assessment framework for sustainable children’s furniture design;
• Integrated expert and user perspectives to form empirical evidence to guide design trade-offs;
• Produced practical modular configuration solutions to reduce resource waste by extending product life cycles, providing a practical roadmap for sustainable, growth-adaptive children’s furniture design.

2. Materials and Methods

2.1. Construction of the GSOWCELM Evaluation Framework

This study proposes the GSOWCELM model, a user-perception-driven evaluation framework tailored for children’s storage cabinets, with core focuses on sustainability, modularity, and growth adaptability. The framework comprises eight main dimensions covering 24 secondary indicators (three for each dimension;

Table 1. GSOWCELM model perception evaluation index system.

Code	Dimension	Indicator	Indicator Definition
G	Growth adaptability	G1: Adjustable size	Dimensions can be adjusted to align with children’s height and physical development.
		G2: Functional evolution	Functional modules evolve with age, e.g., from toy storage to study use.
		G3: Multi-scenario adaptation	Facilitates seamless integration into diverse domestic environments, such as bedrooms, studies, or shared living spaces.
S	Safety	S1: Stable structure	Reinforced structure and anti-tip design ensure safety during use.
		S2: Material safety	Eco-friendly, non-toxic materials meet kids’ safety standards; sustainable, low emissions.
		S3: Protective details	Rounded corners and anti-pinch elements mitigate accidental harm.
O	Organization	O1: Functional zoning	Interior is divided into logical compartments for item classification.
		O2: Storage label guidance	Labels or icons facilitate children’s independent item storage.
		O3: Visual storage	Transparent or open compartments enhance accessibility and visibility.
W	Warmth	W1: Surface softening treatment	Soft-touch finishes enhance tactile comfort and warmth perception.
		W2: Color affinity	Warm or neutral hues foster psychological comfort.
		W3: Gentle lighting atmosphere	Built-in lighting fosters a cozy, reassuring nighttime setting.
C	Convenience	C1: Child-independent usability	Allows children to operate the cabinet independently, without adult assistance.
		C2: Smooth operation	Ergonomic mechanisms enable minimal-effort opening and closing.
		C3: Convenient maintenance	Surfaces are easy to clean and parts easy to assemble/disassemble.
E	Emotionality	E1: Emotional attachment space	Provides personalized storage zones for meaningful objects.
		E2: Interactive design	Includes features like magnetic or graffiti boards for emotional expression.
		E3: Personality expression	Supports customization in color, form, or decorations.
L	Learning support	L1: Time and order support	Includes calendars or planners to help children manage tasks.
		L2: Learning guidance module	Built-in components support educational engagement, like whiteboards.
		L3: Knowledge display	Enables children to showcase achievements, creative work.
M	Modularity	M1: Interface standardization	Unified module connections allow for consistent upgrades or extensions.
		M2: Flexible module combinations	Cabinets can be stacked or reconfigured to fit space or use needs.
		M3: Function module expansion	Allows expansion with add-ons like lighting, bookshelves, or smart tech.

).

Indicator design adheres to principles of developmental psychology [17,18,19], ergonomics [20,21,22], emotional design [23,24], and modular theory [25,26,27], ensuring observability, operability, and implementability of evaluation criteria. By capturing users’ functional and perceptual needs, the model facilitates modular, dynamic adaptation of children’s cabinets, enabling sustainable use across the entire childhood growth cycle and advancing green, developmentally aligned furniture design.

2.2. Data Collection and Participant Demographics

A two-pronged data collection approach was employed—expert judgment and user perception.

• Expert Sample (AHP): Ten experts were selected through purposive sampling from disciplines including children’s furniture design, child psychology, education, and pediatric health. Selection criteria included a minimum of five years of professional experience, relevant publications or documented project involvement, and recognized expertise in child-centered design or development. This sample size aligns with common AHP practice, where panels of 7–12 experts are typically sufficient to achieve reliable consensus while avoiding cognitive overload during pairwise comparisons.
• Parent Sample (EWM): Twenty parents of children aged 1–12 were surveyed online to evaluate all 24 indicators using a five-point Likert scale. This sample size is methodologically adequate for the entropy weight method (EWM), as it reliably captures sufficient variation in responses across key dimensions enabling robust entropy calculation. Furthermore, the sample reflects a focused and homogeneous participant group, enhancing internal consistency and reducing noise often associated with broader but less targeted samples.

Demographic characteristics of both participant groups—including age, gender, profession, and household structure—are summarized in

Table 2. Demographic information of participants.

Category	Experts (n = 10)	Parents (n = 20)
Age group	Evenly distributed (60s–00s)	Majority under 35 (75%)
Gender	Male, female (50% each)	Male, female (50% each)
Profession	Furniture, psychology, health, education, entertainment (20% each)	–
Experience	More than five years (80%)	–
Monthly household income	–	Between ¥5001–50,000 (85%)
Number of children	–	One or two children (75%)
Age of children	–	All children aged 0–12 years

to ensure sampling diversity and representativeness.

2.3. Subjective Weighting via Analytic Hierarchy Process (AHP)

To determine expert-based priorities, the Analytic Hierarchy Process (AHP) was applied following these steps:

• Construct a hierarchical model;
• Experts apply Saaty’s 1–9 scale to compare indicator importance;
• Build a judgment matrix, normalize data, and test consistency;
• Compute subjective weights for criterion and indicator layers.

Based on the GSOWCELM model, a hierarchical needs model for children’s cabinets was developed via the Analytic Hierarchy Process. It employs eight dimensions as the criterion layer and 24 indicators under them as the indicator layer (Figure 1).

AHP’s core is comparing cross-level indicators via expert judgment. This study employs Saaty’s 1–9 rating scale (

Table 3. 1 to 9 scale values.

Scale	Degree of Importance	Definition of Scale Values
1	Equally important	The two elements are of equal importance.
3	Marginally more important	The former element is marginally more important than the latter.
5	Substantially more important	The former element is substantially more important than the latter.
7	Decisively more important	The former element is decisively more important than the latter.
9	Paramount	The former element is paramount compared to the latter.
2,4,6,8	Median value	The importance lies between the two.

) to compare the criterion and indicator layers, constructing a judgment matrix.

Then, use the judgment matrix (Formula (1)) to calculate these indicators, where $C_i$ and $C_j$ represent the score values of the two indicators being compared in the expert evaluation. $C_{ij}$ represents the relative importance value of $C_i$ relative to $C_j$. If the result is the opposite, it is represented as 1/$C_{ij}$.

$$C=(C_{ij}{)}_{n\times m}\left(\begin{array}{ccc}{C}_{11}& \dots & C_{1n}\\ ⋮& \ddots & ⋮\\ C_{m1}& \dots & C_{mn}\end{array}\right)$$

(1)

Each column in matrix C is normalized, and the result is noted as $b_{ij}$, see Equation (2):

$$b_{ij}={\displaystyle \frac{C_{ij}}{{\sum }_{i=1}^n{C}_{ij}}}$$

(2)

The approximate method of finding the eigenvectors of the judgment matrix is used to sum the normalized matrix by rows, denoted as $a_i$, see Equation (3):

$$a_i={\sum }_{j=1}^n b_{ij}$$

(3)

The vectors were normalized to find the subjective weight of each indicator, denoted ${\omega }_i$, see Equation (4):

$${\omega }_i={\displaystyle \frac{a_{ij}}{{\sum }_{i=1}^n a_{ij}}}$$

(4)

Before performing the consistency test of the results, the maximum characteristic root ${\lambda }_{max}$ must be calculated, see Equation (5):

$${\lambda }_{max}={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n {\displaystyle \frac{(a{\omega )}_i}{{\omega }_i}}(i=\mathrm{1,2},\dots \dots ,n)$$

(5)

A consistency test was conducted on the results. The calculation formula for the consistency index (CI) is given by Equation (6):

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

(6)

The random consistency index (RI) of the judgment matrix was introduced and presented in

Table 4. The RI value of the random consistency index.

n	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

. The Consistency Ratio (CR) is an important indicator used to assess the consistency of a judgment matrix. When $CR={\displaystyle \frac{CI}{RI}}<0.1$, the judgment matrix is consistent, and the weighting results are reasonable.

Finally, multiply the weights of the criteria layer by the corresponding weights of the indicator layer to complete the calculation of subjective weights.

2.4. Objective Weighting via Entropy Weight Method (EWM)

To complement expert judgment with user perception data, the Entropy Weight Method (EWM) was applied to the 20 valid questionnaire responses. The steps included:

• Normalization of raw Likert-scale data;
• Calculation of entropy values for each indicator;
• Derivation of objective weights based on information utility.

The entropy weight method serves to minimize subjective biases in factor weights, aligning evaluation results more closely with objective reality. In entropy weight analysis, indicators with lower entropy values convey more information, have greater information utility values, exhibit higher variability (variance), and are assigned greater weights in the evaluation.

Firstly, this study quantified parental feedback using a five-point Likert scale. The scale divided satisfaction into five levels: “dislike”, “tolerate”, “indifferent”, “worth it”, and “like”. Twenty users were guided to evaluate 24 secondary indicators at the indicator level through a questionnaire.

Secondly, n evaluation objects and m evaluation indicators are constructed, and the judgment matrix based on the entropy weight method is $A=(a_{ij}{)}_{n\times m}$, and $a_{ij}$ denotes the probability value that the ith evaluation indicator belongs to the jth evaluation level. The judgment matrix A is standardized to obtain the standardized matrix, notation q. The elements of the standardized matrix q are shown in Equation (7):

$$q_{ij}={\displaystyle \frac{a_{ij}-a_{min}}{a_{max}-a_{min}}}$$

(7)

The normalization matrix q is normalized to give an eigenweight of $f_{ij}$, see Equation (8):

$$f_{ij}={\displaystyle \frac{q_{ij}}{{\sum }_{i=1}^n q_{ij}}}$$

(8)

Calculate the entropy value of the evaluation indicator, denoted $e_i$, see Equation (9):

$$e_j=-{\displaystyle \frac{1}{\ln m}}{\sum }_{j=1}^m f_{ij}\mathrm{l}\mathrm{n}{f}_{ij}$$

(9)

The objective weight of each indicator is recorded as ${\omega }_j$, $1-e_j$ is the information utility value, see Equation (10):

$${\omega }_j={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^m (1-e_j)}}$$

(10)

2.5. Composite Weighting Calculation

In order to achieve an organic balance between the subjective judgment of experts and the objective data of experienced users, this study is based on the matrix theory, which deeply integrates the weighting results of AHP and EWM, and then scientifically calculates the comprehensive weights.

Using $\alpha$ and $\beta$ to denote the relative degree of importance of subjective and objective weights, respectively, the importance coefficients ${\alpha }_i$ and ${\beta }_i$ of the subjective and objective weights can be calculated accordingly using the idea of matrices, where i = 1, 2, …, n, with the formulas as follows:

$$\left\{\begin{array}{c}{\alpha }_i=v_i/(v_i+w_i)\\ {\beta }_i=w_i/(v_i+w_i)\end{array}\right.$$

(11)

where $v_i$ is the weight obtained by AHP and $w_i$ is the weight obtained by EWM. After obtaining the importance coefficients ${\alpha }_i$ and ${\beta }_i$ for the subjective and objective weights, we are able to obtain the composite weight $Q_i$ for each indicator with the following formula:

$$Q_i={\displaystyle \frac{v_i{\alpha }_i+w_i{\beta }_i}{{\sum }_{i=1}^n (v_i{\alpha }_i+w_i{\beta }_i)}}$$

(12)

3. Results

3.1. Subjective Weight Calculation Results

The judgment matrix and weight distribution of the standard layer are shown in

Table 5. Criterion layer weights.

Dimension	G	S	O	W	C	E	L	M	Weight
G	1	1/2	4	3	4	1/2	2	2	0.1648
S	2	1	4	4	2	3	2	2	0.2389
O	1/4	1/4	1	1/2	1/4	1/4	1/4	1/2	0.0363
W	1/3	1/4	2	1	1/4	1/3	1/4	1	0.0525
C	1/4	1/2	4	4	1	1/3	1	1/3	0.0978
E	2	1/3	4	3	3	1	2	4	0.1986
L	1/2	1/2	4	4	1	1/2	1	2	0.1220
M	1/2	1/2	2	1	3	1/4	1/2	1	0.0892

, and the judgment matrix and weight results of the index layer are shown in Appendix A. The consistency test results are shown in

Table 6. Consistency test results.

Dimension	λ	CI	RI	CR
D	8.8330	0.1190	1.4100	0.0844
G	3.0292	0.0146	0.5200	0.0280
S	3.0092	0.0046	0.5200	0.0089
O	3.0092	0.0046	0.5200	0.0089
W	3.0183	0.0092	0.5200	0.0176
C	3.0542	0.0271	0.5200	0.0521
E	3.0092	0.0046	0.5200	0.0089
L	3.0325	0.0163	0.5200	0.0313
M	3.0293	0.0146	0.5200	0.0281

. The CR values are all less than 0.1, indicating that the weight calculation results are reliable.

Based on the weights of the criterion layer in Table 5 and the weights of the indicator layer in Appendix A, the system derives the subjective composite weights of the 24 indicators, as shown in

Table 7. Subjective Weights.

Criterion Layer	Criterion Layer Weight	Indicator Layer	Indicator Layer Weight	Subjective Weight	Subjective Weight Ranking
G	0.1648	G1	0.5750	0.0947	3
		G2	0.0819	0.0135	18
		G3	0.3431	0.0565	9
S	0.2389	S1	0.4577	0.1093	1
		S2	0.4160	0.0994	2
		S3	0.1263	0.0302	11
O	0.0363	O1	0.5390	0.0196	14
		O2	0.1638	0.0060	24
		O3	0.2973	0.0108	20
W	0.0525	W1	0.3202	0.0168	16
		W2	0.5571	0.0292	12
		W3	0.1226	0.0064	23
C	0.0978	C1	0.1822	0.0178	15
		C2	0.7028	0.0687	7
		C3	0.1149	0.0112	19
E	0.1986	E1	0.4577	0.0909	4
		E2	0.1263	0.0251	13
		E3	0.4160	0.0826	5
L	0.1220	L1	0.0796	0.0097	21
		L2	0.6555	0.0800	6
		L3	0.2648	0.0323	10
M	0.0892	M1	0.1741	0.0155	17
		M2	0.1033	0.0092	22
		M3	0.7225	0.0644	8

. Expert ratings prioritize safety and functionality, with core metrics including Stable structure, Material safety, and Adjustable size. This reflects professionals’ emphasis on physiological safety and growth adaptability and is consistent with design standards oriented toward sustainability.

3.2. Objective Weight Calculation Results

The objective weights derived from user perceptions (

Table 8. Objective weights.

Criterion Layer	Indicator Layer	Index Entropy Value	Information Utility Value	Objective Weight	Objective Weight Ranking
G	G1	0.9029	0.0971	0.0218	20
	G2	0.8993	0.1007	0.0226	18
	G3	0.7891	0.2109	0.0473	4
S	S1	0.8729	0.1271	0.0285	12
	S2	0.8736	0.1264	0.0283	13
	S3	0.8233	0.1767	0.0396	5
O	O1	0.8715	0.1285	0.0288	11
	O2	0.8764	0.1236	0.0277	14
	O3	0.8660	0.1340	0.0300	10
W	W1	0.8393	0.1607	0.0360	7
	W2	0.8471	0.1529	0.0343	8
	W3	0.9011	0.0989	0.0222	19
C	C1	0.8934	0.1066	0.0239	15
	C2	0.8978	0.1022	0.0229	17
	C3	0.9035	0.0965	0.0216	21
E	E1	0.5074	0.4926	0.1104	1
	E2	0.7739	0.2261	0.0507	3
	E3	0.5074	0.4926	0.1104	1
L	L1	0.8958	0.1042	0.0234	16
	L2	0.5074	0.4926	0.1104	1
	L3	0.8305	0.1695	0.0380	6
M	M1	0.8305	0.1695	0.0380	6
	M2	0.8616	0.1384	0.0310	9
	M3	0.7661	0.2339	0.0524	2

) reveal a stark contrast to the expert-driven subjective weights. Whereas experts consistently prioritized functional and safety metrics—a focus that aligns with the conventional ergonomics and safety-centric paradigm of children’s furniture design—parents assigned significantly higher importance to emotional and personalized indicators, such as “Emotional attachment space” and “Personality expression”. This divergence underscores a critical gap between traditional design standards and contemporary user expectations. It suggests that beyond meeting baseline safety requirements—which remain non-negotiable—products that successfully incorporate emotional utility are perceived as more valuable by end-users. This emotional connection can be a key driver in promoting product longevity and achieving sustainability goals, an aspect previously underexplored in a field dominated by physical and ergonomic considerations.

3.3. Comprehensive Weight Calculation Results

Table 9. Combined subjective and objective weighting results.

Indicator Layer	Subjective Weight	Objective Weight	Comprehensive Weight	Comprehensive Weight Ranking
E1	0.0909	0.1104	0.0902	1
E3	0.0826	0.1104	0.0874	2
L2	0.08	0.1104	0.0867	3
S1	0.1093	0.0285	0.0822	4
S2	0.0994	0.0283	0.0742	5
G1	0.0947	0.0218	0.072	6
M3	0.0644	0.0524	0.0524	7
C2	0.0687	0.0229	0.0508	8
G3	0.0565	0.0473	0.0464	9
E2	0.0251	0.0507	0.0375	10
S3	0.0302	0.0396	0.0315	11
L3	0.0323	0.038	0.0314	12
W2	0.0292	0.0343	0.0284	13
M1	0.0155	0.038	0.0279	14
W1	0.0168	0.036	0.0265	15
M2	0.0092	0.031	0.0231	16
O1	0.0196	0.0288	0.0223	17
O3	0.0108	0.03	0.0221	18
O2	0.006	0.0277	0.0212	19
C1	0.0178	0.0239	0.0189	20
L1	0.0097	0.0234	0.0172	21
G2	0.0135	0.0226	0.017	22
W3	0.0064	0.0222	0.0165	23
C3	0.0112	0.0216	0.016	24

shows the comprehensive weight rankings of children’s cabinet evaluation indicators. The top six of 24 are: Emotional attachment space, Personality expression, Learning guidance module, Stable structure, Material safety, and Adjustable size. These rankings reveal three key trends:

• Emotional Resonance Prioritization: Top-ranked indicators for emotional support and personalized expression signify a shift toward psychologically engaging products, transcending traditional utilitarian functions.
• Educational Functionality Integration: The Learning guidance module’s prominence reflects parents’ evolving demands for a family education ecosystem. Its expanded functionality sustains educational continuity while strengthening growth adaptability—reducing premature replacement and aligning with sustainable design.
• Safety and Adaptability Baseline: Safety indicators are the primary benchmark for design, and the priority of adjustable dimensions highlights growth adaptability, supporting sustainability through dynamic development.

3.4. Mapping of Perceptual Dimensions to Children’s Cabinet Design

Based on the key indicators identified through the GSOWCELM model—including Emotional attachment space, Personality expression, and Learning guidance module—this section establishes a mapping framework between key perceptual dimensions and tangible design elements (Figure 2). This mapping mechanism facilitates the translation of subjective user perceptions into concretely designable and evaluable modular solutions, thereby establishing a closed-loop logic that integrates “perception, function, and module” into a coherent design process.

3.5. Age-Based Modular Configuration Strategy

Integrating anthropometric data, behavioral patterns, and functional requirements of children aged 1–12, this study operationalizes the identified design elements into a two-module configuration strategy (Basic Module + Functional Modules). The Basic Module is fixed, while Functional Modules are user-configurable, enabling adaptive customization. Each module is engineered to harmonize structural dimensions with developmental milestones, addressing emotional, cognitive, and educational needs through an age-adaptive growth mechanism. Module configurations are tailored to three developmental stages (

Table 10. Configuration of age-specific modules for children’s cabinets.

Age Stage	Characteristics of Children’s Growth	Basic Modules	Functional Modules (Optional)
1–3 years	Physical: 7–8 cm/year growth, crawling/standing dominant, emerging hand-eye coordination Behavioral: High exploratory drive via touch/grasping; frequent toy storage needs Functional: Safety, entertainment storage, parent–child interaction	S1: Rounded rectangular cabinet (≤60 cm side length) + non-slip base (friction coefficient ≥ 0.6) S2: Soft fabric surface (Shore hardness ≤ 30 A), formaldehyde emission ≤ 0.03 mg/m3 C2: Open toy compartments (≤50 cm height), no complex mechanisms	E1: Magnetic photo clips (3–5 photos) E2: Removable wipeable graffiti panel G3: Lockable casters for living/bedroom transitions
4–6 years	Physical: 100–120 cm height, fine motor skills (e.g., writing, building), basic spatial cognition Behavioral: Growing autonomy, beginning organization skills, rule-learning needs Functional: Zoned storage, basic education, social interaction	G1: Shelf height adjustable in 10 cm increments S1: Anti-pinch drawers with damping guides L2: Magnetic letter storage tray	M3: Height-adjustable desk (50–70 cm) with learning light interface E3: Interchangeable thematic panels O2: Graphical label system for categorization
7–12 years	Physical: 120–150 cm height, independent learning ability, spinal health/sitting posture focus Behavioral: Academic demands increase, privacy/esthetic expression priorities Functional: Dedicated study space, private storage, personalized display	G3: Horizontally stackable cabinets (60 cm width), combinable as bookshelf + desk L2: Erasable planning board (Length > 40 cm and width > 30 cm) E1: Lockable drawer (≥15 kg load capacity) for diaries/souvenirs	M3: Integrated charging ports E3: Programmable LED lighting strips S1: Foldable extension desk panel

).

4. Conclusions

This study focuses on user perception and modular design construction of growth-oriented children’s cabinets, proposing a multi-dimensional perception evaluation framework centered on the GSOWCELM model. It combines the AHP-EWM integrated weighting model to identify key design elements, forming a complete closed-loop path from “perception evaluation → indicator ranking → design mapping → module configuration,” aiming to support the product’s growth adaptability, sustainability, and environmental friendliness. Key findings include:

• Users place significantly greater emphasis on furniture growth adaptability, interactivity, and emotional investment than the traditional “safety + size” model, highlighting the critical importance of growth adaptability and long-term usability value for sustainability;
• The model integration analysis method demonstrates strong data interpretability and contextual adaptability in modeling user perception, providing support for dynamic adaptation during growth stages;
• Age-based configuration strategies validate the feasibility and stage-appropriate rationality of the “modular growth” design approach for children’s furniture. Modular characteristics reduce resource waste and align with green and environmentally friendly principles.

This research offers substantial managerial and practical implications for advancing sustainable children’s furniture design. Managerially, the GSOWCELM framework provides manufacturers and brand managers with a data-driven approach to align product development with sustainability targets and market demands. Prioritizing high-impact indicators (e.g., Emotionality, Learning support, Safety) enables:

• Streamlined R&D through focus on user-centric sustainable features, reducing costs from design misalignment;
• Product differentiation via Emotionality and Growth adaptability advantages, increasingly prioritized over basic functionality;
• Enhanced brand sustainability through waste-minimizing modular designs that meet regulatory standards and eco-conscious consumer expectations.

Practically, the framework delivers actionable implementation strategies:

• Designers translate abstract user needs into tangible solutions (e.g., Emotional attachment space → personalized display racks; Learning guidance module → built-in whiteboards) using the closed-loop workflow;
• Educators apply age-specific configurations (e.g., lockable casters for ages 1–3; erasable planners for ages 7–12) to support developmental needs while reducing replacement frequency in learning environments.

The GSOWCELM model and its underlying methodology offer a transferable framework for sustainable and user-centered design. While this study specifically validates the model in the context of growth-adaptive children’s cabinets, the core principles—integrating perceptual needs, modularity, and lifecycle efficiency—are conceptually applicable to other product categories. These may include other children’s furniture types (e.g., beds, desks) or products for other user groups requiring adaptive solutions (e.g., elderly furniture). However, such extensions would require future empirical validation, including the definition of context-specific indicators and weightings through a similar hybrid AHP-EWM process tailored to the new application domain.

However, some limitations should be recognized. Due to the ethical sensitivity of children’s data, their limited cognitive and expressive abilities, and the fact that parents are the primary decision-makers for children’s furniture, perception data is primarily sourced from domain experts and parents, which may make it difficult to fully capture children’s direct experience feedback. The internal structural relationships between GSOWCELM dimensions have not yet been validated by statistical modeling techniques such as factor analysis or SEM. In addition, the proposed modularization strategy remains at the theoretical level without comprehensive prototyping or longitudinal user testing.

Future research could focus on four areas: introducing child user testing, obtaining direct feedback from 1 to 12 year olds through eye-tracking, behavioral observation records, etc., and constructing a multidimensional perception system by combining parental proxy data; applying quantitative modeling methods to validate the dimensional framework; developing and testing physical prototypes in a real-world environment; and exploring the use of AIGC and parametric design tools to support intelligent generation and personalized modular configuration. These directions aim to further advance perception-driven design towards greater adaptability, emotional resonance and user-specific customization.