An Affordance-Based Model for the Sustainable Design of Community Food Waste Management Facilities

Abstract

This study addresses the issue of low utilization rates of kitchen waste recycling facilities in urban communities in China, caused by insufficient resident willingness to participate and sustain use, which impacts the efficiency of urban sustainability. A system design model based on Affordance Theory (BIATM) is proposed to connect behavioral insights with design and decision-making tools to improve sustainability outcomes at the local level. This model includes three components: demand classification identification based on Affordance Theory and the Best-Worst Method (BWM), hierarchical relationship analysis of demand based on Interpretive Structural Modeling (ISM), and design transformation based on Affordance Theory. Unlike prior approaches that apply affordance theory as a post hoc interpretive lens, this study operationalizes it as a generative design tool integrated with quantitative demand analysis. The results indicate that “Incentive Feedback” is a key factor directly influencing sustained participation behavior, while factors related to hygiene anxiety and operational burden are fundamental constraints to residents’ use of community kitchen waste recycling facilities. This suggests that the explicit needs expressed by residents may not always be the primary drivers of behavior initiation and continuity. To promote sustained participation, design should prioritize meeting deeper behavioral prerequisites and maintain participation through superficial feedback and incentive mechanisms. Based on this analysis, three design proposals for community kitchen waste management facilities are presented, evaluated, and optimized using the TOPSIS method. The study concludes that combining demand weight identification, hierarchical relationship analysis, and Affordance Theory translation effectively supports the identification, design generation, and optimization of community kitchen waste management facilities, providing a methodological reference for the sustainable design of public service facilities and practical pathways for promoting resident participation in community resource recycling.

1. Introduction

With the acceleration of urbanization and changes in residents’ lifestyles, the amount of municipal solid waste continues to grow, and the requirements for waste classification and resource utilization have become more demanding [1,2]. As an important organic component of urban waste, food waste typically has characteristics such as high moisture content, easy spoilage, and high organic matter content. Improper disposal not only affects end-management efficiency but also leads to environmental pollution and resource waste [3,4]. If properly recycled and processed, food waste can realize its potential as a resource. Research indicates that food waste can be converted into fertilizers through resource recovery methods like composting and further used as organic nitrogen fertilizer or soil improver in agricultural production [5,6].

Regarding the recycling and reutilization of community food waste, existing studies have begun to explore decentralized processing, community composting, and the construction and application of community-based processing facilities, aiming to promote on-site reduction and resource utilization of food waste at the community level [7,8]. These facilities have certain technical and environmental feasibility, but their actual operational effectiveness depends not only on the equipment itself but also heavily on residents’ participation rate, willingness to use, and sustained waste disposal behavior [9].

The proper disposal of food waste at the source by residents plays a critical role in promoting resource recycling and enhancing urban governance [10]. The key lies in how to translate the experiential factors involved throughout the entire process of food waste disposal into actionable design strategies [11], so as to address the problem that residents have access to facilities but lack the willingness to use them, and to sustain continued usage behavior in everyday life.

Existing studies have explored this challenge from the perspectives of community governance, resident participation, and behavioral guidance. Case studies indicate that the success of community waste management is not only determined by the proper installation of facilities but also by multi-party collaboration, the completeness of supporting mechanisms, and the foundation of resident participation [12,13,14]. Further studies suggest that, in addition to policy constraints, the formation and maintenance of waste sorting and proper disposal behaviors among residents are influenced by various factors such as infrastructure conditions, governance perceptions, and community atmosphere [15,16,17]. This highlights that, within the context of sustainable community governance, public food waste management facilities are no longer confined to simple collection or temporary storage functions. Instead, these facilities are increasingly tasked with more complex governance requirements, including classification guidance, hygiene assurance, resource conversion, and the facilitation of public participation [18,19,20,21,22].

In research on waste sorting behavior, Linda et al., based on the Theory of Planned Behavior (TPB) and related behavioral models, demonstrated that factors such as attitude, social norms, and perceived behavioral control influence both the intention and behavior of waste sorting [23]. Although TPB has proven highly effective in explaining the formation of pro-environmental behaviors, a significant intention–behavior gap persists in the context of waste sorting. This gap suggests that merely assessing individuals’ intentions is insufficient for ensuring sustained behavior, as resource limitations or perceived physical and cognitive burdens may impede the translation of intentions into consistent actions [2].

However, while existing research has identified numerous influencing factors, it typically treats them as parallel “demands” or “influencing variables”, failing to discuss the causal relationships among them and rarely distinguishing between those that represent residents’ explicitly expressed preferences and those that serve as the underlying prerequisites for the initiation and continuation of behavior. In the context of community public facility design, if design resources are allocated solely based on explicitly high-weighted demands, it may prioritize surface features like incentives, feedback, or visual appeal, neglecting the foundational conditions that truly determine whether residents are willing to approach, engage with, and complete the disposal behavior. This can lead to an inability to maintain residents’ long-term willingness to use the facilities, affecting the sustainable management of food waste in the community. In addition, although the studies mentioned above are valuable for explaining why waste-sorting behavior occurs, they are not sufficient on their own for guiding the design of public food waste management facilities. Even when facility-related factors are considered, they are usually treated as general external conditions such as convenience or accessibility, rather than being translated into concrete design attributes that users can directly perceive, understand, and act upon in public settings.

Therefore, the research question addressed in this study is: In the design of community-based food waste management systems, do the key needs explicitly expressed by residents correspond to the fundamental drivers of behavioral initiation and maintenance? If the two are not consistent, how should design identify this discrepancy and systematically translate it into facility design strategies that promote sustained participation? Addressing this issue, this study proposes the BIATM based on the affordance theory. It identifies residents’ needs from an affordance perspective, introduces BWM to prioritize residents’ explicit needs, and further uses ISM to reveal the hierarchical relationships and causal pathways among these needs. Finally, drawing on Affordance theory, the structural differences among these needs are translated into facility design elements, forming actionable design rules across five dimensions: physical, perceptual, cognitive, functional, and social. The contribution of this study does not lie in proposing yet another explanatory framework, but in combining behavioral insights with multi-criteria decision-making tools in the perspective of affordance to improve sustainable outcomes at the local level. Pilar Buil and Olga Roger Loppacher propose that waste recycling is an environmental behavior highly correlated with human behavior, requiring the construction of a “dialogue space” between people and the environment through design analysis to further narrow the gap between residents’ willingness to recycle and their actual actions [24], suggests that affordance design helps promote the occurrence of behavior.

Finally, this study applies the model to the design of community food waste management facilities and uses TOPSIS for solution evaluation and optimization to preliminarily verify the potential of this model in promoting sustained resident participation in resource recycling. By combining the exploration of participation behavior motivations with design and decision-making tools, the study aims to improve sustainable development outcomes at the local level and provide practical pathways for guiding and maintaining residents’ participation in the sustainable resource recycling process.

2. Materials and Methods

2.1. Affordance Theory

Affordance Theory was first proposed by American ecological psychologist James Gibson in the 1970s and later introduced to the design field by cognitive psychologist Donald Norman. Its core idea is that objects or elements in the environment not only have their inherent functions but also provide cues for users to operate and utilize them. These “cues” or “opportunities” guide users’ behavior based on the environment and individual characteristics. Building on this, Hartson further proposed four types of affordances—physical, perceptual, cognitive, and functional—clarifying how design features support users in perceiving, understanding, operating, and completing tasks, thus providing a solid foundation for interaction design [25].

Affordance Theory emphasizes how objects and the environment shape the possibilities for human behavior through actionable cues, meaning that the characteristics of objects and human cognition work together to stimulate the spontaneity and sustainability of user behavior [26]. This theory provides a systematic analytical tool for the design of community support services, enabling design to activate or inhibit the possibility of specific behaviors, thereby supporting the flexible adjustment and sustainable development of community services. Duffy and Verges (2009) [27] validated this theory through a study on the design of public recycling container openings. They found that designing more directional openings significantly increased the correct disposal rate by users [27]. This finding suggests that the form of the container itself can serve as a behavioral cue, influencing users’ disposal behavior. Nemat et al. also noted that the shape, size, texture, and visual communication properties of objects affect users’ judgments about their categorization methods, indicating that people’s classification judgments are directly influenced by the affordances of design [28]. Both studies emphasize the core concept of Affordance Theory, where objects provide “behavioral cues” to users, showing that design optimization can effectively guide user behavior. In theoretical research, Hsiao et al. proposed an affordance evaluation model from the design methodology perspective, demonstrating that affordances can be broken down, evaluated, and used for product optimization [29]. These studies suggest that applying Affordance Theory to food waste management product design can effectively guide user behavior through clear perceptual and operational cues, enabling users to complete food waste disposal without significantly deviating from their existing cognition and daily habits, thus promoting the initiation and sustainability of food waste disposal behavior among community residents.

For public facilities designed for community food waste management, whether residents are willing to approach the facility, able to complete waste disposal smoothly, capable of quickly understanding the logic of use, and willing to continue participating after receiving feedback correspond to behavioral support needs at different levels. In community public settings, user behavior is influenced not only by the availability of facilities but also by factors such as the social context, visibility to others, and relevant feedback [30,31]. Therefore, building upon Hartson’s theoretical framework, this study incorporates social affordance as an additional analytical dimension, defining it as the potential for social interaction or socializing provided by the physical environment, focus on whether facilities can serve as mediators of social activities to facilitate the emergence of behavior and sustain the willingness to participate [32]. Taking into account the context of community public facility use, this study categorizes the affordance theory into two main levels—physical and psychological—and further breaks them down into five dimensions: physical, perceptual, cognitive, functional, and social, as shown in

Table 1. Classification of Affordance Dimensions.

Level	Dimension	Description
Physical	Physical Affordance	Refers to the support provided by the availability and usability of facility structures, components, and other material elements.
	Perceptual Affordance	Refers to the support derived from the convenience of facility size, component arrangement, and other perceptible design features.
Psychological	Cognitive Affordance	Refers to the extent to which the form, color, and other design attributes of the facility align with residents’ cognitive understanding of public facilities.
	Functional Affordance	Refers to the actual effectiveness of the facility in enabling activities such as waste disposal, processing, and feedback.
	Social Affordance	Refers to the extent to which the facility fosters communication, interaction, and sustained participation within the community context.

.

These five dimensions of affordance represent distinct design attributes and are directly related to residents’ experience of using the facilities. Therefore, guided by these five dimensions, this study will develop a system of design need indicators and translate these need indicators into specific design elements.

2.2. Best-Worst Method

The Best–Worst Method (BWM), proposed by the Dutch scholar Rezaei, is a quantitative weighting method that enables the relative importance of different needs to be represented intuitively through pairwise comparisons among indicators [33]. In new product development research, Li et al. introduced fuzzy BWM into the screening process for new product ideas to determine weights across dimensions such as finance, marketing, engineering, manufacturing, and sustainability, thereby supporting front-end concept selection [34]. Ayvaz-Çavdaroğlu et al. likewise applied BWM to assign weights to dimensions of smart service quality, with the aim of identifying the more critical quality attributes in service experience design [35]. The above studies demonstrate that BWM can effectively support the identification of priority needs and inform design decision-making in the context of complex requirements. However, these studies primarily focus on general consumer products or market-oriented services, whereas community food waste management facilities are public service products embedded in residents’ everyday life contexts, characterized by stronger publicness, shared use, and behavioral guidance. Their design not only affects functional performance but also shapes residents’ attitudes, willingness to participate, and sustained disposal behavior. In this context, different needs not only vary in importance but may also have hierarchical relationships and interact with one another. Since BWM alone is insufficient to reveal the underlying interaction mechanisms among needs or the prerequisite conditions for their realization, Interpretive Structural Modeling (ISM) is introduced to conduct a hierarchical analysis of key needs.

2.3. Interpretive Structural Modeling

Interpretive Structural Modeling (ISM) is a method for addressing the relational structure of complex systems. Proposed by Warfield in 1974, it can decompose and construct relationship matrices among multiple factors, thereby identifying core factors by clarifying their internal influence relationships. Praneashram et al. applied ISM in smart product development to conduct a structured analysis of 20 factors affecting smart product development and identified key driving factors, including data storage and the facilitation of user interaction [36]. Liu et al. introduced an ISM approach based on online reviews in consumer satisfaction research, constructing a hierarchical structure of how product and service attributes influence satisfaction to reveal the pathways through which different attributes interact [37]. By using ISM to further supplement the interrelationships among factors identified through the Best–Worst Method (BWM), more accurate design priorities can be obtained.

2.4. Research Framework

Affordance Theory can provide guidance for design; however, relying solely on it might be difficult to address key decision-making issues, such as the prioritization of needs, the interrelationships among needs, and the allocation of design resources. This limitation hinders its effective translation into design practice. To address this gap, this study constructs a systematic design strategy, as illustrated in Figure 1, which is grounded in Affordance Theory and integrates BWM and ISM for demand analysis. Specifically, BWM is employed to compare demand elements and determine their relative weights, while ISM is used to establish a hierarchical structure of needs to guide the prioritization of affordance-based design. By combining BWM and ISM, the study establishes a needs-analysis framework linking need importance, structural hierarchy, and design priority, and ultimately achieves a systematic translation from user needs to design elements based on affordance theory.

3. Demand Analysis

3.1. Identification of Initial User Needs

User needs were identified through three methods: literature review, expert consultation, and user interviews. The interviews covered the respondents’ experiences with handling food waste, as well as their opinions on the installation of food waste management facilities within the community, which were elicited through guided questions.

The interviews selected community residents with experience in disposing of daily household food waste as participants. To ensure the widest possible coverage of diverse user experiences, a combination of purposive and convenience sampling was used to recruit participants. The inclusion criteria were as follows: Respondents must be permanent residents of the community with experience in daily waste disposal. The initial sample included residents of different age groups, genders, and lifestyles. Through the interviews, we obtained samples of food waste disposal behavior patterns and identified related needs. Additionally, property management staff, community workers, and volunteers were included as interview subjects to gather management-related feedback on the design and disposal of food waste bins within the community. All interviews were recorded with the respondents’ consent for analysis. At the outset of each interview, the purpose of the study and the recording process were explained to the respondents, and their identities were kept anonymous. The interviews were conducted until thematic saturation was reached, defined as the point at which no new themes or insights emerged from subsequent interviews [38]. Specifically, saturation was reached after conducting 40 interviews, which led to a comprehensive understanding of the main themes and patterns discussed by participants. Among the 40 participants, middle-aged adults aged 31–45 constituting a significant proportion of the sample. This demographic represents the community’s primary resident group and typically bears the primary responsibility for managing food waste within households. The interview sample is shown in

Table 2. Interview Sample.

Category	Item	n	Proportion
Gender	Male	20	50%
	Female	20	50%
Age	Under 18	3	7.5%
	18–30	9	22.5%
	31–45	13	32.5%
	46–60	8	20%
	Over 60	7	17.5%
Attribute	Residents	19	47.5%
	Property Management Personnel	10	25%
	Community Staff	8	20%
	Volunteers	3	7.5%
Household Food Waste Disposal	Main Responsible Person	27	67.5%
	Occasional Responsible Person	13	32.5%
Food Waste Classification	Frequent	6	15%
	Occasional	22	55%
	Rare	12	30%

.

The usability of public facilities for food waste disposal in communities depends not only on their physical functions but also on whether residents can identify action cues, perceive the task as relevant to their own roles, and integrate the behavior into their daily routines. Based on the interactive relationship between people and objects as outlined in usability theory, the interviews posed guided questions across three dimensions: cognition, identity and responsibilities, and behavioral habits:

(1)

Cognitive dimension: To understand the interviewee’s awareness of and opinions regarding the food waste disposal process and the public facilities for waste disposal within the community;

(2)

Role and responsibility dimension: To understand the interviewee’s identity within the community and whether they are responsible for food waste disposal within their household;

(3)

Behavioral habits: To understand the interviewee’s habits regarding food waste disposal and the challenges they face.

During the analysis of interview data, to avoid relying solely on intuitive judgments when classifying indicators, this study incorporated the aforementioned five dimensions of affordance into the process of categorizing needs, using them as a guiding framework to identify and integrate different types of needs. In this way, the theory of affordance provided conceptual boundaries for the selection and integration of indicators within the interview data.

Statements regarding product hardware configuration were primarily interpreted from the perspective of physical accessibility; statements concerning the ease of product use were categorized under perceived accessibility. Together with physical accessibility, these represent the physical conditions that support the occurrence of interactive behaviors at the physical level.

Confusions, uncertainties, or difficulties in understanding related to waste disposal are classified as cognitive affordances. Expectations regarding the actual effectiveness of product functions, such as waste disposal, disposal feedback, and incentive rewards, are categorized under functional affordances. Statements related to joint participation and community interaction are understood from the perspective of social affordances. Together, these three types of affordances represent users’ surface preferences from a psychological standpoint.

After a preliminary review of the interview transcripts, the researchers discussed the content with significant repetition. This content was seen as reflecting the residents’ straightforward needs regarding food waste management facilities within the community. These needs specifically include the experience of the waste disposal process and the operational efficiency of the facility. The purpose of the discussion was to establish a coding scheme based on affordance theory. To ensure the smooth progress of the coding process, when disagreements arose among team members, the disputed content was temporarily set aside, and further discussions were held to check for similar content. During the discussion, in addition to the previously mentioned interview content, one team member suggested that there were related need-based insights in the literature. After joint research by the team, it was found that the content from the literature could supplement the interview findings, and therefore, the literature reviewed by the team was incorporated into the scope of the coding content.

After finalizing the research design, the researchers conducted open-coding of the interview data. During the open-coding phase, initial concepts related to waste disposal experience and willingness to use the facilities were extracted from both the respondents’ original statements and relevant literature. These concepts were then categorized into two groups based on the design objectives:

(1)

Reducing users’ operational burden;

(2)

Guiding users toward sustained participation.

In the axial coding phase, these concepts were further clustered based on Affordance Theory to identify the corresponding types of behavioral support, that is, the actions the facility can enable across five dimensions: physical, perceptual, cognitive, functional, and social. Based on this, the construction of demand indicators considers two aspects:

(1)

The key behavioral goals that residents face in sustaining food waste disposal behavior, such as reducing the use burden and promoting continued participation;

(2)

The primary affordance support types on which these goals depend.

The coding process is shown in Figure 2. For example, the researchers first extracted open codes from the original statements such as “fear of touching stains”, “concern about contamination”, and “inconvenience due to hands being occupied”, then in the axial coding phase, they aggregated these into themes such as “hygiene anxiety” and “low-burden operation demand”, ultimately converging into the A₂ indicator. Further analysis based on Affordance Theory indicates that this indicator primarily reflects perceptual affordance, as the cleanliness of the facility surface or its surrounding environment influences whether users are willing to approach and use the facility, as well as their judgment of the action’s feasibility. At the same time, it also involves physical affordance, specifically whether the facility supports residents in completing the disposal action through low-contact interaction.

Ultimately, two primary indicators, four secondary indicators, and sixteen tertiary indicators were identified, as shown in

Table 3. Demand and Metrics Compilation Table.

First-Level Indicator (Goal Layer)	Second-Level Indicator (Criterion Layer)	Third-Level Indicator (Indicator Layer)	Affordance Dimension	Source
Low Use Burden	A Hygiene Anxiety	A1 Reduced Odor Impact	Physical	“Every time I pass by the bin, I can smell a bad odor from a distance”.
		A2 Contact-Free Disposal	Perceptual / Physical	“There is often leaked waste liquid on the lid when others throw away garbage. It feels greasy, and I really do not want to touch it”.
		A3 Smooth Waste Entry into the Bin	Perceptual	“Sometimes when I throw the waste away in a hurry, it gets caught by the lid, and I have to push it in again myself”.
		A4 Post-Use Cleaning	Physical	“Sometimes I accidentally get my hands dirty when disposing of waste, which is quite troublesome”.
	B Ease of Operation	B1 Large Capacity	Perceptual / Physical	[40]
		B2 Effort-Saving Use	Perceptual	“The bin lid is difficult to open”.
		B3 Automatic Processing	Functional	“I only know how to throw the waste away; I do not know how it is processed afterward”.
		B4 Complete Supporting Facilities	Physical	[39]
Sustained Participation	C Willingness to Use	C1 Simple and Clear Appearance	Cognitive	[41]
		C2 Incentive Feedback	Functional / Social	[42]
		C3 Visibility of Processing Results	Functional	“I do not know what the waste can be used for after it is processed”.
		C4 Social Interaction	Social	“When chatting with neighbors, we sometimes talk about what dishes we have cooked recently”.
	D Use Cost	D1 Material Durability	Physical	“Will this thing be durable enough?”
		D2 Short Disposal Distance	Social	“Food waste contains a lot of liquid, and it is quite tiring to carry it all the way to dispose of it”.
		D3 Maintenance Responsibility	Social	“Who is going to manage this thing?”
		D4 Simple Usage Method	Cognitive	[41,43]

. The secondary indicators represent factors that influence residents’ use of food waste management products, while the tertiary indicators represent the design factors that need to be incorporated.

3.2. Demand Analysis Based on the Best–Worst Method (BWM)

Ten experts from relevant fields were invited, including two industrial designers, three community property management staff, and five neighborhood committee representatives. Based on the criteria layer in Table 3, a factor set D = {d₁, d₂, … d_n} was established. The experts then identified the best criterion d_B and the worst criterion d_W from this set based on subjective judgment. Subsequently, a 1–9 rating scale was adopted, with the degree of importance increasing progressively from 1 to 9. The best criterion d_B was compared pairwise with each of the other criteria to obtain the corresponding preference values, which were expressed as the comparison vector A_B = (a_B1, a_B2, … a_Bn). Here, a_Bj denotes the importance of the best criterion d_B relative to another criterion d_j, where 1 indicates equal importance and 9 indicates that the best criterion is absolutely more important than criterion d_j. Similarly, each of the other criteria dj was compared with the worst criterion d_W, yielding the comparison vector A_W = (a_W1, a_W2 … a_Wn), where a_Wj represents the importance of criterion d_j relative to the worst criterion d_W. The best/worst criterion selected by each expert, along with their respective B-O and W-O coefficients are as shown in

Table A1. The Best Criteria Selected by Experts and B—O Vectors.

Expert	The Best Criterion Selected	B—O Vectors
		Hygiene Anxiety	Ease of Operation	Willingness to Use	Use Cost
1	Willingness to Use	4	3	1	8
2	Willingness to Use	2	3	1	7
3	Hygiene Anxiety	1	7	5	3
4	Hygiene Anxiety	1	5	7	5
5	Willingness to Use	3	2	1	5
6	Ease of Operation	7	1	5	7
7	Willingness to Use	1	2	1	7
8	Ease of Operation	5	1	2	4
9	Use Cost	2	5	1	1
10	Willingness to Use	4	3	1	5

and

Table A2. The Worst Criteria Selected by Experts and W—O Vectors.

Expert	The Worst Criterion Selected	W—O Vectors
		Hygiene Anxiety	Ease of Operation	Willingness to Use	Use Cost
1	Use Cost	7	5	8	1
2	Use Cost	6	8	9	1
3	Willingness to Use	7	7	1	7
4	Willingness to Use	6	4	1	7
5	Use Cost	4	6	5	1
6	Use Cost	8	6	7	1
7	Use Cost	5	5	7	1
8	Hygiene Anxiety	1	5	4	1
9	Ease of Operation	4	1	5	5
10	Use Cost	3	2	5	1

in the Appendix A.

A linear model was employed to derive the optimal weights of the indicators as follows:

$$\begin{array}{l}\min \max \left\{\right|w_B^{\prime }-a_{Bj}{w}_j^{\prime }|,|w_j^{\prime }-a_{jW}{w}_w^{\prime }\left|\right\}\\ s.t.\left\{\begin{array}{l}{\displaystyle \sum _{j=1}^n{w}_j^{\prime }}=1\hfill \\ w_j^{\prime }⩾0\hfill \end{array}\right.\end{array}$$

(1)

where w’_B represents the subjective weight of the best indicator, and w’_W represents the subjective weight of the worst indicator.

Accordingly, the model can be reformulated as follows:

$$\begin{array}{l}\min \xi \\ s.t.\left\{\begin{array}{l}|w_B^{\prime }-a_{Bj}{w}_j^{\prime }|⩽\xi \hfill \\ |w_j^{\prime }-a_{jW}{w}_w^{\prime }|⩽\xi \hfill \\ {\displaystyle \sum _{j=1}^n{w}_j^{\prime }}=1\hfill \\ w_j^{\prime }>0\hfill \end{array}\right.\end{array}$$

(2)

The symbol ξ denotes the consistency deviation coefficient. A smaller value of ξ indicates a higher degree of consistency in expert judgments and, consequently, greater reliability. In addition, the consistency ratio (C_R) must be less than 0.1. The formula for calculating the C_R coefficient is shown in Equation (3).

$$C_{\mathrm{R}}={\displaystyle \frac{\xi }{C_{\mathrm{I}}}}$$

(3)

In the BWM, the C_I value is fixed, as shown in

Table 4. Consistency Index (C_I) Value Table.

Preference	1	2	3	4	5	6	7	8	9
CI	0	0.44	1	1.63	2.3	3	3.73	4.47	5.23

.

After the calculations were completed, all results passed the consistency test (C_R < 0.1), as shown in

Table 5. Consistency Ratio (C_R) Value Table.

Expert	1	2	3	4	5	6	7	8	9	10
CR	0.0363	0.0419	0.0731	0.0659	0.0513	0.0808	0.0182	0.0281	0.0217	0.0458

.

The collected data was analyzed to determine the subjective weights for each indicator, which were then ranked according to their weights, as shown in

Table 6. Subjective Weights Determined by BWM.

Criterion Layer	Weight	Indicator Layer	Relative Weight	Absolute Weight	Ranking
A	0.2727	A1	0.2709	0.0739	5
		A2	0.3028	0.0826	3
		A3	0.1952	0.0532	11
		A4	0.2311	0.0630	8
B	0.2588	B1	0.2314	0.0599	10
		B2	0.3058	0.0791	4
		B3	0.2025	0.0524	12
		B4	0.2603	0.0674	6
C	0.3408	C1	0.2828	0.0964	2
		C2	0.3402	0.1159	1
		C3	0.1844	0.0628	9
		C4	0.1926	0.0656	7
D	0.1279	D1	0.2089	0.0267	15
		D2	0.2711	0.0347	14
		D3	0.1822	0.0233	16
		D4	0.3378	0.0432	13

.

The BWM weight results show that “C: Willingness to Use” is at the highest priority in residents’ explicit needs, with “C₂: Incentive Feedback” and “C₁: Simple and Clear Appearance” ranking high. This reflects that residents wish to quickly understand the facility with low cognitive cost and develop sustained participation motivation after receiving positive feedback. However, the weight ranking alone is insufficient to guide the allocation of design resources. When designing public facilities, it is essential to first meet the needs that influence the willingness to use at a deeper level to establish stable, continued usage behavior.

3.3. Further Demand Analysis Using Interpretive Structural Modeling (ISM)

To further reveal the intrinsic relationships among various requirements and clarify the priority of functional elements, this study introduces the Interpretive Structural Modeling (ISM) approach. To ensure that the ISM analysis results can more accurately inform subsequent design translation, this study focuses on key requirements that can be translated into design. Based on the weight rankings of design indicators calculated using the BWM, as well as considerations of controllability across design, technology, and operations, factors that can be directly addressed within the design scheme itself were excluded. Subsequently, the 16 indicators at the indicator layer were screened according to the criteria shown in

Table 7. Screening Criteria for Indicators.

Indicator	Design Controllability	Technical Controllability	Maintenance Controllability	Implementation Approach	Included in ISM	Reason
A1	√	√	×	Exhaust component	Yes	Achieved through functional design
A2	√	√	×	Pedal-operated lid-opening structure	Yes	Achieved through functional design
A3	√	√	×	Waste inlet structure	No	Can be implemented simultaneously with A2
A4	√	√	×	Washbasin	Yes	Achieved through functional design
B1	√	√	×	Large container body	Yes	Achieved through structural design
B2	√	√	×	Height of waste inlet	Yes	Achieved through structural design
B3	×	√	×	Internal fermentation management	No	A functional and technical element
B4	√	√	×	Placement of auxiliary tools	Yes	Achieved through component configuration
C1	√	√	×	Appearance design	Yes	Achieved through appearance design
C2	√	√	√	Provision of rewards	Yes	Achieved through functional design
C3	√	√	×	Display of processing results	No	Can be implemented simultaneously with C2
C4	×	√	√	Provision of communication opportunities	No	Not achieved through design alone
D1	√	√	×	Material selection	No	Can be implemented simultaneously with C1
D2	√	√	√	Disposal location	No	Related to property management
D3	√	√	√	Property logistics	No	Related to property management
D4	√	√	×	Simplified use	Yes	Achieved through functional design

. Ultimately, nine indicators—A₁, A₂, A₄, B₁, B₂, B₄, C₁, C₂, and D₄—were selected for extended ISM calculation and hierarchical structure analysis.

Before conducting a hierarchical structure analysis, it is necessary to determine the degree of influence among the various factors. In addition to the 10 experts mentioned earlier, residents from three different communities were selected through purposive sampling to participate in the questionnaire survey. A total of 120 questionnaires were collected. Combined with the questionnaires completed by the 10 experts, this yielded a total of 130 sets of questionnaire data. After excluding invalid questionnaires (such as those with contradictory or conflicting responses), 113 valid questionnaires were ultimately obtained. To avoid a single source of samples, this study selected three communities with slight differences when conducting the community selection. These differences were primarily reflected in the composition of residents, age structure, and management models. An overview of the communities is shown in

Table 8. Overview of the Selected Communities.

Community Code	Community Attribute	Population Composition	Age Structure	Management Model
A	Old Urban	Local Residents	Predominantly Middle-aged and Rlderly	Resident Self-management
B	Commercial Housing Community	Migrant Workers and Tenants	Predominantly Middle-aged and Young	Property Management
C	Commercial Housing Community	Long-term Resident Owners	Predominantly Middle-aged, with a Balanced Ratio of Elderly and Young	Property and Neighborhood Committee Management

.

Respondents were asked to rate the degree of influence of each factor on another using a 0–4 scale, with the degree of influence increasing from 0 to 4, where 0 indicates no influence and 4 indicates a strong influence. Unlike the binary 0/1 scale used in the classic ISM, the 0–4 impact rating scale adopted in this study allows respondents to precisely express the varying degrees of influence between factors, thereby revealing more nuanced relationships among them. A direct influence matrix X was constructed from the data, as shown in

Table 9. Direct Influence Matrix X.

Indicator	A1	A2	A4	B1	B2	B4	C1	C2	D4
A1	0	2	1	3	2	4	3	1	0
A2	3	0	4	2	3	3	2	1	3
A4	3	1	0	0	1	4	3	2	3
B1	3	2	0	0	1	2	3	3	3
B2	1	4	3	3	0	2	3	3	3
B4	4	2	4	1	3	0	3	4	2
C1	1	3	3	3	3	3	0	2	4
C2	2	0	2	3	2	3	2	0	2
D4	0	3	1	2	3	1	4	4	0

.

The direct influence matrix X was normalized using Equation (4) to obtain the normalized direct influence matrix Z.

$$Z={\displaystyle \frac{X}{\underset{1\le i\le n}{max}{\displaystyle \sum _{j=1}^n{x}_{ij}}}}$$

(4)

Further calculations were then performed using Equation (5), where A denotes the identity matrix, to derive the comprehensive influence matrix T for representing the influence relationships among the factors, as shown in

Table 10. Comprehensive influence matrix T.

Indicator	A1	A2	A4	B1	B2	B4	C1	C2	D4
A1	0.382	0.459	0.453	0.492	0.483	0.626	0.608	0.486	0.449
A2	0.58	0.472	0.656	0.541	0.616	0.706	0.7	0.596	0.664
A4	0.500	0.433	0.424	0.394	0.466	0.643	0.63	0.542	0.567
B1	0.488	0.463	0.412	0.391	0.457	0.556	0.618	0.566	0.56
B2	0.525	0.64	0.642	0.602	0.522	0.69	0.756	0.69	0.696
B4	0.647	0.577	0.692	0.545	0.65	0.638	0.774	0.737	0.662
C1	0.526	0.612	0.644	0.603	0.643	0.725	0.648	0.665	0.734
C2	0.438	0.368	0.462	0.478	0.464	0.568	0.558	0.433	0.502
D4	0.404	0.53	0.489	0.499	0.56	0.554	0.688	0.637	0.494

.

$$T=Z{(A-Z)}^{-1}$$

(5)

Based on the comprehensive influence matrix T, the intercept λ is set to λ = α + β, where α is the mean of all elements in matrix T and β is the standard deviation of matrix T; λ is calculated to be 0.664. By introducing the intercept λ to evaluate the off-diagonal elements, an adjacency matrix F = [f_ij] n × n is constructed. When t_ij ≥ λ, f_ij is set to 1, indicating that an influence relationship exists between the two factors; When t_ij < λ, f_ij is set to 0, indicating that no influence relationship exists between the two elements. The adjacency matrix F is added to the identity matrix A, as shown in Equation (6), to obtain the initial Reachability Matrix D₀. Capture all indirect paths using the power operation until the matrix no longer changes. The iterative formula is as shown in Equation (7), yielding the final Reachability Matrix D, as shown in

Table 11. Reachability Matrix D.

Indicator	A1	A2	A4	B1	B2	B4	C1	C2	D4
A1	1	0	0	0	0	0	0	0	0
A2	0	1	1	0	0	1	1	1	1
A4	0	0	1	0	0	0	0	0	0
B1	0	0	0	1	0	0	0	0	0
B2	0	0	1	0	1	1	1	1	1
B4	0	0	1	0	0	1	1	1	1
C1	0	0	1	0	0	1	1	1	1
C2	0	0	0	0	0	0	0	1	0
D4	0	0	1	0	0	1	1	1	1

.

$$D_0=F+A$$

(6)

$$D_0^{k+1}=D_0^k\otimes D_0$$

(7)

Based on the reachability matrix D, Equation (8) was used to derive the reachable set U, the antecedent set V, and the intersection set W, thereby enabling the hierarchical structuring of the influencing factors. As a result, the hierarchy of influencing factors shown in

Table 12. Hierarchical Classification of Influencing Factors.

Indicator	Reachable Set U	Antecedent Set V	Intersection Set W = U ∩ V
A1	A1	A1	A1
A2	A2, A4, B4, C1, C2, D4	A2	A2
A4	A4	A2, A4, B2, B4 C1, D4	A4
B1	B1	B1	B1
B2	A4, B2, B4, C1, C2, D4	B2	B2
B4	A4, B4, C1, C2 D4	A2, B2, B4, C1 D4	B4, C1, D4
C1	A4, B4, C1, C2, D4	A2, B2, B4, C1, D4	B4, C1, D4
C2	C2	A2, B2, B4, C1, C2 D4	C2
D4	A4, B4, C1, C2, D4	A2, B2, B4, C1, D4	B4, C1, D4

and the hierarchical structure of influencing factors shown in

Table 13. Hierarchy structure table of influencing factors.

Level	Indicator	Degree of Influence
Level 1 (Top Level)	A1, A4, B1, C2	Surface Indicator
Level 2	B4, C1, D4	Shallow Indicator
Level 3 (Bottom Level)	A2, B2	Root Indicator

were obtained.

$$\begin{array}{l}U(S_i)=\{S_j\mid S_j\in S,r_{ij}=1\}\\ V(S_i)=\{S_i\mid S_i\in S,r_{ij}=1\}\\ W(S_i)=U(S_i)\cap V(S_i)\end{array}$$

(8)

Finally, based on the ISM analysis results (Table 12 and Table 13), a hierarchical structure diagram of the influence relationships among the design factors of the community food waste management product was constructed, as shown in Figure 3.

3.4. Integrated Data Analysis

Building upon the use of BWM for demand prioritization, ISM was further applied to analyze the structural hierarchy of key demands, the results are as shown in

Table 14. Integrated Date Analysis Table.

Indicator	BWM Weight	BWM Ranking	ISM Level
A2	0.0826	3	Root Indicator
B2	0.0791	4
C1	0.0964	2	Shallow Indicator
B4	0.0674	6
C2	0.1159	1	Surface Indicator
A1	0.0739	5
A4	0.0630	8
B1	0.0599	10
D4	0.0432	13
C4	0.0656	7	Other Surface
C3	0.0628	9
A3	0.0532	11
B3	0.0524	12
D2	0.0347	14
D1	0.0267	15
D3	0.0233	16

. The results indicate that “C₂: Incentive Feedback”, which ranked highest in the BWM analysis, is located at the surface result layer in the ISM analysis, while “A₂: Contact-Free Disposal” and “B₂: Effort-Saving Use” are root-level factors. These factors determine whether residents are willing to approach and use the system and influence whether the delivery process proceeds smoothly. Therefore, “A₂: Contact-Free Disposal” and “B₂: Effort-Saving Use” must be prioritized.

The result also shows that “A₁: Reduced Odor Impact” and “B₁: Large Capacity” are structurally isolated under the selected threshold. Specifically, neither “A₁: Reduced Odor Impact” nor “B₁: Large Capacity” has off-diagonal values exceeding λ in terms of outgoing or incoming influence relationships after thresholding. Therefore, their reachable sets, antecedent sets, and intersection sets contain only themselves. It also suggests that these two indicators function as relatively independent design needs within the selected indicator system. This does not mean that reduced odor impact or large capacity is unimportant in design practice. Rather, it means that these indicators act more as independent basic design constraints than as relational drivers within the causal hierarchy. In subsequent design translation, they are still retained as necessary design elements.

The demand priorities derived from the BWM analysis and the subsequent ISM analysis seem to be contradictory. In fact, the results reflect that the most direct user need, “Willingness to Use”, is identified as the design goal through the BWM. However, fulfilling this design goal requires addressing the “Hygiene Anxiety” and “Ease of Operation” identified through the ISM analysis as prerequisites. These two secondary indicators, along with the other tertiary indicators under them, serve as the foundation for ensuring user engagement and the link to sustained participation motivation.

In the initial stage of constructing requirement metrics for analysis, the concept of affordance was used to distinguish between “the physical conditions that enable interactions to occur” and “superficial user preferences”. This distinction is crucial for understanding why certain metrics carry greater weight in BWM analysis, while others function as root-cause prerequisites within the ISM hierarchy.

4. Research Model and Design Strategies

4.1. Development of the Model

4.1.1. Translation Model of BWM—ISM—Affordance

To translate demand analysis into executable design elements, a community food waste management product design Translation Model of BWM—ISM—Affordance (BIATM) is constructed, as shown in Figure 4. The BWM is used to determine the weights of users’ explicitly stated needs. Subsequently, the ISM is employed to identify the prerequisites and causal hierarchy required to fulfill these needs. Based on the theory of affordance, these prerequisites and objectives are translated into design elements across five dimensions—physical, perceptual, cognitive, functional, and social—divided into physical and psychological levels. BIATM analysis enables the linking of explicit demand priorities with the underlying prerequisites for behavioral occurrence. This model is suitable for community service facilities characterized by public nature, shared usage attributes, behavior-driven demands, and goals of sustained participation.

The affordance on the physical level mainly refers to the physical affordance and perceptual affordance presented to users by various physical information. The key design considerations focus on the product’s functional hardware, typically referring to components and dimensions designed based on user habits and ergonomics to ensure the product is usable by the user [44,45].

On the psychological level, affordance corresponds to cognitive affordance, functional affordance, and social affordance. The primary design considerations involve product design semantics that align with users’ cognitive habits, as well as how the product’s work efficiency meets users’ psychological expectations and the emotional changes this evokes in users [46,47].

4.1.2. Mapping Process

To avoid the mapping of needs to affordance dimensions relying solely on the researchers’ subjective judgment, this study further introduces a theoretical “user needs—affordance dimensions” mapping process, which is carried out in three steps.

(1) Based on Gibson’s theory of primal affordances—which explains the relationship among people, objects, and the environment—each user need is reformulated as “what obstacles the user encounters in achieving their goal”. This approach interprets needs as manifestations of how certain action possibilities are constrained within the user-facility-environment relationship, thereby identifying the factors influencing the generation of supportive actions through a reverse-engineering approach.

(2) Based on Norman’s interpretation of perceptible affordance in design and Hartson’s classification framework for affordance, this study categorizes the factors influencing supportive actions into five specific dimensions: physical execution, perceptual acquisition, cognitive understanding, task feedback, and social participation.

To avoid any bias in judgment resulting from individual differences in the interpretation of the evaluation criteria, the researchers, following discussion, established the following five evaluation questions and engaged in further deliberation after reaching their conclusions to determine the classification of the affordance dimensions, as shown in

Table 15. Table of Questions and Dimensions.

Questions	Obstacle Dimension	Affordance Dimension
Does the need primarily concern whether the facility provides sufficient components to ensure users can use it smoothly?	Physical Execution	Physical Affordance
Does the need primarily concern whether the facility provides sufficient components to ensure users can use it smoothly?	Perceptual Acquisition	Perceptual Affordance
Does the need primarily concern whether the facility allows users to understand its purpose, rules, and procedures?	Cognitive Understanding	Cognitive Affordance
Does the need primarily concern whether the facilities truly support task completion and feedback on results?	Task Feedback	Functional Affordance
Does the need primarily concern whether the facility fosters social interaction, a sense of identity, and shared participation?	Social Participation	Social Affordance

.

(3) Additionally, since the same user need may involve multiple factors that support action initiation, to avoid indiscriminate multi-label categorization, this study determines the dominant affordance based on the primary action barriers that the design aims to address. When there is another clear supporting layer, it is classified as a secondary affordance. Following the principle of “prioritizing dominant affordances and supplementing with secondary affordances”, the needs are classified into affordance dimensions, and the systematic translation from needs to design elements is completed accordingly. Specifically, four researchers within the team independently judged the affordance dimension associated with each need according to the above procedure. When disagreements arose, they were resolved through repeated communication, ultimately determining the affordance dimension associated with each need. For example, when analyzing the usability dimensions of “B₂: Large Capacity”, the research team first considered that the capacity of waste disposal facilities affects the smoothness of residents’ waste disposal process and imposes an operational burden on them. However, fulfilling this need requires large container components as support, so it was categorized under perceptual/physical affordance.

4.2. Design Strategies

4.2.1. Physical Affordances

Public facilities deployed in communities prioritize convenience and ease of use. The key lies in minimizing the number of steps required to achieve a goal and reducing the duration of the process. Physical affordance and perceptual affordance are the foundations for activating public participation and usage. Therefore, while integrating the necessary functions for food waste disposal, it is essential to consider both the functional requirements and the physical conditions needed for users to complete the process. This helps improve the efficiency of the food waste disposal process and ensures the sustainability of food waste management.

For example, through affordance analysis, this study adapted the “A₂: Contact-Free Disposal” concept into a foot-operated pedal mechanism. This design retains the operational logic of traditional trash cans while preventing users from touching potentially contaminated surfaces with their hands. This design element alleviates users’ hygiene concerns when they approach and dispose of waste, while also aligning with their established habits regarding trash can usage. It communicates the possibility of opening the lid via foot operation, thereby lowering the barrier to use.

4.2.2. Psychological Affordances

From the perspective of cognitive affordance, perceptual guidance can be used to reduce users’ decision-making costs during operation by means of intuitive cues. Jeremiah et al. examined the formation mechanism and functional pathway of cognitive affordance from the perspective of the characteristics of the human cognitive system, thereby providing a theoretical basis for cognitive affordance [41]. In everyday situations where no direct contact occurs between a person and an object, visual cues are typically the primary source of information used for recognition and judgment [48]. Moreover, according to the BWM-based analysis, “C1: Simple and Clear Appearance” is a high-weight demand. Therefore, when guiding participation and use through cognitive affordance, priority should be given to visual guidance [49]. In this study, a nature-associated color scheme dominated by white and green [50] is adopted to abstractly map the process of ecological organic circulation and to provide users with cues that encourage their participation in the ecological cycle [51].

From the perspective of functional affordance, low-threshold and progressive task and feedback mechanisms can be established [52] to enable residents to gradually develop stable participation habits through repeated engagement, situational cues, and positive reinforcement [53]. By providing users with the material basis for completing food waste management through affordance-based design, such an approach can facilitate the emergence of proactive food waste disposal behavior and sustain users’ continued engagement through various forms of incentives [54]. Therefore, translating “C₂ Incentive Feedback” into a low-threshold, progressive feedback mechanism, centers on converting processed food waste into reusable resources, making the results of food waste processing visible—thereby fulfilling the “C₃ Visibility of Processing Results” requirement—and distributing these resources to residents who participate in food waste processing. This effectively incentivizes community residents to engage in the food waste processing process and enhances their governance capabilities as key actors in environmental and social governance [8,55].

From the perspective of social affordance, ecological identity reinforces value alignment [56]. By enhancing the visibility of behavioral outcomes, fostering a sense of identity, and reinforcing alignment with sustainable values—with the goal of stimulating intrinsic motivation—through cognitive restructuring and achievement feedback. Therefore, by incorporating interactive app design into the user-facility interaction process—serving as a vehicle for social and identity verification functions—food waste disposal is transformed into a process through which individual residents affirm their own eco-friendly identity [57]. Combined with the labeling effect, this encourages users to actively align with the eco-conscious identity that has been evoked [58].

Ultimately, a summary table of design strategies was compiled, as shown in

Table 16. Affordance Dimensions, Strategies, and Design Approaches.

Level	Dimension	Strategies	Approach
Physical	Physical Affordance	Enhancing convenience	Functional component configuration
	Perceptual Affordance	Enhancing ease of use	Ergonomic design
Psychological	Cognitive Affordance	Reducing decision-making costs	Appearance design
	Functional Affordance	Progressive task design	Process design for obtaining incentive feedback
	Social Affordance	Strengthening value identity through ecological identity	Identity authentication and community interaction

.

4.3. BIATM-Based Affordance Translation

The user need priorities were identified from the preliminary analysis. According to the BIATM affordance translation rules, the needs were classified into five dimensions of affordance, resulting in the design elements required to guide residents within the community to actively and continuously engage in accurate food waste disposal, as shown in Figure 5, where * denotes high priority.

Based on Figure 5, to ensure that users can carry out food waste disposal actions as expected, affordance cue analysis is introduced. Affordance cues are generated for each high-priority need, aligned with residents’ cognition, and expanded into a mapping table of key needs and affordance translation design elements, as shown in

Table 17. Mapping between Key Needs and Affordance-Translated Design Elements.

Key Need	Dimension	Design Intent	Affordance Cue	Design Element	Design Rule
A1 Reduced Odor Impact	Physical Affordance	Transform odor emission into a controllable factor	Install an exhaust component	Exhaust fan installed at the rear	Avoid direct orientation toward residents
A2 Contact-Free Disposal	Perceptual/ Physical Affordance	Reduce residents’ resistance and anxiety regarding hygiene	Following the logic of conventional waste bins, install a pedal-operated lid-opening device at the bottom	Pedal-operated lid opening	The installation position should accommodate different user groups
A4 Post-Use Cleaning	Physical Affordance	Enable users to clean their hands after waste disposal	Establish a cleaning area	Washbasin with sufficient depth	Installed together with the main body of the product
B1 Large Capacity	Perceptual/ Physical Affordance	Accommodate a larger volume of food waste	Exterior design conveying a sense of large volume	Container body	The volume should be configured as large as possible
B2 Effort-Saving Use	Perceptual Affordance	Reduce users’ physical burden	Waste inlet height within easy reach	Lid-opening height set at 1.2 m	Height range should be determined based on ergonomics
B4 Complete Supporting Facilities	Physical Affordance	Provide tools to support the entire process of waste disposal and management	Provision of auxiliary tools	Washbasin, storage for planting tools	Priority should be given to essential tools
C1 Simple and Clear Appearance	Cognitive Affordance	Enable rapid recognition of the product as a food waste bin	Guiding colors and simple geometric form	Waste icon signage and gradient green decoration	Multiple visual cues should be used to reinforce guidance
C2 Incentive Feedback	Functional/Social Affordance	Transform occasional behavior into sustained habits	Dispose of waste, collect fertilizer, and visualize outcomes	Compost box and public planting area	Should be low-threshold, practical, and sustainable
D4 Simple Usage Method	Cognitive Affordance	Enable direct use without additional instruction or training	Reduce the number of usage steps	Interaction flow design	Retain only the necessary steps for waste disposal and incentive acquisition

.

Based on the aforementioned analysis results, this study organizes the key design elements and divides the facility into three main functional areas: the food waste disposal area, the public planting area, and the cleaning area, forming the functional module layout diagram shown in Figure 6. The waste disposal inlet is located in the center of the facility and is designed in a different color from the facility body to guide residents in quickly finding the correct waste disposal location. Waste enters the container through the disposal inlet, undergoes fermentation, and is converted into compost, which then falls into the fertilizer storage box. Residents can open the fertilizer storage box to collect the compost and use it in the public planting area. After completing the process, they can clean their hands in the cleaning area.

5. Design Practice

5.1. Design Scheme Generation

The purpose of this design project is to conduct a preliminary assessment of the feasibility of applying the design strategy—based on affordance—to community food waste management facilities through the processes of scheme generation, design presentation, and expert evaluation, thereby demonstrating its potential for application in the design of such facilities. Furthermore, it aims to explain how to translate the deep influencing factors of residents’ willingness to use into executable design elements to improve the efficiency of food waste recycling and resource reuse.

Based on the above theoretical framework and demand analysis, a product is designed to support residents in developing more accurate waste sorting habits and to promote the recycling and reuse of food waste. After residents dispose of food waste, the product processes it through crushing and mixing, followed by high-temperature sterilization, and then conducts decomposition and fermentation to produce compost for their use. Residents can collect the compost and either apply it directly in the public planting area or take it home for personal use.

To evaluate the differences in performance of the key design elements derived from BIATM in solution selection, three design schemes are initially proposed, as shown in Figure 7. Based on the weight ranking and affordance-based design element analysis, and to ensure that the evaluation results mainly reflect the differences in key needs derived from BIATM, the three schemes remain consistent in basic functions (such as “A₁: Reduced Odor Impact” and “B₁: large capacity”), processing procedures (such as “A₄: post-use cleaning” and “D₄: ease of use”), and in how they address the need of “C₂: incentive feedback”, which influences sustained user motivation. Differences are introduced in four needs that affect users’ willingness to use: “A₂: Contact-Free Disposal”, “B₂: Effort-Saving Use”, “B₄: complete supporting facilities” and “C₁: simple and clear appearance”.

Among them, Scheme A focuses on providing affordance cues by addressing “A₂: Contact-Free Disposal”, “B₂: Effort-Saving Use” and “B₄: complete supporting facilities”. Scheme B focuses on “B₂: Effort-Saving Use” and “B₄: complete supporting facilities”, while Scheme C focuses solely on achieving “C₁: simple and clear appearance”.

5.2. Selection of the Optimal Design Scheme Using TOPSIS

To conduct a rigorous evaluation and achieve optimal selection among the design schemes, seven evaluation criteria were established: Odor Impact, Hygiene Anxiety, Physical Burden, Ease of Use, Clarity of Appearance, Sustained Willingness to Use, and Learning Cost. Ten experts were invited to evaluate the three schemes, including three industrial designers, three community property management staff, and four neighborhood committee representatives. After providing explanations of the functions and evaluation criteria, the experts rated the performance of each scheme under different criteria using a 100-point scale. The evaluation was primarily based on the experts’ judgment of the conceptual schemes, resulting in the Original Decision Matrix Q, as shown in

Table 18. Original Decision Matrix Q.

Indicator	Scheme A	Scheme B	Scheme C
Odor Impact	90.5	84.8	85.3
Hygiene Anxiety	86.9	76.2	72.9
Physical Burden	80.5	83.2	74.5
Ease of Use	87.2	77.1	72.4
Clarity of Appearance	92.0	80.1	90.3
Sustained Willingness to Use	81.5	74.7	78.6
Learning Cost	83.3	88.5	80.9

.

After obtaining the original scoring matrix, to assess the reliability of the expert ratings used in the TOPSIS evaluation, Kendall’s coefficient of concordance (Kendall’s W) was calculated for each criterion, with adjustments applied for tied rankings when necessary. Cronbach’s alpha was also used to examine the overall internal consistency of the expert rating matrix. The results are shown in

Table 19. Consistency Analysis.

Indicator	Kendall’s W	Chi-Square	p-Value
Odor Impact	0.832	16.632	0.000245
Hygiene Anxiety	0.977	19.538	0.000057
Physical Burden	0.977	19.538	0.000057
Ease of Use	1	20	0.000045
Clarity of Appearance	0.79	15.8	0.000371
Sustained Willingness to Use	0.977	19.538	0.000057
Learning Cost	1	20	0.000045
Overall internal consistency		Cronbach’s α = 0.994

. The Kendall’s W values for the seven criteria range from 0.790 to 1.000, and all values are statistically significant (p < 0.001). In addition, the overall Cronbach’s alpha coefficient is 0.994, indicating a high level of internal consistency among the ten experts. This confirms that the expert ratings are highly reliable and suitable for subsequent scheme selection based on TOPSIS.

The original scoring data were normalized using the square root of the sum of squares method to obtain the Standardization Matrix M, as shown in

Table 20. Standardization Matrix M.

Indicator	Scheme A	Scheme B	Scheme C
Odor Impact	0.6012	0.5634	0.5667
Hygiene Anxiety	0.6359	0.5576	0.5335
Physical Burden	0.5847	0.6043	0.5412
Ease of Use	0.6361	0.5625	0.5282
Clarity of Appearance	0.6062	0.5278	0.5950
Sustained Willingness to Use	0.6008	0.5507	0.5794
Learning Cost	0.5705	0.6062	0.5541

. The Weighted Normalization Matrix K was then calculated by multiplying the absolute weights in Table 3 with the normalized matrix, as shown in

Table 21. Weighted Normalization Matrix K.

Indicator	Scheme A	Scheme B	Scheme C
Odor Impact	0.0444	0.0416	0.0419
Hygiene Anxiety	0.0525	0.0461	0.0441
Physical Burden	0.0463	0.0478	0.0428
Ease of Use	0.0429	0.0379	0.0356
Clarity of Appearance	0.0584	0.0509	0.0574
Sustained Willingness to Use	0.0696	0.0638	0.0672
Learning Cost	0.0246	0.0262	0.0239

.

After the Weighted Normalization Matrix K had been obtained, the positive and negative ideal solutions were defined. Specifically, the maximum value in each row was taken as the positive ideal solution, whereas the minimum value was taken as the negative ideal solution, yielding the positive ideal solution $k_j^{+}=(k_1^{+}$, $k_2^{+}$, …, $k_m^{+})$ and the negative ideal solution $k_j^{-}=(k_1^{-}$, $k_2^{-}$, …, $k_m^{-})$. The Euclidean distances were then calculated according to Equations (9) and (10), after which the relative closeness coefficient Z between each scheme and the ideal solutions was calculated using Equation (11). The schemes were subsequently ranked according to their relative closeness coefficients to determine the optimal scheme: the higher the relative closeness coefficient, the higher the priority of the scheme, and vice versa. The calculation results are shown in

Table 22. Relative Closeness Coefficient.

Scheme	${\mathit{D}}_{\mathit{i}}^{\mathbf{+}}$	${\mathit{D}}_{\mathit{i}}^{\mathbf{-}}$	Zi	Ranking
Scheme A	0.0022	0.0153	0.8747	1
Scheme B	0.0128	0.0063	0.3296	3
Scheme C	0.0129	0.0073	0.3622	2

.

Euclidean Distance to the Positive Ideal Solution:

$$D_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^m{(k_j^{+}-k_{ij})}^2}}$$

(9)

Euclidean Distance to the Negative Ideal Solution:

$$D_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^m{\left(k_j^{-}-k_{ij}\right)}^2}}$$

(10)

Calculation of the Relative Closeness Coefficient:

$$Z_i={\displaystyle \frac{D_i^{-}}{D_i^{-}+D_i^{+}}}$$

(11)

Based on the relative proximity results calculated using the TOPSIS method, the differences in overall performance among the various schemes are quite pronounced. The results indicate that Scheme A, which meets the requirements that influence users’ initial willingness to use the product—namely, “A₂: Contact-Free Disposal”, “B₂: Effort-Saving Use”, and “B₄: Complete Supporting Facilities”—possesses a clear advantage under multi-criteria evaluation and is the optimal choice. This demonstrates that the design scheme derived from the BIATM exhibits good application potential in the comparison of conceptual designs.

What should be noted is that the purpose of using TOPSIS to conduct a comparative evaluation of the conceptual designs in this study was to verify whether the key design elements derived from the BIATM could yield relatively consistent preferred results at the design level, thereby providing preliminary support for the practical application of this model.

5.3. Presentation of the Final Design Scheme

The overall scheme stands 1.8 m tall, with the waste disposal opening located 1.2 m above the ground. It adopts a white–green color palette. The white base symbolizes cleanliness, while green corresponds to the natural cycle in which food waste is fermented into fertilizer and supports plant growth, conveying a visual sense of environmental sustainability and vitality. In terms of form, smooth and fluid wave-like curves run throughout the design. The curved contour of the waste bin on the left naturally connects with the layered wave structure of the planting area on the right, forming an integrated visual composition. This design metaphorically represents the dynamic process of material circulation and communicates, through intuitive visual language, the vision of waste resource utilization and community sustainability, as shown in Figure 8, and the internal components are shown in Figure 9.

5.4. Public Participation in the Sustainable Food Waste Management Process

Intelligent interaction has become an integral part of everyday life and urban environments [59]. Integrating intelligent interaction into public facilities helps foster positive interactions between residents and facilities [60,61]. Therefore, to support residents’ active participation in the food waste management process and enhance their sense of gain, deservedness, and identity, a mechanism is introduced—within the process shown in Figure 10—where simple identity verification is required before rewards can be obtained. Centered on the process of “food waste disposal—fertilizer acquisition—fertilizer use for planting”, additional functions such as a community communication platform, knowledge dissemination, and activity records are incorporated. These functions are integrated into an APP for residents’ use, as shown in Figure 11.

6. Discussion

6.1. Main Findings

This study aims to guide residents to participate in food waste resource recycling and promote sustainable development, with a focus on maintaining residents’ willingness to continuously use community food waste management facilities. A system design strategy based on Affordance Theory is constructed to deeply analyze the intrinsic relationships underlying residents’ willingness to use. The results show that, at the level of explicit expression, residents place the greatest emphasis on “C: willingness to use”, particularly the needs of “C₂: incentive feedback” and “C₁: simple and clear appearance”. However, according to the hierarchical analysis of needs, the root factors that determine whether behavior can occur are not incentives themselves, but “A₂: Contact-Free Disposal” and “B₂: Effort-Saving Use” which are directly related to hygiene and operational burden. This indicates that, in the context of community food waste management, residents’ sustained participation is not determined solely by the added value of facilities, but depends on whether there are hygiene risks during approach, contact, and disposal, as well as the physical burden imposed during operation.

The study shows that residents’ explicit preference needs are not fully aligned with the prerequisites for behavior initiation. For community public facility design, it is necessary to first address deeper constraints such as hygiene anxiety and operational burden. Only then can incentive mechanisms and positive feedback function effectively to sustain continued use behavior. In this way, the study extends existing research on community waste classification, which tends to emphasize publicity, institutional constraints, or incentive mechanisms, by highlighting the critical role of facilities themselves as mediators that trigger behavior.

This result can also be understood in relation to the specific characteristics of the study sample. The reason why needs related to hygiene and operational burden appear as root-level driving factors may not only reflect the inherent characteristics of food waste disposal behavior but also relate to the sociodemographic structure of participating residents. In communities where residents are more sensitive to hygiene and physical contact, or where there is a higher proportion of elderly residents, hygiene anxiety and physical burden are likely to play a more prominent role in influencing whether residents are willing to approach and use the facilities. In this sense, the root-level positions of “A₂: Contact-Free Disposal” and “B₂: Effort-Saving Use” carry both behavioral significance and contextual sensitivity. Discussing this possibility helps to more rigorously define these findings as context-dependent design implications and provides directions for further validation across different population groups and community settings.

6.2. Theoretical Contributions

This study proposes and preliminarily validates a more explanatory design proposition: in the design of community public service facilities, residents’ explicitly expressed important needs are not necessarily the fundamental drivers of behavior initiation and sustainability. Design aimed at promoting sustained participation should first address deeper behavioral prerequisites, and then maintain engagement through surface-level feedback and incentive mechanisms.

Existing studies indicate that hygiene concerns and convenience barriers are important factors influencing waste disposal behavior [62,63]. Consistent with these findings, this study also shows that needs related to hygiene and operational burden play a significant role. In this study, the BWM results indicate that “C₂: incentive feedback” and its related needs have higher weights in explicit expressions, while the ISM results further reveal that “A₂: Contact-Free Disposal” and “B₂: Effort-Saving Use” are the root-level prerequisites that determine whether behavior occurs. Together, these findings suggest that the ranking of explicit needs does not always align with the underlying behavioral drivers. Therefore, public facility design should not rely solely on surface-level preferences for resource allocation, but should first address the fundamental conditions required for behavior initiation.

In the application of Affordance Theory, compared with existing studies on affordance and behavior guidance, this study not only uses affordance as an explanatory framework for existing design outcomes, but also positions it as a link between demand analysis methods and design intervention. In addition, social affordance is incorporated into the analytical framework. Through this approach, the application of Affordance Theory is further extended to the design context of sustained participation in community public service facilities, where it is used to explain and guide how facilities support the initiation and continuation of behavior through physical, perceptual, cognitive, functional, and social cues.

Therefore, the contribution of this study does not lie in repeatedly identifying the determinants of pro-environmental or waste sorting behavior that have already been extensively examined in existing studies. Instead, it lies in reconstructing these factors from “behavioral influencing factors” into “design objectives with hierarchical differences”, and revealing how the relationships among these factors influence users’ willingness to use. These are then systematically translated into public facilities design strategies through the BIATM, expanded the scope of application of affordance theory in the field of sustainability. The significance of BIATM is not limited to demand ranking or scheme selection. Its core lies in uncovering the structural relationship between explicit preferences and deeper behavioral prerequisites, and further translating this relationship into an affordance-oriented design logic. Accordingly, this study defines BIATM as a design research model with contextual transfer potential. It is more suitable for community service facilities characterized by publicness, shared use, behavior guidance objectives, and requirements for sustained participation, rather than as a universal model applicable to all design problems.

6.3. Policy Discussion

The results of this study indicate that the key factors influencing residents’ sustained participation are not fully aligned with their explicitly expressed preferences. Conditions such as hygiene assurance, including Contact-Free Disposal, and basic usability are more likely to serve as prerequisites for behavior initiation. Therefore, facility provision standards should shift from “whether facilities should be installed” to “whether they can be continuously used”.

In addition, relevant policies should incorporate coordinated facility operation and maintenance into the design scope, rather than treating them as secondary issues after installation. Without stable cleaning and maintenance, equipment repair, clear responsibility allocation, and feedback mechanisms, even facilities with strong behavioral guidance potential at the conceptual design level may lose user trust and the basis for sustained use due to negative user experiences.

Although incentive feedback helps enhance residents’ willingness to participate continuously, its effectiveness depends on appropriate boundaries and stable operation and maintenance. On the one hand, if rewards are designed to be overly immediate or excessively frequent, they may induce utilitarian participation or even opportunistic misuse, thereby undermining the fairness of community public resource allocation [64]. On the other hand, if the facility lacks stable cleaning, consumable replenishment, fault maintenance, and status management, the Hygiene Anxiety formed during residents’ initial experience may be reinforced, quickly eroding trust and interrupting continued use [65,66]. Therefore, the incentive mechanism should place greater emphasis on low-threshold participation, phased feedback, and the visualization of outcomes, so as to avoid reducing participation to a short-term exchange behavior. Meanwhile, the operation and maintenance mechanism should rely on coordination among the community, property management, and residents to ensure the continuous and reliable functioning of the facility in terms of hygiene, functionality, and feedback [67,68], as shown in Figure 12.

6.4. Limitations and Future Research Directions

This study conducts demand ranking and identifies the relationships among needs for community food waste management facilities, and constructs a pathway from demand analysis to design translation. However, several limitations remain. The interview and survey samples are mainly drawn from specific community contexts, and the sample size and geographic coverage are limited. Differences in needs across varying urban governance models, community densities, and resident structures have not been fully compared. In addition, factors such as population density, age distribution, management approaches, and cognitive levels within communities may influence both the priority and structural position of different user needs. Future research can enhance external validity by including more communities, larger samples, and cross-regional comparisons.

In addition, although the qualitative coding process was conducted collaboratively and disagreements between researchers were resolved through discussion to improve coding consistency, a formal inter-rater reliability index, such as Cohen’s kappa, was not calculated or reported. Therefore, the reliability of the coding process was ensured primarily through consensus-based discussion rather than through a statistically verifiable measure of coder agreement. This may limit the methodological rigor and reproducibility of the qualitative analysis to some extent.

In the ISM analysis, this study adopts a 0–4 influence scale instead of the traditional binary 0/1 scale. While the 0–4 scale allows for a more nuanced distinction of influence levels and captures partial or moderate relationships between factors, it also introduces greater subjectivity. Respondents may interpret intermediate values differently, which could affect the hierarchical structure of needs. Future research may combine quantitative ratings with actual behavioral data to further validate the relationships among factors.

Similarly, when using TOPSIS for scheme selection, the results still rely to some extent on expert judgment and subjective ratings rather than actual user behavior data from real communities. Therefore, this study remains at the level of conceptual validation of schemes rather than empirical verification of residents’ willingness to use, sustained participation behavior, or community engagement outcomes. And cannot fully replace long-term behavioral data from real usage contexts. It has not yet been tested through real community deployment to examine the long-term effects of the facilities on disposal frequency, misclassification rate, dwell time, sustained usage rate, and community interaction.

7. Conclusions

To enhance residents’ willingness for sustained participation in food waste management and to promote environmental and social sustainability, this study proposes a system design strategy that integrates demand weight identification, hierarchical relationship analysis, and Affordance Theory. On this basis, the BIATM demand translation model is constructed and applied to the design of community food waste management facilities, with TOPSIS used to complete scheme evaluation and selection.

The results show that integrating demand weight identification, hierarchical analysis of demand structure, and affordance-based design translation helps more accurately identify the factors influencing residents’ participation and sustained use of community public facilities. It also enables community food waste management facilities to function as behavioral mediators that promote resident participation, support community resource circulation, and contribute to sustainable development.

The broader significance of this study lies in its relevance to the United Nations Sustainable Development Goals. By integrating demand analysis, hierarchical decomposition of needs, and Affordance Theory, this study provides a practical pathway for public facility design to support more sustainable patterns of consumption, disposal, and resource reuse. It offers preliminary theoretical support in areas such as hygiene assurance, usability, resident participation, and community-level resource circulation, thereby contributing to the achievement of SDG 11 (Sustainable Cities and Communities). These aspects are essential for improving the quality and sustainability of everyday urban living environments. In addition, by promoting food waste management and resource recovery at the source, and by transforming disposal behavior into a more sustainable and responsible form of participation, this study also contributes to the achievement of SDG 12 (Responsible Consumption and Production).

Finally, this study provides a methodological reference for the design of community food waste management facilities and offers new practical evidence for the application of Affordance Theory in sustainable public product design. It contributes to the recycling and reuse of food waste within communities, improves resource recovery rates and value realization, and promotes the harmonious development of society and the environment.