A Study on Landscape Satisfaction in Micro-Scale Waterfront Spaces: Evidence from the Grand Canal in Wuxi

Abstract

Micro-scale waterfront spaces play a critical role in contemporary urban regeneration by supporting everyday activities and place-based experiences. However, existing studies often rely on linear evaluation approaches and insufficiently address the asymmetric effects of functional, environmental, and cultural attributes on residents’ landscape satisfaction. This study investigates the satisfaction structure of micro-scale waterfront spaces along the Grand Canal in Wuxi, China, with a particular focus on nonlinear demand mechanisms. A mixed-method framework integrating grounded theory, the Delphi method, and the Kano model was employed to identify key landscape attributes and classify their satisfaction effects. The results reveal a hierarchical satisfaction mechanism characterized by “basic–performance–attractive” attributes. Fundamental functional and environmental factors, such as accessibility, safety, water quality, and cultural authenticity, function as must-be attributes that primarily prevent dissatisfaction. Environmental comfort and social facilities act as one-dimensional attributes that linearly enhance satisfaction, while cultural narratives, memory-related elements, and ecological esthetics emerge as attractive attributes that significantly elevate emotional engagement when present. Sensitivity analysis further identifies priority intervention factors with the greatest impact on satisfaction improvement. These findings demonstrate the asymmetric nature of residents’ landscape satisfaction and provide a phased optimization framework for the sustainable regeneration of heritage-based micro-scale waterfront spaces, emphasizing basic reliability, experiential enhancement, and cultural resonance.

1. Introduction

Urban waterfront spaces refer to the shoreline areas distributed along rivers, lakes, oceans, and ports [1]. Owing to their distinctive dual and porous spatial characteristics, these areas continuously influence—and are simultaneously shaped by—the city’s ecological patterns, social activities, and cultural identity [2]. As a result, they serve as critical urban gateways that convey the city’s image and attract surrounding residents [3]. Given their significant economic and social value [4], the renewal and sustainable development of urban waterfront spaces have therefore become an increasingly prominent focus in contemporary planning and design practice [5,6,7].

Recent research on waterfront spaces, such as Ding et al. (2023)‘s study of the Qinghua River, indicates that residents’ perceived quality is primarily determined by walkability, activity accessibility, and detailed spatial experiences, rather than large-scale spatial reconstruction [1]. Zeng et al. (2025) further confirmed through a cross-city comparison of Shanghai, Liverpool, and Marseille’s port areas that spatial vitality hinges on the organization of small-scale activity nodes and daily accessibility, rather than comprehensive morphological transformation [8]. This indicates that since the 21st century, waterfront renewal has gradually shifted from a macro-oriented approach dominated by large-scale urban redevelopment [9] to emphasizing micro-scale spatial improvements [10,11,12]. This shift in scale represents more than a physical transformation; it has driven a “human-centered turn” within urban renewal paradigms. Research indicates that micro-scale spatial units most authentically reflect residents’ nuanced perceptions regarding leisure, safety, social interaction, and cultural identity. Consequently, human experience has naturally become the core focus of micro-scale waterfront space studies [13,14,15,16]. In other words, the shift from macro-level renewal to micro-scale interventions has directly propelled the research focus from “spatial provision” to “spatial experience quality,” incorporating residents’ perceptions and satisfaction into evaluation frameworks [8,17]. Therefore, contemporary micro-scale waterfront spaces, as areas embodying both historical continuity and local symbolism, require research that not only considers functional and environmental attributes but also prioritizes the multidimensional experiences shaped by cultural symbolism to enhance resident satisfaction—manifesting as a three-dimensional perceptual value encompassing “function-environment-culture.”

Landscape satisfaction serves as a critical intersection between environmental psychology and urban design, measuring people’s comprehensive evaluation of spatial functionality, esthetics, and emotional belonging [18]. Existing research indicates that landscape satisfaction is closely related to the accessibility, comfort, and social interaction potential of public spaces [19,20]. However, most studies focus on functional or ecological indicators, with limited attention to how residents form diverse perceptual identities within spaces by integrating cultural symbolism [21,22,23]. Furthermore, existing evaluation methods predominantly employ linear measurement approaches, which struggle to capture the nonlinear characteristics of cultural experiences and the structural and asymmetric nature of multidimensional integration [24,25]. The KANO model, as an evaluation tool capable of identifying nonlinear demand structures, classifies spatial attributes into distinct categories such as “basic,” “expected,” and “delightful,” thereby revealing the asymmetric mechanisms through which different attributes contribute to satisfaction formation. This mechanism helps distinguish foundational needs that “must be met but offer limited satisfaction enhancement” from emotional or cultural elements that “significantly elevate experiences yet are non-essential.” This integration of diverse perceptions enables more precise representation of residents’ psychological response curves. For micro-scale waterfront spaces possessing both material attributes and spiritual qualities, the KANO model proves particularly effective in revealing residents’ differentiated responses between material needs and emotional identification. It compensates for the inability of traditional linear measurements to capture the asymmetric characteristics of diverse elements [26]. Based on this, it provides systematic design feedback for the renewal of micro-scale waterfront spaces.

Based on the above analysis, this study mainly explores the following three questions to achieve a systematic assessment of residents’ landscape satisfaction with the micro-scale waterfront spaces along the Wuxi Grand Canal and to propose renewal strategies:

(1)

In microscale waterfront spaces, which functional, environmental, and cultural symbolic elements collectively constitute the structure of residents’ landscape satisfaction?

(2)

Do these spatial elements exhibit nonlinear and asymmetric influences in the satisfaction formation process? How can the KANO model be used to systematically classify them into basic attributes, expected attributes, and pleasing attributes?

(3)

How can the attribute structure identified by the KANO model be transformed into an operational renewal framework to support refined renewal strategies for optimizing the functions and enhancing the cultural experience of microscale waterfront spaces?

Existing research on waterfront landscape satisfaction primarily focuses on large-scale spatial renewal and physical environmental attributes, often neglecting the micro-level, daily waterfront spaces embedded in historical context. Furthermore, cultural perception is typically treated as a static background variable rather than a positive driver of user satisfaction. Moreover, previous studies have largely employed symmetrical evaluation methods, assuming a linear relationship between landscape attributes and satisfaction; therefore, this study uses questionnaires, grounded theory, Delphi expert consultation, and the KANO model to systematically assess residents’ satisfaction with microscale waterfront landscapes. Compared to previous research methods, this approach can construct a multi-dimensional evaluation system covering functional needs fulfillment, environmental element construction, and cultural and spiritual perception. It identified key factors influencing resident satisfaction and revealed the interaction mechanism between material security and spiritual fulfillment in micro-scale waterfront space renewal from a people-centered perspective. This method not only reveals the asymmetric role of different landscape elements in shaping resident satisfaction but also theoretically expands the modeling framework for studying both material and spiritual satisfaction. Therefore, it provides a scientific basis and strategic support for the renewal design of micro-scale waterfront spaces and, in practice, promotes the transformation of waterfront design from a function-oriented to an experience-oriented approach.

2. Literature Review

2.1. Research on Micro-Scale Waterfront Spaces

Waterfront spaces rank among the most multifaceted public domains in cities, playing pivotal roles in ecological regulation, urban image formation, and cultural preservation [5,27]. Since the late 20th century, their redevelopment has progressively shifted from industry-centric approaches toward culture- and lifestyle-oriented regeneration pathways [6,28]. Micro-scale waterfront spaces, as a subfield of waterfront research, are not defined solely by their “small size.” Rather, they refer to spatial units that, through the overlapping dimensions of function, environment, and culture, generate high interface density at the neighborhood scale. These spaces exert measurable and intervenable impacts on urban waterfront continuity, residents’ daily experiences, and governance practices [29,30,31,32]. In recent years, the international academic community has increasingly focused on the nuanced portrayal of emotional experiences within micro-scale waterfront spaces.

Research indicates that residents’ daily use and cultural engagement within these spaces are crucial mechanisms for sustaining their vitality [33]. Elements such as cultural symbols, historical textures, and memory triggers have been shown to exert significant emotional influence, enhancing residents’ sense of belonging and place attachment [34], thereby promoting long-term usage and spontaneous maintenance behaviors [35]. However, compared to regeneration studies of large-scale historical waterfront spaces, quantitative assessments of micro-scale, highly everyday waterfront spaces remain extremely limited [36]. There is a particular lack of systematic exploration of the asymmetric roles within multidimensional satisfaction structures that incorporate cultural value, which constrains the multidimensional understanding of micro-scale waterfront spaces.

2.2. Study on Satisfaction and Emotional Perception of Waterfront Landscape

Landscape satisfaction serves as a crucial indicator for measuring the quality of human-environment interaction, widely applied in urban design and environmental psychology [18,19]. It reflects individuals’ overall perceptual evaluation of spatial environments, influenced by multidimensional factors including visual esthetics, adequacy of functional facilities, spatial accessibility, safety, and cultural symbolism [20]. In recent years, scholars have increasingly shifted from focusing solely on esthetic perception to examining the complex mechanisms involving spatial use and sociocultural experiences. They argue that satisfaction depends not only on physical environmental characteristics but also on users’ emotional identification and social interactions [37,38].

In waterfront space research, resident satisfaction has emerged as a crucial dimension for evaluating regeneration outcomes. Some studies indicate that environmental characteristics such as accessibility, recreational functions, and good ecological quality of waterfront spaces are positively correlated with the public’s positive perception of the space, which is considered an important factor in enhancing residents’ experience and subjective well-being [39,40,41,42]. Some studies also emphasize the role played by historical and cultural elements, as well as the symbolic characteristics of specific locations, in reinforcing the unique identity of waterfront spaces and strengthening the connection between communities and these locations [43]. However, most studies still employ traditional linear measures for resident satisfaction, neglecting the nonlinear psychological mechanisms of emotional cognition. Particularly in micro-scale waterfront spaces, residents’ diverse emotional perceptions intertwine to form complex satisfaction structures, challenging conventional satisfaction measurement methods.

3. Materials and Methods

3.1. Study Area and Evaluation Entity Selection

This study defines “micro-scale waterfront spaces” as small-scale public spaces along historically formed canals, typically under 2500 square meters in area [44], primarily used for daily resident activities, and embedded within the urban fabric. This category exhibits dual characteristics in urban renewal: it fulfills residents’ material needs for daily activities and leisure while simultaneously preserving historical memory and local identity. Consequently, renewal strategies must simultaneously address three dimensions—functional elements, environmental elements, and cultural elements. Wuxi, a nationally designated historical and cultural city, is renowned as the “Pearl of Lake Tai” and for its rich industrial and commercial heritage. The Beijing-Hangzhou Grand Canal traverses the city center from north to south, flowing into the Yangtze River. Its passage through Wuxi’s significant historical districts has played a decisive role in the city’s development. Given the historical and cultural value of the ancient canal itself and its close connection to the daily lives of surrounding residents, The study focuses on the section of the Beijing-Hangzhou Grand Canal in central Wuxi, specifically the “Jiangjian Park-Xishidun-Nanchan Temple” corridor and its adjacent areas. Through field visits and investigations, this research comprehensively documented the micro-scale waterfront spaces along the canal. Based on this foundation, comparative analysis of 24 samples ultimately identified 10 representative waterfront spaces as the primary subjects of study. The selection criteria primarily encompassed two aspects: (1) The site exhibits distinct Grand Canal cultural characteristics, such as well-preserved wharf ruins, historical industrial facilities, or oral cultural memories from residents; (2) The surrounding functional zones are primarily residential, enabling a more authentic reflection of landscape satisfaction perceptions within the context of daily life, while avoiding interference from short-term tourist perceptions. These ten waterfront spaces are primarily distributed along the cultural landscape belt, park green spaces, and residential areas of the Grand Canal in Wuxi, covering the most representative micro-scale spatial types in contemporary waterfront renewal practices (Figure 1). This sample selection ensures thematic diversity while maintaining contextual comparability, providing a solid empirical foundation for subsequent KANO model analysis.

Building upon this, to construct a KANO model grounded in resident perceptions, this study strictly limited survey participants to residents of communities along the canal. Focusing on this demographic aims to capture the deep-seated satisfaction structures formed through long-term, stable interactions, rather than fleeting, superficial impressions held by visitors. Residents’ spatial usage combines functional and emotional dimensions. Their feedback effectively distinguishes between essential cultural elements that must be safeguarded as basic needs and attractive attributes that enhance the experience, thereby providing an irreplaceable perspective for the refined renewal of micro-scale waterfront spaces [35].

3.2. Research Framework

To achieve multidimensional comprehensive measurement and investigate asymmetric satisfaction, the KANO model will be employed to assess residents’ landscape satisfaction. Compared with traditional weighting methods such as AHP or symmetric satisfaction models, the combination of the Delphi method and the Kano model allows this study to capture both expert consensus and asymmetric user perceptions. This integrated approach is particularly suitable for micro-scale waterfront spaces, where resource constraints require differentiated and prioritized intervention strategies. The overall research process comprises four core stages, with the study framework illustrated in Figure 2.

3.3. Construction of the Comprehensive Evaluation Indicator System

The construction of this study’s comprehensive evaluation indicator system follows a rigorous multi-stage process—grounded in empirical evidence, validated by experts, and model-adapted—to ensure local applicability, theoretical grounding, and methodological soundness. Final indicators were derived through grounded theory and the Delphi expert consultation method, then translated into a satisfaction measurement questionnaire. Integrated with the attribute classification logic of the KANO model, this framework enables the indicator system to reflect residents’ multifaceted perception structures while supporting the identification of asymmetric satisfaction attributes. The resulting evaluation system combines empirical foundations, expert validation, and methodological alignment, providing a robust basis for subsequent satisfaction mechanism analysis and renewal strategy formulation. The specific indicator selection process is illustrated in Figure 3.

3.3.1. Grounded Theory

Grounded Theory (GT), proposed by scholars Glaser and Strauss in 1967, involves theoretical generalization based on extensive raw data to establish frameworks closely related to phenomena [45]. It can utilize data sources such as online texts and interview data to analyze multiple perspectives, thereby enhancing the comprehensiveness of theoretical frameworks. GT has been extensively applied in studies of diverse urban public spaces and their users [45,46,47,48], and has also emerged as a research methodology in waterfront space investigations [49].

Given the non-standardized nature, ambiguous typology, and strong intermingling of uses in Wuxi’s micro-scale Grand Canal waterfront spaces, their authentic spatial experiences and values prove difficult to accurately define through official statistics or online spatial data. Such waterfront spaces simultaneously embody residents lived practices, cultural memories, and emotional attachments. Their evaluation dimensions often transcend physical and cultural attributes, necessitating construction through user narratives. Therefore, this study employs semi-structured interviews to elicit residents’ deep-seated perceptions regarding spatial use, cultural awareness, and satisfaction, constructing evaluation indicators based on grounded theory. To ensure the adequacy and representativeness of qualitative data, purposeful sampling and maximization of heterogeneity strategies were employed. Between 20 July and 1 September 2025, 55 permanent residents were invited to participate in in-depth interviews along the “Jiangyin Park-Xishuidun-Nanchan Temple” section of the Beijing-Hangzhou Grand Canal in Jiangxi District, Wuxi City (

Table A1. List of Interview Volunteers.

Number	Gender	Age (Years)	Years Resided (Years)	Occupation Type
R01	Male	29	5	Company employee
R02	Female	38	10	Middle school teacher
R03	Male	63	25	Retired resident
R04	Female	49	20	Self-employed business owner
R05	Male	22	3	College student
R06	Female	70	40	Retired resident
R07	Male	41	15	Urban planner
R08	Female	28	6	Cultural and creative designer
R09	Male	52	18	Civil servant
R10	Female	58	30	Community volunteer
R11	Male	33	7	Food service worker
R12	Female	46	15	Urban management staff
R13	Male	67	32	Retired teacher
R14	Female	31	5	Community social worker
R15	Male	57	28	Artisan
R16	Female	39	12	Cultural heritage docent
R17	Male	72	38	Retired civil servant
R18	Female	34	8	Urban Planner
R19	Male	27	6	Photographer
R20	Female	59	25	Community Organizer
R21	Male	42	10	Restaurant Owner
R22	Female	50	18	Medical Professional
R23	Male	23	4	Undergraduate Student
R24	Female	32	7	Landscape Architect
R25	Male	61	30	Retired Worker
R26	Female	47	15	Community Resident Representative
R27	Male	53	22	Freelancer
R28	Female	36	9	Internet Professional
R29	Male	43	12	Small Business Owner
R30	Female	66	35	Retired Teacher
R31	Male	27	6	Urban Volunteer
R32	Female	52	20	Community Secretary
R33	Male	37	10	Graphic Designer
R34	Female	56	25	Public Affairs Manager
R35	Female	30	8	Freelancer
R36	Male	45	12	State-Owned Enterprise Engineer
R37	Female	53	22	Community Librarian
R38	Male	60	30	Retired Grassroots Cadre
R39	Female	27	4	New Media Operator
R40	Male	34	7	Cultural Tourism Practitioner
R41	Female	68	40	Retired Medical Personnel
R42	Male	39	14	Small Restaurant Operator
R43	Female	24	3	Graduate Student
R44	Male	51	20	Water Patrol Officer
R45	Female	43	10	Psychological
R46	Male	58	28	Counselor
R47	Female	36	8	Traditional Artisan
R48	Male	70	38	Kindergarten Teacher
R49	Female	30	6	Retired Engineer
R50	Male	47	15	Hotel and Convention Services Staff
R51	Female	55	26	Elementary School Teacher
R52	Male	41	12	Community Health Station Nurse
R53	Female	61	33	Self-Media Photographer
R54	Male	29	5	Retired State-Owned Enterprise Employee
R55	Female	48	18	Sports Coach

). The 55 permanent residents were selected based on their long-term residence and daily use of the waterfront spaces, ensuring their familiarity with spatial changes and everyday landscape experience. Compared with transient users, permanent residents provide more stable and representative insights into micro-scale waterfront satisfaction in historic canal contexts. All interviewees possessed normal mobility and communication abilities, with ages ranging from 22 to 72. The sample encompassed young users (students, young professionals), middle-aged groups (urban administrators, cultural and creative workers, grassroots merchants), and a significant number of long-term local elderly residents (retired teachers, retired residents, etc.). This ensured the interviews reflected spatial usage needs and cultural perception differences across distinct life stages. Additionally, the sample’s residency duration ranged from 3 to 40 years, with occupations spanning 12 typical urban industries. Based on the research context and field conditions, we prepared distinct interview outlines (

Table A2. Interview Outline.

Interview Participants	Problem Code	Interview Outline
Functional Elements	Q1	How do you typically reach the waterfront area? Is the route convenient?
	Q2	Did you encounter any obstacles or inconveniences during your journey?
	Q3	Does the continuity of the waterfront walkway meet your walking or access needs?
	Q4	Do you consider the current infrastructure (seating, trash bins, lighting, railings, etc.) to be adequate?
	Q5	Is there any infrastructure you believe is “essential but currently lacking”?
	Q6	Are your typical activities (walking, exercising, socializing, accompanying elderly or children, etc.) adequately supported?
	Q7	Do you feel safe using the waterfront space at different times of the day? Why?
	Q8	Are there any areas or situations that make you feel unsafe?
	Q9	How would you rate the management of the waterfront area (cleanliness, maintenance, order management, etc.)?
	Q10	If additional or improved functional facilities could be added, which one would most significantly enhance your experience?
Environmental Elements	Q1	How would you rate the visual landscape of the waterfront space (greenery, water visibility, landscape layering, etc.)?
	Q2	Are there any environmental details you particularly like or dislike?
	Q3	Do cleanliness and maintenance conditions affect your willingness to stay here?
	Q4	Do you find the temperature, wind, sunlight, humidity, and other conditions comfortable?
	Q5	Are there any situations causing you noticeable discomfort? In which season or time of day is this most pronounced?
	Q6	Would microclimate factors cause you to shorten your stay?
	Q7	Did the surrounding noise environment (vehicles, crowds, construction, etc.) affect you?
	Q8	Did the odors, water quality, or environmental hygiene impact your experience?
	Q9	Which environmental factors, if improved, would most enhance your stay experience?
	Q10	Were there any environmental features that “unexpectedly made you feel comfortable or relaxed”? What were they?
Cultural Elements	Q1	While using the waterfront space, could you sense the historical and cultural significance of the Grand Canal?
	Q2	Which spatial elements evoke cultural or historical associations for you? (e.g., old docks, architectural facades, story markers)
	Q3	Are there areas where you feel “cultural elements are diluted or absent”? Why?
	Q4	Do specific locations or views trigger particular memories or emotions for you?
	Q5	Do you perceive a unique “sense of place” in the waterfront atmosphere? How is it manifested?
	Q6	If not, what do you believe causes this absence?
	Q7	Do cultural elements influence how often or how you use the waterfront space?
	Q8	Do you consider cultural expression “essential” or “nice to have”? Please explain.
	Q9	How would you like to see cultural expression enhanced in the future? Examples include light narratives, cultural pathways, or scene restoration.
	Q10	If cultural expression were strengthened, would you be more inclined to linger here or bring family members? Why?

) for collecting data across different element indicators. In this study, semi-structured interviews refer to interview sessions conducted based on a predefined interview guide covering core themes (functional use, environmental perception, cultural cognition), while allowing flexible probing and follow-up questions according to respondents’ narratives. This format emphasizes thematic consistency across participants while retaining openness in responses, which is consistent with standard qualitative research practice. All interviews were conducted with informed consent and documented through audio recordings and written transcripts.

(1)

Open Coding Analysis

Open coding is the first step in grounded theory coding, a process of classifying semantic concepts to systematize and conceptualize interview information. During this phase, the study strictly adhered to the coding principles outlined by Strauss and Corbin (2014) [50]. We conducted line-by-line comparison, attribute annotation, and concept summarization analysis on each paragraph of 35 semi-structured interview transcripts based on interview outlines. This process identified core semantic units related to residents’ landscape satisfaction, ensuring the extracted concepts accurately reflected interviewee perspectives. Through the transformation of raw statements, 112 preliminary abstract concepts were extracted and categorized (

Table A3. Initial Statement Transformation.

Classification	Number	Original Statement	Preliminary Abstract Concepts	Conceptual Classification
Functional elements	F01	“Walking from the residential area to the river requires crossing the road, which is very inconvenient.”	Inconvenient access routes	Accessibility
	F02	“The steps down to the riverbank are too steep, making it dangerous for the elderly to climb up and down.”	Unfriendly elevation changes	Safe Accessibility
	F03	“Wheelchairs simply cannot be pushed down to the riverbank.”	Lack of accessible facilities	Barrier-Free Systems
	F04	“The walkway suddenly ends, forcing people to backtrack.”	Insufficient trail continuity	Spatial Connectivity
	F05	“The passage under the bridge is too narrow for two people to pass side-by-side.”	Inadequate walking width	Walking Comfort
	F06	“There are too few lights at night, making people feel uneasy walking.”	Insufficient nighttime lighting	Nighttime Safety
	F07	“The lights are bright, but the color is odd—hard to see the path.”	Mismatched lighting color temperature	Nighttime Lighting Quality
	F08	“There are too few places to rest; you get tired after walking a short while.”	Insufficient rest facilities	Rest Stop Provision
	F09	“Some benches are too low for the elderly to sit comfortably.”	Unreasonable seating dimensions	Ergonomic Adaptation
	F10	“Trash cans are too far away, so people just leave their trash on the ground.”	Uneven distribution of sanitation facilities	Sanitation System Completeness
	F11	“There’s no shelter from the rain by the river—you have to run when it rains.”	Absence of rain shelters	Weather Protection Facilities
	F12	“There aren’t enough places for children to play.”	Insufficient children’s play equipment	Family-Friendly Design
	F13	“The fitness equipment is all broken and unusable.”	Aged fitness equipment	Fitness Facility Quality
	F14	“There’s nowhere to stretch or start jogging.”	Unclear exercise organization	Sports Route Design
	F15	“No guardrails along the water—it feels dangerous.”	Inadequate safety protection	Waterfront Safety Protection
	F16	“The guardrails are too high, blocking the view.”	Unreasonable guardrail height	Safety and Visibility Balance
	F17	“I didn’t see any emergency rescue equipment.”	Absence of first-aid facilities	Emergency Systems
	F18	“There are too few parking spots—it’s impossible to park.”	Insufficient parking facilities	Parking Accessibility
	F19	“Shared bikes are parked haphazardly, crowding the walkways.”	Inadequate shared transportation management	Spatial Order Management
	F20	“Construction barriers remain up, making walking inconvenient.”	Disorganized construction management	Spatial Governance
	F21	“Severe water pooling after rain.”	Insufficient drainage systems	Rainwater Management
	F22	“Loose paving tiles wobble underfoot.”	Unstable paving structures	Pedestrian Safety
	F23	“Surfaces are too slippery, especially dangerous when wet.”	Poor slip resistance on surfaces	Walking Safety
	F24	“Public restrooms are nearly impossible to find.”	Inconspicuous sanitation facilities	Public Sanitation Facilities
	F25	“Restroom conditions are so poor I wouldn’t dare use them.”	Low-quality sanitation facilities	Sanitary Experience
	F26	“Public Wi-Fi is too weak to load pages.”	Unreliable communication infrastructure	Communication Support System
	F27	“Signage is too vague to understand directions.”	Unclear wayfinding signage	Wayfinding System
	F28	“Signs are placed in particularly hidden locations.”	Inefficient wayfinding placement	Wayfinding System Layout
	F29	“Bicycles, e-bikes, and pedestrians are all crammed together.”	Mixed traffic flow	Traffic Organization
	F30	“Patrols are too infrequent, making it unsafe.”	Insufficient patrol management	Public Safety Management
	F31	“Few people at night, feels unmanaged.”	Lack of nighttime order maintenance	Nighttime Management
	F32	“Some dark corners along the river make people hesitant to approach.”	Existence of safety blind spots	Spatial Safety
	F33	“Weekends are too crowded.”	Inadequate pedestrian capacity	Capacity Management
	F34	“Street vendors severely obstruct pathways.”	Interference from street vendors	Order Maintenance
	F35	“Lacks intimate spaces for sitting and chatting.”	Scarce social spaces	Social Support
	F36	“Insufficient drinking water stations along the river.”	Insufficient public drinking water facilities	Lifestyle Convenience
	F37	“No designated areas for pet activities.”	Absence of pet-designated zones	User Zoning Management
	F38	“Poorly marked walking routes make it easy to get lost.”	Inadequate pedestrian navigation	Pedestrian Guidance System
Environmental elements	E01	“Winter winds feel exceptionally cold.”	Uncomfortable wind conditions	Microclimate Experience
	E02	“It’s too sunny in summer with nowhere to escape the heat.”	Insufficient shade	Microclimate Regulation
	E03	“The area near the water is extremely humid and stuffy.”	Unpleasant humidity	Climate Suitability
	E04	“There’s a musty smell in the air.”	Unpleasant waterfront odors	Olfactory Environment
	E05	“The river water looks very murky.”	Low water transparency	Water Feature Quality
	E06	“Trash often floats on the water surface.”	Poor water cleanliness	Water Sanitation
	E07	“Sometimes you can smell sewage.”	Unpleasant water odors	Water Quality Issues
	E08	“The greenery along the riverbank is trimmed unevenly.”	Inadequate vegetation maintenance	Greenery Management
	E09	“Plants are too monotonous with no layering.”	Landscape species homogeneity	Landscape Diversity
	E10	“Lacks plants with distinct flowering seasons.”	Lack of seasonal landscape variation	Seasonal Expressiveness
	E11	“Very few birds, lacking ecological vibrancy.”	Insufficient biodiversity	Habitat Quality
	E12	“Tall buildings in front block river views.”	Poor visual permeability	Spatial Visuality
	E13	“Nighttime lighting is too bright and glaring.”	Light pollution	Nighttime Lighting Environment
	E14	“Some areas are too dark to see clearly.”	Insufficient nighttime illumination	Nighttime Visual Safety
	E15	“Excessive noise along the river from constant traffic.”	Traffic noise disturbance	Acoustic Environmental Stress
	E16	“Construction noise is exceptionally loud.”	Construction noise pollution	Soundscape Environment
	E17	“It gets extremely noisy when crowded, unsuitable for relaxation.”	Intense human noise disturbance	Soundscape Comfort
	E18	“There’s dust in the air.”	Poor air quality	Air Environment
	E19	“The ground colors are chaotic and visually uncomfortable.”	Inconsistent pavement color schemes	Pavement Visual Experience
	E20	“The paving style clashes with the surrounding environment.”	Unified pavement styles	Landscape Cohesion
	E21	“Lacks waterfront platforms, too distant from the water.”	Insufficient water accessibility	Spatial Waterfront Experience
	E22	“The riverbank slopes are too steep to approach safely.”	Low shoreline accessibility	Shore Line Friendliness
	E23	“The nighttime scenery is monotonous, lacking depth.”	Monotonous nighttime scenery	Nighttime Aesthetics
	E24	“During the day, it looks ordinary and unremarkable.”	Lacking site character	Visual Appeal
	E25	“The plaza area feels too open and oppressive.”	Imbalanced spatial scale	Spatial Proportionality
	E26	“Small spaces are crowded; sitting down gets interrupted.”	High spatial crowding	Spatial Capacity Experience
	E27	“The breeze across the water feels pleasant.”	Good natural wind sensation	Sensory Comfort (Positive)
	E28	“Hearing the water at night is actually relaxing.”	Good water sound experience	Soundscape Pleasure
	E29	“Sunlight reflecting on the water feels warm.”	Good light and shadow experience	Light and Shadow Comfort
	E30	“The shaded areas under the bridge are too dark and cold.”	Poor shaded area environment	Microclimate
	E31	“Mosquitoes and insects by the river are very bothersome.”	Significant insect disturbance	Habitat Management
	E32	“On rainy days, the puddles reflect light intensely.”	Surface glare issues	Light Pollution
	E33	“The pavement is uneven.”	Low ground surface evenness	Walking Comfort
	E34	“There’s no natural sound by the river, only traffic noise.”	Lack of natural sound sources	Soundscape Naturalness
	E35	“The cultural landscape blends poorly with the environment.”	Low environmental and cultural integration	Landscape Integration
	E36	“The color scheme is too chaotic.”	Chaotic color schemes	Landscape Style Consistency
	E37	“Moldy walls along the riverbank.”	Poor building facade maintenance	Visual Environment Quality
Cultural elements	C01	“Hard to tell this is the Grand Canal Cultural District.”	Weak historical ambiance	Historical Context Presentation
	C02	“Lacks tangible artifacts representing the canal’s history.”	Lack of physical cultural carriers	Heritage Display
	C03	“The old wharf only has a monument left, no real relics.”	Insufficient historical artifacts	Heritage Authenticity
	C04	“Cultural signage is too academic, hard to understand.”	Difficult-to-understand cultural interpretations	Cultural Accessibility
	C05	“No storytelling approach.”	Inadequate narrative expression	Cultural Narrative System
	C06	“Too few cultural markers, hard to spot while walking.”	Poor placement of cultural markers	Cultural Orientation System
	C07	“The historical wall content is outdated and hasn’t been refreshed.”	Outdated cultural displays	Cultural Renewability
	C08	“There are too few festive events; it lacks liveliness.”	Weak festival culture	Cultural Activity
	C09	“No one tells the stories of the canal.”	Discontinuity in oral traditions	Community Cultural Heritage
	C10	“Modern architecture disrupts the traditional ambiance.”	Diminished cultural landscape	Cultural Visual Integrity
	C11	“The building facades don’t match the canal-side style.”	Inconsistent traditional appearance	Characteristic Features
	C12	“There’s no traditional music or sounds.”	Absence of sound culture	Soundscape Culture
	C13	“More old photographs would be appreciated.”	Insufficient historical imagery	Historical Visualization
	C14	“The cultural route is disjointed.”	Discontinuous cultural pathways	Cultural Path System
	C15	“Didn’t see any intangible cultural heritage projects.”	Inadequate intangible cultural heritage presentation	Intangible Cultural Heritage Experience
	C16	“No cultural guidebook or map.”	Lack of cultural navigation	Cultural Information System
	C17	“Exhibition panels are too superficial.”	Shallow cultural interpretation depth	Cultural Depth Presentation
	C18	“Some sculptures have no connection to the canal.”	Misaligned cultural symbols	Symbolic Consistency
	C19	“The cultural atmosphere feels overly commercialized.”	Overwhelming commercial atmosphere	Cultural Purity
	C20	“Young people don’t connect with the canal.”	Weak cultural identity	Cultural Participation
	C21	“Surrounding restaurants lack canal-themed offerings.”	Lack of culturalized operations	Local Cultural Operations
	C22	“The nightscape lacks culturally themed lighting.”	Weak cultural light and shadow presentation	Light and Shadow Culture
	C23	“Too few cultural performances.”	Insufficient performance activities	Cultural Experience Activities
	C24	“The narrative feels pieced together and disjointed.”	Incomplete narrative structure	Cultural Narrative Logic
	C25	“Lacks introductions to historical figures.”	Absence of character stories	Historical Image System
	C26	“Historical maps are unavailable.”	Inadequate historical spatial cognition	Historical Sense of Space
	C27	“Cultural elements don’t integrate with modern facilities.”	Low cultural environment integration	Cultural Integration
	C28	“The canal’s points of interest are too weak.”	Insufficient memory symbols	Place Memory
	C29	“Many stories from elderly community members remain unrecorded.”	Uncollected folk narratives	Community Cultural Resources
	C30	“Lacks experiential cultural activities.”	Lack of interactive cultural activities	Experiential Culture
	C31	“Children lack understanding of canal culture.”	Absence of cultural education	Public Cultural Education
	C32	“Cultural displays are too static.”	Monotonous expression forms	Multimodal Cultural Presentation
	C33	“Materials along the riverbank don’t resemble traditional canal styles.”	Inadequate material cultural expression	Material Culturalist
	C34	“No showcase of traditional craftsmanship.”	Absence of craft culture	Artisan Culture Presentation
	C35	“Hope for light shows with cultural narratives.”	Need for light and shadow storytelling	Cultural Nightscape Presentation
	C36	“No community-participatory cultural events.”	Weak community co-creation culture	Cultural Participation Mechanisms
	C37	“Insufficient cultural-themed landscapes.”	Insufficient landscape cultural symbols	Cultural Landscape Shaping

). Following mutual comparison, reorganization, and deletion of duplicates or highly similar entries, the study refined these into 76 original concepts, which were further consolidated into 39 initial categories (

Table A4. Results of Open Coding Analysis.

Original Concepts (Cpt)	Initial Categories (F)	Dimension
Cpt01 Inconvenient Access Routes	F1 Accessibility	Functional Elements
Cpt02 Poor Safety When Crossing Streets	F2 Pathways and Connectivity
Cpt03 Discontinuous Walkways	F3 Barrier-Free Systems
Cpt04 Insufficient Walkway Width	F4 Nighttime Lighting and Safety
Cpt05 Lack of Accessible Passage	F5 Rest Facilities
Cpt06 Improper Placement of Accessibility Features	F6 Sanitation and Hygiene Facilities
Cpt07 Inadequate Nighttime Lighting	F7 Emergency and Security Facilities
Cpt08 Inappropriate lighting quality	F8 Transportation and Connections
Cpt09 Insufficient seating	F9 Construction and Maintenance Management
Cpt10 Uncomfortable seating	F10 Paving and Walking Safety
Cpt11 Inadequate sanitation facilities	F11 Wayfinding and Information Systems
Cpt12 Insufficient/unclean public restrooms	F12 Social and Family Micro-Spaces
Cpt13 Lack of emergency equipment (life rings, AED)	F13 Space Capacity and Order Management
Cpt14 Inadequate security monitoring and patrols	F14 Nighttime Management and Order
Cpt15 Inconvenient parking/shuttle access	F15 Communication and Information Infrastructure
Cpt16 Disorganized shared bicycle management	E1 Microclimate (Shade/Wind)
Cpt17 Poor construction barrier management	E2 Humidity and Thermal Comfort
Cpt18 Aging/damaged facilities	E3 Water Body Visual Quality
Cpt19 Poor ground slip resistance	E4 Olfactory and Air Quality
Cpt20 Uneven/potholed pavement	E5 Acoustic Environment
Cpt21 Unclear wayfinding system	E6 Vegetation and Landscape Diversity
Cpt22 Inappropriate signage placement/language	E7 Nightscape and Lighting Design
Cpt23 Insufficient social micro-spaces	E8 Visual Connectivity and Material Coordination
Cpt24 Inadequate children’s activity facilities	E9 Water Accessibility and Shoreline Access
Cpt25 Insufficient site capacity (crowding)	E10 Spatial Scale and Proportion
Cpt26 Street vendors disrupting order	E11 Ecological/Habitat Quality
Cpt27 Lack of nighttime management	E12 Seasonal Expression and Attractiveness
Cpt28 Inadequate communication/network coverage	E13 Building/Facade Maintenance
Cpt29 Insufficient Shade	C1 Historical Context Presentation	Environmental Elements
Cpt30 Excessive Wind Along Waterfront	C2 Cultural Interpretation and Legibility
Cpt31 Excessive Humidity/Dampness	C3 Cultural Circulation and Narrative Coherence
Cpt32 Strong Summer Heat Island Effect	C4 Community Memory and Oral Traditions
Cpt33 Turbid Water Quality	C5 Cultural Activities and Site Activation
Cpt34 Floating Debris in Water	C6 Cultural Identity and Intergenerational Transmission
Cpt35 Unpleasant Water Odor	C7 Cultural Information Dissemination
Cpt36 High Airborne Dust	C8 Nighttime Cultural Presentation
Cpt37 Road/construction noise	C9 Cultural Facilities and Operations
Cpt38 Lack of natural sound sources	C10 Forms of Cultural Presentation
Cpt39 Monotonous greenery layering	C11 Cultural Preservation and Renewal
Cpt40 Insufficient greenery maintenance	Initial Categories (F)
Cpt41 Monotonous nightscape lighting design	F1 Accessibility
Cpt42 Light pollution/glare	F2 Pathways and Connectivity
Cpt43 Poor visual permeability (obstructed by buildings)	F3 Barrier-Free Systems
Cpt44 Paving/materials incompatible with surroundings	F4 Nighttime Lighting and Safety
Cpt45 Insufficient waterfront access platforms	F5 Rest Facilities
Cpt46 Excessively steep shoreline gradients	F6 Sanitation and Hygiene Facilities
Cpt47 Imbalanced spatial scale	F7 Emergency and Security Facilities
Cpt48 Ground glare/reflections	F8 Transportation and Connections
Cpt49 Low biodiversity	F9 Construction and Maintenance Management
Cpt50 Ecological disturbances (mosquitoes, etc.)	F10 Paving and Walking Safety
Cpt51 Weak seasonal variation	F11 Wayfinding and Information Systems
Cpt52 Slow maintenance response	F12 Social and Family Micro-Spaces
Cpt53 Facade soiling/mold stains	F13 Space Capacity and Order Management
Cpt54 Conflict between materials and historical texture	F14 Nighttime Management and Order
Cpt55 Weak historical atmosphere	F15 Communication and Information Infrastructure	Cultural Elements
Cpt56 Lack of physical historical relics	E1 Microclimate (Shade/Wind)
Cpt57 Insufficient cultural interpretation	E2 Humidity and Thermal Comfort
Cpt58 Superficial cultural narratives	E3 Water Body Visual Quality
Cpt59 Disrupted cultural circulation routes	E4 Olfactory and Air Quality
Cpt60 Misaligned cultural symbols	E5 Acoustic Environment
Cpt61 Unrecorded community oral histories	E6 Vegetation and Landscape Diversity
Cpt62 Absence of intangible cultural heritage/craftsmanship displays	E7 Nightscape and Lighting Design
Cpt63 Scarce festive events	E8 Visual Connectivity and Material Coordination
Cpt64 Activities heavily commercialized	E9 Water Accessibility and Shoreline Access
Cpt65 Weak cultural identity among youth	E10 Spatial Scale and Proportion
Cpt66 Lack of cultural experiences for children	E11 Ecological/Habitat Quality
Cpt67 Insufficient cultural guide materials	E12 Seasonal Expression and Attractiveness
Cpt68 Absence of historical maps/archival photographs	E13 Building/Facade Maintenance
Cpt69 Lack of culturally themed nighttime lighting	C1 Historical Context Presentation
Cpt70 Missing nodes of place memory	C2 Cultural Interpretation and Legibility
Cpt71 Insufficient operation of cultural facilities	C3 Cultural Circulation and Narrative Coherence
Cpt72 Lack of community co-creation participation mechanisms	C4 Community Memory and Oral Traditions
Cpt73 Monotonous/static cultural presentation formats	C5 Cultural Activities and Site Activation
Cpt74 Materials/details incompatible with historical context	C6 Cultural Identity and Intergenerational Transmission
Cpt75 Lagging cultural content updates	C7 Cultural Information Dissemination
Cpt76 Local businesses lacking cultural relevance

). Through this process, the study extracted structured concepts from residents’ spontaneous semantic expressions, thereby ensuring the local relevance of the constructed indicators.

(2)

Principal Component Analysis

Based on the 76 raw concepts and 39 initial categories derived from open coding, this study conducted axial coding according to the Strauss & Corbin (2014) paradigm [50], reconstructing the initial categories within the “causal conditions-situational conditions-mediating conditions/interventions-action/interaction strategy outcomes” framework. This further established a foundational logical framework among the initial categories to reveal causal and process relationships between them. Based on this, iterative comparison and cluster analysis were conducted, ultimately consolidating the 39 initial categories into 24 main-axis codes (

Table A5. Spindle Encoding Results.

Initial Categories	Main Axis Codification	Axis Coding Logical Positioning
Accessibility, Pathways & Connectivity, Barrier-Free Systems	F1 Spatial Accessibility and Continuity	Conditions (Directly Impact Usability)
Nighttime Lighting & Safety, Nighttime Management & Order	F2 Nighttime Accessibility and Safety	Context (Determines Temporal/Spatial Availability)
Rest Facilities, Social & Family-Friendly Micro-Spaces	F3 Rest and Social Support System	Action/Interaction Strategy (Promotes Staying and Interaction)
Sanitation & Hygiene Facilities, Public Toilets	F4 Environmental Hygiene Support System	Context (Influences Usage Comfort)
Paving & Pedestrian Safety	F5 Walking Comfort and Pedestrian Safety	Conditions (Directly Impact Physical Experience)
Emergency & Security Facilities	F6 Safety Response and Risk Protection System	Action/Interaction Strategy (Responds to Emergencies)
Transportation & Connectivity, Shared Mobility Management	F7 Slow Mobility–Transportation Coordination System	Context (Influences Overall Traffic Distribution)
Communication & Information Infrastructure, Wayfinding Systems	F8 Information and Navigation Support System	Action/Interaction Strategy (Enhances Spatial Understandability)
Space Capacity & Order Management, Street Vending Management	F9 Spatial Order and Capacity Management	Outcome (Order Enhancement → Experience Improvement)
Microclimate (Shade/Wind), Thermal Comfort	E1 Microclimate Comfort System	Context (Influences Extended Stay)
Water Body Visual Quality, Olfactory & Air Quality	E2 Water Environment and Air Quality Experience	Conditions (Influences Sensory Experience)
Acoustic Environment (Traffic/Natural Sounds)	E3 Soundscape Environment and Noise Regulation	Context
Vegetation Diversity & Greenery Maintenance	E4 Vegetation Landscape and Habitat Quality	Action/Interaction Strategy (Ecological Enhancement)
Nightscape Lighting & Glare Control	E5 Nightscape Lighting Environment and Light-Shadow Experience	Action/Interaction Strategy
Visual Transparency, Material Harmony, Building Facade Maintenance	E6 Visual Accessibility and Landscape Consistency	Context (Influences Aesthetic and Recognizability)
Waterfront Platforms, Shoreline Accessibility	E7 Water Accessibility and Waterfront Approachability	Conditions (Determines Waterfront Experience Potential)
Spatial Scale and Experience Capacity, Seasonal Expression	E8 Spatial Scale and Seasonal Expression	Outcome (Influencing Staying Behavior)
Historical Context Presentation, Authenticity of Historical Remnants	C1 Historical Context Reconstruction and Cultural Authenticity	Conditions (Cultural Experience Foundation)
Cultural Interpretation and Legibility, Narrative Depth	C2 Cultural Interpretation and Narrative System	Action/Interaction Strategy (Enhancing Understanding and Immersion)
Cultural Circulation Continuity, Cultural Symbol Consistency	C3 Cultural Circulation and Cultural Symbol Coordination	Context (Influencing Narrative Coherence)
Community Memory, Intangible Heritage Crafts, Oral Traditions	C4 Community Cultural Resources and Local Memory System	Conditions (Cultural Authenticity)
Cultural Events, Festival Activation, Cultural Participation	C5 Cultural Activities and Participation Mechanisms	Action/Interaction Strategy (Activating Place Culture)
Nighttime Cultural Lighting, Themed Cultural Nightscapes	C6 Nighttime Cultural Presentation System	Context (Nighttime Cultural Atmosphere)
Cultural Information Dissemination, Educational Function, Cultural Renewal	C7 Cultural Dissemination and Sustainable Operation System	Outcome (Enhanced Cultural Identity)

). Through this process, the study progressively distills “measurable evaluation indicators” from “residents’ experiential language,” establishing preliminary logical chain relationships among these indicators. This ensures robust logical coherence and traceability in indicator formation. Simultaneously, the framework analysis thoroughly explores micro-scale waterfront spaces, providing the theoretical foundation for constructing the final KANO indicator system.

(3)

Selective Coding Analysis

The purpose of selective coding is to extract the core logical chains from axial coding that can explain the research phenomenon. Through the aforementioned open coding and axial coding, the following logical chain emerges: “The three interlinked dimensions of cultural embeddedness—accessibility, environmental comfort, and governance—are amplified through social activities and cultural narratives, thereby determining residents’ satisfaction and cultural identity in micro-scale waterfront spaces.” Accessibility, environmental comfort, and governance form the foundational conditions; while safety and emergency preparedness, water accessibility and ecological quality, and cultural narratives act as mediators and amplifiers, collectively determining residents’ usage frequency, satisfaction, and cultural identity. During selective coding, building upon the core logical chain and the two coding phases (open coding and axial coding), the core categories of this study were extracted. The 24 axial categories from axial coding were categorized into 6 core categories (

Table A6. Selective Encoding Results.

Core Category Code	Core Categories	Axial Coding Classification
A	Accessibility and Mobility Systems	(F1) Spatial Accessibility and Continuity
		(F5) Walking Comfort and Safety
		(F7) Slow Mobility-Transport Coordination System
		(E8) Spatial Scale and Seasonal Expression
B	Safety and Resilience	(F2) Nighttime Accessibility and Safety
		(F6) Safety Response and Risk Protection System
		(E7) Water Accessibility and Waterfront Approachability
C	Environmental Comfort and Ecological Quality	(E1) Microclimate Comfort System
		(E2) Water Environment and Air Quality Experience
		(E3) Soundscape Environment and Noise Regulation
		(E4) Vegetation Landscape and Habitat Quality
D	Cultural Narratives and Heritage Authenticity	(C1) Historical Context Reconstruction and Cultural Authenticity
		(C2) Cultural Interpretation and Narrative System
		(C3) Cultural Circulation and Symbolic Coherence
		(C4) Community Cultural Resources and Local Memory System
E	Social Activation and Activity Orchestration	(F3) Rest and Social Support System
		(C5) Cultural Activities and Participation Mechanisms
		(C6) Nighttime Cultural Presentation System
F	Governance, Information, and Cognitive Mediation	(F8) Information and Navigation Support System
		(F9) Spatial Order and Capacity Management
		(E5) Walking Comfort and Pedestrian Safety
		(E6) Visual Accessibility and Landscape Coherence
		(F4) Environmental Sanitation Support System
		(C7) Cultural Dissemination and Sustainable Operations System

).

(4)

Theoretical Saturation Test

To ensure the “resident landscape satisfaction framework” constructed based on grounded theory possesses sufficient theoretical support, ontological completeness, and category stability, this study conducted systematic theoretical saturation testing after completing open coding, axial coding, and selective coding. The entire testing process involved category review and recoding of the retained 8 questionnaire datasets to compare coding consistency, thereby confirming the integrity of the indicators. The study first invited two urban planning experts and three design specialists in urban public spaces and cultural landscapes to conduct a tiered review of the completed 76 original concepts, 39 initial categories, 24 main-axis categories, and 6 core categories. The expert panel conducted independent reviews without knowledge of each other’s judgments, cross-verifying concept naming, category boundaries, and logical chain structures. The review results demonstrated clear conceptual distinctions and logical progression between categories, with no new semantic units or structurally necessary categories identified. This indicates the current theoretical framework possesses sound logical closure and stability.

Second, building upon the original 55 in-depth interview transcripts, this study reserved an additional 8 interview materials (

Table A7. Reserved Interview Samples.

Interview Number	Respondent Profile	Original Interview Material
R1	Female, 47 years old, local resident, evening walker	I take a walk along the Grand Canal every evening, but recently some sections have been quite dimly lit. Areas with dense tree cover especially make me feel a bit unsafe. The riverside path is generally continuous, though a few corners require detours. I find the scenery quite pleasant overall, though there aren’t many resting spots, which can be tiring after walking for a while. Additionally, I really enjoy viewing old photographs of the canal, but there are currently few display points; it would be better if the stories of the old streets were told more comprehensively. The experience is generally comfortable, but a richer cultural atmosphere would enhance it further.
R2	Male, 32 years old, new migrant resident, weekend user	I’ve lived here less than a year and find the Grand Canal environment pleasant, though shared bikes park chaotically on weekends, sometimes blocking paths. The river occasionally has an odor in summer. Cultural markers are abundant, but the text feels academic—as an outsider with little historical background, I struggle to understand it. Audio or video guides would be helpful. Overall good, but details need better management.
R3	Male, 68 years old, local resident, long-term user	I’ve lived by the river for over thirty years and witnessed tremendous changes. It’s much better now, though some banks remain steep—my wife hesitates to get too close. Summer pavilions have limited seating, often filling up quickly. Cultural displays feel lacking; many focus on grand narratives rather than stories tied to local life. Our community elders know countless tales—compiling these would truly capture the canal’s authentic spirit.
R4	Female, 25 years old, local university student, night photography user	I enjoy evening strolls and photography here. The nightscape is generally okay, but the colors of some lights are jarring and don’t photograph well. Some lights are too bright and glaring. I do read the cultural display boards, but the content feels overly official and isn’t very engaging. Interactive elements like AR or short videos would be great. The trash bins are spaced too far apart; sometimes I have to walk a long way with my trash.
R5	Male, 54 years old, jogger, frequent user during early morning and evening	I come here to run every day. The path is generally good, but watch out for areas where tree roots have pushed up the surface—it’s easy to twist an ankle. The section under the bridge is quite dark, and running through it before dawn can feel a bit scary. The breeze feels great, but the humidity can be quite heavy at times. Some signs aren’t very visible; I ended up walking for a while looking for the restroom. The sound of the water is very soothing—it’s the most relaxing part for me.
R6	Female, 30 years old, family user with young children	I often bring my kids here to play, but there aren’t enough dedicated children’s activity areas. The open spaces look nice but aren’t very kid-friendly. The railing height is appropriate, but there are gaps in some spots that are too wide, so you have to keep a close eye on the kids. The greenery is nice, but mosquitoes can be a problem in summer. It would be great to have cultural trails suitable for kids, like stamp collecting or small games, combining play with learning about the canal.
R7	Male, 41 years old, freelancer, daytime work and leisure user	I often come to the riverbank for coffee—the atmosphere is pleasant, though construction noise can be loud at times. The wooden platforms are nice but limited in number. Some platforms are close to the water; integrating cultural information like canal history or old wharves would enhance the experience. The cultural displays feel scattered; they could be connected into a cohesive route. Trash accumulates more on weekends, so cleanup frequency could be increased.
R8	Female, 62 years old, retired teacher, plant observer and leisurely walker	I enjoy observing plants, but the variety here isn’t extensive, and seasonal changes aren’t very noticeable. Some pavilion facades look worn, affecting the overall appearance. The cultural display panels present information in a straightforward manner without a strong narrative thread. A more layered storytelling approach would make them more engaging. Evening strolls have a pleasant atmosphere, but lighting in some areas is too uniform, lacking depth and dimension.

) that had not undergone any coding process for an independent recoding procedure. These supplementary samples were drawn from permanent residents within the same canal cultural context, representing diverse age groups and spatial usage characteristics, ensuring sample heterogeneity and data comparability. Following an open-main-selective coding process, the research team recoded these 8 materials. Results revealed: (1) All newly emerging raw narratives could be fully mapped onto the existing 76 original concepts, with no new concepts or semantics incompatible with existing categories emerging; (2) Both the 39 initial categories and 24 main-axis categories were consistently reproduced, with the six core categories demonstrating consistent structural explanatory power in the supplementary sample. This confirms the coding system fully captures the functional, environmental, and cultural experiential structures of residents in micro-scale waterfront spaces.

In summary, through iterative validation across three dimensions—expert review, supplementary sample recoding, and coding consistency testing—the theoretical framework constructed in this study has achieved theoretical saturation. Supplementary samples (Table A7) introduced no new concepts or categories and did not alter the logical structure among existing categories. Consequently, the theoretical system is confirmed to possess sufficient generality and explanatory power, enabling progression to the next phase of quantitative indicator development and model validation.

3.3.2. Delphi Expert Consultation Method

To ensure the scientific validity, representativeness, and operational feasibility of the indicator system derived from grounded theory, two rounds of Delphi expert consultations were conducted to refine and validate the proposed dimensions and attributes. The Delphi method is a structured, iterative consensus-building technique widely applied in urban planning, public health, and environmental design research to construct reliable evaluation frameworks [51,52,53,54]. It systematically integrates expertise through anonymous, multi-stage feedback, promoting collective judgment while minimizing group bias [55,56].

Given the multidimensional nature of micro-scale waterfront spaces—encompassing physical, ecological, cultural, and social dimensions—expert input was crucial for balancing residents’ empirical insights with professional judgments regarding spatial functionality, historical authenticity, and design feasibility [57,58,59]. While resident interviews captured lived experiences, expert validation ensured the final indicators were grounded in practicality, theoretically sound, and applicable across similar waterfront contexts.

This study convened a panel of 24 experts selected for their specialized knowledge and practical experience in urban waterfront renewal within historic environments. To ensure interdisciplinary breadth, the panel was divided into three complementary groups: Group A (8 members): Government officials and urban planners responsible for heritage protection policies and public space governance in Wuxi and other nationally designated historic and cultural cities; Group B (11 members): Scholars and researchers from relevant academic disciplines specializing in urban morphology, cultural landscape conservation, and human-environment interactions; Group C (5 members): Practicing architects and landscape designers with field experience in micro-renewal projects for historic districts, including several individuals involved in the design of the Wuxi section of the Grand Canal (

Table A8. Expert Advisory Group Composition.

Group	Number of People	Expert Composition
Group A	8	Government officials and urban planners responsible for heritage protection policies and public space governance in Wuxi and other nationally designated historical and cultural cities
Group B	11	Scholars and researchers from universities specializing in urban morphology, cultural landscape preservation, and human-environment interactions
Group C	5	Practicing architects and landscape designers with hands-on experience in micro-renewal projects for historic districts, including several individuals who participated in the design of the Wuxi section of the Grand Canal.

). All participants possessed over five years of relevant professional experience, meeting established criteria for Delphi expert panel composition [60,61,62]. Consultations were conducted from 10 September to 20 September 2025, through both in-person and online sessions to ensure anonymity and facilitate iterative revisions.

(1)

Survey Design and Expert Consultation

This study conducted two rounds of expert consultation. In the first round, experts were presented with six core categories and 24 axial codes derived from grounded theory analysis. Experts were asked to evaluate each item based on the following aspects: (1) Importance (rated on a 5-point Likert scale: 1 = Not Important, 5 = Very Important); (2) Clarity of definition; (3) Potential redundancy or overlap; (4) Suggestions for improvement or supplementation. Indicators requiring modification were identified based on mean importance, standard deviation, and coefficient of variation. Definitions were refined directionally according to clarity, redundancy, and explicit expert suggestions. New themes emerging from open-ended comments were integrated where appropriate.

In the second round, revised indicators were resubmitted to experts alongside aggregated group feedback from the first round. Experts were invited to reassess their ratings based on peer feedback to facilitate reflective reconsideration. Consensus definitions were established as follows. In the first round, 24 questionnaires were distributed, with 24 returned on time, achieving a 100% response rate. In the second round, 24 questionnaires were distributed, with 24 returned, again achieving a 100% response rate.

(2)

Expert Feedback and Indicator Revision

Based on questionnaire data, this study calculated the arithmetic mean (hereafter referred to as “Mean”), standard deviation (hereafter referred to as ‘SD’), and coefficient of variation (hereafter referred to as “CV”) to evaluate expert scoring, thereby forming an analysis of each indicator. These statistical metrics are widely used to assess and quantify the consistency of expert judgments. The mean is used to evaluate central tendency, with Mean ≥ 3.5 indicating sufficient importance for a category or indicator. Standard deviation measures convergence; SD ≤ 1 indicates high reliability of the indicator. Coefficient of variation measures dispersion; CV ≤ 0.25 indicates high consensus among experts on a specific item [63,64,65,66]. Therefore, this study defines Mean ≥ 3.5, SD ≤ 1, and CV ≤ 0.25 as conditions for consensus attainment. Items with Mean < 3.5, SD > 1, or CV > 0.25 are flagged for revision or deletion [63,67]. Overall coordination of expert opinions is assessed using Kendall’s coefficient of concordance, ranging from 0 to 1, where higher values indicate stronger agreement.

Table A9. Results of the First Round of Expert Consultation.

Spindle Category Code	Mean (1–5)	SD	CV	Experts Lacking Clear Definitions	Experts Noting Redundancy	Retention Decision
F1	4.45	0.52	0.12	1	1	Retain
F2	4.1	0.68	0.17	0	2	Retain
F3	4.3	0.55	0.13	0	0	Retain
F4	4.35	0.47	0.11	0	0	Retain
F5	4.4	0.5	0.11	0	0	Retain
F6	4.05	0.74	0.18	0	1	Retain
F7	4	0.82	0.21	0	2	Retain
F8	3.4	0.76	0.22	8	6	Revise
F9	4.15	0.6	0.15	0	1	Retain
E1	4.25	0.58	0.14	0	0	Retain
E2	4.5	0.45	0.1	1	0	Retain
E3	4.05	0.7	0.17	0	1	Retain
E4	4.2	0.61	0.15	0	0	Retain
E5	3.3	0.95	0.29	7	8	Revise
E6	4.1	0.66	0.16	0	1	Retain
E7	4.35	0.49	0.11	0	0	Retain
E8	3.95	0.88	0.22	0	3	Retain
C1	4.55	0.4	0.09	0	0	Retain
C2	4.2	0.58	0.14	0	1	Retain
C3	4	0.72	0.18	0	2	Retain
C4	4.3	0.57	0.13	0	0	Retain
C5	4.05	0.69	0.17	0	1	Retain
C6	3.2	0.9	0.28	8	7	Revise
C7	4	1.05	0.26	6	4	Revise

presents the results of the first round of expert consultation. Analysis and synthesis of the feedback revealed four major indicators requiring revision after this round: (1) F8 (Information and Navigation Support Systems): Mean = 3.40 (<3.5). Experts recommended revision or consideration of merging with F9 or F4 while refining details (some experts noted overlap with wayfinding/operations). (2) E5 (Nighttime Lighting Environment): Mean = 3.30 (<3.5), CV = 0.29 (>0.25). Experts disagreed on whether “nightscape/lighting” should be an independent category. (3) C6 (Nighttime Cultural Presentation): Mean = 3.20 (<3.5), CV = 0.28 (>0.25). Disagreement centered on balancing cultural expression with commercialization/light pollution risks. (4) C7 (Cultural Dissemination and Sustainable Operation): SD = 1.05 (>1), CV = 0.26 (>0.25), Significant disagreement and low consensus; experts differed on operational feasibility and primary responsibility.

Based on the first round of expert consultation, the following specific revisions were made for the second round: (1) For F8 (Information and Navigation), although the first round recommended splitting F8, this study decided to maintain it as a single indicator after comprehensive evaluation. However, its internal definitions were differentiated (Information Visibility + Technical Communication Support), and a revised unified description was provided for expert assessment in the second round. (2) For E5 (Nighttime Light Environment) and C6 (Nighttime Cultural Presentation), two alternative texts were presented in the second round for expert selection: (A) Merge both into “Nighttime Light/Cultural Presentation,” (B) Maintain separate indicators but refine definitions and add a “Light Pollution and Commercialization Risk” control item. This facilitated expert consensus on their boundaries. (3) For C7 (Cultural Dissemination and Sustainable Operations), supplement three operational specifications in the second round: operational entity (who), funding sources (how), and evaluation frequency (evaluation interval), to reduce expert disagreement (SD).

This study conducted a second round of expert consultation on the revised indicator system. All data in the second round must meet the consensus criteria (Mean ≥ 3.5; SD ≤ 1; CV ≤ 0.25) after modifications from the first round (

Table A10. Results of the Second Round of Expert Consultation.

Indicator Classification	Indicator Code	Mean	SD	CV	Processing
First Round of Met Indicators	F1	4.6	0.49	0.11	Retain
	F2	4.35	0.62	0.14	Retain
	F3	4.5	0.51	0.11	Retain
	F4	4.55	0.46	0.1	Retain
	F5	4.45	0.54	0.12	Retain
	F6	4.25	0.65	0.15	Retain
	F7	4.2	0.7	0.17	Retain
	F9	4.4	0.55	0.13	Retain
	E1	4.45	0.5	0.11	Retain
	E2	4.65	0.4	0.09	Retain
	E3	4.2	0.63	0.15	Retain
	E4	4.4	0.56	0.13	Retain
	E6	4.3	0.62	0.14	Retain
	E7	4.55	0.48	0.11	Retain
	E8	4.2	0.68	0.16	Retain
	C1	4.7	0.35	0.07	Retain
	C2	4.4	0.59	0.13	Retain
	C3	4.25	0.65	0.15	Retain
	C4	4.5	0.52	0.12	Retain
	C5	4.3	0.6	0.14	Retain after modification
First Round of Unmet Indicators	F8	4.1	0.71	0.17	Retain after modification
	E5	4.05	0.75	0.19	Retain after modification
	C6	4	0.78	0.2	Retain after modification
	C7	4.15	0.7	0.17	Processing

). Results indicate that consensus was reached for all 24 indicators in the second round. The four indicators requiring revision in the first round (F8, E5, C6, C7) underwent supplementary definitions, illustrative examples, and “clarification of boundaries with adjacent indicators.” In the second round, their Mean values were all greater than or equal to 3.5, their coefficients of variation decreased (SD ≤ 1), and all fell within the consistency range (CV ≤ 0.25), meeting the study’s predefined triple consensus criteria.

Finally, the SPSS 25.0 software was used to calculate consistency coefficients to assess the degree of expert consensus on the importance of each indicator. The second Delphi round revealed (

Table 1. Kendall’s W Consistency Statistics for the Second Round of Expert Consultation.

Core Categories	Number of Indicators Included	Kendall’s W	χ2 (df)	p-Value
Functional Elements	9	0.63	121.4 (df = 8)	<0.001
Environmental Elements	8	0.68	114.2 (df = 7)	<0.001
Cultural Elements	7	0.71	102.2 (df = 6)	<0.001
Comprehensive Indicator System	24	0.67	369.8 (df = 23)	<0.001

) that all six core categories achieved significant consistency (p < 0.001), with Kendall’s W values ranging from 0.63 to 0.71, indicating “moderate to high consistency.” The cultural dimension exhibited the highest consistency (W = 0.71), indicating strong expert consensus on interpreting spatial qualities within the cultural dimension. The environmental dimension followed (W = 0.68), while the functional dimension also maintained stable consistency (W = 0.63). The overall system achieved Kendall’s W = 0.67, meeting the international Delphi research standard for “consistency convergence” and demonstrating sufficient expert reliability in the indicator structure after the second round [68]. Comparing these results with the first-round Delphi consistency statistics (

Table 2. Kendall’s W Consistency Statistics for the First Round of Expert Consultation.

Core Categories	Number of Indicators Included	Kendall’s W	χ2 (df)	p-Value
Functional Elements	9	0.57	109.4 (df = 8)	<0.001
Environmental Elements	8	0.60	100.8 (df = 7)	<0.001
Cultural Elements	7	0.63	90.7 (df = 6)	<0.001
Comprehensive Indicator System	24	0.59	325.7 (df = 23)	<0.001

) reveals a significant increase in W values from the first to the second round, with both rounds showing statistical significance (p < 0.001). This indicates a marked improvement in expert consensus [69], eliminating the need for further consultation rounds.

(3)

Final confirmation of the indicator system

Following two rounds of Delphi consultations, this study refined the micro-scale waterfront space demand assessment indicator system based on expert evaluation scores and recommendations. All 24 indicators in the final evaluation system were retained (including 4 revised indicators), ultimately forming a framework comprising 6 core categories and 24 indicators (

Table 3. Final Indicator System.

Element Number	Factor Classification	Indicator Number	Indicator Name
F	Functional elements	F1	Spatial Accessibility and Continuity
		F2	Nighttime Accessibility and Safety
		F3	Rest and Social Support Systems
		F4	Environmental Hygiene and Maintenance Support
		F5	Walking Comfort and Pedestrian Safety
		F6	Safety Response and Risk Protection Systems
		F7	Slow Mobility and Access Coordination Systems
		F8	Information and Navigation Support Systems
		F9	Spatial Order and Capacity Management
E	Environmental factors	E1	Microclimate Comfort
		E2	Water and Air Quality Experience
		E3	Soundscape Environment and Noise Regulation
		E4	Vegetation Landscape and Ecological Habitat
		E5	Nighttime Lighting Environment and Light Pollution Control
		E6	Visual Accessibility and Landscape Consistency
		E7	Water Accessibility and Waterfront Approachability
		E8	Spatial Scale and Seasonal Expression
C	Cultural elements	C1	Historical Context Reconstruction and Cultural Authenticity
		C2	Cultural Interpretation and Narrative Systems
		C3	Cultural Circulation Organization and Cultural Symbol Coordination
		C4	Community Cultural Resources and Local Memory Systems
		C5	Cultural Event Capacity and Participation Mechanisms
		C6	Nighttime Cultural Presentation Systems
		C7	Cultural Dissemination and Sustainable Operations Systems

). This final indicator set can serve as direct input for subsequent KANO model analysis.

3.3.3. KANO Model Evaluation

(1)

KANO Model

The KANO model, proposed by Noriaki Kano et al., is a quality attribute classification framework designed to identify the nonlinear characteristics of user needs. It is widely applied in service design, product innovation, and public space experience research [70,71,72]. Its core contribution lies in challenging the linear assumptions of traditional satisfaction studies, emphasizing that different types of attributes exhibit asymmetry and non-equivalence in their impact on satisfaction [73,74,75]. In studies of urban public spaces, cultural landscapes, and waterfront environments, the KANO model demonstrates particular strengths: it not only identifies elements whose absence causes dissatisfaction but also reveals attributes that, while not essential, significantly enhance satisfaction when provided. This capability makes it an ideal tool for evaluating micro-scale spaces [76]. The KANO model posits that each attribute can be cat{egorized into one of six quality characteristics (Figure 4):

(1)

Must-be (M)

Attributes users consider “expected to be present.” Their absence significantly reduces satisfaction, but their presence does not markedly increase it.

(2)

One-dimensional (O)

Attributes exhibiting a linear relationship with satisfaction: better performance increases satisfaction, while poorer performance decreases it.

(3)

Attractive (A)

Not considered essential, but significantly enhances satisfaction when provided; absence typically does not cause dissatisfaction.

(4)

Indifferent (I)

Has limited impact on satisfaction; users hold no clear expectations.

(5)

Reverse (R)

The presence of the attribute causes dissatisfaction; absence aligns better with user preferences.

(6)

Questionable (Q)

Results from inconsistent reactions or misinterpretations; should be excluded.

Figure 4. KANO Model Quality Attributes (Author’s Own Illustration).

Figure 4. KANO Model Quality Attributes (Author’s Own Illustration).

This multi-category structure enables KANO to capture the complexity of needs, making it particularly suitable for the micro-scale waterfront spaces examined in this study, where user demands often exhibit contextual, nonlinear, and diverse characteristics.

The KANO model’s analytical process involves several key steps. First, a dual questionnaire must be constructed, with each attribute featuring two paired questions: (1) Functional question (How would you feel if this attribute “exists/performs well”?), and (2) Reverse functional question (How would you feel if this attribute “does not exist/performs poorly”?). Second, respondents typically select from five-point options (Like/Must Have/Indifferent/Acceptable/Dislike). This dual-logic approach is fundamental to KANO’s ability to identify asymmetric demand structures. Third, based on respondents’ dual answers (functional and anti-functional), each attribute is mapped to the aforementioned A/O/M/I/R/Q types (

Table 4. KANO Model Type Table.

Type	Like	Must	Indifferent	Acceptable	Dislike
Like	Q	A	A	A	O
Must	R	I	I	I	M
Indifferent	R	I	I	I	M
Acceptable	R	I	I	I	M
Dislike	R	R	R	R	Q

).

To further quantify the results of the KANO classification and enable subsequent prioritization, the Better-Worse coefficients must be calculated using the following formulas:

Satisfaction Improvement Coefficient Better = (A + O)/(A + O + M + I)

Dissatisfaction Reduction Coefficient Worse = (O + M)/(A + O + M + I) × (−1)

A higher better value indicates greater satisfaction enhancement potential for the attribute, while a lower Worse value (larger negative value) signifies that the absence of the attribute will cause greater dissatisfaction.

The first quadrant represents “One-dimensional Quality (O)”, where providing this feature or service enhances user satisfaction, and its absence reduces satisfaction; The second quadrant is “Attractive Quality (A)”, where optimizing a feature or service significantly boosts user satisfaction, while its absence causes only a marginal decline in satisfaction; The third quadrant is “Indifferent Quality (I)”, where users show little sensitivity to the refinement level of a feature or service, and it has minimal impact on satisfaction; The fourth quadrant is “Must-be Quality (M)”, where user satisfaction does not increase with improvements to this feature or service, but its absence leads to a significant decline in satisfaction (Figure 5). The priority for addressing needs is as follows: Must-be Quality (M) > One-dimensional Quality (O) > Attractive Quality (A) > Indifferent Quality (I).

(2)

Questionnaire Design

To capture the classification and prioritization of factors influencing residents’ satisfaction with micro-scale waterfront landscapes, thereby providing robust support for subsequent design recommendations, this study designed a questionnaire based on the KANO model and the final indicator system derived in the preceding section (

Table 5. Sample Questionnaire.

F1 Spatial Accessibility and Continuity		Like (5)	Must (4)	Indifferent (3)	Acceptable (2)	Dislike (1)
Functional issues	If this factor (spatial accessibility and continuity) performs well, how would you feel?
Dysfunctional issues	If this factor (spatial accessibility and continuity) is poorly implemented or absent, how would you feel?

). Before the formal survey, we first conducted a pilot test with 30 respondents to assess the clarity and comprehensibility of the Kano questionnaire. Based on the feedback, we made notes and explanations on the ambiguous wording in both functional and non-functional questions to reduce respondents’ misunderstandings of positive and negative questions. To ensure the questionnaire’s timeliness, response rate, accuracy, and balance, fieldwork was conducted from 7 October to 21 October 2025, in communities along the Wuxi Grand Canal section spanning “Jiangjian Park–Xishuidun–Nanchan Temple.” Basic personal information was collected from all participants upon signing informed consent forms. In the selected micro-waterfront spaces, the number of questionnaires collected at each location ranged from 40 to 50, ensuring balanced representation of different spatial types. Given that the spatial scales and usage intensities of the selected locations were comparable, all sample points were assigned the same weight in subsequent analysis. A total of 436 questionnaires were collected, with 398 deemed valid (91.3% validity rate). To avoid interference from short-term visitor experiences, the sample was restricted to residents with over one year of residency and usage frequency exceeding twice monthly. The sample structure showed males accounting for 47.4% and females for 52.6% in the gender dimension; Age distribution showed 22% aged 18–30, 41% aged 31–50, and 37% aged 50+. Spatial usage revealed 63% were nighttime users and 28% participated in cultural activities. The sample structure closely aligned with the resident population characteristics of the study area, ensuring representativeness.

3.4. Data Sources and Data Processing

The indicators used in this study were derived from multiple data sources to comprehensively capture landscape satisfaction in micro-scale waterfront spaces. These data sources can be classified into three categories: subjective perception data, qualitative interview data, and spatial environmental data.

Subjective perception data were obtained through a questionnaire survey based on the Kano model and a five-point Likert scale. Each indicator was measured using paired functional and dysfunctional questions, and responses were classified into Kano attributes (Must-be, One-dimensional, Attractive, Indifferent) following standard evaluation rules. Better–Worse coefficients were calculated to quantify the positive and negative effects of each indicator on landscape satisfaction.

Qualitative data were collected through semi-structured interviews with local residents and visitors. Grounded theory was applied to analyze the interview transcripts through open, axial, and selective coding. The resulting conceptual categories were used to inform the development and refinement of the evaluation indicators.

In addition, spatial and environmental data were obtained through field surveys and secondary planning documents. These data were processed using GIS-based spatial analysis to identify the distribution and characteristics of micro-scale waterfront spaces. All quantitative data were standardized prior to analysis to ensure comparability among indicators.

4. Result

4.1. Kano Classification Results of Landscape Satisfaction Indicators

Based on the aforementioned 398 valid questionnaires, this study categorizes 24 landscape satisfaction indicators into different Kano attribute categories. The classification results are determined according to the dominant percentage principle, meaning that the category with the highest response proportion in each indicator is designated as the final attribute type. The analysis reveals that the 24 indicators are classified into different attributes based on feedback from surrounding residents. This study conducts separate statistics based on their feedback, as shown in

Table 6. Questionnaire Results Presentation.

Indicator Number	M%	O%	A%	I%	R%	Q%	Result
F1	68.1	21.4	6.3	3.2	0.8	0.2	M
F2	72.6	18.9	5.1	2.8	0.5	0.1	M
F3	12.3	58.7	20.5	6.8	1.2	0.5	O
F4	65.4	23.1	7.2	3.5	0.6	0.2	M
F5	70.3	20.1	6.7	2.4	0.3	0.2	M
F6	75.2	16.8	5.4	2.1	0.4	0.1	M
F7	18.5	52.3	22.7	5.9	0.4	0.2	O
F8	9.8	48.6	30.1	9.2	1.8	0.5	O
F9	60.7	25.4	8.3	4.7	0.7	0.2	M
E1	22.4	50.6	18.9	6.5	1.1	0.5	O
E2	67.8	21.7	6.9	2.8	0.6	0.2	M
E3	15.2	54.3	23.4	5.7	1	0.4	O
E4	10.5	46.2	35.8	6.2	0.9	0.4	A
E5	58.9	24.1	10.2	5.3	1.2	0.3	M
E6	28.7	42.5	21.3	6	1	0.5	O
E7	14.8	51.6	26.4	5.8	1.1	0.3	O
E8	8.9	44.7	38.2	6.7	1	0.5	A
C1	63.5	24.8	8.1	2.9	0.5	0.2	M
C2	11.6	49.3	31.2	6.5	0.9	0.5	A
C3	13.4	50.1	28.6	6.4	1	0.5	A
C4	9.2	42.7	39.8	6.8	1.1	0.4	A
C5	7.8	40.5	43.6	6.7	0.9	0.5	A
C6	6.5	38.2	46.3	7.2	1.3	0.5	A
C7	12.1	47.4	32.5	6.6	1	0.4	O

, which details the proportion of project classifications. For example, for indicator F1, 68.1% of residents consider it as a Must-be Quality (M), which has the highest percentage among all feedback proportions. Therefore, in the resident category, B1 is classified as “Must-be Quality (M)”. For indicator C6, 46.3% of residents consider it as an Attractive attribute (A), which has the highest percentage. Therefore, C6 is classified as “(Attractive, A)”. Similarly, all projects will be classified in this manner. To enhance the robustness of the Kano classification results, a consistency check was conducted by comparing dominant attribute categories across sub-samples. The results showed a high level of stability in the classification outcomes, indicating that the frequency-based Kano judgments were reliable.

According to Table 6, functional and safety-related indicators such as spatial accessibility (F1), nighttime accessibility and safety (F2), environmental health support (F4), and safety response systems (F6) are primarily classified as “essential attributes,” indicating that the absence of these indicators can lead to significant dissatisfaction among residents. In contrast, many cultural perception indicators—including cultural interpretation (C2), community cultural memory (C4), cultural activities (C5), and nighttime cultural displays (C6)—are primarily identified as “attractive attributes,” suggesting that when these indicators are met, they have strong potential to enhance residents’ satisfaction.

4.2. Better–Worse Coefficient Analysis and Two-Dimensional Quadrant Distribution

To further quantify the asymmetric effects of landscape attributes on satisfaction, the satisfaction improvement coefficient (Better) and dissatisfaction reduction coefficient (Worse) were calculated for each indicator. The detailed coefficient values are presented in

Table 7. Better-Worse Calculation Values.

Indicator Number	Indicator Name	Better	Worse
F1	Spatial Accessibility and Continuity	0.284	−0.917
F2	Nighttime Accessibility and Safety	0.249	−0.947
F3	Rest and Social Support Systems	0.815	−0.736
F4	Environmental Hygiene and Maintenance Support	0.312	−0.911
F5	Walking Comfort and Pedestrian Safety	0.274	−0.928
F6	Safety Response and Risk Protection Systems	0.228	−0.944
F7	Slow Mobility and Access Coordination Systems	0.785	−0.737
F8	Information and Navigation Support Systems	0.808	−0.607
F9	Spatial Order and Capacity Management	0.346	−0.887
E1	Microclimate Comfort	0.725	−0.759
E2	Water and Air Quality Experience	0.294	−0.922
E3	Soundscape Environment and Noise Regulation	0.805	−0.721
E4	Vegetation Landscape and Ecological Habitat	0.86	−0.631
E5	Nighttime Lighting Environment and Light Pollution Control	0.353	−0.855
E6	Visual Accessibility and Landscape Consistency	0.669	−0.737
E7	Water Accessibility and Waterfront Approachability	0.807	−0.689
E8	Spatial Scale and Seasonal Expression	0.868	−0.562
C1	Historical Context Reconstruction and Cultural Authenticity	0.338	−0.909
C2	Cultural Interpretation and Narrative Systems	0.841	−0.634
C3	Cultural Circulation Organization and Cultural Symbol Coordination	0.815	−0.66
C4	Community Cultural Resources and Local Memory Systems	0.864	−0.548
C5	Cultural Event Capacity and Participation Mechanisms	0.878	−0.511
C6	Nighttime Cultural Presentation Systems	0.888	−0.476
C7	Cultural Dissemination and Sustainable Operations Systems	0.829	−0.619

.

Based on the calculated Better and Worse values, a two-dimensional quadrant analysis was conducted using the Satisfaction Increase Index (SII) and Dissatisfaction Decrease Index (DDI). As illustrated in Figure 6, indicators were distributed across four quadrants corresponding to different Kano quality types.

Indicators located in the Must-be quadrant exhibited high absolute DDI values, indicating strong dissatisfaction when unmet, while their SII values remained relatively low. Conversely, indicators in the Attractive quadrant, particularly those related to cultural activities, community memory, and nighttime cultural experiences, demonstrated high SII values, reflecting their strong capacity to enhance residents’ satisfaction. The distribution pattern of indicators in the quadrant analysis was consistent with the Kano classification results, supporting the validity of the analytical outcomes.

4.3. Indicator Sensitivity and Priority Ranking

To determine the relative importance of landscape attributes, the Sensitivity Index (S) was calculated for each indicator based on the combined effects of Better and Worse coefficients. The sensitivity values and corresponding rankings are presented in

Table 8. Calculated Values for Indicator Sensitivity.

Factor Classification	Indicators	Sensitivity
Functional elements	(F1) Spatial Accessibility and Continuity	0.960
	(F2) Nighttime Accessibility and Safety	0.979
	(F3) Rest and Social Support System	1.097
	(F4) Environmental Hygiene and Maintenance Support	0.963
	(F5) Walking Comfort and Safety	0.968
	(F6) Safety Response and Analytical Protection System	0.971
	(F7) Slow Mobility and Access Coordination System	1.077
	(F8) Information and Navigation Support System	1.011
	(F9) Spatial Order and Capacity Management	0.951
Environmental factors	(E1) Microclimate Comfort	1.050
	(E2) Water Quality and Air Quality Monitoring	0.967
	(E3) Soundscape Environment and Noise Regulation	1.080
	(E4) Vegetation Landscape and Ecological Habitat	1.067
	(E5) Nighttime Lighting Environment and Light Pollution Control	0.924
	(E6) Visual Accessibility and Landscape Consistency	1.002
	(E7) Water Accessibility and Waterfront Approachability	1.061
	(E8) Spatial Scale and Seasonal Expression	1.034
Cultural elements	(C1) Historical Context Reconstruction and Cultural Authenticity	0.969
	(C2) Cultural Interpretation and Narrative Systems	1.053
	(C3) Cultural Circulation Organization and Symbolic Coherence	1.048
	(C4) Community Cultural Resources and Local Memory Systems	1.022
	(C5) Cultural Event Capacity and Participation Mechanisms	1.016
	(C6) Nighttime Cultural Presentation Systems	1.007
	(C7) Cultural Dissemination and Sustainable Operation Systems	1.035

.

The results indicate that several indicators across functional, environmental, and cultural dimensions exhibit high sensitivity values, suggesting that residents are particularly responsive to changes in these attributes. Functional indicators related to rest and social support systems (F3) and slow mobility coordination (F7), environmental indicators such as soundscape regulation (E3) and vegetation quality (E4), and cultural indicators including cultural interpretation (C2) and historical authenticity (C1) ranked relatively high in sensitivity.

These results provide a quantitative basis for understanding the differentiated influence of landscape attributes on residents’ satisfaction and form the empirical foundation for subsequent discussion on optimization strategies for micro-scale waterfront spaces.

5. Discussion

5.1. Reinterpreting Waterfront Satisfaction: Beyond Linear Evaluation Frameworks

Existing studies on waterfront landscape satisfaction have largely relied on linear analytical frameworks, such as regression models or structural equation modeling, which assume that spatial attributes exert symmetrical and additive effects on overall satisfaction [77,78,79]. Within this paradigm, improvements in accessibility, environmental quality, or esthetic design are generally expected to proportionally enhance user satisfaction. While such approaches have significantly advanced urban waterfront evaluation research, they often overlook potential threshold effects and asymmetric psychological responses embedded in user perception [80,81,82].

The findings of this study challenge this assumption of symmetry. The Kano-based classification demonstrates that satisfaction in micro-scale waterfront spaces operates through differentiated attribute logics: mandatory (M-type), one-dimensional (O-type), and attractive (A-type). Rather than functioning as parallel contributors, these attributes exhibit structurally distinct roles in satisfaction generation. Mandatory elements primarily prevent dissatisfaction but contribute limited positive gains; one-dimensional attributes produce linear improvements; and attractive attributes trigger satisfaction leaps once foundational needs are fulfilled [83,84,85].

This asymmetric structure suggests that waterfront satisfaction is not a simple additive outcome of spatial quality but a hierarchical process governed by threshold mechanisms and differentiated marginal returns. Compared to prior research emphasizing general environmental quality or esthetic enhancement, this study highlights the importance of identifying attribute-specific psychological functions when evaluating micro-scale public spaces.

5.2. Theoretical Advancement: An Asymmetric Chain Mechanism of Satisfaction Generation

Building upon the attribute classification results, this study proposes a chain mechanism of satisfaction generation structured as:

“Basic Safeguards—Experiential Enhancement—Cultural Enrichment.”

This conceptualization contributes to landscape evaluation theory in three primary ways.

First, it introduces an asymmetric satisfaction framework into micro-scale waterfront research. By integrating Kano classification with sensitivity analysis (SII/DDI and S-values), the study demonstrates that spatial attributes operate through non-equivalent psychological pathways. This advances beyond traditional satisfaction models that treat indicators as uniformly weighted contributors [86,87].

Second, the study conceptualizes satisfaction as a sequential chain rather than a simultaneous aggregation process. Foundational attributes (e.g., safety, accessibility, ecological health, cultural authenticity) function as prerequisite conditions. Only after these thresholds are met can experiential attributes generate stable marginal gains, and subsequently, cultural attributes induce emotional and identity-based enhancement [88,89,90]. This staged logic provides a more dynamic understanding of satisfaction formation.

Third, the findings suggest that renewal strategies should prioritize attribute types rather than categorical domains. Conventional planning frameworks often divide interventions into functional, environmental, and cultural categories [91,92,93]. However, the present results show that elements across these domains may share similar psychological roles (e.g., authenticity and ecological health both function as threshold conditions). Therefore, strategic prioritization based on attribute type may yield more efficient resource allocation outcomes.

Together, these contributions extend satisfaction theory within landscape and urban design research by embedding psychological asymmetry and threshold logic into spatial evaluation models.

5.3. Empirical and Contextual Contributions: Micro-Scale Canal Spaces in High-Density Heritage Cities

This study also provides empirical contributions by focusing on micro-scale waterfront spaces within the Grand Canal context of Wuxi. Unlike large-scale waterfront redevelopment projects commonly examined in Western cities—characterized by expansive promenades and landmark-driven regeneration—micro-scale canal spaces in high-density historic urban environments operate under spatial constraints, layered cultural narratives, and incremental renewal processes [94,95,96,97].

In such contexts, spatial capacity is limited, historical textures are preserved, and redevelopment interventions must balance ecological health, cultural authenticity, and everyday usability [98,99,100]. The findings reveal that cultural authenticity functions as a mandatory attribute rather than merely an enhancement factor, underscoring the existential role of heritage continuity in canal-based environments. This diverges from studies where cultural programming primarily acts as a value-added component.

Moreover, the sensitivity analysis indicates that experiential elements—such as microclimate regulation, path comfort, and soundscape optimization—exert substantial marginal effects in constrained environments. This highlights the critical role of fine-grained environmental regulation in micro-scale settings, where small interventions can significantly alter perceptual outcomes.

By situating the analysis within a historic canal system embedded in a high-density Chinese urban fabric, the study enriches international waterfront scholarship with evidence from a context where spatial renewal must operate within heritage protection constraints and limited land availability. Such conditions provide a distinctive laboratory for understanding layered satisfaction mechanisms.

5.4. Practical Implications for Sustainable Micro-Renewal

The chain-based framework offers practical implications for sustainable waterfront regeneration.

First, foundational safeguards should be prioritized. Essential attributes—including safety, sanitation, ecological quality, and cultural authenticity—constitute credibility conditions for public use. Without stabilizing these baseline elements, further esthetic or cultural investments yield limited effectiveness. This underscores the importance of infrastructure maintenance and ecological restoration as primary steps in sustainable renewal [101,102,103].

Second, experiential enhancement strategies should target high-sensitivity O-type attributes. Improvements in environmental comfort, mobility coordination, and information systems generate the most stable marginal returns and can efficiently elevate user satisfaction within limited budgets [104,105,106]. These interventions align with sustainable design principles emphasizing human-scale optimization.

Third, cultural enrichment strategies should be implemented after functional and environmental stability is achieved. Attractive attributes—such as narrative continuity, memory reconstruction, and symbolic imagery—deepen place attachment and identity formation. In heritage waterfronts, such interventions support long-term cultural sustainability by strengthening residents’ emotional bonds with space.

This phased prioritization model provides a structured pathway for decision-makers facing resource constraints, offering a more rational sequencing strategy for micro-scale waterfront renewal [107,108].

6. Conclusions

This study investigated landscape satisfaction in micro-scale waterfront spaces along the Grand Canal in Wuxi by integrating qualitative indicator development and quantitative satisfaction evaluation. The results indicate that landscape satisfaction is not determined by the simple accumulation of physical environmental elements, but by the differentiated roles of landscape attributes. Basic functional attributes primarily influence dissatisfaction when absent, while experiential and perceptual attributes play a stronger role in enhancing overall satisfaction. These findings highlight the importance of prioritizing experiential quality in the improvement of small-scale waterfront spaces.

Beyond empirical findings, this study contributes a reusable analytical framework that integrates grounded theory, the Delphi method, and the Kano model for evaluating landscape satisfaction at the micro scale. Compared with existing studies that rely mainly on static indicator weighting or overall satisfaction scores, the proposed framework emphasizes attribute sensitivity and asymmetric user responses, enabling planners to distinguish between “necessary conditions” and “value-added factors” in waterfront landscape design. This decision-making logic can be transferred to other historic canals or urban waterfront contexts where spatial resources are limited and renewal pressures are high.

From a practical perspective, the findings provide actionable guidance for sustainable waterfront regeneration. The proposed framework supports planners and designers in prioritizing interventions by identifying which landscape elements should be strictly protected, which require basic improvement, and which can be strategically enhanced to maximize user satisfaction. In the context of historic waterfronts such as the Grand Canal, this approach helps balance heritage conservation, everyday usability, and experiential quality, thereby offering a practical decision-support tool for sustainable micro-scale landscape planning.