Resilience Regeneration Priorities for Old Blocks Based on Public Satisfaction: A Case Study of Beijing, China

Abstract

In the process of urban development, old blocks face issues such as long construction times, outdated buildings and facilities, and poor environmental quality, which do not meet the current requirements for the construction and development of resilient cities. Resilience regeneration of old blocks is an important measure to improve public satisfaction and an important way to achieve high-quality and sustainable development of old blocks. Therefore, the priority of resilience regeneration is an important research issue that deserves attention. This study combines the three-factor theory with the asymmetric impact–performance analysis (AIPA) to explore the asymmetric impact relationship between resilience regeneration attributes and public satisfaction, in order to determine the priority order of resilience regeneration of old blocks to improve public satisfaction. Firstly, the main attributes affecting the resilience regeneration of old blocks were summarized and sorted into five dimensions. Secondly, representative old blocks in Beijing were selected, and relevant data were collected through questionnaire surveys, followed by data organization and analysis, to gain a deep understanding of the key issues of resilience regeneration elements in old blocks. Finally, the asymmetric impact–performance analysis was employed to explore and quantify the asymmetric impact relationship between resilience regeneration factors and public satisfaction. The results show that the resilience regeneration attributes can be divided into three categories: spatial texture as a basic factor, environment and emotional experience as excitement factors, and infrastructure and operation and maintenance management as performance factors. This study provides a scientific basis for determining the priority order of resilience regeneration of old blocks and offers a reference for managers to develop targeted resilience regeneration strategies, which is conducive to further improving public satisfaction and promoting the revitalization of old blocks.

1. Introduction

1.1. The Important Position of Old Blocks in Cities

Old blocks in cities are a historical product of changing times and demonstrate the course of urban construction and development [1]. Old blocks retain their unique regional environment, historical culture, and architectural style and integrate multiple functions such as providing residences, commerce, tourism, and cultural inheritance. As such, these old blocks most clearly reflect the rise and fall of an old town district [2,3]. In recent years, the state and local governments of China have placed substantial emphasis on the protection, inheritance, and regeneration of old blocks and have issued several relevant guiding documents. For example, the General Office of the Ministry of Housing and Urban-Rural Development of the People’s Republic of China issued the “Notice on the effectively strengthening of historical and cultural protection and resolutely stopping vandalism activities in urban renewal and renovation” in August 2020 [4], proposing that urban renewal and renovation projects incorporating old blocks, old industrial areas, and old buildings should protect history and culture. The General Office of the Ministry of Housing and Urban-Rural Development of the People’s Republic of China also issued the “Notice on further strengthening the protection of historical and cultural blocks and historic buildings” in January 2021 [5], stating that restoration and repair should be strengthened to give full play to the use value of historic and cultural districts and historic buildings; moreover, public opinion should be fully heard in the protection and restoration work. The General Office of the Communist Party of China Central Committee and the General Office of the State Council jointly issued the “Opinions on strengthening the protection and inheritance of history and culture in urban and rural construction” in September 2021 [6], noting that the historical texture, historic street, spatial scale, and landscape environment of old blocks should be protected and that incongruous architectures and landscapes should be regulated to continue the historical appearance.

Additionally, both domestic and foreign experts and scholars have conducted relevant research on several types of old towns. A literature review revealed that research on old residential districts [7,8,9] and old industrial areas [10,11] is relatively extensive, whereas research on old blocks is relatively weak. More specifically, most studies have mainly focused on historical and cultural blocks [12,13], whereas there are relatively few studies on general old blocks. Most studies on the subject have focused on the spatial regeneration and optimization design of old blocks from various perspectives. For example, Sun et al. (2014) [14] proposed planning measures and recommendations for the microcirculation treatment of old blocks through project investigations, current situation analyses, and experience-based references. Wu and Yang (2015) [15] used the renovation project of an old block as an example and summarized the governance paths and methods of old blocks, providing a reference for future regeneration implementation efforts. Ji et al. (2017) [16] studied the regeneration strategy of an old commercial block against the background of open blocks through case analysis. Liu and Wang (2021) [17] analyzed the practical path of the park city concept in the regeneration of old blocks and summarized the advantages of this regeneration process in terms of ecological, humanistic, and social values. Xiao et al. (2022) [18] explored practical strategies for applying urban acupuncture in old blocks and provided a new method for the regeneration of high-density old blocks. Wang et al. (2022) [19] chose an old block with historical and cultural relics as the research object to conduct research on site optimization and the regeneration design of public spaces within the living circles of old blocks. Xian et al. (2023) [20] studied old blocks with a substantial historical context and provided a reference for guiding the regeneration of various old towns, historical heritage protection, and landscape construction.

Old blocks embody the profound historical heritage and characteristic style of a city and have precious value [21]. Figure 1 lists several representative old blocks in China, such as Nanluoguxiang in Beijing and Tianzifang in Shanghai, that have become popular tourist attractions which are highly sought after and enjoyed by many domestic and international tourists.

1.2. Important Role of Resilience Regeneration

In recent years, with the frequent occurrence of natural disasters such as urban floods and sandstorms, as well as the impact of epidemics, the concept of resilience has been gradually applied to the field of urban construction, development, and regeneration [22,23]. At present, great importance has been attached to the construction of resilient cities. Old blocks are the weak link in the construction of resilient cities, but they are also the key link in the construction of resilient cities. Against the backdrop of urban renewal, urban disaster prevention and reduction, and ecological construction, reasonable resilience regeneration is an important development direction [24]. The resilience regeneration of old blocks is an upgraded version of simple regeneration, representing a groundbreaking application of the resilience concept in the regeneration field of old blocks. Some scholars have conducted studies on the resilience regeneration of old blocks. Yuan et al. (2016) [25] used two historical blocks as examples to research the conservation and regeneration of historical blocks from a resilience perspective. Ma (2020) [26] conducted a resilience evaluation using the public spaces of historic blocks as the research object and identified the problems existing in these spaces. Zhang et al. (2020) [27] proposed block space planning strategies from the perspective of resilient cities based on four aspects: flood control, fire prevention, earthquake mitigation, and community safety. This approach, in turn, fostered improvements in block disaster prevention and mitigation capacities as well as promoted sustainable development. Yan et al. (2021) [28] used a pressure–state–response (PSR) model to construct a fire resilience assessment system for historic blocks, which can provide decision support for promoting the protection and development of historic blocks in China. Han and Han (2021) [29] dissected policies and measures for conservation and regeneration from the perspective of resilience planning using a historic block as an example. Shen (2022) [30] addressed the spatial resilience of urban blocks under flood hazards using three dimensions: ontological robustness, time and space, and functional satisfaction. This study also provided a reference basis for improving future block resilience. Wang et al. (2023) [31] conducted a resilience evaluation of fire protection engineering in historical and cultural blocks from three dimensions—buildings, sites, and roads—which shed some light on exploring the relationship of disaster prevention resilience between cities and blocks. Chu et al. (2023) [32] analyzed the current situation of traditional Guanzhong dwellings in a historical block and proposed strategies to enhance the external and internal resilience of these dwellings in terms of style, space, and culture.

In addition to analyzing the research and application of resilience concepts in the regeneration of old blocks from different angles, these scholars have also achieved certain research results. By combing the literature, it was found that these studies mainly focus on two aspects: resilience planning and resilience evaluation. Currently, research on the resilience regeneration of old blocks is relatively superficial, and there is less focus on the analysis of the priority of resilience regeneration elements in old blocks. Therefore, there are many aspects worthy of in-depth exploration and research to promote a safe, comfortable, environmentally friendly, and esthetically pleasing resilient spatial environment in old blocks.

1.3. Important Impact of Public Participation and Satisfaction

Resilience regeneration of old blocks is an important initiative in facilitating urban renewal and resilient city construction and has gained the attention of the public and various sectors of society. Among them, public satisfaction and well-being are important considerations in judging the beauty of the spatial environment [33] (Kostas, 2021). In recent years, with the introduction of China’s “people-oriented” urban renewal philosophy, more emphasis has been placed on improving the living environment and enhancing urban vitality in the transformation. In the governance of urban resilience enhancement, Beijing is increasingly focusing on the interests of diverse groups. Therefore, the needs of the public are becoming more and more important, and the residents living in old blocks and tourists visiting for sightseeing are very important audience groups. The resilience transformation of old blocks aims to provide residents with a better living environment and tourists with a more comfortable sightseeing environment, so public satisfaction is a key element in evaluating the effectiveness of the resilience transformation of old blocks.

Public satisfaction mainly refers to the fact that “when the needs of the public are met, the expectations of the public are consistent with their actual feelings, so that the public can have a positive mindset of affirmation, pleasure, and satisfaction”. Presently, public satisfaction has been applied in many fields [34,35,36], and relevant studies have pointed out that public satisfaction has a close relationship with related decision attributes [37,38,39]. Its theoretical core is to divide up the basic, performance, and excitement factors to provide a basis for subsequent analysis and decision-making activities.

Therefore, it is important to study the influence of public satisfaction on resilience regeneration in old blocks. Based on this approach, from the perspective of public satisfaction combined with the three-factor theory and asymmetric impact–performance analysis (AIPA) method, this study examines the influence of the relationship between resilience regeneration attributes and public satisfaction to determine the priority order of resilience regeneration activities in old blocks.

2. Materials and Methods

2.1. Satisfaction Three-Factor Theory

Originally developed by Kano based on the two-factor theory, the three-factor theory states an asymmetric relationship between attributes and satisfaction; specifically, it suggests that the other side of satisfaction is not dissatisfaction [40]. The core idea of the three-factor theory is that different attributes influence public satisfaction in variable ways, and it has been applied within several research fields [41,42,43]. The three-factor theory divides attributes into three types—basic, performance, and excitement factors [44]—as shown in Figure 2.

The core of the three-factor theory lies in analyzing the impact of different factors on satisfaction to provide a basis for decision-making. This is highly consistent with the goal of studying the relationship between the resilience regeneration factors of old blocks and satisfaction, and can provide scientific theoretical support for regeneration projects. Through the analysis of the three-factor theory, the basic factors, performance factors, and excitement factors of the resilience regeneration of old blocks can be clarified, thereby determining the priority of regeneration, reasonably allocating resources, and improving satisfaction.

The basic factors are considered to be deservedly met; therefore, the public will be very dissatisfied when the basic factors are not met or fall below expectations and the public will not be satisfied either when the basic factors are met or exceed expectations,. The excitement factors are considered to be beyond public expectations; therefore, the public will be very satisfied when the excitement factors are met or exceed expectations, and the public will not be dissatisfied when the excitement factors are not met or fall below expectations. The performance factors can affect both satisfaction and dissatisfaction; therefore, the public will be very satisfied when performance factors are met or exceed expectations, and the public will be dissatisfied when performance factors are not met or fall below expectations [45]. As shown in Figure 2, there is a negative asymmetric relationship between basic factors and public satisfaction, a positive asymmetric relationship between excitement factors and public satisfaction, and a symmetric relationship between performance factors and public satisfaction [46].

2.2. Asymmetric Impact–Performance Analysis (AIPA)

The asymmetric impact–performance analysis (AIPA) should be understood within the context of the importance–performance analysis (IPA). Importance–performance analysis (IPA) [47], also known as importance–satisfaction performance analysis, has an important place in studies of the influence of attributes on satisfaction, but its basic premise assumes a linear and symmetric influence between attributes and satisfaction [48,49,50]. With the deepening and extensive research, numerous scholars observed an asymmetric relationship between attributes and satisfaction [51,52] and successively proposed some analysis methods to study this asymmetric relationship, such as the penalty–reward contrast analysis (PRCA) [53,54], impact asymmetry analysis (IAA) [55,56], and asymmetric impact–performance analysis (AIPA) [41,57]. Among these analysis methods, IAA is an extension of PRCA, and AIPA is an extension of IAA. The main steps of the AIPA as utilized in this study are as follows:

Step 1: Create two sets of dummy variables for each attribute and code the dummy variables [46].

The quartiles of each attribute score were first calculated, and then the attribute scores were divided into three performance levels: high, medium, and low. Two dummy variables were set using the medium level as the reference class. The first dummy variable was used to measure the influence of high-level attribute performance on overall satisfaction; attributes with scores ≥ the 75% quantile were encoded as 1, with 0 encoded for all other cases. The second dummy variable was used to measure the influence of low-level attribute performance on overall satisfaction; attributes with scores ≤ the 25% quantile were encoded as 1, with 0 encoded for all other cases.

Step 2: Conduct a regression analysis of dummy variables.

Taking two sets of dummy variables for each attribute as independent variables and overall satisfaction as the dependent variable, a regression model of dummy variables was constructed as follows [58]:

$$OS={\beta }_0+{\displaystyle \sum _{i=1}^n\left({\beta }_{ri}{d}_{ri}+{\beta }_{pi}{d}_{pi}\right)}+\epsilon$$

(1)

where $OS$ represents overall satisfaction; ${\beta }_0$ is a constant term; $\epsilon$ is a residual; $d_{ri}$ and $d_{pi}$ are dummy variables for the i-th attribute at a high-level and low-level performance, respectively; and ${\beta }_{ri}$ and ${\beta }_{pi}$ are the influence of the i-th attribute at high-level and low-level performance on the overall satisfaction, also known as reward indicators and penalty indicators, respectively.

Step 3: Carry out the AIPA.

As AIPA is an extension of IAA, the relevant formula for IAA was used to obtain the asymmetric indicator IA as follows [59]:

$$RIO{S}_i={\beta }_{ri}++\left|{\beta }_{pi}\right|$$

(2)

$$SG{P}_i={\beta }_{ri}/RIO{S}_i$$

(3)

$$DG{P}_i=\left|{\beta }_{pi}\right|/RIO{S}_i$$

(4)

$$I{A}_i=SG{P}_i-DG{P}_i$$

(5)

where $RIO{S}_i$ represents the influence range of attribute performance on overall satisfaction; $I{A}_i$ represents the comparison of the potential to generate satisfaction ($SG{P}_i$) and the potential to generate dissatisfaction ($DG{P}_i$) for the i-th attribute, and it is a quantification of the asymmetric influence degree on the attribute performance.

$IA$ is a valid indicator for categorizing attributes, with a threshold value ranging from −1 to 1. During $IA<-0.1$, attributes should be categorized as basic factors; during $-0.1<IA<0.1$, attributes should be categorized as performance factors; and during $IA>0.1$, attributes should be categorized as excitement factors.

Step 4: Draw an AIPA matrix.

First, the average value of the measured items in each dimension was calculated as the attribute performance score, and then a two-dimensional matrix was formed using the attribute performance score as the horizontal axis and the $IA$ value as the vertical axis. Next, each attribute was positioned on a two-dimensional matrix, from which the next step of the resilience regeneration strategy analysis was carried out.

AIPA, as an advanced analytical tool, can effectively identify and quantify the complex relationship between the resilience regeneration elements of old blocks and public satisfaction, and provide a scientific basis for determining the priority of resilience regeneration. Firstly, AIPA can identify and quantify the asymmetric impact of different attributes on public satisfaction, which is particularly important in the resilience regeneration of old blocks, as the impact of different attributes on residents’ satisfaction may vary greatly. Secondly, traditional regression analysis methods have limitations in dealing with multicollinearity, which may lead to inaccurate estimation results. AIPA combines gradient boosting decision trees (GBDTs) and can effectively deal with multicollinearity. At the same time, AIPA intuitively shows the impact of different attributes on satisfaction through a matrix diagram, enabling decision-makers to clearly see which attributes need to be prioritized for improvement and which attributes can be considered secondary.

3. Data Collection

3.1. Attribute Identification

Based on studies in the literature and field investigation, we referred to the Beijing local standard “Community Resilience Evaluation Guidelines” and other standards in this study, and summarized and extracted the main attributes (namely the main influencing factors) of the resilience regeneration of old blocks from five dimensions—spatial texture, infrastructure, environmental, emotional experience, and operation and maintenance management—as shown in Figure 3.

3.2. Questionnaire Survey

This study used a questionnaire survey to collect relevant basic data. The questionnaire consisted of two main parts. The first part contained basic information about the person completing the questionnaire. The second part was a survey on the satisfaction of each attribute and overall satisfaction, which is based on the Likert scale, with a scale of 1 to 5 indicating “very dissatisfied, dissatisfied, general, satisfied and very satisfied”. The interviewees judged and scored several questions set according to their actual experience and satisfaction levels.

Beijing is the capital of China, one of the first batch of national historical and cultural cities, one of the four ancient capitals of China, and the city with the largest number of world cultural heritages in the world, with a long history and splendid culture. As of November 2023, Beijing, China, has designated 49 historical and cultural blocks, 26 traditional cottage areas, 58 historical streets, and 1119 traditional alleys. On top of that, there are countless old blocks in Beijing. Considering the convenience and practicality of questionnaire distribution and collection, this study used a combination of online and offline methods to distribute questionnaires and collect data from typical old block projects in Beijing. Those who completed the questionnaire included residents, businesses, and tourists in old blocks. All of the questionnaires were anonymous. A total of 230 questionnaires were collected and 185 valid questionnaires were returned after screening, with an effective response rate of 80.43%. The sample profile obtained from the analysis of the recovered questionnaires is presented in

Table 1. Sample profile.

Items	Categories	Frequency	Percentage
Gender	Male	87	47.03%
	Female	98	52.97%
Age	18~35 years old	54	29.19%
	36~60 years old	95	51.35%
	≥61 years old	36	19.46%
Education level	High school education and below	31	16.76%
	Junior college degree	50	27.03%
	Undergraduate degree	63	34.05%
	Graduate degree	41	22.16%
Personal monthly income	<2500	19	10.27%
	2501~5000	65	35.14%
	5001~7500	56	30.27%
	7501–10,000	32	17.30%
	≥10,000	13	7.03%

. The questionnaire sources were diverse, indicating that the data were representative and referable.

The old town blocks in Beijing can be categorized into three major types from the perspective of locational function and development positioning: residential blocks, commercial–residential blocks, and cultural–tourism blocks. Residential blocks are primarily focused on living functions, such as the Xinjiekouxi block, Zhangzizhonglunan block, and Xintaicang block. Commercial–residential blocks are dominated by commercial and residential functions, with a concentration of commercial formats that stimulate the vitality of surrounding blocks through commercial streets, such as the Shichahai block, Nanluoguxiang block, and Dashilan block. Cultural–tourism blocks contain open cultural relic protection units as attractions either within or on the edges of the block, or there are numerous unopened cultural relic protection units within the block, with typical examples including the Guozijian block and Fuchengmennei block. Representative blocks from the three types of old town blocks in Beijing were selected as the locations for the questionnaire survey, as shown in Figure 4.

4. Results Analyses

4.1. Reliability and Validity Tests

4.1.1. Reliability Test

Cronbach’s α coefficient is commonly used for reliability testing. In general, the larger the Cronbach’s α, the higher the reliability level. The reliability test results are listed in

Table 2. Results of reliability testing.

First-Level Attribute	Number of Items	Cronbach’s α
Spatial texture	5	0.923
Infrastructure	5	0.914
Environment	5	0.931
Emotional experience	5	0.886
Operation and maintenance management	5	0.909
Total scale	25	0.917

.

As can be seen from Table 2, the Cronbach’s α of the total scale is 0.917 and the Cronbach’s α of each dimension ranges from 0.886 to 0.931, all of which are greater than 0.8. Therefore, the questionnaire had a high reliability level and could meet reliability requirements.

4.1.2. Validity Test

This questionnaire was designed according to the attributes described in Section 3.1 and demonstrated a high content validity. Therefore, this study primarily tested structural validity to determine whether it was suitable for factor analysis. The Kaiser–Meyer–Olkin (KMO) and Bartlett sphericity tests are generally used as structural validity tests. The basic rules are as follows: when the KMO value > 0.6, it is suitable for factor analysis; when the KMO value is closer to 1, it is increasingly suitable for factor analysis; and when the p value of the Bartlett sphericity test reaches the significance level (p < 0.050), it is most suitable for factor analysis. Sample test results are listed in

Table 3. KMO and Bartlett sphericity test values.

Testing Coefficient	KMO Value	Bartlett Sphericity Test Value
		Approximate χ2	df	p
Testing value	0.902	2357.78	276	0.000

.

As can be seen from Table 3, the KMO value is 0.902, greater than 0.6, and the p value of the Bartlett sphericity test is close to 0, meeting the significance level of p < 0.050. The results demonstrate that the questionnaire had a high validity level and was suitable for factor analysis.

4.2. Exploratory Factor Analysis

Following the reliability and validity tests, we conducted an exploratory factor analysis, requiring that the factors with an eigenvalue >1 and factor load >0.5 were retained. In this study, five common factors were extracted using principal component analysis. The cumulative variance contribution rate after rotation was 78.728%, which was greater than the standard requirement of 60%. This indicates that the extracted five common factors explained 78.728% of the information and were consistent with the expected classification in the questionnaire, specifically corresponding to the five first-level attributes.

4.3. Confirmatory Factor Analysis

A confirmatory factor analysis was used to conduct a structural stability test of the five common factors (five first-level attributes). Relevant fitting indices were obtained using the maximum likelihood estimation method, with twenty-five measured items as observation variables and five common factors (five first-level attributes) as latent variables. The fitting results for the confirmatory factor analysis are listed in

Table 4. Fitting results.

Fitting Index	χ²/df	GFI	RMSEA	RMR	CFI	NFI	NNFI	IFI
Judgment criteria	<3	>0.9	<0.10	<0.05	>0.9	>0.9	>0.9	>0.9
Fitting value	1.986	0.921	0.067	0.032	0.947	0.925	0.938	0.953
Requirements met?	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

, all of which meet the judgment criteria for indicating a good fit.

4.4. Asymmetric Impact–Performance Analysis

In this study, the AIPA was used to determine the asymmetric influence of attributes on public satisfaction. According to the steps in Section 2.2., we created two sets of dummy variables for each attribute and coded the dummy variables, and then carried out regression analysis of the dummy variables to obtain reward indicators and penalty indicators. The results of the regression analysis are presented in

Table 5. Regression analysis results.

First-Level Attribute	Regression Coefficient of Dummy Variables
	Reward Indicator (${\mathit{\beta }}_{\mathit{r}\mathit{i}}$)	Penalty Indicator (${\mathit{\beta }}_{\mathit{p}\mathit{i}}$)
Spatial texture	0.246 **	−0.412 **
Infrastructure	0.531 *	−0.557 **
Environment	0.758 **	−0.613 *
Emotional experience	0.489 **	−0.341 ***
Operation and maintenance management	0.621 ***	−0.598 ***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

The asymmetric index was then calculated, and the attributes were classified into three factor categories according to the discrimination criteria.

Table 6. Asymmetric impact–performance analysis results.

First-Level Attribute	$\mathit{R}\mathit{I}\mathit{O}\mathit{S}$	$\mathit{S}\mathit{G}\mathit{P}$	$\mathit{D}\mathit{G}\mathit{P}$	$\mathit{I}\mathit{A}$	Category	Means	Difference
Spatial texture	0.658	0.374	0.626	−0.252	Basic factor	4.157	0.417
Infrastructure	1.088	0.488	0.512	−0.024	Performance factor	3.525	−0.215
Environment	1.371	0.553	0.447	0.106	Excitement factor	3.941	0.201
Emotional experience	0.830	0.589	0.411	0.178	Excitement factor	3.203	−0.537
Operation and maintenance management	1.219	0.509	0.491	0.019	Performance factor	3.876	0.136

Note: “Difference” represents the difference value between the performance score of each attribute and the total average performance score.

presents the results of the analysis.

To display the above results more visually, we drew an AIPA matrix, as shown in Figure 5.

5. Discussion

5.1. Priority Analysis of Resilience Regeneration

The above results show that spatial texture is categorized as a basic factor; environment and emotional experience are categorized as excitement factors; and infrastructure, operation, and maintenance management are categorized as performance factors. There is an asymmetric influence relationship between the resilience regeneration attributes of old blocks and public satisfaction, and their specific influences vary widely. In addition, the above research also indicates that the work can be carried out according to the priority order determined in this study when making decisions on resilience regeneration.

(1) Analysis of the basic factors. Spatial texture is a basic factor of a high performance level, indicating that, in general, the management department pays the most attention to spatial texture in old blocks and invests more in the improvement and management of this aspect. In this context, all aspects of spatial texture are in relatively good condition and have reached a high performance level. However, they must be maintained and further improved because the basic factors are considered deservedly met, and when they are not met or fall below expectations, it will cause strong dissatisfaction with the public.

(2) Analysis of the performance factors. Infrastructure, operation, and maintenance management are performance factors, indicating that they are all important in improving public satisfaction. In this study, infrastructure showed a low performance level, indicating that the current infrastructure situation in old blocks is relatively poor; various facilities are imperfect and incomplete and cannot meet the basic needs of the public, thereby underscoring the necessity of major improvements. Operation and maintenance management showed a high performance level, indicating that the operation and maintenance of old blocks are perceived as relatively satisfactory at present, especially in terms of enhancing the influence or visibility of old blocks, providing a comfortable experience to the public. Because performance factors can affect both satisfaction and dissatisfaction, and there is a symmetric relationship between performance factors and satisfaction, increases and decreases in performance levels will cause significant changes in the same direction of satisfaction. To ensure public satisfaction, infrastructure, operation, and maintenance management must maintain high performance levels.

(3) Analysis of the excitement factors. Environment is an excitement factor with a high performance level, indicating that the current environmental quality is maintained very well and has brought tangible satisfaction to the public. Emotional experience is an excitement factor with a low performance level, indicating that there are still some problems with emotional experiences; however, they will not cause public dissatisfaction. Considering that the excitement factor is outside the expectation of the public, improvements to the environment and emotional experience are not the primary concern and are given lower priority than other factors.

In addition, this study also has certain limitations that can be addressed through future research. For example, there will be a certain correlation between these different attributes, which may be a possible topic for future research.

5.2. Optimization Strategies of Resilience Regeneration

(1) Optimization of spatial texture

The spatial texture is the skeleton of the spatial order of the old blocks, which needs to be controlled and guided by material and spatial elements such as plan layout, regional landscape, architectural style, and building density in the block. At the same time, there are a large number of old buildings in old blocks that are in a state of disrepair and have potential safety hazards. Therefore, the quality of the old building structure should be tested, and measures such as structural reinforcement of the building, heat insulation, and other measures should be taken to improve the structural resilience of the buildings in old blocks.

(2) Improvement of infrastructure

Old blocks, due to the age of their construction, are unable to meet current road traffic demands. Cars have nowhere to park in old blocks but on the roads, causing congestion. Therefore, the density of the road network should be increased to open up the internal roads of the blocks and increase the emergency access of the roads in case of congestion. In addition, the high spatial density of buildings in old blocks makes it easy for fires to spread, so fire hydrant systems should be improved and fire stations should be equipped. In the event of a disaster, the evacuation and sheltering of the crowd should be taken into account by setting up safe evacuation routes and arranging emergency shelters in combination with the original street and squares. Old blocks where a large number of elderly people live should promote the improvement of barrier-free facilities by installing additional barrier-free ramps and elevators to meet the needs of residents in their daily life. At the same time, emphasis can be placed on improving convenient facilities in old blocks, such as providing resting seats that can meet the needs of different public activities, creating multi-functional activity spaces for the public, and improving the livability and functionality of old blocks.

(3) Enhancement of environmental quality

The natural environment, as an important part of old blocks, is the basic condition for creating a healthy living environment while improving people’s quality of life. Old blocks are densely populated, and the increasing amount of waste emitted from people’s production and life threatens the natural environment. In the face of the existing problems of soil degradation, water waste, light pollution, and air pollution in old blocks, it is necessary to strengthen the planning and management of land, improve the utilization rate of water resources, and adopt non-polluting energy as the energy source for winter heating. Old blocks with limited space can make use of rooftop space for planting green plants, which can not only alleviate the heat island effect of old blocks but can also increase the greening rate of old blocks. In addition, vertical greening can be used in the form of selecting shallow-rooted, barren, and drought-resistant rattan and hanging plants, setting up grills for plants to climb, and making full use of the building facades and slopes to expand the coverage of the green landscape.

(4) Mining of emotional experience

Old blocks have unique historical cultures which can bring people different emotional experiences. Tapping the experience economy of old blocks is an important way to revitalize old blocks in the new era. We should protect and pass on the culture of old blocks, including material and non-material culture. For material culture, it can be publicized through cultural exhibitions and cultural skits, for example, displaying sculptures or paintings of different themes on the walls or monuments of the blocks, telling the history of the development of the old block, anecdotes of celebrities, and stories of traditional old businesses. Intangible culture mainly includes neighborhood, folk culture, and celebrity culture. First of all, it should pay attention to the protection of aboriginal people, and protect the way of life and customs of residents. Folk culture should be selectively retained and updated to create a distinctive culture of the old block. Celebrity culture should be integrated into the space of the block and the former residences of celebrities should be displayed to highlight the cultural characteristics of the old block.

(5) Improvement of operation and maintenance management

Currently, the old blocks are dominated by restaurants and retail. Due to the transfer of urban centers, industrial restructuring, and other reasons, the original traditional industries have declined, high-end innovative industries are difficult to enter, and the economic development of the old blocks is more limited. In the face of these problems, a tripartite communication and coordination approach among the government, developers, and original residents should be adopted to promote the renovation and upgrading of old block projects. Another solution is to combine traditional services such as commerce, catering, and entertainment with modern services such as information technology and knowledge-based economy for different consumption levels and consumer groups. It is important to focus on the development of new business models but also to incorporate the characteristics of old blocks. Eventually, it will form a distinctive block with a certain taste that is mainly consumed by local residents and radiates to the surrounding areas.

As the realistic conditions and main features of different old blocks are variable, the management department or personnel should also consider the actual situation when adopting corresponding strategies according to the resilience regeneration priority. Only by taking measures according to local conditions and circumstances can the quality of old blocks be effectively upgraded and public satisfaction improved.

5.3. Limitations

Despite achieving certain results in the resilience regeneration of old blocks, this study still has some limitations. Firstly, this study mainly analyzes based on the local standards of Beijing and specific cases, which may have certain limitations when applied to other regions. Secondly, when proposing optimization strategies, this study mainly explores from a technical perspective and lacks comprehensive consideration of multiple aspects such as policy, economy, and society. We hope that we can further deepen and improve on this basis to provide more effective solutions for the resilience regeneration of old blocks.

6. Conclusions

Resilience regeneration of old blocks is important for the quality development of cities. This study explored the asymmetric influence relationship between resilience regeneration attributes and public satisfaction from the perspective of public satisfaction by combining the three-factor theory and the AIPA method to determine the priority order of resilience regeneration of old blocks. The research conclusions are as follows. The main attributes of resilience regeneration of old blocks can be divided into five dimensions: spatial texture, infrastructure, environment, emotional experience, and operation and maintenance management. There is an asymmetric relationship between these attributes and public satisfaction, and their influence varies greatly. Spatial texture is categorized as a basic factor, meaning it has a negative asymmetric influence on public satisfaction; environment and emotional experience are categorized as excitement factors, meaning they have a positive asymmetric influence on public satisfaction; and infrastructure and operation and maintenance management are categorized as performance factors, meaning they have a basically symmetric influence on public satisfaction. This study provides the proper priority order for resilience regeneration activities in old blocks and proposes optimization strategies for the resilience reconstruction of old blocks, including optimizing spatial texture, improving infrastructure, enhancing environmental quality, exploring emotional experiences, and improving operation and maintenance management. Relevant managers and planners can adopt resilience-targeted regeneration strategies to further enhance public satisfaction and promote the revitalization of old blocks.