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With the continuous acceleration of urbanization, the link between the level of urban community governance and the well-being of residents is becoming closer, and the unique position and function of urban community governance in the process of urbanization and modernization are becoming more and more prominent. This study establishes the mediation effect and moderating effect models based on the behaviors of multiple subjects participating in community governance, and proposes the testing methods of the two effect models for urban community governance. On this basis, the urban governance operation model and organizational structure are constructed based on the collaborative governance of multiple subjects. Through empirical analysis, in the analysis of the mediation model of urban governance, the coefficients of determination R² are 0.62, 0.71 and 0.75, respectively, and in the analysis of the moderating effect model, the coefficients of determination R² are 0.55, 0.518 and 0.553, respectively. After community governance, the scores of urban community’s satisfaction with public services, sense of well-being, social support, sense of belonging to the community, motivation for public services and sense of political efficacy of the community are all greater than 4 points. The scores of the six aspects are all greater than 4. The urban governance model in this paper has significant effects and provides an effective method for improving the effectiveness of urban community governance.
n recent years, in the practice of epidemic prevention and control, transformation of old neighborhoods, etc., the level of urban community construction and governance has been greatly improved [1,2], however, a number of problems are also highlighted in the governance practice: excessive administrative community governance, low effective participation of community residents, imbalance of community power structure, insufficient development of community social organizations, and inefficient synergy of multiple subjects [3-5].
Traditional community governance is centered on neighborhood committees and established at the level of the community as a whole, and this governance pattern often fails to respond effectively to the various needs and problems of residents [6,7]. Moreover, for a long time, under the combined influence of factors such as lack of governance resources, heavy assessment tasks and imbalance of matching power and responsibility, as well as the pursuit of speed and efficiency for decomposing administrative tasks, the effective enhancement of the governance capacity of urban communities has always been faced with the structural development dilemma such as domination of administrative power and instrumental rationality [8-10]. In the context of the new era, the downward shift of the center of gravity of governance is the essential requirement to promote the modernization of the governance system and governance capacity [11]. The downward shift of the center of gravity of urban community governance is a long-term and complex systematic project, which not only requires the state to provide institutional policy support, and governments at all levels to promote the implementation of policy innovations, but also needs to strengthen the construction and improvement of the urban community society itself, and requires equal consultation and orderly interaction among multiple subjects [12,13].
Adhering to and improving the grassroots mass self-governance system, safeguarding people’s democratic rights, and ultimately realizing the fundamental interests of the masses are the consistent foothold of urban community governance [14]. Urban community consultative governance is an important strategy to realize the modernization of the national governance system and governance capacity in the new era, and it is necessary to gradually improve the effectiveness of community consultative governance in practical exploration [15]. Based on this, emotional empowerment can fully link community members together, strengthen the residents’ sense of belonging to the community, thus mobilizing their enthusiasm to participate in the consultation of community affairs; scientific and technological empowerment to enhance the effectiveness of community consultative governance to provide information support, the enhancement of the digital governance capacity of the urban community can effectively resolve the traditional community management of the suspension of democratic consultation problems at the same time, but also for the optimization of the service supply to achieve digital management [16-18]. Democratic empowerment provides an implementation framework for the construction of urban community consultative governance mechanism, guides the direction of community consultative governance optimization, and injects kinetic energy through emotional, technological and democratic empowerment to promote the modernization of urban community governance system and governance capacity [19,20].
Oriented to urban community governance, this paper constructs a mediating effect model and a moderating effect model from community governance cognition, community governance behavior and community governance results, and explores the methods to test the mediating effect and the moderating effect. Then, considering the organizational guarantee and operation mechanism of urban governance, an operation model of urban governance based on the synergy of multiple subjects is established. Then, the theoretical mechanism and influence effects of urban community governance are analyzed from macro and micro perspectives, and the organizational structure based on the synergy of multiple subjects is constructed. Finally, the intermediary and regulating effects of urban community governance are empirically analyzed, and then the effects of this paper’s urban governance model are investigated and analyzed to study the effective ways to improve the effectiveness of urban community governance and the actual effects of this paper’s governance model.
Oriented to urban community governance, the logical relationship of community governance cognition as the independent variable, the behavior of multiple subjects participating in community governance as the mediator variable, and the effect of community governance as the dependent variable. Thus, the mediating effect model for urban community governance in order to improve all aspects of the community by each subject to form the mediating effect model of increasing residents’ satisfaction and ultimately realizing the increase in the willingness of residents to participate in the mediating effect model for urban community governance is shown in Figure 1.
When the independent variable \(X\) influences the dependent variable \(Y\) through another variable \(M\), this variable \(M\) is called the mediating variable, and \(X\) indirectly influencing \(Y\) the model is called the mediating effect. The statistical method uses a causal step approach. The steps are divided into three parts: first, the regression analysis of independent variable \(X\) on dependent variable \(Y\) to test whether the regression coefficient \(c\) is significant. Second, regression analysis of independent variable \(X\) on mediator variable \(M\) to test whether coefficient \({\rm a}\) is significant. Finally, regression analysis of independent variable \(X\) and mediator variable \(M\) on dependent variable \(Y\) to test whether coefficients \(b\) and \(c{'}\) are significant. If coefficients \(a\), \(b\), and \(c\) are significant, this proves that there is a mediating effect in the regression equation. It is worth stating that if coefficient \(c{'}\) is not significant, this mediation effect is considered to be a full mediation effect. If regression coefficient \(c{'}\) is significant but \(c{'} <c\), then this mediation effect is referred to as a partial mediation effect. For this empirical analysis, community improvement identity was used as the independent variable \(X\) and residents’ willingness to participate as the dependent variable \(Y\) and residents’ satisfaction as the mediating variable \(M\) to plot the mediation effect model below.
Human activities cannot be separated from community management and support, and urban community governance is an important mechanism to influence regional activities. The moderating effect model oriented to urban community governance is shown in Figure 2, where individuals, society and capital have key roles in the governance process, and correctly generated and guided behaviors are the main sources. On the one hand, specialized and systematic correct cognition has an important guiding role in governance decision-making. On the other hand, through the effective combination of cooperation mechanism, information mechanism, and constraint mechanism, it can promote the efficiency of urban community governance, improve the effectiveness of community governance, internalize governance opinions, and reduce the cost of governance. Under the premise that people form correct cognition, they can take more efficient actions on urban community governance issues and guarantee the possibility of solving community governance problems.
The term mediating effect means that a variable mediates between the independent and dependent variables. If there is an effect of independent variable \(X\) on dependent variable \(Y\) and \(X\) affects \(Y\) by affecting variable \(M\), then \(M\) is said to be the mediating variable. The formula for this is expressed as: \[\label{GrindEQ__1_} Y=cX+e_{1}.\tag{1}\] \[\label{GrindEQ__2_} M=aX+e_{2} .\tag{2}\] \[\label{GrindEQ__3_} Y=c'X+bM+e_{3}.\tag{3}\]
According to the decomposition of effects in path analysis, the mediating effect should belong to a kind of indirect effect. In the middle 4-, \(c\) is the total effect of \(X\) on \(Y\), \(a\) and \(b\) are the indirect effects generated by the mediating variable \(M\), and \(c{'}\) is the direct effect. In this simple mediation effect model, the relationship between the total effect, the direct effect and the indirect effect can be expressed as follows: \[\label{GrindEQ__4_} c=c{'} +ab .\tag{4}\]
The testing process of the mediation effect is shown in Figure 3, which can be realized by both multiple regression and structural equations, but compared with the structural equations, the testing method is more scientific and convenient.
Specifically, there are three main steps:
The first step should be to determine whether \(X\) has a significant effect on \(Y\). In fact, the insignificant effect of \(X\) on \(Y\) can still be analyzed as a mediation effect. In many cases, \(c\) is not significant but there will still be substantial mediation effect, that is, the so-called inhibition model or generalized mediation model. However, in this case, the analysis of mediation effect should have a practical or theoretical basis, and special attention should be paid to the masking problem and the so-called distal relationship in the analysis, i.e., whether the mediation effect is an “illusion” brought by the data, and whether it has any substantive significance.
The second step should test the coefficient \(a,b\). If both are significant, it means that \(X\) affects \(Y\) at least partially through \(M\). Whether it is a full mediator needs to be tested separately (cf. \(c{'}\)). If one is not significant, a Sobel test is required, with a statistical test value of \(z=\hat{a}b/s_{ab}\), where the formula for \(s_{ab}\) is given in (5). Compare the \(z\) value with the critical \(z\) value based on the standard normal distribution, if the \(z\) value is greater than the critical \(z\) value, the mediating effect is present, if the \(z\) value is less than the critical \(z\) value, the mediating effect is not present. \[\label{GrindEQ__5_} s_{ab} =\sqrt{\hat{a}^{2} s_{b}^{2} +\hat{b}^{2} s_{a}^{2} } .\tag{5}\]
The third step should test the coefficient \(c{'}\). If \(c{'}\) is significant, it indicates a fully mediated process, and vice versa for a partially mediated process. Of course, in the actual research, may use more than \(X\) or more than \(M\) models, there are more than \(M\) models are called multiple mediation model. In the multimediation model, since \(X\) has to be mediated by more than \(M\), because the concept of “full mediation” has little meaning, that is, we do not need to consider the test of full mediation, but only need to state whether there is a mediation effect or not. The size of the mediating effect can be explained by the ratio of the mediating effect to the total effect \((ab/c)\) or the ratio of the mediating effect to the direct effect \(\left(ab/c{'} \right)\).
Moderating effect means that a certain quantity plays a moderating role between the independent variable and the dependent variable. In general, if the independent variable \(X\) has an effect on the dependent variable \(Y\), and the relationship between the two is a function of variable \(M\), variable \(M\) is called the moderating variable and can be expressed as follows: \[\label{GrindEQ__6_} Y=f(X,M)+e .\tag{6}\]
The moderating effect is commonly shown in Eq. (7), which is a linear regression of \(Y\) on \(X\) for a fixed \(M\). The relationship between \(Y\) and \(X\) is portrayed by the regression coefficient \(\beta _{I} +\beta _{3} M\), which is a linear function of \(M\). If \(\beta _{3}\) is not equal to zero, \(M\) is the moderating variable, and \(\beta _{3}\) reflects the magnitude of the moderating effect, which is the basis for understanding the moderating effect. \[\label{GrindEQ__7_} Y=\beta _{0} +\beta _{1} X+\beta _{2} M+\beta _{3} MX+e.\tag{7}\]
Moderator variables can accept a variety of data types, either qualitative (e.g., gender, race, political affiliation, etc.), ordinal (e.g., very good, good, fair, not so good, very bad), or quantitative (e.g., age, years of schooling).
Residents are the most important component of community formation. The community serves the residents, and the residents’ participation can help the community to carry out governance. Residents, such as workers, policemen, doctors, retirees and teachers, can participate in community governance through the extension of their own professions, and the community’s participation is the most direct means and way of reflecting the residents’ self-governance. Community governance is a daily channel for public political participation, and although the public has a certain understanding of participation and the ability to participate, participation in whatever way requires a certain degree of internal and external support, as well as institutional conditions to ensure public participation, and organizational safeguards and operational mechanisms to support the implementation of the system, in order to achieve the good functioning of the mechanism of civic governance.
The operational framework of community governance is shown in Figure 4. The operation of the community governance model in this paper takes grass-roots party building as the top-level mechanism, through which the grass-roots party organization carries out macro-control of the services and management of the four organizations, namely, the community neighborhood committee, the owners’ committee, the property company, and the volunteers, which are under the community, so as to enable them to play their due roles in the community governance. Taking property companies, owners’ committees, volunteer organizations, community neighborhood committees and other organizations as the middle level response, they mainly meet the diversified needs of residents through their own services, and provide diversified services for the residents while giving full play to their own strengths. Residents’ participation is the bottom layer guarantee, through the improvement of service level and life governance, residents’ awareness of participation in grassroots governance is enhanced. This model in the community governance process, the government, enterprises, social organizations and residents to organic integration, forming a pattern of multi-party governance, fully mobilize the enthusiasm of all parties. The government leads the community governance model through grassroots party building and plays the role of social organizations. The market plays a decisive role in the process of community governance, and the organic integration of the forces of society, government, community and residents is the basic operating logic of the community governance model. The most important feature of this model is that it uses party building to lead community governance and services, promoting the organic integration of resources and forces, and enhancing residents’ willingness to participate and their sense of self-governance.
In the context of the era of collaborative governance by multiple subjects, modernized urban governance is crucial. Based on the macro and micro perspectives, an in-depth exploration of the effects of community governance is carried out at the regional, enterprise and individual levels, specifically covering three theoretical mechanisms, firstly, the impact effect, which helps to resolve community governance problems and thus empower the urban region. Secondly, the indirect conduction and regulation of the role of strengthening, urban development and community governance on the regional efficiency of the impact of the existence of complementary effects. The second is the direct influence effect of micro-business society on corporate environmental responsibility. In addition, the indirect conduction effect of the enterprise through the government’s behavior on the corporate responsibility to play, that is, the society through the influence of the government’s environmental subsidies and then on the enterprise to perform the responsibility of the indirect conduction effect. Third, the direct influence effect of micro-individuals on community governance participation, i.e., public environmental participation can be promoted directly based on cooperation, information and constraint guidance mechanisms, and cognitive ability affects the positive effect of urban governance on public participation. In summary, the organizational structure based on collaborative governance of multiple subjects constructed in this paper is shown in Figure 5.
The questionnaire was conducted by the questionnaire, which was conducted by the questionnaire form of the questionnaire, and the survey was conducted to investigate the students, teachers and enterprises of the three different residents in the city. Therefore, the author of the questionnaire, which generated three different groups of questionnaires, collected the data, and the basic information of the subjects was slightly different, and the other guidelines and the measurements of the main body were evenly distributed. Among them, the number of 404 copies was recovered from the group of students. In the group of teachers, the number of 402 copies was recovered. From the enterprise group, the number of 420 copies is collected. In this study, according to the experience of the questionnaire, the results of the analysis of the prediction of the questionnaire were initially removed, and the invalid sample number was obtained in the same number of results, and the results were obtained by the student group of 371, and the sample efficiency of the recovery questionnaire was 91.83%. The sample efficiency of the questionnaire was 93.53%. The enterprise group was 386, and the sample efficiency of the recovery questionnaire was 91.90%.
Through the questionnaire survey data of community residents in City H who use the governance model of this paper, we test the mediation relationship model with urban community governance cognition as the independent variable, community governance behavior involving multiple subjects as the mediator variable, and community governance effect as the dependent variable, and the control variables include gender, age, education level, monthly household income, political profile and ethnicity of the respondents, and the statistical analysis adopts the hierarchical regression analysis method.
Model 1 estimates the total effect of the influence of community governance perception on community governance outcomes by using the gender, age, education level, monthly household income, political profile and ethnicity of the respondents as the control variables, community governance outcomes as the dependent variable and community governance perception as the independent variable.
Model 2 takes gender, age, education level, monthly household income, political profile and ethnicity of the respondents as control variables, community governance behavior as dependent variable and community governance cognition as independent variable, and estimates the effect of community governance cognition on community governance behavior, i.e., it estimates the coefficients of the first half of the path of the mediating effect of community governance behavior.
Model 3 uses respondents’ gender, age, education, monthly household income, political affiliation, and ethnicity as control variables, community governance outcomes as dependent variables, and community governance perceptions and community governance behaviors as independent variables, and this model estimates the effects of community governance perceptions and community governance behaviors on community governance outcomes, controlling for the effects of demographic sociological variables, i.e., it estimates the mediating community governance behavioral The second half of the path coefficients of the role of community governance and also estimates the status of the direct effect of community governance perceptions on community governance outcomes.
The results of the analysis of the mediation model of urban community governance are shown in Table 1. From the results of Model 1, the regression coefficient of community governance cognition on the results of community governance is 0.72, and the accompanying probability P \(\mathrm{<}\) 0.001.The regression coefficient of community governance cognition on the results of community governance is statistically significant. In Model 2 and Model 3, the regression coefficient of community governance cognition on the behavior of multiple subjects participating in community governance is 0.81, and the regression coefficient of community governance behavior on governance outcomes is 0.56 with accompanying probability P \(\mathrm{<}\) 0.001, and the coefficients of determination R² of the three models are 0.65, 0.71, and 0.75, respectively. so in general, the governance behavior has a partial mediating effect on the relationship between governance cognition and community governance outcomes. The relationship between governance behavior has a partially mediating role, and the higher the community governance cognition and residents’ participation in community governance, the better the community governance results.
| Model | Model 1 | Model 2 | Model 3 | |||
| Governance results | Governance behavior | Governance results | ||||
| Non-standardized coefficient (standard error) | Normalization factor | Non-standardized coefficient (standard error) | Normalization factor | Non-standardized coefficient (standard error) | Normalization factor | |
| Constant | 0.95(25)*** | 0.65(23)** | 0.51(2)* | |||
| Gender | 0.04(01)** | 0.02 | -0.02 | -0.01 | 0.04(02)** | 0.03 |
| Age | 0.04(01)** | 0.10 | 0.01(01) | 0.03 | 0.03(01)* | 0.07 |
| Educational level | 0.05(01)** | 0.10 | 0.01(01) | 0.02 | 0.04(01)** | 0.08 |
| Income | 0.00(00) | -0.06 | 0.00(00) | 0.02 | 0.00(00)* | -0.04 |
| Politics status |
-0.23
(08)* |
-0.11 | -0.15(07) | -0.07 | -0.19(06)* | -0.04 |
| Nation | 0.00(03) | 0.00 | 0.00(03) | -0.01 | 0.01(02) | -0.01 |
| Community governance cognition | 0.72(02)*** | 0.73 | 0.81(02)*** | 0.80 | 0.24(05)** | 0.24 |
| Community governance behavior | 0.56(03)** | 0.58 | ||||
| R² | 0.65 | 0.71 | 0.75 | |||
| F | 85.15*** | 114.156*** | 121.89*** | |||
Bootstrap method was used to test the size of the behavioral mediating effect of the participation of multiple subjects in community governance, the repeated sampling sample size was set at 5000, Percentile confidence interval estimation was used, the confidence level was 95%, and the results of the mediating effect test are shown in Table 2. From the table, it can be seen that the total mediation effect value of the impact of community governance cognition on community governance effectiveness is 0.80, and the confidence interval is [0.62, 0.85]. The direct effect value of community governance cognition and governance effect path is 0.29 with a confidence interval of [0.13, 0.39]. The indirect effect value of community governance cognition, the behavior of multiple subjects yielding to governance and the governance outcome path is 0.51, with confidence intervals of [0.41, 0.60], respectively, and none of the confidence intervals include 0, indicating that the total effect, the direct effect and the indirect effect are statistically significant. The behavior of multiple subjects participating in community governance has a partial mediating effect on community governance cognition and community governance effect, in which the relative effect of the direct effect on the total effect is 36.22%, and the relative effect of the indirect effect on the total effect is 63.78%, which indicates that most of the influence of community governance cognition on community governance effect is formed through the mediating path of the behavior of multiple subjects participating in community governance.
| Effect type | Effect value | Standard error | Fiducial interval | Relative effect | |
|---|---|---|---|---|---|
| Floor limit | Superior limit | ||||
| Total effect | 0.80 | 0.03 | 0.62 | 0.85 | |
| Direct effect | 0.29 | 0.04 | 0.13 | 0.39 | 36.22% |
| Indirect effect | 0.51 | 0.03 | 0.41 | 0.60 | 63.78% |
In order to further delve into the moderating effect of community governance, this study uses three regression analyses for testing. The results of the analysis of the moderating effect are shown in Table 3, where the moderating effect in the effects of community governance cognition, community governance behavior and community governance outcomes are verified in an analytical manner in turn, with coefficients of determination R² of 0.455, 0.518, and 0.553 for Model 1, Model 2, and Model 3, respectively.The correlation coefficient of community governance cognition on community governance behavior is 1.305, the correlation coefficient of community governance cognition on The regression coefficient of governance behavior is 0.875, and the regression coefficient of community governance behavior on governance outcomes is 1.305, and all P is less than 0.001. It indicates that governance cognitive control has a direct influence relationship on community governance behavior’s, and its moderating role in the relationship of indirect influence through community governance cognition on community governance behavior.
| Model | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| Governance results | Governance behavior | Governance results | ||||
| correlation index | P | correlation index | P | correlation index | P | |
| Constant | 53.452 | 0.000** | 50.124 | 0.000** | 51.486 | 0.000** |
| Gender | -1.202 | 0.075 | -0.789 | 0.202 | -0.858 | 0.201 |
| Age | -2.456 | 0.000** | -1.867 | 0.000** | -1.789 | 0.000** |
| Educational level | -0.487 | 0.231 | -1.059 | 0.153 | -1.054 | 0.166 |
| Income | -0.592 | 0.521 | -0.644 | 0.376 | -0.567 | 0.352 |
| Income | -3.642 | 0.000** | -3.252 | 0.001** | -3.235 | 0.001** |
| Politics status | -2.567 | 0.587 | 0.521 | 0.000** | -0.617 | 0.531 |
| Nation | 0.123 | 0.001** | -2.515 | 0.354 | -2.462 | 0.000** |
| Community governance cognition | 0.875 | 0.000** | 1.305 | 0.000** | 1.558 | 0.000** |
| Community governance behavior | 1.252 | 0.000** | 1.305 | 0.000** | ||
| R² | 0.455 | 0.518 | 0.553 | |||
| F | 20.954 | 25.859 | 25.967 | |||
| P | 0.000 | 0.000 | 0.001 | |||
This section investigates and analyzes City H, which uses the community governance model of this paper, from six aspects: satisfaction with public services in urban communities, residents’ happiness, social support, sense of belonging to the community, motivation to provide public services, and sense of community effectiveness enhancement. After the questionnaire passed the reliability and validity test, it was conducted using a scoring system from 1 to 5, with the higher the score the better the effect. A total of 600 questionnaires were distributed in the study, 589 questionnaires were recovered, and after screening the questionnaires, a total of 568 valid questionnaires remained, and descriptive statistics were performed on the questionnaire data in order to achieve a visual examination of the research variables.
The results of the descriptive statistics of the Urban Community Governance Survey are shown in Table 4, which shows that among the six explicit variables, namely, satisfaction with public services in the urban community A, well-being B, social support C, sense of belonging to the community D, motivation for public services E, and sense of community effectiveness enhancement F, the motivation for public services scored the highest (4.723\(\mathrm{\pm}\)1.072) and the social support scored the lowest (4.104\(\mathrm{\pm}\)0.255). Community public service satisfaction (4.535\(\mathrm{\pm}\)0.883), well-being (4.253\(\mathrm{\pm}\)1.065), sense of community belonging (4.162\(\mathrm{\pm}\)0.789), and sense of community political efficacy (4.157\(\mathrm{\pm}\)0.829) ranked the second to the fifth, respectively. The results indicate that community residents themselves have high motivation for public service and high perception of community governance. Meanwhile, the mean value of the scores of each dimension is 4 points higher, verifying that the level of urban community governance has been effectively improved.
| Variable | Min | Max | Mean | Standard deviation | Skewness | Kurtosis | ||
|---|---|---|---|---|---|---|---|---|
| Statistical magnitude | Standard error | Statistical magnitude | Standard error | |||||
| A | 1 | 5 | 4.535 | 0.883 | -0.109 | 0.215 | -0.187 | 0.322 |
| B | 1 | 5 | 4.253 | 1.065 | -0.103 | 0.215 | 0.019 | 0.325 |
| C | 1 | 5 | 4.104 | 0.255 | 0.117 | 0.215 | -0.577 | 0.321 |
| D | 1 | 5 | 4.162 | 0.789 | 0.076 | 0.215 | -0.451 | 0.325 |
| E | 1 | 5 | 4.723 | 1.072 | -0.592 | 0.215 | 0.595 | 0.322 |
| F | 1 | 5 | 4.157 | 0.829 | -0.107 | 0.215 | -0.152 | 0.321 |
The actual effect of this paper’s urban community governance model based on the synergy of multiple subjects is further investigated through the statistics of the seven dimensions of community public service satisfaction, including basic community security A1, community cultural and sports activities A2, community safety A3, community transportation A4, community education A5, community environment A6 and community living facilities A7. The scores of each dimension of community governance happiness are shown in Table 5. Among the seven latent variables of community public service satisfaction, the scores are, in descending order, basic community security (4.792\(\mathrm{\pm}\)0.942), community environment satisfaction (4.714\(\mathrm{\pm}\)0.886), community education satisfaction (4.563\(\mathrm{\pm}\)1.029), community transportation satisfaction (4.519\(\mathrm{\pm}\)0.846), community safety satisfaction (4.431\(\mathrm{\pm}\)0.614) , community cultural and sports service satisfaction (4.412\(\mathrm{\pm}\)0.822), and community living facilities satisfaction (4.395\(\mathrm{\pm}\)0.411). The mean values of all seven dimensions are greater than 4 points, which indicates that community residents have a high level of satisfaction with the community of this paper’s governance model. Significant results were obtained in basic community security, community environment and community education. Meanwhile, the lowest score of community living facilities indicates that there is still room for improvement in community living facilities to address the issue of living services and facility conditions.
| Variable | Min | Max | Mean | Standard deviation | Skewness | Kurtosis | ||
|---|---|---|---|---|---|---|---|---|
| Statistical magnitude | Standard error | Statistical magnitude | Standard error | |||||
| A1 | 1 | 5 | 4.792 | 0.942 | -0.118 | 0.104 | -0.196 | 0.211 |
| A2 | 1 | 5 | 4.412 | 0.822 | -0.112 | 0.102 | -0.028 | 0.226 |
| A3 | 1 | 5 | 4.431 | 0.614 | -0.126 | 0.096 | -0.586 | 0.223 |
| A4 | 1 | 5 | 4.519 | 0.846 | -0.085 | 0.103 | -0.46 | 0.216 |
| A5 | 1 | 5 | 4.563 | 1.029 | -0.601 | 0.105 | -0.604 | 0.216 |
| A6 | 1 | 5 | 4.714 | 0.886 | -0.116 | 0.108 | -0.161 | 0.216 |
| A7 | 1 | 5 | 4.395 | 0.411 | -0.26 | 0.106 | -0.263 | 0.216 |
This paper constructs the mediating effect and regulating effect model of urban community governance and proposes an urban community governance model based on the synergy of multiple subjects, and tests it through empirical analysis.
According to the results of mediation effect analysis, the regression coefficient of community governance cognition on community governance effect is 0.72, the regression coefficient of community governance cognition on the behavior of multiple subjects participating in community governance is 0.81, and the regression coefficient of multiple subjects participating in community governance behavior on governance results is 0.56, and the accompanying probability P is less than 0.001. The total mediating effect size of community governance cognition on the effect of community governance was 0.80, and the direct effect size was 0.29. The indirect effect value of community governance cognition, the behavior of multiple subjects participating in community governance and the governance effect path was 0.51. The results show that the influence of community governance cognition on the effect of community governance is mainly reflected through the participation of multiple subjects in community governance, and the higher the governance awareness, the better the community governance results.
The coefficient of determination R² of model 1, model 2 and model 3 in the moderating effect analysis were 0.455, 0.518 and 0.553, respectively. The correlation coefficient of community governance cognition to the behavior of multiple subjects participating in community governance was 1.305, the regression coefficient of community governance cognition to the behavior of multiple subjects participating in community governance was 0.875, and the regression coefficient of multiple subjects participating in community governance was 1.305, and P was less than 0.001. It is further proved that governance cognitive control has a direct impact on the behavior of multiple subjects participating in community governance, and has an obvious moderating effect in the indirect influence relationship of community governance cognition on the behavior of multiple subjects participating in community governance.
In this paper, urban community governance has the highest score of public service motivation (4.723\(\mathrm{\pm}\)1.072) and the lowest score of social support (4.104\(\mathrm{\pm}\)0.255), and the mean value of the scores of all dimensions is 4 points higher. And among the seven latent variables in community public service satisfaction, basic community security (4.792\(\mathrm{\pm}\)0.942) and community environment satisfaction (4.714\(\mathrm{\pm}\)0.886) scores are higher, and district amenities satisfaction score is the lowest (4.395\(\mathrm{\pm}\)0.411). It indicates that the governance model in this paper can improve the community residents’ own cognition of community governance thereby improving the effectiveness of urban community governance.
2023 Guangxi Public Security Department Special Topic: Research on Management and Control Strategies for the New Accommodation Business Model of “Homestay+” in Guangxi Border Areas, Project Number:(2023GAYB048).
