# The Embedding of Sports Social Organizations in Rural Governance Based on the Collaborative Governance Model of Multiple Subjects

Authors: Yanglu Yu; Kuiting Gao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Rural governance is an important part of national governance, and the participation of social organizations is also very important. This research explores the multiple relationships between rural governance subjects and governance methods through the basic requirements of “co-construction and sharing” of multi-governance and the status quo of creation activities, integrates an innovative rural community governance evaluation and supervision system, and builds rural community governance evaluation and supervision system mechanism. Mutually exclusive constraints on the diversity of governance subjects will provide strategic guidance for sports social organizations to participate in diversified management.


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## Body

## 1. Introduction

The participation of social organizations in rural community governance has a clearer theoretical and practical guidance, and participation in governance practice has a better macroenvironment. However, in the practice of sports social organizations’ participation in rural community governance, there are still contradictions such as institutional deviation and lack of practice. Therefore, it is necessary to further grasp the requirements of the times and the development logic of sports social organizations’ participation in rural community multi-governance; to explore the pluralistic relationship between rural community governance subjects and governance means; to explore the innovative mechanism and implementation path of sports social organizations’ participation in rural community multi-governance; and to provide strategic references, suggestions, and inspirations for sports social organizations’ participation in community multi-governance for the modernization of rural community governance.In the new era, “building and sharing” is the essential attribute of governance modernization. In terms of the object of participation in governance, it is conducive to increase the proportion of physical fitness services in public affairs and public activities in the community. In terms of the motivation for participation in governance, it is conducive to motivate the individual fitness service needs of community residents and the awareness of public fitness services, and to the formation of multiple participation motivations. In terms of the subjects involved in governance, it is conducive to the integration of the government, community, social organizations, community residents, and other subjects involved in the governance of fitness services to develop collaboratively. In terms of the goal of participation in governance, it is conducive to the development of sports and fitness services in the community and the linkage of multiple interests.As an important subject in community governance, sports social organizations have accelerated their reform under the influence of the reform and innovation of the power mechanism of Healthy China. Provinces and municipalities will gradually decouple from the government to achieve the goal of “de-hierarchization” and “de-administration.” This article explores the multiple relationships between rural governance subjects and governance methods, integrates an innovative rural community governance evaluation and supervision system, and builds a rural community governance evaluation and supervision system mechanism.


## 2. Related Work

The theory of collaborative governance is a crossover theory between the theory of synergy in natural sciences and the theory of governance in social sciences [1]. By collaborative governance, it means that with the support of network information technology, multiple elements of society, such as government, nongovernmental organizations, enterprises, and individual citizens, coordinate with each other to cooperate in the governance of social public affairs to pursue maximum governance effectiveness and ultimately achieve the purpose of maximizing the maintenance and promotion of public interests [2, 3]. Collaborative governance breaks through the traditional government-led governance model and emphasizes the participation of multiple subjects and capacity integration, which can effectively compensate for government and market failures [4].Collaborative management of rural ecological environment can provide basic advantages, information fusion, and improve management efficiency [5]. The highest value of the collaborative governance model is in the reasonable participation, positive response and benign interaction among subjects, changing the traditional government monocentric governance pattern, expanding the scope of decision-making subjects, implementation subjects and supervision subjects, enhancing the legality and rationality of decision making, the effectiveness of implementation, and the timeliness of supervision [6, 7]. Collaborative governance, as an open governance network, can promote the flow of information from multiple parties, and real information on environmental pollution and governance can quickly enter the decision-making system, laying the foundation for scientific decision-making and effective implementation [8]. Collaborative governance provides open governance channels, which manifests itself in action as active participation and equal cooperation of multiple subjects and enhances governance efficiency through multiple autonomous governance [9].The new concept of international health extends human health from the biological dimension to the social dimension. As a national medium- and long-term health master plan, “Healthy China” is a new level of health evaluation in a macro sense, and healthy cities and healthy communities, as sub-cells of the construction of “Healthy China,” are conducive to promoting the regional practice strategy of “Healthy China.” As a sub-cell of the construction of “Healthy China,” healthy cities and healthy communities are conducive to the regional practice strategy of “Healthy China,” and also bring development opportunities for community multi-governance [10].Firstly, the evaluation level of healthy cities and healthy communities brings a healthy orientation to the development of community foundations [11]. Secondly, the evaluation levels of healthy cities and healthy communities bring new entry points for community creation [12]. The core of community creation is community reconstruction, and community creation activities with health as the entry point are conducive to the stable operation of community sports and fitness self-organization, expanding the edge effect, enhancing residents’ consciousness of participating in scientific fitness, attracting more dynamic youth groups, fitness-loving groups and other multilevel and multifaceted people to participate in residents’ autonomy, and realizing the sustainable supply of fitness services [13].The more successful community-building activities have provided a good opportunity for sports social organizations to participate in rural community governance [14]. Sports organizations can promote community-building activities that use health and fitness as an entry point and then work with communities to support the development of grassroots sports and fitness organizations, strengthen community interaction, and enhance the fit between community sports service supply and demand [15]. The promotion of “health and fitness” as the entry point for community-building activities, the development of community-based sports organizations, the improvement of the management mechanism of community sports organizations, and the role of community sports organizations for the public good and service, to achieve self-connection, self-management, self-supply, enhance the emotional identity of community residents, and increase community participation [16].Collaborative governance between the government and the third sector enhances the effectiveness of rural environmental governance through interaction and cooperation between government internal agencies and between the government and society [17]. Big data application governance means to apply big data technology to community governance to enhance personalized services for community residents [18].Firstly, to strengthen residents’ self-governance in rural communities, the interaction and cooperation between government departments, social organizations, markets, and other governance bodies are needed. Secondly, social organizations and enterprises empower community residents in the fields of culture, sports and health through government purchase of public sports services, corporate social responsibility and public welfare investment, and cultivate residents’ self-governance organizations in rural communities, to strengthen the talent team of rural community governance and enhance residents’ awareness of their own needs [19, 20].


## 3. Methods

Firstly, we need to clarify the relationship between multiple subjects and the specific incentive-compatible constraint mechanism of multiple subjects of rural community governance, as shown in Figure1.Figure 1
Incentive compatibility constraint mechanism of multiple subjects of rural community governance.As shown in Figure1, through the research on the basic requirements of “co-construction and sharing” and the status quo of the creative activities of multi-governance, the article sorts out the era requirements and development logic of sports social organizations’ participation in rural governance, and on this basis, explores the multiple relationships between rural governance subjects and governance methods. Finally, the innovative rural community governance evaluation and supervision system and rural governance system will be integrated, and then, a rural community governance evaluation and supervision system, incentive compatibility, and multiple constraints will be formed.On this basis, this article divides the influencing factors of CGBMP into internal and external categories. Among them, the internal factors include related, sensory, interactive, and effective factors, whereas external factors include internal, external, complex, social, and technical factors, as well as meta-analysis and fuzzy set. See Table1 for detailed definitions.Table 1
Influencing factors of CGBMP.
ClassificationInfluencing factorsMetrics and definitionsInternal factorsRFTrust: the extent to which actors trust the government, experts, or other participating subjectsInterdependence: the ability of multiple actors to share resources or enhance cooperation to strengthen their respective behavioral capabilitiesCompetition: the psychological needs and behavioral activities of actors that attempt to outperform or overpower other actorsEmotion: a psychological activity mediated by the desires and needs of an actorPTFRisk perception: the subjective judgment of the actor about the characteristics and severity of a particular riskQuality perception: the degree to which the actor’s sensory needs for quality characteristics are satisfiedValue perception: the actor’s overall evaluation of the utility of the perceived benefits weighed against the cost of acquiring the product or serviceExternal factorsTIFInteractive communication: the act of dynamic feedback and timely dialogue between subjectsInformation acquisition: the process of activity of the actor to obtain original information through technical means and ways and meansExpression of opinion: the actor can express his or her views without hindrance, without constraintsEFSelf-efficacy: subjective judgment of whether actors can successfully engage in collaborative governance behaviorsParticipation efficacy: subjective judgment of whether actors are willing to participate in public crisis governance behaviorsCLFTask complexity: the complexity of public crisis eventsCollaboration complexity: the complexity of the collaborative governance process of multiple subjectsDemand complexity: the dynamics and uncertainty of the governance process, which leads to the complexity of the decision-making process of the governance bodyRegion of occurrence: the region where the public crisis event occursFrom the internal factors, we can see that relational factors include trust, interdependence, competition, and emotion; perceptual factors include risk perception, quality perception, and value perception; interactive factors include interactive communication, information acquisition, and opinion expression; efficacy factors include self-efficacy and participation efficacy. In terms of external factors, complexity factors include task complexity, collaboration complexity, demand complexity, and occurrence area; social factors include the degree of institutional perfection, spiritual cohesion, and synergy model fit; technical factors include emergency organization, monitoring and early warning, material distribution, and emergency rescue. Based on the above, the following hypotheses are proposed in this study.(1)
RF ratio coefficient has a significant positive effect on CGBMP(2)
Sensitive factors have a significant positive impact on CGBMP(3)
TIF factor interaction has a significant positive effect on CGBMP(4)
Effective factor (EF) has a significant positive effect on CGBMP(5)
CLF complexity coefficient has a significant positive impact on CGBMP(6)
Social factor (SF) has a significant positive impact on CGBMP(7)
TNF technology has a significant positive effect on CGBMPThe research model of this article is shown in Figure2.Figure 2
Research model.To illustrate the methodology used in this article, the key influencing factors are derived from a retrospective study of multiple literatures completed by meta-analysis as shown in Figure3, and then the multicausal relationships presented by the key influencing factors are studied according to the fuzzy-set QCA to form an action path to cope with the collaborative governance behavior of multiple rural subjects.Figure 3
Logical illustration of meta-analysis and qualitative comparative analysis of fuzzy sets.The data coding objects in this study include literature description items and effect value statistics items. For the coding of the selected literature, the study description item contains basic information such as author information, year of publication, and type of publication; the effect value statistics item includes sample size, variable name, correlation coefficientr, reliability, and path coefficient β. Through a close reading of the 35 selected empirical research articles, the effective values of the influencing factors of CGBMP as independent variables were collected, and the influencing factors described above were classified and integrated to facilitate the analysis of the combined effect of each influencing factor. In the coding process, multiple coding was performed when the literature contained multiple independent samples, and the arithmetic mean was calculated as the overall effect value when the correlation coefficient was from the same sample. According to Lipsey and Wilson’s coding procedure, firstly, three researchers coded the screened literature, and to exclude the defects of subjectivity, the coding process of other meta-analyses was also referred to, and the whole process was ensured to be conducted independently; secondly, the researchers conducted cross-checking, and the first coding consistency was 78.9%, and after further review and discussion, the parts with differences were corrected to ensure the coding uniformity.


### 3.1. Publication Bias and Heterogeneity Test Analysis

For the analysis of the publication bias test, both the funnel plot test and the Egger test were used in this article. Also focusing on the Egger test results, it can be found that theP-values of the significance tests are all greater than 0.05, indicating that no publication bias exists.The heterogeneity test in this study was conducted byQ-test, as shown in Table 2, The p-values of all variable relationship studies were less than 0.05, and all the heterogeneity tests reached the significance level, which indicates the existence of heterogeneity between effect sizes. In addition, there are moderating variables among the factors influencing CGBMP, and for this case, this study used a random-effects model for the relationship between each factor and CGBMP, followed by a moderating effects analysis.Table 2
Heterogeneity test (Q-test) of the influencing factors of CGBMP.
VariableNumber of studiesMTotal sample sizeNHeterogeneity test (Q-test)Tau-squaredQ valueP valueI-squaredSEVarianceTauRF2222858769.223<0.0197.5520.0290.0010.221PTF1010980783.558<0.0198.6520.0580.0030.298TIF177180228.888<0.0192.5680.0210.0020.185EF7159881822.777<0.0199.8250.2520.0000.505CLF18131802258.502<0.0133.2980.0990.0650.198SF2224380798.089<0.0197.2430.0270.0120.208TNF116888439.876<0.0197.7220.0580.0020.289Note:M is the number of independent study samples; N is the total sample size of M study samples; P-value is the significance value of the heterogeneity test, if P<0.05, the homogeneity hypothesis is rejected, indicating the existence of heterogeneity among studies [21–23].


### 3.2. Results of Hypothesis Testing of Combined Values of Effects

As shown in Table3, the combined effect values of the seven influencing factors and CGBMP were 0.498, 0.628, 0.380, 0.545, 0.603, 0.383, and 0.539, respectively, and the p-values were less than 0.05, indicating that there was a significant positive relationship. According to the correlation coefficient assessment method, the correlation coefficients r between RF, PTF, EF, CLF, TNF, and CGBMP were all greater than 0.4, which could be judged as a strong correlation and an important influencing factor; the correlation coefficients r between TIF, SF, and CGBMP were located between 0.25 and 0.4, and the correlations were moderate. Therefore, the hypotheses were all verified. The systematic model of influencing factors of CGBMP is shown in Figure 4.Table 3
Results of random effects model test for OGBMP influencing factors.
VariableNumber of studiesMTotal sample sizeNEffect value and 95% confidence intervalTwo-tailed testModelEffect combined valuer0.0290.001Z valueP valueRF22228580.4890.4250.56811.055<0.01RandomPTF10109800.6370.6350.7258.758<0.01RandomTIF1771800.3790.3850.4588.668<0.01RandomEF7159880.5450.2050.7682.9650.003RandomCLF18131800.6050.4550.7256.587<0.01RandomSF22243800.3850.3090.4589.097<0.01RandomTNF1168880.5560.4150.6580.655<0.01RandomFigure 4
Systematic model of CGBMP influence factors.


### 3.3. Results of Moderating Effect Test

With reference to the existing studies, the random-effects model was used to further test whether there were moderating effects of sample size, geographical attributes, and publication type on the relationship between each factor and CGBMP influence. The results of the moderating effect analysis showed that sample size had a moderating effect on RF (Q = 315.990, P=0.000<0.05), PTF (Q = 14.600, P=0.001<0.05), TIF (Q = 73.389, P=0.000<0.05), EF (Q = 9.851, P=0.000<0.05), CLF (Q = 49.547, P=0.000<0.05), SF (Q = 96.628, P=0.000<0.05), and TNF (Q = 46.488, P=0.000<0.05) with CGBMP.Geographical attributes had a significant moderating effect on the relationship between RF (Q = 95.988, P=0.000<0.05), PTF (Q = 253.010, P=0.000<0.05), and TIF (Q = 16.309, P=0.000<0.05) and CGBMP. There was a significant moderating effect between TIF and CGBMP in the context of domestic studies (r = 0.299) and a high correlation between TIF and CGBMP in the context of foreign studies (r = 0.583) as far as TIF was concerned. Publication type had a moderating effect on the relationship between RF (Q = 79.709, P=0.000<0.05), PTF (Q = 142.637, P=0.000<0.05), TIF (Q = 8.700, P=0.003<0.05), and CLF (Q = 4.034, P=0.045<0.05) and CGBMP. In terms of TIF, the publication type for dissertations (r = 0.277) was smaller than that for journal articles (r = 0.496), indicating that journal articles had a stronger moderating effect on TIF and CGBMP than dissertations. However, there was no moderating effect of publication type on the relationship between other influencing factors (P>0.05) and CGBMP. However, we also cannot ignore the possibility that the moderating effect was not significant due to the insufficient number of documents included in the meta-analysis.


## 3.1. Publication Bias and Heterogeneity Test Analysis

For the analysis of the publication bias test, both the funnel plot test and the Egger test were used in this article. Also focusing on the Egger test results, it can be found that theP-values of the significance tests are all greater than 0.05, indicating that no publication bias exists.The heterogeneity test in this study was conducted byQ-test, as shown in Table 2, The p-values of all variable relationship studies were less than 0.05, and all the heterogeneity tests reached the significance level, which indicates the existence of heterogeneity between effect sizes. In addition, there are moderating variables among the factors influencing CGBMP, and for this case, this study used a random-effects model for the relationship between each factor and CGBMP, followed by a moderating effects analysis.Table 2
Heterogeneity test (Q-test) of the influencing factors of CGBMP.
VariableNumber of studiesMTotal sample sizeNHeterogeneity test (Q-test)Tau-squaredQ valueP valueI-squaredSEVarianceTauRF2222858769.223<0.0197.5520.0290.0010.221PTF1010980783.558<0.0198.6520.0580.0030.298TIF177180228.888<0.0192.5680.0210.0020.185EF7159881822.777<0.0199.8250.2520.0000.505CLF18131802258.502<0.0133.2980.0990.0650.198SF2224380798.089<0.0197.2430.0270.0120.208TNF116888439.876<0.0197.7220.0580.0020.289Note:M is the number of independent study samples; N is the total sample size of M study samples; P-value is the significance value of the heterogeneity test, if P<0.05, the homogeneity hypothesis is rejected, indicating the existence of heterogeneity among studies [21–23].


## 3.2. Results of Hypothesis Testing of Combined Values of Effects

As shown in Table3, the combined effect values of the seven influencing factors and CGBMP were 0.498, 0.628, 0.380, 0.545, 0.603, 0.383, and 0.539, respectively, and the p-values were less than 0.05, indicating that there was a significant positive relationship. According to the correlation coefficient assessment method, the correlation coefficients r between RF, PTF, EF, CLF, TNF, and CGBMP were all greater than 0.4, which could be judged as a strong correlation and an important influencing factor; the correlation coefficients r between TIF, SF, and CGBMP were located between 0.25 and 0.4, and the correlations were moderate. Therefore, the hypotheses were all verified. The systematic model of influencing factors of CGBMP is shown in Figure 4.Table 3
Results of random effects model test for OGBMP influencing factors.
VariableNumber of studiesMTotal sample sizeNEffect value and 95% confidence intervalTwo-tailed testModelEffect combined valuer0.0290.001Z valueP valueRF22228580.4890.4250.56811.055<0.01RandomPTF10109800.6370.6350.7258.758<0.01RandomTIF1771800.3790.3850.4588.668<0.01RandomEF7159880.5450.2050.7682.9650.003RandomCLF18131800.6050.4550.7256.587<0.01RandomSF22243800.3850.3090.4589.097<0.01RandomTNF1168880.5560.4150.6580.655<0.01RandomFigure 4
Systematic model of CGBMP influence factors.


## 3.3. Results of Moderating Effect Test

With reference to the existing studies, the random-effects model was used to further test whether there were moderating effects of sample size, geographical attributes, and publication type on the relationship between each factor and CGBMP influence. The results of the moderating effect analysis showed that sample size had a moderating effect on RF (Q = 315.990, P=0.000<0.05), PTF (Q = 14.600, P=0.001<0.05), TIF (Q = 73.389, P=0.000<0.05), EF (Q = 9.851, P=0.000<0.05), CLF (Q = 49.547, P=0.000<0.05), SF (Q = 96.628, P=0.000<0.05), and TNF (Q = 46.488, P=0.000<0.05) with CGBMP.Geographical attributes had a significant moderating effect on the relationship between RF (Q = 95.988, P=0.000<0.05), PTF (Q = 253.010, P=0.000<0.05), and TIF (Q = 16.309, P=0.000<0.05) and CGBMP. There was a significant moderating effect between TIF and CGBMP in the context of domestic studies (r = 0.299) and a high correlation between TIF and CGBMP in the context of foreign studies (r = 0.583) as far as TIF was concerned. Publication type had a moderating effect on the relationship between RF (Q = 79.709, P=0.000<0.05), PTF (Q = 142.637, P=0.000<0.05), TIF (Q = 8.700, P=0.003<0.05), and CLF (Q = 4.034, P=0.045<0.05) and CGBMP. In terms of TIF, the publication type for dissertations (r = 0.277) was smaller than that for journal articles (r = 0.496), indicating that journal articles had a stronger moderating effect on TIF and CGBMP than dissertations. However, there was no moderating effect of publication type on the relationship between other influencing factors (P>0.05) and CGBMP. However, we also cannot ignore the possibility that the moderating effect was not significant due to the insufficient number of documents included in the meta-analysis.


## 4. Case Study

With the above influencing factors, this article uses the fuzzy-set QCA method to analyze the CGBMP action paths of different types of rural governance situations in terms of seven aspects: relational, perceptual, interactive, effectiveness, complexity, social, and technical factors. Therefore, this article selects different types of rural governance situations occurring in China from January 2019 to December 2020 for a fuzzy-set qualitative comparative analysis to analyze the multiple groupings that lead to CGBMP, and these different groupings indicate the action paths formed by the concurrence of different factors to achieve the same outcome. A total of four types of cases were constructed, namely natural disasters (earthquakes, fires, and floods), accidents and disasters (traffic accidents and production accidents), public health (food safety and infectious diseases), and social security (violence).The case selection follows the following rules: (1) the selected cases have a certain degree of similarity/homogeneity/comparability. That is, the cases must share some background or characteristics that are used as “constants” in the specific analysis. (2) The selected cases should also have diversity. That is, the minimum number of cases should be selected to achieve the maximum heterogeneity among cases. (3) The selected cases should have both “maximum similarity” and “maximum difference.” “Maximum similarity” refers to the process of grouping and matching similar cases as much as possible in the case study, which can significantly improve the “internal validity” of the observed relationships; “maximum difference” refers to seeking the maximum heterogeneity in the selected cases to ensure that the “external validity” of the hypothesized causal relationship can be extended for analysis. Based on the above rules, 22 cases were selected for generalization. This study strives for the comprehensiveness and diversity of the selected cases to ensure the rhyme of the multiple action paths formed by using the fuzzy-set QCA to explore the collocation and combination of factors influencing CGBMP [26, 27].Figures5 and 6, respectively, show the importance of external and internal factors after modeling and analysis. It can be seen that both external and internal factors have different degrees of importance.Figure 5
The importance of different internal factors.Figure 6
The importance of different external factors.This study uses fuzzy-set QCA to build a combination of conditions of CGBMP influencing factors in a complexity perspective to derive a multivariate action path for this research problem. In fuzzy-set QCA, each condition and outcome is a set, and each case has an affiliation score in the set, and this process of assigning affiliation scores to the set of cases is known as calibration. According to the requirements of fuzzy QCA for variable assignment, a six-value assignment scheme is used in this study. To reduce the subjectivity of variable assignment, this study follows the procedural credibility principle of qualitative text analysis. The Delphi method was used for variable assignment, and the general process was as follows: firstly, several experts in the field were selected, and after unifying the assignment rules, the experts’ opinions were solicited on each variable assignment, and the assignment opinions were collated, summarized, and counted; secondly, anonymous feedback was given to each expert, and opinions were solicited again, centralized, and fed back until a consensus variable assignment opinion was obtained. Since some of the conditions and the degree of occurrence of the results do not have obvious distinction, the actual values taken do not fully cover the six scores, and the settings of specific variables and assignments are shown in Table4.Table 4
Setting of variables and assignments.
Variable typeVariable nameNumber of variables statisticsAssignmentResult variableCGBMPMultiple subjects participate in governance continuously, and the degree of participation is high and the governance effectiveness is good1Multi-organizers can actively participate in governance, but there are limitations, and the effectiveness of governance is good0.8Multiple subjects participate in governance but not actively, and the governance effect is average0.8Single-actor governance0Conditional variableRFSubjects trust the government, experts or other participating subjects and are willing to share resources or strengthen their own behavior to improve cooperation0.8The subject trusts the government, experts, or other participating subjects in general, and can provide certain resources1Subjects try to outperform or overpower other subjects’ psychological needs and behavioral activities and have a competitive mindset for cooperation0.6Lack of trust among subjects, unwillingness to share resources and strengthen their own cooperative behavior0PTEThe subject is able to make rapid judgments about the characteristics and severity of risk events and is satisfied with the quality and needs of governance1The subject is able to judge the characteristics and severity of the risk event and is basically satisfied with the quality and needs of the governance0.8The subject is able to make basic judgments about the characteristics and severity of the risk event and is partially satisfied with the quality and needs of the governance0.6The subject can hardly judge the characteristics and severity of the risk event and is not satisfied with the quality and needs of governance0TIFEffective dynamic feedback and timely dialogue between subjects, and access to all information0.8Dynamic feedback and dialogue between subjects and access to relevant information1Subjects can have limited dialogue with each other and obtain some necessary information but not all0.6Subjects are constrained to express their views and keep each other’s information confidential0EFSubjects actively participate in the act of public crisis management and do their best to accomplish the task1The subject has the will to participate in public crisis management behavior0.8Subjects are unwilling to participate in public crisis management behavior0CLFThe dynamic and uncertain nature of the public crisis management process leads to a very complex decision-making process and uncertain results for the governing body1The decision making of public crisis management subjects is complex and the results are uncertain0.8The decision making of public crisis management actors is relatively complex and the results are uncertain0.4The decision making of public crisis management actors is simple but the results of management are uncertain0.6The decision making of public crisis management actors is simple, and the results of management are controllable0SFThe system of public crisis management is well established, and the cooperation among the subjects involved in collaborative management is good, and the connection between the subjects is strong1There is a positive team spirit in the process of public crisis management and it can stimulate the cooperation among the subjects0.8The links between the subjects involved in collaborative management are relatively strong0.6Independent action of the subjects involved in governance0TNFThe ability of participating governance entities to monitor risk information in real-time, deal with security incidents quickly and ensure the safety of people’s lives and property to the maximum extent possible1Participating governance entities can monitor risk information, deal with security incidents, and complete risk analysis results0.8The ability of participating governance entities to ensure accurate and timely distribution of relief materials in the event of a public crisis0.6The participating governance body can complete the rescue work after the occurrence of the public crisis event0.6The governance body cannot ensure the safety of people’s lives and property in the event of a public crisis0The software fsQCA 3.0 was used to calculate the consistency of the variables (see Table5), and the results showed that the consistency of the single condition necessity was generally low (<0.9), which shows that the CGBMP in the complexity perspective is not determined by a single variable, but is the result of the combination of several variables acting together.Table 5
Univariate necessity test for CGBMP influencing factors.
ResultCGBMPVariable nameConsistencyRF-RF0.7750.715PTF-PTF0.6790.789TIF-TIF0.8890.672EF-EF0.6880.762CLF-CLF0.8990.644SF-SF0.8890.632TNF-TNF0.8450.637In this study, the intermediate solution with better generalizability and revelation will be used to explain the CGBMP level from the complexity perspective. The results are organized using the logical path table proposed by Larkin, as shown in Table6, and the CGBMP from the complexity perspective can be realized by the following four behavioral paths.Table 6
Multiple action paths for CGBMP in complexity perspective.
ConditionsCGBMPPath 1Path 2Path 3Path 4RF•⊗⊗⊗PTF—⊗⊗⊗TIF•⊗•⊗EF•⊗⊗⊗CLF●⊗●●SF•⊗⊗•TNF●●⊗⊗Consistency10.87808970.933Original coverage0.6010.4530.4750.556Unique coverage0.1980.0190.0090.055Overall consistency0.862Overall coverage0.762Source: fsQCA3.0 software processing results and case-specific analysis results. Note: ● = core condition exists;• = edge condition exists; ⊗ = edge condition is missing; “—” indicates the condition without influence.


## 5. Conclusion

The requirements of various strategies in the new era have been reflected. The participation of sports social organizations in the management of rural diversification is a process of continuous development. Improve the rural governance evaluation and supervision system, establish incentive mechanisms and mutually exclusive constraints for the diversification of governance subjects, provide strategic guidance for sports social organizations to participate in diversified management, realize the modernization of the rural governance system, and ensure the diversification of governance subjects. In addition, it is necessary to continuously improve rural governance policies and support security systems, financial security, and public participation.



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*Source: 2084606-2022-06-16.xml*