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Article

Collaborative Governance Model for Fitness-Health Integration in Smart Communities: Framework and Outcome Measurement

by
Huimin Song
1,
Jinliu Chen
2,3,4,*,
Mengjie Wu
1 and
Wei Zeng
1
1
College of Tourism, Huaqiao University, Quanzhou 362021, China
2
School of Arts and Innovation Design, Suzhou City University, Suzhou 215000, China
3
Purple Academy of Culture & Creativity, Nanjing University of the Arts, Nanjing 210013, China
4
China-Portugal Joint Laboratory of Cultural Heritage Conservation Science, Suzhou 215000, China
*
Author to whom correspondence should be addressed.
Systems 2025, 13(9), 755; https://doi.org/10.3390/systems13090755 (registering DOI)
Submission received: 3 July 2025 / Revised: 19 August 2025 / Accepted: 23 August 2025 / Published: 1 September 2025
(This article belongs to the Section Systems Practice in Social Science)
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Versions Notes

Abstract

Compared to non-smart communities, smart communities expand the boundaries of community management and provide a platform for the deep integration of fitness and health. However, a single-entity governance model reduces the management efficiency of smart communities and hinders the realization of fitness-health integration within them. A collaborative governance model involving governments, businesses, social organizations, and residents replaces the traditional linear governance model that relies on a single entity through resource integration. This study, based on collaborative governance theory, employs three scenario-based experimental designs and quantitative analysis, with Xiamen’s smart city community and non-smart urban village community as research subjects. It explores the multistakeholder collaborative governance model for the deep integration of fitness and health, compares the differences in fitness-health integration between smart communities and non-smart communities, and measures the effectiveness differences between multistakeholder collaborative governance and single-entity governance models. The findings indicate: (1) Residents in smart communities have higher satisfaction with comprehensive fitness-health services; (2) Residents in smart communities perceive shorter psychological distances when engaging in fitness-health activities compared to non-smart environments; (3) The governance model moderates the impact of psychological distance on service satisfaction. Compared to the single-actor model, multiactor collaborative governance more effectively enhances perceived psychological proximity and improves satisfaction. The research findings contribute theoretically to advancing understanding of collaborative governance theory while expanding the application of the technology acceptance model (TAM) and the construal level theory in the context of community governance. Practically, they offer insights for public policymakers to optimize resource allocation and for community managers to strengthen digital governance.

1. Introduction

Amid the global push for sustainable urban development outlined by the Sustainable Development Goals (SDGs), integrating fitness and health has become essential for fostering well-being [1]. Globally, countries are adopting policies to promote active lifestyles and health synergies, with initiatives such as the World Health Organization’s Global Action Plan on Physical Activity 2018–2030 encouraging cross-sector collaboration to address lifestyle-related diseases [2,3]. Smart communities rely on IoT and big data technologies to rebuild their service ecosystems. For example, they use smart devices and software platforms to build information platforms that share information, integrate services, and optimize resources, ultimately achieving intelligent management and innovative services within the community [4]. However, as smart communities mature, challenges persist in realizing effective fitness-health integration through digital platforms [5]. Current smart systems often overlook specialized support for fitness and health services [6,7]. Additionally, cross-sectoral governance remains inefficient, with misaligned resource allocation and fragmented service delivery, limiting the potential of smart technologies to enhance urban health [8,9]. These gaps highlight the need for a more coordinated governance framework that leverages multiactor collaboration to bridge service and resource divides.
Existing literature frequently conceptualizes fitness-health integration as a mere intersection of two sectors, overlooking both the transformative potential of modern technologies and the complexities inherent in community structures and multiactor governance [10,11]. Smart communities, leveraging information technologies, present an innovative governance paradigm by centralizing diverse services within unified smart management systems [12]. Such systems provide critical infrastructure to operationalize fitness-health integration in daily life. Specifically, smart platforms facilitate not only the convergence of fitness and health services but also real-time data dissemination to residents, thereby improving managerial efficiency and resource allocation [13]. For example, using data from mobile fitness apps to understand when and where residents exercise provides an excellent opportunity to identify the fundamental factors that influence urban residents’ exercise activities [14]. Despite these advancements, implementation gaps persist. Current systems lack robust interaction mechanisms between fitness and health services, while the dearth of specialized integration platforms hinders meaningful synergy [15]. These limitations underscore the necessity to reconceptualize fitness-health collaboration—transcending technological instrumentation to align governance frameworks with the operational realities of smart communities [16,17].
Globally, multiactor collaborative governance has emerged as a predominant community management model [18]. Its efficacy hinges on government leadership, clear role delineation, and active engagement of social organizations and citizens to foster co-construction, co-governance, and shared outcomes [19]. Among these, the government is responsible for policy formulation and monitoring community dynamics [20]; businesses provide technical equipment and manage service supply chains [21]; social organizations bridge policy implementation gaps by designing effective public service projects, helping communities become more intelligent [22]; and residents promote the sustainable development of smart communities through active participation in their construction [23]. Multistakeholder collaboration ensures resource liquidity and a closed-loop responsibility system. Yet, within smart communities, divergent priorities among governmental, societal, market, and resident stakeholders often impede coordinated governance [24]. Compounding this issue, an overreliance on rudimentary technical tools diminishes governance efficacy, ultimately constraining communities’ ability to advance fitness-health integration.
Existing studies on fitness-health integration have focused primarily on its significance, pathways, and mechanisms, yet few address how such integration can be realized within smart communities [25]. This gap leaves unanswered the critical question of how to operationalize fitness-health synergies under digital governance frameworks. As modern governance increasingly demands both collaborative management and technological modernization, there is an urgent need to explore practical solutions [26]. As an important vehicle for technology-enabled governance, smart communities significantly improve the efficiency and accuracy of public service responses through digital platforms such as one-stop services and online feedback mechanisms, thereby enhancing resident satisfaction [1,2,5]. At the same time, intelligent facilities such as remote health monitoring and online activity reservations shorten the physical and cognitive distance between residents and services, effectively reducing the “psychological distance” between them and the governing entities [3,6,10], laying the foundation for building trust and a sense of participation. Consequently, based on the collaborative governance theory, in this study, we compare smart urban communities in Xiamen, China, with traditional urban villages, investigating governance models that promote fitness-health integration. We measure the outcomes of multiactor collaboration, analyze residents’ perceived psychological distance from and satisfaction with governance, and provide insights into how collaborative frameworks can effectively advance fitness-health integration within smart communities.
The findings of our study help to restructure the collaborative governance framework for fitness-health integration in smart communities, offering a foundation for enhancing participatory decision-making among multiple stakeholders. At the theoretical level, the findings elucidate how smart fitness facilities and health services influence residents’ satisfaction and psychological distance, expanding the application of the technology acceptance model (TAM) and the Theory of Perceived Scenario in community governance. At the practical level, through scenario-based experiments, this study helps develop an ecological public service model for integrated fitness-health governance, expanding the theoretical scope of community management. Herein, we propose and evaluate a multiactor governance model that drives the deeper integration of fitness and health in smart communities. The model leverages big data, the internet, and cloud computing to streamline service delivery and product demand, fostering efficient resource integration and seamless information sharing. This approach offers valuable insights for advancing “sports+” initiatives, providing a practical reference for promoting fitness-health synergies in the digital era.

2. Literature Review and Research Hypotheses

2.1. Collaborative Governance Theory and Its Application in Smart Communities

Smart communities leverage digital technology to foster harmonious environments through innovative governance. Technology serves as the operational foundation and driving force for their development [12]. The governance of smart communities aligns with the principles of collaborative governance, as it involves multiple stakeholders working toward common objectives [27]. Unlike traditional top-down directives, smart community governance is driven by resident needs, reflecting the participatory nature of multiactor collaboration [28]. This approach invites external organizations, such as NGOs, to contribute new resources and social capital, moving beyond the conventional reliance on neighborhood committees [29]. It empowers residents to shift from passive recipients to active participants in community management, generating greater momentum for collaborative efforts [30].
The concept of collaborative governance, which emerged in the 1970s, has gained prominence as economies have expanded and social structures have grown more complex [31]. As governments struggle to address diverse societal demands efficiently, authority is gradually decentralized, and private actors assume an increasingly significant role in public affairs [32,33]. Collaborative governance emphasizes the involvement of multiple stakeholders, the participation of nongovernmental organizations (NGOs), the pursuit of public interests, cooperative problem-solving, interorganizational interaction, and shared resources [34]. Emerson et al. define the concept as “a process and structure for public policy-making and management that transcends institutional and sectoral boundaries to achieve public purposes that cannot be accomplished by any single entity” [35]. This definition underscores the importance of the concept in addressing public issues through cross-sectoral collaboration.
In recent years, scholars have applied collaborative governance theory to the field of fitness-health studies [36]. Meier and García analyzed the integration of competitive sports and public fitness through this framework, emphasizing the role of government leadership in policy-making and institutional reform [37]. Other studies have examined the mechanisms for opening school sports facilities to the public, arguing that a multiactor governance structure, with the government at the helm and the market and civil society as partners, is essential for fostering cooperation across sectors [38]. Additionally, the concept of collaborative governance has been used to articulate the governance structure, value orientation, and interrelationships among the multistakeholder dimensions of public fitness [39]. Research has further highlighted how the alignment of public sports interests under government leadership motivates diverse stakeholders to engage in service provision [40]. Collectively, these studies demonstrate the applicability of collaborative governance in managing sports-related public affairs, confirming its effectiveness as a governance model for public sports management. However, existing research has yet to address how collaborative governance frameworks can be systematically applied within smart communities. The literature lacks insight into the mechanisms that drive governance dynamics and stakeholder collaboration in these contexts. This study aims to fill that gap by examining how fitness and health integration can be achieved through collaborative governance in smart communities, assessing the effectiveness of multistakeholder coordination, and exploring its impact on residents’ satisfaction and engagement levels.

2.2. Integration of Public Fitness and Health

The literature on integrating public fitness and health primarily addresses several key areas. First, scholars have explored the meaning and intrinsic mechanisms of this integration. For example, one study has identified a multifaceted approach to achieving deep integration by considering department fusion, policy alignment, talent development, organizational collaboration, resource sharing, and industry integration [41]. Second, from a sports perspective, scholars have examined sports development and industry growth, emphasizing how public fitness advancements contribute to health outcomes. Research has indicated the presence of a symbiotic relationship between public fitness and the health industry, suggesting that reforms in the sports sector’s supply side can enhance market conditions, thereby stimulating fitness consumption and promoting overall public health [42]. Third, investigations into industry integration have highlighted key drivers such as national demand, technological convergence, management innovation, and relaxed government regulation. However, challenges such as supply-demand mismatches and lagging sports service industries have been shown to hinder the effective merging of the fitness and health sectors [5]. Fourth, the role of government and policy frameworks in facilitating this integration has been critically analyzed. The consensus is that government entities serve as both the architects of systems and rules and the primary promoters of public fitness and health initiatives [43]. Fifth, from the perspective of fitness-health integration, research has underscored the necessity of collaboration between the sports and health sectors. It posits that a scientific fitness model—which incorporates insights from both domains—is essential for creating robust health barriers for the public [25]. Finally, the literature has addressed the challenges faced in achieving this integration, identifying issues such as administrative dysfunction, inadequate policies, a shortage of qualified personnel, weakened sports organizations, slow development in health services, and inefficient resource allocation [44].
Despite the clarity regarding the theories, significance, paths, and mechanisms of public fitness and health integration, the research on collaborative governance within smart communities remains sparse. In the current study, scholars explore the problem of collaborative governance of smart communities from both the pros and cons. On the one hand, scholars point out that the use of wisdom means to reshape the relationship between multiple governance subjects, promote the construction of the community governance system, and improve the quality of public services; on the other hand, the wisdom of the community is also for the traditional grassroots management system, the existence of the concept of governance, institutional mechanisms, platform construction and resource security, and other aspects of the contradiction [25]. In response to these contradictions, it has become important to explore multi-body collaborative governance of smart communities, such as Tian and Wang proposed improving collaborative governance of smart communities through subject-culture embeddedness, both technology-resource-driven and system-talent-driven, and emphasizing the combination of specific cultural elements of the community and modern technology, which plays an important role in facilitating the participation of stakeholders, enhancing the decision-making process and improve service delivery [25]. These studies provide useful insights for communities to better utilize smart tools to achieve effective collaborative community governance; however, there is still a lack of specific and in-depth discussion on how collaborative governance systems can effectively promote the integration of fitness and health within smart communities. With the advancement of smart city initiatives and the establishment of smart community platforms, urgent academic tasks have emerged, namely, to explore how to implement multistakeholder collaboration in fitness and health integration on these smart platforms and how to measure the effectiveness of such collaborative governance. Focusing on these tasks is crucial for expanding the application of public fitness and health integration within smart communities.

2.3. Collaborative Governance Model for Fitness-Health Integration in Smart Communities

The theory of collaborative governance emphasizes an interactive and equitable partnership between the government and social organizations [45]. It advocates for a governance model based on mutual trust, negotiation, and shared interests, incorporating the government, the market, and society as key stakeholders [46]. In light of the literature and the development of smart communities, in this study, we propose a multistakeholder collaborative governance model, as illustrated in Figure 1. This model centers on smart communities, supported by technology in a horizontal, radiating grid structure.
In this framework, the government acts as the nurturer of community organizations and the coordinator of community affairs. Community committees serve as the primary leaders and implementers of community initiatives, whereas social groups and residents function as supervisors and beneficiaries of community activities [47]. Enterprises related to fitness and health provide the products, services, technologies, and resources necessary for addressing public fitness and health issues [48]. Under this model, information technology facilitates the collection of resident needs and disseminates this information to all stakeholders. Equipped with technological support, these stakeholders can effectively allocate resources to meet residents’ high-quality demands for fitness and health services.
Moreover, governance theory aims to compensate for the shortcomings or failures of state and market mechanisms in allocating social resources [49]. Rather than establishing hierarchical relationships, the model fosters cooperation among diverse entities. It integrates smart devices, information technology, the government, social organizations, community residents, businesses, and online and offline services to delineate clear roles and responsibilities among stakeholders [50]. This approach strives to create a harmonious community where “everyone participates, contributes, and benefits.” Communities represent the fundamental units of public governance in China and serve as primary venues for integrating fitness and health [51]. The government guides the entire governance system, nurtures community committees, and directs actions [52]. Community committees execute policies, lead community organizations, and manage social affairs. Health and fitness enterprises are market actors who provide the necessary products, services, technologies, and resources to support the integration of fitness and health within smart communities [53]. Residents, as beneficiaries, enjoy the policies and serve as overseers of implementation. Social organizations act as bridges between individuals and the state, contributing to policy formulation and complementing national governance [54]. Smart devices and information technology not only underpin the operations of smart communities but also support the integration of fitness and health [55]. These tools record residents’ fitness and health data, analyze their needs, and communicate relevant information to appropriate departments, ensuring timely responses to resident demands. The collaborative governance model depicted in Figure 1 encapsulates these dynamics, paving the way for a comprehensive approach to fitness and health integration in smart communities.

2.4. Model Construction of the Collaborative Governance Effect for Fitness-Health Integration in Smart Communities

2.4.1. Level of Residents’ Satisfaction with Collaborative Governance Outcomes

The level of residents’ satisfaction with collaborative governance is a critical measure of community service effectiveness and the overall success of urban management, reflecting how well communities meet the needs of their citizens [56]. It serves as a focal point in community studies, marking both the ultimate goal of community governance and its effectiveness as perceived by residents. This level of satisfaction directly reflects the level of urban management and development [57]. Research consistently shows a direct correlation between community spatial resources and resident satisfaction levels. Moreover, the dynamics of community interaction, particularly those experienced through neighborhood effects, significantly influence one’s level of life satisfaction [58,59]. Residents’ expectations and perceptions of community services further shape their satisfaction levels [60,61]. Factors such as one’s level of community awareness, the degree to which one’s needs are met, one’s perceptions of the community, and one’s levels of participation all contribute to this subjective evaluation of community services [62]. Thus, one’s level of satisfaction with collaborative governance in fitness-health integration emerges as a nuanced perception that encompasses both individual characteristics and environmental contexts. It ultimately reflects how well a smart community meets the fitness and health needs of its residents through its collaborative governance framework.

2.4.2. Smart Community Development

Smart communities leverage information and digital technologies to foster harmonious environments through innovative governance models. These technologies serve as the foundation for effective operation and act as primary drivers of development [11]. With the support of digital infrastructure, community departments and organizations have become more streamlined, significantly enhancing efficiency, governance capacity, and service delivery [63]. In this context, the integration and management of resources ensure that diverse resident needs are met. Smart governance embodies core characteristics, including connectivity, multistakeholder engagement, smart management, and refined operational processes [64]. By optimizing service workflows, smart communities enhance the level of resident satisfaction with collaborative governance in fitness and health integration [65]. Implementing smart technologies greatly simplifies residents’ lives, providing easier access to fitness and health services. Therefore, the following hypothesis is proposed:
H1. 
The degree of smartness positively influences residents’ satisfaction levels. Specifically, residents experience greater satisfaction with public fitness and health services in communities that are characterized by smart governance than in those that are not.

2.4.3. Psychological Distance

The concept of psychological distance, which was introduced by Liberman and Trope in 1998, refers to individuals’ subjective perception of how far an event is from themselves in terms of time, space, social interaction, and certainty [66]. These dimensions include time distance, which is related to individuals’ perception of the temporal proximity of events; spatial distance, which concerns the perceived proximity of events in physical space; social distance, which reflects individuals’ perceptions of differences between themselves and others; and certainty, which involves the likelihood of events occurring [67]. From a temporal perspective, smart technologies offer residents more accessible public services for fitness and health, enabling quicker access to these resources and consequently influencing their perceived time distance from community health services [68]. Spatially, the Internet of Things (IoT) enhances connections between residents and fitness facilities within the community, thereby reducing the perceived spatial distance [69]. Socially, smart systems create broader communication platforms, thereby fostering interactions among residents and between community staff and residents, which, in turn, help to diminish the perceived social distance [70]. In terms of certainty, advanced information technologies facilitate the integration and analysis of fitness-related information and services, allowing communities to address residents’ health needs better and enhancing the perceived reliability of services [71].
Thus, as the degree of smartness within a community increases, residents perceive a shorter psychological distance between themselves and fitness and health services, leading to greater satisfaction with these public services. These findings suggest that psychological distance mediates the relationship between community smartness and residents’ level of satisfaction with fitness and health services. On the basis of this analysis, the following hypotheses are proposed:
H2. 
The degree of smartness positively influences residents’ psychological distance, such that as community smartness increases, residents perceive a shorter psychological distance between themselves and public fitness and health services.
H3. 
Psychological distance serves as a mediator between smartness and the level of satisfaction with public fitness and health services.

2.4.4. Moderating Role of Governance Models

Collaborative governance among multiple stakeholders plays a crucial role in enhancing community engagement. By defining the government’s leading role and delineating responsibilities, this approach encourages participation from social organizations and residents alike [72]. Such cooperation harnesses the collective strength of various community entities, facilitating a co-governance mechanism that aims for sustainable community development and meets residents’ needs [50]. This multistakeholder collaborative model significantly boosts governance efficiency, resource allocation, and the quality of public services. Consequently, it reduces residents’ time costs associated with locating fitness and health facilities while improving accessibility to relevant resources [73]. As a result, it diminishes both the temporal and spatial distances that residents perceive in relation to community fitness and health activities [74].
Moreover, collaborative governance introduces more cooperative entities into the community, empowering residents to shift from passive management recipients to active participants [75]. This transformation enhances residents’ agency, control, and choice regarding their needs. The improved quality of public fitness and health services, alongside more effective resource allocation, fosters a closer psychological distance between residents and community services [76]. This proximity cultivates a stronger sense of belonging, trust, and identification with the community, ultimately leading to increased satisfaction with integrated fitness and health services under collaborative governance. On the basis of this framework, the following hypothesis is proposed:
H4. 
The governance model of a smart community moderates the relationship between psychological distance and satisfaction with integrated fitness and health services under collaborative governance. Specifically, the multistakeholder collaborative governance model enhances the positive influence of psychological distance on residents’ satisfaction with community health services compared with a single-entity governance model (Figure 2).

3. Research Design

First, in Study 1, we employ a scenario-based experimental method, which allows for the controlled manipulation of experimental conditions [77]. The specific manipulation materials are detailed in Appendix A Table A1. This approach minimizes information distortion compared with simple recall methods, thereby enhancing the credibility of the findings and providing robust internal validity [77]. Additionally, to enhance external validity, field investigations were conducted in Studies 2 and 3. To conduct a comprehensive examination of the research questions and enhance the overall robustness of the study, we employed quantitative analysis for empirical validation. By conducting repeated or cross-tests on different variables and samples, we can better control the target variables, test corresponding hypotheses, and improve the verifiability and reproducibility of the results [78].
Xiamen Datang Shijia smart city community and Xiaodongshan non-smart traditional community were selected for the study (Appendix C). The Datang Shijia Smart City Community performs the functions of grid management, statistical analysis, and residents’ interaction in the community, in addition to information sharing and business collaboration. In contrast, the Xiaodongshan community has not realized intelligent management and retains the non-smart community work mode. These two communities were selected not only to better compare the variability of the impact of the community’s level of smartness on residents’ satisfaction with psychological and fitness and health services, but also to effectively compare the effect of the collaborative governance mechanism in communities with different levels of smartness.

4. Study 1

4.1. Level of Resident Satisfaction with Collaborative Governance Outcomes

In Study 1, a scenario experiment was conducted to explore the impact of community intelligence levels on residents’ psychological distance and satisfaction with public health services. To ensure randomness, this study utilized the built-in random grouping module of the questionnaire platform, automatically assigning hidden groups when participants accessed the link.
  • Research design: The eight items used to measure community smartness were adapted from previous research [79]. The public service satisfaction scale referenced prior studies, included six items [80]. The psychological distance scale, which was modified from the literature, included six items [81]. All questionnaires utilized a seven-point Likert scale (1 = strongly disagree; 7 = strongly agree), with higher scores on the psychological distance scale indicating closer psychological proximity. The questionnaire also collected demographic information such as gender and age.
  • Pretest: Fifty participants were randomly recruited through an online questionnaire platform and presented with text and image materials depicting both smart and non-smart community scenarios (62% female). The participants then rated the smartness level of the community. The results indicated significant differences in perceived smartness levels, thereby validating the materials for subsequent experimental stimuli.
  • Main experiment: The main experiment simulated various community smartness levels while controlling for participants’ psychological distance to assess satisfaction. 69 residents (53.6% female) from Xiamen participated. The participants were randomly assigned to one of two experimental groups. In the smart group, stimuli featured images of community fitness and health services enriched by smart technology, whereas the non-smart group viewed ordinary fitness scenes without such technological support. After reviewing the relevant materials, the participants completed questionnaires measuring perceived smartness, psychological distance, and satisfaction. The main experiment used tiered rewards (1–3 RMB) to increase participation rates, designed as follows: Completing over 80% of the basic questions earned a base reward of 1 RMB, ensuring basic participation rates. Passing the attention detection questions earned an additional quality reward of +1 RMB, filtering out invalid samples. After manual review and validation, a contribution reward of +1 RMB was distributed to incentivize deeper thinking.

4.2. Research Results

A total of 69 data samples were collected for analysis (Appendix C). The results of the manipulation check (Figure 3) indicated that the smart group had a mean perceived smartness score of 5.688 (SD = 0.199), whereas the non-smart group scored an average of 3.760 (SD = 0.284), thereby demonstrating a significant difference between the two groups (F = 5.549, p < 0.001) and confirming the successful manipulation of smartness levels. The participants in the smart group reported higher levels of satisfaction (M = 5.409) than those in the non-smart group (M = 4.430; F = 3.665, p < 0.001), thus supporting H1. Additionally, the psychological distance was greater in the smart group (M = 5.307) than in the non-smart group (M = 4.654; F = 2.669, p < 0.001), confirming H2.
Additionally, in this study, we position community smartness level as the independent variable, residents’ satisfaction with fitness and health public services as the dependent variable, and psychological distance as the mediating variable. The control variables include gender, age, occupation, monthly income, and educational level. Following the established mediation analysis models, a 95% confidence interval was calculated on the basis of 5000 bootstrap samples. The findings indicated that psychological distance mediates the relationship between community smartness and public service satisfaction levels (β = 0.462, SE = 0.068, LLCI = 0.322, ULCI = 0.590, excluding zero), thereby providing preliminary support for H4.

5. Study 2

5.1. Research Process-Study 2

To assess the differences in collaborative governance models between smart communities and urban villages in terms of fitness and health integration, Study 2 used field research methods to explore the effect of smart communities on residents’ satisfaction with collaborative governance outcomes. A questionnaire survey was randomly distributed in Datang Shijia smart city community (smart community) and Xiaodongshan non-smart traditional community (urban village) in Xiamen to compare residents’ satisfaction levels with public services post integration (Appendix C).
  • Research design: A one-way between-subjects design was used to compare satisfaction levels (smart vs. non-smart) during the integration of fitness and health services. To enhance data reliability and external validity, selections were made from Xiamen Datang Shijia smart city community (characterized by high smartness levels) and Xiaodongshan non-smart traditional community (characterized by low smartness levels). On-site surveys were conducted with residents.
  • Questionnaire design: Fifty participants were randomly recruited and presented with text and image materials depicting both smart and non-smart community scenarios. The participants then rated the smartness level of the community. The results indicated significant differences in perceived smartness levels, validating the materials for subsequent experimental stimuli.

5.2. Research Results-Study 2

The results from the one-way ANOVA (Table 1) indicated that residents in smart communities perceive their community’s smartness level as being significantly higher than those in urban villages (M_smart = 5.759 > M_urban village = 4.228, F = 3.221, p < 0.05). Moreover, the level of satisfaction with community fitness and health services was found to be notably greater among residents of smart communities (M_smart = 5.864 > M_urban village = 4.693, F = 0.457, p < 0.05), demonstrating the positive influence of community smartness on satisfaction with public services. This result confirms H1.
Control variables (gender, age, occupation, monthly income, and educational level) were subsequently incorporated into the regression model. Initially, none of the control variables demonstrated a significant relationship with satisfaction (p > 0.05). Upon introducing community smartness into the model, the R2 value increased from 0.051 to 0.716, indicating a significant positive impact of community smartness on public service satisfaction, with an explanatory power of 66.6%. Adding psychological distance to the third model further increased R2 to 0.83, showing that psychological distance accounted for an additional 11.4% of the variance in satisfaction. Thus, greater community smartness is correlated with greater satisfaction with public services, confirming H1 and validating H3 with the significant positive impact of psychological distance on resident satisfaction level.
In this study, community smartness serves as the independent variable, resident satisfaction level serves as the dependent variable, and psychological distance serves as the mediating variable. The control variables include gender, age, occupation, monthly income, and educational level. To examine the mediating effect of psychological distance, we employed a bootstrap sampling method with 5000 samples to calculate a 95% confidence interval. Table 2 presents the results, which indicate a significant mediating effect of psychological distance within the confidence interval (indirect effect = 0.308, SE = 0.046, LLCI = 0.227, ULCI = 0.408, excluding zero). This finding confirms that psychological distance mediates the relationship between community smartness and resident satisfaction level, thereby validating H4.

6. Study 3

6.1. Research Process-Study 3

Study 3 investigates the moderating effects of multi-agent collaborative governance and single-agent governance models on the relationships among community fitness, health integration, psychological distance, and resident satisfaction level (H4). Our research team randomly invited local residents in smart communities and urban villages in Xiamen to participate in the survey. After obtaining their consent, we distributed questionnaires to first measure their perceptions of the smart community and governance model in their local area, and then measure their psychological distance and satisfaction with their local area. The study focused on how smart and collaborative governance elements influence community governance outcomes. A total of 194 participants were recruited and yielded 164 valid responses, resulting in an 84.54% response rate.
The scales for community smartness, psychological distance, and satisfaction mirrored those used in the previously described studies. To operationalize the multi-agent and single-agent governance models, a modified scale with six items was adapted from the literature (Appendix B Table A2). The questionnaire employed a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree) and included demographic variables such as gender and age.

6.2. Research Results-Study 3

  • Manipulation check: The manipulation of the smart community scenario was successful (Table 3). The participants in both governance models perceived a high level of smartness, with no significant differences found in the mean smartness scores (M_multiagent = 5.869, SD = 0.540; M_single agent = 5.768, SD = 0.618), thereby confirming effective manipulation of smartness levels. When examining the governance effectiveness between the two models, the results shown in Table 3 indicate that participants in the multi-agent collaborative governance scenario reported a greater perception of governance quality (M_multiagent = 5.482, SD = 0.802; t = 4.543, p < 0.01). In contrast, those in the single-agent governance model experienced a lower perceived governance level (M_single-agent = 4.779, SD = 1.156; t = 4.543, p < 0.01). These findings demonstrate the successful manipulation of both governance models.
Table 3. Manipulation test of governance effectiveness.
Table 3. Manipulation test of governance effectiveness.
Fp Valuetp Value (Dual Tailed)
Treatment Effect21.3480.0004.5050.000 ***
4.5430.000 ***
*** < 0.001.
  • Mediating effect of psychological distance: To examine the mediating effect of psychological distance, in this study, community smartness level serves as the independent variable, whereas residents’ satisfaction with fitness and health public services serves as the dependent variable. Psychological distance is treated as the mediator, with gender, age, occupation, monthly income, and education level as control variables. Employing a mediation analysis model with 5000 bootstrap samples, the 95% confidence interval was calculated to test the mediating effect of psychological distance. The results displayed in Table 4 indicate a significant mediating effect of psychological distance (indirect effect = 0.438, SE = 0.138, LLCI = 0.1815, ULCI = 0.7197, excluding zero). Consequently, H4 is reaffirmed.
Table 4. Mediating test.
Table 4. Mediating test.
EffectsetpLLCIULCI
Total effect0.5820.1184.9170.000 ***0.3480.816
Direct effect0.1440.1580.9140.362−0.1680.456
Indirect effects0.4380.138//0.1820.720
*** < 0.001.
  • Moderating effects of governance models: The investigation further assessed the moderating effects of governance models via process analysis. As shown in Table 5, psychological distance was found to significantly and positively influence residents’ satisfaction level with community fitness and health public services (p < 0.01). Furthermore, the interaction effect between psychological distance and governance effectiveness was found to be significant (p < 0.01), indicating that the impact of psychological distance on satisfaction is moderated by the effectiveness of different governance models.
Table 5. Tests of moderating effects.
Table 5. Tests of moderating effects.
VariablecoffsetpLLCIULCI
Constant7.4281.0896.8220.000 ***5.2779.579
Smart level0.1150.0901.2900.199−0.0610.292
Psychological distance−0.1620.169−0.9560.340−0.4960.172
Governance effectiveness−1.6820.304−5.5320.000 ***−2.282−1.081
Psychological distance x governance effect0.1730.0513.4080.001 **0.0730.273
Gender−0.1400.099−1.4180.158−0.3350.055
Age0.1430.1011.4280.155−0.0550.342
Occupation0.0240.0240.9920.323−0.0240.071
Educational level−0.0170.072−0.2320.817−0.1580.125
*** < 0.001, ** < 0.05.
Table 6 shows that governance models function as a moderating variable, enhancing the influence of psychological distance on residents’ satisfaction with fitness and health public services. Specifically, as the governance effectiveness score increases, the positive impact of psychological distance on satisfaction strengthens. The results of the analysis revealed the following clear trend: with varying levels of governance effectiveness, the moderating effect intensifies as scores rise, leading to an increase in the magnitude of the mediating effect. Consequently, the effectiveness of different governance models significantly amplifies the mediating role of psychological distance. This finding corroborates the validation of H4, which is supported by relevant analytical methods.

7. Conclusions, Discussion, and Implications

7.1. Conclusions and Discussion

First, our study reveals significant differences in residents’ satisfaction levels with fitness and health public services between smart and non-smart community models (H1). Residents in smart communities report higher satisfaction levels than those in non-smart communities. This conclusion supports the view put forward by Zavratnik et al. that technology can enhance the value of public services [12], while also deepening Granier’s research on citizen participation mechanisms. Spatial analysis reveals the optimization path of smart technology for grassroots health services [30]. This finding underscores the notion that technology is a key differentiator, enabling smart communities to leverage information technology in management and service delivery. By breaking down temporal and spatial barriers, these technologies foster interactive and personalized information exchanges, effectively advancing governance practices.
Second, this study reveals significant differences between smart community models and non-smart community models in terms of residents’ psychological distance from the community (H2). Specifically, residents of smart communities participate more directly in community affairs through digital public services and interactive platforms, such as fitness and health monitoring systems, thereby enhancing their sense of belonging [56]. Meanwhile, smart governance models optimize the layout of public spaces and the efficiency of facility responses, thereby reducing the physical and psychological barriers between residents and community resources, significantly narrowing the psychological distance [50,58]. This conclusion aligns with Trope and Liberman’s psychological distance theory, which emphasizes that technological interventions can influence individual cognition by regulating spatial, temporal, and social dimensions [66]. It also expands on Zavratnik et al.‘s research on “community-centric” smart development, moving from technological application to empirical validation of residents’ psychological perceptions [12].
Third, the impact of smart communities on resident satisfaction is mediated by psychological distance (H3). In smart community settings, residents perceive a closer psychological proximity to participation in fitness and health activities. Consequently, their level of satisfaction with community public services increases. Previous research has focused predominantly on the psychological distance of residents in online virtual communities, leaving a gap in understanding the psychological distance experienced by residents in real communities, particularly within smart communities [82]. This study bridges that gap by demonstrating the mediating role of psychological distance between the level of community smartness and residents’ satisfaction in two real community contexts, namely, smart communities and urban villages.
Finally, the relationship between psychological distance and residents’ satisfaction with public services is moderated by the community governance model (H4). Compared with a single-actor governance model, the multiactor collaborative governance approach strengthens the positive impact of psychological distance on residents’ satisfaction. These findings reinforce the perspective that diverse and collaborative governance enhances community management effectiveness, aligning with established views on community governance [83].
In summary, this study confirmed the positive impact of the smart community model on resident satisfaction and psychological distance through a scenario experiment and two field surveys, while also verifying the moderating role of collaborative governance in the relationship between psychological distance and satisfaction. Specifically, smart communities leverage their technological advantages to precisely match residents’ needs, directly enhancing their satisfaction. Additionally, they can reduce residents’ psychological distance through the design of emotional engagement programs, thereby indirectly improving their satisfaction. In this context, multistakeholder collaborative governance amplifies the impact of psychological distance on residents’ satisfaction.

7.2. Theoretical Contributions

The findings of this research help to advance the understanding of collaborative governance theory by establishing a model for integrating fitness and health services in smart communities. This model highlights the necessity of smart technologies and diverse stakeholder participation in the convergence of community fitness and health, elucidating the distinct roles played by various stakeholders in meeting residents’ needs.
First, it advances the understanding of technology-driven governance by quantitatively validating the link between smart community development and residents’ satisfaction with public services. While prior research emphasizes the infrastructural benefits of smart communities [84], this study empirically demonstrates that technology-enhanced governance significantly improves satisfaction with health services compared to traditional communities. This finding extends the technology acceptance model (TAM) in a community governance context, suggesting that smart platforms not only facilitate service integration but also enhance perceived service quality, thereby bridging the gap between fitness and health initiatives [85].
Second, the study introduces psychological distance as a novel mediator between smart community development and resident satisfaction. Whereas existing literature predominantly examines objective measures of community performance [14], this research reveals that residents’ subjective perceptions of accessibility, trust, and engagement critically shape their satisfaction levels. This aligns with construal level theory [66], positing that reduced psychological distance, through transparent governance and responsive technology, fosters stronger resident-community connections. By identifying this mechanism, the study shifts the discourse from purely structural factors to resident-centric governance models [86,87,88].
Third, the research refines collaborative governance theory by contrasting single-actor and multiactor approaches. Previous work highlights the conceptual benefits of multiactor collaboration; however, this study provides empirical evidence that such governance moderates the psychological distance-satisfaction relationship. Specifically, it shows that when governments, social organizations, and residents co-manage services, psychological barriers diminish, leading to higher satisfaction. This finding challenges the techno-determinism prevalent in smart city literature, underscoring that effective governance requires both technological infrastructure and institutional cooperation.

7.3. Practical Implications

First, leveraging information technology to enhance residents’ health is essential. Establishing a data platform for community fitness and health can facilitate efficient data flow, addressing the barriers posed by ineffective communication. Increasing the prevalence of smart technologies in urban communities, including the development of more smart gyms and the introduction of advanced health monitoring devices, can significantly improve the quality of fitness and health services provided to residents. Moreover, creating effective connections between hospitals and communities, allowing healthcare providers to issue fitness prescriptions on the basis of residents’ uploaded data, can further enhance health outcomes.
Second, minimizing the psychological distance in accessing fitness and health services is crucial. By utilizing residential data, communities should ensure that public services are within a 15-minute reach for all residents. Underpinning this approach with Internet of Things (IoT) technology will enable the centralization of detailed information on fitness and health services, thereby making such information accessible through community information platforms. Additionally, establishing online fitness platforms can encourage residents to log their activities and share suggestions for service improvements, thus fostering community engagement.
Third, developing a collaborative governance model that involves multiple stakeholders is vital [89,90,91]. Engaging market players and nurturing social organizations can help streamline the establishment of fitness initiatives and the organization of community sporting events. Providing policy support, such as tax reductions and financial incentives for enterprises, will empower businesses to contribute effectively to community health and fitness offerings. Furthermore, enhancing talent development mechanisms in educational institutions to better align with the needs of the health and fitness sectors will continuously drive innovation within smart communities. By establishing a comprehensive industry chain that connects communities, governments, residents, and businesses, the integration of fitness and health services can be advanced, thereby enriching the available products and services for residents.

7.4. Limitations and Future Research

In this study, which is grounded in collaborative governance theory, we explore the impact of a smart community’s integration of fitness and health services on residents’ satisfaction levels and assess the effectiveness of the multiactor governance model. The main limitations are as follows: firstly, the study mainly used a sample of Xiamen community residents for the online experiment, which may cause problems in terms of generalizability, and this should be further verified in future studies by using samples from different urban communities. Second, the study did not further disaggregate public services and health services, and thus could not compare residents’ satisfaction with different types of services with perceived psychological distance. Future research could delve deeper into satisfaction levels across different types of public fitness and health services. Additionally, while psychological distance serves as a key mediator, further qualitative investigations could provide insights into the psychological factors influencing residents’ satisfaction, thereby enriching the understanding of this dynamic. Finally, in terms of methodology, this study does not provide an in-depth analysis of the dynamic process of multistakeholder collaborative governance. In the future, multi-source data can be integrated for detailed analysis.

Author Contributions

Conceptualization, H.S.; Data curation, M.W. and W.Z.; Formal analysis, M.W. and W.Z.; Funding acquisition, H.S.; Investigation, H.S. and M.W.; Methodology, H.S.; Project administration, H.S. and J.C.; Resources, H.S.; Software, W.Z.; Supervision, H.S. and J.C.; Validation, H.S. and J.C.; Visualization, J.C., M.W., and W.Z.; Writing—original draft, H.S., J.C., M.W. and W.Z.; Writing—review and editing, H.S., J.C., M.W., and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [the Youth Project of the National Social Science Fund of China] grant number [20CTY013].

Institutional Review Board Statement

This work was evaluated and approved by the Ethics Committee of Huaqiao University (Protocol HQU-j2024006).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Pictures of Smart and Non-Smart Community Experiments

Table A1. Pictures of smart and non-smart community experiments.
Table A1. Pictures of smart and non-smart community experiments.
NO.Smart CommunitiesNon-Smart Communities
1Systems 13 00755 i001Systems 13 00755 i002
2Systems 13 00755 i003Systems 13 00755 i004
3Systems 13 00755 i005Systems 13 00755 i006
4Systems 13 00755 i007Systems 13 00755 i008
5Systems 13 00755 i009Systems 13 00755 i010

Appendix B. Specific Questionnaire Scales

Table A2. Scales and items.
Table A2. Scales and items.
VariableItems
Intelligence levelFacilitating residents’ access to fitness and health information
Ability to intelligently monitor residents’ fitness and health data
Having an intelligent technology system
Equipped with an intelligent service platform
Specialized intelligent management organization
Shared fitness and health resources
High degree of intelligent development
Strong ability to provide intelligent services
Psychological distanceGood health and fitness services
I like the health and fitness products in this community
Wishing to continue using it
Fitness and health services meet expectations
Being able to obtain the fitness products or services one desires
Willing to exercise in the community for a long time
Collaborative governance modelEfficient communication
Clear division of labor
High professional ability
Trust each other
High efficiency in resource allocation
High management and operational efficiency
Satisfaction levelSatisfied with the venue facilities
High service quality
Fitness and health guidance are very scientific
There is a complete supervision system
There is a complete supervision system
Trust this community

Appendix C. Introduction to the Samples Used in the Study

Table A3. Sample Overview.
Table A3. Sample Overview.
VariableSample 1 (N = 69)Sample 2 (N = 50)Sample 3 (N = 164)
Frequency%Frequency%Frequency%
Gender
Male3246.424488048.8
Female3753.626528451.2
Age(years)
<1857.236%127.3
19–304362.331629859.8
31–401217.49183320.1
41–50710.1612137.9
>5022.91284.9
Education
Junior high school and below68.736106.1
High School57.2510127.3
Junior college1014.57141811.0
Undergraduate3652.220408048.8
Master’s degree and above1217.415304426.8
Occupation
Current student3144.927547143.3
Research and education personnel811.65102213.4
Government/public institution workers68.7482012.2
Corporate employee1826.18163521.3
Freelancer22.948106.1
Retired personnel45.82463.7
Average monthly income ¥
<30002536.222446640.2
3001–60001521.716325131.1
6001–90002231.99182917.7
>9000710.1361811

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Systems 13 00755 g001
Figure 1. Collaborative governance model with multiple actors.
Figure 1. Collaborative governance model with multiple actors.
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Figure 2. A model of the level of resident satisfaction with multiactor collaborative governance in the integration of fitness and health in smart communities.
Figure 2. A model of the level of resident satisfaction with multiactor collaborative governance in the integration of fitness and health in smart communities.
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Figure 3. Differences in psychological distance and public service satisfaction between the two scenarios.
Figure 3. Differences in psychological distance and public service satisfaction between the two scenarios.
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Table 1. Stepwise regression analysis.
Table 1. Stepwise regression analysis.
Model 1Model 2Model 3
(Constant)5.631−0.929−2.265
Gender−0.0720.0560.056
Age−0.296−0.0010.005
Work0.2040.378 **0.342 ***
Income0.2210.2440.168
Education−0.3890.0160.08
Smart degree/0.707 ***0.4 ***
Psychological distance//0.588 ***
R20.0510.7160.83
ΔR20.0510.6660.114
F1.12844.144 ***72.738 ***
*** < 0.001, ** < 0.05.
Table 2. Mediating test of psychological distance.
Table 2. Mediating test of psychological distance.
EffectsetpLLCIULCI
Total effect0.7070.04515.6900.000 ***0.6180.797
Direct effect0.4000.0517.8800.000 ***0.2990.501
Indirect effect0.3080.046//0.2270.408
*** < 0.001.
Table 6. Moderated mediator.
Table 6. Moderated mediator.
Treatment EffectEffectBootSEBootLLCIBootULCI
1.82120.1210.0741−0.02130.2664
2.8780.26550.07930.10610.4176
3.93490.40990.11280.18030.6149
Moderated intermediaries0.13670.05020.03440.234
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Song, H.; Chen, J.; Wu, M.; Zeng, W. Collaborative Governance Model for Fitness-Health Integration in Smart Communities: Framework and Outcome Measurement. Systems 2025, 13, 755. https://doi.org/10.3390/systems13090755

AMA Style

Song H, Chen J, Wu M, Zeng W. Collaborative Governance Model for Fitness-Health Integration in Smart Communities: Framework and Outcome Measurement. Systems. 2025; 13(9):755. https://doi.org/10.3390/systems13090755

Chicago/Turabian Style

Song, Huimin, Jinliu Chen, Mengjie Wu, and Wei Zeng. 2025. "Collaborative Governance Model for Fitness-Health Integration in Smart Communities: Framework and Outcome Measurement" Systems 13, no. 9: 755. https://doi.org/10.3390/systems13090755

APA Style

Song, H., Chen, J., Wu, M., & Zeng, W. (2025). Collaborative Governance Model for Fitness-Health Integration in Smart Communities: Framework and Outcome Measurement. Systems, 13(9), 755. https://doi.org/10.3390/systems13090755

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