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Article

How Can Smart Digital Technology Improve the Security Resilience of Old Urban Communities? The Chain Mediating Effect of Residents’ Sense of Safety and Safety Behaviors

by
Chengcheng Zhang
1,2,
Linxiu Wang
1,*,
Chenyang Wang
1 and
Tiantian Gu
1
1
School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
School of Management Engineering, Xuzhou University of Technology, Xuzhou 221018, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(17), 7921; https://doi.org/10.3390/su17177921
Submission received: 23 July 2025 / Revised: 26 August 2025 / Accepted: 29 August 2025 / Published: 3 September 2025
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Abstract

Old communities are the weak link in urban security resilience, and smart governance could be a useful tool to address this issue. However, the existing research does not provide a definitive explanation of the mechanisms through which smart governance affects resilience. Based on the Accident Causation Theory and the ABC Theory of Emotion, a mixed-methods approach utilizing NCA and SEM is used to analyze the impact of smart digital technology on the security resilience of old urban communities and to explore the mediating roles of residents’ sense of safety and safety behaviors. The findings from on old urban communities in China reveal that smart digital technology and residents’ safety compliance behaviors are essential for community security resilience. Smart digital technology significantly and positively influences the security resilience of old urban communities. Residents’ sense of safety and safety participation behaviors mediate the relationship between smart digital technology and security resilience of old urban communities; residents’ sense of safety, safety compliance behaviors, and safety participation behaviors also exhibit a chain mediating role in the relationship between smart digital technology and security resilience of old urban communities. Therefore, old urban communities need to strengthen the application of smart digital technologies, while considering the human factor and emphasizing the facilitating role of residents’ sense of safety and safety behaviors.

1. Introduction

The United Nations Office for Disaster Risk Reduction (UNDRR) has initiated the “Making Cities Resilient” campaign, and cities such as New York, Tokyo, London, Paris, Rotterdam, Beijing, and Shanghai have implemented resilient city development strategies. The United Nations International Strategy for Disaster Reduction (UNISDR) defines “urban security resilience” as the capacity of a system, community, or society exposed to a disaster to resist or adapt in order to achieve and sustain an acceptable level of functioning. China’s Security Resilient Cities Evaluation Guide (2022) defines security resilience as the ability of cities, communities, and other social systems to withstand, adapt, and recover from disaster environments. As the fundamental unit of the city, communities have become the bearers of various risks, perturbations, and pressures and serve as governance venues for perceiving and responding to risks [1]. Old communities refer to urban residential areas that have been in existence for an extended period of time, with relatively underdeveloped infrastructure and public service systems, reflecting the aging of both the physical space and governance mechanisms. These communities often face many challenges, such as an aging population, and are particularly vulnerable to “grey rhinoceros” and “black swan” events, which represent weak links in the city’s security resilience and must be addressed promptly. China’s urban renewal initiatives, as outlined in the 14th Five-Year Plan, to renovate 216,000 old communities prioritize enhancing security resilience within the renovation of these areas [2].
Concurrently, another urban development initiative is underway: the smart city agenda. The smart city agenda seeks to address large-scale environmental and socioeconomic challenges through technology-driven urban retrofitting and development, primarily leveraging smart digital technologies [3]. These encompass technologies such as ICTs, sensors, big data, and the Internet of Things (IoT). These technologies are employed to monitor urban systems and enhance their efficiency through real-time monitoring and big data analytics [4]. Within smart city frameworks, smart governance in old communities relies on smart digital technologies to integrate safety monitoring, emergency responses, and resident participation. Many cities have attempted to use smart digital technologies to address security resilience challenges in old urban communities, but the outcomes have been mixed, indicating that the impact of smart technologies on security resilience in old communities is complicated. As security incidents persist and achieving security resilience remains challenging [5], a practical dilemma exists in smart governance, particularly concerning the application of smart digital technologies. Why has the application of smart digital technologies been ineffective in certain old communities, leading to governance failure? In the complex context of community governance, it is of significant practical importance to explore how smart digital technology can influence the security resilience of old communities.

2. Literature Review

A number of scholars in academia have examined this issue. Many scholars who argue that the interrelation between smartness and resilience leads to predominantly positive outcomes either view smart city development as the key to improving urban resilience [6,7] or propose that specific data-driven solutions and digital tools should be used to enhance the resilience of cities or specific urban systems [8,9]. Opposing views also exist: some scholars suggest that the interrelation between smartness and resilience may result in negative outcomes overall [10,11]. These scholars argue that, rather than improving urban resilience, smart digital technologies introduce new vulnerabilities into the urban context, a phenomenon referred to as the vulnerability paradox, which refers to the phenomenon where smart digital technologies, while designed to enhance security, may introduce new vulnerabilities due to factors such as an over-reliance on technology, system failures, or the exclusion of specific groups [12]. In practice, this may manifest as smart access control systems requiring mobile apps excluding elderly residents without smartphones, increasing their risk of unauthorized entry (impairing the resistance capacity); over-reliance on automated fire alarms reducing human patrols, delaying responses when systems malfunction (impairing the adaptation capacity); and technical exclusion preventing some residents (e.g., the elderly) from obtaining emergency information through smart platforms, delaying post-disaster recovery efficiency (impairing the recovery capacity). Thus, the relationship between smartness and resilience remains contested. Other scholars, such as Cañavera-Herrera et al., have reached a more neutral conclusion. They argue that smart digital technology can play an enabling role in urban resilience-building processes if implemented appropriately. Furthermore, they emphasize that smart digital technology must be viewed as part of an integrated ‘solutions package,’ rather than as standalone solutions, with greater attention paid to embedding these technologies within specific social contexts [13]. Among these social factors, the role of humans is particularly important. While there is a consensus that smart digital technology is a key tool for enhancing the smartness of cities, concerns regarding the people who operate the technology and manage the policy are mounting [14,15]. Smart cities can use smart digital technologies to involve people, improve city services, and enhance the urban system, resulting in an improved resilience ability of the city and improved urban sustainability [16]. Smart digital technologies can facilitate urban development and capacity-building by fostering technology-enabled human capital [3,4]. In the social context of old urban communities, residents are the most vital human capital, and security resilience cannot be enhanced without their involvement. A community can achieve security resilience when people in those communities demonstrate strong agency, possess a sense of place, and can effectively access various forms of capital [17].
Scholars have made significant advancements in understanding the relationship between smart digital technology and security resilience. Although they have offered diverse insights and acknowledged the importance of human factors, they have not yet elucidated how humans mediate the relationship between the two. In summary, ① the existing studies debate the relationship between smart digital technology and community security resilience and have failed to clarify the mediating mechanism of “human factors”; ② although some studies identified residents as key subjects of community security resilience, they have not elucidated the chain transmission path between the “psychological level and behavioral level”; and ③ traditional studies mostly use methods such as SEM to verify correlations between variables, but they cannot identify “which factors are necessary conditions for security resilience”. Therefore, in this study, we try to propose targeted solutions: ① taking old urban communities in China as the research scenario and using the Accident Causation Theory and the ABC Theory of Emotion, a chain mediation model of “smart digital technology → residents’ sense of safety → safety behavior → community security resilience” is constructed to fill the gap in the two-dimensional mediating mechanism of “psychology–behavior”; ② a mixed method using NCA and SEM is adopted to not only verify the correlation between the variables (SEM) but to also identify the necessary conditions to address the deficiencies of traditional studies that “focus on correlation while neglecting the necessary conditions”. This study seeks to address the following questions: Can smart digital technology effectively enhance the security resilience of old urban communities? What is the mechanism through which resident factors mediate the relationship between smart digital technology and security resilience? What is the relationship between these factors? This study aims to unravel the ‘black box’ in the process of using smart digital technology to govern the security resilience of old communities, address the gaps in the existing research, and provide practical recommendations for enhancing the security resilience of old urban communities.

3. Methodology and Analysis

3.1. Theory and Hypothesis

3.1.1. Smart Digital Technology and Security Resilience of Old Urban Communities

Many scholars have discussed the concept of urban security resilience, such as Meerow et al. who argued that security resilience is the ability of a socio-ecological and socio-technical network composed of an urban system and its components to maintain or rapidly restore desired functions as well as to rapidly transform in the face of disruptions across spatial and temporal scales [18]. Bozza et al. viewed cities as “hybrid socio-physical networks” and utilized existing complex network theory to study urban resilience, which describes a “positive response” of the system to external perturbations, and is considered to be “positive” when a suitable equilibrium is achieved between the natural and artificial environments (e.g., a balance between physical systems and residents’ needs). When a city’s natural and artificial environments reach a suitable balance (e.g., between the physical system and the needs of its inhabitants), the “response” can be considered “positive” [19]. Huang Hong et al. defined security resilient city as a sustainable city that can effectively respond to shocks and pressures from internal and external sources that affect its economic, social, and technological systems and infrastructures, and can maintain the basic functions, structures, and systems of the city after suffering a major disaster, as well as quickly recover and adapt after the disaster [20]. The security resilience of urban communities is influenced by a variety of factors. Various natural disasters and safety accidents are external disturbances to security resilience. The existing research has primarily focused on disasters associated with natural hazards, such as earthquakes, tornadoes, fires, and floods [21], as well as human-made hazards [13]. The condition and coping capacity of a city’s own systems are also prerequisites for ensuring security resilience. Factors such as technology, infrastructure, organization, society, environment, and emergency response capacity all play important roles in influencing the security resilience of a community. In recent years, urban renewal has become a prominent area of urban research, with old communities identified as weak points in urban security resilience [22], and the influence mechanisms of urban renewal, particularly within the context of innovative technology, have garnered significant attention [23].
Smart digital technologies are commonly used to monitor and connect elements of the physical city or enhance the capacities of those elements [13]. While there is ongoing debate regarding the ability of smart digital technology to improve security resilience, most research acknowledges their potential to enhance the capabilities of physical cities [16]. Technological change is viewed as something that can contribute to adaptive capacity—or at least a resource to be drawn upon [24]. In addition, smart digital technology can play a role in the resilience process of predicting, monitoring, responding, and learning to improve emergency management capabilities [25]. For example, digital models and simulation tools based on cloud computing and big data can help to anticipate potential risks, develop emergency scenarios, and test emergency responses. In old urban communities, infrastructure and emergency response capabilities represent key shortcomings in security resilience; smart digital technology is widely recognized to be able to play a role in facilitating improvements in both areas.
Building upon this, the present study posits the following hypotheses:
Hypothesis H1a.
Smart digital technology has a positive impact on the security resilience of old urban communities.
Hypothesis H1b.
Smart digital technology is a necessity for the security resilience of old urban communities.

3.1.2. Mediating Role of Residents’ Safety Behaviors

The Accident Causation Theory proposed by Heinrich states that accidents occur due to unsafe behaviors of humans and/or things are in unsafe states, which stem from human limitations, with 88% of accidents attributed to unsafe human actions [26]. Subsequent scholars, including Frank Bird and John Adams et al., further developed accident causation theories, reinforcing the notion that unsafe human behaviors remain the primary cause of accidents [27,28]. Unsafe behaviors encompass two aspects: actions that increase the likelihood of an accident and behaviors that hinder disaster loss mitigation during an accident [29]. Thus, positive safety behaviors contribute to better safety outcomes, and enhanced safety practices help minimize near misses and injuries [30]. Neal and Griffin [31] distinguished between two types of safety behaviors: safety compliance and safety participation. Safety compliance stems from accident prevention compliance and involves strict adherence to rules, regulations, and established safety procedures. In contrast, safety participation is rooted in the theory of civic engagement and entails actively engaging in safety management by assisting and supervising others, as well as providing safety-related feedback and recommendations to superiors.
Technological advancements positively influence safe behaviors [32]. Smart digital technologies can regulate and monitor human behaviors in various ways, contributing to behavior identification [33], behavior monitoring [34], behavioral warning and correction [35], and the dissemination of group norms [36]. Additionally, smart digital technologies can enhance residents’ digital literacy [37,38], which further promotes safe behaviors [39]. In urban communities, these technologies can foster both safety compliance [40] and safety participation [41] among residents.
Building upon this, the present study posits the following hypotheses:
Hypothesis H2a.
Residents’ safety compliance mediates the relationship between smart digital technology and the security resilience of old urban communities.
Hypothesis H2b.
Residents’ safety compliance is a necessary condition for the security resilience of old urban communities.
Hypothesis H3a.
Residents’ safety participation mediates the relationship between smart digital technology and the security resilience of old urban communities.
Hypothesis H3b.
Residents’ safety participation is a necessary condition for the security resilience of old urban communities.

3.1.3. Mediating Role of Residents’ Sense of Safety

The ABC Theory of Emotions proposed by Albert Ellis posits that an evocative event (A) is merely an indirect cause of a person’s emotional and behavioral response (C), whereas the beliefs, perceptions, and interpretations that an individual holds regarding the evocative event (B) serve as the more direct causes of the emotional and behavioral responses (C) [42]. In the context of smart digital technology facilitating the security resilience of an old community, smart digital technologies act as the precipitating event (A), the resident’s sense of safety can be understood as their beliefs, perceptions, and evaluations of the application of smart digital technologies (i.e., B), while the residents’ safety behaviors are the result of their actions within the context of smart digital technologies (i.e., C). Consequently, to gain a deeper understanding of the residents’ safety behaviors, it is essential to first comprehend the residents’ sense of safety. The sense of safety refers to a feeling of confidence, safety, and freedom that satisfies various needs, both immediate and future, of an individual [43]. It encompasses aspects such as certainty and control [44]. Safety technologies can enhance the sense of safety [45], while an increase in urban security measures can substantially reduce residents’ sense of insecurity [46]. In the development of a smart city, individuals form their own perceptions of safety [47]. Additionally, the application of intelligent monitoring systems and digital intelligence devices, such as smartphones, provides users with an enhanced sense of safety [48].
Regarding the potential impact of the sense of safety on security, Goldberg argued that a sense of safety forms the foundation for individuals to develop new skills, explore new opportunities, and establish personal interests as they mature [49], Moreover, a sense of safety influences an individual’s attention [50], subjective feelings [51], and danger recognition [52]. In contrast, a sense of insecurity impairs individual performance, which can subsequently affect the organizational culture and climate [53], and safety climate has been shown to significantly affect accidents and injuries [54]. A sense of insecurity hinders the harmonious and stable development of social organizations [55] and citizen safety, and perceptions of safety are fundamental prerequisites for a city to be both viable and sustainable [56]. In smart communities, residents’ sense of safety is essential to community development and directly influences the effectiveness of such development [57].
Building upon this, the present study posits the following hypotheses:
Hypothesis H4a.
Residents’ sense of safety mediates the relationship between smart digital technology and the security resilience of old urban communities.
Hypothesis H4b.
Residents feeling a sense of safety is a necessary condition for the security resilience of old urban communities.

3.1.4. Chain Mediating Effect of Residents’ Sense of Safety and Safety Behaviors

Psychological capital theory posits that an individual’s psychological capital influences their behavior and performance, aligning with the ABC Theory of Emotion. Human behavior is influenced by psychological states [58], and human perceptions of risk have been identified as an important driver of individual risk-reducing behaviors [59]. The community safety climate, shaped by individuals’ sense of safety, provides a framework to guide human behavior [60] and is positively correlated with safety compliance and participation [61]. On the one hand, a higher sense of safety for individuals is associated with better compliance with safety systems [62], while chronic agitation can lead to erratic and unpredictable behavior [63]. On the other hand, perceptions and emotions about the health of the community help to increase residents’ participation in community building efforts [64]. However, when residents’ identification with the management functions of their community weakens, their participation in safety governance also diminishes [65].
In summary, the present study posits the following hypotheses:
Hypothesis H5.
Residents’ sense of safety and safety compliance play a chain mediating role in the relationship between smart digital technology and the security resilience of old urban communities.
Hypothesis H6.
Residents’ sense of safety and safety participation play a chain mediating role in the relationship between smart digital technology and the security resilience of old urban communities.
Based on the above hypotheses, this study constructs a chain mediation model, as illustrated in Figure 1.

3.2. Research Design

3.2.1. Data Collection

This study focused on old urban communities constructed before 2005 and distributed questionnaires to both community residents and managers to collect data. The information provided by these two groups of respondents was processed together in the initial data analysis. The formal survey was conducted from October to December 2024, with the surveyed communities drawn from provinces with varying levels of development, including Shanghai, Jiangsu, Shandong, Anhui, Henan, and others. After distributing 393 questionnaires, a total of 278 valid responses were recovered. After the exclusion of incomplete responses, extreme item values, and uniform answers across the items, the effective recovery rate was 70.7%.

3.2.2. Variable Measurement

This study involved five variables. To ensure the reliability of the study, the measurement scales for all the variables were based on established, validated scales and were revised to fit the specific scenarios of this research. The original scales (in English or Chinese) were translated using the “back-translation” method: first, two bilingual researchers independently translated the English scales into Chinese; then, a third researcher compared and revised the discrepancies to ensure semantic consistency. A pre-test was conducted by distributing 59 questionnaires to select old urban communities to assess the clarity, logical consistency, and validity. The final questionnaire was refined after several revisions. Based on the pre-test (59 questionnaires), we revised ambiguous items. The pre-test data were analyzed for reliability and face validity, ensuring that the scales were suitable for old urban communities.
The measurement of smart digital technology was based on the study by Xinjing Dong et al. [66] using four measurement items, including “Is the community equipped with digital intelligence tools for safety management?” The measurement of the residents’ sense of safety was based on the study by Yulin Zhou [67] and consisted of three items, including “Compared with your expectation of safety, do you feel safe in this community?” The residents’ safety compliance and participation were measured using the Safety Behavior Scale developed by Xinfeng Ye et al. [68], which consisted of 3 and 4 items, such as “I strictly follow the community’s safety instructions” and “I actively make suggestions for the community’s safety work.” The measure of security resilience of the old urban communities was adapted from the CART scale by Pfefferbaum et al. [69] using four items, including “The community is able to provide emergency services in the event of a safety incident.” All variables were measured using a 5-point Likert scale, with scores ranging from 1 to 5. A score of 1 indicates very poor compliance, while a score of 5 indicates very good compliance.

3.3. Data Analysis

3.3.1. Reliability Analysis

In this study, SPSS 27 and AMOS 28 software were used to assess the reliability, convergent validity, and discriminant validity of the data. Reliability was evaluated using Cronbach’s alpha (Cα), composite reliability (CR), and average variance extracted (AVE). The Cα and CR values exceeded the threshold of 0.70, and the AVE values surpassed the recommended threshold of 0.50, indicating that the indicators demonstrated good reliability (see Table 1). Additionally, all factor loadings were greater than 0.60, and the square root of the AVE for each factor exceeded the correlation coefficient between it and other factors, indicating that the measurement model possesses good convergent validity and discriminant validity.

3.3.2. Descriptive Statistics and Correlation Analysis

The descriptive statistics and correlation analysis results for each variable are presented in Table 2. There was a significant positive correlation among the five variables (smart digital technology, residents’ sense of safety, safety compliance, safety participation, and community security resilience), providing preliminary support for this study’s research hypotheses.

3.3.3. NCA Necessity Hypothesis Testing

In this study, Necessary Condition Analysis (NCA) was employed using R4.4.2 software to test if the five variables are necessary. Additionally, the Ceiling Regression-Free Disposal Hull (CR-FDH) method was applied to determine the effect sizes when X and Y were discrete variables with multiple levels of magnitude [70] (see Table 3). According to Dul et al. (2020) [71], an antecedent variable must satisfy two conditions to be deemed necessary: an effect size of at least 0.1 and a p-value of less than 0.01. Accordingly, the analysis revealed that smart digital technology and residents’ safety compliance are necessary for the formation of community security resilience, supporting hypothesis H1b and hypothesis H2b. In contrast, residents’ sense of safety and safety participation do not meet the criteria to be deemed necessary, rendering hypothesis H3b and hypothesis H4b invalid. Table 4 further presents the results of the bottleneck level (%) analysis of the necessary conditions, indicating that 15.10% residents’ safety compliance and 1.90% safety participation are required to attain a 60.00% level of security resilience. No bottleneck levels were observed for any other variables.

3.3.4. SEM Adequacy Hypothesis Testing

Since there are three types of multiple mediator models—pure chain mediator models, juxtaposed mediator models, and composite mediator models—this study first constructed Model A (see Figure 1). Next, the direct effect paths between smart digital technology and community security resilience were removed from the hypothetical Model A, converting it into the full chain mediation Model B. Similarly, the paths of residents’ sense of safety and safety behaviors were removed from Model A, transforming it into the juxtaposed mediation Model C. The test results are presented in Table 5. When the difference in ΔX2 is significant, the more complex model with a better goodness-of-fit is considered the optimal model. Model A exhibited a significant change in its chi-square value compared to Model B and Model C, confirming it as the optimal model. Therefore, Model A was selected as the hypothesis-testing model for this study.
To test the hypotheses proposed in this study, the Bootstrap method in AMOS 28 software was applied, resampling 5000 times to establish 95% confidence intervals to assess the multiple chained mediating roles of residents’ sense of safety and safety behaviors.

4. Results

The results of the optimal mediation model are presented in Table 6 and Figure 2, which led to the following findings:
(1)
The path coefficient between smart digital technology and community security resilience was significant (β = 0.239, p < 0.01), confirming hypothesis H1a.
(2)
The path coefficient between smart digital technology and residents’ safety compliance (β = 0.012, p = 0.907 > 0.05) was not significant, whereas the path coefficient between residents’ safety compliance and community security resilience (β = 0.381, p < 0.001) was significant. However, the mediating role of residents’ safety compliance in the relationship between digital technology and community security resilience was not significant (β = 0.005, p = 0.878 > 0.05), with a confidence interval of [−0.068, 0.091], which includes zero. Therefore, hypothesis H2a is not supported.
(3)
The path coefficients between smart digital technology and residents’ safety participation (β = 0.402, p < 0.001) and between residents’ safety participation and community security resilience (β = 0.215, p < 0.001) were significant. The mediating role of residents’ safety participation in the relationship between smart digital technology and community security resilience was also significant (β = 0.086, p < 0.01), with a confidence interval of [0.021, 0.162], which does not include zero. Thus, hypothesis H3a is supported.
(4)
The path coefficients between smart digital technology and residents’ sense of safety (β = 0.613, p < 0.001) and between residents’ sense of safety and community security resilience (β = 0.331, p < 0.001) were significant. The mediating role of residents’ sense of safety in the relationship between smart digital technology and community security resilience was also significant (β = 0.203, p < 0.001), with a confidence interval of [0.062, 0.291], which does not include zero. Thus, hypothesis H4a is supported.
(5)
The path coefficient between residents’ sense of safety and safety compliance was significant (β = 0.445, p < 0.001), and the chained mediation effect of residents’ sense of safety and safety compliance between smart digital technology and community security resilience was significant (β = 0.104, p < 0.001), with a confidence interval of [0.041, 0.171], which does not include zero. Thus, hypothesis H5 is supported. Since hypothesis H1 is also supported, this indicates that residents’ sense of safety and safety compliance play partial mediating roles in the relationship between smart digital technology and community security resilience.
(6)
The path coefficient between residents’ sense of safety and safety participation was significant (β = 0.224, p < 0.05), and the chained mediation effect of residents’ sense of safety and safety participation between smart digital technology and community security resilience was also significant (β = 0.030, p < 0.05), with a confidence interval of [0.003, 0.069], which does not include zero. Thus, hypothesis H6 is supported. Additionally, residents’ sense of safety and safety participation partially mediated the relationship between smart digital technology and community security resilience.

5. Discussion

(1)
Smart digital technology significantly and positively influences security resilience of old urban communities, and it is a necessity for security resilience in these areas. This finding aligns with the research of scholars such as Huang Jie [72] and further demonstrates that implementing smart digital technology is a crucial prerequisite for enhancing the security resilience of old urban communities. This suggests that the relationship between smartness and resilience has been affirmatively established, at least within the context of old urban community governance. Moreover, within the broader framework of constructing both smart and security-resilient cities, smart digital technology does not contradict security resilience; rather, it serves as a crucial mechanism for strengthening it.
(2)
Residents’ sense of safety mediates the relationship between smart digital technology and security resilience of old urban communities. A stable safety environment is a prerequisite for the security resilience of old urban communities. In a smart community, residents recognize the positive effects of smart digital technology, believing that its use fosters a safer environment. Simultaneously, it enhances their sense of personal efficacy and alleviates negative emotions, such as panic, thereby contributing to a more stable community safety environment. This suggests that promoting awareness of smart digital technology is essential. Educating the public on its functionalities serves as an implicit reinforcement mechanism for its adoption and integration into community security strategies.
(3)
Resident safety participation mediates the relationship between smart digital technology and the security resilience of old urban communities, whereas the mediating role of resident safety compliance was not confirmed, though it remains a necessary condition for security resilience. Smart digital technology accelerates information exchange and broadens the scope of information dissemination. Many community affairs engage a greater number of residents through online platforms, enabling them to influence community decisions through participation. This increased engagement allows residents to access more information and it fosters stronger community cohesion, ultimately enhancing community security resilience. Furthermore, the inability of smart digital technology to directly promote resident safety compliance partially supports the ABC Theory of Emotion. However, resident safety compliance was identified as a necessary condition for the security resilience of old urban communities, aligning with the findings of the Accident Causation Theory. This suggests that achieving overall community security resilience is dependent on individual safety practices and collective cooperation.
(4)
Residents’ sense of safety and safety behaviors serve as a chain mediator between smart digital technology and the security resilience of old urban communities. Residents’ sense of safety fosters both safety compliance and safety participation behaviors. In particular, safety compliance, as a necessary condition, exerts a significant indirect effect, which is driven by a heightened sense of safety, ultimately confirming the applicability of the ABC Theory of Emotion in this context. This suggests that smart digital technology is more effective in promoting residents’ safety compliance when it aligns with their needs, enhancing acceptance and fostering a greater sense of safety, thereby becoming a necessary component of community security resilience.

6. Conclusions

This study took old urban communities in China as the research focus, integrated the Accident Causation Theory and the ABC Theory of Emotion, and adopted a mixed-methods approach combining NCA and SEM to systematically explore the impact mechanism of smart digital technology on the security resilience of old urban communities. It clarified the chain mediating role of residents’ sense of safety and safety behaviors, and identified smart digital technology and residents’ safety compliance behaviors as necessities for community security resilience. The research not only provides empirical evidence for solving the practical dilemma of smart governance in improving the security resilience of old communities, but also offers a referable theoretical framework and methodological paradigm for subsequent research in related fields.

6.1. Theoretical Contributions

(1)
Unveiled the chain mediating role of human factors in the relationship between smartness and resilience. This study explored the relationship between smartness and resilience in the context of old urban communities governance, confirming the positive impact of smart digital technology on security resilience [6,73]. By constructing a chain mediation theoretical model, the research revealed the mediating role of residents’ sense of safety and safety behaviors. It also identified smart digital technology and residents’ safety compliance as necessities for the security resilience of old urban communities. Furthermore, this study broadens the research scope on the application of smart digital technology and urban security resilience, offering a new perspective on how urban managers can enhance community governance through human resource strategies in the digital intelligence era.
(2)
Further enrichment of Accident Causation Theory. This study extends the Accident Causation Theory by confirming its relevance from a safety perspective, demonstrating that safety behaviors not only reduce accident occurrence but also contribute to resilience in accident prevention, response, and recovery. This finding uniquely broadens the dependent variable of the Accident Causation Theory, which was not commonly achieved by previous studies [74]. Additionally, by distinguishing between safety compliance and safety participation behaviors, the study highlighted their respective roles in enhancing security resilience. This contributes to a deeper understanding of safety behavior dynamics, enriching the theoretical foundation of the Accident Causation Theory and advancing research in the field of safety and behavior studies.
(3)
Integration of the ABC Theory of Emotion and Accident Causation Theory. While the Accident Causation Theory acknowledges the role of human factors, it overlooks the influence of cognitive and emotional processes. The human mind serves as the foundation for safety perceptions, shaping psychological states that ultimately influence specific behaviors. This represents a theoretical limitation in the development of the Accident Causation Theory [74,75]. To address this gap, the present study integrated the ABC Theory of Emotion with the Accident Causation Theory, emphasizing the role of residents’ sense of safety in shaping safety behaviors. This integration not only fills an existing theoretical void but also expands the application of the ABC Theory of Emotion, offering new insights into how smart digital technology influences residents’ behaviors in old urban communities through a sense of safety.

6.2. Management Insights

(1)
To enhance security resilience of old urban communities through smart digital technology, urban managers should integrate smart digital technology into urban renewal projects, targeting common issues in aging communities.
Short-term: Government urban management departments should fund 360° high-definition surveillance cameras at key locations and property management should connect them to a monitoring platform.
Medium-term: Fire departments should collaborate with tech enterprises to install smart smoke detectors and sprinklers in elderly people’s homes.
Long-term: Urban construction authorities should renovate outdated facilities, replace aging wires with flame-retardant ones, upgrade elevators with smart maintenance systems, and install streetlights with emergency buttons. IoT and big data could enable precise management, optimizing resource allocation and emergency responses to enhance disaster resilience.
(2)
Residents’ sense of safety should be strengthened through smart digital technology, with specific strategies tailored to different stakeholders and stages.
Initial stage: Community committees should host safety lectures and tech demos. Experts should simplify device explanations for elderly residents.
Medium-term: Property management should use a user-friendly community APP for real-time feedback to enable timely issue resolution and updates.
Long-term: Government propaganda departments should publicize smart community success stories via local media to boost trust, fostering compliance and a positive safety atmosphere.
(3)
Residents’ safety participation should be promoted through smart digital technology. An inclusive online platform with clear channels and incentives should be established.
Platform construction: Community committees should partner with IT companies to develop a user-friendly platform with offline help for elderly residents.
Operation: Property management should offer rewards for verified hazard reports and the platform should publish issue-handling statuses to ensure transparency.
Long-term: Participatory governance should be enhanced via a joint safety committee using the platform for discussions, plan formulation, and supervision to strengthen resilience through a collaborative culture.

6.3. Research Insights and Outlook

This study has several limitations that should be addressed in future research. First, the mediating effects of smart digital governance may differ across different community types, for example, the proportion of elderly residents in the sample may limit the generalizability of the findings to communities with extremely high aging rates. Future research should investigate the applicability of these findings to different community contexts. Second, there is the possibility of digital divide bias: we acknowledge that elderly residents with low digital literacy may have underreported their use of smart technologies, potentially affecting the accuracy of the model. Thirdly, this study relied on cross-sectional data for analysis, which limits the ability to capture long-term causal relationships. Future studies could employ longitudinal tracking surveys. Finally, while this study examined the mediating roles of residents’ sense of safety and safety behaviors, future research should explore additional social boundary conditions.

Author Contributions

All authors contributed to the study conception and design. C.Z. analyzed the data and wrote the original manuscript. C.W. participated in the investigation and provided the research direction. L.W. and T.G. contributed to the methodology and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China [Grant Number: 72104233] and the Philosophy and Social Science Research Project of Jiangsu Universities [Project Approval Number: 2022SJYB1211].

Institutional Review Board Statement

The study was approved by the School of Management Engineering Research Committee (protocol code: MEIRB-2024-08-002; date: 14 August 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 07921 g001
Figure 1. Theoretical model.
Figure 1. Theoretical model.
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Figure 2. Results of model testing. *** denotes significant correlation at the 0.001 level (two-sided); ** denotes significant correlation at the 0.01 level (two-sided); * denotes significant correlation at the 0.05 level (two-sided), same below.
Figure 2. Results of model testing. *** denotes significant correlation at the 0.001 level (two-sided); ** denotes significant correlation at the 0.01 level (two-sided); * denotes significant correlation at the 0.05 level (two-sided), same below.
Sustainability 17 07921 g002
Table 1. Reliability test for each variable.
Table 1. Reliability test for each variable.
FactorCαCRAVEFactor Loading Interval
SDT0.800.800.500.63~0.77
RSS0.790.790.560.71~0.81
RSC0.790.790.550.72~0.78
RSP0.840.840.580.69~0.79
OCSR0.810.820.530.67~0.79
Note: SDT denotes smart digital technology, RSS denotes resident sense of safety, RSC denotes residents’ safety compliance, RSP denotes residents’ safety participation, and OCSR denotes old community security resilience.
Table 2. Descriptive statistics and correlation analysis results.
Table 2. Descriptive statistics and correlation analysis results.
FactorAverage ValueStandard DeviationSDTRSSRSCRSPOCSR
SDT3.7750.7171
RSS3.6920.7230.586 **1
RSC4.1940.6760.469 **0.338 **1
RSP3.5890.8350.554 **0.367 **0.367 **1
OCSR3.8240.7390.892 **0.580 **0.540 **0.554 **1
Note: ** denotes significant correlation at the 0.01 level (two-sided).
Table 3. NCA results.
Table 3. NCA results.
FactorC-AccuracyCeiling ZoneScoped
Effect Size		p-Value
SDT0.9390.1060.9500.1120.000
RSS0.9960.0440.9400.0470.009
RSC0.9530.1230.9200.1340.000
RSP0.9930.0150.9400.0150.327
Note: (1) Calibrated fuzzy affiliation value; (2) 0 ≤ d < 0.1 represents a “low level”; 0.1 ≤ d < 0.3 represents a “medium level”, and 0.3 ≤ d < 0.5 represents a “high level”; (3) the p-value is from the permutation test from the NCA using a resampling count of 10,000.
Table 4. Results of bottleneck level analysis of NCA method.
Table 4. Results of bottleneck level analysis of NCA method.
OCSRSDTRSSRSCRSP
0.00NNNNNNNN
10.00NNNNNNNN
20.00NNNNNNNN
30.00NNNNNN0.50
40.00NNNN0.800.90
50.00NNNN8.001.40
60.00NNNN15.101.90
70.0012.90NN22.302.40
80.0027.90NN29.402.90
90.0043.0023.4036.503.40
100.0058.0049.1043.703.90
Note: (1) Method of analysis is CR; (2) NN indicates not necessary.
Table 5. SEM fit indicators.
Table 5. SEM fit indicators.
ModelX2DFX2/DFNFIIFITLICFIRMSEA
A227.383126.0001.8050.9000.9530.9420.9520.054
B236.960127.0001.8660.8960.9490.9380.9480.056
C248.276128.0001.9400.8910.8700.9440.9320.058
Table 6. Mediation effect estimates and 95% confidence intervals for bootstrap methods.
Table 6. Mediation effect estimates and 95% confidence intervals for bootstrap methods.
PathIndirect Effect Estimates (Standardized)95% Confidence Intervals
Upper LimitLower Limit
Total indirect effects0.427 ***0.2270.500
Decomposition of specific indirect effects
SDT→RSS→OCSR0.203 ***0.0620.291
SDT→RSC→OCSR0.005 −0.0680.091
SDT→RSP→OCSR0.086 **0.0210.162
SDT→RSS→RSC→OCSR0.104 ***0.0410.171
SDT→RSS→RSP→OCSR0.030 *0.0030.069
Note: *** denotes significant correlation at the 0.001 level (two-sided); ** denotes significant correlation at the 0.01 level (two-sided); * denotes significant correlation at the 0.05 level (two-sided), same below.
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Zhang, C.; Wang, L.; Wang, C.; Gu, T. How Can Smart Digital Technology Improve the Security Resilience of Old Urban Communities? The Chain Mediating Effect of Residents’ Sense of Safety and Safety Behaviors. Sustainability 2025, 17, 7921. https://doi.org/10.3390/su17177921

AMA Style

Zhang C, Wang L, Wang C, Gu T. How Can Smart Digital Technology Improve the Security Resilience of Old Urban Communities? The Chain Mediating Effect of Residents’ Sense of Safety and Safety Behaviors. Sustainability. 2025; 17(17):7921. https://doi.org/10.3390/su17177921

Chicago/Turabian Style

Zhang, Chengcheng, Linxiu Wang, Chenyang Wang, and Tiantian Gu. 2025. "How Can Smart Digital Technology Improve the Security Resilience of Old Urban Communities? The Chain Mediating Effect of Residents’ Sense of Safety and Safety Behaviors" Sustainability 17, no. 17: 7921. https://doi.org/10.3390/su17177921

APA Style

Zhang, C., Wang, L., Wang, C., & Gu, T. (2025). How Can Smart Digital Technology Improve the Security Resilience of Old Urban Communities? The Chain Mediating Effect of Residents’ Sense of Safety and Safety Behaviors. Sustainability, 17(17), 7921. https://doi.org/10.3390/su17177921

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