Keywords

1 Introduction

Growing urbanization, climate change consequences and the recent Covid-19 pandemic have highlighted the vulnerabilities of cities across their economic, social and environmental systems [1,2,3]. In this context, Urban Resilience, defined as the ability of a city to absorb, adapt and transform in face of disruptions [4], emerges as a possible solution to minimize its vulnerabilities. Assessing urban resilience in the face of these events has gained importance among urban planners, policymakers, experts and researchers engaged in the urban planning and resilient initiatives [1, 5, 6]. In fact, scientific literature reveals that enhancing urban resilience allows cities to improve their performance and to better face the challenges the cities encounter. Being a city a complex system where many sub-systems are connected together, acting on urban resilience needs cities establish a resilience plan that encompasses the measurement and analysis of resilience across their economic, social and environmental systems [7]. In literature several frameworks exist and contribute to building city resilience plan, but they still present different limitations [8].

Due to the inherent complexity of the city and the concept of resilience itself, as well as to the variety of research perspectives, the current theoretical frameworks and indices are quite confusing and fail to reveal how different urban factors influence resilience and the way it acts on the urban scale [7, 9]. [6] and [10] also highlight that present frameworks underestimate the importance of including the perspectives of urban stakeholders who contribute to a more conscious city resilience building process. Recognizing these existing gaps, we propose a comprehensive framework designed to overcome these limitations by examining the extent to which urban factors influence resilience, incorporating perspectives from various urban stakeholders.

The framework proposed is based on resource-based view perspective (RBV). This theory originates within organizations and has been further expanded into different contexts, including the urban one in which it is possible to think about the city as a collection of both tangible and intangible resources and capabilities which can significantly impact the resilience [11]. The RBV perspective represents a valuable lens through which to analyze the complex interactions occurring in urban system, offering insights into how cities can enhance their strength various challenges.

2 Literature Review

The concept of resilience is characterized by the absence of a single definition [13]. Three main perspectives have been developed on this concept: engineering, ecological and socioecological. From the engineering perspective, systems have a single state of equilibrium and resilience is considered as the ability to absorb change to return to the previous state of equilibrium; from the ecological perspective, systems have multiple states of equilibrium and resilience is seen as the ability to adapt [2] and reach one of the equilibrium states after the interruption occurs [14]; finally, from the third perspective, the systems do not have any equilibrium states, but are seen as constantly changing [12] and resilience is seen as the ability to transform to respond to disruption [2].

According to these three perspectives, [15] proposed an analytical framework that breaks down resilience into three capacities that must be considered jointly. Absorptive capacity is implemented when the event has a low intensity, adaptation capacity refers to the changes made with the aim of persisting or resuming functioning after the interruption occurs [15, 16] and the transformation capacity is introduced when the required change exceeds the system’s ability to adapt [15].

Several frameworks have been developed to build the city resilience. For example, the Hyogo Framework provides guidance on reducing disaster risk at the country level [8]. The Sendai Framework highlights the importance of governments and communities in reducing vulnerabilities and enhancing community resilience [13]. The City Resilience Framework, developed by Arup and the Rockefeller Foundation, provides cities with a tool for assessing their resilience level, understanding contributing factors, identifying critical areas and determining actions to enhance resilience [13].

While these frameworks advance knowledge and awareness about urban resilience by outlining key features and areas of actions that cities can use in the form of checklists [13], they fail in providing instructions and guidelines about urban characteristics essential to build resilience. Furthermore, many scholars emphasize the importance of promoting the participation of multiple stakeholders in the city’s resilience-building process. In fact, the need to involve the community, policy makers, professional groups, local experts and stakeholders such as local businesses and civil society groups in the assessment of resilience has often been highlighted in the literature [6, 7, 10]. Based on the above considerations, significant gap exists in bridging the transition from theory to practice by trying to understand how resilience concepts can be truly implemented in cities [2] and what urban systems should do to move from a vulnerable to a resilient state [17].

3 Theoretical Background and Hypothesis Development

According to the RBV perspective, we identified the city’s resources that are subdivided in three systems: economic, social and environmental. [18] and [19] highlight the role of urban systems and their resources in dealing with shocks, proving their significant impact on the three resilience capacities.

  • H1: The economic system positively influences urban resilience, i.e. absorptive capacity (H1a), adaptive capacity (H1b) and transformative capacity (H1c).

  • H2: The social system positively influences urban resilience, i.e. absorptive capacity (H2a), adaptive capacity (H2b) and transformative capacity (H2c).

  • H3: The environmental system positively influences urban resilience, i.e. absorptive capacity (H3a), adaptive capacity (H3b) and transformative capacity (H3c).

Various evidence supports the fact that urban abilities influence resilience capacities, and this is consistent with the RBV perspective adopted. [13] building upon the Resilient Maturity Model delineate urban abilities to enhance urban resilience. Governance plays a central role in defining strategies to enhance city resilience [20, 21]. [22] support that the notion of resilience is intricately connected to both strategic preparedness planning and proactive measures to mitigate disturbances as they arise. [23] sustain that infrastructures play a vital role in maintaining the quality of critical services during shocks, simultaneously enhancing adaptive capacity to respond to disruptions [24]. [25] underscore the importance of adopting a comprehensive and collaborative approach to fortifying resilience.

  • H4: Leadership & Governance positively influences urban resilience, i.e. absorptive capacity (H4a), adaptive capacity (H4b) and transformative capacity (H4c).

  • H5: Preparedness positively influences urban resilience, i.e. absorptive capacity (H5a), adaptive capacity (H5b) and transformative capacity (H5c).

  • H6: Cooperation positively influences urban resilience, i.e. absorptive capacity (H6a), adaptive capacity (H6b) and transformative capacity (H6c).

  • H7: Infrastructure & Resources positively influences urban resilience, i.e. absorptive capacity (H7a), adaptive capacity (H7b) and transformative capacity (H7c).

In the literature different studies highlight the connections between urban system resources and its abilities. In particular, the cases of [23] and [24] highlight how critical infrastructures foster the development of partnerships and preparedness plans to increase their overall efficiency. The relationship between systems and these two urban abilities is also highlighted by [26] which explores the relationship between the healthcare system and preparedness, cooperation and leadership & governance. Furthermore, in the research works the relationship between the economic system and infrastructure emerges [27], while [28] highlight how the economic system requires government policies for its development.

  • H8: The economic system positively influences urban abilities, i.e. Leadership & Governance (H9a), Preparedness (H9b), Cooperation (H9c), Infrastructure & Resources (H9d).

  • H9: The environmental system positively influences urban abilities, i.e. Leadership & Governance (H9a), Preparedness (H9b), Cooperation (H9c), Infrastructure & Resources (H9d).

  • H10: The social system positively influences urban abilities, i.e. Leadership & Governance (H10a), Preparedness (H10b), Cooperation (H10c), Infrastructure & Resources (H10d).

Through hypotheses H4, H5, H6, H7, H8, H9, H10, we propose that economic, environmental and social system might indirectly affect absorptive, adaptive and transformative capacity through urban abilities, namely Leadership & Governance, Preparedness, Cooperation and Infrastructure & Resources. The indirect effect of urban systems on resilience capacities can be understood based on the RBV perspective. In this perspective, abilities enable the utilization of resources [29]. Hence, we suggest that economic, environmental and social systems might positively and indirectly affect resilience capacities through urban abilities. We suggest the following hypotheses:

  • HM1: The urban systems resources might positively and indirectly affect resilience capacities through Leadership & Governance

  • HM2: The urban systems resources might positively and indirectly affect resilience capacities through Preparedness

  • HM3: The urban systems resources might positively and indirectly affect resilience capacities through Cooperation

  • HM4: The urban systems resources might positively and indirectly affect resilience capacities through Infrastructure & Resources

The conceptual framework is shown in Fig. 1.

Fig. 1.
figure 1

Conceptual Framework

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4 Research Method

Data was gathered through an online survey conducted among policymakers, experts and academics.

Prior to distribution, the survey was first reviewed during a meeting with academics and then was sent to a small sample of scholars and experts. The collected feedback has been evaluated and integrated in the questionnaire. The final version of the survey consists of four sections, in which all questions, except those related to demographic information about respondents, are measured with a 5-point Likert scale.

In the data cleaning phase five questionnaires were discarded due to incompleteness and inconsistency of the answers, reaching a total of 190 questionnaires considered acceptable.

“A priori” and “Post-hoc” power analysis were conducted, the number of collected questionnaires revealed a statistical power of 0.92. Through subsequent analysis no problems of non-response and common method bias were found.

We tested the model through PLS-SEM methodology because it is considered suitable for the analysis of complex models [30] which have an exploratory nature [31], does not require a normal distribution of the data and yields improved outcomes when working with significantly smaller sample sizes [32].

5 Results

The implementation of the (PLS-SEM) methodology comprises two steps: the assessment of the validity of the measurement and the structural model.

5.1 Assessment Measurement Model

The first step in the assessment of the measurement model is the evaluation of the factor loadings, since their values varying in a range from 0.605 to 0.949 are all considered acceptable. Regarding the descriptive statistics of the measurement model to assess the internal consistency reliability and convergent validity, we tested composite reliability (CR) Cronbach’s alpha coefficient (CCA), and evaluated the average variance extracted (AVE).

For all constructs of the model the CCA values exceed the minimum value of 0.6, the CR values exceed the minimum of 0.7, and the AVE values are greater than 0.5 [33], hence we can conclude that the model has good internal consistency reliability.

Discriminant validity was assessed through [34] criterion. The findings show that this criterion is met for all the constructs of the model, as the shared variance for all model constructs does not exceed their respective Average Variance Extracted (AVE) values.

5.2 Assessment Structural Model

The second step is the evaluation of the structural model. First a variance inflation factor (VIF) is checked, since VIF values don’t exceed the threshold of 5 [32], collinearity issues are not a problem in this study.

To check the model’s quality, it is necessary to assess R2 and path significance values. Results show that R2 values vary in the range 0.481–0.756. Given that ranges of 0.25–0.5 and 0.5–0.75 represent weak and moderate explanatory power respectively, we can state that only preparedness presents weak explanatory power.

The assessment of the magnitude and statistical significance of the path coefficients was conducted and results are shown in Table 1.

Table 1. Path coefficients.
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To test the mediating hypothesis about the role played by the four abilities of the urban system in the relationship between urban systems and resilience capacities, we evaluated the statistical significance of both the direct and indirect effects.

Following the indications of [35] we investigated on the type of mediation and the results obtained are presented in the following Table 2.

Table 2. Analysis of the mediation type
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6 Discussion

Using a RBV perspective, it is possible to identify which factors allow to improve the urban resilience. The model is built considering the resources distributed in the three systems: environmental, social and economic and the four urban abilities, through which resources impact differently on resilience capacities (absorption, adaptation and transformation) and therefore on urban performance.

Since the direct effect of urban systems on resilience capacities is not statistically significant, as illustrated in Table 1, we can state that urban systems do not seem to directly influence resilience, this allows us to deduce that even highly performing urban systems alone cannot improve resilience directly.

The direct effect of the three urban systems on the four urban abilities, in contrast, is found to be statistically significant. This allows us to say that investments made to enhance the three urban systems, also have positive effects on the four urban abilities.

As shown in Table 1, the resilience of cities is not influenced by all urban abilities directly: investments in Preparedness and Infrastructure & Resources, would improve all resilience capacities; investments in Cooperation would only improve the Adaptive Capacity. In contrast, it appears that investments in Leadership & Governance do not have direct effects on resilience performance.

As can be seen in Table 2, two types of mediation emerged: no effect and full mediation. When mediation is full, the urban ability is necessary to allow the urban system to influence resilience. Hence, we can say that it is important to invest in urban abilities so that the resources of the urban system can influence resilience.

From the results on the type of mediation we can state that Leadership & Governance does not play any mediating role in the relationship between the three urban systems and the three resilience capacities. While Preparedness e Infrastructure & Resources play a full mediating role in the relationship between the three urban systems and the three resilience capacities. Therefore, it is important to invest in the development of elements that help increase these two abilities.

The development of early warning, emergency response and disaster management systems, as well as communication actions allow to increase the Preparedness ability. While investments intended for IT infrastructure and their security, critical infrastructures and their maintenance and continuity, and for compliance with standards help improve the Infrastructure & Resource ability.

In accordance with what emerges from Table 2, Cooperation plays a mediating role in the relationship between the three urban systems and adaptive capacity. Therefore, urban systems should invest in the development of partnerships with urban stakeholders and commit to building a cohesive and supportive community that encourages the active participation of citizens, to improve cooperation ability and, consequently, increase the influence of urban resources on the adaptive capacity.

The results obtained support the choice to implement the model through an RBV perspective as they highlight that urban resources can have an impact on resilience only through urban abilities, and a different level of development of these abilities translates into different performance in responding to disruptions, in accordance with what emerges from the definition of RBV itself.

7 Conclusions

Cities are facing numerous challenges, which will increase over decades and further try out urban communities. These issues underlined the importance of preparing contingency plans to respond promptly to disruptions and cope with a rapid reorganization of resources. In this scenario, the literature has highlighted how urban resilience has a central role in the decision-making process and in the formulation of response strategies. Existing frameworks offer qualitative insights but often lack a comprehensive consideration of all urban stakeholders. Consequently, we propose a model with the objective of identifying the primary factors within the urban system and assessing their impact on resilience through a resource-based view. The model explores the connections between resources, abilities, and the capacity for urban resilience, providing valuable managerial insights.

7.1 Policy Implications

The provided theoretical framework holds significant potential as a valuable asset in the field of urban planning. [36] asserts the critical role of urban resilience in the formulation of urban policies, emphasizing its contribution to the development of a resilient city capable of responding promptly to shocks. The presented model facilitates the identification of areas for improvement within the urban system, thereby ensuring a more robust formulation of strategies and optimal allocation of investments by political decision-makers.

7.2 Limitation and Further Research Directions

Study is not without limitations. First, the conceptual model considers a specific subset of resources and abilities, overlooking the extensive array of resources and abilities inherent in a city. Second, the response rate could be enhanced to ensure a larger amount of data to analyze and increase validity of the results. Third, for further insights, it would be beneficial to explore and employ alternative methodologies to validate the obtained results. Lastly, cross case analysis on different countries could be done in order to increase the generalizability of findings.

The results open new research possibilities, prompting a comprehensive exploration of the model’s mediators to better understand their role in urban systems’ resilience capacities. Such an investigation intends to furnish political decision-makers with precise insights, empowering them to steer policies and investments more effectively.