Developing a Model for Assessing the Performance Outcome for Building Urban Community Resilience Through Public–Private Partnership

Abstract

The vulnerabilities of critical infrastructure and other disruptive events expose urban communities to severe risks. Public–private partnership (PPP) is an intensive cooperation between public and private actors with enhanced and more innovative services and policy outputs that can be achieved in building urban community resilience. Considering the potential of building urban community resilience through PPP, there is a need to assess the performance of using PPP in urban community resilience building. This study aims to develop a model for assessing the performance outcome for building urban community resilience through PPP. A questionnaire survey was conducted with experienced practitioners globally. The fuzzy synthetic evaluation method was used to develop an evaluation tool that could be used to objectively assess performance outcomes of PPP in urban community resilience building. The tool consists of five critical assessment indicators with defined coefficients: “Resilient urban community physical capital (0.270)”, “Well-developed community stakeholder engagement and training policies” (0.215), “Strong urban community disaster resilience PPP policy” (0.202), “Restriction and preservation” (0.197), “Existence of effective urban disaster risks database and PPP communication plan” (0.116). This performance assessment model can be used as a baseline for measuring the performance of PPP in urban community resilience building.

1. Introduction

Natural and man-made disasters cause significant damage to urban communities [1]. A community is composed of different elements such as population, companies, public infrastructure, housing, and networks [2]. When essential buildings or infrastructure systems are damaged, it disrupts the functioning of an urban community [1]. Damages to food, energy, and water infrastructure systems affect the capacity of urban residents to recover from these disasters [3]. Urban community resilience (UCR) is highlighted under Sustainable Development Goal 11, which aims to make “cities and human settlements inclusive, safe, resilient, and sustainable” [4]. The capability of a community can be enhanced by ensuring its resilience, which will reduce the impact of potential disasters [5]. Resilience is an indicator of a system’s ability to withstand disruption within acceptable degradation parameters and recover within a specified time frame [6]. Poorly planned urban development makes it vulnerable to disasters [7].

An urban community is a dynamic and place-based area tied together by infrastructure that enables it to thrive [8]. Community resilience refers to a community’s ability to cope with, recover from, or adapt to hazards [9]. Chuang, Garmestani [10] define community resilience as the capacity of a community to resist disasters and implement actions that achieve an expected level of protection. Norris, Stevens [11] defines community resilience as linking a network of adaptive capacities to adaptation after being subjected to disruptive events such as disasters. A resilient community requires a resilient building portfolio that comprises various building sectors, with their functionalities interdependent in maintaining a functioning community [12]. Community resilience refers to the actions that enable community–societal services to remain functional even after disruptive events [13]. This means infrastructure systems must be designed to continue functioning under hazardous conditions to provide services to the communities.

Public–private partnerships (PPP) are highly recommended for consideration in disaster management, particularly in building urban community resilience [14,15]. PPP is a strategic collaboration within urban communities to strengthen resilience by addressing vulnerabilities, enhancing resource allocation, and promoting community empowerment [16,17,18,19]; a PPP agreement can include resilient policies such as tax rebates and subsidies for private investors, technical support for infrastructure operators, and community disaster awareness programmes. Measuring the performance of PPP is essential to achieving the success of urban community resilience [20]. Indicators are a good measure of measuring performance with the aim of improving efficiency and effectiveness [21]. They are a good measure of assessing the performance of PPP in achieving their goal of building urban community resilience. An example is when “readily available emergency services” becomes a performance indicator for PPP in urban community resilience. This means that when a PPP ensures the provision of readily available emergency services in communities, it can be deemed as performing activities that contribute to providing a resilient community. On the other hand, when there are no readily available emergency services, then it means the PPP has a shortfall in their performance towards achieving urban community resilience. Another example is when “assessing and evaluating vulnerabilities” is an indicator of a PPP performance; it implies that the PPP must assess and evaluate vulnerabilities within communities and implement mitigating strategies to offset the impact of these vulnerabilities. On the other hand, when the PPP fails to do this, then there is a shortfall in their performance towards achieving urban community resilience. Overall, an objective measure of PPP performance in urban community resilience involves using a list of indicators to develop an index that quantifies PPP performance mathematically, which can help communities reduce vulnerabilities and adapt to future challenges. Although PPP has been championed in research studies as a collaborative approach for building urban community resilience, the area of PPP performance assessment in urban community resilience has not been explored. Fell and Mattsson [22] studied the role of public–private partnerships in housing as a potential contributor to sustainable cities and communities. Reference [23] examines public–private partnerships for resilient Italian communities. Casady, Cepparulo [24] conducted a literature review into public–private partnerships for low-carbon, climate-resilient infrastructure. Harman, Taylor [25] conducted a review of traditional partnerships and emergent forms of urban partnerships, examining their key challenges and gaps. In their study, they characterized partnership under five families: PPPs for critical infrastructure contracts, PPPs for urban regeneration and development, PPPs for disaster risk mitigation, PPPs for regional collaboratives for adaptation, and PPPs for local government networks. There are numerous studies on the performance measurement of PPP as a procurement route for constructing large-scale projects, but in the area of urban community resilience, there is a gap.

The aim of this research is to use fuzzy set theory to develop a tool for assessing the performance of PPP in achieving the goal of building urban community resilience against unexpected disruptions. This study is poised to contribute to achieving Sustainable Development Goal 17, and its outcome can also contribute to achieving SDG 11, which aims to make communities safe and resilient. The results of this study will provide policymakers, local government authorities, and private developers with valuable information, particularly during the planning and execution phases of disaster management and urban community resilience building, on the key resilience indicators they should focus on when evaluating the success of their collaboration. Additionally, this study will contribute to international discussions on the effectiveness of PPP in disaster management and resilience building for critical infrastructures and flood management. Given the importance of enhancing the resilience of urban communities against unexpected disruptions, there is an urgent need to adopt the PPP concept for urban community resilience and to identify indicators for assessing its performance.

2. Review of Literature on Urban Community Resilience Indicators

Urban community resilience refers to a community’s ability to mitigate risks and recover quickly from disasters [26]. Studies have been conducted to examine urban communities’ resilience indicators, with an emphasis on either social vulnerability, economic vulnerability, or environmental vulnerability. A systematic literature review was conducted by Ampratwum, Tam [27] and a list of critical risks in PPP in critical infrastructure resilience was developed. Also, Lwin, Pal [28] assessed the social resilience of flood-vulnerable communities in Myanmar. Their study found that communities in high-flood-prone areas had a higher awareness of flood risk than those in low-flood-prone areas. Scherzer, Lujala [29] developed a baseline community resilience index in Norway for comparing communities and tracking changes over time. Their results showed considerable variations in the relative levels of resilience. Zhong, Lin [30] identified urban community flood-resilience indicators peculiar to Nanning using neighbourhood, college, and village communities as case studies. Their results showed that the neighbourhood community had the most significant community resilience to urban flooding, followed by the college and village communities. Kwok, Doyle [31] examined the perspectives of hazard researchers, emergency management practitioners, and policymakers from New Zealand on social resilience. In their study, they developed social indicators applicable to the New Zealand’s Wellington region.

Furthermore, Chen, Liu [32] assessed the comprehensive resilience level in various types of communities. Their results show that built-up environmental factors such as topography, riverfront, building coverage ratio, green-space rate, and land-use diversity have a significant impact on community resilience. Eadie and Su [33] examined the effect of disaster rehabilitation interventions on bonding social capital within a community in the Philippines. The results show that the inequitable distribution of relief goods and services generated discontent within communities. It was also discovered that the communities’ perception of resilience is high. Mohamad, Jusoh [34] used a qualitative approach to localize the resilience community indicators to measure the resilience level of the urban community. In their study, they develop resilience indicators based on three broad themes: social capital, environmental capital, and economic capital. These indicators were tested using a case study.

Osei-Kyei, Tam [35] adopted a systematic literature review to explore the indicators of urban community resilience; however, the authors did not empirically test these indicators. Their study highlighted infrastructure and housing resilience indicators, institutional resilience indicators, community capital indicators, spatial characteristics of community indicators, and socio-demographic indicators as key indicators. Kammouh, Zamani Noori [36] proposed an indicator-based method for measuring urban community resilience. In their study, the researchers developed a tool that can be used to quantify the resilience of urban communities. Their developed tool was applied to the city of San Francisco. A successful urban community resilience would mean that stakeholders must be involved and knowledgeable about the vulnerability of residents [37].

From the above literature review on urban community resilience indicators, it could be seen that none of the studies attempted to employ the concept of PPP in building urban community resilience. More importantly, none of the studies explored the performance indicators for adopting a PPP concept in building urban community resilience.

3. Research Methodology

This study adopted a quantitative research approach, using a questionnaire survey to gain insight into its aim. The research workflow for this study is shown in Figure 1 below.

3.1. Sampling of Respondents

To select research participants that are most likely to yield appropriate and useful information, a purposive sampling with pre-defined criteria was adopted [38]. The predefined criteria included: (1) respondent should have basic knowledge about PPP and/or urban community resilience; (2) respondent should have at least one peer-reviewed journal article as lead author, and/or one year of hands-on industry experience in PPP and/or urban community resilience/disaster management. Considering these criteria, relevant research databases such as Scopus, Google Scholar, and Web of Science, local government websites, institutional and government reports, LinkedIn profiles, architectural, engineering, and construction companies’ websites, non-governmental organizations’ portals, and industry blogs on professional bodies websites were searched to retrieve the contacts of potential respondents. A total of 250 potential experts were identified.

3.2. Prior Literature and Pre-Testing

A list of 16 performance resilience indicators for building urban community resilience through PPP was adapted from a previous study by Osei-Kyei, Tam [35], (see

Table 1. Performance indicators for building urban community resilience through PPP.

S/N	Performance Indicators	Sources (Refer to Appendix A)
1	Properly developed disaster-resistant urban community buildings	5,9,20
2	Existence of permanent channels for sharing information between the urban population and disaster response units/departments	1,4,6,13,15,16,19,20,
3	Well-established collaboration among private sectors, government, and community residents	23,26,27,28,30,31,33
4	Well-developed database on the current numbers of urban households exposed to hazards	1,6,15,12
5	Readily available effective and well-distributed evacuation plans	13
6	Readily available emergency services for urban communities	1,3,7,15,23,26,34
7	Regularly maintained community infrastructure	11,14,17,24,25,31,32
8	Availability of disaster training programmes for emergency workers	13
9	Existence of well-crafted urban disaster management policies for PPP	2,3,27,30
10	Readily available risks and vulnerabilities database of urban communities	15,16,19
11	Readily available trained personnel stationed at identified hazardous urban communities	11,13
12	Substantial percentage of urban households with comprehensive insurance policies including coverage of catastrophic events	6,25
13	Frequent disaster risk reduction training for communities	3,4,5,8,10,11,15,18,
14	Limited access to hazard areas for settlement development	21,26,27,28,29,34
15	Substantial percentage of urban population not facing extreme poverty situations	25
16	Reserved private sector funding and budget for community disaster management	13,34

). The list of indicators was derived from a thorough search of papers in Scopus, Web of Science, and Google Scholar [35]. Keywords such as PPP, community resilience, and urban community were used to retrieve 35 relevant journal articles. A preliminary list of indicators relevant to developing urban community resilience through PPP was derived through a content review of the articles. This research aims to use fuzzy set theory to develop a tool for assessing the performance of PPP in achieving the goal of building urban community resilience against unexpected disruptions. To achieve this aim, a questionnaire survey was developed using the 16 performance resilience indicators to assess PPP performance in building urban community resilience. A Likert scale was provided for each of the indicators, where research participants had to rate the level of importance for each of the indicators in assessing the performance of PPP in building urban community resilience. A pilot study was conducted to ensure the applicability and genuineness of the indicators. Five (5) experts with more than five years of experience were selected for pre-testing.

Specifically, the list of indicators was sent to three experienced academics and two industry PPP practitioners to ascertain their practicality and authenticity. The three academics have PhD degrees and more than 5 years of research experience in PPP, and the two practitioners with master’s degrees have more than 7 years of experience in urban planning and PPP practices. Five (5) experts are deemed adequate for this study because similar studies in construction research, such as [39] used three (3) experts in the pre-testing of the questionnaire. Ref. [40] also used two (2) respondents in testing their questionnaire before their main survey. Ref. [41] used four (4) experts to vet and scrutinize their research questionnaire.

The five experts confirmed the relevance of the indicators. They suggested minor changes to the wording of three indicators (namely ‘readily available emergency services for urban communities’, ‘Well-developed database on the current numbers of urban households exposed to hazards’ and ‘Limited access to hazard areas for settlement development’). The final list of indicators was used to develop the questionnaire for the study.

3.3. International Questionnaire Survey

An international online questionnaire survey was conducted with experienced experts in PPPs and urban community resilience. This survey was adopted because urban community resilience building has become a global concern, especially with the use of the PPP concept [27]. Further, because PPP has become a global public policy, it is prudent and effective to ascertain the perceptions of practitioners from different cultures to explore the salient indicators associated with PPP in urban community resilience development.

The questionnaire was developed using the online Qualtrics tool, and the web link was sent to respondents by email. The questionnaire had two sections: the respondents’ background and the indicators’ Likert scale questions. After several weeks of reminders, 43 valid responses were received for further analysis. International online-based surveys have several limitations and challenges; therefore, the response rate is unsurprising, especially in PPP research [42]. According to Fan and Yan [43], the average response rate for online-based surveys is 11% lower than that of other survey methods. However, compared with other past studies in PPP research that adopted international surveys, the sample size is considered adequate for further analysis. For example, Ameyaw and Chan [40] obtained 35 responses out of 356, Kukah, Owusu-Manu [41] obtained 48 responses in their factor analysis study, and Osei-Kyei, Chan [44] received 48 responses out of 320 questionnaires. The above justifications indicate that the sample size for this study is considered reasonable for further analysis.

4. Statistical Analysis Methods Adopted

The Statistical Package for the Social Sciences (SPSS) was used for statistical analysis, including reliability testing, mean score ranking, and factor analysis (FA), while Microsoft Excel was used for fuzzy synthetic evaluation (FSE). Cronbach’s alpha was conducted to test the consistency and reliability of the survey instrument for this study. The Cronbach alpha value obtained was 0.80, signifying the strong reliability of the dataset.

Mean score ranking is a widely used analysis method in engineering and construction research to determine the relative importance of variables. This technique was used to establish the importance and criticality of the 16 indicators for assessing the resilience of public–private partnerships (PPPs) in urban community resilience building. The mean score for each performance indicator was used in the FSE analysis to calculate weightings for each performance indicator and weightings for each component derived from the factor analysis.

Next, factor analysis was used to group the critical indicators into a smaller number of categories and to identify the underlying relationships among the variables. Factor analysis was used to reduce the large number of indicators to smaller groupings, where a composite equation can be developed that captures all the individual 14 indicators. Reducing the list to a small number of groups will help develop the fuzzy assessment model [42]. Before conducting the FA, preliminary statistical tests were performed to assess the suitability and appropriateness of the FA for the data. These tests include Bartlett’s test of sphericity and Kaiser–Meyer–Olkin (KMO). The value for KMO is 0.636, which is greater than the recommended threshold of 0.50 [45] This means that the FA was considered appropriate for the survey data.

Finally, the FSE technique was utilized to develop the model because it can objectify experts’ subjective opinions and perceptions, making it an appropriate technique to create an objective and reliable assessment model [43]. The FSE technique has been commonly used in previous studies across various disciplines to develop multi-criteria decision-making models, which have proven practical and useful [44]. Therefore, it was suitable to employ this tool to develop the assessment model for PPP in urban community resilience building. The FSE technique was also employed to develop the assessment tool. FSE is a fuzzy logic approach for multi-criteria decision-making in several disciplines. It is useful in developing a performance index. Studies like [46,47,48,49] have used FSE in developing a performance index. The following summarized steps were adopted in using the FSE [50]:

• Determine the appropriate weightings for performance indicators (PIs) and performance indicator groups (PIGs).
• Determine the membership function (MF) of each PIG component (first level) and PI (second level).
• Computing the index for each PIG component.
• Developing an assessment model for assessing the performance outcome for building urban community resilience through PPP.

5. Results and Discussion

5.1. Background of Respondents

Background information guarantees the reliability of survey responses.

Table 2. Sectors and years of experience of respondents.

Sectors	Private sector	18	41.9
	Academic/consultants	18	41.9
	Public sector	7	16.2
	Total	43	100
Years of experience in PPP and/or urban community resilience/disaster management	0–5 years	14	32.6
	6–10 year	10	23.3
	11–15 years	5	11.6
	16–20 years	5	11.6
	21 years or above	9	20.9
	Total	43	100

shows the sectors and years of experience of respondents. The table indicates that around 58% of respondents are industry practitioners from public-sector organizations (16.2%) or private-sector organizations such as construction companies and developers (41.9%). This indicates that the majority of the respondents are exposed to technicalities and daily operations of PPPs in urban community resilience building, which therefore enriches the reliability of the survey responses. Further, more than 67% of respondents have more than six years of experience in PPP and/or urban community resilience/disaster management. This indicates that well-experienced practitioners and academics were involved in this study. Therefore, the outputs of this study are valuable for future reference.

Table 3. Respondents’ countries/jurisdictions.

Countries/Jurisdictions	Frequency	Percentage
Australia	12	27.9
India	6	14
United Kingdom	4	9.3
United States of America	4	9.3
Philippines	3	7
Sweden	3	7
Belgium	2	4.7
Ghana	2	4.7
Nigeria	2	4.7
Spain	2	4.7
Hong Kong	1	2.3
Ireland	1	2.3
New Zealand	1	2.3
Total	43	100

shows the countries of the respondents. The table shows that Australia had the highest response rate, 27.9%. The 2nd highest response rate was from India with 14%. The United Kingdom and the United States of America were ranked the third-highest countries with a 9.3% response rate. The Philippines and Sweden were ranked 4th with a 7% response rate. Belgium, Ghana, Nigeria, and Spain were ranked 5th with a 4.7% response rate. The diverse cultural backgrounds of respondents show the richness of the survey responses. Furthermore, it shows the reliability of the survey responses for further analysis.

5.2. Mean Ranking Analysis and Normalization of Indicators

Table 4. Mean score ranking for performance indicators for building urban community resilience through PPP.

S/N	Performance Indicators	Mean	Std. Deviation	Normalization
1	Properly developed disaster-resistant urban community buildings	4.63	0.578	1.0
2	Existence of permanent channels for sharing information between the urban population and disaster response units/departments	4.35	0.650	0.9
3	Well-established collaboration among private sectors, government, and community residents	4.21	0.638	0.9
4	Well-developed database on the current numbers of urban households exposed to hazards	4.07	0.737	0.8
5	Readily available effective and well-distributed evacuation plans	4.02	0.740	0.8
6	Readily available emergency services for urban communities	3.95	0.785	0.7
7	Regularly maintained community infrastructure	3.84	0.652	0.7
8	Availability of disaster training programmes for emergency workers	3.79	0.742	0.7
9	Existence of well-crafted urban disaster management policies for PPP	3.72	0.766	0.7
10	Readily available risks and vulnerabilities database of urban communities	3.65	0.783	0.6
11	Readily available trained personnel stationed at identified hazardous urban communities	3.47	0.735	0.6
12	Substantial percentage of urban households with comprehensive insurance policies including coverage of catastrophic events	3.44	0.590	0.5
13	Frequent disaster risk reduction training for communities	3.30	0.832	0.5
14	Limited access to hazard areas for settlement development	3.23	0.947	0.5
15	Substantial percentage of urban population not facing extreme poverty situations	2.84	0.785	0.3
16	Reserved private sector funding and budget for community disaster management	2.07	1.055	0.0

Normalized value = (actual value − minimum value)/(maximum value − minimum value).

shows the mean score analysis for the performance indicators in using PPP to build urban community resilience. It is noticeable that the mean values range from 4.63 to 2.07, indicating more balanced scores from respondents. To select the critical performance indicators, only indicators with normalized values equal to or greater than 0.5 are considered [51]. Of the 16 indicators, 14 had normalized values above 0.5 and were considered critical. Among the critical indicators, the five top-ranked indicators include ‘properly developed disaster resistant urban community buildings’ (with a mean of 4.63), ‘existence of permanent channels for sharing information between the urban population and disaster response units/departments’ (with a mean of 4.35), “well-established collaboration among private sectors, government, and community residents” (with a mean of 4.21), “well-developed database on the current numbers of urban households exposed to hazards” (with a mean of 4.07), and “readily available effective and well-distributed evacuation plans” (with a mean of 4.02).

“Properly developed disaster-resistant urban community buildings” is ranked first, which is unsurprising. Undoubtedly, for an urban community to resist natural disasters and further ensure the minimization of disruptions, urban buildings should be able to resist unexpected natural events such as earthquakes, cyclonic winds, typhoons, etc. Urban community buildings should be able to resist natural events to minimize destruction to occupants and residents. To achieve this, in recent times, designers and developers have attempted to adopt design-resistant concepts in urban buildings. For example, architects are now employing flexible foundations, vibration–deflection technologies and moment-resisting frames in urban buildings to make them more resilient.

“Existence of permanent channels for sharing information between the urban population and disaster response units/departments” is ranked second among the critical indicators. For urban communities to be resilient through PPP, there must be clear and robust permanent communication channels and platforms. This is critical to ensure the free flow and rapid dissemination of information among the local government authorities, urban residents, and the private sector. This will help in the rapid response and recovery of urban communities to disruptions in a timely and effective manner.

“Well-established collaboration among private sectors, government, and community residents” is ranked third. Many previous studies on public–private partnerships (PPPs) have highlighted the importance of strong collaboration among PPP stakeholders to ensure the achievement of the objectives and goals of PPPs [27]. Effective collaboration among all stakeholders is necessary for urban communities to be resilient under the PPP concept. All parties, especially the local government authorities, the private sector, and urban community residents, should have common goals and objectives in building urban community resilience. Additionally, stakeholders should maintain transparency to ensure effective information sharing and disaster response strategies.

5.3. Factor Analysis

To ascertain the underlying relationship among the fourteen critical performance indicators identified, a factor analysis (FA) using principal component analysis was conducted (see Table 4).

The principal component extraction with varimax rotation yielded a five-factor solution, with eigenvalues greater than 1.00, accounting for 72.012% of the total variance (see

Table 5. Factor analysis results for performance indicators for building urban community resilience through PPP.

S/N	Principal Components	Factor Loadings	Eigenvalues	Variance Explained	Cumulated Variance Explained
Strong urban community disaster resilience PPP policy			4.414	31.531	31.531
	Existence of well-crafted urban disaster management policies for PPP	0.831
	Availability of disaster training programmes for emergency workers	0.778
	Readily available emergency services for urban communities	0.702
	Substantial percentage of urban households with comprehensive insurance policies including coverage of catastrophic events	0.686
	Readily available trained personnel stationed at identified hazardous urban communities	0.569
Existence of effective urban disaster risks database and PPP communication plan			2.203	15.738	47.269
	Readily available risks and vulnerabilities database of urban communities	0.794
	Well-developed database on the current numbers of urban households exposed to hazards	0.771
	Well-established collaboration among private sectors, government, and community residents	0.698
	Existence of permanent channels for sharing information between the urban population and disaster response units/departments	0.670
Resilient urban community physical capital			1.286	9.187	56.456
	Properly developed disaster-resistant urban community buildings	0.826
Restriction and preservation			1.175	8.396	64.852
	Limited access to hazard areas for settlement development	0.812
	Regularly maintained community infrastructure	0.748
Well-developed community stakeholder engagement and training policies			1.002	7.159	72.012
	Frequent disaster risk reduction training for communities	0.797
	Readily available effective and well-distributed evacuation plans	0.656

). The factor solutions exhibit strong item loadings (≥0.569) on each component, which exceeds the recommended 0.50 threshold [51,52]. The factor loadings of each performance indicator show its contribution to the underlying components. In addition, the high factor loadings of variables on the significant components indicate the appropriateness of the survey data for factor analysis (FA).

Based on the literature and the interpretations of the variables within each principal component, the five principal components are labelled as follows (Table 5):

Component 1: Strong urban community disaster resilience PPP policy.

Component 2: Existence of effective urban disaster risks database and PPP communication plan.

Component 3: Resilient urban community physical capital.

Component 4: Restriction and preservation.

Component 5: Well-developed community stakeholder engagement and training policies.

5.4. Establishing a Fuzzy Synthetic Evaluation Tool for Evaluating Urban Community Resilience Through PPP

Fuzzy Synthetic Evaluation is used to develop an assessment tool for ascertaining the resilience of PPP in urban community resilience building. This index can assess the success or performance of building urban community resilience through PPP. This will help stakeholders identify any resilience gaps that can be addressed to improve urban community resilience. In fuzzy modelling, there are two levels of membership; the first level is component groupings, and the second level is urban community resilience indicators. Given these levels, the fuzzy evaluation model is demonstrated in the steps below;

• Step 1—Determine the appropriate weightings for performance indicators (PI) and performance indicator groups (PIG).

The weightings for 14 indicators and five principal components are established (

Table 6. Weightings for performance indicator (PI) and performance indicator group (PIG) for building urban community resilience through PPP.

Principal Component		MS for Indicator	Weightings for Each Indicator	Total MS for Each Indicator Group	Weighting for Each Indicator Group
Strong urban community disaster resilience PPP policy				18.37	0.263
	Existence of well-crafted urban disaster management policies for PPP	3.72	0.203
	Availability of disaster training programmes for emergency workers	3.79	0.206
	Readily available emergency services for urban communities	3.95	0.187
	Substantial percentage of urban households with comprehensive insurance policies including coverage of catastrophic events	3.44	0.189
	Readily available trained personnel stationed at identified hazardous urban communities	3.47	0.189
Existence of effective urban disaster risks database and PPP communication plan				32.56	0.465
	Readily available risks and vulnerabilities database of urban communities	3.65	0.224
	Well-developed database on the current numbers of urban households exposed to hazards	4.07	0.250
	Well-established collaboration among private sectors, government and community residents	4.21	0.259
	Existence of permanent channels for sharing information between the urban population and disaster response units/departments	4.35	0.267
Resilient urban community physical capital				4.63	0.066
	Properly developed disaster-resistant urban community buildings	4.63	1
Restriction and preservation				7.07	0.101
	Limited access to hazard areas for settlement development	3.23	0.457
	Regularly maintained community infrastructure	3.84	0.543
Well-developed community stakeholder engagement and training policies				7.32	0.105
	Frequent disaster risk reduction training for communities	3.3	0.451
	Readily available effective and well-distributed evacuation plans	4.02	0.549
	Total			69.95

) using the mean score values acquired from the questionnaire survey. The weightings for the performance indicators and five performance indicator components are computed using the following equation [53].

W_i = M_i

$$\sum Mii$$

where W_i is the weightings of a performance indicator (PI) and performance indicator group (PIG); M_i is the mean score value of a PI/PIG group, $\sum Mii$ is the summation of mean score values of all PIs under each PIG group. For example, Component 5 has two PIs with a total mean of 7.07. The weightings for the indicators under Component 5 are as follows:

Limited access to hazard areas for settlement development

$$W = {\displaystyle \frac{3.23}{3.23+3.84}} = 0.457$$

Regularly maintained community infrastructure

$$W = {\displaystyle \frac{3.84}{3.23+3.84}} = 0.53$$

• Step 2—Determine the membership function (MF) of each PIG component (first level) and PI (second level).

The membership function of each PI and each PIG component was calculated. The membership function of each PI is obtained from the evaluation by experts using the Likert scale (1 = least important, 2 = fairly important 3 = important, 4 = very important, 5 = extremely important).

Using “Properly developed disaster resistant urban community buildings” as an example, the results indicate that the ranking of this PI had the following response: 0.0% as ‘least important’, 0.0% as ‘fairly important’, 4.7% ‘important’, 27.9% as ‘very important’, 67.4% ‘extremely important’. The MF is expressed as (0.00, 0.00, 0.047, 0.279, 0.674). Using similar steps, the MFs of other indicators are computed, respectively, as shown in

Table 7. Membership function for performance indicators for building urban community resilience through PPP.

S/N	Principal Groups	Weightings for Each Indicator	Membership Functions of Level 2 (Indicators)	Membership Functions of Level 1 (Principal Components)
Strong urban community disaster resilience PPP policy				0.00, 0.08, 0.41, 0.34, 0.15
	Existence of well-crafted urban disaster management policies for PPP	0.20	0, 0.023, 0.395, 0.419, 0.163
	Availability of disaster training programmes for emergency workers	0.21	0, 0, 0.395, 0.419, 0.186
	Readily available emergency services for urban communities	0.19	0, 0.023, 0.256, 0.465, 0.256
	Substantial percentage of urban households with comprehensive insurance policies including coverage of catastrophic events	0.19	0, 0.326, 0.581, 0.023, 0.07
	Readily available trained personnel stationed at identified hazardous urban communities	0.19	0.01, 0.07, 0.465, 0.395, 0.07
Existence of effective urban disaster risks database and PPP communication plan				0.00, 0.01, 0.19, 0.05, 0.24
	Readily available risks and vulnerabilities database of urban communities	0.22	0, 0.01, 0.465, 0.349, 0.163
	Well-developed database on the current numbers of urban households exposed to hazards	0.25	0, 0.023, 0.163, 0.535, 0.279
	Well-established collaboration among private sectors, government, and community residents	0.26	0, 0, 0.116, 0.558, 0.326
	Existence of permanent channels for sharing information between the urban population and disaster response units/departments	0.27	0, 0, 0.04, 0.2, 0.19
Resilient urban community physical capital				0.00, 0.00, 0.05, 0.28, 0.67
	Properly developed disaster resistant urban community buildings	1	0, 0, 0.047, 0.279, 0.674
Restriction and preservation				0.00, 0.15, 0.43, 0.31, 0.11
	Limited access to hazard areas for settlement development	0.46	0, 0.326, 0.581, 0.023, 0.07
	Regularly maintained community infrastructure	0.54	0, 0, 0.302, 0.558, 0.14
Well-developed community stakeholder engagement and training policies
	Frequent disaster risk reduction training for communities	0.45	0, 0.14, 0.512, 0.256, 0.093
	Readily available effective and well-distributed evacuation plans	0.55	0, 0.023, 0.186, 0.535, 0.256

.

After determining the MF for all fourteen indicators, these set the basis to determine the MF for the five principal components. The following equation was used to calculate the MF for all five groupings.

$$\mathrm{D}={\mathrm{W}}_i\circ {\mathrm{R}}_{\mathrm{i}}$$

where $R_i$, is the fuzzy evaluation matrix [51]

The MFs for all the five categories are shown in Table 7 (Column 5).

After computing the MFs for all the principal groupings, the index for each PIG component was computed using the formula below.

Index for each PIG

$$component=\sum _{i=1}^5\mathrm{D}\times \mathrm{E}$$

where D is the final FSE matrix for each component and E is the linguistic variables (i.e., 1, 2, 3, 4, 5).

Therefore, the indices for the five components are determined as follows:

Strong urban community disaster resilience PPP policy = (0.00, 0.08, 0.41, 0.34, 0.15) × (1, 2, 3, 4, 5) = 3.47

Existence of effective urban disaster risks database and PPP communication plan = (0.00, 0.01, 0.19, 0.05, 0.24) × (1, 2, 3, 4, 5) = 1.99

Resilient urban community physical capital = (0.00, 0.00, 0.05, 0.28, 0.67) × (1, 2, 3, 4, 5) = 4.63

Restriction and preservation = (0.00, 0.15, 0.43, 0.31, 0.11) × (1, 2, 3, 4, 5) = 3.38

Well-developed community stakeholder engagement and training policies = (0.00, 0.08, 0.33, 0.41, 0.18) × (1, 2, 3, 4, 5) = 3.70

• Step 3—Developing an assessment model for assessing the performance outcome for building urban community resilience through PPP.

A composite indicator was computed to assess the performance outcome for building urban community resilience through PPP. A composite indicator is derived from the notion that it is a linear and additive model [54]. Therefore, deriving a linear and additive model is rational and valid. This is because the units of measurement for the five components differ, so there is unlikely to be any multiplier effect between them.

Before deriving the composite indicator, the indices of the five principal components, which functioned as coefficients in the linear equation, were normalized to sum up to one, as shown in

Table 8. Indices and coefficients for PIGs for building urban community resilience through PPP.

Principal Indicators Groups	Index	Coefficients a
Resilient urban community physical capital	4.63	0.270
Well-developed community stakeholder engagement and training policies	3.70	0.215
Strong urban community disaster resilience PPP policy	3.47	0.202
Restriction and preservation	3.38	0.197
Existence of effective urban disaster risks database and PPP communication plan	1.99	0.116
Total	17.17

^a Coefficient = (Index for Factor Group/$\sum$ Index for Factor Grouping).

[51]. This normalization is very important so that different Likert scales of measurements could be used for the model in the future.

The model for assessing the performance outcome for building urban community resilience through PPP is expressed by the following equation:

(Resilient urban community physical capital × 0.270) + (Well-developed community stakeholder engagement and training policies × 0.215) + (Strong urban community disaster resilience PPP policy × 0.202) + (Restriction and preservation × 0.197) + (Existence of effective urban disaster risks database and PPP communication plan × 0.116)

5.5. Application of the Model in Practice

The assessment model could be used practically to ascertain the performance level of a PPP in building urban community resilience. This will certainly inform practitioners of the progress made in employing the PPP concept in this area. The following simple steps can be considered for the practical use of this model.

• Practitioners or stakeholders from the public and private sectors within a designated urban community should consider the list of assessment indicators under each of the five components. Based on Likert scale ratings, a score reflecting the level of achievement or compliance of an indicator in building urban community resilience should be assigned to each resilience indicator under each category.
• The average score for each of the five categories should be determined.
• The average score of each category should then be used in the linear equation to derive the overall score or performance level of using PPP for building urban community resilience.

Indeed, a higher score indicates that the urban community resilience strategies or practices implemented are effective. Therefore, such an urban community will have the capacity to be resilient against any unexpected disruptions in the future, and vice versa.

5.6. Discussion of FA and FSE Results

5.6.1. Resilient Urban Community Physical Capital

This component consists of properly developed disaster-resistant urban community buildings with a mean of 4.63. This indicator has the highest mean, and it is not surprising. This is because the evidence of a functioning public–private partnership is to ensure that urban communities are resilient. In many communities, especially poor and developing countries, the gradual erosion of the structural integrity of a building from a lack of maintenance translates into low community resilience and vulnerability [55]. Communities would benefit from buildings that maintain structural integrity and contribute to function after disasters [56]. Building codes that have stipulations for resilient designs would ensure that buildings are built to be disaster resilient. The goal of building codes is to reduce the likelihood of structural failure and to offer some level of property protection [56]. Building resilient buildings has the added benefit of reducing vulnerabilities in a community’s physical infrastructure systems, which lowers economic losses [1]. Ribeiro and Gonçalves [57] agree that building resilient buildings is important for communities that are most at risk.

5.6.2. Well-Developed Community Stakeholder Engagement and Training Policies

This component consists of “Frequent disaster risk reduction training for communities” and “Readily available effective and well-distributed evacuation plans”. Community capacity building must be enhanced in urban communities to equip them with the needed skills and information to assess and manage their own risk [58]. Training is the best way of equipping urban communities with information and strategies to prepare for disasters. An example is an earthquake drill simulating the shallow 7.4 earthquakes in Rongguang Township. This exercise allowed inhabitants to practice evacuation techniques and skills that would be needed in an actual disaster [59]. This is similar to Feofilovs and Romagnoli’s [60] study, where they developed a natural hazards assessment tool to be able to compare different urban resilience scenarios to help determine urban resilience strategies that will suit communities. Resilience training can be delivered as a series of thematic lectures and interactive group exercises to provide them with hands-on learning experience to understand and assess risks in their communities [61]. Resilience capacity training helps residents respond to and recover from disasters. A community should have effective and well-distributed evacuation plans and be able to prioritize both resilience preparations before a disaster and recovery efforts after a disaster occurs to provide and maintain a community’s basic needs [37].

5.6.3. Strong Urban Community Disaster Resilience PPP Policy

This component consists of “Existence of well-crafted urban disaster management policies for PPP”, “Availability of disaster training programmes for emergency workers”, “Readily available emergency services for urban communities”, “Substantial percentage of urban households with comprehensive insurance policies including coverage of catastrophic events”, and “Readily available trained personnel stationed at identified hazardous urban communities”. In Woodruff, Bowman’s [62] study, they asserted that resilience policies seeks to address the underlying drivers of urban vulnerabilities. Disaster-prone areas heighten the residents’ vulnerability, leading to significant financial losses for the community [63]. Positioning trained personnel at identified hazardous urban communities will increase the speed in aiding disaster-affected communities [31]. Government organizations are the central decision-making bodies for managing resources during disasters [64]. The knowledge, skills, and competency serve as a basis for efficient disaster recovery [31]. Various skills are needed to perform the necessary activities before, during, and after a disaster [65]. Comprehensive insurance policies that cover disasters such as flooding enhance the recoverability of affected households from flooding disasters. Comprehensive guidelines, such as recovery goals and restoring building and infrastructure systems in a cost-effective manner, are needed to proactively prepare communities to be resilient against disasters [1].

5.6.4. Restriction and Preservation

This component consists of “Limited access to hazard areas for settlement development” and “Regularly maintained community infrastructure”. Geographic location determines the vulnerability of urban communities [63]. Residing in areas prone to natural disasters increases the vulnerability of the inhabitants to the impacts of natural disasters [63]. Budget allocations for disaster-related activities impact the recovery process of communities when hit by disruptive events [66]. Building performance during disasters depends on multiple factors, including maintenance, upkeep, and retrofit [56]. Community resilience depends on the capacity of the built environment to maintain acceptable levels of functionality during and after disruptive events and to recover quickly to full functionality. Design and construction requirements are focused on preventing loss of life, with little to no consideration for building damage after disasters. Regularly maintaining buildings with consideration for vulnerable areas helps increase the resilience of these buildings [37]. Dhar and Khirfan [67] also believe that post-disaster recovery and reconstruction can provide a unique opportunity for communities to resolve any structural issues in their building and build better to prevent vulnerabilities.

5.6.5. Existence of Effective Urban Disaster Risks Database and PPP Communication Plan

This component consists of “Readily available risks and vulnerabilities database of urban communities”, “Well-developed database on the current numbers of urban households exposed to hazards”, “Well-established collaboration among private sectors, government and community residents” [67], and “Existence of permanent channels for sharing information between the urban population [67] and disaster response units/departments”. Delivering accurate information through communication channels is key to ensuring the safety of inhabitants in urban communities or disaster-prone areas [63]. A lack of established communication channels would exacerbate the vulnerabilities of these communities during disasters. The capacity of urban communities can be enhanced by providing timely and relevant information, including floodwater heights, floodwater management plans, accurate weather forecasts, and provision of emergency services [68]. In fact, urban resilience requires access to external resources in addition to their local efforts, and a delay in providing these resources will delay the recovery time of the community [55]. Communities may not have adequate knowledge of how disasters affect their buildings. It is necessary to have a database on vulnerable buildings in communities and define their level of risk exposure with strategies. Information gathering must be a continual effort where disaster management is contextualized for different communities, where their distinct vulnerability and resilience needs are studied [69].

6. Implications for Practice and Significance of Study

Studies on the adoption of PPP in building urban community resilience are limited; therefore, this paper offers valuable information and outputs for future practice and research.

First, the output of this study, particularly the assessment model, will help stakeholders from both the public and private sectors to evaluate the performance outcome and level of adoption of a PPP approach in building the resilience of urban communities against unexpected events. These indicators are proactive pointers guiding a PPP in urban community resilience to implement measures before any disruptive event impacts community infrastructure. In assessing the performance outcomes, resilience areas that should be considered include the robustness of any existing urban community resilience building through PPP policies or frameworks, the level of stakeholder engagement in building urban community resilience, the resilience of physical capitals in urban communities, the robustness of any existing restrictions and urban facilities’ maintenance culture, policies, and frameworks, and the strength of any existing risk database and PPP communication plans.

Second, the outputs of this study will inform stakeholders in urban communities of the potential resilience strategies they should adopt to ensure the effective and successful adoption of the PPP concept for urban community resilience building. Specifically, some key strategies practitioners could consider drawing on the findings include continuous stakeholder engagement and a communication plan, the existence of a PPP framework for building urban community resilience and the establishment of a comprehensive disaster risk database with the assistance of the private sector.

7. Conclusions and Limitations

Urban communities rely on critical infrastructure services, including transportation, electricity, telecommunications, and water supply. The vulnerabilities of these services and other disruptive events expose urban communities to significant risks. Public–private partnership (PPP) involves close cooperation between public and private entities to deliver enhanced and innovative services and policy outcomes, ultimately contributing to building resilience in urban communities. Urban community resilience indicators are performance assessment guidelines for assessing the performance of PPP in building the resilience of urban communities. The study quantitatively used urban resilience indicators to assess the performance outcome of using PPP to build urban community resilience. Mean score ranking, factor analysis, and fuzzy synthetic evaluation were used to analyze respondents’ survey responses globally.

The research has limitations that affect the generalization of the results. The sample size in this study was small despite efforts to increase the response rate. Nevertheless, the responses were from diverse countries with different cultural and societal differences to augment the findings. The performance model has not been tested in real case studies. It is recommended that these performance assessment indicators be tested on case studies that have used PPP in their urban community resilience projects. This will help determine the practicality of this model. Further, future studies should consider investigating the specific contract arrangement models suitable for using PPP in urban community resilience building and disaster management. Respondents were sampled from relevant databases as Scopus, Google Scholar, and Web of Science, local government websites, institutional and government reports, LinkedIn profiles, architectural, engineering, and construction companies’ websites, non-governmental organizations’ portals, and industry blogs on professional bodies. The possibility of missing out on some potential respondents due to their absence on these platforms would mean their responses were not captured in this research. It is recommended that future studies include more databases to widen the scope for respondent sourcing.

A performance index equation was developed to help assess the performance of PPP in building urban community resilience. The performance assessment model would help streamline the activities of the PPP to ensure that all their efforts are tailored towards building the resilience of urban communities