Modelling National Resilience through a Whole-of-Government Framework Using ISM–MICMAC Analysis

Modelling National Resilience through a Whole-of-Government Framework Using ISM–MICMAC Analysis

Rayyan Cheong Tian Ming, Liley Afzani Saidi, Nurhafizza Rahaman

Faculty of Management, UPNM, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000509

Received: 13 August 2025; Accepted: 20 August 2025; Published: 18 September 2025

ABSTRACT

National resilience is increasingly recognised as a strategic imperative for safeguarding societal stability and continuity in the face of complex and evolving threats. The Whole-of-Government (WoG) approach provides an integrated governance framework that unites multiple sectors to strengthen resilience capabilities. This study aims (i) to determine national resilience through a WoG framework based on expert consensus and (ii) to propose a structured national resilience model grounded in that consensus. A qualitative design was applied, involving six subject matter experts from relevant public sector domains. Data were analysed using Interpretive Structural Modelling (ISM) to establish the hierarchical structure of resilience factors, followed by MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis to classify them by driving and dependence powers. The analysis yielded seven interrelated themes: Anticipation and Preparedness, Robustness and Redundancy, Adaptation and Flexibility, Coordination and Integration, Community and Stakeholder Engagement, Knowledge and Information Sharing, and Policy and Governance Frameworks. Findings indicate that Policy and Governance Frameworks hold the highest driving power, exerting systemic influence on all other factors, while the remaining variables function as linkage elements characterised by high mutual dependence. The MICMAC mapping underscores governance as the primary leverage point for initiating and sustaining resilience enhancements. The proposed model offers a policy-oriented, empirically derived tool to guide prioritisation, resource allocation, and cross-sectoral coordination, thereby enabling the design of coherent, adaptive, and sustainable resilience strategies within a WoG paradigm. This integrated approach provides actionable insights for national policymakers and strategic planners seeking to operationalise resilience in complex governance environments.

Keywords:  Whole-of-Government, National Resilience, Interpretive Structural Modelling (ISM), MICMAC Analysis

INTRODUCTION

National resilience is a multifaceted concept that encompasses the ability of a nation to anticipate, prepare for, respond to, and recover from various adverse events, ensuring stability and adaptability in the face of challenges. This concept has gained significant importance in recent years due to the increasing frequency and severity of natural disasters, geopolitical threats, and other crises. The resilience of a nation is crucial for maintaining the welfare and development of its society, especially in the context of “wicked problems” that are complex and multifaceted (Coaffee et al., 2009). The integration of resilience into national policy frameworks is essential for safeguarding the socio-economic stability and security of nations (Dewaele & Lucas, 2022; Eljaoued et al., 2020). This article aims to explore the modeling of national resilience through a Whole-of-Government (WoG) framework using ISM–MICMAC analysis, providing a comprehensive approach to understanding and enhancing national resilience.

The concept of resilience has evolved from its origins in material science and ecology to become a central theme in security studies and public policy. Resilience is now recognized as a critical component of national security strategies, as evidenced by its inclusion in key policy documents such as the European Union Global Strategy and the US National Security Strategy (Hémond & Robert, 2012). Research has highlighted the importance of resilience in addressing a wide range of threats, including terrorism, pandemics, and climate change (Eljaoued et al., 2020; Kalra et al., 2024). Various studies have emphasized the need for integrated and holistic approaches to resilience, incorporating elements such as political leadership, economic stability, social cohesion, and infrastructure robustness (Coaffee et al., 2009; Kumaraswamy et al., 2025; Medland et al., 2024). Despite the growing body of literature, there remains a gap in the development of practical frameworks for assessing and enhancing national resilience, particularly in the context of complex socio-technical systems.

Several key studies have contributed to the understanding of national resilience. For instance, research has shown that resilience strategies must be tailored to the specific needs and vulnerabilities of different governance levels, from local communities to national governments (Dewaele & Lucas, 2022). The use of ISM–MICMAC analysis has been proposed as a method for identifying and prioritizing key strategies for building resilience, particularly in the context of climate change and sustainable development (Kalra et al., 2024). This approach allows for the classification of strategies based on their influence and dependence, providing a structured framework for decision-making and policy implementation. Additionally, the concept of functional resilience, which focuses on the adaptive capacity of systems to maintain functionality under stress, has been highlighted as a critical area for further research and application (Coaffee et al., 2009; Schneider et al., 2015). The development of resilience assessment frameworks, such as those proposed by the National Infrastructure Commission and the Ministry of Defence, underscores the need for comprehensive tools that can evaluate and enhance the resilience of critical infrastructures and societal systems (Kumaraswamy et al., 2025; Žilinskas, 2017).

LITERATURE REVIEW

National resilience has become an essential component of public policy, especially in safeguarding national security and enhancing disaster preparedness (Coaffee et al., 2009). The concept, which gained prominence after the September 11 attacks, emphasises a nation’s ability to anticipate, prepare for, respond to, and recover from disruptions such as terrorism, pandemics, and climate-related hazards (Dewaele & Lucas, 2022). It encompasses political, economic, military, social, and psychological capacities, ensuring stability and adaptability at all levels of governance (Dias & Viswakula, 2020) Many national strategies, including those in the EU, US, and UK, recognise resilience as a critical factor in sustaining societal stability under crisis conditions (Hémond & Robert, 2012).

Efforts to strengthen resilience have led to the development of various assessment frameworks (Kumaraswamy et al., 2025). Analytical methods such as Interpretive Structural Modelling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) have been employed to identify relationships and interdependencies among resilience variables (Larkin et al., 2015). However, in certain contexts, fragmented approaches where agencies create separate frameworks have resulted in duplication and inconsistency (Dewaele & Lucas, 2022).  Effective resilience building requires integrated frameworks across governance levels, as reflected in multi-sectoral models that incorporate robustness, redundancy, and adaptability (McClelland et al., 2022).  Initiatives such as the Hyogo Framework also highlight the role of knowledge sharing, institutional learning, and adaptive governance (Prihartanto et al., 2024).

Despite growing attention, operationalising and measuring resilience remains challenging (Dias & Viswakula, 2020; Larkin et al., 2015). The absence of standardised policies and unified assessment methods often leads to inconsistent outcomes (Shimizu & Clark, 2019).  Moreover, the limited impact of some resilience initiatives signals the need to reassess current practices (Stewart et al., 2009).  A Whole-of-Government (WoG) approach that synchronises formal governance mechanisms with community-led efforts offers a pathway to more coherent and effective resilience strategies (Vâlsan et al., 2025).  Public–private collaboration is also vital, as much of the critical infrastructure lies in private hands (Dewaele & Lucas, 2022; Žilinskas, 2017).

Research objective

There are two main objectives in writing this article;

1. To determine National Resilience through a Whole-of-Government Framework based on expert consensus
2. To propose a National Resilience model through a Whole-of-Government Framework based on experts’ consensus.

METHODOLOGY

This research uses ISM and MICMAC analysis, with expert opinion included, to find and understand the connection between the strategies that generate a model of National Resilience through a Whole-of-Government Framework. Due to these procedures, a hierarchical link among the factors recognized by the experts will emerge. To better assist and resolve complicated problems or systems comprised of several aspects and their interplay, ISM was expanded by Warfield (1974) and Sage (1977). Strategies for group problem-solving that include organized discussion, such as the Nominal Group Technique (NGT), Focus Group Technique (FGT), brainstorming, focus groups, etc., are ideal for implementing ISM (Prasad et al., 2020). An organized hierarchical model may be constructed using the ISM method from a collection of variables or components that may have both direct and indirect effects on each other (Attri, Singh, & Mehra, 2017). Since ISM is a procedure that calls for interpretation and decision-making in groups, it might be considered interpretative. Since ISM simplifies the complicated system or issue’s structure, it may be considered structural. Modelling is an integral part of ISM as each model or diagraph represents a different structure. A wide range of fields are making use of ISM, including manufacturing (Singh & Khamba, 2011), education (Muhammad Ridhuan et al., 2014), policy (Kumar et al., 2018), environment (Chandramowli et al., 2011), and the aviation industry (Pitchaimuthy et al., 2019). The ISM approach follows a structured sequence of steps:

1. Identify the appreciating diversity competency (ADC) through a structured review and discussions with expert panels or by synthesizing relevant literature.
2. Develop the Structural Self-Interaction Matrix (SSIM) by conducting pair-wise comparisons of variables agreed upon and ranked by experts during the Nominal Group Technique (NGT) session. Variables are denoted by symbols V, A, X, and O, which indicate the direction of relationships between variables i and j: (i) to produce an appropriate Digital Ethics Guideline (DEG) model for educational leaders; (ii) V means ADC i is more important than ADC j; (iii) A means DEG j is more important than ADC i; (iv) X means ADC i and j are equally important and related; and (v) O means DEG i and j are unrelated.
3. Construct the final Reachability Matrix (RM) from the SSIM by converting V, A, X, and O into binary values based on these rules: (i) if (i, j) is V, then (i, j) = 1 and (j, i) = 0; (ii) if (i, j) is A, then (i, j) = 0 and (j, i) = 1; (iii) if (i, j) is X, then both (i, j) and (j, i) = 1; (iv) if (i, j) is O, then both (i, j) and (j, i) = 0.
4. Perform level partitioning of the matrix.
5. Develop the hierarchical relationship digraph from the final RM to form the ISM model.

• Apply Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis to categorize variables into clusters based on their driving and dependence power.

Figure 1: ISM Step

Sample

For this study, six subject matter experts were engaged to participate in the ISM sessions, ensuring informed input across the thematic scope of national resilience and the Whole-of-Government framework. Consistent with the expert selection approach outlined by Prasad et al. (2020), these individuals possessed extensive professional experience and specialised knowledge relevant to public policy, governance, national security, crisis management, and strategic studies. The experts, drawn from diverse government sectors and public sector organisations, are profiled in Table 1 according to their specific fields of expertise, academic qualifications, and professional backgrounds.

Table 1: List of experts

Data Analysis

Finding from step 1

For the first step, the researcher interviewed experts and reviewed some literature to determine the elements or steps that can be taken to implement counsellors’ competencies in digital counselling practice. The results are as follow;

Table 2: Elements /Guidelines for National Resilience through a Whole-of-Government

Figure 2: Elements of NRWG

Table 3: Structural Self-Interaction Matrix (SSIM) Structural Self-Interaction Matrix (SSIM)

** Output from SmartISM software

Finding from step 2

The Structural Self-Interaction Matrix (SSIM) illustrates the perceived contextual relationships among seven key variables of national resilience within a Whole-of-Government framework. The entries show that Anticipation and Preparedness (1) exerts a strong driving influence, directly affecting Robustness and Redundancy (2), Adaptation and Flexibility (3), and Coordination and Integration (4), while maintaining mutual reinforcement (X) with Community Engagement (5) and Knowledge Sharing (6), and being shaped by Policy and Governance Frameworks (7). Robustness and Redundancy (2) exhibits mutual influence with Adaptation (3) and Coordination (4) but is also governed by Policy and Governance. Adaptation and Flexibility (3) mutually interacts with Coordination (4), Engagement (5), and Knowledge Sharing (6), reflecting its integrative role. Coordination and Integration (4) shares two-way influence with Engagement (5) and Knowledge Sharing (6), highlighting the feedback loop between operational cohesion and stakeholder interaction. Community Engagement (5) and Knowledge Sharing (6) are closely linked, yet both are ultimately influenced by the overarching Policy and Governance Frameworks (7), which emerges as the primary enabling factor shaping all other variables. This pattern suggests a hierarchical structure where governance provides the strategic foundation, operationalised through preparedness and coordination, and reinforced by adaptability, engagement, and knowledge flows.

Table 4: Reachability Matrix (RM) For Analysis Final Reachability Matrix (FRM)

Findings from step 3 (Reachability matrix)

The Final Reachability Matrix (FRM) reveals the consolidated direct and transitive relationships among the seven national resilience variables, highlighting their respective driving and dependence powers. Six variables, Anticipation and Preparedness, Robustness and Redundancy, Adaptation and Flexibility, Coordination and Integration, Community and Stakeholder Engagement, and Knowledge and Information Sharing, share equal driving power (6) and high dependence power (7), indicating strong interconnectedness and mutual reinforcement within the system. In contrast, Policy and Governance Frameworks demonstrate the highest driving power (7) and the lowest dependence power (1), positioning it as the primary independent driver that influences all other variables while being minimally affected by them. This structure suggests a hierarchical influence pattern in which governance frameworks serve as the foundational enabler, cascading their impact across operational, adaptive, and collaborative dimensions of resilience.

Finding from step 4 &5

The level partitioning (LP) table iterations table presents the hierarchical decomposition of variables based on their reachability and antecedent sets in the ISM process. Elements 1 to 6 (covering Anticipation and Preparedness through Knowledge and Information Sharing) share identical reachability sets comprising variables 1–6, and their intersection with the antecedent sets matches these same elements, placing them collectively at Level 1. This indicates that these six variables occupy the top layer of the ISM hierarchy, as they are mutually interconnected and do not directly depend on lower-level variables within the model. In contrast, Element 7 (Policy and Governance Frameworks) has a reachability set including all variables (1–7) but an antecedent set containing only itself, leading to an intersection set of just {7}. This positions it at the foundational or bottom level in the ISM hierarchy, signifying its role as the primary driver influencing all other variables while remaining largely independent from them.

Table 5: Level Partitioning (LP)

The ISM hierarchical digraph illustrates the structural relationship among the seven national resilience variables, positioning Policy and Governance Frameworks (Variable 7) as the sole foundational driver at Level 2. This placement reflects its pivotal role in influencing all six upper-level variables—Anticipation and Preparedness (1), Robustness and Redundancy (2), Adaptation and Flexibility (3), Coordination and Integration (4), Community and Stakeholder Engagement (5), and Knowledge and Information Sharing (6)—which occupy Level 1. The direct arrows from the foundational driver to each dependent variable indicate that robust governance structures provide the strategic direction, institutional capacity, and enabling environment necessary for operational readiness, adaptability, coordination, community involvement, and effective knowledge exchange. This configuration underscores the principle that strengthening policy and governance mechanisms is essential for enhancing the overall resilience capacity of the system, as improvements at this foundational level cascade through and positively impact all other components.

Figure 3: Model digraph (SmartISM output)

Figure 4: National Resilience Whole of Government (NRWG) Model

Finding from step 6 (MICMAC analysis)

The cross-impact matrix multiplication applied to classification (MICMAC) analysis in this study follows a similar approach to that used by Attri et al. (2017) and Pitchaimuthu et al. (2019). The primary aim of MICMAC analysis is to identify the key DEGs (variables) that drive the system. In this process, the driving power of each SI is plotted on the Y-axis, while its dependence power is plotted on the X-axis. Each NRWG is then categorized based on the combination of these two measures as follows:

Figure 5: MIMAC Analyze

The MICMAC analysis scatter plot maps the seven national resilience variables according to their driving and dependence powers, categorizing them into four strategic clusters. Policy and Governance Frameworks (Variable 7) is positioned in Quadrant IV (Independent Variables) with the highest driving power (7) and the lowest dependence power, signifying its role as the strategic foundation for building national resilience. This position reflects its capacity to exert a cascading influence on all other variables while being minimally influenced itself. In contrast, Anticipation and Preparedness (1), Robustness and Redundancy (2), Adaptation and Flexibility (3), Coordination and Integration (4), Community and Stakeholder Engagement (5), and Knowledge and Information Sharing (6) cluster in Quadrant III (Linkage Variables), displaying both high driving and high dependence powers. These variables are dynamic and highly interconnected, meaning any improvement or disruption in one can significantly impact the others, potentially creating reinforcing or destabilizing loops. Notably, Quadrants I (Autonomous Variables) and II (Dependent Variables) are empty, indicating there are no isolated, weakly connected factors or purely reactive elements in the system. Overall, the distribution underscores a resilience framework where all variables are strategically interlinked, with governance acting as the principal leverage point for systemic enhancement.

Key Findings

The analysis of the seven national resilience variables through the ISM–MICMAC framework reveals several critical insights into their structural relationships and strategic significance within a Whole-of-Government (WoG) context. The seven approaches—Anticipation and Preparedness, Robustness and Redundancy, Adaptation and Flexibility, Coordination and Integration, Community and Stakeholder Engagement, Knowledge and Information Sharing, and Policy and Governance Frameworks, collectively define the operational and strategic dimensions of resilience.

Firstly, Policy and Governance Frameworks emerged as the sole independent variable in Quadrant IV of the MICMAC analysis. With the highest driving power (7) and lowest dependence power, it is the foundational driver of the entire resilience system. Its strategic role lies in setting the legal, institutional, and procedural foundations that shape the effectiveness of all other resilience components. Improvements in governance through coherent policies, inter-agency coordination mandates, and resource allocation are likely to create cascading positive effects across operational, adaptive, and collaborative resilience measures.

Secondly, the remaining six variables: Anticipation and Preparedness, Robustness and Redundancy, Adaptation and Flexibility, Coordination and Integration, Community and Stakeholder Engagement, and Knowledge and Information Sharing are clustered in Quadrant III (Linkage Variables). These elements exhibit both high driving and high dependence powers, making them dynamic and sensitive to changes in the system. This interdependence means that strengthening one area can yield multiple benefits, but weaknesses in any of them can disrupt the entire network. For instance, enhanced knowledge sharing can boost preparedness, coordination, and engagement, while poor coordination can undermine both adaptability and stakeholder trust.

Thirdly, the ISM hierarchical model positions Policy and Governance Frameworks at the base level, directly influencing all other variables. This confirms that governance acts not only as a directional enabler but also as a stabilising force for resilience development. The dependent variables, positioned at the top of the hierarchy, represent the operational outcomes and adaptive capacities that manifest as a result of sound governance structures.

Finally, the absence of variables in Quadrant I (Autonomous) and Quadrant II (Purely Dependent) highlights a system where all components are active contributors to resilience. There are no isolated or passive factors; rather, the framework represents a highly integrated and mutually reinforcing network.

CONCLUSION

This study, applying the ISM–MICMAC framework to seven national resilience variables within a Whole-of-Government (WoG) context, provides a clear structural understanding of their interrelationships and strategic importance. The analysis identified Policy and Governance Frameworks as the sole independent driver, exerting the highest influence across the system while remaining minimally dependent on other factors. Positioned at the base of the ISM hierarchy and in Quadrant IV of the MICMAC analysis, governance emerges as the fundamental enabler, providing the legal, institutional, and strategic direction necessary for building and sustaining resilience.

The remaining six variables, Anticipation and Preparedness, Robustness and Redundancy, Adaptation and Flexibility, Coordination and Integration, Community and Stakeholder Engagement, and Knowledge and Information Sharing, are all linkage variables, characterised by high driving and high dependence powers. Their interdependent nature signifies that advancements or disruptions in one will likely trigger corresponding effects across the others, creating either reinforcing synergies or destabilising feedback loops.

The absence of autonomous or purely dependent variables indicates a highly interconnected system, where every component plays an active role in shaping resilience outcomes. This integrated structure underscores the importance of coordinated policy interventions, ensuring that improvements in governance are complemented by capacity-building in operational and adaptive domains.

Ultimately, the findings highlight that strengthening governance frameworks is the most effective leverage point for enhancing national resilience. When combined with targeted efforts to reinforce the six interlinked operational dimensions, such reforms can produce systemic, sustainable improvements in resilience capacity, supporting national security, societal stability, and long-term development.

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