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This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

Supply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.

The study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.

All three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.

The authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.

Cyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve cyber resilience that focuses on critical assets and reduce the attack surface.

The approach follows two main cyber resilience design principles that focus on common critical assets and reduce the attack surface for this purpose. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles. The critical assets include Cyber Digital, Cyber Physical and physical elements. We consider Logistic Regression, Decision Tree, Naïve Bayes and Random Forest classification algorithms in a Majority Voting to predicate the results. Finally, we mapped the threats with known attacks for inferences to improve resilience on the critical assets.

The paper contributes to CSC system resilience based on the understanding and prediction of the threats. The result shows a 70% performance accuracy for the threat prediction with cyber resilience design principles that focus on critical assets and controls and reduce the threat.

Therefore, there is a need to understand and predicate the threat so that appropriate control actions can ensure system resilience. However, due to the invincibility and dynamic nature of cyber attacks, there are limited controls and attributions. This poses serious implications for cyber supply chain systems and its cascading impacts.

ML techniques are used on a dataset to analyse and predict the threats based on the CSC resilience design principles.

There are no social implications rather it has serious implications for organizations and third-party vendors.

The originality of the paper lies in the fact that cyber resilience design principles that focus on common critical assets are used including Cyber Digital, Cyber Physical and physical elements to determine the attack surface. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles to reduce the attack surface for this purpose.

Supply chain resilience capabilities are usually considered in light of some anticipated events and are as passive assets, which are “waiting” for use in case of an emergency. This, however, can be inefficient. Moreover, the current COVID-19 pandemic has revealed difficulties in the timely deployments of resilience assets and their utilization for value creation. We present a framework that consolidates different angles of efficient resilience and renders utilization of resilience capabilities for creation of value.

We conceptualise the design of the AURA (Active Usage of Resilience Assets) framework for post-COVID-19 supply chain management through collating the extant literature on value creation-oriented resilience and practical examples and complementing our analysis with a discussion of practical implementations.

Building upon and integrating the existing frameworks of VSC (Viable Supply Chain), RSC (Reconfigurable Supply Chain) and LCNSC (Low-Certainty-Need Supply Chain), we elaborate on a new idea in the AURA approach – to consider resilience as an inherent, active and value-creating component of operations management decisions, rather than as a passive “shield” to protect against rare, severe events. We identify 10 future research areas for lean resilience integrating management and digital platforms and technology.

The outcomes of our study can be used by supply chain and operations managers to improve the efficiency and effectiveness by turning resilience from passive, cost-driving assets into a value-creating, inclusive decision-making paradigm.

We propose a novel approach to bring more dynamics to the notion of supply chain resilience. We name our approach AURA and articulate its two major advantages as follows: (1) reduction of disruption prediction efforts and (2) value creation from resilience assets. We offer a discussion on ten future research directions towards a lean resilience.

Critical infrastructures (CIs) for essential services such as water supply and electricity delivery are notoriously vulnerable to disruptions. While extant literature offers important insights into the resilience of CIs following large-scale disasters, our understanding of CI resilience to the more typical disruptions that affect CIs on a day-to-day basis remains limited. The present study investigates how the interorganizational (supply) network that uses and manages the CI can mitigate the adverse consequences of day-to-day disruptions.

Longitudinal archival data on 277 day-to-day disruptions within the Dutch national railway CI were collected and analyzed using generalized estimating equations.

The empirical results largely support the study’s predictions that day-to-day disruptions have greater adverse effects if they co-occur or are relatively unprecedented. The findings further show that the involved interorganizational network can enhance CI resilience to these disruptions, in particular, by increasing the overall level of cross-boundary information exchange between organizations inside the network.

This study helps managers to make well-informed choices regarding the target and intensity of their cross-boundary information-exchange efforts when dealing with day-to-day disruptions affecting their CI. The findings illustrate the importance of targeting cross-boundary information exchange at the complete interorganizational network responsible for the CI and to increase the intensity of such efforts when CI disruptions co-occur and/or are unprecedented.

This study contributes to our academic understanding of how network-level processes (i.e. cross-boundary information exchange) can be managed to ensure interorganizational (supply) networks’ resilience to day-to-day disruptions in a CI context. Subsequent research may draw from the conceptual framework advanced in the present study for examining additional supply network-level processes that can influence the effectiveness of entire supply networks. As such, the present research may assist scholars to move beyond a simple dyadic context and toward examining complete supply networks

Amidst unpredictable and turbulent periods, such as the COVID-19 pandemic, service organization’s responses are required to be innovative, adaptable and resilient. The purpose of this study is to explore the utilization of both reconfiguration and transformational strategies as instruments for cultivating resilience and advancing sustainability in service organizations.

The study examines a proposed resilience model using fuzzy logic. The research also used a semantic differential scale to capture nuanced and intricate attitudes. Finally, to augment the validity of the resilience model, a measurement scale was formulated using business mathematics and expert opinions.

Although investing in resilience training can help organizations gain control and maintain their operations in times of crisis, it may not directly help service organizations understand the external turmoil, seek available resources or create adaptive remedies. Conversely, high levels of reconfiguration and transformation management vigour empower a service organization’s revolutionary, malleable vision, organizational structure and decision-making processes, welcoming talented and innovative employees to enhance capabilities during crises.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations identifying the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research guides service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. The study elaborates on the enhancement of resilience, increasing innovation, improving efficiency and enhancing customer satisfaction for service organizations to remain competitive and contribute to positive social and economic outcomes through the adoption of both reconfiguration and transformational strategies.

The study also guides the service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. Rapid innovation and business model innovation are essential components, enabling service organizations to foster a culture of innovation and remain competitive. In addition, the adoption can lead to improved financial performance, job creation and economic growth, contributing to positive social and economic impacts.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations. It identifies the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research also provides a foundation for further investigation into the effectiveness of these strategies and their impact on organizational performance and sustainability. By better preparing service organizations for disruptions and uncertainties, this research triggers ameliorated organizational performance and sustainability.

Within the realm of the service industry, the present investigation has undertaken the development, quantification and scrutiny of both resilience and tenacity. In addition, it has delved into the intricate dynamics surrounding the influencing factors and antecedents that bear upon resilience, elucidating their consequential impact on the operational performance and outlook of service-oriented organizations. The findings derived from this research furnish valuable insights germane to enhancing operational efficacy and surmounting impediments within the sector.

The purpose of this paper is to empirically appraise the health of banking systems by applying a new theoretical framework based on resilience and stability simultaneously. In line with complex system theories, the authors will consider the dynamics of the banking system as a whole, analysing not only banks individually but also the broad environment in which they operate. For doing so, the authors propose a composite indicator (CI) for analysing the resilience and stability of banking systems of developed countries. The main purpose of the indicator is not to make predictions on future banks’ behaviour, but rather to use it as a tool for appraising the overall health of the most salient banking systems.

The authors have designed a theoretical framework of resilience and stability taking into account the review of previous literature. The authors have identified the main factors underlying these two concepts that can be appraised as complementary targets. The authors have applied multiple factor analyses to identify the main determinants of banks’ resilience and stability, and the authors have constructed a CI giving different weights to the relevant dimensions previously identified. The authors have tried different model specification and the authors have chosen the simplest model that render better empirical results. The authors construct the resilience and stability indicator for the group of G7 countries, Spain and Portugal, from 2004 up to 2015.

First, resilience–stability indicators for the group of countries analysed reveal quite different patterns in the aftermath of the financial crises. While some countries have improved its relative position within the ranking, the authors find others evolving just in the opposite direction. Second, the relative position of countries in terms of the resilience–stability indicator allows the authors to identify Canada and the USA as examples of best practices. Third, by analysing countries individually the authors will be better able to identify potential weakness and areas for improvement in each case.

The evolution of the resilience and stability indicator will serve as an early warning system for policy makers and supervisors in identifying signs of weakness, as well as a useful tool to identify the best practices. Furthermore, this indicator will allow to better assessing the potential vulnerability of banking systems in the advent of a forthcoming crisis. Therefore, this measurement should not be interpreted as an absolute value but as a warning signal of potential weakness in each case.

The main contribution of this paper to the existing literature is that it introduces a new reconceptualization of the health of the banking system in line with complex theories. The theoretical background is based on a comprehensive framework of resilience and stability as complementary targets. The CI summarises into a single figure a multidimensional concept like resilience and stability. The variables that the authors have used for the construction of the indicator have been validated by applying multiple factor analysis. The authors have empirically appraise the resilience and stability of a group of advanced economies that encompass the group of the more developed countries in the world and the two European cases that have receive financial support in order to see if there are remarkable differences.

This study examines the importance of financial literacy in explaining financial resilience in four continental European countries during the coronavirus disease 2019 (COVID-19) crisis while controlling for a wide set of additional determinants.

Variable importance may vary with the technique applied. Therefore, different classification techniques, such as logistic regression, partial proportional odds regression, and conditional random forest, have been employed. The analysis relies on the Survey of Health, Ageing and Retirement in Europe in the context of COVID-19, collecting 4,781 observations from France, Germany, Italy, and Spain.

In line with previous studies, financial resilience is found to increase with financial literacy that consistently ranks in the midfield in terms of variable importance among all explanatory variables.

The findings reveal the most important features that improve financial resilience. Financial literacy is one of the few determinants of financial resilience that can be actively shaped. To increase preparedness for future crises, a policy mix of financial education, regulation, and nudging may help increase financial literacy and, subsequently, financial resilience.

The better the financial literacy, the more protected individuals are from macroeconomic shocks. However, most previous studies do not rely on data samples that cover such crises. Moreover, most of the previous studies rely on single classification techniques, while this study applied traditional and data-mining techniques to assess feature importance.

The COVID-19 pandemic has significantly affected service small- and medium-sized enterprises (SMEs), increasing the importance of understanding how these businesses can become more resilient and how service innovation can be an effective strategy to increase their adaptive capacity and survival. This study aims to examine the role of dynamic capabilities in service innovation as a factor explaining the resilience of SMEs in Puerto Rico and the Dominican Republic during the COVID-19 crisis and its impact on service innovation. Additionally, the authors assess whether service innovation has a significant impact on value cocreation in these businesses.

This study used a quantitative method by surveying 118 SME owners in Puerto Rico and the Dominican Republic. The data were analyzed using partial least-squares structural equation modeling.

The results reflect important theoretical contributions by analyzing resilience from an innovation perspective instead of a retrospective approach, which is an area that has not been analyzed in the literature. Additionally, theoretical contributions to marketing services in SMEs are discussed, which is an underresearched topic. The results advance by discussing the role of service innovation through the reconfiguration of resources and how this can be an effective strategy to increase value cocreation with customers during crises.

This study is original in that it analyzes resilience from the perspective of innovation, and not from a retrospective approach. It offers a vision in response to the need for studies that provide a clearer conceptualization of resilience in small businesses. This highlights the importance of considering regional differences and service innovation as effective strategies to enhance resilience and value cocreation with customers.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.