The use of machine learning techniques for assessing the potential of organizational resilience

Aug 4, 2026

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Organizational resilience strengthens when companies can anticipate, plan, decide, and respond quickly to disruptions. This study proposes the most accurate machine learning technique for predicting attributes of organizational resilience potential using data derived from a 48-item questionnaire aligned with ISO 22316:2017. Regression and machine learning approaches are compared, and ensemble methods emerge as optimal for accuracy. A k-nearest neighbor-based preprocessing step is applied for a stacked ensemble combining Random Forest, Naive Bayes, and Support Vector Machine, supporting managerial decision quality and identifying key resilience attributes.

LanguageEnglish

CategoryResearch & Report

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Pages26

Reading Time1 h 6 min

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Abstract

Introduction

Central European Journal of Operations Research [https://doi.org/10.1007/s10100-023-00875-z](https://doi.org/10.1007/s10100-023-00875-z) ORIGINAL PAPER The use of machine learning techniques for assessing the potential of organizational resilience Tomasz Ewertowski1 · Buse Çisil Güldoğuş2 · Semih Kuter3 · Süreyya Akyüz4 · Gerhard‑Wilhelm Weber1,5 · Joanna Sadłowska‑Wrzesińska1 · Elżbieta Racek1 Accepted: 12 July 2023 © The Author(s) 2023 Abstract Organizational resilience (OR) increases when the company has the ability to anticipate, plan, make decisions, and react quickly to changes and disruptions. Thus the company should focus on the creation and implementation of proactive and innovative solutions. Proactive processing of information requires modern technological solutions and new techniques used. The main focus of this study is to propose the best technique of Machine Learning (ML) in the context of accuracy for predicting the attributes of the organizational resilience potential. Based on the calculations, the research includes estimating them through the applications of regression and machine learning methods. The dataset is obtained from the results of the our survey based on the questionnaire consisting of 48 items mainly established on OR attributes formed on ISO 22316:2017 standard. Based on the outcomes of the study, it can be stated that the optimal technique in the context of accuracy for predicting the attributes of the organizational resilience potential is ensemble methods. The k-nearest neighbor (KNN) filtering-based data pre-processing technique for stacked ensemble classifier is used. The stacking is achieved with three base classifiers namely Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) . The chosen ensemble method should be implemented in an organization systemically according to the circle of innovation, and should support the quality of managerial decision-making process by increasing the accuracy of organizational resilience potential prediction, and indication of the importance of attributes and factors affecting the potential for organizational resilience. Keywords Organizational resilience · Decision-making process · Regression · Machine learning · Artificial intelligence Extended author information available on the last page of the article 1 3 1 Introduction Disruptions such as the COVID-19 pandemic and other crises can affect the organization’s performance. In a crisis situation, managers must make rapid, high-risk decisions based on available information (Rauner et al. 2018) . To be able to withstand any kind of disruption, an organization needs three essential components: organizational resilience (OR), crisis management (CM), and business continuity management (BCM) . These components work together to create a comprehensive concept of integrating corporate recovery management systems. The foundation of this concept is the element of organizational resilience (Ewertowski 2022) . The pandemic crisis revealed varying levels of organizational resilience of enterprises (Ewertowski and Butlewski 2021) . Companies try to cope with increasing uncertainty to survive the crisis, adapt to a new situation and ensure stability and safety (Ma et al. 2018) . To become more resilient, organizations should anticipate and respond to threats and opportunities arising from sudden or gradual changes in both the internal and external context. Effective risk management help to achieve it (Ewertowski and Butlewski 2022) . Proper utilization of relevant information regarding past and current disruptions, as well asthe perception of organizational resilience characteristics by employees, is crucial in effectively managing the risks linked with issues related to organizational resilience. The approach allows to set and modify properly the solution based on the obtained information (Nehézová et al. 2022) . Enabling the refinement of the decision-making procedure in relatio

FAQs

What does organizational resilience require according to the document?

Organizational resilience increases when an organization can anticipate, plan, make decisions, and react quickly to changes and disruptions.

How is organizational resilience potential measured in this study?

The study uses a dataset created from a questionnaire with 48 items, primarily based on organizational resilience attributes defined by ISO 22316:2017.

Which machine learning approach is reported as most accurate for prediction?

Ensemble methods are reported as optimal for accuracy. A stacked ensemble is built using Random Forest, Naive Bayes, and Support Vector Machine with k-nearest neighbor filtering-based preprocessing.

Aug 4, 2026

Upload by Liam