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Article

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Title

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

Authors

[ 1 ] Instytut Inżynierii Bezpieczeństwa i Jakości, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee | [ D ] phd student

Scientific discipline (Law 2.0)

[6.6] Management and quality studies

Year of publication

2023

Published in

Central European Journal of Operations Research

Journal year: 2023 | Journal volume: 2023

Article type

scientific article

Publication language

english

Keywords
EN
  • Organizational resilience
  • Decision-making process
  • Regression
  • Machine learning
  • Artificial intelligence
Abstract

EN 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. 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 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 a 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 the managerial decision-making process by increasing the accuracy of organizational resilience potential prediction and indicating the importance of attributes and factors affecting the potential for organizational resilience.

Date of online publication

07.08.2023

Pages (from - to)

1 - 26

DOI

10.1007/s10100-023-00875-z

URL

https://link.springer.com/article/10.1007/s10100-023-00875-z

License type

other

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Release date

07.08.2023

Date of Open Access to the publication

at the time of publication

Full text of article

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Access level to full text

public

Ministry points / journal

70

Impact Factor

1,7 [List 2022]

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