Fuzzy Business Models and ESG Risk: Offering a Sustainable Perspective on Companies and Financial Institutions (Palgrave Studies in Impact Finance) 3031405749, 9783031405747

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PALGRAVE STUDIES IN IMPACT FINANCE

Fuzzy Business Models and ESG Risk Offering a Sustainable Perspective on Companies and Financial Institutions Edited by Magdalena Ziolo

Palgrave Studies in Impact Finance

Series Editor Mario La Torre, Department of Management, Sapienza University of Rome, Rome, Italy

The Palgrave Studies in Impact Finance series provides a valuable scientific ‘hub’ for researchers, professionals and policy makers involved in Impact finance and related topics. It includes studies in the social, political, environmental and ethical impact of finance, exploring all aspects of impact finance and socially responsible investment, including policy issues, financial instruments, markets and clients, standards, regulations and financial management, with a particular focus on impact investments and microfinance. Titles feature the most recent empirical analysis with a theoretical approach, including up to date and innovative studies that cover issues which impact finance and society globally.

Magdalena Ziolo Editor

Fuzzy Business Models and ESG Risk Offering a Sustainable Perspective on Companies and Financial Institutions

Editor Magdalena Ziolo University of Szczecin Szczecin, Poland

ISSN 2662-5105 ISSN 2662-5113 (electronic) Palgrave Studies in Impact Finance ISBN 978-3-031-40574-7 ISBN 978-3-031-40575-4 (eBook) https://doi.org/10.1007/978-3-031-40575-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Ivary Inc./Alamy Stock Photo This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgements

Research results presented in this paper are an integral element of research project implemented by the National Science Centre Poland under the grant OPUS16 no UMO-DEC-2018/31/B/HS4/00570.

v

Contents

1

Introduction Magdalena Ziolo

1

2

Theoretical Framework of Sustainable Business Models Anna Spoz

5

3

Fuzzy Logic Concept Iwona B˛ak and Maciej Oesterreich

29

4

Fuzzy Logic in Finance Anna Spoz and Magdalena Zioło

53

5

Fuzzy Logic in Business Ethics Beata Zofia Filipiak

73

6

Cooperation Between Financial Institutions and Companies: Fuzzy Business Models ESG-Oriented Beata Zofia Filipiak and Magdalena Ziolo

7

Conclusion and Recommendations Magdalena Zioło

105 133

References

137

Index

151

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Notes on Contributors

Iwona B˛ak is an Associate Professor, Ph.D. at West Pomeranian University of Technology Szczecin, Poland. She is an expert in the field of quantitative methods, specializing in analyses regarding the use of quantitative methods in economic research, with particular emphasis on international comparisons in the area of sustainable development, competitiveness of the national economy and regional development, with experience in working with advanced statistical packages: STATISTICA, R program, etc. Author and co-author of scientific articles published in scientific journals from the JCR list, also having practical experience in the implementation of projects carried out on behalf of public institutions. Beata Zofia Filipiak is Professor at University of Szczecin, Poland. She is the head of the Department of Sustainable Finance and Capital Markets at the University of Szczecin, as well as a Member of the University Council of the University of Szczecin. Her research and teaching scope focus on finance, finance management in public sector and sustainability. She has extensive experience gained in financial institutions and financial market. Scholarship holder of the Flemish government the University of Antwerp in 1999. She has received scholarships from DAAD (2007–2008 and 2008–2009) and DPWS (2016–2018), Erasmus +. She is a Member of the Financial Sciences Committee of PAS (the Polish Academy of Sciences), Expert of The National Centre for Research and Development (NCBR). She was an Expert of Polish Accreditation Commission and a board member of the Polish Association of Finance and Banking. She has ix

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been a licensed financial advisor since 1998. She was involved in 26 scientific projects regarding, corporate financial strategies, financial strategies of LGU’s and sustainable development. She carries out research supported by National Science Centre Poland in the scope of financing sustainable development. She is the author and editor of numerous books, mostly about financial management and financing sustainable development. Research interests: sustainable finances; use of financial instruments (“green financial instruments”) in creating sustainable development; ESG risk; financial strategies and their connection with business models; policy of public authorities toward sustainable development (instruments, decisions, effects). Maciej Oesterreich Ph.D., is an Assistant Professor at the Department of Applied Mathematics in Economics at the West Pomeranian University of Technology in Szczecin and specializes in econometric modeling and forecasting, as well as the application of quantitative methods in the analysis of socio-economic phenomena. He is the author or co-author of over 40 scientific papers (articles, chapters of monographs) and a member of the Polish Statistical Society and the Polish Economic Society. Anna Spoz Assistant Professor, Ph.D. at the Department of Finance and Accountancy at the John Paul II Catholic University of Lublin, Poland. Her research and teaching scope focus on finance, particularly corporate finance, accounting and tax and sustainable finance. Author and co-author of numerous publications on finance, accounting, reporting and management. She is reviewer of international publications. She combines teaching and scholarly activities with work in the business. Magdalena Ziolo is Professor at University of Szczecin, Poland. Her research and teaching scope focus on finance, banking and sustainability. She has extensive experience gained in financial institutions. She has received scholarships from the Dekaban-Liddle Foundation (University of Glasgow, Scotland) and Impakt Asia Erasmus + (Ulan Bator, Mongolia). She is a Member of Polish Accreditation Commission, Member of the Financial Sciences Committee of PAS (the Polish Academy of Sciences), Member of the Advisory Scientific Committee of the Financial Ombudsman, Expert of the National Centre for Research and Development, Expert of the National Science Centre and the National Agency for Academic Exchange, Expert of the Accreditation Agency of Curacao. She was a member of State Quality Council, Kosovo Accreditation Agency

NOTES ON CONTRIBUTORS

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and visiting professor of University of Prishtina (Kosovo). She is Principal Investigator in the research projects funded by National Science Centre, Poland in the field sustainable finance. She is the author and editor of numerous books, mostly about financing sustainable development.

List of Figures

Fig. 2.1

Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7

Fig. 3.8 Fig. 3.9 Fig. 4.1 Fig. 4.2 Fig. 4.3

Environmental, social and governance pillars of ESG (Source https://www.techtarget.com/whatis/definition/ environmental-social-and-governance-ESG [Accessed 14 June 2023]) Graphical representation of the A 1 set (Source own study) Graphic representation of the triangular membership function (Source own study) Graphic representation of the trapezoidal membership function (Source own study) Graphic representation of the membership function with the shape of Gaussian distribution (Source own study) Graphic representation of the S-shaped membership function (Source own study) Graphical representation of the linguistic variable “Age” (Source own study) The number of publications containing the phrase “fuzzy logic” in the Web of Science Core Collection database and the number of their citations in the years 1990–2022 (Source own study based on Web of Science) Map of links between keywords (Source own study) Classification of fuzzy MCDM methods (Source Kaya et al. [2019]) Risk management scheme (Source own elaboration) Examples of membership functions (Source own elaboration) Scheme of fuzzy inference system (Source own elaboration)

6 31 32 33 33 34 37

39 42 45 57 59 61

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LIST OF FIGURES

Fig. 4.4 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4

Fig. 5.5

Fig. 5.6

Fig. 6.1 Fig. 6.2 Fig. 6.3

Fig. 6.4

Fig. 6.5

Summary of fuzzy logic applications in crisis prevention (Source Own elaboration) Two areas are presented, taking into account which decisions are made (Source Own elaboration) The schematic diagram of the process (Source Own elaboration Imran and Alsuhaibani [2019]) Inclusion of ESG risk in the risk management process in entities (Sources Own elaboration) The postulates of including ESG risk in process risk management using different method (Sources Own elaboration) Consolidation of key areas of classic and ESG risks toward fuzzy approach using (Sources Own elaboration on Zou et al. [2014]) The basic areas (groups) of ESG risk common to enterprises from various sectors and institutions constituting potential “fuzzy base” areas (Sources Own elaboration on Society for Corporate Governance [2020]) The evolution of factors in choosing a financial institution by enterprises (Source Own elaboration) The passive adaptation level cooperation between financial institution by enterprises (Source Own elaboration) The creative cooperation level cooperation between financial institution by enterprises (Source Own elaboration) The holistic approach development of cooperation allowing to improve the quality of the decision-making process between financial institutions and enterprises, taking into account the impact of the ESG risk and the impact of climate change (Source Own elaboration on [KPMG, 2021]) Fuzzy business models (Source Own elaboration on Sen, 2017)

69 75 78 89

90

95

97 109 114

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120 126

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1 Table 3.2 Table 3.3 Table 4.1 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 6.1

Table 6.2

Research subjects in the field of ESG Publications regarding sustainable business model Challenges in SBM development and author who described them Financial sustainable business model archetypes Colors: green, red and yellow with RGB system codes The most frequent keywords in works containing the phrase “fuzzy logic” Advantages and disadvantages of using fuzzy logic Summary of models typically combined with fuzzy logic The matrix of the general use of the fuzzy approach is presented—the types (typology) of decisions made The matrix of the general use of the fuzzy approach is presented Application of the fuzzy approach in solving various decision problems Identification of common risks (classic and ESG): methods, tools, and techniques The factors determining mutual cooperation between enterprises and financial institutions toward a sustainable perspective Areas of interaction between financial institutions and enterprises on decision-making processes from the perspective of sustainability end ESG risk

8 12 16 19 35 41 43 65 80 81 84 92

111

117

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LIST OF TABLES

Table 6.3 Table 6.4

The levels of integration of ESG risk and sustainability in the decisions of enterprises and financial institutions The Triple Layered Business Model Canvas—elements

121 125

CHAPTER 1

Introduction Magdalena Ziolo

Contemporary finance and business are characterized by increasing complexity and an interdisciplinary approach. The best example is the growing impact of ESG (Environmental, Social, Governance) factors on finances and companies. This impact is noticeable primarily in the context of climate change and the recent experience of the COVID-19 pandemic, which significantly affected public finances. In the period before the pandemic, as well as now, finance is transforming and adapting toward ESG risk, especially climate change. To varying degrees, financial markets and institutions are exposed to the impact of ESG risk and its negative consequences. The insurance and banking markets are particularly exposed to ESG risk. Considering the impact of ESG risk on their operating activities and financial situation, financial institutions undertake several adaptation measures and adjust their business models toward the so-called sustainable business models. The reaction of financial institutions to new challenges related to the new

M. Ziolo (B) Institute of Economics and Finance, Faculty of Economics, Finance and Management, University of Szczecin, Szczecin, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_1

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M. ZIOLO

sphere of risk and an attempt to adapt to different conditions are visible. In particular, financial institutions take numerous actions to change business models and make them more sustainable, and this is connected with the need to include ESG risk in decision-making processes or, more broadly, risk analysis and valuation processes. The consequence of this approach is a change in the instruments and methods of risk management and monitoring. Climate stress tests, balanced benchmarks and ratings, taxonomy, labels, and balanced reporting appear, which are supposed to reduce information asymmetry and ensure effective risk management. Risk estimation in a new formula determines the need to develop a new approach to capital adequacy (including liquidity regulations), valuation of risk-weighted assets, or the offer of products and services, and calculating prices for financial services. These issues also impact changing the nature and levels of competition between financial institutions. The business sector also includes sustainability issues in its strategies, especially in the mission and vision, and, in practice, applies solutions that are part of the sustainable development trend. Entrepreneurs in their activities for sustainable development take into account i.e., participation in “green” public procurement, recycling, selection of social distribution channels, avoidance of orders that are socially and environmentally harmful, use of innovations with a positive impact on the environment, limiting the share of water in production, selection of suppliers from the CSR group, and several other activities contributing to building sustainable business models. Every business sector nowadays considers the impact of ESG factors on its management and finances. In sum, ESG factors are considered in financial performance, financial prediction, financial decisions, accounting, reporting, broadly understood risk management, and bankruptcy forecasting. There is a direct relationship between finance, primarily financial performance, and ESG. At the same time, factors affecting finances, including ESG factors, are difficult to measure, often needing to be fully defined and precise. Therefore, there are challenges related to the analysis and study of financial phenomena precisely and comprehensively. Therefore, there is a need for methods of analyzing business and finance that consider their current specificity expressed through inaccurate, incomplete data or fuzzy data. It applies to all finance and business, from financial data prediction to financial risk management and bankruptcy forecasting. With such challenges, fuzzy logic is the best solution. Fuzzy logic has its history in studying monetary phenomena, from analyzing the value

1

INTRODUCTION

3

of money in time to more complex formulas such as risk management, financial predictions, financial crises, or insolvency forecasting. The scope of fuzzy logic in finance is broad, and its role will grow along with the importance of ESG risk in finance, which is already being observed. Fuzzy logic is also an excellent tool for analyzing business models. However, research on business models and finance with fuzzy approach still needs to be completed. There are few papers published in this scope. This book covers the research gap of the fuzzy logic approach in business and finance. The aim of the book is to fill in the gap in knowledge about relationship between fuzzy business models and ESG risk. The book consists of seven chapters. This chapter is introduction. Chapter 2 presents the issue of ESG and defines the state of research in this field. The second part of the chapter is devoted to the issue of a sustainable business model and the challenges that companies face in this scope. The last part of the chapter describes a sustainable business model in financial institutions. The chapter especially points out the importance of the concept of sustainable development which makes the incorporation of ESG factors into the business model a natural necessity for modern entities that want to gain or maintain a competitive advantage on the market. The chapter discusses the role of ESG factors in the decisionmaking processes of the companies and its operating strategy. Chapter 3 presents information on fuzzy sets, in particular the concept, assumptions, applicability, and limitations. Differences between classical and fuzzy logic are shown as well. Attention is paid to the spheres of application of fuzzy logic in many areas of life, e.g., it has been shown to be extremely useful to many people involved in research and development. It is widely used in sciences, e.g.: chemical, aviation, agricultural, biomedical, computer, environmental, geological, industrial, mechatronic, and economic. An important part of the considerations is the presentation and typology of fuzzy MCDA—multi-criteria decision analysis. Chapter 4 indicates the possibilities of using the fuzzy approach in finance, particularly in the example of banking and crisis prevention. In particular, the fuzzy approach is described on a sample of essential processes such as risk management of financial institutions. The element of the original approach concerns not only the use of fuzzy logic itself but also the presentation of risk management from the perspective of financial risk and ESG risk. Chapter 5 discusses the possibilities of using the fuzzy approach in decision-making processes and risk management in business. This chapter

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M. ZIOLO

identifies the components of the fuzzy approach as an important approach to making decisions in conditions of reduced information quality. The chapter presents the possibilities of using and the role of the fuzzy approach in decision-making processes in enterprises, taking into account their impact on decision-making process. Attention was drawn to the differences in the approach to enterprises from the production, services, and trade sectors and the enterprise’s exposure to ESG risk. The stages and tools supporting risk management in the classical approach, and a specific approach and methods that may contribute to a better understanding of ESG risk were included in the process. The position of the fuzzy approach in the ESG risk management process was also shown. Chapter 6 presents the levels of cooperation between enterprises and financial institutions and the directions of mutual influence on decisionmaking processes from the perspective of spreading the sustainability process. Standard decision fields referred to business models of financial institutions and enterprises and the possibilities of using the fuzzy approach were shown. Fuzzy business models and research in this area are discussed. Attention was paid to possible cooperation scenarios between financial institutions and enterprises in building sustainable business models with fuzzy logic variables. Chapter 7 is the conclusion and recommendations. The book is addressed to a wide range of recipients (readers) ranging from scientists, students, doctoral students, practitioners, and others interested in the financial sector from the perspective of sustainability, ESG, and fuzzy logic, dealing with those issues as part of their professional work. The group of recipients can also include those who want to acquire or deepen their knowledge of fuzzy logic in finance because the first part of the monograph explains in detail and defines the meaning of the fuzzy logic approach for finance, business, and management.

CHAPTER 2

Theoretical Framework of Sustainable Business Models Anna Spoz

2.1

ESG Framework, Factors and Risks

The concept of sustainable development assumes intergenerational solidarity consisting in finding such solutions that guarantee further growth, which allows for active inclusion in development processes of all social groups, while giving them the opportunity to benefit from economic growth. In 2015, the United Nations (UN) developed and introduced a program for sustainable development. The 2030 Agenda included 17 Sustainable Development Goals (SDGs) and 169 tasks related to them, which reflect the three dimensions of sustainable development—social, environmental and economic sustainability (Herrero et al., 2021). ESG issues can be divided into three main areas: environment, society and corporate governance (Fig. 2.1). Each of them includes a number of aspects that can be assessed by stakeholders.

A. Spoz (B) Department of Finance and Accountancy, John Paul II Catholic University of Lublin, Lublin, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_2

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3 pillars of ESG Environmental • • • • •

Energy usage and efficiency Climate change strategy Waste reduction Biodiversity loss Greenhouse gas emissions Carbon footprint reduction

Social • • • • • • •

Fair pay and living wages Equal employment opportunity Employee benefits Workplace health and safety Community engagement Responsible supply chain partnership Adhering to labor laws

Governance • • • • • •

Corporate governance Risk management Compliance Ethical business practices Avoiding conflicts of interest Accounting integrity and transparency

Fig. 2.1 Environmental, social and governance pillars of ESG (Source https:// www.techtarget.com/whatis/definition/environmental-social-and-governanc e-ESG [Accessed 14 June 2023])

Environmental factors include the scope and methods of using renewable and non-renewable resources, including renewable energy, the amount of greenhouse gas emissions, efficiency in the management of natural resources, the amount of waste generated and the method of its disposal, as well as the impact on the natural environment and biodiversity. Social factors include those through which enterprises affect the social environment, i.e., employees, customers, suppliers and the local community. They cover issues such as employee management, employee rights, consumer rights, health and occupational health and safety issues. The COVID-19 pandemic that has affected the world in recent years has highlighted the importance of the risks associated with the spread of disease and infection. The concept of governance should be understood as the company’s internal supervision system, which consists of policies and procedures as well as standards and control mechanisms created and implemented in enterprises in order to streamline decision-making processes and improve management efficiency, comply with the law and take into account the needs of a wide range of stakeholders, including in particular investors.

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THEORETICAL FRAMEWORK OF SUSTAINABLE BUSINESS …

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Every company that wants to maintain or improve its position on the market needs efficient corporate governance rules adequate to its size and scope of operation as well as individual circumstances. The key links between all ESG areas and sustainability are people, risk and capital. People are at the heart of climate and resilience, well-being, diversity, equity and inclusion (DEI) and sustainability issues. Companies that engage their employees in achieving their ESG goals are more successful than those that do not. The risk of non-financial factors is gaining importance year by year. The Global Risks Report published every year shows that in recent years there has been a shift in the focus of risks, i.e., from the category of economic risk (so far leading) toward environmental and social risk. The latter in the cost of living crisis sub-category topped the most likely risks in the short term. The next risks were environmental. In the long-term perspective, the most important risks include four environmental risks (failure to mitigate climate change, failure of climate-change adaptation, natural disasters and extreme weather events, biodiversity loss and ecosystem collapse) and one social risk (large-scale involuntary migration) (World Economic Forum, 2023). Non-financial risk management includes monitoring and assessing the impact of ESG factors on the company and recording the costs of potential actions or their inaction. The entity should consider how and to what extent ESG issues may affect the process of creating the company’s value and which of them significantly impacts the company’s market valuation, its image and revenues generated from operations. Effective ESG risk management requires a comprehensive and integrated approach to this issue from the company, including the inclusion of non-financial risks in the company’s risk management system. The concept of capital includes both socially responsible investments made by the company and the financial involvement of the entity in the implementation of programs aimed at supporting the needs and development of its employees and the local community. The influence of ESG elements on modern firms’ operation has led to many research in a variety of fields being conducted on this problem. A synthetic summary of research problems related to ESG along with research results is presented in Table 2.1. Identification of the impact of ESG factors on the company and its proper management, in addition to the need to incur expenses, brings many benefits. If the entity does not control the ESG risk, as well as

– ESG companies are characterized by: • Lower credit spreads (Bauer & Hann, 2010) • Lower interest rates on loans (Chava et al., 2009; Goss & Roberts, 2011); • Lower cost of equity financing (Albuquerque et al., 2013; Dhaliwal et al., 2011; Giakoumelou et al., 2022; Salvi, et al., 2020, 2021; Sharfman & Fernando, 2008) • Lower cost of foreign capital (Dunne & McBrayer, 2019; Eliwa et al., 2021; Hamrouni et al., 2020; Raimo et al., 2021; Wong et al., 2021) – ESG companies are characterized by: • Positive relation between stakeholder welfare (such as employees, customers and communities) and positive correlated with Tobin’s Q (Deng et al., 2013; Guenster et al., 2011; Ioannou & Hawn, 2016; Jiao, 2010; Wrong et al. 2021) • Negatively correlated with Tobin’s Q (Baron et al., 2011) • Performance of SRI indices is comparable to conventional indices (Lee & Faff, 2013; Schröder, 2007) • Portfolios with a high CSR rating perform better than those with a low rating (Van de Velde et al., 2005), • “An unequivocally positive” contribution to risk-adjusted returns and ESG firms (Verheyden et al., 2016; Jin, 2018); • ESG factors show better investment performance over traditional size and value-based (Maiti, 2021) • Socially Responsible Investment (SRI) and conventional funds produce similar alphas (Derwall et al., 2005) • SRI mutual funds do not, on average, hold socially responsible firms to a greater extent than conventional funds (Utz & Wimmer, 2014)

Cost of capital

Future return, risk return

Valuation

The nature of dependence and an example of research

Research subjects in the field of ESG

Subject of study

Table 2.1

8 A. SPOZ

– ESG companies are characterized by: • Positive and significant relation between environmental and the firm’s return-on-assets ratio • ESG scores have a positive association with subsequent stock returns and return on equity (ROE) (Aydo˘gmu¸s et al., 2022) • Significantly increases corporations’ operating performance, measured by their return-on-assets (Guenster et al., 2011) • pozytywny zwi˛azek mi˛edzy ESG and financial performance of corporate (Janah & Sassi, 2021) • Corporate social responsibility (CSR) disclosure decreases the cost of capital (Chen & Jian, 2006; Chi et al., 2020; DeBoskey et al., 2017; Guidara et al., 2014; Khanchel & Lassoued, 2022; Talbi & Omri, 2014) • Integrated reporting decreases cost of debt (Gerwanski, 2020; Muttakin et al., 2020)

Accounting and financial performance:

Source Own elaboration based on Henriksson, R., Livnat, J., Pfeifer, P., Stumpp, M., & Zeng, G. (2018). ESG literature review. SSRN Electron J , 1–14

Disclosure and Financial Reporting

The nature of dependence and an example of research

Subject of study

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other risks, it may expose it to losses, the amount of which will significantly exceed the sum of the costs of actions taken for preventive purposes and related to proactive management. Additionally, the awareness of a wide range of stakeholders and their sensitivity to issues related to sustainable development may ensure that entities that integrate ESG factors at the strategic level in their activities and are characterized by high level of responsibility and transparency in running a business have a better chance of gaining the trust of the environment (customers, investors, capital providers). The trust can actually translate into an increase in revenues and increasing the possibilities and reducing the costs of obtaining equity and foreign capital, and thus also the possibility of achieving market success. In this context, it is worth mentioning the role of the financial sector, which, apart from providing capital for business entities, directs its flow. Financial institutions can support and stimulate the flow of capital to projects that fit the concept of sustainable development. The need of considering ESG factors in their operations is faced by entities providing investment services, i.e., financial sector entities, and is related to the entry into force of Commission Delegated Regulation (EU) 2021/1253 of April 21, 2021. Taking into account ESG factors in the process of providing investment services aims not only at supporting sustainable investments already in progress, but also at increasing demand for them, which indirectly contributes to supporting the implementation of sustainable development goals, including those related to climate. For this reason, ESG factors are included in the requirements for product governance, and therefore also in the study of customer investment preferences in the ESG area. The changes introduced to the MiFID II package are aimed at strengthening the impact of the provisions on disclosure of information related to sustainable development in the financial services sector (SFDR) and the recently introduced EU Taxonomy, which is a reference point in the process of qualifying a given activity as environmentally sustainable activity. Investment firms dealing with the creation and distribution of financial instruments have been obliged to take into account ESG factors in the process of product approval, management and supervision. When determining the target group for a given financial instrument, financial institutions are required to consider the sustainability objectives with which the given financial instrument is compliant. Sustainable development factors are also taken into account in the process of examining

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whether the financial instrument meets the identified needs and objectives of the target group. In this way, investors are provided with the opportunity to invest in financial instruments related to the implementation of projects in line with the concept of sustainable development and, consequently, increase both the demand for these instruments and the flow of capital for the implementation of projects related to sustainable development. The ESG factors included in a given financial instrument must be presented to clients in a transparent and understandable manner so that their presentation allows for proper consideration of any sustainability objectives of the client or potential client. In practice, therefore, when conducting an adequacy test of a given financial instrument, investment firms are obliged to obtain information from clients regarding their preferences in relation to investment products that take into account ESG factors. In this way, investment firms are obliged to obtain information from the client regarding his knowledge, experience in investing on the financial market, financial situation, including the ability to bear losses and investment objectives, taking into account the appetite for sustainable investments, including the level of acceptable risk. In addition to specifying the target group, investment firms are obliged to carry out regular reviews of the instruments offered. This requirement is to ensure that the coherence of a given financial instrument with the needs, characteristics and objectives of the target group, identified at the time of its creation, is not lost over time. This means that the initial categorization of an ESG financial instrument to a given target group will not be final and will be subject to periodic review. Investment firms will be required to regularly review the financial instruments they create, taking into account all events that could significantly affect the potential risk for a specific target group. A financial instrument qualified for a given target group will have to be consistent with the needs, characteristics and objectives of the target group, including its objectives related to the concept of sustainable development. Based on the scientific database Elsevier’s Scopus, the number of publications on the sustainable business model in the years 2003–2023 was analyzed (Table 2.2). During this time, a total of 273 papers (containing a “sustainable business model” in the title or abstract) were published. The most papers have been published so far in 2020, 45 publications, while before 2018 a total of 46 papers were published. Publications most often concerned topics from the areas of Business, Management and Accounting, followed by Environmental Science, Energy and Social Sciences.

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Table 2.2 Publications regarding sustainable business model

Total number of publications

273

Period 2023 2022 2021 2020 2019 2018 Subject area Business, Management and Accounting Environmental Science Energy Social Sciences Engineering Computer Science Economics, Econometrics and Finance Decision Sciences Other Document type Article Conference paper Book chapter Review Book Editorial Keyword Sustainable Business Model (s) Sustainable Development Sustainability Business Model(s) Sustainable Business Business Model Innovation Innovation Sustainable Business Model Innovation Business

2003–2023 35 44 37 45 26 40 35 163 144 109 92 89 35 30 21 46 186 33 29 23 1 1 131 120 100 95 86 43 38 22 27

Note One publication may cover several Subject Areas and several Keywords Source Own elaboration based on Scopus database

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Increasing number of publications on the sustainable business model in recent years shows how actual this topic is. Although this is not a systematic increase, the number of publications has an upward trend.

2.2 Sustainable Business Modes---State of the Art, Concept and Components Anthropogenic climate changes and the growing awareness of the impact of human activities on the environment and the living conditions of current and future generations mean that the importance of the concept of sustainable development is systematically increasing. The introduced legal regulations in this area as well as the awareness and sensitivity of consumers to social, environmental and governance issues made it difficult for conventional business models to respond to market needs. In search of a competitive advantage, enterprises began to include ESG factors in conventional business models, to achieve the goals of sustainable development while maintaining productivity and profitability (Schaltegger et al., 2016). Modifying existing business models (BM) to deal with challenges related to sustainable development or creating completely new sustainable business models is one of the most difficult challenges for enterprises today (Moratis et al., 2018). The concept of the business model gained popularity in the 1990s (Zott et al., 2011). In the literature on the subject, there are many definitions of the business model describing this concept from different perspectives. Timmers (1998) and Chesbrough and Rosenbloom (2002) and Magretta (2002) perceive the business model as a configuration of company elements (resources, technology, information) that allows it to generate benefits for various business entities, and in consequence also profits. It is also understood as a “simplified and aggregated representation of the relevant activities of the company” (Lemus-Aguilar et al., 2019) reflecting the implemented business strategy that will allow the entity to be competitive on the market (Casadesus-Masanell & Ricart, 2010; Richardson, 2008). Teece (2010) pointed out that designing business models enables the reconfiguration of a company’s business capabilities to adapt it to a changing environment. Most often, the business model is perceived through the value creation process. Chesbrough and Rosenbloom (2002), Magretta (2002), Kaplan and Winby (2007), Teece (2010) and Osterwalder and Pigneur (2010) define a business model as a description (justification) of how an organization creates, delivers

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and captures value. Creating a business model is about determining how the company will create value (for the customer) to gain a competitive advantage and succeed in the market. The process of developing an innovative business model that will provide end users with products or services that have not been available on the market so far is understood as business model innovation (BMI) (Mitchel & Bruckner Coles, 2004). According to Labbe and Mazet (2005), BMI is an innovative configuration of elements of an existing business model or consists in creating new business models in start-ups. Such an approach to BMI allowed Geissdoerfer et al. (2016) to distinguish its four configurations, i.e., start-ups, business model transformation, business model diversification and business model acquisition. A sustainable business model can also be defined as an innovation of a conventional business model (Girotra & Netessine, 2013), where innovation adds some characteristics and goals to it. They either: (1) include sustainability-related ideas, values or objectives; or (2) include sustainability into their value proposition, value creation, value delivery and/or value capture processes (Lemus-Aguilar et al., 2019). Creating an SBM is not only about considering ESG factors, but also about focusing on the needs of stakeholders (Evans et al., 2017). In this approach, the term stakeholders are extremely broad, as it includes both employees, shareholders, investors, suppliers, consumers and public stakeholders (governments, universities and local communities). Stubbs and Cocklin (2008), the stakeholder also included the environment (nature) and society. In this approach, the needs and expectations of stakeholders are very important, because the measure of a company’s success is meeting the needs of stakeholders. One of the main elements of SBM is building lasting relationships with stakeholders based on mutual trust (Gulati & Kletter, 2005). Stubbs and Cocklin (2008) made six proposals to characterize SBM, starting with the organizational purpose expressed in terms of ecological, social and economic outcomes. The main goal is to achieve sustainable development, while financial profits are treated only as a means to achieve sustainable development goals. The company’s goal is to meet the needs of stakeholders, which is why the method of creating value is extremely difficult and requires a multi-faceted and integrated approach, because the group of stakeholders is not homogeneous. In turn, Boons and LüdekeFreund (2013) use an approach based on the four pillars of the “Business Model Canvas” concept of Osterwalder and Pigneur (2010) and claim

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that customer value propositions should provide measurable ecological and/or social value in line with customer value and that it should balance the needs of the client and society (Lüdeke-Freund & Dembek, 2017). A combination of the above-mentioned approaches is the definition of SBM proposed by Bocken et al. (2014). In their view, sustainable business models (SBM) include a triple bottom-line approach and take into account a wider range of stakeholder interests (considering the environment and society as stakeholders). SBM are important in driving and implementing corporate sustainability innovation; they can help embed sustainability in business goals and processes and serve as a key competitive advantage. Sustainable business models can be used as a tool to coordinate technological and social innovation with sustainability at the system level. Despite different methodological approaches, the common characteristics of SBM can be distinguished. These are (i) a clear focus on sustainability, combining ecological, social and economic aspects, (ii) an expanded notion of the process of value creating, (iii) an expanded concept of capturing value in terms of those for whom value is created, (iv) increasing the importance of meeting the needs of stakeholders, the concept of which has been significantly expanded and (v) an expanded perspective on the system in which SBM is embedded and its impact on the surrounding (Lüdeke-Freund & Dembek, 2017). The challenges when developing a sustainable business model are presented in Table 2.3. To introduce the method of implementing sustainable business models, Bocken et al. (2014) proposed a categorization of nine archetypes of sustainable business models. One of the latest areas of research on a sustainable business model is sustainable business model innovation. Schaltegger et al. (2016) defined it as “modification or completely new business models that can serve the achievement of sustainable development goals, i.e., counteracting negative or creating positive externalities for the natural environment and society” (Schaltegger et al., 2016). Similar to business model innovation, these models are seen as a process of exploration, customization, redesign, acquisition and transformation. A process qualifies as sustainable business model innovation or business model innovation for sustainable development when it aims at: (1) sustainable development or a positive, appropriately reduced, negative impact on the environment, society and long-term well-being of the organization and its stakeholders, or

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Table 2.3 Challenges in SBM development and author who described them Challenges

Authors

Triple bottom line The co-creation of profits, social and environmental benefits and the balance among them is challenging for moving toward SBMs Mind-set The business rules, guidelines, behavioral norms and performance metrics prevail in the mind-set of firms and inhibit the introduction of new business models Resources Reluctance to allocate resources to business model innovation and reconfigure resources and processes for new business models Technology innovation Integrating technology innovation, e.g., clean technology with business model innovation is multidimensional and complex External relationship Engaging in extensive interaction with external stakeholders and business environment requires extra efforts Business modeling methods and tools Existing business modeling methods and tools, e.g., Osterwalder and Pigneur (2010) and Johnson et al. (2008), are few and rarely sustainability driven

Hart and Milstein (2003), Stubbs and Cocklin (2008), and Schaltegger et al. (2016)

Johnson et al. (2008), Yu and Hang (2010), Boons and Lüdeke-Freund (2013)

Chesbrough (2010), Zott et al. (2011), and Björkdahl and Holmén (2013)

Hart and Milstein (2003), Yu and Hang (2010), and Zott et al., (2011)

Stubbs and Cocklin (2008), Vladimirova (2012), and Boons and Lüdeke-Freund (2013) Björkdahl and Holmén (2013), Girotra and Netessine (2013), and Yang et al. (2014)

Source Evans, S., Vladimirova, D., Holgado, M., Van Fossen, K., Yang, M., Silva, E. A., & Barlow, C. Y. (2017). Business model innovation for sustainability: Toward a unified perspective for creation of sustainable business models. Business Strategy and the Environment, 26(5), 597–608

(2) adopting solutions or features that foster sustainability in its value proposition, creating and capturing elements or their value networks. Geissdoerfer et al. (2016) similarly to the innovative business model, proposed four types of innovations in the field of sustainable business model: (1) sustainable start-ups: a new organization with a sustainable business model is created; (2) transformation of a sustainable business model: the current business model is changing, resulting in a sustainable business model; (3) sustainable diversification of the business model: no major changes to the organization’s existing business models and an additional sustainable business model is established; (4) acquiring a sustainable

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business model: an additional sustainable business model is identified, acquired and integrated into the organization. Creating and implementing sustainable business models and sustainable business model innovation is an extremely difficult process that requires an integrated approach in many areas. However, enterprises that want to maintain or strengthen their position on the market must take this fight.

2.3 Sustainable Business Models of Financial Institutions A business model describes the way of doing business by a firm. It is a conceptual tool that can be used for analysis, comparison and performance assessment, communication, management and innovation (Osterwalder et al., 2005). In turn, sustainable business models (SBMs) are tools that enable businesses to concurrently achieve social, environmental and economic goals (Nosratabadi et al., 2020). The financial industry was not the first industry to turn to sustainability. Due to the nature of their operations, financial institutions do not have such a negative impact on the natural environment as companies from the production or transport industries. Greenhouse gas emissions, waste generation and raw material consumption by the financial sector are relatively low. Despite this, the possibility of achieving benefits and the expectations of the stakeholders made financial institutions implement the principles of sustainable development. The most noticeable benefits are improved reputation, increased employee satisfaction, reduced costs thanks to saving energy, water and materials, and the ability to promote the brand as environmentally friendly (Nosratabadi et al., 2020). Currently, financial institutions can not only boast of conducting sustainable activities, but also encourage their clients to conduct sustainable activities. This is done mainly by supporting green projects and restricting access to services and funds to entities whose activities are harmful to the environment. The transformation of the financial sector toward sustainability can be done in various ways. Initially, sustainability was implemented independently of the strategy and business model of the companies. Over time, it was discovered that effective transformation, ensuring sustainable economic growth, is possible only through the transformation of the organization’s business model into a sustainable one. This approach

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applies not only to the financial industry, but to companies from all industries. The need to transform business models into sustainable business models (SBM) arose when it became apparent that the introduction of eco-design and eco-efficiency by organizations was not sufficient to offset the growing demand for resources (Bocken et al., 2014) and to stop environmental degradation. Fundamental changes in the way companies operate were therefore needed. Although presence of many definitions of SBM in the literature, but most of them include economic value capturing while delivering sustainable values to stakeholders or contributing to the sustainable development of both the company and society (Lüdeke-Freund et al., 2019). In some definitions, society and environment are considered as stakeholders (Stubbs & Cocklin, 2008). The transition to SBM is related to business model innovation which goal is to identify new business strategies that will challenge the established competitive landscape of a market and result in the creation of new business models (Ireland et al., 2001). In order to describe mechanisms and solutions that can help create a business model for sustainability, sustainable business model archetypes for banking industry have been developed. Yip and Bocken (2018) proposed eight financial SBM archetypes, grouped in three categories: Technological, Social and Organizational. The archetypes are presented in Table 2.4. with description of three components of business models, proposed by Richardson (2008): value proposition, value creation and delivery, and value capture. Before sustainable business models were introduced in financial institutions, the concept of corporate social responsibility (CSR) emerged. According to a wide group of researchers, bank’s path to sustainability began with social responsibility adoption. Another approach to the bank’s sustainability includes activities contributing to mitigating the negative impact on the environment, which include saving energy, water and materials as well as reducing the amount of waste. According to the third approach, banks can achieve sustainability goals by offering sustainable products that contribute to sustainable development (Nosratabadi et al., 2020). Sustainable operation of banks requires taking environmental social and governance (ESG) factors into account. The role of banks as financial intermediaries means that the challenges arising from ESG factors for the financial system require them to take adjustment measures. These measures include the development and implementation of ESG risk

3. Encourage sufficiency (Social)

Solutions that seek to reduce demand (which was generally inflated before) by correct assessment of customer needs and reducing mis-selling of financial products and moral hazard in lending. The focus is on the customer relationship and reward system

Services that use fewer resources, generating less waste and emissions than the services that deliver similar functionality 2. Substitute with digital Reduce environmental impacts processes (Technological) and increase business resilience in terms of speed, convenience, cost and accuracy by using electronic means in service delivery process

Vale proposition

Financial sustainable business model archetypes

1. Maximize material and energy efficiency (Technological)

Archetype

Table 2.4

This may involve changing the frontline sales staff’s remuneration to a higher portion of fixed salary, promoting need-based selling by correct matching of products and advocating sensible borrowing

Innovation in service delivery design (e.g., delivery channels) enhances the speed, convenience, cost and accuracy of service delivery to customers

Focus is on the internal operational process innovation

Value creation and delivery

(continued)

Costs are reduced through increased operational efficiency leading to increased profits Revenue is enhanced by providing customers more convenience, which may result in more frequent transactions Cost saving is achieved by reducing manpower and related expenses Customer satisfaction and loyalty may increase that may lead to more business. Compliance risk is lowered and reduces the chance of penalties by regulators. Societal benefit is captured: customers get what they really need in the right quantity and quality

Value capture

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(continued) Vale proposition

Inclusive value is created through product and service innovations that serve previously un-served markets or high-risk customers

Creating societal benefits and environmental benefits through specializing in providing banking services that match the needs of the customers

5. Inclusive value creation (Social)

6. Repurpose for society/ environment (Organizational)

4. Adopt a stewardship role Provision of services intended to (Social) genuinely and proactively engage with stakeholders to ensure their long-term well-being. Broader benefits to stakeholders often become an important aspect of the values proposition by engaging customers better

Archetype

Table 2.4 Value capture

Stewardship strategies can generate brand value, potential cost savings and secure future business. Stakeholders’ well-being generates long-term business benefits. For example, healthy and happy staff may claim less sick days and be more productive Process innovation is the key to Increase of market share. reduce the risks associated, for More business opportunities example, using credit scoring and may be secured by customer portfolio management methods to loyalty when customers manage the risks in SME lending become more profitable in the future. Societal benefit is also captured Banks are using sustainability as a Only provide banking services criterion for selecting customers; to sustainable companies and being an expert in providing the disadvantaged, including banking services to this particular “positive screening” against segment and achieving economies social and environmental of scale benchmarks

Ensuring activities and partners are focused on delivering stakeholders’ well-being. The value chain is ensured to deliver environmental or social benefits

Value creation and delivery

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Use loan approval process innovation with sustainability criteria to screen out unsustainable business (i.e., negative screening) Asset-side and liability side products are created for savers/ investors and borrowers respectively

7. Resilience in loan granting (Organizational)

8. Sustainable financial products (Organizational)

Source Yip and Bocken (2018)

Vale proposition

Archetype

Value capture

Lending to customers with no/minor sustainability risk. This can directly reduce financial resources to companies with adverse sustainability impacts Product innovation opens up new Product innovation enables markets in sustainable finance and participation in sustainability, supports sustainable development which may provide a platform for both savers and borrowers to pursue financial return in sustainable business

As the sustainability risk is lowered, the cost of capital and the potential bad debt could be reduced

Value creation and delivery

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management systems, as well as the adaptation of the product and service offer so that it supports activities for sustainable development and ensures the implementation of the assumptions of the business model based on creating sustainable value (Zioło et al., 2020). ESG risk management by incorporating the risk of non-financial factors in the decision-making process of financial institutions requires the development and implementation of an ESG management strategy whose shape depends on the adopted model of ESG factor integration. When developing this strategy, the level of expectations and integration of ESG factors in the decision-making process should be specified and the types of ESG risks to which the financial institution is exposed should be identified. Strategy development takes place in five stages (Zioło et al., 2020): 1. Determining the level of expectations regarding the degree of integration of ESG factors; 2. Risk exposure identification; 3. Determining the level of ESG risk acceptable to the institution; 4. Response to the risk; 5. Development of the ESG policy framework and implementation of the ESG strategy. Innovative services and products offered by a financial institution are of great importance for mitigating the ESG risk. They should additionally support sustainable development, in accordance with the assumptions of the sustainable business model. The inclusion of ESG factors and the consideration of ESG risk in the management processes of a financial institution, together with the transformation of the business model into a sustainable business model, lead to achievement of sustainable goals. In the case of banks, such goal may be sustainable banking. The concept of sustainable banking was created, based on three pillars of sustainable development: environmental, social and economic. Sustainable banking includes three concepts (Zioło, 2020): – green banking–environmental pillar, – ethical banking–environmental, social and economic pillars,

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– socially responsible banking–environmental, social and economic pillars. Sustainable banking refers to financial services and products that have been created to address social needs, protect the environment and still generate profits for the running activities of sustainable banks (Yip & Bocken, 2018). Sustainable business models have great potential for integrating sustainability principles and integrating the Sustainable Development Goals into the value proposition, value creation and value capture activities of the entity. According to Lüdeke-Freund et al. (2019), sustainable business models are tools to ensure sustainable development taking into account ESG factors. They aim to apply proactive multilateral governance, innovation and a long-term perspective to achieve the Sustainable Development Goals. Sustainable business models therefore effectively contribute to reducing the harmful impact of business activities on the environment and society by providing solutions that help entities (enterprises and financial institutions) to simultaneously achieve their economic and sustainable development goals. Creating sustainable models is a difficult process, but every modern market player has to face this challenge.

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CHAPTER 3

Fuzzy Logic Concept Iwona Bak ˛

and Maciej Oesterreich

3.1 Basic Theoretical Information About Fuzzy Sets Decision-making is one of the most important activities of a human being, who realizes that people’s opinions or preferences are permeated with vagueness and imprecision. Many scientists have tried to solve this problem by looking for new methods of dealing with uncertainty. At the beginning of the twentieth century, the Polish scientist Jan Łukasiewicz proposed a system of three-valued logic, which is the basis for fuzzy logic, which is an extension of classical logic. Nevertheless, Lotfi A. Zadeh, who in 1965 published the article “Fuzzy Sets” (Zadeh, 1965), is considered to be the author of the theory of fuzzy sets and fuzzy logic. He proposed a new methodology for dealing with vagueness and imprecision, based on

I. B˛ak (B) · M. Oesterreich Faculty of Economics, Department of Mathematical Applications in Economy, West Pomeranian University of Technology, Szczecin, Poland e-mail: [email protected] M. Oesterreich e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_3

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the concept of “fuzzy or gradual membership” to a set, which he called “fuzzy set” (Farhadinia & Chiclana, 2021). Zadeh applied Łukasiewicz’s logic to each element of the set and derived a complete algebra of fuzzy sets. Fuzzy set theory and fuzzy logic are important technologies forming the area of computational intelligence that identify a set of methods to solve real-world problems that are not satisfactorily solved by traditional (classical) methods (Herrea-Viedma, 2015). Classical (crisp) logic is based on two values, most often represented by 0 and 1 or true and false. The boundary between them is clearly defined and unchanging. Fuzzy logic is an extension of classical reasoning. It puts values between the standard 0 and 1; it “blurs” the boundaries between them, giving the possibility of values between this range (e.g., almost false, half true) (Sadowski et al., 2018). Thanks to this, it is possible to describe ambiguous phenomena that cannot be captured by classical theory and two-valued logic. The fuzzy set A in some numerical space of considerations X is the set of pairs: A = {(μ A (x), x)}; x ∈ X,

(3.1)

where: μ A is the membership function of the fuzzy set A, which assigns to each element x ∈ X the degree of its membership in the fuzzy set. The degree of membership assigns each element x of a given variable a certain value from the range [0;1] in the fuzzy set A : μ A (x) :→ [0, 1]. This value informs to what extent the element x belongs to the fuzzy set A (Ross, 2010, pp. 34–35). To describe the fuzzy set, we usually use the following notation: A = {μ A (x)/x, x ∈ A, μ A (x) ∈ [0, 1]}

(3.2)

In this case, the “/” symbol does not mean division, but only informs about the value of the membership function assigned to a given element of the set (Bojadziev & Bojadziev, 2007, p. 10).

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Example The set A1 describes the similarity of numbers to the number 3: A1 = {0.33/1, 0.66/2, 1/3, 0.66/4, 0.33/5}

(3.3)

This notation means that the values of x ∈ A1 : 1, 2, 3, 4 and 5 were assigned the following values of the membership function μ A1 (x): 0.33, 0.66, 1, 0.66 and 0.33. Figure 3.1 presents the A 1 set in a graphical form. A fuzzy set is defined as normalized when the membership function for at least one of the elements of this set A reaches a maximum value of 1, otherwise, the set is called unnormalized. An unnormalized set can be transformed into a normalized one by dividing the value of the membership function of individual elements by the maximum value (max μ A (x)) in the set (Bojadziev & Bojadziev, 2007, p. 10): μ A (x) max μ A (x)

(3.4)

The membership function (μ A (x)) is a curve that determines the degree to which an element of the set (x) (input space) is assigned to the fuzzy set A and takes values between 0 and 1. The most commonly used mathematical forms of the membership function are (Chaira, 2019, pp. 6–7):

Membership function

1

0 1

2

3

4

x

Fig. 3.1 Graphical representation of the A 1 set (Source own study)

5

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• triangular functions (Fig. 3.2): ⎧ ⎪ 0, a ≤ x ⎨ x−a ,a ≤ x ≤ b μ(x) = b−a ⎪ ⎩ c−x , b ≤ x ≤ c c−b • trapezoidal functions (Fig. 3.3): ⎧ ⎪ 0, a ≤ x ⎪ ⎪ ⎪ x−a ⎪ ⎨ b−a , a ≤ x ≤ b μ(x) = 1, b ≤ x ≤ c ⎪ ⎪ ⎪ d−x ,c ≤ x ≤ d ⎪ ⎪ ⎩ d−c 0, x > d • with the shape of Gaussian (normal) distribution (Fig. 3.4):   1 (x − m)2 μ(x) = √ exp − 2σ 2 σ 2π

(3.5)

(3.6)

(3.7)

where: σ —the standard deviation, m—mean.

1

0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20

Fig. 3.2 Graphic representation of the triangular membership function (Source own study)

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1

0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20

Fig. 3.3 Graphic representation of the trapezoidal membership function (Source own study) 1

0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20

Fig. 3.4 Graphic representation of the membership function with the shape of Gaussian distribution (Source own study)

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1

0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20

Fig. 3.5 Graphic representation of the S-shaped membership function (Source own study)

• S-shaped functions (Fig. 3.5): ⎧ ⎪ 0, a ≤ x ⎪ ⎪ 2  ⎪ ⎪ (x−a) a+b ⎨ 2· (b−a) , a < x < 2 μ(x) =  2 ⎪ (x−b) a+b ⎪ ⎪ 1 − 2 · (b−a) , 2 < x < b ⎪ ⎪ ⎩ 1, x > b

3.2

(3.8)

Fuzzy Logic

As already mentioned, in classical logic, the so-called intermediate values are not taken into account. This means that a given statement can be true or false. Fuzzy logic, in turn, due to its multi-valued nature, is closer to the natural human thought process, which is not always “zero–one”.

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Table 3.1 Colors: green, red and yellow with RGB system codes Green

Red

Yellow

R: 0; G: 255; B: 0

R: 255; G: 0; B: 0

R: 255; G: 255; B: 0

Source own study

Example Table 3.1 shows the three colors—green, red and yellow—with their corresponding RGB system codes. Based on this information, the statement that: Gr een = Y ellow according to classical logic is false—green and yellow are two different colors. However, looking from the point of view of fuzzy logic, such a statement is not entirely false, nor is it entirely true—green is, after all, a component of yellow. The degree of this similarity will be defined by the value of the membership function. A detailed description of logical operations on fuzzy sets, which include, among others: • union, • intersection, • complementation, can be found in: (Piegat, 1999, pp. 112–154; Chen & Pham, 2001, pp. 69–75; Buckley & Eslami, 2002, pp. 21–29; Valášková et al., 2014). Due to the multi-valued nature of fuzzy sets, in their description linguistic variables are used. The values of these variables are words, expressions or sentences in a natural or artificial language (Zadeh, 1975). These variables are characterized by five parameters (Klir & Yuan, 1995, p. 102):

 x; T (x), U, G, M˜ (3.10) where: • x —name of the linguistic variable, • T (x)—a set of linguistic values (definitions) assumed by the variable, • U —space of considerations (universe),

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• G—semantics/grammar, generating linguistic values T, ˜ • M—a semantic rule assigning to each linguistic value t ∈ T an appropriate fuzzy set m(T ), as a subset of the space X.

Example Using the notations presented in formula (9), let us define the linguistic variable “Age”. For this purpose, let us use the methodology of Eurostat, which distinguishes three main groups of the population in terms of age: • young people: 0–14 years old, • working age people: 15–64 years old • elderly people: 65 and over. The linguistic variable “Age” can take the following form: v = Age;

T ∈ “young”, “working age”, “older” ; X ∈ [0, 100]; G ∈ ∅; 

M˜ young = u, μyoung (u), u ∈ [0, 100] ⎧ 1, u ∈ [0, 14] ⎨ μyoung (u) 1 − x−14 11 , u ∈ [15, 24] ⎩ 0, u ∈ [25, 100]  

M˜ working age ⎧ = u, μworking age (u), u ∈ [0, 100] ⎪ 0, u ∈ [0, 14] ⎪ ⎪ ⎪ x−14 ⎪ ⎨ 11 , u ∈ [15, 24] μworking age (u) 1, u ∈ [25, 59] ⎪ x−59 ⎪ ⎪ 1 − ⎪ 6 , u ∈ [60, 64] ⎪ ⎩ 0, u ∈ [65, 100] ˜ {u, M(older) = μ older (u), u ∈ [0, 100]} ⎧ 0, ∈ [0, 59] ⎨ μolder (u) x−59 , u ∈ [60, 64] ⎩ 6 1, u ∈ [65, 100]

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Figure 3.6 presents a graphical representation of the linguistic variable “Age”. In the classical approach, relationships between variables can be represented by correlation coefficients, whose absolute value and sign indicate its strength and direction. In fuzzy logic, due to the fact that the values of the membership function for individual elements of sets can take values in the range [0,1], it is difficult to build a measure that determines the strength of relationships between variables (see Chiang & Lin, 1999; Wu & Hung, 2016). In this case, the description of the process or phenomenon is most often defined by the researcher using a rule base. This means that it must independently determine the importance of individual variables, the strength and direction of their interconnections and the impact on the described phenomenon. Such a fuzzy base rule consists of single rules of the form “if -> then” (Shepherd & Shi, 1998): (3.11)

IF x is A THEN y is C

Membership function

where the elements X and  Y are related by the levels of the membership function (µA (x) i µC y ) and A and C are some fuzzy variables.

1

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

0

Age young

working age

older

Fig. 3.6 Graphical representation of the linguistic variable “Age” (Source own study)

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Example Suppose we want to describe the demand for some goods. This phenomenon would be described by a linguistic substitute: UDemand ∈

decrease, no change, increase . We assume that demand will be affected by two variables described by two linguistic variables: AType of good ∈

basic, higher order oraz BPrice change ∈ {decrease, increase}. In this case, the rule base can take the following form: IF AType of good = basic AND BPrice change = increase THEN UDemand = no change IF AType of good = basic AND BPrice change = decrease THEN UDemand = no change IF AType of good = higher AND BPrice change = increase THEN UDemand = decrease IF AType of good = higher AND BPrice change = decrease THEN UDemand = increase Inference based on the rule base can be carried out in two ways (Kuniszyk-Jó´zkowiak, 2012, pp. 64–65): 1. Aggregation and then inference (FATI). 2. First Infer Then Aggregate (FITA). A very important stage related to fuzzy reasoning is the process of defuzzification (sharpening), in which the fuzzy value is transformed into a specific numerical value (Rotshtein & Shtovba, 2002). For this purpose, the center of gravity method can be used, which is described by the formula (Van Broekhaven & De Beats, 2006): • in the case of a continuous membership function:  μc (x) · xdx x∗ =  μc (x)dx

(3.12)

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• for the discrete membership function: n i=1 μc (x i ) · x i ∗ x =  n i=1 μc (x i )

39

(3.13)

3.3 The Spheres of Fuzzy Sets and Fuzzy Logic: Advantages and Disadvantages of Its Application

2500

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10000

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5000

Citations

Publications

Fuzzy logic is a frequently used research tool, as evidenced by the number of published scientific papers—only in the Web of Science Core Collection database, 31,429 papers containing in key words the phrase “fuzzy logic” are indexed. Figure 3.7 graphically presents their number and the number of citations in individual years, starting from 1990. Figure 3.7 shows that both the number of publications and their citations have been steadily increasing, reaching their maximums in 2019 (2064) and 2014 (23,294), respectively. After 2019, the number of publications decreased to 1808 in 2022, the number of citations to 5217.

0 2022

2020

2018

2016

2012

2014

2010

2008

2006

2002

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2000

1998

1996

1994

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1990

0

rok Publications

Citations

Fig. 3.7 The number of publications containing the phrase “fuzzy logic” in the Web of Science Core Collection database and the number of their citations in the years 1990–2022 (Source own study based on Web of Science)

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The analysis of keywords in works containing the phrase “fuzzy logic” performed using the VOSviewer program, version 1.6.19, shows that out of 58,649 expressions, 38 are repeated at least 200 times. They are presented in Table 3.2, along with information on the number of occurrences and the assigned cluster. Their connections in graphical form are presented in Fig. 3.8. Fuzzy logic is used in many areas of life. It is widely used, for example, in device control (Filo, 2010; Sadowski et al., 2018), as a teaching aid (Das et al., 2019; Machado et al., 2016). It is extremely useful for many people involved in research and development, including engineers (electrical, mechanical, civil, chemical, aerospace, agricultural, biomedical, computer, environmental, geological, industrial and mechatronic), mathematicians, computer software developers and researchers, natural scientists (biology, chemistry, earth sciences and physics), medical researchers, sociologists (economics, management, political science and psychology), public policy analysts, business analysts and lawyers (Gupta, 2022; Singh et al., 2013). Hernández and Hidalgo (2020) conducted, based on the Scopus database, a bibliographic analysis of the applications of fuzzy logic in business, administration and accounting. At the same time, they pointed to the increasingly common use of quantitative methods in decision-making. They also emphasized that the future lies in the application of modern techniques in these fields, e.g., based on artificial intelligence, neural networks, genetic algorithm and others. Kuniszyk-Jó´zkowiak (2012, pp. 136–144) presented a review of proposals for using fuzzy logic in medical diagnostics. They concern e.g., analysis of ECG records, processing of medical images (MRA examinations), monitoring of vital functions, drug dosing and control of medical equipment, as well as systems supporting the diagnosis of diseases. In turn (Phuong & Kreinovich, 2001), they presented an example of the operation of a diagnostic system for lung diseases based, among others, on fuzzy logic. In the work of Suganthi et al. (2015), an attempt was made to review the applications of models based on fuzzy logic in renewable energy systems. The authors found that these models support the optimization of the level of generated power, the choice of location or the monitoring of the functioning of systems in the field of speed control (wind energy) or temperature. It will also highlight the wide range of fuzzy modeling research related to this type of energy.

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Table 3.2 The most frequent keywords in works containing the phrase “fuzzy logic” Keyword

Occurrences

Cluster

Keyword

Occurrences

Cluster

anfis artificial neural network fuzzy logic control fuzzy logic controller fuzzy logic controller (flc) genetic algorithm membership function mppt optimization particle swarm optimization pid controller simulation artificial intelligence artificial neural networks data mining expert systems fuzzy logic fuzzy logics genetic algorithms machine learning neural networks soft computing

239 321

1 1

adaptive control clustering

379 299

3 3

1908

1

fuzzy control

336

3

1962

1

fuzzy logic system 538

3

400

1

326

3

778

1

fuzzy logic systems nonlinear systems

200

3

206

1

270

3

242 522 262

1 1 1

227 437 227

3 3 3

225 282 458

1 1 2

sliding mode control type-2 fuzzy logic uncertainty wireless sensor networks expert system neural network Decision-making

285 621 237

4 4 5

282

2

fuzzy sets

349

5

216 218 20,661 203 515

2 2 2 2 2

image processing classification

218 280

6 7

312 952 226

2 2 2

Source own study

As pointed out by Cádiz (2020), fuzzy logic can be helpful in many areas of musical creativity, such as music composition, sound synthesis, gesture mapping in electronic instruments, parametric control of sound synthesis, audiovisual content generation or sonification. Valášková et al. (2014) constructed a fuzzy model of car safety taking into account the vehicle, its manufacturer and intelligent transport

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Fig. 3.8 Map of links between keywords (Source own study)

systems (ITS) to show how fuzzy sets help to make informed purchasing decisions. Kumar et al. (2013) proposed a fuzzy logic based model for supplier evaluation in 66 Indian textile enterprises. Zimmermann (2001) presented examples of fuzzy logic applications in decision-making, management and engineering. They are concerned, among others, solving linear and dynamic programming problems, fuzzy logic applications in logistics, marketing and banking. The most frequently recurring advantages and disadvantages of using models (systems) based on mutation logic are listed in Table 3.3.

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Table 3.3 Advantages and disadvantages of using fuzzy logic Advantages

Disadvantages

• simple construction • models (systems) require a small amount of data • simple results interpretation • the model (system) can work with any type of input data, regardless of whether it is imprecise, distorted or noisy information • Provides a highly effective solution to complex problems in all areas of life as it resembles human reasoning and decision-making

• fuzzy logic works on both precise and imprecise data, therefore the accuracy of the obtained result should be approached with caution • lack of a systematic approach to describing and solving problems using fuzzy logic • lack of a precise, mathematical description of problem by the model (system)

Source Own study based on MasterClass (2022), FTL (2023)

3.4

Sets and Fuzzy Logic in Decision-Making

Multi-criteria decision making (MCDM) was introduced as a promising and important field of research in the early 1970s. MCDM is concerned with constructing and solving multi-criteria decision and planning problems to support the complex decision-making processes that people develop in their daily lives. MCDA is a set of methods and mathematical tools that enable the comparison of decision variants, taking into account various, often contradictory, criteria. The aim is to achieve such an effect that will maximize the multi-criteria objective function of the form (Zioło et al., 2020): F(x) = max( f 1 (x), f 2 (x), . . . , f j (x)

(3.14)

on the assumption x ∈ Adop , where: Adop —set of admissible solutions, f j (x)—individual partial criterion functions for j = 1, 2, . . . J . The process of using MCDA usually consists of several steps: 1. Selection of decision variants that will be analyzed during the decision procedure. 2. Selection of criteria (measurements) to be used as the basis for evaluation and prioritization of criteria according to their importance by assigning weights to them. 3. Selection and application of the appropriate method.

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There is a whole range of multi-criteria methods and categories of their division, as well as a rich literature describing their application. Carlsson and Fuller (1996) list four families of MCDM methods: • methods based on outranking relations (e.g., group of ELECTRE methods, PROMETHEE methods, TOPSIS, ORESTE, TACTIC methods). • multi-attribute utility theory methods (e.g., MAUT, AHP, DEMATEL, UTA). • Analytical Hierarchy Process (AHP). • theory of group decisions, consensus and negotiation. Many decision situations typically involve imprecise, uncertain, indefinite and subjective data that are difficult to represent and manage. Fuzzy tools turned out to be useful in modeling and solving such problems. The first fuzzy approach to decision-making was introduced by Bellman and Zadeh (1970). Since then, many other fuzzy approaches have been defined for each of the four families of MCDM methods listed above (Fodor & Roubens, 1994; Kacprzyk & Fedrizzi, 1990; Kahraman et al., 2015; Zimmermann, 1987). According to Kaya et al. (2019), Fuzzy MCDM methods can be classified by using methods based on distance, outranking, pairwise comparison and others (Fig. 3.9). According to the authors: • fuzzy AHP and ANP are used to calculate the relative importance values of criteria and alternatives using a pairwise comparison matrix, • fuzzy TOPSIS and VIKO methods use the principle that in these methods alternatives are evaluated on the basis of their distance from ideal solutions, • fuzzy ELECTRE and PROMETHEE methods belong to the outranking methods, where the Fuzzy ELECTRE method uses advantage relations to evaluate alternatives, and the PROMETHEE method is also an overestimation method used for partial and full ranking of several alternatives, • other methods are: fuzzy DEMATEL method that is used to determine interrelationships among criteria, fuzzy Axiomatic Design that is used to rate alternatives and criteria by expressing quantitatively and semantically and fuzzy Choquet Integral that is used to determine conjunctive or disjunctive behaviors between criteria methods.

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AHP Pairwise Comparison Based Methods

ANP

MACBETH

PROMETHEE Outranking Methods ELECTRE Fuzzy MCDM Methods VIKTOR Distance Based Methods TOPSIS AXIOMATIC DESIGN Other Methods

DEMATEL CHOQUET INTEGRAL

Fig. 3.9 Classification of fuzzy MCDM methods (Source Kaya et al. [2019])

Different types of fuzzy MCDM methods and their applications can be found in the literature. These methods offer many ways to model and manage problems and variables that are difficult to quantify in economics. The MCDM model is a popular strategy that is successfully used in many sectors to make optimal location decisions based on several competing criteria. The fuzzy TOPSIS method was used, for example, to select industrial areas in rural areas in central Iran (Chu, 2002), the location of the Yong plant (2006) and the location of shopping centers (Erdin & Akbas, 2019). The fuzzy method of AHP Lee et al. (2008) was used to assess the country’s competitiveness in the hydrogen technology sector, and Wang et al. (2011) used this method to assess the environmental impact of energy consumption. Cavallaro and Ciraolo (2013) used the

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fuzzy PROMETHEE method to compare a group of solar energy technologies. Kaya and Kahraman (2010) used the integrated VIKOR and AHP methodology in a fuzzy environment to determine the best energy policy for Istanbul. Puška et al. (2022) used fuzzy MCDM methods to select the green suppliers (GSS) that will best help agricultural producers to adopt green agricultural production using uncertainty in decision-making. As part of the work, the validation of the results and the sensitivity analysis of the model were carried out by carrying out the procedure of comparing the obtained results with the results obtained by other MCDM methods and changing the criteria weight coefficients. The article uses the methods of multi-criteria analysis (MCDA), namely: the fuzzy LMAW (Logarithm Methodology of Additive Weights) method and the fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method. Su et al. (2023) presented an improved, fuzzy, multi-attribute decisionmaking method to realize the green selection of supply chain members as part of the green innovation vision. According to the authors, the multiattribute decision method proposed in their work takes into account the shortcomings of the original, fluctuating, fuzzy multi-attribute decision method, taking into account the optimization of attribute weights, and then proposes a three-point estimation method for the ranking of schemes and optimizes the attribute weights by quantifying the equilibrium coefficients of the original decision method. Solo (2012) proposes the use of fuzzy sets for the application of asking and answering queries about quantitatively defining imprecise natural language linguistic terms in politics and public policy. Fuzzy logic is needed to properly ask and answer the question of how to quantify the “rich”. An imprecise natural language word like rich should be considered to have qualitative definitions, crisp quantitative definitions and fuzzy quantitative definitions. MCDA is also successfully used in medicine. Kumar (2023) used a multi-criteria decision-making technique to diagnose diseases and rank them among patients. In his opinion, the applied method is very effective in introducing appropriate treatment of the diagnosed disease. Kumar and Jain (2018) proposed a fuzzy medical decision-making system for identifying the type of malaria. Ortiz-Barrios et al. (2023) presented a hybrid, fuzzy, multi-criteria decision-making model for evaluating emergency department (ED) performance and creating targeted improvement

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interventions. In recent years, many studies have been published using the MCDM-based approach to assess the readiness of healthcare facilities in the event of a COVID-19 outbreak or any disaster (Gul & Yucesan, 2021; Hosseini et al., 2019). Al Mohamed et al. (2023) used a fuzzy multi-criteria decision model in choosing the location of a pandemic hospital. Kaya et al. (2019) reviewed articles that use fuzzy MCDM methods to solve problems related to energy policy and decision-making, in terms of some characteristics, such as: types of fuzzy sets, year, journal, fuzzy MCDM method, country and document type of papers that use fuzzy MCDM methods to solve energy policy and decision-making problems have been analyzed with respect to some characteristics such as types of fuzzy sets, year, journal, fuzzy MCDM method, country and document type. According to the authors, there are many different areas of application of fuzzy MCDM techniques in energy decision-making problems. These include, among others: selection of the location of the power plant (nuclear, solar, wind, etc.), assessment of energy storage options, assessment of alternative methods of electricity generation and determination of energy policy for various countries. In addition, the authors found that Turkey and China are the countries that have the largest number of publications related to fuzzy MCDM methods in energy problems.

References Al Mohamed, A., Al Mohamed, S., & Zino, M. (2023). Application of fuzzy multicriteria decision-making model in selecting pandemic hospital site. Abstract Future Business Journal, 9(1). https://doi.org/10.1186/s43093023-00185-5 Bellman, R. E., & Zadeh, L. A. (1970). Decision making in a fuzzy environment. Management Sciences, 17 , 141–164. https://doi.org/10.1287/mnsc. 17.4.B141 Bojadziev, G., & Bojadziev, M. (2007). Fuzzy logic for business, finance, and management (2nd ed.). World Scientific Publishing. Buckley, J. J., & Eslami, E. (2002). An introduction to fuzzy logic and fuzzy sets. Springer. Cádiz, R. F. (2020). Creating music with fuzzy logic. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00059 Carlsson, C., & Fuller, R. (1996). Fuzzy multiple criteria decision making: Recent developments. Fuzzy Sets and Systems, 78(2), 139–153. https://doi. org/10.1016/0165-0114(95)00165-4

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Cavallaro, F., & Ciraolo, L. (2013). Sustainability assessment of solar technologies based on linguistic information. In F. Cavallaro (Ed.), Assessment and simulation tools for sustainable energy systems, theory and applications (pp. 3–25). Springer. https://doi.org/10.1007/978-1-4471-5143-2 Chaira, T. (2019). Fuzzy set and its extension. The intuitionistic fuzzy set. John Wiley & Sons. Chen, G., & Pham, T. T. (2001). Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press. Chiang, D.-A., & Lin, N. P. (1999). Correlation of fuzzy sets. Fuzzy Sets and Systems, 102(2), 221–226. https://doi.org/10.1016/S0165-0114(97)001 27-9 Chu, T. C. (2002). Selecting plant location via a fuzzy TOPSIS approach. International Journal of Advanced Manufacturing Technology, 20, 859–864. https://doi.org/10.1007/s001700200227 Das, K., Samanta, S., Naseem, U., Khan, S. K., & De, K. (2019). Application of fuzzy logic in the ranking of academic institutions. Fuzzy Information and Engineering, 11(3), 295–306. https://doi.org/10.1080/16168658. 2020.1805253 Erdin, C., & Akbas, H. E. (2019). A comparative analysis of fuzzy TOPSIS and geographic information systems (GIS) for the location selection of shopping malls: A case study from Turkey. Sustainability, 11, 3837. https://doi.org/ 10.3390/su11143837 Farhadinia, B., & Chiclana, F. (2021). Extended fuzzy sets and their applications. Mathematics, 9(7), 770. https://doi.org/10.3390/math9070770 Filo, G. (2010). Modelling fuzzy logic control system using the Matlab Simulink program. Czasopismo techniczne. Mechanika, Politechnika Krakowska, 8, 73– 81. Fodor, J. C., & Roubens, M. (1994). Fuzzy preference modelling and multicriteria decision support. Springer. https://doi.org/10.1007/978-94-0171648-2 FTL. (2023). Explain advantages and disadvantages of fuzzy logic system. https://www.freetimelearning.com/software-interview-questions-and-ans wers.php?Explain-Advantages-and-Disadvantages-of-Fuzzy-Logic-System.& id=1449, 1 June 2023. Gul, M., & Yucesan, M. (2021). Hospital preparedness assessment against COVID-19 pandemic: A case study in Turkish tertiary healthcare services. Mathematical Problems in Engineering, 2931219. https://doi.org/10.1155/ 2021/2931219 Gupta, A. K. (2022). Fuzzy logic and their application in different areas of engineering science and research: A survey. International Journal of Scientific Research in Science and Technology, 8(2), 71–75. https://doi.org/10.32628/ IJSRST218212

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Hernández, A., & Hidalgo, D. (2020). Fuzzy logic in business, management and accounting. Open Journal of Business and Management, 8, 2524–2544. https://doi.org/10.4236/ojbm.2020.86157 Herrea-Viedma, E. (2015). Fuzzy sets and fuzzy logic in multi-criteria decision making. The 50th anniversary of prof. Lotfi Zadeh’s theory: introduction. Technological and Economic Development of Economy, 21(5): 677–6383. https://doi.org/10.3846/20294913.2015.1084956 Hosseini, S. M., Bahadori, M., Raadabadi, M., & Ravangard, R. (2019). Ranking hospitals based on the disasters preparedness using the TOPSIS technique in western Iran. Hospital Topics, 97 (1), 23–31. https://doi.org/10.1080/001 85868.2018.1556571 Kacprzyk, J., & Fedrizzi, M. (1990). Multiperson decision-making using fuzzy sets and possibility theory. Kluwer Academic Publisher. Kahraman, C., Çevik, S., & Öztay¸si, B. (2015). Fuzzy multicriteria decisionmaking: A literature review. International Journal of Computational Intelligence Systems, 8(4), 637–666. https://doi.org/10.1080/18756891.2015. 1046325 Kaya, I., Colak, M., & Terzi, F. (2019). A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Reviews, 24, 207–228. https://doi.org/10.1016/j.esr.2019.03.003 Kaya, T., & Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHS methodology: The case of Istanbul. Energy, 35, 2517–2527. https://doi.org/10.1016/j.energy.2010.02.051 Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic. Theory and applications. Prentice Hall. Kumar, D., Singh, J., & Singh, O. P. (2013). A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Mathematical and Computer Modelling, 58(11–12), 1679–1695. https://doi.org/10.1016/j.mcm.2013.07.003 Kumar, V. (2023). VlseKriterijumska Optimizacija I Kompromisno Resenj (VIKOR) method: MCDM approach for the medical diagnosis of vectorborne diseases. Journal of Computational and Cognitive Engineering, 1–11. https://doi.org/10.47852/bonviewJCCE3202484 Kumar, V., & Jain, S. (2018). Alternate procedure for the diagnosis of malaria via intuitionistic fuzzy sets. In B. K. Panigrahi, M. N. Hoda, V. Sharma, & S. Goel (Eds.), Nature inspired computing: Proceedings of CSI 2015 (pp. 49–53). Springer. https://doi.org/10.1007/978-981-10-6747-1 Kuniszyk-Jó´zkowiak, W. (2012). Algorytmy logiki rozmytej. UMCS. Lee, S. K., Mogi, G., Kim, J. W., & Gim, B. J. (2008). A fuzzy analytic hierarchy process approach for assessing national competitiveness in the hydrogen technology sector. International Journal of Hydrogen Energy, 33(23), 6840–6848. https://doi.org/10.1016/j.ijhydene.2008.09.028

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Machado, M. A. S., Moreira, T. D. R. G., Gomes, L. F. A. M., Caldiera, A. M., & Dantos, D. J. (2016). A fuzzy logic application in virtual education. Procedia Computer Science, 91, 19–26. https://doi.org/10.1016/j.procs.2016.07.037 MasterClass. (2022). Fuzzy logic explained: Real-life fuzzy logic applications. https://www.masterclass.com/articles/fuzzy-logic#1rA2bzYQEdkC BVq4sRIaBO, 1 June 2023. Ortiz-Barrios, M., Jaramillo-Rueda, N., Gul, M., Yucesan, M., JiménezaDelgado, G., & Alfaro-Saiz, J. J. (2023). A fuzzy hybrid MCDM approach for assessing the emergency department performance during the COVID-19 outbreak. International Journal of Environmental Research and Public Health, 20(5), 4591. https://doi.org/10.3390/ijerph20054591 Phuong, N. H., & Kreinovich, V. (2001). Fuzzy logic and its applications in medicine. International Journal of Medical Informatics, 62(2–3), 165–173. https://doi.org/10.1016/S1386-5056(01)00160-5 Piegat, A. (1999). Modelowanie i sterowanie rozmyte. Akademicka Oficyna Wydawnicza EXIT. Puška, A., Božani´c, D., Nedeljkovi´c, M., & Janoševi´c, M. (2022). Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model. Axioms, 11(9): 427. https://doi.org/10.3390/axioms11090427 Ross, T. J. (2010). Fuzzy logic with engineering applications (3rd ed.). John Wiley & Sons. Rotshtein, A. P., & Shtovba, S. D. (2002). Influence of defuzzification methods on the rate of tuning a fuzzy model. Cybernetics and Systems Analysis, 38(5), 782–789. Sadowski, E., Marek, T., Pniewski, R., & Kowalik, R. (2018). Wykorzystanie logiki rozmytej w sterowaniu ogniwem Peltiera. Autobusy, 6, 704–707. https://doi.org/10.24136/atest.2018.160 Shepherd, D., & Shi, F. C. (1998). Economic modelling with fuzzy logic. IFAC Proceedings Volumes, 31(16), 435–440. https://doi.org/10.1016/S1474-667 0(17)40518-0 Singh, H., Gupta, M. M., Meitzler, T., Hou, Z. G., Garg, K. K., Solo, A. M. G., & Zadeh, L. A. (2013). Real-life applications of fuzzy logic. Advance in Fuzzy System, 581879. https://doi.org/10.1155/2013/581879 Solo, A. M. G. (2012). Warren, McCain, and Obama needed fuzzy sets at presidential forum. Advances in Fuzzy Systems, 319718. https://doi.org/10. 1155/2012/319718 Su, J., Xu, B., Li, L., Wang, D., & Zhang, F. (2023). A green supply chain member selection method considering green innovation capability in a hesitant fuzzy environment. Axioms, 12(2), 188. https://doi.org/10.3390/axi oms12020188

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Suganthi, L., Iniyan, S., & Samuel, A. A. (2015). Applications of fuzzy logic in renewable energy systems—A review. Renewable and Sustainable Energy Reviews, 48, 585–607. https://doi.org/10.1016/j.rser.2015.04.037 Valášková, K., Kliestik, T., & Mišanková, M. (2014). The role of fuzzy logic in decision making process. In Conference: 2nd International Conference on Management Innovation and Business Innovation (ICMIBI 2014), Bangkok, Thailand, 44. https://doi.org/10.5729/lnms.vol44.143 Van Broekhaven, E., & De Beats, B. (2006). Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets and Systems, 157 (7), 904–918. https://doi.org/10.1016/j. fss.2005.11.00 Wang, L., Xu, L., & Song, H. (2011). Environmental performance evaluation of Beijing’s energy use planning. Energy Policy, 39(6), 3483–3495. https://doi. org/10.1016/j.enpol.2011.03.047 Wu, B., & Hung, Ch. F. (2016). Innovative correlation coefficient measurement with fuzzy data. Mathematical Problems in Engineering, 2016, 9094832. https://doi.org/10.1155/2016/9094832 Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, 28, 839–844. https:// doi.org/10.1007/s00170-004-2436-5 Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X Zadeh, L. A. (1975). he concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249. https://doi. org/10.1016/0020-0255(75)90036-5 Zimmermann, H. J. (1987). Fuzzy sets, decision-making and expert systems. Kluwer Academic Publisher. Zimmermann, H. J. (2001). Fuzzy set theory—And Its Applications (4th ed.). Springer. Zioło, M., B˛ak, I., Sinha, R., & Datta, M. (2020). ESG Risk Perception in sustainable financial decisions. Quantitative methods perspective. In K. Nermend, & M. Łatuszynska ´ (Eds.), Experimental and quantitative methods in contemporary economics (pp. 157–172). Springer.

CHAPTER 4

Fuzzy Logic in Finance Anna Spoz

and Magdalena Zioło

4.1 Application of Fuzzy Logic Approach in Finance and Banking A review of research on fuzzy logic in finance indicates that this is not a popular research trend, and there are relatively few publications in this field. Due to its specificity, fuzzy logic is suitable for researching financial phenomena due to the possibility of using imprecise, incomplete, and unclear data in the analysis (Sanchez-Roger et al., 2019). Fuzzy logic was originally used in finance to study how money changes over time. Buckley was the first to study the mathematics of fuzzy logic in finance in this area. Among other areas of financial research with fuzzy logic are bankruptcy

A. Spoz (B) Department of Finance and Accountancy, The John Paul II Catholic University of Lublin, Lublin, Poland e-mail: [email protected] M. Zioło Faculty of Economics, Finance and Management, University of Szczecin, Szczecin, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_4

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forecasting, stock market predictions, and portfolio optimization. Fuzzy logic is used in banking research to analyze categories such as: • • • • •

risk, credit scoring, banking crises, bank restructuring processes, liquidation of banks.

Fuzzy logic allows for a more in-depth understanding of these phenomena, which is important because the consequences of banking crises have an impact on public finances and taxpayers because, as practice shows, states engage the bulk of public funds to rescue banks and, more broadly, financial institutions and counteract the socio-economic consequences crises (Sanchez-Roger et al., 2019), which ultimately burdens their budgets and results in an increase in public debt and an increase in the cost of servicing it. Sanchez-Roger et al. (2019), reviewing the literature on the use of fuzzy logic in finance, diagnosed that most articles in this area were devoted to: • • • • •

financial markets (60% of all publications), corporate finance (35%), public finance (about 3%), household finances (1%), other (2.52%).

Bahrammirzaee (2010), analyzing the research methodology used in finance, pointed to better and more accurate results of analyzes obtained using methods such as artificial intelligence methods, such as fuzzy logic, or neural networks compared to the methodology based on: parametric statistical methods such as—discriminant analysis and regression; or nonparametric statistical methods including decision trees (Sanchez-Roger et al., 2019). When reviewing selected articles on fuzzy logic in finance, it is worth paying attention to research topics related to banking. Darwish and Abdelghany (2016) proposed a fuzzy logic model for credit risk rating

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commercial banks in Egypt. The model uses indicators such as profitability, debt repayment capacity, operational capacity, and liquidity, which allows us to determine their impact on the credit rating. As a result of the study, the authors indicated that the fuzzy logic technique is more scalable, reliable, stable, and different from classical methods. Costea (2014) used fuzzy logic and machine learning techniques in financial performance predictions. Fuzzy C-means clustering and artificial intelligence algorithms were made to compare the assessment of the financial performance of non-banking financial institutions (NFIs) in Romania. Mohamed and Salama (2013) proposed a fuzzy logic-based model for predicting commercial banks’ financial failure. The model allows for the effective prediction of bankruptcy of commercial banks. Based on the model, financial decision-makers can determine the risk of default in commercial banks, take preventive measures, and strengthen the values of financial ratios. Hachicha et al. (2011) used fuzzy logic to explain price dynamics by taking into account the essential explanatory variables (profit, systematic risk), microstructural (SMBt effect size and HMLt book-to-market effect), and behavioral approach (investor sentiment). As a result of the study showed that modeling the return using the optimized fuzzy system improved compared to the classical logic system (both for the emerging market and the international market). Salih and Hagras (2018) developed a novel genetic type-2 fuzzy logic model for decision support to minimize financial default in the banking sector. Brkic et al. (2017) dealt with fuzzy logic as a tool supporting the assessment of corporate customer credit risk in the commercial banking environment. The results of this paper present a new approach to the use/assessment of soft data to incorporate them into a new and excellent model of fusion of soft and hard data for customer credit risk assessment. Hernández and Hidalgo (2020) assume that fuzzy logic in business, management, and accounting applications has specific characteristics. Fuzzy logic help decentralize decision-making processes to be standardized, repeatable, and documented. Fuzzy methods play a very important role in business because they help to reduce costs and thus generate more profits. They can also help companies compete effectively and reduce costs. Fuzzy logic is also used for the financial analysis of enterprises. Korol (2018) uses fuzzy logic in forecasting financial ratios and predicts the

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financial standing of companies. The approach used by Korol (2018) “greatly enhances the predictive power of financial analysis and makes it an economically useful tool for the management of enterprises”.

4.2

Risk Management and Fuzzy Logic Risk Management Process

Every business activity is exposed to various types of risk. Therefore, dealing with it is an integral part of the business. Risk analysis and management is the process of understanding risk and proactively dealing with it in order to minimize threats. Although risk management may vary from organization to organization, certain general steps in the risk management process are common to the vast majority of cases. These steps are: – risk identification and analysis—all external and internal risk factors that may affect organization should be identified and analyzed regarding their category, scope, and severity; – risk assessment—identified risks should be ranked regarding possibility or frequency of their occurrence as well as their impact on the organization; – risk treatment—at this stage a risk mitigation strategy should be defined; – risk monitoring and reporting—as not all risk can be avoided or eliminated, there is a need of constant monitoring and reporting them. As shown in Fig. 4.1, risk management is a continuous process. Risk management, like many other processes in modern companies, is often supported by digital tools. Although at all stages of risk management process such tools are useful, the risk assessment is a task for which the support of computer tools is particularly useful. More advanced tools offer not only the assessment of risk but also support decision-making based on the assessment. In practice, risk may be often a result of many factors, therefore mathematical models, and most often statistical models, are used to assess, predict, and estimate the risk. Accurate risk assessment is critical to risk management processes, so using the best approach is of the utmost importance.

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Fig. 4.1 Risk management scheme (Source own elaboration)

Models in Risk Management Statistical models, based on classical probability, are most often used types of models in risk management. They represent a quantitative approach, allowing for risk assessment taking into account many factors. Financial institutions commonly use models to assess credit or investment risk. Statistical models are also used for risk assessment in projects, e.g., in construction. Another use of models is to predict the occurrence of events such as a financial crisis or company bankruptcy.

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The types of statistical models used for risk assessment depend on the type of risk. For example, in credit risk assessment, regression models are most popular. Such statistical methods typically make assumptions about the variables involved, such as (i) normal distribution, (ii) lack of relationship between independent variables, (iii) high discriminating ability of sorting creditworthy from non-creditworthy customer, (iv) complete information, and (v) clear and exclusive definition of membership into one of the groups (creditworthy and non-creditworthy). Discriminant analysis and logistic regression, often applied to build credit scoring models, assume, multivariate normality and homoscedasticity, which are features not often present in datasets of real-world credit institutions (Fonseca et al., 2020). Assumptions of statistical models are one of their shortcomings. There are also other issues with methodologies for assessing the risks of companies or projects, as they are often based on common economic parameters and quantitative indicators, which do not capture the so-called soft factors. Among others, the above disadvantages of assessment methods were motivation to introduce different approach, combining numerical and qualitative parameters. Qualitative parameters allow to describe the realworld variables using human language. While quantitative variables take values from strictly defined numeric ranges, more natural characteristics such as low, medium, or high can be assigned to qualitative variables. However, this requires methods different from classical probability, such as soft computing techniques. Soft computing techniques of model approximation allow to solve complex problems. Most popular soft computing techniques are: fuzzy logic, genetic algorithms, artificial neural networks, machine learning, and expert systems. Main feature of soft computing, unlike statistical methods, is their ability to handle imprecisely defined problems, and incomplete data, which commonly appear, for example, in credit requests and business bankruptcy prediction. Soft computing techniques are also appropriate for dealing with contextual changes in dynamic and evolving contexts. Statistical modeling assumes variable precision, reliability, but precision and certainty generate cost; thus, the assumption of soft computing is that decision-making should be more “tolerant” to aspects such as imprecision, vagueness, and incompleteness whenever possible (Fonseca et al., 2020).

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Fuzzy Sets and Fuzzy Logic One of soft computing techniques, fuzzy logic is broadly perceived as a system for dealing with reasoning in approximate way rather than exact way (Klir, 1995). Thanks to successful practical applications, fuzzy logic has gained significant attention. Fuzzy logic was introduced by scientist L. A. Zadeh, along with fuzzy sets which he defined as classes of objects with a continuum of grades of membership. Fuzzy sets are “characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one” (Zadeh, 1965). This is the main difference from traditional logic, where an object can only be a member or non-member of a set. In fuzzy logic, a variable’s membership in one set can be for example 80% true, and at the same time, its membership in a different set can be for example 10% true. In other words, it is a member of the first fuzzy set to degree 0.8, and a member of the second fuzzy set to degree 0.1. The grade to which a variable is a member of a fuzzy set is determined by a membership function. Membership functions are in most cases linear, with triangular or trapezoidal shape. However, other shapes like Gaussian, generalized bell, sigmoidal, or polynomial can also be used. The example of combination of trapezoidal (left-sided and right-sided) and triangular (two-sided) membership function is presented in Fig. 4.2. The membership functions in Fig. 4.2 define three fuzzy sets: low, medium, and high. Variable x = 2.75 is a member of medium set with μ (level of truth)

1 Low Medium 0.45 0.3

High x

0 1

2

2.75 3

4

5

Fig. 4.2 Examples of membership functions (Source own elaboration)

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truth level µmedium (x) = 0.45 and a member of high set with truth level µhigh (x) = 0.3. Applying membership functions give several values as an output, which is not useful in practice. To obtain a single output value further action is required, which involves operations on fuzzy sets. Operations on fuzzy sets are different from operations on classical sets, as they are based on membership functions. Instead of AND operator for conjunction minimum function is used, and for disjunction, maximum function is used instead of OR. Negation operator NOT is replaced by 1 − µ(x). In case of the above example of membership function, the output indicating that the variable x belongs to the conjunction of “medium” and “high” sets, will take the value 0.3, which is the minimum of the values 0.45 and 0.3. If the output were to indicate that the variable belongs to the disjunction of both sets, the result would be the maximum value, which is 0.45. Fuzzy Models in Risk Management Fuzzy Inference Systems Thanks to their specificity, fuzzy logic models can be used to calculate the risks in cases with incomplete knowledge and inadequate data. Fuzzy logic provides a framework for risk analysis using such data and human rational (Fakhravar, 2020). The knowledge of risk may be developed in two ways with use of fuzzy logic models: – The model allows risk managers and experts not to deal with the inference part for many risks, but to focus on cause-and-effect relationships based on their experience and knowledge. – The results of risk assessment are transferred to the risk decisionmaking process, and the outcome of the decision can then be fed back into the model to improve the fuzzy sets, rules, and understanding (Shang & Hossen, 2013). Fuzzy logic models can be divided into three common classes: (1) models in fuzzy continuous-time (MFC)—employed in estimates to make real financial decisions using trapezoidal numbers;

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(2) fuzzy pay-off method (FPOM)—based on triangular distributions, scenario-based real option valuation method; (3) models in fuzzy discrete-time (MFD)—adapt the binomial model to the fuzzy logic allowing to predict the upward and downward movements (Díaz Córdova et al., 2017). In risk management, fuzzy logic models are expected to support risk assessment as well as decision-making. Such models are implemented into fuzzy inference systems (FIS) that offer automation of decision-making, thanks to the fact that, based on their own assessment, they qualify the risk as acceptable or not. However, great knowledge and experience of experts are required to build an effective model, which is one of fuzzy logic systems’ disadvantages. Simplified fuzzy inference system is presented in Fig. 4.3. The majority of fuzzy systems used in risk management, are based on Mamdani FIS (Mamdani & Assilian, 1975) or Sugeno FIS (Takagi & Sugeno, 1985). They are slightly different but general concept of both is the same. Mamdani FIS operates in the following steps: 1. A set of fuzzy rules is determined 2. The input variables are fuzzified using the input membership functions 3. The fuzzified inputs are combined according to the fuzzy rules to establish a rule strength (Fuzzy Operations)

Fuzzy Rule Base

Input variables

Fuzzifier

Fuzzy Inference Engine

Defuzzifier

Fig. 4.3 Scheme of fuzzy inference system (Source own elaboration)

output variables

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4. The consequence of the rule is found by combining the rule strength and the output membership function (implication) 5. The consequences are combined to get an output distribution (aggregation) 6. The output distribution is defuzzified (this step is only if a crisp output (class) is needed) (Mamdani & Assilian, 1975). The fuzzy rules are “IF-THEN” rules, and they define the output values of dependent variables based on values of independent variables. For example, if the model has three independent variables x1, x2, and x3, and each can have the value “low”, “medium” or “high”, it requires to define rules for all combinations of these values and determining the value of dependent variable y for each of these combinations, e.g., IF (x1 is low) AND (x2 is low) AND (x3 is low) THEN (y is low); IF (x1 is medium) AND (x2 is low) AND (x3 is low) THEN (y is low), etc. In practice, for multivariable models, number of such rules can be large. Development of fuzzy inference system is not a one-time effort but an iterative process. The first and possibly most significant step in developing a fuzzy model is to discover the parameters that influence a risk and decision based on risk assessment (Dahal et al., 2005). In this step, based on subject matter experts’ and business managers’ knowledge and experience, key factors that may cause any risk, the value of each factor for existing business, any known cause-and-effect relationship, any risk measures that could be used, and any relationship with other risk types need to be identified. In the second step, collected information is analyzed for and any conflicting or inconsistent opinions are consulted with experts for explanation. Then, a fuzzy logic model is proposed to experts and their feedback is gathered. After this stage the model is finalized, and risk monitoring starts. The fuzzy logic model is used to generate regular reporting on the present risk exposure. The reports are presented to experts for feedback and information. Experts’ opinions may be revised based on model results, previous experience, a changing environment, or greater understanding. The model must be reviewed and updated on a regular basis. In addition to the selection of factors that will become independent variables of the fuzzy model, it is extremely important to define appropriate membership functions. This is not an easy task because it requires translating the qualitative description into a quantitative measure. What is worse, there may not be enough data for the majority of risks handled

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by fuzzy logic models. The model’s rationality is primarily determined by experts or corporate management. Comments on the inference rules or the membership functions may have a significant impact on the risk assessment outcome. However, if experience data is available, back testing can be utilized to validate or improve the models. After developing the fuzzy logic system, one approach is to compare actual experience with the model. The membership functions can be changed or calibrated based on the experience data to better predict the output variable. Tracking each expert’s inputs may also reveal how well they suit the experience data and allow to adjust the weight on each expert’s opinions accordingly. Furthermore, when enough data is obtained, it may have an impact on the experts’ understanding of the issue and may affect their inputs, such as inference rules and membership functions. Finally, with enough data, fuzzy logic models may be transferred to models based on probability theory. The ultimate purpose of any risk-assessment system is to enable decision-makers make informed decisions to better manage the risk. Although fuzzy logic systems can be used to predict risk exposure numerically, the ranking of risks is more relevant. This allows decision-makers to identify the main risks and better understand the relative size of the risks (Shang & Hossen, 2013). Fuzzy logic systems of course have several disadvantages, and probably the biggest of them is the dependence of the model on the knowledge and experience of experts and the proper use of this knowledge when creating fuzzy rules. The emerging discrepancies in the opinions of experts regarding the significance of the risk and its impact on the organization cause problems when developing fuzzy inference systems. Fuzzy logic models also require broad validation, which in their case is qualitative rather than quantitative, making them more open to interpretation. Applications of Fuzzy Logic in Risk Management Fuzzy logic models for risk assessment are useful in cases where statistical models do not perform well or cannot be used. The advantage of fuzzy over those model logic is ability to handle with inaccurate or incomplete data. It is obvious that the use of fuzzy logic models by companies may vary depending on the type of their business activity. For example, in organizations related to accounting fuzzy logic can be applied in five areas of

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business, with problems concerning: portfolio selection, financial mathematics, capital budget, technical analysis, credit analysis, and financial analysis (Díaz Córdova et al., 2017). Financial institutions use fuzzy logic in operational risk (OR) management. OR exposures are often complex, diverse, and context-dependent, thus, their assessment, differently from market risk and credit risk, requires models that do not rely on quantitative data. The use of a fuzzy inference systems is a worthwhile alternative since it uses human reasoning and expert knowledge to explain qualitative and quantitative inputs while addressing the multifactor, highly non-linear system that underlies OR. FIS enables for the integration of OR measurement with the other stages of OR management. Fuzzy model allows not only for identification of OR sources, but also for evaluating risk management decisions ex-ante (León, 2009). Fuzzy logic systems allow you to simplify large and complex risk management frameworks. They help model cause-and-effect relationships for risks that lack a proper quantitative probabilistic model. Fuzzy models allow for risk assessment as well as its ranking based on data and expert knowledge. Fuzzy inference systems are particularly useful in companies with diversified business, operating in different locations around the world, which makes them exposed to many types of risk. Monitoring and analyzing risk in such companies is costly and resource-intensive, especially when there are many different risk factors that are interrelated. The use of fuzzy logic in risk management systems saves resources and at the same time provides information about the connections and dependencies between the monitored risk factors. This significantly facilitates the identification of the most important risk factors and allows to focus on them when developing a risk mitigation strategy (Shang & Hossen, 2013). One of the typical applications for fuzzy logic inference systems is credit risk assessment, both for individual and corporate clients. Although FIS are rarely used compared to other methods, they are often more effective than the most commonly used regression models or even other soft computing methods (Louzada et al., 2016). In order to achieve better results in risk management, fuzzy logic can be combined with other techniques such as decision trees, artificial neural networks, or Bayesian networks. Depending on the application, a method which advantages will complement the advantages of fuzzy models should be chosen. A summary of the features of the models most commonly combined with fuzzy logic models is presented in Table 4.1.

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Table 4.1 Summary of models typically combined with fuzzy logic Model

Advantages

Artificial Neural Network

– Allows for sophisticated pattern recognition, prediction and classification – Learning algorithms offer extremely wide range of applications – Easy to understand – Suitable for discrete variables – Directly helps in decisionmaking with limited choices

Decision Tree

Bayesian Network

Disadvantages

– Requires large amount of data – Relationships detected only on the basis of data are often unintuitive – The degree of complication makes it difficult to understand – Suitable only for decision-making (not for risk assessment) – Not suitable for complex problems with multiple variables and relationships – Poor at identifying linear relationships – Shows – Not suitable for relationships complex problems between with multiple variables in variables easy to – Finding understand way relationships and conditional – Provides probability may be estimation of expansive conditional probability and – Determination of distribution conditional probability may be difficult without experience data

Applications – Modeling complex issues where the relationships between the variables are not well known, but the amount of collected data is sufficient – Issues with multiple explanatory variables

– Decision-making for noncomplex issues, where number choices are limited (mostly binary—accept or reject)

– Modeling noncomplex issues – Decision-making for noncomplex issues

Source Own elaboration based on Shand and Hossen (2013)

One of the most interesting hybrid systems is neuro-fuzzy systems, combining the advantages of fuzzy logic and artificial neural networks (Sreekantha & Kulkarni, 2012). They are particularly useful in cases where it is difficult to obtain expert knowledge needed to model risk. Thanks to

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the learning ability of neural networks all necessary rules can be learned from data (Konrad & Philip, 1994; Santana et al., 2018).

4.3

Fuzzy Logic and Financial Crisis Preventing Crisis and How to Prevent It

Financial crisis is an unstable situation, when asset prices see a sharp decrease in value, firms, and individuals are unable to pay their loans, and financial institutions face a shortage of liquidity. A panic or bank run that occurs when investors sell off their assets or remove cash from savings accounts out of fear that their assets’ value will decline if they remain in a financial institution is frequently linked to a financial crisis. Multiple factors may contribute to a financial crisis. In general, an overvalued asset or institution can trigger a crisis, which can then be worsened by irrational or herd-like investor behavior. The ability to predict future financial conditions and identify impending economic crises has become essential for preparing countries for an economic downturn. Crisis prediction is a first step to its successful prevention and the earlier and more accurately the prediction is provided, the more adequate and effective measures can be taken to counteract the crisis. Preventing a crisis requires the identification of risk factors that may cause a crisis or indirectly contribute to its occurrence. Therefore, it is impossible to prevent a crisis without risk analysis and identification and assessment of risk factors that may affect the crisis. In the next stage, it is necessary to take action to eliminate or reduce the risk, and finally monitor risk factors. Crisis prevention is largely about managing the risks that may cause a crisis. In addition to identifying and assessing risk factors, an equally important element of crisis prevention is anticipating its occurrence. This requires the preparation of appropriate methods to detect the impending crisis. Such methods are based on the assessment of selected factors influencing the possibility of a crisis. Despite risk monitoring and the implementation of a crisis prediction system, it is not always possible to avoid it. During a crisis, efficient management is of significant importance, the key aspect of which is decision-making. Therefore, proper crisis preparedness should also include the development of a decision support system.

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Fuzzy Logic in Crisis Preventing Fuzzy logic is an excellent tool for simulating imprecise, uncertain, and ambiguous occurrences. Because a company’s financial situation is influenced by many factors (economic, political, psychological, etc.) that cannot be precisely defined by quantitative measures, the fuzzy logic approach improves the accuracy of prediction of financial analysis and transforms it into a practical tool supporting enterprise management. In the area of enterprise management, fuzzy logic is used to assess company’s stability and predict its bankruptcy. Financial ratios such as current assets, short-term liabilities, revenues from sales, dynamics of short-term liabilities, ratio of fixed capital to equity, current liquidity ratio, and many others can be used for this purpose. Korol and Korodi (2011) and Korol (2018) showed, that the fuzzy logic model used to analyze financial ratios was able to predict the bankruptcy of companies with high accuracy. Thanks to the fuzzy logic model, which takes into account the dynamics of changes in factors, it was possible to identify risks resulting from an inappropriate capital structure in the analyzed companies. It is significant that these risks were visible two years before the bankruptcy. Interestingly, the traditional statistical model did not detect a significant change in these factors. This means that the fuzzy logic model allows you to identify risk factors affecting bankruptcy well in advance. The link between the crisis and financial situation of companies is obvious; therefore, their analysis can be a way of predicting the impending crisis. Of course, for that purpose, the analysis needs to cover a large part of the market, and not just individual companies. Factors other than financial indicators can also be used to develop a fuzzy model for crisis prediction. A relatively simple early warning system for a financial crisis was proposed by Sztojanov et al., 2016) based only on two describing variables: “annual credit growth rate” and “annual growth of real estate prices”. The output of the system indicated a crisis warning or its absence. This shows that although fuzzy models are suitable for multiple variable issues, they also can be successfully used in noncomplex issues. The effectiveness of the system has been validated on test data. Such systems can serve as additional support in financial crises prediction. They also have the advantage of being improved by using a wider set of reference data and by combining them with another technique, such as

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neural networks. In addition, the use of data from a specific country or region allows the model to be adapted to local market conditions. Decision-making is crucial in crisis management. Fuzzy inference systems (FIS) can be extremely useful in such situations. The advantage of FIS is their ability to handle imprecisely defined problems and incomplete data. During crisis, it is often difficult to obtain precise numerical description of problems and complete data, therefore, fuzzy logic is well suited to supporting decisions in crises. Various methods based on fuzzy logic are used to build decision support systems, e.g., fuzzy computation for crisis classification, fuzzy rules to control decision variables, and fuzzy-multicriteria decision-making methods for ranking decision scenarios (Nokhbatolfoghahaayee et al., 2010). Interesting implementation of fuzzy logic in crisis management is crisis modeling. Such applications, where characteristics of crisis environment have been modeled and fuzzy inference techniques employed to add temporal modalities, allowed to simulate the flow of events during crisis which can be helpful in decision-making and resource management. Modeling future environment state by use of prediction algorithm provided temporal relationship description without need of experts’ involvement (Alnahhas & Alkhatib, 2012). Since risk prevention is one of the most important ways to prevent a crisis, it is of great importance to assess the ability of an organization or authority to deal with risk. The ability to effectively manage risk may determine whether the risk will be identified, properly assessed and whether the necessary preventive measures will be undertaken. The risk management capability means the ability of administration to reduce, adapt, or mitigate risk which were identified in risk assessment to acceptable levels. This capability is assessed regarding financial, technical, and administrative capacity allowing adequate: – risk assessments; – risk management planning for prevention and preparedness; – risk prevention and preparedness measures (Zlateva et al., 2015). Fuzzy logic is suitable for assessment of risk management capability as it allows using linguistic description of variables. This allows the description of variables to be based on questions that are easy to understand and answer (precise numerical answers are not required). The role of financial

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institutions, especially banks, in preventing a financial crisis is crucial. The disturbed financial stability of banks can trigger a crisis, and high stability can prevent a crisis caused by factors coming from outside the financial system (pandemic, natural disasters, etc.). Fuzzy logic methods can be used to assess the stability of the financial system. The usefulness of the fuzzy inference system in assessing the stability of the banking system was demonstrated by Blahun et al. (2020) on the example of the Ukrainian banking system. In the analysis, they took into account selected factors (state of assets and liabilities formed by banks, the level of efficiency of banking operations, the volume of formed assets and liabilities in foreign currency, and the state of the interbank market) and created the stability index from them. The summary of selected applications of fuzzy logic methods in crisis prevention is presented in Fig. 4.4. The results showed that the indicator of the state of the banking system determined by the fuzzy model coincided with the actual events from the analyzed period that had a negative or positive impact on the banking system. This confirmed that the fuzzy model can effectively

Assessment of the risk responsible for the financial crisis Crisis prediction based on assessment of financial or non-financial factors Decision support systems CRISIS PREVENTION Crisis simulations

Assessment of risk management capability

Assessment of financial system stability

Fig. 4.4 Summary of fuzzy logic applications in crisis prevention (Source Own elaboration)

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predict the state of the banking system based on selected factors. Identification of the relationship between the stability of the banking system and the economic situation on the domestic or global market would allow predicting the occurrence of a crisis in the future based on the assessment of the condition of banks.

References Alnahhas, A., & Alkhatib, B. (2012). Decision support system for crisis management using temporal fuzzy logic. In 2012 6th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1–5). https://doi.org/10.1109/ICAICT.2012.6398525 Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19, 1165–1195. Blahun, I. S., Blahun, I. I., & Blahun, S. I. (2020). Assessing the stability of the banking system based on fuzzy logic methods. Banks and Bank Systems, 15(3), 171–183. https://doi.org/10.21511/bbs.15(3).2020.15 Brkic, S., Hodzic, M., & Dzanic, E. (2017). Fuzzy logic model of soft data analysis for corporate client credit risk assessment in commercial banking (November 29, 2017). In Fifth scientific conference with International Participation “Economy of Integration” ICEI 2017 , Available at SSRN: https://ssrn. com/abstract=3079471 Costea A. (2014). Applying fuzzy logic and machine learning techniques in financial performance predictions. 7th International Conference on Applied Statistics. Procedia Economics and Finance, 10, 4–9. Dahal, K., Hussain, Z., & Hossain, M. A. (2005). Loan risk analyzer based on fuzzy logic. In 2005 IEEE International Conference on E-Technology, eCommerce and e-Service (pp. 363–366). https://doi.org/10.1109/EEE.200 5.88 Darwish, N. R., & Abdelghany, A. S. (2016). A fuzzy logic model for credit risk rating of Egyptian commercial banks. International Journal of Computer Science and Information Security, 14, 11–18. Díaz Córdova, J. F., Coba Molina, E., & Navarrete López, P. (2017). Fuzzy logic and financial risk. A proposed classification of financial risk to the cooperative sector. Contaduría y Administración, 62(5), 1687–1703. https://doi.org/ 10.1016/j.cya.2017.10.001 Fakhravar, H. (2020). Research project quantifying uncertainty in risk assessment using fuzzy theory.

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Fonseca, D. P., Wanke, P. F., & Correa, H. L. (2020). A two-stage fuzzy neural approach for credit risk assessment in a Brazilian credit card company. Applied Soft Computing, 92, 106329. https://doi.org/10.1016/j.asoc.2020.106329 Hernández, A. B., & Hidalgo, D. B. (2020). Fuzzy logic in business, management and accounting. Open Journal of Business and Management, 8(6). Klir, G. J. (1995). Fuzzy logic. IEEE Potentials, 14(4), 10–15. https://doi.org/ 10.1109/45.468220 Konrad, F., & Philip, T. (1994). Intelligent systems in finance. Applied Mathematical Finance, 1(2), 195–207. https://doi.org/10.1080/135048694000 00011 Korol, T. (2018). The implementation of fuzzy logic in forecasting financial ratios. Contemporary Economics, 12(2), 165–188. Korol, T., & Korodi, A. (2011). An evaluation of effectiveness of fuzzy logic model in predicting the business bankruptcy. Journal for Economic Forecasting, 3, 92–107. https://EconPapers.repec.org/RePEc:rjr:romjef:v::y: 2011:i:3:p:92-107 León, C. (2009). Operational risk management using a fuzzy logic inference system. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1473614 Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, 21(2), 117–134. https://doi.org/ 10.1016/j.sorms.2016.10.001 Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7 (1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2 Mohamed, A. A., & Salama, A. S. (2013, October). A fuzzy logic based model for predicting commercial banks financial failure. International Journal of Computer Applications (0975-8887), 79(11). Hachicha, N., Jarboui, B., & Siarry, P. (2011). A fuzzy logic control using a differential evolution algorithm aimed at modelling the financial market dynamics. Information Sciences, 181(1), 79–91. ISSN: 0020-0255, https:// doi.org/10.1016/j.ins.2010.09.010 Nokhbatolfoghahaayee, H., Menhaj, M. B., & Shafiee, M. (2010). Fuzzy decision support system for crisis management with a new structure for decision making. Expert Systems with Applications, 37 (5), 3545–3552. https://doi. org/10.1016/j.eswa.2009.10.011 Salih, A., & Hagras, H. (2018). Towards a Type-2 fuzzy logic based system for decision support to minimize financial default in banking sector. In 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, UK (pp. 46–49). https://doi.org/10.1109/CEEC.2018.8674212 Sanchez-Roger, M., Oliver-Alfonso, M. D., & Sanchís-Pedregosa, C. (2019). Fuzzy Logic and its uses in finance: A systematic review exploring its potential

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to deal with banking crises. Mathematics, 7 , 1091. https://doi.org/10.3390/ math7111091 Santana, P. J., Lanzarini, L., & Bariviera, A. F. (2018). Fuzzy credit risk scoring rules using FRvarPSO. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(1), 39–57. https://doi.org/10.1142/S02184 88518400032 Sreekantha, D. K., & Kulkarni, R. V. (2012). Expert system design for credit risk evaluation using neuro-fuzzy logic. Expert Systems, 29(1), 56–69. https://doi. org/10.1111/j.1468-0394.2010.00562.x Shang, K., & Hossen, Z. (2013). Applying fuzzy logic to risk assessment and decision-making sponsored by CAS/CIA/SOA joint risk management section. Sztojanov, E., Stamatescu, G., & Sztojanov, I. (2016). Early-warning of financial crises based on fuzzy logic (pp. 1109–1118). https://doi.org/10.1007/9783-319-18416-6_90 Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC, 15(1), 116–132. https://doi.org/10.1109/TSMC.1985. 6313399 Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X Zlateva, P., Velev, D., & Raeva, L. (2015). A fuzzy logic method for assessment of risk management capability. International Journal of Innovation, Management and Technology, 6(4), 260–266. https://doi.org/10.7763/IJIMT.2015. V6.612

CHAPTER 5

Fuzzy Logic in Business Ethics Beata Zofia Filipiak

5.1

Fuzzy Logic in Decision-Making Processes of Enterprises

Does the integration of environmental, social, and governmental aspects become an important aspect of decision-making in entities and their risk assessment? The answer to this question is important not only from the point of view of climate change but also from the point of view of the decision-making process and the market position of entities in the environment. Adequate risk management, including ESG risk, in many cases requires a different approach due to the specificity of the industry as well as the lack of structured information, especially such that allows for unambiguous formulation of recommendations. Entities (enterprises of various industries—commercial or manufacturing services, as well as institutions, especially financial ones) should take into account the incorporation of climate—and environmental risks in their activities. Due to the occurrence of ESG risk, in particular with high

B. Z. Filipiak (B) University of Szczecin, Szczecin, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_5

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exposure to environmental risk related to climate change, and due to the characteristics of the industry and the markets of operation they should pay particular attention to: (1) directional guidelines for the conducted activity and the quality of information that underlies the construction of business models, strategy, and current operational decisions; (2) development of procedures to integrate ESG risk and environmental risk into decision-making and risk management; (3) change your risk management attitude to incorporate climate-related and ESG risks into their risk management framework, with a view to identifying, assessing, managing, and monitoring these risks over a sufficiently long-term horizon; (4) change the value system and take into account ESG factors as responding to changes in the environment that are positively perceived by customers. Entities decision-makers need to know how they should react to ESG, in which direction decisions should go and transform their business models due to ESG risk and the impact of climate change. Based on this knowledge, the support system adjusted toward ESG risk is worth reconsidering. Entities (enterprises of various industries—commercial or manufacturing services, as well as institutions, especially financial ones) benefit from learning about sustainable business models in the contexts of risk management and ESG factors. Finally, companies use knowledge to further the adaptation process toward corporate sustainability. It has been inferred that good decision-making model in such situations must be able to function in unstructured problems and must tolerate vagueness, ambiguity, or inaccurate data (Lumbroso & Vinet, 2012; Škoda et al., 2021). The way that companies incorporate ESG into their decision-making process is determined by each company’s sector, size, and geographical location (Zioło et al., 2023). To minimize errors, risk resulting from the activities conducted in the environment, related to the complexity of ESG risk, methods based on fuzzy approach are extensively used in such situations (Dominiak, 2013; Dwivedi et al., 2017; Shahzad et al., 2017; Chou et al., 2020; Agarwala & Chaudhary, 2021; Feng et al., 2022a, 2022b; Xu et al., 2022; Wang et al., 2022). The transition to a circular economy, but also a change in the relationship between enterprises and financial institutions based on the creation of a common value that takes sustainability into account is a complex process. This process makes it possible to create and maintain positive relationships between the company’s goals, its resources, the environment, and the changing environment toward sustainability (Yu et al., 2011; Ziolo et al., 2021). This process includes both the traditional

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approach typical of the decision-making process, which means that it consists of a set of guidelines for decisions and actions taken by decisionmakers in a specific time, in specific areas, and in relation to specific resources. (Pajak, 2008) But it also covers new areas that support the circular economy and the implementation of sustainable development goals. The last two decades of literature studies, analyses and numerous empirical studies, unequivocally that the main paradigm of contemporary development is paradigm od sustainable development, including quality of life (Izdebski & Jacyna, 2018; Cie´sla et al., 2020; Ziolo et al.,2021, Kaczorek & Jacyna, 2022).The latter area consists of a set of decisions regarding the use of circular economy principles, ESG risk reduction, cooperation with financial institutions based on the principles of responsible business, or the use of GRI taxonomies and principles. In Fig. 5.1. Two areas are presented, taking into account which decisions are made. One should also remember that business based on the circular economy, due to its scale and implemented solutions to achieve SDG’s goals, covers numerous interactions. In the literature, a trend can be observed to group these impacts as follows (Ziolo et al., 2023): • • • •

social impact, environmental impact, social responsibility, and economic impact.

•a specific time the traditional approach typical of the decision- •a pecific areas making process •a specific resources

the sustainable approach typical of the decision-making process in circular economy

•principles of circular economy, •ESG risk, •cooperation with financial institutions based on the principles of responsible business, •GRI standard, •taxonomy, •business responsibility.

New standards of business towards sustainability by enterprises

Fig. 5.1 Two areas are presented, taking into account which decisions are made (Source Own elaboration)

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Decision-making is becoming an important activity in the changing environment where companies operate in the face of crises and climate change. New solutions supporting this process are constantly being sought. (Abdullah, 2013) The general theory of decision-making formed a basis for more systematic and rational decision-making especially in the situation where multiple criteria need to be accounted for, and these criteria take into account uncertainty and striving for change toward sustainability. The general decision theory making is defined as follows (Chaudhuri et al., 2013; Kapoor, 2013): 1. A process that results in the selection from a set of alternative courses of action, that course of action which is considered to meet the objectives of the decision problem more satisfactorily than others as judged by the decision-maker. 2. The process of logical and quantitative analysis of all factors that influences the decision problem assists the decision-maker in analyzing these problems with several courses of action and consequences. The literature on the subject indicates that fuzzy logic is a logic trying to be as close as possible to human thinking and perception. It is based on the assumption that people are not thinking in the exact variables (yes/no), but distinguish a range of “fuzzy” values (rather yes, much yes, maybe no, and yes, and no). This means that decision-makers in the decision-making process operate with cloudy concepts and blurred boundaries, criteria, and judgments. The problems can be presented by ˇ some degree of truth and falsity, which makes them fuzzy (Duraˇ ciová, 2014; Valášková et al., 2014; Naeem et al., 2023). The fuzzy approach takes into account three key elements (Zadeh, 1965; Mckone & Deshpande, 2005): • Fuzzy sets. In contrast to classical sets, fuzzy sets include objects with partial membership. • Fuzzy logic. Fuzzy logic provides rules for operations on fuzzy sets and is therefore key to building models of fuzzy systems. • Fuzzy arithmetic. In addition to logic operations, interval arithmetic operations apply to fuzzy sets.

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Fuzzy logic is mainly used as a form of decision-making in conditions of uncertainty (Buckley & Eslami, 2006; Naeem et al., 2023). This theory is determined “how much” element belongs to the set or not (variable x and its set membership are defined as µ (x) in the range from 0 to 1, 0 means completely non-membership and 1 full membership in the set). (Zadeh, 1965). The decision-maker is unaware of all available alternatives and the risks related to the consequences of each alternative. Each element in the fuzzy set (FS) is given a membership degree, ranging from 0 to 1, indicating its quality or effectiveness. Indeed, quality or effectiveness makes FSs important in human decision-making. (Naeem et al., 2023). The fuzzy approach allows for situations in which an element x may belong to the set only to some degree, or it may also belong to the set and its complement at the same time. According to the formulated definition, the fuzzy set FS in the space X is the set defined as follows: F S = {(x, µ A (x)); x ∈ X, µ A (x) ∈ [0, 1]} where µ A (x) is a function of membership of the element x ∈ X to the fuzzy set FS. For the fuzzy logic approach, it is important to present the concept of a linguistic variable. This term should be understood as a variable whose values are words or sentences in a natural or artificial language. We call the above words or sentences the linguistic values of a linguistic variable. (Zadeh, 1975) We describe the mathematical form of a linguistic variable as follows (Konopka, 2013): (X, T (X ), U, G, M) where X—he name of the linguistic variable (e.g. ESG risk), T(X)—set of linguistic terms, e.g. {“low”, “moderate”, “high”}, U—universe of discourse (e.g. variable development interval), G—grammar creating linguistic values T(X), M—meaning, where M(X) is a fuzzy subset in the space U. Carrying out inference, i.e. basing the decision-making process on fuzzy logic, requires the introduction of an inference algorithm (model) based on a set of rules and an inference module with at the same time the

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so-called input module. Fuzzification is the first input element and at the output follows the so-called sharpening (defuzzification) as the final element. The schematic diagram of the process is shown in Fig. 5.2. Blurring involves assigning a “sharp” value of the input to the appropriate subset of terms of the linguistic variable, specifying the value of the membership function. Sharpening computes the sharp value of the output based on the resulting membership function. This value is computed using defined calculation methods. The set of rules is a set of logical sentences using the plural operators IF, AND/OR, THEN, which describe the relationships between the “input” crisp values of the reasoning model and the “output” values of this model. (Smolarkiewicz, 2010; Kayacan & Khanesar, 2016). Usually simple logical rules in the form of operators are used (Kaczorek & Jacyna, 2022):   IF (x1 is A) AND (x2 is B) THEN y is C where x1 and x2 – “input” crisp variables; y – “output” crisp variables; A, B, C – linguistic terms; AND, THEN – plural operations on membership functions. Inference using a set of rules, which allows the calculation of the resulting membership function, is a three-stage process. Initially, the so-called

„x1” & „x2” crisp variables

Fuzzyfication

Rule evaluation

Aggregation of the rule outputs

Defuzzyfication

„y” crisp variables

Fig. 5.2 The schematic diagram of the process (Source Own elaboration Imran and Alsuhaibani [2019])

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aggregation, i.e. it determines the degree of fulfillment of the predecessors of individual rules. Then, the so-called activation, i.e. the degree of membership of the successor of individual rules is determined. The third stage is the so-called accumulation, i.e. determining the resultant membership function based on the degree of activation of successors. It should be remembered that fuzzy logic should be treated as a kind of automata that base their operation on the laws of logic fuzzy in order to make a decision under uncertainty. The system has a knowledge base (base of the rules) and inference rules. On the basis of “observations, i.e. registered data in the knowledge base”, he makes a decision. (Imran & Alsuhaibani, 2019) From the point of view of enterprises and their decision-making process, it is important to include in the “observations” database-specific variables (observations) for a given entity useful for the decision-making process. These specific variables (observations) should concern the specificity of the activity, environment, relations with the environment, or internal processes of entities.

5.2 Similarities and Differences in the Application of the Fuzzy Approach in Enterprises from the Production, Services, and Trade Sectors The purpose of introducing the concept and theory of sets and the very application of fuzzy logic fuzzy was the need to mathematically describe these phenomena and concepts that are ambiguous and imprecise. In the case of enterprises, increasing decision-making certainty is of fundamental importance. The fuzzy logic theory allows to determine the partial affiliation of a point (object, phenomenon, variables) to the considered decision set. Instead of sentences taking the values of true or false, we use linguistic variables that take imprecise concepts of spoken language as values. However, certain limitations of an objective nature are pointed out. Gorzałczany (1987) states that formal fuzzy set representation is not often adequate. It may be difficult for a decision-maker to provide an exact value of the degree of membership of an element. (Hanine e al., 2021) In many real-world issues, decision-makers may express their opinions even when they are not certain about them, inducing a potential hesitation degree (Xu, 2007; Xu et al., 2008) and may additionally imply decisionmaking errors. To tackle this challenge, Atanassov (1995) introduces the

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intuitionistic fuzzy set (IFSs) as another extension of the fuzzy set. This eliminates this inconvenience for all types of decision-makers, in all types of enterprises. The fuzzy approach can be used in various decision-making areas both in enterprises from the production, services, and trade sectors. The matrix of the general use of the fuzzy approach is presented in Tables 5.1 and 5.2. The division was made based on the types (typology) of decisions made and the decision spheres (internal and external). Making a decision using fuzzy logic is, therefore, associated with obtaining a lot of important information and directing the decision to specific solutions. It allows for a broad look at the decision-making process itself (as shown in Table 5.1.). In addition, industry differences are shown, as different information will be collected at the level of the Table 5.1 The matrix of the general use of the fuzzy approach is presented— the types (typology) of decisions made Typology of decisions made

Entities of production sectors

Entities of services sectors

Entities of trade sectors

Production decisions Service creation decisions

YES

NO

NO

NO

YES

Trade decisions

NO

Financial decisions Investment decisions HR decision

YES YES

YES (restricted by sector specifics YES YES

YES (restricted by sector specifics YES

Logistical decisions Technical decisions

Technological decisions Source Own elaboration

YES (in terms of strategic HR modeling) YES YES

YES (in terms of strategic HR modeling) YES YES (restricted by sector specifics)

YES

YES (restricted by sector specifics)

YES YES YES (in terms of strategic HR modeling) YES YES (restricted by sector specifics) YES (restricted by sector specifics)

Competitiveness Development of the production and market structure Development of the service and market structure Development of the trade and market structure Changes in organizational forms or management structure Creating essential components of the environment

External and strategic decision

X – YES (restricted by sector specifics and the scope of tasks resulting from the strategy) X –

X

X

X

X

– X YES (restricted by sector specifics and the scope of tasks resulting from the strategy) X X

–

–

X

X

Entities of services sectors

X

X

X

X

YES (restricted by sector specifics and the scope of tasks resulting from the strategy) X –

–

X

Entities of trade sectors

FUZZY LOGIC IN BUSINESS ETHICS

Source Own elaboration

Decision-making areas Development of the sphere of services Development of the sphere of production Ongoing implementation of the strategy

Internal

Entities of production sectors

The matrix of the general use of the fuzzy approach is presented

The decision spheres

Table 5.2

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production industry and production companies, while others will be dedicated to service or commercial companies. Fuzzy approach allows you to obtain two types of information: • prospective—used at the strategic level, regarding the future and • retrospective—relating to the past and applicable to operational activities in both trade, service, and production companies. There are four basic areas of strategic decision-making in enterprises and it is in them that the role of the fuzzy approach should be noticed, which allows to determine the optimal strategic directions in relation to the external sphere. Matrix presented in Table 5.2. indicates a significant involvement of fuzzy logic in strategic decisions. It can be put as follows (Dominiak, 2013; Dwivedi et al., 2017; Shahzad et al., 2017; Chou et al., 2020; Agarwala & Chaudhary, 2021; Feng et al., 2022a, 2022b; Xu et al., 2022; Wang et al., 2022): 1. The area of “achieving, maintaining, strengthening, and consolidating the competitiveness of the enterprise” through the appropriate shaping of the company’s resources and skills, appropriate use of sources of competitiveness and shaping the current market competition strategy; 2. The area of development of the production and market structure of the company for production companies or the service and market structure for a service company or the trade and market structure for a company with a commercial profile, through the implementation of new products, access to new markets, or new segments thereof; 3. The area of changes in organizational forms and management structures is to lead to expansion, but also mergers and acquisitions; 4. The area of formation, according to long-term goals, of sophisticated components of the environment; 5. The organizational culture, entrepreneurship, IT, industry orientation, technology adoption decisions, social media, and consumer behavior; 6. The area of sustainability, an important element of influencing the environment, shaping business responsibility, constituting the basis of the business model, and building bonds with financial institutions and potential clients.

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Different methods that are used in the fuzzy approach solve different decision problems. The approach to the problem builds a distinguishing feature of different types of decisions made in different industries. In Table 5.3application of the fuzzy approach in solving various decision problems was presented. All three sectors have a common element that they must analyze in their decision-making processes. It is the environment and the impact of the ESG factor on commercial, production, and sales processes. However, due to the specificity of these industries, the knowledge base (rule base) may differ in the content of variables. However, the common application is mitigation against climate change, compliance of decisions with environmental requirements, or taking into account ESG factors and reporting in operations. It should be pointed out that, in addition to the use of different methods to solve various problems in the production, service, and trade sectors, differences in the input-knowledge base (base of the rules) should be acknowledged. This fact is indicated by the approach used by Li et al. (2018), which shows two approaches to trade and production. The procedure for applying the fuzzy approach is the same, i.e. as described in Fig. 5.1. It should not be forgotten that some decisions are shared in the process itself. These decisions include personnel decisions or decisions in the field of logistics. Here, both the methods and the scope of decision-making, or some of the knowledge bases (base of the rules), may be similar. However, the differences will always result from the specificity of the industry, region, as well as the specificity of the entity itself (including its size). At the end of the presented analysis of the problem of differences and similarities in the application of the fuzzy approach in enterprises from the production, services, and trade sectors, general barriers that all three sectors may encounter in the fuzzy approach application should be pointed out. The studies analyzed in this chapter show the following problems as barriers to the use of the fuzzy approach: limited resources, bureaucracy, lack of appropriate competences of decision-makers, inappropriate organizational structure, size of the company, and the legitimacy of using the approach in relation to the inputs related to the achieved effects, the occurrence of conflicts (competency conflicts or in the selection and selection of variables for the knowledge base).

Services sectors

Determining the order of execution of orders. Orders prioritizing in manufacturing and service providing enterprises based on financial and beyond financial factors, METLAB was used Enterprise inventory management, logistics Application of fuzzy approaches to production planning in complex industrial environments A fuzzy approach for production planning was used A Fuzzy Approach for Solving Production System Problem was used Solving an integrated production, mathematical models with multi-objectives (including optimizing the production cost, processing time, and customer satisfaction), NSGA-II algorithm, a crowd density sorting method based on improved niche dimensions were used A production and transportation scheduling problem was analyzed, using a mixed integer programming model was established An approach to comparative research was used, in particular, the assessment of banks respecting the relative importance of financial performance and their values was made; model based on Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) Usage of FAHP application is growing, especially in the situation of global financial crisis.

Marek-Kolodziej & Lapunka (2020)

Production sectors

Bhattarai and Yadav (2009); Chatterjee and Mukherjee, (2010);

Jakši´c et al. (2016)

Zegordi et al. (2010)

Chen (2019) Solomon et al. (2019) Li et al. (2018)

Rogowska Adenso-D´ıaz et al. (2004)

Description of the problem being solved (method)

Authors

Application of the fuzzy approach in solving various decision problems

Sector type

Table 5.3

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Trade sectors

Sector type

Rodríguez-Cándido et al. (2021) Makhazhanova et al. (2022) Tomasiello & Alijani (2021) Nuro˘glu & Kunst (2012)

Hallerbach (2004)

Kowalczuk & Orłowski (2014)

Ziyadin et al. (2019)

(continued)

Showing the application of different fuzzy-based approaches for agri-food supply chains Applying a fuzzy approach to analyze the effects of exchange rate volatility on international trade flows

Presented a fuzzy multi-criteria approach for measuring consumer perceived travel risk Using the fuzzy multi-criteria decision-making model (FMCDM) for the selection of hotel locations by international tourist Models of sustainable tourism development strategic management were constructed, combination of the results of economic benefits with environmental and social indicators was shown, Fuzzy Logic Toolbox environment of MATLAB was used for the modeling process Described the model of information technology management (MITM) and its component models (contextual, local) describing initial processing (IPP) and the Client–Supplier/Provider–Project (CSP) maturity capsule as well as a decision-making system represented by a multi-level sequential model (MSM) of IT technology selection, which eventually acquires a fuzzy rule-based implementation Suggested a multi-criteria decision framework for managing an investment portfolio in which the investment opportunities are described in terms of a set of attributes, and part of this set is intended to capture the effects on society Applying a fuzzy approach to the decision-making process on stock and cryptocurrency markets Lending to small businesses operating in trade

Hsu i Lin (2006)

Chou et al. (2008)

Description of the problem being solved (method)

Authors

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Fuzzy modeling of stock trading The use of fuzzy approach in improving overall supply chain performance, coordination of supply chain of trading partner plays a crucial role, application of TOPSIS and AHP Solving an integrated distribution, mathematical models with multi-objectives (including optimizing the production cost, processing time, and customer satisfaction), NSGA-II algorithm, a crowd density sorting method based on improved niche dimensions were used

Naranjo et al. (2018) Shukla et al. (2014)

Li et al. (2018)

Description of the problem being solved (method)

Authors

(continued)

Source Own elaboration

Sector type

Table 5.3

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5.3 Implications of the Fuzzy Logic Approach in ESG Risk Management Process ESG (Environmental, social, and corporate governance) parameters are involved: in investing-related decision-making, concerning alternative business models, choices of alternative policies or strategies. Factors, and especially their impact, recognized as ESG factors are related represents a complex task, which should be studied under severe information shortage (which is why a fuzzy approach is used). It is increasingly difficult to treat ESG factors as less important. But not every ESG factor will have the same impact on a small or large enterprise, on a given industry or region. Thus, individual ESG factors should also be considered as parameters completely or it takes it as less important. (Škapa et al., 2023). It is indicated that for uncomplicated decision-making problems concerning climate impact or environmental issues, including ESG risk, simple, straightforward, and easily understandable common sense algorithms, of different natures, represent a significant advantage (Miller et al., 2013; Škapa et al., 2023). ESG experts, especially at the very beginning of any analysis, a decision-making process, do not use mathematical/formal models as the basic framework for their reasoning for just uncomplicated environmental goals and decisions, which means that the basis (draw heavily) on knowledge represented by common sense of the decision-making process. (Bredeweg & Sales, 2009). The impact growing of non-financial factors, crisis situations, as well as the unpredictable course of classic risks burdening the activities of business entities means that ESG risk is increasingly identified as part of the risks to which enterprises are exposed. Among the factors that determine ESG risk, the impact of environmental risk was identified the earliest and the following factors were referred to: climate change, environmental pollution (air, water, land), environmental degradation, and resource scarcity (Bua et al., 2022; Escrig-Olmedo et al., 2019). In numerous studies, environmental risk is presented in the context of risk management process (Annamalah et al., 2018; Poon et al., 2022; Srinivas, 2019). The second element of ESG risk is social risk, the impact of which began to be analyzed separately, and only later was it recognized as an important non-financial factor affecting the scope of economic activity of enterprises. Currently, it is analyzed in the context of the impact on the decision-making process in a broad concept and has the potential to grow the fastest. (Society for Corporate Governance, 2020; Cohen, 2022).

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Governance risk is closely related to the decision-making process and is considered as an important factor affecting the quality of the decisions themselves in the context of: employee relations, relevant staff compensation, tax, and legal compliance, (Karwowski & Raulinajtys-Grzybek, 2021) the prevention of corruption and bribery, transparency (MuñozTorres, 2019, Society for Corporate Governance, 2020) risk management (Srinivas, 2019). Decision-makers, politicians as well as entrepreneurs need environmental data and ESG risk modeling data. The demand for data in the decision-making process should be based on the ability to choose from potentially competitive policies, from potentially competitive investments or development programmes. There is also a need for specific decisions, burdened with ESG risk, regarding changes toward sustainability. Appropriate methods for comparative assessment of such policies, investments, or programs are therefore needed (Browne & Ryan, 2011; Chalabi et al., 2017). These methods include cost-effectiveness analysis (CEA), cost–benefit analysis (CBA), and multi-criteria decision analysis (MCDA). The literature on the subject points to the imprecision of the ecological impacts and the frequent lack of quantitative information (especially in the area of ESG risk and the programming of legal rules regarding the ESG area), fuzzy theory provides a useful approach to the environmental impact evaluation. It is advisable to use a fuzzy approach to define the environmental parameters through fuzzy numbers (Enea & Salemi, 2001). It is also advisable to use a fuzzy approach when decisionmakers should use flexible approaches to decisions designed to improve environmental quality having regard to uncertainty. (Fisher, 2006). Considering the fact that risk management is a planned and a structured process aimed at helping the managers (decision-makers) and teams makes the right decision at the right time to identify, classify, quantify the risks and then to manage and control them. A particular risk is ESG risk, the theory of which is still developing and needs to be refined. The aim is mitigation against risk indicators, in particular limiting the impact of ESG risk and climate risk on the entity’s operations. Risk management is a continuous process which is to be implemented in any project from inception to completion. However, in order to realize the full potential of entities (enterprises or institutions), ESG risk management should be implemented together with the classic risk management process. Risk is an uncertain event or condition that, if occurs, has a positive or negative effect on entities objectives. (Srinivas, 2019) This postulate is presented

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in Fig. 5.3. Taking into account the definition formulated by Zou et al. (2014), indicating that the risk management process is “a systematic way of looking at areas of risk and consciously determining how each should be treated”. Moreover, they point out that this process provides tools that allow identifying sources of risk and uncertainty, determining their impact, and developing appropriate management responses. Thus, the indicated approach in Fig. 5.3. Requires the inclusion in the decision-making process of new tools that will strengthen the cognitive and decision-making value. Most of the ESG ratings and data providers provide reports and data. This increased transparency undoubtedly paves the way for a better opportunity to scrutinize approach to ESG risk, with the focus being on: (1) the differences across firms on what ESG factors are considered material, (2) the measurement of ESG factors, (3) the weight given to ESG factors, and (4) the sources used to carry out the evaluation. This creates an opportunity to build a decision support area in various types of entities for fuzzy approach. Because independent ESG ratings and independent data provider function side by side, there is a lack of consistency and uniform presentation rules of provide reports and data. (Berg et al., 2019) This creates uncertainty and makes it possible to use fuzzy approach for decision-making purposes.

ESG risk factors

a new approach to risk management

classical factors, recognized in the risk management process

risk factors taking into account ESG risk and climate risk in the risk management process

Fig. 5.3 Inclusion of ESG risk in the risk management process in entities (Sources Own elaboration)

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The introduced ESG reporting standards and agreement on what should be deemed as material for each sector has led to ESG data regarding risk are difficult for companies to manage their narrative on sustainability and determine how best to allocate internal resources regarding sustainability reporting. It can be stated that there are some common and separate approaches (elements) regarding the inclusion of ESG risk in the risk management process using a fuzzy approach. The postulates of including ESG risk in process risk management using different method are presented in Fig. 5.4. Effective management of risks would be possible if these risks are managed using a fuzzy approach. Accordingly, classic (i.e. previously recognized risks, such as customer risk, market risk, or financial risk) are allocated into different project phases of production, service, or commercial as per their possible time of occurrence. Many risks may arise over time, and in addition, the fuzzy approach allows you to consider risks both typical for a given industry and related to the market, as well as allows you to estimate the ESG risk (even by using a fuzzy approach). Given the increasing importance of ESG data and ratings, the use of a

Plan risk management in entieties (enterprices, institutions)

qualitative risk assesment (evaluate of risk, ranking), posible using of fuzzy base)

using the knowledge bases (base of the rules) - using a fuzzy approach

identify risk traditional and ESG risk (use info. from fuzzy base)

qualitative risk assesment (likelihood, impact, level of risk, factors, ESG)

risk respons planning (idenrtify options, select the best strategy or choosing the optimal path

supplementing the base

Outcome of the risk response

monitoring and controlling of the risks

fuzzy approach application directions

Fig. 5.4 The postulates of including ESG risk in process risk management using different method (Sources Own elaboration)

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fuzzy approach in risk management allows for: (1) an overview of the ESG data and ratings landscape; (2) key takeaways for companies to navigate this increasingly difficult issue; and (3) combine traditional risk (so far included in the analyses, referring to the specificity of the activity) with ESG risk. The use of the database will be supplemented with information from the analyses carried out, which will additionally affect the quality of decision-making in the future (as shown in Figs. 5.2. and 5.3). The literature on the subject points to methods typical for risk management. In order for the approaches indicated in Fig. 5.4. were full, they should be supplemented with a presentation of methods, tools, and techniques that will be characteristic of combined risks, i.e. ESG risk and classic risk, typical for the industry. This approach is presented in Table 5.4. As already indicated, the fuzzy approach gives the opportunity to look more broadly, including its processes, products, and services, but also customers, contractors, government relations, or external economic entities, in order to diagnose and use gaps that entities have not yet noticed (Fig. 5.5). These key risks, affecting the production process or the provision of services, are categorized into important areas from the point of view of the decision-making process, but also from the point of view of the product life cycle. It is easy to judge that a majority of risks occur in the pre-operation stages, but also ESG risk factors show that they have a significant impact in the first and last phases, when products and services reach the final recipients. It should be remembered that each type of enterprise (trade, service, or production) has its own specific key risks. It also has specific processes that are affected by risk. On the other hand, customers (clients), suppliers, contractors, or stakeholders (owners) are the common area of risk impact in various types of enterprises. In addition, regulations related to exclusion, social responsibility, ESG factors, and externalities are a new, poorly structured area that requires special support. It should be remembered that the use of a fuzzy approach is to lead to the achievement of decision-making goals in operational and tactical terms. The indicated areas carry a potential risk, but are also burdened with the possibility of a potential occurrence of uncertainty resulting from inference (abductive reasoning), or data uncertainty resulting from the fact that they are missing, incomplete, or incorrect. The use of fuzzy approach variables in this respect allows to take into account both data

Methodology—traditional (classic) approach Structured review of documentation, study of history of execution of similar processes and projects, and quality of plans as well as the consistency between those plans and activities requirements/assumptions would be an indicator of risks; taking into account the relationship between activities and the strategy and business model.

Documentation reviews (Inputs)

These requirements confirm the need to conduct a reliable analysis of the financial and business impact of ESG factors on the company’s value and strategy. Documentation and existing procedures are analyzed. First of all, it will be necessary to include non-financial factors in an orderly and holistic manner in operational processes. Without a long-term management vision and a connection with the business strategy, there is no value security. The use of the fuzzy approach gives the opportunity to look more broadly, including the company’s operations, its processes, products, and services, to diagnose and use gaps that the competition has not yet noticed (Fig. 5.5.) The result will be the reconstruction of documentation and processes and the inclusion of ESG in existing processes

Methodology—fuzzy approach

Identification of common risks (classic and ESG): methods, tools, and techniques

Parameter

Table 5.4

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• • • • • • •

Information gathering techniques

Brainstorming Delhi technique Checklist analysis Cause and effect diagram Questionnaires SWOT analysis Expert judgment

Methodology—traditional (classic) approach

Parameter

(continued)

• Classical Zadeh’s (1965) fuzzy approach • Cost-effectiveness analysis (CEA) • Cost–benefit analysis (CBA) • Multi-criteria decision analysis (MCDA) • TOPSIS • AHP • NSGA-II • MITM describing initial processing (IPP) • Client–Supplier/ Provider–Project (CSP) • Multi-level sequential model (MSM) • Fuzzy multi-criteria decision-making model (FMCDM) • Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS)

Methodology—fuzzy approach

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Sources Own elaboration onSrinivas (2019)

Analysis results

• Risks list that poses the greatest threat or presents the greatest opportunity • List of the risk with the greatest impact on decisions made in entities • Overshooting stated objectives to acceptable levels • Establishing a trend that leads to conclusions affecting risk responses • Historical data analysis gives performance reflects new insights gained through quantitative process • Preparation of the form of quantitative risk analysis report

The output of quantitative risk

Prioritized list of quantified risk

Methodology—traditional (classic) approach

(continued)

Parameter

Table 5.4

• Insights gained through fuzzy logic and fuzzy arithmetic, using knowledge bases (base of the rules)—using a fuzzy approach and effect of fuzzy approach • Preparation the ESG report

• Positioning on the list of ESG factors • Clustering of ESG and climate-related risks

Methodology—fuzzy approach

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Fig. 5.5 Consolidation of key areas of classic and ESG risks toward fuzzy approach using (Sources Own elaboration on Zou et al. [2014])

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on historical results and relationships, as well as expert knowledge. Identifying the possible occurrence of risks in each stage of the decision-making process and making appropriate actions to cope with them are significant. On the other hand, the process of mitigating and managing the effects of crises is important as well as predicting the impact of non-financial factors related to the impact of ESG risk on all areas of activity. In doing so, a consolidation of key risks, stakeholders, ESG risk, and key areas of the entity’s activity is shown in Fig. 5.5. ESG risk has a feature that is described by the principle of double significance (as shown in Fig. 5.4. This principle indicates that (1) the subject (enterprise or institution) affects the environment and (2) the environment affects the subject. This means that not all risk factors can be quantified, so a descriptive approach is required, and this creates a field for the use of a fuzzy approach. In particular, it is the risk factors typical of the “S” and “G” factors that require a qualitative approach and description. The basic areas (groups) of ESG risk common to enterprises from various sectors and institutions are shown in Fig. 5.6. But fuzzy logic has at least two limitations for decision-making process and impacts ESG factors for processes in entities. One problem is its strong reliance on subjective inputs. The literature indicates that this problem in any type of assessment, and fuzzy methods might provide more opportunities for the misuse of subjective inputs. (Mckone & Deshpande, 2005) Moreover, it is indicated that good effects for the decision-making process can also be achieved from the use of standard statistical descriptions. The second limitation is the lack of certainty that fuzzy logic will provide a full guarantee of obtaining the best solution to a given problem. Based on fuzzy logic, we get closer to the optimum, but there is no certainty that the provided risk solution will be the most optimal. Take into account the fact that taking into account ESG factors in the risk analysis regarding all aspects of the functioning of entities on the market is a global trend to transform the enterprises sector and develop new sustainable business models, so as to protect the environment, but also act with respect for the rights of society and while maintaining corporate social responsibility.

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Groups of risk factor environment "E" •climate change, described by the impact of: CO2 (I), greenhouse gases (I), energy consumption (I) and energy efficiency (I), reduction in waste (I), reduction in single-use plastics (I), recycling (I) , Emissions Management (D), Climate Threats (D), Climate Opportunities (D) •green products (I+D), •Natural resources: water use (I), supply chain and third-party contractors (I+D), impact on biodiversity (D), waste and pollution (I+D)

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Groups of risk factor society "S" •human capital management (I+D) •environmental, health, and safety (EHS) (I+D) •diversity: diversity on supervisory boards (I), gender equal pay index (I) •employment: job rotation (I), freedom of association and collective bargaining (I), job security (I+D) •human rights (D)

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Groups of risk factor governmental "G" •security (I+D) and cybersecurity (I +D) •corporate governance (D) and policy (D+I) •business ethics: good practices (D), code of ethics (D), violations (D)

D - descriptive I – indicator

Fig. 5.6 The basic areas (groups) of ESG risk common to enterprises from various sectors and institutions constituting potential “fuzzy base” areas (Sources Own elaboration on Society for Corporate Governance [2020])

5.4

Conclusion

In recent years, many entity activities that need to run their business taking into account environmental factors have changed, both trading, service, and manufacturing enterprises have changed. Their links with financial institutions have also changed. The basic feature of this change is the growing influence of ESG factors with very high uncertainty of information and business conditions. (Feng et al., 2022a, 2022b; Xu et al., 2022; Wang et al., 2022). Enterprises are moving away from their traditional business models, taking into account ESG factors, but also seeing the need for creating new value and new business connections, as well as fostering new values toward sustainability. Growing risk in the classical sense and ESG risk creates a new space for the development of decision support methods, but also for filling databases with information for decision-making purposes. A valuable solution, the use of which is growing, is the use of a fuzzy approach in decision-making processes regarding the impact of ESG

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risk. As indicated by the literature review, the applications of the fuzzy approach in the decision-making process are constantly increasing and different methods are used. Despite the ever-growing stock of new challenges, including expected regulatory changes, commercial, manufacturing, and service enterprises approach ESG risks, change toward sustainability and environmental activities in a structured manner. Topics that enterprises are already doing and should be doing with include, but are not limited to: (1). building new business models toward sustainability, taking into account the modeling of the impact of ESG risk on the conducted business; (2). defining the potential impact of ESG risk based on a fuzzy approach (fuzzy logic); (3). they should define their degree of integration of sustainability into business and the impact of ESG risk on their business (threats, the need to mitigate risks) using a fuzzy approach; (4). Impact of ESG factors on pricing (integrated value) and evaluation for stakeholders, which is important for managing relations with stakeholders. Decision-making is becoming an important activity in the turbulent environment where ESG risk is present, despite being applicable with various updated technology advancements-assisted decision tools. Technology alone sometimes fails to deliver a decision without considering human cognitive capability. But also cognitive abilities without adequate quality of information will not be fully optimal. Fuzzy approach enabling continuous measurement and correction of values, enabling the description of phenomena and processes that cannot be described by logic in the classical sense.

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CHAPTER 6

Cooperation Between Financial Institutions and Companies: Fuzzy Business Models ESG-Oriented Beata Zofia Filipiak

and Magdalena Ziolo

6.1 The Levels of Cooperation Between Enterprises and Financial Institutions Toward a Sustainable Perspective Cooperation with banks is an integral element of enterprise operations. However, the needs, as well as models of cooperation can be diverse. This diversity is related to the specificity of the activity: trade, services, production. Diversity can also be the result of factors such as politics, culture

B. Z. Filipiak (B) · M. Ziolo Department of Sustainable Finance and Capital Markets, University of Szczecin, Szczecin, Poland e-mail: [email protected] M. Ziolo e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_6

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and tradition, supplier specificity. An important factor differentiating cooperation may be the ESG risk exposure factor. Many authors see the success of doing business in business models, but also in the effective cooperation of enterprises and financial institutions. However, this approach or introducing the idea of sustainability ˇ et al., 2012; to business (Zoot & Amit, 2010; Gerster, 2012; Cihák Saebi & Foss, 2014) is nothing new, for example: how to “do business” (Zott et al., 2011) and how to create financial value (Teece, 2010; Wirtz et al., 2016), how to integrate into one whole basic elements such as systemic analysis, planning, mapping communication as well as configuration (Doleski, 2015) and how to use to create value a strategic asset for competitive advantage and performance of company (Afuah, 2004). Models of cooperation toward sustainability financial institutions and enterprising are also indicated (Zioło et al., 2023). In view of the rapid changes in the environment caused by climate change, significant exposure of the business to ESG risk, the need to adapt to the changing requirements, trends or expectations of the society, the answers to a number of decision-making issues are unclear and ambiguous. They require the search for more effective methods than before, and the existing business models, even those adapted to sustainability, require verification. Enterprises will increasingly look for financing instruments, green products in which they could invest, as well as examples of good practice or new knowledge that financial institutions can provide. The elements of green finance require more transparency, more information from and to the client, to the investor than is the case with traditional instruments. (Ziolo et al., 2021). Cooperation with financial institutions is a very important managerial domain. The scope of this cooperation can be classified into various categories, as listed below: • Programmed decisions, which can be described as routine and repetitive decisions taken by managers. (Kozioł-Nadolna & Beyer, 2021) These decisions are short-term, less tactical. They refer to the issue of constant cooperation with financial institutions, sometimes long-term cooperation. • Non-Programmed decisions, which are typically one-shot decisions. (Kozioł-Nadolna & Beyer, 2021) Their nature depends on the subject of the decision. If they concern an investment that is related

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to a strategic decision (concerning in particular development), they are highly structured and prepared. Changing the financial institution is related to the optimization of financing. “Non-Programmed” decisions may also apply to the tactical sphere, when enterprises are looking for a partner and want to verify a financial institution in cooperation. These decisions are usually less structured. • Decisions can also be made under pressure, in crisis situations and can be about both short-term and tactical cooperation, but they are less structured than programmed and are made in unforeseen conditions. The essence of continuous cooperation between companies and financial institutions can be analyzed from the point of view of the needs associated with conducted business activity, in different time horizons. According to the classic division of financial services provided by banking institutions and financial markets, they include (Kwiecien, ´ 2019): • the provision of daily services for the company in the sphere of various financial operations, • the possibility to use various kinds of available cash accumulation forms • and seeking sources of financing, both for current and investment activity. The literature on the subject points to many factors that guide customers when choosing a financial institution. It is also indicated that it is necessary for financial institutions to understand the preferences of the customers to offer the services. (Kamakodi & Khan, 2008; Aliero et al., 2018; Joseph & Mung’atu, 2018; Kwiecien, ´ 2019; Zelie, 2023). There are many factors that can be classified as economic (offer activity, price conditions, costs of providing services for products and services), but also those related to access to services (capital potential, location, the speed and flexibility of decision-making) and promotion and marketing issues (security and prestige, recommendations). The evaluation of the selection criteria is visible, which can be defined as constant factors: economic, but those that have developed along with a change in awareness, under the

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influence of the current situation and market pressure, as well as financial institutions themselves. The Fig. 6.1 shows the evolution of factors in choosing a financial institution. As indicated in Fig. 6.1 changes occurring in the environment of enterprises and institutions caused by the European Action Plan for Financing Sustainable Growth of 2018 and the Renewed Sustainable Finance Strategy recently adopted by the European Commission introduced a number of new regulations for financial institutions such as banks, stock exchanges and financial markets. Responsibilities such as: reporting information on the topic of environmental impact, decarbonization strategy and estimation of ESG risks and the impact of climate risk on business mean not only additional costs, but also significant organizational changes on the part of financial institutions as well as enterprises and institutions. These challenges affect the decision-making process regarding the choice of the financial institution with which the company will cooperate, but also the new business model of financial institutions, especially banks, will make it necessary to influence the companies with which the financial institution will cooperate. In addition, the most important actors in the financial system (central banks, commercial banks, pension funds, insurance companies or government entities) are taking action to change financial products toward sustainability. This influence is manifested in regulatory activities as well as in the attitudes of financial institutions, such as banks or financial markets. Changes are indicated by ESG benchmark reports and assessments made by independent institutions. This responsible action changes the level of cooperation between financial institutions and enterprises. It can be pointed out that changes in the financial market ( Schoenmaker & Tilburg, 2016; Stern, 2016), striving to change the business models of financial institutions and the impact of central banks in response to the actions of international institutions for the climate (Zioło et al., 2020), as well as the classic approach to the factors of choosing financial institutions allow for formulation of two levels of cooperation between enterprises and financial institutions toward a sustainable perspective: • passive adaptation to new business models of financial institutions; • creative cooperation.

old criteria

Governance / Social responsibility

Promotional and Security

ESG Risk

Behavioral

elimate changes

Ecosystem/ Environment

new criteria

Economics

adaptive transformation towards circular economy

17 SDG's

good practices in financial institutions

new legal regulations towards sustainability

taxonomy

new funds for transformation

new funds for sustainability

Fig. 6.1 The evolution of factors in choosing a financial institution by enterprises (Source Own elaboration)

Access to services

Promotion and marketing issues

Economics

externalities

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In Table 6.1 factors that are determinants of mutual cooperation between enterprises and financial institutions toward a sustainable perspective are included. Passive adaptation to new business models of financial institutions is an activity based on existing cooperation, and changes toward sustainability result from the changing business models of financial institutions (Fig. 5.2). It is financial institutions and their stakeholders that force companies to change toward sustainability. Financial institutions are changing their cooperation requirements, changing the assessment criteria (access to financing products), pushing out customers who are "brown". Along with the change of the financial institution’s business model, the motives for cooperation, the criteria for cooperation change, but also the activities stimulating changes that are transferred to customers begin. Awareness is changing, the factor of social responsibility is gaining importance, the importance of ESG factors is growing and the product offer is being modified, which is an important factor in influencing customers (Fig. 6.2). The level of creative cooperation includes mutual cooperation in creating changes toward sustainability (Fig. 6.3). At this level, cooperation covers both traditional needs, but taking into account the needs resulting from changes in enterprises toward sustainability, as well as supporting partners in managing climate and ESG risks, and exchanging data (information). Entities cooperating create new opportunities for products and services. They create cooperation networks for the development of new financial products and services. An important area is promotion, proper reporting, taxonomy and responsible promotion of good practices. Under the influence of mutual cooperation and attitudes toward sustainability, the values and business models of cooperating partners— financial institutions and enterprises—are changing. This cooperation means that the attitudes of other enterprises, stakeholders of financial institutions and enterprises may change through good practices. It should be remembered that the area of climate change, ESG risk management, as well as focusing on the problems of achieving goals related to sustainability raise a number of decision-making problems that require support and it is not always possible to obtain clear data or obtain unambiguous answers. Therefore, cooperation between enterprises and financial institutions toward a sustainable perspective must be based on innovative tools and use a different approach to the way of making decisions. This area allows for a fuzzy approach.

Adjusting the offer to new needs

Safety

Costs

Channels

Safety of fund; safety of transaction; reliability Innovative product; sustainable products; green products

M

O

M

Enterprises

A

N

O->M (relative - the strategy of M higher service costs for customers qualified as toward-unsustainability) O M

M

O

M

Selection of services; ease of obtaining loan/bonds issue; wide range of product Working hours of the bank, speed of service and decision, convenient branch location; availability of parking space; convenient ATM location Collaboration (access) using internet links; mobile Low interest rate on loan/ bonds; low service charges; low penalty charges

Offer

Availability

Financial institutions

The point of view of

Characteristics of the factors

Group of factors in cooperation between enterprises and financial institution

A

A

A

M->C

A

M->C

(continued)

Level of cooperation

Table 6.1 The factors determining mutual cooperation between enterprises and financial institutions toward a sustainable perspective

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(continued)

Characteristics of the factors

Good complaint handling; service efficiency; service quality; friendliness personnel Benefit Paying higher interest rate on savings account; automatic procedures for managing financial surpluses Promotion / special offers Friendly relations, additional offers, shaping social relations Reputation Social responsibility; reliability; recommendation.

Quality/service friendliness

Group of factors in cooperation between enterprises and financial institution

Table 6.1

A->C C

M N

M (relative – toward sustainability) M

M->C

A

M

Enterprises

Level of cooperation

O->M (relative -changes toward M shaping benefit toward sustainability)

O

Financial institutions

The point of view of

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N

M

Enterprises

C

C

Level of cooperation

Designations: M—qualitative changes of the cooperation factor toward sustainability; O—factor remaining unchanged; N—a new factor of cooperation toward sustainability; A—level of adaptation; C—level of creative cooperation; M–>C—the factor of change leads to modification of the level of cooperations; A–>C—flexible of change leads to modification of the level of cooperation; O–>M—flexible of change of factors Source Own elaboration

N

M

Additional services offered to clients, e.g., training, new knowledge, consulting Risk management, support in the mitigation of ESG risk, shaping changes in terms of impact on society and improving the quality of governance

Additional services

Cooperation in the mitigation of risks

Financial institutions

The point of view of

Characteristics of the factors

Group of factors in cooperation between enterprises and financial institution

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climate changes

ESG risk

SDG's

impact of legislative changes towards sustainability (national/international )

stakeholders building the idea of sustainability

Enterprise

Bank Spheres of influence: strategy

strategy business partners

distribution channels

business model

business model key activities / spheres of activity customer segments

strategic resources creative changes to existing products

new customer service and evaluation procedures towards sustainability

taxonomy

new green product and services

ne w s ustainable business models => cha nges i n products a nd s e rvi ces

knowledge base

ESG risk reporting

cre a ti ng changes i n mutual coope ration towa rds s us tainability

mentoring activities

ta xonomy and re porting

ne w cooperation model - passive adaptation

Fig. 6.2 The passive adaptation level cooperation between financial institution by enterprises (Source Own elaboration)

6.2 Mutual Influence of Enterprises and Financial Institutions on Decision-Making Processes from the Perspective of Sustainability End ESG Risk Since climate change causes unpredictable disturbances both in the environment and in the business environment, the decision-making process must be focused on taking into account climate risk factors. A number of activities related to the implementation of the SDGs require time to learn about their effects. Actions taken by financial institutions and enterprises aimed at sustainability and ESG risk mitigation must be longterm actions for two reasons. Firstly, one-off actions do not bring the intended effects or benefits for the environment, society or entities undertaking them (e.g., creating new value). Long-term operation allows for

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climate changes

ESG risk

SDG's

impact of legislative changes towards sustainability (national/international)

stakeholders building the idea of sustainability

Enterprise

Bank Spheres of influence: strategy

strategy business partners

distribution channels

business model

business model key activities / spheres of activity customer segments

strategic resources creative changes to existing products

new customer service and evaluation procedures towards sustainability

taxonomy

new green product and services

new s ustainable business models => cha nges i n products a nd s ervi ces

knowledge base

ESG risk reporting

crea ting changes i n mutual cooperation towa rds s us tainability

mentoring activities

ta xonomy and reporting

new cooperation model - passive adaptation

Fig. 6.3 The creative cooperation level cooperation between financial institution by enterprises (Source Own elaboration)

increasing awareness, consolidating desired patterns of behavior, but also consolidating bonds of cooperation and building a portfolio of products and services based on the inclusion of sustainability. The cooperation of financial institutions and enterprises allows for the development of joint

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activities for the recognition of ESG risk, its mitigation and impact assessment, so that stakeholders will appreciate additional activities, which is a side effect of investing in sustainable development. Secondly, enterprise does not have to take such steps alone. It is looking for business partners (financial institutions) who will financially support the undertaken activities, have experience in mitigating climate risk and ESG risk and additionally also often share the same environmental ideas, i.e., their business models also consider the SDGs and activities for ESG risk mitigation (ESG reporting, increasing transparency, impact on biodiversity). As indicated in the literature (Ziolo et al., 2020), it should be expected that both financial institutions and enterprises will have business models tailored to the SDGs and ESG risk. This activity directing business models to the SDGs and ESG risk will allow to achieve a business advantage is experienced in sustainability and implementation of new environmentally friendly development projects. Recognizing the strength of cooperation, both financial institutions and their customers, and in particular enterprises, will seek common elements, or even common ground of understanding, which may be the area of ESG risk management and presenting your achievements in this area. Financial institutions, as participants in the Sustainable Banking Network (sustainability knowledge network) interact with companies to (SBN, 2023): (1) increase the efficiency of ESG risk management, (2) increase the flow of capital toward financing pro-environmental and socially responsible investments, (3) increase the level of knowledge about sustainability, ESG risks by sharing experience and transferring good practices. In this area, financial institutions are influencing the dedications of companies in terms of both short-term and long-term perspectives. Table 6.2 provides a summary of the directional impact areas of financial institutions and enterprises in the short and long term. ESG risks must be analyzed in various perspectives of the activities of financial institutions but also their impact on the enterprises themselves and their activities should be expected. Therefore, cooperation and cooperation for the creative implementation of the idea of sustainable development requires taking into account all the above-mentioned perspectives. This requests a holistic approach when embedding them into the risk management framework both on the side of the financial institution and the enterprise. This makes the ESG risk area and the sustainability area an area of creative cooperation starting with a sound risk governance and a sensible risk strategy before implementing these

Financial institution

(1) Action for development, implementation, measurement; (2) Adaptation to national and regional specificities, inclusion of metrics in decision-making procedures for cooperation with actors

(3) Building instruments toward sustainability (4) Development of credit risk assessment procedures for different financing instruments (5) Analysis of the bond market, social and environmental financing instruments

Areas of interaction on decision-making processes from the perspective of sustainability end ESG risk

Developing measures of sustainability and progress toward sustainability

Instruments for sustainable financing

(continued)

(1) Knowledge of the metrics makes it possible to adapt to the requirements of the institutions, but also to use this instrument to build benchmarks with other players in the market in order to better position themselves; (2) A better assessment position gives a better bargaining position for obtaining funding to develop toward sustainability. (3) Decisions to adapt one’s own situation toward the requirements of financial institutions. (4) Strategic decisions to link the object of investment in sustainable development with the offer of financing instruments (5) Debt impact assessment (6) Environmental impact assessment and consideration of ESG factors related to the planned investment toward sustainability.

Enterprises

Table 6.2 Areas of interaction between financial institutions and enterprises on decision-making processes from the perspective of sustainability end ESG risk

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(6) Collection and management of data on ESG disclosures and risks (7) Improvement of procedures

(8) Assessing the implementation of climate change financing procedures and regulations (9) Raising ESG risk management standards (10) Engaging the private sector in financing sustainable climate change projects and activities

Data and disclosures related to sustainability and ESG risk

Assessment of the implementation of the sustainable funding framework

Source Own elaboration on (SBN, 2023; Stojan & Iorgulescu, 2019)

Financial institution

(continued)

Areas of interaction on decision-making processes from the perspective of sustainability end ESG risk

Table 6.2

(7) Use of good practice data on ESG disclosures and risks (8) Broadening its own knowledge base with information and good decision-making practices on the impact of ESG risks. (9) To direct decisions toward existing solutions, to take advantage of information offers (10) Benefit from good risk management practices and the opportunity to present their own solutions (11) Be involved as a partner in efforts to finance alternative sustainability projects

Enterprises

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risks into the risk management cycle. To this end, common areas for coordination should be prepared, especially financial ones, as well as those related to sustainability and ESG risk. In Fig. 6.4 a holistic approach was presented and development of changes taking into account the ESG risk and the impact of climate change. Both financial institutions as well as enterprises and their stakeholders are interested in developing sustainable solutions that allow the inclusion of both financial and non-financial (ESG) factors in common standards, allowing to raise cooperation to a new level. The developed solutions increase the decision-making area, but also affect the quality of decisions made, as financial institutions and enterprises increase the amount of information by using a common knowledge base, which will contribute to a better implementation of these risks into the risk management cycle. In addition, an important area related to the risk management cycle is the possibility of using a fuzzy approach in the processes of ESG risk management and environmental risks. Including ESG factors in the decision-making processes of both financial institutions and enterprises themselves is a long-term process that evolves and the scope of ESG-related information included in decisions increases at its various levels. The use of a holistic approach as well as support for this fuzzy approach process takes time and must take place at the operational and strategic level. The use of a holistic approach as well as support for this fuzzy approach process takes time and must take place at the operational and strategic level. It requires a period of transition after the first stage, at the level of which actions are preventive and reconnaissance. The highest level is the level that allows the implementation of new solutions. The Table 6.3 shows the levels of integration of ESG risk and sustainability in the decisions of enterprises and financial institutions. Cooperation in the inclusion of ESG factors from the sustainability perspective in management, decision-making and risk management processes brings measurable benefits to both partners. These benefits apply not only to the mitigation and reduction of potential losses related to the risk of non-financial factors. They make it possible to capture the achievement of goals in the area of sustainability, faster adaptation to changes caused by the transition to a circular economy, but above all, they result in the selection and implementation of decisions that result in the creation of a positive impact on society and the environment.

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FINANCIAL INSTITUTIONS

Financial ratios basic impact

STAKEHOLDERS

Equity ratio

New financial ratios (towards sustainability end ESG risk- basic

Financial ratios

Industry factors

Climate change causing excessive risk exposure

impact impact / force of change estimation

the power of influence estimation

Cash flow figures

ENTERPRICES

Exposure to ESG risk

Cash flow figures

change-inducing risk factors

investments

Operating result

Equity ratio

Cash flow Revenue

risk factors that may trigger changes

Short-term debt

changes that have occurred

Gross margin

?

other

prices changes in the industry caused by ESG risk factors other

?

Revenue

Revenue

Lock-up of capital

Short-term debt

Depreciation

Gross margin

Other

Other

Financial ratios dedicated to cooperation (limitation and specificity of cooperation)

Financial ratios dedicated to cooperation (limitation and specificity of cooperation)

ESG ratios (new assessment of adaptation to sustainability requirements)

Credit rating / Evaluations

Taxonomy, GRI, ESG reporting, new collaboration solutions, other and currently unknown factors

Knowledge bases supporting decisions, including bases based on fuzzy approach

ESG ratios (new assessment of adaptation to sustainability requirements)

Credit rating / Evaluations

Fig. 6.4 The holistic approach development of cooperation allowing to improve the quality of the decision-making process between financial institutions and enterprises, taking into account the impact of the ESG risk and the impact of climate change (Source Own elaboration on [KPMG, 2021])

Characteristics of activities • Adjustment actions taken as a result of the occurrence of ESG risk and caused by climate risk; • Fragmented integration of activities with the areas of climate risk, sustainability and ESG risk; • Law-enforced adaptation (e.g., through the need to implement taxonomies or GRI standards);

Level 1—Basic response to climate change and ESG risk factors (operational) (type of cooperation passive)

(continued)

• Institutional adjustment of financial entities under the influence of supervisory institutions and institutional regulations; • Financial institutions affect the adjustment changes of enterprises; • Decision-making compulsion toward sustainability, co-operation compulsion toward sustainability, knowledge blurs the impact of ESG risk, gathering information and good practices; • Base cooperation;

Decision-making cooperation approach

The levels of integration of ESG risk and sustainability in the decisions of enterprises and financial institutions

The levels of integration

Table 6.3

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Characteristics of activities • Fragmentary perception of the need to implement SDG goals on the part of enterprises, greater awareness of financial institutions in this decision-making area; • Active imitation, development of standards, striving to integrate ESG risk into the risk management process, e.g., by developing policies dedicated to high-risk sectors and integrating these policies with the risk management process; • Increased awareness of the impact of risk on the operations and shaping of the financial institution–enterprise relationship; • Development of mechanisms for controlling ESG risk and climate risk in the activities and cooperation of entities; cooperation in the field of ESG risk as well as the implementation of SDG objectives is fragmentary and unsystematic;

Level 2—Active imitation (operational advantage over tactical) (type of cooperation passive)

(continued)

The levels of integration

Table 6.3

• Implementation of good practices; • Implementation of standards; • Searching for information and building knowledge bases; • Lack of clear decision-making standards; • Lack of an integrated decision-making approach; • Cooperation in the field of products and services offered by financial institutions;

Decision-making cooperation approach

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Characteristics of activities • Risk is fully incorporated in partners’ activities and their decision-making processes; • Addressing the issues of sustainability and ESG risk in a tactical and strategic framework; • Inclusion of non-financial risk and the implementation of ESG objectives as an element of business models with the processes of creating new value; • Implemented taxonomy procedures, ESG reporting and promotion of environmental activities;

The levels of integration

Level 3—Systematic observation and development (tactical toward strategic) (type of cooperation passive toward creative)

(continued)

• Application of integrated decision-making processes; • Inclusion of ESG risk management procedures filling knowledge gaps based on available sources; • No information exchange; • Use of decision support technologies; • Cooperation in the field of products and services offered by financial institutions;

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• The implementation of the SDG objectives has become the dominant strategic decision-making area; the impact of ESG risk is a dominant strategic issue; • Process integration focused on ESG; • Integration with financial institutions in the field of mitigation, ESG perception and reporting; • Exchange of knowledge bases based on a fuzzy approach; • All decision areas have been ESG-oriented (financial, organizational, HR, investment, cooperation with the environment, value creation, etc.); • Creating new solutions and products (services) for sustainability and ESG risk mitigation; • Promotion and communication, taking into account sustainability and ESG; • Cooperation with the environment based on values based on sustainability

Level 4—Creative cooperation and impact on the environment (strategic) (type of cooperation creative)

Source Own elaboration

Characteristics of activities

(continued)

The levels of integration

Table 6.3

• Creative inclusion in sustainable and ESG decision-making processes; • Applying fuzzy approach in decision-making processes; • Exchange of good practices between enterprises and financial institutions; • Creative imitation toward sustainability; • Exchange of approaches to improving the ESG risk management process; • Permanent changes in decision-making processes based on the “idea of a learning organization” and flexible adaptation to changes in the areas of sustainability and ESG; • Supporting changes toward sustainability and ESG with modern integrated decision-making methods; • Using only financial products and services toward sustainability and ESG

Decision-making cooperation approach

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The cooperation of financial institutions and the enterprise will not only lead to qualitative changes toward the sustainability of the latter, but will also bring systemic solutions, increase the cognitive value of knowledge bases based on the fuzzy approach and improve the decision-making process based on sustainability priorities, but also allow for the achievement of social goals. Mutual ties based on common pro-environmental values create a platform for building new values toward sustainability.

6.3

Fuzzy Business Models

Using fuzzy logic to study business models is one of the research trends in their analysis. Research on the fuzzy approach in business models is modest, and more needs to be published in this area. Fuzzy logic is suitable for analyzing business models due to many parameters of business models that are imprecise; this applies, in particular, to sustainable business models in which fuzzy parameters can be used in particular for the analysis of non-financial components, i.e., environmental, social and governance included among other things in The Triple Layered Business Model Canvas (Table 6.4). The Triple Layered Business Model Canvas is a tool for analyzing innovative, sustainability-oriented business models. It extends the original business model approach with two layers: an environmental layer based on a life cycle perspective and a social layer based on a stakeholder perspective Table 6.4 The Triple Layered Business Model Canvas—elements Economic business model canvas

Environmental life cycle business model canvas

Social stakeholder business models canvas

Partners Activities Resources Value proposition Customer relationship Channels Costs Revenues

Suppliers and out-sourcing Production Materials Functional value End-of-life Distribution Use Phase Environmental impacts Environmental benefits

Local communities Governance Employees Social value Societal culture End-user Scale of outreach Social impacts Social benefits

Source Own elaboration based on: A. Joyce, R. L., Paquin: The triple layered business model canvas: A tool to design more sustainable business models. Journal of Cleaner Production, 135 (2016) pp. 1474–1486

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(Joyce & Paquin, 2016). The TLBMC was proposed by Osterwalder and Pigneur (2010). TLBMC offers an easy-to-use tool to support the innovation of sustainable business models. First, as a multi-layered business model canvas, TLBMC provides a straightforward and relatively easy way to visualize and discuss a business model’s multiple and diverse implications (Joyce & Paquin, 2016). In each of the TLBMC layers, sustainable value is built, which can be achieved through pro-environmental activities, e.g., implementation of eco-innovations, social activities, e.g., the performance of social impact investment, or in the field of governance, e.g., gender quota in the area of board composition, each of these activities can be analyzed using fuzzy logic tools, which gives a more precise image of the organization and its achievements than traditional research. Sen (2017, p. 104) directly referred to fuzzy business models, pointing out that “The formal principle structure of a business model can be represented with a set of causative (input, antecedent) and a single result (output, consequent) variable” (Fig. 6.5). Other research approach uses fuzzy maps to analyze business models. Fuzzy Cognitive Maps (FCM) are used by Glykas (2004) as a primary performance modeling tool to simulate the operational performance of complex and imprecise functional relationships and to quantify the impact of process reorganization activities on the business model. Preliminary research indicates that the proposed hierarchical and dynamic network of interconnected FCMs supports setting performance quantifications that complement typical Performance Driven Change (PDC) projects’ strategic planning and business analysis phases. Vatankhah et al. (2019) used the fuzzy AHP method to evaluate the composition of business model attributes in the aviation sector and its corresponding hierarchy of Trade Economy Cooperation Customer

Fuzzy

Expected output

business models

Market

Fig. 6.5 Fuzzy business models (Source Own elaboration on Sen, 2017)

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evaluation index. The paper proposes a procedure to diagnose the most crucial attributes of airline business models including low-cost carriers. Leppänen (2017) analyzes the interdependence and complementarity of business model design themes and competitive strategies. Based on a fuzzy set qualitative comparative study, the author analyzed high- and low-performing configurations of 232 publicly traded firms of two sorts; online businesses and computer companies. The paper concludes that the absence of cost leadership is required for high performance and that the expected high-performing combinations of the design themes and strategies do not appear consistently effective. Husain et al. (2021) rank business models for successful adoption of the circular economy based on criteria using the appropriate Multi-Criteria Decision-Making Method (MCDM). The research is based on a fuzzy technique of determining the order of preferences according to similarity to the ideal solution (Fuzzy TOPSIS). As a result, eleven business models were identified and analyzed based on nine business model success criteria (Husain et al., 2021). Córdova et al. (2017) applied fuzzy logic to analyze financial risk (CAMEL model and risk rating) based on selected financial indicators. The Authors conclude that fuzzy logic allows “understanding the business information in a broader context, and not only evaluate the quantity but also the qualities of the different ranges”. Both financial institutions and the business sector can use fuzzy logic to analyze their business models and build sustainable value. In the case of financial institutions, activities aimed at creating sustainable business models include, among others, the digitization of processes; using financial innovations to reduce the level of ESG risk; the economical use of materials and energy; using modern technologies; pro-social approach to customers and employees; care for staff; ethical action; offering sustainable products and services; incorporating ESG risk into decision-making processes. The postulate of developing the risk assessment methodology with ESG components is emphasized by the Environmental Program Financial Initiative (UNEP FI). Fuzzy logic may be used in several scenarios of cooperation between financial institutions and the business sector, considering the impact of financial institutions on enterprises’ business models through the transfer of good practices. Examples of variables considering in analyzing fuzzy business models are as follows (both for companies and financial institutions) (Zioło et al., 2020):

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offering environmentally friendly products and services; use of energy-saving technologies; reducing water consumption in production; supporting charity campaigns; saving office materials through the use of electronic technologies; application of employee-friendly policy; using innovation to reduce the risk of environmental pollution; selection of suppliers only from the group of CSR companies; selection of environmentally friendly distribution channels; using innovation to reduce negative impact on the environment; cooperation only with entities using the CSR strategy - corporate social responsibility.

As part of their cooperation with clients, financial institutions are competent to define the terms of such collaboration (financial and nonfinancial), considering the principles of partnership, ethics and good business practices under the conditions dictated by the competitive market. The terms of cooperation depend on many factors, including the type of client, the scope of collaboration, risk and profitability. Considering that every enterprise conducting economic activity must have a bank account for settlements, the scale of the potential impact of banks on entrepreneurs is wide. Scenario 1 assumes that banks primarily influence the transformation of companies’ business models toward sustainability. These institutions will provide financing for social and environmental projects. However, depending on the market and demand, sustainable adaptation and funding of sustainable development will mainly concern either pro-environmental or pro-social activities (development of finance for environmental protection or development of social financing, respectively). Scenario 2 assumes the dynamic development of sustainable business models of companies based on the capital market. In this case, it is also recognized that this development can be sustainable (applies to implementing projects in both social and environmental pillars in parallel) and unsustainable (projects from the environmental pillar dominate over the social one or vice versa). In the case of the development of sustainable business models based on the capital market, the dominant role is be played by the green bond market and the social bond market. Scenario 3 predicts that the development of sustainable business models will progress moderately. The financial sector will be highly differentiated regarding the degree of advancement of individual institutions in adopting and

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implementing voluntary regulations for sustainability. It is assumed that some institutions will only adopt partial regulations due to their cost and market potential (e.g., lack of customers reporting demand for sustainable financial products and services). Compulsory laws will be implemented following the requirements and will cover a significant part of the market. Leading banks/institutions in sustainable financing will emerge, which will dictate the strategies of operation and funding. Common elements characterize the business models of financial institutions and enterprises. Entrepreneurs with sustainable business models select suppliers according to the same key (having a sustainable business model), which means they cooperate with sustainable financial institutions. At the same time, financial institutions with a sustainable business model cooperate with a sustainable “clean” business because the cost of losing reputation risk is incommensurable with the costs created by the risk of cooperation with clients from the “dirty business” sector.

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Joseph, N., & K. Mung’atu, D. J. (2018). Identifying factors influencing selection of banks by customers in Rwanda: Principal components analysis approach. International Journal of Sciences: Basic and Applied Research (IJSBAR), 39(1), 229–247. Joyce, A., & Paquin, R. L. (2016). The triple layered business model canvas: A tool to design more sustainable business models. Journal of Cleaner Production, 135, 1474–1486. Kamakodi, N., & Khan, B. A. (2008). An insight into factors influencing bank selection decisions of Indian customers. Asia Pacific Business Review, 4(1), 17–26. https://doi.org/10.1177/097324700800400102 Kozioł-Nadolna, K., & Beyer, K. (2021). Determinants of the decision-making process in organizations. Procedia Computer Science, 192(6), 2375–2384. https://doi.org/10.1016/j.procs.2021.09.006 KPMG. (2021). ESG risks in banks effective strategies to use opportunities and mitigate risks. https://assets.kpmg.com/content/dam/kpmg/xx/pdf/ 2021/05/esg-risks-in-banks.pdf Kwiecien, ´ A. (2019). Relationships between enterprises and banks. Scientific Papers of Silesian University of Technology Organization and Management Series, 136(339), 351. Leppänen, P. (2017). A fuzzy set theoretic approach to business model design and strategy. Academy of Management. https://journals.aom.org/doi/abs/ 10.5465/AMBPP.2017.17816abstract (Accessed 20 June 2023). Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A Handbook for visionaries, game changers, and challengers. John Wiley & Sons. Saebi, T., & Foss, N. J. (2014). Business models for open innovation: Matching heterogenous open innovation strategies with business model dimensions. European Management Journal, 3(3), 201–213. SBN 2023: Sustainable Banking Network. (2023). https://www.worldbank.org/ en/who-we-are Schoenmaker, D., & Tilburg, R. V. (2016). What role for financial supervisors in addressing environmental risks? Comparative Economic Studies, 58, 317–334. Sen, Z. (2017). Intelligent business decision-making research with innovative fuzzy logic system, International Journal of Research Innovation and Commercialisation, 1(1), 93. Stern, N. (2016). Climate change and central banks. Bank for International Settlements. Stoian, A., & Iorgulescu, F. (2019). Sustainable capital market In Ziolo, M. & Sergi, B. S. Cham (Eds.), Financing sustainable development: Key challenges and prospects, Plagrave Macmillan. Teece, D. (2010). Business models, business strategy and innovation. Long Range Planning, 43, 172–194.

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Vatankhah, S., Zarra-Nezhad, M., & Amirnejad, G. (2019). Tackling the fuzziness of business model concept: A study in the airline industry, Tourism Management, 74, 134–143, ISSN 0261–5177. https://doi.org/10.1016/j. tourman.2019.01.022. Wirtz, B. W., Pistoia, A., Ullrich, S., & Göttel, V. (2016). Business models: Origin, development and future research perspectives. Long Range Planning, 49(1), 36–54. Xirogiannis, G., & Glykas, M. (2004). Fuzzy causal maps in business modeling and performance-driven process re-engineering. In Vouros, G.A., Panayiotopoulos, T. (Eds.), Methods and applications of artificial intelligence. SETN 2004. Lecture Notes in Computer Science, (LNAI)3025. Springer. https://doi.org/10.1007/978-3-540-24674-9_35 Husain, Z., Maqbool, A., Haleem, A., Pathak, R. D., & Samson, D. A. (2021, December). Analyzing the business models for circular economy implementation: A fuzzy TOPSIS approach, Operations Management Research, 14(3), 256–271. Zelie, E. (2023). Factors determining bank selection by micro- and smallsized enterprises: Evidence from Ethiopia. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-08-2022-0380 Zioło, M., B˛ak, I., Cheba, K., & Spoz, A. (2020). Sustainable business models of enterprises—actual and declared activities for ensuring corporate sustainability. Procedia Computer Science, 176, 1497–1506. Zioło, M., B˛ak, I., Cheba, K., Filipiak, B. Z., & Spoz, A. (2023). Environmental, social, governance risk versus cooperation models between financial institutions and businesses. Sectoral approach and ESG risk analysis. Frontiers in Environmental Science, 10, 1077947. https://doi.org/10.3389/fenvs.2022. 1077947 Ziolo, M., Filipiak, B. Z., & Tundys, B. (2021). Sustainability in bank and corporate business models.The link between ESG risk assessment and corporate sustainability. Palgrave, Macmillan. https://doi.org/10.1007/978-3030-72098-8 Zott, C., & Amit, R. (2010). Business model design: An activity system perspective. Long Range Planning, Business Models, 43(2–3), 216–226. Zott, C., Amit, R., & Massa, L. (2011). The business model: Recent developments and future research. Journal of Management, 37 , 1019–1042.

CHAPTER 7

Conclusion and Recommendations Magdalena Zioło

The ongoing demographic, climate, and socioeconomic changes have a financial dimension and create a new type of risk, the ESG risk (Environmental, Social, and Governance). Mitigating ESG risk and related negative consequences like climatic, demographic, and socioeconomic changes is done by implementing and respecting the postulates of sustainable development, especially implementing sustainable development goals (SDGs). Such action requires a high degree of coordination and cohesion between the activities of all market actors. Modern economies are in the process of transformation to the requirements of sustainable growth and development, and in this process, they face many challenges. For example, this applies to issues such as the internalization of external costs, the problem of measuring and valuing ESG risk, the lack of a comprehensive approach to sustainable financial reporting, and the dominant short-termism in the decision-making process.

M. Zioło (B) Faculty of Economics, Finance and Management of the University of Szczecin, Szczecin, Poland e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4_7

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The transformation of the economy toward sustainability requires financing, hence the critical role of financial markets in this process. Currently, no market sector has been specified or has yet to undergo adjustment measures to function in the conditions of ESG risk. Numerous regulations of a mandatory nature facilitate this (EU taxonomy for sustainable activities, the European green deal, The Sustainable Finance Disclosure Regulation, others) and optional (numerous international initiatives, e.g., The Principles for Responsible Investment). Regulations substantially impact large entities, including financial institutions such as banks or insurance institutions. This results in a change in the business models of these institutions toward sustainable ones. The diffusion of the sustainability process in financial markets results in sustainability spilling over to other business sectors cooperating with the financial market. Therefore, there are visible interdependencies between sustainability in financial institutions’ business models and enterprises’ business models. This phenomenon is associated with many unexplored threads; in particular, it concerns the challenges in examining changes in business models under the influence of ESG risk. It is indicated that traditional research methods need to entirely precisely allow to study of ESG risk and its impact on business models due to deficiencies in databases, and inaccurate or unclear data, hence the field for such methods as fuzzy logic. Fuzzy business models are a phenomenon that needs to be better researched and described; this field requires systematic research to bridge the research gap. The monograph attempts to do so. As a result of the considerations, three types of recommendations were formulated—general, for companies, and financial institutions. The general recommendations include the following issues: inclusion of ESG risk in the risk management system and decision-making processes; ensuring the compatibility of financial and non-financial databases; adapting information systems to collect, process, and store non-financial data; adapting accounting systems to the needs of nonfinancial data, in particular, management accounting and changing the perception of the role of accountants in organizations—a business partner; using and offering sustainable product and services; building business models based on the Triple Layer Business Model Canvas; building a market advantage based on sustainability; using sustainable value to build market position; inclusion of sustainability in the management system, including strategic management; effective monitoring of sustainability regulations; providing financing for business transformation toward

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sustainability; communicating actions taken in the field of sustainability and their consequences for the business; customer segmentation taking into account the ESG risk criterion. Recommendations for enterprises focus on activities in the field of: monitoring business models in terms of ESG and creating ESG benchmarks; cooperation with financial institutions for ensuring financing for the transformation of business models toward sustainability; ensuring the bankability of investment projects; reducing reputation risk; using the relationship banking model to ensure sustainability; implementation of early warning systems and internal ESG risk ratings; taking systematic actions to reduce the ESG risk in business; if possible, submit to a sustainable rating assessment. Recommendations for financial institutions concern the following issues: conducting climate stress tests; analysis of acts in terms of ESG risk burden; complying with international ESG rules and recommendations; participation in organizations associating environmentally friendly and pro-social financial institutions; transfer of sustainable knowledge for the enterprise sector; active role in stimulating the processes of greening the economy.

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Index

A accounting applications, 55 advantages, 42, 43 application, 3, 40, 43–47, 59, 64, 65, 68, 69, 79, 83–86, 98, 123, 128 archetype, 15, 18, 19

B banking, 1, 3, 18, 20, 22, 23, 42, 54, 55, 69, 70, 107, 135 banking crises, 54 bank restructuring processes, 54 business, 1–4, 10, 12, 13, 15–21, 23, 40, 55, 56, 58, 62–64, 75, 82, 85, 87, 92, 97, 98, 106–108, 114, 116, 126–129, 134, 135 business model, 1–4, 13–18, 22, 74, 82, 87, 92, 97, 98, 106, 108, 110, 116, 123, 125–129, 134, 135 Business Model Innovation (BMI), 12, 14–16, 18

C Canvas, 125, 126 Challenges in SBM development, 16 circular economy, 74, 75, 119, 127 climate risk, 88, 108, 114, 116, 121, 122 company, 7, 13, 14, 18, 57, 67, 74, 82, 83, 92, 106–108 company’s internal supervision system, 6 consolidation, 96 conventional business model, 13, 14 corporate social responsibility (CSR), 2, 9, 18, 128 COVID-19, 1, 6, 47 credit scoring, 20, 54, 58 crisis preventing, 66, 67

D decision making process, 4, 85 disadvantages, 42, 58

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ziolo (ed.), Fuzzy Business Models and ESG Risk, Palgrave Studies in Impact Finance, https://doi.org/10.1007/978-3-031-40575-4

151

152

INDEX

E entity, 7, 13, 23, 79, 83, 88, 96, 97 environmental factors, 6, 97 environmental impact, 45, 75, 88, 108, 117, 125 Environmental, Social, Governance (ESG), 1–11, 18, 22, 74, 87–92, 94, 108, 116, 118, 119, 123, 124, 127, 135 ESG factors, 2, 3, 7, 8, 10, 11, 13, 14, 18, 22, 23, 74, 83, 87, 89, 91, 92, 94, 96–98, 110, 117, 119 ESG risk, 1–4, 7, 18, 22, 73–75, 77, 87–91, 96–98, 106, 108, 110, 113, 114, 116, 118, 119, 121–124, 127, 133–135 ethical banking, 22 ethics, 128 EU Taxonomy, 10, 134 evaluation, 42, 43, 88, 89, 98, 107, 127 external, 16, 56, 80–82, 91, 133 F finance, 1–4, 21, 53, 54, 106, 118, 128 financial crisis, 57, 66, 67, 69, 84 financial institution, 1–4, 10, 17, 18, 22, 23, 54, 57, 64, 66, 69, 74, 75, 82, 97, 106–108, 110–117, 119, 121–125, 127–129, 134, 135 financial ratios, 55, 67 financial standing, 56 fizzy logic disadvantages, 43, 61, 63 forecasting, 2, 3, 54, 55 fuzzification, 78 fuzzy base rules, 37 fuzzy business model, 3, 4, 126, 134 fuzzy inference systems (FIS), 61–64, 68, 69

fuzzy logic, 2–4, 29, 30, 34, 35, 37, 39–43, 46, 53–55, 58–65, 67–69, 76, 77, 79, 80, 82, 94, 96, 98, 125–127, 134 fuzzy logic advantages, 64, 65 fuzzy logic applications, 42 fuzzy logic applications in crisis prevention, 69 fuzzy logic disadvantages, 39 fuzzy MCDM, 44–47 fuzzy MCDM classification, 44 fuzzy models in risk management, 60 fuzzy pay-off method (FPOM), 61 fuzzy set, 3, 29–31, 35, 36, 41, 42, 46, 47, 59, 60, 76, 77, 79, 80, 127 G Gaussian shaped membership function, 33 general decision theory making, 76 green banking, 22 GRI taxonomies and principles, 75 I internal, 19, 56, 79–81, 90, 135 L linguistic variables, 35–38, 77–79 liquidation of banks, 54 M machine learning techniques, 55 matrix, 44, 80, 82 membership function, 30, 31, 35, 37, 38, 41, 59–63, 78, 79 methodology, 29, 36, 46, 54, 127 MiFID II package, 10 models in fuzzy continuous-time (MFC), 60

INDEX

models in fuzzy discrete-time (MFD), 61 models in risk management, 57 monitoring, 2, 7, 40, 56, 62, 64, 66, 74, 134, 135

N non-financial factors, 7, 22, 87, 92, 96, 119 non-financial risk, 7, 123 Non-financial risk management, 7 normalized fuzzy set, 31

P pandemic, 1, 6, 47, 69 parameter, 35, 58, 62, 87, 88, 125 prediction, 2, 3, 54, 55, 58, 65–68 production, services and trade sectors, 4, 80, 83 prospective, 82

R recommendations, 4, 73, 112, 134, 135 research, 3, 4, 7, 15, 39, 40, 43, 53, 54, 84, 125–127, 134 Research subjects in the field of ESG, 8 retrospective, 82 risk management, 2–4, 7, 22, 56, 61, 64, 68, 73, 74, 87, 88, 90, 91, 110, 113, 116, 118, 119, 122, 123, 134 risk management process, 4, 56, 88–90, 119, 124

153

S sector, 2, 4, 10, 17, 45, 55, 74, 80, 81, 83, 90, 96, 118, 122, 126–129, 134, 135 SFDR, 10 social factors, 6 social impact, 75, 125, 126 socially responsible banking, 23 S-shaped membership function functions, 34 sustainability, 2, 4, 5, 7, 10–12, 14–18, 20, 21, 23, 74, 76, 82, 88, 90, 97, 98, 106, 108, 110, 112–119, 121, 123–125, 128, 129, 134, 135 sustainable business model (SBM), 1–4, 11–18, 22, 23, 74, 125–129 Sustainable Development Goals (SDGs), 5, 10, 14, 15, 23, 75, 114, 116, 133 sustainable value, 18, 22, 126, 127, 134 T The financial industry, 17, 18 The Global Risks Report, 7 The transformation of the financial, 17 three pillars, 22 trapezoidal membership function, 33 triangular membership function, 32 Triple Layered Business Model Canvas, 125 typology, 3, 80 V value, 2, 7, 13–16, 18, 20, 23, 30, 31, 34–38, 44, 55, 58, 60, 62, 66, 74, 76–79, 84, 89, 92, 97, 98, 106, 110, 123–125