Fuzzy Cognitive Map Approach Research Articles

A fuzzy cognitive map approach to selecting the best knowledge management strategy

Purpose Selecting an appropriate strategy can significantly influence an organization’s direction, but numerous interrelated factors make this decision complex and uncertain. Identifying these relationships and making informed decisions often involves navigating uncertainty. This paper aims to assist managers in leveraging the organization’s intellectual assets to select the most suitable knowledge management strategy. Design/methodology/approach This research proposes fuzzy cognitive maps to model and select the appropriate strategy. Given that knowledge is a company’s most valuable asset and its management is critical, the proposed method for selecting a knowledge management strategy was tested in one of Iran’s largest logistics companies. The factors influencing strategy adoption were treated as concepts in the fuzzy cognitive network, with evaluation criteria applied to three strategies: human-oriented, system-oriented and dynamic. Findings The model’s output identifies the most suitable strategy based on knowledge management principles and the specific characteristics of the target project within the organization. Compared to the most cited paper in this field, the proposed model shows a 0.15% improvement in sensitivity and a 0.22% improvement in separation degree. Originality/value This study proposes a novel method using fuzzy cognitive maps to assist organizations in selecting the best knowledge management strategy, even amidst multiple influencing factors and uncertain conditions.

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

Learning-based framework for industrial accident prevention: fuzzy cognitive mapping approach

PurposeDespite efforts to improve safety management practices in industrial companies, major accidents seem to be inevitable. Many accidents still occur because companies are unable to learn from past occurrences due to ineffective incident and accident learning processes. This study proposes a learning-based framework for industrial accidents investigation and contributes to accident prevention research.Design/methodology/approachThe proposed learning process includes the analysis of the industrial accident using the Event Tree Analysis (ETA) method, capitalisation of causative factors using the Swiss Cheese Model (SCM), and finally modelling the relationships among the accident causative factors and analysing their causality using the Fuzzy Cognitive Mapping (FCM) technique and running learning scenarios.FindingsThe proposed learning process was applied to an industrial accident, and the results showed that human unsafe behaviours and unsafe supervision were the principal causative factors of the blowout accident.Practical implicationsThe proposed learning-based framework provides a structured approach for oil and gas companies to systematically analyse and learn from past accidents, enhancing their prevention strategies. Theoretically, the framework bridges the gap between theory and practice by demonstrating how established accident analysis methods can be combined and applied in a real-world industrial context.Originality/valueThe proposed learning process combines accident analysis and investigation techniques with simulations for an in-depth and robust learning-based framework for accident prevention.

Fuzzy Cognitive Map for Evaluating Critical Factors Causing Rockbursts in Underground Construction: A Fundamental Study

The rockburst phenomenon in excavation endeavours reveals a multitude of complexities and obstacles that significantly impact both the technical and financial dimensions of project execution. Investigating critical rockburst factors in underground excavations is of considerable importance for addressing pivotal safety issues and operational complexities within the field of underground excavation projects. This research proposes an innovative approach based on an expert-based fuzzy cognitive map (FCM) framework, aiming to identify and prioritize the key critical rockburst factors prevalent in underground excavations and tunnelling. A tailored cognitive map of the parameters of problem was constructed, integrating 56 critical and critical factors meticulously curated by a team of seasoned managers, engineers, deputy managers, trainee engineers and assistant managers. The structured cognitive map was meticulously developed, considering the relative weights of the identified critical factors and their intricate interrelationships—all informed by the invaluable insights and expertise of seasoned engineers in the field. Subsequently, the cognitive map underwent a systematic solution process, whereby the causal relationships and influences amongst the identified critical factors were analysed and factored in. The outcomes of the comprehensive analysis unveiled several critical factors: lack of rockburst risk assessments, high in situ stress, presence of rock seams and weak layers, rock quality variations, and geological heterogeneity as the most paramount concerns demanding immediate attention and strategic intervention. By adopting the proposed FCM approach and leveraging the collective expertise of industry professionals, this research offers a robust and systematic framework for comprehensively assessing and addressing the key challenges associated with rockburst events in underground excavations and tunnelling projects, thereby fostering enhanced project performance and efficacy within the field.

Supporting underrepresented students in health sciences: a fuzzy cognitive mapping approach to program evaluation

BackgroundThe Students Training in Academia, Health, and Research (STAHR) Program at the University of Missouri-Kansas City (UMKC) strives to help students from low-income families that have experienced educational challenges due to poverty and prepare them to enter, persist, and graduate from a health sciences degree program at UMKC. Students in the program participated in fuzzy cognitive mapping (FCM) sessions to ensure that all voices of the program were heard to improve program implementation, and student success, and contribute to an equitable educational environment.MethodsFuzzy Cognitive Mapping sessions for the 2020–2021 cohort of students (n = 52) were conducted to assess the strengths and weaknesses in program implementation, especially through the beginning of the COVID-19 pandemic. Students’ maps were coded by a team of researchers and then confirmed using confirmatory factor analysis.ResultsStatistical analyses reveal that mentorship, workshops, and social support helped students to work toward their goal of obtaining a professional health sciences degree, while a lack of time, remote learning, and outside stressors inhibited their opportunities for success.ConclusionsThe findings from a multipronged analysis of mapping data demonstrate the value of this innovative approach to the field, especially when looking to incorporate student voices.

Decision‐making analysis in post‐fire and explosion aftermath assessment tool: A fuzzy cognitive mapping approach

AbstractThis assessment tool was created to generate a decision‐making support system and assessment approach for a post‐fire (aftermath) and blast explosion impact. It uses a multidecision analysis neural network. Multidecision analysis is very crucial as the judgment of the assessment relies not only on engineering input but also on many contributing factors, such as lead time for replacement. This article outlines the framework for post‐incident explosion and fire‐damaged assessment using evaluation acceptance criteria and judgment that streamlines the process. Grounded theory (GT) and fuzzy cognitive mapping (FCM) techniques are presented and evaluated. Overall, this study aims to develop how the project organizations can ensure safety and asset integrity decision‐making by identifying hindrances and later developing a multicriteria analysis framework for the post‐incident assessment phase of implementation.

What affects the corporate social responsibility practices of Chinese international contractors considering dynamic interactions? A hybrid structural equation modeling–fuzzy cognitive map approach

PurposeCorporate social responsibility (CSR) practices frequently result in increased costs for Chinese international contractors (CICs), where profitability is the primary objective; therefore, internal corporate drivers and external pressures play a crucial role in encouraging them to engage in sustainable CSR practices. This study systematically examines the dynamic impact of internal and external stakeholders on the CSR practices of CICs.Design/methodology/approachThis study adopted a structural equation model (SEM) to identify and validate a correlation between stakeholders and CSR practices. Standardized causal coefficients estimated in SEM were used to construct a fuzzy cognitive map (FCM) model to illustrate the effect of stakeholders on CSR practices with linkage direction and weights. Predictive, diagnostic, and hybrid analyses were performed to dynamically model the variation in stakeholders on the evolution of CSR practices.FindingsThe empirical results demonstrate that (1) employee participation in CSR has the greatest impact on CSR practices, followed by CSR strategies, partner and customer expectations, and finally government regulations. (2) In the early stage of CSR fulfillment, CSR strategies have the greatest influence on CSR practices; in the later stage of CSR fulfillment, employee participation in CSR has the greatest influence on CSR practices. (3) In the long run, the most effective and economical integrated interventions are those that address employee participation in CSR, partner expectations and customer expectations, and intervention in CSR strategies is needed if the level of CSR practice needs to be improved in the short term.Originality/valueThis study contributes to the research on the influence mechanisms of CSR practices of CICs and systematically analyzes their dynamic influence on CSR practices of CICs from the perspective of stakeholders.

Risk spillover measurement of carbon trading market considering susceptible factors: A network perspective

AbstractAn objective and robust network‐based data‐driven strategy is proposed to analyze risk spillovers in carbon markets. First, we characterize the causality network between the carbon market and potential associated markets using a data‐driven fuzzy cognitive map approach. Second, network‐based community detection is conducted to explore community structures that include carbon trading markets, and five market factors belonging to the same community as EU Allowances (EUA) are identified. Next, we conduct downside and upside‐tail measurements of EUA risk spillover levels within the community based on estimates and fits of marginal and joint distributions for different market pairs. Finally, we point out that the market factor having the most significant upper‐tail spillover effects on EUA is OILFUTURE, besides, EURUSD asset is found to be the best hedge for EUA futures among the detected market factors.

Factors Influencing the Financing of Renewable Energy Projects for Sustainable Development Goals with a Fuzzy Cognitive Map Approach

Objective: The aim of this study is to analyze the factors influencing the financing of renewable energy projects to achieve sustainable development goals using a fuzzy cognitive map approach. Methodology: This applied research was conducted in both qualitative and quantitative sections. In the qualitative section, content analysis with a conventional and directed approach was employed, and in the quantitative section, a causal method was used. Data were collected through semi-structured interviews in the qualitative phase and pairwise comparison questionnaires in the quantitative phase. The participants were senior managers and experts from the Renewable Energy and Energy Efficiency Organization (SATBA), selected using purposive and theoretical sampling (a total of 21 participants). Findings: The qualitative findings revealed key influencing factors, including "social responsibility," "direct and indirect government support," "sustainable economic development," "environmental sustainability," "support from financial funds," "commercial creditworthiness of financial suppliers," "foresight and capital planning," "financial capacities," "investment return policies," "empowerment of financial institutions," "renewable energy development policies," "investment culture in renewable energy," and "optimal management of investment risks." The fuzzy cognitive map results showed that "direct and indirect government support for the energy industry" had the highest output and influence, while "commercial creditworthiness of financial suppliers" had the highest input power and susceptibility to influence. Conclusion: This study provides a comprehensive analysis of the factors affecting the financing of renewable energy projects. It highlights the critical roles of government support policies and the commercial creditworthiness of financial suppliers in the success of these projects, suggesting that improvements in these areas can contribute to achieving sustainable development goals.

Conceptualizing and Modeling Factors Influencing Digital Twin Performance in Industrial Contexts: A Fuzzy Cognitive Mapping Approach (November 2024)

Conceptualizing and Modeling Factors Influencing Digital Twin Performance in Industrial Contexts: A Fuzzy Cognitive Mapping Approach (November 2024)

The Cybernetic Artificial Intelligence (CAI): Α new scientific field for modelling and controlling Complex Dynamical Systems

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. The Fuzzy Cognitive Map approach is used for creating new knowledge. An algorithm for developing the CAI approach is proposed. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence (CAI). The importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values and the acceptance of the CAI as a viable new scientific field.

An analysis of consumer opinions on waste medicine management utilizing fermatean fuzzy cognitive mapping

The management of post-consumer medicine waste has become increasingly important due to the high levels of consumer purchasing and stockpiling of drugs brought on by the pandemic. Environmental, social, and financial sustainability are directly influenced by the specifics of how pharmaceutical waste should be disposed of or recycled. Hence, this study provides a comprehensive approach that employs text mining with the inclusion of 894 participants for preliminary research to extract consumer-centric waste medicine awareness criteria, and proposes a Fermatean Fuzzy Cognitive Mapping (F-FCM) approach to examine the relationships among the concepts to identify the most important ones, and compares F-FCM with the classical FCM to demonstrate the effectiveness of the improved approach. The findings of the example from Turkey reveal that waste medicine management applications prioritize (1) establishing a trusted system for collecting and recycling medicine, (2) ensuring the reliability of business models, and (3) increasing societal awareness. The F-FCM ranking differs slightly from the classical FCM ranking when uncertainty is introduced. The present study contributes to the literature by proposing a novel approach and utilizing text mining as a data-extracting tool. A practitioner can also understand the perspectives of real consumers, identify long-term plans, and comprehend the requirements of a service network with several stakeholders.

A fuzzy cognitive map approach to understand agricultural system and food prices in Türkiye: Policy recommendations for national food security

AbstractOnce one of the few self‐sufficient food countries in the world, Turkey has become dependent on imports to feed its population. Food prices have climbed to among the highest in the world, severely threatening the food security of the country. Most researchers generally attributed the high prices to the increased input costs of agriculture. Although the role of input prices cannot be denied, this paper focuses on a neglected problem that can account for food price inflation: the attitudes and behaviours of farming communities towards agriculture. Through fuzzy cognitive map methodology, known to be very effective in understanding complex networks of problems, we identify and map the relationships among the factors affecting the agriculture system, develop interview and literature‐driven scenarios, and test these scenarios to demonstrate their role in explaining the relationship between attitudes and behaviours of farming communities and food prices in Turkey. Our findings provide recommendations to policymakers.

Fuzzy Cognitive Mapping Approach to Conduct Deficiency Investigation under SIRE 2.0 Inspection

Ship Inspection Report Programme (SIRE) 2.0 has recently become operational as a new vessel inspection regime in the tanker industry. This study proposes a methodology to analyse and address multiple deficiencies observed during SIRE 2.0 inspections. The methodology is structured based on Fuzzy Cognitive Mapping (FCM) to identify and analyse the causes of deficiencies derived from the International Maritime Organization (IMO) classification scheme, including the factors under key dimensions, such as diminished human performance, marine environment, safety administration, and management. An illustrative case study on a set of deficiencies has been conducted to ascertain the utility of the methodology. The results specifically reveal that inadequate situational communication and awareness, inadequate knowledge of ship procedures, regulations, and standards, inadequate supervision, being unaware of role or task responsibility, poor maintenance, etc. are the potential causes that might lead to the occurrence of deficiency items. Considering the dimension-based distributions of causes, the study highlights integrated preventive action recommendations specific to the analysed deficiency cases. Consequently, the study might help tanker shipping companies manage key challenges with SIRE 2.0 implementations.

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

The CO2 reduction promise must be widely adopted if governments are to decrease future emissions and alter the trajectory of urban mobility. However, from a long-term perspective, the strategic vision of CO2 mitigation is driven by inherent uncertainty and unanticipated volatility. As these issues emerge, they have a considerable impact on the future trends produced by a number of exogenous and endogenous factors, including Political, Economic, Social, Technological, Environmental, and Legal aspects (PESTEL). This study’s goal is to identify, categorize, and analyze major PESTEL factors that have an impact on the dynamics of urban mobility in a rapidly changing environment. For the example scenario of the city of Larissa, Greece, a Fuzzy Cognitive Map (FCM) approach was employed to examine the dynamic interactions and behaviors of the connected criteria from the previous PESTEL categories. An integrative strategy that evaluates the interaction of linguistic evaluations in the FCM is used to include all stakeholders in the creation of a Decision Support System (DSS). The methodology eliminates the uncertainty brought on by a dearth of quantitative data. The scenarios in the study strands highlight how urbanization’s effects on sustainable urban transportation and the emergence of urban PESTEL actors impact on CO2 reduction decision-making. We focus on the use case of Larissa, Greece (the city of the CIVITAS program), which began putting its sustainable urban development plan into practice in 2015. The proposed decision-making tool uses analytics and optimization algorithms to point responsible authorities and decision-makers in the direction of Larissa’s sustainable urban mobility and eventually the decarbonization of the urban and suburban regions.

Understanding expectations from and capabilities of climate-economy models for measuring the impact of crises on sustainability

The multi-faceted emergencies of our time (recession, pandemics, and international conflict) can disrupt progress towards sustainable development goals (SDGs). While climate-economy models can show pathways out of these disruptions, numerous stakeholder-informed enhancements are required to navigate through the interconnected SDG landscape. We introduce a novel Fuzzy Cognitive Maps (FCMs) approach that integrates Monte Carlo simulations, aiming to capture and understand the differences among modellers' and experts' perceptions of the impacts of such crises on broader sustainability, considering models’ existing/planned capacity and uncertainty over perceived magnitude and propagation of impacts across SDGs. Our application shows that expert stakeholders appeared more concerned over the implications of these crises for sustainable development than modellers, but expectations were considerably constrained by model capabilities. SDGs 8 (growth), 7 (energy-sector sustainability), and 13 (climate action) were robustly found as most prone to examined shocks. Our proposed Monte Carlo Fuzzy Cognitive Mapping (MCFCM) approach also highlighted potentially important propagation effects as well as gaps in integrated assessment model capabilities to holistically study the SDG spectrum.

Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach

Crowdfunding as an alternative source of finance to banking is the subject of numerous research works. This research proposes the guidelines of a Decision Support System (DSS) in the area of crowdfunding based on a Fuzzy Cognitive Maps (FCM) approach. A framework consisting of three phases (ten steps) has been proposed to design the guidelines of a DSS in crowdfunding. The first phase focusses in the construction of the FCM. FCM-based scenarios have been built in the second phase. Finally, in the third phase the guidelines of the DSS in crowdfunding have been defined. Results show that DSS using FCM-based scenarios could be useful tools to promoters, funders and managers of crowdfunding projects.

Assessing Blockchain-Based Innovation for the “Right to Education” Using MCDA Approach of Value-Focused Thinking and Fuzzy Cognitive Maps

Assessing Blockchain-Based Innovation for the “Right to Education” Using MCDA Approach of Value-Focused Thinking and Fuzzy Cognitive Maps

Fuzzy cognitive mapping as a tool to assess the relative cumulative effects of environmental stressors on an Arctic seabird population to identify conservation action and research priorities

Abstract In the Arctic, chemical contaminants, shipping, oil pollution, plastic pollution, changing habitats in relation to climate change and fisheries have been identified as environmental stressors to seabirds such as Fulmarus glacialis (northern fulmar; qaqulluk; ᖃᖁᓪᓗᖅ), but rarely have these stressors been considered within a cumulative effects framework in this species which is currently showing a declining populations trend. As a novel tool to understand cumulative effects within a conservation context, we applied a fuzzy cognitive mapping (FCM) approach that allows experts to arrange key factors and their interrelationships, organizing their understanding of the components of a complex issue into a graphical representation; a ‘cognitive map’. This process was grounded in local environment concerns as documented in several Nunavut‐specific reports and discussions, and worked with western‐trained seabird experts with knowledge of northern fulmar populations to assess the inter‐related environmental threats to fulmars as a way to combine these stressors in a cumulative effects framework and identify conservation actions and knowledge gaps. We found strong agreement that the main stressors affecting northern fulmar populations in Canada include pollution (11% total influence (TI)), shipping activities (16% TI), hunting and fishing (18% TI) and mining/oil and gas exploitation activities (22% TI). The indirect influence of threats on northern fulmar population size (57% TI) exceeded the total direct influence (43% TI), emphasizing the value of cognitive mapping in cumulative effects assessment for a more holistic understanding of interacting stressors. Participants expressed substantial uncertainty regarding the strong relationships leading from the concepts, commercial fishing activity in the BBDS and the North Atlantic fisheries activity, indicating that these potential stressors require more research. Similarly, uncertainty was expressed about the potential effects of zodiac traffic, ship strikes of northern fulmar, number of oil spills and magnitude of oil spills on northern fulmar. By characterizing individual factors as manageable or not, we determined that stressors are largely manageable with enforcement of existing policies (58% TI)—importantly, fishing activities were both highly influential on fulmars and deemed manageable, which will inform ongoing co‐management planning in the region.

The analysis of the electronic customer relationship management system based on marketing performance and knowledge management of the company using the Fuzzy cognitive map approach

The analysis of the electronic customer relationship management system based on marketing performance and knowledge management of the company using the Fuzzy cognitive map approach