Search Results (89)

The social–ecological nexus (SEN) offers a framework to capture the complex and dynamic interactions and interdependencies between human communities and the natural systems that support them. This study analyzed the SENs within a village tank cascade system (VTCS), a social–ecological system (SES) located in the dry zone of Sri Lanka. The study adopted a participatory approach, combining fuzzy cognitive mapping (FCM) to determine key SES properties of the VTCS. The FCM process identified 49 nodes (elements) and 434 edges (connections) within the study landscape that contribute to system performance. Network graphs were generated using centrality metrics—degree, betweenness, and eigenvector centrality—to identify the most influential nodes and edges contributing to system sustainability and productivity. The study identified nine nodes as the most influential elements in the SEN which are critical for balancing trade-offs between sustainability and productivity in the VTCS. Three distinct clusters of elements influencing sustainability and productivity emerged from the SEN graph: (i) ecological cluster, (ii) social–ecological cluster, and (iii) social cluster. Understanding the role of SES elements and their positions in the SEN is crucial for identifying gaps within the system and informing tailored management interventions. These findings offer a theoretical basis for optimizing sustainability strategies aimed at enhancing the overall productivity and resilience of SES. Consequently, this approach exposes the complexities of the SEN, making it widely applicable to similar SESs globally. Full article

Despite the growing importance of Environmental, Social, and Governance (ESG) performance in shaping brand perception and consumer trust, limited empirical evidence exists on how ESG indicators translate into measurable digital consumer engagement outcomes, particularly in ethically driven markets such as the vegan food sector. This study addresses this gap by examining how ESG performance translates into digitally observable consumer engagement and frames this relationship as a strategic performance measurement and decision-support problem. Building on the sector’s reliance on ethical positioning, trust, and online visibility, we integrate ESG indicators with digital marketing and web analytics metrics (e.g., traffic and engagement proxies) for a panel of five leading vegan food firms [Nestlé SA (Vevey, Switzerland), Kellanova (Chicago, IL, USA), Beyond Meat Inc. (El Segundo, CA, USA), Danone SA (Paris, France), and Conagra Brands Inc. (Chicago, IL, USA)], using data from the Semrush web analytics platform and the Eikon Refinitiv ESG database for the period January–December 2024. We employ a mixed-method design combining descriptive analytics with correlation analysis and simple linear regression to estimate the direction and strength of ESG–digital performance links, and we extend inference through Fuzzy Cognitive Mapping (FCM) using the MentalModeler platform to simulate “what-if” scenarios that support managerial foresight under digital uncertainty. Results indicate that stronger ESG profiles are associated with more favorable digital outcomes, with specific ESG mechanisms (e.g., human-capital and environmental initiatives) aligning with deeper engagement signals. The FCM scenarios further suggest that coordinated ESG improvements can amplify digital traction and reinforce sustainable brand growth. The proposed framework contributes to strategic management by operationalizing an ESG-enabled digital performance measurement system and a lightweight Decision Support System (DSS) that can guide resource allocation, KPI monitoring, and risk-aware positioning in sustainability-oriented markets. Full article

The fashion industry is one of the most resource-intensive sectors, generating major environmental impacts such as greenhouse gas emissions, excessive water and land use, and pollution from waste and microplastics. Fast fashion intensifies these issues through overproduction and overconsumption. However, growing consumer awareness and regulatory pressure are pushing brands to adopt Circular Economy (CE) and sustainability strategies, including resale platforms, recycling programs, and sustainability frameworks. Despite these efforts, their real effectiveness remains uncertain. This study investigates which CE and sustainability strategies are most common among fast fashion companies and how they can mitigate key environmental impacts. Using a Fuzzy Cognitive Maps (FCM) model, the research quantitatively evaluates the effects of various circular and sustainable strategies across the supply chain. Ten key strategies were identified, revealing that isolated actions are often ineffective. Instead, an integrated, systemic approach combining multiple initiatives is essential to achieve meaningful sustainability improvements. Full article

This study addresses the gap between predictive optimization and monitoring-oriented risk concentration by introducing a hierarchical Fuzzy Cognitive Map (FCM) framework for financial risk assessment. Financial distress prediction models are employed to estimate firm-level default probabilities and are required to comply with regulatory standards. IFRS 9 and Basel III/IV frameworks emphasize model explainability, scenario analysis and causal transparency, which are essential for compliance purposes. The methodology aggregates correlated financial ratios into financial concepts through unsupervised clustering. Concepts interact through a learned coupling matrix and a controlled multi-step propagation, which enables the amplification of risk signals. A small residual correction is applied at the final readout, preserving the interpretability of the proposed framework. The framework was applied to two severely imbalanced benchmark bankruptcy datasets. It achieved higher precision–recall performance than Logistic Regression (PR–AUC $\approx 0.32$ vs. $0.27$), improved calibration (Brier score $\approx 0.046$ vs. $0.089$) and maintained competitive Recall@Top–K under tight supervisory monitoring budgets. Hierarchical FCM achieved predictive performance comparable to nonlinear models while maintaining concept-level interpretability. Our findings demonstrate that structured concept aggregation combined with interaction-based propagation provides a transparent alternative to purely predictive black-box models in financial distress assessment and is aligned with regulatory frameworks. Full article

This study examines the statistical associations between commercialization-related cost structures and financial outcomes on revenue growth, profitability, and scale within a centralized financial system. We estimate four OLS models (M1–M4) using aggregated annual data from 2020 to 2025 and enhance our analysis with a fuzzy cognitive map (FCM) scenario assessment. The findings demonstrate that revenue growth correlates positively with both SG&A growth and commercialization efficiency (revenue per unit of SG&A); however, SG&A intensity exhibits a negative relationship with net margins. Logarithmic estimations indicate a robust co-scaling between operational expenses and revenues, implying growth driven by capacity rather than operating leverage. Lagged analysis also reveals an intertemporal trade-off, wherein phases of accelerated SG&A expansion are succeeded by diminished subsequent growth. The findings underscore the necessity of differentiating between commercialization intensity and efficiency, and advise against viewing SG&A growth as a consistent alignment of financial performance. Full article

In an era where environmental consciousness increasingly shapes consumer behaviour, the tourism industry faces the dual challenge of promoting destinations while ensuring ecological sustainability. This study explores how web analytics and big data can be leveraged to enhance the visibility and attractiveness of eco-friendly destinations. Building upon digital marketing and sustainability frameworks, the authors develop a data-driven methodology that integrates website performance metrics, search behaviour patterns, and social media engagement indicators. After data collection, statistical and content analyses were implemented, followed by a Fuzzy Cognitive Map (FCM) to visualise the interrelationships between online user behaviour, environmental awareness, and destination appeal. Full article

Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network with built-in meaning in their architecture, originally devoted to modeling and scenario simulation tasks. These knowledge-based neural systems support feedback loops that handle static and temporal data. Over the last decade, there has been a noticeable increase in the number of contributions dedicated to developing FCM-based models and algorithms for structured pattern classification and time series forecasting. These models are attractive since they have proven competitive compared to black boxes while providing highly desirable interpretability features. Equally important are the theoretical studies that have significantly advanced our understanding of the convergence behavior and approximation capabilities of FCM-based models. These studies can challenge individuals who are not experts in Mathematics or Computer Science. As a result, we can occasionally find flawed FCM studies that fail to benefit from the theoretical progress experienced by the field. To address all these challenges, this survey paper aims to cover relevant theoretical and algorithmic advances in the field, while providing clear interpretations and practical pointers for both practitioners and researchers. Additionally, we will survey existing tools and software implementations, highlighting their strengths and limitations towards developing FCM-based solutions. Full article

Preeclampsia (PE) and intrauterine growth restriction (IUGR) are obstetric complications associated with placental dysfunction, which represent a public health problem due to high maternal and fetal morbidity and mortality. Early detection is crucial for timely interventions. Therefore, this study proposes the development of models based on fuzzy cognitive maps (FCM) optimized with metaheuristic algorithms (particle swarm optimization (PSO) and genetic algorithms (GA)) for the prediction of PE and IUGR. The results showed that FCM-PSO applied to the PE dataset achieved excellent performance (accuracy, precision, recall, and F1-Score = 1.0). The FCM-GA model excelled in predicting IUGR with an accuracy and F1-Score of 0.97. Our proposed models outperformed those reported in the literature to predict PE and IUGR. Analysis of the relationships between nodes allowed for the identification of influential variables such as sFlt-1, sFlt-1/PlGF, and uterine Doppler parameters, in accordance with the pathophysiology of placental disorders. FCM optimized with PSO and GA offer a viable clinical alternative as a medical decision support system due to their ability to explore nonlinear relationships and interpretability of variables. In addition, they are suitable for scenarios where low computational resource consumption is required. Full article

Urban inefficient land redevelopment (UILR) is crucial for sustainable urban development, yet its progress is driven by the interplay of multiple factors. To systematically uncover the driving mechanisms and dynamic patterns of these factors, an integrated analytical approach combining Structural Equation Modeling (SEM) and Fuzzy Cognitive Map (FCM) is developed in this study. Based on 222 valid survey responses from professionals across eight cities in China’s Yangtze River Delta region, five key factors are identified within the “drivers–pressure–enablers” conceptual framework: economic incentives, environmental objectives, social needs, policy guidance, and implementation conditions. SEM is first employed to examine static causal relationships, and the quantified pathway effects are subsequently incorporated into an FCM model to simulate the long-term evolution. The results reveal the following: (i) All five factors exert significant direct effects, with economic incentives, environmental objectives, and policy guidance also demonstrating notable indirect effects. (ii) The factors exhibit distinct temporal characteristics: policy guidance acts as a “fast variable” enabling short-term breakthroughs; economic incentives serve as a “strong variable” driving medium-term progress; and social needs function as a “slow variable” with long-term benefits. (iii) Policy guidance is essential, as its absence leads to persistently low effectiveness, while its synergy with implementation conditions can achieve satisfactory performance even without economic incentives. The combined SEM–FCM approach validates static hypotheses and simulates dynamic scenarios, offering a new perspective for analyzing complex driving mechanisms of UILR and providing practical insights for targeted redevelopment strategy design. Full article

Digital transformation has reshaped how cruise firms acquire, engage and retain customers. However, existing research rarely integrates these behavioural dimensions within a unified analytical framework. This study applies a hybrid regression–Fuzzy Cognitive Mapping (FCM) approach to examine how acquisition channels, engagement indicators and online reputation metrics jointly shape website performance and digital innovation among leading global cruise operators. Using multi-source web-analytics data, regression models identify the direct predictive effects of organic, paid, referral and email channels, while FCM captures their non-linear feedback dynamics. Results reveal that visibility does not equate to engagement: organic and referral traffic drive exposure but not depth, whereas authority and reputation mediate engagement–performance relationships. Scenario simulations reveal asymmetric responses within the digital ecosystem. Consequently, balanced, knowledge-driven channel diversification emerges as a key strategic advantage. The findings extend the Knowledge-Based View (KBV) by conceptualising behavioural analytics as organisational knowledge resources that enable adaptive learning and digital innovation. The proposed framework contributes to both tourism analytics and information systems research, offering a scalable model for understanding how data-intensive service firms convert behavioural information into strategic knowledge and sustainable competitive advantage. Full article

Mesozooplankton are pivotal for Black Sea food webs, yet they are highly vulnerable to hydrographic variability, eutrophication, and human pressures. This study analysed mesozooplankton dynamics along the Romanian coast (2013–2020) across three sectors (north, central, and south) and two distinct periods (cold and warm seasons), integrating Abundance–Biomass Comparison (ABC) curves with Fuzzy Cognitive Mapping (FCM). Results revealed a clear disturbance gradient: the Danube-influenced north supported high abundances of small-bodied taxa; the central sector maintained the most resilient and functionally diverse assemblages; and the southern sector showed chronic degradation with Noctiluca scintillans dominance. ABC curves quantified disturbance, with curve convergence in the north and near overlap in the south during summer, while FCM highlighted network simplification and reduced functional redundancy. Climate scenario simulations projected further declines in cladocerans and meroplankton under warming and freshening, whereas copepods showed relative resilience. Collectively, the findings demonstrate progressive simplification of mesozooplankton and declining energy transfer efficiency, underscoring the need to integrate zooplankton-based indicators into Black Sea monitoring and management frameworks. Full article

This study investigates the systemic interrelations between logistics performance, environmental performance, sustainable development progress, and institutional governance. While the existing literature often examines these dimensions separately, this research conceptualizes them as co-determined drivers of national development. Using data from 123 countries, the analysis integrates four composite indices—Logistics Performance Index (LPI), Environmental Performance Index (EPI), Sustainable Development Goals Index (SDG), and Worldwide Governance Indicators (WGI)—alongside GDP per capita. Methodologically, this study applies multiple linear regressions and correlation analyses to assess the associations among these variables and employs Fuzzy Cognitive Mapping (FCM) to simulate scenario-based systemic interactions. Results show that all ESG indicators are positively and significantly associated with LPI, with WGI exerting the strongest effect. In turn, LPI, EPI, SDG, and WGI jointly explain 81.7% of the variance in GDP per capita, confirming their integrated role in shaping economic performance. FCM simulations further reveal that both environmental and institutional improvements generate reinforcing effects on logistics capacity and GDP outcomes. This study’s originality lies in its multiple-method approach and its synthesis of ESG and logistics performance metrics into a unified explanatory framework. It contributes to development studies by highlighting the structural embeddedness of logistics within broader institutional and sustainability ecosystems. Its policy implication lies in suggesting that integrated reforms—combining infrastructure, regulatory quality, and environmental stewardship—are essential for enhancing long-term national competitiveness and resilience. Full article

In agile, adaptive, and hybrid project management, the Weighted Shortest Job First (WSJF) technique is increasingly being used to prioritize the most relevant business opportunities for organizations. However, this decision-making process often involves the evaluation of multiple interconnected factors whose interactions can influence outcomes in unforeseen ways. Traditional decision-making models tend to assume independence between variables for the sake of simplicity and tractability. In real-world contexts, however, variables rarely operate in isolation; their interdependence introduces complexities that challenge the validity, robustness, and practical applicability of conventional decision-making tools. The objective of this research is to address the problem of interdependence among WSJF variables. To achieve this, Fuzzy Cognitive Mapping (FCM) was applied to evaluate the impact and influence of interdependencies during the decision-making process. The findings demonstrate that incorporating FCM into WSJF yields a 76% correlation in prioritization order with the best outcomes, compared to linear WSJF, while revealing a 24% variation that highlights areas for further study. This evidence indicates that accounting for interdependence leads to more efficient and reliable decision-making than traditional approaches. Full article

In the digitally saturated hospitality environment, research on digital transformation remains dominated by macro-level adoption trends and user-generated content, while the potential of micro-level web-behavioural data remains largely untapped. Recent systematic reviews highlight a fragmented body of literature and note that hospitality studies seldom address first-party behavioural data or big-data analytics capabilities. To address this gap, we collected clickstream, navigation and booking-funnel data from five luxury hotels in the Mediterranean and employed big-data analytics integrated with simulation modelling—specifically fuzzy cognitive mapping (FCM)—to model causal relationships among digital touchpoints, managerial actions and customer outcomes. FCM is a robust simulation tool that captures stakeholder knowledge and causal influences across complex systems. Using a case-study methodology, we show that first-party behavioural data enable real-time insights, support knowledge-based decision-making and drive digital service innovation. Across a 12-month panel, visitor volume was strongly associated with search traffic and social traffic, with the total-visitors model explaining 99.8% of variance. Our findings extend digital-transformation models by embedding micro-level behavioural data flows and simulation modelling. Practically, this study offers a replicable framework that helps managers integrate web-analytics into decision-making and customer-centric innovation. Overall, embedding micro-level web-behavioural analytics within an FCM framework yields a decision-ready, replicable pipeline that translates behavioural evidence into high-leverage managerial interventions. Full article

The Mediterranean Sea, a biodiversity hotspot with over 8500 marine species, faces escalating threats from climate change, pollution, overfishing, and habitat degradation. This study introduces a novel Decision Support System (DSS) integrating a modified PESTEL framework (BESTEL: Biological, Economic, Social, Technological, Environmental, Legal) with Fuzzy Cognitive Mapping (FCM) to assess and mitigate risks to marine species. Leveraging expert knowledge from 34 specialists, we identified 30 key factors influencing extinction risk, analyzed through Principal Component Analysis (PCA) to reduce dimensionality. The resulting FCM model simulated four policy scenarios, evaluating the impacts of climate change and dam proliferation on biodiversity. Findings reveal that mitigating both drivers significantly reduces extinction risk (−0.14), while unchecked climate change offsets gain from dam removal. The DSS highlights the dominance of climate stressors, with pollution and temperature shifts (−0.45, −0.42) as critical variables. Biological traits like reproductive frequency and longevity respond strongly to environmental improvements. This integrative approach bridges qualitative expertise and quantitative modeling, offering actionable insights for conservation planning. The study underscores the need for holistic strategies combining climate mitigation and habitat restoration to safeguard Mediterranean marine ecosystems. The FCM-based DSS provides a scalable tool for policymakers to prioritize interventions and assess trade-offs in complex socio-ecological systems. Full article