Fuzzy Sets Research Articles

Measurement of livelihood resilience and its influencing factors for farmers in poverty alleviation areas: a case study of Yunnan Province, China

ABSTRACT This paper focuses on farmers in poverty alleviation areas of Yunnan Province, China, and constructs an evaluation index system for livelihood resilience based on buffering, self-organization, and learning capacities. Utilizing methods such as the entropy method, obstacle degree model, and fuzzy set qualitative comparative analysis (fsQCA), we measure livelihood resilience and explore its influencing factors. The results indicate significant differences in livelihood resilience levels among different livelihood types. Diversified farmers demonstrate the strongest resilience, while farming-oriented farmers show the weakest. Buffering capacity is identified as the core element for enhancing livelihood resilience, with business-oriented and diversified farmers performing notably well, while labour-oriented and farming-oriented farmers are relatively weaker. Differences in self-organization and learning capacities also significantly affect the livelihood resilience levels of various types of farmers. The obstacle degree model analysis reveals that arable land area, labour force, income level, information acquisition capacity, and social networks are the main obstacles affecting farmers’ livelihood resilience. The configurational analysis proposes two pathways to achieve high livelihood resilience, further emphasizing the critical role of buffering capacity. This study offers empirical and theoretical support for targeted policies to enhance farmers’ capacity to manage livelihood risks in poverty alleviation areas.

What influences teaching methods of K-12 inclusive school teachers? An fsQCA study on teacher professional development projects at a group school in Beijing

ABSTRACT Inclusive education demands flexible and effective teaching methods to accommodate diverse student needs. However, existing teaching approaches often fall short in supporting personalized development. This study investigates the complex causal mechanisms influencing teaching method effectiveness in inclusive schools by adopting a fuzzy set qualitative comparative analysis (fsQCA) framework. Drawing on a survey of 254 elementary and middle school teachers participating in a professional development program in a Beijing group school, the study explores how eight professional competence dimensions—disciplinary thinking, knowledge system, learning effectiveness, core concepts, disciplinary methods, metacognition, content organization, and curriculum standards—interact to shape effective teaching modes. Results indicate that content organization is a necessary condition for high teaching effectiveness. Furthermore, five distinct configurations of conditional factors were identified, each capable of producing highly effective teaching methods. Among them, disciplinary thinking, disciplinary methods, and content organization consistently play a central role. The findings highlight multiple equivalence pathways to effectiveness and reveal substitution relationships between factors such as curriculum standards, knowledge systems, and metacognition. This research contributes to refining theoretical models of teaching effectiveness and offers practical implications for designing teacher training programs tailored to inclusive education contexts.

A configurational exploration of how organization ownership influences their substantive innovation: evidence from Chinese high-tech companies

ABSTRACT The substantial innovation and performance disparity between state-owned and private enterprises have garnered significant attention from researchers and the general public. This study aims to integrate these topics and explore the intricate interdependencies between government subsidies, innovation outputs, and their differential impacts on state-owned and private Chinese high-tech companies. Drawing upon configuration theory and prior innovation research, we adopt a configurational approach to identify the various combinations of influences, driven by company characteristics, organisational conditions, and the macro environment, that lead to high levels of substantial innovation. Utilising fuzzy set qualitative comparative analysis with a matched pair sample of state-owned and private enterprises, we discover multiple configurations of influence conditions that contribute to high levels of substantial innovation in both types of enterprises. Through inductive analysis, we uncover four distinct influence mechanisms: government-led, information-driven, subsidy-driven, and property rights-driven. These mechanisms shed light on how the conditions for influence in state-owned and private firms synergize or strengthen each other in different ways. Furthermore, we discuss the theoretical and research implications concerning equitable opportunities and high levels of innovation for state-owned and private firms.

Classification of possible solutions regarding business engineering problems by using complex Pythagorean fuzzy rough WASPAS approach

Business engineering creates novel business solutions using a social and technological system. Business engineering is facing several challenges like (1) complexity management (2) rapid technological advancements (3) Resource constraints (4) Interdisciplinary collaboration etc., and there is a need to classify the solutions for these issues faced by the business community. To cover more advanced data and overcome the chance of data loss, in this article, we have developed the idea of a complex Pythagorean fuzzy rough set based on Tamir’s idea of a complex fuzzy set. We have developed basic operational laws for the proposed idea under the notion of Yager’s t-norm and t-conorm. Additionally, we have initiated the theory of complex Pythagorean fuzzy rough Yager weighted average and geometric aggregation operators. To discuss the utilization of the initiated work, we have introduced the WASPAS technique that can help us tackle the MADM problems. Moreover, we have provided an illustrative example for the classification of the solutions regarding the problems in business engineering. Also, a comparative analysis of the initiated theory shows the advantages of the introduced work.

A Novel Multi-Q Valued Bipolar Picture Fuzzy Set Approach for Evaluating Cybersecurity Risks

A Novel Multi-Q Valued Bipolar Picture Fuzzy Set Approach for Evaluating Cybersecurity Risks

How AI policies influence urban innovation in China: analysis based on feature extraction and fsQCA

PurposeThis study aims to understand the patterns that characterize the impact of artificial intelligence (AI) policies on urban innovation performance, and reveal how these patterns vary across different regions, thereby helping AI policy-making and promoting the urban innovation.Design/methodology/approachThis research focuses on how AI policies influence innovation using the city as unit of analysis. AI policy and patent data were collected from 156 Chinese cities over a decade. Coding and machine learning methods were applied to extract policy features, including three types of policy instruments, policy continuity, policy intensity, and policy count. The fuzzy set Qualitative Comparative Analysis (fsQCA) method is used to identify patterns that explain how AI policies influence urban innovation performance and to further explore regional differences.FindingsComparing four models for extracting policy instruments, ERNIE 3.0 has been proven to be the most accurate and effective model. Three patterns are found using fsQCA: the environment-safeguard, demand-pull, and supply-environment-demand triple-drive patterns. Moreover, these patterns reflect the development distinction of the eastern, middle, and western cities, respectively. Hence, governments should focus on the intricate interplay and synergistic application of multiple policy levers, and enhance creativity in policy formulation based on their specific developmental characteristics.Originality/valueThis research analyzed the patterns that AI policies influence urban innovation from the national and regional perspective. Automated methods were introduced for policy feature extraction, particularly in identifying policy instruments, thereby significantly cutting down on labor and enhancing the efficiency of data analysis. Besides, this research highlights the interplay among various factors, utilizing fsQCA to reveal the collaborative dynamics at work, which compensates for the deficiency of independent assumptions in regression analysis, and analyze the synergistic effects of different factors from a systematic perspective.

How Does Revenue Diversification Affect the Financial Health of Sustainable Entrepreneurship Organizations in China? A Fuzzy Set Qualitative Comparative Analysis

The past decade has witnessed the bourgeoning development of sustainable entrepreneurship organizations (SEOs) that are engaging in advancing sustainable development in China. Revenue diversification is often considered by policymakers, scholars, and practitioners to be a desirable strategy for improving the financial health and organizational sustainability of SEOs and other types of hybrid organizations. However, previous studies on the benefits of revenue diversification for hybrid organizations have not reached a definitive conclusion, and the empirical literature has devoted little attention to the financial outcomes of revenue diversification in the SEO context. To address these knowledge gaps, this study uses fuzzy set qualitative comparative analysis to investigate how revenue diversification and organizational conditions interact to conjunctively affect the multidimensional financial health of SEOs in the Chinese context. This study identifies divergent configurations for high and low levels of financial health in four dimensions and across different types of SEOs. The results show that revenue diversification generates benefits primarily for large, established for-profit SEOs in terms of enhancing their financial flexibility but produces no observed improvements in financial flexibility, efficiency, profitability, or growth among nonprofit SEOs. These findings contribute to the sustainable entrepreneurship and sustainability literature in diverse ways, with valuable practical implications for SEO practitioners and major stakeholders.

The causal and interactive approach to drive sustainability: role of green dynamic capabilities, ambidextrous green innovation strategy and resource orchestration capability

PurposeGrounded in dynamic capabilities and ambidexterity theories, this study investigates how causal and interactive mechanisms promote sustainable outcomes via the prisms of green dynamic capabilities (GDC), ambidextrous green innovation strategy (AGIS) and resource orchestration capability (ROC).Design/methodology/approachThe study employed partial least squares-structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to assess data gathered from 304 respondents from Chinese manufacturing firms.FindingsThe findings show that GDC significantly impacts different aspects of sustainable performance and AGIS serves as a significant mediating mechanism. High ROC amplifies GDC’s indirect impact on sustainable performance via AGIS. FsQCA results reveal multiple synergistic pathways of how GDC, AGIS and ROC can bring about better sustainability outcomes.Practical implicationsThe research offers valuable insights and a novel strategy for manufacturing firms and policymakers to mitigate environmental degradation and attain sustainable performance by stimulating AGIS through GDC.Originality/valueThis paper contributes to sustainability research by integrating dynamic capabilities and ambidexterity theories. It provides actionable insights for manufacturing firms to engage in appropriate green innovation strategies and resource orchestration for sustainable growth.

Foundational concepts of circular intuitionistic fuzzy soft sets applied to sustainable decision-making for cement plant site selection

The optimal selection of a site for cement plant development is a multifaceted decision-making process that demands careful consideration of environmental, economic, and social dimensions. This research delves into the utilization of Circular Intuitionistic Fuzzy Soft Sets (CIFSS) as an advanced mathematical framework to enhance the precision and reliability of sustainable decision-making in cement plant site selection. The CIFSS approach adeptly manages the inherent uncertainties and ambiguities associated with evaluating potential locations, offering a comprehensive methodology for assessing various criteria. By embedding CIFSS within the context of sustainable development, this technique provides decision-makers with a robust and adaptable tool for identifying the most appropriate site, thereby ensuring long-term viability and minimizing environmental impacts. The results underscore the effectiveness of CIFSS in facilitating complex, multi-criteria decision-making in industrial site selection, underscoring its broader applicability in sustainable infrastructure planning.

The Synergy of Fuzzy Logic and Multi-Criteria Decision-Making: Application Areas and Global Trends

This study conducts a bibliometric analysis to understand the evolution and application of Multi-Criteria Decision Making (MCDM)/Fuzzy MCDM methods in scientific literature. The literature review process, utilizing the Scopus database, analyzed 13278 articles out of 30144 published between 2014-2024. The analysis reveals substantial academic output in MCDM/Fuzzy MCDM fields with an annual growth rate of 13.38%. However, the declining citation-per-paper ratio suggests that increasing publication volume may negatively impact individual studies' visibility. Journals like Sustainability, PLOS One, and IEEE Access emerge as top publishers, highlighting the rise of open-access platforms while cautioning that rapid acceptance processes may introduce methodological weaknesses. Leading authors such as Zeshui Xu and Dragan Pamučar have made significant theoretical and practical contributions. Xu's operators for intuitionistic fuzzy sets and Pamučar's novel MCDM methods for healthcare supplier selection represent key milestones. The University of California System demonstrates strong research infrastructure as the most prolific institution. China dominates as the most productive country (14545 articles, 122430 citations), followed by India, Pakistan, and Turkey, whose collaborations enhance scientific interaction. Yager's (2013) IEEE Transactions on Fuzzy Systems paper, "Pythagorean Membership Grades in Multicriteria Decision Making," remains the most cited (2433 citations). Science mapping identifies key trends: fuzzy logic's role in decision model evolution and integration with machine learning. Applications in energy efficiency, maritime safety, and sustainability are growing. Future integration with AI/ML is projected to enhance decision-making efficiency.

A Decade of Picture Fuzzy Sets in Multi-Criteria Decision-Making: A Comprehensive Review of Trends, Gaps, and Future Directions

Picture fuzzy sets (PFSs) are extensively utilized in medical diagnostics and multi-criteria decision-making (MCDM) due to their enhanced flexibility, distinguishing capability, and applicability in handling uncertainty and hesitation, particularly in complex domains such as healthcare, transportation, environmental decision-making, artificial intelligence (AI), and machine learning. Guided by four research questions, this review employed descriptive statistics to analyse the extent of research focused on PFSs. An intensive literature search was performed across leading publishers, including IEEE Xplore, SpringerLink, ScienceDirect, the Association for Computing Machinery (ACM), and the Multidisciplinary Digital Publishing Institute (MDPI). Findings revealed significant scholarly efforts to adopt PFSs in healthcare (22.6%), transportation (24.5%), environmental decision-making (7.5%), AI and machine learning (5.7%), and other domains (39.6%). Research from 2013 to 2024 demonstrated notable advancements in mathematical operations and extensions (25%), aggregation operators and similarity measures (30%), hybrid approaches and MCDM applications (20%), domain-specific implementations (15%), and theoretical developments (10%). MCDM emerged as a prominent tool for enhancing decision-making across diverse fields. The study highlights the need to explore additional areas of application for PFSs, particularly in refining distance measures to further enhance their utility.

An Aczel-Alsina T-Spherical Fuzzy Framework for the Electric Vehicle Selection

Among many other selection criteria, multi-attribute decision-making is a tool used in various industries, including risk control, numerical examination, cybercrime investigation, networking, etc. Aczel-Alsina aggregation operators are suitable to mitigate the effects of inconsistent data. In this article, we present a few novel methodologies to account for T-spherical fuzzy set data using Aczel-Alsina aggregation techniques. The T-spherical fuzzy Aczel-Alsina weighted average operator and the T-spherical fuzzy Aczel-Alsina ordered weighted average are two examples of these novel, unique approaches. The T-spherical fuzzy Aczel-Alsina weighted geometric and T-spherical fuzzy Aczel-Alsina ordered weighted geometric operators are novel strategies that we also introduce. We look at a few unique situations and noteworthy properties to show the persistence and effectiveness of the techniques that are provided. A method for leveraging the T-spherical fuzzy information system to resolve the electric vehicle selection problem is also described. Due to the high cost of petrol and the current financial difficulties faced by middle-class households, this problem is chosen. An experimental case study is also built. To calculate the accuracy and authority of the present developments, we contrast the outcomes of formerly used methods with the aggregation operators that are presently available.

A Configurational Model of Green Consumerism Integrating Ethical Obligations, Collective Efficacy, and Environmental Concerns Using Fuzzy Sets Qualitative Comparative Analysis

ABSTRACTThe pressing demand for sustainable consumption stems from environmental challenges such as global warming and resource depletion. However, many studies fail to capture the complex relationships among personal, social, and technological factors influencing green purchase behavior. Building on the Theory of Planned Behavior, this research explores how these factors interact to promote or hinder sustainable consumption. Using survey data from 400 Indonesian consumers with experience in purchasing green products, the study employs fuzzy‐set qualitative comparative analysis to identify configurational pathways. The results highlight unique combinations of intrinsic factors, such as ethical obligation and environmental concern, alongside extrinsic influences, including social media engagement and subjective norms, that drive green purchasing. While various configurations lead to high green purchase behavior, the absence of critical elements, particularly environmental knowledge, emerges as a significant barrier. By adopting a configurational perspective, this study moves beyond linear models, uncovering dynamic pathways that shape green purchase behavior. It provides an integrated framework that considers personal, social, and technological dimensions to address the context‐specific challenges of fostering sustainable consumption.

Fuzzy Reliability Assessment of Photovoltaic Power Plant System with Generalized Intuitionistic Fuzzy Sets

Fuzzy Reliability Assessment of Photovoltaic Power Plant System with Generalized Intuitionistic Fuzzy Sets

Exploring the value creation mechanism of agricultural heritage creative tourism based on SEM and fsQCA

Creative performance is essential for tourists to engage in community activities at heritage sites. However, there is a theoretical gap in how creative performance affects the willingness of tourists and residents to co-create values together in an important agricultural heritage site. Therefore, the research combines two methods, SEM (structural equation modeling) and fsQCA (fuzzy set qualitative comparative analysis), to investigate the antecedent paths of tourists’ willingness to co-create value at tea cultural heritage sites. The results show that self-identity and cultural identity play a complex chain mediating role in creative performance, influencing tourists’ value co-creation. Meanwhile, “cultural identity * creative performance * awakening of enjoyment * cultural learning” is the optimal pathway for achieving tourists’ value co-creation. This study advances research on the conservation and development of creative agriculture and cultural heritage communities. Finally, it provides a realistic basis for research on the interaction between tourists and residents.

On Generalized Overlap and Grouping Indices in n-Dimensional Contexts

Overlap and grouping indices are functions measuring, respectively, the fuzzy intersection and fuzzy union of two fuzzy sets. They have been applied successfully in several fields, such as in interpolative fuzzy systems, fuzzy rule-based classification systems and comparison of fuzzy inference rules. Overlap and grouping indices can be built employing overlap and grouping functions, respectively, which are possibly non-associative aggregation functions with features that provide good results when applied to practical bivariate problems. Many studies have generalized the concepts of overlap and grouping functions to be applied in n-dimensional problems. However, the concepts of overlap/grouping indices have not been generalized in similar pattern. Since the associative property may not hold, their application in n-dimensional domains, for comparing more than two fuzzy sets at a time, is not immediate, which limit their application in such contexts. The objective of this paper is to introduce the concepts of n-dimensional and general overlap/grouping indices, with special attention to the development of their construction methods based on generalized overlap/grouping functions. As an application example, we introduce the concept of n-dimensional Jaccard index, with a construction method based on n-dimensional overlap/grouping indices, providing an n-dimensional fuzzy set similarity score.

An integrated approach for design concept evaluation based on interval-valued Pythagorean fuzzy set and consensus model

PurposeDesign concept evaluation overwhelmingly impacts product development. This study aims to enhance uncertainty representation in the design concept evaluation process, consider the impact of evaluators’ cognitive bias on the evaluation results and improve the credibility of the evaluation results.Design/methodology/approachThis study proposes an integrated approach for design concept evaluation based on interval-valued Pythagorean fuzzy set (IVPFS) and consensus model. In this study, we introduced IVPFS to represent the evaluation and preferences of the evaluators, then built a consensus model based on mathematical operators to measure the degree of cognitive consistency among evaluators, applied the modification of non-consensus opinions through a feedback mechanism to reach consensus, and finally, ranked all design concepts by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).FindingsThe results indicate that the proposed approach is an objective and effective solution for representing individual differences and reducing the impact of group heterogeneity.Research limitations/implicationsThe number of evaluators in the numerical example is insufficient for realistic design activities. We aim to address this issue in future studies.Practical implicationsThe proposed method contributes to the smooth implementation of design concept evaluation and the construction of harmonious interpersonal relationships within organizations.Social implicationsThe proposed method contributes to the smooth implementation of the design concept evaluation as well as the construction of harmonious interpersonal relationships within the organization.Originality/valueThis method addresses the subjectivity of the evaluator and the uncertainty of information in the evaluation process, reduces the impact of evaluators’ cognitive differences on the evaluation results, and ultimately selects a robust design alternative under these circumstances.

Confidence Level-Driven Dombi Aggregation Operators within the p,q-Quasirung Orthopair Fuzzy Environment for Sustainable Supplier Evaluation in Automotive Industry

Selecting sustainable suppliers in the automotive industry is crucial for fostering environmental responsibility, cost efficiency, and ethical sourcing, all of which enhance long-term competitiveness and compliance with global sustainability standards. However, this process is a complex decision-making problem due to vague, uncertain, and imprecise data stemming from subjective expert judgments, incomplete information, dynamic market conditions, evolving regulations, and diverse stakeholder expectations. Traditional methods often fail to adequately capture these intricacies, necessitating more flexible and intelligent evaluation frameworks. To address this challenge, this study leverages p,q-quasirung orthopair fuzzy sets (p,q-QOFSs) to effectively model hesitation and ambiguity in expert assessments, while incorporating confidence levels to enhance reliability by accounting for varying decision-maker expertise. We propose confidence level-based Dombi weighted averaging (geometric) aggregation operators for p,q-QOFSs and develop a multi-criteria group decision-making model, with attribute weights determined using the Analytic Hierarchy Process (AHP). The model is validated through a case study in which three experts evaluate five automotive suppliers across eight sustainability criteria. A comparative analysis with existing methods demonstrates the superiority of the proposed approach, while sensitivity analysis confirms its robustness and stability under parameter variations.

MHF: A multi-task hybrid fusion method for underwater image enhancement based on biological vision.

Enhancement of underwater images is a new challenge in image research because low image visibility and contrast due to wavelength attenuation of underwater light and the effect of suspended particles in the water are most obvious. These problems can lead to difficulties in underwater information extraction and affect the development of underwater research, so we propose a multi-task hybrid fusion method (MHF) for underwater image enhancement based on biological vision. In terms of technological innovation, we designed an improved type II fuzzy set computation module based on the foundation of biological vision to improve the visibility of images. Meanwhile, we designed an adjustable contrast stretching module to improve image visibility. In addition, inspired by the fusion approach, we introduce a visual fusion module which fuses the results of the above two modules with a weight ratio. Therefore, this method focusing on multi-task synchronization can overcome the limitations of previous methods and effectively solve the problems of white balance distortion, color shift, low visibility, and low contrast in underwater images, and achieve the best results in the application tests of geometric rotation estimation, feature point matching, and edge detection. The experimental results demonstrate that the application results of this research method on 2 datasets outperform the top 14 existing algorithms. The wide applicability and excellent performance of the method are verified through application tests on various underwater vision tasks. By explicitly addressing the limitations of existing methods, the method becomes an advantageous solution in underwater image processing, providing enhancements in image quality and task-specific applications in a concise and efficient manner.

Road to glory or highway to hell? Uncovering the consequences of corporate greenwashing in Latin America

Purpose The purpose of this paper is to investigate the effect of corporate greenwashing on the reputation, innovation and market value of companies in Latin America. Design/methodology/approach To achieve the research purpose, the authors examined the greenwashing of 428 companies based in 14 Latin American countries and selected three organizational factors: corporate reputation, innovation capacity and market value. To analyze the collected data, the authors combined a symmetric technique (panel data analysis) with an asymmetric technique (fuzzy set qualitative comparative analysis) to give greater robustness to the findings. Findings The findings revealed that greenwashing practices affect the corporate reputation, innovation capacity and market value of companies. More specifically, companies that commit more greenwashing practices tend to have a higher reputation, make more investments in research and development, as well as a higher market value. The findings can be useful for managers, investors, governments and public policymakers. Originality/value To the best of the authors’ knowledge, this is the first work that investigates the consequences of greenwashing in companies based in Latin America. The paper challenges the difficulties of measuring greenwashing by following the methodology of Ghitti et al. (2023) and finds that the consequences of greenwashing in emerging economies may be different from findings in developed economies.