Search Results (426)

This study investigates the application of Bayesian probability models in the diagnostic assessment of learning disabilities. The objective of this study was to determine whether specific conditions identified in expert reports could predict subsequent diagnoses. The sample consisted of 201 expert reports on children diagnosed with learning disabilities, which were analysed using qualitative content analysis, fuzzy set qualitative comparative analysis (fsQCA), and Bayesian conditional probability models. Variables such as vocabulary, working memory index, processing speed, and visuomotor coordination were examined as potential predictors. The analysis demonstrated that Bayesian networks captured conditional links, such as the strong association between working memory and perceptual inference, as well as an unexpected negative link between vocabulary and verbal comprehension. The study concludes that Bayesian networks provide a transparent and data-driven framework for pre-screening and risk assessment in special education settings. The limitations of this study include the absence of a control group and exclusive reliance on SNI cases. Future research should explore the integration of abductive reasoning into automated diagnostic software to enhance inclusivity and support decision-making. Full article

Plant-based meat alternatives (PBMAs) are gaining momentum in response to rising demand for sustainable and healthier diets. Drawing on an integrated framework combining the Theory of Planned Behavior (TPB) and the Value–Attitude–Behavior (VAB) model, this study explores key determinants shaping consumers’ purchase intention towards PBMAs in Taiwan. This study performed Partial Least Squares Structural Equation Modelling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) to evaluate the formation of consumers’ PBMA purchase intention. The PLS-SEM results revealed that both environmental consciousness and health consciousness exert a significant influence on consumer attitudes, which, together with subjective norms and perceived behavioral control, positively predict purchase intention. fsQCA revealed six distinct combinations of conditions leading to high purchase intention, while NCA identified environmental consciousness, health consciousness, and the three TPB components as necessary conditions. The results highlight the mediating role of attitude and underscore the value of integrating multiple analytical perspectives to capture the complexity of consumer decision-making. This research advances both theoretical understanding and practical application by elucidating the psychological mechanisms underpinning PBMA adoption and by providing evidence-based implications for strategic marketing within the plant-based food sector. Full article

The resilience of the tourism economy plays a pivotal role in sustaining regional economic stability across the nine provinces along the Yellow River. This study examines the spatio-temporal evolution and configurational pathways of tourism economic resilience across the nine provinces along the Yellow River during 2012–2022 by applying the Standard Deviation Ellipse and Fuzzy Set Qualitative Comparative Analysis. The results showed that: (1) From 2012 to 2019, the tourism economic resilience exhibited a steady upward tendency overall, with a slight fluctuation in the short term in 2020. (2) High and relatively high-level regions experienced a belt-like high-value zone, eventually extending to Sichuan Province, Henan Province, and Shandong Province. (3) The standard deviation ellipse exhibited a distribution pattern along the northeast-southwest axis, with its center of gravity situated in the middle reaches of the Yellow River, having shifted a total of 146.81 km. (4) Four driving pathways were identified: resistance-dominated, recovery-dominated with restructuring synergy, renewal-driven, and multi-resilience synergy-driven. Three barriers also appeared: renewal-constrained, restructuring-lagged, and overall resilience-deficient. Full article

With the rapid advancement of artificial intelligence technology, the role of Artificial Intelligence Generated Content (AIGC) applications within digital learning communities has become increasingly significant. Enhancing the level of knowledge innovation through the integration of human and artificial intelligence has emerged as a critical issue. Grounded in social cognitive theory, this study utilizes a sample of 407 Super Star Learn community learners as a case study. It applies the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method to investigate the synergistic effects of technological environment, cultural context, and individual cognitive factors in promoting learners’ knowledge innovation capabilities. The results show the following: (1) No single condition constitutes a prerequisite for learners to achieve high-level knowledge innovation when acting in isolation. However, enhancing technical capabilities has a relatively universal impact on promoting learners to achieve these results. (2) The multiple concurrency of the technological environment, cultural environment, and individual cognitive conditions has generated multiple configuration patterns that promote knowledge innovation, indicating that the paths leading to learners’ high-level innovation exhibit the characteristic of numerous concurrency. Therefore, it is suggested that digital learning communities actively explore new paths for sustainable knowledge innovation and development driven by generative artificial intelligence technology, thereby injecting sustainable impetus into the development and innovation process of learners, contributing to the goals of sustainable education. Full article

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

Evaluating and enhancing system resilience is essential to strengthen the regional ability to external shocks and promote the synergistic development of environment, economy and society. Taking the Upper Yellow River (UYR) as an example, this paper constructed a resilience evaluation index system for the environment—economy—society (EES) composite system. A three-dimensional space vector model was built to calculate the resilience development index (RDI) of three subsystems and the composite system from 2009 to 2022. Pathways supporting high resilience levels of the composite system were examined using the fuzzy-set qualitative comparative analysis (fsQCA) method from a configuration perspective. The results revealed that (1) the RDI of three subsystems and the composite system in the UYR showed an increasing trend; relatively, the environment and economy subsystems were lower, and their RDI fluctuated between 0.01 and 0.06 for most cities. (2) The emergence of high resilience is not absolutely dominated by a single factor, but rather the interaction of multiple factors. To achieve high resilience levels, all the cities must prioritize both environmental protection and economic structure as core strategic pillars. The difference is that eastern cities need to further consider social development and life quality, while western cities need to consider social development, life quality, and social security. Other cities including Lanzhou, Baiyin, Tianshui, and Ordos should focus on social construction and social security. Exploring the interactive relationship between various influencing factors of the resilience of the composite system from a configuration perspective has to some extent promoted the transformation from a single contingency perspective to a holistic and multi-dimensional perspective. These findings provide policy recommendations for achieving sustainable development in the UYR and other ecologically fragile areas around the world. Full article

The integration of artificial intelligence (AI) in the manufacturing industry is increasingly prevalent, presenting both ongoing opportunities and challenges for organizations while also significantly impacting worker behavior and psychology. Drawing on data from 405 workers in China, this study employs hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the influence mechanism of AI-enabled job characteristics on work-related flow. Key findings reveal that: AI-enabled job characteristics positively predict work-related flow by increasing perceived challenge stress, yet simultaneously exert a negative influence by exacerbating perceived hindrance stress; techno-efficacy significantly alleviates the relationship between AI-enabled job characteristics and perceived hindrance stress but does not moderate the path via perceived challenge stress; fsQCA identifies four distinct causal configurations of antecedents leading to high work-related flow. This research elucidates the complexities of AI-enabled job characteristics and their dual-faceted impact on work-related flow. By integrating AI into the study of worker psychology and behavior, it extends the contextual scope of job characteristics research. Furthermore, the application of fsQCA provides novel insights into the antecedent conditions and configurational pathways for achieving work-related flow, offering significant theoretical and practical implications. Full article

With accelerating digital transformation, firms must renew how they create, deliver, and capture value to remain competitive and to advance sustainable competitiveness. This study examines how ambidextrous market orientation drives digital business model innovation (DBMI) through the mediating role of digital resource bricolage and the moderating effect of environmental turbulence. Using survey data and structural equation modeling (SEM), we find that both proactive and responsive market orientations positively affect DBMI. Digital resource bricolage partially mediates both relationships, with a stronger mediation effect for responsive orientation. Environmental turbulence strengthens the association between ambidextrous market orientation and digital resource bricolage. Complementing variable-centric tests, fuzzy-set qualitative comparative analysis (fsQCA) identifies three configurational pathways sufficient for high DBMI, revealing alternative routes to business-model renewal under different contextual conditions. The findings extend ambidextrous market orientation research to digital contexts, enrich the resource-recombination perspective on DBMI, and provide actionable guidance for firms seeking to orchestrate data, platforms, and legacy assets to reconfigure activity systems. By clarifying when and how market sensing and shaping translate into effective digital recombination, this study informs strategies for sustainable competitiveness in turbulent environments. Full article

Exploring the influencing factors and mechanisms of willingness to adopt GAI for collaborative decision-making in the generative artificial intelligence context is of significant importance for advancing the application of collaborative decision-making between human intelligence and generative AI. This study builds upon the traditional Technology Acceptance Model (TAM) and the Task–Technology Fit (TTF) models by introducing factors of human–GAI trust and collaborative efficacy to construct a theoretical model of the influencing factors of willingness to adopt GAI for collaborative decision-making. Empirical analysis is conducted using Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The results show that perceived usefulness and collaborative efficacy emerge as key determinants of willingness to adopt GAI for collaborative decision-making. Attitude and human–GAI trust exert significant direct positive effects, while perceived ease of use and task–technology fit demonstrate significant indirect positive influences. The fsQCA results further identify three distinct configuration pathways: perceived value-driven, functional compensation-driven, trust in technology-driven. Full article

Travel policies during crises strongly reshape mobility patterns, raising the challenge of protecting public health while minimizing socio-economic disruption—an essential concern for sustainable development. Most evaluations quantify changes in travel volume, which hampers cross-city comparison and policy monitoring. This study proposes a distance-based sustainability metric—distance equivalence (DE)—that translates policy-induced mobility frictions into interpretable “added distance” within a gravity framework, enabling consistent measurement and monitoring of policy impacts. Using inter-city flows for 358 Chinese cities during the Stay-in-Place for Lunar New Year (SIP) guidance, we map DE, test spatial dependence (Moran’s I; LISA), and apply fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify city-level configurations associated with high DE. DE exhibits significant spatial clustering, concentrating east of the Hu line, where dense urban networks amplify advisory checks. Three recurrent configurations—combining case counts, health-care capacity (hospital beds), and average inter-city distance—are linked to high DE. As a sustainability assessment tool, DE supports adaptive management, region-differentiated strategies, and ex-ante risk assessment for governments, public-health authorities, and transport agencies. The framework generalizes to short-term mobility interventions under crisis conditions, advancing the quantification of policy impacts on sustainable mobility and urban resilience. Full article

This study combines Structural Equation Modeling (SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) methods to systematically analyze the key factors affecting the digital transformation of construction enterprises, and to propose differentiated implementation paths and strategies based on these factors. The results of the fsQCA analysis show that the four combination configurations affecting the effectiveness/success of digital transformation of construction enterprises from a group perspective are identified as/can be categorized as “technology-organization dual-driven” and “environment-capability leverage”. The study proposes countermeasures based on the results of the model and the current challenges, in order to offer insights for/serve as a reference for the successful implementation of digital transformation in construction enterprises. Full article

This study investigates how AI-driven virtual anchors affect consumers’ purchase intentions by identifying their key attributes, underlying mechanisms, and configurational interplay. We integrate latent Dirichlet allocation (LDA), structural equation modeling (SEM), and fuzzy-set qualitative comparative analysis (fsQCA) into a unified methodological framework. Empirical evidence demonstrates that the public visibility of virtual anchors exerts a significant positive impact on purchase intention, whereas professionalism, responsiveness, and personalization primarily cultivate consumer pleasure and trust, yet exert limited direct influence on purchase decisions. Emotional states—arousal, pleasure, and trust—mediate the relationship between anchor characteristics and purchase intention. fsQCA further reveals that high purchase intention emerges when responsiveness serves as a necessary condition, trust operates as a pivotal hub, and arousal/pleasure function as emotional conduits; conversely, low purchase intention is chiefly attributable to deficiencies in visibility, responsiveness, and trust. By synthesizing the SOR (stimulus-organism-response) model with the PAD (Pleasure-Arousal-Dominance) emotion theory, this research extends theoretical insights into consumer behavior within e-commerce live-streaming contexts and provides actionable guidance for optimizing virtual anchor strategies, thereby advancing both standardization and innovation in the industry. Full article

The digital economy plays a pivotal role in advancing green productivity; however, the specific configurations driving this relationship remain underexplored. Employing the TOE theoretical framework alongside k-means clustering and fuzzy-set qualitative comparative analysis (fsQCA), we systematically examine the heterogeneous pathways through which digital economy configurations enhance green productivity in China’s Beijing–Tianjin–Hebei region. The results reveal that (1) green productivity exhibits distinct temporal evolution phases and spatial distribution patterns; (2) five characteristic digital economy city clusters emerge from the clustering analysis; (3) improvements in green productivity require specific synergistic combinations of technological, organizational, and environmental factors; and (4) antecedent conditions demonstrate complex substitution patterns across different development stages. These findings offer a configurational perspective on how digital economy architectures differentially influence regional green productivity development. Full article

The hydrogen fuel cell vehicle (HFCV) market is growing rapidly, but technological limitations, high costs, and market constraints are hindering enterprise performance. Existing studies often analyze isolated factors, overlooking their configurational interactions. This study applies the Technology–Organization–Environment (TOE) framework and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how R&D capability, human capital level, scale of enterprise, attention allocation, and government support shape high performance in 40 Chinese HFCV enterprises. The consistency of all antecedents does not exceed 0.9, indicating that high performance does not depend on any single factor. The sufficiency analysis identifies three effective configurations: technology-driven, internal–external synergy, and organization–policy-driven, with an overall solution consistency of 0.9206 and a coverage of 0.4167. Without adequate government support and human capital, achieving high performance in HFCV enterprises appears improbable. These findings reveal multiple pathways toward high performance and highlight the importance of condition combinations over isolated effects, offering theoretical and practical insights into sustainable development strategies for emerging green industries. Full article