Search Results (459)

Artificial intelligence (AI) has become increasingly embedded in technology-enhanced learning environments, where peer assessment now serves both instructional and analytic purposes. Beyond allocating feedback and grades, it also produces data that is later interpreted through learning analytics systems. In practice, visible indicators such as students’ fairness perceptions and the degree of agreement among peer raters are often treated as signs that the assessment process is functioning effectively. However, these indicators do not necessarily correspond to grading validity. Students may regard a peer assessment process as fair even when peer-generated ratings remain weakly aligned with expert judgement. This study, therefore, examines whether the socio-technical configurations associated with high perceived fairness in a digitally mediated peer assessment environment also correspond to criterion-referenced grading validity. Data were collected from 215 undergraduate students enrolled in an Artificial Intelligence Foundations course over two consecutive semesters at a university in Taiwan, with instructor ratings serving as an external expert reference within the course context, rather than as a universal ground truth. Because anonymity conditions and semester were fully confounded in the study design, differences linked to anonymity should not be interpreted as isolated causal effects. A two-stage fuzzy-set Qualitative Comparative Analysis (fsQCA) was used. In the first stage, three equifinal configurations associated with high perceived fairness were identified. In the second stage, these configurations were examined against four grading objectivity outcomes: peer–instructor alignment, peer convergence, familiarity bias, and leniency bias. The findings show that fairness perception and grading validity are only partially aligned. Configurations anchored in explicit criterion transparency consistently supported both experiential legitimacy and evaluative accuracy. By contrast, one configuration was associated with high peer convergence while remaining weakly aligned with instructor standards, a pattern described here as false objectivity; this context-dependent configurational finding warrants further investigation across other settings. The study contributes to research on digitally enhanced assessment and learning analytics by showing that fairness perception, peer convergence, and grading validity should be treated as analytically distinct dimensions of assessment quality. Full article

The transition to a circular economy creates winners and losers, challenging the assumption that green growth is inherently inclusive. While environmental benefits are documented, the distributional consequences remain poorly understood. This study analyzes how socioeconomic and labor inequalities shape the effectiveness of circular economy policies. Using panel data from 90 countries (2019–2024) combined with global governance indicators, we employ fixed-effects models, instrumental variables, and configurational analysis (fsQCA) to identify causal mechanisms. The results reveal a critical institutional threshold: circular economy policies reduce inequality only in countries with high institutional quality (WGI > 1.39). In contexts with weak institutions or positive Skill Structure Balance (SSB), these policies are regressive. Social protection and digital financial inclusion moderate these effects, acting as buffers against distributional risks. These findings challenge the “automatic social benefits” narrative, suggesting that environmental ambition requires parallel investments in institutional capacity and human capital to achieve a just transition. Full article

Despite the popularity of AI-powered virtual streamers in live streaming commerce as persistent and customizable digital intermediaries, the dynamic role of virtual streamer features across the decision journey remains unclear. Grounded in the integrated AIDA-HSM framework, this study aims to systematically investigate the multi-stage mechanism through which virtual streamer features guide consumers from attention to action in virtual live streaming commerce (VLSC) marketing. We adopt a three-stage hybrid research approach, integrating a systematic literature review, structural equation modeling (SEM), and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results reveal the differential impact of distinct virtual streamer features across various stages of the consumer journey. Furthermore, the fsQCA indicates that every sufficient configuration must draw upon factors from each of the AIDA stages. This study not only pioneers the validation and contextualization of the AIDA-HSM framework in VLSC marketing, but also offers actionable guidance for practitioners to optimize their virtual streamer strategies. Full article

The research landscape in tourism and hospitality often feels like a house divided. On one side, there is the quantitative camp searching for broad, linear patterns; on the other side, there are qualitative scholars who prefer deep, contextual dives. This division suggests that scholars may overlook valuable insights in the middle. Therefore, this study examines how Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) are transforming the landscape and bridging the methodological and analytical divide. For this purpose, authors analyzed 91 peer-reviewed articles using PRISMA 2020 systematic review principles from six databases. The findings highlight that this multi-methodological triangulation addresses causal asymmetry, acknowledging that the drivers of success are not necessarily mirror images of those of failure. The study implies that, in theory, it bridges the gap between qualitative nuance and quantitative rigor, moving from universal linear assumptions to complexity theory. Methodologically, it allows for a prioritized roadmap in which NCA pinpoints exact operational thresholds and fsQCA provides strategic flexibility. In practice, the findings offer a two-tiered decision-making framework for industry managers: first, addressing non-negotiable bottlenecks, and second, selecting the strategic configuration that best aligns with their unique resource base. The review concludes that, while challenges such as data calibration and interpretative complexity remain, integrating these paradigms offers a more authentic and comprehensive understanding of the volatile landscape of tourism and hospitality. Full article

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

The sustainable enhancement of regional science and technology industrial competitiveness is an important objective of artificial intelligence (AI) policy. However, how different AI policy instruments can be combined to achieve this goal remains insufficiently understood. This study aims to address this issue by identifying the configurational pathways through which combinations of AI policy instruments contribute to the sustainable enhancement of regional science and technology industrial competitiveness. Based on a policy instrument framework, we analyze AI policies issued by provincial-level governments in China and apply fuzzy-set qualitative comparative analysis (fsQCA), which is appropriate for examining the combined effects of multiple policy instruments. The results show that no single policy instrument is sufficient to produce high regional science and technology industrial competitiveness. Instead, sustained competitiveness is achieved through multiple equivalent configurations of policy instruments. Three driving pathways are identified—(supply and demand)-environmental resonance, demand-driven (supply-environmental) assurance, and supply–demand complementarity—covering five specific configurations. The variation across configurations indicates that effective AI policy mixes are contingent on regional resource endowments and development conditions. Technology R&D support, talent cultivation and collaboration, and application demonstration and promotion emerge as the most recurrent core conditions across configurations. Accordingly, local governments should develop coordinated AI policy mixes, align differentiated policy pathways with regional conditions, and prioritize technology R&D support, talent cultivation and collaboration, and application demonstration and promotion to sustain long-term regional competitiveness. Full article

In recent years, cross-border e-commerce and digital trade activities in transition economy countries and regions have continued to grow. Based on resource orchestration theory and empowerment theory, this paper examines the influence mechanism of digital leadership on international entrepreneurial performance and investigates the moderating effect of platform support. Analyzing survey data from 227 Chinese cross-border e-commerce enterprises using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the study finds that: (1) Digital leadership positively influences the international entrepreneurial performance of cross-border e-commerce enterprises through the mediating roles of brand management capability and product innovation capability; (2) Platform support plays a positive moderating role in the relationship between brand management capability and international entrepreneurial performance in cross-border e-commerce; (3) Platform support moderates the mediating effect of brand management capability in the relationship between digital leadership and international entrepreneurial performance of cross-border e-commerce enterprises; (4) Based on fsQCA analysis, two antecedent configurations for achieving high international entrepreneurial performance in cross-border e-commerce are identified. These findings hold significant theoretical implications for research on cross-border digital platforms and international new ventures, while also providing robust empirical support for enterprises seeking to achieve international entrepreneurial success through the implementation of digital strategies. Full article

While GenAI is transforming education, it remains unclear how it shapes students’ behavior, especially concerning AI literacy. The purpose of this study is to examine which factors positively affect students’ learning behavior and whether AI literacy moderates this effect, using the COM-B model. An online survey of 438 participants was analyzed using covariance-based structural equation modeling (CB-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The CB-SEM results indicate that independent learning ability, receptive ability, learning environment, AI support equipment, and both intrinsic and extrinsic motivations significantly shape student learning behavior. Notably, AI literacy moderates the relationship between GenAI and learning behavior. Furthermore, fsQCA reveals seven configurations of these factors that favorably impact learning behavior. Together, these findings provide theoretical and practical insights for universities, highlighting actionable ways universities can support students’ adoption of GenAI. Full article

The escalation of geopolitical tensions has forced global manufacturers to reconfigure their supply chains. While Digital Transformation (DT) is widely touted as a primary driver of resilience, traditional variance-based research often assumes a symmetric, linear relationship that applies universally across firms. This study challenges this assumption through the lens of Complexity Theory. Viewing supply chains as Complex Adaptive Systems (CASs), we employ Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on a stratified sample of 928 manufacturers in a geopolitical high-risk zone (Taiwan). We identify equifinal pathways to Organizational Resilience, revealing a fundamental asymmetry between organizational types. The results suggest that while large enterprises rely on a resource-intensive strategy—which we term the “Digital Fortress” configurational metaphor (combining high digital maturity and agility as a core condition)—SMEs can achieve high resilience through an “Agile Dodger” configuration, leveraging operational agility and niche positioning without necessitating high digital maturity. This study contributes to the systems literature by mapping the “topology of resilience” and offering tailored configurational pathways that complement traditional variance-based perspectives in volatile ecosystems. Full article

Green innovation is central to industrial ecological transition, yet heavily polluting firms often exhibit low-quality green innovation investment. Grounded in the government–enterprise–research–intermediary Quadruple Helix innovation ecosystem framework, this study integrates Necessary Condition Analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) to examine 66 publicly listed heavily polluting manufacturing firms in China. The results show that fiscal subsidies and environmental taxes are necessary but not sufficient conditions for achieving high-quality green innovation investment. Moreover, high-quality outcomes arise through three equifinal pathways: the Government–Intermediary Dual-Drive Model, the Government–Enterprise–Intermediary Co-Directional Model, and the Government–Enterprise Symbiotic Model. Six configurations lead to non-high-quality green innovation investment, which cluster into Resource-Scarcity and Regulatory-Constrained models. A favorable macro environment further strengthens high-quality outcomes. These findings clarify how policy instruments and multi-actor collaboration jointly shape green innovation investment quality and provide actionable implications for heavily polluting firms and policymakers seeking sustainable development. Full article

Under the dual strategic imperatives of carbon neutrality and digital transformation, the power industry plays a pivotal role in advancing green and low-carbon development. Green Total Factor Productivity (GTFP) provides a comprehensive measure of efficiency in the power sector under energy and environmental constraints. Using panel data from 31 Chinese provinces over the period 2012–2023, this study employs a super-efficiency Slacks-Based Measure (SBM) model, kernel density estimation, standard deviation ellipse analysis, the Gini coefficient, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the spatiotemporal evolution, regional disparities, and digital-driven improvement pathways of power industry GTFP. The results indicate that national power-sector GTFP exhibits a fluctuating upward trend, accompanied by pronounced regional heterogeneity. A distinct spatial pattern has emerged, characterized by rapid improvement in the western region, relative stability in the eastern region, contraction in the central region, and persistent lagging in the northeastern region. Spatially, the distribution has evolved from an initial east–west dual-core structure to a three-tier gradient pattern led by the west, stabilized in the east, and depressed in the central region. Kernel density estimation reveals a clear multi-peak polarization trend, while standard deviation ellipse analysis shows a relatively stable spatial center with continuously expanding dispersion along the northeast–southwest axis. Further analysis demonstrates that interregional differences remain the primary source of overall inequality, with rapidly widening intraregional disparities in the western region. Configurational analysis identifies five digital-economy-driven pathways to high GTFP, highlighting that no single optimal configuration exists. Instead, multiple combinations of technological, organizational, and environmental conditions jointly facilitate GTFP enhancement. These findings provide empirical evidence to support differentiated and precision-oriented policy design for promoting coordinated digital transformation and green development in China’s power industry. Full article

With the increasing constraints of resource and environmental factors and the prominent issues of regional development imbalance, how to scientifically measure the level of agricultural sustainable development and reveal its spatial-temporal differentiation patterns has become a key scientific question that urgently needs to be addressed in optimizing land use layout and promoting rural revitalization. This study takes the human-land spatial systems coupling theory as the core framework and constructs an evaluation index system for agricultural sustainable development covering five dimensions: economy, society, resources, ecology, and technology. Based on provincial panel data in China from 2001 to 2024, the entropy method is employed to measure agricultural sustainable development, while Dagum’s Gini coefficient, kernel density estimation, and convergence models are applied to analyze its spatial–temporal evolution. Furthermore, the fuzzy-set qualitative comparative analysis (fsQCA) method is introduced to identify multi-factor configurational driving pathways. The results indicate that the overall level of agricultural sustainable development in China shows a steady upward trend, exhibiting a regional gradient pattern characterized by “central region leading, eastern region steadily advancing, and western region gradually catching up”. The overall disparity presents a weak convergence trend, with inter-regional differences as the primary source, although their contribution is gradually declining. The development structure has evolved from regional fragmentation to a more complex spatial interaction pattern. The overall distribution shifts rightward with evident stage-based differentiation, accompanied by significant positive spatial dependence, with “high–high” and “low–low” clustering coexisting over the long term. Convergence analysis shows that σ-convergence exists at the national level. After accounting for spatial effects, significant absolute β-convergence is observed in the eastern and western regions, while the central region does not exhibit significant convergence. Conditional β-convergence further confirms the existence of regional convergence trends, although the convergence speeds vary. The fsQCA results indicate that agricultural sustainable development is not driven by a single factor but by multiple configurational pathways formed through the interaction of various conditions. These findings provide empirical evidence for optimizing agricultural spatial layout, strengthening land factor support, and promoting regionally coordinated agricultural sustainable development. Full article

Achieving high-quality employment for retired athletes is essential for promoting the holistic development of athletes and accelerating the construction of a strong sports nation. From the perspective of capital collaboration, this study develops a comprehensive analysis framework by incorporating human capital, social capital, and psychological capital to systematically investigate the influencing factors and configuration pathways for high-quality employment of retired athletes. Utilizing Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study discovers three main findings. First, no single condition variable independently constitutes the necessary condition for high-quality employment. Second, three configuration pathways for achieving high-quality employment are identified, including human capital–social capital synergy, human capital–psychological capital synergy, and human capital–social capital–psychological capital integration. Third, vocational skill, as a component of human capital, emerges as an important condition in configurations associated with high-quality employment. Based on the findings, this research recommends improving the athlete security policy system, promoting the accumulation of human capital, strengthening the development of psychological capital, constructing diverse social support networks, and optimizing the pathways for retired athletes to achieve high-quality employment. These aims will support retired athletes in navigating career transitions effectively while securing stable and high-quality employment. Full article

As artificial intelligence (AI) changes educational practices, understanding what sustains pre-service teachers’ generative AI use beyond initial adoption becomes important. However, existing research mainly focuses on initial acceptance rather than continuance intention, which is a more realistic indicator for sustainable technology integration. This study drew on an integrated framework including psychological (GAI anxiety, GAI self-efficacy), contextual (facilitating conditions, social influence), and perceptual factors (perceived ease of use, perceived usefulness) to examine pre-service teachers’ continuance intention toward GAI in future teaching. Survey data from 549 Chinese pre-service teachers were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Results showed that GAI self-efficacy had the strongest positive associations with both perceived ease of use and perceived usefulness. GAI anxiety negatively influenced both perceptions. However, facilitating conditions did not significantly relate to perceived usefulness. The fsQCA identified six configurational pathways clustered into the following three patterns: intrinsic value driven, efficacy capability driven, and external support driven. These findings suggest that teacher education programs should prioritize building GAI self-efficacy and supportive peer environments and not focus solely on infrastructure provision. Full article

While existing research analyzes AI’s impact on platform enterprises’ competitive advantages from technological or organizational perspectives, it fails to adequately account for how multiple factors combined shape competitive advantages. From the perspective of situated AI theory, this study examines how the combinations among AI application characteristics, situated AI activities, platform enterprise attributes, and environmental characteristics collaboratively build platform enterprises’ competitive advantages. Drawing on panel data from Chinese listed platform enterprises and employing fuzzy-set Qualitative Comparative Analysis (fsQCA), this study reveals that (1) AI technology innovation and recasting AI are necessary conditions for platform enterprises to establish competitive advantages; (2) AI-enabled competitive advantages emerge from three types of configurations, the situated AI dominance type, the situated AI subsidiary type, or the collaborative drive type; (3) the AI-enabled combinations result in competitive advantages by three paths, AI internalization, AI leverage, and AI collaboration; and (4) the AI-enabled competitive advantages are more likely to be achieved by innovation platforms than by transaction platforms. These research findings fill the knowledge gap in AI-enabled competitive strategy, enrich the literature on situated AI theory, and offer practical guidance for platform enterprises’ AI applications. Full article