Young Spanish university students facing employment: a qualitative comparative analysis of the conditions that lead to greater participation in selection processes

How digital transformation shapes employee creativity: Insights from the ability-motivation-opportunity framework and qualitative comparative analysis

How does digitalization affect the urban-rural income gap? A study using panel fsQCA and NCA in developing economies

Riding the wind: environmental dynamism and the sustainable takeoff of Chinese electric vehicle manufacturers

Industrial park planning from the perspective of productive service industries: based on the mechanism of “form-flow synthesis”

Green innovation pathways and sustainable development performance in China’s logistics industry: an integration of TOE framework and resource orchestration theory

Factors influencing disinformation avoidance behavior among generative artificial intelligence users: A cognition-affect-conation framework.

Differentiated pathways for enhancing green innovation quality in the automotive industry guided by ESG frameworks and energy policies

Multiple pathways of institutional environment driving the reduction of veterinary antibiotic overuse: Evidence from dairy farmers in Heilongjiang Province, China.

Fraud and improper payments across U.S. federal benefits programs impose substantial fiscal and social costs, with the Government Accountability Office estimating annual fraud-related losses of $233–521 billion and cumulative improper payments exceeding $2.7 trillion since fiscal year 2003. Traditional "pay-and-chase" recovery models have proven insufficient, prompting growing interest in preventive strategies that leverage cross-agency data fusion and predictive intelligence. This paper presents a qualitative comparative analysis of four dominant institutional and technical models used to integrate data and apply predictive analytics for fraud reduction: centralized federal data hubs, federated and privacy-preserving architectures, vendor-managed identity and fraud detection systems, and state-level or program-specific predictive analytics. Each model is evaluated against five criteria derived from federal oversight guidance and academic literature: legal and institutional authority, data governance and privacy protection, technical architecture and scalability, analytic effectiveness, and transparency and accountability. The analysis demonstrates that no single model adequately balances these competing demands. Centralized hubs offer statutory grounding but limited analytic flexibility; federated approaches strengthen privacy at the cost of governance complexity; vendor systems raise accountability concerns; and program-specific models lack cross-jurisdictional reach. The paper proposes a hybrid framework combining centralized authoritative checks, federated analytics for sensitive data, and program-level predictive modeling under unified governance with human-in-the-loop decision-making. Policy recommendations address statutory authorization, oversight structures, model validation, vendor accountability, and workforce capacity to support responsible, equitable, and scalable fraud prevention.

ABSTRACT In its original form, Qualitative Comparative Analysis (QCA) struggles with cases that evolve over time. Temporal QCA (TQCA) and Time-Series QCA (TS/QCA) are among the older variants that try to incorporate time in the comparative analysis. Recently, many more alternative strategies have been proposed. This article provides an overview of all known strategies to account for time and process in QCA, so that researchers can make informed research design choices. We present the main strategies regarding: the research aspect to which it pertains (casing, calibration, and/or truth table analysis), data requirements, the nature of the results, and the type of research questions that can be addressed. The following eight strategies are discussed: a conventional QCA in a mixed-methods design, using temporal conditions, conducting multiple QCA analyses for different time periods, conducting one QCA for different time periods, Trajectory-Based QCA (TJ-QCA), TQCA, TS/QCA, and Linear Growth QCA (LG-QCA).

Amidst growing concerns over research misconduct in academic institutions, this study examines the complex causal mechanisms that influence research integrity among university faculty. In this article, we explore how the interplay between personal attributes and institutional environments influences ethical research behavior, drawing on perspectives from both individual and organizational dimensions. We analyzed data from 109 university teachers across six key conditions, including factors related to individual endowment and the organizational environment, using fuzzy-set Qualitative Comparative Analysis (fsQCA). The results revealed that no single factor is a necessary condition for ensuring research integrity. Four distinct causal pathways were identified as sufficient to promote integrity, reflecting the principle of equifinality. These findings revealed the various combinations of internal and external factors that can yield similar positive outcomes. Based on the results, promoting research integrity requires a contextualized approach, where universities should tailor interventions and institutional policies to the specific characteristics and needs of their faculty members. This configurational understanding enhances theoretical insights into ethical behavior in academic settings and offers practical guidance for fostering a culture of integrity in higher education.

Artificial intelligence (AI)-driven chatbots showed great potential in medical education. However, users' willingness to adopt them varied considerably, hindering their broader implementation. This study aimed to identify the combinations of factors influencing health professional students' use of AI chatbots in learning and to determine the pathways leading to a high willingness to adopt them. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to examine configurations of factors influencing health professional students' high willingness to use AI chatbots, including performance expectancy, effort expectancy, and social influence. Four configurational pathways leading to high chatbot usage intention were identified: (1) Performance expectancy-Effort expectancy-Hedonistic motivation; (2) Performance expectancy-Facilitating conditions-Hedonistic motivation-Higher training levels; (3) Social influence-Facilitating conditions-Hedonistic motivation-Higher training levels; (4) Effort expectancy-Social influence-Hedonistic motivation-Lower training levels-female. The overall solution consistency was 0.94, with a coverage of 0.76, indicating that high usage intention was not attributable to a single factor but emerged from the joint effects of multiple conditions. Notably, hedonistic motivation emerged as a core condition across all four pathways, underscoring its central role in promoting chatbot usage intention. The findings suggested that intervention strategies should account for multiple pathways leading to the intention to use AI chatbots.

BackgroundClinical nurses significantly contribute to sustaining healthcare systems worldwide. However, they experience multiple adverse situations that affect their job satisfaction, which in turn influences the quality of nursing services.AimTo analyze the pathways influencing clinical nurses’ job satisfaction from the perspective of the dual‐factor theory.MethodsWe recruited 202 clinical nurses from August to September 2024 using a convenience sampling method for this cross‐sectional questionnaire survey assessing job burnout, job satisfaction, and fear of missing out. The fuzzy‐set qualitative comparative analysis (fsQCA) method was used to examine the factors, their synergistic effects, and the configurational pathways through which different combinations of factors influence clinical nurses’ job satisfaction.ResultsThe average score for job satisfaction in this study was 60.28 ± 10.51 points. Single‐factor analysis showed that age, fear of missing out, and job burnout were significantly associated with job satisfaction. However, fsQCA necessity analysis indicated that no single factor was a necessary condition for high job satisfaction (consistency < 0.9). The configuration analysis extracted six sufficient condition combinations, categorized into two types: hygiene‐dominated and motivation‐health configurations. Among these, age (as a moderating variable) emerged as a core condition in several configurations.ConclusionThe fsQCA method, grounded in the two‐factor theory, illustrated the influence of multiple interacting and synergistic factors on clinical nurses’ job satisfaction and identified the specific elements contributing to high job satisfaction within various configurations of their roles. This insight helps hospitals and nursing managers identify optimal combination of factors that enhance the stability of clinical nurses’ careers and clarify potential improvement pathways from a configuration perspective. Overall, our findings provide novel evidence and a framework for the development of effective interventions to enhance job satisfaction.

This study investigates the configuration paths of value co-creation in the green supply chains of Chinese new energy vehicle (NEV) enterprises. Drawing on value co-creation theory, a four-dimensional analytical framework comprising consensus, sharing, resonance, and integration is developed to explain multi-actor collaboration under sustainable development. Using fuzzy-set qualitative comparative analysis (fsQCA) based on data from 18 representative NEV enterprises, the study identifies four synergistic configurations-Digital Leverage, Collaborative Dual-Drive, Digital Sharing, and Consensus-Driven-that lead to high-level green supply chain development. The results reveal that consensus, sharing, and resonance frequently co-occur as mutually reinforcing conditions, while information-sharing platforms constitute the core infrastructure for effective collaboration. Moreover, the findings challenge the prevailing assumption that green transformation requires short-term profit sacrifice, demonstrating the feasibility of achieving both sustainability and profitability through coordinated multi-actor value co-creation.

ABSTRACT Tourism is increasingly viewed as a context that may support psychological well-being, yet little is known about how travel functions following emotional disruption. Drawing on the process model of emotion regulation, this study examines how tourism may operate as a situational resource for emotional adjustment after romantic relationship dissolution and how regulatory processes during travel are associated with psychological recovery and posttraumatic growth. Using survey data from 607 individuals who travelled after a recent breakup, the study integrates structural equation modelling, serial mediation analysis, fuzzy-set qualitative comparative analysis, and analytical case evidence. Results suggest that emotional distress does not directly determine recovery or growth. Instead, distress appears to initiate divergent regulatory trajectories shaped by rumination and self-concept clarity, which are associated with outcomes through cognitive reappraisal and perceived restorativeness. By conceptualising post-breakup travel as a conditional situational resource, this study advances process-oriented tourism research and extends emotion regulation theory to self-selected travel contexts.

Managers are increasingly confronted with decisions, particularly in environments marked by uncertainty, ethical dilemmas, and competing priorities. These compressed, high-stakes “micro-moments” expose not only technical skills but also the deeper dispositions of managerial character. Yet, despite recognition of character as a vital dimension of leadership, research in management studies has tended to reduce it to traits or virtues, leaving underexplored how character is enacted and cultivated in practice. This study addresses that gap by examining how attention, critical reflection, and peer learning, embedded in a film-based pedagogy, foster the development of managerial character. Drawing on social learning theory, we position film as a pedagogical “safe laboratory” that immerses managers in vivid, emotionally engaging scenarios of hard decision-making, provoking dialogue and reflective inquiry without real-world consequences. Using a mixed-method design, including phenomenological interviews and fuzzy-set Qualitative Comparative Analysis (fsQCA) of survey data from 110 managers, we identify multiple causal configurations that lead to heightened motivation to apply new decision-making skills in the workplace. Our findings advance a processual view of character, showing it to be dynamic and socially constructed rather than a fixed attribute. We demonstrate how cognitive and relational mechanisms, particularly reflective dialogue and peer interaction, translate film-based experiences into the enactment of dispositional qualities such as humanity, humility, and integrity. For management education, this highlights the value of pedagogies that combine attention, reflection, and social learning to cultivate character in ways conventional analytical tools neglect. By reframing character as something performed and refined in micro-moments of choice, this study contributes both theoretically and pedagogically. It offers educators and organizations actionable pathways to prepare leaders capable of navigating ambiguity with judgment, resilience, and integrity.

How AI coach voice anthropomorphism influences exercise adherence: A PLS-SEM and fsQCA analysis.

Will the advancement of GAI diminish international students' reliance on Chinese language teachers? Evidence from SEM and FsQCA.

Type of the article: Research ArticleAbstractAs the global economy increasingly emphasizes sustainable development, small and medium-sized enterprises (SMEs) in emerging economies face growing pressure to integrate environmental responsibility with long-term business performance. This study aims to examine how environmental leadership and corporate social responsibility contribute to sustainable success in women-owned SMEs through the roles of green innovation and green competitive advantage. The paper analyzes survey data collected from 536 female business leaders in Vietnam using partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis. The results reveal that environmental leadership and corporate social responsibility do not exert a direct influence on sustainable success (p &amp;amp;gt; 0.05). Instead, their effects operate through a full serial mediation mechanism in which corporate social responsibility stimulates green innovation, which subsequently strengthens green competitive advantage and ultimately enhances sustainable success. The structural model explains 52.1% of the variance in sustainable success. In addition, market orientation significantly strengthens the relationship between green competitive advantage and sustainable success (β = 0.176, p &amp;amp;lt; 0.001). Configurational analysis further identifies two alternative strategic pathways leading to high sustainable success, both highlighting the central role of green innovation and green competitive advantage. These findings demonstrate that ethical leadership values must be transformed into concrete green capabilities to generate sustainable competitive performance in resource-constrained environments.AcknowledgmentThis research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 504.05-2023.05.