Generative AI holds promise for venture-creation curricula, yet faculty adoption remains hindered by poorly understood incentives and barriers. This study employs a three-stage mixed-methods design to clarify those drivers. A systematic review identified 28 factors, refined by expert panel to 16 key variables. A fuzzy-DEMATEL survey revealed that faculty training, institutional support, and curricular integration exert the strongest causal influence. Clustering these factors yields three intervention domains—pedagogical, organizational, and technological—suggesting a phased adoption strategy. This framework shifts focus from tool access to educator-led implementation, offering academic leaders an evidence-based roadmap for cost-effective AI integration.

As artificial intelligence (AI) has undergone rapid development, large-scale financial models have become the core support mechanisms for the digital intelligence transformation of financial institutions. With the deep integration of AI technologies such as generation algorithms, pretraining models, and multimodal data analyses, their application scenarios in finance continue to expand, and they have been widely applied in intelligent investment decision-making, intelligent customer service, intelligent risk prediction, etc. In this paper, a financial technology company is used as an example to analyze the logic of large model architecture designs in detail. Seven large models are selected from the financial field, six aspects of evaluation tasks are designed, and the ability of large models to perform financial tasks is evaluated, considering that during the rapid evolution process, large models face pressure related to computing power costs, model accuracy, data security, privacy protection, etc.

As digital transformation (DT) accelerates, ensuring its quality has become a pressing challenge for firms. This study explores how executives' IT imprints affect DT quality by drawing on data from Chinese listed companies between 2008 and 2023. Executives' personal resumes are manually collected to identify IT imprints, and text analysis is employed to quantify DT quality, with the underlying mechanisms further tested. Empirical results show that IT imprints significantly improve DT quality, with IT work experience exerting stronger influence than IT education. Moreover, the effect is amplified when executives hold greater power, demonstrate higher stability, or when firms possess abundant redundant resources. Mediating analysis indicates that IT imprints cultivate forward thinking and innovative awareness, thereby enhancing DT quality. This study extends imprinting theory to the digital era and offers managerial implications for executive recruitment and talent management in driving digital transformation.

In the context of globalization, how enterprises can achieve high-value creation through cross-border collaborative innovation (CbCI) is a pressing issue. Drawing on social network theory and resource orchestration perspective, this study employs Fuzzy-set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) to analyze the complex relationships among network structure, network relationships, and network participants' characteristics from a configurational perspective on CbCI value creation. The findings are as follows: (1) Individual antecedent conditions are not necessary conditions that affect CbCI value creation. (2) Top management teamwork capabilities and network relationship management drive CbCI value co-creation; poor relationship management and prominent competitive relationships, combined with unfavorable network positions, excessive cooperative motivations or network diversity, lead to CbCI value co-destruction. The findings offer important implications for enterprises to achieve value co-creation and avoid value co-destruction in CbCI.

In the context of deepening digital integration in manufacturing and the growing emphasis on sustainable development, this study investigates how human–digital symbiosis (HDS) influences employees' organizational perceived green intelligent manufacturing performance. Using robust organizational-level data from two-year, multi-period surveys of 315 manufacturing firms in China's Yangtze River Delta, the authors find that HDS significantly enhances organizational green intelligent manufacturing outcomes. This effect is further strengthened by organizational digital technology acceptance, digital technology proficiency, and firms' financial slack. Heterogeneity analysis reveals variation across job positions, an inverted U-shaped relationship with firm sizes, and stronger effects in high-tech industries. Robustness checks confirm the validity of these findings. Moving beyond unidirectional digital empowerment, this study conceptualizes HDS as a dual enablement mechanism, offering new theoretical insights and practical guidance for firms simultaneously pursuing green and digital transformation.

This study examines how regional rule of law affects corporate supply chain resilience. Drawing on contract theory and institutional economics, the authors argue that stronger rule of law enhances resilience through direct and indirect pathways, with intellectual property protection as a key mediator. Using 18,450 firm-year observations from Chinese listed firms (2010–2020), they find that a one-standard-deviation improvement in provincial rule of law is associated with a 0.30 standard deviation increase in supply chain resilience, with IP protection transmitting 39.3% of this effect. Heterogeneity analyses reveal stronger effects among SMEs, non-state-owned enterprises, and firms in less-developed regions. These findings remain robust across alternative measurements and specifications. The study contributes to the dialogue between institutional economics and operations management by demonstrating that macro-level legal institutions shape micro-level operational outcomes.

In developed countries, a significant gap persists in the absence of empirical, survey-based rubrics to measure AI's technical characteristics and predictive analytics in the public service sector. This study fills this gap by developing and validating survey-based rubrics through a comparison between Canada and the United States (US). Following MacKenzie, Shiau, and Huang's scale development procedures, this research utilized data from Canadian and US government AI websites twice, employing structural equation modeling (SEM) and PLS methods to examine the scale properties and test the relationships between AI's technical characteristics and predictive analytics. Findings show that supervised and unsupervised machine learning, along with deep learning, are positively associated with predictive analytics across public service sectors in both countries. Conversely, artificial neural networks are not positively associated with predictive analytics in Canada, whereas they are in the US. The relationship between artificial neural networks and predictive analytics varies across countries.

Live streaming commerce, integrating real-time interaction with online retailing, is reshaping consumer behavior. While prior studies have focused on sales drivers, how to attract viewers remains underexplored. This study fills this gap by analyzing how short videos drive traffic acquisition in live streaming commerce, using a large Douyin dataset and OLS regressions with multiple robustness checks. Results show that sociability (likes, comments) and demographic match between streamers and viewers (gender, geography) enhance traffic acquisition, whereas commercialization through sponsored ads weakens it. The interval between video release and live streaming shows an inverted U-shaped effect on traffic acquisition. Moreover, streamer influence dampens the positive effects of sociability and matching, while production input mitigates the negative impact of commercialization. This study advances research on short video marketing and live streaming commerce and offers guidance for global digital commerce participants to optimize short video strategies for greater engagement and value.

Artificial intelligence-enabled diagnostics promise to transform hearing healthcare, yet real-world adoption remains limited. This study identifies and prioritizes barriers to AI integration in clinical audiology through a three-phase mixed-methods approach. Phase I reviewed literature, surfacing 20 obstacles across workflow, infrastructure, culture, and ethics. Phase II involved expert interviews, refining these into nine context-specific barriers. In Phase III, a fuzzy-DEMATEL survey and thematic coding revealed a causal hierarchy: algorithmic inaccuracy, privacy concerns, and lack of training erode clinician trust and widen the knowledge gap. Cost, integration issues, and resource limitations add systemic stress, while ethical concerns emerge downstream. Cluster analysis groups the barriers into three blocs: Clinical Workflow, Governance and Trust, and Tech Infrastructure. This is the first study to apply fuzzy-DEMATEL to AI barriers in audiology, producing a causal map and cluster framework that offer both theoretical insights and practical guidance for adoption strategies.

Generative AI can enhance venture creation education, yet faculty adoption remains limited. This study explores why through a three-stage mixed-methods approach. Stage 1 reviewed 2020–25 literature to identify 23 barriers across pedagogical, technical, institutional, and ethical domains. Stage 2 involved interviews with experienced entrepreneurship educators, refining and reducing the list to 15 context-specific challenges. Stage 3 used a fuzzy-DEMATEL survey to capture expert causal judgments, while thematic coding of interviews added narrative depth. The resulting influence map highlights a clear hierarchy: lack of staff training, unclear governance, and weak technical support are key upstream barriers, while concerns like plagiarism and over-reliance are downstream effects. Cluster analysis groups drivers into pedagogical, organisational, and infrastructural clusters, suggesting a phased response: begin with training and transparent policy, then invest in tools and assessments.