Purpose: Based on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors, and SC characteristics.Design/methodology/approach: Drawing on the innovation diffusion theory, a model consisted of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data was collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.Findings: The empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and SC connectivity, can significantly moderate the direct relationships between driving factors and the adoption intention.Research limitations/implications: Given the fact that big data (BD) usage in logistics and SC management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.Practical implications: This paper tests the organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.Social implications: This research helps top managers assess the benefits of BDA as well as how to adjust their business strategy along the changes of environment and SC maturity.Originality/value: This paper contributes to the literature of organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.

As a representative of the sharing economy business model, TikTok, a short-form mobile video-sharing social networking platform, has numerous consumers worldwide. Based on the background of the development of information technology and value co-creation theory, this paper analyzes the relevance between information and communication technology and the sharing economy; studies the internal mechanism between consumer participation behaviors and consumer stickiness under different emotions; and expounds the impact of consumer participation behaviors on consumer perceived value. The structural equation model was used to analyze 902 questionnaires collected from TikTok consumers. The research results reveal that positive consumer participation behavior has a positive effect on consumer perceived value, negative consumer participation behavior harms consumer perceived value, and consumer perceived value has a positive impact on consumer happiness and stickiness. Consumer happiness plays an intermediary role between consumer perceived value and consumer stickiness.

In recent times, there has been a growing interest in the role of food trade in achieving food security. This study presents a comprehensive analysis of the relationship between food trade and food security within the context of the Belt and Road Initiative (BRI). This study employs complex networks alongside the quadratic assignment procedure (QAP) regression method on trade data of 46 countries under BRI for the period 2008 to 2018. The resulting complex network showed an enhancement in the food trade relationships and food security levels after BRI, indicating a mutually positive effect. In addition, the closeness centrality measure showed a strong correlation with food security, indicating that food trade has positive spillovers on food security in the proximate countries. Using geographical distance as an instrumental variable, the QAP regression model proved the positive effects of food trade on food security. More investment in trade infrastructure is recommended to reduce the negative effect of geographical distance on food security.

Generative artificial intelligence (GenAI) is increasingly integrated into higher education, where it supports writing, feedback, problem solving, and research-related tasks while also raising concerns about cognitive offloading and learner dependence. This scoping review maps the literature on the relationships among GenAI, cognitive offloading, and learner agency in higher education. Following PRISMA-ScR guidance, searches were conducted on 24 June 2026 across six database sources: Web of Science Core Collection, Scopus, EBSCOhost ERIC, EBSCOhost Education Source, APA PsycInfo via ProQuest, and ProQuest Education Collection via ProQuest Social Science Premium Collection. English-language peer-reviewed or scholarly journal literature published from 2022 to 24 June 2026 was eligible for inclusion. Data were charted using a structured extraction matrix and synthesized through descriptive mapping and theory-informed configurative synthesis. After duplicate removal, 5327 records were screened by title and abstract, 691 full-text reports were assessed, and 123 studies were included in the final charting matrix and synthesis. The review shows that learner agency is conceptualized as a multidimensional construct involving self-regulation, reflective judgement, intentionality, and responsible action. Across the literature, GenAI is best interpreted through an interpretive dual-pattern account: agency-supportive patterns are associated with self-regulated learning, self-efficacy, feedback literacy, and reflective engagement, whereas agency-eroding patterns are associated with cognitive offloading, overreliance, dependence, uncritical uptake, and weakened judgement. These patterns should be interpreted as a configurative synthesis of heterogeneous evidence rather than as causal effect estimates. Overall, the findings suggest that the educational value of GenAI depends less on the technology itself than on how it is pedagogically embedded, with augmentation-oriented and scaffolded use being more supportive of learner agency than replacement-oriented use.

With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors.