Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies.

E-commerce virtual anchors’ live-streaming sales represent a critical intersection of virtual human technology and artificial intelligence (AI), paving the way for new developments in digital marketing. Recent advancements in AI have significantly enhanced the intelligence of virtual anchors, driving increased consumer acceptance. As intelligence is a key determinant of user adoption, this study employs a mixed-methods approach combining grounded theory and quantitative analysis to conceptualize, structure, and measure virtual anchor intelligence. The grounded theory results reveal that virtual anchor intelligence encompasses multiple capabilities—manifested in guidance, recognition, analysis, and feedback during human–computer interaction, which enable effective hosting and sales performance. Four core dimensions of intelligence are identified: guidance intelligence, recognition intelligence, analysis intelligence, and feedback intelligence. Following established protocols for scale development, we constructed and validated an 18-item measurement scale, demonstrating strong reliability and validity. Empirical findings indicate that while guidance intelligence exerts an adverse effect on consumer participation, the remaining dimensions have positive effects, mediated by perceived shopping value. This study provides a comprehensive framework for understanding and measuring virtual anchor intelligence, elucidating its underlying mechanisms. The findings lay a theoretical foundation for future research on e-commerce virtual anchors but also offer practical implications for optimizing live-streaming strategies and advancing the design of virtual humans.

Media convergence is considered an effective way to alleviate the decline of traditional media audiences. Although media convergence has nearly swept through the world’s foremost traditional media, studies reflecting its effects on individuals at the micro level are lacking. Therefore, this study established a multidimensional perceived media convergence scale to measure the actual perception of media convergence from the audience’s perspective. To achieve this, a comprehensive field survey was conducted in a county in southwestern China, yielding a sample of 966 participants. Study 1 used exploratory factor analysis and a multidimensional graded response model (MGRM) to optimize the item pool. Study 2 then tested the reliability and validity of the scale using confirmatory factor analysis and a MGRM. Moreover, the entire sample was tested and analyzed for the measurement accuracy of this scale. The final reliable scale contains 24 items in six dimensions: communication power, influence power, guiding power, credibility power, political propaganda power, and service power. The scale is double validated and has good robustness and extensibility. Based on this scale, future research can provide a deep understanding of which groups of people perceive media convergence in what way and conduct causal inference or mechanism analysis in conjunction with the audience’s behaviors.

With the rapid development of e-commerce, Taobao Villages have emerged as a representative form of rural e-commerce in China, exerting a profound influence on rural economic transformation and urban–rural integration. However, their spatiotemporal distribution is uneven and exhibits a complex interaction with urbanization. Drawing on data from 178 cities between 2017 and 2022, this study employs the Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM) to examine both the direct and spillover effects of urbanization on Taobao Villages. The results indicate that Taobao Villages display significant spatial clustering across China. While urbanization exerts a positive short-term effect on the number of local Taobao Villages, this effect weakens in the long term and under spatial interaction, and higher levels of urbanization in one region impose significant negative spillover effects on neighboring areas. These findings highlight the dual nature of urbanization in simultaneously promoting and constraining rural e-commerce development. Accordingly, policy efforts should focus on rational administrative spatial adjustment and boundary reorganization, optimizing the urban–rural spatial structure, supporting the development of peripheral and disadvantaged regions, enhancing the balanced and efficient flow of factors across regions, and fostering differentiated development pathways to strengthen the resilience of Taobao Villages and promote healthy and sustainable urban–rural integration.

Consumer digital hoarding is becoming increasingly common in agricultural live social e-commerce, where the abundance of product information, seasonal promotions, and origin-based narratives make consumers more inclined to accumulate digital content such as product links, coupons, and live-stream screenshots. This phenomenon not only affects consumers’ digital mental health, consumption behavior, and decision-making ability, but also poses challenges to agricultural merchants and platforms in terms of customer conversion, precision marketing, and supply chain management. Drawing on the SOR model and integrating construal level theory, this paper constructs a research framework to analyze the key factors influencing consumers’ willingness to digitally hoard in the context of agricultural live social e-commerce. Based on a questionnaire survey of 322 consumers, and using the Ordered Probit (O-Probit) model, the empirical results show that emotional interaction significantly influences digital hoarding intention through the chain mediating effects of emotional attachment and fear of missing out (FOMO). Furthermore, social distance and immersion serve as boundary conditions in this mechanism. Our findings not only deepen the understanding of consumer digital hoarding behavior in agricultural live e-commerce, but also provide new insights for agricultural merchants and platforms to better design interaction strategies, balance consumers’ digital accumulation with actual purchasing conversion, and enhance the efficiency of agricultural product marketing.