In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.

With the widespread application of AI-generated content (AIGC) tools in creative domains, users have become increasingly concerned about the ethical issues they raise, which may influence their adoption decisions. To explore how ethical perceptions affect user behavior, this study constructs an ethical perception model based on the trust–risk theoretical framework, focusing on its impact on users’ adoption intention (ADI). Through a systematic literature review and expert interviews, eight core ethical dimensions were identified: Misinformation (MIS), Accountability (ACC), Algorithmic Bias (ALB), Creativity Ethics (CRE), Privacy (PRI), Job Displacement (JOD), Ethical Transparency (ETR), and Control over AI (CON). Based on 582 valid responses, structural equation modeling (SEM) was conducted to empirically test the proposed paths. The results show that six factors significantly and positively influence perceived risk (PR): JOD (β = 0.216), MIS (β = 0.161), ETR (β = 0.150), ACC (β = 0.137), CON (β = 0.136), and PRI (β = 0.131), while the effects of ALB and CRE were not significant. Regarding trust in AI (TR), six factors significantly negatively influence it: CRE (β = −0.195), PRI (β = −0.145), ETR (β = −0.148), CON (β = −0.133), ALB (β = −0.113), and ACC (β = −0.098), while MIS and JOD were not significant. In addition, PR has a significant negative effect on TR (β = −0.234), which further impacts ADI. Specifically, PR has a significant negative effect on ADI (β = −0.259), while TR has a significant positive effect (β = 0.187). This study not only expands the applicability of the trust–risk framework in the context of AIGC but also proposes an ethical perception model for user adoption research, offering empirical evidence and practical guidance for platform design, governance mechanisms, and trust-building strategies.

This study explores the impact of artificial intelligence (AI) on the efficiency of 3D animation production through Network Data Envelopment Analysis (NDEA). While AI’s adoption in content creation is on the rise, its actual effect on different production stages remains unclear. This research examines ten animation projects from commercial, educational, and entertainment sectors, focusing on four key stages: pre-production, asset creation, animation production, and post-production. The findings indicate that AI’s influence varies significantly across these stages, with post-production demonstrating consistently high efficiency (mean: 0.91275). AI integration proved most effective in standardized processes rather than in creative tasks, with commercial projects achieving the highest efficiency scores. This study highlights that successful AI adoption relies on strategic integration and organizational capability rather than on mere technological implementation. Optimal efficiency gains were observed with AI usage between 30 and 70%. These insights suggest that organizations should focus on phased AI implementation, starting with standardized processes to maximize efficiency. This research contributes to both the theoretical understanding and practical application of AI in creative production, offering empirical guidance for optimizing AI integration in animation workflows.

In the context of globalization, cross-cultural research is essential for understanding behaviors and values across different cultural backgrounds. The way audiences from diverse cultures interpret and accept film content significantly impacts the international dissemination and market performance of films. This study, grounded in Hofstede’s six cultural dimensions—power distance index (PDI), individualism vs. collectivism (IDV), uncertainty avoidance (UAI), masculinity vs. femininity (MAS), long-term vs. short-term orientation (LTO), and indulgence vs. restraint (IVR)—incorporates additional variables such as visual aesthetic appeal (VAA), narrative complexity (NCI), viewing motivation (VM), behavioral intentions (BIs), and brand loyalty (BL) to construct a multidimensional research framework. This framework aims to comprehensively examine the acceptance of American animated films among Chinese audiences and the cultural differences influencing such acceptance. Using structural equation modeling (SEM), this study analyzed the interrelationships between variables based on a sample of 507 participants with prior viewing experience. The findings reveal that different cultural dimensions significantly impact VM. PDI, UAI, and IDV exert significant negative influences on VM, with PDI being the most influential. Conversely, LTO and IVR do not demonstrate significant negative effects. In contrast, MAS, VAA, and NCI exhibit significant positive impacts on VM. Additionally, VM strongly influences audience acceptance, which, in turn, promotes the formation of BIs and repeated VM. This study extends the application of Hofstede’s cultural dimensions to the domain of cross-cultural media consumption, enriching the theoretical framework with additional dimensions and offering a novel perspective for cross-cultural research. Furthermore, the study uncovers the intricate interactions between cultural context and film content, proposing strategies to enhance the acceptance of cross-cultural films. These findings not only provide valuable insights for the production and marketing of animated films but also offer strategic guidance for filmmakers in diverse markets.

With the widespread integration of Artificial Intelligence-Generated Content (AIGC) into e-commerce platforms, understanding how users perceive, evaluate, and respond to such content has become a critical issue for both academia and industry. This study examines the influence mechanism of AIGC Content Quality (AIGCQ) on users’ Purchase Intention (PI) by constructing a cognitive model centered on Trust (TR) and Perceived Risk (PR). Additionally, it introduces two moderating variables—Ethical Concern (EC) and Perceived Platform Responsibility (PLR)—to explore higher-order psychological influences. The research variables were identified through a systematic literature review and expert interviews, followed by structural equation modeling based on data collected from 507 e-commerce users. The results indicate that AIGCQ significantly reduces users’ PR and enhances TR, while PR negatively and TR positively influence PI, validating the fundamental dual-pathway structure. However, the moderating effects reveal unexpected complexities: PLR simultaneously amplifies both the negative effect of PR and the positive effect of TR on PI, presenting a “dual amplification” pattern; meanwhile, EC weakens the strength of both pathways, manifesting a “dual attenuation” effect. These findings highlight the nonlinear cognitive mechanisms underlying users’ acceptance of AIGC, suggesting that PLR and EC influence decision-making in more intricate ways than previously anticipated. By uncovering the unanticipated patterns in moderation, this study extends the boundary conditions of the trust–risk theoretical framework within AIGC contexts. In practical terms, it reveals that PLR acts as a “double-edged sword,” providing more nuanced guidance for platform governance of AI-generated content, including responsibility frameworks and ethical labeling strategies.

This study examines the influence of Artificial Intelligence Generated Content (AIGC) technology on the workflows and career trajectories of designers. Using the Technology-Organization-Environment (TOE) framework, a mixed-methods approach was employed, integrating expert interviews and quantitative surveys. Semi-structured interviews identified ten primary influencing factors, which were further analyzed through a survey of 531 users to quantify the interrelationships among these factors. Results indicate that career paths and industry environment exert the most significant positive impact on designers’ behavioral intentions, highlighting the rising demand for AIGC and its potential to enhance career prospects. Output availability and cross-functional collaboration demonstrate practical benefits in improving content quality and team efficiency. Technological maturity and public acceptance serve as key adoption drivers, while social construction and innovation underscore AI’s capacity to foster creativity and design diversity. Ethics and privacy reflect user concerns over data security, whereas learning costs negatively affect adoption due to perceived complexity. This study confirms the utility of the TOE framework in guiding the deployment of AIGC technology and underscores the necessity of promoting AI’s sustainable development in design. It provides theoretical, practical, and policy recommendations, including improving the usability and innovation of AIGC tools, strengthening support for team collaboration, and offering career development training for designers. Furthermore, policymakers are urged to address privacy and ethical challenges to ensure the long-term sustainable adoption of AI. This research delivers critical theoretical and practical insights into integration of AIGC in the design field.