This research aims to reveal the deep dilemma faced by South Africa's broad basic black economic empowerment system in the process of implementation. The research found that the original intention of the B-BBEE system was to follow the broad purpose to achieve the dual goals of historical compensation and economic development. However, the system shows the remarkable characteristics of 'generalized vision but narrow implementation' in some practices. This implementation alienation is specifically reflected in four key dimensions: the formal compliance of the scorecard mechanism, the logical mandatory simplification of the mining charter, the structural defects caused by the delay in land reform, and the systemic obstacles caused by these factors to the optimization of the industrial structure. This article believes that the fundamental reason why the B-BBEE policy has failed to effectively alleviate South Africa's economic inequality and continued stagnation is that there is a fundamental contradiction that is difficult to reconcile between its historical compensation logic and the economic development logic, and the tendency of 'narrowing' in policy implementation further exacerbates this contradiction.

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The digital economy is driving the global film industry to shift from sole reliance on box office revenue to a multi-channel monetization model integrating "box office + streaming subscription + copyright licensing + derivative product sales". In this context, the three major regions, the United States, China, and Europe, exhibit differentiated development characteristics. This article takes the film industries of the United States, China, and Europe as the research objects, adopting case study methods, cross-regional comparison methods, and literature research methods to explore the key influencing factors and cross-border mechanism differences of the multi-channel performance of the film industry in the digital economy era. Research findings show that high-quality content, digital variables driving, multi-channel synergy, traditional factors interacting with digital variables are the commonalities of cross-border multi-channel performance; the United States focuses on global IP operation and cross-channel linkage as the core, China relies on social media traffic to achieve rapid monetization; Europe relies on public funds to ensure the small-scale dissemination of art films. The differences arise from the fundamental differences in industry foundation, market environment, and policy orientation.

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With the rapid growth of live streaming e-commerce, impulse buying has become a common and influential consumer behavior in digital marketplaces. This study aims to systematically identify and analyze the key factors driving impulse purchases in live streaming contexts through a systematic literature review. The research synthesizes findings from academic journals, industry reports, and theoretical frameworks published between 2018 and 2024. Specifically, it addresses the central research question: What are the primary factors influencing consumers' impulse buying behavior in live streaming e-commerce, and how do they interact? Findings reveal that impulse buying in live streams is influenced by a combination of streamer characteristics, platform design features, consumer emotional states, and social influences. The study concludes that impulse buying in live streaming is a multidimensional phenomenon driven by technological, social, and psychological triggers. It contributes to the theoretical understanding of digital consumer behavior and offers practical insights for streamers and e-commerce platforms.

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Price forecasting holds crucial decision-making value across various sectors, such as finance, commodities, retail, and energy. Accurate price forecasts are essential for optimizing inventory management, mitigating risks, formulating effective strategies, and improving overall profitability. This paper provides a systematic view of the evolution of price forecasting methods, tracing their development from traditional statistical time series models to modern machine learning approaches, as well as cutting-edge deep learning and their hybrid models. Using a comprehensive literature review and comparative analysis methods, different categories of forecasting models are organized and compared. The study finds that a single forecasting model is difficult to adapt to all market conditions. In contrast, hybrid models, especially those that integrate the feature extraction capabilities of deep learning with the interpretability of traditional models or combine multi-source data, demonstrate higher accuracy and robustness. These approaches are therefore identified as the mainstream research direction for future research in price forecasting.

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