The convergence of big data and social media is having a profound impact on the marketing logic and consumer behavior within the cosmetics industry. For theoretical support in discussing these issues in a more organized fashion, a Stimulus-Organism-Response (S-O-R) framework approach is adopted in this paper.By analysis of relevant literature surveys, it has been revealed how big data-based marketing practices like UGC marketing, influencer marketing algorithms, and other forms of algorithmic recommendations work as "stimuli" in the context of S-O-R theory.To be precise, through intrinsic processes like Social Proof Theory and Signal Theories/Quasi-Social Relations, these practices affect consumers in a significantly great manner at the "organism" level of the theory, and hence lead to a "response" in terms of buying decisions and brand loyalty.This paper also identifies some of the ethical implications of this model in relation to Privacy Computing and Distributed Justice Theories.
Entering the third decade of the 21st century, global supply chains reveal considerable systemic vulnerabilities due to the cumulative effects of pandemic disruptions, geopolitical conflicts, and recurrent extreme weather phenomena. Conventional supply chain models focused on cost efficiency and lean operations encounter ongoing difficulties. This study establishes a comprehensive framework for evaluating global supply chain hazards from a multidimensional risk viewpoint, incorporating political, economic, and natural concerns. It methodically examines the initiating mechanisms, transmission routes, and interconnected impacts of each risk category. The study provides a streamlined risk assessment methodology that equips organizations with actionable analytical tools, including an indicator system and a multidimensional risk matrix, to ascertain risk exposure levels and comprehend risk structures. This study formulates a supply-chain resilience strategy system tailored for complex and uncertain situations, which is based on four essential dimensions: structural robustness, process adaptability, information-driven capability, and collaborative governance. The research demonstrates that by recognizing risks from various dimensions and integrating conceptual quantitative assessment tools, enterprises can more efficiently pinpoint critical vulnerabilities, improve supply chain resilience, and bolster their shock resistance and enduring competitive advantage in high-uncertainty contexts.
Predicting the volatility of crude oil prices is of crucial significance, yet it poses challenges because of the intricate interaction process between geopolitical factors and economic factors. In this study, a systematic and comparative analysis is carried out on traditional econometric models and modern machine-learning models for the volatility of WTI crude oil. This study starts by making use of ARIMA and GARCH-family models (including GARCH and APGARCH) to capture the linear patterns and the asymmetric volatility clustering phenomenon. In order to deal with the nonlinear limitations of these parametric models, this study then puts forward a brand-new hybrid framework that combines the APGARCH model with an Artificial Neural Network (ANN). By using the WTI spot price data, the empirical results of this paper show the better performance of the hybrid approach. Specifically speaking, the APGARCH-ANN model reduces the forecast errors by more than 80% when it comes to RMSE in comparison with the standalone APGARCH model. This notable improvement emphasizes the synergy that exists between the econometric feature extraction process and the machine learning’s ability to perform nonlinear approximation. The findings offer a robust and advanced forecasting tool for energy firms as well as financial institutions, which can enhance the risk management process and the strategic decision - making process when facing the volatile oil markets.
This study examines the relationship between China's export performance and exchange rate dynamics against the backdrop of global value chain reorganization and intensified external shocks. The study employed monthly data (2016-2024) and time series regression methods, and found that the explanatory power of the real effective exchange rate (REER) is limited. Time series regression confirmed that the REER coefficient was not statistically significant, which contradicted the simple "devaluation promotes exports" model. The research explains this pattern through recent evidence: (i) the role of processing trade and imported intermediate goods in shaping China's export results, where the exchange rates of supply chain economies may have as significant an impact as the RMB exchange rate; (ii) The exchange rate effect varies under different product complexities and qualities, which may weaken the average exchange rate flexibility, especially for higher-end export product baskets. (iii) The partial and asymmetric exchange rate transmission effect is influenced by the restrictions at the enterprise level and the amplification of market frictions. These findings are consistent with the reconfiguration of the global production network and the adjustment of response strategies after the pandemic, and also include "risk reduction" as well as the adjustment of trade patterns and incentive mechanisms due to geopolitical changes. Overall, these findings suggest that China's export resilience is increasingly driven by structural factors embedded in the global value chain rather than short-term exchange rate fluctuations. This indicates that models that merely focus on price competitiveness may not fully reflect the dynamic changes in modern, upgrade-oriented manufacturing exports.
