High-tech industry has a powerful impact on developing countries’ regional economy development. The diffusion effect of high-tech industry as the main driving force of economic development on some developed regions has been examined. However, in developing countries, especially, in China, the literature is less focused. Besides, the mechanism of factor flow is still unclear in China. We use the panel data of high-tech industries in 28 provinces of China from 2004 to 2016. This article analyzes the mechanism of industrial diffusion and factor mobility on the coordinated development of the regional economy by establishing the system GMM model and threshold model empirically. The system GMM results show that under the conditions of controlling R&D capital investment, deposit and loan balance, property rights structure, etc., capital flow can promote the coordinated development of regional economy, industrial diffusion, labor mobility and technology flow inhibit the coordinated development of regional economy; threshold regression results show The factor flow has a double threshold effect based on industrial diffusion for the coordinated development of regional economy. This study integrates the factor flow of knowledge into high-tech industry development, examining the impacts of industrial diffusion effect. The findings offer an industry developing frame to explain regional economy growth, which is beneficial to the local government’s economic decisions.

This study aims to investigate the impact of sentiment and policy on the volatility of educational stock prices by using HAR (Heterogeneous Auto Regressive) and LSTM (Long Short-Term Memory) models. We construct a weighted educational index volatility composed of nine publicly traded educational companies from the Shenzhen Stock Exchange and Shanghai Stock Exchange, and analyze the impact of sentiment and policy variables on the volatility of educational stock prices. We use OLS regression models and LSTM prediction models to analyze the data by developing various of models to investigate the impact of sentiment, education policies and their intersection effect. The empirical results show that the sentiment index and policy index have significant impacts on different time horizons of educational stock price volatility. The LSTM model confirms the effectiveness of including sentiment and policy variables in predicting educational stock price volatility. These findings carry several practical implications, particularly for investors, education-listed companies, and policymakers. And this study contributes to the literature by providing new evidence on the impact of sentiment and policy on the volatility of educational stock prices and by demonstrating the usefulness of combining HAR and LSTM models in predicting stock price volatility.

Live streaming with e-commerce as a significant feature of the new retail model is providing a new interpretation and significance for the elements of people, goods, and the marketplace. This study focuses on how the atmosphere in e-commerce live broadcasting elements affects and enhances brand value, especially from the perspective of brand intimacy in brand value. Through empirical research using questionnaire survey data and structural equation models, this paper explores the mechanism by which the factors of people, goods, and venues in e-commerce live broadcasting affect consumer brand intimacy. The study found that the atmosphere support of the venue affects brand intimacy. This study constructs a theoretical framework for the relationship between the factors of people, goods, and venues in online celebrity live broadcasting and brand intimacy.