The impact of different types of capital flows on China’s economic growth has been widely studied to determine whether the type of capital significantly affects the Chinese economy. The purpose of this study is to investigate the relationship between long-term capital flows and economic growth in China, considering factors such as Foreign Direct Investment (FDI), portfolio equity, portfolio bonds, and external debt. All secondary data were collected from the World Bank database. The paper also investigates which type of capital flow has the most significant relation with the economic growth of China. A quantitative approach was chosen for the study. Moreover, to overcome the bias output of ordinary least squares, this paper deployed a Two-Stage Least Squares (2SLS) estimation method. This study has found a relatively stable positive relationship between FDI and growth, where the coefficient of 0.9699 indicates that a 1% increase in FDI is associated with a 0.97% growth in Gross Domestic Product (GDP). Similar to FDI, portfolio equity has a positive impact on GDP growth, with a coefficient of 2.1419. In contrast, portfolio bond and debts have a negative coefficient of –1.7752 and –0.2831. These findings contribute to a deeper understanding of China’s development experience, particularly regarding the role of capital flow. The paper explores two key limitations that need to be explored in the future, i.e., the causal relation between each type of long-term capital flow and economic growth, and the impact of COVID-19 on the economic growth relationship.

This study investigates the impact of SERVQUAL dimensions (Assurance, Reliability, Tangibles, Empathy, and Responsiveness) and review scores on customer sentiment. We analyze a large dataset of 920,407 UberEats reviews from the Google Play Store, classifying sentiment based on star ratings and using a Long Short-Term Memory (LSTM) model to predict sentiment from review content. Using text mining and sentiment analysis, the study employs robust feature engineering techniques to extract and quantify SERVQUAL components from customer reviews. The LSTM model demonstrated high accuracy (89.64%) in predicting sentiment, validating the alignment between predicted and assigned sentiments. Our analysis reveals that all SERVQUAL dimensions and review scores have a positive and significant impact on overall sentiment. Specifically, the Ordinary Least Squares (OLS) regression results highlight Empathy as the most influential SERVQUAL component, followed by Responsiveness, Reliability, Tangibles, and Assurance. Furthermore, review score emerged as the strongest predictor of customer sentiment. These findings provide actionable insights for service providers aiming to enhance customer satisfaction by optimizing key SERVQUAL dimensions and addressing review score trends.