The purpose of this paper is to identify key determinants of foreign direct investment (FDI) inflows in developing countries by using unbalanced panel data set pertaining to the years 1990-2012. This study considers 20 developing countries from the whole of South, East and South-East Asia. Using seven explanatory variables (market size, trade openness, infrastructure, inflation, interest rate, research and development and human capital), the authors have tried to find the best fit model from the two models considered (fixed effect model and random effect model) with the help of Hausman test. Fixed effect estimation indicates that market size, trade openness, interest rate and human capital yield significant coefficients in relation to FDI inflow for the panel of developing countries under study. The findings reveal that market size is the most significant determinant of FDI inflow. Like any other study, this work also has some limitations. Lack of data on key determinants such as labor cost, exchange rate, corruption, natural resources, effectiveness of rule of law and political risk may be considered one such limitation. Further, controlling for variables such as exchange rate, corruption, labor cost and political risk could make significant improvements to this study. This study has significant implications for policy makers, mangers and investors. Policy makers would be able to understand the importance of the major determinants of FDI mentioned in the paper, and take steps to formulate policies that encourage FDI. Such measures could include developing market size, making regulations more international trade friendly and investing in the nation’s human capital. Further, steps could be taken to keep interest rates and inflation rates under control as these factors have been found to influence FDI. The sample of 20 developing nations chosen for this study has not been considered by any study earlier. This is a unique contribution to existing body of research, and highlights the originality value of this paper.

Purpose: This study examines the long-term and causal relationship among foreign direct investment (FDI) inflows, trade openness and economic growth from India. Design/methodology/approach: This study has used annual time series data from the period 1985–2018 and applied the Johansen cointegration and vector autoregression (VAR) model. Findings: The results of Johansen's cointegration confirm no long-term relationship among all the above three variables. Further, the results of VAR Granger causality indicate that FDI causes economic growth and economic growth causes FDI, which confirms the bi-directional causality. In contrast, this study found that there is no bi-directional causality between trade openness and economic growth. Social implications: Through this study, the government could take the decisions related to foreign investment after adopting more trade openness because the study results revealed that if India follows more trade openness, then how FDI will flow (upward and downward). With impulse analysis, researchers, government and policymakers take the decision-related FDI inflows for the forthcoming ten years after 2018. Originality/value: This study has found the most exciting results from the impulse functions of FDI inflows, trade openness and economic growth, which showed the situation of these three variables as increase and decrease in the forthcoming ten years.

Effective consumer behavior prediction can play a crucial role in online marketing, especially in the COVID19 scenario. In this work, we have analyzed consumer behavior to understand consumer needs and predict future requirements. For the same, we have applied the machine learning models on an amazon dataset collected from Kaggle. The dataset consists of reviewers' comments, ratings, many other parameters for the product. The model's outcome indicates that the proposed Random Forest model performs exceptionally well, and its Accuracy is approx. 98.73%. A comparative study has been done to show the efficacy of the work, and it has been observed that the performance of the proposed model is quite remarkable, and it can be a competent model for effective consumer behavior prediction.

This article discusses the question, does trade openness (TO) lead to economic growth in the Quadrilateral Security Dialogue (QUAD) countries (including the USA, Japan, India and Australia). Being driven by the paucity of the existing empirical data that targets QUAD countries as a strategic economic bloc, thus the study fills a valuable gap in the work on trade and economic growth. For the filling gap, this article studied a panel data set for 23 years (2000–2022). The analysis comprises a combination of both short and long-run dynamics of the variables using annual panel data through the autoregressive distributed lag (ARDL) framework, including pooled mean group (PMG), mean group (MG) and dynamic fixed effects (DFE) estimators. The empirical findings indicate that TO and foreign direct investment (FDI) inflows have positive and significant effects on economic growth in the long run and the macroeconomic stability, which is indicated by the controlled inflation and proper reserves, has a complementary effect. It has been found that the PMG estimator is the most efficient, which means that there are homogeneous long-run relations over all the economies of the QUAD countries with heterogeneous short-run adjustment. These results are solid arguments that trade-led growth theory applies in relation to developed and emerging markets in the framework of QUAD countries. The results point to trade liberalization, FDI magnetism and exchange rate stability as essential drivers of sustained economic growth. Policymakers can use them to create targeted policies that are less likely to drive trade barriers, create investor-friendly conditions, and stabilize the monetary balance. This study also points to the importance of restrained inflation and institutional transparency in long-term economic resilience and stability.