Role of Big Data Analytics in Belt and Road Initiative (BRI): Multivariate Analysis with Gaussian Distribution of Data

Abstract

This manuscript focuses on the Belt and Road Initiative (BRI) of China, whereby the focus is on the engagement of big data analytics to comprehend logistics exertion. China is the trendsetter for revolutionary practices in trade, logistics, and technology. The recent progress the nation is thriving is on ‘One Belt One Road’ project whereby 65 countries are involved. It aims to connect continents and circulate smooth trade between them. This paper addresses the role of the database to identify the inter-model logistics in BRI. The merits of this project in the perspective of economic growth are measured through a quantitative study with 112 samples. Goal-setting theory is used to construct a conceptual framework for the research. Multivariate analysis is executed with SmartPLS 3.3.3 followed by an in-depth structural equation modeling. Normal distribution of data was given importance as in statistics the real-value of random variables whose distributions are not known, thus Gaussian distribution of data was used. Out of 6 Hypotheses, it is noted that five are significantly positive. Hypothesis testing is concluded based on p-value and t-statistics. The outcome of research suggests that big-data analytics is a major contributor in determining the significant model on logistics in Belt and Road Initiative.