The Influence of Big Data Analytics on E-Commerce: Case Study of the U.S. and China

Table 19

Regression models for China’s theoretical research and data companies founded.

Dependent variable	Independent variable							Statistics
	X04	X09	X28	X60	X64	X66	X68
Y14					0.418	0.596		, both , the standardized residuals are approximately normally distributed
Y15	1.136			-0.480				, both , the standardized residuals are approximately normally distributed
Y16					0.530	0.452		, both , the standardized residuals are approximately normally distributed
Y17		-0.210			0.351	0.755		, , , the standardized residuals are approximately normally distributed
Y18			1.069		0.418		-0.469	, , the standardized residuals are approximately normally distributed

, , and ; low VIF values indicate low collinearity; all coefficients are standardized coefficients; X04: searching subject term “E-Commerce” in CNKI (periodical); X09: searching subject term “E-Commerce” and then “Big data” in CNKI (periodical); X28: searching subject term “Machine Learning” and “Big Data” in CNKI (periodical); X60: searching subject term “Online Consumer Behavior & Big Data” classified by the field of “topic” in CNKI (periodical); X64: searching subject term “Mobile Technology & E-Commerce” in CNKI (periodical); X66: searching subject term “Cloud Computing & E-Commerce” in CNKI (periodical); X68: searching subject term “Artificial Intelligence & Big Data & E-Commerce” in CNKI (periodical); Y14: founded number of data companies in China (total); Y15: founded number of data companies in China classified as data/technology; Y16: founded number of data companies in China classified as business analytics; Y17: founded number of data companies in China classified as industrial application; Y18: founded number of data companies in China classified as research/consulting.