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

Table 20

Coefficients for Y06^a.

Model		Unstandardized coefficients		Standardized coefficients		Sig.	Correlation			Collinearity statistics
			Std. error	Beta			Zero-order	Partial	Part	Tolerance	VIF
1	(Constant)	-89.898	208.579		-0.431	0.670
	Y18	168.642	19.750	0.859	8.539	0.000	0.859	0.859	0.859	1.000	1.000
2	(Constant)	-440.918	243.502		-1.811	0.082
	Y18	100.833	34.095	0.513	2.957	0.007	0.859	0.509	0.274	0.286	3.500
	X04	2.584	1.098	0.408	2.353	0.027	0.842	0.426	0.218	0.286	3.500

^aDependent variable: Y06, where model 1 is 0.737 and model 2 is 0.785; low VIF values indicate low collinearity; the standardized residuals are approximately normally distributed. X04: searching subject term “E-Commerce” in CNKI (periodical). Y18: founded number of data companies in China classified as research/consulting. Y06: retail e-commerce sales for China.