Table 4 Determinants of fintech usage...

Table 4

Determinants of fintech usage

IVs	SEM	OLS	Logit
IVs	(1)	(2)	(3)
Financial literacy	0.073***	0.073***
	(3.527)	(3.499)
Financial literacy: lower vs			−0.727***
higher than average			(10.459)
Gender (Male = 1)	0.073	0.073
	(0.525)	(0.521)
Female vs male			−0.066
			(0.129)
Age	−0.008	−0.008
	(−0.112)	(−0.112)
Under 25 vs over 45 years old			0.203
			(0.792)
Education	0.114	0.114
	(0.677)	(0.671)
University vs Master or higher			0.397
			(1.676)
Marital status (Married = 1)	−0.248*	−0.248*
	(−1.605)	(−1.693)
Single vs married			0.384*
			(3.504)
Work experience	0.210***	0.210***
	(2.667)	(2.646)
Work experience: less vs more			−0.146
			(0.055)
Explorer trait	0.243***	0.243***
	(2.971)	(2.948)
Explorer trait: less vs much			−0.649*
			(3.249)
Intercept/-2 Log Likelihood		2.087***	896.40
R²/Adjusted R²/Pseudo R²	0.101	0.084	0.146
Chi-square/F Change	0.094***	6.869***	826.22
Df	1	7	16

IVs	SEM	OLS	Logit
IVs	(1)	(2)	(3)
Financial literacy	0.073***	0.073***
	(3.527)	(3.499)
Financial literacy: lower vs			−0.727***
higher than average			(10.459)
Gender (Male = 1)	0.073	0.073
	(0.525)	(0.521)
Female vs male			−0.066
			(0.129)
Age	−0.008	−0.008
	(−0.112)	(−0.112)
Under 25 vs over 45 years old			0.203
			(0.792)
Education	0.114	0.114
	(0.677)	(0.671)
University vs Master or higher			0.397
			(1.676)
Marital status (Married = 1)	−0.248*	−0.248*
	(−1.605)	(−1.693)
Single vs married			0.384*
			(3.504)
Work experience	0.210***	0.210***
	(2.667)	(2.646)
Work experience: less vs more			−0.146
			(0.055)
Explorer trait	0.243***	0.243***
	(2.971)	(2.948)
Explorer trait: less vs much			−0.649*
			(3.249)
Intercept/-2 Log Likelihood		2.087***	896.40
R²/Adjusted R²/Pseudo R²	0.101	0.084	0.146
Chi-square/F Change	0.094***	6.869***	826.22
Df	1	7	16

Note(s): ***: p < 1%; **: p < 5%, *: p < 10%. Dependent variable: fintech usage. t-test in the parenthesis. SEM indicator: AGFI = 0.998; RFI = 0.993, TLI = 1.074; RMSEA = 0.004

Source(s): Authors’ own work

[ViewLarge]