Table 4.

Coefficient estimates and diagnostics from hierarchical binary logistic regression explaining consumers’ revisit intentions (study 1; n = 1053).

	Model 1: Consumer Profiling Variables					Model 2: Consumer Profiling and Attitudes
Variable	B	S.E.	Wald	p	Exp(B)	B	S.E.	Wald	p	Exp(B)
Socio-demographic
Age	−0.001	0.005	0.039	0.843	0.999	0.009	0.005	3.099	0.078	1.009
Gender (1 = male)	0.488	0.142	11.910	0.001	1.630	0.204	0.154	1.760	0.185	1.226
Education (1 = higher)	−0.141	0.151	0.872	0.350	0.868	−0.159	0.163	0.958	0.328	0.853
Past behaviour
Visit frequency	0.335	0.049	47.305	<0.001	1.398	0.300	0.050	36.555	<0.001	1.349
Attitudes
F1(1) Hygiene						−0.479	0.138	12.043	0.001	0.620
F2(1) Avoidance						−0.449	0.121	13.727	<0.001	0.638
F3(1) Organisation						−0.505	0.122	17.197	<0.001	0.604
Constant	−0.256	0.276	0.862	0.353	0.774	4.849	0.578	70.427	<0.001	127.613
Model
Likelihood ratio	101.971			<0.001		244.007			<0.001
Nagelkerke R2	0.125					0.279

Note: Predictive accuracy of 63.6% (Model 1) and 69.3% (Model 2) compared to 59.8% in the ‘null’ model; dependent variable (revisit intention) is a dummy variable: postpone visit (0), retake visit (1); bold indicates significant coefficients (p < 0.05); FX(Y) with X = number of factor, Y = number of study.