The determinants of green consumption: a study of socio-demographics factors as determinants

3.3.7 H5: The intention to buy green product is positively linked the act of purchasing green product

For this hypothesis the null hypothesis is:

H0 = The intention to buy is not explaining the consumption of green products H1 = The intention to buy permits to explain the consumption of green product

Table 3.35 H5 Model Summary

Récapitulatif des modèles

Modèle	R	R-deux	R-deux ajusté	Erreur standard de l'estimation
1 dimensi on0	,856^a	,733	,731	,56914

a. Valeurs prédites : (constantes), intention

For this hypothesis, we could observe that the correlation between the variables, the store type and the consumption of green products is 0.856, which indicates a high correlation. Moreover, R-square is equal to 0.733 this means that 73.3% of the variance of green consumption could be explained because of the intention to buy green products; which is very large; therefore it seems that most of the time, those who have the intention to buy green

Table 3.36 ANOVA Table

ANOVA^b

Modèle	Somme des carrés	ddl	Moyenne des carrés	D	Sig.
1 Régression Résidu Total	131,732 47,941 179,673	1 148 149	131,732 ,324	406,676	,000^a

a. Valeurs prédites : (constantes), intention

b. Variable dépendante : green_consump

The part of variance none explain by the independent variable is less important,
47.941, than the part explain by the independent variable, 131.732. So it seems that

people who have the intention to buy are finally buying green products, they are following their intention.

In this case, the D (F) value is 406.676 and is significant at p < 0.0005. In other words, at the p = 0.05 level of significance, there exists enough evidence to conclude that the slope of the population regression line is not zero and, hence, that the intention to buy green is useful as a predictor of green consumption. Therefore we reject the null hypothesis formulated above. So there is a statistically significant relationship between the green consumption and the intention to buy green.

We can conclude that the model with a predictor (intention to buy green) permits to predict the variable (green consumption) better than a model without a predictor.

Table 3.37 Coefficients Table

Coefficients^a

Modèle			Coefficients
	Coefficients non standardisés		standardisés
	A	Erreur standard	Bêta	t	Sig.
1 (Constante)	,411	,158		2,602	,010
intention	1,004	,050	,856	20,166	,000

a. Variable dépendante : green_consump

For this hypothesis, the regression equation could be drawn as followed: Green consumption = 0.411+1.004*green knowledge

For the p-value, in this case p = .000 therefore we get .000 > 0.05, as a consequence we reject H0 and the relationship is reliable and can be used to make predictions. (Jeff Sinn 2008)