The Impact of Social Media Marketing on Consumer Purchase Intention: Consumer Survey in Saudi Arabia (original) (raw)

Abstract

The main purpose of the current research was to find out the impact of social media marketing on the purchase intention of the customers. The study was conducted in Saudi Arabia. It is a quantitative research based primary data collected through structured close ended questionnaire, distributed online. Results were drawn based on 121 responses using factor analysis, ANOVA and regression analysis. The results showed that 92.4% of the customer purchase intention has been explained by Social Media Marketing. The results are statistically significant.

Figures (12)

The main goal of correlation matrix is to check the pattern of the relationship; variables are reasonably well correlated. Table 1 showed that Pearson correlation between the independent variable and dependent variable is strong and positive in nature, that is reflected by the high value of 0.520, which means increase in social media marketing is associated by increase in customer purchase intention and vice versa. There is no problem of Multicollinearity because the determinant is greater than 0.00001. Data analysis and interpretation Factor Analysis

The main goal of correlation matrix is to check the pattern of the relationship; variables are reasonably well correlated. Table 1 showed that Pearson correlation between the independent variable and dependent variable is strong and positive in nature, that is reflected by the high value of 0.520, which means increase in social media marketing is associated by increase in customer purchase intention and vice versa. There is no problem of Multicollinearity because the determinant is greater than 0.00001. Data analysis and interpretation Factor Analysis

KMO matrix measured the sampling adequacy, acceptable range value of KMO analysis is ‘0.5 and 0.7’, the results in table 2, is 0.796. Besides Bartlett's Test, showed statistically significant results and thus the data is appropriate for factor analysis. The significance of Bartlett’s Test of Sphericity, which is below 0.05 and equals 0.000, showed that the data that have been gathered is suitable and valid.

KMO matrix measured the sampling adequacy, acceptable range value of KMO analysis is ‘0.5 and 0.7’, the results in table 2, is 0.796. Besides Bartlett's Test, showed statistically significant results and thus the data is appropriate for factor analysis. The significance of Bartlett’s Test of Sphericity, which is below 0.05 and equals 0.000, showed that the data that have been gathered is suitable and valid.

Journal of Marketing and Consumer Research ISSN 2422-8451 An International Peer-reviewed Journal Vol.56, 2019

Journal of Marketing and Consumer Research ISSN 2422-8451 An International Peer-reviewed Journal Vol.56, 2019

Table 5, showed that the first six factors explain large amount of variance specially factor 1, whereas subsequent factors explain only small amount of variance.

Table 5, showed that the first six factors explain large amount of variance specially factor 1, whereas subsequent factors explain only small amount of variance.

The regression model well-fits the data due to that adjusted R square is less than R square with a very small difference, where it equals 0.037. The Durbin-Watson is equal to 1.991 which is between the two critical values of 1.5<d<2.5. Therefore, we can assume that there is no first order linear auto-correlation in our multiple linear regression data. OD. FICQICLOIs. (UONStant), INDAIN TaClOr sCOre =z LOT dildlysis J, INDUIN TdClOr sCOlre = LOT dildlysis 5 ANOVA is analysis to tests the effect of Independent variables on dependent variables, large value of F like what we have in the above value indicate that null hypothesis is wrong, so we reject the null hypothesis of equal population means and conclude that there is a (statistically) significant difference among the population means (Table 8).

The regression model well-fits the data due to that adjusted R square is less than R square with a very small difference, where it equals 0.037. The Durbin-Watson is equal to 1.991 which is between the two critical values of 1.5<d<2.5. Therefore, we can assume that there is no first order linear auto-correlation in our multiple linear regression data. OD. FICQICLOIs. (UONStant), INDAIN TaClOr sCOre =z LOT dildlysis J, INDUIN TdClOr sCOlre = LOT dildlysis 5 ANOVA is analysis to tests the effect of Independent variables on dependent variables, large value of F like what we have in the above value indicate that null hypothesis is wrong, so we reject the null hypothesis of equal population means and conclude that there is a (statistically) significant difference among the population means (Table 8).

_Table 10: Residuals Statistics*

_Table 10: Residuals Statistics*

Journal of Marketing and Consumer Research ISSN 2422-8451 An International Peer-reviewed Journal Vol.56, 2019 Fig.l examines the multivariate normality of the dependent variable using histogram, which denotes normal distribution of collected data.

Journal of Marketing and Consumer Research ISSN 2422-8451 An International Peer-reviewed Journal Vol.56, 2019 Fig.l examines the multivariate normality of the dependent variable using histogram, which denotes normal distribution of collected data.

Also using P-P plot, we can check for normality of residuals. Fig. 2 shows that the points follow the normal line with no deviations. This indicates that the dependent variable i is normally distributed. ee a a ae ee eT Os. 48

Also using P-P plot, we can check for normality of residuals. Fig. 2 shows that the points follow the normal line with no deviations. This indicates that the dependent variable i is normally distributed. ee a a ae ee eT Os. 48

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