It is evident from the table 2 that equal number of respondents from both the gender taken for the analysis, all respondents (100%) were using social networking sites like facebook, LinkedIn etc. 53%, 42% and 21% photo/video sharing sites (YouTube), Micro blogging (Twitter) and blogs/forum respectively. In order to check the usage of social media; respondents categorized into light user (1-3 hrs/day), medium user (4-9 hrs/day) and heavy user (10 hrs or more/day). The majority of respondents come under the category of medium user which is using social media only 4-8 hrs in a day (47%) and 42.64%, 9.85 light user and heavy users respectively. The next question was asked about whether they use social media for online shopping at least once in a while; 100% respondents say that they use social media for online shopping.Reliability analysisReliability is used to check the consistency of the research instrument; questionnaire. Here in the research reliability of the multi-item scale for each construct was measured using Cronbach alphas. Measures of reliability were above the recommended minimum standard of 0.60. For all five dimensions, measures of reliability were above 0.60 (see table 3). Hence the instrument meeting with the criteria of validity and reliabilityTable 3 Reliability analysisS.no Construct Items Cronbach alpha (?)1 Perceived Usefulness (PU) 5 0.8452 Perceived Value (PV) 3 0.8213 Perceived Risk (PR) 6 0.8064 Purchase Intention (PI) 4 0.839Correlation among PU, PV and PRAccording to Libguides.library.kent.edu, (2018) Pearson Correlation evaluates whether there is statistical evidence for a linear relationship among the same pairs of variables in the population, represented by a population correlation coefficient, ? (“rho”).It is concluding from the table 4 that there is a statistically significant correlation among Perceived usefulness (PU), Perceived value (PV) and Perceived risk (PR). That means, increases or decreases in either of the variable significantly relate to increases or decreases in your other variable (Statistics-help-for-students.com, 2018).Table 4 Correlation table among PU, PV & PR PU PV PRPU Pearson Correlation 1 .613** -.276** Sig. (2-tailed) 0.000 0.000 N 265 265 265PV Pearson Correlation .613** 1 -.403** Sig. (2-tailed) 0.000 0.000 N 265 265 265PR Pearson Correlation -.276** .403** 1 Sig. (2-tailed) 0.000 0.000 N 265 265 265Multiple RegressionsMultiple regression uses when there will be more than one independent variable and one dependent variable; in this research Perceived usefulness (PU), Perceived value (PV) and Perceived risk (PR) act as independent variable and Purchase intention (PI)) act as dependent variable. In the model summery (table 5) The “R” column represents the value of R, the multiple correlation coefficients. R can be considered to be one measure of the quality of the prediction of the dependent variable; in this case, PI. A value of 0.660, in this research, indicates a good level of prediction. The “R Square” value represents the R2 value (also called the coefficient of determination), which is the proportion of variance in the dependent variable that can be explained by the independent variables (technically, it is the proportion of variation accounted for by the regression model above and beyond the mean model). You can see from our value of 0.435 that our independent variables explain 43.5% of the variability of our dependent variable, PI. Table 5 Multiple Regression AnalysisModel SummarybModel R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Changedimension0 1 .660a .435 .429 2.65564 .435 67.069 3 261 .000a. Predictors: (Constant), PR, PU, PVb. Dependent Variable: PIThe F-ratio in the ANOVA table (see table 6) tests whether the overall regression model is a good fit for the data. The table shows that the independent variables statistically significantly predict the dependent variable, F (3, 261) = 67.069, p < .0005 (i.e., the regression model is a good fit of the data).Table 6 ANOVA ANOVAbModel Sum of Squares df Mean Square F Sig.1 Regression 1418.994 3 472.998 67.069 .000a Residual 1840.683 261 7.052 Total 3259.678 264 a. Predictors: (Constant), PR, PU, PVb. Dependent Variable: PIUnstandardized coefficients indicate how much the dependent variable varies with an independent variable when all other independent variables are held constant. The unstandardized coefficient, B1, for PU is equal to 0.272 (see Coefficients table 7). This means that for each one unit increase in PU, there is an increase in PI of 0.272. Likewise if PV increases by one unit PI will increase by 0.451 units and if PR changes in one unit the PI will increase but very less by 0.084. The t-value and corresponding p-value are located in the "t" and "Sig." columns; indicate that the effect of PU, PV and PR on PI is significant as the P is less than 0.05.Table 7 CoefficientsCoefficientsaModel Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF1 (Constant) 3.028 .845 3.583 .000 PU .272 .049 .327 5.547 .000 .624 1.604 PV .451 .080 .348 5.627 .000 .566 1.768 PR -.084 .036 .118 2.316 .021 .837 1.195a. Dependent Variable: PITable 8 Summary of Multiple RegressionsVariables Beta T Significance Tolerance VIFPerceived Usefulness (PU) .327 5.547 .000 .624 1.604Perceived Value (PV) .348 5.627 .000 .566 1.768Perceived Risk (PR) .118 2.316 .000 .837 1.195Notes: Overall Model F= 67.069; p < 0.01; R2= .435; adjusted R2= .429A multiple regression was run to predict purchase intention (PI) from Perceived usefulness (PU), Perceived value (PV) and Perceived risk (PR). These variables statistically significantly predicted PI, F(3, 261) = 67.069, p < .05, R2 = .435. All three variables added statistically significantly to the prediction, p < .05.