Capstone presentation
- 1. SOCIAL AND ENVIRONMENTAL FACTORS WHICH EFFECT THE GREEN PURCHASE BEHAVIOUR OF YOUNG CONSUMER’S Prepared By Manish (11306737) Somil Maurya (11303816) Shashwat Vishwakarma (11304305)
- 2. THE NEED OF STUDY SCOPE OF THE STUDY Green Marketing is very popular in developed countries. In India there is less research on consumer purchasing patterns and environmental behavior of consumers. In North India people are not concern about the environment. This research will throw light on the effective factors which can influence young consumer in North India to purchase Green products.
- 3. LIMITATION OF THE STUDY The study will be covering youth only the age group between 15-29 years of age group. This study is limited to students studying in Lovely Professional University, Jalandhar, Punjab. All the respondents are from two state and one Union Territory of North India Punjab, Haryana and New Delhi respectively. Hence, it has geographical limitations.
- 4. OBJECTIVE: The present study mainly focus on – To find out the important factors which effect the green purchase behaviour in young consumers in North India. Social Influence. Environmental Attitude. Environment Concern. Perceived Seriousness of Environmental Problems. Perceived Environmental Responsibility. Perceived Effectiveness of Environmental Behavior. Concern for Self-image in Environmental Protection.
- 5. OBJECTIVE: To identify effective factors which can motivate North Indian young people to engage in green purchase behavior.
- 6. ENTRY METHOD The standard method of entry is simultaneous (a.k.a. the enter method); all independent variables are entered into the equation at the same time. This is an appropriate analysis when dealing with a small set of predictors and when the researcher does not know which independent variables will create the best prediction equation. Each predictor is assessed as though it were entered after all the other independent variables were entered, and assessed by what it offers to the prediction of the dependent variable that is different from the predictions offered by the other variables entered into the model
- 7. STATISTICAL REGRESSION METHODS OF ENTRY: Stepwise selection involves analysis at each step to determine the contribution of the predictor variable entered previously in the equation. In this way it is possible to understand the contribution of the previous variables now that another variable has been added. Variables can be retained or deleted based on their statistical contribution. Backward elimination (or backward deletion) is the reverse process. All the independent variables are entered into the equation first and each one is deleted one at a time if they do not contribute to the regression equation.
- 9. FINDING’S R:Multiple regression coefficient between all the independent variables also called predictor variables in the model and the response variables, also called dependent variable. R Square: It determines the predictability of dependent variable that how much proportion of dependent variable is determine with the help of predictor variable. R square= Variation Explained / Total Variation.
- 10. R SQUARE & ADJUSTED R SQUARE R square value in our analysis is 0.523 and Adjusted R square value is 0.512. In multiple regression, we generally go for adjusted R square value rather than R square value. R square measures the proportion of total variability of dependent variables explained by independent variable. Adjusted R-square in above case is 0.512 which means that 51.2 percentage of total variability of dependent variable i.e. Green purchase behaviour is explained with the help of seven independent variables (Social Influence, Environmental Attitude, Environmental Concern, Perceived Seriousness, Perceived Environmental Response, Perceived Effectiveness of Environmental Behaviour and Supporting Environmental Protection). The difference in values of R square and adjusted R square is due to some independent variables were included in the regression model. Due to some redundancy in independent variable, difference occurs.
- 11. ANOVA ANOVA (Analysis of Variance) help us to determine the means of two or more groups are different or not. ANOVA used F-test to statistically determine the equality of means. In ANOVA table, we majorly focus on “F” value which we obtain with the help of previous three columns i.e. Sum of squares, df and Mean square. F-test is the ratio of two variance. In ANOVA table, another important parameter is Significance. Here, significance is 0.00 means significance of error is within the limit i.e. less than 0.05 (5%).
- 13. CO-EFFICIENT TABLE ANALYSIS In co-efficient table, Beta (Unstandardized Co-efficient) values are the regression equation i.e. values of B0, B1and B2 and so on. Standard Error help us to define the range between which Beta can lie. For Example, calculate range for Social Influence (SI) y = 2.36*0.321=0.75756 Range of Social Influence (SI) is in between 0.83344 to 2.34856,and as the Significance is 0.00,which is less than the Level of Significance (0.05) means we can say that 95 % confidence interval range for beta in case of Social Influence lies between 0.83344 to 2.34856. Another important parameter we can determine from the Co-efficient table is “t” score, which is the ratio of Beta to the Standard Error. Here, t-score for Social Influence (SI) = Bo / Std. Error = 1.591/0.321 t- score = 4.958, which is a correct value.
- 14. CO-EFFICIENT TABLE ANALYSIS Significance level help us to determine whether t-score will be accepted or will be rejected. Here, value of alpha i.e. level of significance is 5%.We can get the value from alpha table for 5% level of significance i.e. 2.36.If t-score lies between -2.36 and 2.36 then accept the value. Significance also called as p –value. If p value is less than 0.05 reject the t-score. Standard Co-efficient is defined as the ratio of Values to Standard Deviation. R square in Model Summary can be calculated from the ANOVA table. It is the ratio of sum of square value to the total sum of square values.