CHI-SQUAREQUESTIONNAIRE:

What is your family income?1. Upto 200002. 20001-300003.30001-400004.40001-500005.50001 and above

What is your total savings?1. Upto 50002. 5001-100003.10001-150004.15001-200005.20001 and above

5.5.1.8.3. Family income and Total Savings

Family income includes salary of the respondents, salary of spouse & children and income from all other sources. The family saving otherwise called as total savings includes discretionary savings and contractual saving. To understand the relationship between income and saving, the correlation analysis is done. TABLE 5.5.26RELATIONSHIP BETWEEN FAMILY INCOME AND TOTAL SAVINGSS. NoParticulars

1Average Total SavingsRs.8238

2Average Family IncomeRs.30706.12

3% of Total Savings in Family Income28.1

4Ch-Square Value128.683

5Sig. Value0.000

6R (Correlation) value0.438

7Sig. Value0.000

8 F Value34.834

9Sig. Value0.000

10 Duncan subsets4 sub sets

The r value for the relationship is 0.438 and its significant value is 0.000. This shows that there is a positive relationship between family income and family savings and that relationship is significant at 1 percent significant level.

TABLE 5.5.27TOTAL SAVINGS AND FAMILY INCOMEFamily IncomeTotal SavingTotal

Up to 50005001-1000010001-1500015001-20000Above 2000

Up to 2000067(34%)*42(23%)13(11%)2(5%)124(22%)

20001-3000089(45%)*80(43%)*42(38%)6(15%)2219(40%)

30001-4000025(13%)32(17%)26(24%)*15(37%)*5103(17%)

40001-500008(4%)21(11%)10(9%)11(27%)*454(10%)

50001 and above7(4%)9(5%)19(17%)*71052(9%)

Total1961841104121552

The cross tabulation between family income and family saving indicates that families having less than Rs.20000 income account for 22 percent of total population but their population is 34 percent in less than Rs.5000 saving category. The families having income between Rs.20000 and Rs.30000 are saving to the extent of Rs10000. Likewise higher income families are associated with higher saving categories. This shows that there is a clear trend in the association, and the final inference can be that the size of saving increases with the rise in income. The chi-square test indicates that there is a significant association between income and saving.

TABLE 5.2.26CHI-SQUARE TEST VALUE FOR VARIOUS VARIABLESS. NoVariableChi-Square valueSig. ValueSignificance or not

1Type of Institution1.3490.853Not Significant

2Department54.1020.141Not Significant

3Designation5.1170.745Not Significant

1Age3.2870.511Not Significant

2Experience7.4420.282Not Significant

3Family Size7.5940.269Not Significant

4Total Income3.9310.863Not Significant

5Gender1.6730.433Not Significant

6Source of Information21.8940.000Significant

7Perception19.1650.001Significant

8Choice Criteria38.1840.000Significant

9Awareness Level9.9790.041Significant

10Risk Category9.6750.046Significant

11Frequency of Investment12.8080.012Significant

12Channel of Investment9.3250.009Significant

To understand the characteristics of these three motive segments, association among the segments with various demographic and investment related variables are analysed. Cross tabulation is used to find out the associations and the chi-square test is applied to test the significance of associations. The chi-square values along with their level of significance are given in the following table. From the chi-square test it is found that age, experience, family size, gender and income per month have no significant association with motive based segments. The chi-square test table also reveals that there is a significant association between motive segments and source of information, perception of investors, investment behaviour, choice criteria, and awareness level and risk category of investors. This signifies that the demographic variables do not have association with motive segments whereas investment related variables have association. This shows that motive segmentation is mainly based on investment related variables and not on demographic variables.

5.2.1.1.9 Characteristics of Motive SegmentsIn the previous section, investors have been classified into three categories namely highly motivated, self centered and least motivated on the basis of their saving motives. It has also been noticed that the highly motivated people will save more than the least motivated people. As the motivation level of highly motivated people is high, convincing them to make investment will not require much effort. To sell more with limited effort, the institutions who offer investment products can concentrate on highly motivated segment. To device effective marketing strategy, it is necessary for them to understand the characteristics of this motive segment. If necessary, they can also alter the existing features of their products and service levels to suit the requirements of a particular segment. In this section of the research, the characteristics of motive segments are identified through chi-square test along with correspondence analysis and analysis of variance. TABLE 5.2.26CHI-SQUARE TEST VALUE FOR VARIOUS VARIABLESS. NoVariableChi-Square valueSig. ValueSignificance or not

1Type of Institution1.3490.853Not Significant

2Department54.1020.141Not Significant

3Designation5.1170.745Not Significant

1Age3.2870.511Not Significant

2Experience7.4420.282Not Significant

3Family Size7.5940.269Not Significant

4Total Income3.9310.863Not Significant

5Gender1.6730.433Not Significant

6Source of Information21.8940.000Significant

7Perception19.1650.001Significant

8Choice Criteria38.1840.000Significant

9Awareness Level9.9790.041Significant

10Risk Category9.6750.046Significant

11Frequency of Investment12.8080.012Significant

12Channel of Investment9.3250.009Significant

To understand the characteristics of these three motive segments, association among the segments with various demographic and investment related variables are analysed. Cross tabulation is used to find out the associations and the chi-square test is applied to test the significance of associations. The chi-square values along with their level of significance are given in the following table. From the chi-square test it is found that age, experience, family size, gender and income per month have no significant association with motive based segments. The chi-square test table also reveals that there is a significant association between motive segments and source of information, perception of investors, investment behaviour, choice criteria, and awareness level and risk category of investors. This signifies that the demographic variables do not have association with motive segments whereas investment related variables have association. This shows that motive segmentation is mainly based on investment related variables and not on demographic variables.