Group 7

Charan Kamal Singh (7)

Maneesha Gautam (15) Manjalika

Manjalika Raj (16)

Customer Perception about Coaching Institutes for MBA

Index

S. No. Contents Page No.

1. Conceptual Model

Research Objectives:

To study the impact of Service Performance parameters on Customer’s (student) Perception.

To study the effect of demographics on relationship between Service Performance and Customer

Perception.

Research Hypothesis:

From the research objectives we define our null research hypothesis as follows:

H1: Service Performance factors have no significant impact on the customer’s perception

about coaching institutes.

H1.1: Reliability has no significant impact on customer’s perception.

H1.2: Responsiveness has no significant impact on customer’s perception.

H1.3: Assurance has no significant impact on customer’s perception.

H1.4: Empathy has no significant impact on customer’s perception.

H1.5: Tangibles has no significant impact on customer’s perception.

.

H2: Service Performance parameters are independent of demographics.

H2.1: Service Performance parameters are independent of Age.

H2.2: Service Performance parameters are independent of Gender.

H2.3: Service Performance parameters are independent of Family Income.

H2.4: Service Performance parameters are independent of Educational background.

H2.5: Service Performance parameters are independent of Work Experience.

H3: Customer Perception is independent of demographics.

H3.1: Customer Perception is independent of Age.

H3.2: Customer Perception is independent of Gender.

H3.3: Customer Perception is independent of Educational background.

H3.5: Customer Perception is independent of Work Experience.

Independent Variables: Reliability, Responsiveness, Assurance, Empathy, Tangibles

Dependent Variable: Customer Perception

Conceptual Model

SERVICE PERFORMANCE

PARAMETERS DEMOGRAPHICS

RELIABILITY

RESPONSIVENESS

TANGIBLES

EMPATHY

ASSURANCE

H1 H2

AGE

SEX

FAMILY INCOME

EDUCATIONAL

BACKGROUND

WORK EXPERIENCE

CUSTOMER

PERCEPTION

Research Objectives:

To study the impact of Service Performance parameters on Customer’s (student) Perception.

To study the effect of demographics on relationship between Service Performance and Customer

Perception.

Research Hypothesis:

From the research objectives we define our null research hypothesis as follows:

H1: Service Performance factors have no significant impact on the customer’s perception

about coaching institutes.

H1.1: Reliability has no significant impact on customer’s perception.

H1.2: Responsiveness has no significant impact on customer’s perception.

H1.3: Assurance has no significant impact on customer’s perception.

H1.4: Empathy has no significant impact on customer’s perception.

H1.5: Tangibles has no significant impact on customer’s perception.

.

H2: Service Performance parameters are independent of demographics.

H2.1: Service Performance parameters are independent of Age.

H2.2: Service Performance parameters are independent of Gender.

H2.3: Service Performance parameters are independent of Family Income.

H2.4: Service Performance parameters are independent of Educational background.

H2.5: Service Performance parameters are independent of Work Experience.

H3: Customer Perception is independent of demographics.

H3.1: Customer Perception is independent of Age.

H3.2: Customer Perception is independent of Gender.

H3.3: Customer Perception is independent of Educational background.

H3.5: Customer Perception is independent of Work Experience.

Independent Variables: Reliability, Responsiveness, Assurance, Empathy, Tangibles

Dependent Variable: Customer Perception

Demographic variables: Age, Gender, Family income, Educational background, Work experience

3. Data Analysis

3.1 Descriptive Analysis of the Sample:

3.1.1 Frequency analysis with respect to Age.

Table: Frequency distribution of sample with respect to Age

Indicate your age bracket

Frequency Percent Val id Percent

Cumulative

Percent

Val id BELOW 20 8 6.7 6.7 6.7

20-25 109 90.8 90.8 97.5

26-30 3 2.5 2.5 100.0

Tota l 120 100.0 100.0

Figure: Graphical representation of sample with respect to Age

Interpretation: Above graph shows that 6.67% of the respondents are in age group below 20,

90.83% of the respondents are in age group 20-25 and 2.5% of the respondents are in age group

26-30.

3.1.2 Frequency analysis with respect to Gender.

Table: Frequency distribution of sample with respect to Gender

Gender: Male or Female

Frequency Percent Val id Percent Cumulative Percent

Val id Male 62 51.7 51.7 51.7

Female 58 48.3 48.3 100.0

Tota l 120 100.0 100.0

Figure: Graphical representation of sample with respect to Gender

Interpretation: Above graph shows that 48.33% of the respondents are females, 51.67% of the

respondents are males.

3.1.3 Frequency analysis with respect to Family income.

Table: Frequency distribution of sample with respect to Family income.

Indicate your family income bracket

Frequency Percent Val id Percent

Cumulative

Percent

Val id LESS THAN 1,00,000 3 2.5 2.5 2.5

1,00,000 - 2,00,000 9 7.5 7.5 10.0

2,00,001- 5,00,000 39 32.5 32.5 42.5

5,00,001 -10,00,000 36 30.0 30.0 72.5

Above 10,00,000 33 27.5 27.5 100.0

Tota l 120 100.0 100.0

Figure: Graphical representation of sample with respect to Family income.

Interpretation: Above graph shows that 2.5% of the respondents have annual income of family

less than Rs 100000, 7.5% of the respondents have annual income of family between Rs 100000-

200000, 32.5% of the respondents have annual income of family between Rs 200000 - 500000,

30% of the respondents have annual income of family between Rs 500000 - 1000000 and

27.5% of the respondents have annual income of family more than Rs 1000000.

3.1.4 Frequency analysis with respect to Education background.

Table: Frequency distribution of sample with respect to Educational Background.

Indicate your Education Background

Frequency Percent Valid Percent

Cumulative

Percent

Valid ENGINEERING 77 64.2 64.2 64.2

SCIENCE 7 5.8 5.8 70.0

COMMERCE 27 22.5 22.5 92.5

MANAGEMENT 3 2.5 2.5 95.0

OTHERS 6 5.0 5.0 100.0

Total 120 100.0 100.0

Figure: Graphical representation of sample with respect to Educational Background

Interpretation: Above graph shows that 64.17% of the respondents are engineering students,

5.83% of the respondents are science students, 22.5% of the respondents are commerce students,

2.5% of the respondant are management students and 5% of the respondents are with other

preference students.

3.1.5 Frequency analysis with respect to Work experience.

Table: Frequency distribution of sample with respect to Work experience.

Indicate your Work Experience

Frequency Percent Val id Percent

Cumulative

Percent

Val id NIL 101 84.2 84.2 84.2

0-12 MONTHS 11 9.2 9.2 93.3

13-24 MONTHS 5 4.2 4.2 97.5

25-36 MONTHS 1 .8 .8 98.3

ABOVE 36 MONTHS 2 1.7 1.7 100.0

Tota l 120 100.0 100.0

Figure: Graphical representation of sample with respect to Work Experience.

Interpretation: Above graph shows that 84.17% of the respondents are having no work

experience, 9.17% of the respondents are having work experience 0-12 months, 4.17% of the

respondents are having work experience 13-24 months, 0.82% of the respondents are having work

experience 25-36 months, 1.67% of the respondents are having work experience above 36moths.

3.3 Tests of normality

Table: Normality values for different variable.

Interpretation: For each of the variables mentioned above significant value (p) is ≤ 0.05, that

means data is not normally distributed.

3.4 Correlation Analysis

Table: Correlation between parameters of Service Performance, Average Service Performance and Customers

Perception.

Results of Correlation Analysis:

1. The correlation coefficient between Customer perception and Reliability is 0.622, with p= 0.00. Thus there is a significant positive association between Customer perception and Reliability. Hence, we

reject our Hypothesis H1.1.

2. The correlation coefficient between Customer perception and Responsiveness is 0.634, with p= 0.00. Thus there is a significant positive association between Customer perception and Responsiveness.

Hence, we reject our Hypothesis H1.2.

3. The correlation coefficient between Customer perception and Assurance is 0.595, with p= 0.00. Thus there is a significant positive association between Customer perception and Assurance. Hence, we

reject our Hypothesis H1.3.

4. The correlation coefficient between Customer perception and Empathy is 0.627, with p= 0.00. Thus there is a significant positive association between Customer perception and Empathy. Hence, we

reject our Hypothesis H1.4.

5. The correlation coefficient between Customer perception and Tangibility is 0.638, with p= 0.00. Thus there is a significant positive association between Customer perception and Reliability. Hence, we

reject our Hypothesis H1.5.

Hence, from above we find that all the five service performance parameters are significantly

affecting the Customer Perception.

3.5 Regression Analysis

Applying the regression test between all the five service performance parameters factors and

Customer Perception.

Table: Summary of the regression model

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .757a .573 .554 .538

a. Predictors: (Constant), AVG_TAN, AVG_REL, AVG_ASU,

AVG_EMP, AVG_RES

Interpretation: We got the value of R = 0.757 and R square = 0.573. The value of R square tells

us what percent of the variance in the dependent variable has been explained by the considered

independent variables. Thus, in our research, service performance parameters i.e. Reliability,

Responsiveness, Assurance, Empathy, Tangibles can explain 75.7% of the variance in the

Customer Perception.

Table: ANOVA table giving the p value

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 44.278 5 8.856 30.596 .000a

Residual 32.996 114 .289

Total 77.274 119

a. Predictors: (Constant), AVG_TAN, AVG_REL, AVG_ASU, AVG_EMP, AVG_RES

b. Dependent Variable: AVG_CP

Interpretation: We got the value of p = 0.000. Since, p value is less than 0.05, thus the relationship is

significant at 100 % confidence level which is very high.

Table: Coefficients of the significant factors in the regression equation

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 44.278 5 8.856 30.596 .000a

Residual 32.996 114 .289

Total 77.274 119

a. Predictors: (Constant), AVG_TAN, AVG_REL, AVG_ASU, AVG_EMP, AVG_RES

Interpretation: The above table gives us the coefficients of the independent variables in the required regression equation. We get, Y = -0.004 + 0.320*X1 + 0.186*X2+0.251*X3+0.100*X4+0.164*X5

Where, Y = Customer Perception X1= Average Reliability

X2 = Average Responsiveness X3= Average Assurance X4= Average Empathy

X5= Average Tangibles