Case Processing Summary

N %

Cases Valid 50 100.0

Excluded(a)

0 .0

Total 50 100.0

a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha N of Items

.905 21

The Cronbach Alpha value of our model is 0.905 which tells us that our questionnaire is reliable.

Model Summary(b)

Model R R Square Adjusted R

Square Std. Error of the Estimate Durbin-Watson

1 .843(a) .711 .685 .59797 2.271

a Predictors: (Constant), AA4, AA2, AA1, AA3 Table 1 b Dependent Variable: DV

Table 1 is the model summary table providing us the summary of regression statistics the value of R2. This

shows the magnitude of relationship between the independent and the dependent variables. The above

value of R2 i.e. 0.711 shows that 71.1% variation in dependent variable can be ascribed to the set of

independent variables. The value of R2 lies between 0-1. Higher values of R2 shows stronger impact of IVs

on DV and vice versa.

ANOVA(b)

Model Sum of

Squares Df Mean Square F Sig.

1 Regression 39.589 4 9.897 27.679 .000(a)

Residual 16.091 45 .358

Total 55.680 49

a Predictors: (Constant), AA4, AA2, AA1, AA3 Table 2 b Dependent Variable: DV

Table 2 the ANOVA table provides us with the F statistics. The significance value 0.000 tells us that we can

apply regression on our model. If this value were to be more than 0.05 to it would have shown that

regression could not be applied to our model

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

T Sig. B Std. Error Beta

1 (Constant) .080 .568 .141 .888

AA1 .504 .168 .347 3.000 .004

AA2 -.301 .107 -.227 -2.818 .007

AA3 .197 .173 .148 1.136 .262

AA4 .529 .131 .456 4.051 .000

a Dependent Variable: DV Table 3

Table 3 is the coefficient table providing us the values for beta coefficients and t-statistics. The

Beta coefficients of constant is .080 revealing the value of dependant variable keeping all the IVs

are equal to 0 but the t-statistics for this value is .888(p > 0.05) which means that the constant

value is insignificant.

The quality of advertisements (AA1) has a positive relationship with effectiveness of ad

campaigns (DV) as shown by the positive beta sign. The beta value shows the variation in

effectiveness of ad campaigns (DV) caused by quality of advertisements (AA1). The beta value of

quality advertisements is 0.504that tells us that a 1 degree change in quality of advertisements

(AA1) will bring a 50.4% change in the effectiveness of ad campaigns (DV). The t value of this

beta coefficient is 3.000 (p < 0.05) which means that the relationship between quality of

advertisements and the effectiveness of ad campaigns is significant.

The competitors (AA2) have an inverse relationship with effectiveness of ad campaigns (DV) as

shown by the negative beta sign. The beta value of competitors is -0.301 that tells us that a 1

degree change in quality of advertisements (AA1) will bring a 30.1% negative change in the

effectiveness of ad campaigns (DV). The t value of this beta coefficient is -2.818 (p < 0.05) which

means that the relationship between quality of advertisements and the effectiveness of ad

campaigns is significant. This negative value represents that as the competition increases the

effectiveness of ad campaigns diminishes.

The pricing and promotion (AA3) has a positive relationship with effectiveness of ad campaigns

(DV) as shown by the positive beta sign. The beta value of pricing and promotion (AA3) is 0.197

that tells us that a 1 degree change in quality of advertisements (AA1) will bring a 19.7% change

in the effectiveness of ad campaigns (DV). The t value of this beta coefficient is 4.051 (p < 0.05)

which means that the relationship between Research and the effectiveness of ad campaigns is

significant.

The Research (AA4) has a positive relationship with effectiveness of ad campaigns (DV) as shown

by the positive beta sign. The beta value of Research (AA4) is 0.529 that tells us that a 1 degree

change in quality of advertisements (AA1) will bring a 52.9% change in the effectiveness of ad

campaigns (DV). The t value of this beta coefficient is 1.136 (p < 0.05) which means that the

relationship between pricing and promotion and the effectiveness of ad campaigns is

insignificant.

Correlations

AA1 AA2 AA3 AA4 DV

AA1 Pearson Correlation 1

Sig. (1-tailed)

AA2 Pearson Correlation .059 1

Sig. (1-tailed) .342

AA3 Pearson Correlation .710(**) .086 1

Sig. (1-tailed) .000 .276

AA4 Pearson Correlation .578(**) .112 .688(**) 1

Sig. (1-tailed) .000 .218 .000

DV Pearson Correlation .702(**) -.143 .688(**) .732(**) 1

Sig. (1-tailed) .000 .161 .000 .000

** Correlation is significant at the 0.01 level (1-tailed).

The relationship of Quality of Advertisement (AA1) and Competitors (AA2) is insignificant

as shown by the significance value .342.

The relationship of Quality of Advertisement (AA1) and Pricing and Promotion (AA3) is

significant as shown by the significance value 0.000. Hence a 1% change in Quality of

Advertisement (AA1) will cause a 71% change in the variable Pricing and Promotion

(AA3).

The relationship of Quality of Advertisement (AA1) and Research (AA4) is significant as

shown by the significance value 0.000. Hence a 1% change in Quality of Advertisement

(AA1) will cause a 57.8% change in the variable Research (AA4).

The relationship of Quality of Advertisement (AA1) and Effectiveness of Ad Campaigns

(DV) is significant as shown by the significance value 0.000. Hence a 1% change in Quality

of Advertisement (AA1) will cause a 70.2% change in the variable Effectiveness of Ad

Campaigns (DV).

The relationship of Competitors (AA2) and Pricing and Promotion (AA3) is insignificant as

is shown by the significance value .276.

The relationship of Competitors (AA2) and Research (AA4) is insignificant as is shown by

the significance value .218.

The relationship of Competitors (AA2) and Effectiveness of Ad Campaigns (DV) is

insignificant as is shown by the significance value .161.

The relationship of Pricing and Promotion (AA3) and Research (AA4) is significant as

shown by the significance value 0.000. Hence a 1% change in Pricing and Promotion

(AA3) will cause a 68.8% change in the variable Research (AA4).

The relationship of Pricing and Promotion (AA3) and Effectiveness of Ad Campaigns (DV)

is significant as shown by the significance value 0.000. Hence a 1% change in Pricing and

Promotion (AA3) will cause a 68.8% change in the variable Effectiveness of Ad Campaigns

(DV).

The relationship of Research (AA4) and Effectiveness of Ad Campaigns (DV) is significant

as shown by the significance value 0.000. Hence a 1% change in Research (AA4) will cause

a 73.2% change in the variable Effectiveness of Ad Campaigns (DV).

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

AA1 50 1.00 4.75 3.3100 .73290

AA2 50 2.25 5.00 3.9750 .80377

AA3 50 1.00 4.75 3.2150 .79861

AA4 50 1.00 4.50 3.2050 .91848

DV 50 1.00 5.00 2.8800 1.06599

Valid N (listwise) 50

The descriptive stats reveal that the ad campaigns of Warid are ineffective. The mean value of

the dependent variable is less than 3 which signify that majority of the people in the survey

were in disagreement of the effectiveness of ad campaigns of Warid. If this value were to be

more than 3 then it would have meant that the ad campaigns of Warid are effective.

Conclusion After analyzing all the results and findings I would like to conclude this chapter by saying that all

the independent variables except for pricing and promotion impact the dependent variable. The

hypothesis of pricing and promotion has been found to be null i.e. any change in pricing and

promotion will have no effect on the effectiveness of ad campaigns.

Also after analysis we also came to found out that competition has an inverse relationship with

the dependent variable i.e. an increase in competition will lead to reduced effectiveness of ad

campaigns and vice versa. The rest of the variables (quality of advertisement and research) have

a direct relationship with the dependent variable i.e. and increase in any of these will increase the

effectiveness of ad campaigns and vice versa.

Pie Charts of Items