Impact of Balanced Scorecard Components

This assignment explores the impact of various Balanced Scorecard components (e.g., financial performance, customer satisfaction) on decision-makers. It utilizes statistical methods like eigenvalue analysis and Durbin-Watson test to determine the significance and influence of each component. The analysis reveals differing degrees of impact among components and highlights potential autocorrelation issues over time.

SPSS ANALYSIS

TABLE OF CONTENTS
Frequency table............................................................................................................................3
Descriptive statistics....................................................................................................................8
Correlation analysis.....................................................................................................................8
Regression and multi regression analysis....................................................................................9
REFERENCES..............................................................................................................................14

INTRODUCTION
Balanced scorecard is the one of the most important method that is used by the managers to
make different time of decisions. There are four components of balanced score card which help
managers in making business decisions. Current research report focused on the impact that
balanced scorecard method have on the decision making of the managers. In this regard
descriptive statistics is applied on the given data. Further, regression analysis is also applied on
data set in order to derive useful conclusions. In this way research work is done in the report.
Data screening
Screening of data is done and there are no error in same. Thus, it can be said that accurate
and reliable data is used in the present research study and on same descriptive and regression is
applied.
Sampling profile
Stratified sampling method is used in the current research study. Under this, all sample
units are classified in to different categories like manager, team leader, supervisor and engineer.
It can be said that best sampling method is used in current research study.
Hypothesis testing
Frequency table
Frequency Table
AverageFP
Frequency Percent Valid Percent Cumulative
Percent
Valid 1.40 1 1.9 1.9 1.9
1.60 1 1.9 1.9 3.8
2.60 1 1.9 1.9 5.8
3.00 3 5.8 5.8 11.5
3.20 2 3.8 3.8 15.4
3.40 3 5.8 5.8 21.2
3.60 4 7.7 7.7 28.8
3.80 8 15.4 15.4 44.2
4.00 18 34.6 34.6 78.8

4.20 6 11.5 11.5 90.4
4.40 3 5.8 5.8 96.2
4.60 1 1.9 1.9 98.1
5.00 1 1.9 1.9 100.0
Total 52 100.0 100.0
Interpretation
It can be seen from the table that majority of respondents are selecting fourth option which
is agree. Out of 52 respondents there are 18 who are agree with the fact that FP which is part of
balanced scorecard heavily affect the decision making of the business firms. It can be said that
FP as part of balanced score card have heavy influence on decision making of business firms.
AverageCP
Frequency Percent Valid Percent Cumulative
Percent
Valid 1.60 1 1.9 1.9 1.9
2.00 1 1.9 1.9 3.8
2.80 4 7.7 7.7 11.5
3.00 1 1.9 1.9 13.5
3.20 7 13.5 13.5 26.9
3.40 1 1.9 1.9 28.8
3.60 4 7.7 7.7 36.5
3.80 2 3.8 3.8 40.4

4.00 22 42.3 42.3 82.7
4.20 3 5.8 5.8 88.5
4.40 2 3.8 3.8 92.3
4.60 2 3.8 3.8 96.2
5.00 2 3.8 3.8 100.0
Total 52 100.0 100.0
Interpretation
In case of variable average CP highest number of respondents are on 4 which means that
majority of number of respondents are agree on the fact that CP affects decision making of
business firms on balanced score card. This is because customers are those from which firm is
earning revenue in its business.
AverageIP
Frequency Percent Valid Percent Cumulative
Percent
Valid 1.20 1 1.9 1.9 1.9
2.00 1 1.9 1.9 3.8
2.20 1 1.9 1.9 5.8
2.60 1 1.9 1.9 7.7
2.80 1 1.9 1.9 9.6
3.00 1 1.9 1.9 11.5
3.20 1 1.9 1.9 13.5

3.40 4 7.7 7.7 21.2
3.60 1 1.9 1.9 23.1
3.80 2 3.8 3.8 26.9
4.00 18 34.6 34.6 61.5
4.20 5 9.6 9.6 71.2
4.40 8 15.4 15.4 86.5
4.60 3 5.8 5.8 92.3
4.80 1 1.9 1.9 94.2
5.00 3 5.8 5.8 100.0
Total 52 100.0 100.0
Interpretation
In case of variable average IP also same scenario is observed and on value 4 there are higher
number of respondents which are 18. It is concluded that most of respondents are either agree or
strongly agree on the fact that IP as part of balance score affects decision making of business
firms.
AverageLG
Frequency Percent Valid Percent Cumulative
Percent
Valid 2.20 2 3.8 3.8 3.8
2.60 1 1.9 1.9 5.8
2.80 1 1.9 1.9 7.7
3.00 3 5.8 5.8 13.5
3.20 2 3.8 3.8 17.3
3.40 1 1.9 1.9 19.2

3.60 8 15.4 15.4 34.6
3.80 7 13.5 13.5 48.1
4.00 17 32.7 32.7 80.8
4.20 2 3.8 3.8 84.6
4.40 4 7.7 7.7 92.3
4.60 1 1.9 1.9 94.2
5.00 3 5.8 5.8 100.0
Total 52 100.0 100.0
Interpretation
Like other cases in this case also same thing is observed and on value 4 there are highest
number of respondents. This reflects that large proportion of same units are agree on the fact
that variable LG is considered while making decisions by the company.
AverageDV
Frequency Percent Valid
Percent
Cumulative
Percent
Valid 2.00 1 1.9 1.9 1.9
2.70 1 1.9 1.9 3.8
2.80 2 3.8 3.8 7.7
3.00 2 3.8 3.8 11.5
3.40 4 7.7 7.7 19.2
3.50 3 5.8 5.8 25.0
3.60 3 5.8 5.8 30.8
3.70 3 5.8 5.8 36.5

3.80 2 3.8 3.8 40.4
3.90 5 9.6 9.6 50.0
4.00 15 28.8 28.8 78.8
4.10 3 5.8 5.8 84.6
4.20 1 1.9 1.9 86.5
4.30 3 5.8 5.8 92.3
4.40 2 3.8 3.8 96.2
4.60 2 3.8 3.8 100.0
Total 52 100.0 100.0
Interpretation
There are 15 respondents out of 50 who select option 4 which is agree. Thus, it can be said
that most of respondents are agreed on asked statement.
Descriptive statistics
Statistics
AverageFP AverageCP AverageIP AverageLG AverageDV
N Valid 52 52 52 52 52
Missing 0 0 0 0 0
Mean 3.7692 3.7346 3.9154 3.8000 3.7846
Median 4.0000 4.0000 4.0000 4.0000 3.9500
Std. Deviation .62796 .65046 .73602 .59541 .49720
Interpretation
Mean indicate average value of the specific variable as it can be seen that mean value of all five
variables is in range of 3.70 to 3.80 which is nearby to 4. This means that most of respondents
are agree on the fact that all components of balance score card have impact on decision makers.

Median value in case of all variables is 4 which means that 50% of observations are above value
4 and 50% are below 4. This means that at least 50% of sample size are agree or strongly agree
on the fact that balance score card have any sort of impact on decision makers. Thus, it can be
said that balance score card must be carefully used by the mangers because it heavily affects
their decision making. Standard deviation value is very low in case of all variables either it is
average FP or any other variable. This means that respondents are almost giving similar response
on asked questions. It can be assumed that large percentage of sample units are having same
opinion on the asked statements.
Correlation analysis
Correlations
AverageDV AverageFP AverageCP AverageIP AverageLG
Pearson Correlation
AverageDV 1.000 .565 .663 .593 .560
AverageFP .565 1.000 .573 .763 .541
AverageCP .663 .573 1.000 .652 .610
AverageIP .593 .763 .652 1.000 .621
AverageLG .560 .541 .610 .621 1.000
Sig. (1-tailed)
AverageDV . .000 .000 .000 .000
AverageFP .000 . .000 .000 .000
AverageCP .000 .000 . .000 .000
AverageIP .000 .000 .000 . .000
AverageLG .000 .000 .000 .000 .
N
AverageDV 52 52 52 52 52
AverageFP 52 52 52 52 52
AverageCP 52 52 52 52 52
AverageIP 52 52 52 52 52
AverageLG 52 52 52 52 52
Interpretation
Correlation value of average DV and average FP is 0.565 which reflects that there is a
moderate relationship among the variables. Level of significance 0.00<0.05 validate this fact.
Apart from this, correlation value is 0.663 in case of variables average DV and CP. Level of
significance is 0.00<0.05 which indicate there is significant relationship between dependent and
independent variable. Correlation in case of variables IP and DV is 0.593 which is nearby to
0.600. It can be said that there is high or moderate relationship among the variables. Like other

cases level of significance of 0.00<0.005 support the asked statement. Value of correlation
between average LG and average DV is 0.560 which again reveal moderate relationship among
the variables followed by level of significance 0.00<0.005. Overall, it can be concluded that
there is a moderate relationship of average DV with all other variables.
Regression and multi regression analysis
H0: There is no significant difference between mean values of average LP, CP, IP, FP and
average DV.
H1: There is significant difference between mean values of average LP, CP, IP, FP and average
DV.
Model Summaryb
Model R R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics Durbin-
WatsonR Square
Change
F
Change
df1 df2 Sig. F
Change
1 .716a .512 .471 .36172 .512 12.339 4 47 .000 2.196
a. Predictors: (Constant), AverageLG, AverageFP, AverageCP, AverageIP
b. Dependent Variable: AverageDV
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 6.458 4 1.615 12.339 .000b
Residual 6.150 47 .131
Total 12.608 51
a. Dependent Variable: AverageDV
b. Predictors: (Constant), AverageLG, AverageFP, AverageCP, AverageIP
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 1.353 .369 3.664 .001
AverageFP .135 .127 .170 1.064 .293 .405 2.470
AverageCP .306 .110 .400 2.777 .008 .500 2.000
AverageIP .070 .120 .103 .578 .566 .327 3.055

AverageLG .134 .116 .160 1.153 .255 .537 1.861
a. Dependent Variable: AverageDV
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition
Index
Variance Proportions
(Constant) AverageFP AverageCP AverageIP AverageLG
1
1 4.952 1.000 .00 .00 .00 .00 .00
2 .018 16.397 .60 .05 .01 .17 .02
3 .013 19.458 .13 .22 .53 .03 .11
4 .010 22.434 .09 .02 .44 .01 .81
5 .006 27.652 .18 .71 .02 .78 .07
a. Dependent Variable: AverageDV
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 2.4091 4.4398 3.7846 .35585 52
Residual -1.19934 .67003 .00000 .34725 52
Std. Predicted Value -3.865 1.841 .000 1.000 52
Std. Residual -3.316 1.852 .000 .960 52
a. Dependent Variable: AverageDV

Interpretation
Durbin Watson test is revealing that there is negative autocorrelation among the residuals as
value of statistical test is more than 2. This means that residuals values of dependent variables is
increasing and decreasing for specific number of lags (What is the Durbin Watson test, 2017).
Value of R is 0.716 which means that there is high degree of relationship between dependent and
independent variables. Along with this, value of R square is 0.512 which means that with change
in independent variable 52% change comes in the dependent variable. Adjusted R square is 0.471
which means that with addition of value of variable 47% change may come in the dependent
variable. Level of significance is 0.00<0.05 which means that there is significant difference
between dependent and independent variables and due to this reason null hypothesis is rejected.
It can be said that there is a huge difference in the rate of change of dependent and independent

variable. Value of beta in case of variable average FP is 0.135 which means that with change in
value of the mentioned variable value of dependent variable will change by 0.135 points. Beta
value of average CP is 0.306 and it can be interpreted that with slight percentage change in
relevant variable dependent variable will get changed by 0.306 points. In case of average IP
value of relevant statistical tool is 0.070. This reflects that with variation in mentioned variable
value huge even moderate change will not come in the dependent variable. It can be observed
that beta value for variable average LG and average FP is almost same at 0.134. This reflects that
variables average LG and average FP which are important component of the balance score card
have similar impact on decision makers. Eigenvalue is the one of the most important tool which
reflects the degree up to which change may come in dependent variable due to variation in
independent variable. Beta reflects the points by which value of coefficient may change but
eigenvalue indicate extent to which change may take place in value of dependent variable with
change in independent variable. If value of eigenvalue is zero or nearby to same then it means
that with small change in independent variable big variation comes in the dependent variable
(IBM knowledge center, 2017). In case of average LG and CP there is very low value.
Eigenvalue for LG is 0.07 and for CP it is 0.02. Thus, it can be said that if slight variation will
happened in these variables big impact will be observed on decision makers. In case of other
variables such a small gap between value of eigenvalue and zero is not observed. Thus, it can be
interpreted that remaining variables does not have a very high impact on the decision makers.
Conditional index is another most important component of the collinearity diagnostics table. It is
the element which is computed by using a specific approach (Huber, 2011). Under this, square
root of ratios of largest eigenvalue to each next eigenvalue is computed. If value of conditional
index is above 15 then it means that there is a problem with collinearity. Further, if it is identified
that value is more than 30 then it means that there is a very serious problem with coolinearity.
Value of all variables in conditional index is range of 15 to 30 which means that there is an issue
in terms of coolinearity.
Summary
It is concluded that there is significant mean difference among independent variable which
are components of the balance score card. It can be said that the degree of impact of all
components is different from each other. However, consistency in same for different time period
is not observed as value of Durbin Watson test is revealing that there is negative autocorrelation

among the variables on which study is undertaken. It can be said that with passage of time
impact of these components on decision maker’s decision also get changed.

REFERENCES
Books and journals
Huber, P.J., 2011. Robust statistics. Springer Berlin Heidelberg.
Online
What is the Durbin Watson test, 2017. [Online]. Available through :<
http://www.statisticshowto.com/durbin-watson-test-coefficient/>.[Accessed on 11th February
2016].
IBM knowledge center, 2017. [Online]. Available through :<
https://www.ibm.com/support/knowledgecenter/SSLVMB_20.0.0/com.ibm.spss.statistics.cs
/reg_cars_collin_01.htm>. [Accessed on 11th February 2016].