Thesis of BBA-04
Chapter 4
 |
|
|
4.2 Cross Tabulations
Income*Mostly_Purchased
Crosstabulation
|
Count
|
|||||||||||
|
Mostly_Purchased
|
Total
|
||||||||||
|
Bashundara
|
Shah
|
Crown
|
Holcim
|
Lafarge
Surma
|
Fresh
|
Tiger
|
Akij
|
Others
|
|||
|
Income
|
lessthan
20000
|
1
|
7
|
5
|
4
|
0
|
2
|
0
|
2
|
10
|
31
|
|
20000-50000
|
3
|
7
|
3
|
1
|
3
|
5
|
1
|
2
|
8
|
33
|
|
|
51000-75000
|
2
|
3
|
0
|
2
|
0
|
2
|
3
|
1
|
2
|
15
|
|
|
76000-125000
|
0
|
0
|
2
|
0
|
1
|
1
|
1
|
1
|
4
|
10
|
|
|
126000-200000
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
1
|
|
|
Total
|
6
|
17
|
10
|
7
|
4
|
10
|
5
|
6
|
25
|
90
|
|
Table: 19
According to this table customer
mostly purchased shah cement when their income is less than 50000tk.
Respondent purchased crown cement when income is less than 20000
taka. Respondent purchased shah cement when income is 76000-125000
taka.
|
Profession
* Which_Cement_Suggest_Others Crosstabulation
|
|||||||||||
|
Count
|
|||||||||||
|
Which_Cement_Suggest_Others
|
Total
|
||||||||||
|
Bashundara
|
Shah
|
Crown
|
Holcim
|
Lafarge
Surma
|
Fresh
|
Tiger
|
Akij
|
Others
|
|||
|
Profession
|
Engineers
|
1
|
0
|
0
|
0
|
1
|
1
|
1
|
1
|
1
|
6
|
|
Business
Man
|
0
|
4
|
0
|
1
|
1
|
3
|
1
|
1
|
5
|
16
|
|
|
Masons
|
1
|
3
|
1
|
2
|
0
|
0
|
1
|
1
|
5
|
14
|
|
|
Students
|
0
|
1
|
0
|
0
|
1
|
0
|
0
|
0
|
2
|
4
|
|
|
Job
|
1
|
3
|
2
|
3
|
1
|
1
|
0
|
2
|
17
|
30
|
|
|
Housewife
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
1
|
|
|
Retired
|
3
|
3
|
1
|
1
|
0
|
1
|
0
|
1
|
9
|
19
|
|
|
Total
|
6
|
14
|
4
|
7
|
4
|
6
|
3
|
6
|
40
|
90
|
|
Table: 20
In this table we found
that business men are mostly suggest shah cement. Masons suggest others cement.
Job holder suggests Holcim and others cement. Retired mostly
suggest Bashundara and shah cement.
|
Profession
* Most_Preferred_Cement Crosstabulation
|
|||||||||||
|
Count
|
|||||||||||
|
Most_Preferred_Cement
|
Total
|
||||||||||
|
Bashundara
|
Shah
|
Crown
|
Holcim
|
Lafarge
Surma
|
Fresh
|
Tiger
|
Akij
|
Others
|
|||
|
Profession
|
Engineers
|
1
|
0
|
0
|
0
|
1
|
1
|
1
|
1
|
1
|
6
|
|
Business
Man
|
1
|
4
|
0
|
1
|
1
|
3
|
2
|
1
|
3
|
16
|
|
|
Masons
|
1
|
4
|
1
|
2
|
0
|
1
|
2
|
1
|
2
|
14
|
|
|
Students
|
0
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
2
|
4
|
|
|
Job
|
1
|
3
|
4
|
4
|
2
|
4
|
0
|
2
|
10
|
30
|
|
|
Housewife
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
1
|
|
|
Retired
|
2
|
5
|
4
|
0
|
0
|
1
|
0
|
1
|
6
|
19
|
|
|
Total
|
6
|
17
|
10
|
7
|
4
|
10
|
5
|
6
|
25
|
90
|
|
Table: 21
According to this table
Retired parsons are mostly preferred Shah Cement. Business
man mostly preferred Shah and Fresh cement. Masons are mostly
preferred Shah, Holcim, Tiger. Job holders mostly preferred crown, Holcim and
other cement.
|
Profession*
Factor_1
|
||||||||||
|
Count
|
||||||||||
|
Factor_1
|
Total
|
|||||||||
|
Family
Member
|
Peers
|
TV
Ads
|
Point
Of sales
|
Engineers
|
Masons
|
Real
Estate Companies
|
Retailers
|
|||
|
Profession
|
Engineers
|
0
|
0
|
1
|
0
|
1
|
2
|
0
|
2
|
6
|
|
Business
Man
|
2
|
0
|
2
|
0
|
3
|
7
|
1
|
1
|
16
|
|
|
Masons
|
1
|
1
|
0
|
0
|
6
|
1
|
0
|
5
|
14
|
|
|
Students
|
0
|
0
|
2
|
0
|
0
|
1
|
1
|
0
|
4
|
|
|
Job
|
5
|
1
|
1
|
1
|
7
|
13
|
1
|
1
|
30
|
|
|
Housewife
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
|
|
Retired
|
1
|
0
|
3
|
0
|
2
|
9
|
1
|
3
|
19
|
|
|
Total
|
10
|
2
|
9
|
1
|
19
|
33
|
4
|
12
|
90
|
|
Table: 22
In this table business
man and job holder mostly heard information from Masons. Masons are mostly
heard information from engineers and retailers. Retired are mostly heard
information from Tv Ads and masons.
|
Profession*
Factor_2
|
||||||||||
|
Count
|
||||||||||
|
Factor_2
|
Total
|
|||||||||
|
Family
members
|
peers
|
TV
Ads
|
Point
Of Sales
|
Engineers
|
Masons
|
Real
Estate Companies
|
Retailers
|
|||
|
Profession
|
Engineers
|
0
|
0
|
1
|
1
|
2
|
1
|
0
|
1
|
6
|
|
Business
Man
|
2
|
2
|
4
|
1
|
2
|
3
|
1
|
1
|
16
|
|
|
Masons
|
0
|
1
|
6
|
1
|
2
|
1
|
1
|
2
|
14
|
|
|
Students
|
1
|
2
|
0
|
0
|
0
|
0
|
0
|
1
|
4
|
|
|
Job
|
6
|
3
|
7
|
0
|
5
|
5
|
1
|
3
|
30
|
|
|
Housewife
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
1
|
|
|
Retired
|
5
|
0
|
5
|
0
|
3
|
5
|
0
|
1
|
19
|
|
|
Total
|
14
|
8
|
23
|
3
|
14
|
16
|
3
|
9
|
90
|
|
Table: 23
According to this table
factor -2 masons mostly heard information from TV Ads. Retired mostly heard
information from family members, masons and TV Ads. Job holders are mostly
heard information from family members, engineers, masons and TV Ads.
|
Profession*
Factor_3
|
|||||||||||
|
Count
|
|||||||||||
|
Factor_3
|
Total
|
||||||||||
|
Family
Members
|
Peers
|
TV
Ads
|
Point
Of Sales
|
Engineers
|
Masons
|
Real
Estate Companies
|
Retailers
|
9
|
|||
|
Profession
|
Engineers
|
2
|
2
|
1
|
0
|
0
|
0
|
1
|
0
|
0
|
6
|
|
Business
Man
|
1
|
1
|
4
|
4
|
1
|
3
|
0
|
2
|
0
|
16
|
|
|
Masons
|
0
|
3
|
4
|
0
|
1
|
2
|
2
|
2
|
0
|
14
|
|
|
Students
|
1
|
0
|
2
|
0
|
0
|
0
|
0
|
1
|
0
|
4
|
|
|
Job
|
7
|
0
|
11
|
2
|
0
|
5
|
2
|
3
|
0
|
30
|
|
|
Housewife
|
0
|
0
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
|
|
Retired
|
1
|
0
|
9
|
0
|
1
|
3
|
1
|
3
|
1
|
19
|
|
|
Total
|
12
|
6
|
32
|
6
|
3
|
13
|
6
|
11
|
1
|
90
|
|
Table: 24
In this table factor -3
Job holders are mostly heard information from TV Ads, family members, masons.
Retired mostly heard information from masons and TV Ads. Business man are
mostly heard information from point of sales, masons and TV Ads.
|
Income
* Standard_Price_Range Crosstabulation
|
||||||
|
Count
|
||||||
|
Standard_Price_Range
|
Total
|
|||||
|
300-350
|
351-400
|
401-450
|
451-500
|
|||
|
Income
|
Less
than 20000
|
11
|
12
|
8
|
0
|
31
|
|
20000-50000
|
7
|
13
|
11
|
2
|
33
|
|
|
51000-75000
|
1
|
5
|
8
|
1
|
15
|
|
|
76000-125000
|
1
|
5
|
2
|
2
|
10
|
|
|
126000-200000
|
0
|
1
|
0
|
0
|
1
|
|
|
Total
|
20
|
36
|
29
|
5
|
90
|
|
Table: 25
According to this table
respondents think standard price of cement is 351 to 400 when their income
level is less than 50000. Here 29 respondent think standard price of cement is
401-450 when income is 20000-50000 taka.
4.3 Hypothesis Testing
4.3.1
t-Test Analysis
4.3.2
Regression Analysis
Based on the dependent
variable of overall employee satisfaction with the performance appraisal system
and a total of 10 independent variables, regression analysis has been done to
check the degree of relationship among the variables.
|
Model
Summary
|
||||
|
Model
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
|
1
|
.329a
|
.109
|
-.004
|
.601
|
|
a. Predictors: (Constant), Ads_Change_Buying_Decision,
High_Price_Quality_good, Avilable, Consider_Country_Of_Origin,
Benefits_Expected, Attractive_Pack, Reasonable_Cost,
Pack_Important_Purchasing, Quality_Depend_Brand_Name, Durability
|
||||
|
ANOVAb
|
||||||
|
Model
|
Sum
of Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
|
|
1
|
Regression
|
3.477
|
10
|
.348
|
.962
|
.483a
|
|
Residual
|
28.568
|
79
|
.362
|
|||
|
Total
|
32.045
|
89
|
||||
|
a.
Predictors: (Constant), Ads_Change_Buying_Decision, High_Price_Quality_good,
Avilable, Consider_Country_Of_Origin, Benefits_Expected, Attractive_Pack, Reasonable_Cost,
Pack_Important_Purchasing, Quality_Depend_Brand_Name, Durability
|
||||||
|
b. Dependent Variable:
Satisfaction_bangladesh_Cement
|
||||||
The table shows that the
value of R-square is .109 that indicates more than 10% of the variation in the
dependent variable can be accounted for by the variation in independent
variables. The adjusted R-square is -.004that indicates data
representation of independent variables will be around .04% if the number of
independent variables and sample size is adjusted. The overall significance
score of ANOVA is 0.483 that exceeds the limit to reject the null hypothesis at
0.05 level. This situation along with the score of F value shows that the
variables might have interrelationships that need to be reduced to some
factors.
|
Coefficientsa
|
||||||
|
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
|
B
|
Std.
Error
|
Beta
|
||||
|
1
|
(Constant)
|
3.318
|
.756
|
4.387
|
.000
|
|
|
High_Price_Quality_good
|
.047
|
.062
|
.086
|
.761
|
.449
|
|
|
Attractive_Pack
|
.059
|
.079
|
.087
|
.747
|
.457
|
|
|
Quality_Depend_Brand_Name
|
-.036
|
.075
|
-.059
|
-.485
|
.629
|
|
|
Consider_Country_Of_Origin
|
.045
|
.072
|
.071
|
.628
|
.532
|
|
|
Benefits_Expected
|
.032
|
.082
|
.044
|
.389
|
.698
|
|
|
Reasonable_Cost
|
.094
|
.074
|
.146
|
1.265
|
.209
|
|
|
Pack_Important_Purchasing
|
.032
|
.068
|
.054
|
.472
|
.638
|
|
|
Avilable
|
-.093
|
.088
|
-.127
|
-1.056
|
.294
|
|
|
Durability
|
-.023
|
.090
|
-.032
|
-.259
|
.796
|
|
|
Ads_Change_Buying_Decision
|
.087
|
.071
|
.144
|
1.226
|
.224
|
|
|
a. Dependent Variable:
Satisfaction_bangladesh_Cement
|
||||||
The table shows
different coefficients associated with each variable. From the table it can be
seen that 10 variables have significance score over than 0.05 indicating the
rejection of null hypothesis that independent variables have no impact on the
employee satisfaction of performance appraisal system. The variables can be
shown in an equation as follows:
Satisfied with cement
brand = 3.318 + 0.047 (High price) + .059(Attractive Pack) - 0.036
(Cement quality) + 0.045 (country of origin) + 0.032 (expected
benefit) + .094 (Reasonable Cost) + 0.032 (important of packaging)
- .093 (Available) - 0.023 (Durability) + .087
(Ads_Change_Buying_Decision)
4.2 Factor Analysis
In this study, 10
variables have been taken into consideration. The variables are derived from
reviewing the literature on related subject matter. And for the factor analysis
to be appropriate, the variables must have to be correlated. These variables
are as follows:
V1 – High Price
V2 – Attractive Packing
V3 – Cement Quality
V4 – Country of origin
V6 – Reasonable Cost
V7– Importance of
packaging
V8 – Availability
V9 – Durability
V10 – Advertising
Bartlett’s test of
sphericity has been used to test the null hypotheses that the variables in the
study are not correlated. In other words, the null hypothesis states that the
population correlation matrix is an identity matrix. In an identity matrix, all
the diagonal terms are 1 and all off-diagonal terms are 0. The test statistic
for sphericity is based on a chi-square transformation of the determinant of
the correlation matrix. A large value of the test statistic will favor the
rejection of the null hypotheses. If this hypothesis cannot be rejected, the
appropriateness of the factors will be questioned. Another useful statistic is
the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. This index compares
the magnitudes of the observed correlation coefficients to the magnitudes of
the partial correlation coefficients. Small values (below 0.5) of the KMO
statistic indicate that the correlations between pairs of variables cannot be
explained by other variables and that factor analysis may not be
appropriate.
|
KMO
and Bartlett's Test
|
||
|
Kaiser-Meyer-Olkin
Measure of Sampling Adequacy.
|
.431
|
|
|
Bartlett's
Test of Sphericity
|
Approx.
Chi-Square
|
75.595
|
|
Df
|
45
|
|
|
Sig.
|
.003
|
|
Table 2: KMO and
Bartlett’s Test
Consequently, from the
above table, it is apparent that factor analysis is appropriate. Here, the KMO
value is .431, which is between 0.5 and 1.0, and the approximate
chi-square statistic is 75.595 with 45 degrees
of freedom, which is significant at the 0.05 levels.
Therefore, the null hypotheses can be rejected and the alternative hypotheses
that all variables are correlated to each other can be accepted. To analyze the
variables ranging from V1 to V5, factor analysis has been used for data
reduction. This analysis divulges the most important factors that contribute to
satisfaction of Cement brand preference in Bangladesh.
|
Total Variance Explained
|
|||||||||
|
Component
|
Initial Eigenvalues
|
Extraction Sums of Squared Loadings
|
Rotation Sums of Squared Loadings
|
||||||
|
Total
|
% of Variance
|
Cumulative %
|
Total
|
% of Variance
|
Cumulative %
|
Total
|
% of Variance
|
Cumulative %
|
|
|
1
|
1.754
|
17.539
|
17.539
|
1.754
|
17.539
|
17.539
|
1.558
|
15.583
|
15.583
|
|
2
|
1.373
|
13.729
|
31.268
|
1.373
|
13.729
|
31.268
|
1.319
|
13.190
|
28.773
|
|
3
|
1.304
|
13.037
|
44.305
|
1.304
|
13.037
|
44.305
|
1.307
|
13.074
|
41.847
|
|
4
|
1.259
|
12.593
|
56.899
|
1.259
|
12.593
|
56.899
|
1.282
|
12.816
|
54.663
|
|
5
|
1.003
|
10.029
|
66.928
|
1.003
|
10.029
|
66.928
|
1.226
|
12.265
|
66.928
|
|
6
|
.910
|
9.102
|
76.030
|
||||||
|
7
|
.753
|
7.525
|
83.555
|
||||||
|
8
|
.723
|
7.228
|
90.784
|
||||||
|
9
|
.519
|
5.191
|
95.975
|
||||||
|
10
|
.403
|
4.025
|
100.000
|
||||||
|
Extraction
Method: Principal
|
|||||||||
Table 3: Total Variance
Explained
From the above table,
only 5 factors have been extracted, as cumulative percentage is greater than
70% at this point and eigenvalue is greater than 1.0 (it is recommended that
factors with eigenvalues greater than 1.0 should be retained) that indicates
the adequacy of the analysis using derived factors.
|
Rotated
Component Matrixa
|
|||||
|
Component
|
|||||
|
1
|
2
|
3
|
4
|
5
|
|
|
High_Price_Quality_good
|
-.173
|
.560
|
-.253
|
.065
|
-.198
|
|
Attractive_Pack
|
.059
|
.847
|
.081
|
.078
|
.053
|
|
Quality_Depend_Brand_Name
|
.129
|
.205
|
.256
|
-.371
|
.692
|
|
Consider_Country_Of_Origin
|
.042
|
-.249
|
-.109
|
.290
|
.791
|
|
Benefits_Expected
|
.198
|
.147
|
-.110
|
.781
|
.051
|
|
Reasonable_Cost
|
.721
|
-.197
|
.026
|
.219
|
-.165
|
|
Pack_Important_Purchasing
|
.622
|
.336
|
.071
|
-.144
|
.121
|
|
Avilable
|
-.277
|
.439
|
.583
|
.599
|
-.031
|
|
Durability
|
.123
|
-.066
|
.896
|
-.108
|
.050
|
|
Ads_Change_Buying_Decision
|
.684
|
-.081
|
-.002
|
.028
|
.179
|
|
Extraction Method: Principal
Component Analysis.
Rotation Method: Varimax
with Kaiser Normalization.
|
|||||
|
a. Rotation converged in 9
iterations.
|
|||||
Table 4: Rotated Component
Matrix
The extracted 5 factors
can be interpreted in terms of the variables that load high coefficients. From
the rotated component matrix table, factor 1 has high coefficients for
Reasonable cost (.510), Important of packaging (.546), Advertising (.639).
Thus, factor 1 can be entitled as “Promotional Activities”. Factor
2 has high coefficients for High Price (.489). Factor 3 has
high coefficients for Cement Quality (-.531), Expected Benefits
(.754). Hence, this can be tagged as “Expected value”.
Again, Factor 4 has high coefficients for Attractive Packaging (.638)
Availability (.557) and Durability (.508). So, this factor can be named “”. Finally, Factor 5 has very high coefficients for Country of origin
(.717).
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