The regression model predicts customer satisfaction with 13 predictor variables. It explains 80.4% of the variance in satisfaction (R Square = .804). Delivery speed, technical support, product quality, e-commerce activities, and salesforce image were significant predictors of satisfaction, with salesforce image having the largest effect. The model was a good fit for the data (ANOVA p < .001).

1. HATCO
REGRESSION (ENTER)
Model Summary
Model R R Square Adjusted R Std. Error of the
Square Estimate
a
1 .895 .801 .784 .3979
a. Predictors: (Constant), Usage Level, Price Level, Salesforce Image,
Product Quality, Price Flexibility, Manufacturer Image, Delivery Speed,
Service
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
b
Regression 58.060 8 7.257 45.843 .000
1 Residual 14.406 91 .158
Total 72.466 99
a. Dependent Variable: Satisfaction Level
b. Predictors: (Constant), Usage Level, Price Level, Sales force Image, Product Quality, Price
Flexibility, Manufacturer Image, Delivery Speed, Service
a
Coefficients
Model Unstandardized Standardized t Sig. Collinearity
Coefficients Coefficients Statistics
B Std. Error Beta Tolerance VIF
-
(Constant) -.575 .458 .212
1.257
Delivery Speed .240 .181 .370 1.326 .188 .028 35.747
Price Level .176 .188 .245 .933 .353 .032 31.636
Price Flexibility .293 .049 .474 6.018 .000 .352 2.844
1
Manufacturer
.428 .060 .567 7.144 .000 .347 2.880
Image
Service .139 .361 .122 .384 .702 .022 46.008
-
Salesforce Image -.195 .086 -.176 .026 .364 2.751
2.268

2. -
Product Quality -.046 .032 -.084 .164 .606 1.651
1.404
Usage Level -.001 .009 -.009 -.088 .930 .225 4.443
a. Dependent Variable: Satisfaction Level

3. REGRESSION(STEPWISE)
Model Summary
Model R R Square Adjusted R Std. Error of the
Square Estimate
a
1 .711 .505 .500 .6049
b
2 .781 .611 .603 .5393
c
3 .830 .689 .679 .4848
d
4 .857 .735 .724 .4496
e
5 .885 .784 .772 .4082
f
6 .885 .783 .774 .4070
g
7 .892 .795 .785 .3971
a. Predictors: (Constant), Usage Level
b. Predictors: (Constant), Usage Level, Manufacturer Image
c. Predictors: (Constant), Usage Level, Manufacturer Image, Delivery
Speed
d. Predictors: (Constant), Usage Level, Manufacturer Image, Delivery
Speed, Price Flexibility
e. Predictors: (Constant), Usage Level, Manufacturer Image, Delivery
Speed, Price Flexibility, Service
f. Predictors: (Constant), Manufacturer Image, Delivery Speed, Price
Flexibility, Service
g. Predictors: (Constant), Manufacturer Image, Delivery Speed, Price
Flexibility, Service, Salesforce Image

4. a
Coefficients
Model Unstandardized Standardized t Sig. Collinearity
Coefficients Coefficients Statistics
B Std. Error Beta Tolerance VIF
(Constant) 1.653 .318 5.203 .000
1
Usage Level .068 .007 .711 10.001 .000 1.000 1.000
(Constant) .658 .343 1.916 .058
Usage Level .061 .006 .636 9.782 .000 .950 1.053
2
Manufacturer
.252 .049 .333 5.128 .000 .950 1.053
Image
(Constant) .800 .310 2.583 .011
Usage Level .035 .008 .368 4.593 .000 .506 1.976
3 Manufacturer
.283 .045 .374 6.339 .000 .931 1.074
Image
Delivery Speed .248 .051 .383 4.904 .000 .532 1.881
(Constant) -.100 .362 -.276 .783
Usage Level .022 .008 .236 2.908 .005 .425 2.352
Manufacturer
4 .332 .043 .439 7.700 .000 .859 1.164
Image
Delivery Speed .213 .048 .328 4.454 .000 .514 1.946
Price Flexibility .171 .042 .277 4.080 .000 .605 1.652
(Constant) -.784 .361 -2.172 .032
Usage Level -.006 .009 -.067 -.676 .501 .237 4.222
Manufacturer
.314 .039 .415 7.979 .000 .851 1.176
5 Image
Delivery Speed .106 .049 .163 2.146 .034 .399 2.506
Price Flexibility .308 .048 .499 6.378 .000 .375 2.664
Service .480 .104 .421 4.608 .000 .275 3.633
(Constant) -.743 .355 -2.093 .039
Manufacturer
.309 .039 .408 8.015 .000 .881 1.135
Image
6
Delivery Speed .104 .049 .161 2.125 .036 .400 2.501
Price Flexibility .287 .037 .465 7.800 .000 .643 1.555
Service .433 .077 .380 5.587 .000 .494 2.023
(Constant) -.824 .348 -2.371 .020
Manufacturer
7 .419 .059 .555 7.071 .000 .354 2.827
Image
Delivery Speed .106 .048 .164 2.226 .028 .400 2.502

5. Price Flexibility .293 .036 .474 8.134 .000 .640 1.561
Service .430 .076 .378 5.692 .000 .494 2.024
Salesforce Image -.204 .085 -.184 -2.411 .018 .375 2.665
a. Dependent Variable: Satisfaction Level

6. CORRELATION
Correlations
Satisfactio Deliver Price Price Manufactur Servic Salesforc Produc Usag
n Level y Leve Flexibilit er Image e e Image t e
Speed l y Quality Level
Pearson
** ** ** ** ** ** **
Correlatio 1 .651 .028 .525 .476 .631 .341 -.283 .711
Satisfaction n
Level Sig. (2-
.000 .779 .000 .000 .000 .001 .004 .000
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson -
** * ** ** ** **
Correlatio .651 1 .349 .509 .050 .612 .077 -.483 .676
*
Delivery n
Speed Sig. (2-
.000 .000 .000 .618 .000 .446 .000 .000
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson
** ** ** ** **
Correlatio .028 -.349 1 -.487 .272 .513 .186 .470 .082
n
Price Level
Sig. (2-
.779 .000 .000 .006 .000 .064 .000 .418
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson -
** ** * ** **
Correlatio .525 .509 .487 1 -.116 .067 -.034 -.448 .559
*
Price n
Flexibility Sig. (2-
.000 .000 .000 .250 .510 .735 .000 .000
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson *
**
.272 ** ** * *
Correlatio .476 .050 *
-.116 1 .299 .788 .200 .224
Manufactur n
er Image Sig. (2-
.000 .618 .006 .250 .003 .000 .046 .025
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson *
** **
.513 ** * **
Service Correlatio .631 .612 *
.067 .299 1 .241 -.055 .701
n

7. Sig. (2-
.000 .000 .000 .510 .003 .016 .586 .000
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson
** ** * *
Correlatio .341 .077 .186 -.034 .788 .241 1 .177 .256
Salesforce n
Image Sig. (2-
.001 .446 .064 .735 .000 .016 .078 .010
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson *
** **
.470 ** *
Correlatio -.283 -.483 *
-.448 .200 -.055 .177 1 -.192
Product n
Quality Sig. (2-
.004 .000 .000 .000 .046 .586 .078 .055
tailed)
N 100 100 100 100 100 100 100 100 100
Pearson
** ** ** * ** *
Correlatio .711 .676 .082 .559 .224 .701 .256 -.192 1
Usage n
Level Sig. (2-
.000 .000 .418 .000 .025 .000 .010 .055
tailed)
N 100 100 100 100 100 100 100 100 100
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
FACTOR ANALYSIS
a
Rotated Component Matrix
Component
1 2 3
Service .952 .191 .127
Usage Level .848 -.285 .198
Delivery Speed .702 -.582 .034
Price Level .366 .871 .089
Price Flexibility .292 -.782 -.009
Product Quality -.170 .713 .200
Salesforce Image .111 .053 .941
Manufacturer Image .145 .154 .915
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.

8. HBAT
MULTIPLE REGRESSION (ENTER)
Model Summary
Model R R Square Adjusted R Std. Error of the
Square Estimate
a
1 .897 .804 .774 .5663
a. Predictors: (Constant), X18 - Delivery Speed, X8 - Technical
Support, X6 - Product Quality, X15 - New Products, X7 - E-Commerce
Activities, X10 - Advertising, X13 - Competitive Pricing, X16 - Order &
Billing, X17 - Price Flexibility, X14 - Warranty & Claims, X12 -
Salesforce Image, X9 - Complaint Resolution, X11 - Product Line
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
b
Regression 113.044 13 8.696 27.111 .000
1 Residual 27.584 86 .321
Total 140.628 99
a. Dependent Variable: X19 - Satisfaction
b. Predictors: (Constant), X18 - Delivery Speed, X8 - Technical Support, X6 - Product Quality,
X15 - New Products, X7 - E-Commerce Activities, X10 - Advertising, X13 - Competitive Pricing,
X16 - Order & Billing, X17 - Price Flexibility, X14 - Warranty & Claims, X12 - Salesforce Image,
X9 - Complaint Resolution, X11 - Product Line
a
Coefficients
Model Unstandardized Standardized t Sig. Collinearity
Coefficients Coefficients Statistics
B Std. Error Beta Tolerance VIF
-
(Constant) -1.336 1.120 .236
1.192
X6 - Product Quality .377 .053 .442 7.161 .000 .598 1.672
1
X7 - E-Commerce -
-.456 .137 -.268 .001 .354 2.823
Activities 3.341
X8 - Technical Support .035 .065 .045 .542 .589 .328 3.047

9. X9 - Complaint
.154 .104 .156 1.489 .140 .207 4.838
Resolution
X10 - Advertising -.034 .063 -.033 -.548 .585 .646 1.547
X11 - Product Line .362 .267 .400 1.359 .178 .026 37.978
X12 - Salesforce Image .827 .101 .744 8.155 .000 .274 3.654
X13 - Competitive
-.047 .048 -.062 -.985 .328 .584 1.712
Pricing
X14 - Warranty &
-.107 .126 -.074 -.852 .397 .306 3.268
Claims
X15 - New Products -.003 .040 -.004 -.074 .941 .930 1.075
X16 - Order & Billing .143 .105 .111 1.369 .175 .344 2.909
X17 - Price Flexibility .238 .272 .241 .873 .385 .030 33.332
X18 - Delivery Speed -.249 .514 -.154 -.485 .629 .023 44.004
a. Dependent Variable: X19 – Satisfaction

10. MULTIPLE REGRESSION (STEPWISE)
Model Summary
Model R R Square Adjusted R Std. Error of the
Square Estimate
a
1 .603 .364 .357 .9554
b
2 .738 .544 .535 .8129
c
3 .868 .753 .745 .6020
d
4 .879 .773 .763 .5802
e
5 .889 .791 .780 .5595
a. Predictors: (Constant), X9 - Complaint Resolution
b. Predictors: (Constant), X9 - Complaint Resolution, X6 - Product
Quality
c. Predictors: (Constant), X9 - Complaint Resolution, X6 - Product
Quality, X12 - Salesforce Image
d. Predictors: (Constant), X9 - Complaint Resolution, X6 - Product
Quality, X12 - Salesforce Image, X7 - E-Commerce Activities
e. Predictors: (Constant), X9 - Complaint Resolution, X6 - Product
Quality, X12 - Salesforce Image, X7 - E-Commerce Activities, X11 -
Product Line
a
Coefficients
Model Unstandardized Standardized t Sig. Collinearity
Coefficients Coefficients Statistics
B Std. Error Beta Tolerance VIF
(Constant) 3.680 .443 8.310 .000
1 X9 - Complaint
.595 .079 .603 7.488 .000 1.000 1.000
Resolution
(Constant) 1.077 .564 1.909 .059
X9 - Complaint
2 .550 .068 .558 8.092 .000 .989 1.011
Resolution
X6 - Product Quality .364 .059 .427 6.193 .000 .989 1.011
(Constant) -1.569 .511 -3.069 .003
X9 - Complaint
3 .433 .052 .439 8.329 .000 .927 1.079
Resolution
X6 - Product Quality .437 .044 .512 9.861 .000 .956 1.046

11. X12 - Salesforce Image .530 .059 .477 8.992 .000 .916 1.092
(Constant) -1.106 .518 -2.134 .035
X9 - Complaint
.423 .050 .429 8.430 .000 .923 1.084
Resolution
4 X6 - Product Quality .435 .043 .509 10.177 .000 .956 1.046
X12 - Salesforce Image .736 .091 .663 8.074 .000 .356 2.813
X7 - E-Commerce
-.395 .137 -.232 -2.890 .005 .372 2.692
Activities
(Constant) -1.151 .500 -2.303 .023
X9 - Complaint
.319 .061 .323 5.256 .000 .588 1.701
Resolution
X6 - Product Quality .369 .047 .432 7.820 .000 .728 1.373
5
X12 - Salesforce Image .775 .089 .697 8.711 .000 .347 2.880
X7 - E-Commerce
-.417 .132 -.245 -3.162 .002 .370 2.701
Activities
X11 - Product Line .174 .061 .192 2.860 .005 .492 2.033
a. Dependent Variable: X19 - Satisfaction

12. CORRELATION
FACTOR ANALYSIS
a
Rotated Component Matrix
Component
1 2 3 4 5
X18 - Delivery Speed .945 .024 .170 .001 .056
X9 - Complaint Resolution .928 .078 .111 .053 .001
X16 - Order & Billing .869 .017 .096 .092 .022
X6 - Product Quality .039 .823 -.010 -.048 .054
X17 - Price Flexibility .490 -.758 .221 -.147 .079
X11 - Product Line .559 .714 -.037 .133 .008
X13 - Competitive Pricing -.117 -.688 .223 -.232 .035
X12 - Salesforce Image .129 -.159 .902 .074 -.013
X7 - E-Commerce Activities .054 -.106 .879 .038 -.103
X10 - Advertising .160 -.050 .722 -.071 .144
X8 - Technical Support .025 .102 -.023 .936 -.068
X14 - Warranty & Claims .099 .123 .060 .932 .057
X15 - New Products .051 -.001 .020 -.008 .987
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.

13. NIKE
MULTIPLE REGRESSION(ENTER)
Model Summary
Model R R Square Adjusted R Std. Error of the
Square Estimate
a
1 .767 .588 .541 1.164
a. Predictors: (Constant), Attitude, Intention, Preference, Awareness
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
b
Regression 67.592 4 16.898 12.482 .000
1 Residual 47.383 35 1.354
Total 114.975 39
a. Dependent Variable: Loyalty
b. Predictors: (Constant), Attitude, Intention, Preference, Awareness
a
Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) .526 .692 .760 .452
Awareness .031 .172 .034 .180 .858 .328 3.045
1 Preference .059 .156 .055 .380 .706 .571 1.752
Intention .784 .117 .757 6.728 .000 .930 1.075
Attitude -.034 .166 -.038 -.206 .838 .339 2.948
a. Dependent Variable: Loyalty

14. MULTIPLE REGRESSION(STEPWISE)
Model Summary
Model R R Square Adjusted R Std. Error of the
Square Estimate
a
1 .765 .585 .574 1.121
a. Predictors: (Constant), Intention
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
b
Regression 67.222 1 67.222 53.494 .000
1 Residual 47.753 38 1.257
Total 114.975 39
a. Dependent Variable: Loyalty
b. Predictors: (Constant), Intention
a
Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) .737 .483 1.526 .135
1
Intention .792 .108 .765 7.314 .000 1.000 1.000
a. Dependent Variable: Loyalty

15. CORRELATION
Correlations
Loyalty Awareness Preference Intention Attitude
**
Pearson Correlation 1 .068 .193 .759 .081
Loyalty Sig. (2-tailed) .664 .215 .000 .604
N 44 43 43 43 43
** **
Pearson Correlation .068 1 .596 .031 .790
Awareness Sig. (2-tailed) .664 .000 .846 .000
N 43 44 43 43 43
** **
Pearson Correlation .193 .596 1 .226 .601
Preference Sig. (2-tailed) .215 .000 .145 .000
N 43 43 44 43 43
**
Pearson Correlation .759 .031 .226 1 .102
Intention Sig. (2-tailed) .000 .846 .145 .513
N 43 43 43 44 43
** **
Pearson Correlation .081 .790 .601 .102 1
Attitude Sig. (2-tailed) .604 .000 .000 .513
N 43 43 43 43 44
**. Correlation is significant at the 0.01 level (2-tailed).
FACTOR ANALYSIS
a
Rotated Component Matrix
Component
1 2
Awareness .922 -.080
Attitude .914 -.023
Preference .798 .282
Intention .031 .984
Extraction Method: Principal
Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization.
a. Rotation converged in 3 iterations.