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Amr

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  • 1. HATCOREGRESSION (ENTER) Model SummaryModel R R Square Adjusted R Std. Error of the Square Estimate a1 .895 .801 .784 .3979a. Predictors: (Constant), Usage Level, Price Level, Salesforce Image,Product Quality, Price Flexibility, Manufacturer Image, Delivery Speed,Service a ANOVAModel Sum of Squares df Mean Square F Sig. b Regression 58.060 8 7.257 45.843 .0001 Residual 14.406 91 .158 Total 72.466 99a. Dependent Variable: Satisfaction Levelb. Predictors: (Constant), Usage Level, Price Level, Sales force Image, Product Quality, PriceFlexibility, Manufacturer Image, Delivery Speed, Service a CoefficientsModel 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 2.8441 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 4.443a. Dependent Variable: Satisfaction Level
  • 3. REGRESSION(STEPWISE) Model SummaryModel R R Square Adjusted R Std. Error of the Square Estimate a1 .711 .505 .500 .6049 b2 .781 .611 .603 .5393 c3 .830 .689 .679 .4848 d4 .857 .735 .724 .4496 e5 .885 .784 .772 .4082 f6 .885 .783 .774 .4070 g7 .892 .795 .785 .3971a. Predictors: (Constant), Usage Levelb. Predictors: (Constant), Usage Level, Manufacturer Imagec. Predictors: (Constant), Usage Level, Manufacturer Image, DeliverySpeedd. Predictors: (Constant), Usage Level, Manufacturer Image, DeliverySpeed, Price Flexibilitye. Predictors: (Constant), Usage Level, Manufacturer Image, DeliverySpeed, Price Flexibility, Servicef. Predictors: (Constant), Manufacturer Image, Delivery Speed, PriceFlexibility, Serviceg. Predictors: (Constant), Manufacturer Image, Delivery Speed, PriceFlexibility, Service, Salesforce Image
  • 4. a CoefficientsModel Unstandardized Standardized t Sig. Collinearity Coefficients Coefficients Statistics B Std. Error Beta Tolerance VIF (Constant) 1.653 .318 5.203 .0001 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 1.0532 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 1.9763 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 Manufacturer4 .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 1.1765 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 Image6 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 Manufacturer7 .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 2.665a. 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 .711Satisfaction nLevel 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 nSpeed 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 nPrice 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 nFlexibility 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 .224Manufactur ner 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 .256Salesforce nImage 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 -.192Product nQuality 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 1Usage nLevel 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 3Service .952 .191 .127Usage Level .848 -.285 .198Delivery Speed .702 -.582 .034Price Level .366 .871 .089Price Flexibility .292 -.782 -.009Product Quality -.170 .713 .200Salesforce Image .111 .053 .941Manufacturer Image .145 .154 .915Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 5 iterations.
  • 8. HBATMULTIPLE REGRESSION (ENTER) Model SummaryModel R R Square Adjusted R Std. Error of the Square Estimate a1 .897 .804 .774 .5663a. Predictors: (Constant), X18 - Delivery Speed, X8 - TechnicalSupport, X6 - Product Quality, X15 - New Products, X7 - E-CommerceActivities, X10 - Advertising, X13 - Competitive Pricing, X16 - Order &Billing, X17 - Price Flexibility, X14 - Warranty & Claims, X12 -Salesforce Image, X9 - Complaint Resolution, X11 - Product Line a ANOVAModel Sum of Squares df Mean Square F Sig. b Regression 113.044 13 8.696 27.111 .0001 Residual 27.584 86 .321 Total 140.628 99a. Dependent Variable: X19 - Satisfactionb. 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 CoefficientsModel 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 1.6721 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 44.004a. Dependent Variable: X19 – Satisfaction
  • 10. MULTIPLE REGRESSION (STEPWISE) Model SummaryModel R R Square Adjusted R Std. Error of the Square Estimate a1 .603 .364 .357 .9554 b2 .738 .544 .535 .8129 c3 .868 .753 .745 .6020 d4 .879 .773 .763 .5802 e5 .889 .791 .780 .5595a. Predictors: (Constant), X9 - Complaint Resolutionb. Predictors: (Constant), X9 - Complaint Resolution, X6 - ProductQualityc. Predictors: (Constant), X9 - Complaint Resolution, X6 - ProductQuality, X12 - Salesforce Imaged. Predictors: (Constant), X9 - Complaint Resolution, X6 - ProductQuality, X12 - Salesforce Image, X7 - E-Commerce Activitiese. Predictors: (Constant), X9 - Complaint Resolution, X6 - ProductQuality, X12 - Salesforce Image, X7 - E-Commerce Activities, X11 -Product Line a CoefficientsModel Unstandardized Standardized t Sig. Collinearity Coefficients Coefficients Statistics B Std. Error Beta Tolerance VIF (Constant) 3.680 .443 8.310 .0001 X9 - Complaint .595 .079 .603 7.488 .000 1.000 1.000 Resolution (Constant) 1.077 .564 1.909 .059 X9 - Complaint2 .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 - Complaint3 .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 Resolution4 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 1.3735 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 2.033a. Dependent Variable: X19 - Satisfaction
  • 12. CORRELATIONFACTOR ANALYSIS a Rotated Component Matrix Component 1 2 3 4 5X18 - Delivery Speed .945 .024 .170 .001 .056X9 - Complaint Resolution .928 .078 .111 .053 .001X16 - Order & Billing .869 .017 .096 .092 .022X6 - Product Quality .039 .823 -.010 -.048 .054X17 - Price Flexibility .490 -.758 .221 -.147 .079X11 - Product Line .559 .714 -.037 .133 .008X13 - Competitive Pricing -.117 -.688 .223 -.232 .035X12 - Salesforce Image .129 -.159 .902 .074 -.013X7 - E-Commerce Activities .054 -.106 .879 .038 -.103X10 - Advertising .160 -.050 .722 -.071 .144X8 - Technical Support .025 .102 -.023 .936 -.068X14 - Warranty & Claims .099 .123 .060 .932 .057X15 - New Products .051 -.001 .020 -.008 .987Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 5 iterations.
  • 13. NIKEMULTIPLE REGRESSION(ENTER) Model SummaryModel R R Square Adjusted R Std. Error of the Square Estimate a1 .767 .588 .541 1.164a. Predictors: (Constant), Attitude, Intention, Preference, Awareness a ANOVAModel Sum of Squares df Mean Square F Sig. b Regression 67.592 4 16.898 12.482 .0001 Residual 47.383 35 1.354 Total 114.975 39a. Dependent Variable: Loyaltyb. Predictors: (Constant), Attitude, Intention, Preference, Awareness a CoefficientsModel 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 3.0451 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 2.948a. Dependent Variable: Loyalty
  • 14. MULTIPLE REGRESSION(STEPWISE) Model SummaryModel R R Square Adjusted R Std. Error of the Square Estimate a1 .765 .585 .574 1.121a. Predictors: (Constant), Intention a ANOVAModel Sum of Squares df Mean Square F Sig. b Regression 67.222 1 67.222 53.494 .0001 Residual 47.753 38 1.257 Total 114.975 39a. Dependent Variable: Loyaltyb. Predictors: (Constant), Intention a CoefficientsModel Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF (Constant) .737 .483 1.526 .1351 Intention .792 .108 .765 7.314 .000 1.000 1.000a. Dependent Variable: Loyalty
  • 15. CORRELATION Correlations Loyalty Awareness Preference Intention Attitude ** Pearson Correlation 1 .068 .193 .759 .081Loyalty Sig. (2-tailed) .664 .215 .000 .604 N 44 43 43 43 43 ** ** Pearson Correlation .068 1 .596 .031 .790Awareness Sig. (2-tailed) .664 .000 .846 .000 N 43 44 43 43 43 ** ** Pearson Correlation .193 .596 1 .226 .601Preference Sig. (2-tailed) .215 .000 .145 .000 N 43 43 44 43 43 ** Pearson Correlation .759 .031 .226 1 .102Intention Sig. (2-tailed) .000 .846 .145 .513 N 43 43 43 44 43 ** ** Pearson Correlation .081 .790 .601 .102 1Attitude 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 2Awareness .922 -.080Attitude .914 -.023Preference .798 .282Intention .031 .984Extraction Method: PrincipalComponent Analysis.Rotation Method: Varimax with KaiserNormalization.a. Rotation converged in 3 iterations.

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