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Transcript of MR group 4
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A STUDY ON INFLUENCE OF PRODUCT ATTRIBUTES ON THE CUSTOMER SATISFACTION AND THEIR REPURCHASE
INTENTION IN BIKE INDUSTRY
SUBMITTED TOWARDS PARTIAL FULFILLMENTOF
POST GRADUATE DIPLOMA IN MANAGEMENT
(APPROVED BY AICTE, GOVT. OF INDIA)
ACADEMIC SESSION
2009-11
Submitted to: - Submitted by :-
Dr. Sanjay Jain Ashutosh Sarkar (BM 09260)
IMS Ghaziabad Amritanshu Kumar (BM 09253)
Anshul Tomar (BM 09257)
Pawandeep Singh (BM 09275)
Kunal Pupneja (BM 09271)
Rajat Tyagi (BM 09279)
Sikha Srivastav (BM 09196)
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OBJECTIVES OF THE STUDY
To measure the influence of product attributes on the consumer satisfaction, for the bike company, which
they are using.
To measure the repurchase intention of the bike users for the company whose bike they are using, based
on Product attributes.
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RESEARCH METHODOLOGY: -
Research Design - Descriptive research.
Sampling design:-
Sample unit - Bike user.
Sample size - 120.
Sampling plan - Convenience sampling.
Sampling area IMS Hostel, AKG College, INMANTEC College.
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FACTOR ANALYSIS: -
Here factor analysis has been used for minimizing the total number of variables. As here in this research the total number of variable is
28. So it is hard to find out the impact of the 28 variables individually on the consumer satisfaction towards the bike company so theyare clubed together with the help of factor analysis which aids in data reduction and variables of similar nature are grouped together into a common factor and thus the study can be carried out more easily.
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .672
Bartlett's Test of Sphericity Approx. Chi-Square 1137.982
df 378
Sig. .000
Interpretation: - As the value of kmo test is .672 so here the data are adequate for performing factor analysis.
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Communalities
Initial Extraction
Durability 1.000 .503
Look of bike 1.000 .744
Riding comfort 1.000 .574
Color 1.000 .786
Pickup 1.000 .434
Height 1.000 .707
Spare parts availability 1.000 .853Resale value 1.000 .807
After sales service 1.000 .856
Price 1.000 .748
New model 1.000 .710
Engine efficiency 1.000 .703
Brand popularity 1.000 .755
Tyre size 1.000 .644
Status 1.000 .634
Gear number 1.000 .684
Head light power 1.000 .515Footbrake life 1.000 .632
Maintenance expense 1.000 .715
Maintenance ease 1.000 .652
Body design 1.000 .479
Body strength 1.000 .668
Bodyweight 1.000 .547
Overall functioning 1.000 .626
Load capacity 1.000 .540
Travel convenience 1.000 .547
Foot break power 1.000 .596Fuel efficiency 1.000 .563
Extraction Method: Principal Component
Analysis.
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Total Variance Explained
Component
Initial Eigenvalues Extract ion Sums of Squared Loadings Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 5.716 20.414 20.414 5.716 20.414 20.414 3.093 11.047 11.047
2 2.459 8.781 29.195 2.459 8.781 29.195 2.507 8.955 20.002
3 1.916 6.842 36.037 1.916 6.842 36.037 2.462 8.793 28.795
4 1.850 6.606 42.642 1.850 6.606 42.642 1.950 6.965 35.760
5 1.483 5.295 47.937 1.483 5.295 47.937 1.905 6.802 42.562
6 1.372 4.900 52.837 1.372 4.900 52.837 1.797 6.418 48.9807 1.291 4.609 57.446 1.291 4.609 57.446 1.704 6.087 55.067
8 1.087 3.884 61.330 1.087 3.884 61.330 1.506 5.380 60.447
9 1.051 3.753 65.082 1.051 3.753 65.082 1.298 4.635 65.082
10 .989 3.532 68.615
11 .917 3.277 71.891
12 .900 3.213 75.104
13 .748 2.670 77.774
14 .734 2.621 80.395
15 .706 2.523 82.918
16 .623 2.224 85.14217 .586 2.091 87.233
18 .529 1.890 89.123
19 .456 1.628 90.750
20 .402 1.437 92.187
21 .389 1.389 93.576
22 .362 1.292 94.868
23 .335 1.195 96.063
24 .331 1.182 97.245
25 .288 1.029 98.274
26 .201 .717 98.99127 .171 .609 99.600
28 .112 .400 100.000
Extraction Method: Principal Component Analysis.
INTERPRETATION : From the Total variance explained table we can infer that the 9 factors thus extracted are explaining 65.082% of the total variance with the help of
eigen values and component 1 and component 2 has the maximum loading in the total variance that means they are contributing the most to the variance obtained.
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Component Matrixa
Component
1 2 3 4 5 6 7 8 9
Durability .063 -.345 .001 .382 .357 .307 .011 .023 .108
Look of bike -.073 .134 .785 .046 -.277 -.076 .111 .081 .014
Riding comfort .144 -.226 .275 .509 .197 .205 -.043 .130 -.262
Color -.124 .114 .759 .121 -.226 -.154 .059 .097 .280
Pickup .140 -.206 -.119 .388 .241 .276 -.009 .234 .135
Height .244 .048 .220 -.028 -.087 .410 .544 -.306 -.174Spare parts availability .163 .789 -.101 -.158 .013 .202 -.037 .350 .060
Resale value .449 .512 -.039 .456 .116 -.044 .117 -.308 .104
After sales service .361 .637 -.100 -.096 .041 .298 .130 .439 .021
Price .524 .402 -.190 .417 -.088 .063 .010 -.261 .147
New model .447 .348 -.164 .203 .053 -.290 -.247 .159 -.384
Engine efficiency .319 -.107 .158 .230 .444 -.455 .253 .187 -.094
Brand popularity .540 -.227 -.085 .024 -.183 -.275 .424 .337 -.044
Tyre size .566 -.312 -.302 .117 -.278 -.044 .116 .027 .164
Status .583 -.244 .010 .335 -.171 -.255 .037 .023 -.162
Gear number .395 .140 .199 -.417 .378 -.288 -.141 -.096 -.201Headlight power .583 -.101 -.138 -.299 .077 -.043 .069 -.178 .111
Footbrake life .626 -.199 -.157 -.162 -.260 -.044 .030 .021 .281
Maintenance expense .591 -.078 .061 .150 .114 -.102 -.373 .020 .413
Maintenance ease .530 .120 .197 -.274 .455 .008 .104 -.050 .151
Body design .560 -.176 -.001 -.229 .019 -.024 .117 .143 .217
Body strenghth .488 -.266 -.004 -.037 -.255 .374 -.049 .167 -.350
Bodyweight .361 -.240 .296 -.093 .077 .161 -.466 .078 -.088
Overallfunctioning .511 -.206 .259 -.224 .267 .339 -.031 -.125 .051
Loadcapacity .565 -.140 -.095 -.334 .014 -.024 .106 -.102 -.241
Travelconvinience .569 -.038 .205 -.097 -.294 .113 -.222 .142 .033Footbreakpower .492 .158 .027 .108 -.305 -.019 -.379 -.255 -.116
fuelefficiency .598 .286 .209 .090 -.040 .018 .102 -.204 -.133
Extraction Method: Principal Component Analysis.
a. 9 components extracted.
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Rotated Component Matrixa
Component
1 2 3 4 5 6 7 8 9
Durabiliy -.012 .011 .070 -.011 -.156 -.083 .682 -.025 .029
Lookofbike -.106 -.024 -.017 .120 .004 .821 -.121 .067 .157
Ridingcomfort -.131 .075 -.045 .283 -.106 .137 .574 .303 .134
Color -.054 .012 -.019 -.033 -.029 .881 -.038 -.025 -.053
Pickup .112 .031 -.047 .014 .079 -.105 .629 .027 -.066
Height .085 .173 .154 .005 .023 .110 .058 -.092 .789Sparepartsavailability -.096 .196 .061 .003 .876 -.003 -.166 -.054 -.059
Resalevalue .016 .839 .117 -.145 .178 .023 .105 .139 .077
Aftersalesservice .100 .167 .109 .042 .890 -.020 .019 .044 .102
Price .202 .809 -.005 .017 .187 -.083 .097 -.005 .038
Newmodel .005 .390 .017 .297 .269 -.202 -.121 .551 -.195
Engineefficiency .137 .059 .306 -.215 -.076 .111 .226 .683 -.071
Brandpopularity .715 -.042 .008 -.018 .078 .040 .014 .460 .153
Tyresize .731 .197 -.051 .120 -.095 -.173 .113 .039 .020
Status .466 .304 -.028 .270 -.201 .029 .119 .441 .027
Gearnumber -.062 .037 .686 .154 .040 -.044 -.288 .303 -.080Headlightpower .448 .156 .472 .074 -.024 -.210 -.106 -.023 .068
Footbrakelife .729 .146 .187 .165 .011 -.058 -.055 -.095 -.047
Maintenanceexpense .375 .359 .348 .216 -.015 .092 .246 -.034 -.455
Maintenanceease .150 .131 .749 -.039 .184 .041 .072 .082 .050
Bodydesign .564 -.019 .375 .080 .100 .010 .051 .031 -.001
Bodystrenghth .339 -.060 -.007 .625 .090 -.132 .154 .068 .324
Bodyweight .044 -.056 .329 .603 -.069 .090 .176 -.013 -.160
Overallfunctioning .175 .019 .617 .300 -.019 .020 .251 -.136 .207
Loadcapacity .365 .029 .391 .254 -.018 -.254 -.177 .173 .250
Travelconvinience .400 .129 .161 .531 .150 .192 -.024 -.034 -.030
Footbreakpower .143 .522 .037 .510 -.037 -.012 -.184 -.011 -.073
fuelefficiency .151 .515 .286 .217 .135 .134 -.067 .184 .268
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 10 iterations.
INTERPRETATION : With the help of the Component Matrix table we come to know about the classification of the variables among the 9 factors obtained depending on
the values of the values of the variables among the 9 factors and this can be cross validated more effectively from the Rotated Component Matrix table and thus the 28
variables are classified in 9 factors which are listed below :
Factors Variables
1) Brand value Body design, maint exp, foot break life, status, tyre size,brand popularity
2) Economy Fuel efficiency, Foot break power, price, resale value
3) Functionality Load capacity, Overall functioning, maint ease, headlight
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power, gear number
4) Body specifications Travel convenience, Body weight, Body strength
5) After sales service After sales service, spare part availability
6) Appearance color, look of bike
7) Performance Pick up, durability, riding comfort
8) Delighters Engine efficiency, new model
9) Height height
Component Transformation Matrix
Component 1 2 3 4 5 6 7 8 9
1 .605 .436 .455 .374 .158 -.066 .088 .229 .082
2 -.363 .486 -.001 -.164 .685 .102 -.353 -.002 -.014
3 -.233 -.074 .294 .223 -.120 .872 .047 .068 .1474 -.097 .519 -.494 -.053 -.147 .136 .596 .274 -.069
5 -.398 -.077 .631 -.314 .032 -.260 .439 .241 -.140
6 -.140 -.061 -.016 .307 .313 -.130 .471 -.581 .456
7 .285 -.086 -.030 -.613 .076 .113 .024 .195 .691
8 .214 -.518 -.212 .089 .602 .152 .248 .331 -.276
9 .366 .114 .131 -.452 .047 .292 .186 -.570 -.430
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
INTERPRETATION : Component Transformation Matrix table tells us about the correlation between the components obtained before and after rotation. Thus we can
interpret that from this table obtained C1 has highest correlation with C3 (ignoring its correlation with itself) and same is applied for other components.
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DISCRIMINANT ANALYSIS:- Here discriminant analysis has been used to know the repurchase intention of the consumers. Here this is a two dimensional
discrimination analysis. In the previous factor analysis 9 components have been extracted and based on those 9 components repurchase intention of the consumers has been
measured.
Analysis Case Processing Summary
Un weighted Cases N Percent
Valid 120 100
Excluded Missing or out-of-range
group codes
0 0
At least one missing
discriminating variable
0 0
Both missing or out-of-
range group codes and at
least one missing
discriminating variable
0 0
Total 0 0
Total 120 100.0
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Group Statistics
Repurchase Mean Std. Deviation
Valid N (listwise)
Unweighted Weighted
yes Brand Value 2.3011 .60005 88 88.000
Economy 2.0483 .45305 88 88.000
Functionality 2.0966 .87381 88 88.000
Body specifications 2.3250 .53846 88 88.000
After sales service 2.2121 .58762 88 88.000
Appearance 2.2443 .65212 88 88.000Performance 2.3492 .63796 88 88.000
Delighters 2.2727 .70674 88 88.000
Height 2.7614 .97077 88 88.000
no Brand Value 3.0365 .48773 32 32.000
Economy 3.1172 .59563 32 32.000
Functionality 2.3438 1.09572 32 32.000
Body specifications 3.0625 .58240 32 32.000
After sales service 2.7917 .72710 32 32.000
Appearance 2.6563 .82733 32 32.000
Performance 2.4032 .58013 32 32.000
Delighters 2.6875 .60575 32 32.000
Height 3.4063 .87471 32 32.000
Total Brand Value 2.4972 .65714 120 120.000
Economy 2.3333 .68395 120 120.000
Functionality 2.1625 .93970 120 120.000
Body specifications 2.5217 .63843 120 120.000
After sales service 2.3667 .67557 120 120.000
Appearance 2.3542 .72296 120 120.000
Performance 2.3636 .62113 120 120.000Delighters 2.3833 .70333 120 120.000
Height 2.9333 .98504 120 120.000
INTERPRETATION : From this Group statistics table we can classify the respondents in to the categories the respondents who areresponding Yes if they are going to buy that company bike again and No if they are not going to buy that company bike again. Andthis is classified according to their mean and the standard deviation values for the 9 factors so the factor which is having the highestseparation of the mean value is the most preferred for the grouping of the respondents for predicting their repurchase intention for thebike from the same company. So in this case Economy has the highest separation of the mean value so the Economy is the basis onwhich respondents repurchase intention can be predicted by classifying their repurchase intention in to Yes or No.
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Pooled Within-Groups Matrices
Brand
Value Economy Functionality Body specifications
After sales
service Appearance Performance Delighters Heigh
Correlation Brand Value 1.000 .131 -.190 .345 .369 -.100 .153 .258
Economy .131 1.000 -.107 -.021 .056 .220 .048 .283
Functionality -.190 -.107 1.000 -.173 -.015 -.055 -.036 -.085
Body
specifications
.345 -.021 -.173 1.000 .308 .031 -.009 .213
After sales service .369 .056 -.015 .308 1.000 -.023 .154 .059Appearance -.100 .220 -.055 .031 -.023 1.000 -.157 .114
Performance .153 .048 -.036 -.009 .154 -.157 1.000 .122
Delighters .258 .283 -.085 .213 .059 .114 .122 1.000
Height -.039 -.014 .067 .091 .002 .027 .044 -.154
INTERPRETATION : From the Pooled within-Groups Matrices table we can find out the correlation between the various factors obtained. Since Aftersales service is having a correlation coefficient of 0.369 with Brand Value so it is a these two factors are having a moderate correlation with eachother so to some extent /moderate positive extent after sales service is related with the Brand Value of the bike company . so a company having agood brand value also provides good after sales service.
Similarly, Delighters are having a moderate positive correlation (correlation coefficient of 0.283) with Economy so to some extent if the bikecompany is able to offer more economy to the consumers (i.e. More mileage, cheap and quality spares parts) they will be more satisfied with thebike company.
Log Determinants
Repurchase Rank
Log
Determinant
yes 9 -8.721
no 9 -9.034
Pooled within-groups 9 -8.113
The ranks and natural logarithms of determinants
printed are those of the group covariance matrices.
Test Results
Box's M 81.407
F Approx. 1.602
df1 45
df2 11965.803
Sig. .006
Tests null hypothesis of equal
population covariance matrices.
Ei l
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Eigenvalues
Function Eigenvalue % of Variance Cumulative %
Canonical
Correlation
1 1.612a 100.0 100.0 .786
a. First 1 canonical discriminant functions were used in the analysis.
INTERPRETATION : Only one function is obtained which is having an eigen value of 1.612 and is explaining 100% variance. So one function is able
to discriminate between the two categories of respondents having repurchase intention of either Yes or No.
Wilks' Lambda
Test of Function(s) Wilks' Lambda Chi-square df Sig.
1 .383 108.961 9 .000
INTERPRETATION : Wilks lambda is having a value of 0.383 which is close to zero so the Group means seems to be different and thus the twocategories of respondents can be discriminated for their future prediction of their repurchase intention of either Yes or No. Also the significancevalue is 0.000 which is less than level of significance of 5% i.e. 0.05 so Null hypothesis is rejected and thus it can be inferred that the group means
seems to be different and thus the respondents can be properly classified in to two categories according to their repurchase intention.
Standardized Canonical
Discriminant Function
Coefficients
Function
1
BrandValue .284
Economy .782
Functionality .285
Bodyspecifications .428
Aftersalesservice .061
Appearance .072
Performance -.027
Delighters -.129
Height .182
INTERPRETATION : From this table we can see that only one function is obtained and among which Economy is having the highest coefficient of0.782 so Economy is a sufficient base to discriminate between the two categories of respondents depending upon their repurchase intention ofeither Yes or No.
Structure Matrix
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Structure Matrix
Function
1
Economy .759
Body specifications .471
Brand Value .451
After sales service .325
Height .239
Delighters .214
Appearance .206
Functionality .093
Performance .030
Pooled within-groups correlations
between discriminating variables
and standardized canonical
discriminant functions
Variables ordered by absolute size
of correlation within function.
INTERPRETATION : From the Structure Matrix it can be cross validated that whether the function obtained from the Standardized table is able tocategorize respondents and in this table also highest value is obtained for Economy again which is 0.759 thus, Economy can be used todifferentiate between the two categories of respondents having repurchase intention of either Yes or No.
Canonical Discriminant Function
Coefficients
Function
1
BrandValue .497Economy 1.582
Functionality .304
Bodyspecifications .777
Aftersalesservice .098
Appearance .103
Performance -.044
Delighters -.189
Height .193
(Constant) -8.034
Unstandardized coefficients
Functions at Group
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Functions at Group
Centroids
Repurchase
Function
1
yes -.759
no 2.088
Unstandardized canonical
discriminant functions
evaluated at group means
INTERPRETATION : From the Canonical Discriminant Coefficient matrix table the all the 9 coefficients are obtained along with the value of thecentroids value for the categories Yes and No and than putting the value of these coefficients, Variables and constant in the Discriminant scoreequation the Discriminant score of all the 120 respondents can be found out and the obtained score is close to which ever centroid value willbelong to that group. Likewise for the first respondent the D value came out to me -0.547 which is close to centroid value of Yes i.e. -0.759 sofirst respondent will be placed in the category of customer who is having a repurchase intention for the same company bike.
Prior Probabilities for Groups
Repurchase Prior
Cases Used in Analysis
Unweighted Weighted
Yes .500 88 88.000
No .500 32 32.000
Total 1.000 120 120.000
Classification Resultsb,c
Repurchase
Predicted Group Membership
TotalYes no
Original Count Yes 82 6 88
No 1 31 32
% Yes 93.2 6.8 100.0No 3.1 96.9 100.0
Cross-validateda Count Yes 79 9 88
No 3 29 32
% Yes 89.8 10.2 100.0
No 9.4 90.6 100.0
a. Cross validation is done only for those cases in the analysis. In cross validation, each
case is classified by the functions derived from all cases other than that case.
b. 94.2% of original grouped cases correctly classified.
c. 90.0% of cross-validated grouped cases correctly classified.
INTERPRETATION : From the Classification results table we obtained the predicted group membership. On the basis of this we can infer that out ofthe 88 respondents who said that they are having a repurchase intention for the same company bike 82 (93.2%) have a probability or prediction ofrepurchasing the same company bike and 6 (6.8%)are not having the prediction of repurchasing the same company bike.
Similarly out of the 32 respondents who replied that they are not having a repurchase intention for the same company bike 31 (96.9%) are having
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y p p y g p p y ( ) ga chance of staying with their response and 1 (3.1%) is not having a chance of staying with his response.
Thus it can be inferred that a majority of the consumers are satisfied with the attributes offered by the bike company which they are using and amajority of them are having a repurchase intention of buying the same company bike.
CONCLUSION: -
Customers are satisfied with Brand Value, economical attributes, and functionality.
Economical attributes affect more to create differentiation in the repurchase intention of the consumers.
RECOMMENDATION: -
As economical factors are very important to create differentiation among the loyal and disloyal customers so companies should pay morefocus on economical factors.
Consumers are not satisfied with the appearance , riding comfort, height of the bike so companies should focus more on these attributes.
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ANNEXURE: -
Name -
1) Age:
15-20.
20-25.
25-30.
30-35.
35 or above.
2) Academic Qualification:
HSC.
GRADUATE.
POST GRADUATE
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POST GRADUATE.
Other
3) Occupation:
Student.
Private sector.
Public sector.
Business man
Retired.
Unemployed.
4) Annual Income
Less than 3 Lakh.
3 to 6 Lakh.
6 to 9 Lakh.
9 to 12 Lakh.
More than 12 Lakh.
5) Which Brand Bike do you have?
Hero Honda
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Hero Honda.
BAJAJ
TVS
HONDA
6) Please tick on the right box related with your satisfaction with the product attributes of yourbike.
Statements
Highlysatisfied
Satisfied
Neutral
Dissatisfied
HighlyDissatisfied
Durabilityof Bike
Look ofBike
RidingComfort ofBike
Color ofBike
Foot breakpower
Pick up ofBike
Fuelefficiencyof Bike
Height of
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gBike
Sparepartsavailability
Re-sale
value
After salesservice
Price ofBike
New modelof Bike
Engineefficiency
Brandpopularity
Tyre size( Stability)
Travelconvenience
Status ofBike
GearNumber
HeadlightPower
Foot brakelife
Maintenance
expenses
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p
Maintenance ease
Bodydesign ofBike
Bodystrength
Bodyweight ofBike
Overallfunctioning
Load
Capacity
7) Would you like to buy the Bike of same company again?
YES
NO