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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


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


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


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



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



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

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