final exam am(1)

7/29/2019 Final Exam AM(1)

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UNIVERSITY OF INDONESIA

BLACKBERRY CUSTOMERS SATISFACTION ANALYSIS

BY USING FACTOR ANALYSIS METHOD

MULTIVARIATE ANALYSIS REPORT

ANDRI MUBARAK (0906489555)

NOVANDRA RHEZZA PRATAMA (0906515793)

NOVELLY SIONITA SIMANJUNTAK (0906557190)

RENALDA KRISSALAM (0906636560)

FACULTY OF ENGINEERING UNIVERSITAS INDONESIA

DEPARTMENT OF INDUSTRIAL ENGINEERING

DEPOK

DECEMBER 2012


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TABLE OF CONTENTS

TITLE PAGE ........................................................................................................... iTABLE OF CONTENTS ........................................................................................ ii

LIST OF PICTURES ............................................................................................. iii

LIST OF TABLES ................................................................................................. iv

CHAPTER 1 INTRODUCTION ......................................................................... 1

1.1 Research Background .......................................................................... 11.2 Research Objectives............................................................................. 31.3 Research Information........................................................................... 3

CHAPTER 2 LITERATURE REVIEW ............................................................. 4

2.1 Concept of Factor Analysis ................................................................. 42.2 Design Factor Analysis Experiment .................................................... 5

CHAPTER 3 DATA PROCESSING ................................................................... 7

3.1 Developing Questionnaires .................................................................. 73.2 Data Collection .................................................................................... 73.3 Steps on Data Tabulation with Factor Analysis .................................. 7

CHAPTER 4 ANALYSIS ...................................................................................12

4.1 Factor Analysis .................................................................................. 124.2 Result Data Tabulation and Analysis ................................................ 124.3 Correlation Between Variables .......................................................... 134.4 Factor Analysis Test .......................................................................... 174.5 AntiImage Analysis ....................................................................... 184.6 Process Data after Variable X3 Turn Out From Tabulation Data ..... 224.7 Communalities of Variables .............................................................. 264.8 Determine Number of Factor ............................................................. 274.9 Interpreting Factor ............................................................................. 304.10Component Transformation Matrix ................................................... 35

CHAPTER 5 CONCLUSION ............................................................................ 36

REFERENCES .................................................................................................... 37

THE ATTACHMENTS CHAPTER ................................................................. 38


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LIST OF PICTURES

Figure 2.1 The Common Factor Model ............................................................... 5

Figure 3.1 Data Reduction .................................................................................... 9

Figure 3.2 Insert Variabel .................................................................................... 9

Figure 3.3 Descriptive Window .......................................................................... 10

Figure 3.4 Extraction Window ........................................................................... 10

Figure 3.5 Rotation Window .............................................................................. 11

Figure 3.6 Score Window ................................................................................... 11

Figure 4.1 Scree Plot for the Data...................................................................... 29

Figure 4.2 Component Plot in Rotated Space ................................................... 35


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LIST OF TABLES

Table 4.1 Correlation Between Variables ......................................................... 14Table 4.2 KMO and Bartletts Test ................................................................... 17

Table 4.3 Anti-Image Metrics ............................................................................ 19

Table 4.4 The New KMO and Bartlett's Test ................................................... 23

Table 4.5 The New Anti-Image Metrics ............................................................ 24

Table 4.6 Communalities .................................................................................... 26

Table 4.7 Total Variance Explained .................................................................. 27

Table 4.8 Initial Eigen Values ............................................................................ 28

Table 4.9 Extraction Sum of Squared Loadings .............................................. 29

Table 4.10 Rotation Sum of Squared Loadings ................................................ 30

Table 4.11 Component Matrix ........................................................................... 31Table 4.12 Rotated Component Matrix ............................................................ 32

Table 4.13 Classified Factor from Variables .................................................... 33

Table 4.14 Component Score Coefficient Matrix ............................................. 34

Table 4.15 Component Transformation Matrix ............................................... 35


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Universitas Indonesia

CHAPTER 1

INTRODUCTION

1.1 Research Background

According to Cronin and Taylor in Baharet al (2009:972), customer

satisfaction is a major factor in assessing the quality of products and services.

Customer assesses the performance of the products and services, they directly

comparing what they received and what they perceived to services and products.

Quality of services or products is determined by the level of compatibility

between the services given to those expected by the consumer. The higher the

received service equality will be higher levels of customer satisfaction. Marketing

managers recognize that customer satisfaction proved to be very useful in the

business so they often held management survey on customer satisfaction with the

product produced by a company.

Customer satisfaction can be used to measure the market a product or

service so that company can further expand or repair their existing production.

company use customer satisfaction as input for subsequent products and services,

because if a customer satisfied with the products or services it wishes to purchase

very high, after the purchase, the customer would recommend the product or

service to other people starting families and the closest so they will be motivated

to buy products or services.

After purchasing the products or services and the customer is satisfied

its possible to buy the products or services back the next time and it could be a

repeat for several times if the level of satisfaction is high. If customers are not

satisfied then it is not going to happen so there should be measurement ofcustomer satisfaction possible by asking their customers and if customers are not

satisfied, the customer will provide a critical commentary.

Many companies believe that customer satisfaction is important. As

reflected from the vision, mission and business plan that they formulated.

Measuring customer satisfaction is a concrete manifestation of the company's

commitment to customer satisfaction. Based on the experience of doing research

for many companies in Indonesia, there are 5 things to optimize the benefits of the


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research. The first step is to involve top management. Prior research conducted,

very important or even should, to seek approval and support from top

management. If top management is still in doubt, it is better to delay the conduct

customer satisfaction research. The second step is to involve the relevant

department. Measuring customer satisfaction often involves many departments

such as marketing department, service, human resources, production, business

development and even financial. In the manufacturing industry, R&D department

is also important to be involved. The higher the company's orientation towards the

customers, the more departments should be involved. Customer satisfaction is the

responsibility of all departments and not just the departments that deal with

customers on a daily basis.

Third, research should have high credibility. Therefore, the measurement

of customer satisfaction research must be carried out to the correct method. The

company can actually do the research yourself if measurements have sufficient

resources, i.e. at least some researchers who controlled research methodology,

statistics and basic concepts of customer satisfaction. When internally is not

possible, it is better to outsource the research firm that has credibility. Without

credibility, let alone take advantage of the results, to simply listen to the

presentation of the results of his research-also, they will be reluctant. Fourth, the

results of this survey should be presented and disseminated. It is important; the

results of this research are communicated to the level of front-line staff.

Fifth, the preparation of a new program based on the results of this

research should be done as quickly as possible. At the latest, one month after the

research is completed; the company should be ready with the customer

satisfaction program after learning how effective customer satisfaction programlength based on research. Making this program should be initiated with the

objective to be achieved such as how much the rate of increase of customer

satisfaction will be achieved. Sixth, scheduling customer satisfaction measure-

ment. Time, depending on the speed of change in the business environment and

how much of the planned program has been implemented. In general, the

measurement of large-scale customer satisfaction can be done once or twice a

year. Seventh, to use the results of customer satisfaction measurement is to


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connect the measurement of customer satisfaction and the reward of the

employee.

In this case, we use Blackberry as our product, we choose this product

because its widely used in Indonesia so that our customer is sufficient for the

survey, and we especially survey Industrial Engineering students. This research is

useful to measure Blackberry next product market especially among industrial

engineering students.

1.2 Research Objectives

The research is done to realize the objectives mentioned below:

Determine the factors or variables that influence customer satisfactions orcustomer preference in Blackberry.

Find the most significant factors that influence the customer preferencefor Blackberry.

Determine the level of customer satisfaction especially Blackberry usersin Industrial Engineering.

1.3 Research Information

The detail informations about this research is mentioned below:

Title : Blackberrys customer satisfaction using factor analysismethod.

Methods : Factor Analysis. Samples : Blackberry users especially Industrial Engineering students

University of Indonesia.

Time : Tuesday, 4th December 2012 Tuesday, 18th December2012.

Place : Mailing list Industrial Engineering 2009 (online),Department of Industrial Engineering, University of

Indonesia.


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CHAPTER 2

LITERATURE REVIEW

2.1 Concept of Factor Analysis

Factor analysis is a statistical method used to study the dimensionality of

a set of variables. In factor analysis, latent variables represent unobserved

constructs and are referred to as factors or dimensions. Factor analysis searches

for such joint variations in response to unobserved latent variables. The observed

variables are modeled as linear combinations of the potential factors, plus "error"

terms. The information gained about the interdependencies between observed

variables can be used later to reduce the set of variables in a dataset.

Computationally this technique is equivalent to low rank approximation of the

matrix of observed variables. Factor analysis originated in psychometrics, and is

used in behavioral sciences, social sciences, marketing, product management,

operations research, and other applied sciences that deal with large quantities of

data.

There are basically two types of factor analysis: exploratory and

confirmatory.

Exploratory Factor Analysis (EFA)Used to explore the dimensionality of a measurement instrument by

finding the smallest number of interpretable factors needed to explain the

correlations among a set of variables exploratory in the sense that it places no

structure on the linear relationships between the observed variables and on the

linear relationships between the observed variables and the factors but only

specifies the number of latent variables. Confirmatory Factor Analysis (CFA)

Used to study how well a hypothesized factor model fits a new sample

from the same population or a sample from a different populationcharacterized

by allowing restrictions on the parameters of the model.


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Figure 2.1 The Common Factor Model

Both types of factor analyses are based on the Common Factor Model,

illustrated in figure 1.1. This model proposes that each observed response

(measure 1 through measure 5) is influenced partially by underlying common

factors (factor 1 and factor 2) and partially by underlying unique factors (E1

through E5). The strength of the link between each factor and each measure

varies, such that a given factor influences some measures more than others.

2.2 Design Factor Analysis Experiment

In designing experiment which uses factor analysis, we need several

steps. Principles of experimental design, following Ronald A. Fisher. Generally

there are many steps to conduct factor analysis experiment. There are steps will be

explained below:

ComparisonIn some fields of study it is not possible to have independent

measurements to a traceable standard. Comparisons between treatments are much

more valuable and are usually preferable. Often one compares against a scientific

control or traditional treatment that acts as baseline.

RandomizationRandom assignment is the process of assigning individuals at random to

groups or to different groups in an experiment. The random assignment of


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individuals to groups (or conditions within a group) distinguishes a rigorous,

"true" experiment from an adequate, but less-than-rigorous, "quasi-experiment".

ReplicationMeasurements are usually subject to variation and uncertainty.

Measurements are repeated and full experiments are replicated to help identify the

sources of variation, to better estimate the true effects of treatments, to further

strengthen the experiment's reliability and validity, and to add to the existing

knowledge of the topic.

BlockingBlocking is the arrangement of experimental units into groups (blocks)

consisting of units that are similar to one another. Blocking reduces known but

irrelevant sources of variation between units and thus allows greater precision in

the estimation of the source of variation under study.

OrthogonalityOrthogonality concerns the forms of comparison (contrasts) that can be

legitimately and efficiently carried out. Contrasts can be represented by vectors

and sets of orthogonal contrasts are uncorrelated and independently distributed if

the data are normal.

Factorial experimentsUse of factorial experiments instead of the one-factor-at-a-time method.

These are efficient at evaluating the effects and possible interactions of several

factors (independent variables).


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CHAPTER 3

DATA PROCESSING

3.1 Developing Questionnaires

To get customer satisfaction data to the Blackberry, we conduct research

with the use of a detailed questionnaire. In developing the questionnaires, weshould decide the characteristics and the variables needed to make good

questionnaires. A good questionnaire should be valid and reliable. Valid means

the study is able to scientifically answer the questions it is intended to answer.

Reliable means the study is consistent and the study may not measure what it

wants to be measuring. Both validity and reliability should be fulfilled so that we

could analyze the real situation accurately.

3.2 Data Collection

We gathered data by distributing questionnaire to Blackberry users. From

the gathered data, we already collect 50 (fifty) data that can represent the

Blackberry users. The distributed questionnaires and the data result will be

attached in The Attachments Chapter.

3.3 Step on Data Tabulation with Factor Analysis

We use SPSS 17.0 software for tabulated data in factor analysis.

Furthermore, step for doing factor analysis using SPSS 17.0 are shown below:

1. Open SPSS 17.02. Create new data.

a.

Click on file toolbar.b. Choose new and the data.c. Insert the data in data sheet.

(by typing data on the data sheet or import data from Ms.Excel)


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1. If data already exist, open the data file.(click Analyze toolbar, choose data reduction and then factor)


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Figure 3.1 Data Reduction

2. Select the variables you want the factor analysis to be based on and movethem into the variable(s) box.

Figure 3.2 Insert Variabel


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3. Click descriptive button.a. On statistic cluster, check initial solution.b. On correlation matrix cluster, check coefficients, significance levels,

determinant, KMO and Bartletts test of sphericity and anti-image.

c. Click continue button.

Figure 3.3 Descriptive Window

4. Choose extraction button.a. On display cluster, check unrotated factor solution, scree plot.b. Then continue.

Figure 3.4 Extraction Window

5. Click rotation button.


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a. On method cluster, choose varimax.b. Click continue.

Figure 3.5 Rotation Window

6. Choose scores button.a. Check save as variable.b. On method cluster, choose Bartlett.c. Check display factor score coefficient matrix.d. Then continue.

Figure 3.6 Score Window

7. Click OK button


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CHAPTER 4

ANALYSIS

4.1 Factor Analysis

Factor analysis is a multivariate method used for data reduction purposes.

The basic idea is to represent a set of variables by a smaller number of variables.

In this case they are called factor.

There are two kinds of factor analysis that often used, which are:

a. Data reduction analysis. Use to remove redundant (highly correlatd)variables from the data file, perhaps replacing the entire data file with a

smaller number of uncorrelated variables.

b. Structure detection analysis. Use to examine the underlying (or latent)relationships between the variables.

We would use data reduction analysis as the procedure in accomplishing

this research, considering the objective of this factor analysis is to reduce 10

attributes into fewer numbers, which are the linear combination of those attributes.

We also used structure detection in order to see the correlation between variables.

4.2 Result Data Tabulation and Analysis

Determinant using to show something or in this case factor that establish.

KMO Bartlett used to determine if data was suitable or not. In the Descriptives

window above, we select KMO and Bartletts test of sphericity. KMO is a statistic

which tells whether you have sufficient items for each factor. Barletts test test is

used to check that the original variables are sufficiently correlated. Whereas, anti

image can be using to seeing when factor model are good or not from the valuenumber of element.

On extraction, correlation matrix and eigenvalue over 1 are checked, it

use to be based on extraction. Raw data are reducted into factor that amount

appropriate with number of data with eigenvalue more than 1. Unrotated factor

solution displayed extraction on side of communalities. It indicates how well a

variable representing on the factor. Scree plot used to display graph of eigenvalue

from each component in the initial solution. The scree plot also helps you to


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determine the optimal number of components. Data then classified into that factor

based on correlation of each variable to factor created. Usually, from the

correlation matrix, there is a data which hard to classified because the correlation

to factor almost same. While, correlation matrix used because variables on our

analysis are measured on different scales.

To solve this, we can used rotation. Rotation method that used in this

data tabulation are Variamax. Variamax simplifying the columns of the factor

matrix so there tend to be some high loading (i.e, close to -1 or +1 ) and some

laodings near to 0 in each column of matrix. The reason to choose this method are

Variamax The reason to choose this method are Variamax has a simple structure

(near -1 or +1 high correlation and near 0 indicating lack of correlation).

Compared with Quartimax which has simpler analytical. Varimax give a clearer

separation of the factor. When different subsets of variables are analyzed, factor

pattern that obtained from Varimax tend to be more invariant than that obtained

by Quartimax method.

Furthemore, data that has been tabulated saved as Bartlett. This factor

will be use again on the develop linear regression as an independent variable.

4.3 Correlation Between Variables

The underlying statisticlal assumptions impact factor analysis to the

extent that they affect the derived correlation. Departures from normality,

homoscedasticity and linearity can diminish correlations between variables.

Examining the correlations among the survey items reveals that there is significant

overlap among various subgroups of items.


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Table 4.1 Correlation Between Variables

Correlation Matrix

X1 -

kepuasananda

terhadapBB

X2 - kecepatanoperasi/penggunaan

program BB yangbaik

X3 -kehandalanBB, dalam

menangani

program dan

gangguaneksternal

sepertiterjatuh

X4 -kesesuaian

service

(program

yang

dimasukkan)dengan

kebutuhananda

X5 -

kesesuaianharga

denganproduk

X6 -service

pascapenjualan

X7 -

kemudahandalam

menggunakanuntuk pemula

X8 -kemudahan

dalammaintenance

X9 -

promosiproduk

yangefektif

X10 -design

yangmenarik

Correlation X1 - kepuasan anda

terhadap BB

1.000 .678 .049 .157 .087 .399 .534 .507 .177 -.002


program BB yang

baik

.678 1.000 .153 .167 .176 .525 .319 .343 .165 -.063

X3 - kehandalan BB,

dalam menanganiprogram dan

gangguan eksternalseperti terjatuh

.049 .153 1.000 .321 .097 .120 -.020 .054 .091 .394

X4 - kesesuaian

service (programyang dimasukkan)dengan kebutuhan

anda

.157 .167 .321 1.000 .514 .136 .169 .115 .443 .432

X5 - kesesuaian

harga dengan produk

.087 .176 .097 .514 1.000 .349 .128 .301 .408 .304


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X6 - service pasca

penjualan

.399 .525 .120 .136 .349 1.000 .359 .569 .189 .136

X7 - kemudahan

dalam menggunakanuntuk pemula

.534 .319 -.020 .169 .128 .359 1.000 .554 -.038 .121

X8 - kemudahandalam maintenance

.507 .343 .054 .115 .301 .569 .554 1.000 .397 .261

X9 - promosi produk

yang efektif

.177 .165 .091 .443 .408 .189 -.038 .397 1.000 .460

X10 - design yang

menarik

-.002 -.063 .394 .432 .304 .136 .121 .261 .460 1.000

Sig. (1-

tailed)

X1 - kepuasan anda

terhadap BB

.000 .368 .137 .274 .002 .000 .000 .110 .493

X2 - kecepatan

operasi/penggunaan

program BB yangbaik

.000 .144 .123 .111 .000 .012 .007 .126 .333

X3 - kehandalan BB,

dalam menangani

program dangangguan eksternalseperti terjatuh

.368 .144 .012 .252 .202 .444 .356 .264 .002

X4 - kesesuaian

service (program

yang dimasukkan)

dengan kebutuhananda

.137 .123 .012 .000 .173 .120 .214 .001 .001

X5 - kesesuaian

harga dengan produk

.274 .111 .252 .000 .006 .188 .017 .002 .016


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X6 - service pasca

penjualan

.002 .000 .202 .173 .006 .005 .000 .094 .174

X7 - kemudahandalam menggunakanuntuk pemula

.000 .012 .444 .120 .188 .005 .000 .398 .201


.000 .007 .356 .214 .017 .000 .000 .002 .034

X9 - promosi produkyang efektif

.110 .126 .264 .001 .002 .094 .398 .002 .000

X10 - design yang

menarik

.493 .333 .002 .001 .016 .174 .201 .034 .000


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Table above shows the correlation between the factors. It shows the

relative value between the variable. As we can see, we cannot conclude anything

since too much variability within the numbers above. Thats why continue our

analysis to the next figure.

4.4 Factor Analysis Test

After examine the correlation between variable, we assess the overall

significance of the correlation matrix using KMO ( Kaiser-Meyer-Olkin) and

Bartletts Test of Sphericity. This test is also effective to check whether factor

analysis is suitable to our data or not. Factor analysis is suitable if there is

correlation between variable that being measured. If correlation between all

variables is low, factor analysis can not be used.

The Kaiser - MeyerOlkin measure of sampling adequacy tests whether

the partial correlations among variables are small. Bartletts test of sphericity tests

whether the correlation matrix is an identity matrix, which would indicate that the

factor model is inappropriate. Use of these method is related to structure detection

analysis.

The result of KMO (Kaiser Meyer Olkin) and Bartletts Test of

Sphericity is shown in table below.

Table 4.2 KMO and Bartletts Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .628

Bartlett's Test of Sphericity Approx. Chi-Square 177.936

df 45

Sig. .000

Value range of KMO test is 0-1. High values (close to 1.0) generally

indicate that a factor analysis may be useful with your data. If the value is less

than 0.50, the results of the factor analysis probably wont be very useful. In table


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above, we get KMO 0.628. It means that factor analysis is very useful to our

variables.

For Bartlett Test, small values (less than 0.05) of the significance level

indicate that a factor a factor analysis may be useful with our data. In table above,

we get significance level 0.000 which is less than 0.05, so we may conclude that

factor analysis may be useful with our data.

4.5 AntiImage Analysis

The anti image correlation matrix contains the negatives of the partial

correlation coefficient, and the image covariance matrix contains the negatives of

the partial covariance. In a good factor model, most of the off- diagonal elements

will be small. The measure of sampling adequacy for a variable is displayed on

the diagonal of the anti-image correlation matrix. If true factors exist in the data,

the partial correlation should be small, because the variables can be explained by

the factors (variates with loadings for each variable). If partial correlation are

high, then there are no true underlying factors, and factor analysis is

inappropriate.

From the result of data tabulation we can look at diagonal from left-up to

right down the data on anti-image correlation matrix which also measures of

sampling adequacy. If the value less than 0.5 then the data must be trun out from

the data tabulation and then repeat the process. As seen in the table below,

Variable X3 - kehandalan BB, dalam menangani program dan gangguan

eksternal seperti terjatuhis less than 0.5, so the data must be turn out from

the data tabulation and we must repeat the process.


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Table 4.3 Anti-Image Metrics

Anti-image Matrices

X1 -

kepuasananda

terhadapBB

X2 -

kecepatan

operasi/penggunaan

program BByang baik

X3 -kehandalanBB, dalam

menangani

program dan

gangguaneksternal

sepertiterjatuh

X4 -

kesesuaian

service

(program yangdimasukkan)

dengankebutuhan anda

X5 -

kesesuaianharga

denganproduk

X6 -service

pascapenjualan

X7 -

kemudahandalam

menggunakanuntuk pemula

X8 -kemudahan

dalammaintenance

X9 -

promosiproduk

yangefektif

X10 -design

yangmenarik

Anti-image

Covariance

X1 - kepuasan anda

terhadap BB

.381 -.227 .018 -.038 .077 .048 -.123 -.087 -.031 .036


program BB yang baik

-.227 .407 -.106 -.002 -.028 -.183 .002 .065 -.054 .105

X3 - kehandalan BB,

dalam menangani

program dan gangguaneksternal seperti

terjatuh

.018 -.106 .710 -.159 .081 -.013 .118 -.044 .138 -.246

X4 - kesesuaian

service (program yang

dimasukkan) dengankebutuhan anda

-.038 -.002 -.159 .497 -.232 .023 -.133 .135 -.163 -.073

X5 - kesesuaian harga

dengan produk

.077 -.028 .081 -.232 .595 -.120 .022 -.062 -.049 -.017

X6 - service pasca

penjualan

.048 -.183 -.013 .023 -.120 .504 .002 -.165 .063 -.032


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X7 - kemudahan

dalam menggunakan

untuk pemula

-.123 .002 .118 -.133 .022 .002 .458 -.187 .214 -.088


-.087 .065 -.044 .135 -.062 -.165 -.187 .357 -.180 -.021


-.031 -.054 .138 -.163 -.049 .063 .214 -.180 .461 -.177

X10 - design yang

menarik

.036 .105 -.246 -.073 -.017 -.032 -.088 -.021 -.177 .551

Anti-imageCorrelation

X1 - kepuasan andaterhadap BB

.697a

-.575 .034 -.088 .163 .109 -.295 -.236 -.074 .079

X2 - kecepatan

operasi/penggunaan


-.575 .629a -.197 -.006 -.056 -.403 .004 .171 -.125 .223

X3 - kehandalan BB,dalam menangani

program dan gangguaneksternal seperti

terjatuh

.034 -.197 .450a

-.268 .125 -.021 .207 -.087 .241 -.394

X4 - kesesuaian

service (program yangdimasukkan) dengan

kebutuhan anda

-.088 -.006 -.268 .598a

-.427 .045 -.279 .321 -.340 -.140

X5 - kesesuaian hargadengan produk

.163 -.056 .125 -.427 .724a -.220 .043 -.135 -.093 -.030

X6 - service pascapenjualan

.109 -.403 -.021 .045 -.220 .733a

.003 -.390 .131 -.060

X7 - kemudahan

dalam menggunakanuntuk pemula

-.295 .004 .207 -.279 .043 .003 .569a -.463 .465 -.176


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X8 - kemudahan

dalam maintenance

-.236 .171 -.087 .321 -.135 -.390 -.463 .632a

-.444 -.048


-.074 -.125 .241 -.340 -.093 .131 .465 -.444 .525a

-.351

X10 - design yang

menarik

.079 .223 -.394 -.140 -.030 -.060 -.176 -.048 -.351 .658a

a. Measures of Sampling Adequacy(MSA)


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4.6 Process Data After Variable X3 Turn Out From Tabulation Data

The result of KMO (Kaiser - Meyer Olkin) and Bartletts Test of

Sphericity after we removed Variable X3 is shown in table below, This is the

processing of the data.


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Table 4.4 The New KMO and Bartlett's Test

Table The New KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .657

Bartlett's Test of Sphericity Approx. Chi-Square 163.760

Df 36

Sig. .000

From the result of data tabulation, we can see the new result of KMO and

Bartletts Test. As seen in the table below, all of them more than 0.5, so all of

data can be included on data tabulation and classified into factors on

extraction.


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Table 4.5 The New Anti-Image Metrics

Anti-image Matrices

X1 -kepuasan

anda

terhadap

BB

X2 -kecepatanoperasi/

|penggunaan

program BB

yang baik

X4 - kesesuaianservice (program

yang dimasukkan)

dengan kebutuhan

anda

X5 -kesesuaian

harga

dengan

produk

X6 -

service

pasca

penjualan

X7 - kemudahan

dalam

menggunakan

untuk pemula

X8 -

kemudahan

dalam

maintenance

X9 -promosi

produk

yang

efektif

X10 -

design

yang

menarik

Anti-imageCovariance

X1 - kepuasan andaterhadap BB

.382 -.233 -.037 .077 .048 -.132 -.087 -.037 .050

X2 - kecepatan

operasi/penggunaanprogram BB yang baik

-.233 .423 -.029 -.016 -.192 .021 .061 -.037 .085

X4 - kesesuaian service(program yang

dimasukkan) dengankebutuhan anda

-.037 -.029 .535 -.234 .021 -.120 .136 -.151 -.163

X5 - kesesuaian harga

dengan produk

.077 -.016 -.234 .604 -.121 .009 -.059 -.069 .013


.048 -.192 .021 -.121 .504 .004 -.168 .070 -.043

X7 - kemudahan dalam

menggunakan untukpemula

-.132 .021 -.120 .009 .004 .479 -.190 .212 -.058

X8 - kemudahan dalammaintenance

-.087 .061 .136 -.059 -.168 -.190 .360 -.183 -.043


-.037 -.037 -.151 -.069 .070 .212 -.183 .489 -.162


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X10 - design yang

menarik

.050 .085 -.163 .013 -.043 -.058 -.043 -.162 .652

Anti-image

Correlation

X1 - kepuasan anda

terhadap BB

.691a

-.580 -.082 .160 .110 -.309 -.234 -.085 .101

X2 - kecepatan

operasi/penggunaan


-.580 .646a -.062 -.032 -.415 .046 .157 -.081 .161

X4 - kesesuaian service

(program yangdimasukkan) dengankebutuhan anda

-.082 -.062 .605a -.412 .041 -.237 .310 -.295 -.277

X5 - kesesuaian hargadengan produk

.160 -.032 -.412 .741a

-.219 .017 -.126 -.127 .021


.110 -.415 .041 -.219 .723a

.008 -.394 .140 -.074

X7 - kemudahan dalammenggunakan untuk

pemula

-.309 .046 -.237 .017 .008 .610a -.457 .437 -.105

X8 - kemudahan dalam

maintenance

-.234 .157 .310 -.126 -.394 -.457 .639a -.437 -.090


-.085 -.081 -.295 -.127 .140 .437 -.437 .579a

-.287

X10 - design yang

menarik

.101 .161 -.277 .021 -.074 -.105 -.090 -.287 .731a

a. Measures of Sampling Adequacy(MSA)


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4.7 Communalities of Variables

Communality shows the amount of variance in a variable that is

accounted for by the two factors taken together. The size of the communality is a

useful index for assessing how much variance variable is accounted for by the

factor solution. Small communalities show that a substantial portion of the

variance in a variable is unaccounted for by the factors. Large communalities

indicate that a large amount of variance in a variable has been extracted by the

factor solution.

Communality table of variables is shown in table below:

Table 4.6 Communalities

Communalities

Initial Extraction

X1 - kepuasan anda terhadap BB 1.000 .711

X2 - kecepatan operasi/penggunaan program BB yang baik 1.000 .601

X4 - kesesuaian service (program yang dimasukkan) dengan

kebutuhan anda

1.000 .580

X5 - kesesuaian harga dengan produk 1.000 .540

X6 - service pasca penjualan 1.000 .552

X7 - kemudahan dalam menggunakan untuk pemula 1.000 .511

X8 - kemudahan dalam maintenance 1.000 .624

X9 - promosi produk yang efektif 1.000 .601

X10 - design yang menarik 1.000 .562

Extraction Method: Principal Component Analysis.

Table Communalities

The first column in table above, which is labeled as initial

communalities, are estimates of the variance in each variable accounted for by all

components or factors. For principal components extraction, this is always equal

to 1.0 for correlation analyses.

The second column is labeled as extraction. This column show the

estimation of the variance in each accounted for by the components. The


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communalities in this table are all high, which indicates that the extracted

components represent the variables well. If any communalities are very low in a

principal components extraction, you may need to extract another component. For

example, lets take a look in variable Kepuasaan Anda terhadap BB. This

variable has value 0.711. It means that 71.1 % variance of variable. Kepuasaan

Anda terhadap BB can be determine by the result factor.

4.8 Determine Number of Factor

In order to determine the correct number of factor, there are 4 methods

that can be used, which are latent root criterion, priori criterion, percentage of

variance, and screen plot criterion. In this step, we focus in latent root method.

Latent root criterion is the most commonly used technique. This

method extracts eigen value to determine how many factors should be included in

the research. Eigen value is value that represents the amount of variance

accounted for by factor. The rationale of Latent Root Criterion is that any

individual factor should account for the variance of at least a single variable if it is

to be retained for interpretation. After the calculation has been done, eigen value

will continue to decrease, as shown in the table below.

Table 4.7 Total Variance Explained

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 3.391 37.679 37.679 3.391 37.679 37.679 2.909 32.324 32.324

2 1.892 21.018 58.697 1.892 21.018 58.697 2.374 26.373 58.697

3 .905 10.057 68.754

4 .786 8.732 77.486

5 .743 8.254 85.740

6 .523 5.807 91.547

7 .336 3.730 95.277

8 .232 2.581 97.858

9 .193 2.142 100.000



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Table 4.8 Initial Eigen Values

Component

Initial Eigenvalues

Total % of Variance Cumulative %

1 3.391 37.679 37.679

2 1.892 21.018 58.697

3 .905 10.057 68.754

4 .786 8.732 77.486

5 .743 8.254 85.740

6 .523 5.807 91.547

7 .336 3.730 95.277

8 .232 2.581 97.858

9 .193 2.142 100.000

The Total column gives the eigen value, or amount of variance in the

original variables accounted for by each component. The % of Variance column

gives the ratio, expressed as a percentage, of the variance accounted for by each

component to the total variance in all of the variables. The Cumulative %

column gives the percentage of variance accounted for by the first n components.

For example, the cumulative percentage for the second components is the sum of

the percentage of variance for the first and second components.

For the initial solution, there are as many components as variables, and in

a correlation analysis, the sum of the eigen values equals the number of

components. We requested that eigen values greater than 1 be extracted in SPSS

step. It means that only the components having eigen values greater than 1 are

considered significant; all components with eigen values less than 1 are

considered insignificant and are disagreed. In table above, only two components

that have eigen values greater than 1. They are first two components with eigen

values 3.391 and 1.892 respectively. So we may conclude that there are 2 factors

that will be retained.

Scree plot will show the calculation graphically. The scree plot for the

data is shown below:


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Figure 4.1 Scree Plot for the Data

From scree plot we can obviously see that there are 2 components

retained. The second section of the table above show the extracted components.

Table 4.9 Extraction Sum of Squared Loadings

Extraction Sums of Squared Loadings

Total % of Variance Cumulative %

3.391 37.679 37.679

1.892 21.018 58.697

The % of Variance column gives the ratio, expressed as a percentage;

between the eigenvalues with overall eigenvalues (the value of overall

eigenvalues is equal to number number of variables, 9). Factor 1 (component 1)


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has 37.679% ratio, and factor 2 has 21.018% ratio. The Cumulative % column

gives the cumulative of % variance. The last row of this column contains value of

58.697%. It means 58.697% variability of level of customer satisfaction in the

original 9 variables can be described b this factor analysis. So we can

considerably reduce the complexity of the data set by using these components,

with 41.303% loss of information.

The third section of the table above contains rotation sum of squared

loading. The rotation maintains the cumulative percentage of variation explained

by the extracted components, but that variation is now spread more evenly over

the components. The small changes in the individual totals suggest that the rotated

component matrix will be harder to interpret than the unrotated matrix. The final

Cumulative % is not different.

Table 4.10 Rotation Sum of Squared Loadings

Rotation Sums of Squared Loadings

Total % of Variance

2.909 32.324

2.374 26.373

4.9 Interpreting Factor

After decide number of factor retained, the next step is classify each

variables into new factor. First of all, we computed the initial unrotated factor

matrix to assist in obtaining a preliminary indication of the number of factor to

extract. The factor matrix contain factor loading for each variable on each factor.Factor loading is correlation between the original variables and the factor and the

key to understanding the nature of particular factor. The high loading factor means

the variables are representative of the factor or have strong correlation. The

unrotated factor matrix is shown below:


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Table 4.11 Component Matrix

Component Matrixa

Component

1 2

X1 - kepuasan anda terhadap BB .695 -.478

X2 - kecepatan operasi/penggunaan program BB yang baik .648 -.426


kebutuhan anda

.504 .571

X5 - kesesuaian harga dengan produk .558 .478

X6 - service pasca penjualan .709 -.222

X7 - kemudahan dalam menggunakan untuk pemula .606 -.380

X8 - kemudahan dalam maintenance .776 -.146

X9 - promosi produk yang efektif .538 .559

X10 - design yang menarik .404 .632


a. 2 components extracted.

X1 factor loading for component (Factor) 1 has 0.695 values. It is

obvious that this value is higher than factor loading for other component. So, wecan easily put X1 Variable into Factor 1. However, factor loading of X4 for factor

1 (0.504) has similar factor loading for factor 2 (0.571). It is difficult to decide

whether X4 is in factor 1 or 2.

Based on facts above, we can conclude that unrotated factor solution may

not provide a meaningful pattern of variable loading. If the unrotated factors are

expected to be meaningful, we have to rotate the data.

Rotation

Rotation method can increase interpretation level by decreasing the

occurrence of ambiguity that often happen on first extraction. Thats why in this

factor analysis we also use rotation method. We conduct rotation for every score

that doesnt show any significance between them. The term rotation means

exactly what it implies. Specifically the reference axes of the factor are turned

about the origin until some other position has been reached.


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The simplest case of rotation is an orthogonal rotation, in which the axes

are maintained at 90 degrees. There are 3 methods of orthogonal rotation,

Varimax, Quartimax, and Equimax. In this case, we chose Varimax. While

quartimax rotation is to simplify the rows of factor matrix, the varimax criterion

centers on simplifying the column of the factor matrix. With varimax rotational

approach, the maximum possible simplification is reached if there are only 1s and

0s in a column. So, with varimax, we will get clear variable classification.

Table 4.12 Rotated Component Matrix

Rotated Component Matrixa

Component

1 2

X1 - kepuasan anda terhadap BB .843 .000

X2 - kecepatan operasi/penggunaan program BB yang baik .775 .016


kebutuhan anda

.091 .756

X5 - kesesuaian harga dengan produk .188 .710

X6 - service pasca penjualan .710 .219

X7 - kemudahan dalam menggunakan untuk pemula .714 .030

X8 - kemudahan dalam maintenance .722 .320

X9 - promosi produk yang efektif .126 .765

X10 - design yang menarik -.026 .749


Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.

We can see on the table above, each factor loading has significant score.

So, it makes us to classify variable into factor. Usually, factor loading score + 0.4

is considered minimum bound, + 0.5 considered better and + 0.6 considered

significant. Since all factor loading scores is over 0.6, we may say that classifying

variable using factor analysis is suitable.


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From 9 variables, 5 variables are put into factor 1, and 4 variables

into factor 2. The next step is giving name for each factor. The name of the new

factors can be seen in table below:

Table 4.13 Classified Factor from Variables

Factor Name Variables

1

Kemudahan

Penggunaan

dan Perbaikan

BB

kepuasan anda terhadap BB

kecepatan operasi/penggunaan program BB yang baik

service pasca penjualan

kemudahan dalam menggunakan untuk pemula

kemudahan dalam maintenance

2Harga dan

Desain

kesesuaian service (program yang dimasukkan) dengan

kebutuhan anda

kesesuaian harga dengan produk

promosi produk yang efektif

design yang menarik

Besides classification variables into factor, the other important result of

factor analysis is factor score. The factor score is composite measures created for

each observation on each factor extracted in factor analysis. The factor score then

can be used to represents the factor in subsequent analysis. The factor score of our

project is shown in table below:


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Table 4.14 Component Score Coefficient Matrix

Component Score Coefficient Matrix

Component

1 2

X1 - kepuasan anda terhadap BB .312 -.092

X2 - kecepatan operasi/penggunaan program BB yang baik .285 -.077


kebutuhan anda

-.049 .333

X5 - kesesuaian harga dengan produk -.008 .302

X6 - service pasca penjualan .239 .022

X7 - kemudahan dalam menggunakan untuk pemula .261 -.064

X8 - kemudahan dalam maintenance .232 .066

X9 - promosi produk yang efektif -.037 .333

X10 - design yang menarik -.091 .343



Component Scores.

For each case and each component, the factor score is computed by

multiplying the cases standardized variable values (computed using listwise

deletion) by the components score coefficients. The resulting four component

score variables are representative of, and can be used in place of, the 9 original

variables.


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Figure 4.2 Component Plot in Rotated Space

4.10Component Transformation Matrix

The factor transformation matrix describes the specific rotation applied to

your factor solution. This matrix is used to compute the rotated factor matrix

(Component Score Coefficient matrix) from the original (unrotated) factor matrix.

Table 4.15 Component Transformation Matrix

Component Transformation Matrix

Component 1 2

1 .824 .567

2 -.567 .824




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CHAPTER 5

CONCLUSION

From the research that we conducted, we take some conclusions, such as:

1. Customer satisfaction is an important for the company sustainabilitybecause the customer is the reason for the company to create their

products and deliver their services.

2. The company must hear their voice of customer by conducting surveysand improve their subsequent product for the higher level of customer

satisfaction. The company not only should conduct the survey but they

also should processing the data with the appropriate method.

3. In this research we use factor analysis to process the data that we hadgotten from the questionaire, which all questions are about aspects that

influencing customer satisfaction.

4. From the questionaire, we know that 9 from 10 factors has greatinfluence on customer satisfaction. This aspect is grouped into two, and

the details shows in the table below.

Factor Name Variables

1Easy to use and easy

to maintenance

Satisfaction for BB

Operation /program speed excellence

After sales service

Easy to use for beginner

Easyto maintenance

2 Price and design

Suitability betwee available service to

customer needsValue for money

Effective product promotion

Attractive design

8. Factor number three about Blackberry reliability is not significantlyaffected customer preference for blackberry.


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REFERENCES

DeCoster, J. (1998). Overview of Factor Analysis. Retrieved from http://www.stat-help.com/notes.html

Hair, Joseph F. Jr., Anderson, Rolph E.Multivariate Data Analysis. Fifth Edition.

1984. New Jersey: Prentice Hall
http://www.stat-help.com/notes.htmlhttp://www.stat-help.com/notes.html


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THE ATTACHMENTS CHAPTER

Kuesioner Pengguna Blackberry

Kami mahasiswa Teknik Industri Universitas Indonesia akan mengadakan

penelitian tentang kepuasaaan konsumen terhadap penggunaan Blackberry untuk

kebutuhan tugas perkuliahaan. Diharapkan saudara/i membantu kami dengan

mengisi kuisioner ini.

Jenis Kelamin :

Usia :

Jenis Blackberry :

Untuk menjawab pertanyaang dibawah ini. Silahkan lingkari jawaban yang sesuai

dengan pilihan anda.

1. Apakah anda puas dengan Blackberry yang anda miliki? Ya Tidak

2. Apakah yang paling anda perhatikan ketika membeli Blackberry? Apakah haltersebut telah terpenuhi? (sebutkan dengan singkat)

3. Berikan skala pada aspek penilaian dibawah ini (1 = sangat tidak puas dan 10= sangat puas) dengan melingkari pilihan yang ada.


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Kepuasan anda terhadap BB1 2 3 4 5 6 7 8 9 10

Kecepatan operasi/penggunaan program Blackberry yang baik.1 2 3 4 5 6 7 8 9 10

Kehandalan Blackberry dalam menangani program dan gangguaneksternal seperti terjatuh.

1 2 3 4 5 6 7 8 9 10

Kesesuaian service (program yang dimasukkan) dengan kebutuhan anda.1 2 3 4 5 6 7 8 9 10

Kesesuaian harga dengan produk.1 2 3 4 5 6 7 8 9 10

Service pasca penjualan.1 2 3 4 5 6 7 8 9 10

Kemudahan dalam menggunakan untuk pemula.1 2 3 4 5 6 7 8 9 10

Kemudahan dalam maintenance.1 2 3 4 5 6 7 8 9 10


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Promosi produk yang efektif.1 2 3 4 5 6 7 8 9 10

Design yang menarik.1 2 3 4 5 6 7 8 9 10

4. Jika ada produk Blackberry baru, apa yang menjadi keinginan anda terkaitfaktor-faktor diatas?

..

5. Jika ada produk Blackberry baru, dengan service yang sama pada saat ini,apakah anda akan membelinya?

Ya Mungkin akan membeli Ragu-ragu Mungkin tidak akan membeli Tidak

6. Jika ada produk Blackberry yang mengalami perbaikan dalam faktor diatas(dalam tabel), apakah anda akan membeli? Misalnya pengurangan jumlah

program yang belum dibutuhkan oleh mahasiswa.

Ya Mungkin akan membeli Ragu-ragu Mungkin tidak akan membeli

final exam am(1)

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