Consumers Buying Behaviour Towards Electonic Home Appliances
Understanding Consumers’ Buying Behavior for Mobiles
-
Upload
gazal-gupta -
Category
Documents
-
view
219 -
download
0
Transcript of Understanding Consumers’ Buying Behavior for Mobiles
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
1/70
1
BRM Final Report:
Understanding Consumers
Buying Behavior for
Mobile Phones
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
2/70
2
ACKNOWLEDGEMENT
If the only prayer you ever say in your whole life is "thankyou," that would suffice,
-Meister Eckhart
We would like to express our deep sense of hearty and special gratitude to our faculty guide ---------for her
valuable suggestions and constant help; encouragement throughout the preparation of this project and for the
valuable time he spent with us, and without whose help it would have not attained its present shape.
We convey our special thanks to all fellow batch-mates for their co-operation in preparing this report
smoothly.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
3/70
3
EXECUTIVE SUMMARY
Mobile phones are necessity of humankind since civilization. As people have become more and more
civilized, their needs have enhanced as well, so as the features in phones. With increase in features the
market players also differentiated its product and preferences of human beings have changed according to
features. Gender also affects the preference of factors. With the increase in choices of brands and variety in
both Indian as well as international market there are a numbers of factors which affect the preference of
consumers. Our objective in this report was to find the major factors that influence the buying decisions of
the youths (both male and female) for mobile phones. In order to do this we did a primary research through
the means of questionnaire of 100 sample sizes. The population of the sample was 1st year MBA students
of IBS, Hyderabad. The sample consisted of 50 males and 50 females. To find out relation between various
factors for the selection of phones among youth of both the genders we analyzed on the basis of Multivariate
Analysis, Cluster Analysis, Factor Analysis and Discriminant Analysis. On doing the Factor Analysis wefound that there are 7 major factorswhich influence the buying decision of the selected samples. On doing
the Discriminant Analysis we didnt get a significant model which could explain significantly the
factors for buying Indian or Foreign phones or factors affecting the buying behavior amongst male
and female. After doing this research we find that there were not many differences in the factors
affecting the buying behavior in male and female. They are more or less influenced by the same
factors.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
4/70
4
INTRODUCTIOnMobile phone
A mobile telephone or cellular telephone (commonly, "mobile phone" or "cell phone") is a long-range,
portable electronic device used for mobile communication. In addition to the standard voice function of a
telephone, current mobile phones can support many additional services such as SMS for text messaging,
email, packet switching for access to the Internet, and MMS for sending and receiving photos and video.
Most current mobile phones connect to a cellular network of base stations (cell sites), which is in turn
interconnected to the public switched telephone network (PSTN) (the exception are satellite phones).
History
The introduction of hexagonal cells for mobile phone base stations, invented in 1947 by Bell Labs engineers
at AT&T, was further developed by Bell Labs during the 1960s. Radiophones have a long and varied history
going back to the Second World War with military use of radio telephony links and civil services in the
1950s, while hand-held cellular radio devices have been available since 1983. Due to their low
establishment costs and rapid deployment, mobile phone networks have since spread rapidly throughout the
world, outstripping the growth of fixed telephony.
In 1945, the 0G generation of mobile telephones was introduced. 0G mobile telephones, such as Mobile
Telephone Service, were not officially categorized as mobile phones, since they did not support the
automatic change of channel frequency in the middle of a call, when the user moved from one cell (base
station coverage area) to another cell, a feature called "handover".
In 1970 Amos Joel of Bell Labs invented the "call handoff" feature, which allowed a mobile-phone user to
travel through several cells during the same conversation. Martin Cooper of Motorola is widely considered
to be the inventor of the first practical mobile phone for handheld use in a non-vehicle setting. Using a
modern, if somewhat heavy portable handset, Cooper made the first call on a handheld mobile phone on
April 3, 1973. At the time he made his call, Cooper was working as Motorola's General Manager of its
Communications Division.
Fully automatic cellular networks were first introduced in the early to mid-1980s (the 1G generation). The
first fully automatic mobile phone system was the 1981 Nordic Mobile Telephone (NMT) system. Until the
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
5/70
5
early 1990s, most mobile phones were too large to be carried in a jacket pocket, so they were usually
permanently installed in vehicles as car phones. With the advance of miniaturization and smaller digital
components, mobile phones got smaller and lighter.
The JOURNEY
Although mobile phoneshave taken over our current society, they have been around for several decades in
some form or another. Beginning in the late 1940s, the technology that would later be used in todays cell
phoneswas created and the idea of a mobile phonewas introduced. This cell technology was first used in
mobile rigs which was mainly used in taxis, police cars and other emergency vehicles and situations.
Truckers also used a form of this technology to communicate with each other. Little did they know how far
their idea would advance to make it accessible to the majority of the population.
The first mobile phones, referred to as First Generation or 1G, were introduced to the public market in
1983by the Motorola Company. These first mobile phones used analog technologywhich was much less
reliable than the digital technologywe use today. The analog phones also had a great deal more static and
noise interference than we are accustomed to today. The first mobile phones during this era were confined to
car phonesand they were permanently installed in the floorboard of automobiles. After a few years, they
became mobile and consumers could take the phones with them outside of the car. However, they were the
size of a large briefcaseand very inconvenient. The main purpose of this First Generation technology was
for voice traffic, but consumers felt insecure about people listening in on their conversations. These newmobile phones were also rather expensive, many of them costing hundreds of dollars. They were more of a
status symbol during the decade rather than a means of convenience.
During the 1990s, great improvements were made in the mobile phone technology. These phones used
Second Generation, or 2G technology. In 1990, the first cell phone call was made using the new digital
technology that became characteristic of this era. The Second Generation cellular phone technology was
faster and much quieter than its analog predecessor. As a result, it became even more popular than previous
models, too. The new technology also made them capable of being smaller rather than the large briefcase-
sized units from the 1980s. Smaller batteriesand other technology that made the phones more energy-
efficient helped contribute to their smaller sizes and their popularity. Companies also strived to make the
prices more affordable than the mobile phones of the 1980s. You could buy a decent cell phone with 2G
technology for approximately $200along with an airtime service. The cell phone industry was beginning to
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
6/70
6
take off.
The Third Generation technology, or 3G, is what many people currently use in their digital cellular phones
today. This technology was created very soon after the excitement that the 2G technology created. This new
technology is not only capable of transferring voice data(such as a phone call), but it is also able to transfer
other types of data, including emails, information and instant messages. These capabilities have helped to
increase the amount of sales and the popularity of these new phones. Many users prefer to use the instant
messaging capabilities to text other users rather than call them in the form of a traditional phone call.
Many cell phone companies offer free and very affordable phones for consumers who sign-up with their
airtime service for a contractual period. Prices for the services range but the competition in the industry is
helping to keep them more affordable than they have been in previous years.
You would think that there is little more that you could do with cellular phone technology. This is, however,
not the case. There are currently plans in place to develop a Fourth Generation4Gtechnology. Goals
for this new set of standards include a combination of technologies that will make information transfer and
internet capabilities faster and more affordable for cellular phones. At this time, there is no one definition
that can be attributed to 4G technology because researchers are still striving to make advances and build
upon the technology that already exists.
The mobile phone industry continues to grow by leaps and bounds as it has in the past few decades. Even
though it started a little more than 20 years ago, manufacturers have created an abundance of new
technologies that keep cell phone users coming back for more. They continue to increase the number of
capabilities and services to accommodate the growing needs of todays on the go culture. Waiting
anxiously is the only way to find out what they will think of next.
As the number and quality of WI/FI points become available and with the growth of Smart Phones that not
only provide the basic functions expected in a mobile phone but provide so much more the market is
changing and brand new players have entered the market including Apple with the successful Iphone andResearch Machines with the equally successful Blackberry. In 2008 a new player enters the market
providing an open source operating system for mobile phones that manufacturers can use and adapt, the new
player is Google who make the Android operating system available and the first phone to appear is the G1
from T-Mobile, because the OS is open source the number of applications available is expected to grow and
sites like The Android Library who provide a library of the latest free and commercial applications will
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
7/70
7
grow. It remains to be seen if this latest entry of an Operating System in the Smartphone market will make a
significant impact but many feel this could be the future for the market
STATEMENT OF PURPOSEThe major focus is to ascertain the factors which lead to the usage or deviate from the usage of mobile
phones among the youth (both male and female). The methods used are Multivariate Analysis, Discriminant
analysis, Factor analysis and Cluster analysis.
RESEARCH PROBLEM
What are the various factors that affect the purchase of a mobile phone among the young consumers
(both male and female) in India?
What are the most important features that are to be incorporated in mobile phone brand which
targets the Youth; specially the professionals of IBS Hyderabad?
To find the preferences amongst youth for Indian and Foreign brands.
OBJECTIVES
To study the factors those are considered by the youth segment while making a buying decision of
mobile phones. Further the youth segment has been divided into male and female to understand
their respective preferences.
To evaluate the features which a consumer looks for in various brands available in the market.
To find the preferences amongst youth for Indian and Foreign brands.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
8/70
8
Research Articles
1. Consumer Behavior Statistics of Mobile Telephone ServicesFrida Ahslund
Master of Sciences Thesis, Stockholm, Sweden 2006
This thesis looks at how the users of mobile telephone services have behaved historically by exploring the
transaction data in the Internet Payment Exchange database. With analyze of variance it was possible
to establish what behavior to expect in the future. Also, the content-providers were clustered with the
unsupervised clustering method self-organizing maps.
It can be shown that
61 % of the users use one or two services per month.
77,6 % of the users use services four month per year or less.
55% of the users use only services that are free.
37,4 % of the users that pay for some of their services spend 10 SEK or less per month.
28 % of the users are responsible for 90% of the spending.
It was possible to find a cluster of content providers that had more transactions as well as higher spending
per user and month, than other content providers. The group had an average of 3,84 transactions, and 51,56
SEK per user and month.
2. Wireless Consumer Behavior
http://www.3g.co.uk/PR/July2003/5644.htm
Campaigns targeting MobileNet consumers must go beyond considerations of location and time focusing on
broader user context in order to be effective, according to a study released today by researchers at the
International University of Japan.
Based on the results of 14,000 mobile user responses nationwide, the researchers have created an approach
that includes user context for developing and deploying MobileNet solutions. Although physical location
and time of day at which users access the MobileNet is important and correlated to some extent with user
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
9/70
9
content choice, the results suggest that such factors provide no true foundation upon which to build effective
marketing campaigns or profitable business models.
Its not enough to know the location of the user, said Prof. Philip H. Sidel, who co-authored the study
with Prof. Glenn E. Mayhew, Ph.D. You have to understand why the user is there to be effective. To truly
understand the MobileNet userand to see the mobile platforms potential -- requires a much richer context
of attitudes and motivations.
The study, which is available for free at www.MoCoBe.com, identifies psychological drivers, specifically
how consumers view their mobile devices, which provide a much clearer segmentation of consumer
behavior and the content choices that they make.
Other findings of the study were that consumers more often accessed the MobileNet in non-mobile locations
such as from home (29 percent) and work (28 percent) rather than while commuting (19 percent) or during
leisure time (22 percent). The most popular MobileNet access location in the home is the living room and
from the office is an individuals desk or primary work space. While the most popular access location while
commuting was on the train or subway.
The portable aspect of the MobileNet, the ability to have it with you wherever you are, is more important
than the ability to use it on the go, said Prof. Mayhew. So places where people spend the most time
become the high volume usage locations.
Other results from the study include:
The locations and times of day from which individuals access the MobileNet do have a relationship with
total usage and the type of content that is accessed, but such relationships are weak.
Prof. Sidel said, Based on what has appeared in the business press, you would expect to find clear patterns
between the content people choose to access relative to time of day, general location such as home or
work -- and specific locations such as a restaurant or a bus -- from which they conduct their MobileNet
sessions. There are some patterns that exist, but definitely not enough clarity supporting them to build an
effective marketing campaign or business model.
While location and time of day had weak relationships with usage, how people feel about their phone had
much clearer interactions. For example, people who value their phones ability to keep them informed areheavier users of news and information. Those who value the convenience of the MobileNet are far less
likely to download ringtones and backgrounds, and are far more likely to use their phones for email and chat
-- 81percent as opposed to 76 percent overall.
These relationships made intuitive sense, but also offered new insights, said Prof. Mayhew. Providing
mobile experiences based on the inherent value that each individual perceives in the mobile platform will
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
10/70
10
not only yield richer experiences for individual users, but is very likely to significantly impact average
revenue per user (ARPU) and overall MobileNet usage.
For the overwhelming majority of people, the MobileNet is primarily a communication platform. Over 75
percent of respondents gave email/chat as their most accessed content. Ringtone/picture downloads was next
at 5 percent. News/information (4 percent), traffic/ transportation information (3 percent), and entertainment
(2 percent) were also categories with 2 percent or more response.
The analysis of the study is continuing and Prof. Sidel will present updated results in November at the IDG-
sponsored 3G Japan; Wireless and Beyond conference in Tokyo.
3. International Marketing Communication in Mobile Phone Industry
Junwen Guo, University essay from Blekinge Tekniska Hgskola/Sektionen fr Management (MAM)
The purpose of this study orients to the discussion of the applicability of Integrated Marketing
Communication (IMC) in Chinese market, typically in the music mobile phone industry.This paper
endeavors in contributing to the analysis of the local consumer behavior characteristics in the process of
purchase decision making as well as shaping long-term attitude towards mobile phone brands, in order to
discuss the effectiveness of the objective marketing strategy and the application of the Integrated Marketing
Communication in the branding strategy.
MethodologyOur approach to the research was as follows:
1. Pilot study:The group will conduct a pilot study inside the IBS campus in order to evaluate the
effectiveness of the questionnaire and to find out the factors that contribute most towards the buying
behavior. A pre-test questionnaire has been prepared and filled up by a small random sample of 30
respondents which will help in identifying the factors which contribute least towards the buying
decision of the youth. These factors will not be considered for the post-test questionnaire.
2. Sample design: Our target sample is 100 students (50Males, 50Females) of 1styear MBA program
of IBS Hyderabad. We took 50 male and 50 female because our research objective was to find out
the differences in their preferences.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
11/70
11
3. Research design: We made use of descriptive research design as our objective was very well
defined. We use this study because we wanted to make specific predictions and wanted to find out
the characteristics of male and female preference patterns. We also made use of the questionnaire in
which we basically used itemized category scale, likert scale. Our major output made use of the
checklist question and likert scale; it contributed to major part of our analysis.
4. Data collection: It is collected from secondary sources in the form of:
a) Research articles: As discussed above.
b) Questionnaires: For primary data collection from the 1styear MBA students of IBS Hyderabad.
The number of field workers used was 7 and the period of data collection was from 21st
December till 27th
December, 2008.
5. Statistical tools used: We basically made use of 3 major statistical tools which are as follows:
A) Discriminant analysis:
Discriminant function analysis is used to classify the cases into values of a categorical dependent variable. It
is used to determine which variables discriminate between two or more naturally occurring groups . It
also called Canonical discriminant analysis.
L = b1x1 + b2x2 + ... + bnxn + c
where, L is the latent variable that is formed by the discriminant function.
The b's represent the discriminant coefficients
The x's being the discriminating variables and c is a constant.
B) Factor analysis:
Factor analysis is astatistical data reduction techniquewhich is used to explain the variability among
the observed random variables. This analysis is done in terms of fewer unobserved random variables
called factors. The observed variables are modeled aslinear combinations of the factors, plus "error"
terms. It is used in behavioral sciences, social sciences, marketing,product management, operations
research, and other applied sciences that deal with large quantities of data. In Factor analysis, there is
nothing like dependent and independent variable. Instead all variables are analyzed at a time irrespective of
which is dependent and which is independent.
It helps in answering four major questions:
http://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Variancehttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Linear_combinationhttp://en.wikipedia.org/wiki/Errors_and_residuals_in_statisticshttp://en.wikipedia.org/wiki/Social_scienceshttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Product_managementhttp://en.wikipedia.org/wiki/Operations_researchhttp://en.wikipedia.org/wiki/Operations_researchhttp://en.wikipedia.org/wiki/Operations_researchhttp://en.wikipedia.org/wiki/Operations_researchhttp://en.wikipedia.org/wiki/Product_managementhttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Social_scienceshttp://en.wikipedia.org/wiki/Errors_and_residuals_in_statisticshttp://en.wikipedia.org/wiki/Linear_combinationhttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Variancehttp://en.wikipedia.org/wiki/Statistics -
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
12/70
12
1. How many different factors are needed to explain the pattern of relationships among these variables?
2. What is the nature of those factors?
3. How well do the hypothesized factors explain the observed data?
4. How much purely random or unique variance does each observed variable include?
Some of the applications of factor analysis are:
To explain a business phenomenon, there are some of the hidden factors that need to be determined.
(Interdependency and pattern delineation)
To find out uncorrelated variables or factors that can be used in multiple regression and other tools
(Parsimony and data reduction)
Methods of Factor Analysis: Two major types of Factor Analysisare Principal Component Analysis
and Principal Axis Factoring (also called as Common Factor Analysis).
Exploratory Factor Analysis is that method which is used to explore or uncover the underlying
structureof relatively large number of variables.
A factor is formed from a set of variables. As said, a factor can be expressed as a linear combination of
a set of variables. Let us see an example.
F1= a1x1+ a2x2+ a3x3
F2= b1x1+ b2x2+ b3x3
Here we have two factors and these two are expressed in terms of three variables x1, x2and x3. The
numbers a1, a2, a3, b1, b2, b3are called as Factor Loadings. They represent the correlation coefficients
of individual variables on the factors. The first step in Factor Analysis is to calculate two important
measures namely Eigen-values and Communalities. Communality exists for variables and Eigen
values exist for the factors.Hence there are 2 Eigen values (in this case) and three communalities.
Eigen Value of F1= (a1)2+ (a2)
2+ (a3)
2
C) Cluster analysis:
Cluster analysis also called segmented analysis or taxonomy analysis which seeks toidentify homogeneous
subgroups in a population. It identifies a set of groups which both minimizes within group variation
and maximizes between group variation.There are three basic types of clustering:
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
13/70
13
1. Hierarchical Clustering:Hierarchical clustering builds (agglomerative), or breaks up (divisive), a
hierarchy of clusters. The traditional representation of this hierarchy is atree(called adendrogram)
2. K- means Clustering:TheK-means clustering assigns each point to the cluster whose center (also
called centroid) is nearest.
3. Two step Clustering:The Two Step Clustering is a scalable cluster analysis algorithm designed to
handle very large datasets. It is Capable of handling both continuous and categorical variables and
attributes. In the first step of the procedure, one has to pre-cluster the records into many small sub-
clusters. Then, cluster the sub-clusters from the pre-cluster step into the desired number of clusters.
If the desired number of clusters is unknown, the Two Step Clustering will find the proper number of
clusters automatically.
PRE Questionnaire Method:PRE TEST RESPONSES ANALYSIS
We have used pre test questionnaire in order to ensure that the accurate variables go for the final analysis. In
this test the questionnaire is split into two parts, first one has the main questions and the other half has its
statements. Then the questionnaire is filled by various respondents and their responses are analyzed and
only those responses are taken into final analysis whose correlation among main and split questions is more
than 65 %. By correlation we mean that the responses towards main and split question must be in the range
of plus or minus 1. For example if a respondent gives a response towards the main question of a product
http://en.wikipedia.org/wiki/Tree_data_structurehttp://en.wikipedia.org/wiki/Tree_data_structurehttp://en.wikipedia.org/wiki/Tree_data_structurehttp://en.wikipedia.org/wiki/Dendrogramhttp://en.wikipedia.org/wiki/Dendrogramhttp://en.wikipedia.org/wiki/Dendrogramhttp://en.wikipedia.org/wiki/K-means_algorithmhttp://en.wikipedia.org/wiki/K-means_algorithmhttp://en.wikipedia.org/wiki/K-means_algorithmhttp://en.wikipedia.org/wiki/K-means_algorithmhttp://en.wikipedia.org/wiki/Dendrogramhttp://en.wikipedia.org/wiki/Tree_data_structure -
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
14/70
14
characteristic as 3 and in split question if he gives 1 or 5 then his responses are not correlated. Such
instances are taken into account and correlation among answers is found.
Analyzing the results of the pre-test questionnaire, we found that of the 28 variables under research, only 20
variables had a significant correlation in their responses by various respondents. Thus, we cut down the 28
variables into 20 final variables which will be included in the final questionnaire to reach the final
conclusion of the research.
FINDINGS OF PRE TEST QUESTIONNAIRE:
The variables in pre test questionnaire are documented under three different questions. Following were the
findings of our survey under each question:
QUESTION NO. 1
The following table shows the variables which were taken that affect consumer behavior at the most primary
level, and hence are taken as the most integral aspect of any mobile phone characteristics (headed under
product characteristics) and respondents response for each variable. Also shown below is a chart showing
correlation of main and split questions for each variable.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
15/70
15
Product characteristics (main
question)
S.No. Mobile Size Mobile color
Shape of the
Mobile
Number of
Mobiles
1 5 5 2 4
2 2 4 1 1
3 3 4 2 3
4 2 2 3 2
5 4 1 4 1
6 4 3 5 1
7 2 4 4 1
8 3 2 4 1
9 5 1 4 4
10 3 2 3 3
11 1 1 2 2
12 5 2 5 1
13 4 1 4 4
14 4 4 4 5
15 5 4 3 2
16 1 3 1 1
17 2 3 5 2
18 5 3 3 3
19 4 3 1 4
20 2 2 2 421 2 2 1 1
22 4 4 5 4
23 4 3 3 2
24 4 1 4 5
25 1 4 3 1
26 2 2 3 4
27 3 3 1 4
28 2 3 4 3
29 2 3 5 3
30 4 1 2 1
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
16/70
16
Split Statement
S.No. Mobile Size Mobile color Shape of the Mobile Number of Mobiles
1 4 2 1 4
2 2 3 3 3
3 1 4 2 2
4 2 3 2 3
5 2 1 2 1
6 3 2 3 4
7 3 2 4 2
8 1 2 1 3
9 4 1 4 4
10 4 3 3 4
11 2 1 3 2
12 2 2 4 3
13 4 3 1 4
14 3 3 2 5
15 2 4 4 4
16 1 3 2 1
17 2 1 5 3
18 4 3 4 1
19 1 4 3 4
20 2 2 3 1
21 3 3 4 2
22 2 4 4 4
23 4 1 2 3
24 3 1 2 2
25 4 4 2 1
26 2 4 5 3
27 3 2 2 1
28 1 5 3 3
29 2 2 2 5
30 2 1 3 2
Discrepencies 9 7 12 10
Correl (%) 70 76.66 60 66.66
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
17/70
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
18/70
18
Split questions
S.No. Screen Type Screen Size Screen Color LED Light Durability
1 1 5 5 1 4
2 4 4 2 2 4
3 2 3 2 4 3
4 3 3 2 5 15 1 1 1 2 2
6 2 2 5 2 4
7 1 2 3 1 2
8 2 4 2 3 3
9 3 2 3 3 4
10 3 4 5 4 5
11 2 3 3 2 3
12 3 2 4 4 4
13 4 2 2 3 3
14 2 3 4 5 515 2 3 4 2 2
16 1 2 2 3 1
17 2 1 2 4 2
18 1 5 2 5 4
19 4 2 3 1 1
20 3 4 1 5 3
21 2 2 2 3 5
22 3 4 5 2 3
23 5 3 1 4 5
24 3 5 4 5 225 2 1 5 3 4
26 4 2 2 5 1
27 4 4 4 1 5
28 3 2 1 3 3
29 4 3 4 2 4
30 4 1 5 4 2
Discrepencies 6 8 14 7 4
Correl (%) 80 73.33 53.33 76.66 86.66
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
19/70
19
Product characteristics
(main question)
S. No.Warranty Presence of calculator Bluetooth Stop Watch Alarm
1 3 3 5 4 1
2 5 1 2 4 5
3 5 4 3 4 4
4 3 1 3 3 4
5 5 3 1 4 4
6 3 1 1 4 3
7 5 2 5 3 5
8 3 5 4 4 3
9 5 4 5 3 4
10 5 2 4 4 3
11 4 2 1 2 5
12 4 3 5 2 4
13 4 1 5 5 414 2 1 3 2 5
15 4 2 2 3 5
16 1 4 2 3 4
17 4 1 1 5 2
18 3 2 1 1 1
19 2 4 1 5 4
20 3 3 5 4 5
21 1 1 3 5 2
22 3 5 4 5 3
23 1 2 2 2 424 5 5 2 4 3
25 1 5 4 1 5
26 5 1 4 1 3
27 4 4 2 4 2
28 3 1 2 3 3
29 4 2 2 3 3
30 5 4 1 1 2
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
20/70
20
Split Questions
S.No. Warranty Presence of calculator Bluetooth Stop Watch Alarm
1 4 1 2 3 3
2 4 2 4 5 4
3 3 2 2 1 2
4 3 2 3 3 4
5 5 2 3 2 1
6 2 4 4 3 4
7 3 5 4 5 3
8 1 4 5 4 1
9 4 3 2 2 4
10 5 4 3 1 4
11 2 5 2 2 5
12 4 4 3 2 1
13 5 2 5 4 2
14 3 3 1 2 4
15 2 1 3 4 3
16 2 1 4 3 3
17 3 2 1 3 4
18 3 4 2 2 2
19 5 5 4 4 3
20 3 2 2 1 3
21 2 1 5 3 3
22 3 2 3 2 5
23 1 3 3 3 4
24 2 3 5 3 1
25 1 4 3 2 4
26 4 2 5 4 3
27 1 1 2 1 4
28 2 2 4 3 4
29 2 4 2 4 4
30 5 2 3 2 4
Discrepencies 9 14 14 13 12
Correl (%) 70 53.33 53.33 56.66 60
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
21/70
21
Product characteristics
(Main questions)
S.No. Water Resistance
Shock
Resistance
Battery
Life Weight
Service Centre
Availability
1 1 3 3 4 1
2 1 4 3 4 4
3 3 1 3 1 24 3 2 1 1 1
5 4 2 3 2 1
6 1 2 4 1 4
7 4 5 1 4 4
8 3 2 1 3 1
9 5 5 4 4 1
10 4 1 4 2 4
11 4 3 4 3 2
12 3 5 3 3 1
13 2 3 1 5 114 1 1 4 3 4
15 4 5 5 4 3
16 2 5 4 4 3
17 2 1 4 2 3
18 5 4 3 1 3
19 3 2 4 2 2
20 2 4 1 1 2
21 4 2 3 4 3
22 3 4 3 4 5
23 5 5 1 2 424 3 3 4 2 5
25 3 2 3 5 2
26 3 3 5 1 3
27 5 5 4 3 3
28 4 3 1 4 4
29 5 3 5 5 1
30 4 3 3 2 4
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
22/70
22
Split Questions
S.No. Water Resistance
Shock
Resistance Battery Life Weight
Service Centre
Availability
1 3 2 1 3 4
2 2 5 3 5 3
3 2 2 5 2 1
4 3 1 2 1 35 4 4 4 3 1
6 1 2 3 2 3
7 3 4 4 4 1
8 5 3 1 1 2
9 4 4 2 5 1
10 4 2 4 3 3
11 5 3 3 2 4
12 3 4 4 4 2
13 4 1 4 4 1
14 3 2 3 2 2
15 2 4 4 3 4
16 3 5 2 5 2
17 1 3 3 3 1
18 4 3 4 3 4
19 3 3 3 1 3
20 2 4 2 2 2
21 4 3 1 3 2
22 3 3 3 4 4
23 4 4 2 1 5
24 2 5 3 3 2
25 5 2 4 2 3
26 2 4 5 1 2
27 5 5 3 2 3
28 4 4 2 5 5
29 1 2 5 4 2
30 4 3 4 1 5
Discrepencies 3 4 7 3 6
correl (%) 90 86.66 76.66 90 80
Responses of 30 respondents towards product characteristics of a mobile phone affecting buying
behavior and their split statement.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
23/70
23
The tables which are given above shows the respondents views on what affects consumer buying behavior
for mobile phones as far as characteristics of a mobile phone are concerned. In bold are those responses
which are not correlated in case of main and split question. As we have taken a range of response plus or
minus 1 for correlation, therefore for a response 3 in main question both 1 and 5 responses in split question
is not correlated.
Given below is a table showing correlation between responses to various product characteristics. In this
table, the characteristics which are shown in bold have correlation coefficient less than 65 % and therefore
are not taken in final analysis.
Product Characteristics Correlation
Mobile Size 70
Mobile color 76.66Shape of the Mobile 60
Number of Mobiles 66.66
Screen Type 80
Screen Size 73.33
Screen Color 53.33
LED Light 76.66
Durability 86.66
Warranty 70
Presence of calculator 53.33
Bluetooth 53.33Stop Phone 56.66
Alarm 60
Water Resistance 90
Shock Resistance 86.66
Battery Life 76.66
Weight 90
Service Centre Availability 80
Correlation between responses for main and split questions in case of product characteristics.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
24/70
24
Correlation among responses to main question and split statements for various product characteristics
affecting consumer buying behavior.
Question 2
The following table shows the variables which were taken that affect consumer behavior at the most
primary level, and hence are taken as sources of information and respondents response for each variable.
Also shown below is a chart showing correlation of main and split questions for each variable.
Sources (Main Questions)
S. No. Advertisement Internet
Promotional Efforts
(Schemes, discounts etc)
Word Of Mouth (Friends,
Work Groups)
1 5 2 5 52 1 2 5 2
3 3 2 2 3
4 2 1 4 2
5 2 3 3 5
6 5 5 1 3
7 4 2 4 3
8 5 4 2 3
0
10
20
30
40
50
60
70
80
90100
Mob
ileSize
Mobilecolor
Shapeofthe
Mobile
NumberofM
obiles
Scree
nType
Scre
enSize
Scree
nColor
LE
DLight
Du
rability
W
arranty
Presenceofcalculator
MarineCompass
Stop
Phone
Alarm
WaterRes
istance
ShockRes
istance
BatteryLife
Weight
ServiceCentreAva
ilability
Correlation
Correlation
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
25/70
25
9 2 5 5 3
10 1 4 1 1
11 2 2 2 5
12 1 5 5 4
13 2 1 1 3
14 5 5 1 5
15 4 5 3 4
16 1 2 5 4
17 4 3 2 5
18 3 4 4 1
19 2 4 1 4
20 2 2 5 2
21 1 1 4 1
22 4 3 5 4
23 1 1 3 1
24 2 1 2 1
25 5 5 1 4
26 4 5 4 1
27 4 1 1 2
28 1 4 4 2
29 2 1 2 5
30 1 3 5 4
Statement (Split Questions)
S. No. Advertisement Internet Promotional Efforts(Schemes, discounts etc)
Word Of Mouth
(Friends, WorkGroups)
1 5 4 4 4
2 2 3 3 3
3 1 5 1 2
4 3 2 5 1
5 1 4 2 5
6 4 4 1 2
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
26/70
26
7 5 2 3 5
8 4 1 3 4
9 1 4 4 3
10 3 4 2 2
11 1 2 1 5
12 2 5 2 3
13 3 1 1 4
14 4 5 2 4
15 2 2 2 5
16 1 4 4 5
17 3 3 1 4
18 4 4 5 1
19 1 1 2 2
20 3 2 4 3
21 1 1 3 2
22 2 3 3 4
23 1 1 4 2
24 2 3 1 1
25 4 3 2 4
26 5 5 3 1
27 3 1 2 3
28 2 1 5 1
29 1 1 3 4
30 3 5 4 5
Discrepencies 5 11 3 2
correl (%) 83.33 63.33 90 93.33
The tables which are given above shows the respondents views on what affects consumer buying behavior
for mobile phones as far as sources of information are concerned. In bold are those responses which are not
correlated in case of main and split question. Given below is a table showing correlation between responses
to various product characteristics. In this table, the characteristics which are shown in bold have correlation
coefficient less than 65 % and therefore are not taken in final analysis.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
27/70
27
Sources Correlation
Advertisement 83.33
Internet 63.33
Promotional Efforts (Schemes, discounts etc) 90
Word Of Mouth (Friends, Work Groups) 93.33
Correlation between responses for main and split questions in case of source of information.
Correlation among responses to main question and split statements for various sources of information
affecting consumer buying behavior.
Question 3
The following table shows the variables which were taken that affect consumer behavior at the most
primary level, and hence are taken as psychological factors and respondents response for each variable.
Also shown below is a chart showing correlation of main and split questions for each variable.
0
10
20
30
40
50
60
70
80
90
100
Advertisement Internet Promotional Efforts
(Schemes, discounts etc)
Word Of Mouth
(Friends, Work Groups)
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
28/70
28
Psychological Factors (Main Question)
S.No. Price Status Change Brand perception
Celebrity
Endorsements
1 4 5 1 3 5
2 4 1 2 3 4
3 1 2 5 1 34 3 3 5 5 5
5 1 4 1 3 3
6 3 4 5 1 2
7 1 1 1 2 4
8 3 4 2 3 2
9 5 1 5 5 4
10 2 2 3 1 2
11 4 3 5 1 5
12 1 3 2 3 1
13 1 4 3 4 4
14 4 1 3 2 4
15 1 5 2 2 4
16 2 5 3 3 4
17 1 3 1 2 5
18 3 1 3 1 4
19 4 3 3 4 5
20 5 4 4 4 1
21 5 4 2 3 5
22 5 3 1 4 2
23 5 2 5 5 1
24 2 1 1 5 1
25 5 5 2 3 3
26 1 4 3 4 2
27 5 2 3 5 1
28 5 2 1 3 4
29 2 2 4 5 2
30 1 5 3 1 3
Statements (Split
Questions)
S.No. Price Status Change
Brand
perception
Celebrity
Endorsements
1 3 4 2 5 4
2 4 2 4 2 3
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
29/70
29
3 2 1 4 1 4
4 2 1 2 3 2
5 1 3 2 4 2
6 2 5 3 2 3
7 1 1 4 1 5
8 2 4 1 3 5
9 4 1 4 2 4
10 3 1 1 2 1
11 4 4 4 4 4
12 3 2 4 2 3
13 2 2 4 1 5
14 3 2 2 3 3
15 2 5 5 2 4
16 1 4 4 5 2
17 2 3 3 1 4
18 3 2 1 2 5
19 5 4 3 3 3
20 4 3 2 5 2
21 4 1 1 5 4
22 5 3 2 3 4
23 4 1 4 4 2
24 3 2 2 5 1
25 4 4 4 1 4
26 2 5 1 3 5
27 4 3 2 4 2
28 5 4 4 5 4
29 3 2 4 4 330 2 4 5 2 1
Discrepa
cies 1 4 14 9 8
Correlati
n(%) 96.66 86.66 53.33 70 73.33
The tables which are given above shows the respondents views on what affects consumer buying behavior
for mobile phones as far as psychological factors are concerned. In bold are those responses which are not
correlated in case of main and split question. Given below is a table showing correlation between responses
to various product characteristics. In this table, the characteristics which are shown in bold have correlation
coefficient less than 65 % and therefore are not taken in final analysis.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
30/70
30
Psychological factor Correlation
Price 96.66
Status 86.66
Change 53.33
Brand perception 70
Celebrity Endorsements 73.33
0
20
40
60
80
100
120
Price Status Change Brand perception Celebrity
Endorsements
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
31/70
31
FINDINGS AND ANALYSIS:
In the study undertaken by us on mobile phone preferences we have taken into consideration 20
independent and 2 dependent variables which were categorical. We made use of two techniques:
1. Dependency technique: In dependency techniques we made use of Discriminant analysis.2. Interdependency techniques: In interdependency techniques we made use of factor as well as
cluster analysis.
Since we are not having any dependant variable having unique value, we were not able to run
multiple regression for our data.
FACTOR ANALYSIS: Factor analysis is a class of procedures used in data reduction or data
summarization. Since the variables which we used in our study were 20, so we used this technique in order
to make our data analysis easier. We were able to reduce the number of variables into few dimensions (7)
called factors which enables us to summarize our data. Now let us discuss about the output of factor
analysis:
KMO- It is an index which is used to measure the appropriateness of factor analysis. KMO value of greater
than 0.6 indicates whether factor analysis is applicable or not. In our study undertaken we found thatKMO value is 0.64 which indicates that factor analysis is applicable for our sample.
KMO and Bartlett's Tes t
.640
416.753
190
.000
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
Approx. Chi-Square
df
Sig.
Bartlett's Test of
Sphericity
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
32/70
32
Significance shall be less than 0.01. In our study the value of significance level is 0.000
Communality (h2)Communalities indicate the amount of variance in each variable that is accounted
for. Initial communalities are estimates of the variance in each variable accounted for by all the
components in the factors.
Extraction communalitiesare estimates of the variance in each variable accounted for by the factors
(or components) in the factor solution.
Communality is amount of variance a variable shares with all the other variables being considered. Thisis also the proportion of the variance explained by the common factor that is all the factors
cumulatively explaining the amount of extraction from that variable.
Analysis:
The value of commonality has to be more than 0.05. In our study each and every variable exhibits this
property.
Communalities
Initial Extraction
MOBILE_SIZE 1.000 .711
MOBILE_COLOUR 1.000 .801
SHAPE_OF_MOBILE 1.000 .704
NUMBER_OF_MOBILE 1.000 .824
SCREEN_TYPE 1.000 .863
SCREEN_SIZE 1.000 .760
SCREEN_COLOUR 1.000 .752LED_LIGHT 1.000 .683
DURABILITY 1.000 .891
WARRANTY 1.000 .728
CALCULATOR 1.000 .738
BLUETOOTH 1.000 .786
STOPWATCH 1.000 .728
ALARM 1.000 .726
WATER_RESISTANCE 1.000 .766
SHOCK_RESISTANCE 1.000 .786
BATTERY_LIFE 1.000 .818
WEIGHT1.000 .757
SERVICE_CENTRE_AVAILABILITY 1.000 .686
PRICE 1.000 .800
Extraction Method: Principal Component Analysis.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
33/70
33
Total variance explained- It is the percentage of variance explained by significant factors in a research
study. In our study we find that factors like mobile size, mobile colour, shape, number of mobiles,
screen-type, screen-size, screen-colour, led-light and durability explains 76.698% of the variance.
Eigen value: The Eigen values reflect the importance of the variables which classify cases of the
dependent variable. Eigen Values are equal for between the group variance and within the group
variance. Ideally, the between variance should be more than within the group variance; hence the
Eigen Value should always be greater than 1.
Analysis:
From the given table above (Total Variance Explained), the first factor explains 12.518% of
total variance. It can be noted that the first few factors explain relatively large amount of
variance whereas subsequent factors explain only small amount of variance.
SPSS then extracts all factors with Eigen values greater than 1, which leaves us with 9 factors.
The Eigen valuesassociated with these factors are displayed in the above mentioned table.
So, by looking at the first panel, we have seven factors which have Eigen Value greater than 1,
the cumulative variance explained by them is 76.698%.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
34/70
34
The Scree plothelps the researcher to decide the number of factors that should be retained for
success. The point after which the curve begins to even out is taken as the final no. of factors
Analysis:
From the output sheet we can say that Scree plot begins to even out after the extraction of 9th
factor therefore only 9 factors should be retained.
2019181716151413121110987654321
Component Number
4
3
2
1
0
Eigenvalue
Scree Plot
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
35/70
35
COMPONENT MATRIXThis table reports the factor loadings for each variable on the unrotated components or factors. Each
number represents the correlation between a variable and the unrotated factor. These correlations can
helps us to formulate an interpretation of the factors or components.
This above table just below the Total Variance Table i.e. Component Matrix reports the factor
loadings for each variable on the unrotated components or factors. Each number represents the
correlation between the item and the unrotated factor. For example 0.661shows correlation between the
screen colour and the second factor; 0.303shows correlation between the battery life and thethird
component.
The variable with highest loadingis grouped under one factor. But in some cases the factor loadings of
one variable may be high in two factors making interpretation difficult. So we go for rotation and get
rotated component matrix. To confirm the highest loadings under one factor only we make the rotated
component matrix.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
36/70
36
Rotated Component Matrix(a)
Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 16 iterations.
ROTATED COMPONENT MATRIX
Through rotation the factor matrix is transformed into a simpler one that is easier to interpret.
As already mentioned if several factors have high loadings with the same variable, it is difficult to
interpret them. Rotation does not affect the communalityand the percentage of total variance explained.
We use orthogonal rotation with the most commonly used method of rotation called Varimaxprocedure which has already been explained.
Through rotation the interpretation becomes easier. The Rotated Component Matrix table shown
above gives the rotated component matrix with only the highest loadings under each factor.
Component
1 2 3 4 5 6 7 8 9
MOBILE_SIZE -.144 -.125 .357 .438 .239 .110 .519 .121 .044
MOBILE_COLOUR .120 .122 -.057 -.004 -.078 -.099 -.001 .862 .101
SHAPE_OF_MOBILE
.048 .004 -.037 -.027 -.041 -.024 .828 -.068 -.083
NUMBER_OF_MOBILE
-.080 .052 .083 .871 -.136 -.065 .032 -.064 .145
SCREEN_TYPE .162 .739 -.382 .245 .134 -.124 -.182 -.083 -.109
SCREEN_SIZE .338 .226 .553 .408 .059 -.206 -.204 .100 -.156
SCREEN_COLOUR .253 .042 .181 -.370 .013 .528 .290 .073 .385
LED_LIGHT -.762 .148 .076 .060 -.024 .213 .096 .112 .062
DURABILITY .034 .860 .192 -.014 .108 .135 .029 .274 .081
WARRANTY .083 -.073 -.133 .127 -.020 .037 .450 -.551 .418
CALCULATOR .066 -.494 .087 .260 .275 .500 -.224 .151 .124
BLUETOOTH .221 .162 -.286 .431 -.050 .459 .222 .084 -.417
STOPWATCH .219 -.133 .257 .137 -.744 .151 -.021 -.024 -.029
ALARM .103 .064 -.831 -.026 .077 .053 -.041 .066 -.071
WATER_RESISTANCE
.033 .168 .298 -.108 .652 .148 -.069 -.421 .090
SHOCK_RESISTANCE
.564 -.138 .003 .121 .647 -.007 .035 .050 -.103
BATTERY_LIFE -.031 .004 .025 .111 .019 -.079 -.062 .038 .890
WEIGHT .779 .229 -.100 .008 -.015 .208 -.090 .184 -.048
SERVICE_CENTRE_AVAILABILITY .089 -.011 .136 .078 .098 -.748 .030 .235 .169
PRICE .792 .148 .103 -.068 -.106 .085 .308 .066 .135
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
37/70
37
This table (called the Pattern Matrix for oblique rotations) reports the factor loadings for each
variable on the components or factors after rotation.
From the above table we find that there are major attributes which affect the buying behavior of our
population. It is shown as below along with their variance:
Long
lastingness
Looks High-end
features
Operational
features
External
appearance
Ease of
maintenance
Monetary
features
Warranty-
.450
Screen type-
.739
Bluetooth -
.459
Weight-.779 Screen size-
.739
Shock-.749
resistance
Price-.611
Durability-
.860
Mobile size-
.519
Water-
resistance -
.652
Led light-
.762
Screen
colour - .528
Service
centre
availiability-
.592
Battery life-
.890
Mobile
colour-.862
Calculator -
.500
No. of
mobiles-.871
Shape of
mobile - .828
Stopwatch -
.744
Alarm - . 831
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
38/70
38
Component Transformation Matrix
Component 1 2 3 4 5 6 7 8 9
1 .856 .395 -.060 .104 .131 .107 .027 .232 -.12
2 .257 -.408 .389 .056 .137 .357 .504 -.290 .35
3 -.098 .178 .634 .545 .026 -.383 -.088 .255 .19
4 .025 -.157 -.062 .285 -.819 .188 .264 .275 -.19
5 -.269 .310 -.218 .596 .268 .266 .267 -.334 -.32
6 -.199 .647 -.055 -.295 -.177 -.014 .460 .044 .45
7 -.259 .118 .307 -.160 .164 .714 -.210 .457 -.09
8 -.043 -.229 -.543 .338 .202 .081 -.063 .408 .56
9 .105 .202 .014 .162 -.349 .303 -.581 -.482 .37
Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.
VARIMAX ROTATION: It makes it easy to identify each variable with a single factor.
1. Component 1 explains variable 1.
2. Component 2 explains variable 1.3. Component 3 explains variable 2
4. Component 4 explains variable3.
5. Component 5 explains variable 4.
6. Component 6 explains variable 6.
7. Component 7 explains variable 5.
8. Component 8 explains variable 9.
9. Component 9 explains variable 9.
FACTOR CLASSIFICATION:
From factor analysis, we were able to break down 20 variables into 9 major factors which influence buying
behavior for mobile phones. They are:
1. Long lastingness
2. Looks
3. High-end features
4. Operational features
5. External appearance
6. Ease of maintenance
7. Price
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
39/70
39
DISCRIMINANT ANALYSIS:It is a technique which is used when the independent variables are interval in nature and dependent variable
is categorical in nature. In our project independent variables are of two types:
1. Male /Female
2. Indian brands/Foreign brands
Both are having categorical values 0 and 1 and there are 20 independent variables.
When the dependent variable is male or female
In this our objective was to find whether there exists any difference between or among the groups.
Analysis:
When we run the discriminant analysis it shows that it totally has examined all the observation of our
sample. This table shows that the discriminant analysis could be used on a particular set as it has included
all the 100 observations.
Analysis Case Process ing Summ ary
97 97.0
0 .0
3 3.0
0 .0
3 3.0
100 100.0
Unw eighted CasesValid
Missing or out-of-range
group codes
At least one missing
discriminating variable
Both miss ing or
out-of-range group codes
and at least one missing
discriminating variable
Total
Excluded
Total
N Percent
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
40/70
40
Group Statistics
M/F Mean Std. Deviation Valid N (listwise)
Unweighted WeightedMALE MOBILE_SIZE 3.79 1.020 47 47.000
PRICE 4.02 .989 47 47.000
SHAPE_OF_MOBILE 3.68 .755 47 47.000
WORD_OF_MOUTH 3.06 1.051 47 47.000
ADVERTIZEMENT 3.53 .929 47 47.000
WARRANTY 4.04 .779 47 47.000
SCREEN_TYPE 4.21 .657 47 47.000
battery life 4.34 .668 47 47.000
WATER_RESISTANCE 4.04 .859 47 47.000
SCREEN_COLOUR 3.66 1.006 47 47.000
BRAND_VALUE 4.19 .876 47 47.000
SCREEN_SIZE 3.89 .814 47 47.000
S resist 3.79 .977 47 47.000
WEIGHT 3.26 .966 47 47.000
S C Availa 3.98 .794 47 47.000
NUMBER_OF_PHONES 2.68 1.065 47 47.000
led light 2.85 1.021 47 47.000
DURABILITY 4.32 .755 47 47.000
prom eff 2.81 .992 47 47.000
celeb end 2.70 .998 47 47.000
FEMALE MOBILE_SIZE 3.98 1.000 50 50.000
PRICE 4.04 .832 50 50.000
SHAPE_OF_MOBILE 3.68 .844 50 50.000
WORD_OF_MOUTH 2.92 1.104 50 50.000
ADVERTIZEMENT 2.92 .944 50 50.000
WARRANTY 3.86 1.107 50 50.000
SCREEN_TYPE 4.00 .990 50 50.000
battery life 4.10 .909 50 50.000
WATER_RESISTANCE 3.96 .903 50 50.000
SCREEN_COLOUR 3.94 .956 50 50.000
BRAND_VALUE 4.18 .800 50 50.000
SCREEN_SIZE 3.88 .940 50 50.000
S resist 3.58 1.090 50 50.000
WEIGHT 3.60 1.125 50 50.000S C Availa 3.94 1.038 50 50.000
NUMBER_OF_PHONES 2.68 .999 50 50.000
led light 2.60 .926 50 50.000
DURABILITY 3.86 1.030 50 50.000
prom eff 2.90 .931 50 50.000
celeb end 2.20 .969 50 50.000
Total MOBILE_SIZE 3.89 1.009 97 97.000
PRICE 4.03 .907 97 97.000
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
41/70
41
SHAPE_OF_MOBILE 3.68 .798 97 97.000
WORD_OF_MOUTH 2.99 1.075 97 97.000
ADVERTIZEMENT 3.22 .981 97 97.000
WARRANTY 3.95 .961 97 97.000
SCREEN_TYPE 4.10 .848 97 97.000
battery life 4.22 .807 97 97.000
WATER_RESISTANCE 4.00 .878 97 97.000
SCREEN_COLOUR 3.80 .986 97 97.000
BRAND_VALUE 4.19 .833 97 97.000
SCREEN_SIZE 3.89 .877 97 97.000
S resist 3.68 1.036 97 97.000
WEIGHT 3.43 1.060 97 97.000
S C Availa 3.96 .923 97 97.000
NUMBER_OF_PHONES 2.68 1.026 97 97.000
led light 2.72 .976 97 97.000
DURABILITY 4.08 .932 97 97.000
prom eff 2.86 .957 97 97.000
celeb end 2.44 1.010 97 97.000
Analysis:
It shows the degree of importance attached by the two samples to various variables. From the above table,
for example, we can see that the degree of importance attached by Male towards Mobile Size is 3.79 while
that by female is 3.98. When we see it in totality it is 3.89.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
42/70
42
Tests of Equality of Group Means
Wilks'Lambda F df1 df2 Sig.
MOBILE_SIZE .991 .883 1 95 .350
PRICE 1.000 .010 1 95 .920
SHAPE_OF_MOBILE 1.000 .000 1 95 .996
WORD_OF_MOUTH .995 .431 1 95 .513
ADVERTIZEMENT .902 10.335 1 95 .002
WARRANTY .991 .872 1 95 .353
SCREEN_TYPE .984 1.535 1 95 .218
battery life .978 2.179 1 95 .143
WATER_RESISTANCE .998 .212 1 95 .646
SCREEN_COLOUR .980 1.981 1 95 .163
BRAND_VALUE 1.000 .005 1 95 .946
SCREEN_SIZE 1.000 .006 1 95 .940
S resist .990 .969 1 95 .328
WEIGHT .973 2.606 1 95 .110
S C Availa 1.000 .042 1 95 .838
NUMBER_OF_PHONES 1.000 .000 1 95 .997
led light .983 1.613 1 95 .207
DURABILITY .939 6.202 1 95 .014
prom eff .998 .219 1 95 .641
celeb end .938 6.319 1 95 .014
Analysis:
The above table shows that none of the variable is significant in case of determining the preferences for
phones when the variable is male or female as none of the variable is having a significance of less than .05.
When the dependent variable is male/female
Under the table of Test of group means, when we look at the Wilkslambda of the independent variables,
we find that the Wilks lambda of advertisement is lowest i.e. 0.902 along with F-distribution of 10.335.
This means that is the most significant variable.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
43/70
43
Eigen value: The Eigen values reflect the importance of the variables which classify cases of the
dependent variable. For a function to be good it should always be greater than 1.
In our study conducted it is found out to be 0.568.
Canonical Discriminant Function: It is a measure of the association between groups formed by dependent
variable and Discriminant function. When it is zero, there is no correlation between the groups. In our
study the value of R is 0.602 which shows that the correlation is not very significant.
Wilks Lambda: It is used to test the significance of the discriminant function as a whole. In our study
the significance level of the discriminant function is 0.008. For a function to be effective the significance
shall be less than .01
Eigenvalues
.568a 100.0 100.0 .602
Function
1
Eigenvalue % of V ariance Cumulative %
Canonical
Correlation
First 1 canonical discriminant functions w ere used in the
analysis.
a.
Wilks' Lam bda
.638 38.225 20 .008
Test of Function(s)
1
Wilks'
Lambda Chi-square df Sig.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
44/70
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
45/70
45
Structure Matrix
Function
1
ADVERTIZEMENT .438
celeb end .342
DURABILITY .339
WEIGHT -.220battery life .201
SCREEN_COLOUR -.192
led light .173
SCREEN_TYPE .169
S resist .134
MOBILE_SIZE -.128
WARRANTY .127
WORD_OF_MOUTH .089
prom eff -.064
WATER_RESISTANCE .063
S C Availa.028PRICE -.014
SCREEN_SIZE .010
BRAND_VALUE .009
SHAPE_OF_MOBILE .001
NUMBER_OF_PHONES .001
Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functionsVariables ordered by absolute size of correlation within function.
Group Centroid
The number of males and females used in our study were 50 each.
The value of Group centroid is 0.23
Analysis:
unctions at Group Centroids
.769
-.723
M/F
0
1
1
Function
Unstandardized canonical discriminant
functions evaluated at group means
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
46/70
46
It means that if we enter the values of independent variables in the discriminant function and we found that
the discriminant score is less than 0.23 it will represent the preferences of male and discriminant score of
more than 0.23 represents the preferences of female.
When the dependent variable is Indian /foreign
Analysis:
When we run the discriminant analysis it shows that it totally has examined all the observation of our
sample. This table shows that the discriminant analysis could be used on a particular set as it has included
all the 100 observations.
Analysis Case Processing Summ ary
97 97.0
0 .0
3 3.0
0 .0
3 3.0100 100.0
Unw eighted Cases
Valid
Missing or out-of -range
group codes
At least one missing
disc riminating variable
Both missing or
out-of-range group codes
and at least one missing
disc riminating variable
Total
Excluded
Total
N Percent
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
47/70
47
Group Statistics
I/F Mean Valid N (listwise)
Unweighted Weighted
INDIAN MOBILE_SIZE 3.86 65 65.000
PRICE 4.00 65 65.000SHAPE_OF_MOBILE 3.58 65 65.000
WORD_OF_MOUTH 3.08 65 65.000
ADVERTIZEMENT 3.17 65 65.000
WARRANTY 3.95 65 65.000
SCREEN_TYPE 4.09 65 65.000
battery life 4.22 65 65.000
WATER_RESISTANCE 3.92 65 65.000
SCREEN_COLOUR 3.77 65 65.000
BRAND_VALUE 4.09 65 65.000
SCREEN_SIZE 3.82 65 65.000
S resist 3.60 65 65.000WEIGHT 3.42 65 65.000
S C Availa 3.91 65 65.000
NUMBER_OF_PHONES 2.66 65 65.000
led light 2.65 65 65.000
DURABILITY 4.15 65 65.000
prom eff 2.75 65 65.000
celeb end 2.45 65 65.000
FOREIGN MOBILE_SIZE 3.94 32 32.000
PRICE 4.09 32 32.000
SHAPE_OF_MOBILE 3.88 32 32.000
WORD_OF_MOUTH 2.81 32 32.000ADVERTIZEMENT 3.31 32 32.000
WARRANTY 3.94 32 32.000
SCREEN_TYPE 4.13 32 32.000
battery life 4.22 32 32.000
WATER_RESISTANCE 4.16 32 32.000
SCREEN_COLOUR 3.88 32 32.000
BRAND_VALUE 4.38 32 32.000
SCREEN_SIZE 4.03 32 32.000
S resist 3.84 32 32.000
WEIGHT 3.47 32 32.000
S C Availa 4.06 32 32.000
NUMBER_OF_PHONES 2.72 32 32.000
led light 2.88 32 32.000
DURABILITY 3.94 32 32.000
prom eff 3.06 32 32.000
celeb end 2.44 32 32.000
Total MOBILE_SIZE 3.89 97 97.000
PRICE 4.03 97 97.000
SHAPE_OF_MOBILE 3.68 97 97.000
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
48/70
48
WORD_OF_MOUTH 2.99 97 97.000
ADVERTIZEMENT 3.22 97 97.000
WARRANTY 3.95 97 97.000
SCREEN_TYPE 4.10 97 97.000
battery life 4.22 97 97.000
WATER_RESISTANCE 4.00 97 97.000
SCREEN_COLOUR 3.80 97 97.000
BRAND_VALUE 4.19 97 97.000
SCREEN_SIZE 3.89 97 97.000
S resist 3.68 97 97.000
WEIGHT 3.43 97 97.000
S C Availa 3.96 97 97.000
NUMBER_OF_PHONES 2.68 97 97.000
led light 2.72 97 97.000
DURABILITY 4.08 97 97.000
prom eff 2.86 97 97.000
celeb end 2.44 97 97.000
Analysis:
It shows the degree of importance attached by the two samples to various variables. From the above table,
for example, we can see that the degree of importance attached by Indian brands towards Mobile Size is
3.86 while that of foreign brands is 3.94. When we see it in totality it is 3.89.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
49/70
49
Standardized Canonical Discriminant Function Coefficients
Function
1
MOBILE_SIZE -.338
PRICE .125
SHAPE_OF_MOBILE.522WORD_OF_MOUTH -.591
ADVERTIZEMENT .206
WARRANTY .189
SCREEN_TYPE .108
battery life -.021
WATER_RESISTANCE .479
SCREEN_COLOUR -.237
BRAND_VALUE .214
SCREEN_SIZE .218
S resist .044
WEIGHT -.071
S C Availa -.068
NUMBER_OF_PHONES -.228
led light .577
DURABILITY -.530
prom eff .515
celeb end -.145
Analysis:
The above table shows that none of the variable is significant in case of determining the preferences for
phones when the variable is Indian or foreign as none of the variable is having a significance of less than
.05.
Log Determ inants
20 -8.812
20 -12.319
20 -7.158
I/F
0
1
Pooled w ithin-groups
Rank
Log
Determinant
The ranks and natural logarithms of determinants
printed are those of the group covariance matrices.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
50/70
50
Eigen value: It reflects the importance of the variables which classify cases of the dependent variable. For a
function to be good it should be greater than 1. Inour study conducted by us it is found out to be 0.223
Canonical Discriminant Function: Itis a measure of the association between groups formed by dependent
variable and discriminant function. When it is zero there is no correlation between the groups. In our study
the value of R is 0.427 which shows that the correlation is not very significant.
Wilks lambda: It is used to test the significance of the discriminant function as a whole. In our study the
significance level of the discriminant function is .644. For a function to be effective it shall be less than
.01.
Test Results
265.861
.922
210
12527.971
.782
Box's M
Approx.
df1
df2
Sig.
F
Tests null hypothesis of equal population covariance matrices.
Eigenvalues
.223a 100.0 100.0 .427
Function
1
Eigenvalue % of Variance Cumulative %
Canonical
Correlation
First 1 canonical discriminant functions w ere used in the
analysis.
a.
Wilks' L am bda
.817 17.137 20 .644
Test of Function(s)
1
Wilks'
Lambda Chi-square df Sig.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
51/70
51
When the dependent variable is Indian /Foreign
Under the table of Test of group means, when we look at the Wilks lambda of the independent variables,
we find that the Wilks lambda of status is lowest i.e. 0.970 along with F-distribution of 2.897. This means
that is the most significant variable.
Standardized Canonical Discriminant Function Coefficients
Function
1
MOBILE_SIZE -.338
PRICE .125
SHAPE_OF_MOBILE .522
WORD_OF_MOUTH -.591
ADVERTIZEMENT .206
WARRANTY .189
SCREEN_TYPE .108battery life -.021
WATER_RESISTANCE .479
SCREEN_COLOUR -.237
BRAND_VALUE .214
SCREEN_SIZE .218
S resist .044
WEIGHT -.071
S C Availa -.068
NUMBER_OF_PHONES -.228
led light .577
DURABILITY -.530prom eff .515
celeb end -.145
Standardized discriminant function:It is used for studying the relative importance of factors.
Analysis:
In our study we found that the most important factors that is differentiating the preference for Indian and
foreign brands are:
LED Light (.577)
Shape of mobile (.522)
Promotional efforts (.515) and so on.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
52/70
52
Structure Matrix
Function
1
SHAPE_OF_MOBILE .370
BRAND_VALUE .344
prom eff .326
WATER_RESISTANCE .268SCREEN_SIZE .248
WORD_OF_MOUTH -.248
S resist .237
led light .236
DURABILITY -.234
S C Availa .168
ADVERTIZEMENT .146
SCREEN_COLOUR .107
PRICE .104
MOBILE_SIZE .075
NUMBER_OF_PHONES .056WEIGHT .050
SCREEN_TYPE .039
WARRANTY -.017
celeb end -.009
battery life .004
Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functionsVariables ordered by absolute size of correlation within function.
Analysis:
It also ranks the variable in their power explain the preferences for Indian and foreign brands. The above
table shows that the individual importance of each of the variable in differentiating the preference between
Indian and foreign brand, ex the contribution of shape of mobile in determining the preferences for Indian
and foreign brands is 37%and so on.
Group Centroid
In our study the preferences for Indian phones are 34 and Foreign phones are 66.
unctions at Group Centroids
-.328
.667
I/F
0
1
1
Function
Unstandardized canonical discriminant
functions evaluated at group means
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
53/70
53
The value of Group centroid comes out to be is .3287
It means that if we enter values of independent variables in the discriminant function and if we found the
discriminant score is more than .3287 it will represents the preferences of foreign phones. Due to the value
we got for judging the sign of Discriminant model like WilksLambda, Eigen value we conclude that our
model is not very reliable in explaining the difference between male and female preferences. So we have
done the cluster analysis of the data collected by us.
CLUSTER ANALYSIS: Clustering is the classification of objects into groups (called clusters) so thatobjects from the same cluster are more similar to each other than objects from different clusters.
Cluster analysis is an exploratory data analysis tool for solving classification problems. Its object is to sort
cases (people, things, events, etc) into groups, or clusters, so that the degree of association is strong betweenmembers of the same cluster and weak between members of different clusters. Each cluster thus describes,
in terms of the data collected, the class to which its members belong; and this description may be abstracted
through use from the particular to the general class or type.
http://en.wikipedia.org/wiki/Statistical_classificationhttp://en.wikipedia.org/wiki/Statistical_classification -
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
54/70
54
INITIAL CLUSTER:
Initial Cluster Centers
Cluster
1 2
MOBILE_SIZE 1 5
PRICE 3 5SHAPE_OF_MOBILE 3 4
WORD_OF_MOUTH 3 5
ADVERTIZEMENT 2 4
WARRANTY 4 5
SCREEN_TYPE 4 5
battery life 4 5
WATER_RESISTANCE 2 5
SCREEN_COLOUR 2 5
BRAND_VALUE 2 5
SCREEN_SIZE 2 5
Shock resist 2 5WEIGHT 2 5
S C Availa 2 5
NUMBER_OF_PHONES 3 5
led light 4 5
DURABILITY 5 5
PROMO EFFECT 1 5
celeb endorsement 5 4
The first step in clustering is finding the initial cluster centers. This is done iteratively. We start with initial
set of centers and modify them until the changes between two iterations are small enough.
After the initial centers have been selected, each case is assigned to the closest cluster based on its distance
from the cluster centers.
Analysis:
It shows that on each of the factor there is a contrast in the preference attached to it, for example on an
average the respondents in sample 1 have given a rating of 1 i.e. least important to the mobile size whereas
sample 2 respondents have given it a rating of 5 i.e. most important and so on.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
55/70
55
Cluster Membership
1 2.572
2 4.297
2 4.548
1 4.148
2 4.929
1 3.513
1 3.685
1 3.120
1 2.837
2 3.883
1 5.612
1 3.423
1 4.743
1 4.577
2 3.160
2 3.275
2 3.098
1 4.280
1 2.847
2 3.405
2 2.849
1 3.679
2 3.634
2 3.160
2 2.517
2 3.971
2 2.465
2 4.835
2 2.643
2 3.105
1 6.395
2 5.012
2 4.407
2 2.716
2 3.288
2 3.537
2 4.015
2 3.027
1 4.025
2 3.658
2 3.201
2 3.854
2 2.534
2 3.091
1 5.813
2 4.377
1 3.538
2 3.792
. .
. .
1 3.207
2 4.025
. .
1 2.962
2 2.411
2 3.966
1 3.040
1 4.018
1 2.699
1 4.303
1 5.150
1 2.522
2 3.860
1 4.553
1 5.150
2 2.619
2 2.924
1 5.835
1 4.480
2 4.703
2 5.012
2 2.772
1 4.556
1 2.565
2 3.780
2 3.153
2 3.347
1 4.632
1 3.795
2 3.288
1 4.603
1 4.664
1 5.331
1 4.342
1 4.190
1 4.290
1 4.110
2 3.580
2 3.622
1 4.497
1 4.076
1 4.882
1 4.774
1 4.651
1 4.093
1 3.766
1 4.413
1 5.473
1 4.269
1 3.823
Case Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Cluster Distance
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
56/70
56
Analysis:
The above table shows us that which respondent falls in which sample, ex respondent 100 falls in sample 1 ,
respondent 88 falls in sample 2 and so on.
FINAL CLUSTER CENTERS:
Final Cluster Centers
Cluster
1 2
MOBILE_SIZE 4 4
PRICE 4 4
SHAPE_OF_MOBILE 4 4
WORD_OF_MOUTH 3 3
ADVERTIZEMENT 3 3
WARRANTY 4 4
SCREEN_TYPE 4 4
battery life 4 5
WATER_RESISTANCE 4 4
SCREEN_COLOUR 4 4
BRAND_VALUE 4 5
SCREEN_SIZE 4 4
S resist 3 4
WEIGHT 3 4
S C Availa 4 4
NUMBER_OF_mobiles 2 3
led light 2 3
DURABILITY 4 4
prom eff 3 3
celeb end 2 3
After iteration stops, all cases are assigned to clusters, based on the last set of cluster centers. After all the
cases are clustered, the cluster centers are computed one last time. Using final cluster centers the clusters
can be described.
Analysis:
After rotating we find that there were only seven factors that were making the people fall in two different
samples which are battery life, brand, shock resistance, weight, no of mobiles, led light and celebrity
endorsements.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
57/70
57
Analysis:
The above table shows the difference between two sample of respondents on the XY plane. They both are at
a distance of 2.828 from each other.
ANOVA
Cluster Error
F Sig.Mean Square df Mean Square df
MOBILE_SIZE 10.873 1 .915 95 11.890 .001PRICE .275 1 .828 95 .332 .566
SHAPE_OF_MOBILE .914 1 .633 95 1.442 .233
WORD_OF_MOUTH 8.662 1 1.077 95 8.042 .006
ADVERTIZEMENT 2.050 1 .952 95 2.154 .145
WARRANTY 12.477 1 .803 95 15.542 .000
SCREEN_TYPE 12.314 1 .596 95 20.649 .000
battery life 13.458 1 .516 95 26.094 .000
WATER_RESISTANCE 16.539 1 .605 95 27.343 .000
SCREEN_COLOUR 6.999 1 .908 95 7.706 .007
BRAND_VALUE 9.887 1 .598 95 16.545 .000
SCREEN_SIZE 13.721 1 .632 95 21.713 .000S resist 12.955 1 .949 95 13.654 .000
WEIGHT 20.162 1 .923 95 21.852 .000
S C Availa 6.877 1 .789 95 8.716 .004
NUMBER_OF_PHONES 11.533 1 .943 95 12.233 .001
led light 7.879 1 .880 95 8.953 .004
DURABILITY 13.705 1 .733 95 18.697 .000
prom eff 5.601 1 .867 95 6.460 .013
celeb end 6.573 1 .962 95 6.834 .010
The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differencesamong cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted astests of the hypothesis that the cluster means are equal.
Analysis:
stances betwe en Final Cluster Center
2.828
2.828
Cluster
1
2
1 2
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
58/70
58
The above table shows the variables which are important in our study which are having a significance value
of less than .05. The above table shows that apart from price, shape and advertizement all other variables are
significant for our study.
Now the F-ratio i.e. Anova can be used to describe the difference between the clusters. If the observed
significance level for a variable is large, it can be deduced that the variable does not contribute much to the
separation of the clusters.
Two Step Clustering
Cluster Analysis seeks to identify a set of groups which both minimizes within-group variation and
maximizes between group variations. The key objective of cluster analysis is to identify similar objects and
group them into relatively homogeneous groups.
Analysis:
After running the cluster analysis on our data set, in order to carry out the segmentation of our sample, wecame to know that there are two segments in our sample of 100 respondents:
Cluster1 consisting of 51 subject
Cluster 2 consisting of 46 subject
By looking the table of f inal cluster centerswe find that there were some factors which have same rating in
both the cluster. They are:
Mobile size
Price
Screen type
Word of mouth
Advertisement
Water resistant
screen size
Mobile Color
umbe r of Cases in e ach Clus te
51.000
46.000
97.000
3.000
1
2
Cluster
Valid
Missing
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
59/70
59
Service centre
Durability
Promotional efforts
By looking at Cluster 2 we find that the following factors are important:
Celebrity Endorsement
LED Light
No. of Mobiles
Shock resistant
Weight
Battery Life
May be this sample represents those group consist of people who are sport loving, high income group etc.
They also focus on look like Led Light etc.
When we look at Cluster 1 we find that theyfocus more on operational factors like:
Battery life
LED Light
No. of mobiles
Weight
Limitations
The research result cannot be considered as a reliable tool for implementation because of the small sample
being surveyed. However it can be used as a basis for getting an idea for carrying out the further research
and an overview of the taste and preferences of the young urban professional for phones
.
Conclusion and results:
We can conclude that there are major seven attributes which influences the buying behavior of our samplewhich are categorized as operational, intangible, maintenance, price, looks, long lastingness, promotional
efforts by the company. However there are not many differences in the buying behavior of mobile phones
by male and female. They both look more or less for the same features. By this we are also able to prove our
hypothesis that there are not many differences in the factors that influences the buying behavior of male and
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
60/70
60
female. We find this out after doing the discriminant analysis. From our analysis we find out that there are
two groups of people in our sample:
1. Adventurous, sports loving and high income group
2. Value for money , functional people
The buying decision of the people in the first category are influenced by celebrity endorsement of a phone,
looks, promotional push given to the phone, shock resistance and screen size.
The buying decisions of the second group of people are influenced by the attributes like warranty, battery
life, screen type etc. They are not much concerned with the looks and promotional efforts given to the
phone. They are the people who want value for money.
We can say that a company who want to tap the young urban professional market of phones can make use of
the study and can focus on the attributes mentioned above for category one people. If they come out with
these features than by doing that they will also be able to serve the category 2 people.
So there are no major differences in the factors which are considered important by male and female while
buying mobile phones. Company shall focus on the seven attributes mentioned above.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
61/70
61
APPENDICES1. Graphs
Reduction of 20 major variables into 7 major factors.
Last attribute considered important was price
Long Lastingness
Warranty
(39.439%)
Durability
(30.327%)
Battery life
(30.234%)
Efforts by
Company
Promotional
efforts
(49.895%)
Celebirirty
endorsement(50.105%)
Intangibility
Shape of
mobile
(59.142%)
Brand
(40.858%)
looks
Screen size
(27.592%)
Mobile size
(25.069%)
Mobile
colour
(23.729%)
Operational
Features
Weight
(35.379%)
LED light
(36.913%)
No. of
mobiles
(27.709%)
Ease Of
Maintenance
Shock
resistance
(55.854%)
Servicecentre
avalability
(44.146%)
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
62/70
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
63/70
63
DISCRIMINAT ANALYSIS:
Function group centroid
When dependent variable is male and female
Dependent variable value
Male -0.723
Female 0.769
When dependent variable is male
and female
Indian and
foreign
dependent variable valueSeries 1(Indian) 0.667
Series 2 (Foreign) -0.328
-1
-0.5
0
0.5
1
male female
Value
value
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Series1
Series2
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
64/70
64
Table of major attributes preferred by our sample
Factor analysis
Major attributes
considered in our
sample
Component 1
long lastingness
warranty 0.844
durability 0.649
battery life 0.647
Component2
Looks contribution
screen size 0.7
mobile size 0.636
mobile colour 0.602
screen type 0.599
Component 3
efforts by company contribution
promotional efforts 0.71
celebrity
endorsement 0.713
Component 4
operational contribution
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
65/70
65
features
Weight
0.738
led light 0.77
no. of mobiles 0.578
Component 5
intangibility contribution
Shape of mobile 0.841
Brand 0.581
Component 6
ease of
maintenance contribution
shock resistance 0.749
service centre
availability 0.592
Lastfactor was price.
-
8/13/2019 Understanding Consumers Buying Behavior for Mobiles
66/70
66
REFERENCES
7e William G Zikmund, Thomson South-Western, Singapore(2003), Business Research Method
Donald R. Cooper and Ramela S. Schindler, New Delhi, (2000), Tata McGraw Hill Publishing
Company Ltd,
Business Research Methods,
www.wikipedia.com\history of phones.htmlat 21:05, 22 December 2007
Preference Does Not Equal Performance, by John S. Rhodes
Exploring Consumer Confusion in the Phone Market, Vincent-Wayne Mitchell Manchester School
of Management, UMIST, Manchester Vassilios Papavassiliou, Manchester School of Management,
UMIST, Manchester, UK
www.saharaparivar.com\mobile phones\1000.html
www.dazzleyellowpages.com\Resources-Guide\200606\phones.asp
Klecka, William R. (1980) Discriminant Analysis. Quantitative applications in the social sciences
series nmber19 Thousand Oaks , CA :Sage Publications
Malhotra Naresh (2001),Tata Magraw Hill Publications Marketing Research Methods
http://www.2.chass.ncsu.edu/garson/pa765/discrimin.htm
Business research met