Understanding Consumers’ Buying Behavior for Mobiles

8/13/2019 Understanding Consumers Buying Behavior for Mobiles

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BRM Final Report:

Understanding Consumers

Buying Behavior for

Mobile Phones


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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.


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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.


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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


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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


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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


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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.


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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


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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


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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.


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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


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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:


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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


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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.


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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


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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


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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


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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


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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


Correl (%) 70 53.33 53.33 56.66 60


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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


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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


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.


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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.


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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


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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


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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


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.


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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)


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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


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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


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.


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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


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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


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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.


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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%.


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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


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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.


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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


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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


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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.






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


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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


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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


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


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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


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.


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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.


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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.


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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


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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


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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




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


led light 2.88 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


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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




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


led light 2.72 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.


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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


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.


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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.


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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


WORD_OF_MOUTH -.591

ADVERTIZEMENT .206

WARRANTY .189

SCREEN_TYPE .108battery life -.021


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.


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Structure Matrix

Function

1


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


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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


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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.


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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


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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.


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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


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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


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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


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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.


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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%)


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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


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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


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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.


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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

Documents

Understanding Consumers’ Buying Behavior for Mobiles