Summer Internship Project Report On ” To Study the factors affecting Internet Leased Line purchase decision in Ahmedabad” Submitted By

Summer Internship Project Report
On
” To Study the factors affecting Internet Leased Line purchase decision in
Ahmedabad”

Submitted By:
Santosh Pandey ( 177290592131 )
MBA Batch 2017 – 19
Submitted To:

L J Institute of Management Studies
Institute Code – 729
Affiliated To

Gujarat Technological University
Under the Guidance of:
Faculty Mentor Corporate Mentor

Dr. Meetali Saxena Mr. Akshay Mathukiya

L J Institute of Management Studies

STUDENT ‘S DECL ARATIO N

I undersigned Santosh Pandey a student of L J Institute of Management Studies MBA 3rd semester,
declare that summer internship project titled ” To Study the factors affecting Internet Leased Line
purchase decision in Ahmedabad . ” is a result of my/our own work and my/our indebtedness to
other work publications, references, if any, have been d uly acknowledged. If I/we are found guilty
of copying any other report or published information and showing as my/our original work, I
understand that I/we shall be liable and punishable by Institute or University, which may include
‘Fail’ in examination, ‘Repeat study & re – submission of the report’ or any other punishment that
Institute or University may decide.

1. Name of Student – Santosh Pandey
Enrolment Number – 177290592131

Signature and Date

L J Institute of Management Studies

Date: __/__/____

“This is to certify that this Summer Internship Project Report Titled “To Study the
factors affecting Internet Leased Line purchase decision in Ahmedabad.” is the
bonafide work of Santosh Pandey ( 177290592131 ), who has carried out his / her
project under my supervision. I also certify further, that to the best of my knowledge
the work reported herein does not form part of any other project report or
dissertation on the basis of which a degree or award was conferred on an earlier
occasion on this or any other candidate. I have also checked the plagiarism extent
of this report which is ……… % and it is below the prescribed limit of 30%. The
separate plagiarism report in the form of html /pdf file is enclosed with this.

Rati ng of Project Report A/B/C/D/E: ______
(A=Excellent; B=Good; C=Average; D=Poor; E=Worst)
(By Faculty Guide)

Signature of the Faculty Guide/s
(Name and Designation of Guide/s)

Signature of Principal/Director with Stamp of Institute
(Name of Princip al / Director)

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Preface

MBA is a stepping – stone to the management carrier and to develop good manger. Summer
internship is an essential and important part of curriculum of MBA program because along with
the theoretical aspects, practical training is also very important. Summer training is an
exposure to corporate environment and help MBA students to get acquainted with original
norms, procedures, practices ethics, and culture. It also gives an insight of actual functioning of
the organization. It helps the student to und erstand and to cooperate with theoretical aspect
with practical reality.
The attractive feature of the MBA course is that along with theory we also get to have the
exposure of the practical environment. This is through the summer training that we have to
u ndergo after the completion of first year. The entire journey from the very idea of this project
report to reality would not have been possible without guidance and support of many people.
The Project Report is based on Market Study on ” To Study the factor s affecting Internet
Leased Line purchase decision in Ahmedabad “. The study was confined geographically to
Ahmedabad.
Consumer behaviour can be defined as the decision making process and physical activity
involved in acquiring, evaluating, using and dispos ing of goods and services. This definition
clearly brings out it is not just the buying of goods / services that receives attention in
consumer behaviour, but the process starts much before the goods have been acquired or
bought. The study Consumer Buying Behaviour is the study of how individuals make decisions
to spend their available resources (time, effort, money) on consumption related item. It
includes the study of what they buy it, where they buy it, how they buy it and how often they
use it. It is im portant to know how consumer reacts towards different products. Buying
behaviour involves a complicated series of stimulus and response.
This particular project has been conducted at Tata Teleservices. In the first phase of the
research project, there is a Research proposal, introduction of Industrial profile, company
profile of Tata Teleservices given. After that a market research is performed with a sample size
of 100 people. The research study was limited to Ahmedabad. Here, in my survey, I have
contacte d the respondents through personal interviews with the help of questionnaires and
used google forms for making questionnaire to fill by respondents at their convenience by any
device like phone or laptop.

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Acknowledgement

Every project big or small is su ccessful largely due to the effort of number of wonderful people
who have always given their valuable advice or lent a helping hand. We sincerely appreciate the
inspiration; support and guidance of all those people who have been instrumental in making this
project success.
I Santosh Pandey the student of Oakbrook Business School , are extremely grateful to “Tata
Teleservices” for the confidence bestowed in me and entrusting our project entitled ” To Study
the factors affecting Internet Leased Line purchase decision in Ahmedabad .” With special
reference to Tata Teleservices
At this juncture, we feel deeply honored in expressing our sincere thanks to Mr. Akshay
Mathukiya for making the resource available at right time and providing valuable insights
leading to the successful completion of my project.
We express our gratitude to college Mr. Siddharth Singh Bist for arranging the summer
training in good schedule. We also extended our g ratitude to our project Guide Dr. Meetali
Saxena who assisted us in completing the project.
We would also like to thank all the faculty member of L J Institute of Management Studies for
their critical advice and guidance without which this project would not have been possible.
Last but not the least we place deep sense of gratitude t o our family member and our friends who
have been constant source of inspiration during the preparation of this project work.

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Executive summary

This report is particularly related with Telecommunication Industry and that too with much
more emphasis given to the Leased Lone Services Provided by the telecom companies. In this
report we are going to make out on if the demographic factors such as age, income, industry
vertical and profession have any impact on factors influencing purchase decision of Le ased
Line for their organization.
About 100 respondents were taken into consideration for the collection of primary data using
which the further process of hypothesis and its testing was carried out. The report also
provides us with information regards to Telecom Industry in India and competitors in this
industry. It gives an insight of overall telecommunication industry too.
So, in summarized manner we can say that this report relates to the overall growth, history and
current situation of Telecommunicatio n in India and has a specific focus over the Leased Line
product available from telecom companies as enterprise solution of internet.

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Sr. No Particular Page No.
Part – 1
Title Page
Student Declaration
Company Certificate
College Certificate
Preface I
Acknowledgement II
Executive Summary III
Table of Content IV
Part – 2
1 Introduction

jjjj
kk 1
2 Industry Overview 5
3 Company Overview 14
4 Literature Review 18
5 Introduction of the topic 21
6 Research Objectives 30
7 Research Methodology 30
Research Design (Res. Approach, Type of Res.)
Sample Design ( Sample Unit, size, Technique)
Limitations of Study
8 Data Analysis 36
9 Hypothesis and Hypothesis Testing 41
10 Findings and Conclusion. 70
11 Bibliography and Reference 73
12 Annexure 74
Table of Content

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1. Introduction
The telecommunications busine ss among the arena info of data , knowledge and
communication technology is formed from all Telecommunications/telephone
corporations and web service suppliers and plays the crucial role within the evolution
of mobile communications and also the information society.
Traditional phone calls still be the industry’s biggest revenue generator, however, due
to advances in network technology, medium n owadays is a smaller amount
concerning voice and progressively concerning text (messaging, email) and pictures
(e.g. video streaming). High – speed web access for computer – based knowledge
applications admire broadband info services and interactive diversion is pervasive.
Digital line (DSL) is that the main broadband medium technology. The quickest growth
comes from (value – added) services delivered over mobile networks.
The telecommunication sector continues to be at the geographical point for growth,
innovati on, and disruption for just about any trade. Mobile devices and connected
broadband property still be additional and additional embedded within the material
of society nowadays and that they area unit key in driving the momentum around
some key trends equi valent to video streaming, net of Things (IoT), and mobile
payments.
India is that the world’s second – largest telecommunications market, with over
one.206 billion subscribers as of March 2018. The wireless section (98.10 per cent of
total phone phone subsc riptions) dominates the market. It’s conjointly been growing
at a brisk pace. Throughout FY07 – 18, wireless subscriptions witnessed a CAGR of
nineteen.62 per cent to succeed in one, 183.41 million. Asian country is additionally
the second largest country in terms of web subscribers with 445.96 million web
subscribers, as of Dec 2017. The country is currently the world’s second largest
smartphone market and can have nearly one billion distinctive mobile subscribers by
2020. Revenues from the telecommunication equipment sector square measure
expected to grow to US$ twenty six.38 billion by 2020. The coming National
telecommunication Policy 2018 has envisaged attracting investments price US$ one
hundred billion within the telecommunications sector by 2022.

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India ‘s telecommunications market is predicted to expertise additional growth, fueled
by accumulated non – voice revenues and better penetration in rural market.
Telecommunication penetration within the nation’s rural market reached fifty nine.05
per cent in Marc h 2018. The emergence of Associate in nursing affluent social class is
triggering demand for the mobile and web segments.
Strong policy support from the govt has been crucial to the sector’s development.
Foreign Direct Investment (FDI) cap within the telec ommunication sector has been
accumulated to one hundred per cent from seventy four per cent. Also, Government
of Asian country is presently planning to embark with a replacement National
telecommunication Policy 2018 in part of speedy technological advance ment within
the sector over the past few years.
The telecommunications industry within the sector of information and
communication technology is made up of all Telecommunications/Telephone
companies and internet service providers and plays the crucial role in the evolution of
mobile communications and the information society.
Indian Telecom Industry is one of the fastest growing telecom market in the world.
The mobile sector has grown from around 10 million subscribers in 2002 to reach 300
million by early 2015. Today India stands as the second – largest telecommunications
market in the world. The mobile phone industry in India would contribute US$ 400
billion in terms of gross domestic product (GDP) of the country in 2014.
According to Telecom Regulatory Au thority of India (TRAI) the rate of market
expansion would increase with further regulatory and structural reforms. The telecom
reforms have allowed the foreign telecommunication companies to enter Indian
market which has still got huge potential. Internat ional telecom companies like
Vodafone have made entry into Indian market in a big way. Currently the Indian
Telecommunication market is valued at around $100 billion. Two telecom players
dominate this market – Bharti Airtel with 27% market share and Relian ce
Communication with 20% along with other players like BSNL & newly entered JIO. The
Ministry of Communications and Information Technology (MCIT) has very aggressive
plans to increase the pace of growth.

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This sector which is growing exponentially is expe cted to generate about 4.1 million
additional jobs by 2020, as per Groupe Speciale Mobile Association (GSMA). The
advancements in the telecom sector have been the primary drivers of growth,
innovation, and disruption in a number of industries across the wo rld over the past
few years. This is also true in India, where smartphones and internet – based services
are getting embedded in the fabric of our society. India has now emerged as the
second largest telecommunications market in the world.
1.1 History of In dian telecommunication :
History of Indian Telecommunications started in 1851 when the first operational land
lines were laid by the government near Calcutta. Telephone services were introduced
in India in 1881. In 1883 telephone services were emerged with the postal system. In
1986, two wholly govt – owned companies were created the Videsh Sanchar Nigam
Limited (VSNL), for international telecommunications and Mahanagar Telephone
Nigam Limited (MTNL) for service in metropolitan areas.
Telecom Regulatory Auth ority of India (TRAI) was created in 1997 to act as a regulator
to facilitate the growth of Telecom Sector. India is the world’s fastest growing industry
in the world in terms of a number of wireless connections after China, with 811.59
million mobile phon e subscribers.
Furthermore, projections by several leading global consultancies indicate that the
total number of subscribers in India will exceed the total subscriber count in the China.
Well, Postal means of communication was the only mean communication until the
year 1850. In 1850 experimental electric telegraph started for first time in India
between Calcutta (Kolkata) and Diamond Harbour (southern suburbs of Kolkata, on
the banks of the Hooghly River).
In 1851, it was opened for the use of the Britis h East India Company. Subsequently,
construction of telegraph started throughout India. A separate department was
opened to the public in 1854. Dr.William O’Shaughnessy, who pioneered the telegraph
and telephone in India, belonged to the Public Works Depar tment and worked
towards the development of telecom. Calcutta or the then Kolkata was chosen as it
was the capital of British India.

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In early1881, Oriental Telephone Company Limited of England opened telephone
exchanges at Calcutta (Kolkata), Bombay (Mumb ai), Madras (Chennai) and
Ahmedabad. On the 28th January 1882, the first formal telephone service was
established with a total of 93 subscribers.
From the year 1902, India drastically changes from cable telegraph to wireless
telegraph, radio telegraph, radio telephone, trunk dialling. Trunk dialling used in India
for more than a decade were system allowed subscribers to dial calls with operator
assi stance. Later moved to digital microwave, optical fiber, satellite earth station.
During British period all major cities and towns in India were linked with telephones.
In the 1990s the telecom sector was opened up by the Government for private
investment . In1995 TRAI (Telecom Regulatory Authority of India) was set up. This
reduced the interference of Government in deciding tariffs and policy making. The
Government of India corporatized the operations wing of DoT in 2000 and renamed
Department of Telecom a s Bharat Sanchar Nigam Limited (BSNL).
In last 10 years, many private operator’s especially foreign investors successfully
entered the high potential Indian telecom market. Globally acclaimed operators like
Telenor, NTT Docomo, Vodafone, Sistema, SingTel, Tata, Maxis, and Etisalat invested
in India mobile operators.
India picked up freedom in 1947, when India had around 84,000 phone lines for its
populace of 350 million. Following thirty after three years, by 1980, India’s telephone
utility expanded with just 2.5 million phones and 12,000 open telephones for a
populace of 700 million. Just 3 percent of India’s 600,000 towns appreciated
telephone utility. Nonetheless, in the late 1990s, a huge change was found in the
media communications situation. By 1999, India had an introduced system of in excess
of 25 million phone lines that spread crosswise over 300 urban communities, 4869
towns, and 310,897 towns, influencing India’s media communications to arrange the
ninth biggest on the planet. Particularly eminen t is the way that in excess of 80 percent
of this national broadcast communications framework, checking up to roughly 20
million phone lines, was included the 1990s alone.

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2. Industry overview
India is right now the world’s second – biggest media communication s advertise with
an endorser base of 1.20 billion and has enlisted solid development in the previous
decade and half. The Indian portable economy is developing quickly and will
contribute generously to India’s Gross Domestic Product (GDP), as per report ar ranged
by GSM Association (GSMA) in a joint effort with the Boston Consulting Group (BCG).
Application downloads in the nation became roughly 215 for every penny in the
vicinity of 2015 and 2017.
The liberal and reformist approaches of the Government of I ndia have been
instrumental alongside solid buyer request in the quick development in the Indian
telecom segment. The administration has empowered simple market access to
telecom gear and a reasonable and proactive administrative structure that has
guarant eed accessibility of telecom administrations to customer at moderate costs.
The deregulation of Foreign Direct Investment (FDI) standards has made the segment
one of the quickest developing and a best five business opportunity generator in the
nation.
The Indian telecom part is relied upon to produce four million immediate and aberrant
occupations throughout the following five years as per evaluates by Randstad India.
The work openings are relied upon to be made because of mix of government’s
endeavors to build entrance in provincial territories and the quick increment in cell
phone deals and rising web utilization.
2.1 Market Size
The versatile business is relied upon to make an aggregate financial estimation of Rs
14 trillion (US$ 217.37 billion) con tinuously 2020. It would create around 3 million
direct openings for work and 2 million roundabout occupations amid this [email protected]
India’s cell phone advertise grew 14 for each penny year – on – year to an aggregate of
124 million shipments in 2017.

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Ascend in c ell phone entrance and decrease in information expenses will include 500
million new web clients in India throughout the following five years, making open
doors for new organizations. The month to month information use per cell phone in
India is required t o increment from 3.9 GB in 2017 to 18 GB by 2023.
2.2 Speculation/Major advancement
With day by day expanding endorser base, there have been a considerable measure
of ventures and improvements in the division. The business has pulled in FDI worth
US$ 30. 08 billion amid the period April 2000 to December 2017, as indicated by the
information discharged by Department of Industrial Policy and Promotion (D IPP).
A portion of the advancements in the ongoing past are:
? Amid the primary quarter of 2018, India tur ned into the world’s quickest
developing business sector for versatile applications.
? Finnish media transmission organization Nokia, will team up with Indian
telecom organizations Bharti Airtel and BSNL to take a shot at the guide for
improvement of 5G inn ovation and making a favorable environment for 5G in
India.
? India media transmission organizations will contribute US$ 20 billion
throughout the following two years for extension of system and activities,
expressed Mr Akhil Gupta, Vice Chairman, Bharti En terprise.
2.3 Government Initiatives
The legislature has optimized changes in the telecom segment and keeps on being
proactive in giving space to development for telecom organizations. A portion of the
other significant activities taken by the administration are as per the following:
The Gov ernment of India is before long going to turn out with another National
Telecom Policy 2018 in lieu of fast mechanical progression in the segment in the
course of recent years. The approach has conceived pulling in ventures worth US$ 100
billion in the are a by 2022.

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The Government of India is attempting to carefully interface the rustic and remote
locales in the nation and has chosen another reasonable duty structure with the
standard of more you utilize, less you pay. The progressions will before long be
reflected in duty changes by specialist co – ops in the nation.
2.4 Street Ahead
India will develop as a main player in the virtual world by having 700 million web
clients of the 4.7 billion worldwide clients by 2025, according to a Microsoft report.
Web e conomy anticipated that would contact Rs 10 trillion (US$ 155 billion) by 2018,
contributing around 5 for every penny to the nation’s GDP. With the administration’s
good direction arrangements and 4G administrations hitting the market, the Indian
media tra nsmission segment is required to witness quick development in the following
couple of years. The Government of India likewise plans to sell the 5G range in groups
like 3,300 MHz and 3,400 MHz to advance activities like Internet of Things (IoT),
machine – to – machine interchanges, moment superior quality video exchange and
additionally its Smart Cities activity. The Indian cell phone industry expects that the
Government of India’s lift to creation of battery chargers will bring about setting up of
365 plants, i n this way producing 800,000 occupations by 2025.
2.5 Players in Telecom Industry.
1. Bharti Airtel:

Airtel is the largest telecom service provider in India and is the third largest telecom
operator in the world. It operates in 20 countries across south Asia, Africa and the
Channel Islands. It offers services like 2G, 3G and 4G depending upon the country of
operation. It has nearly 261 million subscribers out of which 200 million are in India.
It is known as being the first mobile phone company in the world to outsource all its
business operations except marketing.

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2. Reliance Communication:

Reliance communication is a subsidiary of Reliance Anil Dhirubhai Ambani Group and
provides telecom services across the country. Headquartered in Navi Mumbai, it ranks
16th on the global platform in terms of mobile operations and provides 2G and 3G
service s in the country. RCOM also provides National Long Distance and International
Long Distance operations. Its customer base touches 150 million.
3. Vodafone:

Vodafone previously known as Vodafone Essar and Hutchinson Essar is one of the well
renowned te lecom service providers in India headquartered in Mumbai. In 2011,
Vodafone Group agreed to buy the share of its partner Essar from the Indian mobile
phone business.
4. Idea Cellular:

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Idea Cellular is part of the Aditya Birla Group, with its headquarters situated in
Mumbai, India. Previously ran by Tata Cellular, it was bought by Birla – AT&T in 2000.
After This merger of Birla – Tata – AT&T, it was rebranded as IDEA. However, in the
subseque nt years, AT&T and Tata sold their stake in Idea and it became an entity of
Aditya Birla group.
5. BSNL:

BS NL is a state – owned telecom company. Its headquarters are situated in New Delhi,
India. It is the largest provider of fixed telephony and fourth largest mobile telephony
provider in India. It even provides broadband services. It is India’s oldest
communication service provider and enjoys a customer base of 95 million throughout
India.
6. Tata Teleservices Ltd:

Tata Teleservices Ltd is a subsidi ary of renowned Tata group and is headquartered in
Mumbai. It serves over 85 million customers across the country offering Mobile
Services, Wireless Desktop Phones, Public Booth Telephony and Wireline Data
Services across one unified and integrated brand – T ata DOCOMO. The Japanese
telecom giant NTT Docomo bought 26 percent stake in Tata Teleservices in November

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2008 paying a sum of nearly 13,070Crores. It also became the first private sector
telecom operator to launch 3G service in the country.
7. Aircel:

Aircel is the seventh largest telecom service provider among the Indian mobile service
providers (GSM & CDMA) and fifth largest among the GSM mobile service providers.
The company is jointly held by the Maxis Communications and Sindya Securities &
Investm ents Private Limited.
With over 900 million wireless subscribers, India is one of the biggest markets in the
telecom industry globally. From GSM, CDMA and even broadband subscribers, the
overall penetration in India is nearly 75 percent.
2.6 Ways of Communication
Wireless communication :
In 1897 Gugliemo Marconi was the first to demonstrate that it was possible to
establish a continuous communication stream with the ships that were sailing in the
English Channel, by means of radio waves. Since then, t he wireless technologies that
make “on – the – move” communication possible for us have evolved remarkably.
We know, communication means transfer of information from source to recipient. In
traditional telephony, when source and recipient were located in long distance, this
transfer used to happen by connecting source and recipient physically through
conducting wires, which would carry information in the form of electrical signals.
Any transfer of information between points that do not have a physical connect ion,
like wire or cable connection, would be wireless communication.

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EXAMPLES OF WIRELESS COMMUNICATION:
Short Distance –
Long Distance – Space Radio Communication
Cellular communication :
Cellular Mobile Communication systems are wireless systems that divide a given
geographical area into cells and use a large number of transmitters to communicate
wirelessly within those cells. They provide mobility to the user within the cell, and
when he/ she moves from one cell to another, a ‘hand – off’ mechanism takes care of
continuous connectivity.
Evolution of cellular radio communication :
Over the years, we have seen remarkable growth of cellular communication over
radio. With ever increasing subscri ber base and limited radio resource, providing
quality telecom services became difficult. These issues led mobile service providers to
research into technologies and improve the quality of service and be able to support
more users in their systems. Theref ore Cellular communication has been continuously
evolving into newer forms. Here’s a brief look into its journey from 1G to LTE.
?
1980’s
range
?
900 MHz range
? – 114 kbps
?
Mbps based on the technology
? –
to approximately 100 Mbps

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Pager services :
Pager communication successful launched in India in the year 1995. Pagers were
looked upon as devices that offered the much – needed mobility in communication,
especially for businesses. Motorola was a major player with nearly 80 percent of the
market share. The other companies included Mobilink, Pagelink, BPL, Usha Martin
telecom and Easy call. Pagers were generally worn on the belt or carried in the pocket.
The business peaked in 1998 with the subscriber base reaching nearly 2 million.
However, the number dropped to less than 500,000 in 2002. The pager companies in
India were soon struggling to maintain their business. While 2 – way pagers could have
buffered the fall, the pager companies were not in a position to upgrade their
in frastructure to improve the ailing market. The Indian Paging Services Association
was unable to support the industry.
Pager companies in India also offered their services in regional languages also.
However, the end had begun already. By 2002, Motorola st ops making or servicing
pagers. When mobile phones were commercially launched in India, the pager had
many advantages to boast. Pagers were smaller, had a longer battery life and were
considerably cheaper. However, the mobile phones got better with time an d
continuously upgraded themselves.
Mobile communication :
First mobile telephone service on non – commercial basis started in India on 48th
Independence Day at country’s capital Delhi. The first cellular call was made in India
on July 31st, 1995 over Modi Telstra’s MobileNet GSM network of Kolkata. Later
mobile telephone services are divided into multiple zones known as circles.
Competition has caused prices to drop and calls across India are one of the cheapest
in the world.
Most of the operator follows GSM mobile system operate under 900MHz bandwidth
few recent players started operating under 1800MHz bandwidth. CDMA operators
operate under the 800Mhz band, they are first to introduce EVDO based high – speed
wireless data services via USB dongle. In spite o f this huge growth, Indian telecom

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sector is hit by severe spectrum crunch, corruption by India Govt. officials and financial
troubles.
In 2008, India entered the 3G arena with the launch of 3G enabled Mobile and Data
services by Government owned MTNL and BSNL. Later from November 2010 private
operator’s started to launch their services.
Broadband communication :
After US, Japan, India stands in third largest Internet users of which 40% of Internet
used via mobile phones. India ranks one of the lowest pro viders of broadband speed
as compared countries such as Japan, India and Norway. The minimum broadband
speed of 256kbit/s but speed above 2Mbits is still in a nascent stage.
The year 2007 had been declared as “Year of Broadband” in India. Telcos based on
ADSL/VDSL in India generally have sped up to 24Mbit max while those based on newer
Optical Fiber technology offer up to 100Mbits in some plans Fiber – optic
communication (FTTx). Broadband growth has been plagued by many problems.
Complicated tariff structur e, metered billing, High charges for the right of way, Lack
of domestic content, nonimplementation of Local – loop unbundling have all resulted
in hindrance to the growth of broadband. Many experts think future of broadband is
in the hands of a wireless fact or. BWA auction winners are expected to roll out LTE
and WiMAX in India in 2012.
Next generation network:
Next Generation Networks, multiple access networks can connect customers to a core
network based on IP technology. These access networks include fibre optics or coaxial
cable networks connected to fixed locations or customers connected through Wi – Fi as
well as to 3G networks connected to mobile users.
As a result, in the future, it would be impossible to identify whether the next
generation networ k is a fixed or mobile network and the wireless access broadband
would be used both for fixed and mobile services. It would then be futile to
differentiate between fixed and mobile networks both fixed and mobile users will
access services through a single core network.

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3. Company Overview
Tata Teleservices Ltd:

Tata Teleservices Ltd is a subsidiary of renowned Tata group and is headquartered in
Mumbai. It serves over 85 million customers across the country offering Mobile
Services, Wireless Desktop Phones, Public Booth Telephony and Wireline Data
Services across one unified and integrated brand – Tata DOCOMO. The Japanese
telecom giant NTT Docomo bought 26 percent stake in Tata Teleservices in November
2008 paying a sum of nearly 13,070Crores. It also became the first private sector
telecom operator to launch 3G ser vice in the country.
Type
Public
Industry
Telecommunications
Founded
1996
Founder
Ratan Tata
Headquarters Mumbai, Maharashtra, India
Key people Ratan Tata (Interim Chairman)
Srinath Narasimhan (MD & CEO)
Revenue
?3,191 crore (US$480 million) (2017)
Parent
Tata Sons
Subsidiaries T24 Mobile
Viom Networks (26%)

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TTSL also maintains a distribution network across villages, wherein people are
appointed and trained by TTSL – who visit villages on a bicycle or a two – wheeler at
defined times on defined days of the week, selling recharge vouchers and servicing
equipment; each runner covers between 200 and 300 customers?
The company joined hands with Tata Chemicals, Tata Kisan Sansar (TKS) network,
disseminating information through these centers an d using them as local distributors.
I n November 2008, Japanese telecom giant NTT Docomo picked up a 26 per cent
equity stake in Tata Indicom , a subsidiary of Tata Teleservices, for about ?130.7 billion
(US$1.9 billion) or an enterprise value of ?502.69 billion (US$7.5 billion). NTT
DOCOMO announced on 25 April 2014 that they are going to sell 100% of their shares
in Tata Indicom to Tata Telese rvices and exit Indian Telecom. The reason for exit is
because of huge loss of $1.3 billion.
In February 2008, TTSL announced that it would provide CDMA mobile services
targeted towards the youth, in association with the Virgin Group on a franchisee
model basis. By April 1, 2015, all Virgin Mobile CDMA & GSM customers have been
migrated into the umbrella Tata Indicom brand (Tata Indicom for Mumbai NCR).
On 9 October 2017 Tata Teleservices announced it is preparing an exit plan for most
of its 5,000 – odd empl oyees, which includes a notice of three to six months, severance
packages for those willing to leave earlier, a voluntary retirement scheme (VRS) for
elders, while transferring only a small part of its employees to other group companies.
3.1 NTT DoCoMo exi t
NTT DoCoMo announced on 25 April 2014 that they are going to sell 100% of their
shares in Tata Indicom to Tata Teleservices and exit Indian Telecom. The reason for
exit is because of huge loss of $780 million during financial year ending 31 March 2014.
T ata Teleservices Ltd (TTSL) and Tata Teleservices Maharashtra Ltd (TTML) in a debt –
free cash – free deal. The deal will essentially be free for Airtel which will only incur
TTSL’s unpaid spectrum payment liability. TTSL will continue to operate its enterpris e,
fixed line and broadband bu sinesses and its stake in Tower Company Viom Networks.

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The deal received approval from the Competition Commission of I ndia (CCI) in mid –
November 2017
3.2 Product Range Offered By Tata Teleservices.

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4. Literature review
Videsh Sanchar Nigam Limited ( India like many other
countries has adopted a gradual approach to telecom sector reform through selective
privatization and managed competition in different segments of the tel ecom sector.
India introduced private competition in value – added services in 1992 followed by
opening up of cellular and basic services for local area to competition. Competition
was also introduced in National Long Distance (NLD) and International Long Di stance
(ILD) at the start of the current decade.
Indian Tele communication Statistics (2002) in its study showed the long run trend in
supply and demand of Direct Exchange Lines (DEL). Potential demand for telecom
services is much more than its supply. In eventful decade of sect oral reforms, there
has been significant growth in supply of DEL.
Economic Survey, Government of India (2002 – 2003) has mentioned two very
important goals of telecom sector as delivering low – cost telephony to the largest
number of in dividuals and delivering low cost high speed computer networking to the
largest number of firms. The number of phone lines per 100 persons of the population
which is called teledensity, has improved rapidly from 43.6 in March 2001 to 4.9 in
December 2002.
Marketing Whitebook (2005) explains with support of detailed data that bigger
players are close to 20% of the market each. In CDMA market, it is Reliance Infocom
and Tata Teleservices are dominating the scene whereas Airtel is lead in GSM
operators. Betwee n 2003 and 2004, the total subscriber base of the private GSM
operators doubled. It rose from 12.6 million subscribers at the end of March 2003 to
26.1 million by the end of March 2004. And yet that 100% growth rate
notwithstanding, total industry revenue for 2003 – 04 was around Rs. 8308 crores.
Compared to Rs. 6400 crores that industry grossed in 2002 – 2003, that is an increase
of 30%.
Arindham Mukherjee (March, 2006) takes out various case studies like Vodafone,
Maxis, Telekopm Malaysia, Tata tele services e tc. to study the rising interest of

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foreigners for investment in Indian telecom industry. Various reasons of stemming
growth can be rising subscriber base, rising teledensity, rising handset requirements,
saturated telecom markets of other countries, stiff competition, requirement of huge
capital, high growth curve on telecom, changing regulatory environment, conducive
FDI limits in telecom sector.
Hemant Joshi (2014) in his report to the Deloitte and Indian Chamber of Commerce
and Industry had commented th at over the past decade, Indian telecom industry has
witnessed many positive developments. India has attained the second largest
subscriber network after China with the total number of subscribers scaling upto
about 900 million and claiming an urban tele – d ensity in excess of 140 and rural tele –
density of 40. With an estimated base of 67 million smart phone users in 2013, India
also ranks fifth amongst the top countries in this category. With an increasing smart
phone penetration in the country, subscribers accessing internet through mobile
devices stand at 176.50 million.
According to the Annual Report of TRAI (2011) the Indian Telecom sector continued
to register an impressive growth in the year 2010 – 11. During the year, the number of
telephone subscriptions increased from 621.28 million to 846.32 million, registering a
growth of 36.22 per cent. While the wireless subscription base increased by 227.27
million, the wire – line base recorded a decline of 2.23 million. The wireless segment
continued t o dominate with a total base of 811.59 million connections. The overall
tele – density in the country registered an increase from 52.74 at the end of March 2010
to 70.89 at the end of March 2011. The rural tele – density which as on 31st March 2010
was 24.29 i ncreased to 33.79 at the end of March 2011, as compared to the urban
tele – density of 119.77 and 157.32 respectively. However, the growth rate of
subscribers in rural areas during the year was higher at 40.64 per cent compared to
34.11 per cent in urban are as.
Dutt and Sundram (2004) research work revealed that in India in order to boost
communication business, new modes of communication are now being introduced in
various cities of the country by Ministry of Communication. Cellular Mobile Phones,
Radio Pagi ng, E – mail, Voice – mail, Video, Text and Video – Conferencing the modern
communication tools are now operational in many cities. It is a boon to business and

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industries. Value – added hi – tech services, access to internet and introduction of
Integrated Service D igital Network (ISDN) are being introduced in various places in the
country for speedy communication.
“Comment: Politics and economics of Telecom liberalization in India” by Chowdary
T.H. published in the Journal of Telecommunications Policy in 1998, descr ibes the
ideological background to more than 40 – year monopoly of the Department of
Telecommunications over Indian telecommunications. It traces how the monopoly
was eased between 1986 and 1991 and the government had to give up its policy of
central plannin g and control (Chowdary, 1998). This was the phase of pre – reforms in
Indian telecom sector, which plays a vital role in setting the scene for growth post the
1991 reforms, and the Chinese telecom industry underwent a similar phase before the
markets were o pened for reforms. The paper also summarized the events that led to
India opening its doors to Foreign Direct Investment (FDI).
Though the state owned telecom company Bharat Sanchar Nigam Limited (BSNL)
remains as the pioneer in the telecom market of India , private operators obtained a
high market share (Arun, 2011), among which, India’s largest mobile operator Bharti
leads the pack with over one – fifth of the telecom market, followed by 16.71% from
Reliance who is the third largest mobile operator, 16.52% f rom Vodafone as the fourth
largest and 11.16% from the fifth largest mobile transport TATA Group business.
The analysts’ report published by Ernst and Young in collaboration with FICCI titled,
“Enabling the next wave of telecom growth in India – Industry i nputs for National
Telecom Policy 2011” is a comprehensive report about the evolution of the telecom
sector in India over the past decade. This report tracks the changes in terms of
technological advancements, business dynamics and socioeconomic environmen t
over the years. The research program studies in detail all the key segments of the
telecom landscape — wireless, wire line, broadband, infrastructure, NLD, ILD, value –
added services (VAS), equipment manufacturing, infrastructure and con vergence.

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5. Introd uction to the Topic
Topic of Report – To Study the factors affecting Internet Leased Line
purchase decision in Ahmedabad
So we need start with understanding on what Lease Line is and other
re lated things as discussed below:
5.1 Leased Line

A leased line is a private bidirectional or symmetric telecommunications circuit
between two or more locations provided in exchange for a monthly rent. Sometimes
known as a private circuit or data line in the UK.
Unlike traditional PSTN lines they do not have telephone numbers, each side of the
line being permanently connected and dedicated to the other. Leased lines can be
used for telephone, Internet, or other data services. Some are ring down services, and
some connect to a private branch exchange or router.
Typicall y, leased lines are used by businesses to connect geographically distant offices.
Unlike dial – up connections, a leased line is always active. The fee for the connection is
a fixed monthly rate. The primary factors affecting the monthly fee are distance
bet ween end points and the speed of the circuit. Because the connection does not
carry anybody else’s communications, the carrier can assure a given level of quality.

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An Internet leased line is a premium Internet connectivity product, normally delivered
over fiber, which provides uncontended, symmetrical speeds with full duplex. It is also
known as an ethernet leased line, dedicated line, data circuit or private line.
For example, a T1 can be leased and provides a maximum transmission speed of 1.544
Mbit/s. Th e user can channelize the T1 to separate the 24 DS0 circuits for voice
communication, partial the T1 for data and voice communications, or multiplex the
channels into a single data circuit. Leased lines, as opposed to DSL, are being used by
companies and i ndividuals for Internet access because they afford faster data transfer
rates and are cost – effective for heavy users of the Internet
Leased Lines are business grade circuits that provide guaranteed, dedicated and
symmetrical bandwidth. Perfectly suited for organizations that depend on 100%
connectivity, leased lines don’t suffer from shared bandwidth. Leased line is the only
solution that guarantees that the internet connection speed you pay for, is the speed
you get.
Businesses need constant connectivity t o communicate and transact over internet.
Shared internet lines along with multiple users accessing simultaneously, can lead to
inefficiencies. Internet Leased Line service empowers you with dedicated bandwidth
ensuring reliable high – speed communication an d collaboration.
5.2 Advantages of Leased Line
W hile there are multiple ways of accessing the Internet, broadband seems to be the
most popular among them for Internet access in homes and SOHO segments. But,
Internet Leased Lines are preferred over broadband connections for providing
internet connectivity to larger organizations like colleges, corporate offices, hospitals,
etc. Let’s find out why!
An Internet Leased Line is usually a dedicated line which offers direct connectivity to
the Internet. Th is is done by connecting the nearest service provider nodal point and
the customer premises with a dedicated copper line, optical fiber cable, radio links or
any combination of the above.

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While there are business broadband options available at pretty good price points,
Internet Leased Lines are still preferred with a lot of customers for the following
reasons:
? Internet Leased Lines offer same speed for both uploads and downloads (
connectivity ), while in broadband (through DSL technology) the speed s are always
optimized for downloads and the upload speeds are much lesser. Symmetric
connectivity is critical for applications like file sharing between two branches, hosting
websites/ mail servers etc, video conferencing/ surveillance, voice over IP etc. all of
which are used by medium and large businesses regularly.
? Generally, broadband connectivity is shared with multiple users in a locality.
Broadband plans generally denote the best bandwidth that can be attained and not
the assured bandwidth levels. B ut a 2 Mbps Internet Leased Line should give 2 Mbps
of performance as it is a dedicated connection without any sharing.
? The backbone network and performance parameters (like latency, jitter etc) can be
monitored in the case of Internet Leased Lines. So, it is easier to offer SLA (Service
Level Agreements) to the customer and Internet Leased Lines generally come with an
SLA. There are very few broadband plans that offer such performance guarantee. So,
Internet Leased Lines are more reliable .
? There is a wider choice of bandwidth selection (64 Kbps, 128 Kbps,… 2 Mbps,… 155
Mbps etc) with Internet Leased Lines. With broadband, the selection is more
restricted.
? Higher bandwidth (Greater than 8 Mbps) is possible only through Internet Leased
Lines. That’s why it is the default choice for large organizations with hundreds of
internet users.
? Internet Leased lines are provided through multiple media – Copper, Fiber, Radio links
or a combination of the above. Optical media communication is more reliable and
offers bette r fault tolerance/ performance/ monitoring abilities, especially for higher
bandwidths.
? Internet Leased Lines come with unlimited usage plans which enables companies to
add services like video, voice etc over IP in addition to the internet/ data connectivi ty.

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Most of the broadband plans offer usage based billing (based on the bandwidth
consumed). Of course, there are unlimited usage plans with broadband as well.
? Internet Leased Lines offer a pool of permanent IP addresses which enables
organizations to run t heir own mail servers, web servers and other applications.
? Internet Leased Lines offer better QoS (Quality of Service) when compared to
broadband. So, it is more effective to run convergent services like voice, video etc.
over Internet Leased Lines.
? Intern et Leased Lines can be leveraged to form a Virtual Private Network across
multiple branches more effectively than broadband connections.
Of course, broadband connectivity has its own advantages like lesser cost, wider
reach, mobile internet access etc. But the dedicated high performance Internet
Leased Lines (cost of which is coming down every year) is the preferred choice for
larger networks with a considerable internet user base.
5.3 Basic Features of Internet Lease Line (ILL).
? Speed Range : 1Mbps to 1Gbps
? Sharing Ratio: 1:1
? Uptime: 98.5%
? Media: Fiber. Copper and Wireless
? Bandwidth on Demand
? Internet Redundancy
? Dedicated Relationship Manager
Bandwidth, the speed at which you gain Internet access, is not something small
business owners probably put muc h thought into. And that is a mindset worth
changing.The Internet now plays a substantial role in business, probably to the extent
that we take it for granted.
Consider, for example, how extensively used a data – heavy function like video
conferencing is, ho w often we access cloud – based apps, stream videos or download
large files from a website. Your bandwidth is taxed for any of those high – demand

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activities. One video service recommends that you need 5.0 megabits per second, just
to stream an HD video.
If mu ltiple users on your network are streaming multiple videos, uploading large files
or doing other high – demand tasks concurrently, the needs multiply. If you only have
limited bandwidth such as a 20 megabit per second speed, you can see how quickly a
few hea vy demands on your Internet access can eat up your access and slow
everything and everyone down. Still, businesses tend to use the same Internet
connection for years, blithely oblivious to the speed of technology’s progress.
Bandwidth problems can slow dow n your company’s productivity, waste your
employees’ time and result in lost sales.
Before you can figure out how to ensure your operation is Internet optimized, you first
need to know what you’re dealing with. Consider this a primer regarding the three
ba sic materials we use to connect to the Internet:
? Copper
? Fiber optics
? Wireless
These three mediums for Internet connections are available today in the marketplace.
Each has advantages and disadvantages. Let’s look at the pros and cons of each.
Copper Has Dominance
Since the phone’s approach more than 100 years back, the overwhelming method to
“wire” the home included the utilization of copper cabling. The copper telephone wire
is impeccably satisfactory for a voice flag, which is the thing that it was proposed for.
Everything considered, be that as it may, it offers exceptionally restricted data transfer
capacity. All things considered, such a large number of know about copper that they
questioned some other medium could ever supplant it.
Until fiber optics came along.
Fiber optics alludes to innovation that transmits information through thin strands of
an exceptionally straightforward material that more often than not is either glass or

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plastic. Fiber optic correspondences were propelled in th e 1970s, however the main
fiber optic media communications systems were not introduced until the mid – 1980s .
By the mid – 1980s, fiber’s data transmission and separation abilities made it essentially
more affordable than other correspondence mediums, so it has supplanted them.
In the mid – 1990s, satellite TV found fiber could improve execution unwavering quality,
a nd empower the offering of both telephone and Internet benefit on a similar fiber.
Fiber Optics or Copper Cables?
Evaluating which sort of system link is ideal for a specific organization requires thought
of a few elements.
Copper offers favorable circu mstances for those in rustic territories. It as of now exists
(it has been utilized, as noted, to wire phones, so copper effectively discovered its
place in the family unit) and is more affordable when used to associate system
gadgets. Those in provincial territories where no fiber optics have been run may
discover copper the most financially savvy, since they don’t need to pay to run new
cabling.
In any case, fiber optic link offers numerous focal points over copper: Fiber optic
transmission is quicker: F iber optic versus copper wire transmission can be come down
to the speed of photons versus the speed of electrons. Photons travel at the speed of
light, while electrons (as utilized as a part of copper wire) happening in nature travel
at short of what one percent of the speed of light. And keeping in mind that fiber optic
links don’t go at the speed of light, they come close — just around 31 percent slower.
So as should be obvious, there’s a colossal inborn speed contrast.
Fiber optic transmission brings a bout less weakening: When going over a long
separation, fiber optic links encounter less flag misfortune than copper cabling, known
as low constriction. One source assesses that fiber loses just three percent flag quality
going more than 100 meters (around 320 feet) in separate. By differentiate, copper
loses 94 percent over a similar separation. Repeaters or supporters can enhance those
rates, however in its local state, fiber demolishes copper with regards to evading signal
misfortune.

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Remote versus Fibe r Optics and Copper
While fiber optics appear to have the high ground over copper, remote broadband is
picking up in notoriety and utilization.
Remote broadband (or 4G, which remains for fourth era remote), a technique for
broadcasting an Internet associ ation over radio waves, is an expansive term that
speaks to a wide range of advances.
4G expects framework to be worked out so scope can achieve remote regions, and it
is winding up more boundless with each passing year. Consider 4G the sort of
innovation utilized by cell phone bearers — aside from that it has speedier speed,
settling on it a more practical decision for Internet access than more established 3G
cell phone associations.
Remote Has Potential to Remove Costs
With regards to fiber optic or co pper links, costs run the range from buying the cabling,
getting licenses marked, paying for work groups and protection and compensating the
IT wizards who influence the system to work legitimately.
Remote systems may reduce quite a bit of this cost.One o f the most concerning issues,
notwithstanding, is that remote signs debase with separate: the further away the
client is from the communicated station, the weaker the flag. Fiber optics can pass on
an unmistakable flag significantly more remote.
Furthermo re, there are still parts of the United States without remote scope or with
spotty scope, for example, in rustic territories. Without adequate remote towers to
communicate the flag all through provincial territories, remote may not be a suitable
decision i n remote zones. Be that as it may, gave the 4G foundation achieves your
territory, it can be a decent decision.
The two frameworks — fiber optic and remote — can supplement each other, with
numerous correspondences frameworks utilizing both fiber optics a nd remote
transmissions. Australia, for instance, has proposed connecting in excess of 90 percent

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of its populace to a fiber optic system for Internet access, with provincial Australians
accepting remote.
Considering the multifaceted nature required with deciding if fiber optics or copper is
better for your organization, you might need to consider the upsides and downsides
of outsourcing your IT arrange, for a specialist supposition.
Outsourcing gives you a chance to center around what your organization does best.
Staying aware of the innovation required to maintain your business is costly and
tedious. By outsourcing your IT organizing, you can invest your restricted energy and
cash on things that are specifically identified with fulfilling your clients, as opposed to
on the basic framework.
For those in rustic regions, copper – based associations might be the most financially
savvy and handy decision, since it’s now in presence in many spots. In places where
4G innovation foundation has achieved, remote may give a reasonable choice,
particularly as it turns out to be more pervasive and the innovation makes strides.

5.4 Factors for considering Leased Line
Cost – Leased lines are the mos t expensive. VPNs are less costly and come in multiple
flavors – layer 2 VPNs are more secure, layer 3 VPNs are faster to deploy and less
expensive but exposed to the risks and congestion of running over the public Internet.
MPLS increases efficiency compa red to relying on IP – based routing.
Security – Leased lines win out in terms of security, as they are dedicated only to a
specific customer’s traffic. VPNs over the public Internet are the least secure. MPLS
falls somewhere in the middle, as it emulates th e “feel” of a dedicated line but still
relies on shared network elements. MPLS has no inherent encryption and its security
depends heavily on the network core being secure, according to Professor Jose Santos
of the University of Colorado at Boulder’s Telec ommunications Department.
Reliability – Again, leased lines come out in front. VPNs can be subject to the variability
and congestion of the open Internet as traffic makes its way from one network point
to another, as it shares the virtual road with other t raffic. MPLS allows prioritization

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of traffic and establishment of quality of service levels, including the definition of
fallback paths to ensure reliability in the event of outages within the network requiring
traffic to be re – routed.
Scalability – Lease d lines are the most difficult to scale, both because of the time
needed for deployment and the expense. Layer 3 VPNs are quick and easy to deploy,
but can become complex to manage as a business grows. MPLS is widely accepted as
an efficient technology tha t is easily scaled
Operational data needs – This includes the question of the type of data flowing and
the business’ network needs. Does the business require only point – to – point
communication between two locations? Point to multi – point? A mesh network
cove ring multiple locations, where each branch must be able to communicate with all
others? Leased lines again fall to the rear in terms of easily addressing complex
network needs, particularly for medium – sized businesses. MPLS has both point – to –
point and one – to – many capabilities for communication.
Symmetry in connectivity – If you are a person, who uses the internet all the time, for
business purposes, then you would have to face a major catastrophe if your server’s
down. It could even cause you a major loss. Enterprise Leased line internet on the
other hand is your life saver. They provide dedicated and uninterrupted access to the
internet. Hence you don’t need to panic during the peak hours. It also provides same
speed for downloads as well as uploads. Bandwi dth is synchronous which means data
can be sent and received at the same time as well.
Speeds – Leased lines offer far higher speeds; they can be bought at speeds of 10Mbps,
100Mbps, 1000Mbps or even 10,000Mbps. Other connectivity options such as ADSL
simp ly can’t compete with that. For example, ADSL is advertised as being ‘Up to
8Mbps’. ADSL2+ is advertised as being ‘Up to 20′ or Up to 24Mbps’.

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6. Research Objectives

1) To understand overall telecom industry of India.

2) To understand the preferences of busi ness consumer towards purchase
influencing factors Lease d Line.

3) To study the impact of demogra phic factors on purchase influencing
factors with respect to Leased Line.

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7. Research Methodology

Research methodologists study and develop study designs, research methods and
measurement instruments particularly for human – related research, whether they
involve a quantitative, qualitative or mixed methods approach. We are especially
interested in methodology for interdisciplinary research that is th e methodology of
research involving sciences that may have different scientific cultures such as the
sc iences and the social sciences.
Research in common parlance refers to a search for knowledge. Once can also define
research as a scientific and systemati c search for pertinent information on a specific
topic. In fact, research is an art of scientific investigation. The advanced Learner’s
Dictionary of current English lays down the meaning of research as “a careful
investigation or inquiry especially throug h search for new facts in any branch of
knowledge.” Redman and Mory define research as a “systematized effort to gain new
knowledge.” Some people consider research as a movement, a movement from the
known to unknown. It is actually a voyage of discovery
7. 1 Research Design
Research design is the basic framework which provides guidelines for the rest of
research process. It specifies the methods for data collection and data analysis. In this
research project I have used the survey method of data collection, to be more specific
questionnaire method. I condu cted a survey in Ahmedabad. Sample size for this
research was 100. Respondents in the sample size were asked to fill the questionnaire
for purpose of data collection.

Cl assification of Research Design:
Exploratory Research
Exploratory research is defined as the initial research into a hypothetical or theoretical
idea. This is where a researcher has an idea or has observed something and seeks to
understand more about it. An exploratory research project i s an attempt to lay the
groundwork that will lead to future studies or to determine if what is being observed

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might be explained by a currently existing theory. Most often, exploratory research
lays the initial groundwork for future research.

Descriptive Research
Once the groundwork is established, the newly explored field needs more
information. The next step is descriptive research, defined as attempts to explore and
explain while providing additional information about a topic. This is where research is
trying to describe what is happening in more detail, filling in the missing parts and
expanding our understanding. This is also where as much information is collected as
possible instead of making guesses or elaborate models to predict the future – the
‘wh at’ and ‘how,’ rather than the ‘why.’

Causal Research
Causal research, as the name specifies, tried to determine the cause underlying a given
behaviour. It finds the cause and effect relationship between variables. It seeks to
determine how the dependent variable changes with variations in the independent
variable.
? Particularly , this research report is based on Exploratory Research Design. As this
research tries to explore more of how business consumer prefer Internet Leased
Line based on several factor a nd how those factors affec t their purchase decision
so, we can say that it is more of exploring more into purchase decisions with
respect to Leased Line of Tata Teleservices of consumers based on several factors .
Therefore, this research project adopts exp loratory research design.

7.2 Research Approaches
Rese arch approaches are plans nd the procedures for research that span the steps
from broad assumptions to detailed methods of data collection, analysis, and
interpretation. This plan involves several decisions, and they need not be taken in the
order in which they make sense to me and t he order of their presentation here. The
overall decision involves which approach should be used to study a topic. Informing
this decision should be the philosophical assumptions the researcher brings to the

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study; procedures of inquiry (called research de signs); and specific research methods
of data collection, analysis, and interpretation.

A pproaches to research
1) Qualitative research
Qualitative research is an approach for exploring and understanding the meaning
individuals or groups ascribe to a social or human problem. The process of research
involves emerging questions and procedures, data typically collected in the
participant’s se tting, data analysis inductively building from particulars to general
themes, and the researcher making interpretations of the meaning of the data. The
final written report has a flexible structure.

2) Quantitative research
Quantitative research is an approach for testing objective theories by examining the
relationship among variables. These variables, in turn, can be measured, typically on
instruments, so that numbered data can be analyzed using statistical procedures. The
final written report has a set structure consisting of introduction, literature and
theory, methods, results, and discussion. Like qualitative researchers, those who
engage in this form of inquiry have assumptions about testing theories deductively,
buildi ng in protections against bias, controlling for alternative explanations, and being
able to generalize and replicate the findings.

3) Mixed methods research
Mixed methods research is an approach to inquiry involving collecting both
quantitative and qualitat ive data, integrating the two forms of data, and using distinct
designs that may involve philosophical assumptions and theoretical frameworks. The
core assumption of this form of inquiry is that the combination of qualitative and
quantitative approaches pr ovides a more complete understanding of a research
problem than either approach alone .

? Particularly, this research report follows the Quantitative Research Approach.

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7.3 Data Sources
Ther e are two types of data sources
1) Primary Data
It is the data which is newly and first time collected data by marketer. They normally
use Questionnaire and Observation method to collect the primary data.

2) Secondary Data
It is the data which is already collected by someone else and it prevails in the
market.

? In this repo rt, both primary as well as secondary data has been collected. Primary
data from questionnaire and Secondary data from internet (articles, blogs, news)

7.4 Sample Design

A sample design is made up of two elements of sampling method. Sampling method
refers to the rules and procedures by which some elements of the population are
included in the sample . Some common sampling methods are simple random
sampling , stratified sampling , and cluster sampling . Estimator.
Sample design methods generally refer to the technique used to select sample units
for measurement (e.g., select individuals from a population or locations to sample
within a study area). Before sample design methods can be considered, it is necessary
to have thoroughly defined the population, st udy area, sampling unit, and sampling
objective. All of these will have an impact on which sample design methods are
suitable. Selection of a suitable sample design method ensures that the samples you
invest your time and money into collecting can support the inferences you want to
make. Use of a sample design method that is not appropriate can lead to samples that
are biased with respect to your assessment or monitoring objectives. In this case,
inference is valid only for samples/sites that were measured, and not the larger
area/population.

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? Sampling units
The marketing research must define target population that will be sample. In our
report, my tar get population were enterprise customer (users or potential users)
of Leased Line .

? Sample size
It refers to out of target population how much population is to survey as a sample.
In this report sample size from target population was 100 .

? Sampling techniques
There are two methods of sampling techniques

1. Probability sampling: –
In this method of sampling, each and every units of target population gets
known or equal chance to be selected as a sample.
2. Non – probability sampling
In this method of sampling, each and every units of target population does not
get known or equal c hance to be selected as sample.

? For this report, sampling technique used was Non – probability sampling technique
as the respondents were selected randomly and no equal ch ance was given to all
sample s among population.
7.5 Limitations of th e Study :
? The research was limited only to Ahmedabad city, so the result can’t be

? Time factor was the main limitation for the study as the project was restricted to
small period.
? The findings of the current study were applicable
? Resources for the project, as the customers are spread all over the Country; a large
amount of financial resources is required.

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8. Data Analysis
8.1 Bar Charts Analysis – shows the responses of respondents on scale
of 1 to 5 where higher value shows higher level; of agreeableness of the
respondent over accepting that, that particular factor affects their
purchase decision with respect to Leased Line.

Reliability as a factor
affecting Purchase
Decision of Leased Line.

0510152025303540
ReliabilityRe l i a b i l t y B a s e d Re p o n s e s
5 4 3 2 1
010203040
ScalabilityS c a l a b i l i t y B a s e d
Re s p o n s e s
5 4 3 2 1
01020304050
SecurityS e c u r i t y B a s e d Re s p o n s e s
5 4 3 2 1 Scalability as a factor
affecting Purchase
Decision of Leased
Line.

Security as a factor
affecting Purchase
Decision of Leased
Line.

37 | P a g e

05101520253035
CostC o s t B a s e d Re s p o n s e s
5 4 3 2 1 Cost as a factor
affecting Purchase
Decision of Leased
Line.

Speed as a factor
affecting Purchase
Decision of Leased
Line.

Operational Data
Needs as a factor
affecting Purchase
Decision of Leased
Line.
010203040
Operational Data NeedsO p e ra t i o n a l D a ta N e e d s
B a s e d Re s p o n s e s
5 4 3 2 1010203040
SpeedS p e e d B a s e d Re s p o n s e s
5 4 3 2 1

38 | P a g e

Interpretation – Total of 100 Respondent’s responses were been recorded based on
which the above charts were drawn. Above, charts are the way where the Y axis shows
the number respondents who gave their response on the factors as mentioned on the
scale of 1 to 5 on X axis which showed their level of importance they think an air
condition should have.
Higher number on scale shows higher amount of importance of the respondent
towards the particular factor.
Numbers shown on bars refers to the percentage of respondents who ha d selected
that particular importance level .
8.2 Pie Chart Analysis of the data collected
1. Gender
Male 57
Female 43
Total 100

010203040
Symetric ConnectionSy m m e t r i c C o n n e c t i o n
B a s e d Re s p o n s e s
5 4 3 2 1 Symmetric Connection
as a factor affecting
Purchase Decision of
Leased Line.

Chart Title
Male Female

39 | P a g e

2. Profession
Business 47
Employed 53
Total 100

3. Income
Below 5 Lakh 66
Above 5 Lakh 34
Total 100

4. Industry Vertical
Manufacturer 50
Service 50
Total 100

Chart Title
Above 5 Lakh Below 5 LakhChart Title
Business Employed
Chart Title
Manufacturing Sevice

40 | P a g e
5. Age
20 to 30 Years 34
30 to 40 Years 40
Above 40
Years 26
Total 100

So, above pie charts and information table shows the information about the
respondents which are considered for the purpose of this research report.

Chart Title
20 to 30 Years 30 to 40 Years
Above 40 Years

41 | P a g e
9. Hypothesis and Testing
Hypothesis
1. H 0: There is no significant impact of demographic factor age on reliability as factor
of influencing purchase decision of Lease Line.
H1: There is significant impact of demographic factor age on reliability as factor
of influencing purchase decision of Lease Line.

Age and Reliability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 34 6.8 43.7

Row 2 5 40 8 28.5

Row 3 5 26 5.2 9.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 19.73333 2 9.866667 0.363636 0.702547 3.885294
Within Groups 325.6 12 27.13333

Total 345.3333 14

0102030405060
5 4 3 2 1
Age – Reliability
20 – 30 30 – 40 40 above

42 | P a g e
Interpretation – As we can see from the calculated A nova table that, P value is
0.702547 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of age on reliability as factor of influencing purchase decision for
Leased Line.
2. H0: There is no significant impact of demographic factor age on scalability as
factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor age on scalability as factor
of influencing purchase de cision of Lease Line.

Age and Scalability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 126 25.2 662.7

Row 2 5 151 30.2 633.2

Row 3 5 94 18.8 195.7

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 326.5333 2 163.2667 0.328372 0.726366 3.885294
Within Groups 5966.4 12 497.2

Total 6292.933 14
020406080
5 4 3 2 1
Age – Scalability
20 – 30 30 – 40 40 above

43 | P a g e
Interpretation – As we can see from the calculated A nova table that, P value is
0.726366 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of age on scalability as factor of influencing pur chase decision
for Leased Line.
3. H0: There is no significant impact of demographic factor age on security as factor
of influencing purchase decision of Lease Line.
H 1 : There is significant impact of demographic factor age on security as factor of
influencing purchase decision of Lease Line.

Age and Security 020406080
5 4 3 2 1
Age -Security
20 – 30 30 – 40 40 above
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 15 3 2.5

Row 2 5 141 28.2 918.2

Row 3 5 165 33 1170

Row 4 5 106 21.2 501.7

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 2596.95 3 865.65 1.335674 0.29782 3.238872
Within Groups 10369.6 16 648.1

Total 12966.55 19

44 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is 0. 29782
which is more than the significance level 0.05 therefore we can say that our null
hypothesis is accepted and alternative hypothesis is rejected. That means there is no
significant impact of age on securi ty as factor of influencing pur chase decision for
Leased Line.
4. H0: There is no significant impact of demographic factor age on cost as factor of
influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor age on cost as factor of
influencing purchase decision of Lease Line.

Age and Cost
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 111 22.2 202.2

Row 2 5 137 27.4 473.8

Row 3 5 90 18 271.5

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 221.7333 2 110.8667 0.351029 0.710956 3.885294
Within Groups 3790 12 315.8333

Total 4011.733 14
020406080
5 4 3 2 1
Age-Cost
20 – 30 30 – 40 40 above

45 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 710956 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of age on cost as factor of influencing pur chase decision for
Leased Line.
5. H0: There is no significant impact of demographic factor age on operational data
needs as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic fac tor age on operational data
needs as factor of influencing purchase decision of Lease Line

Age and Operational Data Needs
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 130 26 654.5

Row 2 5 146 29.2 574.7

Row 3 5 95 19 301

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 272.1333 2 136.0667 0.266763 0.770279 3.885294
Within Groups 6120.8 12 510.0667

Total 6392.933 14 010203040506070
5 4 3 2 1
Age-Operational Data Needs
20 – 30 30 – 40 40 above

46 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 770279 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of age on opera tional data needs as factor of influencing
pu rchase decision for Leased Line
6. H0: There is no significant impact of demographic factor age on speed as factor of
influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor age on speed as factor of
influencing purchase decision of Lease Line.

Age and Speed
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 145 29 1219

Row 2 5 142 28.4 478.3

Row 3 5 101 20.2 404.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 241.7333 2 120.8667 0.172543 0.843573 3.885294
Within Groups 8406 12 700.5

Total 8647.733 14 01020304050607080
5 4 3 2 1
Age-Speed
20 – 30 30 – 40 40 above

47 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 843573 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of age on speed as factor of influencing purchase decision for
Leased Line.
7. H0: There is no significant impact of demographic factor age on symmetric
connection as factor of influencing purchase decision of Lease Line
H1 : There is significant impact of demographic factor age on symmetric
connection as factor of influencing purchase decision of Lease Line.
.
Age and Symmetric Connection
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 124 24.8 394.7

Row 2 5 156 31.2 875.7

Row 3 5 93 18.6 436.8

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 396.9333 2 198.4667 0.348758 0.712483 3.885294
Within Groups 6828.8 12 569.0667

Total 7225.733 14 01020304050607080
5 4 3 2 1
Age-Symetric Connection
20 – 30 30 – 40 40 above

48 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 712483 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of age on symmetric connection as factor of influencing purchase
dec ision for Leased Line.
8. H0: There is no significant impact of demographic factor industry vertical on
reliability as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor industry vertical on
reliability as factor of influencing purchase decision of Lease Line

Industrial Vertical and Reliability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 188 37.6 999.3

Row 2 5 200 40 1729.5

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 14.4 1 14.4 0.010554 0.920703 5.317655
Within Groups 10915.2 8 1364.4

Total 10929.6 9
020406080100120
5 4 3 2 1
Industry Vertical – Reliabilty
Manufacturing Service

49 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 920703 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of industry vertical on reliability as factor of influencing purchase
decision for Leased Line.
9. H0: There is no significant impact of demographic factor industry vertical on
scalability as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor industry vertical on
scalability as factor of influencing purchase decision of Lease Line.

Industrial Vertical and Scalability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 185 37 1045

Row 2 5 186 37.2 885.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 0.1 1 0.1 0.000104 0.992128 5.317655
Within Groups 7720.8 8 965.1

Total 7720.9 9 0102030405060708090
5 4 3 2 1
Industry Vertical – Scalability
Manufacturing Service

50 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 992128 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of industry vertical on scalability as factor of influencing purchase
decision for Leased Line
10. H0: There is no significant impact of demographic factor industry vertical on
security as factor of influencing purchase decision of Lease Li ne.
H1 : There is significant impact of demographic factor industry vertical on security
as factor of influencing purchase decision of Lease Line.

Industrial Vertical and Security
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 210 42 2396

Row 2 5 186 37.2 1431.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 57.6 1 57.6 0.0301 0.866572 5.317655
Within Groups 15308.8 8 1913.6

Total 15366.4 9 020406080100120140
5 4 3 2 1
Industry Vertical – Security
Manufacturing Service

51 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 866572 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of industry vertical on security as factor of influencing purchase
d ecision for Leased Line.
11. H0: There is no significant impact of demographic factor industry vertical on cost
as factor of influencing purchase decision of Lease Line.
H0: There is no significant impact of demographic factor industry vertical on cost
as factor of influencing purchase decision of Lease Line.

Industrial Vertical and Cost
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 167 33.4 590.8

Row 2 5 171 34.2 845.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 1.6 1 1.6 0.002228 0.963506 5.317655
Within Groups 5744 8 718

Total 5745.6 9 0102030405060708090
5 4 3 2 1
Industry Vertical – Cost
Manufacturing Service

52 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0.920703 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of industry vertical on reliability as factor of influencing purchase
decision for Leased Line.
12. H0: There is no significant impact of demographic factor industry vertical on
operational data needs as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor industry vertical on
operational data needs as factor of influencing purchase decision of Lease Line.

Industrial Vertical and Operational Data Needs
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 192 38.4 1306.8

Row 2 5 179 35.8 875.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 16.9 1 16.9 0.01549 0.904021 5.317655
Within Groups 8728 8 1091

Total 8744.9 9 0102030405060708090100
5 4 3 2 1
Industry Vertical – Operational Data Needs
Manufacturing Service

53 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 904021 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of industry vertical on operational data needs as factor of
influenc ing pu rchase decision for Leased Line
13. H0: There is no significant impact of demographic factor industry vertical on speed
as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor industry vertical on speed
as factor of influencing purchase decision of Lease Line.

Industrial Vertical and Speed
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 203 40.6 1684.8

Row 2 5 198 39.6 1125.3

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 2.5 1 2.5 0.001779 0.967388 5.317655
Within Groups 11240.4 8 1405.05

Total 11242.9 9 0102030405060708090100
5 4 3 2 1
Industry Vertical – Speed
Manufacturing Service

54 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 967388 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of industry vertical on speed as factor of influencing purchas e
decision for Leased Line.
14. H0: There is no significant impact of demographic factor industry vertical on
symmetric connection as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor industry vertical on
symmetric connection as factor of influencing purchase decision of Lease Line.

Industrial Vertical and Symmetric Connection
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 203 40.6 1684.8

Row 2 5 198 39.6 1125.3

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 2.5 1 2.5 0.001779 0.967388 5.317655
Within Groups 11240.4 8 1405.05

Total 11242.9 9 0102030405060708090100
5 4 3 2 1
Industry Vertical – Symmetric Connection
Manufacturing Service

55 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 967388 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of industry vertical on symmetric connection as factor of
influencing purchase decision for Leased Line.
15. H0: There is no significant impact of demographic factor profession on reliability
as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor profession on reliability as
factor of influencing purchase decision of Lease Line.

Profession and Reliability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 184 36.8 1037.2

Row 2 5 204 40.8 1809.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 40 1 40 0.028106 0.871021 5.317655
Within Groups 11385.6 8 1423.2

Total 11425.6 9 020406080100120
5 4 3 2 1
Profession – Reliabilty
Business Employed

56 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 871021 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of profession on reliability as factor of influencing pu rchase
decision for Leased Line.
16. H0: There is no significant impact of demographic factor profession on scalability
as factor of influencing purchase decision of Lease Line.
H1 : Th ere is significant impact of demographic factor profession on scalability as
factor of influencing purchase decision of Lease Line.

Profession and Scalability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 178 35.6 900.8

Row 2 5 193 38.6 1048.8

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 22.5 1 22.5 0.023082 0.883006 5.317655
Within Groups 7798.4 8 974.8

Total 7820.9 9 020406080100
5 4 3 2 1
Profession – Scalability
Business Employed

57 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 883006 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of profession on scalability as factor of influencing purchase
decision for Leased Line.
17. H0: There is no significant impact of demographic factor profession on security as
factor of influencing purchase decision of Lease Line.
H1 : There is significan t impact of demographic factor profession on security as
factor of influencing purchase decision of Lease Line.

Profession and Security
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 190 38 1433.5

Row 2 5 222 44.4 2378.3

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 102.4 1 102.4 0.053728 0.822518 5.317655
Within Groups 15247.2 8 1905.9

Total 15349.6 9 020406080100120140
5 4 3 2 1
Profession – Security
Business Employed

58 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0.822518 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of profession on security as factor of influencing purchase
decision for Leased Line.
18. H0: There is no significant impact of demographic factor profession on cost as
factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor profession on cost as factor
of influencing purchase decision of Lease Line.

Profession and Cost
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 140 28 355.5

Row 2 5 135 27 131

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 2.5 1 2.5 0.010277 0.921745 5.317655
Within Groups 1946 8 243.25

Total 1948.5 9 0102030405060
5 4 3 2 1
Profession – Cost
Business Employed

59 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 921745 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of profession on cost as factor of influencing purchase decision
for Leased Line.
19. H0: There is no significant impact of demographic factor profession on operational
data needs as factor of influencing purchase decision of Lease Line.
H1 : There is sig nificant impact of demographic factor profession on operational
data needs as factor of influencing purchase decision of Lease Line.

Profession and Operational Data Needs
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 176 35.2 1000.7

Row 2 5 183 36.6 1131.8

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 4.9 1 4.9 0.004596 0.947616 5.317655
Within Groups 8530 8 1066.25

Total 8534.9 9 0102030405060708090
5 4 3 2 1
Profession – Operational Data Needs
Business Employed

60 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 947616 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of profession on operational data needs as factor of influencing
purchase decision for Leased Line.
20. H0: There is no significant impact of demographic factor profession on speed as
factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor profession on speed as
factor of influencing purchase decision of Lease Line.

Profession and Speed
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 167 33.4 624.8

Row 2 5 240 48 2866

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 532.9 1 532.9 0.305317 0.595665 5.317655
Within Groups 13963.2 8 1745.4

Total 14496.1 9 020406080100120140
5 4 3 2 1
Profession – Speed
Business Employed

61 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 595665 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of profession on speed as factor of influencing purchase decision
for Leased Line.
21. H0: There is no significant impact of demographic factor profession on symmetric
connection as factor of influencing purchase decision of Lease Line.
H1 : There is sign ificant impact of demographic factor profession on symmetric
connection as factor of influencing purchase decision of Lease Line.

Profession and Symmetric Connection
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 165 33 1118

Row 2 5 184 36.8 1095.7

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 36.1 1 36.1 0.032615 0.861175 5.317655
Within Groups 8854.8 8 1106.85

Total 8890.9 9 0102030405060708090
5 4 3 2 1
Profession – Symmetric Connection
Business Employed

62 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 861175 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of profession on symmetric connection as factor of influencing
purchase decision for Leased Line.
22. H0: There is no significant impact of demographic factor income on reliability as
factor of influencing purchase decision of Lease Line.
H 1 : There is significant impact of demographic factor income on reliability as
factor of influencing purchase decision of Lease Line.

Income and Reliability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 266 53.2 3350.7

Row 2 5 122 24.4 343.3

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 2073.6 1 2073.6 1.122685 0.320281 5.317655
Within Groups 14776 8 1847

Total 16849.6 9 020406080100120140160
5 4 3 2 1
Income – Reliabilty
Below 5 Lakh Above 5 Lakh

63 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 320281 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of income on reliability as factor of influencing purchase decision
for Leased Line.
23. H0: There is no significant impact of demographic factor income on scalability as
factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor income on scalability as
factor of influencing purchase decision of Lease Line.

Income and Scalability
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 245 49 1907

Row 2 5 126 25.2 436.7

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 1416.1 1 1416.1 1.208431 0.303621 5.317655
Within Groups 9374.8 8 1171.85

Total 10790.9 9 020406080100120
5 4 3 2 1
Income – Scalability
Below 5 Lakh Above 5 Lakh

64 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 303621 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant i mpact of income on scalability as factor of influencing purchase decision
for Leased Line.
24. H0: There is no significant impact of demographic factor income on security as
factor of influencing purchase decision of Lease Line.
H0: There is significant impact of demographic factor income on security as factor
of influencing purchase decision of Lease Line.

Income and Security
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 270 54 3074

Row 2 5 142 28.4 974.8

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 1638.4 1 1638.4 0.809326 0.394592 5.317655
Within Groups 16195.2 8 2024.4

Total 17833.6 9 020406080100120140
5 4 3 2 1
Income – Security
Below 5 Lakh Above 5 Lakh

65 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 394592 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of income on security as factor of influencing purchase decision
for Leased Line.
25. H0: There is no significant impact of demographic factor income on cost as factor
of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor income on cost as fac tor of
influencing purchase decision of Lease Line.

Income and Cost
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 229 45.8 1570.2

Row 2 5 109 21.8 157.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 1440 1 1440 1.667246 0.232686 5.317655
Within Groups 6909.6 8 863.7

Total 8349.6 9 020406080100120
5 4 3 2 1
Income- Cost
Below 5 Lakh Above 5 Lakh

66 | P a g e
Interpretation – As we can see from the calculated Anova table that, P value is
0. 232686 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of income on cost as factor of influencing pur chase decision for
Lea sed Line.
26. H0: There is no significant impact of demographic factor income on operational
data needs as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor income on operational data
needs as factor of influencing purchase decision of Lease Line.

Income and Operational Data Needs
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 240 48 1601.5

Row 2 5 131 26.2 743.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 1188.1 1 1188.1 1.013435 0.343556 5.317655
Within Groups 9378.8 8 1172.35

Total 10566.9 9 0102030405060708090100
5 4 3 2 1
Income – Operational Data Needs
Below 5 Lakh Above 5 Lakh

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Interpretation – As we can see from the calculated Anova table that, P value is
0. 343556 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of income on operational data needs as factor of influencing
pu rchase decision for Leased Line.
27. H0: There is no significant impact of demographic factor income on speed as factor
of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor income on speed as factor
of influencing purchase decision of Lease Line.

Income and Speed
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 269 53.8 3244.2

Row 2 5 119 23.8 335.2

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 2250 1 2250 1.257194 0.294709 5.317655
Within Groups 14317.6 8 1789.7

Total 16567.6 9 020406080100120140
5 4 3 2 1
Income- Speed
Below 5 Lakh Above 5 Lakh

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Interpretation – As we can see from the calculated Anova table that, P value is
0. 294709 which is more than the significance level 0.05 therefore we can say that our
null hypothesis is accepted and alternative hypothesis is rejected. That means there is
no significant impact of income on speed as factor of influencing pu rchase decision for
Le ased Line.
28. H0: There is no significant impact of demographic factor income on symmetric
connection as factor of influencing purchase decision of Lease Line.
H1 : There is significant impact of demographic factor income on symmetric
connection as factor of i nfluencing purchase decision of Lease Line.

Income and Symmetric Connection
Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Row 1 5 264 52.8 1901.7

Row 2 5 127 25.4 619.8

ANOVA

Source of Variation SS df MS F P – value F crit
Between Groups 1876.9 1 1876.9 1.488717 0.25716 5.317655
Within Groups 10086 8 1260.75

Total 11962.9 9 020406080100120
5 4 3 2 1
Income – Symmetric Connection
Below 5 Lakh Above 5 Lakh

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Interpretation – As we can see from the calculated Anova table that, P value is 0. 25716
which is more than the significance level 0.05 therefore we can say that our null
hypothesis is accepted and alternative hypothesis is rejected. That means there is no
significant impact of income on symmetric connection as factor of influencing
purchase decision for Leased Line.

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10. Findings and Conclusions
? From the whole research report now we can finally make out certain outcomes
of the tests carried on the data obtained. We could observe from the research
th at the demographic factors were taken as one variable and factors based on
which a purchase decision could get influence were taken as another variable.
? After the collection of data Anova One Sample Test was applied to the data for
further analysis, from w hich the below findings were drawn.

? T here was no significant impact of demographic factor age on reliability as
factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor age on scalability as
factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor age on security as
factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor age on cost as factor
of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor age on operational
data needs as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic fact or age on speed as
factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor age on symmetric
connection as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of dem ographic factor Industry Vertical on
reliability as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Industry Vertical on
scalability as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Industry Vertical on
security as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Industry Vertical on
cost as factor of influencing purchase decision for Leased Line.

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? T here was no significant impact of demographic factor Industry Vertical on
operational data needs as factor of influencing purchase decision for
Leased Line.
? T here was no significant impact of de mographic factor Industry Vertical on
speed as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Industry Vertical on
symmetric connection as factor of influencing purchase decision for Leased
Li ne.
? T here was no significant impact of demographic factor Income on reliability
as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Income on
scalability as factor of influencing purchase decisi on for Leased Line.
? T here was no significant impact of demographic factor Income on security
as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Income on cost as
factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Income on
operational data needs as factor of influencing purchase decision for
Leased Line.
? T here was no significant impact of demographic factor Income on speed as
factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Income on
symmetric connection as factor of influencing purchase decision for Leased
Line.
? T here was no significant impact of demographic fac tor Profession on
reliability as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Profession on
scalability as factor of influencing purchase decision for Leased Line.
? T here was no significant i mpact of demographic factor Profession on
security as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Profession on cost
as factor of influencing purchase decision for Leased Line.

72 | P a g e
? T here was no significant impact of demographic factor Profession on
operational data needs as factor of influencing purchase decision for
Leased Line.
? T here was no significant impact of demographic factor Profession on speed
as factor of influencing purchase decision for Leased Line.
? T here was no significant impact of demographic factor Profession on
symmetric connection as factor of influencing purchase decision for Leased
Line.

? So, to conclude we can say that the factors which actually influence purchase
decision for a Leased Line is not based on demographic factors such as age,
income, industry vertical and profession of a person as per the results obtained
from the test. Instea d we can perceive that the purchase decision majorly rely
on the need of the organization and their severe requirements. Also brand
value and other value added services including all factors mention in report for
influencing the purchase decision of Leased Line do not get impact of any of
the demographic factors mentioned in the test.

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11. References and Bibliograpghy
? Arindham Mukherjee (March, 2006), in his report to Sify Technologies.
? Chowdary . T.H: “Information and Communication Technologies for Classes ;
Masses”, A CTMS Publication, Published by CTMS, P & T colony, Karkhana,
Hyderabad, A. P, India, May, 2004.
? Hemant Joshi (2014), Deloitte and Indian Chamber of Commerce and Industry.
? Jumiati Sasmita, Norazah Mohd Suki, (2015) Young consumers’ insights on brand
equity: Effects of brand association, brand loyalty, brand awareness, and brand
image”, International Journal of Retail ; Distribution Management, Vol. 43 Issue:
3, pp.276 – 292.2015
? Kelle r, K. ;. (2009). Satisfaction is a person of pleasure or disappoint/unhappiness,
resulting from comparing a product’s perceived performance in relation to his or
her expectation. Vol. 52 Issue: 3, pp.16 – 52.2004
? Summersingh Rajput(2012) How to increase mar ket share in wireless broadband
market and increase customer retentively. Vol. 54 Issue: 3, pp.16 – 52.1984
? Videsh Sanchar Nigam LTD (2002), 16 th
Anuual Report of VSNL.
? Fiber, Copper, or Wireless: Which Connection Is Best for Your Company? Up dated:
Dec 3, 2 017 by Ed Lieber. – https://smallbiztrends.com/2015/08/fiber – optic –
copper – wireless – internet – transmission – methods.html
? I ndian Telecom Industry Analysis: July, 20 18 https://www.ibef.org/industry/
indian – telecommunications – industry – analysis – presentation
? India NetZone , History of Indian Telecommunications – https://www.indianet
zone.com /42/history_indian_telecommunications.ht

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12. Annexure
? Questionnaire

Name: _________________________________

Gender: Male Female

? Profession – Employed
Business
? Industrial Vertical – Manufacturing
Service Industry
? Age: 20 years to 30 years
30 y ears to 40 year s
Above 40 Years
Purpose Confidentiality
. Statement
This questionnaire is regarding th e
survey on how several factors affects the
consumer buying behaviour towards
Lease Line. The data in the questionnaire and the
questionnaire itself shall be used purely
for academic research purpose .
? Income – Below 5 Lakh
Above 5 Lakh

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? Please read out t he following factors carefully. Rate them in
the order of your level of agreeableness which would influence
you to take decision regarding purchase of leased line.
1. How much do you think on RELIABILITY as a factor
influence you to take decision for purchase of leased line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree
2. How much do you think SCALABILITY as a factor influence
you to take decision for purchase of leased line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree
3. How much do you think SECURITY as a factor influence you
to take decision for purchase of leased line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree

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4. How much do you think COST as a factor influence you to
take decision for purchase of leased line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree

5. How much do you think OPERATIONAL DATA NEEDS as a
factor influence you to take decision for purchase of leased
line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree

6. How much do you think SPEED as a factor influ ence you to
take decision for purchase of leased line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree

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7. How much do you think SYMETRIC CONNECTION as a
factor influence you to take decision for purchase of leased
line?
Mostly Agree
Agree
Neutral
Disagree
Mostly Disagree