1 | P a g e

Viral Marketing: A Study of drivers of Viral Marketing and factors that influence the receipt and forwarding of viral messages

Dissertation Submitted to the

Padmashree Dr. D.Y. Patil University, Navi Mumbai,

Department of Business Management

in partial fulfillment of the requirements for the award of the Degree of

Master in Philosophy (M.Phil) in (Business Management)

Submitted by:

PRATIMA NISHANT DABHOLKAR Roll No.: DYP-M.Phil-09008

Research Guide:

Dr. PRADIP MANJREKAR

Professor in Management,

Padmashree Dr. D.Y. Patil University

Department of Business Management

Sector 4, Plot No-10, CBD Belapur, Navi Mumbai- 400 614

July, 2011

2 | P a g e

DECLARATION

I hereby declare that the Study titled “Viral Marketing: A Study of drivers of

Viral Marketing and factors that influence the receipt and forwarding of

viral messages” submitted for the M.Phil. Degree at Padmashree Dr. D.Y.

Patil University, Navi Mumbai, Department of Business Management is my

original work and the dissertation has not formed the basis for the award of

any degree, associateship, fellowship or any other similar titles.

Place: Navi Mumbai

Date: July, 2011

Signature of the Student

Pratima Nishant Dabholkar

3 | P a g e

CERTIFICATE

This is to certify that the dissertation titled ―Viral Marketing: A Study of drivers

of Viral Marketing and factors that influence the receipt and forwarding of

viral messages‖ is the bona-fide research work carried out by Pratima Nishant

Dabholkar, student of M.Phil., at Padmashree Dr. D.Y. Patil University, Navi

Mumbai, Department of Business Management, in partial fulfillment of the

requirements for the award of the Degree of M. Phil. and that the dissertation has

not formed the basis for the award previously of any degree, diploma,

associateship, fellowship or any other similar title.

Place: Navi Mumbai

Date: July, 2011

Signature Signature of the Guide Prof.Dr. R. Gopal Prof. Dr. Pradip Manjrekar Director Dean & Head of the Department

4 | P a g e

ACKNOWLEDGEMENTS

I am grateful to Padmashree Dr. D.Y. Patil University, Navi Mumbai, Department of Business

Management for giving me an opportunity to pursue M.Phil. I wish to thank Professor Dr. R.

Gopal, Director, Dean & Head of the Department, Padmashree Dr. D.Y. Patil University, Navi

Mumbai, Department of Business Management who has been a perpetual source of inspiration

and offered valuable suggestions to improve my M.Phil work.

I am beholden to my Research Guide Dr. Pradip Manjrekar, Professor, Padmashree Dr. D.Y. Patil

University, Navi Mumbai, Department of Business Management for abundant guidance, support,

and encouragement throughout my M. Phil Work. I sincerely thank Dr. Pradip Manjrekar for given

me his valuable guidance for the project. Without his guidance, it would have never been possible

for me to complete this project.

I would also like to thank people from different organizations and the students, who have helped

me and participated in collection of data for this project, without which this project could have

never been completed. I wish to express my gratitude to my colleagues for providing me valuable

information and help during my research work.

I would be failing in my duty if I do not acknowledge, with a deep sense of gratitude, the sacrifices

made by my husband Nishant and daughters Shweta and Nikita for allowing and supporting me

to spend my free time on this project work and thus have helped me in completing the project

work successfully.

Place: Navi Mumbai Date: July, 2011

Signature of the student

Pratima Nishant Dabholkar

5 | P a g e

PREFACE

Viral marketing is a powerful marketing tool with untapped potential. Viral

Marketing Communication can bring about benefits to marketers with its

advantages such as low cost, high reach, high credibility, accountability, fast

speed, ease of usage and ability to reach a global audience.

With the increased usage of broadband and internet services like YouTube,

Hotmail and Facebook, there will be an increasing trend for Viral Marketing to be

adopted by companies as part of their promotional mix in the future, thus fuelling

my interest in this topic.

For the success of the viral marketing strategy, viral marketing techniques

applied from diverse platforms needs to be studied. In this study main focus is on

viral marketing via email. This study aims to understand the drivers of viral

marketing and investigates various attributes which influence user to receive and

forward messages.

Limited research has been done on viral marketing and response to such

marketing techniques.

Signature of the Student

Pratima Nishant Dabholkar

6 | P a g e

Executive Summary

Viral marketing, viral advertising, or marketing buzz are buzzwords referring

to marketing techniques that use pre-existing social networks to produce

increases in brand awareness or to achieve other marketing objectives (such as

product sales) through self-replicating viral processes, analogous to the spread

of viruses or computer viruses. It can be delivered by word of mouth or enhanced

by the network effects of the Internet. Viral marketing may take the form of video

clips, interactive Flash games, advergames, ebooks, brandable software,

images, or text messages.

The goal of marketers interested in creating successful viral marketing programs

is to create viral messages that appeal to individuals with high social networking

potential (SNP) and that have a high probability of being presented and spread

by these individuals and their competitors in their communications with others in

a short period of time.

The term "viral marketing" has also been used pejoratively to refer to stealth

marketing campaigns—the unscrupulous use of astroturfing online combined

with undermarket advertising in shopping centers to create the impression of

spontaneous word of mouth enthusiasm (Ref. Wikipedia)

One of the most alluring facets of social media marketing is the potential for sales

and branding messages to ―turn viral,‖ meaning that people start passing them

7 | P a g e

on to friends just because they find it amusing, entertaining, or valuable. But in

order for this to happen, you need to really have an idea of what people in your

market segments are interested in. You also have to have some good ideas

about how to get them to pass your message along. This study aimed to study

drivers of viral marketing and factors which influence user to receive and forward

viral messages.

A lot of research work is done in the field of viral marketing. Key drivers of Viral

Marketing is awareness(Arnaud De Bruyn, Gary L. Lilien, 2008), interest((Arnaud

De Bruyn, Gary L. Lilien, 2008), Access to use this marketing techniques and

experience which decides their final decision((Arnaud De Bruyn, Gary L. Lilien,

2008). The consumer has now taken an observable action, a purchase of a good

or service or the sustained adoption of an innovation. One factor added to this

chain of drivers of viral marketing is access to see whether regular access to

internet has any significant impact on user to get experience of viral marketing.

Due to advancements in computer technology and internet people all over the

world can now interact and communicate with virtually anyone else who has

access to a computer and the internet (Abed Abedniya, 2010).

Drivers of viral marketing give user understanding about viral marketing

phenomena and built their interest in getting information about product or brand

through their social network via internet. This curiosity about getting information

leads them to receive viral messages. User likes to receive message if it is from

8 | P a g e

a trusted source, message is having relevance and user is getting perceived

benefits. These three factors are influencing user to receive messages. One

messages are received it is likely to be forwarded

From the existing research it has been observed that tie strength, sender‘s

benefit, customer satisfaction and altruism are the factors which influence user to

forward viral messages. Tie strength was measured in terms of how often user

saw the contact person over the network. Senders benefit is related to benefit

user will receive in terms of incentives, bonus, free services, and prizes etc.

Customer satisfaction in terms of product or service user used and satisfied.

Ease in forwarding messages in his/her network and having good opinion about

the product. Altruism is the term relates to the users kindness or is the

renunciation of the self, and an exclusive concern for the welfare of others.

These factors are revealed through the prior literature which influence user to

receive and forward viral messages.

It has been found from the earlier research that factors to receive as well as

forward viral messages are not studied together in Indian context. This is one of

the research gap found through literature review. Most of the study is done where

data collected from the college students. Opinion of the working professional is

not taken into consideration. Working professionals are required to study as they

are having purchasing power. There has been little research into finding out the

drivers of viral marketing.

9 | P a g e

Motivation stems primarily in this study because viral marketing is a powerful

marketing tool with untapped potential. Viral Marketing Communication can bring

about benefits to marketers with its advantages such as low cost, high reach,

high credibility, accountability, fast speed, ease of usage and ability to reach a

global audience.

Therefore, our objective for this research is see the influence of factors which are

identified through literature review as well influence of demographic factors to

receive and forward viral messages. Objective for the research are as follows:

1. To understand viral marketing through social network.

2. To identify drivers for viral marketing.

3. To reveal and validate factors which influence user to receive and forward

messages.

4. To understand the impact of demographic factors of user on receiving and

forwarding messages.

Drivers of viral marketing are needed to validate and for that it has hypothesized

that access, awareness and interest have significant impact on the experience of

viral marketing.

Factors which are revealed are needed to validate and for that it has

hypothesized that trusted source, relevance and perceived benefit have

significant impact on user to receive viral messages. To see the impact of

10 | P a g e

demographic factors which are gender and occupation is it also hypothesized

that trusted source, relevance and perceived benefit have significant impact on

user to receive viral messages with respect to gender an occupation. Similarly for

the factors tie strength, sender‘s benefit, customer satisfaction and altruism it has

hypothesized that tie strength, sender‘s benefit, customer satisfaction and

altruism have significant impact on user to forward viral messages. To see the

impact of demographic factors which are gender and occupation is it also

hypothesized that tie strength, sender‘s benefit, customer satisfaction and

altruism have significant impact on user to forward viral messages with respect to

gender an occupation.

Data is collected from the students of the various colleges of various streams like

degree colleges, management colleges and engineering colleges including

undergraduate, graduate and post graduate students. Working professional from

IT sector and non-IT sector included in this study. Data collected from Mumbai

region only.

For data analysis SPSS-12 statistical package is used. Various statistical tests

like descriptive statistics, regression, correlation and independent sample t-test is

performed to validate the hypotheses. After statistical analysis it has been found

that access does not have any significant impact on the experience of viral

marketing. For user access in not a contributing factor whereas awareness and

interest having significant impact on the experience of viral marketing. This

11 | P a g e

shows that whether user get continuous access to internet or not it does not

make any difference but the awareness of viral marketing and interest in getting

information of the products or services over internet makes the significant impact

on experience of viral marketing.

To receive viral messages trusted source, source from where user get

information, relevance i.e. messages of his interest or messages containing

information he is looking for and the perceived benefits have significant impact

on user to receive viral messages. Demographic factor occupation is also

contributes to the message relevance to receive messages but gender does not

contribute any role to receive viral messages.

To forward viral messages tie strength, senders‘ benefits has significant impact

on user to forward viral messages. Customer satisfaction and altruism does not

have significant impact on user to forward viral messages. Demographic factor

occupation is also contributes to the tie strength to forward messages but gender

does not contribute any role to forward viral messages. Customer satisfaction

and altruism is not proven significant may for the user satisfaction form product

or service is not important to forward viral messages. While forwarding messages

user considered only tie strength and senders benefit. User normally forward

messages to his close acquaintances and to get benefit like incentives, prizes,

discount or free services after forwarding messages.

12 | P a g e

To find out the correlation between the drivers of viral marketing which are

access, awareness, interest and experience a Pearson product-moment

correlation was run across the variables to determine the relationship between

the sequences of drivers of viral marketing. It is observed that there is a strong

and positive correlation between access and interest which was statistically

significant (r = 0.252, n = 491, value of P = 0.000 where P < .0005). Whereas

there is negative correlation between access and awareness which was

statistically not significant (r = -0.065, n = 491, value of P = 0.153 where P >

.0005). It is also proved that there is negative relationship between awareness

and access and awareness and interest which was statistically not significant ((r

= -0.065, n = 491, value of P = 0.153, where P < .0005), (r = -0.051, n = 491,

value of P = 0.262 where P > .0005). There is negative correlation between

interest and awareness which was statistically not significant (r = -0.051, n = 491,

value of P = 0.262 where P > .0005) but there is strong and positive correlation

between interest and access which was statistically significant (r = 0.252, n =

491, value of P = 0.000 where P < .0005).

To find out the association between the intention to receive viral messages and

influencing factors which are trusted source, relevance, perceived benefits a

Pearson correlation test is carried out. It is observed that there is strong and

positive correlation between these variables with the intention to receive viral

messages. Similarly To find out the association between the intention to forward

viral messages and influencing factors which are tie strength, sender‘s benefit,

13 | P a g e

customer satisfaction, and altruism a Pearson correlation test is carried out. It is

observed that there is strong and positive correlation between these variables

with the intention to forward viral messages.

Only platform of viral marketing considered in the research study is email. Other

platform of viral marketing like company website, online review, blogs, social

network, online communities, newsgroups, chat rooms, hate sites, needs to be

considered and compare different levels of impact on these eWOM forms on

consumer behavior.

This study may not have identified all the factors which influence user to receive

and forward messages. Therefore, another limitations lies in the limited number

of variables examined in relation to receive and forward messages.

14 | P a g e

CONTENTS

Chapter

No. Title Page Number

From To

Chapter-0 Executive summary

Chapter-1 Introduction

Chapter-2 Literature Review

Chapter-3 Purpose of the Study

Chapter-4 Objectives of the Study

Chapter-5 Hypothesis

Chapter-6 Research Methodology

Chapter-7 Data Analysis and Hypothesis Testing

Chapter-8 Limitations and Future Scope of the Study

Appendices

Appendix-1 Bibliography

Appendix-2 Questionnaire

15 | P a g e

List of Tables and Annexure

Table/

Annexure Title Page Number

Tables From To

Table-1 Demographic Analysis of Sample

Table-2 Table of Responses

Table-3 Major Constructs and sub variables of the

Study

Table-4

Table-5 Frequency Table

Table-6 Summary of Hypotheses

Table-7 Summary of Objectives and outcomes

Annexure

Annexure-1 Reliability Statistics

Annexure-2 KMO and Bartlett‘s Test

Annexure-3 Item-total Statistics

Annexure-4 Descriptive statistics - Sequence of Drivers

of Viral Marketing

Annexure-5 Descriptive statistics – factors influence user

to receive viral messages

Annexure-6 Descriptive statistics - factors influence user

to forward viral messages

Annexure-7 Correlation between Access and Experience

16 | P a g e

List of Tables and Annexure

Table/

Annexure Title Page Number

From To

Annexure-10 Correlation between sequence of drivers of

viral marketing

Annexure-11 Regression – factors influence user to

receive viral messages

Annexure-12 Group statistics between trust and gender

Annexure-13 Independent sample test between trust and

gender

Annexure-14 Group statistics between trust and

occupation

Annexure-15 Independent sample test between trust and

occupation

Annexure-16 Group statistics between relevance and

gender

Annexure-17 Independent sample test between relevance

and gender

Annexure-18 Group statistics between relevance and

occupation

Annexure-19 Independent sample test between relevance

and occupation

Annexure-20 Group statistics between perceived benefits

and gender

Annexure-21 Independent sample test between perceived

benefits and gender

Annexure-22 Group statistics between perceived benefits

and occupation

17 | P a g e

List of Tables and Annexure

Table/

Annexure Title Page Number

From To

Annexure-23 Independent sample test between perceived

benefits and occupation

Annexure-24 Regression – factors influence user to

forward viral messages

Annexure-25 Group statistics between tie strength and

gender

Annexure-26 Independent sample test between tie

strength and gender

Annexure-27 Group statistics between tie strength and

occupation

Annexure-28 Independent sample test between tie

strength and occupation

Annexure-29 Group statistics between sender‘s benefit

and gender

Annexure-30 Independent sample test between sender‘s

benefit and gender

Annexure-31 Group statistics between sender‘s benefit

and occupation

Annexure-32 Independent sample test between sender‘s

benefit and occupation

Annexure-33 Group statistics between customer

satisfaction and gender

Annexure-34 Independent sample test between customer

satisfaction and gender

18 | P a g e

List of Tables and Annexure

Table/

Annexure Title Page Number

From To

Annexure-35 Group statistics between customer

satisfaction and occupation

Annexure-36 Independent sample test between customer

satisfaction and occupation

Annexure-37 Group statistics between altruism and gender

Annexure-38 Independent sample test between altruism

and gender

Annexure-39 Group statistics between altruism and

occupation

Annexure-40 Independent sample test between altruism

and occupation

Annexure-41 Correlation between intention to receive

messages and influencing factors

Annexure-42 Correlation between intention to forward

messages and influencing factors

19 | P a g e

List of Abbreviations

CMC Computer Mediated Communication

ERM Electronic Referral Marketing

eWOM Electronic Word-of-mouth

VM Viral Marketing

VMC Viral Marketing Communication

WOM Word-of-mouth

20 | P a g e

Chapter-1

Introduction

This research project attempts to understand viral marketing strategy adopted by

the marketer and find out drivers of viral marketing. There is evolution of new

marketing tactic which is called viral marketing. Why this new marketing tactic

has been evolved? The reason behind is that there are dynamics or transitions in

marketing. This transition is because changes in marketing techniques and these

changes of marketing techniques are because customer is changing. Why

customer is changing? Because of the advancement of communication

technology and internet technology made his life very simple. Information of the

product is available on internet. Today‘s customer is well informed. Before going

for purchase he likes to know about the product. This knowledge he may get it

from the internet or through his social network. There is lot of ease also in

purchasing product. At any time, from anywhere he can purchased product

online.

As information technology has occupied most of the life of human being. At

workplace, at home he is using this technology for his day to day activity. At

office he is using computer for his official work. At home he is using it for

educational purpose, for entertainment, or for his personal use. Today‘s

customer is spending most of the time on virtual world rather than in real world.

Marketer has to find out a technique to reach to the customer in his virtual world.

This reach is possible with the advancement in communication technology there

21 | P a g e

is evolution of new electronic word of mouth (eWOM) which is called as a viral

marketing.

Viral marketing is a techniques where individual is encouraged to spread

marketing message over internet. It is called as a viral marketing because it

spreads like a virus. Message about the product and its brands or services is

send to a potential buyer over internet. This potential buyer sends this

information to another potential buyer in a way a large network is created swiftly.

What does a virus have to do with marketing? Viral marketing describes any

strategy that encourages individuals to pass on a marketing message to

others, creating the potential for exponential growth in the message's

exposure and influence. Like viruses, such strategies take advantage of rapid

multiplication to explode the message to thousands, to millions (Dr. Ralph F.

Wilson, 2005).

Viral Marketing comprises of diverse platforms and can spread in many forms,

including e-mails, blogs, chat rooms, adver-games, user forums, company

websites, social networks, and viral videos. Through all these platform marketing

messages are send to the user either by the marketer or by the user or potential

buyer to another user. The eWOM is a electronic communication, specifically

using the e-mail medium, emphasizes the direct person-to-person transmission

of the messages.

22 | P a g e

This study mainly comprises of understanding how viral marketing platform such

as email works. It also tries to understand user perception and drives of viral

marketing. As there are various platforms for viral marketing available.

Transmission of marketing messages has been done through email. It has been

decided to focus on this platform. There is a need to investigate the various

factors influencing user to receive and forward message. Further it is noted that

no research on viral marketing in the Indian context is known to exist.

This study attempts to explore various attributes which forms the user‘s

perception about viral marketing and also tries to find out drivers of viral

marketing. Marketing messages are transmitted to user with the help of email.

For the successful of viral marketing strategy user needs to accept message as

well as it is equally important that these messages needs to be forward.

Therefore attempt is made to find of various attributes which influence user to

receive marketing messages and also there is need to find of the attributes which

influence user to forward theses messages.

Motivation stems for viral marketing study primarily from the opinion that viral

marketing is a powerful marketing tool with untapped potential. Viral Marketing

Communication (VMC) can bring about benefits to marketers with its advantages

such as low cost, high reach, high credibility, accountability, fast speed, ease of

usage and ability to reach a global audience. With the increased usage of

broadband and internet services like YouTube, Hotmail and Facebook, there will

23 | P a g e

be an increasing trend for Viral Marketing tactic to be adopted by companies as

part of their promotional mix in the future, thus fuelling interest in this topic. This

study will help marketer to understand the user perception and which factors

needs to address while sending messages so that these messages will get

propagated or forwarded.

Viral marketing and viral advertising refer to marketing techniques that use pre-

existing social networks to increases brand and product awareness. Viral

marketing is analogous to the spread of pathological and computer viruses. It can

be word-of-mouth delivered or enhanced by the network effects of the internet.

Viral marketing is a marketing phenomenon that encourages people to pass

along a marketing message voluntarily (Steve Anderson, 2008).

Purpose of this study is to understand viral marketing concept and how it is

successfully implemented by the marketers‘ right from its inception. Finding out

the drivers of viral marketing and factors which influence user to receive and

forward messages. Success of the viral marketing is largely dependent on the

acceptance and adoption of the viral marketing strategy by the user which is

adopted by the marketer.

24 | P a g e

Chapter-2

Literature Review

What is Viral Marketing1?

Viral marketing is a mix of marketing techniques that use pre-existing social

networks to increases brand awareness or to achieve other marketing objectives

of a business. Viral marketing helps to increase product sales with help of

various processes and modules that resemble viruses.

Video clips, interactive Flash games, advergames, ebooks, brandable software,

images, or even text messages are some of the forms of viral marketing services

to add to the promotion of a website/business. Sometimes, word-of-mouth

communication and network effects of the Internet also work as a tool of viral

marketing.

Viral Marketing is any marketing technique that encourages web site, Internet,

email or wireless users to pass on a message to other sites or users, creating a

potentially exponential growth in the message's visibility and effect. Viral

Marketing is extremely attractive to businesses because it can deliver astounding

results in a relatively short period of time. Advertising and marketing budgets no

1 Viral Internet Marketing, Available at http://www.viralbuzz.com/viral_marketing.html, Accessed

01st January, 2011.

25 | P a g e

longer stretch as far as they used to, and the iperceived savings by using viral

web promotion techniques are too attractive to ignore.

Viral Marketing methods include email marketing, "refer-a-friend", "pass-it-on",

"send-an-article", ecards, ebook distribution, video email, and many more.

Internet experts at ViralBuzz can implement web promotion strategy to virtually

any web site or promotional campaign.

A well known example of of successful viral email marketing is Hotmail, a

company, now owned by Microsoft that promotes its email service and its own

advertisers' messages at the end of every Hotmail user's e-mail notes.

Justin Kirby and Paul Mardsen, September 9, 2005, in his book titled ―Connected

Marketing, the viral, buzz and Word of mouth revolution.‖ explains that people no

longer use the internet only for practical purposes such as research and

shopping. New technologies and and the increase of the bandwidth have made

that people want more and more to be entertained on the web. Besides of that,

people have learned to tune out a lot of marketing communications. These two

points have participated to a big part of the explosion of viral marketing. This type

of marketing focuses on personal experience of the brand and taps into the

power of consumers and their connections to other consumers. It can both

improve brand advocacy and increase brand awareness, but also help generate

26 | P a g e

sales. Contrary to what wrong ideas let think : it is possible to track such

campaigns and viral marketing is also needed by innovative products.

Why go viral?

As the name suggests, Viral marketing works on the principal of a virus and

speedily spreads it influence just within a short period of time. Viral marketing

helps a website to swiftly reach throughout the world with multiplied networking

chain. And once a business maintains the speed and reaches to the target with a

brisk pace, it surely gains lots of advantages over other business.

One example of Viral Marketing is encouraging current and potential customers

to tell others about the company's products and services, and in turn encouraging

those others to tell even more others.

Types of Viral Marketing2

There are different ways to apply viral marketing strategy.

Pass-Along a message

Pass-along is a message which encourages the user to forward it to others. The

crudest form of this is chain letters where a message at the bottom of the e-mail

prompts the reader to forward it to his contacts by highlighting suitable

rewards/punishments for acting upon/inaction.

Incentivized Viral Marketing

2. What is Viral Marketing, Available at: http://www.squidoo.com/what-is-viral-marketing.

Accessed 1st December, 2010.

27 | P a g e

In this a reward or incentive is offered for either passing a message along or

providing someone else's address. This can dramatically increase referrals.

However, this can be effective only when the offer requires another person to

take action.

Undercover Marketing

A viral message is presented as a cool or unusual page, activity, or piece of

news, without obvious incitement to pass along. In this form of viral marketing, it

is not immediately apparent that anything is being marketed.

Edgy Gossip/Buzz Marketing

In which Buzz about the product or services is created. This makes use of

advertisements or messages that create controversy by challenging the readers‘

taste or appropriateness of usage. Discussion of the resulting controversy can

generate enormous buzz and consequent WOM publicity. For instance prior to

the release of a blockbuster, some Hollywood movie stars get married, get

divorced, or get arrested, or become involved in some controversy that directs

conversational attention towards them and the movie.

PepsiCo former CMO and eBay COO Brian Swette says, ―‖Buzzmarketing works.

It‘s not just a nice-to-have, it‘s a must-have!‖ Steve Forbes, Forbes Magazine‘s

Editor-in-Chief calls it, ―…a business book that‘s both entertaining and useful for

big brands and start-ups alike.‖

28 | P a g e

User-Managed Database

Here users themselves create and manage their list of contacts using a database

provided by an online service provider by sending add-a-friend request. By

inviting other members to their community, users create a viral, self-propagating

chain of contacts that naturally grows and encourages others to sign up as well.

A major benefit of viral marketing is that it is very powerful advertising tool and

reaches a large number of people in a short amount of time.

Viral Marketing in India

Like everywhere, people in India pass on and share interesting and entertaining

content online. Viral marketing is popular in India for its ease of execution of

marketing campaign and relative low-cost. It ensures good targeting, and the

high and rapid response rate. Thus, for its speed and effective penetration ability,

viral marketing leaves you with no choice but to go for it. Viral Marketing helps a

business to get a large number of interested people at a low cost. Hotmail's offer

was a free email account. It was special for receiver to get this message. If you

do not add value to receiver, your message will be not speeded from first entry

itself. In India, Monster India or Naukri (a job site) made their presence through

viral marketing only. Latter on they used Television or print advertising3.

3 Viral Marketing : Recommend it, available at http://www.buzzle.com/editorials/3-19-2004-51871.asp

Accessed 20th March, 2011.

29 | P a g e

How to Merge Viral Marketing with Email4?

Viral marketing is a promotion phenomenon that encourages people to pass

along a marketing message voluntarily. The dynamic viral message may arrive in

the form of amusing video clips, interactive Flash games, adver-games, images

or even text messages. The successful viral marketer aims to identify individuals

with high Social Networking potential and create viral messages that appeal to

this audience and have a positive probability of being passed along to friends.

―Viral marketing is one of the most powerful forms of online marketing today, due

to the potential it has to spread your brand‘s message like wild fire across the

Internet,‖ says Grant Fleming, COO of Fontera, leading South African mobile

software development company, that has designed a number of Facebook

applications. ―Viral marketing is an influential tool and if well implemented it can

propel a brand from insignificance to global fame.‖

Some of the examples of Viral Marketing in last decades5:

A look back at the history of online marketing efforts must include Hotmail. In

1996, Hotmail was a particularly unique email service in that it was free, could be

accessed anywhere, and would allow the user to have multiple accounts. One of

the interesting things Hotmail did was it would attach the message "Get your free

4How to merge Viral marketing with email, available at

http://www.ehow.com/how_2132947_merge-viral-marketing-email.html, Accessed 2nd

January, 2011.

5 47 Outrageous Viral Marketing Examples over the Last Decade, available at

http://www.ignitesocialmedia.com/viral-marketing-examples/ , Accessed 2nd

January, 2011

30 | P a g e

email at Hotmail" at the bottom of every email sent by a Hotmail user. Once the

receiving user clicked on the word "Hotmail" they were taken to Hotmail's

homepage where the free email service was further explained. The plan, original

at the time, worked. By 1998, Hotmail had accumulated 12 million subscribers.

Hotmail eventually sold to Microsoft for a cool $400 million.

1999

The Blair Witch Project was released on July 14, 1999. The film cost a about

$350,000 to produce and went on to gross nearly $250 million worldwide, giving

it the highest profit-to-cost ratio of any film in history. The incredible success of

the film could be attributed to its unique website that effectively blurred the lines

between fact and fiction. The website, that still exists today, spoke convincingly

of the mythology behind the Blair Witch, contained a realistic photo of the three

filmmakers/stars with a caption that the photo was taken "less than a week

before their disappearance," along with a sideshow of other rather generic, yet

real photos that made many believe that this site was actually authentic. The

gimmick worked!

2000

John West Salmon ―Bear Fight‖ advertisement, this was one of a series of ads by

John West Salmon that appeared on the Internet in late 2000. Since their

groundbreaking debut, the "Bear Fight" videos have gone on to attract an

astonishing 300 million Internet views according to the BBC, and it is not difficult

to see why. It's hand-held, low budget, realistic feel would become synonymous

with the term "viral" for years to come.

31 | P a g e

2001

BMW launched a series of eight high-cost, high-production short films released

on BMW's website. The films were produced and directed by such acclaimed

filmmakers as David Fincher and Guy Richie and starred actors such as Don

Cheadle, Clive Owen, and even Madonna. Within the first four months of release,

the films attracted over 11 million views and sent BMW sells up 12% in 2001

alone. The success of the BMW series has prompted many other car

manufacturers such as Nissan to adopt a similar internet-based strategy.

2002

Microsoft Xbox - Champagne

Microsoft promoted the XBox launch in Europe with a viral campaign,

―Champagne‖, in the lead up to the console‘s release in March 2002. The

campaign introduced potential gamers to the philosophy of XBox gaming, ―Life is

short. Play More‖.

XBox's shocking and provocative 2002 ad raised eyebrows across Europe when

it appeared on the web. The ad has been described as "graphic," "disturbing,"

and even "morbid" by some; "interesting" and "innovative" by others. Whatever

the proper description, Microsoft continues to generate buzz around the world for

this pithy advertisement.

32 | P a g e

20036

Deloitte Consulting, a US$ 3.5 billion global consulting firm with more than

14,000 employees. The company has long struggled for name awareness in the

consulting field. In June 2003, Deloitte set out to change all that by launching a

viral marketing campaign around a free software program called Bullfighter.

Bullfighter searches electronic documents for business jargon and suggests

plainspoken alternatives. Deloitte devised a public relations campaign around

Bullfighter based on a perceived need for the business community to

communicate more clearly.

2004

The Subservient Chicken for Burger King was introduced in 2004. The branded

micro site consisted of an interactive web cam that filmed a person dressed in a

chicken costume who would perform certain acts the user typed into the site.

Users flocked to the site in droves, accumulating more than 15 million visits in the

first 5 days. Today, the site has attracted over 450 million hits.

2005

The Australian beer company, Carlton Draught, wanted to produce an ad that

would grab the attention of the world. The result: "The Big Ad." The ad went

viral, forcing the beer company to scale back its broadcast television ambitions

due to risk of over-exposure. Within 24 hours of its release, the ad attracted more

than 162,000 views, and after two weeks it had garnered over one million views.

6 Viral Marketing: it‘s infectious!, available at

http://www.brandchannel.com/features_effect.asp?pf_id=173, accessed 25th January, 2010

33 | P a g e

2006

Nike has become a master of viral marketing over the years, but 2006 ad staring

Brazilian soccer superstar Ronaldinho has emerged as one of the greatest viral

ads of all time. The "is it real, or is it doctored" quality of the ad caused many

viewers to send the clip to friends to get a second opinion on whether the feat

was real or computer generated. As of today, the amateur-looking clip has

generated more than 30 million views on YouTube and positioned itself as one of

the most successful and acclaimed viral ads of all time.

2007

The Diet Coke and Mentos Experiment was a viral sensation produced

completely independent of either the Coke or Mentos brands. Though "the

exploding Cokes" had already been an online phenomenon well before 2007, the

release of the "Diet Coke and Mentos Experiment" helped to generate more than

10 million YouTube views and raise the profile of the experiment beyond just a

passing fad and into the annals of Internet lore. Both Coke and Mentos gained a

considerable amount of brand awareness from the clip that has emerged as one

of the most iconic viral sensations of the past decade.

2008

Honda produced the first ever live commercial on British television. In more than

three-minute commercial showed 19 skydivers jumping out of two planes more

than 14,000 feet above the ground. The skydivers linked up to spell H-O-N-D-A

in the sky. The British ad was a traditional television ad in Europe, but became a

YouTube hit in the United States, generating over 400,000 views. Though the

34 | P a g e

effectiveness of this commercial has been debated, it must be noted that the

Accord became the bestselling vehicle in America in April 2009 for the first time

in its history.

Vodafone's ad campaigns featuring the Zoozoo creatures have become an

international sensation. Developed in India, the playful commercials have made

their way to the internet and become viral hits. The campaign, "Make the Most of

Now," has become truly global as a result. The videos have collected millions of

online views worldwide and firmly positioned the Zoozoo creatures as loveable

global icons. This is one of the successful advertising campaigns from Vodafone.

2009

Samsung's clip of LED-illuminated sheep running around the Welsh countryside

continues to generate interest throughout the Internet. The clip has attracted

nearly 8.5 million views on YouTube and continues to be the topic of discussion

on blogs across the web. The "is it real or not" quality proves once again to be

YouTube gold.

20107

Toy Story 3 — which grossed has grossed more than $1 billion worldwide and is

the highest grossing film of 2010 — had a unique viral video campaign this year

that was composed of fake vintage ‘80s commercials for one of the toys

introduced in the movie,

7 The Best Viral Marketing Campaigns of 2010, available at http://www.flowtown.com/blog/the-best-

viral-marketing-campaigns-of-2010, Accessed 25th

January, 2011.

35 | P a g e

When the environmentalist organization Greenpeace wanted Nestle to stop using

palm oil, a kind of vegetable oil used in processed foods, because they claimed it

was fueling deforestation and removing the orangutan from its natural home, they

went viral. Activists teamed up with producers to create a video parodying

Nestle‘s ―Need a Break?‖ catchphrase by showing a stressed office worker

chewing off the finger of an orangutan instead of a Kit Kat. The video is fairly

graphic (it shows blood spewing from the finger) but certainly gets the message

across — it won ―Best Viral Video 2010″ at the Berlin International Short Film

Festival.

One of the most successful viral marketing strategy is viral marketing is through

email. This study is mainly focused on finding drivers of viral marketing and

investigating various attributes which motivates users to receive viral messages

via email and influence them to forward viral messages to another user.

Drivers of Viral Marketing:

This study makes three principal contributions; first, the study generates a

grounded understanding about the drivers of viral marketing. A sequence of

drivers of viral marketing is also found out in previous research on viral marketing

but this sequence is modified with an additional factor. Second, a theoretical

framework is developed that illustrates the factors which influenced user to

receive viral messages.

36 | P a g e

Thus, the proposed framework helps researchers and marketers better

understand the important attributes which influences user to receive viral

messages. The success of viral marketing is depends upon if the viral messages

are propagated. Therefore, efforts have been taken into this study to understand

the factors which influenced user to forward messages. Third, this study

integrates a specific grounded theory with the more formal insights available from

information systems research and marketing literature, developing a more

general framework that will allow researchers and practitioners to explain,

anticipate, and evaluate viral marketing strategies.

Traditional word-of-mouth marketing technique has proven to play a significant

role in consumer buying behavior or decision. Past research also shows that

WOM is more effective than traditional marketing tools of persona; selling and

promotion through convention advertising media like print, Television etc.

Electronic word-of-mouth communication refers to any positive or negative

statement made by potential, actual, or former customers about a product or

company, which is made available to a multitude of people and institutions via

the Internet. It can also be considered as the extension of traditional

interpersonal communication into the new generation of cyberspace.

With the growth and evolution of the internet, electronic peer-to-peer referrals

have become an important phenomenon, and marketers have tried to harvest

37 | P a g e

their potential through electronic referral marketing (ERM) campaigns. At the

same time, spam, email-based viruses and the like have cluttered electronic

communications, making ERM campaigns problematic and challenging to deploy.

The key driver in ERM is the effectiveness of unsolicited, electronic referrals to

create awareness, trigger interest and generate sales or adoption (Arnaud De

Bruy, Gary L. Lilien, 2004).

For the success of viral marketing internet user should be aware of this

marketing technique. The decision to purchase a good or service or to adopt an

innovation, for instance, can be viewed as the end result of electronic word of

mouse, a viral marketing strategy. It has been observed that marketing role

has been changed and Information Technology is promoting that change.

Earlier marketing was one-to-one basis but because of Information

Technology whole scenario has changed.

Key drivers of Viral Marketing is awareness(Arnaud De Bruyn, Gary L. Lilien,

2008), interest((Arnaud De Bruyn, Gary L. Lilien, 2008), Access to use this

marketing techniques and experience which decides their final decision((Arnaud

De Bruyn, Gary L. Lilien, 2008). The consumer has now taken an observable

action, a purchase of a good or service or the sustained adoption of an

innovation. The key driver in viral marketing is the effectiveness of unsolicited,

electronic referrals to create awareness, trigger interest, and generate sales or

product adoption.

38 | P a g e

Sequence of actions and intermediary decisions, a diagrammatic representation

of a sequence which is the driver of viral marketing includes the following steps.

Figure 1 – Sequence of Drivers of Viral Marketing

Access, to get benefit from viral marketing technique which is adopted by the

company, access to use internet plays a very vital role. User should get access

to internet from its home or workplace to receive and forward messages or to

view company website which is uploaded on the company portal for promotional

purpose. Access creates the awareness.

Awareness, The consumer knows the alternative exists, but may have neither

interest in it nor enough information to understand its possible benefits (Rebecca

J. Larson, January 2001). Once the user gets the awareness about the

viral marketing technique it helps them to create interest into it.

Interest, The consumer is aware, develops some interest and hence decides to

learn more about the product (Arnaud De Bruy, Gary L. Lilien, 2004).

Access

Awareness

Interest

Experience

39 | P a g e

The success of viral marketing campaigns depends on how well they are

promoted within the target users, and also the increased usage of internet and

mobile phones by the Indian consumers provides a very good platform for the

retail chains to promote their business by using viral marketing as an effective

and cost efficient tool for marketing (Shailendra Dasari* and B Anandakrishna,

2010).

Once the interest is build user depends upon the internet to get information about

the product which creates their experiences about the viral marketing technique.

Experience: The consumer‘s demands for a personal, interactive and relational

experience have arisen from the opportunity to demand and experience this type

of interaction made possible through improved technology (Rebecca J. Larson,

January 2001).

All above process i.e access, awareness, interest and experience is hierarchical

in the sense that each step is conditional on the positive or favorable outcome of

the previous one. The original sequence in which access was not mentioned

proposed by Rogers (1962) included an evaluation stage and a trial stage that

may not be relevant in all contexts. Other variations of this sequence exist

(Hauser & Urban,1977; Rogers, 1995). For instance, if a consumer becomes

aware through exposure to a very persuasive source (e.g., a very effective ad or

an enthusiastic peer), awareness and interest may occur concurrently (Van den

40 | P a g e

Bulte & Lilien, 2003). Alternatively, interest and evaluation may be combined.

Nevertheless, most models rely on the above three-stage decision-making

framework in one form or another (Arnaud De Bruy, Gary L. Lilien, 2004).

User experienced innovation in viral marketing strategy. Scholarly research

concerning social and communication networks, opinion leadership, source

credibility, uses and gratifications, and diffusion of innovations can provide

insights into viral marketing processes and participants' motivations. Research in

these and other areas has long demonstrated that consumers influence other

consumers (Joseph E. Phelps et. al., 2004)

In a book ―Marketing Moves: A New Approach to Profits, Growth, and Renewal

by Philip Kotler et. al., 2002 mentioned that ―Markets are changing faster than

our marketing. The classic marketing model needs to be future-fitted. Marketing

must be deconstructed, redefined and stretched. Marketing is not going to work,

if its only charge is to pump up sales of existing goods.‖

The rapid growth of internet and other communication channels has opened up a

new arena for WOM communication. Viral marketing is based on WOM and can

be understood as a communication and distribution concept. The term ‗viral‘

describes a type of marketing that infects customers with an advertising message

which passes from one customer to the next ‗like a rampant flu virus‘ (Wolfgang

Palka et. al., 2009).

41 | P a g e

Models on Viral Marketing – from literature review.

The number of people online around the world will grow more than 45 percent to

2.2 billion users over the next five years, according to a new report by Forrester

Research, Inc titled "Global Online Population Forecast, 2008 To 2013".India will

be the third largest Internet user base by 2013 - with China and the US taking the

first two spots, respectively. Forrester estimates number of Internet users in India

currently to be 52 million and expects India to have an average growth rate of 10-

20 % respectively.

According to Forrester While per capita online spending is likely to remain

highest in North America, Western Europe, and the developed markets of Asia

throughout the next five years, the shifting online population and growing

spending power among Asian consumers means that Asian markets will

represent a far greater percentage of the total in 2013 than they do today,‖ said

Forrester Research Senior Analyst Zia Daniell Wigder. ―Multinational

organizations must understand the dynamics of the shifting global online

population to ensure that they are positioned to take advantage of emerging

international opportunities.‖

The first purpose of this study was to study drivers of viral marketing. The second

purpose of this research is to identify factors that impact the willingness to

receive and forward viral messages. Form the extensive literature review two

models on viral marketing have been identified. For the research study these two

42 | P a g e

models have been studied extensively and attributes are identified, having most

significant impact on user to receive and forward messages.

Wolfgang Palka et. al., 2009 developed a basic model of mobile viral marketing

processes. In this model three main conditions which influenced user to receive,

use, and forward messages are identified. These conditions are 1)receive

(intention to open), 2)use (intention to use), and 3)forward (intention to forward).

These conditions are influenced by other factors which are as follows:

The first stage concerns the recipient‘s response to the receipt of a mobile vector

and the decision to open or delete. The receipt of the mobile vector is seen as

the causal condition of the model. As the category intention to open was the

primary issue in the first stage, it was chosen as the core category. The intention

has an impact on the actual behavior (receipt), that is, the action/interaction

strategy. Three types of intervening conditions lead to the intention to open:

security-related conditions dealing with the risk perception of the recipient, social

conditions dealing with his or her relationship to the communicator of the content,

and resource-based conditions dealing with the recipient‘s perceived control of

the receipt. Action and interaction (taken in response to a phenomenon) have

certain outcomes or consequences.

If opened, the second stage concerns the circumstances under what recipients

rely on recommendations and use the mobile viral content. The core category

43 | P a g e

intention to use impacts the actual behavior (action/interaction strategy). Three

types of intervening conditions lead to the phenomenon: social conditions

describe interpersonal influences, attitudinal conditions arise from the personal

assessment of the content, resource-based conditions refer to the recipients‘

perceived behavioral control of its usage. Depending on the action/interaction

strategy one consequence can be the intention to forward the mobile viral

content,

The third stage deals with the decision whether to forward the content to others.

Similar to the previous stages, the core category, that is, intention to forward is

related to the actual behavior (action/interaction strategy). The intention is

influenced by social, attitudinal, resourced based, consumption based, and

personal conditions (intervening conditions). In case of forwarding, the

consequence is the receipt of the mobile vector by a further recipient. Otherwise

the mobile viral process ends.

1) Intention to Open is influenced by following factors: a. Security-related conditions

i. Trust ii. Perceived risk

b. Social conditions i. Sender Recognition ii. Perceptual Affinity

c. Resource based conditions i. Self Efficacy ii. Perceived Cost

2) Intention to use is influenced by following factors: a. Attitudinal conditions

i. Perceived usefulness ii. Perceived ease of use iii. Perceived enjoyment

b. Social conditions

44 | P a g e

i. Perceptual affinity ii. Expertise of communicator iii. Subjective norm iv. expressiveness

c. Resource based conditions i. Self efficacy ii. Perceived cost

3) Intention to forward is influenced by following factors: a. Attitudinal conditions

i. Perceived usefulness of communicator ii. Perceived user friendliness iii. Perceived enjoyment iv. Attitude towards forwarding

b. Social conditions i. Adherence of recipient‘s interests ii. Tie strength iii. Subjective norm iv. expressiveness

c. Resource based conditions i. Perceived cost

d. Personal Conditions i. Altruism ii. Market mavenism

e. Consumption-based conditions i. Customer satisfaction ii. Involvement of communicator

45 | P a g e

Figure 2: Basic model of a mobile viral marketing process.

Source: Journal of Information Technology (2009) 24, 172–185

46 | P a g e

The second model was based on the information adoption model which is

developed by Sussman and Siegal, 2003. This research model was built upon

the information adoption model (Sussman and Siegal, 2003). It examines

individual relationships between argument quality, source credibility, information

usefulness, and information adoption (Christy M.K. Cheung et. al., 2008).

Figure 3- Information Adoption Model

Source: Internet Research Vol. 18 No. 3, 2008 pp. 229-247

Further analysis of the Information Adoption Model regarding the components of

argument quality and source credibility done and a Research Model is

developed. This research model explores the motivations behind adoption of

online opinions. The research model is built on the theoretical model of

information adoption by Sussman and Siegal (2003). In this model there is a

resulting relationship between Information adoption, Information usefulness,

Relevance, Comprehensiveness, Accuracy, Timeliness, Source expertise, and

Source trustworthiness. Information adoption is a process in which people

purposefully engage in using information. Information adoption behavior is one of

47 | P a g e

the principal activities that users seek to conduct in virtual communities (Christy

M.K. Cheung et. al., 2008).

Figure 4 - Research Model for Social Network Site influence on Viral Marketing: Source: Internet Research Vol. 18 No. 3, 2008 pp. 229-247 From the above two models and extensive literature review it has been observed

that trust and relevance is the most significant factor which influenced user to

receive messages. Source credibility and Source Trustworthiness are combined

together from Research model into Trust. Content of the message,

comprehensiveness and sender recognition combined together in relevance of

the message. There are some factors which are described in Basic model of

48 | P a g e

mobile viral marketing such as perceived usefulness, perceived ease of use,

perceived ease of use, perceived enjoyment are combined together in perceived

benefits as these all factors gives perceived benefits like usefulness, enjoyment,

reward/incentive to user.

Following diagram illustrates the factors which influences user to receive viral

messages.

Indicate prerequisite

Indicate relationship

Figure 5: Diagrammatic representation of factors influencing user to

receive messages

Drivers of viral marketing as identified in this study are access to use internet.

Awareness of viral marketing strategy adopted by the marketer, Interest in

getting online information about the product and experience of viral marketing in

Intention to

Receive Viral

Messages

Trust (Source) Relevance

Perceived Benefits

Drivers of Viral

Marketing

49 | P a g e

which user receive and forward (send) messages within their social network as

well as getting information about the products directly from the marketer.

Interest and experience together built up user‘s understanding about viral

marketing. Once they are into social network over the internet they start receiving

viral messages. The first stage concerns the recipient‘s response to the receipt of

a viral messages and the decision to open or delete. As the category intention to

receive (open) was the primary issue in the first stage, it was chosen as the core

category. The intention has an impact on the actual behavior (receipt), that is, the

action/interaction strategy. Three types of intervening conditions lead to the

intention to open: Trust related conditions dealing with the risk perception of the

recipient, Relevance related conditions dealing with his or her relevance to the

content, and perceived benefit related conditions dealing with the user‘s

measurable interest in receiving viral messages (Wolfgang Palka et. al., 2009).

A consumer's expectations and subsequent satisfaction level are often shaped

by marketing communications. Marketing communications, such as advertising,

serve as a source of information and motivation for the consumer before the

purchase is made, and continue to inform prospective, current, and past

customers even while a product is in use. As such, marketing communications

present the focal product or service in the best light (David Aron, 2006). In viral

marketing strategy Marketers has to use effective communication strategy to by

way of sending effective promotional messages to target potential customer.

50 | P a g e

Companies have made use of websites and e-mail to try to reach customers.

However, over the years, people have become more critical towards e-mail

generated by companies, especially when companies send these e-mails without

asking permission of the (potential) customer. Often these e-mails are deleted

without opening them first. This had lead marketers to find new strategies.

In viral marketing strategy marketer communicate with customer with the help of

email messages. Success of viral marketing is not only in receiving viral

messages these messages needs to be forwarded by the user to another

potential user. This research is investigating factors which influence user to

receive and forward viral messages.

Factors influence user to receive viral messages.

Trusted Source

The importance of and source credibility has also been highlighted and strongly

validated in prior research on receiving messages. From the past literature it has

been observed that there is the positive relationship between trust and

acceptance of new technologies (e.g., Gefen et al., 2003). In general, the more

the trust there is, the lower the perceived risk is, the more willing people are to

adopt new technologies. Trust can reduce complexity especially when

innovations are being considered (Gefen, 2002). Trust is the most important and

most influential factor for user to receive viral messages.

51 | P a g e

User normally receives viral messages if it is from a trusted source. Word of

mouth (WOM) marketing is such a successful marketing strategy because it

breeds ―familiarity, personal connection, care and trust‖ between the consumer

and the translator of the information (Datta, et al., 2005). Consumers often hit the

delete key when they know the message is from a marketer. They are much

more reluctant to delete a message from a person they know this fact is a key

component in understanding the potential power of viral marketing. A deeper

analysis of the category revealed that it is also interwoven with perceived risk. If

the content comes from a ‗trusted source‘ the perceived risk is low or not

existent. For instance, participants did not express reservations regarding data

security and privacy issues when they would receive the content from a friend.

Also, they would open viral messages received from well-established brands

(Wolfgang Palka et. al., 2009).

In the online environment, people have almost unlimited freedom to publish and

express their feelings towards certain products or services without disclosing

his/her real identity. It is therefore left up to users to determine the expertise and

trustworthiness of the contributors in order to either adopt or reject the

information presented. If the consumer thinks that the comments are posted by

high-credibility (high degree of expertise and trustworthiness) individuals, he/she

will then have a higher perception of the usefulness of the comments (Christy

M.K. Cheung, 2008).

52 | P a g e

Emails are here to stay, and there is no doubt that peer-to-peer, email-based

communications will continue to play an informational and influential role on

recipients' behavior. The proliferation of spam (i.e., unsolicited bulk emails) and

email-based electronic viruses, however, have made most unsolicited emails

suspicious. Consumers experience a high level of noise in their day-to-day

electronic communications (Arnaud De Bruyn, 2004). Therefore, source is very

influencing factor for user to receive viral messages.

Protecting users‘ privacy is another measure concern for user‘s point of view.

Gold Robert et. al., 2002 quoted that Today, being secure means protecting

privacy. For the most part, firms argue that keeping customer data secure is the

basis for establishing competitive advantage. Marketer‘s need to constantly

demonstrate commitment to privacy so that customers don't demand their identity

be stripped from this information. Marketer can do this by developing and

distributing privacy-focused messages throughout company channels and

materials, and by working with qualified third parties that can endorse their

privacy commitment. Once users get confidence that their personal information

will be protected by the marketer they are always receptive about receiving viral

messages.

Trust can also be related to tie strength. In real life, people maintain a large

number of relationships with varying tie strength: close friends, family, work

53 | P a g e

colleagues, casual acquaintances, and so on. User normally accept information if

it is from a strong tie.

Relevance

Relevance of messages is important as most Internet users are conscious of

their time. Madu and Madu (2002) urged that Internet users rarely read web

pages in detail but rather scan the pages to find the information they need. Users

want to find the information that they want quickly and with little effort (Nah and

Davis, 2002). It is therefore important to have only the most relevant information

present in the online community. Dunk (2004) also suggested that relevance is

an important element in decision making. Therefore, the more relevant the

messages are, the higher the perceived information usefulness of the message

(Christy M.K. Cheung, 2008). Attitude towards receive and accept messages is

also important. Some users like to receive messages even though they are not

from their acquaintances but they have the attitude to receive information

(Wolfgang Palka, 2009). User is interested in getting information about the

product which he may not be required at that very moment.

When consumers engage in goal-directed decision making and are exposed to

information that may not be diagnostic or relevant in nature and content, they

may elicit the negative emotion of irritation because of their wasted time and the

utility of the cognitive effort in processing the information (Sweta Chaturvedi,

2009). This irritation may lead them into not accepting viral messages.

54 | P a g e

User accepts messages from the acquaintances for giving sender recognition

(Wolfgang Palka, 2009). E-mail messages are described as inherently interactive

(Andrew Paul Williams et. al., 2004). Messages with content which users‘ are

looking are likely to get received by user.

A measure of viral advertising effectiveness that is of particular concern to

advertisers, and ultimately the raison d'être of viral campaigns, is the extent to

which an advertisement is passed from one person to another. Researchers

have begun to investigate why ads are passed on. Phelps et al. (2004) found that

messages that spark emotion are more likely to be forwarded. A qualitative study

by Dobele et al. (2007) provides additional support of this view, suggesting that

for a viral advertisement to be passed on, it must elicit both the emotion of

surprise and at least one other emotional response such as joy. They argue that

although such combinations of emotions may be prerequisite, they may be

insufficient to motivate an individual to pass on a viral advertisement. A message

must also capture a viewer's attention "in a unique or unforgettable way" (Mark R

Brown et al, 2010). Appearance of viral advertisement posted on website or

social network plays important role in capturing user‘s attention towards them.

Yih Hwai Lee, 1999 quoted in his research paper that Relevancy refers to the

degree to which an item or a piece of information contributes to the identification

of the primary message communicated. Across two studies that examined

immediate response, He found that information expectancy and relevancy

55 | P a g e

interact to produce different levels of attitude favorability. Although ads with

unexpected-relevant information elicited more favorable attitudes than did ads

with expected-relevant information, ads with unexpected-irrelevant information

yielded less favorable attitudes than did ads with expected-relevant information.

User likes to reciprocate on the messages which are relevant as well as

appealing and interactive to them. For some users looks is not that important

than relevance. Similarity of interests and preferences with those of the

communicators is also important to decision to open messages. Therefore

intention to open is positively associated with perceptual affinity (Wolfgang Palka,

2009).

Personalization is one important way to make the Web work harder. In fact, it's

one of the areas where businesses will see the best return on their online

investments. In Internet terms, personalization means the ability to harness

customer knowledge in order to dynamically create, package and deliver

individualized marketing messages. It's also the ability to listen to customers and

learn from them, delivering content and services tailored to their responses and

actions.

Customer data is collected both online and off, for example, when a customer

clicks on a Web banner ad for the first time, reaches a certain number of

frequent-flier miles or purchases a particular item on their credit card. These are

56 | P a g e

events that, until now, have not been lever-aged to deliver more effective

customer communications (Young Troy, 2001).

Perceived Benefits

Messages which give benefits to the user like enjoyment (Wolfgang Palka, 2009),

information, usefulness (Wolfgang Palka, 2009), ease of use ((Wolfgang Palka,

2009), incentive/reward, certain conditions (Daniels, 2002) to get relevant

information are likely to be received by user. Comic strips and video clips grab

the attention of people (Angela Dobele et. al., 2007).Viral marketing relies on

consumers to extend the reach of a campaign. Most Internet marketers count on

a send-to-a-friend option to help make that happen (Daniels, 2002). Here user is

getting benefited by clicking on send-to-a-friend option.

The perception of usefulness clearly influences the acceptance (Wolfgang Palka,

2009). People would carry individual perception of whether these opinions could

be useful to help them to make a better buying decision. Therefore, if others think

that a comment within an online community is useful, they will have greater

intention of adopting the comment (Christy M.K. Cheung, 2008). Usefulness of

information increases the chances of converting user into potential buyer or

potential customer.

Especially in the starting phase of viral marketing, marketers tried to promote

forwarding messages by giving financial incentives to people that spread the

message. One example of a company that uses this strategy is Amazon.com.

Especially in the starting phase of viral marketing, marketers tried to promote

57 | P a g e

forwarding messages by giving financial incentives to people that spread the

message (Jurvetson & Draper, 1997). User accept viral messages to get benefits

like incentive, free subscription (Jeffrey Boase et. al., 2001), reward, bonus

points etc.

Email viral marketing is a permission marketing strategy used by American

Airlines sends e-mails to registered customers informing them of discount flights

on a weekly basis. Customers first asked the airline for notification of low fares

and receive them regularly. Permission marketing means the supplier has the

consent of the customer to mail him advertisements. It is a means of increasing

the customer base, promotes customer loyalty and trust. Internet users get in

return for this permission a credit entry or a free service such as e-mail service.

Online surveys as well as observing target groups can help to determine what

incentives motivate customers to spread a message. Without a doubt, offering

something that helps the users in their daily life, as was the case with Hotmail, is

a good start. (Skrob, J.-R., 2005).

Marketers needs to provide a steady stream of relevant news, entertainment,

knowledge, free of charge information means viral contents should be offered for

free (Wolfgang Palka, 2009) to encourage user to receive messages. Once the

message is accepted or recived by user, for the success of Viral marketing

strategy, messages needs to be forwarded or ropogated.

58 | P a g e

Factors influence user to forward viral messages.

This type of marketing encourages individuals to pass on information that they

receive in a hypermedia environment to friends and acquaintances. Before a

user makes a purchase they‘ll seek peer reviews and product recommendations.

After the purchase they will experience the product and form their own opinions

upon which they will cycle back comments for new consumers to review.

Viral marketing has been described as ―the process of getting customers to pass

along a company's marketing message to friends, family, and colleagues‖

(Laudon & Traver, 2001, p. 381). Like a virus, information about the company

and its brand message, goods, or services is spread to potential buyers, who

then pass the information along to other potential buyers such that a huge

network is created rapidly (Dobele, Toleman, & Beverland,2005; Lindgreen &

Vanhamme, 2005).

However, the emergence of the Internet along with broadband capabilities have

opened the door to the emergence of a new WOM advertising platform in which

individuals communicate about a brand, product or service in a non oral manner

but rather through a computer-mediated communication (CMC) environment

(Guy J. Golan, et.al, 2008)

From the literature review and basic model of mobile viral marketing it has been

observed that Tie strength ((Wolfgang Palka, 2009, Arnaud De Bruyn, 2008),

59 | P a g e

Sender benefits, Conditions to forward, Customer Satisfaction, and Altruism

(intention to benefit others) (Wolfgang Palka, 2009) are the most influential

factors for user to forward messages. Following diagrammatic representation

shows the prerequisites and relationship of factors which influence user to

forward viral messages.

Jonker, MJ, in his master thesis ―What drives people to forward viral messages?

Message aspects and motivations‖, (2008), mentioned that ―Many people forward

messages to each other that they on their turn have also received from other

people. Some examples are poems, jokes, chain letters and feel good e-mails.

These messages are called pass-along-emails or viral messages. These

messages are forwarded to other people in one‘s own network, often without

altering the message. This has offered opportunities for marketers to spread

messages by using the network of other people. The use of this strategy is called

viral marketing‖. Influencing factors which motivates user to forward viral

messages are as follows:

60 | P a g e

Indicate Prerequisite

Indicate Relationship

Figure 6: Diagrammatic representation of factors influencing user to

forward messages

In this section, we discuss the categories and their conceptual relationships

within the forwarding model that is depicted in Figure 6. As shown in the above

diagram drivers of viral marketing is pre-requisite for the user to receive viral

messages. Drivers of viral marketing give user understanding about viral

marketing phenomena and built their interest in getting information about product

or brand through their social network via internet. This curiosity about getting

information leads them to receive viral messages.

Intention to Receive

Viral Messages

Drivers of Viral

Marketing

Intention to

Forward Viral

Messages

Tie Strength

Sender

Benefit

Customer

Satisfaction

Altruism

61 | P a g e

User likes to receive message if it is from a trusted source, message is having

relevance and user is getting perceived benefits. These three factors are

influencing user to receive messages. One messages are received it is likely to

be forwarded. To forward viral messages receiving of messages is a pre-

requisite. In this study attempt is made to study what motivates or influence

people to forward viral messages.

Tie Strength

Wolfgang Palka, (2009) stated in his research paper that tie strength is a

category was motivated by respondents‘ descriptions of potential recipients.

Strong ties include family members, friends, or neighbors. When the recipient is

identified as merely an acquaintance, colleague, or neighbor, but primarily an

acquaintance, the tie is classified as weak (Brown and Reingen, 1987). The

category tie strength describes the combination of the amount of time, degree of

emotional intensity, level of intimacy, and degree of reciprocity between two

individuals (Granovetter, 1973). Tie strength has been found to be one of the

most significant factors to explain the influence of WOM communications (Arnaud

De Bruyn, 2004).

For viral marketers it is important to know if a message is sent only to people with

whom one remains a strong tie, or if a message is also forwarded to people with

whom one remains a weak tie. By forwarding messages to weak ties social

62 | P a g e

networks are crossed and this makes it more likely that the message reaches a

large audience.

However, as discussed, people interact on a more regular basis with their strong

ties than with their weak ties. It can be argued that this is also true for e-mail

communication. In this paper, the author argues that to whom the message is

forwarded is closely linked to interpersonal motivations for forwarding a

message.

Antti Vilpponen etl al, 2006 quoted in his research paper that, Frenzen and

Nakamoto (1993) have found that individuals tend to allow valued information

that has the potential to provide limited positive benefits to flow through strong

ties only. As information becomes inexpensive and benefits are permitted to

become common, weak ties are developed. As the information and

communication is basically free in electronic online environments (Dellarocas,

2003).

Janghyuk Lee et. al., 2009 in his research paper on viral marketing mentioned

that the tie strength includes closeness, intimacy, support, and association

(Frenzen and Davis, 1990). Strong ties are characterized by the degree of

intimacy and special meaning through a voluntary investment, which have

frequent interactions in multiple contexts under a sense of mutuality of the

relationship [Walker et al. 1994]. Previous studies on viral marketing and social

63 | P a g e

networks mostly focus on the network structure and how it affects the diffusion

process of information.

For example, Watts and Strogatz (1998) explained how a random link can

substantially improve the connectivity of networks by reducing the average length

among network participants. Using mobile telecommunications data, Onnela et

al. (2007) showed the role of weak ties to connect remote communities as well as

that of strong ties to maintain local communities, and simulation results

demonstrated that Information diffusion can be slowed down in a network which

has unevenly weighted links. Because marketing messages have to be timely

and interesting to consumers, the total volume and speed of the viral generated

within a given period time is a critical indicator to gauge the performance of a

viral marketing campaign (Leskovec et al.2007).

For decades, social science has measured relationships between individuals in

the currency of tie strength. Weak ties (loose acquaintances) can help to

disseminate ideas and/or innovations between different groups, help to find a job

or new information; while strong ties (family, trusted friends) hold together

organizations and social groups and can affect emotional health. However, since

information transmission and human communication are concurrent, the temporal

structure of communication must influence the properties of information

spreading (Giovanna Miritello, 2010)

64 | P a g e

Whether it is a weak tie or strong tie, tie strength is having significant impact on

user in forwarding viral messages. Where shopping motives were hedonic in

nature, tie-strength was more important than expertise, but when utilitarian

motives were dominant, both tie-strength and expertise were important.

Moreover, trust mediated the impact of the peer recommender's characteristics

on their perceived influence, propensity to search for product information, and

willingness to recommend the opinions of the peer (Ronald E. Goldsmith et. al.,

2006).

In a book ―Advances in electronic marketing‖ by Irvine Clarke et. al, (2005)

mentioned that Consumers engage in word-of-mouth when they are highly

satisfied or dissatisfied, when they feel committed to a company, or when a

product or service is novel. Further, word-of-mouth is more likely to be at play if

consumers know little about a product category, or if they are deeply involved in

a purchase decision. Lastly, the influence of personal source of information is

higher than that of other sources because of source expertise, tie strength,

demographic similarity, and perceptual affinity.

Message credibility (Wolfgang Palka, 2009) is having significant influence on

user to forward messages. However, people will not just forward any message.

As Porter (2006) outlines, the content of the message is important to provoke the

receiver to forward the information: ―Viral advertising is unpaid peer to peer

communication of provocative content originating from an identified sponsor

65 | P a g e

using the internet to persuade or influence an audience to pass along the content

to others‖ (L. Porter & Golan, 2006). A consumer who thinks he/she is an expert

in a product domain (higher subjective product knowledge) may have a different

motivation from that of a novice consumer when communicating product

information to others (Dongyoung Sohn et. al., 2005).

The hypermedia context might facilitate the spread of the message, it is still the

sender who decides to forward the message or not. Therefore, the message has

to be compelling. Often it is mentioned that the content has to be provocative. It

has to evoke an emotion with the receiver that motivates the receiver to forward

the message to other people. There has to be a persuasive aspect in the

message. Porter and Golan (2006) argued that those messages that are

forwarded are fun and intriguing, emotional, and provoking curiosity (Dongyoung

Sohn et. al., 2005). People in a strong tie always shared information which they

received.

Message developers should note that messages that spark strong emotion—

humor, fear, sadness, or inspiration—are likely to be forwarded. They should

consider crafting messages consistent with those particularly viral strains that are

most appropriate to their cause (Joseph E. Phelps, 2004).

Often in a tie strength user tend to forward messages if he/she received it from a

trusted source. Strong-tie sources are perceived as more credible and

66 | P a g e

trustworthy than weak-tie sources. In other words, strong-tie sources are more

likely to reduce the potential risks of the e-mails they send; hence, opening an e-

mail from a strong tie should be perceived as less risky than opening an e-mail

from a weak tie (Arnaud De Bruyn, 2008).

Sender’s Benefit.

This factor is not studied in a research widely. Messages through which sender is

benefited is most likely to be forwarded. People are interested in doing things

that gives benefit to them. Without a reward they will likely to ignore forward

request. Marketers who offer reward for spreading the message and services to

friends, family, and coworkers who might benefit are forwarded by the user.

There are some messages or services which require user to provide their friends

contact details to get complete benefit of the message or service e.g.

subscription, additional information etc. These messages are called as

incentivized messages.

Jason Y.C., 2010, quoted in his research paper that despite the increasing

popularity of viral marketing, factors critical to such a new communication

medium remain largely unknown. This paper examines one of the critical factors,

namely Internet users' motivations to pass along online content. Conceptualizing

the act of forwarding online content as a special case of a more general

communication behavior, we identify four potential motivations: (1) the need to be

part of a group, (2) the need to be individualistic, (3) the need to be altruistic, and

67 | P a g e

(4) the need for personal growth. Personal growth can be reward or incentive

from marketer, be in the network as a well informed member, availing free

services from the marketer, earning bonus points, etc.

Wilson (2000) explained that while the idea of giving away rewards results in a

loss of profit for a company in the short-term, however, the spread of a message

generates general interest which in the long-term leads to interest in other

desirable things the company is selling. This type of viral sheds some innuendo

on the marketing maxim, "give away something, sell something" (Wilson, 2000).

One of the most common examples of financially incentivized word of mouth

techniques is an affiliate or online referral programme, such as that run by online

retailer Amazon. The system is simple and straightforward: anyone who runs a

website, or who is active on the Web, can recommend products from the

company via the affiliate programme. The recommendations are made in the

form of referrer-personalized weblinks. The referrer can publish them on his or

her website, send them in an email, or post them in Internet forums. When

someone clicks on such a link, the system logs the referrer, and once the

referred visitor completes a purchase, the referrer will get a reward for the

purchase made8. Stefan Wuyts, Marnik G. Dekimpe, Els Gijsbrechts (2010) in his

8 Martin Oetting, How to manage connected Marketing, ESCP–EAP European School of Management/MemeticMinds.com, available at http://www.download-it.org/free_files/filePages%20from%20Chapter%2015%20%20How%20to%20manage%20connected%20marketing.pdf, Accessed on 21st March, 2011.

68 | P a g e

book ―The connected customer: that changing nature of consumer and business

markets‖ mentioned that Many viral marketing campaigns provide incentives,

such as sweepstakes, to motivate consumers to forward messages to friend.

Sender‘s benefit is also a one of the influencing factor for user to forward viral

messages.

Customer Satisfaction

Messages which give user usefulness(Wolfgang Palka et. al., 2009), enjoyment

(Wolfgang Palka et. al., 2009) and personal satisfaction (Wolfgang Palka et. al.,

2009) are more likely to be forwarded. Perceived usefulness includes the

concepts perceived ease of use. Perceived enjoyment where user finds this as

an enjoyable activity. Therefore, customer satisfaction is defined as a

pleasurable level of consumption-related fulfillment (Oliver, 1997). The nature of

satisfaction is defined as ―happiness, ―good feeling‖, or ―pleasure‖. Therefore,

customer satisfaction is defined as a pleasurable level of consumption-related

fulfillment (Oliver, 1997). Higher the customer satisfaction, which derives from

mobile viral content, the higher is the intention to forward this content (Wolfgang

Palka et. al., 2009).

Furthermore, marketers nowadays develop websites containing videos and

games that attract customer attention and interests. These websites usually

facilitate the viral process by providing tools to easily forward emails to friends,

69 | P a g e

such as ‗Tell a Friend‘ or ‗Share Video‘ buttons. Examples of extrinsic

motivations to forward marketing messages are prizes and other monetary

incentives (Biyalogorsky, Gerstner, and Libai 2001). E-mail is a convenient and

efficient way for people to exchange messages through mutually connected

computer networks as well to be connected with the people.

Some of the customer having a behavior that is information-seeking behaviors, or

more specifically, they rely of word-of-mouth communication to make purchase

decision. They seek to take opinion on their purchase decision as they perceive

high risk in decision making on purchase (Arnaud De Bruyn, 2008).

In 2001, Honda UK appointed Wieden and Kenney, an advertising agency

strongly focused on developing innovative and interesting ways to express an

idea (often referred to as the ―creative‖), to find a way to communicate the

intricacy and excellence of its automotive products. However, it was not enough

to have a ―wow‖ factor in the advertisements; Honda sought a unique way to

transmit the message to potential consumers, influencers, and those who would

aspire to the brand, making consumers the instruments of advocacy (Angela

Dobele, 2005).

Customer satisfaction with respect to product or service is a key factor in

forwarding viral messages. Customer who is satisfied with the product would like

to advocate the product on his own pay positing positive review or forwarding

70 | P a g e

messages about the product within his network. We tried to found out in this

study also that whether customer would like to advocate or forward messages of

the product or service even he/she is not using it but having a good knowledge or

opinion about it. Satisfied experts generate the most WOM, but only when they

had choice. Consumer experts may be motivated to self-enhance by talking

about their positive (satisfying) experiences (Andrea C., 2006). (Rebecca, 2009)

quoted in her paper that Popcorn (2005), who confirms that current trends have

―led consumers to reject artificial, highly scripted, top-down marketing‖ and are

instead seeking a personal, conversational experience Not only do consumers‘

opinions about their care experience shared online influence other people‘s

perceptions about a business, they truly impact purchase intent‖ (Barnes, et al.,

2008).

Altruism

Wolfgang Polka, 2009 quoted in his research paper that respondents indicated

that they would forward mobile viral content to give something to or help others.

This resulted in the category altruism that is referred to as the intention to benefit

others as an expression of internal values, regardless of social or motivational

reinforcement (Feick et al., 1995). Desire to help others may be a motivation for

forwarding mobile viral content. Sundaram et al. (1998) have suggested altruism

as a motivation for positive and negative WOM communication. Analyzing WOM

on Web-based opinion platforms Hennig-Thurau et al. (2004) identified a group

71 | P a g e

they referred to as true altruists, as they appear to be both strongly motivated by

helping other consumers and companies.

Those that pass on the information and have influence among the consumers

can be considered online market mavens or ―viral mavens‖ (Phelps, Lewis,

Mobilio, Perry, & Raman, 2004). The researchers discovered that Internet users‘

who are more altruistic and/or more individualistic, tend to share more online

content than others. According to Dempsey (2010) ―While e-mavens want to help

someone by forwarding online content that may interest them, at the same time,

they want to be recognized as an expert,‖ says Dempsey (2010). ―So although e-

mavens carefully choose what will be forwarded, they are also trying to manage

their self-image. E-mavens want to be unique.‖ Viral Mavens and Infrequent

Senders attributed largely positive motivations to the senders.

A desire to connect and share with others was mentioned most frequently.

Consumers may share such practically useful content for altruistic reasons (e.g.,

to help others) or for self-enhancement purposes (e.g., to appear knowledgeable)

(Wojnicki and Godes 2008). Altruism and self interest may have the same

impetus: concern for well-being.

The researchers also found that participants who spend more time online tend to

share more information with others in their social network. Pending more time

surfing the Web also may allow individuals to feel a sense of inclusion. All of

72 | P a g e

these results, according to Dempsey (2010), may help viral marketing campaigns

achieve more success. Viral marketing is here to stay. The tools, technologies,

and support are available to embrace and profit from the incorporation of viral

marketing and social media into an integrated marketing and communications

strategy.

The vast majority of consumers who post online reviewers are overwhelmingly

motivated by goodwill and positive sentiment, according to a Bazaarvoice survey

conducted by the Keller Fay Group, available on

http://www.marketingcharts.com/interactive/online-reviewers-driven-mostly-by-

altruism-cmos-need-not-fear-wom-2527/ which surveyed some 1,300 online

reviewers. Fully 90% of respondents say they write reviews to help others make

better buying decisions, and more than 70% want to help companies improve the

products they build and carry.

The study also found that 79% write reviews in order to reward a company, and

87% of the reviews are generally positive in tone.

Among other findings of the new survey:

Reviewers are active online participants who post reviews as a way

of giving back to the review community (79%).

Reviewers purchase products online (85%), send more than 10

emails a day (77%), and engage in social networks (25%).

73 | P a g e

20% of reviewers post messages on other people‘s blogs or chat

rooms; 19% post on independent product-review sites such as

ePinions or CNET; and significantly more post directly on a

retailer‘s own website.

Highlighting the prevalence of multichannel shopping, the survey

also found that over 65% of reviewers have returned to the

retailer‘s site to leave an online review about an offline purchase.

Nearly 60% of reviewers have told friends and family about their

product experience.

This study attempts to extend the existing research about viral marketing.

Through extensive literature review in the first part of the research it has been

observed that sequence of drivers of viral marketing is access, awareness,

interest and experience.

In the second part of the research it has been examined which type of factors

influence user to receive viral messages of product/service and they are trust,

relevance and perceived benefits. For the success of the viral marketing in the

third part of research it is imperative to study factors which influence user to

forward viral messages of product/service. Factors which are identified as tie

strength, senders benefit, customer satisfaction, and altruism.

74 | P a g e

Concluding the literature review, it is noted that:

1. Not much study of viral marketing known to exists in Indian context.

2. There has been little research into finding out the drivers of viral

marketing.

3. No combine research has been done to investigate the factors which

influence user to receive and forward messages.

75 | P a g e

Chapter-3

Purpose of the Study

Motivation stems primarily in this study because viral marketing is a powerful

marketing tool with untapped potential. Viral Marketing Communication can bring

about benefits to marketers with its advantages such as low cost, high reach,

high credibility, accountability, fast speed, ease of usage and ability to reach a

global audience.

With the increased usage of broadband and internet services like YouTube,

Hotmail and Facebook, we think that there will be an increasing trend for Viral

Marketing to be adopted by companies as part of their promotional mix in the

future, thus fuelling interest in this topic. Limited research has been done on

drivers of viral marketing and response to such marketing techniques.

For the purpose of viral marketing successes, intention to read is of great

importance. Most researchers discussed receive and forward separately (Hsi-

Pen, 2007). . Except while going through prior literature it has been found that

study on mobile viral marketing has been done which identifies the factors which

influence user to receive, use and forward mobile messages. To make sure that

every email is meaningful to receivers, and is transmitted by viral marketing, this

study focuses on viral messages itself, understanding what meaningful viral

messages to users is, being willing to read and exploring the factors of

forwarding viral messages as well.

76 | P a g e

This study also investigates the drivers of viral marketing. It is very essential for

user to know about the viral marketing strategy adopted by the marketer. Only by

understanding what people think, will businesses achieve marketing goals. In

sum purpose of this study are to investigate receiver‘s determinants of reading

viral messages and to explore the factors that affect receivers to read viral

messages and forward after reading.

77 | P a g e

Chapter-4

Objectives of the Study

The current study is attempted to understand viral marketing strategy adopted by

the marketer. Viral marketing strategy on the other hand, is a specifically

coordinate set of activities that are designed to take advantage of potential word-

of-mouth accelerators. Viral marketing consists of a set of tactics designed to

grow attention and increase usage at exponential rates. A successful viral

marketing program should have a massive result in regards to awareness and its

adoption.

In order to achieve virality, one of the platforms of viral marketing is sending viral

messages through email by the marketer or from one user to another. Messages

must have a message that resonates due to strong entertainment or

informational value. This message must, of course, be resident in electronic or

online form so that it can be passed along in an exponential fashion via social

networks or email. It‘s not so easy to activate people‘s desire to share humorous,

entertaining or useful information.

First Objective of the study is to understand viral marketing and how this viral

marketing is taking place through social network? On the basis of research study

which is conducted in 2007 it has been observed that social networking sites

visitors are increasing.

78 | P a g e

Therefore it is very essential to see the response of internet users to this viral

marketing strategy. Accordingly, second objective is to find out the drivers of viral

marketing which drives user to get information about product/service.

During the study it has been observed that internet users are increasing day by

day. A study conducted by Fabernovel consulting in 2007 depicted in following

graph 1, shows the frequency of visitation: on social networking sites.

Graph 1

Source : Ipsos 2007, faberNovel Consulting 2007, Research paper 2007, Social Network websites: best practices from leading services

In India penetration of internet is low as compared to the rest of the world. Mostly

the penetration is mainly in urban area than the rural area. Study in 2010 is

79 | P a g e

conducted by Internet World Stats to see the highest number of users in Internet

top 20 countries which shows in India there is 81.0 millions of user using internet.

China is having highest number of internet user which is 420.0 millions, followed

by Unites States with 239.9 millions of user. Least is a Argentina with 26.6

millions of users followed by Canada with 26.2 millions of user. Graph 2 shows

the diagrammatic presentation of number of users (in millions) in Internet top 20

countries.

Graph 2

80 | P a g e

Some Statistics about Internet Users in India9

A Study of the Indian Telecommunications Industry which is conducted at 2008

shows number of Internet users in Asia is 5,29,701,704. Though Asia has only

16% of populations of the world, 37.6% of total internet users are Asian which is

great. Of them around 60 million are from India. India is 3rd in Asia (1st is China

(220 million) and 2nd is Japan (87.5 million)) and 4th in world ((1st is China (220

million), 2nd is USA (216 million) and 3rd is Japan (87.5 million)) as per as

internet users are concerned. As per the recent study of Internet world Stats

which is conducted at 2010 the Indian users has increased from 60 million to 81

million.

India has 13% of internet users in Asia and 7.36% that of the world. But the

sorrowful fact is only 5.3% of people in India use internet. The reason of this is

most of the people in India don‘t know computer. 70% of people who know

computer have used internet which is a healthy sign.

This study also investigates mainly which age group and which type people use

internet in India. 19-40 years age group is major section (85%) using internet in

India. 85% of internet users in India are male which not a very good sign is.

Among working women, only 11% use internet. The ratio is almost half (6%)

in case of non-working women and even worst in case of house-wives (2%). The

scenario is much better in case of young men (33%). Also 15% older men, 14% 9Available at http://www.indiabroadband.net/india-broadband-telecom-news/11169-

some-statistics-about-internet-users-india.html, Accessed on 15-04-2011.

81 | P a g e

school going kids and 21% college students use internet in India. 46% of net

users are graduate, 26% are post-graduate. Among these, 2/3 rd of user use

internet 2-3 times a week. 62% uses internet from office as in most of the offices,

it‘s free.

Next, this study is carried on to see from which cities most users come. Mumbai

has the maximum number of internet users (3.24 million) in India followed by

Delhi (2.66 million). The top ten cities where people use internets are Mumbai,

Delhi, Bangalore, Kolkata, Chennai, Pune, Hydrabad, Ahmedabad, Surat and

Nagpur. The total numbers of internet users of those 10 cities are 37% of the

total numbers of internet users in India.

Further it also take a look which types of sites majority of users browse. Most of

the users use net for emailing (95%) which is obvious. Next is job searching

(73%) showing crisis of getting job in India followed by chatting sites (62%),

social networking sites (51%) and quite interestingly mathematical sites (48%).

The top ten sites internet users browse in India are the following:

1. Yahoo 6. Youtube

2. Google India 7. Blogger.com

3. Google 8. Windows Live

4. Orkut 9. Rapid Share

5. Rediff 10. Wikipedia

82 | P a g e

So, briefly this is the situation of internet users in India. Though internet

ownership has seen growth of 32% compare to last year which is a delighting

fact, there are some concerning factors too. Those are

Only 5.3% people use internet in India which is very low.

Most of the users are male (85%). The female percentage should

increase.

Maximum number of users is from top 10 cities (37%). So, the internet

usage in urban areas is very less.

Most of the users are male (85%):

A study conducted in 2009, by Internet & Mobile Association of India (IAMAI) and

Indian Market Research Bureau (IMRB) found that there are over 54million users

who are active on internet only in India. However, it was claimed that there were

about 71 million users who used the internet. The number seemed to cross 52

million in September last year from 42 million at the same point of the previous

year i.e September 2008. This means an increase in 19 percent of users in India.

These active users access internet at least once every month to stay in touch

with their online activities. The number of internet users worldwide is expected to

touch 2.2 billion by 2013 and India is projected to have the third largest online

population during the same time, says a report. "The number of people online

around the world will grow more than 45 per cent to 2.2 billion users by 2013 and

Asia will continue to be the biggest Internet growth engine."... India will be the

third largest internet user base by 2013 with China and the US taking the first two

spots, respectively," technology and market research firm Forrester Research

83 | P a g e

said in a report. Globally, there were about 1.5 billion Internet users in the year

2008.

From the above statistical data it has been found that most of internet users are

male compared to female. Therefore, third objective is to find out the factors

which influence user to receive and forward messages with respect to gender.

In viral marketing most of the study is carried on teenagers or college going

students as they are the frequent user of internet. For the success of the viral

marketing it is very essential to know the responses of working professionals also

as they are having money power. It is very essential to know whether viral

marketing strategy influences them to know about the product or service.

Fourth objective is to investigate the factors which influence user to receive and

forward messages with respect to occupation. In this study two occupations are

considered which are student and working professionals.

Therefore, the objectives of the current study are as follows:

1. To understand viral marketing through social network.

2. To identify drivers for viral marketing.

3. To reveal and validate factors which influence user to receive and forward

messages.

4. To understand impact of demographic factors of user on receiving and

forwarding messages.

84 | P a g e

Chapter-5

Hypothesis

Prior research in viral marketing suggests that it is a very effective viral

marketing strategy with lots of benefits for the marketer as well as to the

customers. This study has made an attempt to understand viral marketing

and its communication through viral messages via email over the internet.

This study is divided into three parts. In first part, attempt is made to know

about viral marketing. Social networks make viral marketing and word-of-

mouth marketing much easier than before. The best use out of social

networks is not to make money ‗directly‘ off them, but to harness their

marketing potential and to use them to market your own business. Success of

the viral marketing is dependent on the awareness of the marketing strategy

and benefits user gets from this strategy. Viral marketing is very innovative

technique where user can get information about product/service at anytime,

anywhere over the internet. User‘s like to take benefit of this technology. Viral

Marketing is consumer-to-consumer or business-to-consumer strategy. In this

business sends the information to the user and user share their experiences

with other user.

Communication in this marketing strategy is very convenient and interesting

for the user. Being a member of social network and regular visit on the social

networking sites generates interest of user into knowing about viral marketing.

85 | P a g e

User may be not be aware of the term ―viral marketing‖ but he likes to view or

share information of product of services with his acquaintances. Sequence of

drivers of viral marketing through literature review is identified as access,

awareness, interest and experience. That means regular access on the

internet creates awareness about the viral marketing. Once the user starts

getting benefit it creates interest in it which in turn generates their experience.

To know whether the access, awareness and interest gives experience of

viral marketing to user and drives them to receive and forward information of

product, the null and alternative hypotheses for drivers of viral marketing are

as follows:

Hypothesis - 1:

H01 : Access does not have significant impact on experiences of viral

marketing.

H11 : Access has significant impact on experiences of viral marketing.

Hypothesis - 2:

H02 : Awareness does not have significant impact on experiences of viral

marketing.

H12 : Awareness has significant impact on experiences of viral

marketing.

Hypothesis - 3:

H03 : Interest does not have significant impact on experiences of viral

marketing.

H13 : Interest has significant impact on experiences of viral marketing.

86 | P a g e

Second part of the study identifies influencing factor for user to receive viral

messages. Through research factors identified which are Trusted source,

Relevance and perceived benefits. Further it is hypothesized that trust that

means source of the message, relevance of the message where kind of

information user looking for or information in which he is interested and

benefits which he gets after receiving information is motivational factors for

user to receive viral messages. Therefore, the null and the alternative

hypothesis for all three factors which are trust, relevance and perceived

benefits are formulated as follows:

The null and the alternative hypothesis for the factor trust are as follows:

Hypothesis - 4:

H04 : Trusted source does not have significant impact on user to receive

viral messages .

H14 : Trusted source have significant impact on user to receive viral

messages.

Hypothesis - 5:

H05 : Trusted source does not have significant impact on user to receive

viral messages with respect to gender.

H15 : Trusted source have significant impact on user to receive viral

messages with respect to gender.

87 | P a g e

Hypothesis - 6:

H06 : Trusted source does not have significant impact on user to receive

viral messages with respect to occupation.

H16 : Trusted source have significant impact on user to receive viral

messages with respect to occupation.

The null and the alternative hypothesis for the factor relevance are as follows:

Hypothesis - 7:

H07 : Message relevance does not have significant impact on receiving

viral messages.

H17 : Message relevance has significant impact on receiving viral

messages.

Hypothesis - 8:

H08 : Message relevance does not have significant impact on receiving

viral messages with respect to gender.

H18 : Message relevance has significant impact on receiving viral

messages with respect to gender.

Hypothesis - 9:

H09 : Message relevance does not have significant impact on receiving

viral messages with respect to occupation.

H19 : Message relevance has significant impact on receiving viral

messages with respect to occupation.

88 | P a g e

The null and the alternative hypothesis for the factor perceived benefits are as

follows:

Hypothesis - 10:

H010 : Perceived benefits to user does not have significant impact on

receiving viral messages.

H110 : Perceived benefits to user have significant impact on receiving viral

messages.

Hypothesis - 11:

H011 : Perceived benefits to user does not have significant impact on

receiving viral messages with respect to gender.

H111 : Perceived benefits to user have significant impact on receiving viral

messages with respect to gender.

Hypothesis - 12:

H012 : Perceived benefits to user does not have significant impact on

receiving viral messages with respect to occupation.

H112 : Perceived benefits to user have significant impact on receiving viral

messages with respect to occupation.

Third part of the study is to identify factors which influence used to forward

messages. For the success of viral marketing it is very essential to identify

factors which influence user to forward messages. Through research factors

identified are Tie Strength, senders benefit, customer satisfaction and

altruism Further it is hypothesized Ties strength is the influential factor to

forward messages because, it helps you to be connected with your

89 | P a g e

acquaintances. It gives him the identity as a knowledgeable person. Sender‘s

benefit is another influential factor which gives him incentives or rewards and

enjoyment to forward messages. Altruism is a influencing factor where user

forward messages to benefit other user. Therefore the null and the alternative

hypothesis formulated for these factors are as follows:

The null and the alternative hypothesis for the factor tie strength are as

follows:

Hypothesis - 13:

H013 : Tie strength does not have significant impact on user to forward

viral messages.

H113 : Tie strength has significant impact on user to forward viral

messages.

Hypothesis - 14:

H014 : Tie strength does not have significant impact on user to forward

viral messages with respect to gender.

H114 : Tie strength has significant impact on user to forward viral

messages with respect to gender.

Hypothesis - 15:

H015 : Tie strength does not have significant impact on user to forward

viral messages with respect to occupation.

H115 : Tie strength has significant impact on user to forward viral

messages with respect to occupation.

90 | P a g e

The null and the alternative hypothesis for the factor Sender‘s benefit are as

follows:

Hypothesis - 16:

H016 : Sender‘s benefit does not have significant impact on user to

forward viral messages.

H116 : Sender‘s benefit has significant impact on user to forward viral

message.

Hypothesis - 17:

H017 : Sender‘s benefit does not have significant impact on user to

forward viral messages with respect to gender.

H117 : Sender‘s benefit has significant impact on user to forward viral

messages with respect to gender.

Hypothesis - 18:

H018 : Sender‘s benefit does not have significant impact on user to

forward viral messages with respect to occupation.

H118 : Sender‘s benefit has significant impact on user to forward viral

messages with respect to occupation.

The null and the alternative hypothesis for the factor Customer Satisfaction

are as follows:

Hypothesis – 19:

H019 : Customer Satisfaction does not have significant impact on user to

forward viral messages.

91 | P a g e

H119 : Customer Satisfaction has significant impact on user to forward

viral messages with respect to gender.

Hypothesis – 20:

H020 : Customer Satisfaction does not have significant impact on user to

forward viral messages with respect to gender.

H120 : Customer Satisfaction has significant impact on user to forward

viral messages with respect to gender.

Hypothesis – 21:

H021 : Customer Satisfaction does not have significant impact on user to

forward viral messages with respect to occupation.

H121 : Customer Satisfaction has significant impact on user to forward

viral messages with respect to occupation.

The null and the alternative hypothesis for the factor Altruism are as follows:

Hypothesis – 22:

H022 : Altruism does not have significant impact on user to forward viral

messages.

H122 : Altruism has significant impact on user to forward viral messages .

Hypothesis – 23:

H023 : Altruism does not have significant impact on user to forward viral

messages with respect to gender.

H123 : Altruism has significant impact on user to forward viral messages

with respect to gender.

92 | P a g e

Hypothesis – 24:

H024 : Altruism does not have significant impact on user to forward viral

messages with respect to occupation.

H124 : Altruism has significant impact on user to forward viral messages

with respect to occupation.

93 | P a g e

Various hypotheses (null as well as alternative) are tabulated in TABLE – 7.1

TABLE 7.1 - Research Hypotheses

Sr.No. Null Hypothesis Alternative Hypothesis

H1 Access does not have significant

impact on experiences of viral

marketing.

Access has significant impact on

experiences of viral marketing.

H2 Awareness does not have

significant impact on experiences

of viral marketing.

Awareness has significant impact

on experiences of viral marketing.

H3 Interest does not have significant

impact on experiences of viral

marketing.

Interest has significant impact on

experiences of viral marketing.

H4 Trusted source does not have

significant impact on user to

receive viral messages.

Trusted source have significant

impact on user to receive viral

messages.

H5 Trusted source does not have

significant impact on user to

receive viral messages with

respect to gender.

Trusted source have significant

impact on user to receive viral

messages with respect to gender.

H6 Trusted source does not have

significant impact on user to

receive viral messages with

respect to occupation.

Trusted source have significant

impact on user to receive viral

messages with respect to

occupation.

94 | P a g e

Sr.No. Null Hypothesis Alternative Hypothesis

H7 Message relevance does not have

significant impact on receiving viral

messages.

Message relevance has significant

impact on receiving viral

messages.

H8 Message relevance does not have

significant impact on receiving viral

messages with respect to gender.

Message relevance has significant

impact on receiving viral messages

with respect to gender.

H9 Message relevance does not have

significant impact on receiving viral

messages with respect to

occupation.

Message relevance has significant

impact on receiving viral messages

with respect to occupation.

H10 A perceived benefit does not have

significant impact on user to

receive viral messages.

A perceived benefit has significant

impact on user to receive viral

messages.

H11 A perceived benefit does not have

significant impact on user to

receive viral messages with

respect to gender.

A perceived benefit has significant

impact on user to receive viral

messages with respect to gender.

H12 A perceived benefit does not have

significant impact on user to

receive viral messages with

respect to occupation.

A perceived benefit has significant

impact on user to receive viral

messages with respect to

occupation.

95 | P a g e

Sr.No. Null Hypothesis Alternative Hypothesis

H13 Tie strength does not have

significant impact on user to

forward viral messages.

Tie strength has significant impact

on user to forward viral messages.

H14 Tie strength does not have

significant impact on user to

forward viral messages with

respect to gender.

Tie strength has significant impact

on user to forward viral messages

with respect to gender.

H15 Tie strength does not have

significant impact on user to

forward viral messages with

respect to occupation.

Tie strength has significant impact

on user to forward viral messages

with respect to occupation.

H16 Sender‘s benefit does not have

significant impact on user to

forward viral messages.

Sender‘s benefit has significant

impact on user to forward viral

messages.

H17 Sender‘s benefit does not have

significant impact on user to

forward viral messages with

respect to gender.

Sender‘s benefit has significant

impact on user to forward viral

messages with respect to gender.

H18 Sender‘s benefit does not have

significant impact on user to

forward viral messages with

respect to occupation.

Sender‘s benefit has significant

impact on user to forward viral

messages with respect to

occupation.

96 | P a g e

Sr.No. Null Hypothesis Alternative Hypothesis

H19 Customer Satisfaction does not

have significant impact on user to

forward viral messages.

Customer Satisfaction has

significant impact on user to

forward viral messages.

H20 Customer Satisfaction does not

have significant impact on user to

forward viral messages with

respect to gender.

Customer Satisfaction has

significant impact on user to

forward viral messages with

respect to gender.

H21 Customer Satisfaction does not

have significant impact on user to

forward viral messages with

respect to occupation.

Customer Satisfaction has

significant impact on user to

forward viral messages with

respect to occupation.

H22 Altruism does not have significant

impact on user to forward viral

messages.

Altruism has significant impact on

user to forward viral messages.

H23 Altruism does not have significant

impact on user to forward viral

messages with respect to gender.

Altruism has significant impact on

user to forward viral messages

with respect to gender.

H24 Altruism does not have significant

impact on user to forward viral

messages with respect to

occupation.

Altruism has significant impact on

user to forward viral messages

with respect to occupation.

97 | P a g e

Diagrammatic representation of hypotheses is as follows:

Access

Awareness

Interest

Experience

Of VM

User to get/know

information about

product/service

drives

H2

Intention to open

viral messages

Trust

Relevance

Perceived

Benefits

H4/H5/H6

H7/H8/H9

H10/H11/H12

Intention to forward

viral messages

Tie Strength

Sender‘s

Benefit

H13/H14/H15

H19/H20/H21

Customer

Satisfaction

Altruism

H16/H17/H18

H22/H23/H24

H1

H3

Source: Own Analysis

98 | P a g e

Chapter-6

Research Methodology

The current research attempts to find in first part of the research study the

sequence of drivers of viral marketing and to find out whether this sequence

gives them the experience of viral marketing and drives them to get the

information about products or services through viral messages over the internet.

Second part of research study attempts to find out if there is any relationship

between influencing factors such as trust, relevance and perceived benefits with

intention to open viral messages.

Third part of research study attempts to find out if there is any relationship

between influencing factors such as tie strength, sender‘s benefit, customer

satisfaction and altruism with intention to forward viral messages.

The research process followed in this study is depicted in FIGURE– 6.1. As

already discussed, the diagnostic research design is adopted for this research

study. Under this design, attention has been paid on following aspects:

a) Selection of Sample

b) Method of data collection

c) Data collection

d) Data processing and analysis

e) Interpretation

99 | P a g e

FIGURE 6.1 Diagrammatic representation of Research Process

Define Research Problem

Review the literature

Review Concepts

and Theories

Review previous

research findings

Formulate Research

Objectives and

Hypothesis

Design Research

(including sample

design)

Data Collection

Data Analysis and

Hypothesis Testing

Interpretation of result

100 | P a g e

Design, Instrument Selection or Development, Sampling

Sample Design:

The target population covered under this project is the internet user from the age

group more than 16. To ensure wide cross section of the sample, students as

well as professionals are considered. To further diversify students from diverse

streams like Degree College, engineering college and Management College

selected. Initially, professional data collected by online mode by sharing Google

doc link on facebook and Gmail account. Online response was very low and time

consuming so that data collection shifted to offline mode by distributing

questionnaire to the professionals.

Sampling:

Sampling method used in this study is best described as random sampling. To

ensure a true representative sample, data collected from the internet users.

Demographic data collected from professional whose occupation is service and

non-professional without any occupation i.e. students. Students from various

colleges of Mumbai of different stream lime degree, engineering and

management are selected. Then samples were drawn at random from these

demographics keeping convenience in mind to ensure cross section

representation of each.

.

Demographic analysis of sample for this study and its diagrammatic

representation is depicted in the following tables and graphs.

101 | P a g e

Table 1 (Demographic Analysis of Sample)

Students Professional Total

Male 215 139 354

Female 058 079 137

Total 273 218 491

Research questionnaires were sent to different colleges of Mumbai to collect

data from students. Colleges selected were Degree College, Engineering College

and Management College. For professional data collected from the part-time

MBA students and from their acquaintances from the management college.

Prospective respondents were requested to fill up questionnaire and return the

same. The filled up questionnaire received from each respondents and number

of valid responses are indicated in Table 2 The response rate for valid responses

is 81% which is considered to be excellent.

Table 2

No. of questionnaire

sent

No. of questionnaire

received No. of Invalid questionnaire

No. of valid questionnaire

Student 300 283 10 273

Professional 300 223 5 218

600 506 15 491

Sample Size:

491 persons participated in this study. The technique of data analysis used in this

study are examination of differences between independent samples (e.g.

102 | P a g e

between male and female/students and professional), and as well as association

between variables. The total sample size used in this study is 491(n=491). The

demographic analysis of sample is shown in Table 1 and is depicted graphically

in Graph 3 and Graph 4.

Data Collection Method Primary data collected for which the method used for data collection is self-report

questionnaires. The merits of this method are:

i) This method is economical as compared to other methods like interview.

ii) It is free from interviewer‘s bias.

iii) Respondents get adequate time to give well thought out answers.

v) Large samples can be used.

Instrument Development

The objective of the study is to study drivers of viral marketing and to reveal and

validate factors which influence user to receive and forward viral messages. This

study also focuses on to validate the influence of demographic factors on user to

receive and forward messages. The survey questionnaire for this study purpose

is enclosed herewith in with the information of the viral marketing. It is in four

sections, as described below. Ordinal 5 point likert scale is used where

1.Strongly Disagree, 2.Disagree, 3.Neither Disagree Nor Agree, 4.Agree,

5.Strongly Agree.

103 | P a g e

1 Strongly Disagree

2 Disagree

3 Neither Disagree Nor Agree

4 Agree

5 Strongly Agree

Questionnaire is distributed with little information about viral marketing.

Questionnaire is divided into four parts. First part of the questionnaire consists of

questions related to drivers of viral marketing. Drivers which are revealed through

earlier study are access, interest, access and experience.

Second part of the questionnaire consists of questions related to the factors

which are revealed through earlier study are trust, relevance and perceived

benefits and these factors are require to validate to see whether these factors

having any impact or association with intention to open viral messages.

Third part of the questionnaire consists of questions related to the factors which

are revealed through earlier study are tie strength, senders benefit, customer

satisfaction and altruism and these factors are require to validate to see whether

these factors having any impact or association with intention to forward viral

messages.

Fourth part consists of demographic information like name, age, occupation etc.

104 | P a g e

During extensive literature review sequence of attributes have been identified

which are the sequence of drivers of viral marketing. These attributes are

Awareness, Interest and experience. To this sequence one extra attribute is

added that is access to see that whether regular access to internet has

significant impact on user to get experience of viral marketing. Accordingly

questions were asked by considering these four factors which are access,

awareness and interest and experience.

Part I

Questions Factors

Do you get internet access at home, college, workplace? * Access

Are you a member of any Social Networking site like facebook, twitter, linkedin etc.?

Access

Are you aware of the term ―viral marketing‖? * Awareness

Do you visit Social Networking Sites regularly? * Interest

Are You interested in online purchase? * Interest

I always send messages of product/services over social network. *

Interest

I am interested in receiving email or messages from a friend containing a link of product/service which is relevant to me. *

Interest

I found information regarding product/service available on social network is very useful. *

Experience

My decision to purchase product/service is based on the reviews on social network. *

Experience

I gather information about product/service before going for purchase. *

Experience

I always share information over social network which is not related to marketing. *

Experience

Viral marketing technique is very interesting as there is no middle man required to get information about product. *

Experience

Information about product/service can be viewed on internet as per my convenient time. *

Experience

Second part of the study is to reveal and validate factors which influences user to

receive viral messages of product and service. Factors which identified through

105 | P a g e

literature review are trust, relevance and perceived benefits. To check whether

these three factors have significant impact on intention to open/receive viral

messages, questions are developed for each of the factor which is as follows.

Part II Questions Factors

I don‘t open promotional messages which I get because often it is just a spam which I delete without reading.

trust

I don‘t click on product/service link which I get because of security reason.

trust

I open promotional messages of product/service if it is from the social networking group. (e.g. facebook, linkedin, Twitter etc.)

trust

I open promotional messages of product if it is from relatives or friend.

trust

I open promotional messages of product/service if it is from the reputed organization like Sunsilk, Tata Docomo, Idea, FMCG products etc.)

trust

I open promotional messages of product/service if it is from trusted third party.

trust

I open all messages from my acquaintances trust

If I received a message of product/service from someone who is known to me, l surely give it a try.

trust

I open promotional messages of product/service of my interest and relevant to me.

relevance

I don‘t read message of product/service which is not appealing to me

relevance

While reading message of product/service, look of the message is not important to me

relevance

I read messages of service/product of my interest. relevance

If a message of product/service from third party but of my interest I‘ll surely give it a try.

relevance

I read message of product/services which is read by many users.

perceived benefits

I don‘t read message of product/service which is taking too much time for video & text to display.

perceived benefits

I read message of product/service which gives me incentives/reward.

perceived benefits

I don‘t read messages of product/services which required lot of information from user.

perceived benefits

106 | P a g e

Questions Factors

I read all messages which I received. intention to open

I read messages of product/service which I am looking for. intention to open

I read messages of product/services which is simple and easy to use.

intention to open

Third part of the study is to reveal and validate factors which influences user to

forward viral messages of product and service. Factors which identified through

literature review are tie strength, senders benefit, customer satisfaction and

altruism. To check whether these four factors have significant impact on intention

to forward viral messages, questions are developed for each of the factor which

is as follows.

Part II

Questions Factors

I forward message of product/service to only my close associates.

tie strength

I forward message of product/service to get connected with people.

tie strength

I forward message of product/service to all my contacts. senders benefit

I forward message of product/service to get benefit from company

senders benefit

I forward message of service/product which are conditional to provide contact list in order to get information about product/service.

senders benefit

I forward message of service/product which are conditional to get reward from company.

senders benefit

I forward message of product/service just for a fun. customer satisfaction

I forward message of product/service because I am using and satisfied with product/service.

customer satisfaction

I forward message of product/service because I am not using but having good opinion about it.

customer satisfaction

107 | P a g e

Questions Factors

I forward message of product/service just for the receiver‘s benefits.

altruism

I tend to pass along my contacts ‗positive reviews‘ of product/service.

altruism

I tend to pass along my contacts ‗negative reviews‘ of product/service.

altruism

I forward all messages of product/service which I received. intention to forward

I forward only those messages of product/service which is from trusted source.

intention to forward

I forward only those messages of product/service in which receiver is interested.

intention to forward

Following questions are designed for demographic information of user. Part IV

Gender Male Female

Age (years) 16-20 21-30 31-40

41-50 51 and above

Qualification Graduate Post Graduate Others

Profession Student Working

There are certain pitfalls also of this method, as detailed under:

i. Can be used only with educated respondents.

ii. There is a possibility of ambiguous reply or omission of replies altogether

to certain questions.

iii. It is difficult to know whether willing respondents are truly representatives.

108 | P a g e

However, merits of the self-report questionnaire as method of data collection

outweigh its pitfalls. Therefore, the current study employed this method for data

collection using self-report questionnaire.

Tables 3 of Major Constructs and sub variables of the study: Table 3

Drivers of Viral Marketing

Access

Awareness

Interest

Experience

Intention to Open

Trust

Relevance

Perceived Benefits

Intention to Forward

Tie Strength

Senders Benefit

Customer Satisfaction

Altruism

Frequency Table

Table 4

Demographic Characteristics Frequency Percent Valid Percent

Cumulative Percent

Valid Male 354 71.7 72.1 72.1

Female 137 27.7 27.9 100.0

Total 491 99.4 100.0

Missing System 3 .6

Total 494 100.0

109 | P a g e

Graph 3: Gender Distribution of sample

Table 5

Demographic Characteristics Frequency Percent Valid Percent

Cumulative Percent

Valid Student 273 55.3 55.6 55.6

working 218 44.1 44.4 100.0

Total 491 99.4 100.0

Missing System 3 .6

Total 494 100.0

Graph 4: Occupation distribution of Sample

Data processing and analysis, and interpretation of results are presented in

subsequent chapters.

110 | P a g e

Chapter-7

Data Analysis and Hypothesis Testing

Data Analysis:

Data were collected over a six months period and 491internet user, students and

professional, participated in the study. Of these, 354 were males and 137 were

females. Out of 491, 273 were students and 218 were professionals. Thus the

samples represented a broad cross section of gender and occupation profile

which is required for this study.

The data analysis and hypothesis testing were carried out using computer

software package SPSS ver-12. The relevant result outputs of SPSS are

enclosed under various annexure to this chapter.

Reliability of Scale:

Mainly, reliability is a measure of how a scale can be relied on to produce similar

measurements every time we use the scale. Alpha value is depicted in

Annexure-1. Reliability analysis of a scale is performed on the 13 variables

shown in Table 3. Reliability was assessed by calculating Cronbach‘s Alpha, a

measure of internal consistency, for each measured scale. The internal reliability

of these measures was proven to be acceptable.

111 | P a g e

Annexure-1

Reliability Statistics

Cronbach's Alpha N of Items

.878 13

Alpha value shown in Annexure-1 is 0.828 which is considered a “good scale”.

The KMO statistic assesses one of the assumptions of Principle Components

and Factor Analysis – namely whether there appears to be some underlying

(latent) structure in the data (technically referred to as the Factorability of R). This

is also referred to as Sampling Adequacy, or even lack of Sphericity. The KMO

should be .6 or greater, otherwise any results you get may be unreliable (mere

mathematical illusions). KMO value is depicted in Aneexure-2.

Annexure-2

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .875

Bartlett's Test of Sphericity Approx. Chi-Square 1986.004

df 78

Sig. .000

KMO value is 0.875 which is considered as ―Meritorious and great‖ and data are

likely to factor well based on correlation and partial correlation.

Bartlett‘s measure tests the null hypothesis that the original correlation matrix is

an identity matrix. For factor analysis to work we need some relationship

between variables and if the R-matrix were an identity matrix then all correlation

112 | P a g e

coefficient would be zero. Therefore, we want this test to be significant (i.e. have

a significance value less than 0.05). A significance test tells us that the R-matrix

is not an identity matrix; therefore, there are some relationships between the

variables we hope to include in the analysis. Fort these data, Bartlett‘s test is

highly significant (p < 0.001) and therefore factor analysis is appropriate.

Construct Validity:

As seen in Annexure-3, the alphas of the total 13 variables ranging from 0.799 to

0.848) which is considered as ―good‖ and Cronbach‘s alpha for the whole section

measures came out to 0.875. Overall analyses suggested instrument of the

research study used for data collection is a reliable instrument.

Annexure-3

Item-Total Statistics

Variables

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if

Item Deleted

awareness 34.2844 28.847 -.140 .848

access 34.7498 28.036 .077 .834

interest 33.7467 26.049 .455 .820

experience 32.4511 26.213 .293 .828

trust 32.5922 24.755 .552 .812

relevance 32.4950 24.835 .498 .815

perceived_benefits 32.5570 24.588 .527 .813

intention_to_open 32.6130 24.518 .490 .815

tie_strength 32.9433 21.408 .668 .799

senders_benefit 33.1917 21.946 .639 .802

customer_satisfaction 32.9646 22.213 .672 .799

altruism 32.8438 22.671 .572 .808

intention_to_forward 33.0190 22.422 .588 .807

113 | P a g e

Descriptive Analysis:

Drivers of Viral Marketing

Analysis of Scores of Drivers of Viral Marketing scale (Part-I of Research

Questionnaire): The scores on four variable which are drivers of viral marketing t

was calculated by taking average of individual items on a 5-point scale with 5

representing the strongly agree and 1 representing strongly disagree. The

analysis output of SPSS on these scales is enclosed in Annexure-4.

As can be seen from below SPSS output, the maximum score is on ‗experience‘

(Mean = 3.4199 and standard deviation = 0.59422), and the minimum score is on

‗access‘ (Mean = 1.1212 and standard deviation 0.28226), as summarized below:

Annexure-4

Descriptive Statistics

N Minimum Maximum Mean Std.

Deviation

access 491 1.00 2.00 1.1212 .28226

awareness 491 1.00 2.00 1.5866 .49295

interest 491 1.00 3.25 2.1242 .45101

experience 491 1.67 5.00 3.4199 .59422

Valid N (listwise)

491

Annexure-4 shows, for each of the four variables, the number (N) of participants

with no missing data on that variable. The Valid N (listwise) is the number (491)

who has no missing data on any variable. The table also shows the Minimum and

Maximum score that any participants had on that variable. The table also

provides the Mean or average score for each variable.

114 | P a g e

Factors which influence user to receive viral messages

Analysis of Scores of factors which influence user to receive viral messages

(Part-II of Research Questionnaire): The scores on three variable which are the

factors which influence user to receive viral messages was calculated by taking

average of individual items on a 5-point scale with 5 representing the strongly

agree and 1 representing strongly disagree. The analysis output of SPSS on

these scales is enclosed in Annexure-5.

As can be seen from below SPSS output, the maximum score is on ‗relevance‘

(Mean = 3.3760 and standard deviation = 0.62857), and the minimum score is on

‗trust‘ (Mean = 3.2788 and standard deviation = 0.59050), as summarized below:

Annexure-5

Descriptive Statistics

N Minimum Maximum Mean Std.

Deviation

trust 491 1.25 4.88 3.2788 .59050

relevance 491 1.00 5.00 3.3760 .62857

perceived_benefits 491 1.00 4.83 3.3140 .64040

Valid N (listwise) 491

Annexure-5 shows, for each of the four variables, the number (N) of participants

with no missing data on that variable. The Valid N (listwise) is the number (491)

who has no missing data on any variable. The table also shows the Minimum and

Maximum score that any participants had on that variable. The table also

provides the Mean or average score for each variable.

115 | P a g e

Factors which influence user to forward viral messages

Analysis of Scores of factors which influence user to forward viral messages

(Part-III of Research Questionnaire): The scores on four variable which are the

factors which influence user to forward viral messages was calculated by taking

average of individual items on a 5-point scale with 5 representing the strongly

agree and 1 representing strongly disagree. The analysis output of SPSS on

these scales is enclosed in Annexure-6.

As can be seen from below SPSS output, the maximum score is on ‗altruism‘

(Mean = 3.0272 and standard deviation = 0.89503), and the minimum score is on

‗senders benefit‘ (Mean = 2.6792 and standard deviation = 0.92635), as

summarized below:

Annexure-6

Descriptive Statistics

N Minimum Maximum Mean Std.

Deviation

tie_strength 492 1.00 5.00 2.9277 .96969

senders_benefit 491 1.00 5.00 2.6792 .92635

customer_satisfaction 491 1.00 5.00 2.9063 .85306

altruism 491 1.00 5.00 3.0272 .89503

Valid N (listwise) 491

Annexure-6 shows, for each of the four variables, the number (N) of participants

with no missing data on that variable. The Valid N (listwise) is the number (491)

who has no missing data on any variable. The table also shows the Minimum and

Maximum score that any participants had on that variable. The table also

provides the Mean or average score for each variable.

116 | P a g e

Hypothesis Testing:

Hypothesis testing is carried out with the help of SPSS package. For each

hypothesis, the detailed discussion is as under.

Hypothesis 1: Access has significant impact on experiences of viral marketing.

A Pearson product-moment correlation was run to determine the relationship

between access and experience of viral marketing. The data showed in

annexure-7 no violation of normality, linearity or homoscedasticity. There is a

negative correlation between access and experience which is statistically not

significant (r = -0.082, n = 491, value of P = 0.070 where P > .0005).

Annexure-7 Correlations between Access and Experiences of viral Marketing

access

experience

Pearson Correlation -.082

Sig. (2-tailed) .070

N 491

Hypothesis 1 is rejected as there is no significant impact on experiences of viral

marketing.

Hypothesis 2: Awareness has significant impact on experiences of viral

marketing.

A Pearson product-moment correlation was run to determine the relationship

between awareness and experiences of viral marketing. The data showed in

Annexure-8 no violation of normality, linearity or homoscedasticity. There was a

117 | P a g e

strong, positive correlation between awareness and experiences of viral

marketing which was statistically significant (r = -0.161, n = 491, value of P =

0.000 where P < .0005).

Annexure-8 Correlations between Awareness and Experiences of Viral Marketing

Awareness

experience

Pearson Correlation -.161(**)

Sig. (2-tailed) .000

N 491

** Correlation is significant at the 0.01 level (2-tailed).

From above data It is summarized that Hypothesis 2 is accepted that awareness

does have significant impact on experiences of viral marketing.

Hypothesis 3: Interest has significant impact on experiences of viral marketing.

A Pearson product-moment correlation was run to determine the relationship

between awareness and experiences of viral marketing. The data showed in

Annexure-9 no violation of normality, linearity or homoscedasticity. There was a

strong, positive correlation between awareness and experiences of viral

marketing which was statistically significant (r = 0.289, n = 491, value of P =

0.000 where P < .0005).

Annexure-9 Correlations between Interest and Experiences of Viral Marketing

Interest

Experience

Pearson Correlation .289(**)

Sig. (2-tailed) .000

N 491

** Correlation is significant at the 0.01 level (2-tailed).

118 | P a g e

From above data, it is summarized that Hypothesis 3 is accepted that Interest

does have significant impact on experiences of viral marketing.

From the above statistical analysis it has been observed that access does not

have any significant impact on experience of viral marketing whereas awareness

and interest does have significant impact on experience of viral marketing.

To find out the correlation between the drivers of viral marketing which are

access, awareness, interest and experience a Pearson product-moment

correlation was run across the variables to determine the relationship between

the sequences of drivers of viral marketing. From the Annexure-10 it is observed

that there is a strong and positive correlation between access and interest which

was statistically significant (r = 0.252, n = 491, value of P = 0.000 where P <

.0005). Whereas there is negative correlation between access and awareness

which was statistically not significant (r = -0.065, n = 491, value of P = 0.153

where P > .0005). It is also proved that there is negative relationship between

awareness and access and awareness and interest which was statistically not

significant ((r = -0.065, n = 491, value of P = 0.153, where P < .0005), (r = -0.051,

n = 491, value of P = 0.262 where P > .0005). There is negative correlation

between interest and awareness which was statistically not significant (r = -0.051,

n = 491, value of P = 0.262 where P > .0005) but there is strong and positive

correlation between interest and access which was statistically significant (r =

0.252, n = 491, value of P = 0.000 where P < .0005).

119 | P a g e

Annexure-10

Access Awareness Interest experience

Access

Pearson Correlation 1 -.065 .252(**) -.082

Sig. (2-tailed) . .153 .000 .070

N 491 491 491 491

Awareness

Pearson Correlation -.065 1 -.051 -.161(**)

Sig. (2-tailed) .153 . .262 .000

N 491 491 491 491

Interest

Pearson Correlation .252(**) -.051 1 .289(**)

Sig. (2-tailed) .000 .262 . .000

N 491 491 491 491

Experience

Pearson Correlation -.082 -.161(**) .289(**) 1

Sig. (2-tailed) .070 .000 .000 .

N 491 491 491 491

** Correlation is significant at the 0.01 level (2-tailed).

Hypothesis 4: Trusted source has significant impact on user to receive viral

messages.

The overall multiple regression model which is depicted in Annexure-11 was

found to be significant (2

.AdjR . = .244), F (3, 491) = 53.729, p < .001. Trusted

source (β = 0.149, t = 2.552, p < .05) were found to be having significant impact

on user to receive viral messages. Therefore Hypothesis 4 is accepted that

trusted source has significant impact on user to receive viral messages.

Hypothesis 5: Trusted source has significant impact on user to receive viral

messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is trusted source on the intention to open/receive viral messages

120 | P a g e

with respect to gender. Here, gender is labeled as Male and Female. While

entering data for gender in SPSS value for Male = 1and for Female = 2. Question

about the gender asked in questionnaire was question number 51. Independent

sample t-test compares the means between two unrelated groups on the same

continuous, dependent variable. The results have two main parts: descriptive

statistics and inferential statistics. Descriptive statistics data is displayed in

Annexure-12.

Annexure-12 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

trust Male 354 3.2751 0.58987 0.03135

Female 137 3.2883 0.59420 0.05077

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 345 male, and they

have, on average, 3.275, with a standard deviation of 0.58987. There are 137

female, and they have, on average, 3.2883, with a standard deviation of 0.59420.

The last column gives the standard error of the mean for each of the two groups.

This shows that male trusted source is the important factor for male to intention

to open viral messages than female. In Annexure value of Mean value for trusted

source for male is more than that of female.

The second part of the output gives the inferential statistics which is shown in

Annexure-13.

121 | P a g e

Annexure-13

Independent Samples Test

Trusted Source

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.166

0.684

t-test for Equality of Means

T -0.233 -0.222

Df 489 245.748

Sig. (2-tailed) 0.824 0.824

Mean Difference -0.01325 -0.01325

Std. Error Difference 0.05947 0.05967

95% Confidence Interval Lower of the Difference Upper

-0.13011 -0.13077

-0.10360 -0.10427

From the Annexure-13 it is assumed that Sig. (2-tailed) value is 0.824 is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable trusted source on intention to

open/receive viral messages with respect to gender. A t test failed to reveal a

statistically reliable difference between the mean of trusted source that male has

(M = 3.2751, s = 0. .58987) and that the female has (M = 3.2883, s = 0. .59420),

t(489) = 0.233, p = 0.824, α = .05. It proves that independent variable trusted

source does not have any impact on dependent variable i.e. intention to open

viral messages with respect to gender. Gender was not considered as a

contributing factor in further examinations of intention to receive viral messages.

Therefore hypothesis 5 is rejected as there is no significant impact of trusted

source on intention to open/receive viral messages with respect to gender.

122 | P a g e

Hypothesis 6: Trusted source has significant impact on user to receive viral

messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is trusted source on the intention to open/receive viral messages

with respect to occupation. Here, occupation is labeled as student and working

professional. While entering data for occupation in SPSS value for student =

1and for working professional = 2. Question about the occupation asked in

questionnaire was question number 54. Independent sample t-test compares the

means between two unrelated groups on the same continuous, dependent

variable. The results have two main parts: descriptive statistics and inferential

statistics. Descriptive statistics data is displayed in Annexure-14.

Annexure-14 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

trust Student 273 3.2376 0.60370 0.03654

Working 218 3.3303 0.57075 0.03866

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 273 students, and

they have, on average, 3.2376, with a standard deviation of 0.60370. There are

218 working professional, and they have, on average, 3.3303, with a standard

deviation of 0.57075. The last column gives the standard error of the mean for

each of the two groups. Above table shows that trusted source to viral messages

is important factor for working professionals that the students.

123 | P a g e

The second part of the output gives the inferential statistics which is shown in

Annexure-15.

Annexure-15

Independent Samples Test

Trusted Source

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.646

0.422

t-test for Equality of Means

T -1.731 -1.742

Df 489 475.287

Sig. (2-tailed) 0.084 0.082

Mean Difference -0.09264 -0.09264

Std. Error Difference 0.05353 0.05319

95% Confidence Interval Lower of the Difference Upper

-0.19781 -0.01253

-0.19716 -0.01188

From the Annexure-15 it is assumed that Sig. (2-tailed) value is 0.422 is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable trusted source on intention to

open/receive viral messages with respect to occupation. A t test failed to reveal

a statistically reliable difference between the mean of trusted source that student

has (M = 3.2376, s = 0.60370) and that the working professional has (M =

3.3303, s = 0.57075), t(489) = 1.731, p = 0.084, α = .05. It proves that

independent variable trusted source does not have any impact on dependent

variable i.e. intention to open viral messages with respect to occupation.

Occupation was not considered as a contributing factor in further examinations of

intention to receive viral messages. Therefore hypothesis 6 is rejected as there is

124 | P a g e

no significant impact of trusted source on intention to open/receive viral

messages with respect to occupation.

Hypothesis 7: Message relevance has significant impact on receiving viral

messages.

The overall multiple regression model which is depicted in Annexure-11 was

found to be significant (2

.AdjR = .244), F (3, 491) = 53.729, p < .001. Trusted

source (β = 0.219, t = 4.036, p < .05) were found to be having significant impact

on user to receive viral messages. Therefore Hypothesis 7 is accepted that

message relevance has significant impact on user to receive viral messages.

Hypothesis 8: Message relevance has significant impact on receiving viral

messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is message relevance on the intention to open/receive viral

messages with respect to gender. The results have two main parts: descriptive

statistics and inferential statistics. Descriptive statistics data is displayed in

Annexure-16.

Annexure-16 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

Relevance Male 354 3.3870 0.63205 0.03359

Female 137 3.3474 0.62085 0.05304

125 | P a g e

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 354 male, and they

have, on average, 3.3870, with a standard deviation of 0.63205. There are 137

female, and they have, on average3.3474, with a standard deviation of 0.62085.

The last column gives the standard error of the mean for each of the two groups.

This shows that message relevance is important factor for male to open viral

messages than female.

The second part of the output gives the inferential statistics which is shown in

Annexure-17.

Annexure-17

Independent Samples Test

Message relevance

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.335

0.563

t-test for Equality of Means

t 0.625 0.630

df 489 251.397

Sig. (2-tailed) 0.532 0.529

Mean Difference 0.03956 0.03956

Std. Error Difference 0.06328 0.06279

95% Confidence Interval Lower of the Difference Upper

-0.08478 0.16390

-0.08409 0.06321

From the Annexure-17 it is assumed that Sig. (2-tailed) value is 0.532 is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable message relevance on intention

to open/receive viral messages with respect to gender. A t test failed to reveal a

126 | P a g e

statistically reliable difference between the mean of message relevance that male

has (M = 3.3870, s 0.63205) and that the female has (M = 3.3474, s = 0.57075),

t(489) = 0.625, p = 0.532, α = .05. It proves that independent variable message

relevance does not have any impact on dependent variable i.e. intention to open

viral messages with respect to gender. Gender was not considered as a

contributing factor in further examinations of intention to receive viral messages.

Therefore hypothesis 8 is rejected as there is no significant impact of message

relevance on intention to open/receive viral messages with respect to gender.

Hypothesis 9: Message relevance has significant impact on receiving viral

messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is message relevance on the intention to open/receive viral

messages with respect to occupation. The results have two main parts:

descriptive statistics and inferential statistics. Descriptive statistics data is

displayed in Annexure-18.

Annexure-16 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

Relevance Student 273 3.3084 0.62353 0.03774

Working 218 3.4606 0.62601 0.04240

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is student and working professional). There are

127 | P a g e

273 students, and they have, on average, 3.3084, with a standard deviation of

0.62353. There are 218 working professionals, and they have, on average 3.4606

with a standard deviation of 0.62601. The last column gives the standard error of

the mean for each of the two groups. This shows that message relevance is

important factor for working professionals open viral messages than students.

The second part of the output gives the inferential statistics which is shown in

Annexure-19.

Annexure-19

Independent Samples Test

Message relevance

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.932

0.335

t-test for Equality of Means

t -2.681 -2.680

df 489 464.457

Sig. (2-tailed) 0.008 0.005

Mean Difference -0.15213 -0.15213

Std. Error Difference 0.05674 0.05676

95% Confidence Interval Lower of the Difference Upper

-0.26360 -0.04065

-0.26367 -0.04059

From the Annexure-19 it is assumed that Sig. (2-tailed) value is 0.008 is less

than, so we fail to accept null hypothesis. This implies that independent variable

message relevance has significant impact on intention to open/receive viral

messages with respect to occupation. A t test reveal a statistically reliable

difference between the mean of message relevance that student has (M =

3.3084, s 0.62353) and that the working professional has (M = 3.4606, s =

128 | P a g e

0.62601), t(489) = -2.681, p = 0.008, α = .05. It proves that independent variable

message relevance have impact on dependent variable i.e. intention to open viral

messages with respect to occupation. Occupation was considered as a

contributing factor in further examinations of intention to receive viral messages.

Therefore hypothesis 9 is accepted as there is significant impact of message

relevance on intention to open/receive viral messages with respect to occupation.

Hypothesis 10: Perceived Benefits has significant impact on user to receive viral

messages.

The overall multiple regression model which is depicted in Annexure-11 was

found to be significant (2

.AdjR . = .244), F (3, 491) = 53.729, p < .001. Perceived

benefits (β = 0.303, t = 6.069, p < .05) were found to be having significant impact

on user to receive viral messages. Therefore Hypothesis 10 is accepted that

perceived benefits has significant impact on user to receive viral messages.

Hypothesis 11: Perceived benefit has significant impact on user to receive viral

messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is perceived benefits on the intention to open/receive viral

messages with respect to gender. Independent sample t-test compares the

means between two unrelated groups on the same continuous, dependent

variables. The results have two main parts: descriptive statistics and inferential

statistics. Descriptive statistics data is displayed in Annexure-20.

129 | P a g e

Annexure-20 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

Perceived benefit

Male 354 3.3239 0.63541 0.03377

Female 137 3.2883 0.65477 0.05594

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 345 male, and they

have, on average, 3.3239, with a standard deviation of 0.63541. There are 137

female, and they have, on average, 3.2883, with a standard deviation of 0.65477.

The last column gives the standard error of the mean for each of the two groups.

This shows that for male perceived benefits is the important factor to intention to

open viral messages than female. The second part of the output gives the

inferential statistics which is shown in Annexure-21

Annexure-21 Independent Samples Test

Perceived Benefits

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.361

0.549

t-test for Equality of Means

t 0.552 0.549

df 489 245.748

Sig. (2-tailed) 0.581 0.586

Mean Difference 0.03560 0.03560

Std. Error Difference 0.06448 0.06534

95% Confidence Interval Lower of the Difference Upper

-0.09110 -0.09312

0.16229 0.16432

130 | P a g e

From the Annexure-21 it is assumed that Sig. (2-tailed) value is 0.581 is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable trusted source on intention to

open/receive viral messages with respect to gender. A t test failed to reveal a

statistically reliable difference between the mean of perceived benefit that male

has (M = 3.3239, s = 0.63541) and that the female has (M = 3.2883, s =

0.65477), t(489) = 0.552, p = 0.581, α = .05. It proves that independent variable

perceived benefit does not have any impact on dependent variable i.e. intention

to open viral messages with respect to gender. Gender was not considered as a

contributing factor in further examinations of intention to receive viral messages.

Therefore hypothesis 11 is rejected as there is no significant impact of trusted

source on intention to open/receive viral messages with respect to gender.

Hypothesis 12: perceived benefit has significant impact on user to receive viral

messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is perceived benefit on the intention to open/receive viral

messages with respect to occupation. Independent sample t-test compares the

means between two unrelated groups on the same continuous, dependent

variable. The results have two main parts: descriptive statistics and inferential

statistics. Descriptive statistics data is displayed in Annexure-22.

131 | P a g e

Annexure-22 Group Statistics

Q.51 N Mean Std.

Deviation Std. Error Mean

Perceived benefit

Student 273 3.2845 0.64062 0.03877

Working 218 3.3509 0.63968 0.04332

There are 273 students, and they have, on average, 3.2845, with a standard

deviation of 0.64062. There are 218 working professional, and they have, on

average, 3.3509, with a standard deviation of 0.63968. The last column gives the

standard error of the mean for each of the two groups. Above table shows that

perceived benefits is important factor for working professionals that the students.

The second part of the output gives the inferential statistics which is shown in

Annexure-23

Annexure-23

Independent Samples Test

Perceived Benefits

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.001

0.979

t-test for Equality of Means

t -1.142 -1.142

df 489 465.544

Sig. (2-tailed) 0.254 0.254

Mean Difference -0.06642 -0.06642

Std. Error Difference 0.05815 0.05814

95% Confidence Interval Lower of the Difference Upper

-0.18068 -0.18067

0.04783 0.04783

132 | P a g e

From the Annexure-23 it is assumed that Sig. (2-tailed) value is 0.254is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable perceived benefits on intention

to open/receive viral messages with respect to occupation. A t test failed to

reveal a statistically reliable difference between the mean of perceived benefits

that student has (M = 3.2845, s = 0.64062) and that the working professional has

(M = 3.3509, s = 0.63968), t(489) = -1.142, p = 0.254, α = .05. It proves that

independent variable perceived benefit does not have any impact on dependent

variable i.e. intention to open viral messages with respect to occupation.

Occupation was not considered as a contributing factor in further examinations of

intention to receive viral messages. Therefore hypothesis 12 is rejected as there

is no significant impact of perceived benefits on intention to open/receive viral

messages with respect to occupation.

Annexure 11

Model Unstandardized

Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 1.028 0.180 5.696 0.000

trust 0.149 0.058 0.127 2.552 0.011

relevance 0.219 0.054 0.199 4.036 0.000

Perceived benefits 0.303 0.050 0.281 6.069 0.000

a Dependent Variable: intention_to_open/receive

133 | P a g e

Hypothesis 13: Tie strength has significant impact on user to forward viral

messages.

The overall multiple regression model which is depicted in Annexure-24 was

found to be significant (2

.AdjR . = 0.423), F (4, 490) = 90.742, p < .001. Tie

strength (β = 0.312, t = 6.983, p < .05) were found to be having significant impact

on user to forward viral messages. Therefore Hypothesis 13 is accepted that tie

strength has significant impact on user to forward viral messages.

Annexure 24

Unstandardized

Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

(Constant) 0.755 0.129 5.842 0.000

tie_strength 0.312 0.045 0.331 6.983 0.000

senders_benefit 0.304 0.046 0.308 6.563 0.000

customer_satisfaction 0.100 0.052 0.093 1.909 0.057

altruism 0.026 0.042 0.025 0.613 0.540

a Dependent Variable: intention_to_forward

Hypothesis 14: Tie strength has significant impact on user to forward viral

messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is tie strength on the intention to forward viral messages with

respect to gender. Independent sample t-test compares the means between two

unrelated groups on the same continuous, dependent variables. The results have

two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-25.

134 | P a g e

Annexure-25

Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

Tie Strength Male 354 2.9689 0.97513 0.05183

Female 137 2.8212 0.95436 0.08154

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 345 male, and they

have, on average, 2.9689, with a standard deviation of 0.97513. There are 137

female, and they have, on average, 2.8212, with a standard deviation of 0.95436.

The last column gives the standard error of the mean for each of the two groups.

This shows that for male tie strength is the important factor to intention to forward

viral messages than female. The second part of the output gives the inferential

statistics which is shown in Annexure-26

Annexure-26

Independent Samples Test

Tie Strength

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.366

0.545

t-test for Equality of Means

t 1.515 1.529

df 489 252.235

Sig. (2-tailed) 0.130 0.127

Mean Difference 0.14776 0.14776

Std. Error Difference 0.09754 0.09661

95% Confidence Interval Lower of the Difference Upper

-0.04389 -0.04251

0.33941 0.33803

From the Annexure-26 it is assumed that Sig. (2-tailed) value is 0.130 is not less

135 | P a g e

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable tie strength on intention to

forward viral messages with respect to gender. A t test failed to reveal a

statistically reliable difference between the mean of tie strength that male has (M

= 2.9689, s = 0.97513) and that the female has (M = 2.8212, s = 0.95436), t(489)

= 1.515, p = 0.130, α = .05. It proves that independent variable tie strength does

not have any impact on dependent variable i.e. intention to forward viral

messages with respect to gender. Gender was not considered as a contributing

factor in further examinations of intention to forward viral messages. Therefore

hypothesis 14 is rejected as there is no significant impact of tie strength on

intention to forward viral messages with respect to gender.

Hypothesis 15: Tie strength has significant impact on user to receive viral

messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is tie strength on the intention to forward viral messages with

respect to occupation. Independent sample t-test compares the means between

two unrelated groups on the same continuous, dependent variable. The results

have two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-27.

Annexure-27 Group Statistics

Q.54 N Mean Std. Deviation Std. Error Mean

Tie strength student 273 2.8388 0.96140 0.05819

working 218 3.0390 0.97295 0.06590

136 | P a g e

There are 273 students, and they have, on average, 2.8388, with a standard

deviation of 0.96140. There are 218 working professional, and they have, on

average, 3.0390, with a standard deviation of 0.97295. The last column gives the

standard error of the mean for each of the two groups. Above table shows that tie

strength is important factor for working professional than the students..

The second part of the output gives the inferential statistics which is shown in

Annexure-28

Annexure-28

Independent Samples Test

Tie Strength

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.196

0.658

t-test for Equality of Means

t -2.280 -2.277

df 489 462.834

Sig. (2-tailed) 0.023 0.023

Mean Difference -0.20016 -0.20016

Std. Error Difference 0.08779 0.08791

95% Confidence Interval Lower of the Difference Upper

-0.37266 -0.37291

-0.02767 -0.02741

From the Annexure-24 it is assumed that Sig. (2-tailed) value is 0.023 is less

than 0.05, so we fail to accept null hypothesis. This implies that we observe

impact of independent variable tie strength on intention to forward viral messages

with respect to occupation. A t test reveal a statistically reliable difference

between the mean of tie strength that student has (M = 2.8388, s = 0.96140) and

that the working professional has (M = 3.0390, s = 0.97295), t(489) = --2.280, p =

137 | P a g e

0.023, α = .05. It proves that independent variable tie strength has impact on

dependent variable i.e. intention to forward viral messages with respect to

occupation. Occupation was considered as a contributing factor in further

examinations of intention to forward viral messages. Therefore hypothesis 15 is

accepted as there is significant impact of tie strength on intention to forward viral

messages with respect to occupation.

Hypothesis 16: Sender‘s benefit has significant impact on user to forward viral

messages.

The overall multiple regression model which is depicted in Annexure-24 was

found to be significant (2

.AdjR . = 0.423), F (4, 490) = 90.742, p < .001. Sender‘s

Benefit (β = 0.304, t = 6.563, p < .05) were found to be having significant impact

on user to forward viral messages. Therefore Hypothesis 16 is accepted that

Sender‘s benefit has significant impact on user to forward viral messages.

Hypothesis 17: Sender‘s benefit has significant impact on user to forward viral

messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is sender‘s benefit on the intention to forward viral messages with

respect to gender. Independent sample t-test compares the means between two

unrelated groups on the same continuous, dependent variables. The results have

two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-29.

138 | P a g e

Annexure-29 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

senders_benefit Male 354 2.6850 0.90932 0.04833

Female 137 2.6642 0.97224 0.08306

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 345 male, and they

have, on average, 2.6850, with a standard deviation of 0.90932. There are 137

female, and they have, on average, 2.6642, with a standard deviation of 0.97224.

The last column gives the standard error of the mean for each of the two groups.

This shows that for male sender‘s benefit is the important factor to intention to

forward viral messages than female. The second part of the output gives the

inferential statistics which is shown in Annexure-30.

Annexure-30

Independent Samples Test

Sender’s Benefit

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.632

0.427

t-test for Equality of Means

t 0.223 0.216

df 489 233.363

Sig. (2-tailed) 0.824 0.829

Mean Difference 0.02079 0.02079

Std. Error Difference 0.09330 0.09610

95% Confidence Interval Lower of the Difference Upper

-0.16252 0.20411

-0.16854 0.21013

From the Annexure-30 it is assumed that Sig. (2-tailed) value is 0.824 is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

139 | P a g e

failed to observe impact of independent variable sender‘s benefit on intention to

forward viral messages with respect to gender. A t test failed to reveal a

statistically reliable difference between the mean of sender‘s benefit that male

has (M = 2.6850, s = 0.90932) and that the female has (M = 2.6642, s =

0.97224), t(489) = 0.223, p = 0.824, α = .05. It proves that independent variable

sender‘s benefit does not have any impact on dependent variable i.e. intention to

forward viral messages with respect to gender. Gender was not considered as a

contributing factor in further examinations of intention to forward viral messages.

Therefore hypothesis 17 is rejected as there is no significant impact of sender‘s

benefit on intention to forward viral messages with respect to gender.

Hypothesis 18: Sender‘s benefit has significant impact on user to forward viral

messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is sender‘s benefit on the intention to forward viral messages with

respect to occupation. Independent sample t-test compares the means between

two unrelated groups on the same continuous, dependent variable. The results

have two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-31.

Annexure-31 Group Statistics

Q.54 N Mean Std.

Deviation

Std. Error Mean

Sender‘s Benefit student 273 2.6593 0.91242 0.05522

working 218 2.7041 0.94501 0.06400

140 | P a g e

There are 273 students, and they have, on average, 2.6593, with a standard

deviation of 0.91242. There are 218 working professional, and they have, on

average, 2.7041, with a standard deviation of 0.94501. Above table shows that

sender‘s benefit is important factor for working professional than students.

The second part of the output gives the inferential statistics which is shown in

Annexure-32

Annexure-32

Independent Samples Test

Sender’s Benefit

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.012

0.912

t-test for Equality of Means

t -0.532 -0.530

df 489 457.892

Sig. (2-tailed) 0.595 0.596

Mean Difference -0.04479 -0.04479

Std. Error Difference 0.08420 0.08453

95% Confidence Interval Lower of the Difference Upper

-0.21023 0.12065

-0.21091 0.12134

From the Annexure-32 it is assumed that Sig. (2-tailed) value is 0.595 is not less

than 0.05, so we fail to reject null hypothesis. This implies that we observe no

impact of independent variable sender‘s benefit on intention to forward viral

messages with respect to occupation. A t test reveal a statistically reliable

difference between the mean of sender‘s benefit that student has (M = 2.6593, s

= 0.91242) and that the working professional has (M = 2.7041, s = 0.94501),

t(489) = -0.532, p = 0.595, α = .05. It proves that independent variable sender‘s

141 | P a g e

benefit has no impact on dependent variable i.e. intention to forward viral

messages with respect to occupation. Occupation was not considered as a

contributing factor in further examinations of intention to forward viral messages.

Therefore hypothesis 18 is rejected as there is no significant impact of sender‘s

benefit on intention to forward viral messages with respect to occupation.

Hypothesis 19: Customer Satisfaction has significant impact on user to forward

viral messages.

The overall multiple regression model which is depicted in Annexure-24 was

found to be significant (2

.AdjR . = 0.423), F (4, 490) = 90.742, p > .001. Customer

satisfaction (β = 0.100, t = 1.909, p > .05) were found to be having significant no

impact on user to forward viral messages. Therefore Hypothesis 19 is rejected

that customer satisfaction has no significant impact on user to forward viral

messages.

Hypothesis 20: Customer Satisfaction has significant impact on user to forward

viral messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is customer satisfaction on the intention to forward viral messages

with respect to gender. Independent sample t-test compares the means between

two unrelated groups on the same continuous, dependent variables. The results

have two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-33.

142 | P a g e

Annexure-33 Group Statistics

Q.51 N Mean Std. Deviation Std. Error

Mean

Customer Satisfaction Male 354 2.9360 0.86606 0.04603

Female 137 2.8297 0.81661 0.06977

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 345 male, and they

have, on average, 2.9360, with a standard deviation of 0.86606. There are 137

female, and they have, on average, 2.8297, with a standard deviation of 0.81661.

The last column gives the standard error of the mean for each of the two groups.

This shows that for male customer satisfaction is the important factor to intention

to forward viral messages than female. The second part of the output gives the

inferential statistics which is shown in Annexure-34.

Annexure-34

Independent Samples Test

Customer Satisfaction

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

1.655

0.199

t-test for Equality of Means

t 1.239 1.272

df 489 261.107

Sig. (2-tailed) 0.216 0.205

Mean Difference 0.10629 0.10629

Std. Error Difference 0.08579 0.08358

95% Confidence Interval Lower of the Difference Upper

-0.06227 0.27484

-0.05830 0.27087

From the Annexure-34 it is assumed that Sig. (2-tailed) value is 0.216 is not less

143 | P a g e

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable customer satisfaction on

intention to forward viral messages with respect to gender. A t test failed to

reveal a statistically reliable difference between the mean of customer

satisfaction that male has (M = 2.9360, s = 0.86606) and that the female has (M

= 2.8297, s = 0.81661), t(489) = 1.239, p = 0.216, α = .05. It proves that

independent variable customer satisfaction does not have any impact on

dependent variable i.e. intention to forward viral messages with respect to

gender. Gender was not considered as a contributing factor in further

examinations of intention to forward viral messages. Therefore hypothesis 20 is

rejected as there is no significant impact of customer satisfaction on intention to

forward viral messages with respect to gender.

Hypothesis 21: Customer Satisfaction has significant impact on user to forward

viral messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is customer satisfaction on the intention to forward viral messages

with respect to occupation. Independent sample t-test compares the means

between two unrelated groups on the same continuous, dependent variable. The

results have two main parts: descriptive statistics and inferential statistics.

Descriptive statistics data is displayed in Annexure-35.

144 | P a g e

Annexure-35 Group Statistics

Q.54 N Mean Std. Deviation Std. Error

Mean

Customer Satisfaction student 273 2.8596 0.84370 0.05106

working 218 2.9648 0.86301 0.05845

There are 273 students, and they have, on average, 2.8596, with a standard

deviation of 0.84370. There are 218 working professional, and they have, on

average, 2.9648, with a standard deviation of 2.9648. Above table shows that

customer satisfaction is important factor for working professional than students.

The second part of the output gives the inferential statistics which is shown in

Annexure-36

Annexure-36

Independent Samples Test

Customer Satisfaction

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.219

0.640

t-test for Equality of Means

t -1.359 -1.356

df 489 460.591

Sig. (2-tailed) 0.175 0.176

Mean Difference -0.10525 -0.10525

Std. Error Difference 0.07742 0.07761

95% Confidence Interval Lower of the Difference Upper

-0.25736 0.04686

-0.25777 0.04727

From the Annexure-36 it is assumed that Sig. (2-tailed) value is 0.175 is not less

than 0.05, so we fail to reject null hypothesis. This implies that we observe no

impact of independent variable customer satisfaction on intention to forward viral

messages with respect to occupation. A t test reveal a statistically reliable

145 | P a g e

difference between the mean of customer satisfaction that student has (M =

2.8596, s = 0.84370) and that the working professional has (M = 2.9648, s =

0.86301), t(489) = -1.359, p = 0.175, α = .05. It proves that independent variable

customer satisfaction has no impact on dependent variable i.e. intention to

forward viral messages with respect to occupation. Occupation was not

considered as a contributing factor in further examinations of intention to forward

viral messages. Therefore hypothesis 21 is rejected as there is no significant

impact of sender‘s benefit on intention to forward viral messages with respect to

occupation.

Hypothesis 22: Altruism has significant impact on user to forward viral

messages.

The overall multiple regression model which is depicted in Annexure-24 was

found to be significant (2

.AdjR . = 0.423), F (4, 490) = 90.742, p > .001. Altruism (β

= 0.303, t = 6.069, p > .05) were found to be having no significant impact on user

to forward viral messages. Therefore Hypothesis 22 is rejected that altruism has

no significant impact on user to forward viral messages.

Hypothesis 23: Altruism has significant impact on user to forward viral

messages with respect to gender.

The independent sample t-test is carried out to see the impact of independent

variable which is altruism on the intention to forward viral messages with respect

to gender. Independent sample t-test compares the means between two

146 | P a g e

unrelated groups on the same continuous, dependent variables. The results have

two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-37.

Annexure-37 Group Statistics

Q.51 N Mean Std. Deviation Std. Error Mean

altruism Male 354 3.0377 0.90803 0.04826

Female 137 3.0000 0.86319 0.07375

This gives the descriptive statistics for each of the two groups (as defined by the

grouping variable, in this case is male and female). There are 345 male, and they

have, on average, 3.0377, with a standard deviation of 0.90803. There are 137

female, and they have, on average, 3.0000, with a standard deviation of 0.86319.

The last column gives the standard error of the mean for each of the two groups.

This shows that for male altrism is the important factor to intention to forward viral

messages than female. The second part of the output gives the inferential

statistics which is shown in Annexure-38.

Annexure-38

Independent Samples Test Altruism

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

1.063

0.303

t-test for Equality of Means

t 0.418 0.427

df 489 259.119

Sig. (2-tailed) 0.676 0.669

Mean Difference 0.03766 0.03766

Std. Error Difference 0.09013 0.08814

95% Confidence Interval Lower of the Difference Upper

-0.13943 0.21476

-0.13589 0.21122

147 | P a g e

From the Annexure-38 it is assumed that Sig. (2-tailed) value is 0.418 is not less

than or equal to .05, so we fail to reject null hypothesis. This implies that we

failed to observe impact of independent variable altruism on intention to forward

viral messages with respect to gender. A t test failed to reveal a statistically

reliable difference between the mean of altruism that male has (M = 3.0377, s =

0.90803) and that the female has (M = 3.0000, s = 0.86319), t(489) = 0.418, p =

0.676, α = .05. It proves that independent variable altruism does not have any

impact on dependent variable i.e. intention to forward viral messages with

respect to gender. Gender was not considered as a contributing factor in further

examinations of intention to forward viral messages. Therefore hypothesis 23 is

rejected as there is no significant impact of customer satisfaction on intention to

forward viral messages with respect to gender.

Hypothesis 24: Altruism has significant impact on user to forward viral

messages with respect to occupation.

The independent sample t-test is carried out to see the impact of independent

variable which is altruism on the intention to forward viral messages with respect

to occupation. Independent sample t-test compares the means between two

unrelated groups on the same continuous, dependent variable. The results have

two main parts: descriptive statistics and inferential statistics. Descriptive

statistics data is displayed in Annexure-39.

148 | P a g e

Annexure-39 Group Statistics

Q.54 N Mean Std. Deviation Std. Error Mean

altruism student 273 3.0061 0.89226 0.05400

working 218 3.0535 0.89984 0.06095

There are 273 students, and they have, on average, 3.0061, with a standard

deviation of 0.89226. There are 218 working professional, and they have, on

average, 3.0535, with a standard deviation of 0.89984. Above table shows that

altruism is important factor for working professional than students. The second

part of the output gives the inferential statistics which is shown in Annexure-40

Annexure-40

Independent Samples Test

Altruism

Equal variance assumed

Equal variance not

assumed

Levene‘s Test for Equality of Variances

F Sig.

0.109

0.741

t-test for Equality of Means

t -0.583 -0.582

df 489 463.546

Sig. (2-tailed) 0.560 0.561

Mean Difference -0.04741 -0.04741

Std. Error Difference 0.08135 0.08143

95% Confidence Interval Lower of the Difference Upper

-0.20725 0.11243

-0.20743 0.11260

From the Annexure-40 it is assumed that Sig. (2-tailed) value is 0.560 is not less

than 0.05, so we fail to reject null hypothesis. This implies that we observe no

impact of independent variable altruism on intention to forward viral messages

with respect to occupation. A t test reveal a statistically reliable difference

between the mean of altruism that student has (M = 3.0061, s = 0.89226) and

149 | P a g e

that the working professional has (M = 3.0535, s = 0.89984), t(489) = --0.583, p =

0.560, α = .05. It proves that independent variable altruism has no impact on

dependent variable i.e. intention to forward viral messages with respect to

occupation. Occupation was not considered as a contributing factor in further

examinations of intention to forward viral messages. Therefore hypothesis 24 is

rejected as there is no significant impact of altruism on intention to forward viral

messages with respect to occupation.

The results of hypotheses testing have been summarized into Table 6.

Table 6

Alternative Hypothesis Variable

Statistical Test

Hypothesis Accepted/ Rejected

Independent Dependent

Access has significant impact on experiences of viral marketing.

Access Experience Correlation Rejected Null-Accepted

Awareness has significant impact on experiences of viral marketing.

Awareness Experience Correlation Accepted Null- Rejected

Interest has significant impact on experiences of viral marketing.

Interest Experience Correlation Accepted Null- Rejected

Trusted source does not have significant impact on user to receive viral messages.

Trusted Source Receive viral messages

Regression Accepted Null- Rejected

Trusted source does not have significant impact on user to receive viral messages with respect to gender.

Trusted Source Receive viral messages

Independent sample t-test

Rejected Null-Accepted

150 | P a g e

Hypothesis Variable

Statistical Test

Hypothesis Accepted/ Rejected

Independent Dependent

Trusted source does not have significant impact on user to receive viral messages with respect to occupation.

Trusted Source Receive viral messages

Independent sample t-test

Rejected Null-Accepted

Message relevance does not have significant impact on receiving viral messages.

Relevance Receive viral messages

Regression Accepted Null- Rejected

Message relevance does not have significant impact on receiving viral messages with respect to gender.

Relevance Receive viral messages

Independent sample t-test

Rejected Null-Accepted

Message relevance does not have significant impact on receiving viral messages with respect to occupation.

Relevance Receive viral messages

Independent sample t-test

Accepted Null- Rejected

A perceived benefit does not have significant impact on user to receive viral messages.

Perceived benefits

Receive viral messages

Regression Accepted Null- Rejected

A perceived benefit does not have significant impact on user to receive viral messages with respect to gender.

Perceived benefits

Receive viral messages

Independent sample t-test

Rejected Null-Accepted

151 | P a g e

Hypothesis Variable

Statistical Test

Hypothesis Accepted/ Rejected

Independent Dependent

A perceived benefit does not have significant impact on user to receive viral messages with respect to occupation.

Perceived benefits

Receive viral messages

Independent sample t-test

Rejected Null-Accepted

Tie strength does not have significant impact on user to forward viral messages.

Tie strength Forward message

Regression Accepted Null- Rejected

Tie strength does not have significant impact on user to forward viral messages with respect to gender.

Tie strength Forward message

Independent sample t-test

Rejected Null-Accepted

Tie strength does not have significant impact on user to forward viral messages with respect to occupation.

Tie strength Forward message

Independent sample t-test

Accepted Null- Rejected

Sender‘s benefit does not have significant impact on user to forward viral messages.

Sender‘s benefit Forward message

Regression Accepted Null- Rejected

Sender‘s benefit does not have significant impact on user to forward viral messages with respect to gender.

Sender‘s benefit Forward message

Independent sample t-test

Rejected Null-Accepted

152 | P a g e

Hypothesis Variable

Statistical Test

Hypothesis Accepted /Rejected

Independent Dependent

Sender‘s benefit does not have significant impact on user to forward viral messages with respect to occupation.

Sender‘s benefit Forward message

Independent sample t-test

Rejected Null-Accepted

Customer Satisfaction does not have significant impact on user to forward viral messages.

Customer Satisfaction

Forward

message

Regression Rejected Null-Accepted

Customer Satisfaction does not have significant impact on user to forward viral messages with respect to gender.

Customer Satisfaction

Forward

message

Independent

sample t test

Rejected Null-Accepted

Customer Satisfaction does not have significant impact on user to forward viral messages with respect to occupation.

Customer Satisfaction

Forward

message

Independent

sample t test

Rejected Null-Accepted

Altruism does not have significant impact on user to forward viral messages.

Altruism Forward message

Regression Rejected Null-Accepted

Altruism does not have significant impact on user to forward viral messages with respect to gender.

Altruism Forward message

Independent sample t-test

Rejected Null-Accepted

Altruism does not have significant

Altruism Forward message

Independent sample t-test

Rejected

153 | P a g e

impact on user to forward viral messages with respect to occupation.

Null-Accepted

To find out the correlation between the intention to receive viral messages with

the factors which influence users which are trusted source, relevance and

perceived benefits a Pearson product-moment correlation was run across the

variables to determine the relationship between the influencing factors to receive

viral messages. From the Annexure-41 it is observed that there is a strong and

positive correlation between intention to receive viral messages and trusted

source which was statistically significant (r = 0.373, n = 491, value of P = 0.000

where P < .0005). There is a strong and positive correlation between intention to

receive viral messages and relevance which are statistically significant (r = 0.400,

n = 491, value of P = 0.000 where P < .0005) as well. It is also proved that there

is strong and positive correlation between intention to receive viral messages and

perceived benefits which are statistically significant (r = 0.433, n = 491, value of

P = 0.000 where P < .0005).

Annexure-41

Correlations

Trusted Source Relevance Perceived benefits

Intention to receive

Pearson Correlation

0.373(**) 0.400(**) 0.433(**)

Sig. (2-tailed) 0.000 0.000 0.000

N 491 491 491

** Correlation is significant at the 0.01 level (2-tailed).

154 | P a g e

To find out the correlation between the intention to forward viral messages with

the factors which influence users which are tie strength, sender‘s benefit,

customer satisfaction and altruism a Pearson product-moment correlation was

run across the variables to determine the relationship between the influencing

factors to receive viral messages. From the Annexure-42 it is observed that there

is a strong and positive correlation between intention to forward viral messages

and tie strength which was statistically significant (r = 0.586, n = 491, value of P =

0.000 where P < .0005). There is a strong and positive correlation between

intention to forward viral messages and sender‘s benefit which are statistically

significant (r = 0.576, n = 491, value of P = 0.000 where P < .0005) as well. It is

also proved that there is strong and positive correlation between intention to

forward viral messages and customer satisfaction which are statistically

significant (r = -0.499, n = 491, value of P = 0.000 where P < .0005) and strong

and positive correlation between intention to forward messages and altruism as

well which are statistically significant (r = 0.371, n = 491, value of P = 0.000

where P < .0005).

Annexure-42

Correlations

Tie

Strength Sender‘s Benefit

Customer Satisfaction Altruism

Intention to forward

Pearson Correlation

0.586(**) 0.576(**) 0.499(**) 0.371(**)

Sig. (2-tailed) 0.000 0.000 0.000 0.000

N 491 491 491 491

** Correlation is significant at the 0.01 level (2-tailed).

155 | P a g e

Chapter 8

Conclusion, Limitation and Future Scope

Conclusion This study was designed to study drivers of viral marketing and to reveal and

validate factors which influence user to receive and forward viral messages. In

the first part of the study sequence of drivers of viral marketing is identified.

Existing literature in the area of viral marketing suggest the sequence of drivers

of viral marketing. This sequence is awareness, interest and experience of viral

marketing. Last chain of this sequence is experience. Awareness about the viral

marketing creates interest in product about which user gets information through

viral marketing. Therefore, awareness and interest leads to the experience of the

viral marketing strategy adopted by the business. On top of this sequence extra

attribute is added which is access to identify whether access have any significant

impact on the experience or not.

Analysis was conducted to see the impact of awareness which was not there in

the earlier literature and other attributes which are available in the existing

literature are awareness and interest have any significant impact on the

experiences of viral marketing. After analysis it has been found that access does

not have any significant impact on the experiences of viral marketing. But,

awareness and interest do have significant impact on experiences of viral

marketing. Analysis was done to see the association between these variables. It

has been found that there is strong and positive correlation between awareness

156 | P a g e

and interest with the experience of viral marketing and there is negative

correlation between access and experience of viral marketing.

Descriptive statistic shows that score of experience is interest was higher which

is followed by experience. Reason behind it may be data collected from the

internet user and for them access is not the important criteria to get the

experiences of viral marketing. As internet user do get access regularly on the

internet. But the awareness of the viral marketing they get by having account on

the social networking and other internet portal, search engine creates interest in

getting information about the product which gives them total experience of viral

marketing. This shows that, access is not a contributing factor for the

experiences of viral marketing.

To find out the correlation between the drivers of viral marketing which are

access, awareness, interest and experience a Pearson product-moment

correlation was run across the variables to determine the relationship between

the sequences of drivers of viral marketing. It is observed that there is a strong

and positive correlation between access and interest. There is negative

correlation between access and awareness. It is also proved that there is

negative relationship between awareness and access and awareness and

interest. There is negative correlation between interest and awareness but there

is strong and positive correlation between interest and access. This proves that

157 | P a g e

access does not have any correlation with other attributes of sequence of viral

marketing.

The second part of the study is carried out to reveal and validate the factors

which influenced user to receive viral messages. The success of the viral

marketing lies in the propagation of viral messages. Therefore it was very

essential to identify the factors and validate them. Existing literature in the area of

viral marketing suggests information adoption model and research model. From

these two models three significant factors are identified which are trusted source,

relevance and perceived benefits. According it was hypothesizes that the trusted

source, relevance, perceived benefits have significant impact on user to receive

viral messages. Existing literature also revealed and validates these factors as

influencing factors for user to receive viral messages.

Descriptive statistics shows that the score of relevance is higher that the

perceived benefits and the least score of trusted sources. Regression analysis is

done to see each of these factors have significant on user to receive viral

messages. It shows that trusted source, relevance and perceived benefits have

significant impact on user to receive viral messages. Reason behind it security is

the main concern for the user as most of the promotional mails are considered as

spam. But, users surely give it a try if it is from a trusted source. Message

relevance is also another important factor. If message is what he is looking for

and of his interest it influences user to receive those messages. Another factor

158 | P a g e

which validated was perceived benefits. Perceived benefits in terms of bonus

point, discount, prize, free service influenced user to receive viral messages.

Comparison of influencing factors to receive viral messages of Male &

Female:

Since the gender is a natural variable around which social world is organized, it is

natural to compare persons belonging to these two group viz. males and

females. Literature review and numerous previous studies have revealed that no

Significant difference has been noticed in the behavior & attitude of males and

females in the context of viral marketing. The same conclusion is drawn in the

current study also. Analysis was conducted to compare the mean score of male

and female on various types of influencing factors, and this yielded that there is

no significant difference of the influencing factors on receiving messages by

males and female. Descriptive statistics shows that female are more concerned

with the trusted source that the males as the mean score of female for trusted

score was higher that the male. It also shows that working professional are more

concerned with the trusted source while receiving viral messages that the

students. For male message relevance is important that the female while

receiving messages. For male mean score of perceived benefits is more than the

female shows that perceived benefit is an important factor for male that the

female. This proves that trusted source is important factor for females whereas

relevance, perceived benefits are important factors for male to receive viral

messages.

159 | P a g e

Comparison of influencing factors to receive viral messages of student and

professional:

There was no previous study found in earlier literature whether occupation is

contributing factor for intention to receive viral messages. Therefore it was

decided to see whether occupation has any impact on receiving viral messages.

For this study occupation which is considered was student and working

professional. Like gender independent sample t-test is conducted and the result

shows that occupation is not a contributing factor in receiving viral messages. For

working professional trusted source is important factor that the students as it

shows higher mean value. Whereas working professional are more concerned

with the message relevance that the students. Working professional mean value

shows that to receive viral messages perceived benefits is an influencing factor

as the mean score is higher than the students. This proves that the trusted

source, message relevance and perceived benefits are important factors for

working profession to receive viral messages that the students.

To find out the association between the trusted source, relevance, perceived

benefits with the intention to receive viral messages a regression analysis is

done. Result of the regression analysis shows that all these factors shows the

significant impact on intention to open viral messages where p value is less than

0.005. This proves that trusted source, relevance, perceived benefits are the

factors which influence user to receive viral messages. Gender and occupation

does not contribute any role while receiving viral messages.

160 | P a g e

Third part of the study concentrates on to reveal and validates factors which

influence user to forward messages. In existing literature no combine study of

factors which influence user to receive as well forward message is carried out.

For the success of viral marketing it is very essential to reveal factors which are

responsible to forward messages. Therefore combine study is taken for this

research. From the previous literature factors which are identified as a

influencing factors for forwarding messages are tie strength, sender‘s benefit,

customer satisfaction and altruism. Accordingly it is hypothesizes that the tie

strength, sender‘s benefit, customer satisfaction and altruism have significant

impact on user to forward viral messages. In Descriptive statistics it has been

observed that mean score of altruism is highest score which was preceded by tie

strength, customer satisfaction and sender‘s benefit.

Regression analysis is proved that tie strength is influencing factor to forward

messages. Tie strength is an acquaintance with user is connected. If the tie

strength is strong likelihood of message forwarding increases than the weak tie

strength. Further study is required to see the result between strong

acquaintances and weak acquaintances. This study also proves that sender‘s

benefit also has significant impact on user to forward viral messages. Sender‘s

benefit like incentives, rewards, free services etc. Customer satisfaction and

altruism does not have any impact on user to forward messages. From this

analysis it is concluded that, tie strength and senders‘ benefit are the factors

161 | P a g e

which impact user to forward viral messages whereas customer satisfaction and

altruism does not impact user to forward viral messages.

Comparison of influencing factors to receive viral messages of Male &

Female:

For this study also comparison between male and female is studied to see

whether gender play any role while forwarding viral messages. For this

independent sample t-test is carried out and it has been observed that tie

strength is important factor for male than the female but there is no significant

impact of tie strength on intention to forward viral messages with respect to

gender. Sender‘s benefit is a important factor for male than the female to forward

viral messages. But gender was not considered as a contributing factor in further

examinations of intention to forward viral messages. Therefore hypothesis is

rejected as there is no significant impact of sender‘s benefit on intention to

forward viral messages with respect to gender. Customer satisfaction and

altruism is considered to be most important factor for male than female but it

does not have any significant impact on user to forward viral messages with

respect to gender.

Comparison of influencing factors to forward viral messages of student

and professional:

Tie strength is important factor for working professional that the student. Reason

behind it may working professional have purchasing power therefore they like

162 | P a g e

recommend product in his/her network if he/she is satisfy with the product.

Therefore hypothesis is accepted as there is significant impact of tie strength on

intention to forward viral messages with respect to occupation. Analysis shows

that sender‘s benefit is important factor for working professional than students.

Occupation was not considered as a contributing factor in further examinations of

intention to forward viral messages. Therefore hypothesis rejected as there is no

significant impact of sender‘s benefit on intention to forward viral messages with

respect to occupation. Customer satisfaction and altruism are the important

factor for the working professional that the students to forward viral messages nt

but research study doesn‘t support that customer satisfaction and altruism are

the influencing factor to forward viral messages with respect to occupation.

To find out the association between the intention to receive viral messages and

influencing factors which are trusted source, relevance, perceived benefits a

Pearson correlation test is carried out. It is observed that there is strong and

positive correlation between these variables with the intention to receive viral

messages. Similarly To find out the association between the intention to forward

viral messages and influencing factors which are tie strength, sender‘s benefit,

customer satisfaction, and altruism a Pearson correlation test is carried out. It is

observed that there is strong and positive correlation between these variables

with the intention to forward viral messages.

The summary of objectives and outcomes are illustrated in Table-7.

163 | P a g e

Table-7

Summary of objectives and outcomes

Sr. No.

Objectives Outcomes

1. To understand viral marketing through social network.

Viral marketing is electronic word of mouth technique adopted by the business where message about the product and its brands or services is send to a potential buyer over internet. This potential buyer sends this information to another potential buyer in a way a large network is created swiftly. Viral Marketing Communication can bring about benefits to marketers with its advantages such as low cost, high reach, high credibility, accountability, fast speed, ease of usage and ability to reach a global audience.

2. To identify drivers for viral marketing.

To understand viral marketing, it is very essential to understand the drivers of viral marketing and these drivers are access, awareness, interest and experience. Research study proves that awareness and interest are the major factor which gives the experience of viral marketing whereas access does contribute to the experience of viral marketing

3. To reveal and validate factors which influence user to receive and forward messages.

For the success of viral marketing it is imperative to reveal and validate the factors which influence user to receive and forward viral messages. No combine study known in exists in Indian context. This study is helpful for the marketer while designing their viral marketing strategy.

164 | P a g e

Factors which are revealed to receive viral messages are trusted source, relevance and perceived benefits. These factors are validates by using statistical test and it is observed that trusted source, relevance and perceived benefits have significant impact on user to receive viral messages. Factors which are revealed to forward viral messages are tie strength, sender‘s benefit, customer satisfaction and altruism. These factors are validates by using statistical test and it is observed that tie strength, sender‘s benefit have significant impact on user to receive viral messages whereas customer satisfaction and altruism does not contribute for forwarding messages.

4. To understand impact of demographic factors of user on receiving and forwarding messages.

Demographic factors considered for this study are gender and occupation. No study on this demographic factor known to exists in Indian context. Data is validated with the help of statistical test and it has been observed that: Influencing factor to receive viral messages are trusted source, relevance and perceived benefits. Trusted source does not have any significant impact on user to receive viral messages with respect to gender and occupation. Relevance does not have any significant impact on user to receive

165 | P a g e

viral messages with respect to gender but it has significant impact on user to receive viral messages with respect to occupation. A perceived benefit does not have any significant impact on user to receive viral messages with respect to gender and occupation. Tie strength does not have any significant impact on user to forward viral messages with respect to gender but it has significant impact on user to forward viral messages with respect to occupation. Senders‘ benefit does not have any significant impact on user to forward viral messages with respect to gender and occupation. Customer satisfaction does not have any significant impact on user to forward viral messages with respect to gender and occupation. Altruism does not have any significant impact on user to forward viral messages with respect to gender and occupation. It is proved that demographic factor occupation plays significant role in receiving (relevance) and forwarding (tie strength) viral messages.

166 | P a g e

Chapter-8

Limitations and Future Scope of the Study Current research study examines the drivers of viral marketing and reveal and

validate influencing factors to receive and forward messages. For the current

study factors were identified form earlier study in the area of viral marketing.

These factors were validated by collecting primary data and for that respondents

identified were the internet users.

Data collected through Mumbai only which not a true representation of pan India.

If a different demographic group were used, it is possible that the results could

have been different. Since this the first combine study of identifying factors which

influence user to receive and forward messages, replication of this study would

be essential.

Only platform of viral marketing considered in the research study is email. Other

platform of viral marketing like company website, online review, blogs, social

network, online communities, newsgroups, chat rooms, hate sites, needs to be

considered and compare different levels of impact on these eWOM forms on

consumer behavior.

This study may not have identified all the factors which influence user to receive

and forward messages. Therefore, another limitations lies in the limited number

of variables examined in relation to receive and forward messages.

167 | P a g e

Appendix-1

Bibliography

1. A.Dobele, Why pass on viral messages? Because they connect

emotionally. 2007. Business Horizons, Vol. 50, Iss. 4; 291-304

2. Abed Abedniya, Sahar Sabbaghi Mahmouei, The Impact of Social

Networking Websites to Facilitate the Effectiveness of Viral Marketing,

December 2010, International Journal of Advanced Computer Science

and Applications, Vol. 1, No.6

3. Andrea C. Wojnicki,, Word-of-Mouth and Word-of-Web: Talking About

Products, Talking About Me, 2006, Advances in Consumer Research,

Volume 33,

4. Andrew Paul Williams, Kaye D Trammell, Candidate Campaign E-Mail

Messages in the Presidential Election, 2004, The American Behavioral

Scientist. Thousand Oaks: Dec 2005. Vol. 49, Iss. 4; pg. 560, 15 pgs

5. Angela Dobelea,David Tolemanb, Michael Beverland, Controlled infection!

Spreading the brand message through viral marketing, 2005, Business

Horizons, 48, 143—149

6. Angela Dobele, Adam Lindgreen, Michael Beverland, Joëlle Vanhamme,

Robert van Wijk, Why pass on viral messages? Because they connect

emotionally, 2007, Business Horizons, 50, 291–304

7. Antti Vilpponen, Susanna Winter, and Sanna Sundqvist , Electronic Word-

of-Mouth in Online Environments: Exploring Referral Network Structure

168 | P a g e

and Adoption Behavior, 2006, Journal of Interactive Advertising, Vol.6, No.

2, pp. 63‐77.

8. Arnaud De Bruy, Gary L. Lilien, A Multi-Stage Model of Word of Mouth

Through Electronic Referrals, February, 2004, eBusiness Research

Center, Working Paper

9. Arnaud De Bruyn, Gary L. Lilien, A multi-stage model of word-of-mouth

influence through viral marketing, 2008, International Journal of Research

in Marketing 25, 151-163

10. Arnaud De Bruyn, Gary L. Lilien, February, A Multi-Stage Model of Word

of Mouth Through Electronic Referrals, 2008, International Journal of

Research in Marketing, pp 151-163

11. Barnes, N., & Mattson, E., (2008), Social media in the Inc. 500: The first

longitudinal study [Electronic version]. University of Massachusetts

Dartmouth Center for Marketing Research. Retrieved from:

http://www.umassd.edu/cmr/studiesresearch/blogstudy5.pdf

12. Biyalogorsky, E., E. Gerstner, and B. Libai, Customer Referral

Management: Optimal Reward Programs, 2001, Marketing Science, 20

(1), 82-95.

13. Brown, J. and Reingen, P. Social Ties and Word-of-Mouth Referral

Behavior, 1987, Journal of Consumer Research 14(3): 350–362.

14. Christy M.K. Cheung, M. K., The impact of electronic word-of-mouth The

adoption of online opinions in online customer communities, 2008,

Emerald Internet Research , Vol. 18 No. 3, pp. 229-247.

169 | P a g e

15. Daniels, Chris, I am viral: how Molson successfully spreads its online

marketing message without opting for send-to-a-friend buttons, Jul 2002,

Digital Marketing, pg. 7

16. David Aron, The Effect of Counter-Experiential Marketing Communication

on Satisfaction and Repurchase Intention, 2006, Journal of Consumer

Satisfaction, Dissatisfaction and Complaining Behavior, Vol. 19 pg. 1,

17. Datta, P., Chowdhury, D., & Chakraborty, B., Viral marketing: New form of

wordof-mouth through internet [Electronic version]. 2005, The Business

Review, Cambridge, 3(2), 69-75.

18. Dellarocas, Chrysanthos, The Digitalization of Word of Mouth: Promise

and Challenges of Online Feedback Mechanisms, 2003,Management

Science, 49 (10), 1407-1424.

19. Dobele, A., Toleman, D., & Beverland, M., Controlled infection! Spreading

the brand message through viral marketing. 2005, Business Horizons,

48(2), 143−149.

20. Dongyoung Sohn; John D Leckenby, Product Class Knowledge as a

Moderator of Consumer‘s Electronic Word-of-Mouth…,2005, American

Academy of Advertising. Conference. Proceedings; ABI/INFORM Global

pg. 20

21. Dr. Ralph F. Wilson, The Six simple Principles of Viral Marketing, Web

Marketing Today, February 1, 2005. Originally published 2/1/2000.

170 | P a g e

22. Dunk, A.S, Product life cycle cost analysis: the impact of customer

profiling, competitive advantage, and quality of IS information, 2004,

Management Accounting Research, Vol. 15 No. 4, pp. 401-14.

23. Feick, L.F., Guskey, A. and Price, L., Everyday Market Helping Behavior,

1995, Journal of Public Policy & Marketing 14(2): 255–266.

24. Frenzen, J. K. and H. L. Davis, Purchasing Behavior in Embedded

Markets, 1990, Journal of Consumer Research 17(1), pp. 1-12.

25. Frenzen, Jonathan and Kent Nakamoto, Structure, Cooperation, and the

Flow of Market Information, 1993, Journal of Consumer Research, 20

(December), 360-375.

26. Gefen, D., Customer Loyalty in E-Commerce, 2002, Journal of the

Association for Information Systems 3(2): 27–51.

27. Gefen, D., Karahanna, E. and Straub, D.W., Inexperience and Experience

with Online Stores: The importance of TAM and trust, 2003, IEEE

Transactions on Engineering Management 50(3): 307–321.

28. Giovanna Miritello, Esteban Moro, and Rub_en Lara, The dynamical

strength of social ties in information spreading , November 2010,

arXiv:1011.5367v1 [physics.soc-ph].

29. Gold, Robert, Brown, Andrew Z, Building trust in an increasingly private

world, May 2002, Digital Marketing, pg. 18

30. Granovetter, M.S., The Strength of Weak Ties, American Journal of

Sociology, 1973, 78: 1360–1380.

171 | P a g e

31. Guy J. Golan, Lior Zaidner, Creative Strategies in Viral Advertising: An

Application of Taylor‘s Six-Segment Message Strategy Wheel, 2008,

Journal of Computer-Mediated Communication, 13, 959–972

32. Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D.,

Electronic Word-of-Mouth via Consumer-Opinion Platforms: What

motivates consumers to articulate themselves on the internet, 2004,

Journal of Interactive Marketing 18(1): 38–52.

33. Hsi-Pen, Hsin-Chiau Fu, Chia-Hui Yen, A Study of E-mail Marketing: Why

do People Read and Forward E-mail, 2007, Advances in Information

Systems Development, 239-249

34. Irvine Clarke, Theresa Flaherty, 2005, ―Advances in electronic marketing‖

35. Janghyuk Lee, Jong-Ho Lee, Dongwon Lee, Impacts of Tie Characteristics

on Online Viral Diffusion, January 2009, Communications of the

Association for Information Systems: Vol. 24, Article 32.

36. Jason Y.C. Ho, Melanie Dempsey, Viral Marketing: Motivations to forward

online content, 2010, Journal of Business Research, Vol. 63, Issue: 9-10

37. Jeffrey Boase and Barry Wellman, A Plague of Viruses: Biological,

Computer and Marketing, September 2001, Current Sociology

38. Jonker, MJ, ―What drives people to forward viral messages? Message

aspects and motivations‖, August 2008, Master thesis, Universiteit

Maastricht, Faculty of Economics and Business Administration.

39. Joseph E. Phelps, Regina Lewis, Lynne Mobilio, David Perry, Niranjan

Raman, Viral Marketing or Electronic Word-of-Mouth Advertising:

172 | P a g e

Examining Consumer Responses and Motivations to Pass Along Email,

December 2004, Journal of Advertising Research, 333.

40. Jurvetosn & Draper, 1997, ―Viral Marketing – Viral Marketing Phenomeno

explained‖, Available at: http://www.dfj.com/news/article_26.shtml,

Accesses 18 March, 2011.

41. Justin Kirby and Paul Mardsen, September 9, 2005, in his book titled

―Connected Marketing, the viral, buzz and Word of mouth revolution.‖

42. Laudon, K. C., & Traver, C. G. (2001). E-commerce: Business,

technology, Society. Boston: Addison-Wesley.

43. Leskovec, J., L. A. Adamic, and B. A. Huberman. (2007). ―The Dynamics

of Viral Marketing,‖ ACM Transactions on the Web 1(1) Article 5, pp. 1-39.

44. Madu, C.N. and Madu, A.A., Dimensions of e-quality. 2002, International

Journal of Quality and Reliability Management, Vol. 19 No. 3, pp. 246-58.

45. Mark R Brown, Roop K Bhadury, Nigel K Ll Pope, The Impact of Comedic

Violence on Viral Advertising effectiveness, 2010, . Journal of Advertising..

Vol. 39, Iss. 1; pg. 49, 17 pgs

46. Nah, F.F.-H. and Davis, S., HCI research issues in e-commerce, 2002,

Journal of Electronic Commerce Research, Vol. 3 No. 3, pp. 98-113.

47. Oliver, R.L. (1997). Satisfaction: A behavioral perspective on the

consumer, New York: Irwin/McGraw-Hill.

48. Onnela, J. P., J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski, J.

Kertesz, and A. L. Barabasi, Structure and Tie Strengths in Mobile

173 | P a g e

Communication Networks, 2007, in Proceedings of National Academy of

Science USA 104(18), pp. 7332-7336.

49. Philip Kotler, Dipak C. Jain, Suvit Maesincee, ―Marketing moves: A New

Approach to Profits, Growth, and Renewal‖, 2002, Harvard Business

School Press, Boston, - A Book

50. Phelps, Joseph E., Regina Lewis, Lynne Mobilio, David Perry, and

Niranjan Raman, Viral Marketing or Electronic Word-of-Mouth Advertising:

Examining Consumer Responses and Motivations to Pass Along Email,

2004, Journal of Advertising Research, 44 (4), 333-348.

51. Popcorn, F. (2005). Cultural relevance [Electronic version]. Chief

Executive, 214, 35-37.

52. Porter, L., & Golan, G. J., From Subservient Chickens to Brawny Men: A

Comparison of Viral Advertising to Television Advertising, 2006, Journal of

Interactive Advertising, 6(2), 30-38.

53. Rebecca J. Larson, The Rise of Viral Marketing through the New Media of

Social Media, January 2009, Faculty Publications and Presentations.

Paper 6. http://digitalcommons.liberty.edu/busi_fac_pubs/6.

54. 2 Ralph F Wilson (2005), ―The Six Simple Principles of Viral Marketing‖,

available at www.wilsonweb.com

55. Rebecca J. Larson,The Rise of Viral Marketing through the New Media of

Social Media, January, 2001, Liberty University, available at

http://digitalcommons.liberty.edu/busi_fac_pubs/6/

174 | P a g e

56. Ronald E. Goldsmith and David Horowitz, Measuring Motivations for

Online Opinion Seeking, 2006, Journal of Interactive Advertising, Vol 6 No

2, pp. 3‐14. ISSN 1525‐2019

57. Shailendra Dasari and B Anandakrishna, Viral Marketing of Retail

Products: A Study on the Influence of Attributes of Web Portals and

Incentives Offered on User Registrations, 2010, IUP

58. Skrob, J.-R. (August 2005). Open Source and Viral Marketing. Vienna,

Austria: University of Applied Science Kufstein.

59. Stefan Wuyts, Marnik G. Dekimpe, Els Gijsbrechts (2010), The connected

customer: that changing nature of consumer and business markets

60. Steve Anderson, Using Social Network to Market, Feb 2008, Rough

Notes, 151, 2; ABI/INFORM Global pg. 114

61. Steve Juvetson (2000), ―What is Viral Marketing‖, Red Herring, May 1,

available at http:// www.dfj.com

62. Sundaram,D.S.,Mitra,K. and Webster,C., Word-of-Mouth ommunications:

A motivational analysis, 1998, Advances in Consumer Research 25: 527–

531.

63. Sweta Chaturvedi Thota, Abhijit Biswas, I Want to Buy the Advertised

Product Only!, 2009, Journal of Advertising.. Vol. 38, Iss. 1; pg. 123.

64. Walker, J., S. Wasserman, and B. Wellman, Statistical Models for Social

Support Networks, in Wasserman, S. and J. Galaskiewicz (eds.), 1994,

Advances in Social Network Analysis, Thousand Oaks, CA: Sage., pp. 53-

78.

175 | P a g e

65. Watts, D. J. and S. H. Strogatz, Collective Dynamics of „Small-World‟

Networks, 1998, Nature 393, pp. 440-442.

66. What is Viral Marketing, Available at: http://www.squidoo.com/what-is-

viral-marketing. Accessed 1st December, 2010.

67. Wilson, R. (2000) ―The Six Simple Principles of Viral Marketing? retrieved

October 3, 2005, from http://www.wilsonweb.com/wmt5/viral-

principles.htm

68. Wojnicki, Andrea C. and Dave Godes (2008), ―Word-of-Mouth as Self-

Enhancement,‖ University of Toronto Working Paper.

69. Wolfgang Palka, Key Pousttchi, Dietmar G Wiedemann, Mobile word-of-

mouth – A grounded theory of mobile viral marketing, 2009, Journal of

Information Technology (2009) 24, 172–185

70. Yih Hwai Lee, Charlotte Mason, Responses to information incongruency in

advertising: The role of expectancy, relevancy, and humor, September

1999, Journal of Consumer Research.. Vol. 26, Iss. 2; p. 156.

71. Young, Troy, Providing a personal touch, September 2001, Digital

Marketing, pg. 25

176 | P a g e

Appendix-2

Questionnaire

Viral Marketing Survey

Dear Respondent, This is a survey for a research project on viral marketing i.e. electronic Word of Mouth (eWOM) marketing technique, in which user passes product and its brand

messages to another user which they received from a marketer or through their social network over the internet. This survey is conducted by M.Phil student. The purpose of this study is to examine the drivers of viral marketing and find out the factors which influences user to receive and forward messages. With only a few minutes of your time, you can help me to gather information which would be helpful for a marketer and consumer to send and

receive information about product/service in an effective way by using viral marketing technique. Please begin the survey now by filling one page survey report. And, thank you

for your complete and candid responses to all questions.

1 Are you aware of the term “viral marketing”? * Yes No

2 Do you get internet access at home, college, workplace? * Yes No

3 Are you a member of any Social Networking site like facebook, twitter, linkedin etc.? *Yes No

4 Do you visit Social Networking Sites regularly? * Yes No

5 Are You interested in online purchase? * Yes No

1.Strongly Disagree, 2.Disagree, 3.Neither Disagree Nor Agree, 4.Agree, 5.Strongly Agree

Please tick appropriate option: 1 2 3 4 5

6 I gather information about product/service before going for purchase. *

7 I am interested in receiving email or messages from a friend containing a link of product/service which is relevant to me. *

8 I found information regarding product/service available on social network is very useful. *

9 My decision to purchase product/service is based on the reviews on social network. *

10 I always send messages of product/services over social network. *

11 I always share information over social network which is not related to marketing. *

12 Viral marketing technique is very interesting as there is no middle man required to get information about product. *

13 Information about product/service can be viewed on internet as per my convenient time. *

14 I don’t open promotional messages which I get because often it is just a spam which I delete without reading. *

15 I don’t click on product/service link which I get because of security reason. *

16 I open promotional messages of product/service if it is from the social networking group. (e.g. facebook, linkedin, Twitter etc.) *

177 | P a g e

17 I open promotional messages of product if it is from relatives or friend. *

18 I open promotional messages of product/service if it is from the reputed organization like Sunsilk, Tata Docomo, Idea, FMCG products etc.) *

19 I open promotional messages of product/service of my interest and relevant to me. *

20 I open promotional messages of product/service if it is from trusted third party. *

21 I open all messages from my acquaintances *

22 I read messages of product/service which contain videos, animation and flashy texts or which is entertaining. *

23 I read messages of product/service which gives detailed understanding of product/services. *

24 I don’t read message of product/service which is not appealing to me

25 While reading message of product/service, look of the message is not important to me*

1.Strongly Disagree, 2.Disagree, 3.Neither Disagree Nor Agree, 4.Agree, 5.Strongly Agree

Please tick appropriate option: 1 2 3 4 5

26 I read messages of service/product of my interest. *

27 If I received a message of product/service from someone who is known to me, l surely give it a try. *

28 If a message of product/service from third party but of my interest I’ll surely give it a try. *

29 I read all messages which I received. *

30 I read messages of product/service which I am looking for. *

31 I read message of product/services which is read by many users. *

32 I don’t read message of produc/service which is taking too much time for video & text to display.

33 I don’t read messages of product/services which required lot of information from user. *

34 I read messages of product/services which is simple and easy to use. *

35 I read message of product/service which gives me incentives/reward. *

36 I forward all messages of product/service which I received. *

37 I forward only those messages of product/service which is from trusted source. *

38 I forward only those messages of product/service in which receiver is interested. *

39 I forward message of product/service to only my close associates. *

40 I forward message of product/service to all my contacts. *

178 | P a g e

41 I forward message of product/service to get benefit from company *

42 I forward message of service/product which are conditional to provide contact list in order to get information about product/service. *

43 I forward message of service/product which are conditional to get reward from company. *

44 I forward message of product/service to get connected with people. *

45 I forward message of product/service just for a fun. *

46 I forward message of product/service because I am using and satisfied with product/service. *

47 I forward message of product/service because I am not using but having good opinion about it. *

48 I forward message of product/service just for the receiver’s benefits. *

49 I tend to pass along my contacts ‘positive reviews’ of product/service. *

50 I tend to pass along my contacts ‘negative reviews’ of product/service. *

Please tick appropriate option:

51 Gender * Male Female

52 Age (years) * 16-20 21-30 31-40

41-50 50 and above

53 Qualification * Graduate Post Graduate Others

54 Profession * Student Working

55 If working, specify sector * IT Non-IT

56 Location * Within Maharashtra Outside Maharashtra