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

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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

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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

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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

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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

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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

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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

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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.

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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

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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

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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.

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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,

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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.

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CONTENTS

Chapter Title Page Number No. 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

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List of Tables and Annexure

Table/ Title Page Number Annexure Tables From To

Table-1 Demographic Analysis of Sample

Table-2 Table of Responses

Major Constructs and sub variables of the Table-3 Study Table-4 Frequency Table Table-5

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

Descriptive statistics - Sequence of Drivers Annexure-4 of Viral Marketing Descriptive statistics – factors influence user Annexure-5 to receive viral messages Descriptive statistics - factors influence user Annexure-6 to forward viral messages

Annexure-7 Correlation between Access and Experience

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List of Tables and Annexure

Table/ Title Page Number Annexure From To Correlation between sequence of drivers of Annexure-10 viral marketing Regression – factors influence user to Annexure-11 receive viral messages

Annexure-12 Group statistics between trust and gender

Independent sample test between trust and Annexure-13 gender Group statistics between trust and Annexure-14 occupation Independent sample test between trust and Annexure-15 occupation Group statistics between relevance and Annexure-16 gender Independent sample test between relevance Annexure-17 and gender Group statistics between relevance and Annexure-18 occupation Independent sample test between relevance Annexure-19 and occupation Group statistics between perceived benefits Annexure-20 and gender Independent sample test between perceived Annexure-21 benefits and gender Group statistics between perceived benefits Annexure-22 and occupation

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List of Tables and Annexure

Table/ Title Page Number Annexure From To Independent sample test between perceived Annexure-23 benefits and occupation Regression – factors influence user to Annexure-24 forward viral messages Group statistics between tie strength and Annexure-25 gender Independent sample test between tie Annexure-26 strength and gender Group statistics between tie strength and Annexure-27 occupation Independent sample test between tie Annexure-28 strength and occupation Group statistics between sender‘s benefit Annexure-29 and gender Independent sample test between sender‘s Annexure-30 benefit and gender Group statistics between sender‘s benefit Annexure-31 and occupation Independent sample test between sender‘s Annexure-32 benefit and occupation Group statistics between customer Annexure-33 satisfaction and gender Independent sample test between customer Annexure-34 satisfaction and gender

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List of Tables and Annexure

Table/ Title Page Number Annexure From To Group statistics between customer Annexure-35 satisfaction and occupation Independent sample test between customer Annexure-36 satisfaction and occupation

Annexure-37 Group statistics between altruism and gender

Independent sample test between altruism Annexure-38 and gender Group statistics between altruism and Annexure-39 occupation Independent sample test between altruism Annexure-40 and occupation Correlation between intention to receive Annexure-41 messages and influencing factors Correlation between intention to forward Annexure-42 messages and influencing factors

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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

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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

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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.

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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

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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.

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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.

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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

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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.

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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.‖

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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.

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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

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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.

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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.

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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

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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

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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.

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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.

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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

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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.

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Sequence of actions and intermediary decisions, a diagrammatic representation of a sequence which is the driver of viral marketing includes the following steps.

Access

Awareness

Interest

Experience

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).

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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

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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).

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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

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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

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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

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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

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

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

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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

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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

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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

Trust (Source) Relevance

Drivers of Viral Intention to Marketing Receive Viral Messages

Perceived Benefits

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

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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.

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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.

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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).

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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

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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.

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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

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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

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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

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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.

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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),

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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:

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Drivers of Viral Intention to Receive Marketing Viral Messages

Tie Strength

Intention to Sender Customer Satisfaction Forward Viral Benefit Messages

Altruism

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.

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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

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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

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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)

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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

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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

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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

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(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%20ma nage%20connected%20marketing.pdf, Accessed on 21st March, 2011.

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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,

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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

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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

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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

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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%).

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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.

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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.

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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.

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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.

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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.

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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

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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

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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.

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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

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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

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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 Access has significant impact on

impact on experiences of viral experiences of viral marketing.

marketing.

H2 Awareness does not have Awareness has significant impact

significant impact on experiences on experiences of viral marketing.

of viral marketing.

H3 Interest does not have significant Interest has significant impact on

impact on experiences of viral experiences of viral marketing.

marketing.

H4 Trusted source does not have Trusted source have significant

significant impact on user to impact on user to receive viral

receive viral messages. messages.

H5 Trusted source does not have Trusted source have significant

significant impact on user to impact on user to receive viral

receive viral messages with messages with respect to gender.

respect to gender.

H6 Trusted source does not have Trusted source have significant

significant impact on user to impact on user to receive viral

receive viral messages with messages with respect to

respect to occupation. occupation.

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Sr.No. Null Hypothesis Alternative Hypothesis

H7 Message relevance does not have Message relevance has significant

significant impact on receiving viral impact on receiving viral

messages. messages.

H8 Message relevance does not have Message relevance has significant

significant impact on receiving viral impact on receiving viral messages

messages with respect to gender. with respect to gender.

H9 Message relevance does not have Message relevance has significant

significant impact on receiving viral impact on receiving viral messages

messages with respect to with respect to occupation.

occupation.

H10 A perceived benefit does not have A perceived benefit has significant

significant impact on user to impact on user to receive viral

receive viral messages. messages.

H11 A perceived benefit does not have A perceived benefit has significant

significant impact on user to impact on user to receive viral

receive viral messages with messages with respect to gender.

respect to gender.

H12 A perceived benefit does not have A perceived benefit has significant

significant impact on user to impact on user to receive viral

receive viral messages with messages with respect to

respect to occupation. occupation.

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Sr.No. Null Hypothesis Alternative Hypothesis

H13 Tie strength does not have Tie strength has significant impact

significant impact on user to on user to forward viral messages.

forward viral messages.

H14 Tie strength does not have Tie strength has significant impact

significant impact on user to on user to forward viral messages

forward viral messages with with respect to gender.

respect to gender.

H15 Tie strength does not have Tie strength has significant impact

significant impact on user to on user to forward viral messages

forward viral messages with with respect to occupation.

respect to occupation.

H16 Sender‘s benefit does not have Sender‘s benefit has significant

significant impact on user to impact on user to forward viral

forward viral messages. messages.

H17 Sender‘s benefit does not have Sender‘s benefit has significant

significant impact on user to impact on user to forward viral

forward viral messages with messages with respect to gender.

respect to gender.

H18 Sender‘s benefit does not have Sender‘s benefit has significant

significant impact on user to impact on user to forward viral

forward viral messages with messages with respect to

respect to occupation. occupation.

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Sr.No. Null Hypothesis Alternative Hypothesis

H19 Customer Satisfaction does not Customer Satisfaction has

have significant impact on user to significant impact on user to

forward viral messages. forward viral messages.

H20 Customer Satisfaction does not Customer Satisfaction has

have significant impact on user to significant impact on user to

forward viral messages with forward viral messages with

respect to gender. respect to gender.

H21 Customer Satisfaction does not Customer Satisfaction has

have significant impact on user to significant impact on user to

forward viral messages with forward viral messages with

respect to occupation. respect to occupation.

H22 Altruism does not have significant Altruism has significant impact on

impact on user to forward viral user to forward viral messages.

messages.

H23 Altruism does not have significant Altruism has significant impact on

impact on user to forward viral user to forward viral messages

messages with respect to gender. with respect to gender.

H24 Altruism does not have significant Altruism has significant impact on

impact on user to forward viral user to forward viral messages

messages with respect to with respect to occupation.

occupation.

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Diagrammatic representation of hypotheses is as follows:

Access H1

H2 Awareness Experience Of VM H3 Interest

drives

User to get/know information about product/service

Trust H4/H5/H6

Relevance H7/H8/H9 Intention to open viral messages

Perceived H10/H11/H12 Benefits

Tie Strength H13/H14/H15 H16/H17/H18 Customer Satisfaction Intention to forward viral messages Sender‘s Altruism H19/H20/H21 H22/H23/H24 Benefit

Source: Own Analysis

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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

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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

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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.

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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 No. of questionnaire questionnaire No. of Invalid No. of valid sent received questionnaire 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.

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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.

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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.

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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 Access facebook, twitter, linkedin etc.? 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 Interest network. * I am interested in receiving email or messages from a friend Interest containing a link of product/service which is relevant to me. * I found information regarding product/service available on Experience social network is very useful. * My decision to purchase product/service is Experience based on the reviews on social network. * I gather information about product/service before going for Experience purchase. * I always share information over social network which is not Experience related to marketing. * Viral marketing technique is very interesting as there is no Experience middle man required to get information about product. * Information about product/service can be viewed on internet Experience as per my convenient time. *

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

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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 trust it is just a spam which I delete without reading. I don‘t click on product/service link which I get because of trust security reason. I open promotional messages of product/service if it is from the social networking group. (e.g. facebook, linkedin, Twitter trust etc.) I open promotional messages of product if it is from relatives trust or friend. I open promotional messages of product/service if it is from the reputed organization like Sunsilk, Tata Docomo, Idea, trust FMCG products etc.) I open promotional messages of product/service if it is from trust trusted third party. I open all messages from my acquaintances trust If I received a message of product/service from someone trust who is known to me, l surely give it a try. I open promotional messages of product/service of my relevance interest and relevant to me. I don‘t read message of product/service which is not relevance appealing to me While reading message of product/service, look of the relevance message is not important to me I read messages of service/product of my interest. relevance If a message of product/service from third party but of my relevance interest I‘ll surely give it a try. I read message of product/services which is read by many perceived users. benefits I don‘t read message of product/service which is taking too perceived much time for video & text to display. benefits I read message of product/service which gives me perceived incentives/reward. benefits I don‘t read messages of product/services which required lot perceived of information from user. benefits

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Questions Factors intention to I read all messages which I received. open intention to I read messages of product/service which I am looking for. open I read messages of product/services which is simple and intention to easy to use. 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 tie strength associates. I forward message of product/service to get connected with tie strength people. I forward message of product/service to all my contacts. senders benefit I forward message of product/service to get benefit from senders benefit company I forward message of service/product which are conditional to provide contact list in order to get information about senders benefit product/service. I forward message of service/product which are conditional to senders benefit get reward from company. customer I forward message of product/service just for a fun. satisfaction I forward message of product/service because I am using customer and satisfied with product/service. satisfaction I forward message of product/service because I am not using customer but having good opinion about it. satisfaction

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Questions Factors I forward message of product/service just for the receiver‘s altruism benefits. I tend to pass along my contacts ‗positive reviews‘ of altruism product/service. I tend to pass along my contacts ‗negative reviews‘ of altruism product/service. intention to I forward all messages of product/service which I received. forward I forward only those messages of product/service which is intention to from trusted source. forward I forward only those messages of product/service in which intention to receiver is interested. 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.

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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

Access Awareness Drivers of Viral Marketing Interest Experience

Trust Intention to Open Relevance Perceived Benefits

Tie Strength Senders Benefit Intention to Forward Customer Satisfaction Altruism

Frequency Table

Table 4

Demographic Cumulative Characteristics Frequency Percent Valid Percent 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

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Graph 3: Gender Distribution of sample

Table 5

Demographic Cumulative Characteristics Frequency Percent Valid Percent 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.

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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.

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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

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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

Scale Scale Cronbach's Mean if Variance if Corrected Alpha if Item Item Item-Total Item Variables Deleted Deleted Correlation 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

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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

Std. N Minimum Maximum Mean 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 491 (listwise)

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.

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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

Std. N Minimum Maximum Mean 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.

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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

Std. N Minimum Maximum Mean 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.

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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

experience access 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

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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

experience Awareness 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 Experience Interest Pearson Correlation .289(**) Sig. (2-tailed) .000 N 491 ** Correlation is significant at the 0.01 level (2-tailed).

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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).

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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

2 found to be significant ( RAdj. . = .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

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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.

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Annexure-13

Independent Samples Test

Trusted Source Equal Equal variance variance not assumed assumed Levene‘s Test F 0.166 for Equality of Variances Sig. 0.684 t-test for T -0.233 -0.222 Equality of Df 489 245.748 Means Sig. (2-tailed) 0.824 0.824 Mean Difference -0.01325 -0.01325 Std. Error Difference 0.05947 0.05967 95% Confidence Interval Lower -0.13011 -0.10360 of the Difference Upper -0.13077 -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.

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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.

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The second part of the output gives the inferential statistics which is shown in

Annexure-15.

Annexure-15

Independent Samples Test

Trusted Source Equal Equal variance variance not assumed assumed Levene‘s Test F 0.646 for Equality of Variances Sig. 0.422 t-test for T -1.731 -1.742 Equality of Df 489 475.287 Means Sig. (2-tailed) 0.084 0.082 Mean Difference -0.09264 -0.09264 Std. Error Difference 0.05353 0.05319 95% Confidence Interval Lower -0.19781 -0.19716 of the Difference Upper -0.01253 -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

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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

2 found to be significant ( RAdj. = .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

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.335 for Equality of Variances Sig. 0.563 t-test for t 0.625 0.630 Equality of df 489 251.397 Means Sig. (2-tailed) 0.532 0.529 Mean Difference 0.03956 0.03956 Std. Error Difference 0.06328 0.06279 95% Confidence Interval Lower -0.08478 -0.08409 of the Difference Upper 0.16390 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

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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

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.932 for Equality of Variances Sig. 0.335 t-test for t -2.681 -2.680 Equality of df 489 464.457 Means Sig. (2-tailed) 0.008 0.005 Mean Difference -0.15213 -0.15213 Std. Error Difference 0.05674 0.05676 95% Confidence Interval Lower -0.26360 -0.26367 of the Difference Upper -0.04065 -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 =

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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

2 found to be significant ( RAdj. . = .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.

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Annexure-20

Group Statistics Q.51 N Mean Std. Deviation Std. Error Mean Perceived Male 354 3.3239 0.63541 0.03377 benefit 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 Equal variance variance not assumed assumed Levene‘s Test F 0.361 for Equality of Variances Sig. 0.549 t-test for t 0.552 0.549 Equality of df 489 245.748 Means Sig. (2-tailed) 0.581 0.586 Mean Difference 0.03560 0.03560 Std. Error Difference 0.06448 0.06534 95% Confidence Interval Lower -0.09110 0.16229 of the Difference Upper -0.09312 0.16432

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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.

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Annexure-22

Group Statistics Std. Q.51 N Mean Deviation Std. Error Mean Perceived Student 273 3.2845 0.64062 0.03877 benefit 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 Equal variance variance not assumed assumed Levene‘s Test F 0.001 for Equality of Variances Sig. 0.979 t-test for t -1.142 -1.142 Equality of df 489 465.544 Means Sig. (2-tailed) 0.254 0.254 Mean Difference -0.06642 -0.06642 Std. Error Difference 0.05815 0.05814 95% Confidence Interval Lower -0.18068 0.04783 of the Difference Upper -0.18067 0.04783

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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

Unstandardized Standardized Model Coefficients 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

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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

2 found to be significant ( RAdj. . = 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 Standardized Coefficients 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.

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.366 for Equality of Variances Sig. 0.545 t-test for t 1.515 1.529 Equality of df 489 252.235 Means Sig. (2-tailed) 0.130 0.127 Mean Difference 0.14776 0.14776 Std. Error Difference 0.09754 0.09661 95% Confidence Interval Lower -0.04389 0.33941 of the Difference Upper -0.04251 0.33803

From the Annexure-26 it is assumed that Sig. (2-tailed) value is 0.130 is not less

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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

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.196 for Equality of Variances Sig. 0.658 t-test for t -2.280 -2.277 Equality of df 489 462.834 Means Sig. (2-tailed) 0.023 0.023 Mean Difference -0.20016 -0.20016 Std. Error Difference 0.08779 0.08791 95% Confidence Interval Lower -0.37266 -0.02767 of the Difference Upper -0.37291 -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 =

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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

2 found to be significant ( RAdj. . = 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.

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.632 for Equality of Variances Sig. 0.427 t-test for t 0.223 0.216 Equality of df 489 233.363 Means Sig. (2-tailed) 0.824 0.829 Mean Difference 0.02079 0.02079 Std. Error Difference 0.09330 0.09610 95% Confidence Interval Lower -0.16252 -0.16854 of the Difference Upper 0.20411 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

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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

Std. Std. Error Q.54 N Mean Deviation Mean Sender‘s Benefit student 273 2.6593 0.91242 0.05522 working 218 2.7041 0.94501 0.06400

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.012 for Equality of Variances Sig. 0.912 t-test for t -0.532 -0.530 Equality of df 489 457.892 Means Sig. (2-tailed) 0.595 0.596 Mean Difference -0.04479 -0.04479 Std. Error Difference 0.08420 0.08453 95% Confidence Interval Lower -0.21023 -0.21091 of the Difference Upper 0.12065 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

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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

2 found to be significant ( RAdj. . = 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.

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Annexure-33 Group Statistics Std. Error Q.51 N Mean Std. Deviation 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 Equal variance variance not assumed assumed Levene‘s Test F 1.655 for Equality of Variances Sig. 0.199 t-test for t 1.239 1.272 Equality of df 489 261.107 Means Sig. (2-tailed) 0.216 0.205 Mean Difference 0.10629 0.10629 Std. Error Difference 0.08579 0.08358 95% Confidence Interval Lower -0.06227 -0.05830 of the Difference Upper 0.27484 0.27087

From the Annexure-34 it is assumed that Sig. (2-tailed) value is 0.216 is not less

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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.

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Annexure-35 Group Statistics Std. Error Q.54 N Mean Std. Deviation 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 Equal variance variance not assumed assumed Levene‘s Test F 0.219 for Equality of Variances Sig. 0.640 t-test for t -1.359 -1.356 Equality of df 489 460.591 Means Sig. (2-tailed) 0.175 0.176 Mean Difference -0.10525 -0.10525 Std. Error Difference 0.07742 0.07761 95% Confidence Interval Lower -0.25736 -0.25777 of the Difference Upper 0.04686 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

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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

2 found to be significant ( RAdj. . = 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

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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 Equal variance variance not assumed assumed Levene‘s Test F 1.063 for Equality of Variances Sig. 0.303 t-test for t 0.418 0.427 Equality of df 489 259.119 Means Sig. (2-tailed) 0.676 0.669 Mean Difference 0.03766 0.03766 Std. Error Difference 0.09013 0.08814 95% Confidence Interval Lower -0.13943 -0.13589 of the Difference Upper 0.21476 0.21122

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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.

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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 Equal variance variance not assumed assumed Levene‘s Test F 0.109 for Equality of Variances Sig. 0.741 t-test for t -0.583 -0.582 Equality of df 489 463.546 Means Sig. (2-tailed) 0.560 0.561 Mean Difference -0.04741 -0.04741 Std. Error Difference 0.08135 0.08143 95% Confidence Interval Lower -0.20725 -0.20743 of the Difference Upper 0.11243 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

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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 Statistical Hypothesis Test Accepted/ Alternative Rejected Hypothesis Variable Independent Dependent Access has Access Experience Correlation Rejected significant impact on experiences of Null- viral marketing. Accepted Awareness has Awareness Experience Correlation Accepted significant impact on experiences of Null- viral marketing. Rejected Interest has Interest Experience Correlation Accepted significant impact on experiences of Null- viral marketing. Rejected Trusted source Trusted Source Receive Regression Accepted does not have viral significant impact messages Null- on user to receive Rejected viral messages. Trusted source Trusted Source Receive Independent Rejected does not have viral sample t-test significant impact messages Null- on user to receive Accepted viral messages with respect to gender.

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Statistical Hypothesis Test Accepted/ Hypothesis Variable Rejected Independent Dependent Trusted source Trusted Source Receive Independent Rejected does not have viral sample t-test significant impact messages Null- on user to receive Accepted viral messages with respect to occupation. Message Relevance Receive Regression Accepted relevance does viral not have messages Null- significant impact Rejected on receiving viral messages. Message Relevance Receive Independent Rejected relevance does viral sample t-test not have messages Null- significant impact Accepted on receiving viral messages with respect to gender. Message Relevance Receive Independent Accepted relevance does viral sample t-test not have messages Null- significant impact Rejected on receiving viral messages with respect to occupation. A perceived Perceived Receive Regression Accepted benefit does not benefits viral have significant messages Null- impact on user to Rejected receive viral messages. A perceived Perceived Receive Independent Rejected benefit does not benefits viral sample t-test have significant messages Null- impact on user to Accepted receive viral messages with respect to gender.

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Statistical Hypothesis Test Accepted/ Hypothesis Variable Rejected Independent Dependent A perceived Perceived Receive Independent Rejected benefit does not benefits viral sample t-test have significant messages Null- impact on user to Accepted receive viral messages with respect to occupation. Tie strength does Tie strength Forward Regression Accepted not have message significant impact Null- on user to forward Rejected viral messages. Tie strength does Tie strength Forward Independent Rejected not have message sample t-test significant impact Null- on user to forward Accepted viral messages with respect to gender. Tie strength does Tie strength Forward Independent Accepted not have message sample t-test significant impact Null- on user to forward Rejected viral messages with respect to occupation. Sender‘s benefit Sender‘s benefit Forward Regression Accepted does not have message significant impact Null- on user to forward Rejected viral messages. Sender‘s benefit Sender‘s benefit Forward Independent Rejected does not have message sample t-test significant impact Null- on user to forward Accepted viral messages with respect to gender.

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Statistical Hypothesis Test Accepted Hypothesis Variable /Rejected Independent Dependent Sender‘s benefit Sender‘s benefit Forward Independent Rejected does not have message sample t-test significant impact Null- on user to forward Accepted viral messages with respect to occupation. Customer Customer Forward Regression Rejected Satisfaction does Satisfaction message not have Null- significant impact Accepted on user to forward viral messages. Customer Customer Forward Independent Rejected Satisfaction does Satisfaction message sample t test not have Null- significant impact Accepted on user to forward viral messages with respect to gender. Customer Customer Forward Independent Rejected Satisfaction does Satisfaction message sample t test not have Null- significant impact Accepted on user to forward viral messages with respect to occupation. Altruism does not Altruism Forward Regression Rejected have significant message impact on user to Null- forward viral Accepted messages. Altruism does not Altruism Forward Independent Rejected have significant message sample t-test impact on user to Null- forward viral Accepted messages with respect to gender. Altruism does not Altruism Forward Independent Rejected have significant message sample t-test

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impact on user to Null- forward viral Accepted messages with respect to occupation.

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 Pearson 0.373(**) 0.400(**) 0.433(**) receive Correlation Sig. (2-tailed) 0.000 0.000 0.000 N 491 491 491 ** Correlation is significant at the 0.01 level (2-tailed).

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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 Sender‘s Customer Strength Benefit Satisfaction Altruism Intention to Pearson 0.586(**) 0.576(**) 0.499(**) 0.371(**) forward Correlation 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).

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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

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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

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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

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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.

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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.

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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

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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

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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.

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Table-7

Summary of objectives and outcomes

Sr. Objectives Outcomes No. 1. To understand viral marketing Viral marketing is electronic word of through social network. 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 To understand viral marketing, it is marketing. 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 For the success of viral marketing it is which influence user to receive imperative to reveal and validate the and forward messages. 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.

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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 considered for demographic factors of user on this study are gender and occupation. receiving and forwarding No study on this demographic factor messages. 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

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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.

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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.

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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. * I am interested in receiving email or messages from a friend containing a link of 7 product/service which is relevant to me. * I found information regarding product/service available on social network is very useful. 8 *

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. * Viral marketing technique is very interesting as there is no middle man required to get 12 information about product. * Information about product/service can be viewed on internet as per my convenient 13 time. * I don’t open promotional messages which I get because often it is just a spam which I 14 delete without reading. * 15 I don’t click on product/service link which I get because of security reason. * I open promotional messages of product/service if it is from the social networking 16 group. (e.g. facebook, linkedin, Twitter etc.) *

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17 I open promotional messages of product if it is from relatives or friend. * I open promotional messages of product/service if it is from the reputed organization 18 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 * I read messages of product/service which contain videos, animation and flashy texts or 22 which is entertaining. * I read messages of product/service which gives detailed understanding of 23 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. * If I received a message of product/service from someone who is known to me, l surely 27 give it a try. * If a message of product/service from third party but of my interest I’ll surely give it a 28 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. * I don’t read message of produc/service which is taking too much time for video & text 32 to display. I don’t read messages of product/services which required lot of information from user. 33 * 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. *

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41 I forward message of product/service to get benefit from company * I forward message of service/product which are conditional to provide contact list in 42 order to get information about product/service. * I forward message of service/product which are conditional to get reward from 43 company. * 44 I forward message of product/service to get connected with people. * 45 I forward message of product/service just for a fun. * I forward message of product/service because I am using and satisfied with 46 product/service. * I forward message of product/service because I am not using but having good opinion 47 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

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