Social Media and Social Innovations
Influence is defined as “the capacity or power of persons or things to be a compelling force on or to produce effects on the actions, behavior and opinions of others.”1 In simple terms, it is “the ability to cause a reaction in other people.”2
After studying real-life persuasion techniques in dozens of organizations, Robert Cialdini3 found that we are more influenced by those similar to us (in age, gender, interest, etc.) and those in our social circle. Not all messages influence us the same way: those that follow existing social norms, messages already accepted by many others, and messages communicated by several different sources are more likely to affect us.
For example, we are more likely to start brushing our teeth if two friends recommend that we do so once than if one of our friends recommends it twice.
Cialdini put together six principles of influence summarized in the following table:
|Social proof: People are more likely to change their behavior if they see others doing the same thing. An experimenter had tested the influence of crowds on strangers by asking his assistants to stand on the street and just look at a building. In the first condition, there was only one person looking at the building and only four percent of passersby stooped. In the second condition, there were fifteen people standing on the street and looking at a building; and in that condition ten times more people (forty percent) stopped and looked at the building themselves.4|
|Reciprocity: People are influenced by those who recently did them a favor. This is the technique used in supermarkets, as when people get a free sample of cheese they may feel they received a favor from the person who is sampling and now should return the favor by buying a pack. In general we also pay attention to the messages of those we recently received a favor from. |
|Commitment: When people make a choice on anything, it becomes difficult to change that choice (e.g., a product, political party, favorite music genre). It was found that when people were asked to wear a pin for a worthy cause and later to put a sign on their lawn, many accepted. However, in a similar neighborhood people were directly asked to put a sign on their lawn and the majority rejected.3|
|Authority & Credibility: We are more likely to be influenced by those in authority. In an experiment twenty-six out of forty subjects accepted an order to deliver a deadly shock to a person because they were asked to do so by an authority.5 Many commercials use experts because their opinion is more credible, and thus can make us buy the advertised products.|
|Liking: We are more influenced by those whom we like. Cialdini argues that some companies (e.g., Tupperware and Amway) build their customer base by using mutual liking among people. He also argues that we tend to like attractive people, who also influence us more. A recent study found that when shoppers see an attractive person touching a product in a store, they are more likely to evaluate that product positively.6|
|Scarcity: People tend to be influenced by a sales message that says the product is scarce or the offer is available for only a limited time or to a limited number of people.|
Additionally, young people and women are known7 to be more susceptible to influence, women can influence men more than they influence other women, and married people are less susceptible to influence than single people. Influential people themselves, however, are not as susceptible to influence, and they tend to cluster together with other influential people.
When we asked Twitter users in Japan8 whether they tried a new service or a product after finding out about it on Twitter, we found that females and young users were significantly more likely to report doing so, confirming the age and gender-related propositions mentioned above.
Diffusion of Innovations (DOI) Theory & Social Influence
Everett Rogers,9 who developed DOI theory, argued that diffusion of new ideas, services, and products happens in five stages and each innovation must have five key characteristics. These five stages of the decision to adopt an innovation are as follows:
- Knowledge: Acquiring information about the innovation
- Persuasion: Being persuaded that the innovation is useful
- Decision: Making the decision to adopt the innovation
- Implementation: Using the innovation
- Confirmation: Continue using it without quitting after a few times
Rogers stated that for all innovations there exists an approximate percentage of innovators (2.5%), early adopters (13.5%), and laggards (16%) as shown in the figure below. These percentages, however, may be subject to culture (the percentage of innovators and early adopters is 16% in the United States, 24% in the United Kingdom, and 9% in Spain).10
Figure 8.1 Diffusion of Innovations. Source: Reproduced based on the graph in Rogers, 2004, p. 281. Copyright Simon &Schuster
|Inoovators: Technology enthusiasts, tech bloggers, etc. They are not very rich or opinion leaders. They tend to be young, self-confident, mobile, and not brand-loyal.|
|Early Adopters: These people also can be considered agents of change, as they influence the rest of society. They are heavy users of the product category. Marketers should identify and offer free trials to this group.|
|Early Majority: Once the innovations is adopted by innovators and early adopters, these people feel comfortable trying it. This group relies on ads and expert or celebrity endorsements.|
|Later Majority: This group usually has lower levels of income and adopts innovations just because the product in used by many others.|
|Laggards: Skeptics. They adopt innovations mostly because they don’t have any other alternative. Promotions for this group should emphasize the innovation’s similarity to existing products (it can do everything that similar products can do).|
In order to reach the critical mass, an innovation must be:
- relatively advantageous compared to currently available products and services with similar functions,
- compatible with currently available supplementary products and services,
- simple to use,
- observable, and
- easy to try.
Some authors also argued that social norms, the efforts of change agents, and the density of social networks in a society also play a crucial role when it comes to adoption of new innovations.
A study found that individual innovativeness, along with whether people thing Twitter is compatible, visible, and popular, predicted who will keep using Twitter, and who will quit.13 In the same vein, people who though online social networking is advantageous, simple, and easy to try reported that they were interesting in trying social network services.14
An analysis of the spread of Internet applications among college students via Facebook recommendations showed that passive observation may have as much impact as word of mouth in the adoption of a new application.15
To understand the spread of hobbies in social media, researchers also followed students for four years at a university and found that interest in jazz and classical music is contagious: when people share a residence with or major in the same field as someone who likes classical or jazz music they tend to develop interest in these genres.16 At the same time, people on the same campus who share similar interests and hobbies may also end up becoming Facebook friends.
Similar to two-step flow theory17 (the notion that new ideas are first adopted by opinion leaders, who then pass them on to their network members), DOI theory emphasizes the importance of influentials; it holds that endorsements from opinion leaders and innovators are crucial during the early stages of innovation.9
In other words, innovations have the optimum chance of spreading if early adopters or the early majority consists of influential people.
Recently it was found18 that influentials were responsible for the spread of the Occupy movements across the United States, although hidden influentials (accounts created particularly for that movement) were also effective. The study indicated that influentials not only reach a large number of audiences, they also receive many different messages from a vast number of sources.
As Brian Solis, co-author of The Rise of Digital Influence, explains, it is better for companies with a limited advertising budget to reach influential people who then can influence their network members rather than reaching average users who may or may not influence anybody.19
It is not a coincidence that companies pay Kim Kardashian $10,000 to post a tweet that promotes a brand.20 On the other hand, it may be the case that unpopular people use social media more often than popular people.
Surveying 451 college students, researchers21 concluded that opinion leaders tend to share more brand information but spend less time on Facebook.
There is no clear definition of an influencer/influential, as in the digital age anyone can influence others’ opinion in some particular area or on a particular medium.
According to Brian Solis,19 influencers are “individuals who may possess the capacity to influence based on a variety of factors, such as a substantial or concentrated following in social networks, notable stature, or authority within a community, and the size or loyalty of an audience.”
At the same time, two marketing professors coined the term market mavens, 22 people “who have information about many kinds of products, places to shop, and other facets of markets, and initiate discussions with consumers and respond to requests from consumers for market information” and showed that they also can influence purchase decisions.
Based on the literature review, we introduce the following influence pyramid to explain who influential people are, which may also be applicable to the adoption of commercial products and services.
When it comes to influencing strangers online, perhaps it is better to focus on Twitter, as it stands out among other self-broadcasting tools.23 The problem is, at the time of writing, a search for Twitter + identify + influentials returns more than a dozen papers with different criteria on how to identify influential people.
For instance, a paper2 that successfully explained the spread of new ideas during the Egyptian revolution and the Dominique Strauss–Kahn scandal identified sixteen different criteria (see the table below). Regardless, Paul Adams claims that all influentials should not be treated equally. He puts them into two groups based on their forwarding behavior: innovator hubs and follower hubs.
Innovator hubs are considered to be the main information source by the public, but they are not open to new ideas and innovations and they usually don’t share others’ messages.
Follower hubs, conversely, will accept new ideas and innovations and share people’s messages.
For an innovation to be adopted, it should be shared by follower hubs: easily influenced people who are highly connected to their groups.
|Account Characteristics: Daily tweet rate, followers-to-following ratio, number of lists the account is included in, number of times the account owner favorites other tweets, number of accounts followed, number of accounts followed by, age of the account holder|
|Message Characteristics: has hashtag, has link, mentions another user (@), has exclamation mark, has emoticon, type of sentiment (calm, excited, strong , weak, etc.), polarity of sentiment (positive or negative). Tweet sentiment can be coded according to ANEW Dictionary.25|
Author’s Note: There are even companies that measure people’s online influence such as Klout. The following excerpt from Klout’s own website26 explains its algorithm:
The majority of the signals used to calculate the Klout Score are derived from combinations of attributes, such as the ratio of reactions you generate compared to the amount of content you share.
For example, generating 100 retweets from 10 tweets will contribute more to your Score than generating 100 retweets from 1,000 tweets. We also consider factors such as how selective the people who interact with your content are. The more a person likes and retweets in a given day, the less each of those individual interactions contributes to another person’s Score. Additionally, we value the engagement you drive from unique individuals. One-hundred retweets from 100 different people contribute more to your Score than do 100 retweets from a single person.– Klout, How it works? (http://klout.com/corp/how-it-works)
Adoption of New Technologies & Gartner’s Hype
Gartner’s Hype proposes that there’s always over enthusiasm about new technologies, which inflates potential demand. After quick popularity, however, there is a sharp decline in the visibility of new innovations.27 According to Gartner’s hypothesis, the popularity of new innovations will be determined by time, but at the beginning there’s always artificially created hype.27 This may explain initial negative attitudes toward Facebook in Japan.
In early 2011, we easily found eighteen students who had registered on but stopped using Facebook after announcing our study to only about one hundred people. The participants stated that they would not use Facebook because Mixi was simply better.28
However, just two years after the study, the percentage of students who prefer Mixi over Facebook is zero at our university.29 We think most of the early adopters had mixed feelings about Facebook simply because they did not have many friends on the platform or they did not know what to do with the service.
Social Media and Word of Mouth
Although DOI and social movement theories give us wonderful insights on how ideas spread, they may not explain everything about the diffusion of commercial goods.
Word of mouth (WOM), defined as “all informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services or their sellers,“31 accounts for 80% of all purchase decisions, according to a study that analyzed 6000 business cases.30
Another study that tracked people who signed up for an online network found that the impact of WOM was 20 times higher than traditional marketing activities, 30 times higher than media appearances, and lasted longer that the effects of promotional activities.32 A clear example of word of mouth is Tamagochi.30
The brand first test-marketed the toy among high school girls, and those who received the samples started telling their friends about the toy. Because of word of mouth, the product was sold out as soon as it hit the shelves.
Seth Godin, author of Purple Cow, 33 suggests that WOM is the only way to drive sales because there simply is too much advertising clutter and consumers trust their friends but not necessarily marketers and advertisers.
Overall, WOM is very helpful for consumers because it reduces risk and decision–making time.34 There’s more demand for WOM for new products, expensive products, infrequently bought products, products that are visible and self-expressive (e.g. cars), and risky products (e.g. medical, legal, and investment services).30
There are several reasons people choose to talk about products and services to their network members, as explained in the following table. Although people may think we share info to help others, past studies clearly show that concern for self-image and product involvement are more important than concern for others.35,36
|Product Involvement: If someone is very interested in a product category (for instance, electronics), he or she might enjoy talking about that product category or a brand from that category.|
Self-Involvement: Talking about a product makes someone seem expert, knowledgeable, and superior.
Other Involvement: Positive WOM is mostly to help others improve their lives while negative WOM is for concern for others.
Message Involvement: This happens when advertisements or product messages create discussion among consumers. (Source: Dichter, 1966)
|Altruism: People help others without expecting any return and in general providing information about a useful product is considered to be a helpful act.|
Feeling Better: People may feel better and reduce their anxiety and anger by talking about products and services. (Source: Sundaram et al., 1998)
Just like offline WOM, online WOM also impacts purchase decisions. Recently, a study that analyzed book reviews posted on the Internet found that there’s a very clear relationship between customer reviews on a site and sales of a book.37 However, the effect of a negative (one-star) review is higher than a positive (five-star) review. Similarly, online movie reviews can successfully predict box-office revenues, especially during the opening week.38
In the same way, travel videos, pictures, opinions, and reviews shared in social media before and after vacations are found to have a big impact on consumers’ travel choices.39 Some scholars thing online WOM is more effective than offline WOM because online WOM disseminates so rapidly and it is not limited to one’s direct contacts (about 150 people).40
With the help of social media, online WOM can spread far beyond he initial 150 people. However, recent studies showed that 90% of WOM still occurs offline (not online) and most product-related conversations take place in the home, among family members and friends, etc.41
Additionally, people usually talk about food, technology, and entertainment41 (not every single product category), and their conversations are mostly driven by TV commercials, not social media (yet).
Social Media & Content Sharing
The reasons for sharing things online are quite similar to why people engage in a WOM activity; however, we must understand that sharing a newspaper article, personal message, and branded message are not exactly the same things, thus motivations are likely to differ slightly for each type of content.
A study43 that assessed 7000 articles published on the New York Times found that arousal in key. The study coded each article based on several categories and then looked at how many times each article was shared online:
- practical utility
- word count
- author frame
The results suggested that regardless of interestingness and usefulness, articles that increase arousal are shared more, and articles that are not arousing are shared less. The following figure explains the findings of the study:
Another project44 commissioned by the New York Times that analyzed the sharing motivation for any content found that the top reason for passing online content onto others was “personal interest in social issues and social causes.”
The same study also identified several other motives, including:
- “to bring valuable and entertaining contents to others,”
- “to define ourselves to others,”
- “to grow and nourish our relationships with others,” and
- “for Self-fulfillment.”
By the same token, when researchers45 measured personal traits of people and asked them how much time they spend forwarding electronic content in a typical week, they found that individualistic and altruistic people are more likely to share online content.
Interestingly, one’s need to belong to a group and need for personal growth did not predict sharing behavior. All these findings indicate that online sharing is about self but not others; therefore, we can presume that selfish people are likely to share more and people from collectivistic countries are likely to share less on the Internet.
As much as characteristics of the users, message content also heavily impacts what gets shared in social media. Kaplan and Haenlein40 mention that most common types of posts include:
- memorable and interesting messages,
- true stories about real people,
- practical lists (Top 10 Ways to__________, Top 5 Most Useful __________, etc.),
- hilarious messages,
- sex-related messages,
- posts that trigger emotional responses,
- posts that have surprises or create happiness or fear, and
- posts that are not already known by the user’s friends (if the user thinks her network is aware of the information she won’t post it).
Regarding commercial messages, the authors emphasize the importance of market mavens (those who receive and share a lot of information about products that interest them), salespeople, and social hubs (influentials).
For branded messages to be shared, salespeople should monitor messages related to their brands that are posted by market mavens and then find a way to pass them on to social hubs.
Nevertheless, this formula may not work all the time because according to Kaplan and Heinlein, social media is “more art than science” and does not have ultimate rules. They give two examples where a social media campaign that worked perfectly for one brand was a disaster for another.
P&G had a very successful campaign that asked people to create funny YouTube videos about the problems Pepto-Bismol cures (diarrhea, upset stomach, etc.). A few years later, Heinz Ketchup ran a similar campaign that involved making a funny video about the situations in which people use their ketchup. However, the participation was minimal and a backlash arose against the campaign, charging that the company was trying to take advantage of its users.
Similarly, Starbucks asked people to take pictures while having a cup of coffee in front of the store sign and upload them onto Twitter to get rewards. While some customers participated in the campaign, many uploaded pictures to support a documentary that highlighted bad labor practices at the company.
When it comes to sharing a YouTube video, “surprises and incongruity” are the key components that can explain the differences between commonly shared videos and the rest. Three out of four recent studies indicated that in order to go viral a video must have a surprise. The other crucial elements are “emotional appeal,” “humorousness,” and community engagement, as explained in the table below.
Additionally, it was found that usually extroverted and egocentric people forward videos more (egotists forward videos to show their own taste, their connectedness, and their media savvy). Lastly, as one may expect, videos shared among members of heterogeneous groups (with various interests) are more likely to go viral than those shared in homogenous groups (e.g., niche interests).
|1) Tastemakers/influencers share it. A good example is the “double rainbow video,” which performed very poorly for several months but suddenly went viral because a celebrity tweeted about it.|
2) Communities get involved and each community or a member of a community makes a new parody or a new version of the video.
3) The video has an unexpected ending. (Source: Allocca, 2012)
|Because it has…|
1) Laugh-out-loud funny content
2) Cutting-edge, unique content
3) Thrilling content
4) Sexy or erotic content (Source: Southgate et al., 2010)
|Because it features…|
1) Ordinary people (User-generated videos are more likely to go viral)
2) Flawed masculinity (Males that don’t meet social expectations; weaknesses or stupidity of males shown)
3) Humor and incongruity
4) Simplicity (No editing or less editing, one simple message or one simple action, focused on one person or one object)
5) Repetitiveness (some parts of the video are repeated, making it easier for others to remake the same video)
6) Whimsical content (things related with popular culture, no controversial topics) (Source: Shifman: 2012)
|Because it has…|
1) A surprise bundled with another emotion (most of the time joy)
2) An emotional roller-coaster: emotional ups and downs and different
emotions in the same video (Source: Rockett, 2012)
The Case of the Harlem Shake Video
On February 2, 2013, a YouTube user using the pseudonym Filthy Frank uploaded a video that showed him dancing with three other young males (who looked like college students) to the song “Harlem Shake.”
On the same day two other YouTube users uploaded a similar video that showed them dancing to the same song as a group. One of the second videos garnered as many as 300,000 views in just three days.
Meanwhile, other users started creating parodies of the video that showed a group of people, one of whom wore a helmet or mask, dancing to the same song. Both the original video and the two follow-ups generated more than five million views each in less than two weeks, and it became such a huge phenomenon that at one point all major universities, major brands, and even the (Norwegian) army were making their own versions of the video.
Even though the video is sexist (the gestures of the lead character are considered obscene in some cultures) and not easy to create (the video must be edited in order to match the original version) it went viral.
Why did this happen, why did people create parodies of this video but not many other interesting ones?
It is difficult to answer, as it may be just a herding behavior that randomly became popular among some niche YouTube communities and once perceived as a trend grew exponentially. We may speculate, however, that some of the following factors may have helped its popularity:
- The early versions of the video have a lot of naked college kids. Remember, we subconsciously pay more attention to naked people.
- Just like some other popular videos, the video is about a group of people dancing to the music in an original way.
- It is about surprising the viewers by using creativity. It builds up expectation during the first half. Even though the first half is boring, people still watch it because it is not long and it makes the second half more enjoyable (e.g., from super slow to super-fast).
- It requires teamwork and fosters group spirit. Getting more views on YouTube is an endorsement and social proof of group success and group creativity.
- It is an opportunity for “creative types” to establish their image in their group.
- For a workplace it is a good way to do something crazy that can bring employees together and show an American way of thinking: life shouldn’t be taken that seriously and our workplace is a fun place to be. This is a form of escaping from the stress of daily problems by doing simple, stupid, or crazy things.
- It’s like Halloween. You can do crazy and stupid things but it is OK because everyone else is doing it, or you can cover it by wearing a mask or costume. At the same time, you can somewhat show your real identity.
Regardless, we can never fully understand why things go viral. These are just potential explanations, but it is not guaranteed that another video that has these features can go viral.
Criticisms of Social Media
In 2012, an author named B. J. Mendelson 51 wrote a book titled “Social Media is Bullshit.” Similar to what Kaplan and Haenlein 40 claimed, he argued that most, if not all, social media success stories are big brands who spend a lot of time and money offline to promote their social media accounts.
He also claimed that social movements and meaningful causes cannot be driven by social media because social media itself has a very small impact. Readers of this book should decide whether he is right or wrong but he supported his arguments with the following examples:
- P&G’s Old Spice Campaign is presented as a big social media success but
- many celebrities including Ashton Kutcher, Ryan Seacrest and Ellen Degeneress had a video reply to the Old Spice videos that drove a lot of traffic to the videos
- The videos and the star of the videos were covered in major TV shows in the US
- P&G had a big offline advertising campaign and a coupon campaign for Old Spice within the same year d) Cisco had a very similar campaign but it never took off. (p. 96)
- Dell reported that it made $3 million by using Twitter but
- it never explained in detail how this money was made
- This money is actually smaller than .01% of their annual revenue
- Twitter listed Dell as a suggested account which may have artificially inflated Dell’s followers. Despite the fact that Dell’s extra revenue was not proven, it is already mentioned on the internet more than 13.4 million times (p. 92-94).
- Occupy Wall Street is not a leaderless movement driven by social media: it was started by a Canadian activist (the editor of Adbusters) Kalle Lasn. It was also found that some lead members of the Occupy Wall street movement actually were benefiting from the demonstrations by collecting money from the participants and using it for their own (p. 57)
- Top 50 Facebook pages (except 1 music related page) are about celebrities, corporations, popular brands, Twitter’s top 50 accounts, Youtube’s most popular channels are also famous brands or personalities (p. 13). The author concluded that average people cannot benefit from social media.
1. Sutton, M. (2008). Information citizenship. SA Journal of Information Management, 10(2).
2. Gottron, T., Radcke, O., & Pickhardt, R. (2013). On the Temporal Dynamics of Influence on the Social Semantic Web. In Semantic Web and Web Science. New York: Springer.
3. Cialdini, R. B. (1993). Influence: The Psychology of Persuasion. Boston, MA: Pearson.
4. Milgram, S. B., Leonard &Berkowitz, L. (1969). Note on the drawing power of crowds of different size. Journal of Personality and Social Psychology, 13 (2), 79-82.
5. Milgram, S. (1963). Behavioral study of obedience. The Journal of Abnormal and Social Psychology, 67(4), 371.
6. Argo, J. J., Dahl, D. W., & Morales, A. C. (2008). Positive consumer contagion: Responses to attractive others in a retail context. Journal of Marketing Research, 45(6), 690-701.
7. Aral, S., & Walker, D. (2012). Identifying influential and susceptible members of social networks. Science, 337(6092), 337-341.
8. Adam Acar & Makoto Fukui (2012) . The social media landscape of Japan. Paper Presented at the 2012 IBSM International Conference on Business and Management, Phuket, Thailand.
9. Rogers, E. M. (2010). Diffusion of innovations. New York: Simon and Schuster.
10. de Mooij, M., & Hofstede, G. (2011). Cross-cultural consumer behavior: A review of research findings. Journal of International Consumer Marketing, 23(3-4), 181-192.
11. Moore, G. A. (2002). Crossing the chasm: Marketing and selling disruptive products to mainstream customers. New York: Harper Collins.
12. Chaudhuri, A. (1994). The diffusion of an innovation in Indonesia. Journal of Product & Brand Management, 3(3), 19-26.
13. Coursaris, C. K., Yun, Y., & Sung, J. (2010). Understanding Twitter’s adoption and use continuance: the Synergy between uses and gratifications and diffusion of nnnovations. SIGHCI 2010 Proceedings. Paper 3. Retrieved Dec 26, 2013 from http://aisel.aisnet.org/sighci2010/3
14. Peslak, A, Cecucci, W & Sendall, P (2010). An Empirical Study of Social Networking Behavior Using Diffusion of Innovation Theory. Paper presented at Conference on Information Systems Applied Research, Nashville Tennessee, USA.
15. R. Ermecke, P. Mayrhofer, and S. Wagner. Agents of diơusion insights from a survey of facebook users. In Proceedings of the Forty-second Hawaii International Conference on System Sciences (HICSS-2007), Los Alamitos, CA, 2009
16. Lewis, K., Gonzalez, M., & Kaufman, J. (2012). Social selection and peer influence in an online social network. Proceedings of the National Academy of Sciences, 109(1), 68-72.
17. Katz, E. (1957). The two-step flow of communication: An up-to-date report on an hypothesis. Public Opinion Quarterly, 21(1), 61-78.
18. González-Bailón, S., Borge-Holthoefer, J., & Moreno, Y. (2013). Broadcasters and Hidden Influentials in Online Protest Diffusion. American Behavioral Scientist, 57(7), 943-965.
19. Solis, B., & Webber, A. (2012). The Rise of Digital Influence. Altimeter Group.
20. Grover, R. (2011). Ad.ly: The Art of Advertising on Twitter. Businessweek, January, 6.
21. Acar, A. & Polonsky, M. (2007). Online social networks and insights into marketing communications. Journal of Internet Commerce, 6(4), 55-72.
22. Feick, L. and Price, L. (1987). The market maven: A diffuser of marketplace information. Journal of Marketing, 51, 83-97.
23. Erickson, I. (2010). Geography and community: New forms of interaction among people and places. American Behavioral Scientist, 53(8), 1194-1207.
24. Adams, P. (2011). Grouped: How small groups of friends are the key to influence on the social web. New Riders.
25. Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings (pp. 1-45). Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.
26. Klout (n.d.). How it works? Retrieved Dec 26, 2013, from http://klout.com/corp/how-it-works
27. Fenn, J., Raskino, M., & Gammage, B. (2009). Gartner’s hype cycle special report for 2009. Gartner, Stamford, CT.
28. Acar, A., Nishimuta, A., Takamura, D., Sakamoto, K., & Muraki, Y. (2012). Qualitative Analysis Of Facebook Quitters In Japan. In Proceedings of the Eight International Conference on eLearning for Knowledge-based Society (pp. 23-24).
29. Acar, A & Ohniwa, M. (2023). Understanding Whys and How’s of Marketing on Group-chatting Applications. Paper presented at the 2013 ISIS Conference, Key West, FL.
30. Hanna, N., & Wozniak, R. (2001). Consumer behavior: An applied approach. Upper Saddle River, NJ: Prentice Hall.
31. Westbrook, R. A. (1987) Product/consumption-based affective responses and postpurchase processes. Journal of Marketing Research, 24 (3), 258-270.
32. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of Word-ofMouth Versus Traditional Marketing: Findings from an Internet Social Networking Site.Journal of Marketing, 73(5), 90-102.
33. Godin, S. (2003). Purple cow: Transform your business by being remarkable. New York: Penguin.
34. Chiu, H. C., Hsieh, Y. C., Kao, Y. H., & Lee, M (2007). The determinants of email receivers’ disseminating behaviors on the internet. Journal of Advertising Research, 47(4), 524-534,2007
35. Dichter, E. (1966). How Word-of-Mouth Advertising Works. Harvard Business Review, 44 (November–December), 147–166.
36. Sundaram, D.S., Mitra, K., & Webster, C. (1998). Wordof-Mouth Communications: A Motivational Analysis. Advances in Consumer Research, 25, 527–531.
37. Chevalier, J. A., & Mayzlin, D. (2003). The effect of word of mouth on sales: Online book reviews (No. w10148). National Bureau of Economic Research.
38. Yong Liu (2006) Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing: July 2006, Vol. 70, No. 3, pp. 74-89.
39. Fotis, J., Buhalis, D., & Rossides, N. (2012). Social media use and impact during the holiday travel planning process.
40. Kaplan, A. M., & Haenlein, M. (2011). Two hearts in three-quarter time: How to waltz the social media/viral marketing dance. Business Horizons, 54(3), 253-263.
41. Pollack, Keller (Nov. 10, 2010), Psst, Did You Know Most Word of Mouth is Offline, Not on Social. Web Log Post Retrieved Dec. 26, 2013, from http://www.kellerfay.com/insights/psst-did-you-knowmost-word-of-mouth-is-offline-not-on-social-media/
42. Muminova, O. (Mar. 14, 2013). Music apps, ebooks, conversations and champagne. The Guardian. Retrieved Dec. 26, 2013, from http://www.theguardian.com/advertising/digital-media-trends-ebooksapps-word-of-mouth
43. Jonah Berger, Katherine L. Milkman (2012) What Makes Online Content Viral?. Journal of Marketing Research, 49(2) ,192-205.
44. Customer Insight Group. The psychology of sharing: why do people share online? Slideshare. Retrieved Dec. 26, 2013, from http://www.slideshare.net/HeuvelMarketing/why-do-people-shareonline-11081448
45. Ho, J. Y., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9), 1000- 1006.
46. Rockett, A. (2012). Creating Seed Networks and Video Content to Go Viral: Organizational Strategies for Producing Web Video. Capstone Thesis Project. Retrieved Dec. 26, 2013, from http://www.american.edu/soc/communication/upload/AaronRockett.pdf
47. Ted Talks.com. Why videos go viral? Filmed Nov 2011, posted February 2012. Retrieved Dec. 26, 2013, from http://laughingsquid.com/ted-talk-why-videos-go-viral-by-kevinallocca/
48. Southgate, D., Westoby, N., & Page, G. (2010). Creative determinants of viral video viewing. International journal of advertising, 29(3), 349- 368.
49. Shifman, L. (2012). An anatomy of a YouTube meme. new media & society, 14(2), 187-203.
50. Knowyourmeme.com (n.d./n.a) Harlem Shake. Retrieved Dec. 26, 2013 from http://knowyourmeme.com/memes/harlem-shake 51. Mendelson, B. J. (2012). Social Media is Bullshit. New York: St. Martin’s Press.