Peer Influence & Social Media Research Paper_A Watson

Influence: The Power to Change The topic of influence is not new. Theories and ideas of power, influence, and persuasion have occupied great minds for centuries. It is the stuff of government politics and commerce, of business acumen and self-help psychology, even of history and legends. The question remains: what is influence? This paper will endeavor to examine influence as it pertains to today’s age of technological innovation and online information commerce. Once defined, it will assess the import and correlation which influence has in the post-modern world of social networks, user-generated content, and permission marketing. Finally, it will examine the process by which influence spreads information or entertainment via new media, and the impact this has on the bottom-line of the consumer purchase funnel. Dictionary.com defines influence generally as “the capacity or power of persons or things to be a compelling force on or produce effects on the actions, behavior, opinions, etc. of others”. The Merriam-Webster Dictionary similarly defines influence as “the act or power of producing an effect without apparent exertion of force or direct exercise of command”. Persuasion, in contrast, is defined by Merriam-Webster as “to move by argument, entreaty, or expostulation to a belief, position, or course of action.” So both terms are related as powerful agents of change, but influence is typically indirect whereas persuasion is direct and intentional. In terms of use within marketing and communications, however, influence and persuasion power are often used synonomously. One’s personal power of influence is viewed as the weight or force by which they are able to be an agent of change, whether that is by reputation or title, indirectly or directly. Bruins 1999 introductory work on “Social Power and Influence Tactics” credits the authors French and Raven (1959) with a simple and classic definition of influence from the field of sociology : a force one person (the agent) exerts on someone else (the target), to induce a change in that target. The change could be behavioral or it could be underlying opinions, attitudes, goals, needs, or values (Bruins, 1999). Bruins goes on to define eight basic influence tactics which might be employed by the agent to change the target: assertiveness, ingratiation, rationality, sanctions, exchange, upward appeal, blocking, and coalitions. He also enumerates five variables that affect the relationship between agent and target: uncertainty reduction, expected opposition, desire to be liked, assertion of group membership, and cognitive consistency. Cialdini (1984), a social psychologist by trade, defined six categories of influence based on fundamental psychological principles of human behavior: consistency, reciprocation, social proof, authority, liking, and scarcity. He noted that in our extraordinarily complicated modern environment we must use devices such as stereotypes, shortcuts, and associations in order to manage all the stimuli around us. “We can’t be expected to recognize and analyze all the aspects in each person, event, and situation we encounter in even one day. We haven’t the time, energy, or capacity for it. As the stimuli saturating our lives continue to grow more intricate and variable, we will have to depend increasingly on our shortcuts to handle them all” (Cialdini). It is precisely these shortcuts of associations and networks through which we are influenced. Walter Carl contended that the basic function of communication is to order one’s world and seek confirmation of one’s viewpoint. “Conversations are everyday negotiations of this sense-making process and to the extent people shift the discourse, or engage in efforts to reaffirm a certain discourse, we can say influence has occurred” (Bentwood, 2008). Qualman (2009) observes that “we have shifted from a world where the information and news was held by a few and distributed to millions, to a world where the information is held by millions and distributed to a few (niche markets)”. He also notes “the roots of social media and the social graph come from an offline world (of book clubs, men’s clubs, garden clubs, athletic clubs, gatherings around office watercoolers). It is just that technology has enabled us to go to a whole new level with our networks”. Social media are simply the newest mechanism by which we filter and organize the glut of information available to us. Therefore, they are also the newest carriers of influence. Networks and Filters: Social Media Participation The web log, or blog, was arguably the first user-generated content phenomenon of the internet. Through various user-friendly platforms such as wordpress.com, blogger.com, and typepad.com, anyone can be their own publisher. Technorati.com reports that approximately 175,000 blogs are created daily. There are currently an estimated 120 million blogs, with 7.5 million of them active. An estimated 184 million bloggers create 570,000 pieces of content every day, reaching about 70% of the web’s daily audience. Two-thirds of active bloggers are male, and a majority (60%) between the ages of 18 and 44. 75% of active bloggers hold a college degree, and on average one in three has an annual household income over $75,000. Among bloggers, the leading metrics of success used are personal satisfaction, number of unique visitors, and number of posts or comments (Technorati). Technorati.com continually ranks blogs and assigns a “Technorati Authority” number on a scale of 0-1000, based on calculations including a site’s linking behavior, categorization, and other associated data. Higher authority bloggers are more prolific content creators, posting nearly 300 times more content than lower ranked bloggers. Sites are ranked according to Technorati Authority compared with all other blog sites, and a running list of the Top 100 blogs (across all categories) is published on their website. The top blogs are also listed topically, in nine major categories and 31 sub-categories. See Appendix #1 for Technorati topical directory. Currently, Facebook is the leading platform for social networking worldwide. Facebook.com statistics page reports over 400 million active users, with 50% logging in on any given day. Roughly 70% of Facebook users reside outside of the United States, and more than 70 language translations are currently available on the Facebook website. In addition, there are currently more than 100 million active mobile Facebook users, and people that access Facebook on their mobile devices are twice as active as non-mobile users. The average Facebook user has 130 friends, is connected to 60 pages, groups, or events, and creates 70 pieces of personal content each month. In addition, there are approximately 2.5 billion photos uploaded to Facebook each month. In comparison, Twitter reportedly ended 2009 with just over 75 million users worldwide. Just over 17% of all Twitter users sent a tweet in December 2009, an all-time low. Nearly 6 million users join Twitter each month, with nearly one-third of all accounts having joined within the last four months. A large number of registered Twitter accounts are inactive, with about 25% of accounts having no followers and about 40% of accounts never having sent a single tweet. Furthermore, nearly 80% of registered Twitter users have sent less than 10 tweets. The average Twitter user has 27 followers. Approximately 57% of Twitter users reside in the United States. However, despite the fact that only 20% of Twitter users return to tweet in their second month after registration, those that do return tweet so much that it makes up for all the inactive accounts. So, there is a high amount of loyalty and engagement on the platform from the small percentage of active Twitter users (RJ Metrics data). Blogsites, Facebook, and Twitter are far from the only options available. Other common sites and platforms include YouTube for video content, LinkedIn for professional networking, Flickr for photos, the online user-generated encyclopedic resource Wikipedia.com, and many more. The amount of information and networks available worldwide on the internet is staggering. Each of these networks and platforms offers differing levels of information, interaction, and participation. People are free to consume available information and observe within their social network(s), actively contribute and participate, or lead as they see fit. Bernoff (2009 Forrester report) found that “two-thirds of teens and tweens and more than half of online adults send information back and forth at least once per month”. 56% of adult sharers and nearly two-thirds of youth sharers send information weekly (Bernoff). Interestingly, the factors that motivate people to participate in social media stem from the same basic human behavior patterns that Cialdini references regarding influence. Safko (2009) cites reciprocity, recognition, and social liking in particular as contributing factors. Amerasinghe (2010) summarizes both intrinsic (internal) and extrinsic (external) motivators to participation in a concise model (see Appendix exhibit #2). Craig Newmark (of Craiglist.com) predicts “people use social networking tools to figure out who they can trust and rely on for decision making. By the end of this decade, power and influence will shift largely to those people with the best reputation and trust networks, from people with money and nominal power. That is, peer networks will confer legitimacy on people emerging from the grassroots”. This is a logical conclusion, given that the purpose of online social networks and sharing sites is to bring together people that share a common bond, similar interests, and/or trusted relationships. Influence will reside with those that can provide valuable content or meaningful connections, which in turn can then be spread throughout our social network(s). Trust and Credibility Sources: Peer Influence How important is trust in a relationship? Edelman’s Trust Barometer Survey found that 91% of 25-to-64 year-olds surveyed around the world indicated that trustworthiness was an important factor in the overall reputation of a company. However, trust in traditional media sources continues to decline. “Conversations with friends and peers” was cited as a credible source of business information by 37% of those polled, which rates higher than TV news coverage (36%), newspaper articles (34%), corporate communications (32%), and advertising (17%). Only radio polled slightly higher than peer sources, at 38% credibility. “Academic sources” or “experts” continue to be the most trusted sources of information, with 64% of those surveyed citing them as a credible source. On the heels of a global recession, with numerous headlines of natural disaster, industry bailouts, partisan politics, and more, trust in government and business are at all-time lows (Edelman). At the same time, the proliferation of social media and the changing technological landscape are reinforcing consumer trust in their peer networks. Brogan and Smith (2009) aptly observe that “we are currently living in a communications environment where there is a trust deficit. The result is our tendency to join together into loose networks, or tribes, that gather based on common interest. We are suspicious of anything that comes to us from outside our circle of friends. We form groups of like-minded individuals around those topics, products, or news items that interest us.” Bernoff’s (Forrester 2009) research found that in fact nearly 75% of online adults trust email from people they know, while 60% rely on ratings and reviews, and nearly half of online consumers trust social networking profiles of people they know. Owyang (2010) mapped out various communication roles in a “Rings of Influence” graph and found similar results; corporate branded or sponsored communications are less trusted than the outer rings of peers and prospects. Open-Source Media In fact, our very definition of media is changing. As individuals can now publish anything online, content is undergoing a seismic shift to a more instantaneous, shared model. “We view online social networks as media, not because they help us communicate, but because they extend human relationships. We’ve chosen to make the next media ours, and we’ve shaped our own media to be an extension of our views, our own businesses, and our tribes” (Brogan & Smith). Bob Garfield deems this shift the “Chaos Scenario”. He questions media and marketing professionals alike, “Can you hear it? In the distance? It’s a crowd forming- a crowd of what you used to call the ‘audience’. They’re still an audience, but they aren’t necessarily listening to you. They’re listening to each other talk about you. And they’re using your products, your brands names, your iconography, your slogans, your trademarks, your designs, your goodwill, all of it as if it belonged to them- which, in a way, it all does, because after all, haven’t you spent decades, and trillions, to convince them of just that? Congratulations. It worked.” Garfield describes the current information society as an “open-source world”, in which everyone may contribute and collaborate. So we now look to our trusted networks to find and share information, and we look to our peers to collaborate with us in creating new content as well. Participation Inequality As technology increasingly enables everyone to have a voice in both content production and distribution, it behooves marketers to identify and understand how many people, and which ones, are actively involving themselves in this process. Forrester (2007) created a useful Social Technographics Ladder graph which segments social computing behaviors into seven (overlapping) levels of participation (least to greatest): Inactives (17%), Spectators (70%), Joiners (59%), Collectors (20%), Critics (37%), Conversationalists (33%), and Creators (24%). See Appendix exhibit #3 for detail. Clearly, this categorization of participation indicates that a majority of people online merely consume content as observers, rather than create or modify the community content. This reflects a common theory within internet culture, which originated in the blogosphere in 2006, known as the “90-9-1” rule or the “1% rule”. These theories advance the idea “that more people will lurk in a virtual community than will participate. This term is often used to refer to participation inequality in the context of the internet”(Wikipedia.com). The “90-9-1” version of this theory proposes that 90% of on online community view content without contributing (i.e. “lurk”), while 9% participate by editing or modifying content (i.e. commenting on a post), and only 1% of users create the original content. These theories are similar in concept to the 80/20 rule from information science, also known as the “Pareto principle”, which holds that 20% of a group will produce 80% of the group’s activity. While the actual percentage of participation is likely to vary depending upon subject matter and community, it is a generally held rule that a minority of users will initiate and distribute online content to the majority. These are the mass influencers of social media, the minority that originate content and determine the content that is “remarkable”- literally that which is worthy to be noticed or commented upon. Identifying the Influencers In Jeremiah Owyang’s Dow Jones White Paper on “Tracking the Influence of Conversation”, a meme was defined as “an idea or discussion that grows and spreads from individual to individual into a lengthy commentary”. This relates to Csikszentmihalyi’s earlier work (1996) regarding creativity memes and knowledge domains. A 2008 Edelman White Paper advanced this idea of memes further, to postulate that people of influence are those that may be categorized as either meme starters or meme spreaders. It describes meme starters as a person who “typically is creative, forms opinions, and articulates them well. They have the ability to state a view at the right time. Their readership is not necessarily large but views this individual as trustworthy” (Edelman). These are the content creators. In contrast, the meme spreaders are people who “thrive by sharing opinions and want to do it first. They are trusted and have a large readership”. These are the content distributors. Both types of people have influence, but in different forms. In general, the 2008 Edelman White Paper sought to “understand which individuals are the most trusted or have the loudest voice” by calculating online influence. That paper suggested a rudimentary formula for measuring an individual’s online influence: Volume+Quality of Attention x TimeSize and Quality of Audience Brogan & Smith deem the latter category of influencers (Edelman’s Meme Spreaders) Trust Agents in their 2009 book by the same title. They describe these content distributors as “the power users of the new tools of the Web”. According to Brogan & Smith, these influencers “speak online technology fluently. They recommend more, and more often, on social bookmarking applications than anyone else. They connect with more people than anyone else, and they know how to leave a good impression. As they do so, they build healthy, honest relationships. Trust agents use today’s Web tools to spread their influence faster, wider, and deeper than a typical company’s PR or marketing department might be capable of achieving, and with more genuine interest in people, too”. Ray (2010) creating a Forrester research report titled “Tapping the Entire Online Peer Influence Pyramid”, in which he categorizes online influencers into three large categories: Social Broadcasters, Mass Influencers (sub-categorized as Mass Connectors and Mass Mavens), and Potential Influencers. Forrester named these two sub-categories of Mass Influencers based on a similar analogy described by Malcolm Gladwell in his book The Tipping Point. According to Forrester’s report “Peer Influence Analysis”, the Mass Mavens “are the 13.8% of the online population (24 million people) that creates 80% of all opinions about products and services in blog posts, blog comments, discussion forum posts, and reviews”. This relates to Edelman’s category of Meme Starters. Forrester research indicates that the average Mass Maven is 38 years old and has an average income of $89,800, well above the average for online consumers of $79,100. In contrast, Forrester’s Mass Connectors are the equivalent to Edelman’s Meme Spreaders. Forrester describes Mass Connectors as “the 6.2% of the US online population (11 million people) that generate 80% of all impressions about products and services within social networks. Forrester describes Mass Connectors as young and affluent; with an average age of 32 and average annual household income of $98,100. These Connectors generally have large personal networks online in venues such as Facebook, Twitter, LinkedIn, and MySpace- with an average total of 537 connections across all networks. See Appendix exhibit #4 and #5 for graphs of these Forrester Mass Influencers. Influence Analytics in Social Media: Assessing the 90-9-1 Rule Dr. Michael Wu, Principal Scientist of Analytics at Lithium, has done a great deal of work digging into the dynamics of online communities as well as the mathematical dynamics of social interaction. His work using Lithium’s 10 years of user participation data across 200+ communities has revealed that the 90-9-1 Rule, while a relevant rule of thumb, varies greatly depending on the community, topic, context, as well as the definition of “most of the community content”. Wu’s research found that “hyper-contributors can contribute anywhere from about 30% to nearly 90% of the community content, with an average of 55.95%”. Depending on the definition of “most” of the community content, the ratio between hyper-contributors and occasional contributors may be anywhere from 99:1 (with hyper contributors averaging at least 30% of all content) to 5:1 (with hyper contributors averaging at least 50% of all content). Forrester’s analysis (noted above), found a participation inequality more in line with the 80/20 Pareto Principle than the 90-9-1 Rule. So, mathematical analysis tells us that there is a great deal of variability in the data. However, we do see that “participation in communities is highly skewed and unequal, and there is a small fraction of hyper-contributors who produce a substantial amount of the community contents” (Wu). Wu recommends use of the Lorenz curve (from the field of economics) and the Gini coefficient (from the field of statistics) to specifically analyze the participation inequality in a particular community. Finding the Influencers Wu’s research continues with a breakdown of the process of influence from a data analytics perspective. His simplified model of Social Media Influence involves an influencer and a target. The influencer’s power to influence is dependent of two factors: credibility- expertise in a specific domain of knowledge, and bandwidth- the ability to transmit knowledge through a social media channel. The target’s likelihood to be influenced is dependent on four factors: relevance- the right information, timing (the right time), alignment (the right place), and confidence (the right person) (Wu). High bandwidth users can be identified by: 1) participation velocity data (such as number of posts or tweets), 2) social equity data (such as number of followers or unique blog readers), and 3) social graph data (such as a network analysis of following/followers relationships on Twitter). Credible users can be identified by: 1) reciprocity data (such as online ratings), 2) reputation data (such as online rankings), 3) self-proclaimed data (such as career experience listed on LinkedIn), and 4) social graph data (such as a domain-specific network analysis of conversation on a given topic). Finding the influencers in a particular social media channel, then, is a matter of identifying the credible users and high bandwidth users for a particular topic or field and then refining the data to its intersection point (Wu). See Appendix exhibit #6 for a Venn diagram of this process. Identifying an influencer from a data analytics perspective can be summarized as finding the right person(s), not with the most friends or even the most discussion around a topic, but the one(s) with the most recent discussions about a topic/product. So, in this sense: Relationship+Product Discussion+Timely+Channel Alignment=Best Chance of Success (Wu). First, a marketer should determine their need within the consumer purchase funnel (awareness, consideration, intent, purchase, loyalty) to determine marketing goals and key performance indicators. Then, tools such as Social Network Graphing (Exhibit #7) and Forrester’s Social Technographics Profile (Exhibit #3) can help a marketer identify which social media channel(s) are being utilized by their target audience. Then, depending on the need to create content or distribute content, the marketer can determine relevant metrics such as number of posts or number of connections in a network to refine their search to Mass Mavens or Mass Connectors to align with their marketing goal(s). Finally, the marketer can use those relevant metrics to identify influencers within the appropriate channel(s) using the Social Media Influence analysis process. Power of Peers: Earned Media and Advertising According to Nielsen, “marketers are moving from a broadcast-based marketing relationship with consumers to a relationship that more explicitly considers how traditional paid media drives ‘earned media’- where consumers directly engage with the marketing messages and pass them along to their friends”. As its name implies, “earned media” has to be produced directly by consumers through trust, connection, and engagement between user and brands. Forrester indicates that “the scale of online peer influence compares with online paid media”. In alignment with their Mass Influencer categories, Forrester analyzed peer influence in two categories: influence posts (by the Mass Mavens) and influence impressions (by the Mass Connectors). Forrester estimates that consumers created over 500 billion influence impressions on one another about products and services within social networks last year, which is roughly one-fourth of the paid online ad impressions. Considering that consumers trust peer sources more than any others when making purchase decisions, these social impressions carry significant weight. This is why identifying and targeting social media influencers is imperative. Forrester addresses the challenge of targeting peer influencers in this way, “How do you target millions of influencers in a medium where marketers must earn rather than buy attention? You cannot build one-to-one relationships with millions of influencers, but you can understand the specific characteristics of the Mass Influencers in your particular market. That is the power of Peer Influence Analysis: It arms marketers with knowledge about the Mass Influencers in their specific markets so that they can devise the right word-of-mouth (WOM) strategies”. Nielsen’s research in conjunction with Facebook found “most ‘earned’ impressions have the highest level of impact out of the formats tested, but because this subset of consumers is small, reach is difficult to achieve solely with these impressions”. The study found users exposed to both a paid Facebook ad and an organic (social) impression remembered the ad at three times the rate of those exposed only to a paid ad (Nielsen). Additionally, the study found ad recall, awareness, and purchase intent continued to rise even after 10+ exposures to social impressions, which reinforces the notion that consumers will tolerate and trust peer influence significantly more than traditional forms of advertising (Nielsen). Nielsen’s conclusion was that “the key to success for marketers is creating a mix of social impressions that incorporate both paid and earned media”. Conclusion Jeff Jarvis is quoted in Garfield’s “Listenomics” article with the following statement, “There’s a conversation going on about your brand in the open. You can either join it or not”. Marketers are increasingly going to need to engage in social media and networks, because that is where consumers are spending time and forming their opinions. Garfield suggests that marketers learn to “manage, focus, exploit, maybe even co-opt the open conversation”. Safko (2009) concludes that business owners and marketers “need to transform the way we touch our clients, and integrate ourselves into the very fabric of what they do every day. We have to embrace social networks, digital connections, and the online experience and build an organization that embraces conversation and transparency”. The most efficient way to do this is by leveraging the power of earned media through peer influence. The two key variables that must be analyzed to accomplish this are the percentage of active influencers within a given community channel and the influence type, Mass Mavens (content producers) or Mass Connectors (content distributors). Case Analysis Example: Android Mobile Phone Mass Influencers Forrester’s “Peer Influence Analysis” research (through their Technographics data service) has recently completed research on Mass Influencers for consumers who share opinions about consumer electronics. They found that “about 1.8% of the online population are Mass Connectors for consumer electronics and 4.8% are Mass Mavens.” These groups account for more than 80% of the peer impressions regarding consumer electronics. Forrester found that the demographics of these particular influence groups (electronics Mavens and Connectors) are fairly similar. 61% of the Mavens and 66% of the Connectors are male. On average they are between 32 and 38 years old, with incomes between $95,000 and $106,700 annually. They are also heavy users of mobile internet (54% to 68%) compared to the average online adult population (25%). See Appendix exhibit #8 for Consumer Electronics Mass Influencer Demographic data. Twitter (17%) and Facebook (53%) are the dominant social networks for Connector peer impressions regarding consumer electronics. Forrester found that within the consumer electronics category, compared with the general US online population, they are four times more likely to read others’ tweets, five times more likely to have a Twitter account, and almost 10 times more likely to ask for the opinions of Twitter friends before making a large purchase” (Forrester). In addition, Forrester found that 40% of influence impressions from Mavens regarding consumer electronics occur on blogs within posts and comments, followed by 32% from ratings and reviews on popular online news and information sites such as CNET, Engadget, and Gizmodo. A quick review of Technorati.com’s recent tags (May 6, 2010) reveals that “Android” has been tagged as a topic 31 times on blogs within the last month. Technorati’s Blog Directory reveals that “Technology” is the 2nd most popular blog category, with 8621 blogs in the main category (“Lifestyle” is highest, encompassing 13,703 blogs). Tweetvolume.com indicates that Android has been tweeted about 45,100 times within the last week. Together, these data points suggest it would be possible to identify both types of social influencers: “Mass Mavens” and “Mass Connectors” within the technology field, concerning the topic of Android mobile phones. By definition, the Mass Mavens of Android mobile phones could be found by searching for bloggers and technology critics recently producing content about Android. Technorati’s Top 100 list for Technology could be used to identify high bandwidth content producers, and Twitter could be mined to locate users with high social equity (# of tweets and re-tweets) regarding Android. This data could then be cross-referenced with popular Twitter users and Tech site reviewers to determine the users with highest credibility (through ratings, reputation, and reciprocity data) to determine key Mass Maven Influencers. Mass Connectors regarding Android mobile phones could be found, using the Forrester research as a guide, by analyzing connection data on Facebook and Twitter. A social graph could be constructed to represent recent Facebook and Twitter conversations regarding Android, to pinpoint high bandwidth users. Participation velocity data could also be examined, such as the number of tweets in a given time frame. This data could then be cross-referenced with credibility data from the social network graph as well as from reputation data and self-proclaimed data on websites or networks such as LinkedIn (noting expertise in the domain). These steps would identify the key Mass Connector Influencers for Android phones. Exhibit #1 Technorati Blog Directory (With Blog Count by Topic): Entertainment (8111) ,[object Object]

Books (2514)Business (7496) ,[object Object]

Small Business (1561)Sports (4470) ,[object Object]

Motorsport (78)Politics (5903) ,[object Object]

World Politics (2119)Autos (730) Technology (8658) ,[object Object]

Gadgets (1896)Living (13910) ,[object Object]

Peer Influence & Social Media Research Paper_A Watson

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (18)

Similar a Peer Influence & Social Media Research Paper_A Watson

Similar a Peer Influence & Social Media Research Paper_A Watson (18)

Más de Alexandra Watson

Más de Alexandra Watson (6)

Último

Último (20)

Peer Influence & Social Media Research Paper_A Watson