Speed Summary | Wired Feb 2011 Cover Story on Social Commerce (from SocialCommerceToday.com)

From http://socialcommercetoday.com/speed-summary-wired-feb-2011-cover-story-on-social-commerce/

Commerce Gets Social: How Your Networks Are Driving What You Buy

  • Social Commerce, building “a social layer on top of online commerce”, “turning products into conversations”, is attracting big funding, big buzz and generating big revenue.
    • Flash Sale sites such as Vente-Privée use a social layer to promote the sharing of time-limited online deals (and drive member-get-member referrals)
    • Group-Buy sites such as Groupon and Keynoir use a social layer to to promote the sharing of time-limited local deals
    • Social Shopping sites such as PolyvoreKaboodle and Lockerz use a social layer to promote the sharing of products discovered online – thus encouraging ‘social discovery’
    • Social Shopping apps such as Stickybits and ShopKick use a social layer to promote the sharing of store visits, using scanning technology to encourage store discovery and selection
    • Purchase-Sharing sites such as Blippy and Swipely use a social layer to promote the sharing of products purchased – thus encouraging social product discovery and selection
    • Personal Shopper sites such as GoTryItOn use a social layer to promote smart shopping decisions by allowing shoppers to get a second opinion on what to buy
  • E-commerce is Over. Long Live Social Commerce”: Retail has entered a new phase, where product discovery and purchase decisions are informed by the collective and distributed social intelligence of peoples’ social graphs.
  • Facebook is leading the social commerce charge with a) social plugins that add a social layer to retail sites (so people can share likes and purchases with friends, and get personalised recommendations) and b) Facebook Deals that add a social layer to bricks and mortar stores (allowing people to share store visits – and get deals)
  • Social Commerce enhances the retail experience for shoppers – helping them realize the ‘value-expression’ function of shopping (what psychologists call ‘impression-management’ – aka bragging rights, ego-tripping) whilst also helping shoppers make smarter shopping decisions using their social intelligence (personalized recommendations for product discovery, product choice)
  • Social Commerce enhances the retail business for retailers by turning electronic word of mouth into sales – a share on Facebook generates $2.52 for ticketing site Eventbrite, and $20,000 in additional drinks revenue were generated for London restaurant Preto following a Groupon group-buy promotion
  • Big brands want to cash in on your Facebook Friends”: Volkswagen has added a social layer to its online car configurator allowing people to share personal car configurations and get feedback from their social graph.  Of the 450K people using the configurator in November 2010, nearly 1/4 modified their design based on feedback
  • Word of mouth and imitation have always influenced purchase decisions, but social commerce adds scale and transforms this social influence into sales – leaving a trackable (measurable) digital trace: Social commerce makes word of mouth measurable
  • Social commerce makes use of human psychology – the psychology of sunk costs; consumers change from sceptics to advocates once they own a product (part impression-management – we want to be seen as smart shoppers (which is why the average review is 4+ stars out of 5, and part “Post-decision dissonance management“))
  • 90% of purchases are subject to some sort of social influence – that’s the size of the social commerce market

Social Commerce Factoids

  • $13m – backing for purchase-sharing site Blippy including Twitter’s Evan Williams and Sequoia Capital
  • $2.52 and 11 visits – what a Facebook share generates for ticketing site Eventbrite
  • $250m – funding for sFund for social applications/services from KPCB, Amazon, Facebook and Zynga
  • 1m+ sites that have integrated Facebook’s social layer on their websites
  • 17m – members of social commerce site Lockerz
  • 98m – “haul video” views for 17-year-old juicystar07 YouTube
  • 60m+ “haul video” views for 23-year-old DulceCandy87 from LA
  • 35 – countries that Groupon operates in – with 2 years of launch
  • £20,000 – extra revenue from drinks earned by London restaurateur Dean Knight of Preto, following a Groupon deal (£20 of food for £8 – 3,006 vouchers sold)
  • 90% – proportion of purchases subject to social influence (Econsultancy)
  • 23% – market share of US display ad impressions on Facebook (vs. 2.7% for Google)
  • 1.75m – UK Facebook users referred to top 200 ecommerce sites in October 2010

Social Commerce Smart Quotes

On the potential for social commerce:

  • Mark Zuckerberg (Facebook founder) “If I had to guess, social commerce is the next area to really blow up”
  • Andrew Mason (Groupon founder) “The size of the [social commerce] market is the size of every empty restaurant table”
  • Bing Gordon (KPCB investment (sFund)) “The potential for social commerce today is “infinite”… Every ecommerce site will have to adapt”
  • Christian Hernandez (Facebook) “There was an Econsultancy survey that said that 90 per cent of purchases have some sort of social influence – your friend recommended it, or you saw it on somebody. Until now there’s been no way of getting that ‘girls in the mall’ effect on a large scale. That’s the opportunity: it’s huge and untapped. And we have the benefit of both scale and identity”
  • Danny Rimer (Index Ventures) “It’s this notion that a transaction is more communication than the be-all and end-all. We believe that when you go to checkout, the end of that activity is sharing what you’ve purchased with friends. Everything is going to have to integrate a social layer in commerce. No question.”

On the rationale for social commerce

  • Andrew Mason (Groupon founder)  “Middle-class people sit around trying to think of how to spend money.  One of the most powerful ways to figure that out is looking at what you friends are buying, people you trust”
  • Christian Hernandez (Facebook) “Social recommendations can help you discover things that some algorithm won’t”
  • Danny Rimer (Index Ventures)  “When we buy something, we become its greatest champion. That is what social [commerce] can do. It really is transaction as communication. The internet lends itself to promoting and building validation, confirming that what you’ve bought is great. It’s one of the irrational, unexplainable but inherent bits of human behaviour.”
  • Shervin Pishevar (Social Gaming Network founder, investor) “It’s about ego, sharing your status and accomplishments, a quest for positive validation”

On social commerce business opportunities

  • Reid Hoffman (LinkedIn founder)  People will converse about your brand independently of you. You can’t stop the negative comments. But what you can do is maximise the likelihood of a distributed set of very good conversations. That has to be your strategy.
  • Christian Hernandez (Facebook) “I’m most excited about Deals – so when you check into a physical Gap, it gives away 10,000 pairs of jeans. Imagine you run a Gap promotion on Facebook, and track who walked into the store and cashed it in for a pair of jeans. It’s the advertiser’s holy grail: how does my brand money lead to foot traffic? It means you can figure out the return on your advertising. And users get to discover deals – your friend checks in and cashes in a deal, it gets shared. So if I learn that Tommy Hilfiger is doing an interesting deal nearby, I might just go and check it out myself. It amplifies the coupon model to 500 million people.  There’s discovery, sharing, bragging about what you’ve bought, and redemption at the storefront”.
  • Reid Hoffman (LinkedIn founder) With millions of people in the role of publisher, the challenge for marketers is how you tastemake…Rather than buy [ads in] one TV show, it’s better to be in the fabric of the conversations. It makes more sense to participate in, say, girls displaying their purchase”
  • Reid Hoffman (LinkedIn founder)  “You don’t say, ‘Wow, I bought this really cool toaster,” he says. “But it’s really relevant with music, movies, books – they are part of our identity, and they’re repetitive purchases. We buy maybe three books a month, at least one album a month, see three movies. That’s key. Look for purchases that can be part of people’s everyday lives”
  • Michael Lazero (Buddy Media founder) “Facebook is now ‘This place where 50 per cent or more of our customers live online, where a quarter of all US ad impressions are – if we don’t go there I’ll be fired.’ So all of a sudden, brands are publishers who sell directly through social channels – bookstores, travel companies. And we’re just at the beginning.”
  • Shervin Pishevar (Social Gaming Network founder) “I call it people rank, as opposed to [Google’s] PageRank. Just as PageRank gives more weight to a page with more authority, we can now identify the most influential people in a space. Traditional marketing? It’s dead. It’s real-time social marketing and commerce that really matter now. There will be multibillion companies in the social commerce space. We’re where we were with social gaming two or three years ago.”
  • Sinan Aral (Stern School of Business, chief scientist, SocialAmp) “The companies that will succeed with social shopping are the ones that have science under the hood.”
  • Danny Rimer (Index Ventures) “We picked up a trend on YouTube called v-hauling. We saw these 18- to 25-year-old girls going online with their most recent shopping bag from Topshop and Target, explaining to the camera why these were awesome goods. And some of them had followers in the hundreds of thousands.”
  • Danny Rimer (Index Ventures) “[Privacy], It’s a generational thing: the new generation want to scream from the mountain-tops what they’ve bought and share the value with the largest audience they can get.”

Grooming, Gossip, Language and Social Networking

Summary of the Michael Rogers’ brilliant article:  Michael Rogers, How social can we get? What evolutionary psychology says about social networking,   Special to MSNBC(updated 9/10/2007 10:55:27 AM ET), http://www.msnbc.msn.com/id/20642550/

Robin Dunbar, the British anthropologist,  one of the more influential practitioners of evolutionary psychology:
Robin Dunbar, Gossip, Grooming and the Evolution of Language, Harvard University Press, 1998.

How the human animal behaved in our earliest ancestral environments long before civilization?

Dunbar begins with the premise that back when our Paleolithic ancestors were still more monkey than human, understanding one’s place in the group hierarchy was exceedingly important.  Compared to other creatures, primates are unusually social animals.  And thus knowledge about relationships — who’s mating with whom, who became allies, who just had a fight — was crucial for primates to maintain or advance their place in the pack. It was, Dunbar suggests, the birth of gossip.  But before language evolved, how was gossip transmitted? 

Dunbar speculated that the early hominids maintained and communicated their relationships via the mutual grooming behavior we still see in lower primates.  Baboons and chimpanzees spend 20 percent of their time grooming one another.  But grooming, Dunbar argues — besides tidying one’s fur and feeling good — was a way to establish and maintain friendships, determine the hierarchy within the tribe and signal one’s social connections to other tribe members.  One might almost say that grooming was the first social networking application.

Dunbar speculates that at some point our early ancestors’ tribes began to get too big for even the most energetic primate to get around to grooming everyone.  And thus language emerged to replace grooming as a means of conveying social relationships.  (It’s not clear which came first — language or the larger tribe size — but they grew in tandem.)  An exchange of personal information with language was far quicker than a 20-minute grooming session, and a single individual could converse with several others at one time.  So rather than the traditional anthropologic explanation that language evolved among males to coordinate hunting, Dunbar proposed that language evolved as a way to maintain and identify social relationships. And we haven’t stopped gossiping since. 

Gossip’s primitive significance may explain the unending appeal of celebrity journalism.  We’re still watching the behavior of the alpha males and females in our tribe, only now we identify them as Brad and Angelina (they do look awfully large on the silver screen.)  In a sense, our gossip appetite is a bit like another bit of programming we inherited from our hard-living primate ancestors: the urge that tells us to consume all the food we can when it’s available. Today, of course, surrounded by fat and sugar, that dietary programming rapidly leads many of us to excess poundage.  With professionally-produced gossip now as readily available as fast food, it may be only natural that we overdose on that as well. 

Web-based social networking fills the same need on a personal level: it is an incredibly efficient gossip engine, with an unprecedented ability to establish the precise nature of relationships (limited profiles and privacy settings provide plenty of signals as to who’s close and who is closer). That’s an age-old attraction that’s not going away.  But there’s another element of social networking that may be something altogether new.  

It stems from Dunbar’s observation that, with the new tool of language, humans managed to increase their group size significantly from the 50 or so members that characterize baboon and chimpanzee groupings.  But over the last 10,000 years or so, we seem to have hit another ceiling for an optimum group size in which members are reasonably in touch.  That number is about 150, a figure supported by examples that range from the size of Neolithic villages to military units to corporate management theory.  Organizations, of course, get bigger than that, but as they do they change character: bureaucracies, levels of authority, social stratification begin to emerge.  Dunbar theorizes that this may be due to the limits of how many individuals one can converse with, based on the acoustics of speech, and still have time to take care of life’s essentials.  

So the obvious question about Internet-based social networking is whether we humans are once again increasing the size of our effective groups.  Is this an evolutionary shift that — while certainly not as significant as the advent of spoken language — will ultimately change the way we operate as social creatures?  Will anthropologists of some distant era look back and say that this was the moment when humans once again created much larger social networks than we were able to maintain in the past?  Or perhaps in the end we’ll discover that, once again, about 150 “friends” is as far as our capacities can take us.    

Whether or not Dunbar’s decade-old theory about language’s origin in gossip is correct, it’s a fascinating way to think about what’s important in human communication.  And it suggests that with social software, we’re for the first time arranging the Internet in a way that makes sense to the deeper inclinations of our brains.  While we’re only in the very earliest days, this new twist may well be the beginning of the Internet as it is meant to be.

Social Network Theory vs. Physical Sciences

Types of ties
In the physical sciences, it is not unusual to regard any dyadic phenomena as a network. In this usage, a network and a mathematical graph are synonymous, and a common set of techniques is used to analyze all instances,
from protein interactions to coauthorship to international trade. In contrast, social scientists typically distinguish among different kinds of dyadic links both analytically and theoretically. For example, the typology divides dyadic relations into four basic types—similarities, social relations, interactions, and flows. Much of social network research can be seen as working out how these different kinds of ties affect each other.

 

 

 

 

 The importance of structure.
As in the study of isomers in chemistry, a fundamental axiom of  social network analysis is the concept that structure matters. For example, teams with the same composition of member skills can perform very differently depending on the patterns of relationships among the members. Similarly, at the level of the individual node, a node’s outcomes and future characteristics depend in part on its position in the network structure.Whereas traditional social research explained an individual’s outcomes or characteristics as a function of other characteristics of the same individual (e.g., income as a function of education and gender), social network researchers look to the individual’s social environment for explanations, whether through influence processes (e.g., individuals adopting their friends’
occupational choices) or leveraging processes (e.g., an individual can get certain things done because of the connections she has to powerful others). A key task of social network analysis has been to invent graph-theoretic properties that characterize structures, positions, and dyadic properties (such as the cohesion or connectedness of the structure)  and the overall “shape” (i.e., distribution) of ties.

Research questions
In the physical sciences, a key research goal has been formulating universal characteristics of nonrandom networks, such as the property of having a scale-free degree distribution. In the social sciences, however, researchers have tended to emphasize variation in structure across different groups or contexts, using these variations to explain differences in outcomes. For example, Granovetter argued that when the city of Boston sought to absorb two neighboring towns, the reason that one of the towns was able to successfully resist was that its more diffuse
network structure wasmore conducive to collective action. 

Theoretical mechanisms
Perhaps the most common mechanism for explaining consequences of social network variables is some form of direct transmission from node to node.Whether this is a physical transfer, as in the case of material resources such as money (35), or a mimetic (imitative) process, such as the contagion of ideas, the underlying idea is that something flows along a network path from one node to the other.

Stephen P. Borgatti, et al., Network Analysis in the Social Sciences, Science 323, 892 (2009) http://www.chronicdisease.org/files/public/2009institute_na_track_borgattietal2009science.pdf

Monetizing Social Networks: The four revenue models

I. Display Ads:
II. Branding Certain Elements within an Application
III. Virtual Currency
IV. Virtual Gifts

How You Should Implement These Models:

Display Ads

  • Allocate about 60% of your ad space to high-quality brand ad networks (you’ll need traffic, and a lot of applying)
  • Allocate about 20% of of your ad space to CPA-based Ad Networks
  • Allocate about 10% of your ad space to Job Listing Ad Networks
  • Allocate about 10% of your ad space to product/widget based ad networks (Amazon Affiliates, Widgetbucks)

Brand Certain Elements within Your Application for CPS (Cost Per Share):

  • Get creative with your application and business model by identifying the major value for your users, and evaluating whether or not you can brand pieces of value without hurting the user experience, but enhancing it.

Virtual Currency:

  • Build virtual currency extensions into your application, but be sure to conduct due diligence on who you’re partnering with.
  • Keep an eye on this arena, as I have a feeling some players in this realm are going to be called out and chastised if the recent allegations about misleading users are indeed true.
  • As a Facebook developer, Facebook made it clear that it’s your job to understand what types of offers are being presented to your users. Have someone on your team routinely click the offers and investigate each ad creative.

Virtual Gifts:

  • Virtual gifts are going to continue to be a cash cow well into 2010
  • It’s critical that you somehow find a way to implement Virtual Gifts into your application. Why? Because this will be the primary way to monetize International countries; you don’t want to find yourself with millions of users that make nothing for your application because they’ll end up costing you money in terms of support costs, as well as other expenses.
  • For now Facebook has the monopoly on this monetization route; however, it seems as if they’ll soon be granting developers access to this revenue stream

http://venturedig.com/tech/monetizing-social-networks-the-four-dominant-business-models-and-how-you-should-implement-them-in-2010/

Activity Network (vs. Social Network) and its Evolution in Facebook

Recently, researchers have suggested examining the activity network—a network that is based on the actual interaction between users, rather than mere friendship—to distinguish between strong and weak links.

Initial studies have led to insights on how an activity network is structurally different from the social network itself.

A natural and important aspect of the activity network: whether social links can grow stronger or weaker over time.

In the study of the evolution of activity between users in the Facebook social network, it is found that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.

Interactions between pairs of users who interact infrequently are likely triggered by site mechanisms. For example, over 54% of the interactions between the infrequently interacting user pairs can be directly attributed to Facebook’s birthday reminder feature, which implies that the mechanisms present on the online social networking sites can affect the activity network in unexpected ways.

Even when users do interact frequently, the activity level of user pairs tends to decrease markedly over time, implying that most activity links die out. However, highly active user pairs exhibit this trend to a lesser degree, further emphasizing the strength of these links.

Surprisingly, while the individual user pairs that compose the activity network changes rapidly over time (i.e., over the course of one month 70% of the links in the activity network disappear), many of the graph-theoretic properties (e.g., average node degree, average clustering coefficient, average path length) show remarkable stability over the course of two years.

From Bimal Viswanath, Alan Mislove, Meeyoung Cha, Krishna P. Gummadi, On the Evolution of User Interaction in Facebook,Proceedings of the 2nd ACM SIGCOMM Workshop On Social Networks (WOSN), Barcelona, Spain, August 2009. http://www.mpi-sws.org/~gummadi/papers/wosn23-viswanath.pdf

Idealized virtual-identity hypothesis vs. Extended real-life hypothesis

Two competing hypotheses on the question: Do OSN(Online Social Network) profiles convey accurate impressions of profile owners?

Idealized virtual-identity hypothesis vs. Extended real-life hypothesis

1. Idealized virtual-identity hypothesis: A widely held assumption, supported by content analyses, suggests that OSN profiles are used to create and communicate idealized selves (Manago, Graham, Greenfield, & Salimkhan, 2008). According to this, profile owners display idealized characteristics that do not reflect their actual personalities. Thus, personality impressions based on OSN profiles should reflect profile owners’ ideal-self views rather than what the owners are actually like.

2. Extended real-life hypothesis: OSNs may constitute an extended social context in which to express one’s actual personality characteristics, thus fostering accurate interpersonal perceptions. OSNs integrate various sources of personal information that mirror those found in personal environments, private thoughts, facial images, and social behavior, all of which are known to contain valid information about personality (Ambady & Skowronski, 2008; Funder, 1999; Hall & Bernieri, 2001; Kenny, 1994; Vazire & Gosling, 2004). Moreover, creating idealized identities should be hard to accomplish because (a) OSN profiles include information about one’s reputation that is difficult to control (e.g., wall posts) and (b) friends provide accountability and subtle feedback on one’s profile. Accordingly, the extended real-life hypothesis predicts that people use OSNs to communicate their real personality. If this supposition is true, lay observers should be able to accurately infer the personality characteristics of OSN profile owners.

In the present study, the two competing hypotheses are tested. The results were consistent with the extended real-life hypothesis and contrary to the idealized virtual-identity hypothesis. These results suggest people are not using their OSN profiles to promote an idealized virtual identity. Instead, OSNs might be an efficient medium for expressing and communicating real personality, which may help explain their popularity.

From Mitja D. Back, Juliane M. Stopfer, Simine Vazire, Sam Gaddis, Stefan C. Schmukle, Boris Egloff1, and Samuel D. Gosling, Facebook Profiles Reflect Actual Personality, Not Self-Idealization, Psychological Science 21(3) 372–374. http://www.simine.com/docs/Back_et_al_PSYCHSCIENCE_2010.pdf

The Labour of User Co-Creators: Emergent Social Network Markets?

Co-creative relations among professional media producers and consumers indicate a profound shift in which our frameworks and categories of analysis (such as the traditional labour theory of value) that worked well in the context of an industrial media economy are perhaps less helpful than before. Can this phenomenon just be explained as the exploitative extraction of surplus value from the work of users, or is something else, potentially more profound and challenging, playing out here? Does consumer co-creation contribute to the precarious conditions of professional creative workers?

The implications of networked production, not only for economic and market environments, but for the people who are labouring within the network.
– For waged labour, there is the threat of displacement by unpaid amateurs and the loss or redefinition of work.
– For amateurs there is the question of whether pursuing their passions through creative production is something to be constructed as ‘enabled’ by commercial entities or ‘exploited’ by commercial entities.

If we view networked production as an intersection of enterprise and social networks it becomes possible to identify dynamic relations that are transforming markets.  We have identified the ways in which social economies, which operate through gifting, and the rewards of social status or personal satisfaction and the intrinsic rewards of  reativity do not necessarily seamlessly meld with the processes of enterprise and market economies.
User-led production is messier and often driven by a diverse range of motivations that cannot be marshalled into the institutional forms of industrial style production. The consequences of commercial enterprises coming to rely on this form of production to varying degrees is not necessarily outright cooptation or appropriation, but the emergence of new social network market institutions and processes, in which the commercial entities are changing shape as they seek to harness the productive activities of amateurs.
We have sought to move away from the discursive construction of user-creators as unknowing and exploited people who do not recognize the conditions under which they produce value. We want to take seriously their own, often sophisticated understanding of negotiations with enterprise, and their decision-making in the directions of both
commercial and non-commercial production.  The case study has shown how these negotiations are carried out on the ground. The diverse motivations of player creators must be accounted for in any description of networked production.

In trying for a more nuanced account of labour in a networked production environment, we are not seeking to deny the uneven power relations that exist between enterprise and user creators. However, if there is to be any chance of evening up the power relations, users must be understood as having agency, and the characteristics of that agency and the forms of power it generates must be articulated in order to be mobilized. These forms differ from those of traditional labour relations in industrial-mode economic production. Rather than understanding these relations always through the lens of commercial and monetized property markets, we also want to engage with the legitimacy of social economies.

User co-production relations do not only statically reallocate resources across markets and non-markets, firms and social networks. The cultural economics at work concern dynamic, open, self-organizing networks that generate opportunities for growth, change and innovation. This is not just about user generated content as an outsourcing of labour costs and an associated displacement of paid labour; it is not simply about optimizing allocative efficiencies within the labour relations and productivity frameworks of industrial economies. More provocatively it focuses our attention on how hybrid co-creation relations introduce organizational change and market growth. The value flows here then are not just about cheap labour and content but about the integration of innovations that upset and disrupt established industrial economy business models (Cunningham, 2006: 33–8). It is the liminal, hybrid zone between social networks and established markets that defines the space through which the relations of labour and work play out.
The value of these activities remains uncertain and will be shaped and defined through these market-like processes on social networks. This approach to emergent socio-markets focuses on the dynamics of open systems as new ways of being (including labour) are created and refined rather than on static models that rely on analytic distinctions between markets and non-markets (Potts et al., forthcoming).

User-led labour, in all its uncertainty, is an agent of change that unsettles existent industrial knowledge regimes. The changes wrought by shifting the contributions of users toward the core of commercial business models may well result in open innovation structures and change our understanding of what markets are. It disrupts accepted understandings of the market by questioning what is being exchanged, and what mechanisms of coordination are being used. Who benefits and in what ways?
In terms of policy and regulation the challenge will be to identify where intervention is necessary, particularly to address matters of equity, but not to stifle the innovation processes in doing so. Understanding the changed form of the market requires that regulatory interventions change as well. In the future, we suggest that research could usefully focus on the role of non-professional creators as they navigate and shape the emerging markets. How do we describe and analyse these emerging agents and agencies? What sort of labour relations will these be and how will they articulate with or constitute the creative knowledge economy? What happens to our understanding of the labour theory of value in the context of emerging social network markets? What kinds of job, and social and cultural relations will emerge and how will the interface that creates value be managed? It may be a self-organizing emergent network, and if it is, who will it exclude and how will we balance the opportunities and social costs? In the current shake-up, the answers to these questions are not at all clear.

 From Banks, John A. and Humphreys, Sal M. (2008) The labour of user co-creation. Emerging social network markets? Convergence: The International Journal of Research into New Media Technologies, 14(4). pp. 401-418. http://eprints.qut.edu.au/17705/1/17705.pdf