The Network Economy
In the new economy as more people are connected to a network, greater is the value of the network. They now connect primarily through social media networks where the vast majority of connections happen between consumers and, with increasingly frequency, organizations. These online social networks have changed forever human relations to the world and with brands. The opportunity for business is to make this networked space their operations center so that their brands can engage consumers at scale. People and communities networks are no longer a passive audience as in broadcast; they are active agents, critics with interactive relationships, who want their intelligence respected. They want to belong to something greater than themselves and can only be monetized according to their desire.
Organizations are finding it difficult to understand and get results at scale from online social networks. In our experience, businesses cannot pin down exactly what happens in networks, because they are still geared to simple cause and effect principles of the past. The reality of the new economy is not linear anymore and its network effects are a challenge to visualize or control. An example of this is what happens with some blog articles that though scarcely shared, have a huge amount of readers. I can think of a recent example of an article about Guanabara Bay in Rio and the Olympic Games, from a small Spanish newspaper, which was shared directly from the source very few times in twitter, facebook and linkedin, but had more than 18 million readers in 55 minutes. In our monitoring analysis, we were able to verify that two influencers who linked early to the published article generated the network effects.
In 1981 Robert Metcalfe, inventor of ethernet, proposed that the number of connections in a digital network is roughly the square of the number of participants connected to it. Metcalfe’s law, as it was named in 1993, was used along with Moore’s law by new digital economy businesses, and still reigns supreme in their premises to this day. Metcalfe launched with this law the understanding of the so-called “network effects”, which in the 1990’s influenced sociologists, physicists and virologists to start a new branch of academia: Network Science.
The online social networks have great benefits by being on the internet, where the cost of adding a node (person or machine) and connections (relationships) can become marginal. If you look at the physical world, these same networks are contained by the effects of the “efficiency of Paretto” — a principle whereby for someone to win, another has to lose — that the digital world seems to minimize.
From 1996 onwards, with scientists like Barabási, Dorogovtsev, Mendes and others, networks science made several important discoveries: a) power law distributions of scale-free networks explained by preferential attachment — thus generating a mathematical model explaining the formation of long tail distributions present in many industries, b) the phenomenon of Small World networks and the importance of weak ties — read the next article, and c) how social network architectures with their hidden structures determine node activity and network performance. These and many other findings make up a robust multidisciplinary body in the sciences and help scientists cope with network complexities, allowing computer sciences to build social big data tools to measure, monitor and analyze social networks.
Back when Rolodexes were popular, there was a general feeling that networks formed almost at random. People knew each other by chance connection, exchanged contacts and maybe an important relationship would entail. The term “networking” was almost synonymous with luck. Even though this might be a good personal strategy, it is a very limited way of thinking about networks.
The network economy affects billions of people worldwide and is responsible in part for the current robustness of growth in the US. The drive of the human network economy is interpersonal connections based on affinities — the German writer Goethe was the first to study marriage as a connection of affinities. Most recently in 1954, the Russian mathematician, Rapoport and in 1973, the American sociologist, Granovetter found that these networks are formed by people and groups connected to each other by three types of bonds: strong, weak and absent.
“More novel information flows to individuals through weak than through strong ties. Because our close friends tend to move in the same circles that we do, the information they receive overlaps considerably with what we already know. Acquaintances, by contrast, know people that we do not and, thus, receive more novel information. This outcome arises in part because our acquaintances are typically less similar to us than close friends, and in part because they spend less time with us. Moving in different circles from ours, they connect us to a wider world. They may therefore be better sources when we need to go beyond what our own group knows, as in finding a new job or obtaining a scarce service. This is so even though close friends may be more interested than acquaintances in helping us; social structure can dominate motivation. This is one aspect of what I have called “the strength of weak ties.” (Granovetter, 1973, 1983)
Human social networks are driven by small cohesive communities that are connected to others by weak ties. In this sense, it only takes a bit of interconnection between these groups to have a Small World network, an effect popularized by the challenge named after the American actor Kevin Bacon, where in a few connections you can reach anyone in the planet — or the network. Small World networks are very powerful because they are resilient, resistant to attacks, transport information easily and filter out what is most important.
Essentially networks with this feature reduce distances and make local connections scale globally. Networks of this type are organic, form naturally and the best way to nurture them is not to inhibit them. A recent study on competitiveness between companies in California found that a law that forbade ‘non-compete’ agreements improved exponentially the amount and quality of innovations.
With these researches and the discovery of Scale-free networks in 1998, scientists concluded that networks have internal structures that define important performance characteristics of the network itself. However it is only more recently that the world began to understand about the relationship between specific activities and network architectures — especially after the research on the networks involved in the 9/11 attacks. Since then it was clear that terrorist networks — like health, communication, and all the natural networks — have temporal and hidden structures or architectures that determine how they behave and perform. Several research groups in the US later proved that social networks can be quantified, analyzed and managed.
Above we have the six network architectures of twitter, each map representing a major organization with different outcomes for their network. Each of the six architectures has a typical node behavior and performs functions in a specific way. The formation (or transformation) of these architectures carry a lot of value for business relationships and can be adjusted over time for a certain purpose.
Therefore, if we understand how a network is formed, how the network activity unfolds and know how the network architecture performs — and in what period it manifests itself — we can do experiments and draw strategies to improve the performance of this social network. That is why it is now possible to manage social networks scientifically.
“In the past, the primary role of managers was to increase efficiency. By motivating and monitoring employees, honing the firm’s capital structure and negotiating firmly with customers and suppliers, corporate executives could reduce costs across the value chain and achieve sustainable competitive advantage”.
However today there are no more isolated verticals or industries, but rather widely connected ecosystems with few global borders, as in the digital world. The fact that many do not take into account this change cannot blind us to the fact that these ecosystems already exist, are becoming digital and gain the momentum of dominance through their networks. As in the chart below, for each country we have a rate of inter-connectivity and maturity of ecosystems in their use of content monetization, sharing, and network effects. (World Economic Forum 2015).
The dominance of digital networks as a driving force in economics — from the physical directed connections networks with closed groups — to open shared networks, with interactive connections changes everything. In digital communities, the individuals become more relevant through their collectives. The dynamics of ecosystems that use the digital social networking model support the connection of individuals to new communities, and between communities, generating a frenzy of weak ties in Small World networks. All this is very effective not only for communication, but for any recurring service, for intellectual services and to share limited resources with network effects as do Uber and KickStarter.
Traditional organizations to compete in this new environment need to reach out to the tools of network science, build Small World networks (Watts e Strogatz, 1998), accumulate Social Capital (Ferragina, 2010) and position themselves as Brokers between communities, in what Professor Raymond Burt calls Structural Holes (Burt 1995 and 2004).
In the concept of a Structural Hole, a Broker is the agent making the interconnection of communities (clusters) that without it would have absent ties among themselves. People who connect clusters in social structures are more susceptible to generate innovations, have more influence, greater access to new markets and are more likely to take advantage of trade between clusters. As part of the business intelligence 3.0 stack, social network analysis (SNA) reveals the power of each network, the sub-groups (or communities) and the individuals within it. Networks can be analyzed, monitored and influenced, as the large digital conglomerates and VCs are already doing. Hence, their positioning as the current owners of key ecosystems in mobile and the web, with the likes of AirBnb and Houzz — to name only two — owning their respective markets through brokerage of structural holes and carefully constructing relationship networks. Both structuring and leveraging the opportunity between difficult to find market offerings directly to consumers. A carefully constructed social monitoring and SNA of any vertical will yield a great many insights with threats and opportunities in relation to possible network actions, and should be part of any organization intelligence stack.
The same techniques apply to internal modeling of organizations. The American Government, after the attacks on the twin towers, had to improve internal efficiency, marksmanship (Iraq has weapons of mass destruction?) and improve speed (stop terrorist threats). How to accomplish this in the current hierarchical structure? How can there be more cohesion without centralization? The result generated by an organizational network analysis revealed that of the three possible models the better and more agile was the Small World network from a central decision-making group. See table below for results — the shorter the distance (betweeness) the better, because it means that the information from any point of the network comes faster to decision makers:
The new reality, of networks, presents us with the fact that competitive advantage is no longer only about the sum of all efficiencies, but especially, the resultant of all connections. It is in the collective intelligence of the networks that current organizations using network science find ways to eliminate systemic risks, cut entropies, gain reach, increase speed and knowledge. It is through the strategic use of networks that organizations can build lasting mutually profitable relationships in the digital space.
by Eduardo Mace
Multimedia pioneer and CEO of 18moons inc.