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Innovation and Social Networks

How Social Networks Contribute to the Spread of Unproven Innovations

 

In this article, Valentina Assenova, a management professor at Wharton, discusses the relation between social networks and complex innovations, where the latter is defined as innovations that have uncertain or unproven value for a potential user. Here, Assenova’s research intersects our discussion of information cascades in class. As we learned, when people are connected in some type of network, we are often influenced by other’s decisions and behaviors. Then, when people make decisions after watching the actions of earlier people and inferring something about what those people know, thus abandoning their own information, a cascade results.

 

Assenova later details that her research is a model developed from random networks using a DeGroot naïve learning model, where she uses the spread of microfinance in India to analyze simple randomness. She goes into depth over the manipulation of a network’s density–in other words, the connected-ness of people and the symmetry of the relations. Assenova finds that high density and high asymmetry, as in a more interconnected environment, are optimal for diffusing these complex innovations.  On the other hand, if a technology needs more input, a low density network will not diffuse the technology. Furthermore, she indicates that an individual is more likely to be in a multiplex network, where we are embedded into more than one unique network–and different factors of such networks, such as how broad your span is, can determine your influence. Overall, it’s important to understand the potential users of your product and the need for social validation through other people to understand the value of technology.

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