Leveraging Cumulative Advantage in the Enterprise 2.0

A recent essay in the New York Times Magazine reflects on how cultural blockbusters are made. In it Duncan Watts, a professor of sociology at Columbia University, describes the theory of cumulative advantage: People make decisions based on the opinions of others, rather than independently. As a result, if something happens to be more popular than something else at the right moment in time, it often grows even more popular as a result—as Watts says, it’s “the rich get richer” effect. The trait serves us well in a world full of choices, but it doesn’t always say much for the choices we make.

Watts’ essay is looking at the effects of cumulative advantage on pop culture—why Justin Timberlake is more popular than Justin Vollmar. Watts suggests that cumulative advantage poses a problem for producers of entertainment, who are looking for ways to develop successful content and products, since if decisions aren’t based on “quality” but are relatively random, there’s no real point in developing “good” content in the hopes of appealing to large audiences (the recent success of such critically lambasted films as “300” and “Norbit” attests to this). In fact, there’s no way to know at all what content—good or bad—will appeal, since the mass decision is impacted by relatively arbitrary factors that can change seemingly at random.

Watts’ take-away is a little depressing, at least for would-be Hollywood moguls: “Because the long-run success of a song depends so sensitively on the decisions of a few early-arriving individuals, whose choices are subsequently amplified and eventually locked in by the cumulative-advantage process, and because the particular individuals who play this important role are chosen randomly and may make different decisions from one moment to the next, the resulting unpredictably is inherent to the nature of the market.”

But savvy marketers can leverage cumulative advantage, because one way to generate buzz and drive mass adoption is to identify thought leaders, then market products to them in the hopes of creating a snowball effect. Watts assumes these thought leaders are “chosen randomly,” and in the experiments he did to support cumulative advantage theory, they were. But there’s no reason they have to be. The Internet already identifies thought leaders on popular blogs and social networking sites, for example.

There’s a lesson to be learned here for the enterprise 2.0. When companies introduce social networking and other collaborative technologies, they should expect certain content and networking hubs (social, not literal) to be more popular than others—and not necessarily because they’re “better” or “more valuable” by any objective measure. But they can also try to encourage certain behaviors over others, by relying on thought leaders to help drive adoption. As those early adopters make their choices about what to do and how to do it, the patterns they set should set the stage for the decisions made by everyone else.

Furthermore, according to Watts, the reason cumulative advantage works is because people make choices about what to do and see not so much because they want to enjoy things they know they’ll like but because they want to participate in a shared experience. That’s good news for companies looking to drive collaboration; so long as they can get trail blazers to adopt the technology and business processes that will make the virtual workplace work, they can expect the rank and file to follow suit, precisely because they want to be a part of the experience. And that participation, of course, is what’s required to make collaboration work.