New method predicts which tech startups will thrive

 MIT researchers have developed a method that can project the growth potential of new high-tech firms.

Washington: MIT researchers have developed a method that can project the growth potential of new high-tech firms.

Based on a uniquely comprehensive analysis of businesses in California, researchers at the MIT Sloan School of Management, have created a richly detailed picture of what characteristics high-growth high-tech firms have, and where they exist.

Researchers said that among other factors, firms that formally register, seek capital investment, and make news early in their lives have higher growth potential.

Even a firm's name offers a solid hint of its growth potential, researchers said.

A business whose name includes the name of a founder does not generally expand as much as other types of firms, according to the researchers.

"That combination [of factors] turns out to be a very useful diagnostic for separating out the types of businesses that have a reasonable chance of growing, versus those that are much less likely to grow," said Scott Stern, the David Sarnoff Professor of Management at the MIT Sloan School of Management, who led the study.

The study also highlights which towns are home to startups with higher growth potential.

In California, the municipalities of Menlo Park, Mountain View, Palo Alto, and Sunnyvale top this list, in that order, with startups having characteristics that are associated with offering an Initial public offering (IPO) or being subject of a large acquisition, at about 10 times the state average, researchers said.

To conduct the study, researchers used a comprehensive list of new firms from California's official business registry over the years 2001 to 2011.

For 70 per cent of the firms, they were able to correlate a series of features that characterised high-growth firms over the period from 2001 to 2006; they then used those findings to test their results against the outcomes of the other 30 per cent of new firms in the same period.

They also tested their model against new firms registered in the years 2007 to 2011.

The research was published in the journal Science.