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Wednesday, May 22, 2013

Research on startup teams and startup success

At the ACAC lunch today, Kathy Eisenhardt summarized her decades of research on tech startups. For an academic conference (the Atlanta Competitive Advantage Conference), Prof. Eisenhardt literally needs no introduction: as host Bill Bogner of Georgia State said, “Kathy Eisenhardt needs no introduction: if she does, you didn't pass comps.” (Academics would know her paper that has 20,000+ cites, while tech entrepreneurs might know her as co-director of Stanford Technology Ventures Program or her many STVP videos).

Eisenhardt's focus was on the importance of a startup’s management (“Top Management Team” in strategy jargon) in determining the success of a small or young firm in a highly uncertain environment. She identified three factors that explained that success
  1. Optimal management team
  2. Optimal strategic decision process
  3. Matching strategy and structure (at “the edge of chaos”)
1. Management Team

We know that successful teams need to be larger, diverse and have prior work experience together (and thus trust). However, there is an interaction effect between the team and the sort of opportunities they pursue. As scholars who study tech startups will tell you, firms tend to be veterans of an industry who start firms in that same industry that they know.

Her 1990 paper with Kaye Schoonhoven showed that the best firm growth came where a top team caught a great opportunity. A great opportunity was a market that’s at the takeoff phase of a growth market: at least $20 million of industry revenue and 20+% annual growth. In California-speak, Eisenhardt said this is a great surfer catching a great wave.

In specific domains, she cited the recent research of Anne Fuller and Frank Rothaermel on star faculty entrepreneurs as well as various papers by Sonali Shah on user entrepreneurs. As she noted, Chuck Eesley of Stanford (an MIT alum) who estimated when experience is more valuable than talent, based on a survey of entrepreneurs from among the 100,000+ MIT alumni.

2. Strategic Decision-Making

Her old studies on TMT decision making showed that for fast choices in highly uncertain environments, managers need more information and more alternatives, as well as a decision process that is midway from managerial fiat and (a hopeless search for) total consensus. She also noted later work of researchers who examined improvisation and bricolage.

Her former student, Sam Garg, has studied how CEOs manage their boards. The best CEOs constrain the interactions with the board and don’t give up their power over leading the company, using it to make decisions (not generate ideas), by using a divide and conquer strategy.

3. Strategy and Structure

Summarizing her research with Chris Bingham of UNC, she noted that young firms were most successful when they could use their experience to generate heuristics. Experience was valuable when it created “simple rules” that firms could apply over and over again: such rules were both quicker and often better in solving problems in conditions of high heterogeneity and high uncertainty.

Eisenhardt noted that firms (like parents “raising your teenager”) face a dilemma between too much and too little structure. In a simulation with Bingham and Jason Davis, they found that in a highly unpredictable or turbulent market, too little structure is more dangerous than too much. (This also sounds like raising a teenager).

Finally, in cases of high ambiguity (e.g. nascent markets), success is more determined by luck than skill. Therefore, skillful managers want to reshape the market to fit their skills — rather than leave the outcome to dumb luck.

Wednesday, May 8, 2013

The multi-dimensional 3D printing revolution

At @KeckGrad today, our graduate students are doing their year-end project presentations. In watching the presentation by mechanical engineering students gave me insight into how 3D printing is going to change entrepreneurship.

There are at least three different dimensions of how 3D printing is creating entrepreneurial opportunities. In each case, there are parallels between personal computers almost 40 years ago — and smartphones today — and how they gradually displaced mainframe computers. This is a classic Clay Christensen “disruptive innovation”.

Some of the emphasis on the impact of 3D printing has focused on the 3D printing companies. In fact, Scott Shane published a 2000 research paper on how a variety of companies licensed the original 3D printing technology from MIT. This has been the subject of news has also been on some of the larger and more successful 3D printer manufacturers, whether public companies such as Stratasys or 3D Systems or startups such as Shapeways.

A second opportunity — the one that captures the attention of the popular press — is the print-on-demand business.This nicely fits the mass customization vision of Silicon Valley marketing guru Regis McKenna and German innovation scholar Frank Piller. An example of this is Layer By Layer (@LayerByLayer3D) a company formed by Harvey Mudd students who are graduating next week, who proposed to custom-print iPhone cases.

An advantage of the 3D printing model is easier customization and lower setup costs. However, for now it’s slower and more expensive per unit, and has limitations in product reliability.

However, at the KGI presentation today, I saw a third category of opportunity. This seems like a much broader and more immediate application of 3D printing: changing the process of industrial design.

Among our Team Masters Projects, a team of KGI and Harvey Mudd students spent the academic year to create a mechanism for evenly coating seeds. As in previous projects, they used SolidWorks to design the mechanical components, and had some bent or machined metal components.

However, it became obvious to the team that the default prototype fabrication approach is the 3D printer. The students created two seed picking components that could be sized and shaped to fit whatever requirements they had. Once they had the design, they set the printer going and hard their part ready in the morning.

This reminds me of my first computer experience (pre-PC), when computing job turn-arounds took 10 minutes to several hours. To improve on batch computing, we eventually obtained timesharing — quick but expensive — and then desktop personal computing and handheld computing. Over time, as computing became quicker and cheaper, it allowed computing to permeate and enable every aspect of engineering, science, business and government.

So if 3D printing becomes cheap, ubiquitous and quick, what will that do to physical design? The marginal cost may not become as low as software products, but it will certainly close the gap and thus converge the innovation processes between physical and intangible goods.

Friday, May 3, 2013

Some tech startups are more high-tech than others

In the business press, academic teaching and research, there’s often a discussion of the unique characteristics of “tech startups”, “technology entrepreneurship” and “technology-based firms.” Such startups are the focus of this blog.

Often the distinction between high- and low-tech startups is measured by the proportion of technical employees — such as fraction of R&D employees or fraction of R&D spending (i.e. R&D intensity).

Still, tech startups are not homogeneous. Some of the distinctions that have been draw are science- vs. engineering-based startups, or industry-specific startups like IT, cleantech, or biotech.

This week I attended two business plan competitions here in Claremont: Wednesday’s business plan competition at the Keck Graduate Institute (which I organized) and today’s Kravis Competition across all the Claremont Colleges.

Judges at KGI 2013 Business Plan Competition: George Golumbeski, Stephen Eck, Bob Curry. Not shown: Liam Ratcliffe, Paul Grand
Our KGI business plans (from my ALS 458 class) were all about commercializing patented (or patent-pending) biomedical technologies (therapies, diagnostics, devices) developed by top research institutions such as Caltech, City of Hope, and USC. The Kravis competition included several IT concepts, some low tech businesses, and AccuMab, a cancer diagnostic company from my KGI class.

In comparing the KGI plans to the other Claremont projects — or those in our textbook — it seems to me that — at least from a financial standpoint — there are three types of companies: high tech, medium tech and no tech.

What is dramatically different about our students projects was that (with one exception) is that they’re highly capital intensive, requiring $5 to $50 million in outside funding. For example, the winning team — using technology from Children’s Hospital Los Angeles to repair Shortened Bowel Syndrome — estimated it needs $20 million in equity and $5 million in government grants to get to market. This project — like many others — is building on millions of dollars of NIH/NSF/foundation grants already received to develop the basic science. There is a certain minimum scale required to get FDA approval and thus generate first revenues.
2013 winning KGI team — Hadi Mirmalek-Sani, Porus Shah, Shrina Shah, Rajesh Pareta —
with Bob Curry, chair, KGI Board of Trustees
Think about the story of Mark Zuckerberg, who launched The Facebook in early 2004 and took its first outside investment (of $500k) later that year. (Yes, they didn’t monetize initially, but still they created a compelling product and reached a million subscribers using the founders’ money). The iPhone app startups were launched for tens of thousands of dollars: Rovio had 40 million Angry Birds users before they took their 2011 Series A investment.

Over the years, entrepreneurship researchers (and practitioners) have demonstrated that any new company or product has highest uncertainty and risk up until first customer sale. So from a practical standpoint, I suggest a new metric: how much R&D spending do you need before launching a product? How big a bet — with what scale of outside investment — does it take until the entrepreneur finds out whether (s)he has a viable business?

By this measure, the difference is not the % of the money that goes to R&D but the size of the R&D bet that’s needed to test the marketing hypothesis.

A company that takes 5+ years and $50+ million is fundamentally different from one that can ship a 1.0 (or revenue-generating beta) for less than $1 million. By that standard, after biotech the biggest bets required are for renewable energy. You can start dozens (or hundreds) of software companies for one fully mature biotech, biofuels or solar company.