We are unable to create an online viewer for this document. Please download the document instead.
1 Interview and transcription March 18, 2009 Tom Allen on the Allen Curve: Creating the right space to foster a spirit of innovation [09:58] Thomas Allen, Ph.D. Howard W. Johnson Professor of Management, Emeritus MIT Sloan School of Management I’m Tom Allen and I’m talking about a study that we did in biotechnology, clusters of biology technology companies in the Boston Cambridge area, testing the basic hypothesis that whether clustering of start-up high technology companies together geographically has any benefits or not. It’s been a discussion in the past. We defined an experimental group first of companies that were located in the region behind MIT or Harvard Medical School on the Boston side of the Charles River. We defined it by postal codes. We said if they happened to be in one or more zip codes then they were in our experimental group. If not, they were in our control group, which included companies within one hundred kilometers of where the experimental group is located. One went as far as Wooster, Massachusetts. And we made comparisons between the two of them; now, what we had – and you’ve got some of the data - we set up a web page that listed all of the companies that were doing biotechnology research in the region and we pulled a sample of fifty companies that we were going to gather data from and what we did was set up the web page first for the two hundred or so companies. Then, on randomly chosen days we send e-mails out to scientists in the fifty companies that we’re studying and we asked them to think about what they did on that given day. Had they talked to anybody in any of these other companies about a scientific subject, not business deals, but scientific subjects? Then just take the mouse and click that company, it goes into our data base, we collect that because we do this repeatedly over a period of six months, every week on a different day for six months. Then we plot out a network from the data that we have, we know which companies have had some contact with one another and that includes by the way, five major broad based pharmaceutical companies located in the area. Merck, Pfizer, Wyeth, Astrozac, and Louverdis are located here and we sampled scientists in those companies as well. There were also a number of large well-established biotechnology companies: Biogen, Genzyme, and so forth, five or six companies of that sort and we sampled from them as well. So, we had a pretty complete network of companies because even those we weren’t studying directly, we got references to, somebody said to talk to, somebody in the company that didn’t happen to be one of the ones we were sampling we assumed that that meant there was communication between those two companies. Once Copyright 2009 Betsey Merkel and I-Open. Creative Commons 3.0 Attribution-Noncommercial-No Derivative Works. Institute for Open Economic Networks (I-Open) 4415 Euclid Ave 3rd Fl Cleveland, Ohio 44103 USA 2 you have data like that you can begin to ask a lot of questions about it. For example, one of the basic questions was, “How are the communications within the cluster, within those zip codes than there is with those companies outside?” And the answer is, “Yes.” If you place a simple graph theory measures to it you find the companies within the geographic cluster are more central to the network than are other companies. Then, you can ask things about the role of the Universities, the role of the major pharmaceutical companies, and so forth. You find, of course, that the Universities are a major factor because that’s where most of the companies have their origins. There were actually five Universities nearby doing biotechnology research. But most of the work, most of the companies had their origins in either Harvard Medical School or MIT. But, Boston University is very active, a lot of companies came out of Boston University, Northeastern University less active, Tufts a little bit through the Medical School and so forth; but one of the key things that came out was the role of those major pharmaceutical companies, everybody was connected into to them. Largely, I think, although I can’t really test that, because they were active in going out and trying to make contact with the small companies. They were all obviously trying to expand into biotechnology and there is a lot of technology available in these small companies, they were looking for license opportunities or acquisition opportunities, and they were working hard at it. But, they become a major factor in the network and I think that it really helps the network to develop as a result of their position. We also found the communication was much higher, as I think I said already, of within the geographically defined cluster area than the companies that were farther out. We tested it still another way to see whether there was any affect from physical proximity. We obtained the latitude and longitude of each of the companies in the sample, now that obviously is the location of the front door, but these companies were pretty small and the error there isn’t very great. There is a bigger error in that we measured the distance between the companies but we did it ‘as the crow flies’ - we didn’t measure around corners or anything, it could be done but I wouldn’t get into that level of detail. But we then were able to compute for each company the average distance that company was from all the other companies in our study. So now that’s a character of the company, what’s their mean distance from all the other companies that are in the study? We related that to the level of communication among that same set, how much did that focus company, the company that we’re looking at, how much did that company, the scientists in that company, communicate with all the other companies? And so now you have two measures for each company: they’re average distance and their level of communication with those other companies. And when you relate those two you find a sudden drop within a few kilometers, it’s just about zero, it’s very high in close and drops off very rapidly with distance showing that broadband communication isn’t really a substitute for face-to-face, these people were talking with one another face-to-face. That is how distance impacts the likelihood of communication between companies. Some of that I think is due to the fact that the companies are generally pretty small and as a result they don’t have any dining facilities, people go outside the company for lunch and they go to the lunchrooms and cafes and so forth around Kendall Square, an area which is a neighborhood of Cambridge where most of them are located, and they run into old friends that they knew from their University days and they talk to one another or they ride in on the same subway train or they run into each other in the same parking lot or whatever it may be, I believe it’s a function of small size companies. People get out and Copyright 2009 Betsey Merkel and I-Open. Creative Commons 3.0 Attribution-Noncommercial-No Derivative Works. Institute for Open Economic Networks (I-Open) 4415 Euclid Ave 3rd Fl Cleveland, Ohio 44103 USA 3 bump into one another much more frequently and it is a very interesting phenomenon by the way. As a result, a lot of knowledge was transferred. We also found that people of course move between companies when they are so close together and that promotes communication. Interesting, a little [observation] we ran across, one of the companies that we were studying was very optimistic about their growth for a few years, they were doing very well, but they were a little too optimistic and they over hired and ended up with more staff than the business really could carry and they had a fairly substantial layoff. The scientists who were laid off, interestingly enough, didn’t go away mad - turned out that they brought business back to the company that had fired them! And really built a network, and helped that company to build its network with the companies that were working and they spread out pick up jobs all over the area. So you see things like that going on here that are very interesting. Now, what else did we learn from it? Well, one is, I think, the importance of the major pharmaceutical companies being here, we all know the Universities are important and in different regions where they’ve tried to develop high technology clusters and this sort, of course it has to built around a major University, and I think that’s true, I think that our data certainly supports that. But, the other part of it is the large companies, both the large biotechnology companies by the way and the traditional pharmaceutical companies seem to play a significant role. We need to know more about that, just how it works. But, when we look at the networks, they have central positions in the networks and I think they are critical to developing the network and developing the communication and making these clusters effective. Our generous thanks to Tom Allen. The Institute for Open Economic Networks (I-Open) 4415 Euclid Ave 3rd Floor Cleveland Ohio 44103 USA Creative Commons License Attribution-Noncommercial No Derivative Works 3.0 United States You can view the video interview at http://www.livestream.com/iopen/ondemand/flv_5fc905c1-1982-48c6-acb6-1f7293e7f9de?initthumburl=http://mogulus-user-files.s3.amazonaws.com/chiopen/2009/07/18/5fc905c1-1982-48c6-acb6-1f7293e7f9de_300.jpg&playeraspectwidth=4&playeraspectheight=3 Biographical Information * Margaret MacVicar Faculty Fellow * Howard W. Johnson Professor of Management, Emeritus * Technological Innovation & Entrepreneurship (TIE) * Specializing in organizational psychology and management * http://mitsloan.mit.edu/faculty/detail.php?in_spseqno=267&co_list=F Research * Wikipedia http://en.wikipedia.org/wiki/Thomas_J._Allen Copyright 2009 Betsey Merkel and I-Open. Creative Commons 3.0 Attribution-Noncommercial-No Derivative Works. Institute for Open Economic Networks (I-Open) 4415 Euclid Ave 3rd Fl Cleveland, Ohio 44103 USA 4 * Thomas J. Allen is the Howard W. Johnson Professor of Management at the MIT Sloan School of Management, and the co-director of the MIT Leaders For Manufacturing program. * He is the creator of the Allen curve, an approach to measuring and modeling the performance of cross functional research and development teams. Publications • Tom Allen and the Allen Curve “Creating the right space to foster a spirit of innovation” Irish Times, Ireland, by Frank Dillon (December 8, 2008) • The Organization and Architecture of Innovation: Managing the Flow of Technology, Co-authored by Gunter Henn, Butterworth-Heinemann, October 2006 • Lean Enterprise Value: Insights from MIT's Lean Aerospace Initiative • Co-authored by Earll Murman, Kirkor Bozdogan, Joel Cutcher-Gershenfeld, Hugh McManus, Deborah Nightingale, Eric Rebentisch, Tom Shields, Fred Stahl, Myles Walton, Joyce Warmkessel, Stanley Weiss, and Sheila Widnall, Palgrave Macmillan 2002 Contact information Office: NE25-758 Tel: 617-253-6651 Fax: 617-253-3331 E-mail: email@example.com Support Staff Name: Joanne McHugh Tel: 617-253-0586 E-mail: firstname.lastname@example.org Copyright 2009 Betsey Merkel and I-Open. Creative Commons 3.0 Attribution-Noncommercial-No Derivative Works. Institute for Open Economic Networks (I-Open) 4415 Euclid Ave 3rd Fl Cleveland, Ohio 44103 USA