Mobility of Inventors and Growth of Technology Clusters AT&T Symposium August 3-4 2006 M. Hosein Fallah, Ph.D. Jiang He Wesley J. Howe School of Technology Management Stevens Institute of Technology Hoboken, NJ 07030 MHF/JH August 2006 Slide No. 1 Wesley J. Howe School of Technology Management
Outline Introduction Innovation & geographical clusters Patent inventor network & knowledge spillover Evidence from telecommunications industry of New Jersey vs. Texas Implications of the results Conclusion and future research MHF/JH August 2006 Slide No. 2 Wesley J. Howe School of Technology Management
Evolution of Telecom Industry and its Impact on NJ Growth of Start Ups Declining Leadership Global Leadership Monopoly Deregulation 1982 2001 Industry Crash 2006 MHF/JH August 2006 Slide No. 3 Wesley J. Howe School of Technology Management
NJ Telecom Patents as % of US 0.12 % of US Telecom Patents 0.1 0.08 0.06 0.04 0.02 0 1996 1999 2002 2005 Year Source of Data: USPTO MHF/JH August 2006 Slide No. 4 Wesley J. Howe School of Technology Management
Ranking of the Top 10 State by Telecommunications Patents State 1996 State 2005 California 1 California 1 New Jersey 2 Texas 2 Illinois 3 New York 3 New York 4 Illinois 4 Texas 5 New Jersey 5 Florida 6 Massachusetts 6 Massachusetts 7 Florida 7 Maryland 8 Maryland 8 Arizona 9 Connecticut 9 North Carolina 10 North Carolina 10 MHF/JH August 2006 Slide No. 5 Wesley J. Howe School of Technology Management
Telecom Patents Profiles of NJ & TX 1000 Patent Output Patents/Year 800 600 400 NJ TX 200 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Grant Year MHF/JH August 2006 Slide No. 6 Wesley J. Howe School of Technology Management
Technology Clusters Geographical concentration of related technology firms- competitors, suppliers, distributors, customers Usually around scientific research centers and universities Examples: Silicon Valley, Bangalore MHF/JH August 2006 Slide No. 7 Wesley J. Howe School of Technology Management
Innovation System & Technology Clusters Government University Industry Growth of Private Sector Firms New products & services Job Opportunities Economic growth MHF/JH August 2006 Slide No. 8 Wesley J. Howe School of Technology Management
Why Focus on Clusters? Clustering can bring a wide range of benefits to both businesses and the wider economy. These include: Increased levels of expertise Ability of firms to draw together complementary skills Potential for economies of scale Strengthening social and other informal links Improved information flow within the cluster Enabling the development of the infrastructure and support services MHF/JH August 2006 Slide No. 9 Wesley J. Howe School of Technology Management
Interest in Cluster Development Strategies Strong interest Globally, promoted by Michael Porter s work on cluster and competitiveness Many states have initiatives in cluster development (e.g. Texas) EU has developed guidelines and strategies for cluster development to increase Europe s competitiveness A global survey in 2003 identified 509 cluster initiatives MHF/JH August 2006 Slide No. 10 Wesley J. Howe School of Technology Management
Application of Complex Network Analysis to Technology Clusters Represent the inter-relationships between inventors, their inventions and companies in a cluster as networks of knowledge flow and spillovers Relate network properties to the evolution of these networks Develop models to evaluate the effect of state and regional policies on development of technology clusters MHF/JH August 2006 Slide No. 11 Wesley J. Howe School of Technology Management
Innovation and Knowledge Flow Company 1 Knowledge Flow Company 2 Patent J Citation Patent K Knowledge Flow Inventor A Inventor B Collaboration Inventor C MHF/JH August 2006 Slide No. 12 Wesley J. Howe School of Technology Management
Innovation Networks- Patent Analysis Patent Assignees Patent Inventors One mode network of inventors One mode network of assignees MHF/JH August 2006 Slide No. 13 Wesley J. Howe School of Technology Management
Source of Data 1. Patent data originally collected from USPTO and organized by Jaffe (2002). 2. Jaffe s dataset provides categorized patent records covering year 1975 to 1999 3. For this comparative analysis, we selected the telecom patents granted to inventors in either New Jersey or Texas between 1986 and 1999. 4. USPTO sorts data using patent number as primary index, certain inventors may have multiple names for different patents. Dataset need to be screened to check discrepancies and minimize its impacts. When first and last names matched, check the middle names and their hometowns. MHF/JH August 2006 Slide No. 14 Wesley J. Howe School of Technology Management
Network Construction 1. Develop bipartite networks which consist of patent assignees and patent inventors (patents assigned to individuals were omitted in this study). 2. Transform the bipartite network into two 1-mode networks. 3. Organize the inventors data into a three year moving window. 4. Pajek a social network analysis tool, is used to visualize and explore the patent network. MHF/JH August 2006 Slide No. 15 Wesley J. Howe School of Technology Management
Network Construction (Cont.. d) One-mode network of inventors A tie between two nodes means that the inventors are patenting for the same organization. Network will become too busy to be interpreted as large firms have lots of inventors. One-mode network of Assignees o o o Assignees A and B are linked if an inventor created patents for both companies. Multiplicity of a link occurs if more than one inventor created patents for both companies. A link between two companies indicate that the inventor either moved from one company to the other or worked on a joint R&D project for company B while an employee of company A. MHF/JH August 2006 Slide No. 16 Wesley J. Howe School of Technology Management
0.6 Mean of Node Degree for NJ & TX 0.5 Mean Node Degree 0.4 0.3 0.2 0.1 0 86-88 87-89 88-90 89-91 90-92 91-93 92-94 93-95 94-96 95-97 96-98 97-99 Mean of node degree-nj Mean of node degree-tx MHF/JH August 2006 Slide No. 17 Wesley J. Howe School of Technology Management
Innovation Collaboration Networks New Jersey and Texas NJ Network of Patent Assignees 1997-1999 TX Network of Patent Assignees 1997-1999 MHF/JH August 2006 Slide No. 18 Wesley J. Howe School of Technology Management
7 Percentage of Inventors Connected to two or more Assignees ( NJ vs. TX ) 6 5 % of Nodes 4 3 2 1 0 86-87- 88-89- 90-91- 92-93- 94-95- 96-97- 88 89 90 91 92 93 94 95 96 97 98 99 Year Year % of nodes with m >= 2 in NJ % of nodes with m >=2 in TX MHF/JH August 2006 Slide No. 19 Wesley J. Howe School of Technology Management
% of Connected Nodes for NJ & TX 35 30 25 % of Nodes 20 15 10 5 0 86-87- 88-89- 90-91- 92-93- 94-95- 96-97- 88 89 90 91 92 93 94 95 96 97 98 99 Year % of non-isolated nodes-nj % of non-isolated nodes-tx MHF/JH August 2006 Slide No. 20 Wesley J. Howe School of Technology Management
Observations 1. AT&T has been a key player in the network of NJ; accordingly, it represents a hub in the structure of the inventor Network. We don t see a similar hub structure in TX. The TX network is more distributed 2. Texas telecom inventors were more frequently on the move, changing their employers, starting their own businesses or/and joining other teams for joint R&D activities 3. The distributed structure of TX network is consistent with the more rapid growth of the industry in Texas MHF/JH August 2006 Slide No. 21 Wesley J. Howe School of Technology Management
Potential Application of Research Characterize technology clusters for innovation growth Assess knowledge spillovers in technology clusters Assess the strength of social networks of innovators over time Provide a foundation for simulation and analysis of industry evolution and government policies impacts on innovation in technology clusters MHF/JH August 2006 Slide No. 22 Wesley J. Howe School of Technology Management
Future Research 1. This is a work in progress. 2. Differentiate those mobile inventors from those being involved with joint R&D projects. 3. Further investigate the reasons or motivations for those mobile inventors to move among organzations 4. Study other innovation networks of these clusters 5. Move from a qualitative analysis to a more quantitative analysis of the knowledge flow in these networks MHF/JH August 2006 Slide No. 23 Wesley J. Howe School of Technology Management