Emerging patterns of complex technological innovation

Emerging patterns of complex technological innovation Technological innovation is increasingly concerned with complex products and processes. The trend toward greater complexity is suggested by the fact that in 1970 complex technologies comprised 43% of the 30 most valuable world goods exports, but by 1996 complex technologies represented 84% of those goods. These technologies are innovated by self-organizing networks. Networks are those linked organizations that create, acquire, and integrate the diverse knowledge and skills required to innovate complex technologies. Accessing tacit knowledge (i.e., experienced-based, unwritten know-how) and integrating it with codified knowledge is a particular strength of many networks. Self-organization refers to the capacity networks have for reordering themselves into more complex structures (e.g., replacing individual managers with management teams), and for using more complex processes (e.g., evolving strategies) without centralized, detailed managerial guidance. Case studies of the innovation pathways traced by six complex technologies indicate that innovations can be grouped into three quite distinct patterns. Transformation : the launching of a new trajectory by a new coevolving network and technology. Normal : the coevolution of an established network and technology along an established trajectory. Transition : the coevolutionary movement to a new trajectory by an established network and technology. Policy makers and managers face the greatest challenge during those periods of movement from one innovation trajectory to another. These are periods of turbulence; they are the embodiment of Schumpeter's “gales of creative destruction.” This paper investigates how, in six case studies, core capabilities, complementary assets, organizational learning, path dependencies, and the selection environment varied among the innovation patterns. The paper builds on work reported in a recent book by the authors entitled: The Complexity Challenge: Technological Innovation for the 21st Century , Pinter, London, 1999. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technological Forecasting and Social Change Elsevier

Emerging patterns of complex technological innovation

Loading next page...
 
/lp/elsevier/emerging-patterns-of-complex-technological-innovation-CCAvGUMBIs
Publisher
Elsevier
Copyright
Copyright © 2002 Elsevier Science Inc.
ISSN
0040-1625
eISSN
1873-5509
D.O.I.
10.1016/S0040-1625(01)00171-8
Publisher site
See Article on Publisher Site

Abstract

Technological innovation is increasingly concerned with complex products and processes. The trend toward greater complexity is suggested by the fact that in 1970 complex technologies comprised 43% of the 30 most valuable world goods exports, but by 1996 complex technologies represented 84% of those goods. These technologies are innovated by self-organizing networks. Networks are those linked organizations that create, acquire, and integrate the diverse knowledge and skills required to innovate complex technologies. Accessing tacit knowledge (i.e., experienced-based, unwritten know-how) and integrating it with codified knowledge is a particular strength of many networks. Self-organization refers to the capacity networks have for reordering themselves into more complex structures (e.g., replacing individual managers with management teams), and for using more complex processes (e.g., evolving strategies) without centralized, detailed managerial guidance. Case studies of the innovation pathways traced by six complex technologies indicate that innovations can be grouped into three quite distinct patterns. Transformation : the launching of a new trajectory by a new coevolving network and technology. Normal : the coevolution of an established network and technology along an established trajectory. Transition : the coevolutionary movement to a new trajectory by an established network and technology. Policy makers and managers face the greatest challenge during those periods of movement from one innovation trajectory to another. These are periods of turbulence; they are the embodiment of Schumpeter's “gales of creative destruction.” This paper investigates how, in six case studies, core capabilities, complementary assets, organizational learning, path dependencies, and the selection environment varied among the innovation patterns. The paper builds on work reported in a recent book by the authors entitled: The Complexity Challenge: Technological Innovation for the 21st Century , Pinter, London, 1999.

Journal

Technological Forecasting and Social ChangeElsevier

Published: Jul 1, 2002

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off