Introduction: Special Issue on Tech Mining

Technological Forecasting and Social Change, Volume 73, Issue 8, pg. 915-1060 Elsevier Inc., 2006.

Scott W. Cunningham, Alan L. Porter, and Nils Newman

Published: 2006


Content

In this editorial overview we highlight the main dimensions of tech mining, relating these to the particular themes addressed by our contributors. We also introduce relevant research from disciplines outside of tech mining. The contributors in this special issue discuss the users of tech mining and their organizations. Like our contributors, this overview also discusses designing the tech mining system and the learning process. We consider the nature of ST&I systems – this is, after all, what tech mining is designed to appraise and facilitate. The variety of contributions, which we sample below, shows the health and vibrancy of this emerging field.

Contrast tech mining with traditional ST&I policy and management practices. Traditional processes rely mainly on expertise. This could be anything from asking the "right-hand man" to convening multi-tier expert panels. The former relies excessively on casual knowledge; the latter tends to be slow and expensive. Both neglect the rich intelligence to be gleaned from data resources. Tech mining takes information already compiled, for whatever purposes, and exploits it just when needed. It derives knowledge pertinent to the issue at hand. For instance, today we might search fuel cell patents to help assess how crowded the landscape is to help assess our new program initiative. Tomorrow, we might mine fuel cell patents to identify which other companies are the best prospects to license our intellectual property. In both cases, we get the intelligence immediately; we don’t have to wait for a third party to perform special studies taking months.

Tech mining is not restricted to mining abstract publication and patent records. It combines text and numerical data to best answer the questions confronting us. It draws on multiple content sources; for instance, checking websites after database search result analyses spotlight the key players. It particularly gains from incorporating expertise in formulating analyses and in reviewing preliminary results to spot gaps and offer interpretations. Tech mining is inherently interdisciplinary, drawing on skills of ST&I analysts, information professionals, technical specialists, and technology managers. Probably the best way to get a feel for this field is to read a sample of active research and practice – hence, this issue.

In this editorial overview we highlight the main dimensions of tech mining, relating these to the particular themes addressed by our contributors. We also introduce relevant research from disciplines outside of tech mining. The contributors in this special issue discuss the users of tech mining and their organizations. Like our contributors, this overview also discusses designing the tech mining system and the learning process. We consider the nature of ST&I systems – this is, after all, what tech mining is designed to appraise and facilitate. The variety of contributions, which we sample below, shows the health and vibrancy of this emerging field.

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In summary, we feel that the field of tech mining is well-spawned and beginning to mature. The topic is finding a home in journals like Technological Forecasting and Social Change (and other leading journals). Tech mining practitioners are extending and building their theoretical bases. Practitioners are looking into the organizational impacts of their efforts. Practitioners are also appraising, in a fundamental way, theories of innovation systems, and considering how these theories can be gauged by analyzing search results in leading ST&I databases. The contributions that follow offer a valuable cross-section of ideas in this emerging field.

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