Science and Technology Text Mining: Cross-Disciplinary Innovation

Abstract

Innovation is critical for maintaining competitive advantage in a high tech global economy, especially for organizations or nations that do not possess low cost labor forces. Many studies on innovation attempt to identify endogenous and exogenous variables that impact innovation, in order to better understand the environment that promotes innovation. The author's recent efforts have focused on developing processes for enhancing innovation that exploit the transference of information and insights among seemingly disparate disciplines. The objective of this report is to describe how innovation can be promoted through the enhancement of discovery by cross-discipline knowledge transfer. The approach developed entails two complementary components one literature based, the other workshop-based. The literature-based component identifies the science and technology disciplines related to the central theme of interest, the experts in these disciplines, and promising candidate concepts for innovative solutions. These outputs define the agenda and participants for the workshop-based component. An example of this combined approach is presented for the theme of Autonomous Flying Systems. The hybrid approach appears to be an excellent vehicle for generating discovery and enabling innovation. However, it requires substantial time and effort in both phases.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 4 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.