Strategic Management of Emerging Technologies
Bibliographic record
Abstract
Innovation is widely regarded as a crucial source of competitive advantage in an increasingly uncertain environment. More recently, we have witnessed a substantial rise in interest in emerging technologies in business that have the potential to reshape the economy and boost productivity across all sectors and industries. However, achieving successful development of emerging technologies holds great uncertainty requiring strategic management. Many emerging technologies involve a direct upfront cost, while their benefits may be long-term and unquantifiable, and thus increase the level of uncertainty in establishing market relevance. More theoretical development needs to be done to link higher, macro levels to organizational and individual levels. Studying emerging technologies has critical managerial and policy implications. We are particularly interested in how the consideration of such technologies changes or challenges our theories, methods, and research questions. The proposed symposium seeks to bring together papers that address these issues. Abstraction, Knowledge Flows, and the Rapid Dissemination of Emerging Technologies Presenter: Willy Shih; Harvard Business School Complement, Substitute, or Impediment: Is Government Money Smart Money for Private Innovation? Presenter: Jason Michael Rathje; Stanford U. Presenter: Riitta Katila; Stanford U. Understanding and Managing Uncertainty Surrounding Emerging Technologies Presenter: Rahul Kapoor; U. of Pennsylvania Profiting from Enabling Technologies: A Dynamic Capabilities Perspective Presenter: David J. Teece; U. of California, Berkeley Presenter: Joshua Lathrop; Berkeley Research Group Presenter: Sohvi Heaton; LMU Autonomous Vehicle Technology's Potential to Drastically Transform the Competitive Landscape Presenter: Marvin B Lieberman; UCLA Anderson School of Management Choosing a Technology Strategy Presenter: Joshua Gans; U. of Toronto Presenter: Scott Stern; Massachusetts Institute of Technology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".