From Acting What’s next to Speeding Trap: Co-Evolutionary Dynamics of an Emerging Technology-Leader
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: How does technological innovation emerge and evolve? We approach such an inquiry by synthesizing the perspectives of dynamic capabilities and co-evolutionary dynamics to portray organizational routines and multi-phase strategic renewals of an emerging technology-leader. To untangle the emergence of technological innovation, we conducted a longitudinal case study on the first and the largest dedicated semiconductor foundry, TSMC, located in the emerging economy of Taiwan. The firm-case of TSMC illustrates two unique co-evolutionary paths, that is, transforming from industry-latecomer to technology-leader and from process innovation to product innovation. We found multi-motor co-evolutionary dynamics between TSMC and the semiconductor industry, where its co-evolutionary mechanism of managed selection in its creating phase of mature process-innovation (1987-1998) has migrated to hierarchical renewal in its extending phase of advanced process-innovation (1999-2001), and then to holistic renewal in its modifying phase of product-innovation (2002-2007). During such paths, our research discovered a unique type of organizational routines, acting what’s next because TSMC has proactively searched for potential problems sooner than its competitors. However, such routines, although driving technological innovation, also lead to a unique type of success-trap, that is, speeding trap. When
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it