Trajectory of Technology Upgrading: Intellectual Structure and Future Research Directions
Bibliographic record
Abstract
Technology upgrading is a crucial factor in global economic development and competitiveness. This paper provides a scoping review of technology upgrading research, examining literature from 1995 to 2023. The field, still evolving, is analyzed to understand its intellectual structure, identifying key thematic clusters and central contributors. Four primary research clusters are identified: i) Technology Upgrading and Economic Development; ii) China's Technological Advancement and Impact; iii) Global Value Chains and Knowledge Dynamics; and iv) Productivity and Competitive Dynamics. Each cluster covers diverse aspects of technology upgrading, from policy implications to environmental sustainability. The study highlights the necessity of a holistic perspective to inform policy-making and academic research, advocating for consolidating fragmented knowledge and providing a structured overview of the field. The paper calls for multifaceted future research, exploring governance models, innovation capabilities, and the lifecycle of technology within firms. Findings emphasize the growing academic and practical relevance of technology upgrading, evidenced by rising publication and citation rates. The review advocates for new inquiries, cross-disciplinary collaborations, and policy-oriented studies to advance technology upgrading and integrate it into economic and social frameworks. As technological innovation increasingly drives global development, this field is vital for shaping future progress. The studies is one of the earliest studies based on scoping review of technology upgrading that contributes the new knowledge in the literature of innovation management.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".