Combining scenario analysis, the Delphi method, and the innovation diffusion model for analyzing the development of the light-emitting diode Panel Industry
Bibliographic record
Abstract
According to an industry report, light-emitting diode (LED) technology will replace cold cathode fluorescent lamp (CCFL) technology in the near future. Therefore, for liquid crystal display (LCD) panel-manufacturing companies to allocate their resources efficiently, it is very important that they understand the demand of these two technologies. This study combined scenario analysis, the Delphi method, and innovation diffusion to analyze the situation over the next five years. Scenario analysis was applied twice. The result of the first one showed that the organic light-emitting diode (OLED) TV market will grow slowly in the next 5 years, with the LED TV becoming the leader in the market, and also that the panel is the most critical factor in the development of the LCD TV. Therefore, the second analysis was run to analyze in more detail the competitive situation between LED and CCFL panels. The most optimistic, the most pessimistic, and the most likely scenarios of the LED panel market in the next five years were described. The global sales of CCFL and LED panels were also predicted for the three scenarios above using the innovation diffusion model. According to the forecasting results, the LED panel will replace the CCFL panel as the mainstream product in the second quarter of 2012, in the first quarter of 2013, and in the third quarter of 2012 under the optimistic, pessimistic, and likely scenarios, respectively.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".