FORMALISING THE DEMAND FOR TECHNOLOGICAL INNOVATIONS: RATIONAL HERDS, MARKET FRICTIONS AND NETWORK EFFECTS
Bibliographic record
Abstract
The current paper presents a theoretical model where rational decision makers (DMs) observe credible signals regarding the existence of technologically superior products and generate the demand structure determining their evolution within the market. We will illustrate how consumers may stick to an inferior product when market frictions or their own expectations dictate them to do so. This will be the case even if the newcomer firm credibly guarantees an improvement upon the main characteristics of the incumbent product. Indeed, the prevalence of a suboptimal technology can be the result of the correct choice being made at a given point in time. Moreover, we will compute the expected prevalence of a given product in the market when information regarding the existence of a technologically superior product spreads across consumers following different diffusion processes. The consequences derived from the existence of path dependence phenomena will be analysed from a dynamic perspective by explicitly accounting for the emergence of network effects that may take place after firms signal the availability of a technologically superior set of products.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".