The Relationship between Market Share and Information in a High-Tech Industry
Bibliographic record
Abstract
The role of information in high-technology markets is critical (Dutta, Narasimhan and Rajiv 1999; Farrell and Saloner 1986; Weiss and Heide 1993). In these markets, the volatility and volume of information present managers and researchers with the considerable challenge of monitoring such information and examining how potential customers may respond to it. This article examines the effects of the type and volume of information on the market share of different technological standards in the Local Area Networks (LAN) industry. We identify three different types of information: technological, availability and adoption. Our empirical application suggests that all three types of information have significant effects on the market share of a technological standard, but their direction and magnitude differ. More specifically, technology-related information is negatively related to market share as it demonstrates that the underlying technology is immature and still evolving. Both availability and adoption-related information have a positive effect on market share, but the former is larger than the latter. We conclude that high-tech firms should emphasize the dissemination of information, especially availability-related, as part of their promotional strategy for a new technology. Otherwise, they may risk missing an opportunity to achieve a higher share and establish their market presence.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".