The Impact of IT on Market Information and Transparency: A Unified Theoretical Framework
Bibliographic record
Abstract
With the advent of the Internet, we have seen existing markets transform and new ones emerge. We contribute to the understanding of this phenomenon by developing a unified theory about the role that IT plays in affecting market information, transparency and market structure. In particular, we introduce a new theoretical framework which uncovers the process and the forces that, together with IT, facilitate or inhibit the emerging dominance of transparent electronic markets. Transparent electronic markets offer unbiased, complete, and accurate market information. Our effort to develop a unified theoretical framework begins with a thorough assessment of the prior literature. It also uses an inductive approach involving the case study method, in which we contrast and compare the forces that have led the air travel and financial securities markets to become increasingly transparent. Building on the electronic markets and electronic hierarchies research of Malone, Yates and Benjamin (1987), our findings suggest that IT alone does not explain a move to transparent electronic markets. Instead, we argue that enhanced electronic representation of products, and competitive and institutional forces have also played an important role in the process by which most sellers have come to favor transparent markets.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".