Bibliographic record
Abstract
ABSTRACT Search engines are among the most important information technology (IT) applications and platforms on which to conduct information search. This study contributes by investigating whether and how the search engine-enabled information search is related to accounting information effectiveness. We develop the concept of information traffic to conceptualize investor IT-enabled information search activities and to explore whether the searches captured by this concept provide any insights for understanding and enhancing accounting information effectiveness. Building upon the input-process-output model (Maines and McDaniel 2000) and with a sample of 59 accounting information items, we report that information items with higher information traffic have greater ability to explain and predict firm market value (i.e., higher information effectiveness). The impact of information traffic on information effectiveness is higher for economic upturns than for economic downturns and differs among different types of information. We propose a conceptual measure that integrates both information traffic and information effectiveness to capture information relative importance and to suggest empirically an order in importance of the ten types of information we investigate. Our dynamic analysis of information traffic reveals a significant increase of investor IT-enabled information search in the post-financial-crisis period. It also shows higher search increases for accounting items that received previously scant investor attention.
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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 | 0.004 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".