How does perceived ease of information access affect investors' judgments?
Bibliographic record
Abstract
Abstract This study investigates how using technologies that increase the perceived ease of information access, such as search engines, can affect investors' judgments. Relying on the “Google effect” theory, I predict that using a search engine to access financial information will lead to shallower processing of that information, causing earnings fixation. The results of three experiments support this prediction and the theoretical process. Specifically, investors who access financials via a search engine are less likely to react to the information in the income statement other than earnings compared to those who do not use a search engine. In addition, search condition investors are more likely to mention earnings in their investment reasoning and find earnings more influential than the control condition investors. Furthermore, search condition investors believe that information is more likely to be available in the future and are more likely to reopen the income statement. A second experiment shows that the knowledge that the information will be easily reaccessed increases investors' earnings fixation. Finally, a third experiment provides evidence that using a search engine reduces the depth of information processing. This study extends accounting research by showing that using technologies that increase the perceived ease of information access can reduce investors' processing depth and information acquisition.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".