Can we explain managerial non‐answers during conference call Q&As?
Bibliographic record
Abstract
Abstract Management teams often avoid answering questions during conference call question and answer sessions (Q&As). Viewing this as an information asymmetry issue, accounting scholars have suggested that this behavior is ill‐advised and that non‐answers signal to investors the suppression of bad news. In this article, we demonstrate that this argument lacks nuance. Instead, we argue that answers and non‐answers necessarily coexist and are codependent. Our contribution stems from our social interactionist lens, whereby we draw on interdisciplinary perspectives of workplace silence to make sense of our data. We propose three explanations for managerial non‐answers, namely that they are used (1) defensively, (2) reflectively, and (3) negotiatively. Despite the seeming complexity, analysts claim they can make sense of what managers are able to say in this forum, and by extension, what they do (or perhaps, can) not. From here, we argue that analysts are socialized to managerial non‐answers. Despite this, there is general concern that investors allow an innate fear of “silence” to prejudice their judgment of non‐answers. Thus, we highlight a communication gap between management, intermediary, and investor. On the one hand, this implies a source of market inefficiency, but on the other it points toward a source of potential value in sell‐side analyst work, specifically, their experience and expertise in social interaction.
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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 | 0.016 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".