The role of CEO accounts and perceived integrity in analysts’ forecasts
Bibliographic record
Abstract
Although holding oneself accountable is deemed important for effective leadership, CEOs tend to demonstrate a self-serving tendency when reporting their company’s performance to the financial community. Leaders do so by providing internal accounts for favorable performance and external accounts for unfavorable performance. The effects of this strategy on the financial community’s judgments of a company’s value, however, is frequently mixed. Guided by the actor-observer perspective, we propose that observers (i.e., analysts) are likely to provide higher forecasts for firms whose CEOs attribute unfavorable organizational outcomes to internal factors and favorable outcomes to external factors. Integrating this conceptual perspective with attribution theory, we predicted that CEO accounts will have a stronger influence on analysts’ forecasts when the company performs unfavorably versus favorably. Results of archival data analysis (N = 35,676 quarterly earnings conference calls) generally supported our hypothesis, and were then replicated in a pre-registered follow-up field experiment (Study 2; N = 307), showing that analysts’ perceptions of the leader’s integrity mediated the effects of CEO accounts on analysts’ evaluation of the company. The mediating role of leader integrity was only significant when the company performed unfavorably (versus favorably). The present research adds to theory on causal accounts and perceived leader integrity, while offering guidance on how leaders’ accounts can relate to observers’ evaluations of those leaders and their companies.
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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 | 0.008 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".