The effect of explanations and CEO presence on stock market reactions to downsizing
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether the type of explanation (excuses, justifications, apologies and denials) provided for downsizing and the source of the announcement (CEO vs other organizational members) influences shareholders’ market reactions to downsizing announcements. Design/methodology/approach In total, 388 media-based downsizing announcements from 2006–2015 were coded for explanation type and source of message. Cumulative average return was used to assess the impact of downsizing on market reactions the day after the announcement. Findings As predicted, and consistent with predictions drawn from fairness theory, excuses triggered positive market reactions, whereas justifications, apologies and denials triggered negative reactions. Additionally, shareholders reacted more negatively to excuses and apologies when the announcement came from CEOs vs other organizational members. Research limitations/implications The current research bridges the literature on market reactions to downsizing with the organizational psychology literature to advance a novel theoretical framework for predicting shareholders’ reactions to downsizing announcements. In doing so, the authors provide a more refined understanding of why different types of explanations may differentially influence shareholders’ reactions. The current research also sheds light on when the presence of the CEO in downsizing announcements may have potentially negative consequences for organizations. Originality/value The findings contribute to the sparse literature examining variations in the content of downsizing announcements on shareholders’ reactions. The present research is also the first to examine whether shareholders would react less negatively if downsizing explanations came from top organizational leaders (e.g. CEOs).
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".