When Do Appointments of Chief Digital or Data Officers (CDOs) Affect Stock Prices?
Bibliographic record
Abstract
This article investigates the stock market reaction to appointments of newly created chief digital or data officer (CDO) positions. The analysis is based on a sample of 112 CDO appointment announcements by publicly traded companies listed in the US stock market from 2004 to 2017. We ground our arguments in signaling theory along with the institutional entrepreneurship and synergy and redundancy perspective to understand the factors that could influence the market reaction to CDO appointments. Although the results show that the stock market reacts neutrally to announcements of newly created CDO positions, the market does react positively under certain conditions. The market reacts positively when appointing firms exhibit high growth prospects. The article supports the redundancy perspective, and the results show that the market reacts positively when a potentially conflicting and overlapping role such as chief information officer (CIO) is absent in appointing firms. Our analysis also shows that when CIO is absent, the market reacts more positively to outsider CDO relative to insider CDO, thereby indicating the interaction between redundancy and institutional entrepreneurship perspectives.
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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 | 0.001 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".