CDO Positions: A Study on the Impact of Chief Data Officers on Organizational Performance
Bibliographic record
Abstract
Purpose – The purpose of this paper is to employ a strategic change view, with the appointing of a CDO, to develop theoretical links and empirically examine the association between the CDO’s presence and organizational performance utilizing accounting measures. \n \nDesign/methodology/approach – Adopted for this study is the “matched sample comparison group” methodology, also known as the matched pair analysis, to empirically test the hypotheses, compare the performance of the treatment sample group with CDOs and the control sample group without CDOs, and assess the relationship between CDO presence and firm performance. The financial data for treatment firms with CDOs and control firms without CDOs was collected from the Compustat database. The Wilcoxon signed-rank test and regression analysis were used to analyze the performances for the treatment and control sample groups. \n \nFindings – Overall, the results indicate that firms that have lower performance will be more likely to appoint a CDO and still maintain a competitive cost structure relative to firms without CDOs. Even though the cost ratios of firms with CDOs were not lower after the appointment of the CDO than before the appointment, CDOs promptly improve their profitability relative to peers who do not have CDOs, without significantly increasing cost. Finally, prior year financial performance has very great impact on current year financial performance. \n \nPractical implications – With limited statistical confidence, due to a small sample and short period of performance measurements to analyze, as the CDO position is a quite new to Canada, the conclusion can still be made that: CDOs are hired when performance is low and excel at solutions, which they seem to bring swiftly, by improving revenues and profitability without significantly increasing costs. This research further addresses both the academic and business communities emphasizing that, in this moment in big data history, the position of the CDO is not to be ignored or disregarded, as this role may prove to appreciably advance and elevate corporate Canada. \n \nOriginality/value – The study explores the relationship between the CDO’s presence and firm performance. It is the first attempt to explore the CDO’s impact on the profit and cost performances from a purely Canadian perspective, evaluating only firms listed on the Toronto Stock Exchange – TSX. Prior studies have been from a global standpoint and heavily focused on the United States of America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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; both teacher heads agree on what is shown here.
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".