MétaCan
Menu
Back to cohort
Record W7008574571

CDO Positions: A Study on the Impact of Chief Data Officers on Organizational Performance

2022· other· en· W7008574571 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCollateralized debt obligationSample (material)Profitability indexControl (management)Return on assetsTest (biology)Wilcoxon signed-rank test
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.022
GPT teacher head0.195
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueYork University Digital Library (York University)French-language works237,207