CFO (Chief Financial Officer) Research: A Systematic Review Using the Bibliometric Toolbox
Bibliographic record
Abstract
The chief financial officer (CFO) is a crucial executive position in an organisation, responsible for overseeing the financial operations and strategy of the company. Despite rising interest among academics and practitioners, the literature corpus on CFO research remains largely fragmented, which warrants the unpacking of the underlying intellectual knowledge structure of the domain. In response, this study aims to provide a concise overview of the trends and science relating to CFO research, comprehend potential gaps in the literature, and highlight crucial future research pathways. A quantitative bibliometric overview of 669 research articles from 1982 to 2022 provides a spectrum of intellectual clout that helps decipher performance trends and delineates six significant clusters of knowledge in CFO research. We selectively discuss the empirical findings and theoretical and conceptual advancements within each cluster. This study offers recommendations for future research, emphasising the growing role of CFOs in leadership and addressing the fragmentation in current research. The findings and contributions of this study could further elevate CFOs’ importance in the C-suite.
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 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 | 0.029 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.115 | 0.092 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".