Mapping the Knowledge Domain of Earnings Conference Calls: A Bibliometric Study
Bibliographic record
Abstract
This study uses a bibliometric approach to conduct a critical review of research on earnings conference calls. The goal of the study is to assess the development of the field, identify key research trends and issues, and highlight gaps in the existing literature. This study takes the Web of Science (WOS) academic journal database as the source of literature. It utilizes software such as COOC, DIKW, VOSviewer, and Vismap for bibliometric analysis, mathematical statistics, and knowledge graph drawing. The study constructs knowledge graphs for the research field of earnings conference calls from 2000 to 2025. It covers aspects such as publication volume, research institutions, authors, and keywords, and deeply analyzes their distribution patterns and evolutionary trends to comprehensively trace the research trajectory of this field. The results show that: (1) the publication volume in the field of earnings conference calls presents a linear, year-by-year increase in research papers in this field, indicating that the research on earnings conference calls is in a stage of rapid development. (2) The cooperation intensity among authors is robust, extensive, and tightly knit, indicating the formation of a well-developed collaborative network. In the research field of earnings conference calls, the USA, China, and Canada are the top three contributing countries, with the USA accounting for nearly half of the contributions. (3) Co-occurrence analysis of these keywords reveals a close relationship among them, indicating strong thematic connections within the research domain. (4) The evolution of keywords reveals a notable shift in scholarly focus from traditional economic perspectives to more interdisciplinary approaches, incorporating linguistic and machine-learning perspectives. This trend reflects an increasing emphasis on analyzing financial phenomena through diverse disciplinary lenses. This study underscores the need for more focused research in these perspectives, offering important implications for sustainable earnings conference calls release management. It also underscores the potential for collaborative opportunities within academia to better understand future trends.
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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 | 0.010 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.096 | 0.143 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".