GENDER DIFFERENCES IN MOTIVATION AND PERFORMANCE:A BIBLIOMETRIC ANALYSIS ON RESEARCH TRENDS
Bibliographic record
Abstract
Employee motivation and performance are two fundamental factors in achieving organizational goals, among which the importance of how gender differences impact them stands out. This research analyzes the links between the concepts of "motivation," "performance," "gender differences," and "organization" through a bibliometric approach. A total of 137 scientific documents from the last three decades were investigated, outlining a growing interest in this topic. The methodology includes data collection from the Web of Science database, analysis using Biblioshiny, and interpretation of the results. The bibliometric evaluation highlights the increasing academic attention to the subject, with a peak in publications in 2024. Countries such as the US and Canada are at the forefront of international collaborations, while Michigan State University, Aarhus University, and the University of Amsterdam stand out as key institutions in this field. Regarding the content analysis, this difference in motivation based on gender has been observed in several fields (IT, economics, entrepreneurship, etc.), and we have identified the motivating factors for each gender. The theoretical implications focus on the methodological approach to research in organizational management, while the practical implications provide relevant perspectives for managers and researchers interested in developing more inclusive organizational strategies.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.090 | 0.136 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".