Gender differences in organizational strategy execution: A comparative study between female and male managers in obstacle management
Bibliographic record
Abstract
This paper investigates the differences faced by women and men regarding their abilities to manage obstacles inherent in the organizational strategy execution. A survey questionnaire was administered to 282 managers in order to study the five dimensions of the obstacles inherent in organizational strategy execution identified through a previous research which was designed to validate the set of obstacles faced by managers. We used principal component analysis to categorize the obstacle dimensions and then we used ANOVA to examine the link between dimensions of strategy execution and sociodemographic variables such as age, span of control, number of years in the organization. Past research produced significant gender differences with respect to the strategy execution of the organizational objectives of managers. It also disclosed many gender differences in the set of obstacles related to emotions, immediate action, rules, integrity and initiatives. In contrast with the literature, our first hypothesis that there would be a gender difference in terms of obstacles between female and male managers for the dimension of emotions was not supported. However, this study does show a significant gender difference regarding the obstacle dimensions of immediate action, initiatives and rules: this hypothesis was supported. The study modeled five obstacle dimensions in order to identify differences in practice between female and male managers. On an emotional level, women and men face the same problem-solving in organizational performance development, but for the other dimensions, their capacity to confront the obstacles is different. On a professional level, our study shows that women managers who have reached top management positions can perform as many men managers in terms of the implementation of clear and achievable objectives. At this stage, our model cannot be generalized and further studies that would better identify the causes underlying these differences in practice are needed. Additional work is required in order to be able to generalize our findings. This study proves its contribution by identifying a more refined set of specific obstacles for each facet of organizational strategy execution faced by women and men managers in the execution of their objectives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".