D'Onofrio Consulting Partners

Underestimating one’s leadership impact: Are women leaders more susceptible?

July 1, 2016 • Women in Leadership


While 50% of supervisory and management positions are held by women, female representation drops to less than 5% when it comes to the c-suite. In this paper. the researchers argue that while women self-rate similarly to men on multi-source feedback (i.e. through their own and others feedback ratings), they under-estimate how they are seen by others. Through a series of studies, the researchers set out to assess whether women are less aware of their leadership capabilities than their male counterparts and whether they are failing to appreciate their talents and leadership impact. They found evidence that women under-predicted their leadership competencies relative to men. The discrepancy was particularly pronounced when women leaders were asked to predict their bosses’ ratings on emotional and social competencies and transformational leadership.

The authors recommend areas for future research as well as steps for organizations and their female leaders to take.


  • In their initial study of 240 leaders, assessments of emotional and social competencies of 1700 bosses, direct reports and peers revealed that under-prediction exists among women, but not men. This discrepancy was greatest when women were asked to assess their bosses’ ratings.
  • In a follow up study, using a new sample of 200 leaders and their bosses, women leaders did not self-rate differently from the men. However, they significantly under-predicted their bosses ratings on transformational leadership compared to men. The difference cannot be attributed to self-confidence or differences in their bosses’ feedback.
  • In a follow up to the second study, 76 women leaders were asked about their prediction of their bosses ratings and the potential impact of under-prediction. Women proposed only negative consequences, while failing to offer explanations that the data supported (E.g. lack of self-confidence and feedback frequency).

Practical Implications

The findings raise the question as to whether under-prediction impairs a woman’s leadership performance. The authors note this will be the focus of future research. Under-prediction can negatively impact women and their organizations when qualified women are overlooked for promotion and/or do not speak up. Can this impact self-confidence and performance?

“…our interest and contribution here is to invite organizations to better understand what constitutes leader self-awareness and work to improve this important leader capability for all leaders.”


The authors propose ideas on how organizations and women leaders can foster heightened and more accurate self-awareness, including:

  • Assessing self-awareness in leadership development/education programs
  • Broadening multi-source feedback assessments to include the leader’s predictions of ratings from others
  • Improved quantity and quality of feedback to women leaders, with specifics that enable women to believe rather than discount perceptions

In concluding, the researchers encourage organizations to replicate the study, create forums for discussion among women leaders and take steps to improve prediction accuracy.

Study Methodology

  1. In the first study, 240 leaders (with 15 years’ average experience) were assessed. Information from over 1700 others (including bosses, direct reports, and peers) was collected on these leaders. The leadership behaviors that were measured were emotional and social competencies.
  2. In the second study, data was collected on transformational leadership capability (i.e., intellectual stimulation, idealized influence, individualized consideration) using from a new sample of approximately 200 leaders and their respective bosses online survey techniques.
  3. In a follow up to the second study, the researchers used a qualitative approach to further understand women leaders thinking around self-awareness. They questioned 76 women leaders (from supervisors to senior executives) about predicting boss ratings.

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