Pa ge 1 Pa ge 46 American Journal of Interdisciplinary Research and Innovation (AJIRI) Gender Differences in Career Coaching Outcomes: A Quantitative Analysis using a Logit Model Pratima Adhikari1*, Roshan Thapa2 Volume 4 Issue 2, Year 2025 ISSN: 2833-2237 (Online) DOI: https://doi.org/10.54536/ajiri.v4i2.4785 https://journals.e-palli.com/home/index.php/ajiri Article Information ABSTRACT Received: March 15, 2025 Accepted: April 22, 2025 Published: May 31, 2025 Career coaching is essential in supporting career development and improving workplace adaptability. However, gender-specific differences in coaching experiences remain unexplored, despite evidence that men and women often encounter distinct challenges shaped by societal norms and organizational cultures investigate whether career coaching factors, Career Planning Self-Efficacy, Perceived Gender Supportiveness, and Coaching Satisfaction, can predict participants’ gender using a binary logistic regression model. A quantitative, cross-sectional design was employed with data collected from 120 individuals engaged in career coaching programs. Participants completed a structured questionnaire measuring career self-efficacy, coaching satisfaction, and perceptions of gender supportiveness using validated Likert scales. Binary logistic regression was performed using Jamovi to examine the predictive power of these variables on gender classification, with model fit, classification accuracy, ROC-AUC, and collinearity checks included. The comprehensive model demonstrated statistical significance, χ²(3, N = 120) = 40.6, p < .001, exhibiting an exceptional fit (AUC = 0.888). Career Planning Self-Efficacy was the strongest predictor (OR = 2.89, p < .001), followed by Perceived Gender Supportiveness (OR = 3.65, p = .008), and Coaching Satisfaction (OR = 2.66, p = .005). Accuracy increased from 82.5% (cut- off 0.5) to 84.2% (cut-off 0.798) with improved balance between sensitivity and specificity. Career Planning Self-Efficacy, Perceived Gender Supportiveness, and Coaching Satisfaction significantly predicted gender classification in coaching contexts, with men reporting higher levels across these factors. The findings highlight the need for gender-responsive, inclusive coaching practices tailored to different career needs and perceptions. Keywords Career Coaching, Career Planning Self-Efficacy, Coaching Satisfaction, Gender Supportiveness, Logistic Regression 1 Career Coach Nepal, Kathmandu, Nepal 2 Global College International, Kathmandu, Nepal * Corresponding author’s e-mail: me.pratima.thapa@gmail.com INTRODUCTION In the current scenario, career coaching and counseling have emerged as a sensitive intervention in supporting individuals’ career development, job satisfaction, and workplace adaptability. With increasing globalization and evolving labor market demands, career coaching plays a vital role in helping professionals navigate complex career horizon (Bozer et al., 2013). The effect and impact of coaching and counseling on career outcomes such as job readiness and clarity, decision- making self-efficacy, and career satisfaction is measured. However, gender differences in career experiences necessitate an exploration of how male and female might experience coaching differently. Gender influences career development through societal expectations, organizational practices, and personal perceptions Male and female often faced distinct challenges and opportunities which shape their career trajectories (Ely et al., 2011). As such, career coaching experiences may vary based on gender, with potential differences in satisfaction, supportiveness, preferences, and outcomes (Betz, 2007; Van Nieuwerburgh, 2020). Therefore, it has become important to Understand these differences is essential for designing equitable and effective coaching programs Gender-specific coaching is rooted in the understanding that women often face systemic and structural barriers in their careers that differ from those experienced by men. People often faces difficulties in their career path, like being judged by gender-based expectations, having limited chances to take on leadership roles, and trying to manage the ongoing challenge of balancing work commitments with family life. Coaching strategies that are tailored to address these specific challenges can be more effective in promoting career advancement and personal growth for women. One of the key theoretical underpinnings of gender-specific coaching is the concept of “critical feminist coaching” (CFC), which emphasizes the need to challenge gender hegemony and promote feminist values in the coaching process (Bierema et al., 2022). This approach encourages coaches to adopt a critical lens when working with women, recognizing the systemic inequalities they face and empowering them to navigate these challenges effectively. Another important theoretical framework is the “4 R’s” model, which focuses on reflecting, reforming, raising, and rebuilding. This model provides a well structured approach to coaching that helps female leaders to develop their skills and confidence needed to succeed in male-dominated environments (Bierema et al., 2022). These elements help female leaders through coaching to overcome the barriers that hinder their career progression. Although awareness of gender disparities in career development is increasing, empirical Pa ge 47 https://journals.e-palli.com/home/index.php/ajiri Am. J. Interdiscip. Res. Innov. 4(2) 46-52, 2025 research investigating how gender relates to coaching experiences and preferences remains limited. Most existing literature focuses on overall coaching outcomes, neglecting to explore whether individuals’ gender can be predicted based on their coaching experiences. This gap hinders efforts to create inclusive coaching frameworks that address gender-specific needs. Purpose of the Study The study aims to examine whether career coaching experiences, including satisfaction, self-efficacy, perceived gender supportiveness, frequency, and gender preference in coaching, predict an individual’s gender using a binary logistic regression model. Research Question Does a combination of coaching satisfaction, career planning self-efficacy, perceived gender supportiveness, coaching frequency, and coach gender preference predict the likelihood of a participant being female or male? Hypotheses H1: Higher coaching satisfaction is positively associated with the likelihood of identifying as female. H2: Greater career planning self-efficacy is positively associated with the likelihood of identifying as female. H3: Stronger perceptions of gender supportiveness are positively associated with the likelihood of identifying as female. LITERATURE REVIEW Career Coaching Efficacy Career coaching and counseling helps to enhance individuals’ career management skills, informed decision- making capabilities, and job satisfaction. Research by Bozer et al. (2013) and Feldman and Lankau (2005) affirms coaching’s positive effects on career clarity, adaptability, and work engagement. These outcomes are linked to structured goal setting, reflective practices, and supportive feedback provided in coaching and counseling sessions. it is further supported by recent studies by Van Nieuwerburgh (2020) further highlight the evolving nature of coaching in the digital era, emphasizing the significance of personalization and inclusivity. Gender-Specific Coaching Traditional leadership models often reflect male-centric perspectives, necessitating coaching approaches that address the unique needs of female leaders(Ruderman & Ohlott, 2005). Gender-specific coaching acknowledges that men and women may encounter unique career challenges shaped by social norms and organizational cultures (Ely et al., 2011). Studies indicate that women often face greater obstacles in leadership advancement, negotiation, and work-life balance, necessitating tailored coaching interventions (Athanasopoulou & Dopson, 2018; Lyness & Grotto, 2018). Gender-sensitive coaching environments foster inclusivity and psychological safety, enabling individuals to address gender-related career issues openly. Likely, inclusive coaching practices significantly improve workplace equity and career progression for underrepresented genders (Schneider et al., 2017). Coaching for women in leadership roles should focus on overcoming implicit biases and stereotypes that can impede their progress. In organization, while analyzing the careers of managers it was found that coaching is seen as a successful tool which helped female to compete for top positions traditionally dominated by men. Although the coaching process is not inherently gender-specific, it is particularly beneficial for women aiming to break into higher managerial roles (Żukowska & Miąsek, 2014a). Through the research among people there is gender differences in career success which are evident, with gender acting as a moderating variable in career outcomes. For instance, gender influences the impact of international experience on compensation and career ascendency, highlighting the nuanced role gender plays in career trajectories (Orser & Leck, 2010). Coaching is been a valuable tool for informed career development, particularly for female aspiring to managerial positions. It helps in building the necessary skills and confidence to compete in traditionally male-dominated roles (Żukowska & Miąsek, 2014). Likewise, there was a positive correlation between undergraduate students’ interest in co-curricular activities and their leadership skills, with female students showing higher levels in both areas and emphasized the importance of providing co-curricular opportunities in colleges to enhance leadership skills development among students, regardless of academic stream or locality (Subhrajyoti et al., 2023). Coaching Satisfaction It reflects the participant’s overall contentment with the career coaching sessions they received. It assesses multiple aspects including whether the coaching helped clarify career goals, enhance confidence, strengthen career decision-making, and improve job satisfaction. It was observed that higher levels of coaching satisfaction have been associated to stronger positive career outcomes and wellbeing of an individual (Bozer et al., 2013). It was found that Women in professional psychology are more likely to expect career disruptions due to child- rearing and prefer job flexibility, which impacts their career satisfaction and choices (Singer et al., 2005). As organizations implement gender-competent coaching practices, it becomes imperative to evaluate the long-term impacts on both individual and organizational levels. For instance, by fostering environments where men are not only participants but also advocates for gender equity in coaching, companies can cultivate a more holistic approach to leadership development that benefits all employees. Career Planning Self-Efficacy As organizations strive to implement gender-competent coaching practices, it is equally important to recognize the Pa ge 48 https://journals.e-palli.com/home/index.php/ajiri Am. J. Interdiscip. Res. Innov. 4(2) 46-52, 2025 role of intersectionality in shaping individuals’ experiences within these frameworks. It refers to an individual’s values and belief in their capability and capacity to make effective career-related decisions, set achievable goals, and navigate career transitions. It captures confidence in tackling job search challenges, selecting career paths, and managing work-life balance. High career planning self-efficacy predicts career commitment and proactive career behaviors (Betz & Hackett, 2006). Likely, Self- efficacy plays an important role in career planning, with male generally reporting higher levels of self-efficacy in coaching roles. This difference is predictive of their intention to pursue coaching careers (Majake & Kubayi, 2024). Perceived Gender Supportiveness Furthermore, as organizations seek to refine their coaching frameworks, it is essential to consider the role of mentorship in conjunction with formal coaching practices. Mentorship can provide a complementary layer of support that addresses not only professional development but also personal growth and emotional resilience, particularly for those facing intersectional challenges. Further, there is no significant difference in research self-efficacy between male and female students (Gaoat et al., 2023). It measures participants’ perceptions of the extent to which their coaching environment and sessions are supportive of gender diversity and inclusion. This includes fairness, openness to gender-related concerns, and active encouragement irrespective of gender identity. Supportive environments enhance engagement, learning, and comfort in career coaching contexts (Ely et al., 2011). The perception of gender supportiveness in career environments is crucial for job satisfaction. Female often report higher job satisfaction levels as compared to male, which can be influenced by organizational support and gender-sensitive approaches (Cañizares et al., 2007). Addressing unconscious bias and promoting gender equality in career development can create more inclusive environments, enhancing perceived supportiveness and satisfaction (Costa et al., 2015; Parra-Valencia & Massey, 2023). Theoretical Framework This study is grounded in two key theoretical perspectives: Social Cognitive Career Theory (SCCT) and Gender Role Theory, both of which provide a relevant foundation for examining the role of self-efficacy, satisfaction, and perceived inclusivity in career coaching outcomes. Social Cognitive Career Theory (SCCT) emphasizes the importance of self-efficacy beliefs, outcome expectations, and contextual supports in shaping career development and decision-making (Lent et al., 1994). SCCT posits that individuals’ confidence in their ability to manage career-related tasks which termed career self-efficacy and plays a critical role in career choices, goal-setting, and satisfaction. Within this framework, career coaching can serve as a contextual support system that influences individuals’ self-efficacy beliefs and satisfaction through feedback, guidance, and encouragement. The theory identifies Career Planning Self-Efficacy as a key factor, revealing that individuals with higher confidence in their capabilities are more inclined to take an active role in informed career decisions and view coaching as a valuable resource (Betz & Hackett, 2006). The SCCT framework also supports the inclusion of Coaching Satisfaction as an outcome indicator of the effectiveness of contextual career supports. MATERIALS AND METHODS This study adopted a quantitative approach, cross-sectional research design to anlayze the relationship between career coaching-related factors and gender classification. The data was collected through primary sources through structured self-administered questionnaires and the subsequent application of binary logistic regression analysis to predict the likelihood of gender classification (male or female) based on selected predictor variables. The predictors included Career Planning Self-Efficacy, Perceived Gender Supportiveness, and Coaching Satisfaction. The cross- sectional approach was appropriate as it allowed for the collection of data at a single point in time, enabling the exploration of associations between the study variables without manipulating the study environment (Creswell & Creswell, 2018). The target population for this study comprised individuals participating in career coaching programs and workshops offered within professional and educational settings. The focus was on adults actively involved in career decision-making and coaching services. From this population, a total sample of 120 participants was recruited. The questionnaire included validated Likert-scale items measuring Career Planning Self- Efficacy, Perceived Gender Supportiveness, and Coaching Satisfaction, along with demographic information. Data were analyzed using Jamovi version 2.5.7. Initial data screening involved checking for missing values, outliers, and assumption violations. The main analysis involved: • Binary Logistic Regression Analysis to test the predictive power of the three independent variables on gender classification. • Model Fit Indices including model deviance, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and pseudo R-squared values (Nagelkerke R², Cox & Snell R², and McFadden R²). • Omnibus Likelihood Ratio Tests to examine the overall significance of individual predictors. • Receiver Operating Characteristic (ROC) Curve Analysis and calculation of the Area Under the Curve (AUC)to assess model accuracy at different cut-off points. • Collinearity Diagnostics (Variance Inflation Factor and Tolerance) to test for multicollinearity among the predictors. • All statistical tests were conducted at a significance level of p < .05. Pa ge 49 https://journals.e-palli.com/home/index.php/ajiri Am. J. Interdiscip. Res. Innov. 4(2) 46-52, 2025 RESULTS AND DISCUSSION The fit of the logistic regression model was evaluated using several commonly recommended goodness-of-fit indicators. Table 1: Model Fit Measures Overall Model Test Model Deviance AIC BIC R²McF R²CS R²N χ² df p 1 84.8 92.8 104 0.324 0.287 0.443 40.6 3 <.001 Table 2: Omnibus Likelihood Ratio Tests Predictor χ² df P Coaching Satisfaction 10.70 1 0.001 Career Planning Self-Efficacy 13.02 1 <.001 Perceived Gender Supportiveness 7.38 1 0.007 Table 3: Collinearity Statistics Predictor VIF Tolerance Coaching Satisfaction 1.01 0.993 Career Planning Self-Efficacy 1.18 0.848 Perceived Gender Supportiveness 1.19 0.843 The fit of the logistic regression model was evaluated using several commonly recommended goodness-of- fit indicators. The overall model test was statistically significant, χ²(3, N = 120) = 40.6, p < .001, indicating that the model provided a significantly better fit than a null model with no predictors. This is consistent with recommendations for using likelihood-ratio chi-square tests in evaluating logistic regression models (Acharya et al., 2022; Hosmer et al., 2013). The model reported a Deviance value of 84.8, with an Akaike Information Criterion (AIC) of 92.8 and a Bayesian Information Criterion (BIC) of 104. Lower AIC and BIC values indicate a better and more parsimonious model fit (Vittinghoff et al., 2012). These indices are essential for comparing the relative quality of competing models in logistic regression analysis. To assess explanatory power, three pseudo-R² values were reported. McFadden’s R² was 0.324, which suggests a moderately strong model, as values between 0.2 and 0.4 are typically interpreted as indicative of a good fit in logistic regression (Hosmer et al., 2013; Menard, 2018). Cox & Snell’s R² was 0.287, and Nagelkerke’s R² was 0.443, providing additional measures of the proportion of variance explained by the predictors. Pseudo-R² measures, while not directly comparable to those in linear regression, are widely used for gauging model strength in logistic regression research (Acharya et al., 2022). Overall, these fit statistics demonstrated that the model performed well in explaining and predicting gender classification based on coaching-related experiences. Self-Efficacy was a significant predictor, χ²(1, N = 120) = 13.02, p < .001, representing the strongest individual predictor in the model. This finding aligns with recent work by Jones et al.,(2016) who emphasized the importance of self-efficacy in career-related outcomes and decision-making. Additionally, Perceived Gender Supportiveness made a significant contribution, χ²(1, N = 120) = 7.38, p = .007, suggesting that participants’ perceptions of gender inclusivity in coaching environments play a meaningful role in differentiating gender classifications. These findings highlight the importance of evaluating individual predictors in logistic regression models, as recommended in contemporary applied regression literature (Menard, 2018; Vittinghoff et al., 2012). The contribution of each predictor variable to the logistic regression model was assessed using the Omnibus Likelihood Ratio Test, which compares the full model containing the predictor to a reduced model without it. This test is recommended for determining whether the addition of each individual predictor significantly improves model fit (Hosmer et al., 2013). Results showed that Coaching Satisfaction made a significant contribution to the model, χ²(1, N = 120) = 10.70, p = .001. This indicates that including Coaching Satisfaction significantly improves the model’s ability to predict gender classification. Similarly, Career Planning To assess the presence of multicollinearity among the predictor variables, Variance Inflation Factor (VIF) and Tolerance values were examined. Multicollinearity occurs when independent variables are highly correlated, which can distort regression coefficients and affect the stability of the model (Vittinghoff et al., 2012). The analysis showed that Coaching Satisfaction had a VIF of 1.01 and a Tolerance of 0.993, Career Planning Self-Efficacy had a VIF of 1.18 and a Tolerance of 0.848, and Perceived Gender Supportiveness had a VIF of 1.19 and a Tolerance of 0.843. All VIF values were well below the commonly accepted threshold of 5, and Tolerance values were above 0.2, indicating no significant multicollinearity concerns among the predictors (Hosmer et al., 2021; Menard, 2018). These results suggest that the predictors contributed independently to the model and that the regression estimates were stable and reliable. Examining individual predictors, Career Planning Self- Efficacy was the strongest predictor, with a statistically significant positive association (B = 1.061, SE = 0.289, p < .001, OR = 2.89). This suggests that for each one-unit increase in self-efficacy, the odds of being classified as male increased by approximately 2.89 times, holding other variables constant. Perceived Gender Supportiveness also made a significant contribution to the model (B = 1.295, SE = 0.487, p = .008, OR = 3.65), indicating that higher perceptions of gender inclusivity were associated Pa ge 50 https://journals.e-palli.com/home/index.php/ajiri Am. J. Interdiscip. Res. Innov. 4(2) 46-52, 2025 with an increase in the likelihood of being classified as male. Additionally, Coaching Satisfaction had a significant positive effect (B = 0.980, SE = 0.343, p = .005, OR = 2.66), implying that greater satisfaction with coaching experiences was linked to higher odds of male classification. When looking at each factor separately, Career Planning Self-Efficacy turned out to be the strongest predictor of gender in this study. This means that people who felt more confident about making career decisions after coaching were much more likely to be male. In fact, for every increase in confidence, the chance of being male was about 2.89 times higher, while keeping other factors the same. Perceived Gender Supportiveness also played an important role. Participants who felt that their coaching environment was more supportive and fairer about gender were about 3.65 times more likely to be male. This shows that male participants tended to report more positive feelings about the inclusiveness of their coaching experiences. Lastly, Coaching Satisfaction was also linked to gender. Participants who were more satisfied with their coaching sessions were 2.66 times more likely to be male. This suggests that male participants generally felt more satisfied with their coaching experiences compared to female participants. The classification performance of the logistic regression model was evaluated at two different probability cut- off points: 0.5 and 0.798. At the conventional cut-off of 0.5, the model demonstrated an accuracy of 82.5%, with a specificity of 42.3% and a sensitivity of 93.6%. The area under the curve (AUC) was 0.888, indicating excellent overall discriminatory ability (Acharya et al., 2022; Hosmer et al., 2013). When the cut-off was adjusted to 0.798, classification accuracy improved to 84.2%. Notably, specificity increased substantially to 84.6%, while sensitivity slightly decreased to 84.0%. Despite these changes, the AUC remained constant at 0.888, confirming the model’s Table 4: Model Coefficients of estimated binary logistic regression 95% Confidence Interval 95% Confidence Interval Predictor Estimate Lower Upper SE Z p Odds ratio Lower Upper Intercept -12.562 -18.518 -6.61 3.039 -4.13 <.001 3.50e-6 9.07e-9 0.00135 Coaching Satisfaction 0.980 0.290 1.67 0.352 2.78 0.005 2.66 1.34 5.30919 Career Planning Self- Efficacy 1.061 0.464 1.66 0.305 3.48 <.001 2.89 1.59 5.24553 Perceived Gender Supportiveness 1.295 0.333 2.26 0.491 2.64 0.008 3.65 1.40 9.54442 Note. Estimates represent the log odds of "GENDER = Male" vs. "GENDER = Female" Table 5: Omnibus Likelihood Ratio Tests Cut off Accuracy Specificity Sensitivity AUC 0.5 0.825 0.423 0.936 0.888 0.798 0.842 0.846 0.840 0.888 Figure 1: Cut off Pa ge 51 https://journals.e-palli.com/home/index.php/ajiri Am. J. Interdiscip. Res. Innov. 4(2) 46-52, 2025 strong and stable capacity to differentiate between groups (Vittinghoff et al., 2012). DISCUSSIONS This study aimed to examine whether key career coaching factors predict gender classification among participants using a binary logistic regression model. The findings revealed that Career Planning Self-Efficacy, Perceived Gender Supportiveness, and Coaching Satisfaction were significant predictors of gender. Among these, Career Planning Self-Efficacy emerged as the strongest predictor. Higher levels of self-efficacy were associated with increased odds of male classification, suggesting that men in this sample reported higher confidence in career-related decision-making following coaching. This is consistent with previous findings indicating that men often express greater self-efficacy in professional settings, possibly due to social expectations and differing experiences of career support (Betz & Hackett, 2006; Vittinghoff et al., 2012). Perceived Gender Supportiveness also significantly contributed to gender classification. Participants who reported a more inclusive and gender-supportive coaching environment were more likely to be male. This may reflect varying perceptions of inclusivity across genders, with men potentially perceiving workplace environments as more gender-neutral or supportive compared to women, who may be more attuned to subtle biases or exclusions (Ely et al., 2011). Furthermore, Coaching Satisfaction was positively associated with male classification, indicating that higher levels of satisfaction with coaching outcomes increased the likelihood of being male. This finding may point to differences in how men and women evaluate or experience coaching effectiveness, as men may benefit more from coaching structures currently in place or perceive greater alignment with their career goals (Athanasopoulou & Dopson, 2018; Lyness & Grotto, 2018). In terms of model fit and classification accuracy, the logistic regression model demonstrated excellent predictive ability, with an AUC of 0.888. Accuracy improved from 82.5% at the conventional 0.5 cut-off to 84.2% at the optimal 0.798 threshold. Notably, adjusting the cut-off value improved specificity while maintaining strong sensitivity, suggesting the model effectively discriminates between genders based on coaching- related experiences. These findings reinforce the value of gender-responsive career coaching interventions and highlight the importance of considering gender dynamics in coaching practice. Practitioners should aim to enhance self-efficacy, foster inclusive coaching environments, and regularly evaluate satisfaction to promote equitable outcomes for all participants. CONCLUSION In conclusion, this study provides valuable insights into how career coaching experiences relate to gender classification. The results indicate that Career Planning Self-Efficacy, Perceived Gender Supportiveness, and Coaching Satisfaction are significant predictors of gender, with men in this study reporting higher levels of each. The logistic regression model demonstrated excellent classification performance, especially when adjusting the probability cut-off for improved balance between sensitivity and specificity. These findings underscore the necessity for organizations and coaches to design career coaching interventions that are sensitive to gender-related experiences and perceptions. By fostering environments that actively support gender inclusivity and address diverse needs, career coaching programs can contribute to more equitable and effective career development outcomes. Future research should consider longitudinal designs, larger and more diverse samples, and the inclusion of additional variables such as organizational culture, leadership opportunities, and personal career values to further enrich understanding in this field. REFERENCES Acharya, K. P., Khanal, S. P., & Chhetry, D. (2022). Factors Affecting Poverty in Nepal-ABinary Logistic Regression Model Study. Pertanika J. Soc. Sci. & Hum, 30(2), 641–663. Athanasopoulou, A., & Dopson, S. (2018). A systematic review of executive coaching outcomes: Is it the journey or the destination that matters the most? The Leadership Quarterly, 29(1), 70–88. https://doi. org/10.1016/j.leaqua.2017.11.004 Betz, N. E., & Hackett, G. (2006). Career Self- Efficacy Theory: Back to the Future. Journal of Career Assessment, 14(1), 3–11. https://doi. org/10.1177/1069072705281347 Bozer, G., Sarros, J. C., & Santora, J. C. (2013). The role of coachee characteristics in executive coaching for effective sustainability. Journal of Management Development, 32(3), 277–294. https://doi. org/10.1108/02621711311318319 Cañizares, S. M. a S., García, F. J. F., & Ruiz, C. A. (2007). Analyzing job satisfaction from a gender point of view: Empirical application with logit and probit model. Cuadernos de Gestión, 7(2), 55–67. https://doi. org/10.5295/cdg.19147ss Costa, C., Durão, M., Bakas, F. E., Breda, Z., Carvalho, I., & Caçador, S. (2015). Walking on broken glass: The changing nature of gendered constraints to career development and performance. 23, 1–9. Ely, R. J., Ibarra, H., & Kolb, D. M. (2011). Taking Gender Into Account: Theory and Design for Women’s Leadership Development Programs. Academy of Management Learning & Education, 10(3), 474–493. https://doi.org/10.5465/amle.2010.0046 Feldman, D. C., & Lankau, M. J. (2005). Executive Coaching: A Review and Agenda for Future Research. Journal of Management, 31(6), 829–848. https://doi. org/10.1177/0149206305279599 Gaoat, M. P., Magbitang, T. D., Bumanglag, J. D., & Ramirez, J. F. M. (2023). Research Self-Efficacy among Business Education Students of a Higher Education Institutional in Northern Philippines. American Journal Pa ge 52 https://journals.e-palli.com/home/index.php/ajiri Am. J. Interdiscip. Res. Innov. 4(2) 46-52, 2025 of Interdisciplinary Research and Innovation, 2(1), 7–12. https://doi.org/10.54536/ajiri.v2i1.1145 Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons. Jones, R. J., Woods, S. A., & Guillaume, Y. R. F. (2016). The effectiveness of workplace coaching: A meta- analysis of learning and performance outcomes from coaching. Journal of Occupational and Organizational Psychology, 89(2), 249–277. https://doi.org/10.1111/ joop.12119 Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi. org/10.1006/jvbe.1994.1027 Lyness, K. S., & Grotto, A. R. (2018). Women and leadership in the United States: Are we closing the gender gap? Annual Review of Organizational Psychology and Organizational Behavior, 5(1), 227–265. https://doi. org/10.1146/annurev-orgpsych-032117-104739 Majake, R., & Kubayi, A. (2024). Where does it all start? Gender-differences in athletes’ intention to pursue a coaching career. African Journal of Career Development, 6(1). https://doi.org/10.4102/ajcd.v6i1.155 Menard, S. (2018). Applied logistic regression analysis (2nd ed.). https://doi.org/10.4135/9781544308542 Orser, B., & Leck, J. (2010). Gender influences on career success outcomes. Gender in Management: An International Journal, 25(5), 386–407. https://doi. org/10.1108/17542411011056877 Parra-Valencia, J.-A., & Massey, M. L. (2023). Exploring Gender Inequality and Practical Solutions for an Equitable Environment for Women in Scientific Vocations (pp. 193– 209). Springer Nature. https://doi.org/10.1007/978- 3-031-40635-5_8 Ruderman, M. N., & Ohlott, P. J. (2005). Leading Roles: What coaches of women need to know. Leadership in Action, 25(3), 3–9. https://doi.org/10.1002/LIA.1115 Schneider, B., González-Romá, V., Ostroff, C., & West, M. A. (2017). Organizational climate and culture: Reflections on the history of the constructs in the Journal of Applied Psychology. Journal of Applied Psychology, 102(3), 468. Singer, A. R., Cassin, S. E., & Dobson, K. S. (2005). The Role of Gender in the Career Aspirations of Professional Psychology Graduates: Are There More Similarities Than Differences? Canadian Psychology, 46(4), 215–222. https://doi.org/10.1037/H0087029 Subhrajyoti, T., Kumar, R., Pratima Pradhan, P. P., & Acharya, A. K. (2023). The Interest of Undergraduate Students in Co-curricular Activities in Relation to their Leadership Skills. American Journal of Education and Technology, 2(1), 21–28. https://doi.org/10.54536/ ajet.v2i1.1140 Van Nieuwerburgh, C. (2020). An introduction to coaching skills: A practical guide. Vittinghoff, E., Glidden, D. V., Shiboski, S. C., & McCulloch, C. E. (2012). Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. Springer Science & Business Media. Żukowska, J., & Miąsek, D. (2014). The role of coaching in managerial careers of women. 22, 56–66.