Leadership skills development has become one of the widely approved social interventions for developing leadership abilities, particularly among young females. The goal of the study was to determine how some mentoring strategies affected the development of leadership skills among female students.
The study was guided by the positivist philosophical position and the quantitative approach. The explanatory research design from the quantitative approach was used in this investigation. Data were gathered from a sample of 286 out of 1680 study participants who worked in Ghana’s public and private sectors. Data were gathered with a structured questionnaire and was analysed with partial least square structural equation modelling.
The study found that group mentoring significantly mediated the relationship between formal mentoring and leadership skills development, informal mentoring and leadership skills development and peer mentoring and leadership skills development among female secondary school students in a developing economy.
It was therefore recommended that secondary school authorities should provide orientation and training for mentors in secondary schools in the area of group mentoring. This was needed to help equip mentors with the appropriate skills to handle larger groups of student mentees.
Introduction
Developing leadership is one of the essential skills for twenty-first-century students (Baroudi & David, 2020; Goh & Ransdell, 2024). Leadership development is a part of the secondary school experiences for both female and male students due to its implications on personal development, well-being and self-awareness of students (Lacerenza et al., 2017; Reyes et al., 2019). It is well-known that mentoring helps both professional men and women to overcome obstacles. In America and other countries, mentoring has been a topic of conversation for over a decade and has been used as a widely approved social intervention to better the lives and leadership abilities of disadvantaged populations, particularly young females (Raposa, Dietz, & Rhodes, 2017). Mentoring programmes, particularly in senior secondary schools, have proved to be very beneficial due to the role they play in increasing students’ self-confidence and providing possibilities for leadership development (Haber-Curran, Everman, & Martinez, 2017; Grocutt, Gulseren, Weatherhead, & Turner, 2020; Williams & Shinkai, 2022). Mentoring is described by Carmin (2019) as a process of incorporating socialisation, career and/or educational development, as well as interpersonal or psychosocial development, into a relationship. There are several ways of mentoring. They might consist of peer mentoring, group mentoring, formal mentoring, informal mentoring, traditional mentoring and e-mentoring (Haber-Curran et al., 2017; Wiggins, 2024). Helping female adolescent students develop their leadership abilities through these mentoring strategies might motivate them to serve as role models and also take up leadership roles with confidence in future (Bennett, 2017; Velez et al., 2020; Joo & Cruz, 2024). The question that comes up for scrutiny, however, is to what extent do these mentoring approaches lead to the development of leadership traits among female students in a secondary school setting in a developing country?
There is still a great deal to learn about how different mentoring approaches, especially group mentoring, affect the development of leadership skills among female secondary school students in developing economies, despite the acknowledged significance of leadership development for students in the twenty-first century (Baroudi & David, 2020; Goh & Ransdell, 2024). Previous research has demonstrated the beneficial effects of mentoring on leadership skills and self-confidence (Haber-Curran et al., 2017; Grocutt et al., 2020). However, the precise processes by which these advantages are achieved in a group mentoring setting have not been fully investigated. Female students encounter particular difficulties that impede their chances for leadership and personal growth in many developing nations (Raposa et al., 2017). The underrepresentation of women in leadership positions may be exacerbated by the absence of specialised mentorship programs that tackle these issues. Additionally, even though mentoring has received widespread support as a social intervention, it is still unknown how much each mentoring dimension – peer, formal and informal – contributes to the development of critical leadership qualities (Carmin, 2019; Wiggins, 2024). The purpose of this study is to look into how group mentoring helps female secondary school students in developing economies build their leadership abilities. This study looks at how different mentoring styles and the growth of leadership qualities are related in order to shed light on practical tactics that can enable young women to become self-assured leaders and role models in their communities (Bennett, 2017; Velez et al., 2020). Designing focused interventions that improve female students’ leadership potential and eventually aid in their personal and professional development in a world that is changing quickly requires an understanding of these dynamics.
Regarding the theoretical gap this study aims to address, it is evident that current theories on leadership development frequently ignore gender-specific elements that affect female students’ experiences in mentoring relationships. By integrating gender dynamics into the examination of mentoring’s impact on leadership abilities, this study will deepen theoretical understanding. Although mentoring is acknowledged to be helpful for developing leaders, little is known about the precise characteristics of group mentoring and how it varies from other types of mentoring (such as formal or peer mentoring). This study sheds light on the special features of group mentorship that help female students develop their leadership abilities. There is a knowledge vacuum on how mentorship dynamics function in the unique cultural and socioeconomic contexts of developing economies because the majority of the research that is currently available concentrates on industrialised nations (Haber-Curran et al., 2017; Grocutt et al., 2020; Williams & Shinkai, 2022). Methodologically, there is a dearth of empirical data in much of the research on leadership development and mentorship that is especially targeted at female secondary school students in underdeveloped nations. Prior research frequently looks at mentoring as a composite variable without breaking down the several dimensions of mentoring (peer, formal and informal, for example) and how each contributes to the development of leaders. Earlier studies by Somuah, Kariuki, and Itegi (2019) also examined only the direct relationship between the various dimensions of mentoring, such as peer mentoring, formal mentoring, informal mentoring and leadership skills development, creating a conceptual gap. This study, however, failed to examine the mediating role of group mentoring on the relationship between peer mentoring, formal mentoring, informal mentoring and leadership skills development. The study used a strong methodological framework (PLS-SEM analysis) to examine the collected data and also made a substantial contribution to the literature on mentoring and leadership development by using a comparative analysis of various mentoring styles to comprehend their unique effects in filling these gaps. The findings will be useful for informing future research and real-world interventions in developing economies.
Literature review and hypotheses development
Theoretical review
The social learning theory (SLT) guides this study. Albert Bandura developed the SLT, which highlights the importance of modelling, imitation and observation in the learning process. This theory is especially relevant in educational settings, as mentorship can have a significant impact on how well female secondary school students develop their leadership abilities, particularly in developing nations. The use of SLT in mentoring relationships, its effect on leadership development and the unique difficulties experienced by female students in these settings are all examined in this review of the literature. According to SLT, people pick up behaviours from watching other people as well as from their own experiences. Four essential elements of SLT were recognised by Bandura (1977): motivation, attention, retention and replication. These elements highlight the ways in which people might pick up knowledge from role models, especially in social settings like mentorship. First, students must pay attention to their mentors’ actions in order for learning to take place (Bandura, 1986). Students must retain the observed behaviours in order to repeat them in the future. Effective learning requires the ability to replicate the observed behaviour, which brings us to the following element. Lastly, motivation is crucial; if students believe that certain activities will lead to favourable results, they are more inclined to copy those behaviours (Bandura, 1997).
Mentoring offers a controlled environment for social learning to flourish, especially in educational contexts. According to research, mentoring can help students develop their leadership abilities by providing them with opportunities for observational learning and role models (Haber-Curran et al., 2017; Grocutt et al., 2020). One essential component of this procedure is role modelling. Mentors frequently exhibit the leadership traits that mentees hope to acquire. Since female mentors may connect to the unique difficulties and obstacles faced by women in leadership positions, having female mentors can have a particularly positive effect on female students (Raposa et al., 2017). Furthermore, peer learning is essential to the dynamics of mentorship. Peer relationships are facilitated by group mentoring, allowing students to benefit from one another’s experiences. Students are encouraged to share their triumphs and vulnerabilities in this collaborative setting, which creates a sense of community (Duhigg, 2016; Joo & Cruz, 2024). Another important result of mentoring is the development of self-efficacy. SLT emphasises how crucial self-efficacy is to the learning process. Female students’ confidence in their leadership skills grows when they interact with mentors and get encouraging feedback (Lacerenza et al., 2017). They may be inspired to actively seek leadership positions by this increased sense of self-efficacy.
Female secondary school pupils in poor nations face particular difficulties that may impede their ability to develop as leaders. Significant obstacles include cultural norms, limited opportunities for leadership experiences and restricted access to mentorship programs (Goh & Ransdell, 2024; Bennett, 2017). Women’s responsibilities are frequently dictated by cultural obstacles, which restrict their access to mentorship and leadership opportunities. Culturally sensitive mentoring programs can assist in overcoming these obstacles (Raposa et al., 2017). Furthermore, it might be challenging for female students to locate mentors who can help them on their leadership journeys because many emerging countries lack formal mentorship programs (Williams & Shinkai, 2022). Additionally, the quality of mentoring relationships may be impacted by the lack of educational resources available in schools. Financial limitations may make it difficult for schools in developing nations to put in place successful mentorship programs (Haber-Curran et al., 2017).
A useful framework for comprehending the dynamics of mentoring and its influence on the development of leadership abilities in female secondary school students in developing nations is provided by SLT. SLT emphasises how mentorship programs may empower young women and give them the tools they need to overcome leadership issues by highlighting the value of role modelling, peer learning and self-efficacy. Designing successful mentoring interventions that promote leadership development requires addressing the particular obstacles that these students experience.
Formal mentoring and group mentoring approaches
According to current research, formal and group mentoring approaches are efficient strategies for enhancing young people’s socio-emotional and behavioural outcomes, leading to strengthened group dynamics (Bonneywell, 2017; Joseph and Kuperminc, 2021; Williams & Shinkai, 2022; Joo & Cruz, 2024). These kinds of mentoring offer mentees special interaction while allowing diverse thoughts to be discussed among participants. When a group is well moderated through formally arranged activities, participants are likely to speak out, share their vulnerabilities with one another and make suggestions for improvement (Duhigg, 2016; Joo & Cruz, 2024; Wiggins, 2024). This fosters a sense of community and inspires others to take a chance (Duhigg, 2016; Grocutt et al., 2020; Burbage & Gregory, 2022). The studies of Gorin, Lee, and Knight (2020), Fleischer (2021) and Joseph and Kuperminc (2021) also indicated that group and formal mentorship strategies were effective and efficient interventions that influenced leadership skills development among students and staff. It was based on the foregoing arguments that the current study hypothesised that
Formal mentoring (FM) significantly relates to group mentoring (GM) among female students in a developing economy.
Group mentoring and leadership skills development
GM models serve as a supportive and collegial way to provide mentees with a safe yet challenging space towards leadership skills development (Lennox, 2017; Goh & Ransdell, 2024). According to Komives and Owen (2022), as part of the stages of leadership development, students begin to form some level of commitment to giving back to a group and also begin to mentor other students as a way of practising generativity towards the development of leadership among themselves. An investigation conducted by Bonneywell (2017) revealed that GM fosters personal value and leadership development among female stakeholders. Kaufman et al. (2022) also explored the effect of group online mentoring on the leadership skills acquisition of adolescent girls during COVID-19 in the United States and found that GM benefitted both mentors and mentees in the development of personal and social skills during the pandemic. The results from a study conducted by Gadomska-Lila (2020) on reverse mentoring found that this approach was an effective tool for knowledge sharing, group engagement and leadership development among younger and older groups in an organisation. A similar study by Flowers (2024) also revealed that GM had the potential to increase leadership self-efficacy among participants. The implication is that GM strategies are very powerful tools for the development of leadership among females as they provide the environment for the sharing of ideas and the exchange of experiences. To establish the extent to which GM was related to leadership development skills among female students, the current study hypothesised that:
GM significantly relates to leadership skills development (LSD) among female students in a developing economy.
Informal mentoring versus group mentoring approaches
Though informal mentorship is not as clear as group mentorship due to the various forms the relationship could take, within the GM strategies, informal relationships could spring up leading to the use of informal mentoring of the members this relationship could be formed without a specific time frame or defined roles and responsibilities (Parfitt & Rose, 2020; Christopher & Rose, 2020). Additionally, among mentors and protégés alike, informal mentoring is a more preferred strategy for mentorship even within a group where GM approaches are being used (Deng, Gulseren, & Turner, 2022). Regardless, Abdellah (2021) asserted that both group and informal mentoring approaches were needed for effective succession planning. In describing the importance of informal mentoring and GM approaches, Parfitt and Christie (2019), Hernández-Amorós, Martínez Ruiz, and Sauleda (2023) and Jones and Smith (2019) found that informal mentorship usually built up into GM approaches as they served as the basis for finding people with a common interest and matching them up with the aim of investing in each other. For instance, Hernández-Amorós et al. (2023) reported that first-year students who enjoyed GM developed a significantly higher degree of self-regulation and cooperation in academic activities than their counterparts without such support. Another recent study by Joo and Cruz (2024) on the quality of mentoring approaches found that female mentees benefitted greatly from informal mentoring and networking from their mentors than male mentees. In line with the foregoing arguments, the current study is hypothesising that;
Informal mentoring (IM) significantly relates to GM among female students in a developing economy.
Peer mentoring and group mentoring approaches
Peer mentoring within groups of students offers many different advantages including flexibility, inclusiveness, conversion and diversion of personal identities, interdependence, networking, skills development, personal growth and good friendships (Lim, MacLeod, Tkacik, & Dika, 2017; Leidenfrost et al., 2019; Larose & Duchesne, 2020; Douglas, 2024). Peer mentoring strategies have also been shown to have positive effects on mentored students’ academic performance (Venegas-Muggli, Barrientos, & Alvarez, 2023). In support, Holt and Fifer (2018) also found that the grades of mentored students significantly and positively correlated with the academic support they had received from their mentors. In another instance, Colvin and Ashman (2020) and Douglas (2024) asserted that students felt more comfortable and less intimidated talking to peer mentors than faculty. A study by Larose and Duchesne (2020) revealed that peer mentoring among groups of first-year students increased motivation, career decisions, college adjustment and academic persistence among the participants. Similar to peer mentoring, researchers have asserted that GM approaches had a positive impact on students in transition (Simon, 2021; Bouchard & Wong, 2024; Nabi, Walmsley, Mir, & Osman, 2024). Both Hannon (2022) and Rickards, Hattie, and Reid (2020) found that mentors experienced improved personal and organisational skills and gained a sense of reward as mentees were able to adjust socially and academically. To find out the extent to which peer mentoring and GM were related, the study formulated and tested the following hypotheses;
Peer mentoring (PM) significantly relates to GM among female students in a developing economy.
GM significantly mediates the relationship between formal mentoring (FM) and leadership skills development (LSD) among female students in a developing economy.
GM significantly mediates the relationship between IM and LSD among female students in developing economy.
GM significantly mediates the relationship between PM and LSD among female students developing economy.
The conceptual framework carved at the end of the conceptual review can be found in Figure 1.
The input factors are “P M,” “F M,” and “I M,” all of which converge to influence “G M.” Subsequently, “G M” acts as a direct cause for the final outcome, “L S D.”Conceptual framework of the study. Note: PM= peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development. Source: Figure by authors
The input factors are “P M,” “F M,” and “I M,” all of which converge to influence “G M.” Subsequently, “G M” acts as a direct cause for the final outcome, “L S D.”Conceptual framework of the study. Note: PM= peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development. Source: Figure by authors
Methodology
The study was guided by the positivist philosophical position and the quantitative approach. The explanatory research design from the quantitative approach was used in this investigation. Data were gathered from a sample of 286 out of 1,680 study participants who worked in Ghana’s public and private sectors. The study employed the stratified random sampling technique to ensure that all strata in the data were catered for and that all participants had equal chances to be selected. Data were gathered with a structured questionnaire on a 4-point scale, with 1 denoting strongly disagree, 2 disagree, 3 agree and 4 strongly agree. The scale was adjusted to fit the Ghanaian context. Because this study did not employ an uncertain or neutral because, it wanted each responder to take a stand by agreeing or disagreeing with the assertions, the 4-point Likert scale was used in place of the 5-point assessment. Furthermore, since unsure or neutral responses cannot fall into either of the two extremes (agree or disagree), they may also have an impact on the mean values or outcomes that are obtained. The structured questionnaire consisted of five sections that addressed the following topics: FM, IM, PM, GM, LSD and the demographic characteristics of respondents.
The measuring scale for measuring FM, IM and PM was adopted from Haggard, Dougherty, Turban, and Wilbanks (2011). GM was also measured with an adopted scale from Campbell (2008). Finally, leadership skill development was also measured with an adopted scale from Kouzes and Posner (2007). Detailed descriptions of the items used to measure the variables are indicated in Appendix A of the study for further analysis. The Cronbach alpha coefficient was used to assess the instrument’s validity and reliability, and every variable had a value over the 0.70 minimum coefficient level (Segbenya & Anokye, 2022). Every ethical factor was taken into account, including respondents’ free consent, anonymity, secrecy and the ability to withdraw even if the participant had started the process. PLS-SEM, or partial least squares-structural equation modelling, was employed to analyse the study’s guiding hypotheses.
Compared to more conventional statistical software like SPSS, PLS-SEM has a number of advantages for evaluating quantitative or survey data. PLS-SEM’s capacity to manage intricate models with numerous constructs and interactions at once is one of its main benefits. PLS-SEM enables researchers to model complex interactions between latent variables and their indicators, in contrast to SPSS, which largely concentrates on simpler statistical studies (Hair, Matthews, Matthews, & Sarstedt, 2017). Because it offers a more thorough picture of the data, this capacity is especially helpful for investigations requiring theoretical frameworks that incorporate several dependent and independent variables. In terms of sample size needs, PLS-SEM is noticeably more reliable. PLS-SEM can produce accurate results with smaller samples, but SPSS frequently needs higher sample sizes to produce dependable results in standard statistical analyses (Chin, 1998). This adaptability is especially helpful in domains like exploratory research and investigations involving niche communities where accessing massive datasets might be difficult. The application of PLS-SEM in a variety of research scenarios is increased by its capacity to generate accurate estimates even with little data.
PLS-SEM’s focus on prediction rather than just theory testing is another important benefit. Researchers may effectively estimate outcomes and evaluate the correlations between factors because of this predictive skill (Henseler, Ringle, & Sinkovics, 2016). On the other hand, SPSS does not naturally offer the same degree of predictive insight and instead concentrates on hypothesis testing. PLS-SEM is very useful for applied research since it allows researchers to assess how well the model predicts the data, which facilitates real-world applications in strategic planning and decision-making. In conclusion, PLS-SEM is a strong tool for studying quantitative data, providing clear benefits over conventional SPSS software due to its capacity to handle complex models, resilience to smaller sample sizes and emphasis on predicting results.
Results and findings
The presentation of the results and the findings of the study was done in two parts: demographic characteristics and findings for the main hypotheses of the study. The results for the demographic characteristics of the respondents focused on age, form or level, and leadership position, among others. The results as presented in Table 1 revealed that the majority of the respondents were between 17 and 19 years old (89.9%), were in form three/final year of their secondary school education (98.3%) and never held a leadership position before (60.8%) but do not currently hold leadership positions (72.7%).
Demographic characteristics of respondents
| Demographic characteristics | No | % |
|---|---|---|
| Age | ||
| Between 14–16 years | 20 | 7.0 |
| Between 17–19 years | 257 | 89.9 |
| Over 19 years | 9 | 3.1 |
| Total | 286 | 100.0 |
| Form | ||
| SHS1 | 2 | 0.7 |
| SHS2 | 3 | 1.0 |
| SHS3 | 281 | 98.3 |
| Total | 286 | 100.0 |
| Have you held any leadership positions in the school? | ||
| Yes | 174 | 60.8 |
| No | 112 | 39.2 |
| Total | 286 | 100.0 |
| Do you currently hold any leadership positions in the school? | ||
| Yes | 78 | 27.3 |
| No | 208 | 72.7 |
| Total | 286 | 100.0 |
| Have your parents held any position before | ||
| Yes | 230 | 80.4 |
| No | 56 | 19.6 |
| Total | 286 | 100.0 |
| Demographic characteristics | No | % |
|---|---|---|
| Age | ||
| Between 14–16 years | 20 | 7.0 |
| Between 17–19 years | 257 | 89.9 |
| Over 19 years | 9 | 3.1 |
| Total | 286 | 100.0 |
| Form | ||
| SHS1 | 2 | 0.7 |
| SHS2 | 3 | 1.0 |
| SHS3 | 281 | 98.3 |
| Total | 286 | 100.0 |
| Have you held any leadership positions in the school? | ||
| Yes | 174 | 60.8 |
| No | 112 | 39.2 |
| Total | 286 | 100.0 |
| Do you currently hold any leadership positions in the school? | ||
| Yes | 78 | 27.3 |
| No | 208 | 72.7 |
| Total | 286 | 100.0 |
| Have your parents held any position before | ||
| Yes | 230 | 80.4 |
| No | 56 | 19.6 |
| Total | 286 | 100.0 |
Presentation of the main results
The main results presented in this section to test the hypotheses of the study were preceded by a preliminary analysis. The first preliminary analysis conducted was to check for the construct reliability and validity, and the results are presented in Table 2. The threshold used for interpreting the results was based on Hair et al.'s (2017) minimum threshold of 0.70 for the first three indices (Cronbach’s alpha, rho_A, composite reliability) and 0.50 for the last indicator – average variance extracted (AVE). The results, as presented in Table 2, revealed that all five variables of the study loaded well and met the four indices used to measure the construct reliability and validity. Specifically, values ranging from 0.716 to 0.922 were recorded for Cronbach’s alpha; 0.717 to 0.923 were also recorded for rho_A indices and values between 0.841 and 0.935 for composite reliability. It is also clear that values ranging from 0.610 to 0.677 were also recorded for the last indicator –AVE. The results mean that the PLS-SEM model used for this study met the construct reliability and validity requirements, and the data could be used for further analysis.
Construct reliability and validity
| Cronbach’s alpha | rho_A | Composite reliability | Average variance extracted (AVE) | |
|---|---|---|---|---|
| FM | 0.840 | 0.844 | 0.886 | 0.610 |
| GM | 0.922 | 0.923 | 0.935 | 0.616 |
| IM | 0.880 | 0.885 | 0.913 | 0.677 |
| LSD | 0.716 | 0.717 | 0.841 | 0.638 |
| PM | 0.901 | 0.902 | 0.924 | 0.669 |
| Cronbach’s alpha | rho_A | Composite reliability | Average variance extracted (AVE) | |
|---|---|---|---|---|
| FM | 0.840 | 0.844 | 0.886 | 0.610 |
| GM | 0.922 | 0.923 | 0.935 | 0.616 |
| IM | 0.880 | 0.885 | 0.913 | 0.677 |
| LSD | 0.716 | 0.717 | 0.841 | 0.638 |
| PM | 0.901 | 0.902 | 0.924 | 0.669 |
Note(s): PM = peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development
The second preliminary analysis conducted was to check on the items loading for each variable of the study and the results are presented in Figure 2 of the study. The results mean that all items used in the study to measure each variable of the study loaded with values above the minimum threshold of 0.70 according to Segbenya, Senyametor, Aheto, Agormedah, Nkrumah, and Kaedebi-Donkor (2024) and Hair et al. (2017).
The path diagram shows four interconnected latent variables, represented by blue circles, and their corresponding observed indicators, represented by yellow rectangles. On the far left, there are three latent variables arranged vertically: “P M” (top), “F M” (middle), and “I M” (bottom). Each of these circles is connected to its respective set of indicators: “P M” is linked to “P M 25” through “P M 30,” with path coefficients of “0.777, 0.813, 0.846, 0.827, 0.826, and 0.817,” respectively. “F M” is linked to “F M 12,” “F M 13,” “F M 14,” “F M 15,” and “F M 9,” with path coefficients of “0.789, 0.765, 0.788, 0.832, and 0.728,” respectively. “I M” is linked to “I M 18” through “I M 22,” with path coefficients of “0.764, 0.816, 0.836, 0.878, and 0.816,” respectively. All three of these latent variables are connected by arrows pointing to a central latent variable, “G M.” The arrows from “P M,” “F M,” and “I M” to “G M” have path coefficients of “0.577, 0.165, and 0.127,” respectively. The “G M” circle displays a value of “0.638” within it. This central latent variable “G M” is also connected to its own set of indicators, arranged vertically on the right side of the circle, from “G M 25” down to “G M 42” through “G M 33,” “G M 34,” “G M 36,” “G M 37,” “G M 38,” “G M 39,” “G M 41,” and “G M 42.” The respective path coefficients between these latent variables and “G M” are “0.800, 0.753, 0.764, 0.782, 0.818, 0.816, 0.792, 0.791, and 0.747,” respectively. Finally, a single arrow extends from the “G M” circle to the last latent variable, “L S D,” which is positioned on the far right. This arrow has a path coefficient of “0.443.” The “L S D” circle is connected to its three indicators: “L S D 53,” “L S D 54,” and “L S D 55,” with their corresponding path coefficients being “0.809, 0.799, and 0.788,” respectively.Items loading for each variable of the study. Note: PM = peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development. Source: Figure by authors
The path diagram shows four interconnected latent variables, represented by blue circles, and their corresponding observed indicators, represented by yellow rectangles. On the far left, there are three latent variables arranged vertically: “P M” (top), “F M” (middle), and “I M” (bottom). Each of these circles is connected to its respective set of indicators: “P M” is linked to “P M 25” through “P M 30,” with path coefficients of “0.777, 0.813, 0.846, 0.827, 0.826, and 0.817,” respectively. “F M” is linked to “F M 12,” “F M 13,” “F M 14,” “F M 15,” and “F M 9,” with path coefficients of “0.789, 0.765, 0.788, 0.832, and 0.728,” respectively. “I M” is linked to “I M 18” through “I M 22,” with path coefficients of “0.764, 0.816, 0.836, 0.878, and 0.816,” respectively. All three of these latent variables are connected by arrows pointing to a central latent variable, “G M.” The arrows from “P M,” “F M,” and “I M” to “G M” have path coefficients of “0.577, 0.165, and 0.127,” respectively. The “G M” circle displays a value of “0.638” within it. This central latent variable “G M” is also connected to its own set of indicators, arranged vertically on the right side of the circle, from “G M 25” down to “G M 42” through “G M 33,” “G M 34,” “G M 36,” “G M 37,” “G M 38,” “G M 39,” “G M 41,” and “G M 42.” The respective path coefficients between these latent variables and “G M” are “0.800, 0.753, 0.764, 0.782, 0.818, 0.816, 0.792, 0.791, and 0.747,” respectively. Finally, a single arrow extends from the “G M” circle to the last latent variable, “L S D,” which is positioned on the far right. This arrow has a path coefficient of “0.443.” The “L S D” circle is connected to its three indicators: “L S D 53,” “L S D 54,” and “L S D 55,” with their corresponding path coefficients being “0.809, 0.799, and 0.788,” respectively.Items loading for each variable of the study. Note: PM = peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development. Source: Figure by authors
The third initial analysis conducted for this study was to check for the discriminant validity of the study and the results are presented in Table 3. Two main indicators were used for this analysis. These were the Fornell–Larcker criterion and the heterotrait-monotrait ratio (HTMT) with acceptable threshold values of not exceeding 0.850 according to the suggestion of Hair et al. (2017). The results, as can be seen from Table 3, revealed that all five variables of the study (FM, IM, GM, LSD and PM) all met the acceptable threshold since values recorded were all below the maximum threshold. The results mean that the PLS-SEM model met the discriminant validity criteria.
Discriminant validity
| FM | GM | IM | LSD | PM | |
|---|---|---|---|---|---|
| Fornell–Larcker criterion | |||||
| FM | 0.781 | ||||
| GM | 0.620 | 0.785 | |||
| IM | 0.668 | 0.675 | 0.823 | ||
| LSD | 0.303 | 0.443 | 0.319 | 0.799 | |
| PM | 0.641 | 0.779 | 0.759 | 0.354 | 0.818 |
| Heterotrait-monotrait ratio (HTMT) | |||||
| FM | |||||
| GM | 0.701 | ||||
| IM | 0.774 | 0.746 | |||
| LSD | 0.390 | 0.543 | 0.400 | ||
| PM | 0.736 | 0.853 | 0.850 | 0.437 | |
| FM | GM | IM | LSD | PM | |
|---|---|---|---|---|---|
| Fornell–Larcker criterion | |||||
| FM | 0.781 | ||||
| GM | 0.620 | 0.785 | |||
| IM | 0.668 | 0.675 | 0.823 | ||
| LSD | 0.303 | 0.443 | 0.319 | 0.799 | |
| PM | 0.641 | 0.779 | 0.759 | 0.354 | 0.818 |
| Heterotrait-monotrait ratio (HTMT) | |||||
| FM | |||||
| GM | 0.701 | ||||
| IM | 0.774 | 0.746 | |||
| LSD | 0.390 | 0.543 | 0.400 | ||
| PM | 0.736 | 0.853 | 0.850 | 0.437 | |
Note(s): PM = peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development
The last initial analysis conducted was to check for the presence of multicollinearity, and the inner VIF was used in this analysis; the results are presented in Table 4. The criterion used was a maximum threshold of 3.30. Values obtained as recorded in Table 4 ranged from 1.000 to 2.719, which were all below the maximum threshold of 3.30, suggesting that the PLS-SEM model used for this study did not experience the presence of multicollinearity. The values recorded for each variable of the study, therefore, uniquely measure what they intend to measure. The data, therefore, are qualified to be used for further inferential analysis, such as path relations, to test the hypotheses guiding this study.
Testing of the hypotheses of the study
The results for the hypotheses guiding this study are presented in Table 5. The results revealed that all seven hypotheses of the study were accepted since all these hypotheses assumed a significant relationship. Specifically, hypothesis one revealed that formal mentoring (FM) is significantly related to GM among female students in a developing economy at (β = 0.165, t = 2.415, p < 0.017). GM was also found to be significantly related to LSD among female students in a developing economy for hypothesis two at (β = 0.443, t = 7.546, p < 0.000). The study further found that GM was significantly influenced by IM among female students in a developing economy at (β = 0.127, t = 2.142, p < 0.043). The study again accepted hypothesis four since PM was found to have a significant influence on GM among female students in a developing economy at (β = 0.577, t = 10.199, p < 0.000).
Path coefficients
| Original sample (O) | Sample mean (M) | Standard deviation (STDEV) | T-statistics (|O/STDEV|) | P values | Confidence intervals | ||
|---|---|---|---|---|---|---|---|
| 2.5% | 97.5% | ||||||
| 1. FM → GM | 0.165 | 0.165 | 0.068 | 2.415 | 0.017 | 0.041 | 0.309 |
| 2. GM → LSD | 0.443 | 0.450 | 0.059 | 7.546 | 0.000 | 0.330 | 0.559 |
| 3. IM → GM | 0.127 | 0.130 | 0.059 | 2.142 | 0.043 | 0.005 | 0.237 |
| 4. PM → GM | 0.577 | 0.576 | 0.057 | 10.199 | 0.000 | 0.451 | 0.682 |
| Specific indirect effects | |||||||
| 5. FM → GM → LSD | 0.073 | 0.074 | 0.033 | 2.209 | 0.028 | 0.017 | 0.148 |
| 6. IM → GM → LSD | 0.056 | 0.058 | 0.028 | 2.031 | 0.043 | 0.002 | 0.114 |
| 7. PM → GM → LSD | 0.256 | 0.259 | 0.042 | 6.147 | 0.000 | 0.175 | 0.336 |
| Original sample (O) | Sample mean (M) | Standard deviation (STDEV) | T-statistics (|O/STDEV|) | P values | Confidence intervals | ||
|---|---|---|---|---|---|---|---|
| 2.5% | 97.5% | ||||||
| 1. FM → GM | 0.165 | 0.165 | 0.068 | 2.415 | 0.017 | 0.041 | 0.309 |
| 2. GM → LSD | 0.443 | 0.450 | 0.059 | 7.546 | 0.000 | 0.330 | 0.559 |
| 3. IM → GM | 0.127 | 0.130 | 0.059 | 2.142 | 0.043 | 0.005 | 0.237 |
| 4. PM → GM | 0.577 | 0.576 | 0.057 | 10.199 | 0.000 | 0.451 | 0.682 |
| Specific indirect effects | |||||||
| 5. FM → GM → LSD | 0.073 | 0.074 | 0.033 | 2.209 | 0.028 | 0.017 | 0.148 |
| 6. IM → GM → LSD | 0.056 | 0.058 | 0.028 | 2.031 | 0.043 | 0.002 | 0.114 |
| 7. PM → GM → LSD | 0.256 | 0.259 | 0.042 | 6.147 | 0.000 | 0.175 | 0.336 |
Note(s): PM = peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development
Apart from the direct relationship established for the first four hypotheses of the study, it is also clear that the remaining three indirect hypotheses testing the mediating power or potency of GM were all significant and accepted for this study. That is hypothesis five was accepted because GM significantly mediated the relationship between FM and LSD among female students in a developing economy (β = 0.073, t = 2.209, p < 0.028). GM again was found to have a significantly mediated relationship between IM and LSD among female students in developing hypothesis six at (β = 0.056, t = 2.031, p < 0.043). The last hypothesis (Hypothesis seven) was accepted since GM significantly mediated the relationship between PM and LSD among female students developing at (β = 0.256, t = 6.147, p < 0.000).
The pictorial depiction of the significant relationship established between the variables of the study is further presented in Figure 3. The results revealed that GM significantly mediated PM, FM, IM and LSD. The pictorial presentation also highlighted the contribution of the dependent variables and the dependent variable of the study.
The path diagram shows four interconnected latent variables, represented by blue circles, and their corresponding observed indicators, represented by yellow rectangles. On the far left, there are three latent variables arranged vertically: “P M” (top), “F M” (middle), and “I M” (bottom). Each of these circles is connected to its respective set of indicators: “P M” is linked to “P M 25” through “P M 30,” with path coefficients of “25.535, 31.169, 38.729, 39.113, 37.825, and 33.854,” respectively. “F M” is linked to “F M 12,” “F M 13,” “F M 14,” “F M 15,” and “F M 9,” with path coefficients of “24.344, 19.912, 18.550, 37.248, and 18.281,” respectively. “I M” is linked to “I M 18” through “I M 22,” with path coefficients of “23.326, 31.525, 38.439, 60.360, 60.360, and 37.198,” respectively. All three of these latent variables are connected by arrows pointing to a central latent variable, “G M.” The arrows from “P M,” “F M,” and “I M” to “G M” have path coefficients of “0.577 (0.000), 0.165 (0.017), and 0.127 (0.043),” respectively. This central latent variable “G M” is also connected to its own set of indicators, arranged vertically on the right side of the circle, from “G M 25” down to “G M 42” through “G M 33,” “G M 34,” “G M 36,” “G M 37,” “G M 38,” “G M 39,” “G M 41,” and “G M 42.” The respective path coefficients between these latent variables and “G M” are “29.746, 26.045, 23.601, 23.125, 37.522, 32.695, 30.227, 31.217, and 22.670,” respectively. Finally, a single arrow extends from the “G M” circle to the last latent variable, “L S D,” which is positioned on the far right. This arrow has a path coefficient of “0.443 (0.000).” The “L S D” circle is connected to its three indicators: “L S D 53,” “L S D 54,” and “L S D 55,” with their corresponding path coefficients being “23.121, 22.118, and 26.038,” respectively.”Pictorial presentation of path relations. Note: PM= peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development. Source: Figure by authors
The path diagram shows four interconnected latent variables, represented by blue circles, and their corresponding observed indicators, represented by yellow rectangles. On the far left, there are three latent variables arranged vertically: “P M” (top), “F M” (middle), and “I M” (bottom). Each of these circles is connected to its respective set of indicators: “P M” is linked to “P M 25” through “P M 30,” with path coefficients of “25.535, 31.169, 38.729, 39.113, 37.825, and 33.854,” respectively. “F M” is linked to “F M 12,” “F M 13,” “F M 14,” “F M 15,” and “F M 9,” with path coefficients of “24.344, 19.912, 18.550, 37.248, and 18.281,” respectively. “I M” is linked to “I M 18” through “I M 22,” with path coefficients of “23.326, 31.525, 38.439, 60.360, 60.360, and 37.198,” respectively. All three of these latent variables are connected by arrows pointing to a central latent variable, “G M.” The arrows from “P M,” “F M,” and “I M” to “G M” have path coefficients of “0.577 (0.000), 0.165 (0.017), and 0.127 (0.043),” respectively. This central latent variable “G M” is also connected to its own set of indicators, arranged vertically on the right side of the circle, from “G M 25” down to “G M 42” through “G M 33,” “G M 34,” “G M 36,” “G M 37,” “G M 38,” “G M 39,” “G M 41,” and “G M 42.” The respective path coefficients between these latent variables and “G M” are “29.746, 26.045, 23.601, 23.125, 37.522, 32.695, 30.227, 31.217, and 22.670,” respectively. Finally, a single arrow extends from the “G M” circle to the last latent variable, “L S D,” which is positioned on the far right. This arrow has a path coefficient of “0.443 (0.000).” The “L S D” circle is connected to its three indicators: “L S D 53,” “L S D 54,” and “L S D 55,” with their corresponding path coefficients being “23.121, 22.118, and 26.038,” respectively.”Pictorial presentation of path relations. Note: PM= peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development. Source: Figure by authors
The results for the overall contribution of the variables of the study, represented by R Square, in explaining the variance in the dependent variable of the study are presented in Table 6. The results revealed that the PLS-SEM explained about 64% of GM and 20% of LSD among female students in a developing economy. The results demonstrate that all the dimensions of mentoring considered in this study are very important and significant.
Overall contribution of all variables
| R-square | f Square | |||||||
|---|---|---|---|---|---|---|---|---|
| R-square | R-square adjusted | FM | GM | IM | LSD | PM | ||
| GM | 0.638 | 0.634 | FM | 0.038 | ||||
| LSD | 0.196 | 0.193 | GM | 0.244 | ||||
| IM | 0.016 | |||||||
| LSD | ||||||||
| PM | 0.359 | |||||||
| R-square | f Square | |||||||
|---|---|---|---|---|---|---|---|---|
| R-square | R-square adjusted | FM | GM | IM | LSD | PM | ||
| GM | 0.638 | 0.634 | FM | 0.038 | ||||
| LSD | 0.196 | 0.193 | GM | 0.244 | ||||
| IM | 0.016 | |||||||
| LSD | ||||||||
| PM | 0.359 | |||||||
Note(s): PM = peer mentoring, FM = formal mentoring, IM = informal mentoring, GM = group mentoring and LSD = leadership skills development
Discussion of the results
The findings of the first hypothesis of the study are that FM is significantly related to GM among female students in a developing economy. This meant that structured and official mentoring systems or approaches adopted by educational institutions for secondary schools could be enhanced by GM systems. That is, GM and formalised mentoring approaches can be used by mentoring organisations to scale up a mentoring experience to reach more people while keeping them in an organised manner (Joo & Cruz, 2024). This could be due to the number of students as compared to the number of mentors available. Students or mentees outnumbered mentors in the secondary schools, and the best approach, according to the findings of this study, was to adopt group mentoring within the formal structure of the school system to ensure that more mentees benefit from the mentoring services of one mentor. Thus, a percentage increase in efforts accorded formal mentoring among secondary students would lead to the same percentage increase in GM. The results corroborate the findings of Gorin et al. (2020), Fleischer (2021) and Joseph and Kuperminc (2021), who stipulated that the formal mentoring approach could be greatly enhanced by GM.
The findings for the second hypothesis that GM significantly relates to LSD, mean that the development of leadership skills among female mentees at the secondary school level could be influenced by GM in a developing economy. Confidence and self-awareness, communication and interpersonal skills and collaborative problem-solving of females could be enhanced at secondary schools through GM (Kaufman et al., 2022; Bonneywell, 2017). That is, female students were exposed to a range of viewpoints and concepts from their peers through GM. Female students also got the opportunity to be encouraged to exercise critical thinking, active listening and negotiation skills in this collaborative setting. These abilities are crucial for effective leadership (Flowers, 2024). Additionally, students strengthened their problem-solving skills and learnt how to make use of the group’s strengths when they collaborate to solve problems or take on challenges. The findings further meant that a collaborative and encouraging atmosphere among female mentees during GM could make them feel more confident in their skills and more inclined to assume leadership roles. The findings for this study were in agreement with the studies by Bonneywell (2017), Lacerenza et al. (2017), Reyes et al. (2019), Gadomska-Lila (2020) and Flowers (2024), and that GM influenced LSD among mentees.
Hypothesis three also established that IM was significantly related to GM among female students in a developing economy. The results mean that another perspective for enhancing GM among female secondary school students in a developing economy was through the use of IM (Bouchard & Wong, 2024; Nabi et al., 2024). That is, it was not enough to pay attention to only official and structured approaches established by school authorities for mentoring but also to pay attention to the more organic, spontaneous and unstructured forms of mentoring. GM can organically develop from IM, especially in educational, professional or community contexts (Deng et al., 2022; Wiggins, 2024). For example, depending on common interests, objectives or struggles, a more seasoned person may begin unofficially offering direction and support to a group of younger or less seasoned people. As the IM relationship develops, it may transform into a GM dynamic in which the mentor guides the group’s conversations and offers guidance. The findings of this study agreed with the findings of Bouchard and Wong (2024), Joo and Cruz (2024) and Nabi et al. (2024), who postulated that informal significantly related to GM among mentees.
The findings for the last direct hypothesis of the study that PM significantly related to GM among female students in a developing economy also meant that apart from FM and IFM approaches, one other important approach to mentoring used among female students in secondary school settings in a developing economy was PM. The results suggest that any percentage increase in effort attached to PM would result in the same percentage increase in GM, suggesting that both PM and GM could work together successfully among female students in developing economies. That implied that with the help of GM, PM could produce leadership development, peer support and accountability, collaborative learning and shared peer experience among mentees in secondary schools. The study was in tandem with earlier findings by Douglas (2024), Bouchard and Wong (2024) and Nabi et al. (2024) that PM is related to GM among mentoring.
The first indirect or mediation hypothesis also established that GM significantly mediated the relationship between FM and LSD among female students for hypothesis five. The results meant that the relationship between FM and LSD was significantly enhanced by GM. The two variables thus shared their potency or predictive power with GM in a developing economy. Thus, if leadership at the secondary school level desires to improve the relationship between FM and LSD, then attention should be paid to GM. This could be due to the number of mentees needing assistance in relation to the few experienced mentors in the secondary school. Though formal mentoring programmes might offer an organised framework for mentoring, they might not adequately address the particular demands and difficulties faced by female students in gaining leadership experience. By providing a bridge, GM enabled female students to participate in learning and cooperative problem-solving in a group environment, all while receiving the direction and support of a mentor. Combining GM with formal mentoring could result in a more comprehensive and successful strategy for developing leadership abilities. Though the findings of this study confirmed other studies by Williams and Shinkai (2022) and Joo and Cruz (2024) that FM was significantly related to LSD among female students, this study further added to the existing knowledge by establishing that GM mediated the relationship between FM and LSD.
GM was also found to have significantly mediated the relationship between IM and LSD among female students in developing.
Economy for hypothesis six. The results implied that GM was not only related to FM and LSD as established for hypothesis five, but it also had the potency to influence the relationship between IM and LSD among female students. Thus, apart from educational authorities paying attention to formal mentoring, this study has again proven that IM was also important, but needed to be associated with GM to be very effective among female students considered for this study. The findings of this study, though, corroborate earlier findings of Hannon (2022) and Rickards et al. (2020) that IM related well with LSD, and further added to existing literature that GM mediated the relationship between IM and LSD.
Finally, the last hypothesis (hypothesis seven) also established that GM significantly mediated the relationship between PM and LSD among female students. The findings meant that apart from the mediating potency of GM on FM and IM with LSD, GM again shared its potency with PM and LSD among female students in a developing economy. This was an indication that educational authorities seeking to improve the relationship between PM and leadership skills among secondary students could achieve that through deploying GM. Most previous literature (Larose & Duchesne, 2020; Colvin & Ashman, 2020) only established a relationship between PM and leadership skills without considering the mediating role of GM. Thus, the findings of this present study further contributed to the existing knowledge on the subject that PM and LSD among female students can be enhanced through GM.
Practical and theoretical implications
The findings of this study had several implications for theory and practice. In practice, it is clear that mentoring dimensions such as peer, formal and informal mentoring are very important in secondary schools in terms of developing leadership skills among female students. However, due to the fact that the number of student mentees at both public and private secondary schools in the midst of free senior high school far exceeds the available mentors, this study has found that the best solution is to introduce and pay attention to GM. GM, therefore, allowed mentors to handle several students at the same time, resulting in saving time and resources. Thus, the first practical implication is that school authorities responsible for assigning mentees to mentors should be very knowledgeable about the aspirations of these mentees and be able to assign mentees of similar characteristics and aspirations to respective mentors to enhance the GM approach. Another practical implication of the findings of the study is the need for space and facilities to accommodate several mentees at a time for a mentor to handle. The findings also suggest that the mentors should be knowledgeable about GM dynamics and skills in order to be successful with the GM approach.
The SLT used in this study proposes that people pick up behaviours from watching other people as well as from their own experiences. The four essential elements of SLT, which are motivation, attention, retention and replication, are also upheld in this study. However, this study further found and added to the theory that gender dimensions omitted in earlier studies are important issues for subsequent consideration of this theory. That is, females learn leadership skills through mentoring. Though there are several dimensions of mentoring, such as peer, formal and informal mentoring approaches, this study found that they all shared their potency through GM approaches.
Conclusion and recommendations
This study investigated the mediating role of the GM approach on the relationship between mentoring dimensions and LSD among female secondary school students in a developing economy. The study found that GM is significantly related to formal mentoring, IM, PM and LSD. In terms of the mediating power of GM, it could be concluded that GM significantly mediated the relationship between formal mentoring and LSD, IM and LSD and PM and LSD among female secondary school students in a developing economy.
The conclusion demanded specific action on the part of mentors and mentees in secondary schools in order to enhance LSD among female students. It was therefore recommended that secondary school authorities should provide orientation and training for mentors in secondary schools in the area of GM. This was needed to help equip mentors with the appropriate skills to handle larger groups of student mentees. This would also enable mentors to handle more mentees at the same time to ensure effective use of time. It was also recommended that secondary school authorities should provide adequate resources and facilities to mentors in terms of office space or Internet facilities to help mentors handle large groups of mentees at a time. Without adequate facilities, it would be difficult for the mentors to conduct GM. It was further recommended that PM and informal and formal mentoring should also be encouraged among mentees and mentors in secondary schools. These approaches could be the starting point and the learning point, which could be scaled up to the GM approach in the secondary schools in a developing economy.
Limitations and suggestions for further studies
The findings of this study were limited to only female secondary school students in a developing economy, and further studies could consider the same topic across several developing economies or between developed and developing economies. The findings of this study also highlighted the overall contributions of the dimensions of mentoring to GM as 64%. This meant that there was about 36% variance not explained by the model used for this study hence, further studies could consider other variables that best explained the GM among secondary school students. GM was found to explain about a 20% variance in developing leadership skills among female secondary school students. Further studies could therefore consider other factors or variables that could help explain the remaining 80% variance in LSD among female students.
Ethical approval
The Department of Education, College of Distance Education, granted ethical clearance to the first author of this paper on behalf of the Institutional Review Board of the University of Cape Coast, Ghana.
Informed consent
This study made use of the Google Form version of the questionnaire that was administered to respondents. Respondents owned their handsets or mobile phones and had the freedom to decide to participate in the study. Thus, all respondents who took part in this study read the purpose of the study and voluntarily consented to participate in this study.
Authors’ contribution
All authors contributed equally in terms of conceptualisation, review of literature, methodology, data analysis, writing and review of the final write-up.
The supplementary material for this article can be found online.

