Azman Ismail, Michael Kho Khian Jui. (2014). The Role of Mentoring Program in
Enhancing Mentees’ Academic Performance. Journal of Education and Learning.
Vol.8 (1) pp. 13-22.

The Role of Mentoring Program in Enhancing Mentees’
Academic Performance
Azman Ismail*
Universiti Kebangsaan Malaysia

Michael Kho Khian Jui **
Universiti Kebangsaan Malaysia

Abstract
According to institutions of higher learning literature, mentoring program has two important features:
communication and support. The ability of mentors to appropriately implement comfortable communication and
provide adequate support may ehance positive mentee outcomes, especially academic performance. Although the
nature of this relationship is crucial, little is known about the role of mentoring program as an important
predictor of mentees’ academic performance in the higher education mentoring research literature. Therefore,
this study was conducted to measure the relationship between mentoring program and mentees’ academic
performance using self-administered questionnaires gathered from undergraduate students in Malaysian
institutions of higher learning in Sarawak. The results of SmartPLS path model showed two important outcomes:
firstly, communication positively and significantly correlated with academic performance. Secondly, support
positively and significantly correlated with academic performance. The result demonstrates that mentoring
program does act as an important predictor of mentees’ academic performance in the organizational sample.
Thus, discussion, implications and conclusion are elaborated.
Keywords: Communication, Support, Academic Performance

*

Azman Ismail, Faculty of Economics & Management, Universiti Kebangsaan Malaysia. 43600 Bangi, Selangor
Darul Ehsan. MALAYSIA.
E-mail: azisma08@gmail.com / azisma12@ukm.my

**

Michael Kho Khian Jui, Faculty of Economics & Management, Universiti Kebangsaan Malaysia. 43600 Bangi,
Selangor Darul Ehsan. MALAYSIA.
E-mail: mic6699@gmail.com

Introduction
In an ancient Greek literature, mentoring is first highlighted in the epic story of ‘The Odyssey’
written by Homer. In this story, Odysseus tells his loyal and experienced friend, namely, Mentor (a
person who has great wisdom and trustworthy) to teach his son, namely, Telemachus (a mentee or
protégé who has less experience) about the tips for handling challenging lifestyles before he goes to the
Trojan War (Edlind & Haensly, 1985; Ismail et al., 2005, 2006; Merriam, 1993). Based on this classical
story, mentoring is often related to as an important field of education (Little et al., 2010; Johnson et al.,
1991) and/or counseling (Gregson, 1994; Zuraidah et al., 2004) whereby mentors are the elderly whom
have wisdom, experiences and can be trusted to educate young men who have little experience and
knowledge (Little et al., 2010; Johnson et al., 1991; Russell & Adams, 1997; Wanguri, 1996).
Hence, the traditional mentoring concept has been given new interpretations by contemporary
educationists, social psycologists and management scholars in order to suit it with the current
organizational development and challenges (Dennison, 2000; Ismail et al., 2005, 2006; Ismail &
Ridzwan, 2012; Oliver & Aggleton, 2002).
In today organizations, mentoring is often seen as a learning method where it encourages
comfortable relationship between mentors (i.e., knowledgeable and experienced person) and mentee
(i.e., less knowledgeable and experienced person) as an instrument to develop group and/or individuals’
potentials in carrying out particular duties and responsibilities, familiarize with new techniques, and
care for all aspects of mentees (Cummings & Worley, 2009; Johnson et al., 1991; Long, 2002; Noe et
al., 2002). There is no one best mentoring program model to fit all organizations, but they are designed
and implemented according to the uniqueness of organizational contexts in terms of beliefs, policy,
orientations, stresses, strengths and weaknesses (Irving et al., 2003; Ismail et al., 2005, 2006; Santos &
Reigadas, 2002, 2005). These factors have affected organizations to design and administer the various
types of mentoring program, especially informal relationship (e.g., specific demands, spontaneous and
adhoc) and/or formal relationship (e.g., structured and coordinated relationship between mentor and
mentee, using standard norms, continuously action plans, time frame, and particular objectives). In
organizations, formal and informal mentoring programs are viewed as equally important, but informal
mentoring programs are often implemented to complement and strengthen formal mentoring programs
in order to achieve organizational strategies and goals (Friday & Friday, 2002; Hansford & Ehrich,
2006; Hansford et al., 2003: Ismail et al., 2005, 2006).
A review of current higher education student development program literature highlights that
effective mentoring programs have two salient practices, i.e., communication and support (Bernier et
al., 2005; Ismail & Ridzwan, 2012; Tennenbaum et al., 2001). In the context of university mentoring
program, communication is generally defined as mentors openly delivering information about the
procedures, content, tasks and objectives of the mentoring programs, conducting discussions about tasks
that should be learned, giving detailed explanations about the benefits of attending mentoring programs
and providing performance feedback (Allen et al., 2005; Fox et al., 2010; Ismail et al., 2005, 2006;
Santos & Reigadas, 2005; Stewart & Knowles, 2003). Conversely, support is broadly defined as
mentors provide emotional support (e.g., acquire new knowledge, skills, and attitudes, and guide them
to properly apply in daily life) and instrumental support (e.g., assist mentees to adapt campus
environments) at varying times to mentees (Allen & Finkelstein, 2003; Davis, 2007; Fox et al., 2010;
Stewart & Knowles, 2003; Zuraidah et al., 2004).
Surprisingly, recent studies in university/faculty mentoring programs reveal that the ability of
mentors to appropriately implement such mentoring characteristics may have a significant impact on
positive mentee outcomes, especially academic performances (Bernier et al., 2005; Tennenbaum et al.,
2001). In an institution of higher learning context, academic performance is usually evaluated by the
students’ persistence rates, graduation rates, and grade-point average (Granger, 1995; Levin & Levin,
1991; Santos & Reigadas, 2005). Within a mentoring program model, many scholars think that
communication, support and academic performance are distinct, but strongly interrelated constructs. For
example, the ability of mentors to properly implement comfortable communication and provide
adequate support have been essential factors that may enhance positive mentee outcomes, especially
academic performance (Bernier et al., 2005; Tennenbaum et al., 2001).
The nature of this relationship is interesting, but not much is known the role of mentoring
program as an important predictor of mentees’ academic performance in the higher education mentoring
program research literature (Allen & Finkelstein, 2003; Bernier et al., 2005; Ismail et al., 2005, 2006;
Ismail & Ridzwan, 2012). Many scholars reveal that this situation is due to many previous studies have
much emphasized on the internal properties of mentoring program, employed a simple survey method to
explains different respondent perceptions toward the implementation of mentoring programs and used a
simple correlation analysis to measure the strength of association between mentoring program and
mentees’ academic performance. The findings of these studies have neglected to quantify the effect size

14

The Role of Mentoring Program in Enhancing Mentees’ Academic Performance

of mentoring program as an important predicting variable in the mentoring program research literature.
Consequently, it has not provided adequate information to be used as useful guidelines by practitioners
in formulating strategic action plans to improve the design and management of mentoring programs in
learning organizations (Bernier et al., 2005; Davis, 2007; Ismail & Ridzwan, 2012; Tennenbaum et al.,
2001). Therefore, it motivates the researchers to fill in the gap of literature by measuring the
relationship between mentoring program practices and academic performance.

Literature Review
Relationship between Mentoring Program and Academic Performance
Several recent studies using a direct effects model to discover mentoring activities based on
different samples like perceptions of 189 students in 9 departments at the University of California in
Santa Cruz (Tennenbaum et al., 2001), perceptions of 110 students in Canadian colleges (Bernier et al.,
2005), and 127 students at a defence based university in Malaysia (Ismail & Ridzwan, 2012). These
studies proved that the ability of mentors to properly implement comfortable communication and
provide adequate support in formal and/or informal mentoring activities had enhanced mentees positive
outcomes, especially academic performance (Bernier et al., 2005; Ismail & Ridzwan, 2012;
Tennenbaum et al., 2001).
The empirical studies support the notion of adult learning theories. For example, Chickering’s
(1969) vector theory of identity development highlights seven important vectors to develop yound adult
identities: developing competence, managing emotions, becoming autonomous, developing
interpersonal relationships, establishing identity, developing purpose, and developing integrity. Besides
that, Levinson’s (1978) early adult transition model posits that an individual’s life structure would face
critical situations when he/she goes through the transformation process from childhood into adulthood.
Application of these theories in institutions of higher learning shows that the essence of mentoring
program is to enhance positive young adults identities and life styles. For example, the ability of
mentors to properly implement comfortable communication and provide adequate support in formal
and/or informal mentoring activities may lead to an enhanced positive mentee outcomes, especially
academic performance (Bernier et al., 2005; Ismail & Ridzwan, 2012; Tennenbaum et al., 2001).
Conceptual Framework and Research Hypothesis
The literature has been used as a foundation to establish the conceptual framework for this
study as shown in Figure 1.
Independent Variable
(Mentoring Program)

Dependent Variable

Communication
Academic Performance
Support

Figure 1. Conceptual Framework
Based on the conceptual framework, it can be hypothesized that:
H1: Communication positively related to academic performance
H2: Support positively related to academic performance

Methodology
Research Design
This study used a cross-sectional research design where it allowed the researchers to integrate
the mentoring program literature, the pilot study and the actual study as a main procedure to gather data
for this study. Using such methods may gather accurate data, decrease bias and increase quality of data
being collected (Sekaran & Bougie, 2010; Zikmund, 2000). The location of this study is Malaysian
institutions of higher learning in Sarawak. For confidential reasons, the name of the organizations used
is kept anonymous. At the initial stage of data collection, the survey questionnaires were drafted based
on the information gathered from the mentoring program literature. After that, the pilot study was

Ismail. A, Khian Jui, M.K. (2013). Journal of Education and Learning. Vol.8 (1) pp. 13-22.

15

conducted involving 5 senior year students (2nd year and above) in public institutions and 5 senior year
students (2nd year and above) in private institutions to verify that all questions were importance,
relevance, clear and suitable for an actual study. Hence, a back translation technique was employed to
translate the survey questionnaires into English and Malay languages in order to increase the validity
and ensure the reliability of research findings (Sekaran & Bougie, 2010; Zikmund, 2000).
Measures
The survey questionnaire used in this study had three sections. Firstly, communication was
measured using 3 items that were adapted from mentoring communication system literature (Foxon,
1993; Sullivan, 2000; Yamnill & McLean, 2001; Young & Cates, 2005). The item used to measure the
construct were the importance of mentoring program, approachable and knowledge sharing. Secondly,
support was measured using 5 items that were adapted from mentoring support system literature (Tsai &
Tai, 2003; Chiaburu & Takleab, 2005; Langhout et al., 2004; Rayle et al., 2006; Vieno et al., 2007). The
items used to measure the construct were interpersonal communication skills, giving suggestions, praise
mentee performance in study, understanding the implications of actions taken, and listening. Thirdly,
academic performance was measured using 4 items that were adapted from undergraduate student
performance literature (Campbell & Campbell, 1997; Irving et al., 2003; Rayle et al., 2006). The items
used to measure the construct were able to achieve CGPA, able to identify effective study methods, and
able to improve answering skills in tests/exams. All items used in the questionnaires were measured
using a 7-item Likert scale ranging from “strongly disagree/dissatisfied” (1) to “strongly
agree/satisfied” (7). Demographic variables were used as controlling variables because this study
focused on student attitudes.
Sample
The unit of analysis for this study is undergraduate students in Malaysian institutions of higher
learning in Sarawak, Borneo. The researchers had obtained an official approval to conduct the study
from the management of the organizations and also received advices from them about the rules for
conducting the survey in the organizations. Considering the constraints of the organization rule, as well
as the duration of study and finance, the researchers had distributed 250 survey questionnaires using a
convenient sampling technique to undergraduate students in the public and private institutions of higher
learning. This sampling technique was chosen because the management of the organizations had not
given the list of undergraduate students and this situation did not allow the researchers to randomly
select respondents for this study. From the survey questionnaires distributed, 196 usable questionnaires
from the institutions of higher learning were returned to the researchers, yielding 78.4 percent of the
response rate. The survey questionnaires were answered by participants based on their consents and on
voluntarily basis. The number of this sample exceeds the minimum sample of 30 participants as
required by probability sampling technique, showing that it may be analyzed using inferential statistics
(Sekaran & Bougie, 2010; Zikmund, 2000).
Data Analysis
The SmartPLS 2.0 was employed to assess the validity and reliability of the instrument and
thus test the research hypotheses (Henseler et al., 2009; Riggle et al., 2009). The main advantage of
using this method may deliver latent variable scores, avoid small sample size problems, estimate every
complex models with many latent and manifest variables, hassle stringent assumptions about the
distribution of variables and error terms, and handle both reflective and formative measurement models
(Henseler et al., 2009; Riggle et al., 2009). The SmartPLS path model was employed to assess the
magnitude and nature of the relationship between many independent variables and one or more
dependent variables in the structural model using standardized beta (β) and t statistics. The value of R2
is used as an indicator of the overall predictive strength of the model. The value of R2 are considered as
follows; 0.19 (weak), 0.33 (moderate) and 0.67 (substantial) (Chin, 1998; Henseler et al., 2009). Thus,
a global fit measure is conducted to validate the adequacy of PLS path model globally based on Wetzels
et al.’s (2009) global fit measure. If the results of testing hypothesized model exceed the cut-off value of
0.36 for large effect sizes of R², showing that it adequately support the PLS path model globally
(Wetzels et al., 2009).

Results
Sample Profile
Table 1 shows the respondents’ characteristics. The majority of the respondents were female
(70.9 percent), their ages vary from 22 to 24 years (70.4 percent), the highest education level amongst

16

The Role of Mentoring Program in Enhancing Mentees’ Academic Performance

the respondents were STPM holders (51.0 percent), (68.9 percent) comprises of third year students
being the majority in the respondent group, students achieving CGPA between 3.01 to 3.50 also being
the majority amongst the respondents consists of (48.5 percent), and students who study in a public
institutions of higher learning consists of (85.7 percent).
Table 1. Respondents’ Characteristics (n=196)
Respondents’ Profile

Sub-Profile

Percentage

Gender

Male
Female
19 to 21 years old
22 to 24 years old
25 to 27 years old

29.1
70.9
25.0
70.4
4.6

SPM
STPM
Diploma
Matriculation
Second Year
Third Year
Fourth Year
Fifth Year
CGPA 2.01-2.50
CGPA 2.51-3.00
CGPA 3.01-3.50
CGPA 3.51-4.00
Public Institutions of Higher Learning
Private Institutions of Higher Learning

6.1
51.0
10.8
32.1
6.1
68.9
24.5
0.5
5.6
34.7
48.5
11.2
85.7
14.3

Age

The Highest Educational Level

Current Year of Study

Academic Achievement

Institution
Note:

SPM/MCE : Sijil Pelajaran Malaysia/ Malaysia Certificate of Education
STPM
: Sijil Tinggi Pelajaran Malaysia/ Higher School Certificate

Model Measurement
The confirmatory factor analysis was employed to assess the psychometric of survey
questionnaire data. Table 2 shows the results of convergent and discriminant validity analyses. All
constructs had the values of average variance extracted (AVE) larger than 0.5, indicating that they met
the acceptable standard of convergent validity (Henseler et al., 2009). Besides that, all constructs had
the values of AVE square root in diagonal were greater than the squared correlation with other
constructs in off diagonal, showing that all constructs met the acceptable standard of discriminant
validity (Henseler et al., 2009; Yang, 2009).
Table 2. The Results of Convergent and Discriminant Validity Analyses
Variable

AVE

Communication

Support

Communication
Support
Academic Performance

0.725
0.741
0.779

.851
0.418
0.472

.861
0.437

Academic
Performance

.883

Table 3 shows the factor loadings and cross loadings for different constructs. The correlation
between items and factors had higher loadings than other items in the different constructs, as well as the
loadings of variables were greater than 0.7 in their own constructs in the model are considered adequate
(Henseler et al., 2009). In sum, the validity of measurement model met the criteria.

Ismail. A, Khian Jui, M.K. (2013). Journal of Education and Learning. Vol.8 (1) pp. 13-22.

17

Table 3. The Results of Factor Loadings and Cross Loadings for Different Construct
Construct/ Item
Communication
Objective
Moral values
Critical thinking
Support
Motivation
Listen to suggestions
Praise
Help
Listen to problems
Academic Performance
Cumulative grade point average
Effective study
Skills

Communication

Support

Academic Performance

0.836673
0.897438
0.818922

0.387340
0.393681
0.287202

0.364466
0.439693
0.396970

0.405739
0.340172
0.339573
0.327694
0.384191

0.841673
0.842116
0.875203
0.868722
0.875777

0.365410
0.355063
0.389806
0.394228
0.373948

0.342061
0.458355
0.436621

0.329364
0.435010
0.380145

0.847694
0.914396
0.883913

Table 4 shows the results of reliability analysis for the instrument. The values of composite
reliability and Cronbach’s Alpha were greater than 0.8, indicating that the instrument used in this study
had high internal consistency (Henseler et al., 2009; Nunally & Benstein, 1994). These statistical
analyses confirmed that the measurement scales met the acceptable standard of validity and reliability
analyses as shown in Table 2.
Table 4. Composite Reliability and Cronbach’s Alpha
Construct
Composite Reliability
Cronbach Alpha
Communication
0.888
0.810
Support
0.935
0.913
Academic Performance
0.913
0.859

Analysis of Constructs
Table 5 shows that the mean values for the variables are between 51.1 and 5.3, showing that
the levels of communication, support and academic performance are ranging from high (4) to highest
level (7). The correlation coefficients for the relationship between the independent variable (i.e.,
communication and support) and the dependent variable (i.e., academic performance) are less than 0.90,
showing the data are not affected by serious collinearity problem (Hair et al, 2006).
Table 5. Pearson Correlation Analysis and Descriptive Statistics
Mean
Standard
Pearson Correlation analysis
Deviation
(r)
1
2
4
1.
Communication
5.3
.92
1
2.
Support
5.1
1.17
.42**
1
3.
Academic Performance
5.3
.91
.47**
.43**
1
Note: Significant at **p<0.01
Reliability Estimation is Shown in a Diagonal
Variable

Outcomes of Testing Hypotheses 1 and 2
Figure 2 shows the outcomes of SmartPLS path model for testing the direct effects model. In
terms of exploratory of the model, the inclusion of communication and support in the analysis had
explained 76 percent of the variance in dependent variable. Specifically, the results of testing hypothesis
highlighted two important findings: first, communication significantly correlated with academic
performance (β=0.35; t=4.396), therefore H1 was supported. Second, support significantly correlated
with academic performance (β=0.29; t=3.852), therefore H2 was supported. In sum, the result confirms
that mentoring program does act as an important determinant of mentees’ academic performance in the
organizational sample.

18

The Role of Mentoring Program in Enhancing Mentees’ Academic Performance

Independent Variable
(Mentoring Program)

Dependent Variable
R Square=0.76
H1 (Β=0.35; t=4.396)

Communication

H2 (Β=0.29; t=3.852)

Support

Academic Performance

Note: Significant at t >1.96
Figure 3. The Outcomes of SmartPLS Path Model
In order to determine a global fit PLS path model, we carried out a global fit measure (GoF)
based on Wetzels et al.’s (2009) guideline as follows: GoF=SQRT{MEAN (Communality of
Endogenous) x MEAN (R²)}=0.756, signifying that it exceeds the cut-off value of 0.36 for large effect
sizes of R². This result confirms that the PLS path model has better explaining power in comparison
with the baseline values (GoF small=0.1, GoF medium=0.25, GoF large=0.36). It also provides strong
support to validate the PLS model globally (Wetzel et al., 2009).

Discussion and Implications
he findings of this study confirm that mentoring program does act as an important predictor of
mentees’ academic performance in the studied organizations. In the context of this study, mentors have
appropriately plan and implement mentoring activities based on the organizational policies and
procedures. Majority respondents perceived that comfortable communication, and material and moral
support are actively implemented in formal and/or informal mentoring activities. As a result, it may lead
to enhanced mentees’ academic performance in the higher institutions.
This study presents three major implications: theoretical contribution, robustness of research
methodology, and practical contribution. In terms of theoretical contribution, the results of this study
highlight that communication and support have beenn important predictors of mentees’ academic
performance. This result is consistent with studies by Tennenbaum et al. (2001), Bernier et al. (2005),
and Ismail and Ridzwan (2012).
With respect to the robustness of research methodology, the survey
questionnaires used in this study have met the acceptable standards of validity and reliability analyses.
This may lead to the production of valid and reliable findings. In regards with practical contributions,
the findings of this study may be used to improve the design and management of mentoring programs in
organizations. In order to achieve this objective, management needs to give more attention on
improving the following aspects: firstly, update training content and methods for mentors to in order to
improve their competencies in teaching, counseling and guiding students who have different ability
levels. Secondly, form mentoring groups according to students’ academic achievement in order to ease
mentors fulfilling their needs and expectations. Thirdly, mentors who have high teaching loads and
active in research, but can show high commitment in improving student studies need to be given a high
priority in getting better promotions. Fourthly, plan various kinds of learning activities in order to attract
students who have different interests and capabilities to actively involve in mentoring programs. Fifthly,
students who have actively participated in mentoring activities and show improvement in academic
performance need to be given better recognitions. If these suggestions are heavily considered this may
motivate undergraduate students to enhance their academic performance.

Conclusion
The study developed a conceptual framework based on the higher education mentoring
program research literature. The confirmatory factor analysis confirmed that the instrument used in this
study met the acceptable standards of validity and reliability analyses. Thus, the results of SmartPLS
path model showed that mentoring program does act as an important predictor of mentees’ academic
performance in the organizational sample. This result has also supported and extended mentoring
program research literature mostly published in Western countries. Therefore, current research and
practice within mentoring programs need to consider communication and support as crucial elements in
the higher education student development program. This study further suggests that the capability of
mentors to properly practice comfortable communication and provide adequate support will be essential
factors that may enhance subsequent positive mentee outcomes (e.g., self-efficacy, satisfaction,

Ismail. A, Khian Jui, M.K. (2013). Journal of Education and Learning. Vol.8 (1) pp. 13-22.

19

commitment, career, leadership skills and ethics). Thus, these positive outcomes may lead to maintained
and supported the higher learning institutions’ strategis and goals.
The conclusions drawn from this study should consider the following limitations. First, a crosssectional research design used to gather data at one time within the period of study might not capture
the causal connections between variables of interest. Second, this study does not specify the relationship
between specific indicators for the independent variable and dependent variable. Third, the outcomes of
SmartPLS path model have only focused on the level of performance variation explained by the
regression equations, but there are still a number of unexplained factors that affect the causal
relationship among variables and their relative explanatory power. Finally, the sample for this study was
taken from one institution of higher learning that allowed the researchers to gather data via survey
questionnaires. These limitations may decrease the ability to generalize the results of this study to other
organizational settings.
The conceptual and methodological limitations of this study should be considered when
designing future research. First, several organizational and personal characteristics should be further
explored, as this may provide meaningful perspectives for understanding how individual similarities and
differences influence the mentoring program within an organization. Second, other research designs
(e.g., longitudinal studies) should be used to collect data and describe the patterns of change and the
direction and magnitude of causal relationships between variables of interest (Sekaran & Bougie, 2010;
Zikmund, 2000). Third, to fully understand the effect of mentoring program on mentee attitudes and
behaviors, more organizations need to be used in future study. Fourth, other specific theoretical
constructs of mentoring program, such as learning abilities, decision making, and assignment need to be
considered because they have widely been recognized as an important link between mentoring
relationship and many aspects of individual attitudes and behavior (Davis, 2007; Dutton, 2003). Finally,
other outcomes of mentee like self-efficacy, transfer of knowledge, skills and ability, positive change,
and career help should be considered because they are given more attention in mentoring program
research literature (Fox et al., 2010; Hansford & Ehrich, 2006; Ismail et al., 2006; Ismail & Ridzwan,
2012). The importance of these issues needs to be further explained in future studies.

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The Role of Mentoring Program in Enhancing Mentees’ Academic Performance