Prime Journal of Business Administration and Management (BAM)
ISSN: 2251-1261. Vol. 3(8), pp. 1140-1148, August 31st, 2013
www.primejournal.org/BAM
© Prime Journals
Full Length Research Paper
Academic Workload - Does it affect talent management
in public universities in Kenya?
Alice W. Kamau, Roselyn W. Gakure, and Anthony G. Waititu
Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi CBD Centre, Box 62000 00200, Nairobi,
Kenya.
th
Accepted 8 August, 2013
In spite of higher institutions of learning being regarded as producers of knowledge (Lynch, 2007)
disseminators of knowledge and stimulators of intellectual life (Oketch, 2003) they do not value talent
management as the private sectors (Riccio, 2010). Kenyan universities like many other universities have a slow
pace or lack talent management as noted by (Ngome, 2007). This paper is anchored on a study on factors
affecting talent management in the public universities in Kenya. On this paper, academic workload is focused
on as one of the factors. To achieve this objective, a sample of 251 was selected using stratified random
sampling from public universities in Kenya. Data was analysed using SPSS to get statistical values to
determine the relationship. Q-Q plot, Factor analysis, Bartlett’s test, Cronbach alpha coefficient, and regression
analysis were carried out. The analysis revealed a positive relationship between academic workload and talent
management. The study recommends cut-offs on part-time jobs in addition to the normal workload to ensure
quality is enhanced. The study also recommends work-life balance to increase motivation and talent
management in higher learning institutions.
Key words: Recruitment, training and development, motivation, succession plan, employees retention.
INTRODUCTION
Certified Institute of Personnel Development (CIPD,
2010) defined talent management as the systematic
attraction, identification, development, engagement or
retention and deployment of those individuals who are of
particular value to an organization, either in view of their
high potential for the future or because they are fulfilling
business or operation critical roles. Talent consists of
those individuals who can make a difference to
organizational performance, either through their
immediate contribution or in the longer-term by
demonstrating the highest levels of potential (CIPD,
2010). The concept of talent management was derived
from World War II (Cappelli, 2008) and its strategic
importance was realized when McKinsey Consultants
Group conducted a study on War for talent in late 1990‟s
as cited by Scullion and Collings (2010). The war for
talent was prompted by the realization that talent
shortages were increasingly becoming one of the biggest
human resource concerns for multinational corporations
(Makela and Bjorkman, 2010).
The specific management of talent has been widely
seen as a solution for the HR challenges in today‟s labour
market (Lewis and Heckman, 2006; Ritz and Sinelli,
2010; Schuler et al., 2010). According to Fulmer and
Conger (2004) the purpose of talent management is to
provide a deep supply of valuable employees
continuously throughout the institutions. Collings and
Mellahi (2009) observed that employees‟ knowledge,
skills and competencies are important competitive
weapon; hence talent needs to be recognized and
natured as one of the discrete source of organizational
competitive advantage.
To illustrate the urgency to address talent management
at colleges and universities, one prediction estimated at
least a 50% turnover rate among senior higher education
administrators within the next five to ten years
(Leubsdorf, 2006). A survey by Talent Pulse (2005) of
over 1,400 HR practitioners worldwide by Deloitte
1141 Prim. J. Bus. Admin. Manage.
Consultancy, reported that the most critical people
management issues are attracting and retaining high
calibre workers. A 2007 study conducted by the American
Council on Education (ACE, 2007) addressing the
characteristics of senior officers in higher education found
that less than half (49.0%) of the senior administrators
were promoted to their current positions internally. This
study illustrated the need to improve talent management
within colleges and universities in order to increase the
level of readiness among high potential employees (King
and Gomez, 2007).
Talent management in developing countries
Many African countries have lost some of their highly
skilled professionals to the United States, Canada,
France, the United Kingdom, Australia and the Gulf
States; consequently most universities rely on individuals
who have not acquired their highest level of academic
training as lecturers. Elegbe (2011) observed that it is a
paradox in Africa that although the unemployment rate is
high, organizations are complaining of shortage of talent.
The African Association for Public Administration and
Management (AAPAM, 2008) noted that African
Continent has not been able to recruit and retain needed
well trained and skilled personnel due to challenges
which include among others poor compensation and an
uncompetitive working environment. According to Mihyo
(2007) the most critical element to be given utmost
attention in academic institutions is human capital which
includes academic, administrative and technical staff
resources however all developed countries are engaged
in a struggle to attract talent and reduce the migration of
their skilled professionals to other countries.
Talent management in universities in Kenya
Lewa (2009) noted that in Kenya traditional techniques of
talent management and forecasting that are in use today
became less useful in the 1980‟s because of turbulence
in operating environments. Modern developments in the
world have engendered the use of sophisticated models
in talent management and forecasting. According to
Chacha (2004) most of these institutions are relying on
individuals who have not acquired the highest level of
academic training as lecturers thereby making the quality
of graduates questionable. To improve efficiency and
effectiveness in delivery of services, the academic staff
must be trained continually in relevant areas. It is
therefore prudent for universities to manage talent
properly, among other things, in order to ensure their
future survival.
Theoretical review
Several theories reviewed included the PersonEnvironment Theory, Theory Y and Theory X of
McGregory, Herzberg‟s motivation-hygiene Theory, and
Equity Theory. This paper however concentrates on the
Person-Environment Theory which relates to the subject
under discussion more than the others.
Person-Environment Theory
This theory explains a dynamic approach of matching a
person with an occupation. The P-E fit perspective
explicitly assumes that people and environment change
continually in an ongoing adjustment (Chartland, 1991)
and that people seek congruent environments. Holland
theory is used to illustrate the P-E fit theory.
Holland (1992) described his assumption about people
and environment acting on each other as the interactive
components; he claimed jobs change people and people
change jobs. In this regard Holland theory may be
summarised up in the following assumptions; (i) most
people can be categorised as one of six types realistic,
investigative,
artistics,
social,
enterprising
or
conventional; (ii) there are six model environments,
realistic, investigative, artistics, social, enterprising or
conventional; (iii) people search for environments that will
let them exercise their skills and abilities express their
attitudes and values and take on agreeable problems and
roles. Holland claimed that people seek environments
that are compatible with their values and attitudes and
that allow them to use their skills and abilities; further the
behaviour is determined by interactions between the
individual and the environment and determines
contextual factors such as job satisfaction stability and
achievement, education choice and personal competence
and susceptibility to influence.
A refinement of Holland‟s have emphasised that an
individual heredity and interactions with their environment
contribute to the development of type and that vocational
predictions work better when contextual variables such
as age, gender and social economic status are taken into
account. Holland (1997) discussed the relationship between
an individual and the environment in terms of congruence,
satisfaction and reinforcement; and suggested that
incongruence is resolved by changing jobs, changing
behaviour and perception.
In applying Holland‟s theory and based on the meaning
of talent management, people tend to be attracted to an
institution if they perceive that the environment is compatible
with their personality or individual needs. Those already in
employment tend to remain with the institution if there is
sense of achievement through personal development which
is realised by providing growth opportunities.
However Holland theory remain descriptive with little
emphasis on explaining the causes and timing of
developmental hierarchies of the personal modal styles;
he concentrated on factors that influence career choice
rather than on developmental process that leads to
career choice (Zunker, 1994). It has also been criticized
for not adequately addressing the career development of
women, racial and ethnic and other groups.
Empirical review
According to Howard (1999), faculty workload should be
defined as a mix of three basic areas of faculty activities,
Kamau et al., 1142
the proportion of which can differ just as relative weight
institutions accord these categories does. These areas
include teaching, research and services. Howard explains
the activities as follows;
- Teaching: it consists of far more than what takes place
during the few hours a week that faculty members spend
in their classrooms; many other tasks such as class
design, preparation, grading and meeting with students
make teaching a complex process. Individual instructions
to the students of masters and PhDs require patience,
devotion and skills.
- Research: according to Bowen and Schuster (1981) as
cited by Howard (1991) noted that research is not a
process but a product which is why publication is crucial.
The products of original research, published books and
articles become teaching tools and extend an institutions
mission beyond the campus.
- Services: follow under two categories as suggested by
Howard that is institutional and professional. Institutional
services include administrative duties, committee work
and students activities. Professional service refers to the
work done in support of one‟s academic discipline and
involves activities such as serving in committees and
boards of professional organizations, organizing or
chairing sessions at national or international meetings,
editing or reading manuscripts for professional journals or
participating in on site program evaluations.
Academic workload is the total professional effort, which
comprises the time (and vigor) devoted to class
management, evaluating student work, curriculum and
program deliberation, and research activities. Allen
(1996) defined workload as the total amount of time a
faculty member devotes to activities like teaching,
research, administration, and community services etc.
Faculty workload can be described as the full spectrum of
work commitments of an academic staff member in an
academic unit at an institution of higher education. This
comprises work that contributes to the academic
enterprise and as agreed upon in considerable detail on
an annual basis between the academic staff member and
his/her direct supervisor and/or institution. Porter and
Umbach (2000) and Glazer and Henry (1994) discussed
that faculty workload covers multi factors besides
teaching credit hours: committee involvement, research
time, community service, office hours, student evaluation,
and course preparation. They group the faculty activities
in domains of instruction, scholarship, and service.
Austin (2002) and Tettey (2010) assert that the
workload that accompanies responsibility for large
student numbers imposes significant career-stalling
burdens on young scholars; anxiety that comes with such
a burden, in a context that demands high standards of
research productivity,
can
discourage potential
academics. In order to address this concern, institutions
need to provide relief to those in the early stages of their
careers by giving course releases, not assigning them
the most highly-subscribed courses, and providing
access to professional development opportunities that
enable the acquisition of pedagogical skills and an
aptitude for balancing the multiple demands of academia
and personal life.
In highlighting the benefits of reduced workload,
Shulkin and Tilly (2005), assert that in order to retain
high-talent individuals who value being highly engaged in
both work and personal life, reduced workload
arrangements should be part of human resource
strategies of any employer. Barnett and Hall (2001) also
observed that reduced workload is a new weapon for
winning the war for talent and retaining professionals with
valuable skills. Bond, Thompson, Galinsky, and Prottas,
(2002), acknowledged that reduced- workload helps
organizations adapt to the realities of a changing
workforce and helps foster increased diversity in the
management and professional ranks. The benefits
suggested by Kossek, Lee, and Hall (2007) include cost
savings in pay, increased focus on crucial projects and
tasks when on the job; the ability to attract and retain top
performers;
further
coworker
relationships
and
communication are improved and backup training
systems and subordinate development are also
enhanced.
Today work–life balance has emerged as one of the
highest recruitment and retention criterion; talented
people want to work on what matters most to their firms
but in a way that still enables them to live their total life
dreams or simply be dually engaged in career and family
or personal interests. Employers who do not offer
customized work options or who implement them poorly
when available will not be employers of choice.
Furthermore if the workload of faculty members is higher
it leads to a downfall in job satisfaction and the outcome
would be poor academic quality (Shahzad et al., 2010).
According to Comm and Mathaisel (2003) as
highlighted by Shahzad et al. (2010) Universities ought to
offer a competitive compensation and workload for
attracting and retaining competent workers in higher
learning institutions.
This connection is important
because it enhances the commitment of faculty to
performance and acts as a key factor to improve
academic quality. Comm and Mathaisel (2000) on
employees‟ satisfaction in higher education found
workload, working environment, and pay and benefits to
be the key factors of employees‟ satisfaction. Faculty is
most concerned with salaries and wishes to have stable
job and salary with fair promotion (Chen et al., 2006).
Metle (2003) found that work content is an important
factor in determining employee‟s satisfaction. Hence
there can be a connection between intensity of work and
level of job satisfaction. Faculty who teach more credit
hours which is related to their area of research are more
satisfied than those who are involved in teaching more
credit hours that is not related to their area of interest.
Faculty workload covers the total set of formal and
1143 Prim. J. Bus. Admin. Manage.
Table 1: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Approx. Chi-Square
Bartlett's Test of Sphericity
.677
161.666
Df
21
Sig.
.000
Table 2: Academic Workload Component Matrix
Component Matrixa
Component 1
Comfortable with Lecturing Hours Per week
.700
Enough time for research and publications
.652
Have adequate time for personal engagement
.622
I am overworked at my place of work
.601
Workload not a hindrance to self development
.572
More free time to engage in part timing in other institutions
.329
Engaged in administrative work in my Institution
Extraction Method: Principal component analysis
informal job descriptions. A lot of other factors also affect
this package of job description for example department
size, nature of institution amongst others.
RESEARCH METHODOLOGY
A survey research design was adopted using
triangulation approach on data, analyst, theories and
methodologies. Using stratified random sampling, 251
academic staff was sampled out of 5673 from seven
public universities in Kenya. Questionnaires were
dropped and picked as tools of data collection. The
response rate was 99.2%. SPSS was used to analyse
the collected data.
RESEARCH FINDINGS AND DISCUSSIONS
Kaiser-Meyer-Olkin measure of sampling adequacy
(KMO)
As shown on the table 1, the Kaiser-Meyer-Olkin
Measure of Sampling Adequacy (KMO) has a value of
0.677 greater than 0.5 the minimum value required hence
the sample is adequate for factor analysis. Barlett‟s Test
of Sphericity indicates value of significance as 0.000 a
value less that the predetermined value of 0.05. This
shows that there are some relationships between the
variables and therefore the correlation matrix of academic
workload is not an identity matrix.
Factor analysis
The table 2 shows the factor loadings for the one
component extracted. In this study a threshold of a value
of 0.4 is used. Beaumont (2012) argued to disregard
those loadings below this threshold and therefore two
factors are eliminated from this component; these include
-.054
more "More free time to engage in part timing in other
institutions" (AW5) and "I am engaged in administrative
work in my Institution" (AW3) with threshold of 0.329 and
-0.54 respectively. The remaining factors have a factor
loading of values above 0.4. Higher values mean closer
relationship hence factor analysis is appropriate.
Descriptive analysis
The items under academic workload included: I am
comfortable with the number of lecturing hours/sessions
allocated per week in my institution (normal workload); I
have adequate time for publications and research; the
amount of workload allocated is not a hindrance to self
development; I have adequate time for personal
engagement and I am overworked at my place of work.
Table 3 shows the findings on each item from the
respondents:
I am comfortable with the number of lecturing
hours/sessions allocated per week
Those who agreed comparatively to
those who
disagreed include: 42.6% of the respondents agreeing
they are comfortable with the number of lecturing hours
allocated to them in their institutions as compared to
those who disagreed with 14.1% response rate. Those
who highly agreed that they are comfortable have a
24.1% response rate; this adds up to 66.7% response
rate of those who affirmed and 17.7% who were on the
contrary.
I have adequate time for publications and research
The respondents who disagreed to this factor were
28.9% and those who highly disagreed 12.9%. Those
Kamau et al., 1144
Table 3: Descriptive Analysis - Academic Workload
AW1
AW2
AW4
AW6
AW7
Factors related to academic workload
I am comfortable with the number of lecturing hours/sessions
allocated per week in my institution (normal workload)
I have adequate time for publications and research
The amount of workload allocated is not a hindrance to self
development
I have adequate time for personal engagement
I am overworked at my place of work
Total Mean
HA(5)
A(4)
N(3)
D(2)
HD(1)
24.1
42.6
15.7
14.1
3.6
8.8
13.3
36.1
28.9
12.9
18.5
26.5
24.1
23.7
7.2
10.4
14.1
12.729
17.3
21.7
24.057
34.1
25.7
23.229
22.9
28.1
26.0
15.3
10.4
14.0
Table 4: Pearson correlation analysis
Pearson Correlation
Sig. (1-tailed)
N
Talent Management
Academic Workload
Talent Management
Academic Workload
Talent Management
Academic Workload
who agreed were 21.2% and highly agreed were 8.8%.
The results indicated that majority do not have enough
time for publication with a 41.8% of the respondents‟ rate.
Schulze (2008) found similar results at the South African
higher education (HE) institutions where research output
was at 1.25 articles per academic per year this number. It
is clear from the above that the institution needs to
provide time in order to improve research out.
The amount of workload allocated is not a hindrance
to self development
Majority (45%) agreed that amount of workload was not a
hindrance to self development. The respondents who
disagreed and highly disagreed were 23.7% and 7.2%
respectively. These results partially corroborate Mihyo
(2007) where staff audit revealed workload heavier for
junior staff than senior staff. Similarly Tettety (2010)
noted work overload on young scholars in higher
education institutions. Therefore the senior staff get more
time for research and consultancy which strengthen their
capability to publish.
I have adequate time for personal engagement
These result indicated that work life balance is an issue in
higher institutions of learning. The respondents who
agreed were 17.3% and 10.4% highly agreed there was
no adequate time for personal engagement. Those who
disagreed and highly disagreed were 22.9% and 15.3%
respectively. This compromises Work-life balance which
is explained by Ministry of Business Innovation and
Employment Work-life (2012) as effectively managing the
juggling act between paid work and other activities that
are important to us - including spending time with family,
taking part in sport and recreation, volunteering or
undertaking further study.
Talent management Academic workload
1.000
.302
.302
1.000
.
.000
.000
.
249
249
249
249
I am overworked at my place of work
The results indicate that academic staffs were not over
worked in their respective institutions with 38.5% rate of
respondents. Those who disagreed and highly disagreed
were 28.1% and 10.4% respectively. Mihyo (2007) study
indicated an average workload of staff was eight hours of
teaching a week including lectures and tutorials an
indication that within respective institutions the
academics have normal workload; however he noted that
staff workloads in tertiary institutions were not equitably
distributed. Similarly, Kipketub (2010) found out an
overall work overload due to the additional responsibility
beyond the normal required workload for income
reasons.
Pearson correlation coefficient
Table 4 shows Pearson correlation calculated for the
relationship between academic workload and talent
management. The value of r determines the strength and
direction of a relationship between the two variables. The
correlation coefficient close to 0 represents a weak
relationship. According to Green, Salkind, and Akey,
(2000) correlation coefficient of 0.10, 0.30 and 0.50
regardless of the sign, are interpreted as small, medium
and large coefficients respectively. A moderate positive
relationship was found (r = 0.302, n=249 and p-value
<0.05) indicating a significant moderate linear relationship
between the two variables. This means that if the value of
Academic Workload variable increases, the value of
Talent Management variable also increases and vice
versa.
Goodness of fit model
The R squared indicates how much of dependent
variable (talent management) can be explained by
1145 Prim. J. Bus. Admin. Manage.
Table 5: Model of fit
Model
R
R Square Adjusted R Square
1
.302
.091
.087
Table 6: Regression coefficients
Model
1
Unstandardized Coefficients Standardized Coefficients
Sig.
Std. Error
(Constant)
17.340
1.155
-
15.011 .000
Academic Workload
.655
.132
.302
4.977 .000
independent variable academic workload. In this case
9.1% of the total variation in Talent Management can be
explained by the linear relationship between Academic
Workload and Talent Management. This is an overall
measure of the strength of association and does not
reflect the extent to which academic workload is
associated with Talent management.
Regression analysis
Table 6 shows the regression coefficients of the
previously determined model. The regression model y
=α3 +β3 x3+ e; α3 is the constant represented by 17.34, β3
is represented by 0.655, a value which indicates the
steepness of the regression line or how much the
predicted value of the dependent variable (talent
management) increases when the value of the
independent (academic workload) variable increases.
From table 6 both the constant and academic workload
contributes significantly to the model. The regression
equation takes the form; predicted variable (talent
management) = intercept + slope * academic workload.
According to Field (2005) the slope indicates how steep
the regression line is; the intercept is where the
regression line strikes Y axis. Therefore; Talent
Management= 17.34+0.655* (Academic Workload). For
each Academic Workload value substituted and the
Talent Management that results provides an ordered pair
that falls on the regression line. This means for every unit
increase in academic workload there is a 0.655 change in
talent management; indicating that there was linearity in
the regression model predicting talent management
based on academic workload
To test whether the regression coefficient for academic
workload was significantly different from zero, the null
hypothesis tested was; H0 1 = 0 the H 1 p˃ 0.The
P
coefficient table 5 signifies the calculated t-value for
academic workload equals 4.977, and is statistically
significant at p-value 0.000; the tcrit = t247 (0.975) =
1.960; the null hypothesis is rejected and the conclusion
was that academic workload has a significant positive
influence on talent management.
Beta
t
B
How does academic workload influence talent
management?
This question was answered by regression equation; y =α
3 +β 3
x 3 + e; where β 3 is the coefficient of correlation of
academic workload, x 3 is academic workload and y is
talent management. Appendix 1 represents the
regression line graphically. The line is diagonal reflecting
a positive linear relationship between talent management
and academic workload.
CONCLUSION AND RECOMMENDATION
From the findings, it can be concluded that though
management supported research; individual research
and publications were below the expected levels. The
study also revealed that academic staffs are allocated
normal workloads within their respective institutions;
however the amounts of hours spent in part timing can
amount to work over load emphasizing further the lack of
time for research and publications and personal
engagement. The study recommends cut-offs on parttime jobs in addition to the normal workload to ensure
quality is enhanced. The study also recommends worklife balance to increase motivation and talent
management in higher learning institutions.
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Appendix 1: Academic Workload Vs Talent Management