International Journal of Management, Knowledge and Learning, 9(1), 43–57
Influence of Leadership
on Innovation Efficiency
in Manufacturing Firms in Kenya
Isaac Muiruri Gachanja
Karatina University, Kenya
Stephen Irura Nganga
Karatina University, Kenya
Lucy Maina Kiganane
The Cooperative University of Kenya
The purpose of this paper is to determine the influence of leadership on
Innovation Efficiency (IE) in manufacturing firms in Kenya. A mixed research
methodology was and cross-sectional design was applied. The target popu-
lation was 2484 leaders and multi-stage sampling was used to sample 345
respondents. Primary data was collected. The results revealed that leader-
ship has a significant influence on IE. It is concluded that management should
practice and encourage creativity, exploration, inspiration, exploitation and en-
trepreneurial mindset to promote IE. Further research should be conducted
to find out how leadership should balance the trade-off between risk manage-
ment and IE.
Keywords: leadership, innovation efficiency, exploration, entrepreneurial
mindset
Introduction
The emerging strategic intent of firms is to expand their growth opportuni-
ties by optimising their capacities. Leadership has therefore been tasked
with the responsibility of utilising the resources efficiently for the attain-
ment of current and future goals. Visser et al. (2010) contend that firms
should not only be adaptable to current but also future business (struc-
tural ambidexterity) for sustainability. This calls for leadership to focus on
Innovation Efficiency (IE) for sustained performance improvement.
The global market has been characterised by a turbulent and highly com-
petitive environment. The scenario has been brought about by the rapidly
changing technology, customers’ needs and environmental concerns. In-
novation efficiency has however greatly impacted the growth of emerging
economies such as China (Luo et al., 2011). This is contrary to countries in
Africa, especially sub-Saharan Africa, which rely on exhaustible natural re-
sources and are involved in primary and extractive activities whose outputs
www.issbs.si/press/ISSN/2232-5697/9_43-57.pdf
44 Isaac Muiruri Gachanja, Stephen Irura Nganga, and Lucy Maina Kiganane
are semi-finished products used as raw materials in developed economies.
Sustainable performance in manufacturing firms in Kenya has remained elu-
sive for decades. Several manufacturing firms are struggling to stay afloat;
others have closed down while others have relocated to other countries in
search of competitive advantage.
The growth in the manufacturing sector in Kenya has been dismal, lag-
ging behind the overall economic growth rate and its contribution to the ex-
ports has declined as a result of low competitiveness. The low competitive-
ness is evidenced by the country’s low Competitive Industrial Performance
(CIP) index of 0.011, which is below the world average of 0.079 while other
middle-level industrial countries such as South Africa, South Korea and In-
dia are above the world average (Kenya Institute of Public Policy Research
and Analysis, 2018). The sector’s low competitiveness is manifested in the
declining growth rate and its contribution to the Gross Domestic Product
(GDP).
The dismal performance in the sector is attributed to its decelerating
growth rate. The World Bank (2018) report on Kenya’s economic update
shows that the growth rate in the sector has been declining from 3.6%
to 2.7% and 0.2% in 2015, 2016 and 2017 respectively. The situation is
further worsened by the fact that the last six years have also been char-
acterised by a downward trajectory in terms of the sector contribution to
GDP. The manufacturing sector contribution to GDP has been declining from
11%, 10.7%, 10%, 9.4%, 9.1% and 8.4% from 2012 to 2017 respectively
against the target of 15% as stipulated in vision 2030 (Kenya National Bu-
reau of Statistics, 2018). The low GDP contribution has put the country
at a disadvantage with peer countries from other fast-growing low middle-
income economies due to lack of competitiveness, which emanates from
low IE. The situation has been brought about by the failure of incorporating
local knowledge in the innovation process (Sambuli & Whitt, 2017). This im-
plies that leadership has not harnessed their exploratory and exploitative
capacity to utilise the resources at their disposal.
There are few studies of IE at the micro-level as most of them have
focused on the macro level. The purpose of this paper was therefore to
determine the influence of leadership on innovation efficiency in manufac-
turing firms in Kenya. The objective of the study was evaluated through test-
ing of hypothesis. The null hypothesis in this case was; leadership has no
significant influence on IE in manufacturing firms in Kenya. The alternative
hypothesis is that leadership has a significant influence on IE.
Literature Conceptual Underpinning
The exploration of sustained growth can be addressed by firms aligning
themselves with the changing market trends. Improved IE can enhance sus-
International Journal of Management, Knowledge and Learning
Influence of Leadership on Innovation Efficiency 45
tained growth. Innovation efficiency has been defined as the capacity for
transforming innovation inputs into outputs; it is the ratio between edu-
cation investment in innovation and output (Hollanders & Esser, 2007).
Innovation efficiency can, therefore, be defined as the effectiveness of con-
verting innovation inputs into outputs.
Innovation efficiency is important in several ways. It helps in identifying
the best innovation practices, which can be used for benchmarking. It is
also important in developing innovation policy (Hollanders & Esser, 2007).
This is crucial in evaluating the value of the key players in the innovation
systems. It also focusses on the commercialisation and the economic ben-
efits of innovation activity (Wang et al., 2016). Commercialisation help firms
to develop a new approach of consolidating the local markets while at the
same time regionalising and globalising the market niche to gain access
to the larger customer base for greater sales. Innovation efficiency, there-
fore, enables a firm to value the economic importance of innovation ac-
tivities.
There are several methods of measuring IE. They include Stochastic Fron-
tier Analysis (SFA), Data Envelopment Analysis (DEA) and Two-Stage IE Non-
Radial DEA model. The SFA is a parametric analysis which assumes a par-
ticular relationship between innovation inputs and outputs, but not suitable
when dealing with multiple outputs (Wang et al., 2016). Outputs for inno-
vation are numerous because they include new products, patents acquired,
innovation process, new enterprises and new markets. This approach of
measuring IE is not suitable for this study.
The second method is DEA, which is an improvement of SFA. It utilises
data from multiple inputs and outputs with no prior specification format
(Guan & Chen, 2012). However, it does not capture the operations, inter-
nal systems and processes involved in IE (Wang et al., 2013). This study
utilised the operations, internal systems and processes hence DEA is not
appropriate in this case.
The third approach, which is the two-stage DEA, model built on the sec-
ond method. The approach involves the optimisation of resources (Wang
et al., 2016) and has been used in the manufacturing sector (Bian et al.,
2015). The method is of two types; radial and non-radial DEA. Radial DEA
does not account for inefficiencies in inputs and output (Wang et al., 2016).
The study accounted for the inefficiencies, thus this approach is not appro-
priate in this case. Non-radial DEA provides for optimisation of strategies
and is therefore relevant to this study. The approach recognises innovation
input as comprising human, financial and material resources, while the out-
put is the commercialisation of innovation in terms of the market value and
profits (Wang et al., 2016). The model, therefore, captures the resources at
the disposal of a firm as the input of determining the IE. These resources
Volume 9, Issue 1, 2020
46 Isaac Muiruri Gachanja, Stephen Irura Nganga, and Lucy Maina Kiganane
are under the stewardship, discretion and utilisation of the firm leadership.
Leadership is therefore a critical component in IE.
Leadership can influence how resources are applied and is thus viewed
as one of the core drivers of IE. The appropriate leadership fosters well-
thought decisions and strategic alliances that promote robust plans, devel-
opment and execution, business intelligence and value creation leading to
an improved IE (Murray & Greenes, 2006). The right leadership can, there-
fore, transform the way an organisation is governed, leading to high levels
of IE. Creative leadership develops human and social capital while oper-
ational leadership explores new growth paths (Makri & Scandura, 2010).
Vaccaro et al. (2012) found that transactional leadership is more beneficial
to small firms while transformational leadership is beneficial to large firms.
Transformative leadership enhances creativity, adaptability and interactive
technologies that can derive value from networking, however further clarity
and refinement is required (Desai, 2010). The study interrogated this as-
sertion to verify it and possibly develop a new model. Green and McCann
(2011) proposed a different leadership model to combat uncertainties and
address the new economic revolution. Nevertheless, transformational lead-
ership can inspire an organisation to greater heights of IE.
Leadership that transforms the mindset is directly related to organisa-
tional learning and innovation culture which ultimately influences IE in man-
ufacturing firms, but the complexity of collecting data from multiple informa-
tion should be re-examined (Sattayaraksa & Boon-itt, 2018). The steps of
transformational leadership include; re-imagining customer experience, re-
ducing business ecosystems, promotion of networking and revitalising the
innovation governance which can be achieved through accelerating new op-
portunities, breaking cultural barriers, embracing innovative behaviors and
adopting a global mindset (Ikeda et al., 2016). Leadership, therefore, plays
a critical role in creating an environment that promotes IE by harnessing
knowledge creation, flow and utilisation.
There are various approaches for measuring leadership that has been
advanced by different scholars. The key set of actions that determine effec-
tive innovative leadership is re-imagining customer experience, redefining
the business ecosystem, promoting ecosystem connectivity and revitaliza-
tion of innovation governance (Ikeda et al., 2016).
Re-imagining customer experience can be measured through production
of definitive blueprints, piloting and building foundations of capabilities, pro-
grams for innovation and launches, new expertise, new focus and new ways
of working. Redefining the business ecosystem can be measured by identi-
fying opportunities to collaborate and participate in an ecosystem and also
the development of the capacity to create value for the ecosystem. Promo-
tion of ecosystem connectivity can be measured by networking efficiency,
International Journal of Management, Knowledge and Learning
Influence of Leadership on Innovation Efficiency 47
ecosystem innovation, displacement of value chains and value creation.
Revitalisation of innovation governance can be measured by structures that
are open for new ideas, dedicated teams to prioritise agility and secure
stable innovation funding and quantitative evaluation of innovation initia-
tives. Kuratko et al. (2011) measured leadership in terms of nourishing
entrepreneurial capacity and linking entrepreneurship to strategy. The study
improved on these parameters to measure the leadership variable by in-
corporating the extent of nourishment of entrepreneurial capacity, linking
entrepreneurship to strategy, protection of disruptive innovations, an oppor-
tunity for developing creativity, questioning of the dominant logic and the
level of inspiration provided by leaders. This criterion is more comprehen-
sive and is anchored on the premises of an entrepreneurial mindset that is
crucial in driving IE. These attributes of leadership have been linked to IE in
this study.
Different researchers have attempted to demonstrate the relationship
between Leadership and IE. Oke et al. (2009) found that leadership has
a significant influence on exploratory and exploitative innovation that has
an impact on IE. Exploratory innovation is derived from strategic alliances
while exploitative innovation comes from alignment with the market trends.
Noruzy et al. (2013) found that leadership releases social capital that neu-
tralises the tension between innovation and risk management, thus promot-
ing IE. The social capital empowers innovation teams to be more creative
and productive. Leadership helps a firm to adapt to changes faster, bal-
ance time, cost and value which leads to enhanced IE (Lindgren & Abdullah,
2013). Leadership is therefore a catalyst for IE.
There are two theories on which the study was anchored. The first one is
the (Graen & Uhl-Bein 1995) theory of leader-member exchange. The theory
underscores the significance of leadership in determining the output of fol-
lowers, but it does not recognise the dynamics involved in entrepreneurial
and innovation context (Surie & Hazy, 2006). The theory is also prone to
subjectivity, which can bring about favourism in the leader and the follower
relationship, which may alienate other team members and cause counter-
productivity. The weaknesses of the theory can be addressed in the Gleick
(1987) complexity theory.
The theory is cognizant of the complex context in which leadership finds
it and innovation thrives. The complex nature of IE requires to be matched
with a complex leadership approach (Rosing, 2015). The theory is relevant
in this study because it recognises the dynamics involved in leadership
and IE. These aspects of the interrelationship between leadership and IE
led to the development of a conceptual framework that captured leader-
ship as the independent variable and IE as the dependent variable. The
antecedents of leadership that lead to IE as developed in the literature
Volume 9, Issue 1, 2020
48 Isaac Muiruri Gachanja, Stephen Irura Nganga, and Lucy Maina Kiganane
Leadership
(creativity, exploration,
inspiration, exploitation)
Innovation Efficiency
(market value, profits)
Figure 1 The Conceptual Framework
review are; entrepreneurial capacity, entrepreneurial strategy, exploitation,
creativity, exploration and inspiration. The measure of IE is market value
and profits as indicated in Figure 1.
The conceptual framework is an improvement of the two-stage non-radial
DEA model. Leadership provides crucial input in the innovation process,
therefore it is captured as the independent variable of this study. Innovation
Efficiency is the output of effective leadership and hence it is captured as
the dependent variable.
Methodology
Mixed-method research was used to gain a more comprehensive insight into
leadership and IE. The research design that was used is a cross-sectional
design to allow for observations on characteristics that exist within the
manufacturing sector and make a comparison within the subsector.
The target population was the heads of departments in operations, inno-
vation and marketing of each firm in the 828 manufacturing firms that are
registered with Kenya Association of Manufacturers’ (KAM) across the coun-
try as of the year 2018. The total target population was therefore (828×3)
2484.
A multi-stage sampling strategy was used because the sampling proce-
dure included several steps. The steps included purposive sampling, strat-
ified random sampling purposive sampling once more and simple random
sampling. Purposive sampling was first employed to select the seven major
industrial counties from the forty-seven counties in the country. The major
industrial counties sampled through this procedure are Nairobi, Mombasa,
Kisumu, Nakuru, Kiambu, Machakos and Uasin Ngichu.
The total number of firms sampled using this procedure is the total sum
of numbers from the selected counties, which were 780 out of a total num-
ber of 828 firms in the country. The second step was stratified random
sampling. The procedure was used to sample firms from the seven se-
lected counties in the first step. This was done to provide a proportionate
representative sample of the firms from the selected counties relative to
the total number of firms per county. The stratified random sampling led to
a sample size of 115 firms that were proportionally distributed in the major
industrial countries. This was done to eliminate bias in selecting firms from
the same area.
International Journal of Management, Knowledge and Learning
Influence of Leadership on Innovation Efficiency 49
The third step was to select the respondents. Purposive sampling was
used to sample 3 respondents from the management of each of the 115
firms. The 3 respondents selected were the heads of operations, innovation
and marketing because these are the key personnel who have the responsi-
bility of knowledge management, promotion of innovation efficiency in their
firms. This brought the total respondents to (3×115) 345.
The final step was simple random sampling. This was done to select
firms from the different manufacturing sub-sectors in order to obtain the
number of firms per sector and to keep them proportional relative to the
size of the 12 sub-sectors. The number of firms sampled was therefore
345 from the major industrial counties and the various sub-sectors.
Primary and secondary data, both quantitative and qualitative, was col-
lected using a semi-structured questionnaire, interview schedule and check-
list. The measuring scales were in line with the literature and pre-testing of
the research instruments was done through piloting. Correlation and linear
regression analysis were used to examine the relationship between leader-
ship and IE. Reliability was tested through internal consistency technique
that showed the extent to which the procedures assessed the same charac-
teristics. Prior arrangements were made with management to discuss the
best time the respondents would be available to minimise work interrup-
tions.
Findings and Discussions
The questionnaires received and filled up were 295 against 345 issued rep-
resenting a response rate of 86% from respondents and 101 firms out 115
firms representing 88% of the firms sampled. The male respondents con-
stituted the majority at 58%, which implied that the perspectives expressed
are from a male more than from a female point of view. It means that more
men than women work as head of operations or production, innovation and
marketing in manufacturing firms within the country.
The respondents were also asked to indicate the total sales for each
of the past three years. They were also required to indicate the portion of
sales attributed to innovation. The proportion of sales attributed to innova-
tion formed the percentage sales growth rate brought about by innovations
for the last 3 years as a result of new products, patents acquired, new
processes and new enterprises. The sales growth rate brought about by
innovations was then computed as sales emanating from innovation activi-
ties divided by total sales for a similar period multiplied by 100 to make it
a percentage. This constituted innovation efficiency.
The distribution of innovation efficiency across the manufacturing firms
for the last three years was also examined. This was done by use of range
and standard deviation. The mean innovation efficiency was 0.2971, the
Volume 9, Issue 1, 2020
50 Isaac Muiruri Gachanja, Stephen Irura Nganga, and Lucy Maina Kiganane
Table 1 Innovation Efficiency Distribution across the Manufacturing Firms
Item (1) (2) (3) (4) (5)
N Valid 101 0 0 101 101
Missing 0 0 0 0 0
Mean 0.2971 –0.0006 0.0192 0.2591 0.3348
Stdandard error of mean 0.0194
Stdandard deviation 0.1950 –0.0007 0.0074 0.1794 0.2084
Variance 0.0380 0.0000 0.0030 0.0320 0.0430
Skewness –0.3030 0.0020 0.1650 –0.6550 0.0200
Stdandard error of skewness 0.2400
Range 0.5400
Notes Column headings are as follows: (1) statistics, (2) bias, (3) standard error, (4) lower
95% confidence interval, (5) upper 95% confidence interval.
range was between zero and 0.54 and the standard deviation was 0.19499
as indicated in Table 1.
The mean of 0.2971 implies that on average, each firm had approxi-
mately 30% innovation efficiency. This means that the innovation activities
in manufacturing firms contributed to about a third of sales per firm on the
average. The range implies that the innovation efficiency across manufac-
turing firms varied from zero to 0.54. This means that the lowest IE was
zero and the highest at 0.54. The standard deviation of 0.195 implies that
there was a small spread within the sample. This means that there was a
high degree of uniformity in innovation efficiency across manufacturing firms
and thus a high level of homogeneity in the sample.
The parameters for measuring the latent variable of leadership were the
extent of nourishment of entrepreneurial capacity, linking entrepreneurship
to strategy, protection of disruptive innovations, an opportunity for develop-
ing creativity, questioning of the dominant logic and the level of inspiration
provided by leaders. The responses were captured on a Likert scale which
had six items with a scale of 1 to 5 and thus the expected maximum score
was 30. The score on each of the items was then added up to form the
composite value of leadership in each firm.
The scores of leadership were ranked in terms of their frequency of oc-
currence. The highest frequency of leadership is a score of 27 as indicated
in Figure 2.
This implies that most firms had leadership that promotes innova-
tion. It meant that the nourishment of entrepreneurial capacity, linking
entrepreneurship to strategy, protection of disruptive innovations, provi-
sion of opportunity to develop creativity, questioning of the dominant logic
and acceptable level of inspiration was provided by leaders which promoted
innovation activities.
International Journal of Management, Knowledge and Learning
Influence of Leadership on Innovation Efficiency 51
0
10
20
30
40
50
60
70
80
6 11 14 16 17 18 19 20 21 23 24 25 26 27 28 29
Fr
eq
ue
nc
y
Figure 2 The Distribution of Leadership Score in Manufacturing Firms in Kenya
(mean = 22.49, standard deviation = 4.885, N = 295)
The mean, range and standard deviation were used to analyse the trend
of leadership in the manufacturing sector. The mean score for leadership
was 22.5149, the minimum score was 15, the maximum was 28 giving a
range of 13 and the standard deviation was 4.885.
The mean score of 22 generally implies that manufacturing firms had the
right leadership to nurture a culture of innovation. However, the minimum
score of 15 implies that some firms did not have the right leadership to
encourage innovation. The standard deviation of 4.5 implies that there was
a wide variance in terms of leadership across the firms. It meant that there
was a low degree of uniformity in leadership across manufacturing firms and
therefore a low level of homogeneity in the firms under review. The computed
standard error on leadership and confidence intervals levels ranged from 0
to 2.4 and confidence intervals levels were computed at 95% as indicated
in Table 2. This implies that the data collected was not biased. This led
to a further analysis of how the various subsectors performed in terms of
leadership.
The comparison of how the different subsectors performed was carried
out. The subsector that showed a high level of variance on leadership score
was paper and board. This implies that there was indifference as to whether
leadership promoted innovation in the firms within the paper and board sub-
sector. It means that leadership that promotes innovation was encouraged
in some firms while it was not the case with others within the subsector.
The respondents were then invited to list the factors that affect leader-
ship concerning IE. The main factors were training, openness to new ideas,
partnership and networking abilities, strategy, management style and re-
source provision in descending order. The other factors that were raised
include proactiveness, knowledge management, provision of a conducive
working environment, communication skills, organisation culture, level of
experience, working teams, ability to implement agreed issues, staff en-
Volume 9, Issue 1, 2020
52 Isaac Muiruri Gachanja, Stephen Irura Nganga, and Lucy Maina Kiganane
Table 2 The Distribution of Leadership Score in Manufacturing Firms in Kenya
(1) (2) (3) (4) (5) (6) (7) (8) (9)
6.00 2 0.7 0.7 0.7 0.0 0.5 0.0 1.7
11.00 2 0.7 0.7 1.4 0.0 0.5 0.0 1.7
14.00 9 3.1 3.1 4.4 0.0 1.0 1.4 5.1
16.00 42 14.2 14.2 18.6 0.0 2.1 10.2 18.3
17.00 21 7.1 7.1 25.8 0.0 1.5 4.4 10.2
18.00 14 4.7 4.7 30.5 0.0 1.2 2.4 7.5
19.00 5 1.7 1.7 32.2 0.0 0.8 0.3 3.4
20.00 6 2.0 2.0 34.2 0.0 0.8 0.7 4.1
21.00 16 5.4 5.4 39.7 0.0 1.3 3.1 8.1
23.00 17 5.8 5.8 45.4 0.0 1.3 3.4 8.8
24.00 19 6.4 6.4 51.9 0.0 1.5 3.7 9.5
25.00 15 5.1 5.1 56.9 0.0 1.3 2.7 7.8
26.00 37 12.5 12.5 69.5 –0.1 1.9 9.2 16.6
27.00 72 24.4 24.4 93.9 0.0 2.4 20.0 29.2
28.00 7 2.4 2.4 96.3 0.0 0.9 0.7 4.4
29.00 11 3.7 3.7 100.0 –0.1 1.1 1.7 5.8
Total 295 100.0 100.0 0.0 0.0 100.0 100.0
Notes Column headings are as follows: (1) score (2) frequency, (3) percent, (4) valid per-
cent, (5) cumulative percent, (6) bias, (7) stdandard error, (8) lower 95% confidence interval,
(9) upper 95% confidence interval.
gagement, flexibility, generation gap, risk management, level of technology
savviness, competence, monitoring and evaluation ability. It was observed
that firms with clear evidence of corporate learning, open channels of com-
munication and an operation strategic plan had high levels of IE.
The measures of each of the parameters of leadership were first tested
for reliability to determine the scale stability in providing similar outcomes
in repeated trials. This was done through a scale reliability analysis. The
result was a Cronbach’s Alpha of 0.905. The value is above 0.7, which is
above the recommended threshold of 0.7. It means that the scale used
to measure IE is reliable and can be replicated in other trials with similar
outcomes. This is consistent with Alegre et al. (2006) who found that the
Cronbach’s alpha of the measures of the latent variable should be more
than 0.7.
The next step was to find the Multicollinearity between the leadership
and IE. This is important because Multicollinearity weakens the precision
power of a statistical regression model. The Multicollinearity test was con-
ducted through the application of the Variance Inflation Factor (VIF) and the
level of tolerance. The results produced the highest VIF of 2.793 and the
highest tolerance value of 0.535 as indicated in Table 3.
International Journal of Management, Knowledge and Learning
Influence of Leadership on Innovation Efficiency 53
Table 3 Multicollinearity Test between Measures of Leadership and IE
Item (1) (2) (3) (4) (5) (6) (7)
(Constant) –0.454 0.032 14.305 0.000
Entrepreneurial capacity
nourished
0.005 0.011 0.024 0.474 0.636 0.426 2.35
Protection of disruptive
innovation
0.031 0.010 0.158 2.956 0.003 0.380 2.629
Leadership encourages
creativity
0.075 0.009 0.359 7.972 0.000 0.535 1.870
Dominant logic
questioned
0.031 0.011 0.149 2.702 0.007 0.358 2.793
Leadership provide
motivation to innovate
0.007 0.011 0.034 0.623 0.534 0.367 2.722
Activities linked
to strategy
0.053 0.011 0.268 4.903 0.000 0.363 2.751
Notes Column headings are as follows: (1) B, (2) standard error, (3) β, (4) t, (5) significance,
(6) tolerance, (7) VIF.
Table 4 Correlation between Leadership and Innovation Efficiency
Item Innovation efficiency Leadership
Pearson correlation Innovation efficiency 1.000 0.806
Leadership 0.806 1.000
Significance (1-tailed) Innovation efficiency 0.000
Leadership 0.000
N Innovation efficiency 295 295
Leadership 295 295
The values of VIF for each of the measures of leadership are less than
the cut-off point of 10 and fall between 1 and 3 implying a moderate Multi-
collinearity that does not require corrective action. The tolerance values of
each of the measures of leadership were greater than the required minimum
threshold of 0.10 indicating the absence of Multicollinearity. The findings
are in tandem with Suki & Suki (2015) who found that a VIF of less than 10
and a tolerance value of between 0 and1 does not warrant any action.
Bivariate correlation between leadership and IE was then conducted to
determine their relationship. This was done by running a linear regression
that provided a Pearson correlation coefficient (r); the value was 0.806 as
indicated in Table 4.
The Pearson correlation value between leadership and IE was 0.806,
which is near one, implying that a strong relationship between the two vari-
ables exists. The value is also positive, implying that leadership and IE
move in the same direction hence they are correlated. It means that as
leadership improves so does IE and vice versa among manufacturing firms.
Volume 9, Issue 1, 2020
54 Isaac Muiruri Gachanja, Stephen Irura Nganga, and Lucy Maina Kiganane
Table 5 The Linear Regression between Leadership and Innovation Efficiency
Item (1) (2) (3) (4) (5)
Regression 7.754 1 7.754 543.273 0.000
Residual 4.182 293 0.014
Total 11.935 294
Notes Column headings are as follows: (1) sum of squares, (2) degrees of freedom, (3)
mean square, (4) F, (5) significance.
The findings are in tandem with (Desai, 2010) who found that leadership is
related to IE.
The linear regression between leadership and IE was also conducted to
test for the study hypothesis. The null hypothesis stated that leadership
does not influence IE in manufacturing firms in Kenya. The results showed
that the P value was zero as indicated in Table 5.
The P-value was zero, which is less than 0.05. This led to the rejection of
the null hypothesis and acceptance of the alternative hypothesis. It, there-
fore, means that leadership has a significant influence on IE in manufac-
turing firms in Kenya. This implies that leadership is crucial in determining
the levels of IE and is consistent with Oke et al. (2009) who found that
leadership has a significant influence on IE.
The extent to which leadership influenced IE was also determined. This
was done by observing the value of R square in the linear regression anal-
ysis. The value of R2 was 0.650, which is equivalent to 65%. This implies
that 65% of a unit change in IE can be explained by a unit change in lead-
ership. It means that leadership is a major determinant of IE. This concurs
with Noruzy et al. (2013) who found that Leadership promoted IE. It has
therefore been found that leadership is directly related to IE and has a
significant influence on it.
The findings indicate that leadership has a significant influence on IE in
manufacturing firms in Kenya. Results are obtained in tandem with Xenikou
and Simosi (2006) who found that leadership, especially transformational
one, is directly related to IE. This concurs with Simons and Sower (2012)
who found that good leadership is paramount for enhancing IE. The findings
also concur with Ikeda et al. (2016) who found that transformational lead-
ership has a significant influence on innovative behaviours, thus impacting
on IE. The findings are also consistent with Jia et al. (2018) who found
that transactional leaders inhibit knowledge entrepreneurship while trans-
formational leadership enhances IE. The findings are also consistent with
Sattayaraksa and Boon-itt (2018) who found that good leadership trans-
forms the mind-set, thus influencing IE in manufacturing firms. This concurs
with Naqshbandi et al. (2019) who found that leadership that empowers
employees promotes knowledge entrepreneurship which has a positive in-
International Journal of Management, Knowledge and Learning
Influence of Leadership on Innovation Efficiency 55
fluence on IE. Sufficient evidence, therefore, abounds on the relationship
between Leadership and IE.
However, other scholars found otherwise. Prajogo et al. (2007) found that
leadership does not necessarily influence IE. It is therefore observed that
the type of leadership in manufacturing firms matters in promoting IE. The
study found that a specific type of leadership that contributes to IE is the
one that nurtures creativity encourages exploration, provides inspiration and
promotes exploitation. This can be achieved by promoting entrepreneurial
capacity and strategy, protection of disruptive innovations, encouraging the
questioning of the dominant logic by leaders.
It has a significant influence on the market value of a firm and its profits
that comprise the IE in the manufacturing sector in Kenya. It is therefore
concluded that transformation leadership natures KE which in turn influ-
ences IP.
Conclusions and Recommendations
The study concludes that the various aspects of leadership that nurture cre-
ativity, exploration, inspiration and exploitation have a significant influence
on IE. These aspects are the basis of transformational leadership. Transfor-
mational leadership should, therefore, be encouraged in the manufacturing
firm to improve on IE that plays a key role in enhancing competitiveness.
It is recommended that management should practice and encourage cre-
ativity, exploration, inspiration, exploitation and entrepreneurial mind-set for
higher IE, high innovation performance and ultimately to improve their com-
petitiveness. Further study should be conducted on how leadership should
balance the trade-off between risk management and IE.
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Isaac Muiruri Gachanja is a Ph.D. student in Entrepreneurship and holds a
Masters of Business Management (Entrepreneurship option) from Karatina
University. He holds Bachelor of Education (Arts); Business Studies and Eco-
nomics from the University of Nairobi. He lectures at Strathmore and Mount
Kenya University. igachanja@gokeglobal.org
Dr Stephen Irura Nganga holds Ph.D. in Planning from Maseno University,
Mphil Entrepreneurship from Moi University and MSc in Forest Industries
from Technology University College of North Wales. He is currently the acting
Deputy Vice-Chancellor Finance Administration and planning, Garissa Univer-
sity. Prof. Irura has published widely in several areas. inganga@gmail.com
Dr Lucy Kiganane Maina holds a Ph.D., MSc, and BSc in Entrepreneurship.
She is a senior lecturer and the Director of Postgraduate studies at The Co-
operative University of Kenya. She has published widely in the field of En-
trepreneurship, served as a paper reviewer and member of journal editorial
board. lucynmaina09@gmail.com
Volume 9, Issue 1, 2020