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Entrepreneurial Orientation as Antecedent of Business Model
Innovation in Medium Enterprises in Kenya
Albert Kisiang’ani Simiyu, PhD Candidate
Prof. Elijah Bitange Ndemo, PhD
Prof. Mary Wanjiru Kinoti, PhD
Prof. Gituro Wainaina, PhD
University of Nairobi, Kenya
Doi:10.19044/esj.2023.v19n1p140
Submitted: 29 December 2022
Accepted: 21 January 2023
Published: 31 January 2023
Copyright 2023 Author(s)
Under Creative Commons BY-NC-ND
4.0 OPEN ACCESS
Cite As:
Simiyu A.K., Ndemo E.B., Kinoti M.W. & Wainaina G. (2023). Entrepreneurial Orientation
as Antecedent of Business Model Innovation in Medium Enterprises in Kenya. European
Scientific Journal, ESJ, 19 (1), 140. https://doi.org/10.19044/esj.2023.v19n1p140
Abstract
This study investigated the influence of entrepreneurial orientation on
business model innovation in medium enterprises in Kenya. The study was
grounded on the dynamic capabilities view. A descriptive cross-sectional
survey research design was adopted to achieve the study’s objective. The
sampling frame was the Klynveld Peat Marwick Goerdeler (KPMG) East
Africa and the Nation Media Group annual Top100 companies in
Kenya. Stratified random sampling was utilized to derive 221 companies that
were used in the study. Primary data were collected from single respondents
comprising senior managers of the participating firms. A total of 134
questionnaires were analysed. Ordinary least squares regression analysis
revealed that entrepreneurial orientation positively and significantly
influenced business model innovation in medium enterprises in Kenya. On the
basis of the results, this study concluded that entrepreneurial orientation is
necessary for enterprises to benefit from business model innovation. The study
recommends that managers of medium enterprises in Kenya embrace
entrepreneurial behaviour and attitude to enhance business model innovation
practices. It is further recommended that policymakers should develop and
implement policies that encourage innovation and entrepreneurial behaviour.
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The study clarifies the position of entrepreneurial orientation in relation to
BMI. Additional studies are recommended.
Keywords: Entrepreneurial orientation, business model innovation, medium
enterprises, Kenya
Introduction
Medium enterprises constitute an essential cog in the global economy,
contributing immensely to employment and value creation in many countries
at different levels of development (Muriithi, 2017). They are considered a
critical pillar in Europe’s 2020 roadmap toward reaching smart, sustainable,
and inclusive growth (Rotar et al., 2019). In the Kenyan context, medium
enterprises are regarded as important because of their impact on the Gross
Domestic Product (GDP) and employment generation. Based on Ndegwa et
al. (2015), medium enterprises are essential players in terms of product and
service innovations. Kenya’s Vision 2030, the country’s economic road map
to industrial development recognises medium enterprises as critical in
attaining its industrial development aspirations (the Republic of Kenya, 2012).
Thus, efforts to enhance performance among medium enterprises will
contribute to enabling the country to attain its development agenda by
generating more jobs, solidifying sectors, and evolving business models that
perform. Their importance suggests a need to be sufficiently examined to
comprehend the drivers that can lead to improvement in their performance.
The background of entrepreneurial orientation as a firm-level construct
is traced to the strategic management literature (Wales, 2016) and is applied
to describe a firm whose apex managers exhibit entrepreneurial behaviour and
attitude as demonstrated in their strategic decision making and operating
philosophy (Gupta & Dutta, 2018). Although various conceptualisations have
been advanced in literature (Anderson et al., 2015; Covin & Wales, 2019),
Lomberg et al. (2017) observe that the literature on entrepreneurial orientation
is mainly centered around two conceptualisations, that is, the Covin and Slevin
(1989) and Lumpkin and Dess’ (1996) conceptualisations. According to Covin
and Sleven (1989), entrepreneurial orientation is characterised by a
combination of innovativeness, risk taking, and proactiveness. Lumpkin and
Dess (1996) on the hand view entrepreneurial orientation as a
multidimensional construct exemplified by autonomy and competitive
aggressiveness in addition to innovativeness, risk taking, and proactiveness.
Thus, while Lumpkin and Dess consider the five dimensions of
entrepreneurial orientation as independent from one another and therefore an
enterprise would still be considered entrepreneurial even when only one
indicator exists, Covin and Slevin’s conceptualization considers an enterprise
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to be entrepreneurial if all the three dimensions are evident in the way of
managerial behaviour and action (Okeyo et al., 2016).
According to Wales (2016), researchers can adopt any
concemptualisations that align with their research problem. This study adopted
Covin and Slevin’s conceptualisation in which an entrepreneurial orientated
company is defined as that which “engages in product-market innovation,
undertakes somewhat risky ventures, and is first to come up with ‘proactive’
innovations, beating competitors to the punch” (Miller, 1983, p.771),
According to Anderson et al. (2015), innovativeness characterises an
enterprise’s inclination to embrace new ideas, inventiveness, and
experimentation in developing new products and processes. Proactiveness
entails futuristic and opportunity-seeking tendencies that afford an enterprise
a pioneering benefit over the competition by antedating future market trends.
Risk taking on the other hand is about an enterprise’s inclination to boldly
commit resources towards initiatives portending high but unassured returns.
This conceptualisation has been adopted successfully in preceding studies
such as (Asemokha et al. (2019), Boucken et al. (2016), and Ferreras-Méndez
et al. (2021).
Although entrepreneurial orientation is considered vital to an
enterprise’s enhanced performance (Ndemo & Aiko, 2016; Rauch., et al.,
2009), a number of scholars have argued that entrepreneurial orientation might
not have a direct influence on enterprise performance, hence, calling for the
identification and assessment of immediate outcomes of entrepreneurial
orientation (Markin et al., 2018; Wales et al., 2011). The calls to establish and
assess the immediate outcomes of entrepreneurial orientation are attributed to
mixed findings about the direct effect of entrepreneurial orientation on
enterprise performance where some studies have reported negative or no
significant effect contrary to those that have ascertained positive effect (Soares
& Perin, 2019). According to Covin and Wales (2019), entrepreneurial
orientation is characterised by dynamism and morphing causing a series of
disruptions and network relationships requiring incessant management with
the aim of capturing value, leading to a conclusion that entrepreneurial
orientation independently is not a recipe for sustained enterprise success.
Hence, the need to identify and assess synergistic variables through which
entrepreneurial orientation enhances enterprise performance (Markin et al.,
2018). Concomitantly, business model innovation (BMI), which is described
as a variation in the way a firm does its business that is novel to the firm and
leads to evidential modifications in the way the firm creates, distributes, or
captures value for all participants in the value chain including its customers
(Bouwman et al, 2016), has been advanced as a source of enduring competitive
edge and better performance (Afuah, 2014; Baden-Fuller & Haefliger, 2013;
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Bashir & Verma, 2017; Casadesus-Masanell & Zhu, 2013; Chesbrough, 2010;
Demil & Lecocq, 2010). More importantly, in an environment where product
and process innovations are prone to imitation and therefore, shortened shelf
life (Zott & Amit, 2012). Ndemo and Aiko acknowledge the prevalence of
product imitation in Kenya and on the African continent generally, where
intellectual property protection laws are underdeveloped underpinning the
need for BMI. Kim and Mauborgne (2005) emphasised the need to eschew the
traditional sources of competition for new ways of doing business, that is,
BMI. According to Carayannis et al. (2014), BMI can lead to organisational
sustainability, resilience, and excellence. However, the literature on BMI is
said to be at an infancy stage requiring identification and assessment of its
antecedents (Foss & Saebi, 2017; Lambert & Montemari, 2017; Spieth et al.,
2014). Existing literature asserts a distinction between entrepreneurial
orientation and BMI. While entrepreneurial orientation focuses on product and
process innovation (Veidal & Korneliussen, 2013), BMI addresses how a firm
creates, delivers, and appropriates value to the focal firm and its channel
partners, including its customers (Bouwman et al, 2016; Snihur & Wiklund,
2019). According to Bucherer et al., (2012), BMI is a distinct form of
innovation possessing a higher potential for value creation and capture as it is
difficult to be imitated and implemented by competitors, unlike product and
process innovations (Zott & Amit, 2012). While empirical studies have
identified entrepreneurial orientation as an antecedent of BMI, these studies
are mostly exploratory (Tian et al, 2019) and undeniably scarce (Asemokha et
al., 2019). The purpose of this study, therefore, was to contribute to a better
comprehension of entrepreneurial orientation as an antecedent of BMI by
investigating the impact of entrepreneurial orientation on BMI in medium
enterprises in Kenya.
Research Problem
The current study addresses several gaps identified in previous studies.
First, although entrepreneurial orientation as a construct has been extensively
investigated (Wales, 2016), the focus has mainly been in the context of
developed economies such as the United States of America (USA) (Gupta &
Dutta, 2018). Likewise, BMI studies have mainly been done in Europe, the
USA, and Asia, with minimal effort to understand BMI practices in Africa.
Situating this study in a developing country like Kenya was meant to bridge
the identified contextual gap. Comprehending the nature and impact of
entrepreneurial orientation and BMI in a context different from the developed
countries may help managers develop and adopt strategies and operations
more suitable to local conditions, thus evading possible adverse outcomes
(Knight, 1997). Secondly, this study responds to scholars who have called for
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the identification and assessment of immediate outcomes of entrepreneurial
orientation other than enterprise performance (Covin & Wales, 2019), as well
as antecedents and consequences of BMI (Foss & Saebi, 2017). Prior studies
assessing the outcome of entrepreneurial orientation have largely been
focusing on enterprise performance as a direct outcome with those addressing
other immediate outcomes of entrepreneurial orientation leaning towards
learning orientation and innovativeness as mediator variables (Soares & Perin,
2019). Thus, studies linking BMI to entrepreneurial orientation are scarce
(Asemokha et al., 2019), especially, in the African context. Thirdly, studies
assessing BMI are predominantly case-based or conceptual (Böttcher &
Weking, 2020), leading to a lack of conceptual clarity and generalisability
(Foss & Saebi, 2017). Thus, a survey based on empirical data is timely.
Additionally, this study provided an opportunity to assess Clauss’ BMI
measurement scales in a diverse industry setup as recommended by Clauss
(2017).
Literature Review
Various theoretical perspectives such as the resource-based theory
(Barney, 1991), Schumpeter’s theory of entrepreneurship (Schumpeter, 1912,
1934, 1942), open innovation theory (Chesbrough, 2003), and the general
systems theory (Bertalanffy, 1972) have been applied in business research
assessing the profitability potential and innovation approaches in BMI and
entrepreneurial orientation studies (Afuah, 2014; Wales et al., 2021). The
resource-based theory has been applied in studies to help explain the role of
resources in enhancing enterprise performance while Schumpeter’s theory has
emphasised the essentiality of incessant innovation in the attainment of
competitiveness and superior performance. This study applied the dynamic
capability view (Teece, 2007) to help explain entrepreneurial orientation as an
internal antecedent of BMI. According to Teece (2007), sensing, shaping, and
seizing are dynamic capabilities of a firm. Because a business model is a
reflection of management’s perception of customers’ needs and how to satiate
those needs and get paid (Teece, 2010), sensing (proactiveness) capability
enables the discovery of opportunities or unmet customers’ needs, which is a
ground for innovating a business model of a firm. Shaping (innovativeness)
capability is critical for the design of a new business model while seizing (risk
taking) capability is associated with taking bold steps to exploit the
opportunities. Thus, dynamic capabilities theory is relevant in this study
advancing entrepreneurial orientation as an antecedent of BMI in the context
of medium enterprises in Kenya.
Advancing the argument that entrepreneurial orientation enhances
BMI by creating endogenous shifts in the prevailing conditions within an
enterprise, Kocoglu et al. (2015) assessed entrepreneurial orientation and
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organizational emotional capacity as antecedents of BMI in a cross-sectional
survey targeting 500 Turkish firms. The survey revealed that entrepreneurial
orientation positively influenced firms to innovate their business models. In a
case study of a Chinese high-end equipment manufacturer, Tian et al. (2019)
identified entrepreneurial orientation as an internal antecedent of BMI. They
argued that entrepreneurial orientation influences firms to perceive impending
market trends (proactiveness), recognise and address customer needs
(innovativeness), and act boldly to execute new business models, suggesting
that entrepreneurial orientation is an antecedent of BMI. Similarly, Bouncken
et al. (2016) argued in relation to the entrepreneurial orientation that
innovativeness and risk taking behaviour and attitude cause the innovation of
business models by influencing the generation of new ideas in service firms.
Mütterlein and Kunz (2017) too, identified entrepreneurial orientation as an
antecedent of BMI in a study of 50 media companies in German.
While acknowledging the scarcity of research linking BMI to
entrepreneurial orientation, Asemokha et al. (2019) study of Finish small and
medium enterprises operating in the international market suggested that
entrepreneurial orientation could be an antecedent of BMI. Ferreras-Mendez
et al. (2021) analysed the link between entrepreneurial orientation and new
product development while considering BMI as a mediating variable, thus the
effect of entrepreneurial orientation on BMI. The results of the analysis based
on a survey of 400 small and medium enterprises in Spain established that
entrepreneurial orientation had a positive effect on BMI as well as new product
development.
Based on the foregoing literature review, the conceptual model in
Figure 1 below was adopted to guide this study. Entrepreneurial orientation
and BMI were conceptualised as antecedent and outcome variables,
respectively, and the following null hypothesis was formulated for testing: H₀:
entrepreneurial orientation does not influence BMI in medium enterprises in
Kenya. Antecedent Variable Outcome Variable
Figure 1.The Effect of Entrepreneurial Orientation on Business Model Innovation
Entrepreneurial orientation
Innovativeness
Proactiveness
Risk taking
Business Model Innovation
Value creation innovation
Value proposition innovation
Value capture innovation
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Methods
Research Design
The study employed a positivist philosophy, which according to
Bryman (2008) often leans towards a deductive approach where hypotheses
are developed on the basis of existing knowledge and tested to generate new
knowledge. This study reviewed extant literature, enabling the formulation of
the hypothesis about the study constructs. The study was descriptive as it
sought to provide a description of the characteristics of the study population,
determine the portion of the population possessing those characteristics as well
as establish the association between the study variables (Easterby-Smith et al.,
2008). A survey strategy of the cross-sectional timeframe was adopted,
enabling the attainment of the required quantitative data in a relatively short
time (Saunders et al., 2012).
Target Population and Sampling Strategy
The target population of the study was medium enterprises in Kenya.
The Klynveld Peat Marwick Goerdeler (KPMG) East Africa and Nation
Media Group (NMG) Top 100 companies provided the sampling frame
(http://eastafricatop100.com). They are described as companies that have
outperformed their peers in terms of profitability, revenue growth, and
geographical expansion as well as contributing to employment opportunities,
and have attained annual gross sales of Kenya shillings fifty million to one
billion. Regulated companies such as banks, insurance, companies listed on
the stock market, law, and accountancy firms are excluded from participating
in the survey. According to the KPMG and NMG Top100 companies’ website,
517 companies have been ranked among the Top 100 companies since 2008
when the survey was first initiated up to 2019. The Top 100 companies were
purposively chosen because they have demonstrated excellence; beating their
peers in annual revenue growth, profitability, geographical expansion,
liquidity stability, and contribution to employment opportunities, suggesting
that they have embraced best management practices. The Top 100 companies
have been used in previous studies focusing on SMEs in Kenya (Bor, 2018;
Ndegwa et al., 2015; Irungu & Marwa, 2015; Ng’aru, Mukulu, & Sakwa,
2018). The sample size was determined based on Cochran’s (1977) sample
size determination formula arriving at 221 medium enterprises. A stratified
random sampling technique (Sharma, 2017) was applied in determining the
ideal sample size across the industries.
Industry classification was based on the International Standard
Industrial Classification of All Economic Activities (ISIC) industry
categorisation (United Nations, 2008). Thus, the survey was carried out among
companies from diverse industries including, accommodation and food
service activities (10), administrative support (5), agriculture, forestry and
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fishing (5), construction (37), education (5), electricity, gas, steam, and air
conditioning services (13), finance and insurance activities (24), human health
and social work activities (22), information and communication (27),
manufacturing (96), professional, scientific and technical activities (75), real
estate (8), transportation and storage, including tour activities (61), water
supply, sewerage, waste management and remediation activities (9), and
wholesale and retail trade, repair of motor vehicles and motorcycles (120).
Data Collection
The study was based on primary data obtained by way of a self-
administered questionnaire. A combination of drop and pick and email
strategy which has been used in previous studies to enhance response rate
(Ndegwa et al., 2015) was employed to deliver and collect the questionnaires
from the respondents. One questionnaire targeting the chief executive officer
or a senior manager was delivered to each of the sampled companies. Thus,
the key respondent approach was adopted (Lechner et al., 2006; Hughes et al.,
2015). This approach was deemed appropriate because the chief executive
officer and other senior managers were considered highly knowledgeable
about their companies’ business strategies, making their responses more
credible and reliable (Hussain et al., 2017; Snihur & Wiklund, 2019; Thuo et
al., 2011). Secondary data was considered inaccessible because the
participating enterprises were not listed companies.
Measurement of Variables
The study adapted validated instruments from prior studies.
Entrepreneurial orientation was measured based on Covin and Slevin’s (1989)
nine items assessing proactiveness (three items), risk taking (three items), and
innovativeness (three items) as composite indicators while BMI adapted
Clauss’ (2017) measurement scale comprising three dimensions, that is, value
creation innovation (thirteen items), value proportion innovation (twelve
items) and value capture innovation (eight items). Thus, thirty-three items in
all. A five-point Likert scale was used where respondents were required to
indicate the extent to which they agreed with a given statement on a scale of 1
to 5 representing “Strongly Disagree” to “Strongly Agree”, respectively.
Missing Values, Outliers, and Common Method Variance
A preliminary analysis revealed that there were no cases of missing
values. Further, an examination of the existence of outliers using Box plots
(Walfish, 2006) and Cook’s distance confirmed that there were no outliers
(Hair et al, 2010). An assessment of common method variance based on
Harman’s Single-Factor Test (HSFT) was done revealing total variance
explained by a single factor of 34.28 percent which was within the
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recommended threshold while nine items attained eigenvalues greater than
one (1), meaning that common method variance was not pervasive (Chang et
al., 2010).
Reliability and Validity of the Measures
Although the study adapted validated measurement scales, reliability
and validity tests were done to assure that the instruments can yield the same
results and measure what was purposed to be measured (Cooper and Schindler,
2014) in the context of medium enterprises in Kenya. Cronbach’s alpha
coefficients and composite reliability were computed to determine instrument
reliability while the average variance extracted and the Fornell-Larcker
criterion which weighs the square root of the average variance extracted values
against the latent variable correlations were used to determine convergent and
discriminant validity, respectively (Hair et al., 2021). Principal component
analysis with the oblique rotation method (PROMAX) was performed to
determine sampling adequacy and factor loadings about entrepreneurial
orientation and BMI. The analysis confirmed sampling adequacy as attested
by the Kaiser-Meyer-Olkin measure of sampling adequacy value of 0.896 and
0.747 for BMI and entrepreneurial orientation, respectively, and a significant
Bartlett’s test of Sphericity Chi-square for both constructs as shown in Table
1 below. According to Williams et al. (2012), a Kaiser-Meyer-Olkin value of
0.5 and above is a testament to sampling adequacy.
Table 1.Kaiser-Meyer-Olkin and Bartlett's Test Depicting Sampling Adequacy
Variable
EO
BMI
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
0.747
0.896
Bartlett's Test of Sphericity
Approx. Chi-Square
498.463
2740.386
df
36
528
Sig.
0.000
0.000
To determine the factor loadings, three factors for entrepreneurial
orientation and ten factors for BMI were requested based on existing literature.
The results of the principal component analysis confirmed three factors for
entrepreneurial orientation (Covin & Slevin, 1989) and ten factors for BMI
(Clauss, 2017, Clauss et al., 2019). Based on the confirmed factor loadings,
confirmatory factor analysis was performed using AMOS version 26 with the
Maximum likelihood estimation method to establish the reliability and validity
of the measurement scales. As shown in Table 2 below, the results indicated
that all items in relation to entrepreneurial orientation achieved strong factor
loadings above 0.55. The computed average variance extracted values were
0.61, 0.59, and 0.58 for proactiveness, risk-taking, and innovativeness,
respectively, while composite reliability values were 0.82, 081, and 0.81, in
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the same order. As presented in Table 3 below, the factor loadings in relation
to BMI were all above 0.50, and the calculated average variance extracted as
well as composite reliability values for all items were above 0.50 and 60,
respectively. The square root of each construct’s average variance extracted
was higher than the corresponding highest correlation with any other construct
suggesting discriminant validity (Fornell & Larcker, 1981).
Cronbach’s alpha attained coefficient values of 0.77 and 0.95 for
entrepreneurial orientation and BMI, respectively as shown in Table 4 below.
The literature recommends cut-off values starting from 0.70, 0.50, 0.50, and
0.60 for Cronbach’s alpha coefficients, factor loadings, average variance
extracted, and composite reliability, in that order, for acceptable measures
(Bonett & Wright, 2015; Hair et al., 2021; Nunnally & Bernstein, 1994).
Accordingly, the reliability and validity of the instruments were established,
thus permitting progression to the hypotheses testing stage.
Table 2. Factor Loadings, Average Variance Extracted, and Composite Reliability of
Entrepreneurial Orientation Items
Item code
Item description
Factor
loading
AVE/CR
Proactiveness
PRO_ii
Our firm has always been on the lookout to seize
initiatives whenever possible in our target
market operations.
0.77
AVE = .61
CR = .82
PRO_i
Our firm has always sought to exploit
anticipated changes in future market conditions.
0.65
PRO_iii
Our firm has always acted opportunistically to
shape the business environment in which it
operates.
0.91
Risk Taking
RIS_iii
Our firm’s business strategy has been
characterized by a tendency to commit
significant resources to projects with uncertain
outcomes.
0.80
AVE = .59
CR = .81
RIS_ii
Our firm has shown a great deal of tolerance for
venturing into the unknown
0.82
RIS_i
Our firm has in general tended to invest in high-
risk projects aiming at getting high returns.
0.68
Innovativeness
INN_ii
Our firm has been at the forefront of
technological leadership through new
product/service development.
0.73
AVE = .58
CR = .81
INN_iii
Our firm has constantly experimented with
unique new processes and methods of
production to seek new and unique solutions.
0.79
INN_i
Our firm has continuously promoted new
innovative products/services to meet our
customers’ needs.
0.77
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Table 3. Factor Loadings, Average Variance Extracted, and Composite Reliability of
Business Model Innovation Items
Item code
Item description
Factor
Loading
AVE/CR
New customer relationships
VPR_xii
We have taken many actions in order to
strengthen customer relationships.
0.83
AVE = .67
CR = .86
VPR_xi
We have emphasized innovative or modern
actions to increase customer retention.
0.84
VPR_x
We have tried to increase customer retention
through new service offerings
0.78
New channels
VPR_ix
We have consistently changed our portfolio of
distribution channels
0.77
AVE = .72
CR = .88
VPR_vii
We have regularly utilized new distribution
channels for our products and services
0.83
VPR_viii
Constant changes in our distribution channels
have led to improved efficiency of channel
functions
0.93
New capabilities
VCR_i
Our employees have constantly received
training in order to develop new
competencies
0.8
AVE = .62
CR = .83
VCR_ii
0ur employees have been up-to-date in
knowledge and capabilities.
0.82
VCR_iii
We have constantly reflected on which new
competencies need to be established in order
to adapt to changing market requirements.
0.73
New offerings/New customers and markets
VPR_iv
We have regularly taken opportunities in new
or growing markets.
0.75
AVE = .52
CR = .88
VPR_i
We have regularly addressed new, unmet
customer needs.
0.77
VPR_ii
Our products and services have been very
innovative.
0.86
VPR_v
We have regularly addressed new, unserved
market segments.
0.69
VPR_vi
We have constantly sought new customer
segments and markets for our products and
services.
0.65
VPR_iii
We have solved customer needs by offering
new and unique products and services.
0.75
VCA_i
We have developed new revenue
opportunities (for example, additional sales,
and cross-selling).
0.55
New cost structure
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VCA_vi
We have actively sought opportunities to
reduce production and service costs.
0.64
AVE = .52
CR = .76
VCA_vii
We have constantly examined our production
and service costs and as necessary, amended
them according to market prices.
0.79
VCA_v
We have regularly reflected on our pricing
strategy.
0.72
New partnerships
VCR_vii
We have constantly been searching for new
collaboration partners.
0.8
AVE .68 CR
= .82
VCR_viii
We have regularly utilized opportunities that
arise from the integration of new partners into
our processes
0.91
VCR_x
New collaboration partners have been
regularly helping us to further develop our
business model.
0.76
New processes
VCR_xi
We have been able to significantly improve
our internal processes.
0.72
AVE = .61
CR = .82
VCR_xii
We have been utilizing innovative procedures
and processes during the manufacturing of our
products and delivery of services.
0.77
VCR_xiii
Our existing processes have been assessed
regularly and significantly changed as
needed.
0.84
New revenue models
VCA_iii
We have complemented or replaced one-time
transaction revenues with long-term recurring
revenue models (for example, Leasing).
0.66
AVE = .43
CR = .69
VCA_iv
We have not relied on the durability of our
existing revenue sources.
0.51
VCA_ii
We have been increasingly offering integrated
services (for example, maintenance contracts,
and after-sale service) in order to realize long-
term financial returns.
0.78
New technology/equipment
VCR_v
Our technical equipment has been very
innovative.
0.84
AVE = .61
CR = .82
VCR_vi
We have regularly utilized new technical
opportunities to extend our product and
service portfolio.
0.74
VCR_iv
We have kept the technical resources of our
company up-to-date.
0.76
New cost structure
VCR_ix
We have regularly been evaluating the
potential benefits of outsourcing.
0.55
AVE = .47
CR = .63
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VCA_viii
We have regularly utilized opportunities that
arise through price differentiation.
0.8
Table 4. Cronbach’s Alpha Coefficients Depicting Reliability of the Measurement
Instruments
Variable
Cronbach's Alpha
Number of Items
Entrepreneurial Orientation
0.773
9
Business Model Innovation
0.946
33
Results
Descriptive and Correlation Analysis
Descriptive statistics were applied to ascertain the characteristics of the
respondents and the firms they represented. Out of 221 questionnaires
distributed, 141 were returned, attaining a response rate of 64 percent.
However, seven questionnaires were eliminated because they were not filled
by the target respondents leaving 134 valid responses, 60.6 percent of the
sampled enterprises. The response rate was judged appropriate for regression
analysis based on Hair et al.’s (2010) recommendation that a minimum of one
hundred cases is acceptable. The study revealed that 59 percent of the firms
were family-owned and 41 percent were non-family-owned. 72.4 percent of
the firms had between 1-100 employees, 14.9 percent had between 101-200
employees, and 13 percent had over 300 employees while 4 percent of the
firms had employees ranging from 201-300. According to Baker and Sinkula
(2009), a firm's number of employees for the purpose of determining firm size
depends on the industry in which it functions.
The respondents were 69.4 percent male and 30.6 percent female. They
were senior managers occupying varying positions, such as the chief executive
officer (29.9 percent), finance manager (16.4 percent), human resource
manager (26.9 percent), marketing manager (14.2 percent), operations
manager (5.2 percent), business development manager (4.5 percent) and
procurement manager (3.0 percent). In terms of experience, 47.8 percent
indicated that they had worked in their current industry for over 10 years, 23.1
percent between 5-10 years, and 29.1 percent had below 5 years of experience
in their current industry. Regarding the level of education, 53.0 percent
reported that they had attained an undergraduate degree, 25.4 percent had a
master's degree, and 16.4 percent had obtained a diploma certificate. Those
who reported having attained PhD were 2.2 percent, while those who had
achieved high school and trade test certificates were 1.5 percent, respectively.
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Mean, Standard Deviation, Coefficient of Variation, and Pearson
Correlation
Table 5 below provides descriptive statistics regarding mean, standard
deviations, coefficient of variation, and correlation coefficients among the
study variables. Based on the computed coefficient of variation, the spread
from the mean for all the variables was low, meaning that there was less
variation in the received responses. Pearson’s correlation coefficient which
measures the degree and direction of association between study variables
(Onwuegbuzie et al., 2007; Taylor, 1990) as shown in Table 5 below indicated
the association between BMI and entrepreneurial orientation was positive and
moderate (Cohen & Holliday, 1982, Taylor, 1990), thus, providing initial
expected results of the hypothesis testing.
Table 5. Mean, Standard Deviation, Coefficient of Variation, and Pearson Correlation
Construct
Sample size
Pearson Correlation
Mean
Standard
deviation
Coefficient
of Variation
(Percent)
EO
BMI
EO
134
3.82
0.57
14.92
1
BMI
134
3.97
0.51
12.85
.502**
1
**. Correlation is significant at the 0.01 level (2-tailed).
Hypothesis Testing
The study’s objective was to ascertain the influence of entrepreneurial
orientation on BMI, thus, ordinary least square regression analysis was
performed to test the hypothesis that entrepreneurial orientation does not
influence BMI among medium enterprises in Kenya. The estimated simple
linear regression equation was: BMI = βo + β1EO + ε, where EO is
entrepreneurial orientation, β1 is the regression coefficient for EO, β0 is the
regression constant while ε is the error term. As revealed in Table 6 below, the
influence of entrepreneurial orientation on BMI was weak and positive as
evidenced by the coefficient of determination (R² = 0.252). The overall model
was significant since the P-value was less than 0.05 level of significance. Thus,
the null hypothesis that entrepreneurial orientation does not influence BMI
among medium enterprises in Kenya was not supported by the analysis and
therefore was rejected. On individual significance, both the constant and
entrepreneurial orientation were significant. The consequent estimation
equation was thus, BMI = 2.266 + 0.445EO, meaning that a unit change in
entrepreneurial orientation will on average lead to 0.445 units increase in BMI.
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Table 6. Model Summary, Analysis of Variance and Coefficients of
Entrepreneurial Orientation on Business Model Innovation
Model Summary (Goodness-of-fit)
Model
R
R Square
Adjusted R
Square
Std. Error of the Estimate
1
.502a
0.252
0.246
0.43975
a. Predictors: (Constant), EO
Analysis of Variance (Overall Significance)
Model
Sum of
Squares
df
Mean Square
F
Sig.
1
Regression
8.596
1
8.596
44.452
.000b
Residual
25.526
132
0.193
Total
34.122
133
a. Dependent Variable: BMI, b. Predictors: (Constant), EO
Coefficients (Individual Significance)
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
2.266
0.258
8.790
0.000
EO
0.445
0.067
0.502
6.667
0.000
a. Dependent Variable: BMI
Discussion
This study investigated the impact of entrepreneurial orientation on
BMI in medium enterprises in Kenya. This was in response to calls from
scholars requiring the identification of the immediate outcome of
entrepreneurial orientation other than firm performance (Covin & Wales,
2019; Markin et al., 2018) since most studies had concentrated on the direct
entrepreneurial orientation-performance link attaining mixed results (Soares
& Perin, 2019. Additionally, other scholars contended that BMI literature was
at an embryonic stage requiring the identification of its antecedents and
outcomes (Foss & Saebi, 2017). This study established that entrepreneurial
orientation positively influenced BMI in medium enterprises in Kenya, thus,
suggesting that entrepreneurial orientation is an antecedent of BMI in medium
enterprises in Kenya. However, as indicated by the coefficient of
determination, the statistical power was weak suggesting that other variables
not incorporated in the model contributed to the variance in BMI. The results
suggested that entrepreneurial orientation accounted for about 25.2 percent (R²
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= 0.252) of the variance in BMI while 74.8 percent was attributable to other
variables not included in the model. The findings are consistent with other
studies (Asemokha et al., 2019; Bouncken et al., 2016; Ferreras-Mendez et al.,
2021; Kocoglu et al., 2015; Kunz, 2017; Mütterlein & Kunz, 2017) which
established that entrepreneurial orientation had a positive influence on BMI.
The study contributes to the literature by demonstrating the importance
of dynamic capability theory in explaining the effect of entrepreneurial
orientation on BMI. Empirically, by establishing that entrepreneurial
orientation had a positive influence on BMI, the study partially addressed the
twin concerns raised by scholars calling for the identification and assessment
of antecedents of BMI as well as the immediate outcome of entrepreneurial
orientation other than enterprise performance. This is a novel frontier as there
is no other study in the context of medium enterprises in Kenya, which has
explored entrepreneurial orientation as an antecedent of BMI. Additionally,
the study addressed a call to assess the BMI measurement scale as developed
by Clauss (2017) in a heterogenous industry environment beyond the
electronic sector. Regarding the implication to industry practitioners, the study
highlights the importance of entrepreneurial orientation on BMI, hence,
requiring managers to embrace entrepreneurial behaviour and attitude to
enable BMI in their enterprises. The study suggests a need for the board of
directors as policymakers to create an environment that embraces
entrepreneurial orientation as well as provide resources to enable managers to
embrace innovativeness, proactiveness, and risk taking behaviour and attitude
in their decision making and operations. Additionally, there is a need for the
government of Kenya to formulate policies that are supportive of
entrepreneurial orientation behaviour and attitude in medium enterprises in
Kenya. This study was not without limitations, which on the flip side presents
opportunities for future research. First, the study relied on cross-sectional
survey data. Accordingly, it is recommended that future studies consider
exploring the relationship among the study variables from a longitudinal
perspective. Second, data in the current study was limited to the KPMG East
Africa and NMG annual Top 100 medium enterprises in Kenya meaning that
there are other medium enterprises left out in the study. Thus, there is a need
to extend the sampling frame beyond the surveyed enterprises to incorporate
other qualifying entities. Third, the study relied on self-reported data from
single respondents, thus, it is recommended to conduct a study that
complements self-reported data with secondary as well as using different
respondents for predictor and outcome variables. Finally, the study did not
explore other potential strategic orientations such as market orientation and
learning orientation that could influence BMI and the potential outcome of
BMI and entrepreneurial orientation such as enterprise performance in the
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surveyed medium enterprises. Since the ultimate goal of a business is
performance, there is a need to extend the examined model to incorporate
enterprise performance as the final outcome, thus assessing the indirect effect
of entrepreneurial orientation on performance through BMI as mediating
factor. Despite the limitations, the study employed a robust research design to
mitigate the shortcomings. Thus, the study’s contribution to the theory and
knowledge development in entrepreneurship literature, practice, and policy
formulation is immense.
Conclusion and Recommendation
The aim of this study was to determine the impact of entrepreneurial
orientation on BMI in medium enterprises in Kenya. Based on the findings,
the study concluded that entrepreneurial orientation had a positive significant
impact on BMI in medium enterprises in Kenya. Thus, to enhance BMI in their
enterprises, managers of medium enterprises in Kenya should embrace
entrepreneurial behaviour and attitude among other strategies. Accordingly,
the study recommends that managers of medium enterprises in Kenya should
be entrepreneurially orientated to enhance BMI in their firms. The study
further recommends that the board of directors as policymakers create an
environment that embraces entrepreneurial orientation as well as provide
resources to enable managers to embrace innovativeness, proactiveness, and
risk taking behaviour and attitude in order to enhance BMI in medium
enterprises in Kenya. The government of Kenya should as well formulate
policies that encourage an entrepreneurial culture of innovativeness,
proactiveness, and risk taking so as to promote BMI in medium enterprises in
Kenya. Finally, the study recommends further studies to address the study’s
limitations.
Conflicts of Interests
We confirm that there is no conflict of interest to declare as far as this
paper is concerned.
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