The Effect of Smart University Characteristic on Entrepreneurial
Orientation of Students: The Mediating Role of Knowledge Sharing
AMEL FARHAN SWADI1, AHMAD ABED AL-HAYY AL-DALAIEN2
1Master, Officer of Management Techniques, Department of Office Management Techniques,
Southern Technical University, Department of Office Management Techniques,
Basra Technical Institute, IRAQ
2Faculty of Technology Management, Universiti Tenaga Nasional, MALAYSIA
Abstract: - Smart university is a new concept in education. The characteristic and impact of this university on
creating entrepreneurial oriented community has not received adequate attention in the context of developing
countries. The purpose of this study is to examine the effect of smart university characteristics (course quality,
staff capability, and infrastructure) on entrepreneurial orientation (EO). Knowledge sharing between industry
and university is proposed as a mediator. This study is a quantitative and it collects the data using a
questionnaire. The data collection took place between April 2020 to August 2020. The data was collected from
279 master of business administration (MBA) graduates and students in Iraq. The data analysis was conducted
using smart partial least square (Smart PLS). The findings showed that the effect of smart university
characteristics are significant. In addition, knowledge sharing mediated the effect of the characteristics, except
infrastructure, on EO. More attention has to be paid to the employment of skilful staff and to focus on the
relationship between university and industry.
Key-Words: Smart University, Entrepreneurial Orientation, Knowledge Sharing, Knowledge based View
Received: August 23, 2021. Revised: March 19, 2022. Accepted: April 23, 2022. Published: June 15, 2022.
1 Introduction
The world is changing rapidly and the need for
smart technology is urgent. Recently, several smart
applications and techniques were introduced. This
includes the smart home, smart city, and smart
applications. Among these smart technology, the
smart university is essential to enable the
transformation to smart live [1]. Building smart city,
smart home or smart application is linked to the
smart university. This is because a smart university
is a university that has the potential to improve the
education, research, and work experience of
stakeholders to match the need of the industry. This
can be enabled by utilizing digital, innovative, and
internet based technologies for the goodness of the
society at large [2]–[5].
Smart university is the engine for creating smart
city. Most countries rely on university to change the
behaviour of citizens and to develop the capability
of the stakeholders. However, government is the
main driver of the smart university and its support
for these universities is essential to reduce the gap
between industries and university and to encourage
the collaboration between these entities [6]. The
collaboration between university and industry will
result in a company supporting research that are
conducted in the university [7].
This has changed the role of university from a
knowledge production organization to a product and
service producers with the collaboration with the
industry [8], [9]. Previous studies dealt with the
smart university from technical perspective and
employed technology such as the Radio Frequency
Identification (RFID) and internet of things (IoT)
[10]–[12]. The role smart university in creating EO
stakeholders has not been examined.
Nevertheless, great organizations are established
and build by entrepreneur [13]. There is a firm link
between smart university and the entrepreneurship.
Creating a smart university will lead to an
entrepreneur graduate. Similarly, the creation of
smart university is done by entrepreneurs [13].
However, the link between smart university
characteristic and EO has not been investigated by
previous studies. In addition, in the current
environment where the COVID19 has forced for
lockdown and social distance, the need for a smart
university has become more urgent [14], [15].
Characteristic of smart university include the staff
capabilities as well as the use of the technology. In
addition, the content of courses and its relatedness
to the industry. These characteristics could
potentially increase the entrepreneurship of students
in the university [16], [17]. The current situation in
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DOI: 10.37394/23207.2022.19.102
Amel Farhan Swadi, Ahmad Abed Al-Hayy Al-Dalaien
E-ISSN: 2224-2899
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most of developing countries refer to the increase in
the level of unemployment. These are mainly driven
by weak economic growth and less initiatives as
well as the lack of experience by graduates to
involve in the workplace. Having entrepreneurial
orientation might help in creating more small
projects and reduce the employment [18], [19]. This
requires the graduates to be equipped with the
knowledge and the experience of the marketplace to
achieve better performance and be successful [17].
In this process, the knowledge sharing and
collaboration between industry and university
enables the creation of graduates that suits the
market demand and able to produce relevant product
and services as well as provide adequate services to
community [20]–[23]. Knowledge sharing between
these groups has not been discussed in previous
studies and this study is among the first study to
discuss this issues in the context of developing
countries such as Iraq. In Iraq, the employment
mostly is based on the public sectors and in
particular in the oil and gas industry [24].
Universities are managed in a traditional way and
the courses are outdated. The gap between industry
and course taught in the university is wide.
Therefore, this study aims to understand the effect
of smart university characteristic on the EO in the
context of developing countries such as Iraq.
Further, the study aims to examine the mediating
role of knowledge sharing (university-industry)
between smart university characteristic and EO. In
the next section, the literature as well as the research
methodology, findings, discussion, implication, and
conclusion are given.
2 Literature Review
The literature focuses on the concept of smart
university and EO. It also discusses the theoretical
framework and the conceptual framework of this
study which includes the hypotheses development.
2.1 Smart University
In the 20th century, the role of university has
changed. University turned into “research
university” during the 20th century and this is
followed by the entrepreneurial university. In the
research university, the main purpose is to produce
knowledge [25]. However, in an entrepreneurial
university, the knowledge is commercialized and
sold as product or service to the industry. This has
increased the collaboration between university and
industry and reduced the gap between the graduates
and the market demand [5], [26], [27]. With the
advancement of technology and the introduction of
technology such as Internet of Things and artificial
intelligence as well as the increase collaboration
between industry and university, the need for smart
university has increased and this has resulted in
several initiatives to create smart city in which the
main role is laid on the smart university [1], [28],
[29].
Smart university is characterised by several factors.
It is highly interacted with the industries and
product and services that are desired by the industry.
It also focuses on several aspects that are related to
the society and provide knowledge to all
stakeholders. The contribution of the smart
university and its graduates is not limited to the
present but it is extended to the future generation.
Smart university focuses on several domains that are
the smart campus (which include the required smart
software, hardware, building, and sensors) [4], [30],
smart people (smart staff, students, non-academic
staff) [12], [27], [29], smart education and research
[26], [28], [31]–[33], smart governance [25], [27]
(management, education policies, and budgeting),
and smart influence (on the community) [1], [26],
[27]. In this study the focuses is on the people which
are the staff of the university as well as the
infrastructure. Further, the focus is on the content of
the courses and its quality.
2.2 Theoretical Framework
This study is focusing on the smart university and
its characteristic and their effects of entrepreneurial
orientation (EO). The utilization of a smart
university is an innovative behaviour. According to
the Organization-Technology-Environment
framework developed by [34], the usage of an
innovation is dependent on the organizational
factors as well as the technological factors, and the
environmental factors. In this study, the
organizational factors is operationalized as the
course content quality and information quality. Staff
capabilities are also organizational orientated. The
infrastructure of the university are considered as the
technological factors. Environmental factors can be
the knowledge sharing between university and staff.
Another theory that support the conceptual
framework of this study is the knowledge based
view and the information system (IS) success which
indicate that the information quality, system quality
and service quality affect the satisfaction of users
which in turn affect the benefit that users can gain
from the system [35], [36]. Knowledge based view
indicates that better management of knowledge can
lead to a competitive advantage which in turn affect
the organizational outcome [37], [38].
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2.3 Entrepreneurial Orientation
Entrepreneurial Orientation (EO) is defined as an
organizational willingness to find and accept new
opportunities and taking responsibility to affect
change [39]. Early researchers investigated EO and
operationalized it to include the risk taking,
proactiveness, innovativeness and autonomy [40],
[41]. EO is a multidimensional variable. However,
in this study following the operationalization of
[42], EO is measured as a unidimensional variable.
EO is important for organizations and individuals as
it helps in improve the competitiveness and it helps
in creating competitive advantage for organization
[43]–[46]. On the individual level, being an
entrepreneur is important to initiate new idea and
innovation. Nevertheless, few of the previous
studies examined this variable in the context of
smart university in developing countries. Therefore,
this study will deal with this variable in this context.
2.4 Conceptual Framework
Based on the knowledge-based view, TOE
framework, and IS success, this study proposes that
the smart university characteristic will have
important effect on the EO of students in the Iraqi
university. The study proposes that the knowledge
sharing between university and industry will
mediate the effect of smart university characteristic
on EO. Figure 1 shows the conceptual framework of
this study.
Fig. 1: Conceptual Framework
2.4.1 Smart University Characteristic and EO
Smart university is a new innovation and it deploys
the latest technology to enhance the capabilities of
its stakeholder. This variable is operationalized to
include the quality of courses in the university as
well as the information quality, the staff capabilities,
and the infrastructure. Course content quality is
usually referred to as the information quality and it
is the content that can be generated by the system
[47]. In the IS success model, information quality
was proposed as an important indicators for the
usage and the benefit of a system [35]. Course
quality is important for students to be aware and
updated about the new trend and technology as well
as to be up to date with the market changes [48],
[49]. In this study, the course quality is expected to
affect positively the EO of students.
The staff capabilities is also considered as an
important characteristic of a smart university.
Experienced and highly educated and qualified staff
are able to provide the students with adequate
knowledge about the market changed and the
possible opportunities [50]–[52]. They also can
equip the students with the knowledge that are
required to analyse and understand the events that
occur in an country and deploy these changes in
making accurate decisions [20], [53], [54]. Thus,
this study proposes that when the staff has adequate
capabilities, they are able to develop the
entrepreneurial skills of students.
Infrastructure of the university is critical for
fulfilling the duties of the staff and for students to
access to knowledge and learn about the new
techniques. Building this infrastructure is essential
to start a smart university. Several studies refer to
the importance of infrastructure in a smart
universities [1], [5], [25]–[27], [55]. Infrastructure
also includes smart classroom and easy access to
knowledge from anywhere at any time [27]. Having
the needed infrastructure enable the students and the
staff to be equipped and able to share the knowledge
with each other. Thus, this study proposes the
following hypotheses.
H1: Smart university will lead to a better EO among
students in Iraq.
H2: Course quality has a positive impact on EO
among students in Iraq.
H3: Staff capabilities have positive impact on EO of
students in Iraq.
H4: Infrastructure has a positive impact on EO of
students in Iraq.
2.4.2 Knowledge Sharing as Mediator
Knowledge sharing is defined as the exchange of
knowledge among two parties [56]. It includes a
mutual benefit for both parties [57], [58]. The
quality and quality of the knowledge shared among
the university and industry can affect the course
content, staff capabilities and the infrastructure in
the university [1], [27], [59], [60]. Knowledge
sharing was found to mediate the effect of IT
capabilities and innovation performance [61].
Knowledge sharing also mediated the effect of
human resource on organizational ambidexterity
[62]. Knowledge sharing is also mediated the effect
of information technology on innovation [63]. In
addition, knowledge sharing mediated also the
Smart Universities
Characteristic
Course Quality
Staff Capabilities
Infrastructure
EO
Knowledge
Sharing
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effect of person-organization fit on innovative work
behaviour [64]. In this study, knowledge sharing is
expected to mediate the effect of course quality,
staff capabilities, and infrastructure on EO. Thus,
the following hypotheses are proposed:
H5: Knowledge sharing mediates the effect of smart
university characteristics on EO.
H6: Knowledge sharing mediates the effect of
course quality on EO.
H7: Knowledge sharing mediates the effect of staff
capabilities on EO.
H8: Knowledge sharing mediates the effect of
infrastructure on EO.
3 Research Methodology
This study adopts a quantitative approach to fulfil
the objectives. The study determines the population
to be Master of business administration (MBA)
students and graduates. It is referred to here as MBA
community. The reasons for chosen these groups of
respondents is due to the notion that they are
familiar with the topic of this study, and they have
the required knowledge to answer the questionnaire
of this study. The study uses a convenience
sampling technique. This is because this technique
provides easy access to the respondents who fit in
this study category. The data is collected using a
questionnaire. The questionnaire is adopted from
previous studies. Measurement of EO is adopted
from [42]. Knowledge sharing was adopted from
[8], measurement of smart university characteristic
such as staff capability is adopted from [65],
measurement of infrastructure was adopted from
[66], course quality was adopted from [67]. The
questionnaire was translated into Arabic using back-
to-back translation. The questions were validated by three
experts. Based on the suggestions of experts, a
modification was made on the content of knowledge
sharing, staff capabilities, and course quality.
A pilot study was conducted on 37 students. The
results showed that the Cronbach’s Alphas for all
the variables are greater than 0.70 supporting the
notion that the measurements are reliable. The data
collection took place between December 2020 and
February 2021. Follow up were conducted to
increase the response rate. As a result, a total of 318
responses were collected. The data was collected
using network referral. The data was filtered for
missing values, outliers, and normality as well as
multicollinearity. The findings showed that 30
responses were removed based on missing value
ground. In addition, nine responses were also
removed due to outliers’ issues. The data is
normally distributed because the value of skewness
and kurtosis are less than absolute 1. Further, no
multicollinearity issue in the data because the value
of tolerance is greater than 0.20 and the value of
variation inflation factor (VIF) is less than five.
4 Findings
4.1 Background Information
Among the 279 respondents who took part in this
study, there are 85.7% are males while females
constitute 14.3%. a total of 85.7% of the
respondents are younger than 45 with 67.4% are
graduated with MBA degree and 32.6% are still
studying their master’s degree. Experience of the
respondents are varied and majority (82.5%) of
them have experience of less than 10 years.
4.2 Measurement Model
In the measurement model, there are five criteria
must be examined to assess the measurement model
[68], [69]. The factor loading (FL) for all the items
should be 0.70 or greater. In addition, the composite
reliability (CR) and Cronbach’s Alpha (CA) should
be equal or greater than 0.70. The convergent
validity is achieved if the value of Average Variance
Extracted (AVE) is greater than 0.50. In addition,
the fulfilment of the discriminant validity happens if
the square root of AVE is greater than the cross
loading. The first criterion assessed was the factor
loading and it was found that some of the items of
EO, knowledge sharing (KS), infrastructure (INF)
has weak factor loading. Accordingly, some items
were removed to enhance the reliability and validity
of the model. Table 1 shows that all the criteria were
achieved. All FL of the items is higher than the
threshold of 0.70. CR and CA are higher than 0.70.
Lastly, AVE has value higher than the threshold
supporting the achievement of the convergent
validity.
Table 1. CA, CR, and AVE of Constructs
Cronbach'
s Alpha
Composite
Reliability
AVE
0.942
0.956
0.813
0.944
0.960
0.857
0.868
0.903
0.700
0.947
0.959
0.825
0.942
0.956
0.812
To examine the discriminant validity, the square
root of AVE was calculated and compared with the
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cross loading. Table 2 indicates that the discriminant
validity was fulfilled due to the fact that the number
in bold are greater than the cross loading with other
variables.
Table 2. Discriminant Validity
CQ
SC
INF
KS
EO
Course
quality
0.901
Staff
capability
0.543
0.92
5
Infrastructure
0.445
0.47
3
0.83
7
Knowledge
sharing
0.168
0.14
0
0.28
7
0.90
8
Entrepreneuri
al orientation
0.518
0.64
9
0.38
5
0.13
3
0.90
1
4.3 Structural Model
To assess the structure model, [70] indicates that
there are four criteria. The first criteria are the R-
square and it is widely accepted that a value of
between zero to 0.25 is weak while values between
0.26 to 0.50 is moderate and between 0.51 to 0.75 is
excellent. In this study, the R-square (R2) was found
0.36 for knowledge sharing and 0.46 for EO. The
second criterion is the predictive relevance (Q2).
This value indicates whether or not the variables can
predict the dependent variable. The accepted value
is greater than 0. In this study, it was found that Q2 for
the dependent variables such as knowledge sharing and
EO were 0.26 and 0.35 respectively indicating that the
condition of predictive relevance has been met. The effect
size is acceptable if the value of f2 is greater than 0.02. In
all the paths of this study, the value of f2 was greater than
0.02. The last criterion is the path coefficient, and it is
assessed in the following section.
4.3.1 Direct Effect
In this section, the direct effect of the variables is
tested. The results of hypotheses testing are given in
Table 3. The hypotheses were testing using 5,000
bootstrapping and p-value less than 0.05.
Table 3. Results of Direct Effect Hypotheses
H
Path
β
Std
T
P
H
1
Smart University
Characteristic -> EO
0.6
15
0.0
40
15.4
80
0.0
00
H
2
Course Quality -> EO
0.3
11
0.0
59
5.26
3
0.0
00
H
3
Staff Capability -> EO
0.3
18
0.0
59
6.48
0
0.0
00
H
4
Infrastructure -> EO
0.1
24
0.0
49
2.09
2
0.0
37
Note: H: Hypothesis, β, path coefficient, Std: Standard
Deviation, T= t-value, P: p-value.
The effect of smart university characteristic on EO
was tested and it was found that it is significant with
β= 0.615 and p-value less than 0.001 as shown in
Table 3. This indicates that the smart university
characteristic are main predictors of creating EO
among students. Thus, H1 is supported. The second
hypothesis claimed that course quality affects EO
significantly. The findings in Table 3 shows that it is
true. Course quality is a predictor of EO with β=
0.311 and P-value less than 0.001 supporting the
claim that course quality has a significant effect on
EO. Accordingly, H2 is supported. Staff capability
also predicted to have a significant effect on EO.
Findings in Table 3 indicate that the effect of staff
capability on EO is significant with β= 0.318 and P-
value equal to <0.001 supporting the third
hypothesis (H3) of this study. For the effect of
infrastructure on EO, it was found that the effect is
significant (β= 0.124, P<0.001) supporting H4.
4.3.2 Mediating Role of Knowledge Sharing
The mediating effect of knowledge sharing between
the variables and EO was examined by comparing
the direct and indirect effect. The direct effect
without mediator is given in Table 3. For the direct
effect including the mediator and the indirect effect,
it is given in Table 4.
Table 4. Result of Testing Knowledge Sharing as a
Mediator
H
β
St
d
T
P
Lab
el
H
5
Smart University
Characteristic -> EO
0.
48
7
0.
05
2
9.
34
5
0.
00
0
Part
ial
Sup
port
Smart University
Characteristic -> Knowledge
Sharing ->
EO
0.
12
8
0.
03
0
4.
34
1
0.
00
0
H
6
Course Quality -> EO
0.
24
9
0.
06
0
4.
11
7
0.
00
0
Part
ial
Sup
port
Course Quality ->
Knowledge Sharing -> EO
0.
06
3
0.
02
3
2.
77
3
0.
00
6
H
7
Staff Capabilities ->
Entrepreneurial Orientation
0.
26
1
0.
05
1
5.
06
7
0.
00
0
Part
ial
Sup
port
Staff Capabilities ->
Knowledge Sharing -> EO
0.
05
6
0.
01
8
3.
08
5
0.
00
2
H
8
Infrastructure ->
Entrepreneurial Orientation
0.
09
5
0.
05
5
1.
72
9
0.
08
4
Rej
ecte
d
Infrastructure -> Knowledge
0.
0.
1.
0.
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Sharing -> EO
02
9
01
8
58
1
11
4
Knowledge Sharing ->
Entrepreneurial Orientation
0.
23
7
0.
05
6
4.
20
4
0.
00
0
Table 4 shows the direct effect with mediator
included and the indirect effect through the
mediator. In comparison between Table 3 and Table
4, the effect of smart university characteristic on EO
was reduced to 0.487. The indirect effect is
significant indicating that there is a partial
mediation. Thus, H5 is supported. Similarly, for H6,
knowledge sharing mediated the effect of course
quality on EO. Thus, H6 is supported. For H7, the
effect of staff capability on EO was mediated by
knowledge sharing. Therefore, H7 is supported. In
term of H8, knowledge sharing did not mediate the
effect of infrastructure on EO. The direct effect and
the indirect effect are not significant. Thus, H8 is
rejected. Figure 2 shows the structural model of the
mediating effect of knowledge sharing.
Fig. 2: Structural Model
5 Discussion and Implications
This study investigated the effect of smart university
characteristic on EO of MBA graduates and students
in Iraq. The findings showed that having a smart
university will impact greatly the creation of EO
among MBA graduates and students. The most
important characteristic is the staff capability.
Knowledgeable staff are able to shape the way of
thinking of their students and they have high impact
on their mentality and the way they look into issues
and solving problems. The course quality is also
important. Having updated courses that are
reflective of the market is important for students to
understand the reality of the market and the way of
solving contemporary problems in management and
business administration. The infrastructure has the
least important effect on the EO. This could be due
to the notion that infrastructure is complementary to
the staff capability and the course quality. Staff
cannot do their duties without proper infrastructure.
In line with the above findings, previous studies
found that the staff capability as well as the course
quality and infrastructure are important for the
creation of EO [20], [50]–[54].
The findings also showed that knowledge sharing
mediated the effect of smart university
characteristic, course quality, and staff capability.
This indicates that part of the relationship between
the characteristic and EO can be explained by
knowledge sharing. Knowledge sharing between
industry and university is vital for improving the
content and the quality of the courses provided by
the university. It is also important to sharpen the
capability of the staff and enhance their
understanding of the market mechanisms.
Knowledge sharing did not mediate the effect of
infrastructure on EO. This could be due to the
notion that collaboration between industry and
university in the context of Iraq take a face-to-face
form rather than online form. The findings regarding
the role of knowledge sharing as a mediator are in
agreement with the findings of previous studies [1],
[27], [59]–[64].
Based on the findings of this study, decision makers
are advised to sharpen the skills of their academic
staff. Iraqi universities that wish to be smarter have
to employed academic staff with high capability in
term of research and teaching as well as
relationship. This soft skills of relationship is
usually ignored and adequate attention has to be
paid to this aspect in the employment contract. In
addition, in order for the university to enhance its
intellectual capability, the course quality and its
relevant to the market demand must be periodically
assessed. Case studies approach is good for
improving the skills of students. Relevant case study
from Iraqi context could be developed and students
should be given the opportunity to solve these cases.
The cases can be industry-based. Frequent meeting
should be held between academic and business
organizations. These meetings can be encouraged by
the government and in the public sector at the
beginning and in later stage can be extended to
include the private sector. Such meetings enhance
the knowledge sharing between the two parties
which found to be a critical factor in this study. It
also helps in creating case studies about the current
business problem to train the students.
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This study has contributed to the literature in term
of smart university characteristic in the context of
developing countries. Such studies are missing and
this study is believed to be the first, at least in Iraq,
to deal with smart university characteristic and its
impact on EO. Knowledge sharing as a variable has
been examined in several studies. However, the
contribution of this study is to examine it as a
mediating variable between industry and university.
The study also contributed to the theory by combing
several theories from the technology adoption
context and the business context. The theories that
are included in this study are the knowledge based
view, TOE and IS success. The combination
between these theories managed to explain
significant part of the variation caused by smart
university on EO.
6 Conclusion
This study aimed to explore the effect of smart
university characteristics on EO in the context of
Iraq. The study deployed the MBA community in
Iraq as the population due to the fact that this
community is well informed about the implication
and concept of smart university and EO. Findings of
previous studies and theories were reviewed to
develop the conceptual framework. The findings
indicated that smart university characteristic (course
quality, staff capability, and infrastructure) have
significant effect on EO. The study also showed that
knowledge sharing mediated the effect of smart
university and its characteristic except infrastructure
on EO.
As a limitation of this study, the respondents
included only MBA community. Other graduates or
students from different specialization were not
included. The study also used the convenience
sampling, and this kind of sampling has
generalization limitation. The findings of this study
can be generalized on Iraqi MBA community. As a
way forward, future studies are recommended to
increase the sample and to include MBA and non-
MBA community. Engineers and medical doctors
can be also included. Further, it is suggested for
future studies to conduct a qualitative study by
including industry and university employees to
understand the way of creating effective EO.
References:
[1] R. K. R. Kummitha, “Smart cities and
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