Sustainability 11 00954 PDF
Sustainability 11 00954 PDF
Sustainability 11 00954 PDF
Article
Developing Sustainable Healthcare Systems in
Developing Countries: Examining the Role of
Barriers, Enablers and Drivers on Knowledge
Management Adoption
Jawad Karamat 1, *, Tong Shurong 1 , Naveed Ahmad 1 , Sana Afridi 2 , Shahbaz Khan 3
and Nidha Khan 4
1 School of Management, Northwestern Polytechnical University, Xi’an 710072, China;
stong@nwpu.edu.cn (T.S.); naveedahmad@mail.nwpu.edu.cn (N.A.)
2 Department of Pathology, School of Basic Medical Science, Xi’an Jiaotong University, Xianning West Road,
Xi’an 710049, China; sanaafridi1@sut.xjtu.edu.cn
3 School of Automation, Northwestern Polytechnical University, Xi’an 710072, China;
shahbaz@mail.nwpu.edu.cn
4 Department of Politics and International Relations, The University of Auckland,
Auckland 1010, New Zealand; nkha086@aucklanduni.ac.nz
* Correspondence: jawad@mail.nwpu.edu.cn
Received: 1 January 2019; Accepted: 11 February 2019; Published: 13 February 2019
1. Introduction
Sustainability as an emerging issue and has been widely discussed in the healthcare sector.
Considerable literature focused on the need of the sustainable, efficient, and effective healthcare [1].
However, currently, the healthcare systems are facing multiple challenges to cope with the current
healthcare needs, and sustainability is considered to be a significant requirement to obtain strategic
a fit for the future [2,3]. The world business council for sustainable development (WBCSD) defined
sustainable development as “development that meets the needs of the present without compromising
the ability of future generations to meet their own needs” [4]. Based on the current issues and future
needs, sustainable development captured a great focus in the development of the healthcare system.
Developing a sustainable healthcare system can lead towards improved healthcare performance.
The healthcare sector needs to be able to utilize its current resources more effectively, to find new
resources, manage its finances, improve service, and response to emergency situations [5]. Currently
both the management and treatment of patients are suffering in the public and private sector
healthcare organizations.
Due to intense competition in healthcare, the healthcare industry, as the largest contributor to
the service industry, is facing enormous challenges and developing an effective sustainable healthcare
system has become a difficult task. Therefore, effective and sustainable healthcare systems are key
to providing quality healthcare at a low cost, with large population coverage and effective disease
management. However, cost efficiency and healthcare effectiveness cannot be achieved at the same time
and researchers indicated a trade-off between the increase in efficiency and effective healthcare system.
Healthcare effectiveness shows the potential of the healthcare system to achieve maximum healthcare
output [6]. It is only possible if an effective and sustainable healthcare system such as knowledge
management (KM) is implemented for the better management of extensive data [7]. As healthcare is
a knowledge intensive industry, healthcare professionals cannot possess plenty of new knowledge
because there are over 200,000 medical journals, with over 7,000 types of prescriptions, 800 tests,
1,000 image tests, 1,500 surgical procedures. Therefore, there is a need to utilize, assess, interpret
and share most relevant and appropriate knowledge in healthcare [8]. KM in healthcare focuses on
two aspects; improvement in the management of the hospital and improvement in the treatment
of the patient [5]. It is considered to be one of the most important tools in the healthcare industry
today due to lower utilization of resources, reduced costs, better patient care, and educating patients
with preventive measures [8]. However, the adoption of KM is facing several issues in developing
countries and KM does not get the deserved recognition in healthcare. There are many barriers,
enablers and drivers that influence KM adoption in the healthcare sector. In order to implement KM,
it is important to understand these barriers, enablers and drivers. Several studies have been conducted
on the analysis of barriers [9–11], enablers [12–14] and drivers [15,16]. These studies have multiple
limitations and results cannot be generalized to all the countries. KM adoption in Pakistan is at its
infancy stage, and many different studies suggest that KM adoption in developing countries is at
a slower pace [17–19]. Analyzing the barriers, enablers and drivers of KM adoption plays a crucial
role in understanding how to promote KM. However, research relating to the barriers, enablers and
drivers of KM adoption in developing countries such as Pakistan has been inadequate, as suggested
by different studies [20,21].
In developing countries such as Pakistan, public hospitals make up a significant portion of
healthcare. They are consuming a large amount of resources and have many shortcomings, as in
Pakistan, the bed to patient ratio is 1 bed/1,647 patients. The doctor to patient ratio is 1 to 1,099
and dentist to patient is 1 to 13,441. These indicators are not sufficient to provide quality healthcare.
The government of Pakistan (GoP) spent approximately PKRS 102 billion (2.6% of the budget) in fiscal
year (FY) 2013, which is 29% more as compared to PKRS 76.46 billion (0.57%) in FY 2007 (figures taken
from Economic Survey of Pakistan [22]). The GoP is claiming that by increasing its expenditure in
healthcare it will improve its performance. However, without implementing a sustainable healthcare
system such as KM, the scenario cannot be changed. Therefore, a scientific study is necessary to check
the issues involved in the successful adoption of KM.
Earlier studies employed the interpretive structural modeling (ISM) technique for analyzing
barriers, enablers and drivers to KM adoption. ISM is a technique that helps in defining the
relationships along with the hidden interrelationships that exist between the variables in complex
systems and represents them in a hierarchical form. However, there is a little knowledge about the
quantitative impact of different barriers, enablers and drivers on KM adoption in the healthcare
Sustainability 2019, 11, 954 3 of 31
sector [20,21]. Researcher and policy makers are not only interested in exploring barriers, enablers
and drivers to KM adoption, but also which barriers hinder the KM adoption more and which
enablers and drivers promote KM adoption more. SEM can be used for multivariate data and is
suitable for identifying the relations between exogenous and endogenous latent variables in a single
model [23]. SEM has been acknowledged by many researchers and is used in several studies and
disciplines, social, engineering and management sciences [24]. This technique has also been used in
various studies related to healthcare. Avkiran and Kemal [25] used SEM to analyze the residential
aged care networks combining low-level and high-level care. Mitchell et. al. [26] conducted a study
to develop a predictive model for patients of urinary tract infection. Guo et. al. [27] developed a
predictive model for the intention of administrators in the healthcare of USA to use evidence based
management. Debata et. al. [28] analyzed the interrelationship between service quality and loyalty
for medical tourism. Jacobs et. at. [29] examined how innovation is implemented in healthcare and
its effectiveness. Considering the wide application of SEM in healthcare, this study is the primary
study exploring the quantitative effect of barriers, enablers and drivers to KM adoption in developing
countries especially in Pakistan.
This paper is divided into six sections; Section 2 consists of literature review, Section 3 gives
the research methodology, Section 4 the results are shown and discussed in Section 5 and finally in
Section 6 the conclusion is given.
2. Literature Review
Pakistan currently ranks 149 out of 188 among the United Nations (UN) member countries in terms of
healthcare goals [39]. The healthcare of Pakistan, despite growing, has always been under pressure due
to disease outbreaks, natural disasters, large amount of information available on internet, and alternate
healthcare delivery systems [40]. Pakistan is suffering from many diseases, the major diseases being
neonatal disorders 20.4%, cancer 7%, ischemic heart disease 6.4%, lower respiratory infections 4.94%,
stroke 3.42%, chronic kidney disease 1.45%, malaria 0.43%, etc. (figures according to [41]). Other than
diseases, there are people that suffer due to disasters (earthquake and floods) and terrorism. During
the earthquake of 8th October 2005 there was chaos; there were many patients pouring in and most
of them in critical condition [42]. All the patients had to go through many tests to get their details,
life prevalent conditions, blood type etc. This resulted in loss of time, increase in cost, and loss of life.
If there was an effective knowledge management system these problems could have been overcome.
The GoP has now realized that increasing the budget is not a solution to the problem, they need
to look for new methods. They are now considering the adoption of KM in their healthcare. It helps in
the effective utilization of resources, adoption of best practices, rapid response to change and creating
a competitive advantage [3]. KM helps with the storing and sharing of knowledge. If a new patient
comes to a doctor with an improper record of health, then there is a chance of improper treatment and
wastage of doctor’s time increasing the cost, as research by Hersch W. R. [43] shows that improper
documentation takes up 1/3 of the doctor’s time. KM also helps the healthcare professional keep
updated with the latest knowledge. Generally it is not possible for a doctor to keep up with new
knowledge because there are over 200,000 medical journals, with over 7000 types of prescriptions, 800
tests, 1000 image tests, 1500 surgical procedures [8].
Table 1. Cont.
The literature shows that barriers make it difficult for the stakeholders to adopt KM in their
healthcare; the barriers have a negative influence. Due to this the current study considers the following
hypotheses regarding the barriers:
Hypothesis 1b (H1b). Strategic barriers have negative influence on the adoption of KM in healthcare.
Hypothesis 1d (H1d). Resource barriers have negative influence on the adoption of KM in healthcare.
Hypothesis 1e (H1e). Individual barriers have negative influence on the adoption of KM in healthcare.
ability to identify a knowledge broker, support from the management, organizational structure and
culture as the main enablers of KM.
The knowledge management enablers of healthcare are given in Table 2.
The literature shows that enablers make it easy for the stakeholders to adopt KM in its healthcare;
the enablers have positive influence. Due to this the current study considers the following hypotheses
regarding the enablers:
Hypothesis 2a (H2a). Management related enablers have positive influence on the adoption of KM
in healthcare.
Hypothesis 2b (H2b). Government related enablers have positive influence on the adoption of KM
in healthcare.
Hypothesis 2c (H2c). Information Technological related enablers have positive influence on the
adoption of KM in healthcare.
Hypothesis 2d (H2d). Customer related enablers have positive influence on the adoption of KM
in healthcare.
Hypothesis 2e (H2e). Employee related enablers have positive influence on the adoption of KM
in healthcare.
Drivers also make it easy for the stakeholders to adopt KM in its healthcare like enablers; drivers
have positive influence. Due to this the current study considers the following hypotheses regarding
the drivers:
Hypothesis 3a (H3a). Healthcare related drivers have positive influence on the adoption of KM
in healthcare.
Hypothesis 3c (H3c). Communication related drivers have positive influence on the adoption ofKM
in healthcare.
Hypothesis 3d (H3d). Knowledge related drivers have positive influence on the adoption of KM
in healthcare.
Hypothesis 3e (H3e). Patient related drivers have positive influence on the adoption of KM
in healthcare.
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3. Research Methodology
The aim of this study is to identify and analyze the barriers, enablers and drivers (variables)
of KM adoption in healthcare. This study has been divided into two-steps; first the variables were
identified by conducting a comprehensive literature review by reviewing several peer reviewed journals.
After identifying the variables, the fuzzy Delphi method (FDM) was utilized to narrow down to the most
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relevant variables. In the second step SEM is applied. The step-by-step methodology is given in Figure 1.
foundation to the FDM. In the triangular fuzzy numbers (TFNs) the maximum and minimum values
of the expert opinion are taken into consideration, based on them the geometric mean is calculated to
avoid statistical biasedness based on extreme values. This helps in the correct selection of variables,
it is a simple method and gives proper weightage to the expert’s opinion in the selection process [119].
The FDM was composed of two different rounds. At the end of first round, a facilitator prepared
a summary which could help the experts for further screening (deletion or addition) the barriers,
enablers and drivers. The step-by-step approach to obtain results for FDM are as follows:
(1) Distribute the questionnaire and obtain response and preference for each barrier, enabler or driver
through TFNs.
(2) At the second step, fuzzy weights Wwk obtained through TFNs were transformed into one single
value Vk by utilizing the center of gravity technique:
( Min + GM + Max )
Vk = (1)
3
(Where Vk is the threshold criteria for rejection or selection of the appropriate item, Min represent
the minimum value of TFNs, GM shows the geometric mean, and Max represent the maximum
value of TFNs).
(3) After two rounds, facilitator adopted the questions according to the threshold criteria that were
the part of final questionnaires distributed in respondents.
understanding about the study. These research team members were asked to collect data based on their
contacts. The short fifteen-minute seminars were held in the public and private hospitals, attended
by people willing to participate in the research. They were giving brief information about the current
study and KM. After the seminar, a brief question and answer session was held to remove any queries
and confusion, and after this, the participants were requested to fill out the questionnaire.
The sample size considered sufficient for SEM is 100 to 200 [127]. The response rate of healthcare
is very low, and the experts consider above 42% as acceptable [128,129]. About 500 questionnaires were
circulated among the stakeholders, out of which 255 were received. Of those, 18 of the questionnaires
were removed because they gave invalid answers, 13 were removed because they were incomplete,
resulting in 224 valid questionnaires making the response rate for the current study at 45%. The low
response rate shows that KM adoption in healthcare is at the very initial stages and needs considerable
attention of relevant authorities.
The demographic of the respondents is given in Table 4. The majority of respondents come in
the age bracket of 31–40 which is 36.2%, with 37% respondents below this age and 26.8% above it.
There were more male respondents (57.1%) as compared to females (42.9%). Of the respondents,
26.3% were working for the government in which 12.1 % were in government owned hospitals, while
14.3% were working in teaching hospitals. There were 38.8% of respondents working in privately
owned organizations out of which, 10.7% were working in hospitals, 13.8% were working in teaching
hospitals, 8% in medical centers, and 6.3% in the pharmaceutical companies. However, 34.8% of
the respondents were not considered for this category since they were government employees or
patients. The respondents had various different occupations 8.5% worked in the federal ministry of
health, 12.9% in the provincial ministry of health, 11.2% were doctors, 8.9% dentists, 12.9% nurses,
15.6% administration, 13.4% patients, and 16.5% were technicians. Most of the respondents came in
the experience bracket of 6–9 which is 32.6%, 29% of the respondents had less experience than this,
and 25% had more experience than this.
Table 4. Cont.
4. Results
4.1. Barriers
To check if the results of the latent variables are valid and reliable, the Cronbach’s alpha, composite
reliability and average variance extracted (AVE) were calculated. Since the values of Cronbach’s alpha
in Table 6 are between 0.856 and 0.715, which is more than 0.7, the data is reliable. Composite reliability
is within 0.856 and 0.736, indicating that there is internal consistency in the measurement model since
all values are greater than 0.6 [139]. The AVE values are between 0.605 and 0.527 which is more than 0.5.
This indicates that more than 50% of the latent variables explain the variance in measurement items.
Cronbach’s Composite
Category Code Factor Loading AVE
Alpha Realiability
Organizational barriers OB1 0.925 0.856 0.879 0.605
OB2 0.899
OB3 0.875
OB4 0.489
Strategic barriers SB1 0.917 0.839 0.871 0.583
SB2 0.465
SB3 0.872
Technology barrier TB1 0.973 0.733 0.761 0.529
TB2 0.686
Resource barrier RB1 0.673 0.762 0.785 0.540
RB2 0.505
RB3 0.618
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Table 6. Cont.
Cronbach’s Composite
Category Code Factor Loading AVE
Alpha Realiability
Individual barrier IB1 0.762 0.715 0.736 0.527
IB2 0.812
IB3 0.755
IB4 0.829
IB5 0.756
IB6 0.492
Table 7 shows the correlation of the latent variables and, since the values are less than the square
root of their AVE, proves their validity. Table 8 shows the factor loading of each indicator, and as the
values of the respective indicators are higher than others, the indicators have been correctly grouped.
The structural equation model of barriers and knowledge management (KM) adoption derived from
these calculations is given in Figure 2.
Category OB SB TB RB IB
Organizational barriers (OB) 0.852
Strategic barriers (SB) 0.537 0.759
Technology barriers (TB) 0.482 0.479 0.739
Resource barriers (RB) 0.358 0.384 0.520 0.763
Individual barriers (IB) 0.438 0.258 0.347 0.428 0.628
Note: the bold values show the square root of average variance extracted of each construct, and the other values
show the correlation.
Code OB SB TB RB IB
OB1 0.925 0.475 0.394 0.398 0.413
OB2 0.899 0.463 0.248 0.285 0.311
OB3 0.875 0.418 0.267 0.375 0.479
OB4 0.489 0.049 0.038 0.395 0.021
SB1 0.278 0.917 0.236 0.349 0.408
SB2 0.053 0.465 0.025 0.059 0.06
SB3 0.234 0.872 0.346 0.429 0.016
TB1 0.473 0.394 0.973 0.246 0.279
TB2 0.213 0.386 0.686 0.379 0.197
RB1 0.364 0.349 0.175 0.673 0.264
RB2 0.326 0.149 0.196 0.505 0.151
RB3 0.418 0.285 0.259 0.618 0.230
IB1 0.427 0.349 0.200 0.186 0.762
IB2 0.253 0.259 0.349 0.267 0.812
IB3 0.053 0.281 0.255 0.112 0.755
IB4 0.212 0.351 0.351 0.058 0.829
IB5 0.369 0.247 0.188 0.192 0.756
IB6 0.031 0.035 0.089 0.039 0.492
Note: OB stands for organizational barriers, SB stands for strategic barriers, TB stands for technology barriers,
RB stands for resource barriers, and IB stands for individual barriers. The bold values show the highest values in
their category, implying that they have been correctly grouped.
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Figure 2. Structural equation model of barriers and knowledge management (KM) adoption.
Figure 2. Structural equation model of barriers and knowledge management (KM) adoption.
4.1.2. The Structural Model Validation and Reliability
4.1.2. The Structural Model Validation and Reliability
After checking the measurement model, the structural model was tested. The structural
After
model had checking
five the measurement
exogenous model, the structural
variables; organizational model was
barriers, strategic tested.technology
barriers, The structural model
barriers,
had five exogenous variables; organizational barriers, strategic barriers,
resource barriers and individual barriers, and one endogenous variable which was knowledge technology barriers,
resource barriers
management adoptionand individualThe
in healthcare. barriers, and further
exogenous one endogenous variable three,
had their indicators, whichtwo,
wastwo,
knowledge
three,
management adoption
and five respectively. in healthcare. The exogenous further had their indicators, three, two, two,
three,
The and five respectively.
structure model was validated using the same tests as the measurement model. The χ has 2
The structure
been calculated model
to be 76.51 andwas
thevalidated
degree ofusing the as
freedom same tests asχ2the
40, hence /dfmeasurement model.
= 1.913. The other testThe χ² has
results
been calculated to be
are also given in Table 9. 76.51 and the degree of freedom as 40, hence χ²/df = 1.913. The other test results
are also given in Table 9.
Table 9. Checking the goodness-of-fit for structural model of barriers.
Table 9. Checking the goodness-of-fit for structural model of barriers.
Goodness-of-Fit Recommended Value * Result
Goodness-of-fit
The Chi Square (χ2 )
Recommended
N/A
Value* 76.51
Result
The
degree of Chi Square
freedom (df) (χ²) N/A N/A 40 76.51
χ2 /dfof freedom (df)
degree ≤3 N/A 1.913 40
Standardized root mean χ²/df
square (SRMR) ≤0.1 ≤3 0.052 1.913
Goodness-of-fit index (GFI) ≥0.9 0.975
Standardized root mean square (SRMR) ≤ 0.1 0.052
Adjusted goodness-of-fit index (AGFI) ≥0.85 0.955
Goodness-of-fit
Normed index (GFI)
fit index (NFI) ≥0.9 ≥ 0.9 0.981 0.975
Adjusted
Comparative fit index (CFI) index (AGFI)
goodness-of-fit ≥0.95 ≥ 0.85 0.99 0.955
Root mean square error of approximation
Normed (RMSEA)
fit index (NFI) ≤0.08 ≥ 0.9 0.058 0.981
Comparative
Note: * The recommendedfitvalues
index (CFI)
have ≥ 0.95
been taken from Schermelleh–Engel, et al. [138]. 0.99
Root mean square error of approximation (RMSEA) ≤ 0.08 0.058
To get theNote:
results displayed
* The in Table
recommended 10, the
values bootstrapping
have technique
been taken from was used. The
Schermelleh–Engel, path
et al. coefficient,
[138].
t-value and p-value were calculated. The path coefficient shows the influence of independent variables
To get the results displayed in Table 9, the bootstrapping technique was used. The path
on the dependent variables [23]. If the value of the path coefficient is between 0.1 and 0.3 the influence
coefficient, t-value and p-value were calculated. The path coefficient shows the influence of
is weak, between 0.3 and 0.5 the influence is moderate, between 0.5 and 1 the influence is strong. If the
independent variables on the dependent variables [23]. If the value of the path coefficient is between
t-values are less than 1.65, 1.96 or 2.58, respectively, they are insignificant.
0.1 and 0.3 the influence is weak, between 0.3 and 0.5 the influence is moderate, between 0.5 and 1
Sustainability 2019, 11, 954 15 of 31
The results indicate that organizational barriers and strategic barriers both had a path coefficient
of more than 0.5 and t-value more than 2.58, organizational barriers are statistically significant at 1%
and strategic barriers at 10% respectively. Due to this, hypothesis H1a and H1b were both supported.
The other hypothesis H1c, H1d, and H1e were not supported because the path coefficient and t-values
were less than 1.65, 1.96 or 2.58, so they are insignificant. The results indicate that technology barriers,
resource barriers and individual barriers have a relatively lesser impact on KM adoption in healthcare.
The SEM is given in Figure 2. The R2 , also called the coefficient of determination, was calculated to be
0.386, indicating the accuracy of the model.
4.2. Enablers
The Cronbach’s alpha, composite reliability and average variance extracted (AVE) were calculated.
The Cronbach’s alpha value was greater than 0.7, between 0.803 and 0.752, indicating that the data
was reliable. The composite reliability shows that there is internal consistency in the measurement
model when all values are greater than 0.6 [139], and since the values of our result were between 0.880
and 0.729, it indicates that there is internal consistency. The AVE values were between 0.713 and 0.516
which is higher than the recommended 0.50, indicating that half of the variances have been explained
by the indicators. This showed that the data was strong, reliable and valid. The details of the values
are given in Table 12, validity of constructs for enablers have been checked in Table 13 and their cross
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loadings are given in Table 14. From these values the structural equation model of enablers and KM
adoption is given in Figure 3.
Cronbach’s Composite
Category Code Factor Loading AVE
Alpha Realiability
Management related
MRE1 0.835 0.752 0.758 0.627
enablers (MRE)
MRE2 0.639
MRE3 0.679
MRE4 0.499
MRE5 0.637
MRE6 0.826
Government related
GRE1 0.930 0.803 0.880 0.713
enablers (GRE)
GRE2 0.921
Information Technology
ITRE1 0.839 0.718 0.729 0.697
related enablers (ITRE)
ITRE2 0.518
ITRE3 0.713
ITRE4 0.439
Customer related
CRE1 0.813 0.805 0.815 0.559
enabler (CRE)
CRE2 0.589
ERE1 0.651 0.755 0.763 0.516
Employee related
ERE2 0.695
enablers (ERE)
ERE3 0.756
ERE4 0.718
Since the government related enablers had a path coefficient of more than 0.5, a t-value of more
than 2.58, and was statistically significant at 1%, the hypothesis H2b has been supported. The other
hypothesis H2a, H2c, H2d, and H2e were not supported because the path coefficient and t-values were
less. The results indicate that management related enablers, information technology related enablers,
customer related enabler, and employee related enablers have a relatively less impact on KM adoption
in healthcare. The SEM is given in Figure 3. The R2 , also called the coefficient of determination,
was calculated to be 0.526, indicating the accuracy of the model.
4.3. Drivers
Cronbach’s Composite
Category Code Factor Loading AVE
Alpha Realiability
Healthcare related drivers HCRD1 0.934 0.825 0.869 0.596
HCRD2 0.910
HCRD3 0.859
HCRD4 0.436
HCRD5 0.693
Performance-based drivers PBD1 0.846 0.779 0.806 0.654
PBD2 0.685
PBD3 0.829
Communication related
CRD1 0.759 0.756 0.813 0.643
drivers
CRD2 0.723
CRD3 0.668
Knowledge related drivers KRD1 0.651 0.758 0.856 0.513
KRD2 0.706
KRD3 0.678
KRD4 0.618
Patient related drivers PRD1 0.498
PRD2 0.659 0.635 0.746 0.649
PRD3 0.643
PRD4 0.621
have less impact on KM adoption in healthcare. The SEM is given in Figure 4. The R2 , also called the
coefficient of determination, was calculated to be 0.485, indicating the accuracy of the model.
5. Discussion
5.1. Barriers
Barriers are variables that negatively influence the adoption of KM in the healthcare. Among
the selected barriers, organizational barriers and strategic barriers hinder the adoption of KM
the most. In an organization, the top management plays a vital role. If top management of the
healthcare does not support the implementation of KM in healthcare, then it is one of the most critical
barriers [45–51]. The top management of healthcare must give a clear vision and create an atmosphere
where knowledge sharing is encouraged in order to ensure effective KM adoption in healthcare.
Other than the top management, the structure and culture of healthcare must be considered as well.
Organizational structure helps in task allocation, coordination, and supervision. It also controls the flow
of information [140]. If the structure of the organization does not allow the flow of information then
it will prove to be a barrier [2,13,45,50–54]. Ichijo, et al. [12] pointed out that healthcare firms should
maintain consistency between their structures to put their knowledge to use. Organization culture
can be a critical problem when it comes to successful KM implementation [2,13,45,50–54]. Culture is
very important for the transferring of knowledge between employees [141]. A culture that encourages
knowledge sharing is critical for KM success. Such a culture requires the healthcare employee to get
together and exchange ideas; culture helps in collaboration and motivates the healthcare employees to
work productively. Whenever a new plan is to be launched in an organization, its culture is considered
carefully because the employees are involved. If KM is to be introduced in healthcare, the structure
and culture must be taken into consideration. The infrastructure of Pakistan healthcare is very large.
It is headed by the Ministry of Health (MoH); they should appoint heads of departments that possess
the ability to make decisions on their own, rather than following the bureaucratic procedures. The top
Sustainability 2019, 11, 954 22 of 31
management of each department must create a structure that encourages the flow of information and
create a learning environment. This would make KM adoption easier.
Strategic planning is also very important for the execution of KM. Ineffective strategic planning
will prove to be a barrier [51,55,56]. Without effective strategic planning, it will be impossible to
achieve KM [142] in healthcare. To implement KM in healthcare a clear strategy has to be made,
one that everyone understands, and its goals, purpose, and objectives must be clear. Strategic planning
is crucial for KM implementation in healthcare for sustainable competitive advantage and survival in
the international market. Uncertainty about the effectiveness of KM may also prove to be a barrier.
It is, however, not considered to be very critical, but it cannot be ignored [45,59]. To implement KM
takes a long time, and it takes even longer to see the positive changes it gives. Due to this, the concept
of KM according to the employees is not worth the effort/resources. The Government of Pakistan
(GoP) has tried its best to improve healthcare by developing several strategies [143], but are currently
not successful. The National Health Vision (NHV) [144] was approved in 2016 and is trying their level
best to achieve it. The GoP is looking for new methods and is considering KM as an option.
Technology barriers, resource barriers and individual barriers also hinder the implementation of
KM but it is not considered as significant in the case of Pakistan. The system of KM might be complex
and difficult to implement or there might be difficulty in integrating it with the existing system.
Resource barriers refer to cost of implementation and other resources needed for KM implementation.
The individual barriers refer to conflicts, lack of motivation and resistance to change. Pakistan has
recently invested heavily in getting new technology to improve administration and new machines for
healthcare to improve patient service and has spent $3.04 billion [145]. It has increased its budget over
the years to overcome other issues such as employee motivation and strikes [36].
5.2. Enablers
Enablers are variables that positively influence the adoption of KM in the healthcare. The results
suggest that the government related enablers support the implementation the most. The government
policies highly affect all organizations [89–91] in the public sector, and since the Pakistan healthcare
sector has a large infrastructure it is highly influenced by policies [146]. The GoP provides basic
healthcare through 5334 Basic Health Units (BHUs) and 560 Rural Health Centers (RHCs), secondary
care through 919 Tehsil Headquarter Hospitals (THQs), District Headquarter Hospitals (DHQs) and it
is estimated that there are about 96,430 private health establishments [146]. In Pakistan, the national
polices and strategies are developed by the Federal MoH, which sets the goals and objectives. Whereas
according to the constitution of Pakistan the provincial MoH is responsible for its deliverance and
execution, except in the federally administrated areas. Favorable healthcare sector policies support
the implementation of KM in healthcare. In the healthcare sector, clear long term strategic planning
for implementation of knowledge management is also most critical for success [18,19,92]. Perera
and Peiró [147] have stated that strategic planning is very important for all healthcare organizations,
the short, medium, and long term vision and mission must be clear.
The GoP vision is “health for all”. Pakistan, to improve its healthcare, has developed several
policies over the years such as the National Health Policy (2001) [146], the Health Sector Vision
(2005–2010) [146], and NHV (2016) [144]. The NHV was developed in 2010, it took a long time to
be approved by both federal and provincial MoH. This document states the vision, mission, values
and targets for 2025 of the healthcare very clearly so that Pakistan can improve its health standards.
Pakistan has also signed international treaties such as the Millennium Development Goals (2000)
setting targets for 2015 [148], and Sustainable Development Goals for healthcare setting targets for
2030 made by the United Nations (UN) [149]. The GoP has increased the budget of healthcare over the
years to make sure it is not the shortage of money that is hindering its healthcare service [36]. Pakistan,
realizing its weakness in the healthcare sector, is desperately trying to improve it and is now looking
for new methods and is willing to adopt KM.
Sustainability 2019, 11, 954 23 of 31
There are other enablers to healthcare as well such as management related enablers, information
technology related enablers, customer related enablers, and employee related enablers, but they
are considered as less supportive. If the management is supportive, there is a learning environment,
and the employees trust each other, it will be easy to implement KM. The role of information technology
cannot be avoided since it helps in storage and quick transferring of information. The customers help
the process by giving constant feedback. If the employees are motivated, well trained and empowered
they will perform better and help with implementation of KM in healthcare.
5.3. Drivers
Drivers are variables that positively influence the adoption of KM in the healthcare. The drivers
that most significantly affect the adoption of KM are healthcare related drivers and performance-based
drivers. Almost all the organizations have realized the importance of knowledge as an asset or
commodity and are adopting KM to gain sustainable competitive advantage [16]. Globalization
has increased the sense of competition among organizations, and healthcare is no exception. These
days medical tourism has increased, patients are looking for places where they can get the best
treatment [150]. This is why healthcare should be able to rapidly adjust to changes in the environment.
If the healthcare sector is able to do this, it will certainly be setting the standard for other sectors by
focusing on the best practices and utilizing the minimum of resources [151]. Over time other sectors
will adopt the practices of healthcare [152]. However, the change in the health sector will not come
overnight; it will take its time.
Pakistan progress in the healthcare has always been hindered by diseases and outbreaks [153].
Pakistan also suffers due to its large population, growth rate [35], and limited resources [143].
Nevertheless, Pakistan realizes the problems in its healthcare, and it has developed several polices
and signed international treaties to improve performance. Pakistan is now considering KM because
it understands the advantages that KM has to offer in the healthcare of Pakistan. It wants to bring a
competitive advantage in its healthcare so that it can improve its reputation and get a share of the
international market.
The performance is a critical factor of healthcare, and if there is effective decision making it will
considerably improve the administration of healthcare. Decisions have to be made at many levels;
top, middle and lower levels. The decisions made at the patient level are the most critical, as they
have to be effective to reduce medical errors [154]. Pakistan healthcare generally has a centralized
decision-making process. It will have to adopt a certain level of decentralization to ensure the quick
flow of knowledge, and quick and effective decision making. This will also result in reduced utilization
of resources by quickly dealing with the patients.
The other drivers, communication related drivers, knowledge related drivers and patient related
drivers also support KM adoption but they are not as supportive. Communication is an important
part of knowledge sharing. If there is communication between departments and other organizations,
the knowledge will flow freely. Similarly, if there is a learning environment in the organization, the loss
of knowledge will be less, and will create trust among the employees. This will create improvement in
patient service resulting in less cost.
6. Conclusions
KM adoption has always been considered a source of sustainable competitive advantage. There are
many barriers, enablers, and drivers that will influence its adoption. There have been very few studies
in the area of KM in healthcare of developing countries [20,21]. This study was undertaken to check the
quantitative influence of the variables on the adoption of KM in the healthcare of Pakistan. This study
employs the SEM technique for the analysis of the variables. The data was collected via questionnaires,
by five research representatives with knowledge of KM and several short seminars. The result of the
study shows that organizational barriers and strategic barriers have a negative influence (barriers),
Sustainability 2019, 11, 954 24 of 31
whereas government related enablers (enablers), healthcare related drivers, and performance-based
drivers (drivers) have a positive influence on KM adoption.
The reason of this study is to give a clear idea of KM adoption in the healthcare of Pakistan.
The findings of this research will help the relevant authorities of Pakistan (government, hospitals,
unions, staff, and etc.) get a better understanding of the barriers, enablers and drivers. The results
show that the barriers can be overcome by the enablers and drivers. The organizational and strategic
barriers are the main barriers. They need to be addressed in a way that reduces their influence. It can be
done by developing suitable government policies (enabler) that encourage the flow of knowledge and
make it easier to implement KM. Similarly, the adoption of KM will give the healthcare organizations
a sustainable competitive advantage and improve their performance by effective decision making.
This in return makes the healthcare sector a benchmark for other sectors and developing countries.
In this study there are a few limitations; the sample size and coverage were sufficient for the
current study to apply SEM but they can be increased in future. The study, however, gives a good
idea of how barriers, enablers and drivers influence KM adoption in healthcare. This study can be
considered for other developing countries but it is more relevant to the situation of Pakistan.
In the future this study can be conducted again because barriers, enablers and drivers change
with the passage of time depending on the phase of implementation. These studies might give further
insight into the situation of KM in the healthcare of Pakistan. The same style of study can be used by
researchers to determine the barriers, enablers and drivers in their respective developing countries.
Author Contributions: Conceptualization, J.K. and N.A.; Data curation, J.K., N.A., S.A., S.K. and N.K.; Formal
analysis, J.K. and N.A.; Investigation, N.K.; Methodology, J.K.; Project administration, T.S.; Resources, S.A. and
N.K.; Supervision, T.S.; Validation, J.K. and T.S.; Visualization, N.A.; Writing—original draft, J.K. and S.A.;
Writing—review & editing, J.K., N.A. and S.K.
Funding: This research received no external funding.
Acknowledgments: The authors are very grateful to everyone that participated in the research. The experts that
helped by participating in the fuzzy Delphi method, the hospitals administration that were kind enough to allow
us to hold short seminars, and the staff and patients that attended it. The authors would also like to thank the
respondents that filed and submitted a valid questionnaire. Finally, the authors would like to thank the five
research assistants that helped in the collection of questionnaires.
Conflicts of Interest: The authors declare no conflict of interest.
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