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Grey Systems: Theory and Application
Evaluation of outpatient satisfaction and service quality of Pakistani healthcare
projects: Application of a novel synthetic Grey Incidence Analysis model
Saad Ahmed Javed, Sifeng Liu,
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To cite this document:
Saad Ahmed Javed, Sifeng Liu, (2018) "Evaluation of outpatient satisfaction and service quality of
Pakistani healthcare projects: Application of a novel synthetic Grey Incidence Analysis model", Grey
Systems: Theory and Application, https://doi.org/10.1108/GS-04-2018-0018
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Evaluation of outpatient
satisfaction and service quality of
Pakistani healthcare projects
Application of a novel synthetic Grey Incidence
Analysis model
Saad Ahmed Javed
College of Economics and Management, Nanjing University of Aeronautics and
Astronautics, Nanjing, China and
Institute for Grey Systems and Decision Sciences, GreySys Foundation,
Lahore, Pakistan, and
Sifeng Liu
Institute for Grey Systems Studies, Nanjing University of Aeronautics and
Astronautics, Nanjing, China
Abstract
Purpose –The purpose of this paper is to analyse the relationship between outpatient satisfaction and the
five constructs of healthcare projects’service quality in Pakistan using Deng’s grey incidence analysis (GIA)
model, absolute degree GIA model (ADGIA), a novel second synthetic degree GIA (SSDGIA) model and two
approaches of decision-making under uncertainty.
Design/methodology/approach –The study proposes a new synthetic GIA model and demonstrates its
feasibility on data (N¼221) collected from both public and private sector healthcare projects of Punjab, the
most populous province of Pakistan, using a self-administered questionnaire developed using the original
SERVQUAL approach.
Findings –The results of decision analysis approach indicated that outpatients’satisfaction from the private
sector healthcare projects is higher as compared to the public healthcare projects’. The results from the
proposed model revealed that tangibility and reliability play an important role in shaping the patient
satisfaction in the public and private sectors, respectively.
Originality/value –The study is pioneer in evaluating a healthcare system’s service quality using grey system
theory. The study proposes the SSDGIA model as a novel method to evaluate parameters comprehensively based
on their mutual association (given by absolute degree of grey incidence) and inter-dependencies (given by Deng’s
degree of grey incidence), and tests the new model in the given scenario. The study is novel in terms of its
analysis of data and modelling. The study also proposes a comprehensive structure of the healthcare delivery
system of Pakistan.
Keywords Patient satisfaction, Grey relational analysis, Healthcare service quality,
Healthcare system of Pakistan, Second synthetic degree grey incidence
Paper type Research paper
Introduction
Healthcare service quality is an important issue both in developing and developed countries.
Developed countries are reaping the benefits of having superior healthcare sectors by
nourishing healthier citizens with longer lives, and the developing countries, or more
precisely the poorly managed countries, are still struggling to lift up the quality of lives of
their citizens, on the other. “Health is wealth”and the well-being of human capital, the most
important capital of any country or organisation, is very essential for the development of
a country or an organisation. Pakistan is a developing country of South Asia with a
population of over 207m. Since its creation in 1947, healthcare never remained a priority of
all successive governments ( Javed and Ilyas, 2018; Malkani, 2016). The budget allocated by
Grey Systems: Theory and
Application
© Emerald Publishing Limited
2043-9377
DOI 10.1108/GS-04-2018-0018
Received 27 April 2018
Revised 4 July 2018
Accepted 5 July 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2043-9377.htm
Outpatient
satisfaction
and service
quality
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Pakistani Government for healthcare and the frequent visits of the country’s political
leadership to the healthcare facilities of the developed countries even for the smallest
treatments support this argument ( Javed and Ilyas, 2018). According to the World Bank
statistics, out of 185 countries, Pakistan ranked 180th position in terms of its health
spending as per cent of GDP, in 2014 (GE, 2017). Pakistan is spending 0.5–0.8 per cent of its
GDP, on health for the last 10 years, while WHO benchmark of health expenditure is at least
6 per cent of the GDP (Basharat, 2017). These facts necessitate studying Pakistani
healthcare sectors routinely with staunch proposals for policy implications.
All over the world, public and private sectors play an important role as service provider
and each has different approach to serve the service seekers (Grigoroudis et al., 2012).
Literature highlights a controversy within the perception of Pakistani patients concerning
healthcare quality in public/private sectors. Most studies ( Javed and Ilyas, 2018;
Shabbir et al., 2016; Irfan and Ijaz, 2011; Anwar et al., 2012; Khattak et al., 2012)
demonstrated that patient satisfaction is greater (or patient expectations are lower) in the
private sector healthcare system of Pakistan than in the public sector healthcare system;
however, some studies (see e.g. Nasim and Janjua, 2014) reported the opposite. Irfan and Ijaz
(2011) and Javed and Ilyas (2018) surveyed patients having experience of both sectors and
reported different prioritisation of quality constructs influencing patient satisfaction.
To settle these issues, a new study with entirely different techniques was needed. Therefore,
the study intends to address the following questions:
(1) How different quality constructs/dimensions are associated to patient satisfaction in
public and private sector healthcare projects?
(2) How different quality constructs are predicting the patient satisfaction from the
healthcare projects in each of the two healthcare systems?
(3) Are patients more satisfied with the services of public sector healthcare projects or
with the services of private sector healthcare projects?
(4) If patients having experience of both sectors’hospitals are asked to prioritise
different factors leading to their satisfaction from the health care services, their
prioritisation of service quality attributes for both sectors’projects is likely to be
almost similar. Is it true?
The current study aims to find the answer to these questions using grey incidence
analysis/grey relational analysis (GIA/GRA) models and decision analysis approaches.
The work is novel and important as it not only validates the feasibility of SERVQUAL
approach in evaluating the service quality of Pakistani healthcare projects but also
confirms the significance of GIA models in evaluating the healthcare quality and patient
satisfaction. Further, the study is pioneer in presenting and testing the second synthetic
degree grey incidence analysis (SSDGIA) model.
The paper is mainly organised into six sections. First is about introduction, second is the
review of relevant literature, third reports some definitions knowledge of which is necessary
for the people who are not familiar with grey system theory and its data analysis methods,
fourth is about methodology and hypotheses, fifth is about results and in sixth part, the
paper is concluded.
Literature review
Service quality and service seekers’satisfaction
Healthcare service quality –definition, construct and measurement. According to the online
Oxford Dictionaries, quality means “the standard of something as measured against other
things of a similar kind; the degree of excellence of something”or “a distinctive attribute or
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characteristic possessed by someone or something”(Oxford, 2018). Campbell et al. (2000)
cited the Institute of Medicine for defining quality as the “degree to which health services for
individuals and populations increase the likelihood of desired health outcomes and are
consistent with current professional knowledge”. For Campbell et al. (2000), quality of care
for service seekers is: “whether individuals can access the health structures and processes of
care which they need and whether the care received is effective”.
Quality remained a popular concept in manufacturing/production environment since
long period. However, measuring the quality of services is not as easy as measuring the
quality of a product or good because of intangibility associated with services that is
influenced by customer perceptions and expectations (Irfan and Ijaz, 2011). Therefore, in the
service industries like healthcare, perceived or expected quality is the measure of quality of
the service. Building upon the previous studies, Javed and Ilyas (2018) defined perceived
quality of healthcare services as “the patients’judgment or impression about a healthcare
unit’s overall excellence and superiority”. Healthcare unit can be a hospital, a clinic or a
healthcare project.
Service quality has several dimensions or constructs that have their manifestation in
different service quality models or frameworks. Izadi et al. (2017) mentioned several service
quality assessment models in their study; “client oriental provider efficient, lot quality
assurance sampling, criteria and standards of quality, statistical process control (SPC), and
SERVQUAL and importance performance analysis”. The first part of the series ( Javed and
Ilyas, 2018), of which this paper is the second part, used the original SERVQUAL method
(Parasuraman et al., 1988, 1991) considering its widespread popularity in the studies on
service sector. For instance, Gong (2015) applied the SERVQUAL approach in his study on
hostelling in the USA. Iihan et al. (2015) discussed the SEQVQUAL-based techniques for
information need analysis. Khorshidi et al. (2016) used the SERVQUAL method in their study
involving SPC. Pramanik (2016) studied the patient perception of service quality in the urban
and rural hospitals of India using the SERVQUAL scale. Li et al. (2017) used a modified
SERVQUAL scale in their study on the evaluation of in-flight service quality. Ladhari et al.
(2017) used the SERVQUAL scale in utilitarian service settings. Therefore, for finding this
conceptualization and method still useful and, also, aligned with the research objectives of the
current study, using the SERVQUAL scale was considered an appropriate approach. This
also follows from the idea that service quality isa dynamic, multi-dimensional concept and the
assessment of the scale to measure service quality should be context dependent (Akter et al.,
2013). The SERVQUAL approach originally involves five constructs of service quality:
tangibility, reliability, responsiveness, assurance and empathy. These constructs have been
defined, in the given context, in Table I. For further details concerning each of the five
dimensions/constructs, Parasuraman et al. (1988) can be consulted.
Patient satisfaction. Quality gives a sustainable competitive advantage –a crucial factor
in patient satisfaction, which ultimately increases referrals, service demand and hospital
reputation (Izadi et al., 2017). Providing high-quality services leads to cost savings,
increases market share, profitability and service provider effectiveness (Izadi et al., 2017).
Tangibility “Physical facilities, equipment, and appearance of the physicians, nurses and supporting staff”
Reliability “Ability to perform the promised service dependably and accurately”
Responsiveness “Willingness to help patients and provide prompt healthcare services”
Assurance “Knowledge and courtesy of the physicians, nurses and supporting staff, and their ability to
inspire trust and confidence”
Empathy “Caring, individualised attention the healthcare facility provides its patients”
Source: Javed and Ilyas (2018)
Table I.
The five constructs of
the SERVQUAL scale
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satisfaction
and service
quality
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Public, along with healthcare administrators and policy makers, constitute the most
important body that can assess the healthcare service quality (Li, 1997). Patients’perception
of service quality accounts for 17–27 per cent of variation in financial performance of a
healthcare system, thus undermining the importance of these critical issues can be terrible
for hospitals in the long run ( Javed and Ilyas, 2018).
Patient satisfaction is an important measure of how well the services are provided
(Khan et al., 2013). Health concerns are of prime importance for most of the people and
their decision to continue or discontinue a healthcare service is significantly influenced by
their expectations from the healthcare service quality constructs ( Javed and Ilyas, 2018).
The relationship between expectations and satisfaction is debatable within the healthcare
literature. However, a dominant school of thought proposes “minimising the discord
between patient expectations and the actual result is a key determinant of patient
satisfaction”( Jain et al., 2017). This is also aligned with the gap theory on which the
SERVQUAL scale was originally built. Therefore, in the current study, patients are
more likely be satisfied if their expectations are lower and less likely to be satisfied
if their expectations are higher. To further support this argument, a direct question on
satisfaction–expectation was added in the questionnaire.
Healthcare system of Pakistan
A third of babies in South Asia are born low birth weight, more than in sub-Saharan Africa
(Qureshi et al., 2016). Pakistan is the second largest country of South Asia with a population
of over 200m. Since its creation in 1947, healthcare never remained a priority for the
government. The country’s health profile portrays high maternal and child mortality,
increased population growth rate and the twofold burden of infectious and non-infectious
diseases (Sayani and Feroz, 2017). World Bank statistics put Pakistan at 180th position
among 185 countries in terms of its health spending as per cent of GDP in 2014 (GE, 2017).
The country is spending 0.5–0.8 per cent of its GDP on health for the last 10 years,
thus failing to meet the WHO benchmark of health expenditure (6 per cent of the GDP)
(Basharat, 2017). The United Nations Development Programme ranked Pakistan in the
Human Development Index 146th out of 187 countries (Kumar and Bano, 2017). Therefore,
Pakistan faces a daunting challenge in improving health outcomes for children and adults
alike (Afzal and Yusuf, 2013). Even though Shabbir et al. (2016) claimed that “comparable to
different countries, Pakistan has started a significant emphasis to enhance the quality of
healthcare services”, the current study seconds by Javed and Ilyas (2018) and Afzal and
Yusuf (2013), suggesting that given the past track record of the country’s healthcare
performance and governmental expenditure on health, the country’s emphasis on the
improvement of quality of healthcare services is insignificant, and thus an improvement in
Pakistani healthcare seems unlikely.
Project management has its application in various industries including healthcare
because of the flexibility, fast speed and better management of uncertainties and
complexities. Healthcare projects are becoming integral components of the healthcare
delivery system around the world, including Pakistan. Sayani and Feroz (2017) defined
the health care delivery system as “a structure that provides the best support to a
country’s people with efficient, effective, and equitable distributions of resources, through
well-organised infrastructure”. The modern healthcare system is developed through
projects in large scale (Grönevall and Danilovic, 2014). There are several healthcare
projects being launched in Pakistan in recent decades. World Bank’sstatisticsshow
45 different health projects launched since 1991 across Pakistan (Projects WB, 2017).
Some of the government-funded projects have sub-projects in different areas of the
country. Other than government run-healthcare projects, different non-governmental and
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international organisations (e.g. WHO, UNHCR, USAID, local NGOs like doctHERs,
multinational corporations like Siemens, etc.) are also running healthcare projects in
different areas of Pakistan. Building upon the works of Kumar and Bano (2017), in
particular, and Berendes et al. (2011), Lakha et al. (2017), Bryant et al. (2011) and
Sudhinaraset et al. (2013), in general, the current study proposes a comprehensive
structure of the Healthcare Delivery System of Pakistan, as shown in Figure 1.
Healthcare was primarily the responsibility of the state as per the Constitution of Pakistan;
nevertheless, the federal government transferred this responsibility to provincial governments
after the 18th Amendment in the Constitution in 2010 (Javed and Ilyas, 2018; Malkani, 2016).
Several years have been passed since then but the healthcare delivery system of the country
failed to achieve earnest results. Despite an elaborate and extensive network of health
infrastructure, especially in the public sector, the public sector healthcare facilities lag behind
that of private sector healthcare facilities in Pakistan (Anwar et al., 2012). More than
60 per cent of the aggregate demand for healthcare is provided by private healthcare
providers and less than 5 per cent demand is provided by public primary healthcare
(Malik et al., 2017). “The poor state of public facilities overall has contributed to the diminished
role of public health facilities, while the private sector’s role in the provision of service delivery
has increased enormously”(Afzal and Yusuf, 2013). There is a need to improve the healthcare
services in public sector healthcare system in many low- and middle-income countries like
Pakistan, where the private sector healthcare system is the main provider of primary
healthcare services (Malik et al., 2017; Anwar et al., 2012; Berendes et al., 2011).
Further, because of the deficiencies in accessing primary health services, the utilisation of
healthcare facilities is generally low in rural and peri-urban areas of low- and middle-income
countries like Pakistan (Malik et al., 2017; Irfan et al., 2012). Therefore, the current study was
undertaken to study the satisfaction of the patients from rural areas.
Decision-making under uncertainty (DMUU)
Successes or failures of organisations can be attributed to the decision making of their
managers (Bahrami et al., 2016). With the passage of time, organisational issues are
becoming complex, and thus decision making is becoming a challenging task. Sometimes
issues are such complex that even though multiple alternatives to solve them are available
but rationale to choose one alternative while foregoing the others is not convenient
to develop. In such cases, scholars have stressed to use decision analysis criteria to guide
Military
Hospitals,
Cantonment
Board
Healthcare
facilities
Research
Institutes,
Hospitals,
Vertical
Programs,
etc.
Primary,
Secondary
and
Tertiary care
Homeopathics, Traditional
healers (Arabic/Islamic
medication, Chinese
medication, Indo-Pak
herbal/folk
medication, Hakeems, etc.),
Unani (Greco-Arab) healers
nonmedical general
practitioners,
Lay health workers,
drug sellers and Pharmacists,
ordinary shop keepers,
“quacks”
and religious/spiritual/
superstitious “medication”,
etc.
Other
Ministries
Provincial
Departments
of Health
Public Sector
Healthcare System
Provincial
Governments Formal Informal/
Alternative
Private Sector
Healthcare System
Healthcare Delivery System of Pakistan
Hospitals,
Clinics by
self-employed
practitioners,
Healthcare
projects by
local and
international
“not-for-profit”
and/or “non-
governmental”
Philanthropic,
academic
(medical) and
religious
organizations
Ministry of
Defense
Federal
Government
Figure 1.
The healthcare
delivery system
of Pakistan
Outpatient
satisfaction
and service
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the decision makers in evaluating multiple alternatives and weighing their significance
in the given scenario. There are several decision analysis approaches used in the literature
to solve different issues; however, each approach either guides in making decision under
uncertainty or certainty. DMUU is relativelyhardascomparedtotheDMUCbecauseof
the difference of complexity of the problem and increasing number of uncertainty
associated with the problem. DMUU uses criteria reflecting the decision maker’sattitude
towards risk, ranging from optimism to pessimism (Taha, 2007, p. 489). Here, decisions are
made based on “expected value criterion”in which decision alternatives are compared
based on the maximisation of expected profit or minimisation of expected cost
(Taha, 2007, p. 500). These payoffs (profit or loss) associated with each alternative are
defined by probability distribution (Taha, 2007, p. 500). There are many approaches
to do this; however, two well-known approaches in text books are based on the following
two criteria:
(1) The Conservative (maximin or minimax) criteria.
(2) The Hurwicz criteria.
In this paper, only these two approaches have been used and calculations have been
presented in the section containing results; however, for further details of the methods, the
readers may consult Prasad (2015) or Taha (2007, 2014).
Grey Incidence Analysis (GIA) models
GIA models, also called Grey Relational Analysis (GRA) models, are one of the key parts of
the new framework of grey system theory (see Liu et al., 2016, p. 27). Grey system theory
was first proposed by a Chinese scientist, Professor Julong Deng, in 1982 and since then it is
being used in various fields of knowledge to deal with the problems of uncertainty in a
system with partially known and partially unknown information (Mahmoudi et al., 2018; Liu
et al., 2016; Jian et al., 2011). One of the key benefits of using this theory is that it works quite
well even with small sample of data ( Javed et al., 2018). Professor Deng first proposed the
concept of grey relational grade (GRG) (Deng’s degree of grey incidence) in his GRA model
(Deng, 1985). According to Liu et al. (2016, p. 68), “the grey incidence analysis model is a new
method to analyse systems where statistical methods do not seem appropriate”. It can be
used to express and describe a grey/uncertain system whose operating mechanism and
physical prototype are not clear or lack a physical prototype (Zhang et al., 2012). The basic
concept of GIA is to use the degree of similarity of the geometric curves of available data
sequence to determine whether or not their connections are close (Liu et al., 2016, p. 68); thus,
this model gauges the extent of association between different variables or factors associated
with a system. The more similar the curves are, the higher the degree of incidence between
the sequences and vice versa (Li et al., 2012).
Deng’s degree of GIA model. Many scholars proposed many GIA models but the model
proposed by Professor Julong Deng in 1985 is the most influential one (Liu et al., 2016, p. 68;
Liu et al., 2013) and is one of the most important decision-making methods of grey
system theory (Li et al., 2012). However, considering the infancy of this knowledge area,
there are still many practical and scientific problems need to be solved using this method
(Liu et al., 2016, p. 69).
The computing steps to calculate Deng’s degree of grey incidence (GRG) for two data
sequences X
0
and X
1
are shown in Liu et al. (2016, p. 74) and have been reproduced below:
•Step I: Calculating the initial image (or average image) of X
0
and X
i
,i¼1, 2, …,m,
where:
X0
i¼Xi=xi1ðÞ¼ x0
i1ðÞ;x0
i2ðÞ;...;x0
inðÞ
;i¼0;1;2;...;m:
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•Step II: Computing the difference sequences of X0
0and X0
i;i¼1;2;...;m, as:
DikðÞ¼ x0
0kðÞx0
ikðÞ
;D¼Di1ðÞ;Di2ðÞ;...;DinðÞðÞ;i¼1;2;...;m:
•Step III: Finding the maximum and minimum differences:
M¼maximaxkDikðÞ;
m¼miniminkDikðÞ:
•Step IV: Calculating grey incidence coefficients (also called, grey relational
coefficients or point relation coefficients) by:
g0ikðÞ¼ mþxM
DikðÞþxM;xA0;1ðÞ;k¼1;2;3;...;n;i¼1;2;3;...;m;
where ξis the distinguishing coefficient. Scholars usually assume ξ¼0.5 even
though the rationale behind this assumption is debatable ( Javed et al., 2018).
•Step V: Computing the Deng’s degree of grey incidence (also called, GRG or grey
relational degree) by:
g0i¼1
nX
n
k¼1
g0ikðÞ
;i¼1;2;...;m:
In the above mentioned formula, 1/ncan be replaced by the weights w
k,
as shown
in the formula below, if the effect of each factor on the system is not same, where
Σw
k
¼1. This justification can be confirmed from Kalsi et al. (2013) and Kadier et al. (2015)
as well:
g0i¼X
n
k¼1
g0ikðÞwk
;i¼1;2;...;m:
In short, 1/nimplies that weights of criteria are equally distributed and w
k
implies
that weights are unequally distributed, which is a usual case in real-life problems. In the
case discussed in the current study, the first form of Deng’sGRAmodelcanbe
conveniently applied as all independent variables are equally weighted in both private
and public sectors.
Absolute degree of GIA model. If Deng’s GIA model sheds light on the inter-influences
among the factors (represented by the grey data sequence) (Xuerui et al., 2007; Fung, 2003),
then the absolute degree grey incidence analysis (ADGIA) model (also called, absolute GRA
model) sheds light on the correlations between those factors (Yu et al., 2017; Tung and Lee,
2010). However, both models intend to achieve the objective by estimating the geometric
proximity between the sequence curves in their own way and have their own significance in
the literature. The steps involved in calculating the absolute degree of grey incidence for two
equal-time interval sequences X
0
and X
1
are shown below:
•Step I: Calculate the zero-starting point images (X0
0and X0
1) of the sequences X
0
and X
1
.
•Step II: Calculate |s
0
|,|s
1
|and |s
1
−s
0
|.
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•Step III: Calculate absolute degree of grey incidence (ɛ
01
) between the sequence X
0
and X
1
using the following formula:
e01 ¼1þs0
jjþs1
jj
1þs0
jj
þs1
jj
þs1s0
jj
:
For further details on the two GIA models and their properties, Liu et al. (2016, Chapter 5)
can be consulted. For discussion on Deng’s GRA and absolute GRA, Liu et al. (2013) and
Liu et al. (2017) are also recommended.
Definitions
Definition I: degree of grey relation (or, degree of grey incidence)
Deng’s degree of grey relation, or GRG, is the degree given by Deng’s GRA model, also
called Deng’s grey incidence analysis (DGIA) model. It is given by:
g0i¼X
n
k¼1
g0ikðÞwk
;i¼1;2;...;m;
or:
g0i¼1
nX
n
k¼1
g0ikðÞ
;i¼1;2;...;m;
where g
0i
(k) is grey incidence coefficient (or grey relational coefficient; GRC). The first form
is weighted GRA/DGIA and second one is non-weighted (or equally weighted).
Definition II: absolute degree of grey relation
Absolute degree of grey relation is the degree given by absolute GRA model, also called
ADGIA model. It is given by:
e01 ¼1þs0
jj
þs1
jj
1þs0
jj
þs1
jj
þs1s0
jj
:
Definition III: zero-starting point image
Let X
i
¼(x
i
(1), x
i
(2), …,x
i
(n)) be the data sequence of a system’s behaviour and Dthe
sequence operator which satisfies X
i
D¼(x
i
(1)d,x
i
(2)d,…,x
i
(n)d)andx
i
(k)d¼x
i
(k)d−x
i
(1),
k¼1, 2, …,n.Then,Dis referred to as a zero-starting point operator and X
i
Dis the
zero-starting point image of X
i
.X
i
Dis often written as XiD¼X0
i¼ðx0
i1ðÞ;x0
i2ðÞ;...;x0
inðÞÞ
(Liu et al., 2016, p. 77).
Definition IV: |s
0
|,|s
1
|and |s
1
−s
0
|
Following Liu et al. (2016, p. 79), assume that X
i
and X
j
are one-time-interval sequences of the
same length, and the following are zero-starting point images of X
i
and X
j
:
X0
i¼x0
i1
ðÞ
;x0
i2
ðÞ
;...;x0
in
ðÞ
;
X0
j¼x0
j1
ðÞ
;x0
j2
ðÞ
;...;x0
jn
ðÞ
:
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Then:
si
jj
¼X
n1
k¼2
x0
ikðÞþ
1
2x0
inðÞ
;
sj
¼X
n1
k¼2
x0
jkðÞþ
1
2x0
jnðÞ
;
sisj
¼X
n1
k¼2
x0
ikðÞx0
jkðÞ
þ1
2x0
inðÞx0
jnðÞ
:
Definition V: the second synthetic (SS) degree of grey incidence model
The SSDGIA model, or the Second Synthetic GRA model, is given by:
ϼij ¼yeij þ1–yðÞgij;y∈0;1
½
;
where ϼ
ij
represents the SS degree of grey incidence/grey relation (SSGRG), ɛ
ij
represents
absolute degree of grey incidence/grey relation (absolute GRG) and g
ij
represents Deng’s
degree of grey incidence/grey relation (GRG). Deng’s GRA model relies on grey relational/
incidence coefficient of particular points; however, absolute GRA model relies on integral
(relatively comprehensive) perspective (Liu et al., 2016, p. 69). The SS degree of grey
incidence reflects overall closeness between two sequences based on particular points and
integral perspective. The first synthetic degree grey incidence analysis (SDGIA) model was
developed through absolute degree of grey incidence and relative degree of grey incidence,
and can be found in Liu et al. (2016). The SSDGIA model has been developed in light of the
working principle of SDGIA model. It is recommended to take θ¼0.5 when the decision
maker wants a comprehensive ranking that equally incorporates the benefits of both gand ε
without favouring one over the other. If favouring is necessary, then the value of θcan vary.
If one intends to favour g, then θcan be decreased, and if one desires to favour ε, then θcan
be increased.
Research methodology and hypotheses
Sample and procedure
Our sample consists of service seekers (outpatients) of 12 healthcare projects in different
rural areas of Punjab province of Pakistan. Five projects were by private organisations
(both national and multinational) and seven projects were by the local or provincial
government. The names of the projects are not mentioned in the study because most of the
administrators/project managers of the healthcare projects allowed us to survey in their
premises only on the condition of anonymity.
Construct of the instrument
The research instrument had two parts. The first part recorded basic demographic
information (e.g. gender, type of hospital, etc.). The second part contained 22 items adapted
from Parasuraman et al. (1988, 1991) with slight necessary adjustments, to gauge
expectations of patients concerning the service quality. A five-point Likert scale was used.
Satisfaction with nursing was measured through three items, as used in Aiken et al. (2012).
At the end of the questionnaire, one self-developed direct question (on a five-point Likert
Outpatient
satisfaction
and service
quality
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scale; 5 ¼“extremely satisfied”,3¼“so-so”,1¼“least satisfied”) was added to measure the
Pakistani patients’perception about satisfaction–expectation relationship: “How satisfied
you would be if your expectations from the healthcare unit/project are lower?”In total,
88 per cent patients reported “extremely satisfied”to “so-so”.
Reliability, validity and normality
In total, 278 outpatients were surveyed and 221 questionnaires were considered properly filled
by the authors and utilised for grey data analysis using the Grey System Modelling software
developed by the Nanjing University of Aeronautics and Astronautics, China. For the
SSDGIA model’s execution, Microsoft Excel was deployed. Out of 221 responses, 121 were
related to public sector hospitals and 100 were related to private sector hospitals. To confirm
the normality of the data, kurtosis test was applied. Response rate was good that confirmed
the validity of the instrument. To confirm the reliability of research items and the instrument,
Cronbach’sαvalues were calculated using IBM SPSS (version: 18). Cronbach’sαvalue for the
22 items used to calculate the five dimensions of service quality was turned out to be 0.827.
After incorporating three items patient satisfaction, the overall reliability of the questionnaire
became 0.822. After incorporating the last question on satisfaction–expectation relationship,
the overall reliability of the instrument was 0.823. The reliability of each construct was more
than 0.7; hence, the data collected were considered reliable for data analysis.
Hypotheses
In the light of the research questions and the Parasuraman et al. (1988) based framework
used in Javed and Ilyas (2018), the following hypotheses were proposed for the study:
H1. Patientsatisfaction is significantly associated to, and is being influenced by, tangibility.
H2. Patient satisfaction is significantly associated to, and is being influenced by, empathy.
H3. Patient satisfaction is significantly associated to, and isbeing influenced by, reliability.
H4. Patient satisfaction is significantly associated to, and is being influenced by,
responsiveness.
H5. Patient satisfaction is significantly associated to, and is being influenced by, assurance.
H6. There exists a difference between the patients’expectations from the service quality
among public and private sector healthcare projects.
Results and discussion
Driven by the three GIA models, grey incidence evaluation of the data is shown in Table II.
In grey incidence models, the relationships between data sequences, and thus degrees of
grey incidence, cannot be 0 (Liu et al., 2016); however, their values can vary from
insignificance to significance. Table II shows that the first five hypotheses can be accepted;
however, the extent of acceptance varies. For instance, in the case of public healthcare
projects, the association of patient satisfaction is weakest with empathy and strongest with
tangibility, and in the case of private healthcare projects, the patient satisfaction is most
strongly associated to reliability and least strongly correlated to empathy. Overall, the
patient satisfaction is most significantly related to tangibility and least significantly related
to empathy. In public healthcare sector, responsiveness’s influence on patient satisfaction is
apparently strongest, followed by that of assurance, empathy, tangibility and reliability.
In private healthcare sector, reliability’s influence on patient satisfaction is apparently
strongest, followed by that of assurance, empathy, tangibility and responsiveness. Overall,
responsiveness is influencing the patient satisfaction most strongly, followed by empathy,
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assurance, tangibility and reliability. For the sake of convenience, these results have been
shown in Table III in descending order.
In public healthcare projects (and overall as well), tangibility was most strongly
associated to patient satisfaction and in private hospitals reliability was most strongly
associated with patient satisfaction. Interesting revelation concerning the influence on
patient satisfaction is that in each sector assurance, empathy and tangibility are in the same
position; however, reliability and responsiveness are switching their positions, i.e. in public
sector, responsiveness is most strongly related to patient satisfaction and reliability is least
strongly related to patient satisfactory and opposite is the case in the private sector.
Therefore, the study identified two influential quality dimensions for Pakistani patients on
which they evaluate their satisfaction from the healthcare services of a given sector:
responsiveness in the public sector and reliability in the private sector. Being responsive to
patients need because of market competition is one of the attributes of private sector
healthcare (Basu et al., 2012; Berendes et al., 2011); therefore, the current study suggests, in
private sector, the patients are not expecting responsiveness to significantly influence their
satisfaction (perhaps, because they are already getting it); however, they are more concerned
with the reliability. Lack of responsiveness or timeliness is one of the attributes of public
sector healthcare (Basu et al., 2012); therefore the current study suggests, from the public
sector, healthcare patients are expecting more responsiveness as it is influencing their
Tangibility Empathy Reliability Responsiveness Assurance
PS
Healthcare projects (public)
g0.7747 0.829 0.5867 0.8784 0.8292
ε0.6702 0.5009 0.5751 0.5020 0.5989
ϼ0.7225 0.6650 0.5809 0.6902 0.7141
Healthcare projects (private)
g0.5410 0.7321 0.7976 0.4797 0.7367
ε0.8121 0.5456 0.8659 0.7560 0.6270
ϼ0.6766 0.6389 0.8318 0.6179 0.6819
Overall
g0.7408 0.8497 0.5663 0.8907 0.8349
ε0.7091 0.5004 0.6208 0.7074 0.6696
ϼ0.7250 0.6751 0.5936 0.7991 0.7523
Notes: PS, patient satisfaction.
a
g¼GRG; ε¼absolute GRG; ϼ¼second synthetic GRG (at θ¼0.5)
Table II.
Grey incidence
evaluation
a
Public healthcare projects
gResponsiveness WAssurance WEmpathy WTangibility WReliability
εTangibility WAssurance WReliability WResponsiveness WEmpathy
ϼTangibility WAssurance WResponsiveness WEmpathy WReliability
Private healthcare projects
gReliability WAssurance WEmpathy WTangibility WResponsiveness
εReliability WTangibility WResponsiveness WAssurance WEmpathy
ϼReliability WAssurance WTangibility WEmpathy WResponsiveness
Overall
gResponsiveness WEmpathy WAssurance WTangibility WReliability
εTangibility WResponsiveness WAssurance WReliability WEmpathy
ϼResponsiveness WAssurance WTangibility WEmpathy WReliability
Note: Response variable ¼patient satisfaction
Table III.
Ordered results
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satisfaction
and service
quality
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satisfaction significantly more than reliability. Therefore, it can be argued that public sector
healthcare is supposed to be effective and is expected to enhance its efficiency. On the other
hand, the private sector healthcare is supposed to be efficient and is expected to enhance its
effectiveness. In other words, as far as the influence on patient satisfaction is concerned, the
study suggests that when the patients visit the private sector healthcare projects, they
expect the treatment to be highly reliable, and when the patients visit public sector
healthcare projects, they want to be treated quickly without unnecessary delays. The
findings are aligned with the general presumptions as well, in the given context, because
during the surveying, it was observed that the patients in the public sector hospitals were
significantly more and there were huge queues not only outside ticket office but also outside
physicians’rooms; thus, delay in treatment was expected to be one of the key predictors of
patient satisfaction in those public hospitals. However, in the private sector hospitals, the
queues were smaller and thus delay in treatment (or responsiveness) was presumed to be
one of the least important factors. Therefore, it can be argued that the research findings are
rationale and accurately predicting the patient perception.
According to the SSDGIA model in the public sector hospitals, tangibility and assurance
are most important factors to predict patient satisfaction and reliability is the least
important one. In the private healthcare sector, reliability and assurance are most important
factors to predict patient satisfaction and responsiveness is the least important one. Overall,
responsiveness and assurance are likely to be most important quality dimensions followed
by tangibility, empathy and reliability. Here, it should be noted that since the service
strategy and work environment of public and private healthcare facilities are usually
different, sector-wise results should guide the decision makers (not the overall ones). Here, it
should be noted that when Deng’s GIA and absolute GIA models produce contrary results,
the SSDGIA model (at θ¼0.5) produces moderate results, as can be seen in Figure 2.
In the introductory part of the paper, two questions were raised. In the current study, as
far as the grey influence on patient satisfaction is concerned, in each sector, assurance,
empathy and tangibility are in the same position, while reliability and responsiveness are
switching their positions. As far as grey association is concerned, there is no such
consistency among the orders. Also, according to GIA analysis, there is no obvious
consistency in prioritisation of the factors. Therefore, the current study argues that the
result concerning prioritisation of quality dimensions obtained by Javed and Ilyas (2018)
was more likely to be a coincidence, and thus it is hard to generalise any such priority.
To test H6, the decision analysis was done to measure difference between patient
satisfactions among the two sectors. For this, first of all, states of nature and alternatives
were defined, as shown in Table IV.
(a) (b)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Tangibility
Empathy
Assurance
Reliability
Responsiveness
Tangibility
Empathy
Assurance
Reliability
Responsiveness
0
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Notes: (a) Public healthcare projects; (b) private healthcare projects
Figure 2.
Grey incidence
comparative
evaluation
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Here, m¼2, n¼5, and outcome ¼v(a
i
,s
j
), whereas i¼1, 2 and j¼1, 2, 3, 4, 5. Let a
1
and a
2
represent patient satisfactions from public and private hospitals and s
1
to s
5
represent
tangibility, empathy, reliability, responsiveness and assurance, respectively. Table V
represents the SS degrees of grey incidence-based matrix reporting the “second synthetic
grey relations”between the decision criteria and decision actions.
Afterwards, the two decision analysis approaches were deployed, as shown below.
The Conservative (maximin) criteria
This method has been used following the steps of Prasad (2015) and Taha (2007, 2014). Since
patient satisfaction is to be maximised, it is a maximin criterion:
max aimin sjva
i;sj
¼max ai
0:5809
0:6179
¼0:6179:
The results indicate that the patients are more likely to be satisfied from private sector
healthcare projects.
The Hurwicz criteria
This method has been used following the steps described in Gaspars-Wieloch (2014, pp. 783-784)
and Taha (2007, 2014). Since we need to maximise the patient satisfaction, the decision would
represented by:
max aiamax sjva
i;sj
þ1aðÞmin sjva
i;sj
:
The parameter αis called index of optimism and its value varies between 0 and 1.
For optimist decision makers. For optimistic patients, by considering α¼0.3 (and thus,
1−α¼0.7), we get following results:
a1:0:30:5809þ0:70:7225 ¼0:68002;
a2:0:30:6179þ0:70:8318 ¼0:76763:
For pessimist decision makers. For pessimistic patients, by considering α¼0.7 (and thus,
1−α¼0.3), we get following results:
a1:0:70:5809þ0:30:7225 ¼0:62338;
a2:0:70:6179þ0:30:8318 ¼0:68207:
Goal Measuring the level of patient satisfaction
State of nature/criteria (s
j
);
j¼1, 2, …,n
Tangibility (s
1
), empathy (s
2
), reliability (s
3
), responsiveness (s
4
), assurance (s
5
)
Alternative actions (a
i
); i¼
1, 2, …,m
Patient satisfaction from public healthcare projects (a
1
), patient satisfaction
from private healthcare projects (a
2
)
Table IV.
Defining the
decision parameters
SS degrees s
1
s
2
s
3
s
4
s
5
a
1
0.7225 0.6650 0.5809 0.6902 0.7141
a
2
0.6766 0.6389 0.8318 0.6179 0.6819
Table V.
The criteria-actions
matrix
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For both optimistic and pessimistic patients, the results indicate that Pakistani patients are
more likely to be satisfied from private sector healthcare projects.
In general, the results of both decision-making approaches indicate that patients are
more likely to be satisfied with private healthcare facilities than with the public ones, and
thus there is a greater need to uplift healthcare service quality in public sector, as
compared to private sector. This defines the “predominant utilisation of private healthcare
facilities”in Pakistan (Malik et al., 2017; Afzal and Yusuf, 2013; Mumtaz et al., 2013;
Anwar et al., 2012).
Here, it is worth mentioning that in a healthcare system, the decision making is the key
role of the system’s managers and the quality of their decisions defines the success and
failures of the healthcare system’s ability to attain its objectives (Bahrami et al., 2016). The
feedback of patients, the consumers of the healthcare services, on the level of service in a
healthcare system is important for health managers to make wise decisions to enhance the
organisational efficiency and effectiveness. Another important aspect to improve the quality
of decisions can be the coordinated efforts of all departments within a healthcare system to
enhance their service quality for their patients. Quality improvement is no more the job of
one department and considering quality improvement a job of all functions, it becomes
essential for the managers to improve communication and coordination among caregivers
as well as among all functions associated with the healthcare system (Baker et al., 2004;
Oakland, 2014), and then using the knowledge thus gained to improve the health
management practices within the healthcare sector. Considering the aforementioned results,
the study stresses this on the public sector health managers of Pakistan more raucously as
compared to their contemporaries in the private sector.
Conclusion and recommendations
The literature review revealed the poor ranking of Pakistan in health among other countries
of the world and quite embarrassing proportion of GDP (2.8 per cent) that the government
spends on the healthcare sector. In Pakistan, out-of-pocket expenditure (as percentage of
private expenditure) on health is 86.8 per cent (WHO, 2015), which reveals that the
government’s contribution in maintaining the health of its citizens is relatively
disappointing. Despite this inadequate contribution by the Pakistani Government
towards its population, corruption in public service is further eroding the gains, thus
severely influencing the perception of the citizens. A cross-country study quoted in Gadit
(2011) revealed that 95 per cent of the population of Pakistan “perceives that the health care
system is corrupt”. Thus, gauging the perception of service seekers provides deep insight of
the service and helps policy makers and healthcare managers in making efforts to rectify the
existing inadequacies within the healthcare system. Further, patients’input must be utilised
in designing of healthcare service delivery processes in the developing countries like
Pakistan (Andaleeb, 2001) in order to achieve long-term profitable outcomes.
With this entire context in mind, the current study was designed and the SERVQUAL
method was used to gauge the service quality expectations of Pakistani patients from public
and private sector hospitals. The results revealed the varying degree of association between
patient satisfaction and the five constructs of the service quality as offered by the
SERVQUAL method where responsiveness and reliability turned out to be the strongly
related to patient satisfaction in public and private sectors, respectively. Overall,
responsiveness and empathy revealed a strong relationship to the patients’satisfaction from
the healthcare services. The results of both decision analysis approaches indicated that
patients’expectations from the public sector hospitals are higher and they see more areas of
improvement in this sector. In short, there is greater need to uplift healthcare service quality
in public sector, as compare to private sector, as per the respondents.
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Therefore, to improve health management practices in the underdeveloped countries,
in general, and in Pakistan, in particular, the study recommends the policy makers
and the healthcare managers that when the hospital staff (in both sectors) deal with the
patients, they need to make the patients believe that they genuinely feel the pain and
suffering of the patients and this can be done through proper training of staff on
empathy, business etiquettes, public relations and customer relationship management; for
the patients’satisfaction, these days treatment and nursing are not enough, thus
they need to look after the infrastructure of the hospitals as well, e.g. they should ensure
that there is enough sitting arrangement, the systems of cooling and heating are
functional, the dresses of staff is clean, beds are tidy, for the patients with special needs,
arrangements are appropriate and these issues can be addressed by improving
administration and discipline (in both sectors); for a patient who is in grief, the quick
response from the hospital staff is very important as the delay in entertaining
theneedofpatientismorelikelytoarousethe feelings of dissatisfaction among the
patients which can be overcome by enhancing the operational efficiency (in both sectors,
in general, and in the public sector, in particular); and for the patients the reliability of
prescriptions and treatment is very important the physicians and nursing staff should
manifest highest level of professionalism and competence (especially in the private sector).
Therefore, considering the interconnectedness of these four points, the study also stresses
that quality is not a function of single department but different departments play
their role in enhancing the overall quality of the system. Kuziemsky (2016) also stressed
that many healthcare problems are an effect of the interactive and multi-dimensional
nature of the healthcare system and rarely can be reduced to one root cause or a single
factor. All these facts confirm the well-known hypothesis within the quality
management literature (e.g. Oakland, 2014, p. 16) that quality assurance is a process
and is a job of all functions/departments within an organisation, thus expecting
just from one department or function to improve the overall quality in an organisation
is delusion.
Further, the SSDGIA model, a synthesised model of Deng’s GIA and absolute degree GIA
models, used for grey incidence evaluation of healthcare problem under study turned out to
be a feasible and effective model that can be used as a better alternative to the traditional
approaches of data analysis because the foundation of this model rests upon the grey
system theory, which does not necessarily needs large sample size to make predictions as it
can work well for small sample size as well ( Javed and Liu; 2018; Javed et al., 2018; Javed and
Liu, 2017; Liu et al., 2016). Since sample size is very important issue and most of the
healthcare studies involve large sample size, the approach used in this paper can facilitate
future healthcare scholars to make decisions using small or incomplete samples. Not only in
the healthcare sector the proposed model can be applied in other fields but also where grey
system theory has its applicability.
Acknowledgements
This work was supported by the Funding for Outstanding Doctoral Dissertation in Nanjing
University of Aeronautics and Astronautics (No. BCXJ18-10). The paper is one of the
derivatives of the PhD dissertation of the corresponding author. The authors want to thank
the GreySys Foundation’s GreySys Analytics and the Academy of Young Researchers and
Scholars (AYRS) for their assistance in approaching hospitals, seeking permission for
surveying within their premises and data collection. An older version of the paper was
presented at the Joint Conference of 2018 International Congress of Grey Systems and
Uncertainty Analysis (GSUA) and 32nd Workshop of Grey System Society of China (GSSC)
held in Nanjing, China, from 20 to 23 April 2018.
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quality
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Corresponding author
Saad Ahmed Javed can be contacted at: saad.ahmed.javed@live.com
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