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141
Role of Professional Skills in Promoting Healthcare System: A Performance
Perspective
Rizwan Shabbir a, Muhammad Abrar b, Aysha Batool c
a Assistant Professor, Lyallpur Business School, Government College University, Faisalabad,
Pakistan
Email: rizwanshabbir@gcuf.edu.pk
b Professor, Lyallpur Business School, Government College University, Faisalabad, Pakistan
c PhD student, Lyallpur Business School, Government College University, Faisalabad, Pakistan
ARTICLE DETAILS ABSTRACT
History:
Accepted 10 May 2022
Available Online June 2022
This paper aims to investigate the impact of sustainable practices
especially sustainable production and sustainable supplier management
on supply chain performance. This empirical study demonstrates the
contextual examination of sustainable practices especially with reference
to an emerging economy like Pakistan. Survey was employed to collect
data from 100 Food Manufacturing Firms. Exploratory Factor Analysis
and Structure Equation Modeling were used through AMOS to test
hypothesis. The results reveal that sustainable production and
sustainable supplier management both significantly impact triple
bottom line. However, sustainable production generates stronger impact
on social performance, while, sustainable supplier management
significantly effects environmental performance. Additionally, the
findings provide valuable insights regarding the use of sustainable
production and sustainable supplier management and their impact on
supply chain performance. Finally, it propagates utility of ecological
value chain management mentioning the impact of couple of sustainable
practices on tipple bottom line.
© 2022 The authors. Published by SPCRD Global Publishing. This is an
open access article under the Creative Commons Attribution-
NonCommercial 4.0
Keywords:
Professional Skills, Coproduction,
Healthcare, Performance,
Workflow Efficiency
JEL Classification:
J24, K32
DOI: 10.47067/reads.v8i2.443
Corresponding author’s email address: rizwanshabbir@gcuf.edu.pk
1. Introduction
Pakistan has been striving to improve the healthcare system and has developed many strategies
and reforming programs. Pakistan has also joined hands with international and national NGOs further
to speed up the process of healthcare services betterment. The healthcare sector of Pakistan includes
public and private stakeholders, delivering medical facilities at primary, secondary, and tertiary care
centers. Public hospitals and medical institutions are government-operated. These are supported with
government funding and provide free healthcare services to the citizens. The private healthcare sector
provides health services to above 71% of the population. Because most private hospitals are profit-
oriented, they try to offer better health services to patients than the public sector. The Pakistan Social
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and Living Standards Measurement (PSLM) survey shows that 67.4% of Pakistani families seek
suggestions from private doctors (PBS, 2021).
Pakistan's health service framework is struggling with various rudimentary difficulties. It is
essential to reconsider the structure of the medical services segment to bring dynamic advancements. In
the past few years, the health sector has introduced a promising National Health Vision 2016-2025 to
deliver essential healthcare services to all populations and meet the Sustainable Development Goals
2025 (Kumar & Bano, 2017). With the emergence of the COVID-19 outbreak, it has become critical for
patients living in remote places to receive healthcare services and consultations without necessarily
being present at healthcare centers. As a result, more resilient and improved healthcare systems are
needed in the post-COVID-19 age, with added operational, communication, and technical support
through advanced digital techniques to cater requirements of doctors and the nursing staff (Chigurupati
et al., 2020).
Pakistan is the world's sixth most populated country, and health reserves are insufficient to
meet the needs of the people. Pakistan is one of 57 nations with a significant shortage of health workers
(Rana, Sarfraz, Kamran & Jadoon, 2016). A GALLUP report structured upon the Pakistan Economic
Survey held in 2015-16 indicates that the number of hospitals in the country in 2015 was 1,167. Along
with those hospitals were 5,695 dispensaries, 5,464 basic health units (BHUs) or sub-health centers,
733 centers for child health and maternity issues, and 675 rural health centers. The total number of
beds was 118,869, and its availability for the general public was 1,613 per bed. In Pakistan, the doctor-
to-patient ratio is 1:1300, the doctor-to-nurse ratio is 1:2.7, and the nurse-to-patient ratio is 1:20
(Nishtar, 2006). The Human Development Index (HDI) ranks Pakistan 154th out of 187 countries
(UNCP, 2021). The country's healthcare sector lacks integration and utility of IT health applications,
compulsory healthcare supplies, and electronic health record maintenance systems (Punjani et al.,
2014). Furthermore, the availability of eHealth services is limited in Pakistan due to inconsistent
technological execution and a dearth of eHealth infrastructure development planning (Kumar & Bano,
2017).
With the emergence of technology, the healthcare sector evolved from paper-based to paperless
systems. E-Health uses IT and digital communication to enhance the approach, efficiency, efficacy, and
quality of medical and corporate procedures used by healthcare organizations and the healthcare
workforce. It involves gathering, analyzing, and disseminating information to provide care services.
eHealth, or digital health, is an essential instrument to assist nations in establishing secure, effective,
and sustainable healthcare delivery systems (WHO, 2021); globally, the fast growth of ICT and eHealth
efforts promotes changes in care services systems (Lapão & Dussault, 2017). It is additionally critical
that most of the investigations about eHealth and its successful reception and use have been done in
both developed (Alvarez, 2004; Eysenbach, 2001) and developing states (Braa et al., 2004; Mosse &
Sahay, 2003). The growing use of IT technologies in health care is an emerging, developing, and
underrepresented sector for coaching and capacity development for health professionals.
In today's demanding environment, Information Systems (IS) have received much attention in
the healthcare business to improve healthcare facilities' efficiency and effectiveness (Safdari et al.,
2014). Rapid and comprehensive improvements in medical technology have made electronic health
information system management, or e-HMISs, a priority for care services (Saghaeiannejad-Isfahani et
al., 2015).
The failure to execute a sustainable national health system with increasing population figures
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has emphasized the need for a robust e-HMIS. The pandemic health emergency of COVID-19 in
developing countries also called attention to a robust health performance system. The critical issues
regarding smart governance depend on three attributes: firstly, management should use smart
technological devices for executing different public service tasks (Madon et al., 2007). Likewise, the
Government of Pakistan (GOP) has operationalized "Khidmat Markaz," a national identification system
and computerized passport system. The expansion towards smart governance linked all these databases
for exploring economic development tools. Economic indicators worldwide showed a transformation in
industrialization from service to specialized products with value-added services. Thirdly, governance in
the 21st century calls for a set of rules to supervise citizen protection privacy policies that require a
privacy-based design method. Such smart governance enables private and public institutes to create and
share a free market that utilizes general citizen information with various stakeholders and specific
citizen information with specialized public service providers and strictly uses this information (Cordella
& Willcocks, 2012).
2. Factors for a Sustainable Healthcare System
There is a requirement for social capital and capabilities to advance eHealth. The training,
knowledge, and consciousness of professionals and doctors regarding the utilization of IT applications
and their applicability in hospitals can be developed gradually and sustained by providing appropriate
instruments and apparatus and suitable training on regular spans for progressively immediate access to
the information available on the web. Malik et al. (2009) critiqued that the slight utilization of the web
by the healthcare experts of developing countries like Pakistan is more often a direct result of
inappropriate instruments and gadgets and deficiency of appropriate training programs on the subject
of eHealth systems. Kimaro and Nhampossa (2004) discussed that eHealth ventures in developing
nations are often fruitless because of IT experts' deficiency, information, and capability in eHealth
frameworks. IT experts require training and instruction to utilize all eHealth applications successfully.
Qazi and Ali (2011) illustrated that training is a continuous component, and healthcare experts should
be well-trained.
2.1 Professional skills
Katz (1955) proposed that efficient management systems depend on three essential personal
skills: technical, human, and conceptual. Katz claimed that these abilities are distinct from attributes or
qualities. Individuals acquire skills to function and accomplish their objectives or goals, but
characteristics or traits define their inherent selves. The capacity to use one's knowledge and talents to
achieve goals or objectives is called skill. Katz (1955) first introduced these three skills approaches and
postulated that these skills (Conceptual, human and technical) provide a basis for individual efficiency
and performance in any working environment. Later work by Mumford et al. (2007) supported Katz's
work by further adding that functional or strategic skills are aligned with conceptual skills while
interpersonal or communication skills are aligned with human skills. Katz's model is described as the
best framework for the capacity development of employees (Griffith et al., 2019).
Shiferaw et al. (2020) studied the TS of clinical practitioners in public hospitals. They concluded
that it increases the healthcare provider's capacity to perform the care activities by increasing their
knowledge of the IT system. Bjerrum et al. (2018) recommended that simulation-based IT systems are
beneficial in acquiring TS for healthcare personnel. Robinson and Kersey (2018) concluded that
physicians' training programs on Electronic Health Records (EHR) systems significantly influence care
quality, accuracy, and safety. Melby et al. (1997) also suggested a significant relationship between
healthcare staff CS and care service efficiency. Such as, physician CS must be maintained with the
advancement of health systems into the digital era since the lack of synchronous and asynchronous CS
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limits the technologies' potential added value (Lum et al., 2020; van Galen et al., 2019). Waheed et al.
(2021) examined the influence of functional capabilities on employee effectiveness in the healthcare
industry and found a significant association. In another study, Karatepe et al. (2007) showed that the
service industry's self-efficacious frontline employees (FLEs) perform their jobs at elevated levels. Peña
et al. (2016) also specified that FS could improve workflows, value co-creation, and system performance
in healthcare. Parra-Rizo and Sanchis-Soler (2020) also confirmed that individuals with FS face fewer
difficulties performing their responsibilities and getting along with others.
H1a-c: Professional skills (functional, Technical, Communication) directly impact Health System
Performance
2.2 Workflow Efficiency and Coproduction
Rezai-Rad et al. (2012) explain that to deal with clients' relevant concerns and maintain an
average utilization of ICTs in health associations, medical services providers must be offered a chance to
participate in information systems improvement processes incorporating the IS substance as indicated
by their necessities. Coproduction is often regarded as a viable approach for addressing critical
difficulties in the health sector (McMullin & Needham, 2018; Voorberg et al., 2015), where resources are
severely limited. Coproduction also pressures healthcare systems by posing a long-term threat if
resources are not blended. For this purpose, researchers have pushed for more individualized care
based on new relational models in which informal carers and local communities share duties with care
professionals, allowing people to feel like team members and improving service quality (Marsilio et al.,
2021). First-line healthcare professionals actively collaborate with healthcare stakeholders such as
health providers, general practitioners, social services, and others to coproduce the care services
(Agyepong et al., 2021; Turk et al., 2021).
H2a-c: Professional skills (functional, Technical, Communication) directly impact coproduction in the
healthcare system
Critical organizational issues such as teamwork, work demands, Information Technology, and
structure within healthcare operations can impact clinician efficiency. Furthermore, intensive care's
dynamic nature necessitates doctors to change their jobs often while executing patient care activities
(Bastian et al., 2016). As a result, a deeper understanding of the numerous parts that make up workflow
is an integral part of process optimization. Efficient workflow describes how tasks and responsibilities
in an organization are done with the help of the best available technology and methods to avoid delays
and unnecessary resources (Carlisle et al., 2020). Understanding the interrelationships influencing and
shaping workforce behavior can help healthcare operations drive workflow efforts. Healthcare
practitioners can more quickly identify the leverage points leading to desired workflow results if they
have a deeper grasp of the health information management system and its aspects (e.g., processes,
information).
The work of Denton et al. (2018) explored the effect of electronic health records (EHRs) on
workflow efficiency (WE) of clinics and associated performance standards. The authors determined that
IT-based systems like EHR improve the workflow of clinics, patient safety, and quality of care services.
Similarly, Holman et al. (2016) investigated the primary care physicians' (PCPs) workflow and their
effect on care performance and patient relationships by concluding that ICT platforms played a vital
role in improving care efficiency. Lapão and Dussault (2017) reviewed the past literature on e-health
and its effect on care service staff efficiency. The authors disclosed that e-health improves clinical
decision-making, disease management and control, and workforce efficiency. Moreover, Bastian et al.
(2016) developed a three-phase framework model for hospitals to identify and categorize different
workflows for better quality care. The model proved worth supporting the care staff to improve
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workflow efficiency.
H3a-c: Professional skills (functional, Technical, Communication) directly impact Workflow Efficiency
in the healthcare system
3. Methodology
In line with the quantitative study, this part of the research examines the impact of required
professional skills, workflow efficiency, and coproduction on the performance outcomes of e-HMIS. This
research work is based on primary data collection and quantitative methodological choice. The data is
collected through a structured survey instrument from the frontline employees working in the public
hospitals of big cities in Pakistan through a questionnaire. The study variables were identified, and all
the measurement scales for variables were adopted, i.e., FS (7-items), TS (8-items), CS (7-items), WE
(7-items), CP (11-items), and HP (6-items), were developed and validated by the researchers as a part of
the Higher Education Commission of Pakistan's research project "A Step Towards Smart Hospitals:
Sustainable Health Information System from Big Data Perspective" and the same were adopted for this
study. Each construct was measured on a five-point scale from "1=strongly disagree" to "5=strongly
agree".
The participants of this study were frontline workers who operate computerized healthcare
management information systems and have the necessary computer literacy to use the IT-based
databases. Employees aged 25 years or above were considered the target sample to obtain reliable and
meaningful responses. Almost half of the respondents were 25-30 years old, and the rest were aged
above 30 years. Most of the respondents, i.e. almost 48.4%, had an intermediate education level, and
the rest were qualified with a bachelor's 16.3%, a master's 29%, and above master's 6.3%.
Table 1: Demographics profile
Age in Years Frequency Percent Gender Frequency Percent
25-30 85 53.5 Male 90 56.60
31-35 39 24.5 Female 69 43.40
36-40 10 6.3
Above 40 25 15.7
Experience in Years Education
Less than 5 83 52.21 Intermediate 77 48.4
5-10 30 18.86 Bachelor 26 16.3
11-15 29 18.23 Master 46 29
More than 15 17 10.70 Above master's 10 6.3
4. Results
Exploratory factor analysis is a statistical technique to refine the construct measurement scales
by reducing the number of items utilized. EFA is a necessary procedure in the development of scales for
questionnaires. It is also used to explore the multidimensionality of the constructs. EFA divides the
items of a construct to statistically and theoretically meaningful sub-dimensions to help define the
concepts and better empirical investigation. EFA for this research work was conducted on the collected
data using Statistical Package for Social Sciences (SPSS) V.22. software. The objective of EFA was to
recognize the items in the questionnaire that significantly loaded the constructs. Secondly, future
researchers assessed the underlying multidimensionality of the constructs for better application in the
Pakistani healthcare sector or similar contexts.
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Principal Components Analysis was employed with varimax factor rotation. The number of
items was retained based on the commonalities, which should be ≥ 0.5 (Mitra & Datta, 2014). At the
same time, the number of factors was kept by assessing the (i) eigenvalue criteria (eigenvalue should be
>1) and (ii) cumulative variance explained by the factor, which should be more than 50%.
The Kaiser-Meyer-Olkin (KMO) test value of 0.784 was attained for functio0nal skills, which is
considered suitable for sampling adequacy (≥0.6 value is deemed to be good). Two factors were
achieved with more than one eigenvalue. The first factor with an eigenvalue of 3.201 explained 33% of
the total variance. The second factor with eigenvalue 1.207 explained more than 29% of the total
variance. The first two factors explained more than 62% of the cumulative variance. The KMO test
value of 0.739 was attained for Communication skills considered good for sampling adequacy (Table-2).
The Bartlett's Test of Sphericity for communication skills was also significant, with a Chi-Square value
of 457.306 at the significance level of 99%. Two factors hold eigenvalue above one. The first factor with
an eigenvalue of 3.306 explained 34% of the total variance. The second factor with eigenvalue 1.391
explained more than 32% of the total variance. These two factors explained above 67% of the
cumulative variance.
The KMO test value for the technical skills construct was 0.812, which is considered a good
indicator of sampling adequacy (Table 2). The Bartlett's Test of Sphericity for Technical skills was also
significant, with a Chi-Square value of 676.398 at the significance level of 99%. Varimax factor rotation
with Kaiser Normalization was performed to refine the dimensions of technical skills. The rotated
component matrix presented in table-25 depicts that TS1, TS2, TS3, and TS8 formed component 2.
Component 1 consisted of TS4, TS5, TS6, and TS7. The items loaded on the second factor were related
to the user's knowledge about and familiarity with information and communication technology and e-
HMIS, so it was named "Tech-Familiarity." Sample items include "I am familiar with ICT (information
and communication technologies)" (TS7). Items in the first component were about the system operating
skills of e-HMIS users. For example, "I can troubleshoot software-related issues" (TS5). Thus, the
second factor was termed "Operating Skills."
The KMO test value for the Coproduction construct was 0.762, which is considered suitable for
sampling adequacy (table-2). The Bartlett's Test of Sphericity for Technical skills was also significant,
with a Chi-Square value of 855.737 at the significance level of 99%. The first factor with an eigenvalue
of 4.455 explained >21% of the total variance. The second factor with eigenvalue 1.572 explained more
significant than 20% of the total variance. The third factor with an eigenvalue of 1.454 explained almost
20% of the variance, whereas, the fourth factor with an eigenvalue of 1.034 explained approximately
16% of the total variance. These four factors explained above 77% of the cumulative variance. Varimax
factor rotation with Kaiser Normalization was performed to improve the dimensions of coproduction.
The rotated component matrix below depicts that CP9, CP10, and CP11 formed component 1.
Component 2 consisted of CP4, CP5, and CP6. The third factor contained the items CP1, CP2, and CP3.
The final dimension of coproduction loaded item numbers CP7 and CP8. The items loaded on the first
factor were related to the user's self-efficacy to coproduce in e-HMIS implementation, named "User
Self-efficacy." Sample items include "I am capable of providing constructive suggestion to improve
health information system." (CP9). The second component loaded items related to the consideration by
the organization of the users' role in the coproduction process. For example, "My organization often
asks my opinion for potential changes" (CP5). So, the second component was named "consideration."
The third factor contained questions about the organization, user responsiveness, and knowledge
sharing and hence was called "knowledge sharing." The example item is "My organization promptly
responds to my queries" (CP3). The fourth factor containing items CP7 and CP8 was awareness of
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challenges such as cost, and time needed in the coproduction process. Thus, it was called "awareness."
The sample item is "I know that coproduction process is demanding/effort-full" (CP8)
The KMO test value for the "Healthcare System Performance" construct was 0.775, which is
considered good for sampling adequacy. The Bartlett's Test of Sphericity for Technical skills was also
significant, with a Chi-Square value of 321.866 at the significance level of 99%. To improve the
dimensions of the construct "workflow efficiency," Varimax factor rotation with Kaiser Normalization
was performed (table-43). The rotated component matrix below depicts that HP4, HP5, and HP6
formed the first component. In contrast, the second factor was loaded with HP1, HP2, and HP3. The first
factor loaded items about the operational sustainability of the healthcare system performance, e.g.,
"Improve energy (electricity) efficiency" (HP6). Hence, it was identified as an "Operational
sustainability" performance. Items in the second component were about the increase in the economic
sustainability of the healthcare system due to e-HMIS. For example, "Increased the hospital revenues."
(HP2). The second factor was termed "Economic sustainability" performance.
Table 2: Principal component analysis for Healthcare performance system
Motive (Eigen-value) KMO
Bartlett's
Test
Variance
explained
Cronbach's
Alpha
Composite
Reliability
Functional Skills .784** 351.245** 0.805 0.860
Problem Solving Skills 33.088
Analytical Skills 29.882
Communication Skills .739** 457.306** 0.809 0.862
User-Oriented 32.940
Other-Oriented 34.161
Technical Skills .812** 676.398** 0.876 0.901
Operating Skills 36.474
Tech-Familiarity 32.113
Coproduction .762** 855.737** 0.836 0.870
User Self-Efficacy 21.373
Consideration 20.088
Knowledge Sharing 20.003
Awareness 15.953
Workflow Efficiency .750** 495.753** 0.832 0.875
Efficiency 39.363
Effectiveness 28.275
Healthcare System
Performance
.775** 321.866** 0.772 0.846
Operational sustainability 36.478
Economic sustainability 33.230
The results revealed that Cronbach's alpha values for all the six constructs were greater than
0.70, ranging from 0.772 to 0.876, which fulfilled the statistical cut-off criteria recommended by the
researchers (Rahimnia & Hassanzadeh, 2013). Composite reliability is another measure to assess the
reliability and internal consistency of the construct and the items loaded on it, similar to Cronbach's
Alpha (Netemeyer et al., 2003). It indicates the cumulative shared variance that all the items of a
specific construct combinly explain in that construct. Logically, the observed items intended to measure
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their latent construct should demonstrate a good amount of variance to be called adequate
representatives of the latent construct. According to Hair et al. (2017), the composite reliability value
should be greater than 0.70. the results presented in table-2 depict that all the six measures were found
to have good reliability values of CR>0.70 (Hair et al., 2017). The values ranged from 0.846 to 0.901,
where the construct of technical skills had the highest composite reliability of 0.901.
Table 3: R Square
Construct R Square R Square Adjusted
Coproduction (CP) 0.312 0.299
Workflow Efficiency (WE) 0.333 0.320
Performance of Healthcare system (HP) 0.516 0.500
The value of R-Square is a statistical measure that indicates the proportionate amount of
variance that the independent variable predicts in the independent variable. The R-Square value of 0.25,
0.50, and 0.75, represents a weak, moderate, and substantial variation (Hair et al., 2017). The Table-3
shows that Coproduction, Workflow Efficiency, and healthcare system Performance had an R Square
value of 0.367, 0.312, 0.333, and 0.516, respectively. The total effects Table-4 shows the cumulative
effects of functional skills on coproduction (β=0.362 and p <0.001), workflow efficiency (β=0.404 and
p <0.001), and healthcare system performance (β=0.218 and p <0.001). Thus, the good functional skills
of e-HMIS users can enhance the positive outcomes through increased coproduction and workflow
efficiency, which can, in turn, have positive impacts on overall healthcare system performance through
e-HMIS. Technical skills are also significant predictors of workflow efficiency while utilizing an e-HMIS
with a β coefficient of 0.194 at p > 0.001. Similarly, the most significant impact of technical skills of e-
HMIS users is finally on the improvement of healthcare system performance, which is also previously
seen in the direct and indirect effect results. Thus, the technical skills of e-HMIS users are a source of
better healthcare system performance due to a statistically significant coefficient.
Table 4: Total effect of proposed hypotheses
Hypotheses Sample
Mean
T-value Confidence
Interval
2.5% 97.5%
Functional Skills -> Health System Performance (H1a) 0.21* 2.340 0.023 0.389
Communication Skills -> Health System Performance (H1b) 0.27** 4.431 0.145 0.372
Technical Skills -> Health System Performance (H1c) 0.40** 5.910 0.272 0.546
Functional Skills -> Co-production (H2a) 0.36** 4.239 0.184 0.521
Communication Skills -> Co-production (H2b) 0.31** 4.499 0.168 0.439
Technical Skills -> Co-production (H2c) 0.27* 3.041 0.087 0.428
Functional Skills -> Workflow Efficiency (H3a) 0.40** 4.867 0.228 0.561
Communication Skills -> Workflow Efficiency (H3b) 0.37** 5.696 0.237 0.486
Technical Skills -> Workflow Efficiency (H3c) 0.20* 2.915 0.070 0.322
Haddad (2017) also found that functional competencies (task-related skills) and managerial
competencies (communication skills etc.) increase employee performance.
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5. Discussion and Conclusion
The FS and TS of the Frontline employees, the first line users of e-HMIS, directly enhance the CP
of healthcare services and WE of the employees. It is because better FS improves the efficacy of the
employees to participate as active stakeholders in the CP process of the healthcare system and
efficiently perform workplace tasks. Similarly, as the technology-based system of e-HMIS needs a skill
set of technical abilities to operate such a system, the Frontline employees having TS can better use and
utilize the computerized system. They can participate in the value co-creation and CP process of the
digitalized medical record system by providing feedback about the advantages and disadvantages of the
prevailing system. The e-literacy of the Frontline employees in the form of TS can enhance their WE
through agility, effectiveness, and efficient work management. This is why TS has a direct positive
association with e-HMIS-driven overall healthcare system performance. Studies indicate that the
technical literacy and competency of e-HMIS users is a crucial factors for the better performance of this
technology-based system (Heeks, 2006). Similarly, the person-job fit theory also highlights the
importance of employee skills for achieving better performance.
Thus, better CS can enhance their functional and task performance ability through direct
feedback and interaction with health services clients, which in turn help them in CP of healthcare
services. Similarly, this positive impact of CS on FS of employees also enhances WE because they are
better able to understand and perform the tasks according to the healthcare service consumers' needs.
Technology is the need of time. The new age of the digital world has integrated technological aspects in
every field of life, and it is crucial to follow the trends by allowing a change in the traditional system.
The healthcare sector works as the backbone of the public services structure in any economy. Better
health is the citizens' fundamental right, which is directly dependent upon a better and more efficient
healthcare sector that meets the needs of people cost-effectively.
Government and private institutions should arrange training and education programs to
enhance the functional, communication and technical abilities of the Frontline employees to achieve the
benefits of e-HMIS through co-productivity and operational efficiency. Frontline employees are the
doors for all the inputs by service seekers. They are also the first and foremost users of e-HMIS because
they initiate the record entries in the system. Increasing these employees' literacy and competency level
can enhance the WE and stakeholders' participation in the CP process, strengthening e-HMIS
performance. The study model can be applied to design Frontline employee’s skill-building training
programs through public and private collaboration. Improving workers' skills can enhance their job
satisfaction and bring performance benefits.
The theoretical contributions of the paper include empirical evidence in the context of
technology and healthcare services and e-healthcare system literature. It provides insights into the
antecedents of e-healthcare system performance through a quantitative study. Secondly, the article
extends the research about Frontline employees and their required basic skills in the emerging domain
of e-healthcare system application. Thirdly, the literature about CP and WE is also enhanced by
studying their role as factor of e-HMIS performance. Moreover, the CP process is emphasized by
studying the less researched role of Frontline employees as a stakeholder in service sector performance.
Acknowledgment
This research is sponsored by the "Higher Education Commission" under project No.
9577/Punjab/NRPU/R&D/HEC/2017 titled as "A step towards smart hospitals: Sustainable health
information system from big data perspective"
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150
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