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How Big data Analytics Supports Project Manager in Project Risk Management – Cases from UAE Health Sector

Authors:
  • Hamdan Bin Mohammed SMART University

Abstract

Big data analysis allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or used. Companies can use advanced analytics techniques such as text analysis, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights and insights from previously untapped data sources independently or with existing enterprise data. Significant challenges are still in how to deal with this data and maximize their use, so large data have become the clock that has become the most national, regional, and international institutions, and a broad range of database management, methodologies and measures that can be adopted for the exploitation of large data in all areas of life. This paper investigates the effects of big data analytics on project risk management with examples from Healthcare sector in UAE. Inclusive research has been done by searching approximately more than 20 references resulted in a literature review studied the effect of implementing data analytics in business, technology, industry, and society businesses aspects. A research methodology has been done by interviewing professionals from healthcare field investigating further the role of Data analytics in analyzing and managing data in healthcare, its benefits in predicting risks and improving healthcare outcomes and future insights. This research’s result reveals that collecting data and applying data analytics even in businesses or healthcare represent an important kind of digital transformation. An obvious finding from this research is that business intelligence and data analytics has been implemented widely in UAE healthcare sector by both government and privet sectors resulting in opportunities that develop and emphasize positive changes to this sector.
Intern ational J ournal of Business Analytics and Sec urity (IJBAS ) 3 (1) -2023
How Big Data Analytics Supports Project Manager in Project Risk Management Cases
from UAE Health Sector
Omar Alzaabi¹, Khawla Al Mahri¹, Mounir El khatib², Nouf Alkindi¹
1Graduate Business Management, (20010856@hbmsu.ac.ae, 200106725@hbmsu.ac.ae, 200121339@hbmsu.ac.ae)
2Associate Professor, School of Business & Quality Management, m.elkhatib@hbmsu.ac.ae
1,2School of Business & Quality Management, Hamdan Bin Mohammad Smart University, Dubai. UAE.
* Corresponding author
A R T I C L E I N F O
Keywords:
Project Management, Risk
management, Healthcare,
Digital Transformation,
Digital Disruption,
Disruptive technologies, Big
Data, data analytics.
Received: Jan, 13, 2023
Accepted: Mar, 21, 2023
Published: May, 08, 2023
A B S T R A C T
Big data analysis allows analysts, researchers, and business users to make better
and faster decisions using data that was previously inaccessible or used. Companies
can use advanced analytics techniques such as text analysis, machine learning,
predictive analytics, data mining, statistics, and natural language processing to gain
new insights and insights from previously untapped data sources independently or
with existing enterprise data. Significant challenges are still in how to deal with this
data and maximize their use, so large data have become the clock that has become
the most national, regional, and international institutions, and a broad range of
database management, methodologies and measures that can be adopted for the
exploitation of large data in all areas of life.
This paper investigates the effects of big data analytics on project risk management
with examples from Healthcare sector in UAE. Inclusive research has been done by
searching approximately more than 20 references resulted in a literature review
studied the effect of implementing data analytics in business, technology, industry,
and society businesses aspects. A research methodology has been done by
interviewing professionals from healthcare field investigating further the role of
Data analytics in analyzing and managing data in healthcare, its benefits in
predicting risks and improving healthcare outcomes and future insights.
This research’s result reveals that collecting data and applying data analytics even
in businesses or healthcare represent an important kind of digital transformation.
An obvious finding from this research is that business intelligence and data
analytics has been implemented widely in UAE healthcare sector by both
government and privet sectors resulting in opportunities that develop and
emphasize positive changes to this sector.
1. INTRODUCTION
The data collected by devices connected to the
Internet is often used to identify their users. This is
because the users' devices can capture and store
data (Lee and Peing, 2019). The data collected by
smart objects is used to analyze their users'
activities and interests. Aside from the devices
themselves, the data collected by these gadgets
comes from various sources such as climate data,
scientific data, and energy consumption data (Lee
and Peing, 2019). These data can be used to
identify their users and provide useful information
about them. Due to the increasing number of
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mobile phone and Internet users, the volume of
data collected by these gadgets is growing (Aloini
et al., 2007; Mohandu and Kubendiran, 2021). This
data can be used to extract useful information from
the various systems and equipment that are used
(Mikalef et al., 2020). The Society of Information
aims to provide useful information that can be used
in various political and economic activities (Riahi
and Riahi, 2018).
Big Data refers to the rapid emergence and
evolution of technologies that enable the collection,
analysis, and dissemination of information from a
vast amount of data (Kaisler et al., 2013). The
challenge of managing this massive amount of data
is not only to deal with its increasing complexity,
but also to make sense of it all (Kabanda, 2020).
The concept of a complex polymorphic object, such
as the Big Data, is very different depending on the
community that it belongs to (Begoli and Horey,
2012). For instance, the term big data is very
different from the concept of big in terms of the
volume of data that it collects (Hong et al., 2019).
Although Big Data is not a set of technologies, it is a
broad category that encompasses various
techniques and technologies. As the field continues
to evolve, the definition of Big Data is changing
(Mohd Selamat et al., 2018).
1.1. Characteristics of Big Data
Volume: The rise in data volume is largely
attributed to the increasing number of transactions
and the amount of unstructured data that are
collected and stored in various forms (Aityassine et
al., 2022; Bawaneh et al., 2023; El Khatib et al.,
2020a). This is also caused by the increasing
number of sensors and machine-to-machine data
(I. Akour et al., 2022)(Al-Awamleh et al., 2022; H.
M. Alzoubi et al., 2022e, 2022d). Aside from
reducing storage costs, other factors such as the
use of analytics to derive value from the data are
also becoming more critical (I. A. Akour et al.,
2022).
Velocity: The explosion of data is forcing
organizations to deal with it in a timely manner (El
Khatib and Ahmed, 2018; Khatib et al., 2016). With
the rise of smart meters and RFID tags, the need to
deal with massive amounts of data is becoming
more critical (A. Al-Maroof et al., 2021; Alhamad et
al., 2021).
Variety: Today, data is in various forms, such as
structured and non-structured data (Akour et al.,
2021; Emad Tariq et al., 2022). It can be created
from various sources such as line-of-business
applications and financial transactions. Despite the
variety of formats, managing and governing data
still remains a challenge (Al-Dmour et al., 2023;
Aljumah et al., 2023; Ahmad Ibrahim Aljumah et al.,
2022a; Arshad et al., 2023).
Variability: Due to the variety of data types and
velocities, managing the data flows can be
challenging (A I Aljumah et al., 2022a). Also, with
the increasing volume of data, peak data loads can
occur frequently (Nuseir and Aljumah, 2020).
Complexity: Today, data comes from multiple
sources (Nuseira and Aljumahb, 2020). It is still an
ongoing process to link, cleanse, and transform it
(A I Aljumah et al., 2022b). However, it is also
important to manage the relationships and data
linkages to prevent them from getting out of
control (Aljumah et al., 2021a).
Value: The article also explores how these data can
be used to enhance the living style and business
performance (M T Nuseir et al., 2022a; Nuseir et al.,
2021). Although there are various types of data
that can be generated by various social and
business applications, identifying the appropriate
values still remains a challenge.
1.2. Big Data Analytics
Big Data Analytics is a process that involves
collecting, organizing, and analyzing large data sets
(Nuseir et al., 2020; Nuseir and Aljumah, 2022).
This type of data is often referred to as a massive
amount of data that can be accessed and analyzed
in different ways. It requires new techniques and
technologies to analyze and interpret the data (El
Khatib et al., 2019; El Khatib and Ahmed, 2020).
Big data analytic is a process that helps
organizations make better decisions by analyzing
large amounts of data. For instance, if a company
has a website that sells products online, then it uses
data from various social media platforms to
analyze the customer behavior (Gulseven and
Ahmed, 2022). Big data analytic allows users to
make faster and better decisions by analyzing
previously unusable data (Gaytan et al., 2023;
Khatib et al., 2022; Samra et al., 2020). Using
advanced analytics techniques, such as machine
learning, statistical, and text analytics, businesses
can gain new insights from their data. Big data
analytics can help businesses identify hidden
patterns, improve their customer service, and
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generate new revenue opportunities (Abudaqa et
al., 2021; Ahmed and Nabeel Al Amiri, 2022). It can
also be used to analyze and interpret market
trends.
There are various types of big data analytics that
are commonly used (H. M. Alzoubi et al., 2022a;
Samra and R., 2019). These include prescriptive
analytics, which help users make informed
decisions. For instance, this type of analytics can
help determine the best course of action for a
patient. Predictive analytics is a type of data
analysis that helps predict the future (H. Alzoubi et
al., 2022, 2020; Farrukh et al., 2023). For instance,
if a company decides to launch a new marketing
campaign, then it uses this type of analytics to
identify the most effective strategy (AlDhaheri et
al., 2023; Alteneiji, n.d.).
Other types of big data analytics include market
analysis, customer behavioral analysis, and
weather prediction (Alzoubi and Ahmed, 2019;
Hosam and Abousamra, 2022; Nadzri et al., 2023).
These tools can help organizations improve their
operations and sales by analyzing and predicting
the future (Alfaisal et al., 2022; Alhamad et al.,
2021; Alsharari and Abousamra, 2019; Amiri et al.,
2020).
Due to the increasing complexity of the health care
system, the need for more data has become a major
issue in developing countries and middle-income
regions (Blooshi et al., 2023; Louzi et al., 2022b).
There are four main types of data that can be
collected and used in the field of health care:
medical/clinical Big Data, public health Big Data,
medical experiments, and medical literature (El
Khatib et al., 2021a; Louzi et al., 2022a).
Due to the increasing amount of data collected and
processed in the healthcare industry, Big Data is
expected to grow significantly in the coming years.
2. LITERATURE REVIEW
The below literature review will demonstrate
articles and reports done regarding the effect of
implementing data analytics in projects and risk
management in healthcare industry.
2.1. Business Analytics in project Risk
Management
In the business context, digital transformation in
project management have played a significant role
in developing and lots of Businesses are aware
enough to invest in technology as they aim to
enable their organizations to keep on compliant,
cyber safe, current, agile. According to
(Muhammad Turki Alshurideh et al., 2022c; Varma
et al., 2023), 97% percent of companies in with
revenues of more than 100$ million were found to
apply some form of business analytics in their
businesses. the opportunities that are generating
with the usage of data and analysis in different
organizations have build a significant interest in
Business intelligence and Analytics (Arshad et al.,
2023; E. Khatib et al., 2022; Lee et al., 2023). This is
because that such field concerned on the
techniques, technologies, systems, practices,
methodologies, and applications the work on
business data analyses to provide the
managements with better understand its business
and market and make timely business decisions
(Abudaqa et al., 2022; Alzoubi et al., 2019; H. M.
Alzoubi et al., 2022c).
One of the most important aspects of business
analytics usage in business it the Business
Performance Management (BPM), were it using
scorecards and dashboards to assist analyze and
visualize lots of performance metrics, as well as,
generating well established business reporting
(Alfaisal et al., 2022). These tools will be discussed
later as a kind of Data Analytics approaches that
work on providing lots of benefits in business
applications (H. M. Alzoubi et al., 2022f; Aziz et al.,
2023).
In healthcare, healthcare industry worldwide is
going towards digitalizing every document that
related to provide services for the clients and using
data analytics systems for various reasons (T M
Ghazal et al., 2023b). This is what called today
smart health and wellbeing (T M Ghazal et al.,
2023a). One of the most important reasons is that
this industry generating large volume of data that
should be collected, analyzed effectively for
improving the quality of healthcare, improving
long-term care, patient empowerment, minimizing
cost, and predicting future trends (Abousamra and
Shaalan, 2017; R. S. Al-Maroof et al., 2021b; M.
Alshurideh et al., 2023; M. T. Alshurideh et al.,
2023d).
Framework to identify key initiatives.
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Figure 1 : Technology Transformation
2.2. Technology Analytics Initiatives in Project
Risk Management
In the technology context, today’s advances in
information technologies have provided the
organizations and enterprises with an unlimited
access to an extraordinary amount and variety of
data (Ahmed et al., 2022; H. M. Alzoubi et al., 2022e,
2020; Sakkthivel et al., 2022). In addition, data
management and warehousing are found to be the
fundamental factors of business intelligence and
analysis (M. T. Alshurideh et al., 2023a). Such
concepts have various data marts and tools used to
extract data, transform them, converting and
integrating them to enterprise specific data (M. T.
Alshurideh et al., 2023b; El Khatib et al., 2020b).
The most important approaches that could be used
by organizations to implement data analytics in
managing projects and business application are
Business Visualization Tools, OLAP (Online
Analytical Processing), Interactive visualization,
Predictive Project Analytics (PPA), Data
warehousing, Data mining, Association analysis etc
(Muhammad Turki Alshurideh et al., 2022d; T M
Ghazal et al., 2023c). However, these approaches
usually differ based on the business’s industry and
the goals from performing the analysis. For
example, in healthcare, there are several tools that
could be used in analysis process based on the
outcome expected (Muhammad Turki Alshurideh
et al., 2022a, 2022b; El Khatib and Ahmed, 2019).
Bibliometric analysis, citation network,
coauthorship network, social network theories,
network metrics and topology, mathematical
network models and network visualization are
examples to analytics tools used in healthcare
(Kurdi et al., 2022).
Table 1: List of top three digital initiatives by healthcare competitors:
#
Competitors
Key Digital Transformation Initiatives
1
Electronic Healthcare
Predictive Analytics
(e-HPA) in US
hospitals
Microsoft Heath Vault, an e-health safe, acting as EMR
2
Electronic Health Records (EHRs)
3
Collection large amount of data to understand people’s habits, detect and
predict outcomes
4
Mayo Clinic in
Rochester, London
International appointment offices
5
Healthy Living Programs
Technology that transforms the supply chain of wor kers, clients, vendors, business partners, etc.
BIM
5G Mobile internet
voice driven software
advanced robotics
autonomous
large- scale energy storage
Technology transformation that improves consumer experience
IOT
5G mobile internet
cloud solutions
voice- driven software
3D printing
gene sequencing
Genomics
Mathematical models
Technology transformation that gives correct details to drive
efficient decision-making
AI
5G mobile internet
gene sequencing
Genomics
Mathematical models
social network theories
(SNA)
Technology transformation that enables
accurate and stable transactions in real-
time
blockchain
5G mobile internet
Network visualization
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6
Clinic Voice Apps
7
The Johns Hopkins
Hospital
300 programs and initiatives carried out or supported by administrative,
clinical, and operational departments
8
Paying the medical bills by payment plans and to pay your bill online or
by phone
9
MyChart App
10
Cleveland Clinic, Abu
Dhabi
Virtual Visits
11
Robotic Surgery
12
Muashir Assessment
In UAE, business intelligence and data analytics has
been implemented widely by a range of health
authorities both government and privet sectors (El
Khatib, 2015). Riayati Initiative to National Unified
Medical Record (NUMR), is one of the leading
initiatives that the Ministry of Health and
Prevention is aiming to connect more that 2500
healthcare facilities among the country (Almasaeid
et al., 2022; M. T. Alshurideh et al., 2023c). It is a
digital healthcare platform launched to transform
the current UAE healthcare environment through
the centralization of medical records and the
delivery of an innovative, fully integrated, digitized
clinical information system serving the UAE
population and raising the quality of their life
(Alshawabkeh et al., 2021; Ghazal et al., 2021). The
system will create an efficient and sustainable
healthcare system by reducing overall costs
following in deceasing readmissions, hospital visits
and creating overall savings in prescription costs
(Aljumah et al., 2021b; H. M. Alzoubi et al., 2022b;
E. Khatib et al., 2021).
Another Example of implementing digital
transformation in UAE Healthcare is Malaffi health
information exchange (HIE) platform. It has been
launched through Public Private Partnership
between the Department of Health - Abu Dhabi
(DoH) and Injazat Data Systems for connecting the
Emirate healthcare facilities and facilitating
appropriate and reliable sharing of patient health
information between points of care for better care
coordination and informed decision-making
(Abousamra and Hosam, 2022). From the
analytical aspect, health authorities using the
platform (Malaffi Analytical Portal) in performing
population health analytics and monitoring care
quality trends among Abu Dhabi residents . for
example, and by analyzing inputs, the system
generates reports regarding trends in obesity,
diabetes and chronic diseases (E Tariq et al., 2022).
in contributing to these reports, the government
can obtain real-time and accurate data to examine
the population health needs so as to provide public
health programmes needed in prevention and
management of chronic diseases (Samrah et al.,
2017). Malaffi is the first HIE outside of the US have
been awarded EHNAC accreditation where it has
been assessing for the privacy policies, security
measures, technical performance, business
practices and organizational resources. In addition,
it reflects the governance structure and the HIE
ability to manage, ensure and enhance trust among
healthcare community and patients (Informa
Markets Healthcare, Jun 02, 2021).
Usually, governments initiate such technology for
genomics and sequence analysis and visualization,
EHR association mining and clustering, Health
social media monitoring and analysis, Health text
analytics, Health ontologies, Patient network
analysis, Adverse drug side-effect analysis,
Privacy-preserving data mining (Rasha Abu
Samrah, 2016).
2.3. Industry Analytics Initiatives In Project Risk
Management
In the industry context, operational risk
management (ORM) is one of the most important
approaches that has been applied to the operations
management problems as it utilizes a number of
analytical techniques to enhance making real-time
decisions (Akour et al., 2023; M. Alshurideh et al.,
2022).
Another method for applying data analytics in
project management industry is the Project
Predictive Analytics (PPA). This defined as a
project risk assessment methodology assess
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foresight to predict potential risks at any stage of
the project (Khan et al., 2022). In addition, it
identifies whether the project has enough level of
oversight and governance in its all-execution
stages. Such identifications of these risks allow
organizations to apply adjustments recommended
to improve the project performance and
probability of success (Ahmad Ibrahim Aljumah et
al., 2022b). PPA simply using a proprietary
database that contains information of thousands of
successfully completed projects and then provide
insights to the specific level improvements
required throughout the project stages to achieve
the project objectives (M T Alshurideh et al., 2022;
El Khatib et al., 2021b). This means that your
project can benchmarked against many different
scenarios and best practice. various advantages
can be gained through implements PPA method
where it identifies the complexity level of the
project, mitigate project risk, reducing the
probability of project failure and comparing the
current performance levels against the predicted
expected (Taher M. Ghazal et al., 2023; Nuseir,
2020).
Top three digital disruptions across all the five key areas of disruption that are most relevant in
healthcare sectors
Table 2: Digital Disruptions
2.4. Society Analytics Initiatives in Project Risk
Management
In the society context, this huge amount of data that
is generated and stored has become a major
strength for any knowledge-based society (M. El
Khatib et al., 2022b, 2022a). This big data, if
managed well, can contribute to the acceleration of
economic and social development, as big data helps
people to make the right decisions (Nuseir and
Elrefae, 2022; Nuseir, 2021).
Because big data affects organizations that then
affect the economy and that impact society that
impacts big data technology (I. Akour et al., 2022;
Al-Maroof et al., 2022a). It likes an endless cycle.
The use of big data in manufacturing and
healthcare has increased the level of industrial
automation, privacy, and security. The impact of
big data It could be good or bad on society and only
time will tell if same will affects positively or
negatively in the future (El Khatib and Opulencia,
2015).
Top three initiatives across all five areas of digital disruption in the table below:
Table 3: Digital Initiatives
#
key areas of disruption
Digital initiatives
1
Marketing and
distribution
Blog posts
2
Videos in social media
3
SEO - search engine optimization
AI-assisted surgery
AI
Make prompt, intelligent decisions before, during and after procedures to ensure the best outcomes
Database of potential and current customers
CRM Analytics
It focuses on the role of the schedulers, nurses, and contact centers
The latest developments and insights from the healthcare industry:
Omnia Health
Being better connected for our patients, health information exchange and system includes five of America's Best
Hospitals
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4
Product and service
Access Clinical Information Application
5
CRM Analytics
6
Access Transactional Data Application
7
Processes
Smart Staffing
8
Application On Patients' Care
9
Personnel Management
10
Ecosystems
AI - Artificial Intelligence
11
IoMT
12
Blockchain
13
Supply chains
Oracle
14
Omnia Health
15
McKinsey
Big data is a revolutionary concept that is bound to
affect the companies’ culture that can be compared
to our Stone Age ancestors who underwent
massive cultural changes over time. In case that
organization want to move towards big data
analytics, they must be prepared to make
fundamental changes to their business strategies
includes different approaches such as marketing
culture, trade, finance etc (Al-Maroof et al., 2022b;
M. El Khatib et al., 2021).
As to return to Riayati Initiative and Malaffi the
analytical portal by DoH Abu Dhabi, it is expected
to reward a massive advantage to the UAE society
(Aljumah et al., 2020). Today, UAE is facing the
problem of aging population high life expectancy
rates, and rising prevalence of chronic diseases(M
T Nuseir et al., 2022b). Therefore, technologies
such as business intelligence, AI and big data
analytics continue to play a massive role in sorting
patient information that will provide the essential
data to move toward a preventive and predictive
healthcare system (Mohammed T. Nuseir et al.,
2022). For example, using the analytics part for the
clinical data will assess building models that
predict the likelihood of being readmitted,
developing chronic diseases or even genetic
diseases in future (R. S. Al-Maroof et al., 2021a; H.
Alzoubi et al., 2022). As a result, providers will have
a greater insight into preventive medicine, support
community to better health, directing needed
population health activities and stimulate behavior
change (Informa Markets Healthcare, Feb 02,
2020).
3. RESEARCH METHODOLOGY
Our research methodology will mainly be relying
on the literature review that we have conducted
regarding data analytics in business, especially in
healthcare. Using various sources from articles and
studies in google scholar and HBMSU Library used
as a foundation to further investigate the
importance of data analytics in businesses and
healthcare. Interviews conducted with
professionals in healthcare sector to find out the
role of data analytics in enhancing health care
sector in UAE, its extent to improve healthcare
outcomes and its benefits in predicting risks and
giving insights for future development. Such
further investigation provides us with case studies
from the field.
3.1 Data gathering and Data analysis
To go further with the investigation concerning the
effect of implementing big data analytics on project
risk management and having case studies from
healthcare in UAE, and in addition to the literature
review, interviews were conducted with 1 health
informatics expert, 1 health informatics specialist
and 1 coder from Emirates Health Services (EHS) -
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Clinical Services Sector.
The first question discussed how data analytics and
big data played an important role in enhancing
healthcare sector in UAE. All interviewees agreed
that data analytics is a driving key in management
decision making process regarding the quality of
services, adopting new technologies and global
benchmark to improve healthcare system.
Furthermore, two of interviewees mentioned that
data analytics important for developing UAE
healthcare sector through benchmarking with
other global health sectors.
The second question discussed the interviewee’s
opinion about the extent that data analytics had
improved their organization healthcare outcomes.
All interviewees had similar opinions regarding the
improvement in outcomes after implementing
health information systems that collecting,
gathering, analyzing, and interpreting data
extracted from the available systems in EHS. Such
improvements in outcomes could be seen in the
process of finding gaps, taking actions, and then
applying corrective plans. Moreover, it improves
the health outcomes in regards of patient clinical
management, patient journey, patient follow up
and patient satisfaction. PACE is an example of
implementing such systems at EHS (Appendix 1).
The third question discussed the ability of
implementing data analytics in predicting risks and
giving insight for future development for EHS
management. interviewees stated that predicting
trends by using data analytics is the powerful part
of data analysis at today’s technology
transformation. All agreed that having statistics
from analyzing data assist their organization and
all healthcare sector to predict chronic disease
probability in patients, obesity, or even genetic
diseases, as well as benchmarking KPIs with other
organizations providing same services.
4. DISCUSSION & RESULTS
Based on our literature review and interviews
conducted with experts and specialists from EHS
investigating the effect of big data analytics on
project risk management, it is obvious that
implementing the concept of digital transformation
on businesses can play a substantial role in its
development. Data Analytics for project risk
management and its initiatives can enhance
organizations’ performance and increase the
probability of success in the field.
In the literature review, data analytics and big data
technology allowed an extraordinary of accesses to
data by the organizations and companies. Such
data are already existed, however, by applying
analytics techniques such as data marts and tools
used to extract data, then transform and integrated
them to specific information systems to generate
reports, monitoring performance, and predict risks
and milestones.
Data analytics application can play a massive role
in the development of the countries and
communities. It can affect positively the businesses
and technology values and directions by focusing
on finding new models and approaches through
conducting research and collecting data to serve
clients and provide services through facilitating
easy access and better technical user experience.
Such changes can enhance the economic field,
improve industries standards and processes, and
definitely will serve the society through increasing
knowledge, changing habits and emphasizing
culture of sharing knowledge, communication and
accepting the new digital transformation.
UAE Health sector recently works to maximize the
benefits from adopting business intelligence and
data analytics as an innovative- driven approach in
its care systems. As most of the healthcare facilities
have the required infrastructure of informatics,
patient health records and information systems.
The government represented by Ministry of Health
and Prevision MOHAP, launched Riayati initiative
to link all these facilities’ systems to a unify
patients’ health records through all health care
providers in the country. This linkage facilitates the
health authorities in collecting information
regarding patients’ health and services provided. A
huge number of data could be generated for better
clinical decision making, catching trends, applying
corrective actions, predicting future health
problems, and lunching community health
awareness programs.
4. RECOMMENDATION & CONCLUSION
Based on previous research papers and interviews
that were conducted with specialists in the health
care sector regarding the collection and analysis of
big data, we conclude the importance of modern
technology in the development of this sector
because of its role in improving the quality of
health care and providing much larger and more
accurate information about patients, which helps
O. Alzaabi, K. A. Mahri, M. El. Khatib, & N. Alkindi International Journal of Business Analytics and Security (IJBAS) 3(1) -2023- 19
https://doi.org/10.54489/ijbas.v3i1.201 Publisher: GAFTIM, https://gaftim.com
in predicting It is also noted that the degree of risks
in this area is very low. In view of the many
advantages of using big data analysis in the field of
health care and the few disadvantages of it, we
recommend several points that will develop the
health care system in the United Arab Emirates.
The recommendation of this study are:
Establishing one joint center that collects
and analyzes all data related to the health
care sector at the level of the United Arab
Emirates, which will serve as a reference
for all health care departments in the
country, which will constitute a unified
reference that provides all the necessary
data in the fastest time using advanced
technology.
Urging all components of the health care
sector in the United Arab Emirates to use
and develop their data collection and
analysis systems, which have an effective
role in developing the process of collecting
and analyzing data at the level of the state.
Using the expertise available in developed
countries in the field of health care and
information technology to develop our
system in the United Arab Emirates by
organizing workshops and advanced
courses for workers in the health care
sector and to demonstrate the importance
of data collection and analysis for this
sector.
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Appendix
Interview 1
Q1) : How data analytics and big data played an
important role in enhancing healthcare sector in
UAE? (exp: patient experience, managing data
Q2: To what extent data analytics improve
healthcare outcomes in your organization (exp:
patient experience, managing data easily, EMRs,
research,…etc)
EHS adopt a program called PACE (Performance
and Clinical Excellence) the main scope of PaCE
was to have a program, which can deliver
accurate, timely, clinical, administrative, and
operational data, helps in monitoring and
evaluating the delivery of health care at MoHaP
Hospitals in an efficient manner
As the project revolved around KPIs, we have
encountered several areas of improvement which
include:
Patient waiting time in Outpatient clinics
across all MOHAP hospitals,
Reduction of waiting time in Emergency
department
Improvement in Bed occupancy and bed
O. Alzaabi, K. A. Mahri, M. El. Khatib, & N. Alkindi International Journal of Business Analytics and Security (IJBAS) 3(1) -2023- 24
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utilization, as Hospital management have access
to monthly and real time information to make
changes for improvement.
Reduction in LAMA (left without being
seen) patients, for this data was studied for
category eventually improving the patient
satisfaction in MoHaP facilities.
Improvement in transferring patients
across hospitals as all hospitals has access to live
bed information for whole MOHAP.
Did not Attend rate in Outpatient clinics
went, by using data from each specialty clinic and
moving resources across different clinics.
Q3: How data analytics can be used to predict
risks and give insights for future development?
PACE helps stakeholder to predict the rhythm
and trends of disease over the years. This
prediction helps EHS is preparing plans and be
ready for any crises.
Interview 2
Q1) : How data analytics and big data played an
important role in enhancing healthcare sector in
UAE? (exp: patient experience, managing data
easily, EMRs, research…etc)
It helps in proving clues on healthcare status that
is needed for decision making, also to find the
gaps so that we improve it and work on it leading
to a better patient journey and service, it also
helps to find our situation in comparison to the
global benchmark.
Q2: To what extent data analytics improve
healthcare outcomes in your organization (exp:
patient experience, managing data easily, EMRs,
research,…etc)
It helps in improving the healthcare in regard the
patients journey and their clinical management ,
which made the UAE to jump so fast to be among
the first countries in this sector.
Q3: How data analytics can be used to predict
risks and give insights for future development?
Data analysis gives a clue about the current status
of the healthcare and compare it with the last
status or with the surrounding countries and so
predict the risk in the future if not tackled
properly.
Interview 3
Q1) : How data analytics and big data played an
important role in enhancing healthcare sector in
UAE? (exp: patient experience, managing data
easily, EMRs, research…etc)
from my point of view, healthcare sector is in UAE
is witnessing a huge improvement towards
implementing the technology in its health
services. Data analytics is so important for the
management to make decisions for every single
service provided. This will lead to better patient
experience, increase patient satisfaction, and
increase the competitiveness of the country
among the global community.
Q2: To what extent data analytics improve
healthcare outcomes in your organization (exp:
patient experience, managing data easily, EMRs,
research,…etc)
Analytics reports or even day to day data
extraction from the available systems in EHS help
generally in improving the process of finding
gaps, taking actions, and then applying corrective
plans. Such analytics improve the health
outcomes in regards of patient clinical
management, patient journey from A to Z, patient
follow up and patient satisfaction. Also, analytics
used in EHS for research with cooperation with
national organizations and entities. Also, it shows
the areas or specialities that the hospitals are in
need to be improved either by providing
personnel, clinical training, bed management
..etc.
Q3: How data analytics can be used to predict
risks and give insights for future development?
the systems implemented by EHS can give us an
idea regarding the risks that could happen when
the services or treatment is not correctly
delivered to the patient. we usually work to
correct such unusual trends to avoid any risks in
the future. Also, by having statistics from
analysing data, we can predict chronic disease
probability for the patient, obesity or even
genetic. This help management to go step forward
with the prevention process to limit or avoid risk.
Platforms
Telemedicine, mobile and wireless platforms
have been proven as an effective way to overcome
some of the barriers to delivery of care, especially
for communities located in rural and remote
O. Alzaabi, K. A. Mahri, M. El. Khatib, & N. Alkindi International Journal of Business Analytics and Security (IJBAS) 3(1) -2023- 25
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areas. Additionally, telemedicine can bridge gaps
in providing critical care to those who are
underserved, mainly due to a shortage of
subspecialty providers.
Customer Network
The Customer Relationship Management (CRM)
model offers a fresh look both from the patient
and from the healthcare provider. Some of the
features offered are the robustness of the
systems, the versatility/openness in sharing
information, and the closeness of the patient-
patient healthcare relationship with others. The
model-based system generates value in every
activity for the customer to provide better
service. It also enables customers to access
information.
Big Data
Big data is used to predict diseases before they
appear based on medical records. Public health
systems in many countries now provide
electronic patient records with advanced medical
imaging media. The practice of big data takes the
future to meet the upcoming market needs and
trends in healthcare organizations. Big data
provides a great opportunity for epidemiologists,
clinicians, and health policy experts to make data-
driven judgments that will ultimately advance
patient care.
IOT
The Internet of Things is an important part of the
digital transformation of healthcare, as it allows
new business models to emerge and enables
business process changes, productivity
improvements, cost containment, and improved
customer experiences. Today's wearables and
mobile apps support fitness, health education,
symptom tracking, collaborative disease
management, and care coordination. Sensors can
provide a lot of information to support the
development of pharmaceuticals. Engineering
simulation solutions are making medicine
participatory, personal, predictive and
preventive (P4 medicine) over the medical
Internet of Things (mIoT).
AI
The rapid explosion in AI has made it possible to
use aggregated healthcare data to produce
powerful models that can automate diagnosis and
also enable an increasingly precise approach to
medicine by designing treatments and targeting
resources most effectively in a timely and
dynamic manner. also, it uses in performing
operations for lots of patients with complications
symptoms.
RPA
Robotic Process Automation (RPA) is a new wave
of future technologies. Robotic process
automation is one of the most advanced
technologies in computer science, electronics,
communications, mechanical engineering and
information technology. Robotic Process
Automation suggests physical robots roaming
offices performing human tasks, and RPA is a
software-based solution that has been used
during the Covid-19 pandemic to provide food
and medicine to a virus-infected patient.
XR
Extended Reality has been increasingly used in
healthcare. IT is able to develop the technical
skills, and capable of the professionals. It has
been found that there is a medium to significant
improvement in the skills of learners
participating in virtual reality compared to
traditional or other forms of digital learning. In
addition , this technology used in healthcare
facilities to interactive with the customers and for
customers relationship goals.
List of transformations across the
impact/difficulty matrix
Transformative impact on the healthcare sector
by ensuring that big data technologies are
routinely used across the healthcare sector to
deliver high quality care while reducing costs. In
this sense, the project will:
First transformation initiative :
Contribute to reducing carbon emissions due to
the use of telehealth services driven by big data
technologies and thus contribute to the emissions
goals in our country for the coming years
Second transformation initiative:
Create lasting impact of big data in the healthcare
sector
Third transformation initiative:
Increase the market share of big data technology
providers in the oncology, cardiology, radiology,
O. Alzaabi, K. A. Mahri, M. El. Khatib, & N. Alkindi International Journal of Business Analytics and Security (IJBAS) 3(1) -2023- 26
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hospital logistics and healthcare IT security
sectors.
Fourth transformation initiative:
Play an important role in training UAE’s next
generation of healthcare data innovators.
... Bhattacharya et al. [35] emphasized the role of big data analytics in risk The most used keywords management and decision-making [35]. Furthermore, Alzaabi et al. [13] discuss big data's impact on project risk management in UAE's healthcare sector [13]. Giudici and Mignola [34] highlight big data's role in enhancing decision-making and risk management in the banking and insurance sectors [34]. ...
... Bhattacharya et al. [35] emphasized the role of big data analytics in risk The most used keywords management and decision-making [35]. Furthermore, Alzaabi et al. [13] discuss big data's impact on project risk management in UAE's healthcare sector [13]. Giudici and Mignola [34] highlight big data's role in enhancing decision-making and risk management in the banking and insurance sectors [34]. ...
... Additionally, the integration of big data analytics into risk management often leads to cost reduction by optimizing resource allocation and minimizing losses associated with unforeseen risks. Furthermore, organizations can leverage big data analytics capabilities to identify correlations and dependencies between different risk factors, as highlighted by [13]. This knowledge is invaluable for diversifying risks, enabling organizations to distribute their risks more effectively. ...
Chapter
Full-text available
This paper investigates the influence of big data analytics capabilities on risk management within the insurance sector in Jordan. Employing a quantitative cross-sectional research approach, data were collected from 251 insurance company managers at various administrative levels. Structural equation modeling (SEM) was employed to validate the measurement model and ascertain effect coefficients. The research outcomes underscore a significant positive effect of big data analytics capabilities on risk management, with the dimensions ranked by impact in the following order: infrastructure flexibility, personal capabilities, and management capabilities. As a result, the study offers a comprehensive set of recommendations geared toward both researchers and insurance sector managers.
... The successful implementation of predictive analytics requires robust project management strategies. These strategies encompass stakeholder engagement, resource allocation, and risk management to ensure that predictive tools align with organizational goals and clinical needs [7]. Project management facilitates the integration of predictive analytics into existing systems, such as electronic health records (EHRs), creating seamless workflows and maximizing the impact of analytics [8]. ...
... The use of analytics in healthcare dates back to the early 20th century, when hospitals and public health agencies began collecting data on patient demographics, diseases, and treatment outcomes. Early applications of analytics were primarily descriptive, focusing on summarizing past events to understand healthcare trends [7]. For instance, mortality rates and disease prevalence were analysed to guide public health interventions. ...
Article
Full-text available
Predictive analytics has emerged as a transformative tool in healthcare, offering unprecedented opportunities to enhance operational efficiency, resource management, and patient outcomes. The implementation of predictive analytics in healthcare process improvement initiatives requires robust project management strategies to address the unique complexities of the industry, including data integration challenges, stakeholder alignment, and regulatory compliance. This paper explores the intersection of project management and predictive analytics, focusing on strategies to successfully implement these technologies in healthcare settings. Key project management practices, such as agile methodologies and stakeholder engagement, are analysed for their effectiveness in facilitating predictive analytics adoption. The study highlights the importance of interdisciplinary collaboration among data scientists, clinicians, and administrators to ensure that predictive models align with clinical goals and operational realities. Techniques for data preprocessing, feature engineering, and real-time model updates are also discussed, emphasizing their role in maintaining the accuracy and relevance of predictive tools. Case studies demonstrate the real-world impact of predictive analytics in areas such as patient admission forecasting, resource optimization, and workflow bottleneck detection. These examples underscore the potential of predictive analytics to reduce costs, improve efficiency, and enhance patient care. Challenges, including data privacy concerns and algorithmic bias, are also addressed, with recommendations for ethical and regulatory compliance. This research contributes to a growing body of knowledge on integrating advanced analytics into healthcare processes, providing actionable insights for healthcare leaders and project managers. By leveraging predictive analytics and effective project management strategies, healthcare organizations can drive meaningful improvements in care delivery and operational efficiency.
... AI and machine learning algorithms can analyze large datasets to identify patterns, trends, and anomalies related to environmental impact, occupational hazards, and regulatory compliance. AI-powered predictive modeling and risk assessment tools help automotive manufacturers anticipate and mitigate EHS risks more effectively (Alzaabi et al., 2023). Robotics and automation technologies improve worker safety by automating hazardous tasks, reducing ergonomic strain, and minimizing exposure to workplace hazards. ...
Article
Full-text available
The automotive manufacturing industry plays a pivotal role in global economic development, providing transportation solutions while simultaneously facing multifaceted challenges related to environmental health and safety (EHS) practices. This review investigates the indispensable role of EHS practices within the automotive manufacturing sector, highlighting their significance in mitigating environmental impact, ensuring workplace safety, and complying with regulatory standards. Effective EHS practices are integral to managing environmental sustainability within automotive manufacturing. These practices encompass waste management, emissions reduction, and resource conservation strategies aimed at minimizing the industry's ecological footprint. Additionally, the adoption of eco-friendly technologies and processes, such as renewable energy integration and material recycling, contributes to the industry's overall environmental stewardship. Furthermore, prioritizing workplace safety is imperative in the automotive manufacturing sector due to its inherently hazardous operational environments. EHS initiatives focus on risk assessment, hazard identification, and the implementation of preventive measures to safeguard employees from occupational injuries and illnesses. Moreover, fostering a culture of safety awareness through training programs and regular audits promotes a conducive working environment conducive to employee well-being and productivity. Compliance with regulatory standards is a cornerstone of EHS management in the automotive manufacturing industry. Adherence to local, national, and international regulations ensures operational legality and enhances corporate reputation. Through continuous monitoring and assessment, automotive manufacturers strive to stay abreast of evolving regulatory frameworks, thereby aligning their practices with industry best practices and societal expectations. The integration of robust EHS practices is indispensable for sustainable operations and corporate responsibility within the automotive manufacturing industry. By addressing environmental concerns, ensuring workplace safety, and meeting regulatory requirements, automotive manufacturers can uphold their commitment to environmental stewardship and social accountability while maintaining operational efficiency and competitiveness in the global market. Keywords: Environment, Health, Safety, Automotive, Manufacturing, Industry.
Article
Full-text available
The Underlying study is highly significant for the management of organisations and government to understand the importance of green auditing in controlling the carbon footprint in their country. The primary data collection method has been employed in the underlying research because the information has been accumulated directly from the relevant participants and questionnaire surveys have been conducted with the 300 auditors in the energy sector of the Jordan. The overall findings of the study mainly suggest that there is significant impact of green auditing on organisation performance in Jordan with the moderating effect of auditor's opinion. Therefore, it is recommended to the Jordanian energy sector to carry out green audit on the annual and semi-annual basis for the purpose of maintaining records and take auditor's opinion on the ground on which the audit has to be conducted.
Article
Full-text available
Vector Auto regression model (VAR) a time -varying parameter is applied to study the effect of oil price shocks on the returns of stocks in the LATAM (Latin American) markets. Coherent Wavelet analysis highlights possibilities of connectedness of the oil price and LATAM stock markets through the presence of different patterns in a time series. The structural demand shocks standard deviations during the COVID-19 era remain high and the pass-through effects on stock returns due to oil prices differ for different time frames. The fundamental linkages are demonstrated due to oil market specific demand. The main motive of the research work is to identify the influence of oil price on stocks and identify the fundamental source of contagion. A random effects model is applied to the panel data of LATAM markets with the Global stock market index, MSCI (Morgan Stanley Capital International World Index), domestic money market rates and currency exchange rates during the period of study, 15 March 2019 to 31 July 2021 with 684 observations of controlled non-observed characteristics from individual country. The findings of this research recommend the pass-through effect of the oil prices on the stock market returns are based on time frequency. The contribution of this paper helps the policy makers to restore the confidence amongst the investors in the stock markets and strategies to be adopted by the investors to mitigate the risk by ideal portfolio management.
Book
Business shapes have been changed these days. Change is the main dominant fact that change the way of business operations running. Topics such as innovation, entrepreneurship, leadership, blockchain, mobile business, social media, e-learning, machine learning, and artificial intelligence become essential to be considered by each institution within the technology era. This book tries to give additional views on how technologies influence business and marketing operations for insuring successful institutions survival. The world needs to develop management and intelligent business scenario plans that suite a variety of crisis appears these days. Also, business and marketing intelligence should meet government priorities in individual countries and minimise the risk of business disruptions. Business intelligence - the strategies and technology companies that use it to collect, interpret, and benefit from data - play a key role in informing company strategies, functions, and efficiency. However, being essential to the success, many companies are not taking advantage of tools that can improve their business intelligence efforts. Information technology become a core stone in business. For example, the combination of machine learning and business intelligence can have a far-reaching impact on the insights the company gets from its available data to improve productivity, quality, customer service and more. This book is important because it introduces a large number of chapters that discussed the implications of different Information technology applications in business. This book contains a set of volumes which are: 1- Social Marketing and Social Media Applications, 2- Social Marketing and Social Media Applications, 3- Business and Data Analytics, 4- Corporate governance and performance, 5- Innovation, Entrepreneurship and leadership, 6- Knowledge management, 7- Machine learning, IOT, BIG DATA, Block Chain and AI, 8- Marketing Mix, Services and Branding.
Chapter
The outbreak of the famous Corona Virus in the world at the end of the year 2019 has triggered the world to bring major changes to the everyday walks of life. This virus entered Pakistan in February 2020 and resulted in the lockdown to be enforced throughout the country. Just as many other fields of life, the education sector also had to tolerate a massive hit of this closing down. The traditional approach of education based on the face-to-face interactions between the students and instructors went dependent on online platforms. This was a major and sudden switch, which was found extremely difficult throughout the world as well as in Pakistan. The concept of online education was not anything new. It prevailed earlier also, but that was the one conducted after proper planning, which was not at all in the case Covid-19. This research paper first discusses the perspectives of parents and teachers on the effectiveness of online education by qualitative approach. Secondly, the results of the conducted surveys are evaluated in the quantitative part of the paper. Further, the results of both the research types are compared to discuss and reason the overall scenario of online education in Pakistan.
Chapter
The success of any business continuity today depends on its levels of preparedness to address any disasters that could emerge from the risks it faces in its operations. Most of the businesses in the UAE have adopted information technology and systems to aid them in their operations. Unfortunately, the adoption of such systems enhances the risks that businesses face, hence the need to ensure they adopt reliable disaster management strategies to address any potential challenges they could face. The aim of this research is to review and measure the levels of information system disaster preparedness in both the private and public sector organizations operating in the UAE. A study was conducted to measure the use of some of the popular disaster risk management models such as STRIDE, DREAD, and BCM. The study indicated that many of the firms in the UAE are not fully prepared to tackle disasters emanating from their information management systems but have taken some steps and have invested part of their resources in improving the state of their preparedness. The findings in this paper can benefit researchers in the field of IT, private and public organizations in the region, government entities, and other stakeholders investing in the gulf country.
Chapter
The industrial environment is unpredictably shifting. Technology advancements and pandemics force the corporate environment to evolve even faster in order to stay competitive and thrive. As a result, in today's fast-paced business environment, supply chain management has grown in popularity. The study's major goal is to apply the concept of industrial supply chain management to educational management. The empirical study will be conducted separately and do not cover in this paper. In dynamic and uncertain business operations, the educational model is more appropriate to switch from mass production delivery to industrial experiences in building individual talent excellence. This minimizes the educator's knowledge and experiences delivery gap to the learner in the educational supply chain. From the issues, This study identifies the education attributes and how it affects professional development success. This study further investigates the moderating effect of industrial experiences on the relationship between education attributes and professional development success. Besides, this study aims to propose an educational supply chain model (EduSCM) for 21st-century professional development success in Malaysia higher education to gain competitive advantages. To achieve the objectives, mixed methods research design is proposed. The qualitative data collection will be collected after the completion of quantitative data analysis. The data collected from the interview use to explain, interpret, understand, and triangulate the results of quantitative analysis to produce a potent conclusion. Lastly, this study will provide a unique conceptual diagram expected to aid researchers and practitioners to create a more comprehensive understanding of the linkages between learning competencies, industrial experiences, and professional development.
Chapter
The growth of digital world has become so fast and the data volume turned to more composite in relations to size from terabyte and higher, the variation of data including structured, unstructured and hybrid, pace has gone so high in growth of data. This is called ‘Big Data’ as a worldwide phenomenon. Typically, this is measured as the collection of data from multiple sources has become so huge that it can’t be well exploited or maintained by using regular methods used for data management: e.g., RDBMS the conventional relational database management systems or search engines used conventionally. The experts have been working to solve handle these problems, the traditional relational database management systems are modified by precisely designed a set of substitutes DBMS’s; which are search based systems, New SQL and NoSQL. This goal of this study is to provide characteristics, classification and evaluation of database management system called NoSQL in Big Data Analytics. The study is planned to help people, specifically the companies to gain. The current computation power also falls less against the massive extended storage volume. The fast growth of unstructured data requires a complete model change for the new age to meet the incoming challenges and observe the progress of new proficient techniques for data engineering. The explanation of BigData, RDBMS and NoSQL have also been described in this study to put light on difference of data and requirement of new technology. This study will highlight the differences, capabilities of both RDBMS and NoSQL database management systems in accordance with BigData. With the help of qualitative and quantitative approach the study has emphasized on the limitation of RDBMS and requirement of new technology NoSQL used for unstructured BigData used for analytics.
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The electric commerce (e-commerce) platform in Malaysia has been growing exponentially since the spread of Coronavirus Disease 2019 (COVID-19) pandemic. Many local businesses were badly affected when the first phase of Movement Control Order (MCO) was implemented for several weeks. The increasing number of social media platform users stems from wide internet accessibility and smartphone ownership. As such, this study investigated online purchasing behaviour in Malaysia by linking webrooming and user generated content (UGC) with brand experience and brand engagement. Essentially, this study unravelled consumer purchasing behaviour during the COVID-19 pandemic. This study explored the effects of UGC and webrooming on brand experience and brand engagement among Malaysian online consumers who purchased beauty product. As a result, UGC and webrooming displayed positive correlation with brand experience. However, brand experience was insignificant for brand engagement. Additionally, brand experience was unsuitable to function as a mediator. Thus, the impact of social influencers and shoppertainment on brand engagement should be assessed. Future studies may look into webrooming from the light of Stimulus-Organism-Response (SOR) Theory. Lastly, fake reviews, fake accounts, and bots that influence online purchasing behaviour among Malaysians should be investigated as well.
Article
The study examines the impact of human resource (HR) practices like salary, training and development, performance appraisal and along with remuneration and employee attitude on employee productivity among the government entities in United Arab Emirates (UAE). The data were collected through a structured questionnaire from 396 respondents of different government entities. The findings through structural equation model (SEM) indicates that there is a significant and positive impact of salary, training and development, performance appraisal, remuneration and employee attitude, and innovation on the employee productivity of governmental entities in UAE. This empirical research would be of great support to the HR managers, policymakers, and other stakeholders to make some strategic decisions to improve the productivity of the employees. DOI: https://doi.org/10.1504/IJPMB.2023.128471