What is the current state of ERM implementation in healthcare organizations in the UAE?
ABSTRACT
Enterprise risk management (ERM) has received tremendous attention recently because it establishes a comprehensive approach for managing different types of risks. Although ERM has been increasingly implemented within organizations in the United Arab Emirates (UAE), there remains a lack of knowledge of key factors that must be considered when implementing ERM, especially within the healthcare industry. Therefore, this study aims to empirically determine factors associated with ERM implementation in the UAE. It uses exploratory factor analysis to analyze a sample of 304 responses gathered through an online survey questionnaire. The results of the exploratory analysis support the assumption that several factors are associated with ERM implementation. Smart PLS-SEM along with Ordinary Least Square (OLS) multiple regression were used to identify various factors and their association with ERM implementation within the healthcare sector in the UAE. The results of PLS-SEM showed that education, corporate governance, and strategic governance are significantly associated with the ERM implements in the UAE health-care organizations. Further to these three factors, the OLS regression showed that communication efficacy and quantitative risk assessment are significantly related to ERM implementation. Collectively, these results have several implications for healthcare organizations in the UAE as they showed the factors that currently and significantly affect the implementation of ERM and the factors that need further attention. Overall, the current study’s results are essential for decisionmakers within the healthcare sector in the UAE, since it highlights the most prominent factors associated with the implementation of ERM; thus providing insight into areas for investment within healthcare organizations, to ensure ERM implementation.
INTRODUCTION
Overview
Globally, the healthcare industry has increasingly adopted contemporary risk management practices. The adoption of these practices is particularly informed by the need for increased security and quality in providing care to patients (da Silva Etges et al., 2018). In the United Arab Emirates (UAE), healthcare organizations are exposed to numerous risks that threaten their security systems and the quality of care and services offered to patients. The nature of operations within healthcare organizations in the UAE necessitates effective enterprise risk management (ERM) to reduce the vulnerability of their organizational systems.
The emergence and subsequent declaration of the COVID-19 pandemic by the World Health Organization revealed underlying weaknesses in public health systems globally. Healthcare organizations have increasingly faced challenges and risks that require appropriate corporate governance strategies to identify and analyze these and implement frameworks that protect strategic organizational objectives. The UAE government remains committed to developing a world-class health system that provides quality healthcare and whose primary objective is to improve health outcomes for the population. The UAE is a young nation, and accomplishing the strategic objectives requires substantial investment in established corporate governance to ensure it aligns with these proposed objectives. Therefore, this dissertation explores the different factors are associated with the implementation of an ERM framework. The rapid advancement of the healthcare system stems from rapid economic growth, resulting in a higher number of healthcare facilities, professionals, and service levels in the country. However, there is a need to ensure the existing resources are appropriately used by mitigating potential risks and adversities through strategic management.
There are regulations and policies on infrastructure and organizations requiring all healthcare facilities to obtain external accreditation (Koornneef et al., 2017). The healthcare sector is vulnerable to risk because it relies heavily on confidential patient information and due to the nature of its operations and time constraints. The industry faces unprecedented risks such as cyber threats, physical attacks, regulatory compliance lapses, privacy issues, and information security issues. The multifaceted nature of the risks requires stakeholders and policymakers to incorporate an efficient ERM framework that fosters collaboration and communication to identify and mitigate risks based on the perceived threat to organizational objectives. ERM frameworks help improve economic, financial, and non-financial performance and assist in resource management, including management of the intellectual capital available to healthcare organizations in the UAE.
Background and Context of Enterprise Risk Management (ERM) in United Arab Emirates (UAE) Healthcare Services
Currently, the UAE government is working towards developing a globally competitive healthcare system. According to Koornneef et al. (2017) the government has adopted ERM to enhance the quality of healthcare services and thus improve health outcomes for its population. However, the UAE does not have a specific framework for addressing healthcare risks, instead using ERM depending on the nature of the risk that needs to be addressed. An example of the current situation of ERM in the UAE is how the nation addressed the COVID-19 pandemic, which has threatened its healthcare facilities. Hasan et al. (2020) argue that the UAE deployed data-driven modeling and forecasting as a comprehensive framework to make informed clinical decisions to curb the COVID-19 pandemic. The country has administered more than 4.9 million COVID-19 tests, which is the primary source of data for the ERM process (Hasan et al., 2020). ERM in UAE healthcare facilities extended to the implementation of protective decisions such as the closure of learning institutions and places of worship, imposition of night curfews, and restriction of movement within the country. This form of ERM in UAE healthcare was dependent on the COVID-19 situation in the country. Hasan et al. (2020) argue that the restriction of movement, isolation requirements, and a more comprehensive COVID-19 national screening program in the dramatically reduced the daily reported cases. They conclude that the UAE lacks a specific ERM framework but addresses healthcare risks depending on their nature.
The UAE has made a substantial investment in healthcare over the last decade. The government identified quality healthcare as a core value and objective for the nation. Ever since the country gained independence in 1971, the government has focused on developing healthcare infrastructure in line with international standards. Healthcare in the country is provided by public and private organizations. However, the entire system has experienced challenges due to the immigration-driven population growth and the cost increases per capita in healthcare spending due to increased morbidity, and chronic diseases. The Ministry of Health and Prevention (MOHAP) is responsible for outlining the healthcare goals and the evolution of healthcare services in the country to offer quality care and improve outcomes, which are a top priority in all seven Emirates. Additionally, healthcare development and spending in the UAE reveal the country’s situation. The government has implemented a federal diversification plan for a quality healthcare system aimed at improving health system planning and ensuring availability, accessibility, and quality healthcare services.
The UAE is increasingly becoming a popular tourist destination, and medical tourism is an emerging niche that attracts health tourists and holidaymakers. However, medical tourists are often motivated by various factors, including affordability, waiting list times, local regulations, privacy, the opportunity to travel, religious restrictions, combining wellness with vacation, and insurance agencies. Despite the substantial health investments made by the government, the country is currently still exploring ways to market the UAE as a medical tourism destination. Policymakers and corporate governance of healthcare organizations must collaborate and cooperate through strategic decision-making to ensure the progress of the UAE’s healthcare sector and implement an internationally accepted healthcare system (Al-Talabani et al., 2019). Furthermore, a new threat looms across the global healthcare sector due to the incorporation of health information technology (IT) as a mainstream and critical resource for sustainable growth. The introduction of health IT in the UAE can improve care delivery but also introduces unintended risks and challenges due to cybersecurity threats. The safety of health IT in the clinical setting is a critical challenge that threatens the strategic objectives and reputation of the UAE’s healthcare sector.
Existing Gap and Research Problem
There is a significant potential for the adoption of ERM systems in UAE healthcare organizations. Indeed, effective ERM contributes to introducing holistic frameworks through which one can achieve risk management decisions. There is a need for ERM systems that reduce risk and uncertainty, thereby maximizing value creation and protection within healthcare organizations. While most healthcare organizations in the UAE have structured risk management systems, they do not meet the management’s specific goals and objectives. In part, most of these approaches traditionally focus on clinical events through reactive strategies for risk management (Almansoori & Ali, 2020). Almansoori and Ali (2020) further explain that the dynamic nature of modern healthcare organizations necessitates the integration of systems that prioritize shared responsibilities in the identification, mitigation, and management of risks.
Based on the discussion above, the research study’s objectives are as follows:
1) To understand the current state of ERM implementation in healthcare organizations in the UAE.
2) To explore the factors that lead to different implementation levels.
3) To explore which of these factors significantly determine the implementation of ERM in healthcare organizations in the UAE.
Motivation and Justification
The current study argues that risk management is not a one-size-fits-all approach or proposition. Despite the universality of risks associated with healthcare organizations, the organizational culture and structure in the UAE are uniquely different from those of other countries.
Multiple nations are adopting universal healthcare to promote affordable healthcare coverage for all citizens. One of the key strategic goals of the UAE government since its formation in 1971 has been to improve the quality of healthcare and actual health outcomes for its citizens and residents (Koornneef et al., 2017). The Ministry of Health and Prevention (MOHAP) is responsible for licensing, regulating, and quality assurance in five emirates, while Dubai and Abu Dhabi have their own independent health authorities that fulfill these functions (Brownie et al., 2015). Table 1.1 summarizes the different entities responsible for the healthcare sector in the UAE. Consequently, healthcare organizations warrant a unique approach to risk management.
Table 1. Summary of Healthcare Authorities within UAE
Since the country has multiple healthcare sector administrators, the risk management approach must consider how to integrate all administration types into the plan. In 2006, the government of Abu Dhabi embarked on a significant health system reform program with a focus on the redesign of the healthcare financing and regulatory systems (Koornneef et al., 2017). It split the regulatory function and service provision into two separate entities. The Department of Health (DOH) in Abu Dhabi took responsibility for regulations, and the Abu Dhabi Health Services Company (SEHA) handled service provision and operations of the healthcare facilities.
The fragmentation of the UAE health regulatory system is considered a serious challenge to the future of healthcare in the UAE, highlighting the importance of an ERM system. A study by Koornneef et al. (2017) reported that the lack of regulatory control and lack of competition between insurance companies were the two main obstacles to achieving greater cost efficiency in the healthcare market.
Furthermore, given that healthcare is being moved from the public to the private sector, the risk management approach needs to establish how to integrate auditing to ensure the private healthcare sector remains ethical. The private sector in the northern Emirates is less developed compared to Dubai and Abu Dhabi; consequently, the cost and quality of services vary between these two Emirates and the remainder of the country (Koornneef et al., 2017). This dissertation is based on the inherent benefits of ERM approaches in the healthcare industry. According to Gates (2006), ERM systems holistically assess risks and the associated management practices. Thus, the justification for the dissertation is the need for more efficient programs that facilitate accountability and risk oversight responsibilities within healthcare organizations. Indeed, ERM systems can help healthcare organizations accurately identify, manage, and communicate critical risks within their operations.
Tourism, education, and healthcare are the primary diversification strategies to reduce the UAE’s over-reliance on oil while ensuring an improved quality of life for its citizens. The health sector is complex and requires established corporate governance practices and coordination across all seven emirates to ensure the country achieves the desired outcomes. Existing literature and research highlight that the UAE’s health sector has various competitive strengths and weaknesses that require input from pivotal stakeholders to ensure strategic management of the country’s health infrastructure (da Silva Etges et al., 2018). Proper utilization of resources is crucial for gaining a competitive advantage and requires corporate governance and policymakers to implement a framework aligned with the country’s health objectives and vision. Healthcare resources are sophisticated, and the UAE’s infrastructure might be difficult to imitate, distinguishing the country’s health sector in the developed world. However, as the UAE’s healthcare vision has not yet been accomplished, there is a need to implement various strategic initiatives to leverage the advantage of becoming a global medical tourism hub. Existing and emerging threats such as cybersecurity concerns, fast-paced medical science, and rapidly changing healthcare regulatory, legal, political, and financial concerns jeopardize the country’s efforts to expand the role of its healthcare organizations.
In the contemporary era of globalization, healthcare organizations increasingly rely on ERM to obtain more accurate information and data on which to base their risk management strategies. As a relatively young country, there is a need for transformative leadership and visionary corporate governance in the UAE that capitalizes on existing opportunities while reducing the exposure to risks in the sector. These actors can enforce frameworks that protect the healthcare system’s critical resources and infrastructure. For instance, healthcare organizations in the UAE require specialized and advanced skills in clinical and administrative tasks as the government intends to have world-class healthcare quality (Blair & Sharif, 2013). However, the underlying weakness in the country’s healthcare sector is the lack of a framework and capabilities to utilize existing resources and develop a competitive advantage. Poor management and an inefficient organizational culture of risk management will inevitably reduce the UAE’s healthcare competitive advantage, regardless of its available resources.
Statement of the Problem, Research Questions and Hypothesis
Despite ongoing efforts to implement ERM, the practical difficulty facing many UAE organizations is a lack of information regarding the most important variables to consider while implementing ERM. To address this gap, the research question for this study is the following: How can the UAE improve its ERM implementation? This practical problem and research question prompted the researcher to conduct an exploratory study to understand the factors that influence ERM implementation among healthcare organizations in the UAE and develop actionable knowledge based on the lessons learned and research outcomes. This study poses the following research sub-questions to answer the main research question:
RQ1. What is the current state of ERM implementation in healthcare organizations in the UAE?
RQ2: What are the factors associated with the implementation of ERM systems across UAE healthcare institutions?
RQ3: Which of these factors significantly determine the implementation of ERM in healthcare organizations in the UAE?
The researcher expects to identify statistical associations between certain factors across UAE healthcare institutions that explain variations in the levels of ERM implementation.
Based on these research questions, the research derived the following hypotheses:
H1: The level of ERM implementation varies across healthcare institutions in the UAE.
H2: The levels of ERM implementation are statistically associated with different factors across UAE healthcare organizations.
Research Aim and Objectives
This study’s aim is to explore factors associated with ERM implementation in the UAE. Thus, the primary goals of this research are as follows:
• To identify the aspects required to investigate ERM implementation in healthcare organizations in the UAE.
• To determine the factors that are statistically associated with ERM implementation in healthcare organizations in the UAE.
Overview of Research Methods
This research uses quantitative approaches, mostly surveys and exploratory factor analysis (EFA), to achieve its goals. The ERM dimensions used to build the survey instrument and conduct quantitative analysis were derived from research conducted by da Silva et al. (2018) research. The original dimensions included in the abovementioned study in 2018, were changed and enhanced to concentrate on 30 dimensions covering 6 primary components or domains. A five-point Likert scale was used to assess the level of implementation for each dimension. The researcher established a conceptual model for this study, which was used to investigate the presence of the 30 ERM aspects in healthcare organizations to uncover critical factors influencing the implementation of ERM. The subsequent chapter presents this conceptual model in detail.
The researcher chose to use quantitative methods based on the nature of the research topic, the research methods employed in previous research studies, the accessibility of data, and the researcher’s skills and abilities in gathering and analyzing data. Moreover, this is the first study to investigate the application of ERM in healthcare institutions in the UAE, and therefore, exploratory methods were more appropriate than other methods. These methodologies can also aid in achieving the research’s objectives and producing locally applicable and actionable insights to enhance the implementation of ERM.
This study solely relies on primary data acquired through survey questionnaires completed by a sample of executives and professionals working in healthcare institutions in the UAE who are in some way involved in risk management. A web-based survey instrument was used to collect responses from participants to investigate the degrees of implementation of the 30 ERM dimensions employed in the study.
EFA was used to assess the number of factors influencing ERM implementation. The primary reason for employing EFA is the ambiguity in the literature regarding the number of factors influencing ERM implementation in healthcare organizations in the UAE.
Main Research Findings
This is the first empirical study to explore ERM implementation in healthcare organizations in the UAE. This study is important as it provides a foundational understanding of the key elements influencing ERM implementation and what actions organizations can take to improve ERM. The main research findings are thoroughly discussed in Chapter 5.
Implementation and Contribution
This study aims to identify and evaluate the factors associated with the implementation of ERM in healthcare organizations in the UAE. Healthcare businesses must adopt ERM to manage risks related to healthcare delivery and ensure the safety and quality of patient care. The study provides an important contribution by exploring the variables influencing ERM adoption in healthcare companies, notably in the UAE. It focuses on the UAE, which is distinguished by a fast-changing healthcare industry and a varied population. The study’s conclusions can help healthcare companies in the UAE and other countries with comparable environments successfully adopt ERM.
Thesis Structure
This thesis consists of five chapters, which are as follows:
Chapter one is the introduction, which discusses ERM and explores the different factors that affect the implementation of an ERM framework. The purpose of this chapter is to introduce ERM and describe the many elements that influence the application of ERM in the UAE’s healthcare businesses. The chapter presents the purpose of the research, the research questions, and the hypotheses. It also discusses the implementation of the study and how the study will contribute to managing risks in the healthcare industry. Lastly, it briefly describes the UAE’s healthcare system, emphasizing the difficulties healthcare firms face in risk management.
Chapter two provides an overview of ERM frameworks, theoretical underpinnings, and the preceding empirical research. This chapter reviews the relevant literature and presents the applicable theory. The examination of pertinent literature focuses on risk management, risk management models, how the UAE systems can use ERM, and the risk domains of ERM.
Chapter three discusses the research design and methodology. This chapter begins by reviewing the study’s aims and discussing possible methods that could be used, and then justifies the methods chosen for the current investigation to maintain the objectivity of scientific research. It also describes the study design used to address the research questions.
Chapter four presents the data analysis and discusses the research findings. It thoroughly analyzes the findings, comparing them to prior literature and discussing their theoretical and practical implications. The chapter also discusses the study’s limitations, specifies areas for future research, and concludes on the research.
Chapter five discusses the implications of the study. It also summarizes the information obtained from this study and recommends areas for future research. The study summarizes the methodology and discusses the findings and study implications. It also reflects on the research to determine the practical results of the study and discuss how the results will influence future research on the topic.
LITERATURE REVIEW
Overview
This chapter surveys the relevant theory and provides a literature review. The review of the relevant literature follows the literature map (Figure 2.1), focusing on risk management, risk management models, how ERM applies in the UAE context, and the risk domain of ERM). First, it defines risk management and discusses its various classifications. Second, it discusses the main models of risk management, keeping in mind the UAE context. Following that, it covers ERM and its various elements. Then, it discusses the institutional theory and how it connects to ERM. Finally, it reviews the adoption of ERM in healthcare organizations and the factors that affect its implementation.
The main theory used in this thesis to explain factors that affect ERM implementation in UAE healthcare organizations is institutional theory. The theory is based on various organizational and operational aspects such as strategic governance, corporate governance, communicational efficacy, education, healthcare organization structure, and qualitative risk assessment. Various studies (Meyer, 2007; Phillips & Malhotra, 2008; Zucker, 1987) have explained the connectedness of these aspects to institutional theory and how they influence various operational aspects of organizations.
Risk Management
The ever-evolving nature of business and organizational environments, business growth and development, and diversification imply various dynamics and implications associated with business risks. As such, it is worth noting that all organizations are, in many ways, dealing with business risk management (RM), whether knowingly or not. Therefore, there is always a need for senior managers and investors to recognize the ever-growing demand for businesses and organizations to find alternatives and means to minimize any risks. However, some organizations may lack adequate knowledge and expertise on the best ways to manage business risks. Hence, it becomes paramount for the organizational leadership to constantly and regularly evaluate, review, and examine their business risks. The main aims should be to try and control the various operational processes to ensure the functionality of the organization. Business risks in the UAE are broadly categorized under two main branches: organizational-level risks and process-level risks. According to Sayegh (2014), several UAE organizations have engaged in continuous and constant RM assessments to monitor situations and ensure that they offer the best and most appropriate control initiatives.
Business RM in the UAE entails detecting, managing, and preventing the risks associated with the relevant organizations. Al‐Tamimi and Al‐Mazrooei (2007) mention that business risks have different dynamics, from internal risks to other risks related to natural disasters, legal and regulatory compliance, competition, ever-evolving customer needs, social responsibility, and the fast-changing and advancing nature of IT. RM’s main goal is not eliminating risks. Instead, RM aims to provide manageable remedies to counter risks by identifying the optimal places and areas in which to invest and thus avoid unnecessary costs.
The following sections discuss TRM and ERM models within the UAE context and then briefly compare TRM and ERM.
Traditional Risk Management (TRM) Model
Organizations in the UAE acknowledge that the mismanagement of business risk has serious repercussions, resulting in enormous costs and prices (Stulz, 2008). In recent years, poor management of risks in the UAE has resulted in catastrophic impacts. The risks can lead to significant financial losses, damaged company reputations, decreased shareholder values, dismissal of senior managers, and, in the worst-case scenario, the shutting down of the entire business (Aven, 2016). TRM practices are based on fragmentation, where departments and functional managers are independently responsible for RM. These departments and managers handle the organization’s accounting, treasury, and internal audit concerns. Furthermore, TRM is an ad hoc model, whereby independent managers can implement RM whenever they believe there is a need for it (Lundqvist, 2015). However, the TRM model can also be inefficient due to its narrow focus on using RM resources for basic insurable risks and other financial risks, which means that companies incur considerable costs, leading to closures and loss of profits in many organizations.
Enterprise Risk Management (ERM)
According to Lundqvist (2015), firms using the TRM model do not provide adequate attention to the impacts of failure. Therefore, companies must adopt a new and advanced RM model that can counter the impending environmental risks associated with the volatile nature of their business. Rao (2007) states that to provide a viable solution to the risks that could have far-reaching consequences, UAE organizations have adopted and incorporated the ERM model. The main advantage of ERM is that it provides employees and managers with sensitivity to RM in all steps and levels of business operations (Lundqvist, 2015). The three main aspects of ERM are also what make ERM a better model than the TRM model. ERM integrates RM practices and provides a coordinated RM model, with senior management overseeing the entire process at all times. In doing so, the whole team—employees, managers, and staff—views and perceives RM as an integral part of their job.
Furthermore, ERM is more effective, and dynamic compared to the static nature of TRM. It offers a continuous process of managing business-associated risks in all sectors and procedures of the firm’s operations. Most importantly, ERM allows organizations to focus on all types of business risks and provides an overview of the opportunities available to companies that they can capitalize upon (Naik & Prasad, 2021). The scope of the ERM model encompasses all possible risk events, ranging from internal to external (micro-environment and macro-environmental business risks), that might impede firms from attaining their objectives and visions. Therefore, the ERM model offers an integrated and structurally disciplined approach that aligns all strategies, processes, human resources, technological resources, and the available expertise to achieve the purpose of assessing and managing business uncertainties inherent in an organization’s processes and adding value to their business.
The business climate in most organizations in the UAE generally resembles that of other parts of the world. The UAE comprises seven emirates, of which the most well-known are Abu Dhabi and Dubai. The main industries in these two cities are oil and petrochemicals (Shayah, 2015). However, due to the depleting oil resources in the region, several emirates are diversifying and venturing into an outward-orientation model in their businesses to ensure trade diversification and a working environment conducive to corporate firms that can stimulate trade-related services and relationships. Economic diversification, such as focusing on tourism, healthcare, transportation, communication, and the finance sector, can have numerous risks related to the proper operation of the economy. Furthermore, the UAE has a key role in the Middle East as a major port and commercial capital, leaving it exposed and susceptible to risks due to the rapidly evolving economy.
Firms that systematically implement ERM will reap numerous benefits in managing and controlling any risks. Furthermore, effective implementation of the ERM model in the UAE is often characterized by the accompanying RM consultancy, which provides the necessary assistance to businesses implementing the ERM process.
ERM versus TRM
As mentioned, TRM is a process that recognizes risks, their likelihood, and consequences, devises mitigation methods, and regularly monitors and reports on risks to senior management. TRM is not sector-specific, so it may be used in any industry. Unlike TRM, ERM involves integrating or aggregating all forms of hazards and employing integrated tools and procedures to minimize risks and communicate across business lines or levels. The term “integration” refers to the process of changing a company’s operations, adapting its capital structure, and adopting specific financial products (Meulbroek, 2002). Corporate risk management, enterprise-based risk management, business risk management, holistic risk management, strategic risk management, and integrated risk management are all terms that have been used to describe ERM (Kleffner et al., 2003; Liebenberg & Hoyt, 2003; Manab et al., 2010).
Recent empirical research has also focused on the consequences of ERM, particularly its effects on shareholder value of companies. Kraus and Lehner’s (2012) literature analysis compares the results of 25 studies that examine the relationship between ERM and shareholder value and finds that this relationship varies substantially in the articles. They state that there is a paucity of knowledge about the value-enhancing effect of ERM in general. They also claim that the literature on ERM and value creation is still in its early stages. Pagach and War (2010) used a time-series analysis to investigate the impact of ERM on business performance. They find that ERM’s benefits are contingent on a company’s exposure to reduced tail risks.
According to Stulz (2008), ERM is only effective for companies with low tail risk. As a result, Pagach and War (2010) narrow the sample to companies expected to gain from the adoption of ERM, as judged by positive cumulative abnormal returns around the CRO (Chief Risk Officer) appointment announcement. When comparing pre- and post-CRO appointment data, Pagach and War (2010) discover a considerable reduction in earnings volatility as the only positive benefit for businesses. They hypothesize that the result can be explained by the fact that it takes a long time for the benefits of ERM to become clear or that there is too much noise in the data.
Furthermore, Pagach and War (2010) hypothesized that the scant evidence of improved business performance could be explained by the fact that ERM has no meaningful impact on firm performance, at least as measured by financial statement items. They emphasize the need to give guidance for ERM implementation and identify appropriate metrics to assess the benefits of ERM adoption and, in particular, the long-term impact of ERM adoption.
McShane et al. (2011) distinguish ERM from TRM by emphasizing that the essential idea behind ERM is the aggregation of risks into a risk portfolio. While TRM considers each risk individually, thus using a silo-based approach to risk management, ERM introduces a holistic approach to risk management. COSO’s (2004) study describes the holistic approach to ERM as follows: first, managers assess the many risks that their company unit or department faces. They combine these many hazards into a risk portfolio, which provides an overview of all the various risks that a company faces. Senior management calculates the firm’s residual risk using the risk portfolio that comprises all hazards for each business unit. Instead of hedging each risk, the residual risk of the risk portfolio is hedged according to the firm’s risk appetite.
The healthcare business has been rapidly adopting modern risk management strategies worldwide. These techniques are increasingly adopted due to the demand for greater security and quality in providing treatment to patients Furthermore, healthcare organizations all aim to limit risks and respond to actual risk events (da Silva Etges et al., 2018). Healthcare organizations in the UAE face a variety of threats that jeopardize their security systems and the quality of care and services they can provide to patients. To lower the susceptibility of organizational systems, healthcare organizations in the UAE need comprehensive ERM. There are several opportunities for healthcare firms in the UAE to establish sound ERM systems.
Risk Management Models in the Healthcare Sector in the UAE
This section presents current RM models and then briefly introduces the proposed model and its six components.
Healthcare RM the process of developing and executing strategies aimed at optimizing the well-being of patients while preventing harm and minimizing patient-related hazards and injuries. According to Cagliano et al. (2011), the main objective of RM in the healthcare context is to eliminate or reduce medical and other forms of errors that result in significant costs because they inflict harm, damages, patient discomfort, distress, and disability. This entails establishing operational systems and processes that aim to limit the possibility of error and maximize the probability of intercepting any possible healthcare errors before they arise (Cagliano et al., 2011). Therefore, the UAE healthcare’s RM aims to ensure continuous protection from and treatment and management of unsafe medical actions within the country’s healthcare systems.
There is no set structure in place for managing healthcare risks in the UAE; instead, RM is tailored to the scope of the risk. Examining how the UAE dealt with the COVID-19 epidemic, which jeopardized their healthcare systems, illustrates the current state of ERM in the UAE. According to Hasan et al. (2020), the UAE used data-driven modeling and forecasting as part of a comprehensive framework to make educated clinical decisions to combat the COVID-19 pandemic.
The current study recommends that healthcare institutions implement a new ERM approach. The application of this approach, which uses proactive measures, is likely to bring about changes in RM in the healthcare industry (Etges et al., 2018). In particular, risk reduction in the healthcare industry can lower the susceptibility of healthcare organizations and, as a result, improve performance.
The suggested ERM framework is well-structured and encourages the transfer of RM obligations to all concerned individuals, easing the burden on other stakeholders by spreading RM tasks across management teams responsible for the country’s macro-processes. The framework also suggests an RM plan based on a complete investigation of the scope of the identified risks and the education of affected team members on risk elements such as compliance concerns and resource wasting. The proposed ERM model (Figure 2.2) can be incorporated at the federal and emirate public health management levels, as well as in medical institutions, to guarantee alignment of the country’s healthcare sector with national objectives to become a leading medical tourism destination and improve population health outcomes.
Figure .: Theoretical Model of Enterprise Risk Management
This figure demonstrates the factors associated with ERM, and the elements within each factor.
ERM within the UAE Context
As mentioned, ERM is an RM methodology that entails pinpointing specific incidences relevant to the organization’s objectives, analyzing them in terms of the probability and severity of their effects, developing mitigation strategies, and tracking progress. ERM refers to the procedures and processes that companies employ to improve the capabilities connected to achieving their goals (Hasan et al., 2020). By recognizing and proactively preparing to safeguard against risks and handle opportunities, businesses protect and generate value for their stakeholders. ERM is a risk-based approach to enterprise management that combines internal control and strategic planning ideas to ensure business continuity and recovery from disaster.
The UAE government is currently working hard to establish a globally competitive healthcare system. As per Koornneef et al. (2017), the government adopted ERM to improve the quality of healthcare services and thus better people’s health outcomes. However, there is no set structure in place for handling healthcare risks in the UAE; instead, ERM is used depending on the type of risk that must be handled.
The emergence of the COVID-19 pandemic demonstrated the inherent weaknesses in global public health systems. Healthcare businesses are increasingly confronted with difficulties and dangers that necessitate effective corporate governance strategies for identifying and analyzing risks and putting frameworks in place that preserve strategic organizational goals. The UAE government is dedicated to building a world-class health system with the primary goal of providing quality healthcare and improving population health outcomes. Because the UAE is a young country, achieving its strategic goals will necessitate a significant investment in established corporate governance to ensure alignment with these goals. As a result, the current study examines the feasibility of establishing an ERM framework based on healthcare organization structures, communication efficacy, and strategic corporate governance to ensure consistency throughout all seven emirates (Wynia & Osborn, 2010). Rapid economic expansion has resulted in an increase in healthcare facilities, personnel, and service levels in the country, leading to the rapid advancement of the healthcare system. However, strategic management must guarantee that existing resources are appropriately used by limiting potential risks and adversities.
In the UAE, it is critical to improve the healthcare system and healthcare outcomes. Increased life expectancy and lower mortality are examples of improved health outcomes. Appropriate legislation and policies apply to the infrastructure and organizational structures of all healthcare facilities, requiring them to seek external accreditation (Koornneff et al., 2017). Because of its substantial use of patient personal information, the nature of its operations, and time restrictions, the healthcare sector is particularly vulnerable. Cyber threats, physical attacks, regulatory compliance lapses, diseases, privacy challenges, and information security challenges pose significant industry hazards. Because of the complexity of the risks, stakeholders and policymakers must implement a practical ERM framework that encourages collaboration and communication to identify and manage risks based on the perceived threat to organizational goals. ERM frameworks aid in resource management, including managing the intellectual capital available to healthcare organizations, and help improve economic, financial, and non-financial performance. Furthermore, implementing the proposed ERM framework in the UAE will aid in detecting, monitoring, assessing, mitigating, and preventing hazards in clinical and administrative systems and processes.
The following sections discuss the empirical literature on ERM, the role of ERM in healthcare services, and some of the most important risk domains of ERM.
Empirical Literature on ERM
The empirical literature on ERM is divided into two fields: one focuses on the factors influencing the adoption of ERM, and the other on the consequences of the adoption of ERM (Baxter et al., 2013). Liebenberg and Hoyt (2003) were among the first researchers to examine the many factors of ERM by using a CRO (Chief Risk Officer) as a surrogate for ERM. According to them, a CRO’s primary role is to implement and coordinate ERM, but it also must communicate RM objectives and strategy to external stakeholders. They investigate whether certain firm factors, including earnings volatility, stock price volatility, leverage, and market-to-book ratio, impact ERM adoption. They find that the only significant relationship between these variables and ERM adoption is the positive relationship between leverage and ERM, implying that more leveraged enterprises are more likely to adopt an ERM framework, ceteris paribus.
Beasley et al. (2005) use survey data from a set of companies to examine the implementation of ERM. They created a five-point ERM implementation scale by surveying chief audit executives, ranging from 1 if there is no ERM plan in place to 5 if ERM has been actualized. They investigate the impact of various corporate leadership and other firm features on the implementation of ERM. They conclude that having a Big Four auditor firm, more independent board members, and an appointed CRO has a positive impact on ERM implementation. The significant positive effect of appointing a CRO during the ERM implementation is particularly strong evidence for the use of a CRO as a proxy for ERM.
ERM in Healthcare Services
RM measures, particularly an ERM process, are crucial for healthcare firms in the UAE. Education, corporate governance, and qualitative/quantitative risk assessment frameworks are all part of the proposed ERM. The strategy imposes strategic management of healthcare organizations while maintaining compliance and financial control, which ensures that organizational resources are effectively utilized. The ERM framework is built on a cost-effective healthcare process with crucial RM components.
A unique feature of the UAE’s healthcare sector is the dedication of the UAE’s federal and individual emirate governments to strengthening the healthcare sector through major and strategic investments.
The UAE is becoming a more popular tourist destination, and medical tourism is a growing industry that attracts both health tourists and vacationers. Medical tourists are often motivated by affordability, waiting lists, local rules, privacy and travel options, religious limitations, integrating wellness with vacation, and insurance agencies. The UAE is investing significantly in the healthcare sector and actively researching ways to market the country as a medical tourist destination. The flaws obstruct progress and threaten healthcare organizations, putting government and corporate investments in the country’s healthcare system at risk. Policymakers and healthcare companies must collaborate and cooperate through strategic decision-making to create an internationally acceptable healthcare system in the UAE. Furthermore, the incorporation of health IT for sustainable growth poses a new threat to the global healthcare sector. The UAE’s adoption of health IT aims to improve care delivery, but it has resulted in unforeseen consequences and new issues. The security of health IT in the clinical setting is a critical concern that jeopardizes the UAE’s healthcare sector’s strategic objectives and reputation (Al-Talabani et al., 2019).
The implementation of ERM solutions in UAE healthcare organizations has significant promise. Effective ERM contributes to holistic frameworks that allow organizations to make RM decisions. RM systems within healthcare businesses must reduce risk and uncertainty, thereby optimizing value creation and protection. Although most healthcare organizations in the UAE have organized RM systems, they do not meet the government’s unique aims and objectives. Most RM systems focus on clinical occurrences through reactive risk management strategies (Almansoori & Ali, 2020). However, according to Almansoori and Ali (2020), the dynamic character of modern healthcare organizations requires the integration of systems that emphasize the importance of shared responsibilities in the detecting, avoiding, and controlling risk. While the deployment of ERM systems has benefited some healthcare companies, poor and unresponsive ERM models have limited the efficiency of these companies.
Healthcare organizations in the UAE must recognize the duties required when working in a dynamic health environment and the significance of the external environment in accomplishing national objectives. To inform the decision-making process, the external environment must have a system to critically assess threat levels and opportunity factors, and corporate governance strategies must link the organizational objectives with the identified needs. Furthermore, the external environment, such as economic conditions and regulatory constraints, is out of most healthcare facilities’ control.
The UAE’s rapidly expanding healthcare system has attracted international interest, prompting cybercriminals to target the country’s infrastructure (Almansoori & Ali, 2020). Despite advances in cyber-security defense systems, the UAE remains a possible target for hostile and highly trained actors, who might steal patient data and impose system breakdowns for personal, financial, or political gain. Most businesses’ current low cyber security levels are due to a significant gap in information and expertise among the players involved. As a result, healthcare businesses in the UAE need a corporate governance framework that encourages successful offensive, defensive, and education awareness campaigns about cybersecurity risks to healthcare infrastructure and technology (Almansoori & Ali, 2020). Due to a scarcity of people working in cyber security, government agencies and multinational enterprises frequently fail to fight cyber-attacks (Blair & Sharif, 2013). Current cyber security threats are diverse and sophisticated, and attackers can access sensitive data, such as personal information. Having an ERM framework in UAE healthcare will aid data retention, data encryption, security management, file system security, and better vigilance of healthcare IT systems and enforce legal cybersecurity norms.
There are disparities in communication in various governmental, non-governmental, and commercial healthcare institutions. Experts, international healthcare organizations, and healthcare tourism have all paid attention to the UAE’s focus on strengthening the healthcare industry. Furthermore, there are several difficulties with information transmission between the seven emirates and healthcare organizations. Thus, there is a need to unify healthcare goals across the country by developing standardized and workable communication tactics and frameworks. There are issues concerning communication in clinical and administrative activities, making it difficult to ensure the whole healthcare sector adheres to national goals and ensure compliance by all stakeholders. Instead of performing routine procedures and diagnoses, healthcare organizations should provide patient-centered care that follows best practices. The importance of communication in all aspects of healthcare, including to ensure clinical accuracy in communicating patient information with experts and organizations and collaborating on the treatment of current and new patients. The proposed ERM strategy fosters communication efficacy by encouraging collaboration and cooperation to reduce risks and threats to patients and critical UAE healthcare infrastructure (da Silva Etges et al., 2018).
One of the main strategic aims of the UAE government since its independence in 1971 has been to enhance the quality of care and the overall health benefits for its residents and citizens (Koornneef et al., 2017). The MOHAP is in charge of regulating, licensing, and quality assurance of healthcare organization in five Emirati regions, while Dubai and Abu Dhabi each have their own separate health bodies to carry out these functions (Brownie et al., 2015). As a result, healthcare businesses require a specialized and distinct RM strategy.
McShane et al. (2011) used a sample of enterprises with an identified S&P ERM rating to investigate the relationship between ERM and firm value. They discovered that improving the quality of ERM significantly improves firm value. Several empirical studies have examined the effects of ERM on value creation, with conflicting findings (Baxter et al., 2013; Liebenberg & Hoyt, 2003; McShane et al., 2011; Pagach & War, 2010).
Furthermore, McShane et al. (2011) distinguish ERM from TRM by emphasizing that the essential notion of ERM is the aggregation of risks into a risk portfolio. TRM uses a silo-based approach to RM, in which each risk is individually hedged, whereas ERM’s approach is a holistic approach to RM. Coso (2004) describes the holistic approach to ERM as follows: First, the many risks that each company unit or department faces are assessed by their respective managers. These many hazards are combined in a risk portfolio, which provides an overview of all the risks that a company faces.
Risk Domains of ERM in the UAE
ERM is an organized, coherent, and ongoing discipline. It is possible to save lives, money, effort, and time by correctly identifying risks and taking actions to prevent them. ERM aims to identify the risks associated with opportunities, increase the likelihood of attaining the organization’s core objectives, and reduce the impacts of occurrences that the organization cannot control due to the current uncertain environment and limited resources and capital. A successful ERM process has several vital components, all of which must be in place for the program to be successful. These include management buy-in, efficient stakeholder engagement, clear and simple practices and processes, flexible education and training, an efficient and effective structure, RM applications in operation, and continuing review and evaluation. The commitment of management is the first requirement for a successful ERM system. The administrative team must understand ERM, be proactive, inspire leadership, offer a robust and committed governance framework, and foster a positive culture throughout the company. Management must participate in and contribute to an ERM system. Top-down leadership will promote the ERM system. It must identify an effective governance team to keep the ERM system on track. Finally, the administrative team must create a solid corporate ERM culture and inculcate it in all employees.
In 2006, the Abu Dhabi government began a major health-system reform initiative, aiming to restructure healthcare expenditure and the regulatory environment (Koornneef et al., 2017). It separated the necessary regulatory processes and the supply of services into two distinct entities. The Abu Dhabi Health Authority was in charge of regulations, while the Abu Dhabi Health Service Authority was in charge of service provision and operations at the healthcare institutions. The MOHAP is also considering introducing health insurance but has not yet done so.
The heterogeneity of the UAE’s health regulatory environment is a severe threat to the country’s future healthcare; thus, it is important to have an ERM system. According to a study by Koornneef et al. (2017), the two primary hurdles to obtaining higher cost efficiency in the healthcare industry are a lack of regulatory supervision and a lack of competition amongst insurance firms. Furthermore, because healthcare is shifting from the public to private sectors, the RM strategy must determine how to integrate auditing to verify that private companies are operating. Because the private sector in the northern emirates is less established than in Dubai and Abu Dhabi, the pricing and quality of services vary between these two emirates and the rest of the nation. According to Gates (2006), ERM systems holistically examine risks and associated management strategies. This dissertation is information by the requirement for more efficient systems that promote accountability and risk supervision obligations inside healthcare organizations. For example, ERM solutions can assist healthcare businesses in effectively identifying, managing, and communicating critical risks within their operations.
The key diversification efforts to minimize the UAE’s over-reliance on oil while improving inhabitants’ quality of life include tourism, education, and healthcare. To guarantee that the country achieves its intended goals, it is important to established corporate governance processes and cooperation across all seven emirates in the healthcare sector. According to the existing literature and research, the UAE’s health sector has a variety of competitive strengths and disadvantages. It is necessary to have input from key clients to implement organizational strategies and strategic management of the country’s health infrastructure (da Silva Etges et al., 2018). The resource-based theory claims that proper resource use is critical for creating a competitive advantage. Proper usage of resources depends on governance practices within the corporate environment and frameworks put in place by policymakers that align with the country’s health goals and vision. The UAE’s healthcare resources are advanced, and its infrastructure may be challenging to duplicate, setting the country’s health sector apart from the industrialized world. However, because the vision for healthcare in the country has not yet been realized, various strategic measures must be implemented to capitalize on the acquired advantage of a worldwide medical tourism hub. Furthermore, existing and emerging threats such as cybersecurity concerns, fast-paced medical science, and rapidly changing healthcare regulatory, legal, political, and financial concerns can jeopardize the country’s efforts to improve healthcare organizations.
Healthcare firms have been increasingly relying on ERM to gather more precise information and data on which to base RM strategies in the era of globalization. As a young country, there is a need for transformative leadership and visionary corporate governance in the UAE that capitalize on opportunities while decreasing the sector’s risk exposure. Thus, it is necessary to implement frameworks that preserve the country’s healthcare system’s essential resources and infrastructure. For example, healthcare organizations in the UAE should have specialized and sophisticated clinical and administrative capacities as the government aspires to provide world-class healthcare (Blair & Sharif, 2013). The fundamental weaknesses of the country’s healthcare sector are the lack of structure and inability to use existing resources and establish a competitive advantage. Bad management and an ineffective RM organizational culture will inevitably decrease the UAE’s healthcare competitive edge, regardless of its available resources. The proposed ERM framework encourages critical communication and strategic approaches for detecting and analyzing threats, weaknesses, and vulnerabilities. The strategy guarantees that all stakeholders and management teams implement a mechanism to safeguard and strengthen vital infrastructure, policies, and practices to adhere to national healthcare goals.
Strategic planning is an integral part of achieving long-term growth and corporate and national healthcare goals. Constructive resource use necessitates a grasp of current population health issues and the development of structures that can meet citizens’ health demands while increasing healthcare quality in the country. Corporate governance and leadership are required to promote strategic objectives and execute programmable, methodical, rational, and comprehensive plans encompassing the healthcare organization’s short-, medium-, and long-term goals. After doing this, a business can concentrate on long-term reforms such as strengthening its technical interoperability and security. However, corporate governance may not be able to meet all of these requirements (da Silva Etges et al., 2018). The ERM framework emphasizes the importance of delegating risk identification, mitigation, and treatment procedures to management teams in charge of such infrastructure.
The next section discusses institutional theory and how it is relates to the different components of the proposed ERM framework model.
Institutional Theory
The institutional theory explains how organizations, especially those in the same field, adopt strategies that make them less distinguished from each other. The institutional theory focuses on the economic, social, and political systems in which companies gain and exercise their authenticity. The theory places institutions at the center of organizational design and behavioral analysis (Meyer, 2021) and explains the numerous practices and defense mechanisms used to operate an organization and its structures.
Institutional theory focuses on the deeper and more robust features of social structures in organizations (Scott, 2008). It examines the mechanisms through which frameworks such as plans, rules, norms, and routines establish authoritative social behavioral guides. The institutional theory explains how these aspects are formed, distributed, embraced, and adapted over time and space and how they deteriorate and fall out of favor. Institutions are highly resilient social structures made up of cultural-cognitive, normative, and regulative aspects, which provide stability and significance when combined with related resources and activities (Scott, 2008). Facts, routines, relational systems, and symbolic systems are examples of carriers that transfer institutions. According to Scott (2008), institutional theory is a generally recognized theoretical position that prioritizes efficiency, morality, and validity.
The theory’s primary argument is that institutions, rather than the market, dictate how organizations behave and function within their industries (Krajnovic, 2020). It theory encompasses three elements: coercive, normative, and mimetic forces. This thesis applies this theory to the implementation of the ERM model in the UAE’s healthcare system.
The institutional theory argues that institutions, particularly healthcare organizations, must ensure their operations adhere to industry standards and norms to guarantee their survival. Rather than focusing on delivering superior results, these organizations – such as the state, healthcare institutions, and medical insurance institutions – preserve legitimacy by ensuring that their operations adhere to their industry’s working standards and values. The institutional framework can help researchers identify behaviors and attitudes that impact sector outcomes in the UAE’s healthcare system. To provide consistency across the country’s seven emirates, the ERM framework follows industry certification, licensing, and data security protocols. Coherence is required when implementing the RM strategy to ensure interaction and cooperation between stakeholders and policymakers.
Research is needed due to institutional pressures in the UAE’s healthcare system. The nation currently does not have a framework for dealing with public health threats; as a result, organizations deal with problems on an ad hoc basis. Institutional theory can enhance ERM practice because it builds knowledge on how institutions interact with their environments. Furthermore, isomorphic pressures can assist researchers in developing an ERM implementation plan that aligns with tactics used in other nations to successfully address healthcare issues. Similarly, healthcare stakeholders might follow RM expertise from other countries’ healthcare authorities to solve pressing issues in the UAE’s healthcare system.
The institutional theory focuses on organizational behavior, and through it, researchers can gain a more in-depth understanding of social culture. The theory considers several structures, including norms, routines, and rules, which later become the guidelines for social behavior. It seeks to illustrate why people behave in such a way in a given social setting. While several theories seek to explore the complexity of human and organizational behavior, the institutional theory best explains this complexity. Institutional theory is based on constructionism. Constructionism suggests that what is considered normal and acceptable in a given social reality is based on repeated behavior in an organization and the bestowing of similar meanings on this behavior (Jensen et al., 2009).
Institutional theory plays a substantial role in ERM within the healthcare industry, focusing on how institutional pressures and norms influence structures and activities (Scott, 2014). Healthcare organizations operate under regulatory, normative, and cultural-cognitive pressures to adopt ERM practices. Regulatory pressures stem from formal laws and regulations. For instance, the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. requires healthcare organizations to address risks to patient’s health information, creating an institutional demand for effective ERM practices (Dreachslin et al., 2012). Normative pressures arise from professional standards and best practices. Professional healthcare associations and bodies often embed ERM in their practice to improve patient safety and reduce operational risks (Abu-Rumman et al., 2021).
Cultural-cognitive pressures stem from shared conceptions and understandings that make ERM a ‘taken-for-granted’ practice in healthcare organizations. An example is the widely accepted belief that a systematic, enterprise-wide approach to RM enhances patient safety and organizational resilience (Werner et al., 2021). Further, the process of institutionalization — embedding ERM practices into organizational routines and culture — can improve the efficacy and long-term sustainability of these organizations (Greenwood et al., 2017). By integrating ERM into the organization’s mission and daily operations, healthcare organizations can better manage risks, improve performance, and enhance patient outcomes. Finally, institutional theory provides a comprehensive framework for understanding ERM adoption in healthcare. Healthcare organizations can enhance their risk management capabilities by acknowledging and responding to various institutional pressures, ultimately delivering safer and more efficient patient care.
Institutional forces that affect ERM within the context of UAE healthcare organizations
This section links the different components of the proposed framework to the discussed institutional theory forces.
Strategic Governance
The role of strategic governance in healthcare organizational RM is evident in the use of institutional processes that uncover, prevent, and mitigate risks in medical care systems. Strategic governance in ERM of healthcare institutions encompasses normative institutional aspects such as medical procedures, healthcare practitioners’ codes of conduct, the expectation that every medical expert can fulfil their responsibilities, and the upholding of a high degree of operational standards (Lammers & Garcia, 2017). Ensuring the implementation of these ERM factors in healthcare systems guarantees that risk managers follow their purpose and role following the set standards for risk mitigation and prevention. Therefore, according to Zucker (1987), by employing strategic governance based on the normative and coercive aspects of ERM, healthcare systems can develop efficient operational structures, regulations, standards, and associated routines as guidelines that must be adhered to when minimizing risks.
Over the years, there has been an increased need for the value, purpose, and significance of ERM in healthcare institutions worldwide. As a result, the main purpose for deploying strategic governance in healthcare RM has been centered on the fundamental role of safeguarding the patient’s safety. Similarly, healthcare ERM has reiterated the adoption and implementation of quality operational processes that would minimize medical-related errors, which often jeopardize the ability of the healthcare facility to attain its set of objectives, mission, and vision (Genies et al., 2019). Organizations minimize any financial liability by keeping their errors and risks as low as possible. However, healthcare ERM has become a complex issue because the ever-growing role of healthcare-related technologies has led to the widespread threat of cyber-attacks. Furthermore, the rapid expansion of medical science’s ever-changing regulatory, political, and legal landscape has made it necessary to incorporate strategic governance into healthcare ERM.
In today’s healthcare organizations, reimbursements are shifting from a fee-for-service model and emphasizing value-based and quality-outcome services. For this reason, strategic governance has been widely applied across the globe’s healthcare systems. The purpose of ERM in hospitals is to enable these facilities to expand their RM practices and develop ERM programs to promote patient safety, mitigate legal exposures, and provide a broader insight into associated risks encompassing the healthcare ecosystem (Etges et al., 2018).
Healthcare ERM focuses on using technological advancements to synchronize RM across the healthcare system, thereby eliminating the risks related to risky business units and departments (Genies et al., 2019).
Another significant contribution of strategic governance is the exploitation of healthcare ERM data analytics. Organizational ERM has enabled the analysis of data embedded in the healthcare system to support decision-making, risk prioritization, departmental cohesion, and resource allocation efficacy. According to Gottwald and Mensah (2015), the role of data analytics is to offer monitoring benchmarks that indicate the value and costs incurred for ERM initiatives of the organization. As such, strategic governance can align business operations based on RM programs in the healthcare environment. The responsibilities of a healthcare risk manager have evolved to accommodate the implementation of ERM. Their main duties now entail proactively identifying risks, estimating possible repercussions, and assessing the advantages and disadvantages of the risks in the medical setup. Moreover, they are tasked with developing contingency plans in response to the projected risks to mitigate organizational susceptibility to risk exposures and provide advice on containment plans in the wake of organizational risks (Gottwald & Mensah, 2015).
In conclusion, strategic governance is essential to the success of ERM implementation in UAE hospitals because it influences how the UAE will achieve its national health care goals. Its success depends on the appropriate allocation and utilization of resources based on the population’s health problems and risk factors. To guarantee sustainability, strategic planning must adopt a holistic framework that integrates the health care industry’s long-, medium-, and short-term RM objectives. Strategic governance is exposed to normative forces that stimulate firms to adopt practices to gain legitimacy (Krajnovic, 2020) and exposed to coercive forces.
Coercive forces affect strategic governance in the ERM model because they expose organizations to the beliefs and experiences of other professionals within the nation’s healthcare system and of national regulations and laws. Through the collective effort of stakeholders in the healthcare department, strategic governance might adopt homogenous healthcare goals in RM, which must be aligned with the laws and regulations of the UAE. Adopting these characteristics is necessary to assist organizations in the sector in gaining legitimacy within the national RM structure and ensuring the achievement of the UAE’s national agenda.
Corporate Governance
Organizational corporate governance (CG) is useful for medical practitioners to hold the board and management responsible for the continuous development and improvement of the facility’s operations. Therefore, the leadership, board of directors, and executive management are held accountable to ensure that all the necessary clinical staff operations, financial performance, and CG mechanisms are in place and implemented efficiently to minimize any healthcare risks. Additionally, healthcare facilities can detect, monitor, assess, mitigate, and prevent potential risks and costs by adopting clinical and administrative systems.
Given the ever-evolving healthcare complexities related to CG resulting in proper RM, the board and healthcare executives must establish goals and objectives to provide a roadmap for managing the associated risks. Consequently, healthcare facilities must invest in proper management to ensure that all RM aspects and healthcare governance mechanisms are fulfilled to the required standards. According to Rusydi et al. (2020), the administration’s main aim in hospital RM is to guarantee and safeguard patient safety by ensuring that the patient’s most critical needs are met. As such, the medical facility’s governance is tasked with maintaining the smooth flow of business performance and ensuring that all clinical practices comply with the outlined laws and regulations. Additionally, the ethical standards and moral conduct of all stakeholders and medical practitioners are of great concern in mitigating healthcare-associated risks (Etges et al., 2018). It is paramount that hospitals’ clinical governance effectively eliminates and minimizes potential risk to efficiently incorporate aspects such as revenue cycles and hospital reimbursement policies, which can influence the monitoring and management of risk activities of the healthcare organization.
Etges et al. (2018) mention that the board of healthcare facilities must take all necessary precautions to manage risks, employing the necessary due diligence to identify the possible threat areas. RM assumptions must be avoided at all costs, providing a framework for the healthcare organization to identify risks, prevent them, and earn revenue.
Furthermore, healthcare’s corporate management is responsible for maintaining proper and solid RM practices. Similarly, management must continuously report on their annual financial and other relevant metrics to the organization (Berkowitz, 2001). By assessing the potential major risks to their enterprise, the organization can mitigate and avoid incurring unnecessary costs by addressing risks that might hurt the organization and hamper the healthcare system’s liquidity, solvency, and future operations. By considering the condition of the healthcare system and potential risks, CG must provide a contingency plan that the firm can employ to avert any risks. As a result, the company can foster uninterrupted and continuous operations to attain their expectations, counter any liabilities, and pay attention to any ambiguity and assumptions to encourage proper healthcare ERM. Thus, the overall structure of CG in the healthcare system must focus on overseeing quality RM by employing a professional audit and RM committee.
Another vital role of CG in the ERM of hospitals relates to the manner in which the board evaluates potential reimbursements and associated healthcare clinical risks. The governance systems do this by conducting frequent assessments of hospital billing and monitoring the compliance activities of the healthcare systems. By employing reimbursement specialists, healthcare companies can monitor and report possible risks to the appropriate bodies for further investigation. Corporate responsibility in the healthcare system focuses on compliance and regulatory risks, clinical governance, and RM activities (Rusydi et al., 2020). As a result, healthcare risks can be addressed by using the available technological tools to gather relevant information about certain risks and provide possible suggestions to mitigate them. Furthermore, the appointed reimbursement division in healthcare organizations is pivotal for reducing risks and managing costs related to healthcare complexities. The division can offer streamlined ways and potential strategies to appropriately manage risks in healthcare’s ERM.
With the continued adoption of ERM practices in healthcare facilities across the globe, CG has effectively identified the principal potential risks, estimated the impact of various risks, and provided alternative solutions to mitigate potential threats. In doing so, the process of RM in healthcare has been revolutionized, especially due to the growing demand to employ newly developed technology in patient safety and care. CG is responsible for the entire RM of an organization, particularly hospital risk assessment that provide patient safety and care.
In conclusion, CG strategies help identify and analyze risks that might hinder healthcare organizations from attaining their objectives. A CG framework is especially critical in today’s health industry due to the threats associated with increased technology. Therefore, organizations need a CG structure that guarantees security for sensitive information. CG manages the healthcare practices that govern institutions in the UAE. The department’s leadership, including executive managers and the board of directors, oversees CG and establishes initiatives to improve the treatment process through effective management and delivery care. The CG encompasses compliance with regulations and ethics that guarantee the institution is accountable to its stakeholders.
Coercive isomorphism results from external pressures that force firms to conform to the institutions that they depend on. They encompass formal and informal factors of regulatory bodies, governments, and communities, such as cultural expectations (Krajnovic, 2020). Political influence can create coercive isomorphism, influencing the CG in ERM implementation. Coercive forces that impact CG include regulatory agencies and government mandates on cybersecurity. Additionally, financial control through funding and budget cycles pushes the ERM model toward convergence with its RM approach. Likewise, heightened government mandates for improving care quality affect the development of CG aimed at RM in healthcare.
Communicational Efficacy
Communicational efficacy (CE) is a significant component of the ERM framework because it facilitates effective administration and ensures compliance by stakeholders. According to Connelly (2014), ERM software could make incident reporting easier and provide a mechanism for identifying any source causes. Furthermore, data analytics and dashboards make it possible to obtain the resources required to improve client’s safety. Thus, the incident reporting mechanism must be strong enough to identify steps that can minimize the risk of the same incident reoccurring. Consequently, when all team members work together and recognize their role in RM, there should be a notable improvement in patient safety and quality of care.
For instance, if the team decides to give specialized health education about medical procedures to clients through media such as television to help patients understand their treatment, they may work with the healthcare administration and a staff member from the technology department. Hence, Kreps (2016) states that consulting with a support member could increase the effectiveness of the groups by involving the team in the actual operations in the hospital system and making decisions that could be translated into the real healthcare system, thereby avoiding loss of lives and property.
Vermeir et al. (2015) mention that the critical role played by the risk experts in the healthcare organization is to effectively communicate the risks involved. They need to correctly identify such risks because even though the healthcare system has the best RM policies and systems, there could be harm related to the operations of the organizations if they do not communicate the risks effectively. Thus, senior management ought to clearly understand the risks involved and their consequences. Failing to effectively communicate risks can be the main reason that a healthcare organization has a financial crisis. Therefore, effective communication involves understanding how to listen attentively and provide proper, detailed information. This will build trust and promote teamwork, improving healthcare operations.
Challenges in communication expose the healthcare system to problems associated with ensuring that different sectors pursue similar objectives. An effective national communication channel will facilitate the exchange of accurate information and collaboration among professionals to mitigate healthcare risks. Since transparency is vital to the UAE’s RM approach, healthcare bodies should endorse practices that allow communication among stakeholders, including policymakers and caregivers. Hence, normative isomorphism will influence the operations and become the communication standards for risk mitigation in the nation’s healthcare.
Normative pressures often include internal forces that determine the reinforcement of values and roles in institutions. Normative isomorphism manifests in CE because it involves the diffusion of individual values and skills through socialization or training practices (Krajnovic, 2020). Additionally, normative forces result from collective knowledge centers and educational activities that contribute to the development of professional networks.
It is critical for the whole organization and all departments to be aware of the ongoing RM plan. The risk manager should communicate the RM plan, including the scope, objective, purpose, and protocol to be followed. Furthermore, everyone should note their involvement in the RM and mitigation plans. Abrams et al., (2003) suggest that an organization’s RM programs must include communication plans. Open and spontaneous dialogue is essential as it allows the sharing of information concerning risks. Moreover, a communications plan must document the findings and implement proper reporting strategies. A proper plan should include a requirement that all departments in the organization implement a reporting strategy.
Education
One element of RM in modern healthcare organizations is implementing strategies that educate people on RM. Thus, ERM managers must increase the implementation of these educational strategies in the healthcare system. Organizations should ensure adequate education in the ERM model so that the institutional ERM can be upheld through administrative systems and clinical and operational procedures. Furthermore, organizations should continue to provide constant reports to monitor, evaluate, prevent, and reduce risks after detecting them. ERM also includes educating people in healthcare organizations. Johnstone and Kanitsaki (2007) mention that by employing an educational strategy in ERM, the organization can systematically and actively safeguard and save people in the healthcare system. Similarly, the RM educational practices are important as they could protect the healthcare properties, ensure accreditation, foster brand value, and improve the organization’s market share. As a result, the healthcare organization’s shareholders must ensure that the company provides proper education in the working environment. The collaboration increases awareness among all people in the medical sphere, ensuring they focus on their specific roles, social responsibility, and adherence to the organization’s regulations to mitigate risks.
It is important for healthcare organization experts to have a proper ERM with a suitable education strategy program to focus on better management of people and risks in the healthcare system. Education can enhance the value of the experts’ leadership by preventing reputational and financial losses and increasing the investment in the healthcare system to benefit both the organization and the community (Hahn & Truman, 2015). Proper education across the ERM team can go beyond avoiding legal entanglement and financial losses by helping enhance the healthcare system, increase value for all stakeholders, and ensure organizational stability. Thus, education in ERM contributes to organizational success, which is made possible by implementing ERM. As a result, ERM must use proper educational strategies. Everyone in the healthcare system has a role to play, as per ERM guidelines, to prevent and mitigate risks. Therefore, everyone in the healthcare organization has to acquire the appropriate education to reduce any related medical errors and loss of property and people’s lives.
Suitable education has been deployed in many healthcare systems globally to provide efficient and effective RM. In the event of any failure in the healthcare organization, ERM is solely accountable for not providing proper education to people in the healthcare system. The occurrence of risks implies inefficiency and the inability of the healthcare system to educate people on how to manage, monitor, and prevent potential threats to the organization. Additionally, it shows that the education team of the healthcare board has failed in identifying the risks and the extent to which the risks may affect system operations. According to Capocchi et al. (2018), the executive education team must take all precautions to manage risks by employing the necessary due diligence to understand the possible sections where the problem may arise. The organization must ensure that everyone understands that assumptions in risk management ought to be avoided at all costs and provide a framework for the healthcare system to identify risks, prevent and reduce them, and earn revenue. Therefore, education plays a vital role in implementing ERM in healthcare organizations based on institutional normative and coercive aspects.
Another critical role of education in the ERM of healthcare systems is to provide adequate knowledge to the board on how to evaluate potential reimbursements and any associated organizational risks. The education team can do this by providing the necessary education to the boards and everyone in the organization (Adibi et al., 2012). ERM of healthcare organizations can be addressed by utilizing the available educational tools to identify risks and possible outcomes and learn how to prevent and mitigate them. In addition, education can help identify potential principal risks in the ERM of healthcare sectors. It can help estimate the impact of various risks and identify alternative solutions to addressing company-related risks. Education can revolutionize ERM in the healthcare system, especially with the growing demand to employ better technology to reduce and prevent organizational risks. Moreover, the department of education in healthcare management helps elaborate on the roles within the organization tasked with educating on and evaluating RM (Adibi et al., 2012). The department helps educate people in the healthcare organization on the importance of implementing RM. Therefore, the critical role played by the risk managers in the organization is to ensure that they suitably educate everyone in the organization on the principles of ERM.
The ERM framework’s success depends on effective communication among staff concerning effective RM practice. Education should encompass diverse topics on risk mitigation, including cybersecurity threats in healthcare. Through proper training, workers in the healthcare sector can acquire competitive skills to help agencies identify and prevent threats that might arise from increased technology use in sharing and storing sensitive data. By embracing education, healthcare institutions will equip caregivers with knowledge to identify and prevent risks in the industry.
Mimetic isomorphism results from ambiguity in systems where organizations operate. Since companies are constantly encountering challenges with ambiguous solutions, they tend to mimic problem-solving strategies from other organizations without reflecting on them (Krajnovic, 2020).
The education element of the ERM framework conforms to mimetic isomorphism because it borrows solutions and existing strategies from other industries. Mimetic forces influence hospitals to adopt education strategies regarding RM that have proven successful in other organizations. Successful techniques of risk communication serve as a model for healthcare institutions to copy, equipping their staff with expertise in risk identification and mitigation.
Education and training are fundamental components of RM. They can increase awareness of how to handle different risk management processes and activities. They can also include processes such as conducting employee orientation, annual reviews, in-service training, event-specific training, and competency validation; this can enhance the effectiveness of employees in handling the risk management process.
Healthcare Organization Structure
According to Nilsen et al. (2020), healthcare organizations ought to keep modifying their organizational structure and workforce to provide quality services to strive to maintain standards in a constantly changing work environment. Numerous factors may affect the effectiveness of organizational structure in the healthcare system. Organizational structure has been developed for efficient and effective RM In many healthcare organizations. The ERM aims to prioritize smooth operations and mitigate medical risks. The occurrence of risks in hospitals is the result of inefficiency and the inability of the organizational structure to manage, follow up on, and prevent a potential threat in the system. It indicates that the healthcare organizational team has failed to accomplish their work and responsibility in determining the nature of the risks and the extent to which the risks may affect healthcare processes and operations. The organizational structure of the hospitals ought to take extra-careful measures to manage risks. The organization needs to apply the necessary due diligence to determine where risks may come from. Consequently, assumptions in ERM should be avoided, and organizations should adopt a strong framework to ensure that risks are identified, reduced, or prevented and to promote operational services that are adopted based on the normative aspects of ERM (Tina Dacin et al., 2002). Therefore, the healthcare system should employ an effective organizational structure in ERM to safeguard the organization systematically and effectively.
The organizational structure in the healthcare system provides a framework that can be applied to all the system operations, functions, processes, and organization activities. The organizational structure relies on medical experts in the board and the ERM responsible for constant development and improvement of healthcare operations (Shukri & Ramli, 2015). Additionally, the structure ensures that the system can detect, monitor, control, prevent, and mitigate potential risks and unnecessary costs by adopting effective and good administrative systems. Therefore, organizational structure in the healthcare ERM does encompass people’s safety, help in managing medical operations, and administration.
A significant role of organizational structure in the ERM of healthcare organizations is related to how the organization determines and evaluates any potential reimbursement and hospital-related operation risks. As such, organizations should employ reimbursement experts, and the organizational structure can then monitor and report on potential risks to the concerned teams to ensure prevention and control. The main responsibility of the organizational structure may be to adhere to compliance standards, regulate risks, properly organize the department, and manage healthcare-related risks; thus, the organizational structure should mainly aim to ensure the safety of the people and the system. Therefore, useful technology tools could be implemented to address the organization’s ERM and obtain important information about the risks and possible ways to reduce them. Similarly, the reimbursement team appointed in the hospital are responsible for operations that focus on mitigating risks and controlling expenses related to the organization’s complexities. The organizational structure provides streamlined methods and possible strategies to effectively reduce and manage risks in the organization’s ERM.
The ERM model adopts a unique organizational structure that positions risk mitigation at the core of departmental decisions. The UAE’s healthcare administration has unlimited access to the risk mitigation sector. Seamless interaction between caregivers and administrators is vital for the success of the ERM model because it allows the coordination necessary to identify threats in the industry. The healthcare organization’s ERM relates to coercive isomorphism, which occurs due to firms experiencing pressure when other institutions on which they depend influence their structures (Krajnovic, 2020).
Hospitals in the UAE depend on the nation’s healthcare regulatory departments for structures concerning risk mitigation protocols and regulations. Regulatory requirements concerning the interaction between caregivers and the administration encourage hospitals to adopt this model to ensure efficient communication. Coercive pressures apply to healthcare organizations in the ERM model because hospitals rely on decisions from leadership. They depend on the department for policies and resources that guide their risk management decisions and quality care delivery.
A healthcare setup includes several enterprise risk domains: operational, strategic, clinical/patient safety, financial, human capital, legal/regulatory, technology, and hazard domains (Kaya et al., 2018). The operational domain includes the risk resulting from a failed internal process or a system of people that may affect operations. The healthcare system aims to deliver effective, efficient, safe, and patient-center care in a timely manner to a diverse population. Operational risks may sabotage the delivery of healthcare. Risks could relate to failures in event management, the chain of command, documentation, practice, and credentialing.
In contrast, clinical and patient safety includes the risks associated with the delivery of care to patients, residents, and other customers. Clinical risks may result from the clinician not recognizing or following the evidence-based practice, resulting in medical errors, serious safety events, and hospital-acquired conditions.
Instead, strategic risks include those associated with the direction and focus of the organization. Strategic domain risks primarily stem from the rapid change witnessed in the organization: this creates unpredictability. Other potential risk areas in the strategic domain include contract and administration, advertising, joint ventures, marketing and sales, conflict of interests, and managed partnerships.
The financial domain includes risk associated with decisions concerning an organization’s financial matters; this would include decisions concerning the external financial rating of an organization, expenses, and business ratings. The potential risk areas include litigation, credit, interest rate fluctuations, growth in programs, foreign exchange, and receivable accounts.
The human capital domain is concerned with the organization’s workforce. The economic market and tight labor markets have been concerns in recent times. Risks areas in this domain include employee compensation, selection, staffing, turnover retention, and job-related injuries.
The regulatory domain is concerned with legislation and regulatory concerns of an organization. The risks in this domain are associated with the failure to identify and manage the regulatory requirements within an organization. The risk areas in this domain include management liability, product liability, accreditation, fraud and abuse, and licensure.
The other domain is the technological domain, which encompasses machines, computers, devices, software, and tools. Every organization in the modern world is trying to use the internet and other technologies to enhance the efficiency of their services. Some of the risk areas in the technology domain include asset preservation, electronic health record preservation, social networking, information storage, training, and education.
Finally, the hazard domain is concerned with assets and domains. Risks associated with the hazard domain include facility management, parking, earthquakes, flood, windstorms, construction and renovations, and facility management.
Lastly, the organizational structure of any healthcare organization must ensure the availability of staff to lessen the chances it will encounter any of the mentioned risk domains.
Quantitative Risk Assessment
The quantitative element of the ERM encompasses the economic factors of risk events in healthcare (da Silva Etges et al., 2018). For instance, cost management frameworks offer insight into the economic impacts of risks on the healthcare department. A comprehensive RM plan and risk register are necessary to present quantitative information concerning the mitigation of threats in the UAE’s healthcare system. Knowledge regarding the magnitude and financial implications of risks assists stakeholders in prioritizing threats in the industry and developing corrective measures based on national healthcare goals (Pascarella et al., 2021). The quantitative RM element of ERM conforms to coercive isomorphism because it encompasses pressures and expectations that force hospitals to adopt certain practices.
Coercive forces play a role in economic RM approaches, where influential bodies affect other organizations by bestowing legitimacy on several practices (Krajnovic, 2020). Economic factors significantly impact hospitals’ decisions and models in the UAE’s healthcare sector (Al-Talabani et al., 2019). An RM plan determines healthcare organizations’ approaches to identifying and preventing risks that might prevent them from achieving their objectives. For instance, such a plan could prioritize a specific risk in the industry, thereby influencing the resources and measures that hospitals use to ensure quality care. Likewise, institutions are likely to acquire cost management frameworks that conform to the structures employed by the organization on which they depend.
Organizations can also identify areas where they can invest more time and resources. Quantitative RM employs different techniques and tools to measure and quantify risk, recognizing those that organizations should prioritize.
In conclusion, the institutional theory’s major forces influence the ERM’s elements and their impacts on the UAE’s healthcare industry. Normative pressures encompass the beliefs and experiences of professionals in the industry, which affect strategic governance and communication efficacy. Coercive isomorphism forces organizations to conform to the mechanisms of other organizations, thereby influencing their corporate governance, healthcare organizational structure, and quantitative risk assessment. Mimetic forces encourage companies to copy each other’s structures, a practice that is evident in the education element of ERM (Davidsson et al., 2006).
Adoption of the ERM Framework in Healthcare Organizations
This section discussed the different aspects involved when adopting an ERM framework in different sectors generally and in the healthcare sector specifically.
ERM is becoming increasingly popular among healthcare firms globally. The evolution of healthcare service delivery is partly to blame for the adoption of ERM systems in the healthcare industry. According to da Silva Etges et al. (2018), healthcare facility operations have shifted substantially from a volume-based strategy to a delivery model focused on value generation and sustainability. In their study on the use of RM systems in healthcare transformation, the authors found that ERMs are essential for boosting efficiency and improving quality. This strategy justifies the need for healthcare organizations to align with clinicians, care providers, and other caregivers. Similarly, successful ERM systems concentrate on financial management and efficiency to improve operational efficiency. Effective business RM systems focus on enhancing patient safety and quality of treatment and increasing efficiency.
The role of RM specialists in improving the effectiveness of strategy implementation is also well-documented. According to Rao (2007), RM professionals are positioned to respond to any potential problems when identifying risks and implementing RM. Risk managers’ responsibility is to examine the potential hazards connected with services or practices that are introduced into healthcare organizations. Elamir (2020) evaluates the importance of collaborations, focusing on the specific function of risk managers in avoiding potential risks when delivering services and the common practices of healthcare professionals. In their study on successful RM, the author considers the importance of collaborative efforts in controlling and recognizing hazards in healthcare institutions. The report emphasizes linking RM with strong corporate patient safety strategies.
ERM systems are important for healthcare companies because of their ability to ensure safe service delivery. The entrenchment of RM principles in organizational activities, in turn, demonstrates the importance of competent risk management systems. Furthermore, the study identifies the necessity that risk management approaches are realigned to enterprise strategic planning practices.
Thus, implementing ERM solutions adds value to healthcare businesses through holistic management. Managers can use cross-functional techniques to assess, evaluate, and measure risks. The value of cross-functional approaches in RM, according to Roberta (2017), is that they inform decision-making processes. The author argues that various variables, including changes in corporate governance, have contributed to the shift from traditional and reactive systems to an interactive ERM system. The intrinsic utility of ERM techniques in healthcare institutions partially explains the widespread adoption of the system.
Furthermore, the system can increase the value of healthcare companies by preventing financial losses and enhancing reputational advantages. Investments in comprehensive healthcare services might also benefit from implementing ERM systems. ERM, in turn, helps both the community and the healthcare company. TRM models, which focus on avoidance, control, and risk transfer, are mainly unproductive. According to Kaya et al. (2018), TRM systems prioritize risk transfer through insurance and other mechanisms. TRM approaches are based on risk control activities to ensure the security of businesses by insuring services and assets.
Instead, ERM views hazards as assets by concentrating on the prospect for both gains and losses in risk-producing activities. Furthermore, ERM systems are more efficient because of their ability to predict and avoid hazards in the quest for organizational profitability. ERM thus provides substantial advantages over traditional risk management methodologies. ERM encompasses more than simply identifying and managing clinical risks in healthcare companies. According to Zakaria et al. (2019), ERM systems provide a framework for identifying, measuring, and responding to possible hazards. As a result, ERM is vital to the functioning of healthcare companies since it considers more than just financial losses. Instead, ERM makes it easier to address possible hazards by improving the value of all key participants, such as suppliers, healthcare professionals, and patients.
Similarly, ERM improves managerial effectiveness, resulting in increased organizational stability. In this regard, ERM systems are not restricted to reducing traditional risks such as loss of property or financial resources. Instead, modern ERM solutions are crucial for firms to maintain excellent reputations while also generating confidence in their boards of directors. ERM systems, in other words, are more effective at improving the overall image of healthcare companies.
Furthermore, an ERM system can effectively detect operational and financial risks linked to a company’s core business. ERM systems have been linked to enhanced effectiveness in identifying strategic risks in healthcare businesses. According to Almansoori and Ali (2020), ERM systems can reduce healthcare organizations’ exposure to the negative consequences of strategic decisions such as mergers and acquisitions. Furthermore, ERM can provide a defined framework for minimizing reputational risks by maintaining patient and community performance goals.
To secure the organization and align its planned objectives, the framework recognizes the involvement of multiple stakeholders, such as employees, in risk detection through collaboration and corporate governance. Furthermore, the framework addresses gaps in traditional ERM frameworks, which were constructed to address specific risks and risk scenarios.
Conclusion
This chapter provided a critical analysis of the diverse methods used in previous ERM studies to identify the factors that affect the implementation of ERM and evaluate the level of implementation. The literature review suggests that it is difficult to evaluate the extent of ERM implementation due to the lack of trustworthy and universally acknowledged metrics of what ERM is and what mature ERM looks like. As a result, the results of ERM studies are inconclusive due to discrepancies among researchers regarding how to identify ERM implementation and evaluate the extent of ERM implementation (Lundqvist, 2015). To overcome these inconsistencies in measuring ERM implementation and understand how to apply ERM within an organization, a new stream of ERM research studies is emerging. Researchers adopting this methodology aim to collect adequate information to evaluate and quantify the extent of ERM adoption (Lundqvist, 2015).
As each study seeks to examine different types of ERM dimensions, the process of designing and constructing a complete measure to assess the extent of ERM implementation is still ongoing.
RESEARCH METHODS
Overview
This chapter begins by reviewing the study’s structure and its corresponding elements and discussing the rationale for the methods selected to ensure the rigor of the scientific study (Stockhausen & Conrick, 2015). Then, it discusses the study design that is used to address the research topics outlines in Chapter 1. Lastly, it finishes with a discussion of the ethical considerations made during research.
Research Model
A research model is a conceptual frame of reference that includes views, attitudes, concepts, and hypotheses that assist scientists in structuring and combining theoretical inferences with actual data (Antwi & Hamza, 2015; Babbie, 2010; Corbetta, 2003). Frameworks are crucial because they offer academics the basic guidelines and standards to navigate the relevant problems, methodologies, and procedures, helping them understand the complications that exist in the physical realm (Corbetta, 2003). Researchers have discussed various models that help explain social behaviors (Babbie, 2010; Blaikie, 2007). The two predominant paradigms that have been adopted by the majority of scholars are positivism and interpretivism (Blaikie, 2007; Corbetta, 2003; Johnson & Onwuegbuzie, 2004). The positivist paradigm tackles social investigation in a way comparable to the physical sciences. To avoid bias, researchers should distance themselves from the entities they are investigating; the outputs of social inquiry are generally applicable social laws, and the contributing factors of social phenomena may be precisely identified using a deductive approach (Johnson & Onwuegbuzie, 2004). Alternatively, the interpretivist paradigm asserts that social reality is not objective but constructed by individuals. The researcher must be emotionally connected to the research subjects to acquire in-depth knowledge of people’s perceptions of reality. An inductive approach is used to comprehend and interpret sociological reality, and the findings of social inquiry are time- and context-specific constructed realities (Johnson & Onwuegbuzie, 2004).
This research employs a positivist perspective since it believes that an objective and generalizable reality exists. It uses a deductive method to develop and confirm some assumptions regarding social reality by evaluating the gathered information. The generalizability of the study’s insights can be examined by comparing them with those from other research conducted in different countries.
Research Ontology
According to the literature, ontology concerns the basis and structure of social reality (Antwi & Hamza, 2015; Blaikie, 2007; Corbetta, 2003). There are two opposing perspectives regarding social reality: idealist and realist (Blaikie, 2007). The idealist view is that reality is a subjective fabrication made up of people’s perceptions and assumptions, so reality does not exist independently (Blaikie, 2007; Corbetta, 2003). The realist perspective, in contrast, holds that reality is objective and exists independently without humans needing to perceive it or make assumptions about it (Blaikie, 2007; Corbetta, 2003). It is essential to specify the research ontology when formulating research questions and selecting a research strategy to explain the research questions.
The study is based on a realist ontology as it deems reality as objective and independent of human perceptions. Realism is widely used in social science research. While realist ontology is predominately used in quantitative research, a study by Healy and Perry (2000) showed that this ontology is also reliable and valid for qualitative research.
Research Epistemology
Epistemology is the study of how individuals gain information about the environment around them and evaluate the veracity and acceptability of this knowledge (Antwi & Hamza, 2015; Blaikie, 2007). The two prominent epistemological perspectives in social research are constructionism and empiricism (Blaikie, 2007). The distinction between these perspectives lies in the researcher’s interaction with the social actors or social realities being studied and whether the researcher studies the social actors or phenomena without being influenced by them (Antwi & Hamza, 2015; Blaikie, 2007; Corbetta, 2003).
In the constructionist epistemology, the researcher needs to be intimately connected to the subjects to acquire in-depth knowledge of people’s perception of reality and their suppositions regarding their interactions in society, and investigators actively participate in establishing social reality using these individual evaluations (Antwi & Hamza, 2015; Blaikie, 2007; Corbetta, 2003). Empiricism encourages researchers to be objective, use deductive reasoning, and collect empirical information to establish principles of causation that can explain broad trends and individual behaviors (Antwi & Hamza, 2015; Blaikie, 2007; Corbetta, 2003).
This study employs empiricist epistemology; therefore, it examines social objective reality by gathering and interpreting statistical findings in a dispassionate and universal fashion without affecting or being affected by them.
Research Methodology
Research methodology refers to the approach that the researcher uses to address their research questions (Corbetta, 2003). These methods essentially translate the ontological and epistemological positions of the researchers into the basic standards, practices, and processes for conducting social research (Hanson et al., 2005; Marczyk et al., 2005). As stated in the literature, research techniques are significant because they motivate researchers to organize the study in advance and analyze the significance of their research choices before executing the study. Furthermore, these techniques permit people to judge the quality of the study and its findings (Antwi & Hamza, 2015; Corbetta, 2003; Saunders et al., 2009). When deciding which research methodology to adopt, a researcher must consider several issues, including why they are conducting the research, how to define the research topic, what sort of information to collect, the optimal data collection method, and what techniques to use to analyze the data (Antwi & Hamza, 2015; Saunders et al., 2009).
Qualitative and quantitative methods are the two predominant approaches in social research (Antwi & Hamza, 2015; Marczyk et al., 2005). Scholars who subscribe to an interpretive paradigm often employ qualitative methods, which involve the use of personal interviews, observations, and research reports (extensive examinations of social phenomena or social actors) in lieu of formal assessment (Antwi & Hamza, 2015; Marczyk et al., 2005). Instead, quantitative techniques involve survey questionnaires and experiments and aim to gather precise information and systematically apply statistics to assess their outcomes (Antwi & Hamza, 2015; Marczyk et al., 2005).
This study answers the research questions using a mixed-methods approach. A survey was administered to a subset of personnel to collect data. Surveys have been effectively used in the social sciences to answer research questions in a way similar to collecting numerical representations for rigorous statistical analysis (Myers, 2013; Saunders et al., 2009). Surveys are considered adequate for this positivistic inquiry since they can be used to objectively understand the world using independent measures. Additionally, the study aims to assess the attitudes, perspectives, views, and mindsets of hundreds of workers, which is impossible using other methodologies (Babbie, 2010).
Research Design
To collect research data, the study uses a systematic questionnaire that operationalizes many dimensions in a survey that analyzes individuals’ attitudes, beliefs, conceptions, and behaviors. During the survey period, the study gathered the opinions of personnel of diverse backgrounds (gender, education, and job functions) working in various healthcare divisions. The unit of analysis is the individual employee, and appropriate statistical techniques were used to objectively evaluate the employees’ perspectives and opinions regarding numerous model constructs.
Martin (2005) states that the use of a questionnaire should consider the following: (1) the choice of measurement scales for various constructs; (2) the formatting of the questionnaire; (3) the introduction and explanation of the questionnaire to potential respondents; (4) the pre-testing of the questionnaire; (5) the method of distribution; and (6) the collection of data and the maintenance of the database. While considering these factors, this research adhered to the criteria for constructing questionnaires for survey research suggested by Burgess (2001).
Selection of Measurement Scales
The initial stage in questionnaire design is selecting the appropriate measurement scale for each construct. It takes considerable time to create and validate a new measurement scale (Corbetta, 2003; Swanson & Holton, 2005).
A comprehensive review of the relevant literature led to the selection of measurement scales with numerous indicators for measuring information on corporate strategy, its antecedents, and its effect on ERM. It is important to use multiple indicators to measure the various latent constructs, such as management support, communication efficacy, and organization structure, because it improves understanding of the various features of each construct, enhances the precision of the measures, and eliminates the issues related to single-indicator scales (Bryman, 2015). Appendix 2 contains a comprehensive list of the constructs, their measurement scales, and the sources used.
The questionnaire was organized into five major sections, ordered as follows: (1) general information about the respondent; (2) dichotomous questions; (3) Likert-scale statements on the different variables; (4) employee level in an organization; and (5) Likert-scale statements on the higher management support. The layout of the various sections was intended to encourage people to complete the relatively lengthy questionnaire, which contained 39 statements in total (Burgess, 2001). Respondents were generally aware of and willing to give some thought and consideration to the numerous statements on the factors affecting the implementation of ERM before filling out the questionnaire.
Most of the questions in the questionnaire were formatted as a table consisting of two columns. The left column contained the scale measurement items for each latent variable, while the right column contained five checkboxes for participant responses. The five boxes correspond to a 5-level Likert scale ranging from “Strongly Agree” to “Strongly Disagree,” with “Neither Agree or Disagree” in the middle box. Appendix 3 contains a complete copy of the survey questionnaire, and Appendix 4 contains the approval from the Research Ethics Committee.
The questionnaire was designed using Microsoft Forms, which not only made it easier to participants to fill out the survey but is also a time-efficient method. Microsoft Forms ensures that respondents can check the appropriate boxes with a few mouse clicks. Respondents were not be required to save the file and return it as an attachment to an email, as the software was used to save the responses on the back-end, which only the researcher has complete access to.
Operationalization of the Measurement Scales
The study identified 33 items from the systematic literature review. After conducting the literature review and through discussions, the study identified a total of 6 factors for the ERM model: strategic governance (SG), corporate governance (CG), communication efficacy (CE), education (Ed), health organization structure (HOS), and quantitative risk assessment (QRA; see Table 3.1). The researcher then determined the measurement items for each factor of the ERM model, such as strategic governance (7 items), corporate governance (10 items), communication efficacy (2 items), education (6 items), health organization structure (3 items), and quantitative risk assessment (5 items). Finally, the researcher carried out the necessary tests to validate the model.
Table . Summary of the Operationalization of the Measurement Scales
Introducing the Questionnaire to the Participants
To encourage participation in the survey, a cover letter that included a brief introduction of the researcher and a description of the research topic, its objectives, and its importance was drafted and distributed alongside the questionnaire. The message highlighted that participation in the research is entirely voluntary. The respondents could opt out whenever they wanted to with no penalty. The statement also emphasized that there is no correct or incorrect response to any of the questions and that the responses will be confidential.
Pre-testing the Questionnaire
Before sending the questionnaire to the entire sample, a number of employees with experience in quality and risk management in healthcare conducted a pre-test. Their comments were that, with the exception of a few phrases that required rephrasing to ensure clarity, the survey instrument was clear and easy to grasp, and the measuring scales properly assessed the constructs that they intended to test. They also suggested changing the formatting of the document from a Word document to an online survey that could be shared via a link. The questionnaire was in English since it is considered the official language in almost all healthcare facilities.
Mode of Distribution
The survey questionnaire was sent to the sample group using LinkedIn. There are numerous reasons for using this distribution method. The target population works in multiple healthcare organizations located in different emirates (e.g., Abu Dhabi, Dubai, Sharjah, Ajman, and other emirates). It is incredibly challenging to identify those working in healthcare organizations and specifically within the quality and risk management departments. However, LinkedIn offers a filter to identify the target population and distribute and collect surveys. Second, the researchers used LinkedIn messaging to emphasize the significance of each employee’s contribution to the study, which helped the study obtain a high response rate. Furthermore, the questionnaire was sent digitally, which conserved a substantial amount of paper and made it simpler for each respondent to quickly complete the survey on their computers. Having the material in digital format made it easy for the researcher to archive the data and ensured that paper copies were not lost or damaged. Lastly, this manner of distribution enabled the researcher to send frequent reminders to employees to urge them to complete the survey. Most of the individuals identified participated in the study and were also able to share the link with their colleagues, demonstrating that this manner of dissemination resulted in a greater response rate.
Data Collection
This section discusses the study environment, sample size selection, and data collection and analysis.
Research Context
According to the statistics published by MOHAP (2020), there are 45 hospitals in the government sector and 98 hospitals in the private sector, for a total of 143 in both sectors in the UAE.
The research involves employees working in the quality and risk management departments. The study specifically examines these departments to ensure that participants are directly involved in risk management and its various initiatives within their organization. It was easy to access research participants as they were targeted via LinkedIn. However, it was difficult to track which participants who received the survey completed it.
Research Sample
A correct sample size is crucial in ensuring the quality of the statistical analysis, particularly when researchers wish to determine if a certain correlation or a hypothesis testing is statistically significant. A larger sample size might increase the statistical power of these tests (Cohen, 1992; VanVoorhis & Morgan, 2007). Researchers have discussed the optimal sample size to employ based on the expertise of specialists (Pearson & Mundfrom, 2010). For instance, Cohen (1992) presented tables illustrating the sample sizes necessary to identify significant effect sizes with 80% statistical power. In contrast, Tabachnick and Fidell (2013) recommend following a simple rule of thumb: N > 50 + 8m (where N is sample size and m is the number of independent variables). Other authors support a minimum ratio of sample size to number of variables, such as Gorsuch (1983), who suggests adopting a ratio of at least five, and Nunnally (1978), who suggests having samples that are at least ten times the number of variables (MacCallum et al., 1999). Ticehurst (2009) emphasized the need to identify an absolute sample size that is independent of the research population, hence emphasizing the need to identify a method for selecting sample size, such as a statistical power test. Moreover, Cohen (2007) argued that the sample size must be determined by using an appropriate statistical test of power. In determining the sample size for the study, the test power is an important factor. The power of a statistical test is the likelihood that a given sample size and alpha level can be used to reject the null hypothesis or produce a specified effect size (Cohen, 2008). The test can detect a difference in the general population if one exists. Additionally, according to Sri Ramalu (2010), even if the sample size for a given study has been decided using other approaches, it is still relevant and worthwhile to employ power analysis so that the chance of detecting the effects of various sample sizes is explicitly known. The Yaro Yamane Statistical Formula for Obtaining the Sample Size of a Finite Population is an additional way to determine the sample size of a study. The equation is n = N / [1 + N (e) 2].
Where
n = the sample size
N = the finite population
e = the level of significance or limit of tolerable error
1 = unit or a constant
Consideration was given to the aforementioned sample size estimations when selecting the 500 employees who were invited to participate in the survey. The sample consisted of personnel from quality and risk management departments and other units that support risk management operations.
Data Collection
The survey questionnaire was generated as a Microsoft Form and sent as a link to respondents. A maximum of two reminders were sent via LinkedIn to recipients who did not respond to previous communications. During the data collection period, a total of 304 questionnaires were gathered from participants, which meant a response rate of approximately 60%. The smallest response time recorded was one day, while the longest response time was 59 days.
All responses received were extracted from Microsoft Forms and coded in an Excel spreadsheet to measure progress. This enabled the researcher to run simple quality checks on the responses prior to uploading the data to the SPSS software for further analysis.
Data Analysis
Comprehensive data analysis using both descriptive and inferential statistical studies was conducted to understand the current state of ERM implementation in healthcare organizations, the factors that lead to different implementation levels, and which of these factors determine the implementation of ERM in healthcare organizations in the UAE. The descriptive analysis provides several respondent characteristics, including their gender distribution, highest level of education, length of employment, employment role, and emigration. It also provides numerous survey response parameters, including mean, minimum values, maximum values, standard deviation, skewness, and kurtosis indexes. Data was filtered to ensure its precision, completeness, and quality to use it further in the statistical analysis. All of the analyses were conducted using SPSS.
Due to the model’s complexity and the number of latent and measurable variables, variance-based structure equation modeling (SEM-VB) was employed to investigate the relationships between the numerous model elements. The analysis begins by ensuring the measuring model’s construct validity and reliability. It then evaluates the structural model’s capacity to predict the relationships between constructs.
There are multiple reasons for using this method for data analysis. Variance-based structural equation modeling (SEM-VB) is widely utilized, as indicated by the abundant studies of leading researchers published that use this method (Hair et al., 2014; Hair et al., 2011; Ringle et al., 2014).
Path Model
A path model illustrates the causal connections between various variables. In path analysis, the exogenous variables affect the outcome variables on the right, which are predicted by the variables on the left. The following formula is used to understand a route coefficient: If X varies by one standard error, then Y varies by a standard deviation. The study used the bootstrapping technique to test the proposed research hypotheses (Hair et al., 2011; Ringle et al., 2014). Bootstrapping is a statistical approach that enables researchers to test the statistical significance of a variety of PLS-SEM outcomes, including path coefficients and R2 values. Partial least squares path modeling is a technique for SEM that allows researchers to estimate complex cause-and-effect correlations in path models that contain latent variables. SEM refers to a collection of statistical methods used to measure and analyze the correlations between observable and latent variables (Hair et al., 2014). The following chapter presents the research findings.
Conclusion
This chapter summarized the research paradigm, its related aspects, and the rationale for the research decisions (Coughlan et al., 2007; Stockhausen & Conrick, 2015). As the social inquiry conducted in this study is comparable to physical science, the study used a positivist research paradigm. It regards social reality as universal and generalizable, and believes that it can be derived by a deductive process in which specific hypotheses about social reality are presented and confirmed by studying relevant data. During the collection of the empirical data, the researcher distanced herself from social actors to avoid influencing the results.
The study employed a quantitative methodology by using a structured questionnaire that operationalizes several constructs in the form of statements that quantify and analyze the attitudes, views, hypotheses, and behaviors of the participants. The processes required to create the questionnaire for the survey were outlined. These involve choosing measurement scales from the available literature (Straub, 1989), designing the survey instrument, and pre-testing it to guarantee that it assessed the key aspects of the process.
The chapter also discussed the research’s organization, sample size, and data collection methods to ensure that the response rate was significant. It also presented the data analysis method, which uses variance-based SEM to accommodate the experimental orientation of the study and the intricacy of the paradigm investigated.
The next chapter describes the statistical data analysis results in depth.
RESULTS AND DISCUSSION
Overview
This chapter presents the outcomes of the study, which aims to investigate the relationship between various constructs. The study used SMART PLS-SEM software to assess the measurement factors’ validity, reliability, and adequacy. The software also used a bootstrapping technique to test the path coefficient analysis. In addition, it employed an algorithmic technique to evaluate the validity and reliability of the measurement constructs. To further strengthen the model, EFA was conducted. A regression analysis was also employed to determine the independent variables’ impact on the dependent variables. This chapter presents a thorough analysis of the findings, compares them to the existing literature, and discusses their implications for theory and practice. Furthermore, it addresses any limitations of the study, identifies areas for future research, and concludes on the research process.
Demographic Information
This section discusses the descriptive statistics of the demographic characteristics of participants and inferential statistics of the data. The descriptive analysis presents several characteristics of the respondents, including gender distribution, their highest level of education, length of employment, employment role, and emigration status.
First, the study had 156 (51.3%) male respondents from the healthcare sector and 148 (48.7%) female respondents.
Figure . Respondent’s Gender
This figure demonstrates the comparison of respondents based on their gender.
Furthermore, 65 (21.4%) of respondents had an undergraduate education while 239 (78.6%) had postgraduate degrees. Thus, most of the respondents had postgraduate degrees.
Figure . Respondent’s Level of Education
This figure demonstrates the comparison of respondents based on their level of education.
In addition, 71 (23.4%) of the respondents were linked to both clinical and administrative roles, 31 (10.2%) of the respondents were linked only to clinical roles, and 202 (66.4%) of the respondents were linked only to administrative roles. Thus, most of the respondents were involved in administrative roles.
Figure . Respondent’s Employment Role
This figure demonstrates the comparison of respondents based on their employment role.
In terms of provenance, 116 (38.2%) of respondents were from Abu Dhabi (AD), 154 (50.7%) were from Dubai, 13 (4.3%) were from Sharjah, 7 (2.3%) were from Ajman, and 14 (4.6%) of were from the other emirates. Thus, most of the respondents were from Dubai.
Figure . Respondent’s Emirate of Employment
This figure demonstrates the comparison of respondents based on their emirate for employment.
In terms of experience, 43 (14.1%) of the respondents had less than 1 year of experience, 97 (31.9%) of the respondents had 1–5 years of experience, 70 (23%) of the respondents had 5–10 years of experience, 43 (14.1%) of the respondents had 10–15 years of experience, and 51 (16.8%) of the respondents had more than 15 years of experience.
Figure . Respondent’s Years of Experience
This figure demonstrates the comparison of respondents based on years of experience.
Furthermore, 227 (74.7%) respondents were working in hospitals accredited by the Joint Commission International, 13 (4.3%) were working in hospitals accredited by Accreditation Canada International, only 7 (2.3%) were working in hospitals accredited by the Australian Healthcare Accreditation, 40 (13.2%) were working in hospitals accredited by other accreditation bodies, and 17 (5.6%) were working in hospitals that had not received any international accreditation.
Figure . Respondent’s Healthcare Organization Accreditation Type
This figure demonstrates the comparison of respondents based on their healthcare organization accreditation type.
Descriptive Statistics
The analysis calculated the mean values for the categorical and continuous variables. Data was filtered for further usage in statistical analysis to ensure its precision, completeness, and quality. Furthermore, a measure of the amount of variation is the standard deviation. If the standard deviation is very high or low, then the data points are distributed either very far away from the mean or very close to it, respectively. If the standard deviation is higher than the mean, this may indicate that the data do not follow a normal distribution.
First, the study focuses on the mean score of strategic governance (SG). The study found that commitment and support have the highest mean score (4.04), which means that respondents perceive commitment and support as being the most prioritized elements in the UAE healthcare sector. Top management (TM) seeking middle management’s (MM) opinions has the second-highest mean score (3.81), so respondents perceive it as the second priority of the UAE healthcare sector. Second, within the corporate governance (CG) dimension, the risk committee has the highest score (4.18), while feeling part of the team has the second-highest mean score (4.06). This means that the risk committee and feeling part of the team are the primary factors that respondents consider necessary for the implementation of ERM in the UAE healthcare sector. Third, within the communication efficacy (CE) domain, the emails, circulars, and communication for risk mitigation strategies of communication efficacy have strong mean scores (3.93). This means both factors are essential for good and practical communication skills. Fourth, within the education domain, staff training has the highest mean score (3.90), and employee competency has the second-highest mean score (3.79). This means the UAE healthcare sector should pay more attention to training employees to ensure they have excellent competencies. Fifth, within the healthcare organizational structure (HOS) dimension, support of the employees under the health organization structure has the highest mean score (3.91), and employment role has the second-highest mean score (3.41). Thus, organizational structure must be established to enhance employee support within the organization. Sixth, the RM policy, within the quantitative risk assessment (QRA) domain, has the highest mean score (4.37), and the board recognizes risk (4.15) has the second-highest mean score. The results show that the RM policy is essential and is part of the preferred strategic risk management plan to ensure ERM implementation within the UAE healthcare sector.
Table . Descriptive Statistics
Correlation Analysis
Correlation is used to investigate the direction and strength of the linear link between variables. The value of the correlation coefficient can be anywhere from +1 to -1 (Aleisa, 2017). The coefficient’s value measures how strongly two variables are related to one another; the higher the value, the more significant the link. The fact that the correlation matrix containing all variables produced many matrix correlations between the study variables demonstrates that EFA was an appropriate method for this investigation. Before using path model assessment, the data will be evaluated to detect evidence of very severe correlations across the risk management assessment variables in the healthcare sector and minimize duplicate variables. This is done to check the relationship among the factors of the ERM model in the UAE healthcare industry.
The following sections discuss the correlation test results of the factors relevant to the six components.
Strategic Governance (SG)
Table . Correlation matrix for the factors relevant to strategic governance
Table 4.2, it is evident that there is a statistically significant, weak positive correlation between ERM and whether an organization is involved in distributed risk-taking (r=0.272, P< .05). The result indicates that healthcare organizations with participants who strongly agreed that they are involved in distributed risk-taking were more likely to implement ERM. Secondly, the results show that there is a significant weak positive correlation between the likelihood of implementing ERM and the second factor under the strategic governance component, commitment & support from top management (TM) (r=.311, P< .05). Thus, we can conclude that healthcare organizations with a management team that is committed and supports those they lead, is more likely to implement ERM. Thirdly, the results in Table 4.2 show that there is a significant weak positive correlation between the likelihood of implementing ERM and the second factor under the strategic governance component, TM seeks middle management (MM) opinions (r=.296, P< .05). The results show that healthcare organizations with a top management team that actively seeks middle managers’ opinions and ideas on strategic issues are more likely to implement ERM.
The results in Table 4.2 also show that there is a weak positive correlation between ERM and the remaining four factors under the strategic governance component (p < .05). The four factors are the following: TM is open to new ideas; TM appreciates MM; TM considers MM’s interests; and the RM committee measures TM’s commitment. Therefore, we can conclude that participants from healthcare organizations that have adopted ERM strongly agree that their organizations’ top management teams are open to new ideas and initiatives from ERM team members, appreciate new ideas from middle managers, and account for the interests of middle managers when making strategic decisions. Furthermore, healthcare organizations with a risk management team that measures the commitment of top management are more likely to implement ERM. Lastly, the results in Table 4.2 show that there is a significant positive moderate-to-strong inter-correlation between the factors under the strategic governance component and the implementation of ERM.
Corporate Governance (CG)
Table . Correlation matrix for factors relevant to Corporate Governance
The results in Table 4.3 show that there is a statistically significant but weak positive correlation between ERM and the 10 factors under the corporate governance component (P < .05). These 10 factors are CG1 (assigned CRO), CG2 (management-level risk committee), CG3 (audit committee; AC), CG4 (number of members in AC), CG5 (whether the AC members are internal or external members), CG6 (part of a team), CG7 (a clear set of values), CG8 (solutions achieved during disagreements), CG9 (shared common perspective), and CG10 (management is responsive and changes easily). The results show a significant relationship between the factors under the corporate governance components and the implementation of ERM in healthcare organizations. The results confirm the literature discussed in Chapter 2 on the impacts that the presence of a management-level risk committee, an audit committee comprising more than ten members, and a management team that inspires a team spirit have on the implementation of ERM in organizations.
The results also show that healthcare organizations that have clear and consistent values, offer effective solutions whenever there are disagreements, promote a common perspective, and respond to changes effectively are all more likely to successfully implemented ERM. However, the results show a weak negative correlation between ERM and whether a healthcare organization had internal or external members on its audit committee. The relationship between the two variables was not statistically significant (P = 0.061). Therefore, the variable CG5 (internal/external members of AC) is not a significant determinant of whether a healthcare organization will adopt ERM.
Another notable result shown in Table 4.3 is that there is a significant positive inter-correlation between the ten factors that are positively correlated with ERM. The inter-correlation matrix indicates that organizations that have a management-level risk committee also have an audit committee comprising more than ten members and a clear set of values that guide their everyday operations.
Communication Efficacy (CE)
Table 4.4, below, shows a statistically significant but weak positive correlation between ERM and the factors CE1 (emails/circulars) and CE2 (effective communication on risk mitigation strategies; r = .274, P < .05 for both). The results show that the modes of communication in the organizations are most often emails and circulars. They also agreed that communication is essential in helping clarify how to improve the organization and in identifying the impact of using different risk mitigation strategies. However, the results do not show that such factors are related to the implementation of ERM within the healthcare organization.
Table . Correlation matrix for factors under Communication Efficacy.
The results also show that there is a significant moderate positive correlation between CE1 (emails/circulars) and CE2 (effective communication on risk mitigation strategies; r= .696, P< .05). In other words, respondents from healthcare organizations who strongly agreed that the mode of communication in their organization is emailing/ circulars also agreed that their organizations have effective communication on risk management strategies.
Education (ED)
The results in Table 4.5 show that there is a significant but weak positive correlation between ERM and the six factors under the education component (P < .05). The six factors are ED1 (staff training), ED2 (knowledge tests for ERM practices), ED3 (risk translation team), ED4 (retention of staff with risk-related skills), ED5 (assistance from RM team), and ED6 (employees competence). The results show that respondents from healthcare organizations that effectively implemented ERM agreed that their organizations have effective staff training programs, conduct employee knowledge tests for ERM practices, and have risk translation teams that ensure a better understanding of the ERM approach.
Table . Correlation matrix for factors under Education.
The results also show that such organizations have risk management teams with risk-related skills, and they allow their staff members from different departments to seek assistance from the risk management team in case of unclear information with regard to ERM. Additionally, the results indicate that healthcare organizations implementing ERM have competent employees with the skills and knowledge required to encounter potential risks.
There is also a significant moderate-to-strong positive inter-correlation between the six factors under the education component. The inter-correlation matrix shows that organizations that had implemented one of the factors had also typically implemented the remaining factors. For instance, a respondent who strongly agreed that their organization has an effective staff training program also agreed that the organization conducts knowledge tests on ERM practices. Additionally, the inter-correlation matrix shows that healthcare organizations with a risk translation team tasked with ensuring employees have a better understanding of the ERM approach had a risk management team that was highly skilled in dealing with the risks.
Lastly, the results show that healthcare organizations that allow staff members from different departments to seek assistance from the risk management team in case of unclear information regarding ERM had competent employees who would help the organization deal with potential risks. Thus, the results in Table 4.5 emphasize that healthcare organizations that want to effectively implement ERM must focus on the factors under the education component.
Healthcare Organization Structure (HOS)
The results in Table 4.6 indicate that of the three factors under the health organizational structure component, only one factor, HOS2 organizational structure to support employees, is statistically significantly correlated with ERM (P < .05). The results indicate that there is a weak but positive correlation between ERM and HOS2 (org. structure to support employees). Therefore, respondents from organizations that had effectively implemented ERM agreed that the organizational structure, in the form of the steering committee, provides the concept, guideline, direction, and support to the employees.
The correlations between ERM and the remaining two factors, HOS1 (employment role) and HOS3 (employment Level), are not statistically significant. Specifically, the results show that there is a non-significant weak negative correlation between ERM and participants’ employment role (r = -.018 P = 0.725). This lack of correlation may also be explained by the type of questions asked to assess these factors. As opposed to the question for HOS2, which used a Likert scale, the questions for HOS1 and HOS3 did not use a Likert scale. Furthermore, there is a non-significant weak positive correlation between ERM and participants’ employment level (r = .016 P = 0.751). Therefore, we can conclude that the two factors are not significant determinants of whether an organization will implement ERM.
Table . Correlation matrix for factors under health organization structure.
Quantitative Risk Assessment (QRA)
The five factors measuring the quantitative risks assessment component include QRA1 (RM policy), QRA2 (risk recognized by the board), QRA3 (risk-based decisions are taken at all levels), QRA4 (risk assessments), and QRA5 (risk identification). The results in Table 4.7 show there is a significant weak positive correlation between ERM and the five factors (P < 0.05). They indicate that healthcare organizations that have effectively implemented ERM have a risk management policy, a board that recognizes that the organization is vulnerable to risks, and a top management team that can effectively implement the risk management process. The results also show that such organizations encourage risk-based decision-making at all levels of their organization and conduct risk assessments at the functional and divisional levels to identify valuable information on how to mitigate risks.
The results also show that there is a positive moderate-to-strong inter-correlation between the five factors. Thus, if the organization had adopted one of the factors under the quantitative risk assessment component, it had typically also adopted the remaining four factors. For instance, there is a significant strong positive correlation between QRA4 (risk assessments) and QRA5 (risk identification) (r = .656, P = 0.000) (Freedman et al., 2018). This correlation indicates that respondents who agreed that their organization conducts risk assessment at the divisional and functional levels also concurred with the assertion that their organization identifies risks through brainstorming and focus groups.
Table . Correlation matrix for factors relevant to the quantitative risk assessment.
The correlation test results offer several insights into the relationship between ERM, and the factors related to the six components. In general, the results showed that ERM is statistically significantly correlated with most of the factors used to assess the six components. Only three factors were not significantly correlated with ERM. Therefore, healthcare organizations that want to effectively implement ERM should focus on all six identified components.
Correlation Analysis for the Aggregate Scores
Factor analysis was conducted to determine the correlation of all the variables across the six dimensions. According to Tabachnick and Fidell (2013), a strong indication that factor analysis is an effective statistical tool for a study is a high correlation between research variables. The correlation matrix of all 36 variables revealed a large number of strong correlations between the research variables, validating the use of component analysis in this investigation. However, Tabachnick and Fidell (2013) also state that component analysis is hampered by a high correlation between variables (correlation > 0.90). Thus, before applying EFA, the data were evaluated to identify extremely high correlations amongst ERM aspects to minimize duplicate variables. However, as the results in Table 4.8 show, the correlation between the variables did not exceed 0.9.
The correlation matrix shown above shows the pairwise correlation coefficients that exist between the variables of ERM, SG, CG, CE, ED, HOS, and QRA. The significance level of the correlation coefficients is denoted by the use of asterisks, with a significance level of ‘***’ indicating a high level of statistical significance (p-value less than 0.001). First, there is a significant positive connection between ERM and all variables, with the exception of the HOS. Thus, these elements are closely related to ERM and could potentially have a favorable impact on its adoption. Education has the highest correlation with ERM (0.475), which suggests that it may significantly influence the application of ERM.
The second finding is that HOS does not have a significant correlation with any of the other variables, as seen by the fact that their respective p-values are greater than 0.05 (p > 0.05). This indicates that HOS may not significantly influence ERM or the other factors, which is consistent with the findings of the prior regression study. Third, there is a highly significant strong and positive association between SG, CG, CE, and ED. This suggests that there is a strong mutual interaction between these variables, which in turn suggests that they may collectively influence the ERM implementation. Lastly, QRA, in its final form, has a substantial positive connection with all variables, with the exception of HOS. This indicates that QRA may be a crucial component that favorably impacts other areas besides ERM, with the exception of HOS.
This correlation analysis offers very helpful insights into the links that exist between the variables that were examined. The majority of the factors have a substantial link with ERM, which indicates that they may have an effect on the implementation of ERM. However, it seems that the role of HOS is relatively unimportant. In addition, there is a considerable interaction between SG, CG, CE, ED, and QRA, which suggests that there is a complicated system of correlations between these variables. These findings must be considered when formulating strategies for the efficient implementation of ERM. Further investigation could examine the interrelationships between these parameters and how they jointly influence ERM in healthcare organizations.
Table . Correlation Matrix for all Factors.
Structural Equation Modelling (SEM) using SmartPLS
In the past three decades, SEM has become an important and commonly employed analysis technique in the social and behavioral sciences (Bollen, 2002; MacCallum & Austin, 2000). SEM (Bentler, 1980) is a general multivariate technique that shows the means, variances, and covariances of a set of variables in terms of a reduced number of model parameters.
Normality Distribution
The normality distribution of the measurement scales is based on two parameters: skewness and kurtosis. It is important to check normality because this technique assumes that the data is normally distributed. The normality distribution is checked by examining the skewness and kurtosis values. If the values are close to zero, the distribution can be considered normal. The distribution of responses for a measure is skewed when it leans more toward either the right or the left tails of the distribution. The kurtosis value indicates whether the distributed sample is overly peaked, which refers to a highly narrow distribution with the majority of the responses in the center (Hairr et al., 2014, p. 61). The distribution of answers is normally distributed if both skewness and kurtosis are equal to zero, which is a circumstance that is extremely unlikely to ever happen. If the number is more than +1 or lower than –1, this indicates a strongly skewed distribution (Hair et al., 2011). The results of this study show that the data have a normal distribution.
Table . Normality distribution
Assessment of Measurement Model
Convergent Validity
Factor Loadings
A measure possesses convergent validity when items in the measure converge to reflect the underlying factors of the ERM model. The average variance extracted (AVE) is determined by taking the average of the squared loadings linked with each indication part of a construct. One sub-type of the construct is referred to as convergent validity. The term “construct validity” refers to the degree to which a test is developed to measure specific constructs (Hair et al., 2014). Convergent validity demonstrates a connection between two measurements intended to assess the same underlying construct. Convergent validity is defined by the degree to which one measure relates to other measurements of the same event (Hair et al., 2014). If more than 75% of hypotheses are true or if the correlation with another focused specifically on the same construct is greater than 50%, there is satisfactory convergent validity. The calculated associations between variables in the measurement model are called outer loadings. They establish an item’s absolute contribution to the construct to which it is assigned. The relationship between the two variables studied and the factor is the essence of factor loading. The amount of variance explained by a factor on a given factor can be seen through factor loading. A general rule of thumb when using the SEM methodology is to use a factor loading of (0.7) or above to indicate that the factor removes adequate variance from the variable in question (Hair et al., 2021). Nevertheless, if the loading is less than (0.7), one should remove an item from the list. The study runs a series of algorithmic techniques and finds that 1 item of strategic governance (SG1=0.406), 5 items of corporate governance (CG1=0.596, CG2=0.643, CG3=0.510, CG4=0.501, CG5=0.339), 2 items of health organization structure (HOS1= -0.138, HOS3=0.007) and 1 item of quantitative risk assessment (QRA1=0.645) have lower factor loading than (0.7), thus they have been deleted from the model. The figures of the first run of the factor loadings are provided. The study again runs the algorithm and finds that all items have factor loadings above than (0.7) so there were good factor loadings (Table 4.10).
Table . Factor Loadings
Figure . Factor Loading extracted from SMART-PLS
This figure is a screenshot of the Factor Loading (second algorithm) as extracted from SMART-PLS. A figure of the first algorithm of the Factor loading is presented in Appendix-2
Average Variance Extracted (AVE)
The AVE is determined by taking the mean of the squared loadings linked to each indication that is part of a construct. When the AVE is greater than 0.5, there is convergent validity. It is strongly advised that an AVE of at least 0.5 be achieved for appropriate convergence. An AVE lower than 0.5 indicates that the items in the test explain more error than the variation in the constructs themselves.
Table . Average variance extracted (AVE)
Discriminant Validity
Cross Loadings
This study determines the uniqueness and robustness of the investigated factors of ERM by establishing their discriminant validity. This type of validity demonstrates that the constructs used in the study each have their own unique explanatory power that is not overly associated with the explanatory power of the other constructs in the investigation. Discriminant validity exists when it is shown that measurements of constructs that, in theory, should not have a strong correlation with one another do not, in reality, have a strong correlation with one another. During the cross-loadings phase of the research process, the researcher examined the different items to determine which ones have high loadings on a single construction and which ones have high loadings on many constructions. It is not easy to label all of the factors that share the same variable; this also requires that those variables be distinguishable and reflect separate notions when it is found that a variable has more than one good loading based on the sample size (Richter et al., 2015).
Table . Cross Loadings
Table 4.12 shows the cross-loadings of the investigated factors in the study, which were CE, CG, ED, ERM, HOS, QRA, and SG. This study found that the cross-loadings of one construct had higher values than the other construct’s cross-loadings, so there were good cross-loadings.
Discriminant validity was established to demonstrate that each factor had its own unique identity and was not overly associated with the identities of the other factors used in the investigation. Discriminant validity was measured using cross-loadings and Fornell-Larcker criteria. During the cross-loadings phase, the researcher examined the different items to determine which ones had high loadings on a single factor and which ones had high loadings on many factors. The study found that cross-loadings of one factor had higher values than the other factors’ cross-loadings, indicating good cross-loadings. Specifically, the cross-loadings of CE and CG were relatively high and mostly loaded on their own factors, indicating good discriminant validity. However, the cross-loadings of ED, ERM, HOS, QRA, and SG were less clear and had some degree of overlap with other factors, indicating the need for further examination to ensure discriminant validity. Additionally, the factor ERM had the lowest cross-loadings among all factors, indicating its uniqueness and distinct identity.
Fornell-Larcker Criteria
The Fornell-Larcker criteria are one of the most common methods used in determining whether measurement items have discriminant validity. The validity of the constructs has already been demonstrated following the Fornell-Larcker set of criteria in the following ways: (1) the square root of AVE has a greater value than its correlation among the other constructs in the same table. According to the Fornell-Larcker criteria, the value of the diagonal must be greater than all values in the same row or column (Richter et al., 2015).
Table . Fornell-Larcker Criteria
Construct Reliability
The construct reliability is measured using Cronbach’s alpha, which measures the internal consistency. According to Sarstedt et al. (2019), one way to conceptualize it is that it is equivalent to the whole amount of the actual sample covariance matrix relative to the total scaled variance. Cronbach’s alpha is a popular method for determining item reliability and internal consistency when SEM is used for discriminant validity assessment. When conducting explanatory research, Cronbach’s alpha values should be higher than 0.70 (Hair et al., 2011). As shown in Table 4.14,
Table . Construct Reliability
Multi-Collinearity Statistics
The multi-collinearity in Smart PLS-SEM was tested using the variance inflation factor (VIF) for each predictive variable, which is a straightforward way to identify multi-collinearity in a model (Kock, 2015). In Smart PLS-SEM, the VIF value is analyzed to determine the degree of collinearity present in the data (Hair et al., 2014). There are two general rules for the VIF: if the VIF is 10 or greater, there is a potential collinearity concern. When all other circumstances are perfect, a poor correlation among the variables is indicated by a VIF number less than 10. Only factors whose VIF is less than 10 will be included in the models because the cutoff value for the VIF is set to 10 by default. The VIF is the inverse of the threshold distance. In optimal circumstances, a VIF less than 10 suggests a low correlation between the independent variables (Ringle, 2014).
Model Adequacy and Accuracy
Adjusted R2
R-squared is a statistical measure that determines how well a regression model fits the data. A high R-squared suggests that the model accurately represents the data. R-squared is the proportion of the variance that can be explained by all of the predictors in the model (Cheah et al., 2020). It describes the amount of variation in the endogenous variable that can be attributed to the influence of the exogenous variable. As Table 4.15 reports, around 23% (based on the adjusted- R square) of the variations in ERM implementations can be explained by the variations in strategic governance, corporate governance, education, communication efficacy, quantitative risk assessment, and health organization structure. This study has also calculated the Q-square, which showed a good fitness model as all the values of the Q-squares were higher than 0.
Table . VIF, R Square, and Adjusted R Square Values
Assessment of the Path Model
The path model analyzes linear causal links across variables while simultaneously taking measurement error into account, which makes it a comparable method to regression analysis but one with greater power. This study uses t-values and p-values to test the proposed research hypotheses that are indicated by remarking the beta values (coefficients) by *, **, or *** to indicate significance at 0.1, 0.05 and 0.01, respectively, as the most commonly used significance levels in social sciences (e.g., Hair et al., 2011; Sarstedt et al., 2019).
As shown in Table 4.16 and Figure 12, this dissertation found that SG significantly and positively influences ERM implementation in the UAE healthcare sector (beta = 0.101, t-value = 1.898, p-value = 0.058), suggesting that healthcare organizations with high levels of SG are likely to implement ERM. Therefore, the study supports H1. This result is also consistent with institutional theory and the existing body of literature on the topic. According to institutional theory, businesses operating in the same sector adopt similar organizational structures and practices over time. This is because of regulatory, normative, and cognitive pressures (Scott, 2014). SG can be understood as a normative force that influences the behavior of businesses so that they become more likely to implement ERM. According to Aguilera et al. (2015), institutionalizing good practices within an organization can ensure effective risk management. These good practices include establishing strategic goals, monitoring performance, assigning accountability, and managing risks. Given the complicated and highly regulated environment in which healthcare organizations operate in the UAE, ElKaleh (2019) suggested that robust governance is necessary to guarantee effective risk management in these organizations. In addition, a study by Al-Matari et al. (2014a) on UAE companies revealed that effective corporate governance had a positive impact on risk management, even if the focus of the study was not healthcare. According to the study, this correlation holds for other fields, including the medical profession.
Moreover, corporate governance is significantly correlated with ERM (beta = -0.169, t-value = 1.871, p-value = 0.061), suggesting that corporate governance is an important factor that should be considered when examining ERM implementation in UAE healthcare organizations. The direction of this relationship suggests that firms with more governance components are likely to have significantly higher implementation levels of ERM than other firms. This finding corresponds with both institutional theory and the discussed body of literature. According to institutional theory, an organization should embrace structures and procedures recognized as legitimate within its industry to become more stable and improve its chances of surviving (Scott, 2014). Corporate governance, which is the existence of an established legal structure, helps create an environment that is well-regulated and accountable, which promotes the adoption of ERM. According to Talic et al. (2021), adopting effective ERM requires good corporate governance standards, including robust board supervision, executive accountability, and transparent reporting.
Given the intricate, high-stakes, and highly regulated environment in which healthcare organizations must operate, it is particularly important to have effective governance in the healthcare industry. According to ElKaleh (2019), good corporate governance can provide the strategic direction and oversight necessary for managing various risks, including clinical, financial, operational, and regulatory risks. Furthermore, examining the UAE, Al-Tamimi and Al-Mazrooei (2007) found that having strong corporate governance procedures significantly boosts risk management techniques, highlighting the necessity for a robust internal control system and a powerful board of directors. Even though they did not focus on the healthcare sector, the results are transferrable since corporate governance is important for ERM in a wide variety of fields.
Additionally, education is significantly and positively associated with ERM (beta = 0.527***, t-value = 5.163, p-value = 0.000); thus, this study confirms hypothesis H3. The study findings indicate that education on ERM is significantly and positively associated with the adoption of ERM in UAE healthcare institutions, and this is consistent with institutional theory and the discussed body of literature. According to institutional theory, having information about and understanding the institutionalized practices or structures within a sector or business will increase acceptance and legitimization of these practices and structures. Introducing education on ERM is a potential step toward institutionalizing the practice within the healthcare industry. Education, training, and knowledge of ERM principles and practices are essential components for successfully implementing ERM. According to Nocco and Stulz (2006), the successful adoption of ERM can be facilitated by knowing the significance of ERM, how to carry out risk assessments, and how to mitigate and manage risks. This is especially true in the healthcare industry, which faces various hazards, including clinical, operational, financial, and regulatory risks (Amara & Aljunid, 2014).
In the context of healthcare in the UAE, Al-Matari et al. (2014b) have emphasized the significance of education in introducing good governance practices, such as risk management. Their research highlights the relevance of information and understanding practices in implementing good governance and risk management procedures, which is important in this study’s context even though ERM was not the primary focus of their investigation.
CE did not significantly and positively influence ERM (beta = -0.143, t-value = 1.349, p-value = 0.177), so this study rejects hypothesis H4. Thus, this research found that the efficacy of communication does not have a significant impact on implementing ERM in the healthcare sector of the UAE. Although this may not make sense at first, it is consistent with institutional theory and several findings from previous research. According to the institutional theory, organizations frequently adopt practices or methods due to normative demands and the pursuit of legitimacy rather than their efficacy or instrumental value (DiMaggio & Powell, 1983). Thus, the healthcare sector in the UAE is probably embracing ERM because it views it as an excellent normative practice rather than because there is successful internal communication about the benefits and applications of ERM. This is something researchers should consider when examining this phenomenon. A number of studies have demonstrated that there may be ambiguity in the connection between effective communication and the successful implementation of complex practices such as ERM. For example, Arena et al. (2010) found that communication in risk management tends to be more formal and ceremonial than instructive or influential. Similarly, Power (2009) suggested that ERM frequently becomes a symbolic or ceremonial practice rather than a truly effective one, which suggests that the efficacy of communication may not significantly influence the adoption of ERM.
HOS did not significantly influence ERM (beta = 0.081, t-value = 0.699, p-value = 0.484), so this study rejects hypothesis H5. This finding is consistent with institutional theory and is supported by previous investigations published in the literature. According to DiMaggio and Powell (1983), institutional theory emphasizes how institutional isomorphism molds organizational behavior. This suggests that firms could embrace particular practices, such as ERM, not because their organizational structure is naturally suited to these practices but rather as a response to external demands to conform to accepted norms and standards. As a result, the structure of the healthcare organization may not necessarily affect the implementation of ERM.
Other studies on healthcare organizations support these conclusions. For instance, Smircich (1983) proposes that the structure of an organization is not crucial in determining its practices. Recent research, such as a study conducted by Glendon et al. (2016), has concluded that there needs to be a clear correlation between organizational structure and the adoption of risk management. They, therefore, believe that other elements, such as the business culture and the organization’s leadership, have a more substantial role in influencing the acceptance of risk management methods and the successful implementation of these policies. Nevertheless, more research is needed to completely rule out the possibility that organizational structure affects risk management techniques. Other studies could investigate possible mediators and moderators in this relationship and further investigate these factors to better understand the role organizational structure plays in ERM implementation.
Furthermore, QRA did not significantly influence ERM (beta = 0.129, t-value = 1.344, p-value = 0.179), so the study also rejects hypothesis H6. Having said that, education, or ERM, is only one factor that enhances ERM in the UAE healthcare sector. The fact that extant studies in the sector support and align with the tenets of institutional theory suggests that QRA does not have a substantial influence on enterprise risk management ERM in the healthcare industry in the UAE. According to DiMaggio and Powell (1983), the institutional theory postulates that organizational behaviors and practices are frequently the consequence of compliance with institutional norms, regulations, and expectations rather than the product of objective and evidence-based decision-making. As a result, organizations might not necessarily implement ERM due to QRA but rather due to the desire to conform to industry norms and regulatory requirements.
The body of research that exists lends weight to this point of view. For instance, Power (2009) argues that risk management procedures in firms are frequently ritualistic, driven more by the need to conform with regulatory requirements than by any actual effectiveness in managing risk. Therefore, using QRA in risk management procedures might not necessarily affect the overall ERM implementation.
Nonetheless, this does not diminish the significance of QRA in risk management, which is still important. According to Aven (2016), QRA is a beneficial instrument that can help inform decisions on risk management. However, its impact on the implementation of ERM could be mediated by other factors such as organizational culture, leadership commitment, and resource availability, all of which should be considered in future research.
Table . Path Coefficient
Figure . Structure Equation Modelling (extracted from SMART-PLS)
This figure presents the details of the structure equation modeling as extracted from SMART-PLS
Regression Analysis (Models 1-7)
This dissertation used regression analysis to confirm the PLS-SEM results discussed in the previous section. The results of the regression analysis show how much of the variation in a single response (the dependent variable) can be attributed to a given set of independent factors. The primary uses of regression analysis are predicting, modelling time series, and determining the cause-and-effect connection between variables (Hair et al., 2017).
Table 4.18 presents the results of the seven regression models examining the factors that affect the implementation of ERM. Each model examines the impact of a different independent variable on the dependent variable, ERM. The analysis provides unstandardized coefficients, t-values, and p-values for each independent variable. Additionally, the table reports intercepts, R-squared values, F-statistics, and the number of observations for each model.
Model 1 examines the impact of strategic governance on ERM implementation. The results indicate a significant positive effect (beta = 0.222, t-value = 3.184, p-value < 0.001), so the hypothesis is accepted. This suggests that higher levels of strategic governance are associated with better implementation of ERM. Model 2 examines the impact of corporate governance on ERM. The analysis shows a significant positive effect (beta = 0.643, t-value = 1.786, p-value < 0.001), indicating that strong corporate governance leads to improved ERM implementation, so the hypothesis is accepted.
In Model 3, communication efficacy is found to have a significant positive effect on ERM implementation (beta = 0.395, t-value = 2.347, p-value < 0.001), so the hypothesis is accepted. This suggests that effective communication contributes to successful ERM implementation. Model 4 focuses on the impact of education on ERM implementation. The results show a significant positive effect (beta = 0.578, t-value = 1.815, p-value < 0.001), indicating that higher levels of education are associated with better implementation of ERM. Therefore, the hypothesis is accepted. Model 5 analyzes the impact of health organization structure on ERM. The results are insignificant (beta = 0.138, t-value = 3.453, p-value = 0.596), suggesting that health organization structure may not be a key factor influencing ERM implementation; therefore, the hypothesis is rejected. Finally, in Model 6, quantitative risk assessment is found to have a significant positive effect on ERM implementation (beta = 0.615, t-value = 1.430, p-value < 0.001), indicating that using quantitative risk assessment methods contributes to better ERM implementation. Therefore, the hypothesis is accepted.
Model 7 is a multiple regression model that includes all six independent variables: strategic governance, corporate governance, communication efficacy, education, health organization structure, and quantitative risk assessment. The model investigates the combined effect of these factors on the dependent variable, ERM implementation. In Model 7, the unstandardized coefficient (beta) for strategic governance is 0.08, with a p-value of 0.079. This suggests a positive and marginally significant effect of strategic governance on ERM implementation. Second, the unstandardized coefficient (beta) for corporate governance is 0.299, with a p-value of 0.004. This indicates a positive and statistically significant effect of corporate governance and ERM implementation. Third, the unstandardized coefficient (beta) for communication efficacy is -0.216, with a p-value of 0.030. This suggests a negative and statistically significant effect of communication efficacy on ERM implementation. Fourth, the unstandardized coefficient (beta) for education is 0.313, with a p-value of 0.006. This indicates a positive and statistically significant effect of education on ERM implementation. Fifth, the unstandardized coefficient (beta) for health organization structure is -0.032, with a p-value of 0.596. This suggests no statistically significant effect exists between health organization structure and ERM implementation. Sixth, the unstandardized coefficient (beta) for quantitative risk assessment is 0.308, with a p-value of 0.008. This indicates a positive and statistically significant effect of quantitative risk assessment on ERM implementation.
In addition, the intercept of 1.430 indicates a baseline level of ERM implementation when all the independent variables are equal to zero. However, this result should be taken with caution since the absence of independent variables is unrealistic. The R-square value for Model 7 is 0.285, which indicates that the combined effect of the six independent variables explains approximately 28.5% of the variation in ERM implementation. This value is considered meaningful in social science research (Cohen, 1988). The F-statistic for Model 7 is 19.572, which is statistically significant, indicating that the overall model has explanatory power in understanding the relationship between the independent variables and ERM implementation.
Finally, Model 7 demonstrates that strategic governance, corporate governance, communication efficacy, education, and quantitative risk assessment significantly affect ERM implementation. However, the structure of health organizations does not considerably affect ERM implementation. The R-square value indicates that the model has meaningful explanatory power, but further research could identify additional factors that may improve the model’s explanatory power.
Table . Regression Analysis of all Factors Associated with the Implementation of ERM (Models 1 – 7)
Model Fit Indices
To test model fitness, the coefficient of determination, denoted by the symbol R2, sums up the percentage of variance within the dependent variable associated with the determinant (independent) factors. In addition, the findings also present the values for F-statistics and intercepts. Higher R2 values indicate that the independent variables explain a more significant portion of variability, up to a maximum of 1. The study finds that all 6 ERM factors explain 48.9% of the variance in ERM, and the study has a suitable F2 because the p-value is significant.
Model 1 (Strategic Governance)
An R-squared value of 0.073 for strategic governance indicates that this factor explains approximately 7.3% of the variation in ERM implementation. While this value is relatively low, Cohen (1988) indicates that in the social sciences, R-squared values as low as 0.10 can still be considered meaningful. The F-statistic of 23.727 indicates that the model is significant in explaining the relationship between strategic governance and ERM implementation.
Model 2 (Corporate Governance)
The intercept of 1.786 represents the baseline level of ERM implementation when corporate governance is absent, but this interpretation might not be meaningful in practice. The R-squared value of 0.219 indicates that corporate governance accounts for about 21.9% of the variation in ERM implementation. This value is higher than the value in Model 1, suggesting that corporate governance has a stronger explanatory power. The F-statistic of 84.111 confirms the statistical significance of the model in explaining the relationship between corporate governance and ERM implementation.
Model 3 (Communication Efficacy)
The R-squared value of 0.111 suggests that communication efficacy explains approximately 11.1% of the variation in ERM implementation. This value is considered meaningful according to Cohen’s (1988) guidelines. The F-statistic of 37.689 indicates that the model explains the relationship between communication efficacy and ERM implementation.
Model 4 (Education)
The R-square value of 0.224 indicates that education accounts for about 22.4% of the variation in ERM implementation. This value is the highest among all models, suggesting that education has the strongest effect on implementation. The F-statistic of 87.353 confirms the statistical significance of the model in explaining the relationship between education and ERM implementation.
Model 5 (Health Organization Structure)
The R-square value of 0.014 suggests that health organization structure explains only 1.4% of the variation in ERM implementation. This value is relatively low, indicating weak explanatory power. The F-statistic of 4.439, although significant, indicates a relatively weak relationship between health organization structure and ERM implementation.
Model 6 (Quantitative Risk Assessment)
The R-square value of 0.197 indicates that quantitative risk assessment accounts for about 19.7% of the variation in ERM implementation. The F-statistic of 73.933 confirms the statistical significance of the model in explaining the relationship between quantitative risk assessment and ERM implementation.
The models examining the effects of strategic governance, corporate governance, communication efficacy, education, and quantitative risk assessment on ERM implementation show statistically significant relationships, with R-square values ranging from 0.014 to 0.224. These values, while relatively low, are considered meaningful in social science research (Cohen, 1988). However, the model examining the effect of health organization structure has weak explanatory power, with an R-square value of only 0.014. The F-statistics for all models are statistically significant, indicating that each model has some degree of explanatory power. However, as mentioned, there is no universal threshold for an acceptable F-statistic. Comparing the F-statistics across the models helps identify which factors have the strongest effect with ERM implementation. Based on these findings, this study can conclude that strategic governance, corporate governance, communication efficacy, education, and quantitative risk assessment are essential factors influencing ERM implementation. However, the relatively low R-square values suggest that other factors not included in these models also impact ERM implementation. Future research could aim to identify these additional factors to improve the explanatory power of the models.
Comparative Analysis of SPSS and Smart PLS
The Model 7 findings indicate the relative influence of each independent variable on the dependent variable, ERM. Strategic governance shows a marginally significant positive relationship with ERM (beta = 0.08, p = 0.079). This implies that the higher the strategic governance level, the better the ERM implementation, albeit the relationship is not robustly statistically significant. Corporate governance, however, exhibits a robust positive relationship with ERM (beta = 0.299, p = 0.004). Thus, strong corporate governance leads to better ERM implementation. In contrast, communication efficacy has a statistically significant negative effect on ERM implementation (beta = -0.216, p = 0.030). This indicates that the communication strategies in place may not support ERM practices. Education has a positive, statistically significant relationship with ERM (beta = 0.313, p = 0.006). This result suggests that higher levels of education correlate with better ERM implementation, emphasizing the importance of educating relevant stakeholders to effectively implement ERM practices.
However, health organization structure does not exhibit a statistically significant relationship with ERM (beta = -0.032, p = 0.596). This result suggests that the structure of the health organization might play a minor role in the implementation of ERM. Lastly, quantitative risk assessment has a statistically significant positive effect on ERM (beta = 0.308, p = 0.008), which indicates that quantitative risk assessment methods could lead to better ERM implementation. These findings help understand the various factors that may contribute to the successful implementation of ERM within the healthcare context. However, these relationships can also be influenced by other contextual and environmental factors that are not included in this model.
Comparing the combined model (Model 7) with the separate models (Models 1-6) shows the significant impact of certain independent variables on ERM shifts when all of these variables are studied together. For example, the influence of strategic governance on ERM is far less substantial in Model 7 than in Model 1. This shows that the distinctive effect of strategic governance decreases when it is evaluated with other aspects. It is possible that additional variables in Model 7 have similar explanatory power to strategic governance, which would reduce the impact that strategic governance has on its own. Instead, corporate governance is as important in Model 7 as in Model 2, despite having a lower beta coefficient than Model 2. This illustrates that corporate governance still has a significant effect on the implementation of ERM, even though its relative importance decreases when other factors are considered.
There is a similar pattern in education and quantitative risk assessment. Although both variables maintain their statistical significance in Model 7, the effect sizes of their individual models (Models 4 and 6, respectively) are lower. Instead, communication efficacy has a negative association with ERM in Model 7, which is the opposite of the positive effect found in Model 3. This is likely the result of the suppressor effect, in which the relationship between communication efficacy and ERM is inverted when other variables are controlled. Last but not least, the health organization structure plays a small role in Model 7, which aligns with the findings of Model 5. These results show that the correlations between the independent factors and ERM are complex and may be influenced by the interactions between variables. Due to the variations between the individual models (Models 1-6) and the combined model (Model 7), it is essential to examine the influences of these variables on ERM implementation using an approach that considers all of these elements together.
Comparing the findings from the SPSS regression analysis (Models 1-6) and the Smart PLS analysis, shows some similarities and differences in the relationships between the independent variables and the ERM implementation. In the SPSS analysis, strategic governance (Model 1) has a positive and significant impact on ERM (beta = 0.222, p < 0.001), while in the Smart PLS analysis, the effect is not significant (beta = 0.101, p = 0.058), and the hypothesis is rejected. Therefore, the findings from SPSS indicate that strategic governance positively influences ERM implementation. In addition, corporate governance (Model 2) has a positive and significant impact on ERM in the SPSS analysis (beta = 0.643, p < 0.001), but the effect is not significant in the Smart PLS analysis (beta = -0.169, p = 0.061), and therefore, the hypothesis is rejected. Despite the differences in magnitude and significance levels, the SPSS results suggest that corporate governance is an important factor positively influencing ERM implementation. Communication efficacy (Model 3) shows a positive and significant impact on ERM in the SPSS analysis (beta = 0.395, p < 0.001), while the Smart PLS analysis indicates a negative and non-significant impact (beta = -0.143, p = 0.177). The findings from these two analyses differ, suggesting that the impact of communication efficacy on ERM implementation warrants further investigation. Education (Model 4) demonstrates a positive and significant impact on ERM in both the SPSS analysis (beta = 0.578, p < 0.001) and the Smart PLS analysis (beta = 0.527, p < 0.001). The consistent findings across both analyses indicate that education strongly and significantly influences ERM implementation. Health organization structure (Model 5) shows no significant relationship with ERM implementation in both the SPSS analysis (beta = 0.138, p = 0.000) and the Smart PLS analysis (beta = 0.081, p = 0.484). The consistent findings show that the structure of health organizations does not significantly impact ERM implementation.
Lastly, quantitative risk assessment (Model 6) has a positive and significant impact on ERM in the SPSS analysis (beta = 0.615, p < 0.001), and a positive but non-significant relationship in the Smart PLS analysis (beta = 0.129, p = 0.179). The mixed findings suggest that further research might be needed to clarify the influence of quantitative risk assessment on ERM implementation. In conclusion, the analyses from both SPSS and Smart PLS indicate that education significantly and positively impacts ERM implementation and is the most important predictor of the implementation of ERM. Strategic governance and corporate governance also show positive impacts on ERM implementation, but the significance levels vary between the two analyses. Communication efficacy, health organization structure, and quantitative risk assessment show mixed or non-significant results, highlighting the need for further investigation to better understand their influence on ERM implementation.
Conclusion
This study used several methods to analyze the data. It analyzed respondent demographic information using frequency analysis. It then conducted an EFA to check the validity and reliability of the measurement items. By conducting EFA using IBM SPSS, the study found excellent and acceptable values for KMO and Bartlett’s tests and good factor loadings for the ERM model. Correlation analysis showed that the highest correlation coefficient was between communication efficacy and education. The researcher tested the impacts of all factors on ERM and found that corporate governance, communication efficacy, education, and quantitative risk assessment have significant impacts on ERM implementation. However, strategic governance and health organization structure do not influence ERM implementation. To conclude, the study identified that education has the highest impact on ERM implementation. Moreover, SEM was used to test the validity, reliability, and path coefficients of the factors in ERM. The study also tested for convergent and discriminant validity, and found that the data were normal. Using SEM, the study found that only education significantly and positively influenced ERM, and other factors did not significantly contribute to ERM implementation. The major finding of the different analyses run in this study is that one particular factor has the highest significance when it comes to ERM implementation, which is education. Zou et al. (2010) already highlighted the importance of education in ERM implementation. Although Liebenberg and Hoyt (2003) found that appointing a CRO plays a major role in the implementation of ERM, this study determined that having a CRO is not essential for ERM implementation. This discrepancy in findings could be because Liebenberg and Hoyt’s study was not restricted to the healthcare industry. The appointment of a CRO in healthcare institutions is not as common as it is in business industries. Thus, the uniqueness of healthcare institutions explains why this study yielded different results than studies examining different sectors, such as business or banking. The next chapter further discusses this matter.
Finally, both the SPSS and Smart PLS analyses indicate that education, strategic governance, and corporate governance play important roles in influencing ERM implementation in UAE health organizations. While education has the most significant positive impact, the influence of strategic governance and corporate governance is also positive, although with varying significance levels. Instead, Communication efficacy, health organization structure, and quantitative risk assessment show mixed or non-significant results, suggesting further investigation is needed to better understand their influence on ERM implementation. By focusing on these factors, UAE health organizations can improve their ERM practices and enhance overall organizational performance.
CONLUSION AND RECOMMENDATIONS
Overview of the Purpose of the Study
The healthcare sector is one of the most critical sectors globally, as it is responsible for the well-being of individuals and communities. Healthcare institutions play a significant role in promoting health and preventing diseases, and they are exposed to various risks that can impact their ability to achieve their objectives. As such, it is essential for these institutions to adopt effective risk management practices to identify, assess, and mitigate risks. One of the risk management practices that has gained increasing attention in recent years is ERM.
Despite the growing importance of ERM, the literature on its implementation in healthcare institutions is limited, particularly in the UAE. The UAE is one of the fastest-growing countries in the world, with a rapidly expanding healthcare sector. The healthcare sector in the UAE is diverse, consisting of various types of healthcare institutions, including hospitals, clinics, and medical centers. Despite this diversity, there is a lack of research on ERM implementation in the healthcare sector. This study aims to fill this gap in the literature by examining the factors that influence ERM implementation in healthcare institutions in the UAE.
The purpose of this study is to identify the factors that influence the implementation of ERM in healthcare institutions in the UAE. The study had several objectives, including identifying the factors that influence ERM implementation in the healthcare sector, investigating the relationship between these factors and ERM implementation, and determining the significance of each factor in ERM implementation. The study focused on six factors that were identified through the literature review: strategic governance, corporate governance, communication efficacy, education, health organization structure, and quantitative risk assessment. It found that education has the largest impact on ERM implementation in healthcare institutions in the UAE and that other factors such as corporate governance, communication efficacy, and quantitative risk assessment also play significant roles. This study ends by providing recommendations for further research.
Summary of Methodology
This study employed quantitative methods and used a cross-sectional survey design. The data collection process involved administering a structured questionnaire to healthcare professionals in the UAE, specifically those working in hospitals and clinics. A non-probability purposive sampling technique was used to select the sample of participants, with a total of 304 respondents completing the survey.
SEM was used to analyze the data and test the validity and reliability of the measurement model. The SEM ensured convergent and discriminant validity, and the data were normal. The analysis included testing the path coefficients, which allowed for an investigation into the relationship between the independent and dependent variables. Additionally, regression analysis was conducted to examine the variance in the dependent variable that could be attributed to the independent factors.
EFA was conducted to check the findings of the SEM. The EFA showed excellent and acceptable values for KMO and Bartlett’s test and good factor loadings for the ERM model. The study also used correlation analysis to examine the relationships between the independent variables and ERM.
This study used a rigorous methodology that allowed for the collection of reliable and valid data. The EFA, correlation analysis, SEM, and regression analysis enabled the examination of the relationships between the variables and the identification of the factors that influence ERM implementation in the healthcare sector in the UAE. The results of the analyses were used to make informed conclusions about the impact of the six factors on ERM implementation and to provide recommendations for healthcare institutions in the UAE to improve their ERM practices.
Implications
This study aimed to identify the factors that influence the implementation of ERM in healthcare institutions in the UAE and determine the significance of each factor in ERM implementation. The study found that education has the most significant effect on ERM implementation in the healthcare sector in the UAE. The other factors, namely corporate governance, communication efficacy, and quantitative risk assessment, also had significant impacts on ERM implementation, whereas strategic governance and health organization structure did not have a significant influence on ERM implementation.
The finding that education is the most significant factor influencing ERM implementation is consistent with the literature on ERM. Previous studies have identified education and training as critical factors in the successful implementation of ERM (Beasley et al., 2013; Zou et al., 2010). Healthcare professionals need to be trained on ERM concepts, processes, and techniques to understand and apply ERM effectively in their institutions. Moreover, education on ERM will help build a culture that is aware of risks in healthcare institutions, which is critical for the success of ERM.
The study also found that corporate governance has a significant impact on ERM implementation in healthcare institutions in the UAE. This finding is consistent with the literature on ERM in the business context. Corporate governance provides the structure and accountability necessary for effective RM (COSO, 2004). Moreover, corporate governance practices such as risk oversight, risk appetite, and risk reporting are essential for the success of ERM (COSO, 2004).
Communication efficacy was found to have a significant but negative impact on ERM implementation in healthcare institutions in the UAE. This finding is inconsistent with the literature on ERM, which finds that effective communication is essential for the success of ERM (Hosseini et al., 2016). However, the negative impact of communication efficacy on ERM implementation in this study may be explained by the lack of awareness of risks in healthcare institutions in the UAE. Healthcare professionals may not communicate effectively about risks and their potential impacts because they do not understand or appreciate the importance of ERM.
Quantitative risk assessment had a significant and positive impact on ERM implementation in healthcare institutions in the UAE. This finding is consistent with the literature on ERM, which also finds that quantitative risk assessment is a critical component of ERM (Kaya et al., 2018). Thus, healthcare institutions need to use quantitative risk assessment techniques to identify, measure, and prioritize risks effectively.
Strategic governance and health organization structure did not have a significant impact on ERM implementation in healthcare institutions in the UAE. This finding is slightly inconsistent with the literature on ERM, as studies have provided mixed results on the impact of strategic governance and organizational structure on ERM implementation (Beasley et al., 2005; Bowling & Rieger, 2005). Nonetheless, this study finds that healthcare institutions may need to focus on other factors to improve their ERM practices, such as education, corporate governance, communication efficacy, and quantitative risk assessment.
The study findings have significant implications for healthcare institutions in the UAE. Healthcare institutions need to invest in education and training to build a culture that is aware of risks and thus improve on ERM practices. Healthcare institutions also need to adopt effective corporate governance practices, including risk oversight, risk appetite, and risk reporting. Healthcare institutions should use quantitative risk assessment techniques to effectively identify, measure, and prioritize risks. Effective communication about risks and their potential impacts is also critical for the success of ERM in healthcare institutions., however, less significant than other factors highlighted in this study.
The study’s findings are consistent with some previous studies on ERM implementation, but they contradict some other studies. For example, this study’s finding that communication efficacy had a negative impact on ERM implementation is inconsistent with previous studies that emphasize the importance of effective communication in ERM. This discrepancy may be because effective communication is not the only factor that determines successful ERM implementation in healthcare institutions in the UAE, and other factors may have a more significant impact.
The positive impact of education on ERM implementation found in this study is consistent with previous studies that highlight the importance of education and training in improving ERM practices. For example, Zou et al.’s (2010) study emphasized that education and training are crucial in developing a culture that is aware of risks and increasing risk management competency among employees. This finding suggests that healthcare institutions in the UAE should prioritize education and training programs to improve their ERM practices.
The study’s findings also have practical implications for healthcare institutions in the UAE. The results suggest that healthcare institutions should focus on improving their corporate governance practices and their quantitative risk assessment processes to enhance the ERM implementation. Additionally, healthcare institutions should consider the role of education and training in improving their ERM practices. By improving these factors, healthcare institutions can develop a robust ERM framework that can effectively manage risks and improve organizational performance.
In sum, the study findings contribute to the existing literature on ERM implementation, particularly in the healthcare sector. The study identified the factors that significantly influence ERM implementation in the UAE healthcare sector and provided insights into the impact of each factor on ERM. In particular, it emphasizes the importance of education and training in ERM implementation for improving ERM practices in the UAE healthcare sector.
Recommendations
This study makes the following recommendation for UAE health organizations to enhance their ERM based on the findings from both SPSS and SmartPLS-SEM analyses. Healthcare organizations can offer workshops, seminars, and on-the-job training to increase awareness and knowledge of ERM practices and processes. This study found that strategic governance positively influences ERM implementation, although the significance of this influence varies between the two analyses. UAE health organizations should ensure robust strategic governance structures and promote ERM implementation. This can be achieved by incorporating ERM into strategic planning, setting clear risk management objectives, and assigning specific roles and responsibilities to board members and senior management for ERM oversight. The study also found that corporate governance has a positive impact on ERM implementation. UAE health organizations should review and improve their corporate governance practices to improve their ERM implementation. This could involve developing a clear organizational structure, establishing committees focused on risk management, and creating policies and procedures emphasizing ERM’s importance. As the findings on the impact of communication efficacy on ERM implementation were inconclusive, UAE health organizations should explore the role of communication in ERM implementation. Effective communication channels and practices could be crucial in disseminating information, raising awareness, and fostering a culture that is aware of risks. Since the impacts of health organization structure and quantitative risk assessment on ERM implementation were mixed or non-significant, researchers should further investigate their influence. This may involve conducting additional research or pilot programs to identify best practices or specific aspects of these factors that can be tailored to improve ERM implementation. By focusing on these recommendations, UAE health organizations can improve their ERM implementation, leading to more effective risk management, improved decision-making, and better organizational performance.
Reflection
Undertaking a research study is a complex process that involves a wide range of skills and knowledge. This study on the factors that influence the implementation of ERM in healthcare institutions in the UAE has been a challenging yet rewarding experience that has provided valuable personal and professional development opportunities.
One of the most significant gains from conducting this research has been personal development. This study has allowed me to develop my research skills in various areas, including research design, data collection, and analysis. I have gained experience in using statistical software, which has improved my proficiency in data analysis. Additionally, I have enhanced my critical thinking and problem-solving skills, which are essential for analyzing and interpreting research findings.
Another way I have developed personally from conducting this study is learning to effectively communicate research findings. I have learned how to present information in a clear and concise manner, which is essential for academic writing. Moreover, I have learned how to use in-text citations and reference lists properly, which is important for maintaining academic integrity.
Another significant benefit of conducting research is professional development. This study has provided me with the opportunity to stay up-to-date with the latest developments in the field of risk management. It has also enabled me to develop my project management skills, including setting deadlines, allocating resources, and monitoring progress.
Despite the rewarding experience, conducting a research study has its challenges. One of the significant challenges that I encountered during this study was the lack of literature on ERM implementation in the healthcare sector, particularly in the UAE. This made it difficult to develop a comprehensive understanding of the factors that influence ERM implementation in the healthcare sector.
Another significant challenge was recruiting participants. Healthcare professionals in the UAE have limited time to participate in research studies, which makes it challenging to obtain a representative sample of healthcare institutions in the UAE. Additionally, the study relied on self-reported data from participants, which may be subject to response bias and social desirability bias.
Meticulous planning and execution is needed to ensure that a research study is conducted effectively and efficiently. The process of planning a research study involves identifying a research question that is relevant and feasible, reviewing the relevant literature, selecting appropriate research methods, and designing a reliable data collection process. This study required careful planning to ensure that the research question was clear and relevant to the healthcare sector in the UAE. The research design also had to be appropriate to address the research question, and the data collection process had to be reliable and accurate.
Moreover, the execution of the study required attention to detail and adherence to the study design. The data collection process had to be carried out according to the study design, and the data had to be analyzed accurately and reliably. The statistical analysis had to be appropriate to answer the research question and draw conclusions from the data. The execution of the study required a high level of commitment, patience, and perseverance to ensure that the research was conducted effectively and efficiently.
Furthermore, research studies are not always straightforward, and unexpected challenges may arise. The research process requires flexibility and adaptability to changing circumstances to ensure that the study can be conducted effectively. For example, it was challenging to recruit participants for the study because healthcare professionals in the UAE have significant time constraints. The research team had to adjust the data collection process to accommodate the participants’ busy schedules. The unexpected challenges during the research process highlighted the importance of being flexible and adaptable to ensure that the study could be conducted effectively.
Overall, conducting research is a challenging yet rewarding experience that provides valuable personal and professional development opportunities. This study has enabled me to develop my research skills, critical thinking skills, and problem-solving skills. It has also provided me with the opportunity to stay up-to-date with the latest developments in the field of risk management. Despite the challenges, the study has allowed me to gain valuable insights into the factors that influence ERM implementation in healthcare institutions in the UAE.
Limitations and Future Research
While this study provides valuable insights into the factors that influence ERM implementation in the healthcare sector in the UAE, it has some limitations.
The following limitations were observed during the study:
Each participant self-reported their evaluation of the level of implementation of the 30 ERM dimensions discussed in the online survey tool. Therefore, it is possible that participants’ responses to the survey questionnaire did not precisely reflect the actual level of implementation of each component in their healthcare organizations.
The literature criticizes the use of EFA for data analysis because it is subjective and largely dependent on the researcher’s decisions on the selection of factor extraction and rotation methods. This issue may also affect the final model of the factor structure.
The study was limited by the sample size and the specific context of the healthcare sector in the UAE. Thus, the findings may not be generalizable to other industries or countries with different cultural, economic, and regulatory contexts.
The study relied on self-reported data from participants, which may be subject to response bias and social desirability bias.
The study only focused on six factors that were identified through the literature review. Thus, it did not examine other factors that may influence ERM implementation.
To address these limitations, future research could consider using a larger and more diverse sample of healthcare institutions in different countries and industries. In addition, future research could employ mixed-methods approaches, such as interviews and observation, to complement the self-reported data from surveys. This would provide a more comprehensive understanding of the factors that influence ERM implementation and their interrelationships. Furthermore, future studies could explore the influence of additional factors that were not included in this study to provide a more complete picture of the determinants of ERM implementation.
Another area for future research is the development and testing of interventions to improve ERM implementation in healthcare institutions. This study found that education is a key factor that influences ERM implementation. Future research could investigate the effectiveness of educational interventions, such as training programs and workshops, in improving ERM knowledge and practices among healthcare professionals. Furthermore, future research could explore the potential of using technology, such as ERM software and data analytics tools, to enhance ERM implementation in healthcare institutions.
APPENDICES
Appendix –1: List of Constructs and their Measurement Scale
Appendix –2: Copy of the Survey Questionnaire Document
Appendix –3: Research Ethics Committee Approval
Appendix-4 – First run of factor loadings
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