Original Paper

# The Impact of Human Resource Practices on Nurses' Turnover
## Intention: An Empirical Study of Hospitals in North Lebanon

Joumana A. Younis<fnref n="1" />, Hussin Jose Hejase<fnref n="2" />*, Hala Rashid Dalal<fnref n="3" />, Nabila Abbas Ibrahim<fnref n="4" /> & Ale J. Hejase<fnref n="5" />

Received: September 15, 2021 Accepted: September 26, 2021 Online Published: December 16, 2021

doi:10.22158/jbtp.v9n4p8

URL: http://dx.doi.org/10.22158/jbtp.v9n4p8

Abstract

Nurse turnover has become a continuous and increasing challenging problem in the healthcare system worldwide; and this became a priority that needs to be adequately managed. As a matter of fact, nurses are care givers and represent the frontline services that hospitals deliver to their patients, so it is an indispensable necessity to retain this workforce through both job satisfaction and job motivation. Providing the nurse workforce an appropriate treatment will surely lead to retention of talent that is critical to the continuity of the healthcare organizations. This research aims to assess the impact of Human Resource Management (HRM) practices mainly in relation to job satisfaction on nurses' turnover intention, especially that Lebanon is one of the countries where the healthcare system is suffering from nurses' shortage. This study uses a descriptive correlational study based on a structured questionnaire administered to 100 nurses within North Lebanon Hospitals. The collected data statistical analysis is carried out via the Statistical Product and Service Solutions SPSS-version 25. Results revealed that the absence of efficient HR practices forces nurses toward turnover decision. Thus, the creation of supported and motivated environment will positively affect the nurses' decision to stay, and thus minimize the turnover rate.

Keywords

HRM Practices, nurses' turnover, job satisfaction, healthcare system, Lebanon

1. Introduction

Healthcare organizations evolve and develop with time to meet the requirements of all stakeholders. Therefore, healthcare organizations are complex and dynamic (Thompson, Buchbinder, & Shanks, 2010, p. 2). Consequently, healthcare managers must provide the appropriate leadership not only inside the organization but outside as well. Said in a different manner, healthcare management has opportunities in both direct care and non-direct care settings. “A direct setting is an organization that

<footnote n="1">¹ Faculty of Business, Jinan University, Tripoli, Lebanon & Associate Researcher, Dicen IdF, CNAM, France</footnote>

<footnote n="2">² IEEE Senior Member, Senior Researcher, Professor of Business Administration, Beirut, Lebanon</footnote>

<footnote n="3">³ Faculty of Business Administration, Doctoral Candidate, Jinan University, Tripoli, Lebanon</footnote>

<footnote n="4">⁴ Faculty of Business Administration, Jinan University, Tripoli, Lebanon</footnote>

<footnote n="5">⁵ Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon</footnote>

<footnote>* Hussin Jose Hejase, IEEE Senior member, Senior Researcher, Professor of Business Administration, Beirut, Lebanon</footnote>
offers care services directly to patients, residents or clients who seek the service” (Buchbinder & Thompson, 2010, pp. 33-34). “On the other hand, non-direct setting deals with providing care to individuals via support and products available to direct care settings” (Buchbinder & Thompson, 2010, pp. 33-34). Based on the aforementioned, healthcare management deals with health services that are meant to distribute accountability, responsibility and incentives to patients, physicians, nurses and other employees within the organization. Nevertheless, there exist many complexities that challenge healthcare organization as contended by Bond (2013), “in health care we have knowledge asymmetry, agency problems, lack of price transparency, and biased selection of patients, to name just a few issues” (para 3). Adding to that complexity, “Public and private insurers, and employers have currently argued for incentives to increase care quality, decrease utilization, and improve overall outcomes” (Shih, Davis, Schoenbaum, Gauthier, Nuzum, & McCarthy, 2008). Based on the above, Bond (2013) suggests an integrated management system that considers incentives to stakeholders like nurses, physicians, and other healthcare staff. Such an integrated system necessitates having a competent, organized human resource team who understands the value of integrated incentives. In effect, having a healthcare management team who coordinates a motivated staff will have no problems transforming limited resources into effective outcomes. Actually, coordination involves hospitals’ ability to use different sources together in smooth and efficient ways to achieve the target. However, organizing such resources enables each part of the hospital to clearly do their tasks which influence overall purpose of the organization (Malone, 1994).

It is common sense to associate nurses with patients. Their day to day tasks is closely linked with patients. Therefore, nurse's performance affects greatly the institution resources. This also confirms why institutions lose enormously if a skilled nurse quits her/his job up to several folds to what is paid for the same nurse for 12 months (Hunt, 2009). The abovementioned leads to the responsibility of the healthcare administration to offer nurses, as well as other support teams, a fair contract that offers an incentivized work/life balance. In fact, Magrath et al. (2012) warn that not having a balanced incentive, that is, offering only monetary and quality incentives “might crowd out other sources of motivation such as intrinsic motivation, might undermine the social relationship with patients, and might have detrimental effects on teamwork by fostering competition or envy” (p. 1778). This leads to the fact that an incentivized balance is made up of tangible and intangible incentives. So, according to Elovainio, Steen, Presseau, et al. (2013), intangible or nonfinancial incentives might include “reward and recognition, but individuals may also be motivated by control over lifestyle and work flexibility. At the larger organizational level, characteristics such as organizational justice have been correlated with better performance” (pp. 31-32). According to Bond (2013), “Organizational justice is ultimately rooted in ethically sound practice, professionalism, and model behaviour. Thus, it is best to pay attention to both culture and nonmonetary incentives. Effective efforts to improve culture might include leadership development, accountability for highly professional behaviours, and fostering a focus on the patient” (para 13). In summary, when healthcare stakeholders, in this case nurses, do not sense organizational justice as manifested in fair policies and procedures, they will surely not hesitate to look elsewhere for such an incentivized organization. In a nut shell, the lower nurses’ job satisfaction is the more intense will be their intention to leave, which in turn puts the healthcare institutions/hospitals in more serious problem. Many factors contribute to low satisfaction level among nurses. This research aims to assess the relationship between the various practices of human resource (promotion and recognition, salary and other benefits, training programs, selection and recruitment efficiency, performance appraisal system and career development) and their main determinant of job satisfaction.
## 1.1 Problem Definition

The role of nurse has witnessed a significant evolution throughout the 21st century. “Nurses work in a variety of settings, including the hospital, the classroom, the community health department, the business sector, home health care, and the laboratory” (Tingen, Burnett, Murchison, & Zhu, 2009, p. 167). Therefore, turnover of nursing staff is a significant issue affecting quality, quantity and cost of patient care and healthcare services as well (Battistelli, Portoghese, Galletta, & Pohl, 2013). Notwithstanding, the nursing shortage is still a recurrent problem worldwide, which when coupled with nurse turnover may lead to even worst situation for the healthcare institutions (Valizadeh, Zamanzadeh, Habibzadeh, Alilu, Gillespie, & Shakibi, 2016; American Association of Colleges of Nursing-AACN, 2020). Having that said, the HR department, within a health facility, struggles to address the nurse’s high turnover, which burdens the health institutions worldwide (Hunt, 2009). HR takes the lead in applying the best tactics and policies to keep skilled nurses as part of a health facility staff. HR in their search for answers they should tackle limited career opportunities, poor support, low salary and compensation, weak and routine training, lack of recognition and negative supervisor attitudes (Leone, Bruyneel, Anderson et al., 2015). Starting from the believe that human resource practices focus on transferring human capital into meaningful resource so it is necessary to identify a linkage between satisfaction with HR practice and nurses’ turnover especially that only few researchers took this variables into consideration. Therefore, capitalizing on the aforementioned facts, this research aims to assess the relation between satisfaction with HR practices and nurses’ turnover intention.

## 1.2 Research Significance

The health care workers, especially the nurses have the longest contact with the patients. Moreover, Tingen et al. (2009) contend that nurses play multiple roles and “each role carries different responsibilities, though the primary goal of a professional nurse remains the same: to be the client's advocate and provide optimal care” (p. 167). So a nurse actually builds rapport and has a special bond with patients, for this reason a well-motivated nurse is a pre-requisite when a health system needs to be more functional (Rowe, de Savigny, Lanata, & Victora, 2005). Therefore, this research adds to the scarce body of knowledge about the topic in Lebanon in the context of nurse’ turnover and sheds light on the very few studies that assess nurses’ perception regarding their choice to quit their job and addressing the cause of the turnover phenomena, as well as identifying if human resource management influence nurses’ decision to stay or leave.

## 1.3 Purpose of the Study

This study focuses on the relation between human resource practices and nurses’ turnover intention. It will emphasize the need to create a supportive and motivated work environment as a solution to retain qualified nurses and reduce turnover rate. When tackling nurse turnover in the workplace several factors should be tackled including individual, team and institutional related factors (Chu et al., 2014). This paper sheds the light on factors affecting nurse’ turnover. It offers a wider perspective on what practices are helpful and beneficial not only at the design level but also at a practical level to back up a more maintainable workforce in nursing. Human resources practices including but not limited to recruitment, training, salary, promotion and recognition, appraisal system, supervisor attitude and career advancement are the factors that human resource manager should consider to retain qualified nurses (Hejase, El Dirani, Hamdar, & Hazimeh, 2016; Bugajski, Lengerich, Marchese et al. 2017).
## 1.4 Research Objectives

This research aims to assess the correlation between specific HR practices and nurses' turnover. The objective behind this research is to identify:

1) The impact of recognition and reward in nurses' turnover taking into consideration intrinsic and extrinsic rewards.

2) How nurses' satisfaction is affected by salary.

3) How training affects nurses' satisfaction.

4) How does the recruitment selection system and management style influence nurses' turnover rate.

## 1.5 Research Questions

The major research questions are:

* ✓ How does HR practice influence nurses' turnover level?

The study questions can be summarized by the following:

* ✓ Q1: What is the relationship between efficiency of selection and recruitment process and nurses' satisfaction level?

* ✓ Q2: What is the relationship between training and nurses' satisfaction level?

* ✓ Q3: What is the relationship between promotion and recognition programs and nurses' satisfaction level?

* ✓ Q4: What is the relationship between salary and compensation and nurses' satisfaction level?

* ✓ Q5: What is the relationship between performance appraisal that highlight strength and nurses' satisfaction level?

* ✓ Q6: What is the relationship between work environment and nurses' satisfaction level?

* ✓ Q7: What is the relationship between supervisor practices and nurses' satisfaction level?

## 2. Literature Review

Haddad, Annamaraju, & Toney-Butler (2021) stress the fact that “nurses are a critical part of healthcare and make up the largest section of the health profession” (para 1). In fact, “there are approximately 29 million nurses and midwives globally, with 3.9 million of those individuals in the United States. Estimates of upwards of one million additional nurses will be needed by 2020” (Aiken, Cheung, & Olds, 2009; Slattery, Logan, Mudge, Secore, von Reyn, & Maue, 2016; National Council of State Boards of Nursing, 2020). Hospitals use a significant number of nurses to assist in the day-to-day operations. Nevertheless, according to Carayon & Gurses (2008), “nurses are experiencing higher workloads than ever before due to four main reasons: (1) increased demand for nurses, (2) inadequate supply of nurses, (3) reduced staffing and increased overtime, and (4) reduction in patient length of stay” (p. 203). The aforementioned challenges coupled with the shortage of nurses, create a major pressure on healthcare institutions, especially hospitals, to cope and accordingly create strategic integrated solutions for the sake of continuity. In fact, Porter & Lee (2013) contend that, “We must move away from a supply-driven health care system organized around what physicians do and toward a patient-centred system organized around what patients need” (para 3). Capitalizing on the aforementioned recommendation, Von Grätz (2019) citing Dr Eugene Fidelis Soh, CEO of Tan Tock Seng Hospital in Singapore, asserts that the new transformation is patient-centred, “Healthcare is moving beyond disease towards preserving health, it is moving beyond hospitals towards community-based care, and beyond quality towards value... We need to define hospitals not by numbers of beds but by population served” (para 2). To achieve the sought after transformation,
Salmond & Echevarria (2017) stress that nurses play a new fundamental role requiring a new mind-set and a new set of competencies. “These shifts require a new or an enhanced set of knowledge, skills, and attitudes around wellness and population care with a renewed focus on patient-centred care, care coordination, data analytics, and quality improvement” (p. 12). Consequently, and according to Hejase, Rifai, Tabsh, & Hejase (2012), “HR managers will have to have a holistic view of the organization. As their roles become more strategic, they must be able to define strategic goals, cooperate with employees (nurses or other) to achieve the goals, and be acquainted with the financial aspects of business” (p. 28). Therefore, overall, the Human Resources (HR) department responsibility to meet the challenges is not only to regulate the recruitment process but they also manage compensation, benefits, performance management, recognition, promotion and training of employees (Khaliq, Zia-ur-Rehman, & Rashid, 2011). As a matter of fact, Panayotopoulou, Bourantas, & Papalexandris (2003) assert that there is strategic fit between HRM and organizational strategy leading that HRM give “emphasis to quality and innovation, as well as team spirit creation and skills development” (p. 692). As a result, the HR department plays an important role in boosting nurses’ job satisfaction in order to mitigate turnover intentions (Khaliq et al., 2011). Nowadays, the healthcare industry is facing challenge in term of retaining qualified nurse due to high turnover rate (Wheeler, Halbesleben, & Harris, 2012; Roulin, Mayor, & Bangerter, 2014). Mainly nurses who are dissatisfied with the work environment ultimately leave their workplace; as a result, the efforts of the human resource management to mitigate the nurses’ intention to quit continue to receive attention in the recent research.

## 2.1 Nurses’ Turnover Intention

The definitions of turnover and its causes are often inconsistent, that is why it is challenging to link or take a much broader view across the different researches (Tai, Bame, & Robinson, 1998; Hayesa, O’Brien-Pallasa, Duffield et al., 2006). However, all starts with employees’ intention to leave his/her current jobs. More formally, Vandenberg and Nelson (1999) assert that employees’ intention to quit is an individual’s estimated probability that they are permanently leaving their organization at some point in the near future. So, turnover intention is defined as the total willingness of a staff to leave his/her job. Intention is defined as a staff craving to quit the workplace. In fact, intention to leave is a manifestation and predictor of real turnover (Cohen & Golan, 2007), and an immediate antecedent to actual turnover (Halawi, 2014). Moreover, Meler & Toygar (2020) defined ‘turnover intention’ as, “The destructive and active action of nurses as a result of voluntary or involuntary relocation in their working position and their dissatisfaction with their working conditions” (p. 488). Based on the aforementioned, intention to leave among nurses is really a challenging situation especially when the shortage of nurses has become a serious issue across the globe (Blaauw, Ditlopo, Maseko et al., 2012).

## 2.2 Turnover Causes

Aiken et al. (2002) contend that 41% of the nurses in the USA and 32.9% of the Canadian nurses are dissatisfied with their job. Moreover, according to Alhamwan, Bt Mat, & Al Muala (2015), “the global turnover rate among nurses in the U.S.A was 56% in 2002, while in UK it was 22%, and in Jordan it was 37%” (p. 155). However, more recent data show that the turnover rate is “16.4% in America, 19.9% in Canada and 15.1% in Australia” (Meler & Toygar, 2020, p. 491). In general, Flinkman, Isopahkala-Bouret, & Salanterä (2013) revealed that nurses’ intention to leave the profession varies between 4% and 54% in international studies. Omar, Halim, Yusoff, Ahmad, & Ibrahim (2018) contend that the “literature revealed that some of the most compelling determinants of intention to leave were job satisfaction and organizational commitment” (p. 295). Moreover, according to the literature review performed by Omar et al. (2018), there are many factors that have contributed to
intention to leave feeling among employees in different occupational groups. Among them were "job
satisfaction, leadership styles, organizational commitment, work environment, labor market, moral
obligation, job autonomy, and other personal, organizational and work-related and management issues"
(p. 296). Therefore, organizational, individual and economic factors have been identified as major
reasons (Gebregziabher, Berhanie, Berihu, Belstie, & Teklay, 2020) that lead nurses to think about
leaving their job and seeking for other opportunities outside the organization that match their personal
job goals and/or needs.

2.2.1 Organizational Factors

The internal environment in the organization has both a positive and negative influence on nurses' attitude and behavior. For instance, workload, stress, poor management style including weak performance appraisal system, lack of empowerment and autonomy, absence of promotional opportunities and inflexible work schedules are considered as major reasons for nurses to quit their jobs (Bloom & Alexander, 1992). Moreover, Hayajneh, AbuAlRub, Athamneh, & Almakhzoomy (2009) and Alhamwan et al. (2015) found in their study that leadership and pay level have direct influence on nurses' turnover. Furthermore, promotion opportunities and career advancement (Najera, 2008; Zhao & Zhou, 2008) as well as bonuses were recognized as significant in determining individuals' intention to leave. Another study indicated that the occupational factors that is directly associated with nurse turnover intention are hospital work environment, poor management support, lack of job satisfaction, role conflicts, lack of job promotion opportunities, job stress, and job-reward imbalance (de Oliveira et al., 2017; Hasselhorn et al., 2005).

2.2.2 Individual Factors

The reasons behind employees quitting their jobs became topic of interest for both researchers and managers simply because its consequences cannot be easily resolved. Heinen, van Achterberg, Schwendimann et al. (2013) reported that intention to leave varies from 5 to 17% in European countries up to 62.5% as reported for Lebanon by El-Jardali, Alameddine, Jamal et al. (2013). Moreover, researchers report different personal factors as influencers including age, years of experience and educational level (McNeese & Servellen, 2000), work burnout and Quality of Life (QoL) (Albougami, Almazan, Cruz et al., 2020), as well as “filling staffing gaps, the increasingly burdensome tasks they are expected to do to meet employers’ performance targets, and lack of autonomy” (Merrifield, 2017, para 1).

2.2.3 Economic Factors

Nurses' turnover is costly, disruptive and threats the overall quality of care and patient safety in the healthcare domain. As a matter of fact, Flinkman et al. (2013, p. 2) assert that the recurrent nursing shortage is actually leading to a serious and continuous loss of productivity. In fact, when nurses leave the profession they take their accumulated know-how (tacit knowledge), experience, and contribution from the healthcare institutions and shading away such knowledge from the nursing workforce. Consequently, the economic and financial investments used on nurses' education, orientation, and continuing education are lost. Indeed, the aforementioned is costly to hospitals, clinics, etc. "First, because it results in the direct and indirect costs of filling the positions, and second, because of the loss of organizational productivity and knowledge" (Li & Jones, 2013).

Meler & Toygar (2020) summarized in their literature review about turnover costs the different impacts which are depicted in Exhibit 1 herein.
Exhibit 1. Nurses’ turnover costs

**Nursing Turnover Cost Calculation Methodology (NTCCM)** is widely used to distinguish between direct costs (related to the hiring, temporary replacement and hiring of a new employee) and indirect costs (direct time spent managing the dismissal and turnover process as well as the orientation, training and productivity). In fact, O’Brien-Pallas, Griffin, Shamian et al. (2006) suggested that while turnover accounted for 21% of total costs of direct recruitment costs, indirect costs of lost productivity accounted for 79% of total costs.

**Turnover rate**

= Number of employees who left * 100/the average number of employees during the period. The average number of personnel means the average of the total number of staff at the beginning of the term and the number of staff at the end of the term.

4-12% nurse turnover rate is low;

12-22% are considered medium, and

22-44% high turnover

Heinen et al. (2013) found that nurses’ turnover intention in European countries affects the costs of the institution between 5-17%.

Dawson, Stasa, Roche, Homer, & Duffield (2014) found that the cost of changing a nurse can range from $ 22,000.00 to $ 64,000.00

Garnett et al. (2008) reported that the Northern Territory (Australia) show a 38% turnover rate per nurse and an average turnover cost of $ 10,734 (Australian dollars).

Shaffer & Curtin (2020) asserted that the “Nursing Solutions, Inc., found out in 2019, that each 1% change in nurse turnover will cost (or save) the average hospital an additional $328,400. The turnover rate for hospitals’ bedside nurses grew to 17.2%. The average turnover costs result in hospitals losing $4.4 million to $6.9 million each year. The report also found that the average time it takes to fill a staff nursing position is 85 days and costing about $82,000” (para 3).

Sources: Meler & Toygar, 2020, pp. 491-493; O’Brien-Pallas, Griffin, Shamian et al., 2006; Heinen et al., 2013; Dawson et al., 2014; Garnett et al., 2008; Shaffer & Curtin, 2020.

2.3 Turnover Consequences

It is a common sense to associate nurses with patients. Their day to day tasks is closely linked with patients. Therefore, nurse’s performance affects greatly the institution resources. According to Valizadeh, Zamanzadeh, Habibzadeh et al. (2016), “Shortages of nurses affect health care continuity and have negative effects on patient outcomes. Also, health work force crises seriously impair the ability of many countries in fighting disease and improving health. In fact, turnover is causing problems for nursing and health care in the areas of cost, ability to take care of patients and the quality of care delivered” (p. 169). Moreover, Antwi & Bowblis (2018, p. 131) report that turnover leads to lower quality of healthcare and higher mortality. In addition, the University of New Mexico reports that, “When nurses leave a healthcare organization, they create a vacancy that can affect the cost of operation. The vacancy is also costly to other nurses, who may have to work overtime and can experience burnout due to long hours and a high patient load” (RN to BSN Online, 2016, para 5).

2.3.1 Impact on Nurses Satisfaction

Nurses’ turnover lead to shortage on the staff which in turn increases the work demands for the remaining nurses and thus more nurses are likely to leave (RN to BSN Online, 2016, para 5). However, nurse turnover may reduce employee self-confidence and create more anxiety on those who decided to continue working under unfavorable conditions. The result is a “vicious cycle” where there is a
continuous increase in turnover rate among nurses within the healthcare domain with more and more
shortage with this powerful source. Also, turnover creates a disruptive and unstable work environment
especially that new employee will be hired continuously. This instability in the quality of service
delivered to patient have a major influence on the patients' perception and can be negatively associated
with bad image toward hospital as whole (O'Brien-Pallas, 2010). In addition, Perry, Richter, &
Beauvais (2018) assert that turnover is the result of the lack of five workplace factors (dissatisfaction
factors) that align with fulfillment of nurses' needs and are "priorities in the health care context:
supportive leadership, staffing adequacy, nurse-physician teamwork, nursing care practice, and
advancement opportunities. Moreover, the authors add to the aforementioned the psychological needs
of autonomy, competence, and relatedness" (p. 4945).

2.3.2 Impact on Patient Satisfaction

Several studies stressed on the correlation between nurse job and patient's quality of service received. The quality service provided by nurses is critical as it is associated with human lives, thus recruiting capable and enough nurses is essential in this field of work (Clarke & Aiken, 2003). Moreover, the same factors that lead to nurses' satisfaction especially those factors mentioned by Perry et al. (2018, p. 4945), "supportive leadership, staffing adequacy, nurse-physician teamwork, nursing care practice, and advancement opportunities, autonomy, competence, and relatedness" lead to better quality of care, lower mortality, lower feelings of burn-out to other remaining nurses improving their services, and higher patient comfort. Therefore, energizing and motivating nurses to perform will definitely increase the positive affect on their patients increasing patients' satisfaction and faster recovery (Houston et al., 2012; McHugh et al., 2013).

2.3.3 The Nature of HRM on Healthcare Organization

Dussault & Dubois (2003) complain that “current approaches in human resources suggest a number of
weaknesses including being reactive, using an ad-hoc attitude towards problems of HR, having a trend
to dispersal of accountability within Human Resources Management (HRM), capitalizing on a limited
notion of personnel administration that fails to encompass all aspects of HRM, and finally depending
on a short-term perspective of HRM” (p. 1). On the other hand, based on WHO’s report (2000), the
performance and the benefits the health care system can deliver depend largely upon the knowledge,
skills and motivation of those individuals responsible for delivering health services. Nevertheless,
though health care administrators now a day have an adequate knowledge of resource management,
resources utilization and budgeting, which is a good improvement, but according to Khaliq et al. (2011),
“it needs serious strategic human resource planning and organization to be implemented properly. The
dilemma is that hospitals try to recruit and retain the best nurses, whereas nurses look for good
employers, handsome salary and benefits, flexible schedules, stability recognition and better work/life
balance” (p. 975). Healthcare institutions are compound and energetic. Therefore, Kabene, Orchard,
Howard, Soriano, & Leduc (2006) assert that there should be “a balance between the human and
physical resources. Due to their obvious and important differences, it is imperative that human capital
is handled and managed very differently from physical capital” (p. 2). In fact, Dieleman & Harnmeijer
(2006) report that, “HR strategies are based on interventions that can take place at two levels, the first
at the macro or health-system level, such as HR policy and planning, rural recruitment and training and
bonding; and the second at the micro or facility level, aimed at improving job satisfaction by
addressing working conditions, improve the living conditions, providing incentives and offering
professional development” (p. 1). Moreover, HR strategies need to address, at various levels, staff
members’ performance which is determined by productivity, responsiveness and competence. However,
the aforementioned "elements are influenced by absenteeism, motivation and job satisfaction, obtaining knowledge, skills and attitudes, accountability systems and working conditions, which in turn are all interrelated" (Dieleman & Harnmeijer, 2006, p. 1). Consequently, HR managers' responsibility lies to monitor, coordinate and take the lead at healthcare system aspects. They need to supervise and guarantee that all tasks are performed properly, using the institution resources in the best way to achieve targets and goals. This can be achieved by allowing authority-sharing and power to create decisions that address the quality of service provided as well as the performance of the staff (El-Demerdash & Obied, 2016).

#### 2.3.4 Relation between Nurses' Satisfaction with HR Practices & Nurses' Turnover

Nurses are the largest component of hospitals and public health centers and the long-term healthcare providers (AACN, 2019), with a 7% job outlook that is faster than average (U.S. Bureau of Labor Statistics, 2019). High turnover is associated with scarcity in number of nurses, dissatisfaction and lack of managerial support. Research in the Arab region show trends similar to the western countries for nurse turnover rates and the determinants of such turnover are also congruent. For example, Al-Ahmadi (2009), Albougami et al. (2020) and Abdulaziz, Alsufyani, Alforihidi et al. (2020) showed that in Saudi Arabia the main causes for nurses dissatisfaction, and thus turnover, were related to payment, work conditions, hospitals' policy, recognition system and other administration issues. Moreover, Hannawi & Al Salmi (2014) reported that UAE nurse shortage is "compounded by morale problems, skill imbalances and geographical maldistribution" (p. 332). Another study in the UAE by Underwood (2010) quoted Dr. Fatima al Rifai, the Director of the federal department of nursing at the Ministry of Health and secretary of the new Nursing and Midwifery Council, that there is a need to boost the number of nurses by 25 to 30 per cent over the current 23,000 nurses. Al Salibi (2012) also quoted Al Rifai who added that "this shortage is related to poor image of nurses among themselves, stressful working condition, unspecified job description, poorly paid salary, unclear relationship with other healthcare professional and lack of motivation" (p. 4).

In fact, dissatisfaction with HR practices has regularly been referred to as a main cause for high nurses' turnover intention (Gebregziabher et al., 2000). The research is continuous to discover the real cause of nurse turnover in the context of HR. For example, the Jordanian researchers Amarneh, Raza, Matloob, Alharbi, & Abbasi (2021) contend that the existing Human Resource for Health (HRH) challenges include retention and continuous training. The Iranian researcher Mosadeghrad (2013), in his literature review, quoted several researchers' results stating HR-related factors influencing turnover including, "Less quality of work life, job satisfaction and organizational commitment, organizational culture, job stress, burnout, long shifts, and work-family conflict. In addition, socio-demographic characteristics such as age, marital status, tenure, and education" (p. 169). On the other hand, Mosadeghrad (2013) found that hospital managers must apply appropriate policies to decrease nurses' occupational stressors. They can do that by increasing nurses' satisfaction with policies, work conditions, equal compensation, equal promotion, training to appropriate strategies to cope with stress easily and effectively, Physical activity, meditation, healthy life style and time management" (p. 174). Moreover, Yürümezoğlu, Kocaman, & Haydarİ (2019) showed that "the organizational affective and normative commitment and professional affective commitment play a significant role in influencing nurses' organizational and professional turnover intentions" (p. 274). Furthermore, Roth, Berger, Krug, Mahler, & Wensing (2021) found that "Nurses who migrated to Germany were primarily seeking better working conditions, a higher standard of living, and professional development opportunities" (p. 2). However, results show that "internationally trained nurses reported lower work-related burnout climate than host nurses but
still at a moderately high degree (Safety Culture Domains). Host nurses indicated a higher workload and a lower Work-Life-Balance compared to nurses who trained abroad. The Safety Culture Domains and Engagement Assessment Tool showed room for improvement in both groups” (Roth et al., 2021, p. 2). Furthermore, de Oliveira, Griep, Portela, & Rotenberg (2017) reported in their research in Brazil that “variables associated with the intention to leave were as follows: age, male sex, not holding a leadership position, highly demanding work, passive work, effort-reward imbalance, poor self-rated health, over-commitment to the job, and poor supervisor support” (p. 1). In addition, Sharififard, Asayesh, Rahmani-Anark et al. (2019) in their research with Iranian nurses found that “work climate, type of employment, marital status, and overtime working were significant predictors of nurses' intention to leave” (p. 457).

# 3. Research Methodology

This study is based on a positivism philosophy (Saunders, Philip, Thornhill, & Bristow, 2019), which according to Hejase & Hejase (2013), “the researchers act as objective analysts, independent, and neither affect nor are affected by the subject of the research” (p. 77). Moreover, this research is quantitative following a deductive approach that “is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods” (Elo & Helvi, 2008, p. 107). In the process to find answers to the research questions addressed in our study, a structured survey questionnaire was administered to 150 nurses in order to assess their satisfaction level with HR practices in the hospitals.

## 3.1 Sampling and Sample Size

The questionnaire was distributed to 140 Lebanese nurses chosen conveniently working in private hospitals in Tripoli, North Lebanon, who willingly provided their consent to participate. According to the Order of Nurses in Lebanon (2020), there are 1,698 nurses (79% Females and 21% males) in Northern Lebanon whereby 83% work in hospitals reducing the number to 1,410 nurses. Due to the limitation of resources, we targeted nurses working in private hospitals in the main city of Tripoli in Northern Lebanon with approximately a population of 800 nurses. We used Cochran's (1977) formula to find out the sample size:

$$n = [Z^2(P)(Q)/e^2]$$

Where,

Z = 1.96 (95% Confidence level), P = 79% female nurses, Q = 21% male nurses, and e = 8% error (due to the lack of precise numbers in Lebanese statistics, we opted for less than 10%). Therefore the sample size “n” is 99.58 nurses or 100 nurses to be specific.

Participants were selected from different departments and different hospitals in Tripoli, North Lebanon. Questionnaires were distributed by personnel from the human resource departments and the other by the researchers. 110 questionnaires were collected back. But 10 questionnaires were not filled adequately therefore the researchers discarded them and only 100 questionnaire were analyzed with a response rate of 71.43%.

## 3.2 Questionnaire Design

The questionnaire is divided into three sections. The first section included four demographics questions including gender, age, marital status and years of experience. The second section focuses on the HR practice factors and includes seven variables distributed as follows: Recruitment and selection of staff (3 statements), training (4 statements), salary and benefits (3 statements), promotion and recognition (2 statements), appraisal system (3 statements), supervisor attitude (3 statements) and career advancement
(2 statements). The third section includes four (4) statements about nurses' intention to turnover. Sections two and three use a 5-level Likert scale, with responses coded as follows: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree.

### 3.3 Reliability Analysis

The Internal Reliability of the 24-item scale is assessed using the Cronbach's Alpha technique. Table 1 shows that the 24-item scale produced a Cronbach's Alpha = 0.682, in addition that Cronbach's alpha if items deleted all fall in the range 0.648 to 0.692 matching the range 0.6-0.7 labeled "Moderate" and acceptable (Burns & Burns, 2008, p. 481). In fact, Hejase & Hejase contend that "the generally agreed upon lower limit for Cronbach's alpha is 0.70, although it may decrease to 0.60 in exploratory research" (p. 570). Moreover, Cronbach's alpha for section two (HR satisfaction factors) is 0.621 and for section three (turnover intention measures) is 0.788. The aforementioned indicates a moderate and acceptable "strength of association and proves that the selection of the questions is suitable for the questionnaire purpose" (Chehimi et al., 2019, p. 1915).

<table><caption>Table 1. Reliability Statistics</caption><thead><tr><td>Sections</td><td>Cronbach's Alpha</td><td>N of Items</td></tr></thead><tbody><tr><td>Overall Sections Two &amp; Three</td><td>.682</td><td>24</td></tr><tr><td>Section Two</td><td>.621</td><td>20</td></tr><tr><td>Section Three</td><td>.788</td><td>04</td></tr></tbody></table>

### 3.4 Data Analysis

The collected primary data were analyzed using the Statistical Product and Service Solutions, SPSS-version 25.0 IBM program. Descriptive analysis is used first followed by factor analysis and regression.

### 3.5 Research Framework

Figure 1 depicts the proposed research framework consisting of seven hypotheses which help the assessment of the research questions and at the same time lead to a final inferential model relating the hospitals' human resources practices to nurses' turnover intention.

Next, the hypotheses are as follows:

#### H1. Recruitment & Selection

H1₀: There is no relationship between nurses' satisfaction due to recruitment & selection process and nurses' turnover intention.

H1₁: There is a relationship between nurses' satisfaction due to recruitment & selection process and nurses' turnover intention.

#### H2. Training Program

H2₀: There is no relationship between nurses' satisfaction due training programs and nurses' turnover intention.

H2₁: There is a relationship between nurses' satisfaction due training programs and nurses' turnover intention.

#### H3. Salary & Compensation

H3₀: There is no relationship between nurses' satisfaction due to salary & compensation & nurses' turnover intention.
**H3a:** There is a relationship between nurses' satisfaction due to salary & compensation & nurses' turnover intention.

**H4. Promotion & Recognition**

**H4o:** There is no relationship between nurses' satisfaction due to promotion & recognition practices & nurses' turnover intention.

**H4a:** There is a relationship between nurses' satisfaction due to promotion & recognition practices & nurses' turnover intention.

**H5. Appraisal System**

**H5o:** There is no relationship between nurses' satisfaction due to appraisal system & nurses' turnover intention.

**H5a:** There is a relationship between nurses' satisfaction due to appraisal system & nurses' turnover intention.

**H6. Supervisor Attitude**

**H6o:** There is no relationship between nurses' satisfaction due to supervisor attitude & nurses' turnover intention.

**H6a:** There is a relationship between nurses' satisfaction due to supervisor attitude & nurses' turnover intention.

**H7. Career Advancement**

**H7o:** There is no relationship between nurses' satisfaction due to career advancement opportunities at hospital & nurses' turnover intention.

**H7a:** There is a relationship between nurses' satisfaction due to career advancement opportunities at hospital & nurses' turnover intention.

<figure><img src="image_1.png" /><figcaption>Figure 1. Conceptual Framework</figcaption></figure>
# 4. Results and Discussion

## 4.1 Demographics

Results show that nurses are 30% males, and 70% females. Their average age is 30 years with 50% of the sample's age ranging between 21 and 40. Also, 57% are single (37.5% female & 20% male), 34% are married with children (27.5% female & 7.5% male), and 9% divided evenly between married without children, separated and widowed, respectively (see Table 2). Furthermore, results show that 42% of the nurses have an experience of 5 years or less, 28% have between 6 and 10 years, and 30% have more than 10 years of experience.

<table><caption>Table 2. Cross Tabulation between Gender and Marital Status</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="5">Marital Status</th><th rowspan="2">Total</th></tr><tr><th>Single</th><th>Married with Children</th><th>Married without Children</th><th>Separated</th><th>Widow</th></tr></thead><tbody><tr><td>Male</td><td>% of Total</td><td>20.0%</td><td>7.5%</td><td>2.5%</td><td>0.0%</td><td>0.0%</td><td>30.0%</td></tr><tr><td>Female</td><td>% of Total</td><td>37.5%</td><td>27.5%</td><td>0.0%</td><td>2.5%</td><td>2.5%</td><td>70.0%</td></tr><tr><td>Total</td><td>% of Total</td><td>57.5%</td><td>35.0%</td><td>2.5%</td><td>2.5%</td><td>2.5%</td><td>100.0%</td></tr></tbody></table>

Table 3 shows the correlation between nurses' ages and their feeling of belongingness to the hospital that they are working with. Most of the nurses who are below 21 years old feel that they are not part of the hospital (25%). Similarly, for those who are between 21 and 30 (20%) and between 31 to 40 (10%) feel that they don't belong to the hospital, only 2.5% and 10% of each age range, respectively feels such belongingness. While nurses who are above 40 years old most of them feel that they are part of the hospital (17.5%).

<table><caption>Table 3. Cross Tabulation between Age and Belongingness to Hospital</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">Feel Not Part of the Hospital</th><th colspan="4">Total</th></tr><tr><th>Agree</th><th>Neutral</th><th>Disagree</th><th></th></tr></thead><tbody><tr><td rowspan="4">Age</td><td>less than 20 years old</td><td>% of Total</td><td>25.0%</td><td>5.0%</td><td>0.0%</td><td>30.0%</td></tr><tr><td>21 to 30 years old</td><td>% of Total</td><td>20.0%</td><td>2.5%</td><td>2.5%</td><td>25.0%</td></tr><tr><td>31 to 40 years old</td><td>% of Total</td><td>10.0%</td><td>5.0%</td><td>10.0%</td><td>25.0%</td></tr><tr><td>more than 40 years old</td><td>% of Total</td><td>0.0%</td><td>2.5%</td><td>17.5%</td><td>20.0%</td></tr><tr><td></td><td>Total</td><td>% of Total</td><td>55.0%</td><td>15.0%</td><td>30.0%</td><td>100.0%</td></tr></tbody></table>

Table 3 shows that 55% of nurses are of young age (less than 40 years old) and they agree that they feel that they are not part of the hospital they are in. Thus this may increase the probability of having high turnover rate among this group of nurses. This result is congruent with the findings by Flinkman, Isopahkala-Bouret, & Salanterä (2013) and Ferede, Kibret, Million et al. (2018).
## 4.2 Descriptive Statistics: Likert Scale Statements

The 5-level Likert scale was used to measure the respondents' choices to the questionnaire sections pertaining to HR practices analysis. Table 4 shows the details.

<table><caption>Table 4. Five-point Likert Scale</caption><thead><tr><th>Degree of approval</th><th>Strongly Disagree</th><th>Disagree</th><th>Neutral</th><th>Agree</th><th>Strongly Agree</th></tr></thead><tbody><tr><th>Code</th><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td></tr></tbody></table>

This study adopted the following limits to help analyze the arithmetic mean of the terms and assessing the adoption level. The following scale is used:

$$ \text{Level Difference} = [\text{Upper bound}-\text{Lower bound}]/\text{Number of levels} = [5-1]/5 = 0.80 $$

Thus, the modified scores are distributed as shown in Table 5.

<table><caption>Table 5. Modified 5-level Likert Scale</caption><thead><tr><th>Relative Importance</th><th>Strongly Disagree</th><th>Disagree</th><th>Neutral</th><th>Agree</th><th>Strongly Agree</th></tr></thead><tbody><tr><th>Arithmetic Mean Intervals</th><td>1-1.80</td><td>1.81-2.60</td><td>2.61-3.40</td><td>3.41-4.20</td><td>4.21-5</td></tr></tbody></table>

Next, Table 6 presents all the results generated by grouping “Strongly Agree and Agree” as well as “Strongly Disagree and Disagree” in order to have an easier interpretation of the nurses’ attitude (extent of satisfaction) towards the different HR practices. Results show that the grand majority of the statements received a mean average in the Neutral zone (2.61-3.40). Moreover, the respondent nurses showed less than moderate agreement with the HR practices scoring 35 to 50% (16 out of 20 statements), moderate agreement scoring 51 to 65% (3 out of 20) and only one statement scored 86%. As a matter of fact, the respondent nurses scored a general agreement average of 44.45% in the seven sections representing organizational HR practices. These results lead to the expectation that nurses’ intention to leave is actually a fact. On the other hand, reviewing the results for the questionnaire section on turnover intention, the overall average of the nurses’ agreement on the four statements is 63%. This result confirms the fact that nurses are actually intending to leave their hospitals sooner or later (see Table 7).
<table><caption>Table 6. HR Practices</caption><thead><tr><th>No.</th><th>Statement</th><th>A</th><th>N</th><th>D</th><th>Mean</th><th>Std. Dev.</th></tr></thead><tbody><tr><td colspan="7"><strong>Recruitment & Selection</strong></td></tr><tr><td></td><td>Hire right employee for right position</td><td>42</td><td>14</td><td>44</td><td>3.03</td><td>1.068</td></tr><tr><td></td><td>Wide recruitment system to insure efficiency</td><td>35</td><td>13</td><td>52</td><td>2.85</td><td>1.048</td></tr><tr><td></td><td>Hospital uses fair selection system</td><td>37</td><td>11</td><td>52</td><td>2.88</td><td>1.018</td></tr><tr><td colspan="7"><strong>Training</strong></td></tr><tr><td></td><td>Upon arrival receive general orientation</td><td>86</td><td>3</td><td>11</td><td>3.75</td><td>.730</td></tr><tr><td></td><td>Training at least 3 times per year</td><td>22</td><td>3</td><td>75</td><td>2.41</td><td>.900</td></tr><tr><td></td><td>Trainings received are relevant to work</td><td>53</td><td>8</td><td>39</td><td>3.09</td><td>1.065</td></tr><tr><td></td><td>Training program meet nurses' goals & expectations</td><td>29</td><td>9</td><td>62</td><td>2.66</td><td>1.007</td></tr><tr><td colspan="7"><strong>Salary & Other Benefits</strong></td></tr><tr><td></td><td>Based on nurse's position & ability, salary is fair</td><td>28</td><td>7</td><td>65</td><td>2.58</td><td>.987</td></tr><tr><td></td><td>Satisfied with compensation program at Hospital</td><td>55</td><td>8</td><td>37</td><td>3.15</td><td>1.029</td></tr><tr><td></td><td>Vacation time received is fair</td><td>60</td><td>9</td><td>31</td><td>3.30</td><td>.990</td></tr><tr><td colspan="7"><strong>Promotion & Recognition</strong></td></tr><tr><td></td><td>During the nurse's years of experience, was promoted</td><td>50</td><td>5</td><td>45</td><td>3.02</td><td>1.054</td></tr><tr><td></td><td>Receive the right amount of recognition for work</td><td>32</td><td>9</td><td>59</td><td>2.69</td><td>1.002</td></tr><tr><td colspan="7"><strong>Appraisal System</strong></td></tr><tr><td></td><td>Appraisal is carried out continuously every 6 months</td><td>44</td><td>8</td><td>48</td><td>2.97</td><td>1.010</td></tr><tr><td></td><td>Satisfied with performance feedback provided by appraiser</td><td>42</td><td>6</td><td>52</td><td>2.90</td><td>1.030</td></tr><tr><td></td><td>Evaluation of performance it to highlight strengths & improve weaknesses</td><td>47</td><td>5</td><td>48</td><td>2.97</td><td>1.010</td></tr><tr><td colspan="7"><strong>Supervisor's Attitude</strong></td></tr><tr><td></td><td>Promotes an atmosphere of teamwork</td><td>44</td><td>7</td><td>49</td><td>2.93</td><td>1.057</td></tr><tr><td></td><td>Provides nurses with continuous feedback & support to keep them motivate</td><td>47</td><td>3</td><td>50</td><td>2.94</td><td>1.033</td></tr><tr><td></td><td>Channels of communication are open between nurses and supervisor</td><td>45</td><td>5</td><td>50</td><td>2.95</td><td>1.067</td></tr><tr><td colspan="7"><strong>Career Advancement</strong></td></tr></tbody></table>
<table><caption>Table 7. Turnover Intention</caption><tr><td>Job gives the nurse opportunity to learn</td><td>50</td><td>4</td><td>46</td><td>3.04</td><td>1.014</td></tr><tr><td>Aware of advancement opportunities existing in Hospital</td><td>46</td><td>6</td><td>48</td><td>3.04</td><td>1.063</td></tr></table>

<table><thead><tr><th>No.</th><th>Statement</th><th>A</th><th>N</th><th>D</th><th>Mean</th><th>Std. Dev.</th></tr></thead><tbody><tr><td colspan="7">Turnover Intention</td></tr><tr><td></td><td>Feeling not a part of the Hospital</td><td>56</td><td>16</td><td>28</td><td>3.57</td><td>1.350</td></tr><tr><td></td><td>Considering leaving nursing career due to feeling of exhaustion from the stressful work condition</td><td>76</td><td>3</td><td>24</td><td>3.68</td><td>.952</td></tr><tr><td></td><td>Own prospects might not be good if remaining in the job</td><td>66</td><td>12</td><td>22</td><td>3.58</td><td>1.103</td></tr><tr><td></td><td>Searching for work opportunity outside hospital</td><td>54</td><td>1</td><td>55</td><td>3.03</td><td>1.243</td></tr></tbody></table>

Following the descriptive analysis, it is important next to explore the actual HR factors influencing the nurses' intention to leave using Factor Analysis. Then, after having the actual factors, inferential analysis is conducted using regression analysis.

### 4.3 Inferential Analysis

#### 4.3.1 Common Method Bias (CMB)

This research uses a survey questionnaire, therefore it is necessary to test for “the risk of common method bias” (Jakobsen & Jensen, 2015, p. 3; Farhat, 2020). Hence, data are inspected for CMB risk using Harman Single Factor Test (Younis, Hejase, Abdallah, Haddad, & Hejase, 2021a).

<table><caption>Table 8. Total Variance Explained</caption><thead><tr><th rowspan="2">Component</th><th colspan="3">Initial Eigenvalues</th><th colspan="3">Extraction Sums of Squared Loadings</th><th>Rotation Sums of Squared Loadings</th></tr><tr><th>Total</th><th>% of Variance</th><th>Cumulative %</th><th>Total</th><th>% of Variance</th><th>Cumulative %</th><th>Total</th></tr></thead><tbody><tr><td>1</td><td>3.367</td><td>17.723</td><td>17.723</td><td>3.367</td><td>17.723</td><td><strong>17.723</strong></td><td>2.789</td></tr></tbody></table>

Table 8 shows the result for the Principal Component Analysis. One factor only is tested for the resultant % of variance is less than 50% (Harman Single Factor Test). Component 1 shows a value of 17.723% < 50%, concluding that no CMB is found and data are ready for further analysis. Consequently, the next step is to extract the valid constructs for this research using Factor Analysis.

#### 4.3.2 Factor Analysis

An initial testing of the data is performed using the Principle Component Analysis (PCA) with subsequent rotation (starting with Varimax and followed by Direct Oblimin). The analysis was carried out using 24-item 5-level Likert scale statements. Results from the resultant pattern matrix showed that 5 items had to be eliminated to improve the results.
### 4.3.2.1 Main Factor Analysis Run

The initial run resulted in defining 6 factors only out of the 7 questionnaire constructs and decreasing the 24 items scale to 19 items scale with satisfactory results and improvement in the total variance explained results to 62.879% as well as the weights of all the other items. Consequently, the main run herein is completed.

### 4.3.2.2 PCA using Varimax Rotation

The correlation matrix suitable for factoring consists of all correlations exceeding 0.4 and are statistically significant (less 5%). Table 9 shows that the Bartlett test of Sphericity is statistically significant ($\chi^2 = 512.542$, Sig. =.000) and the Kaiser-Meyer-Olkin measure of sampling adequacy is equal to 0.655 (above .60). This means that variables are correlated to each other, and grouping of variables is possible (Coakes, 2013; Burns & Burns, 2008). Moreover, the anti-image correlation matrix reveals that “all measures of sampling adequacy (MSA) are above the acceptable level of 0.5” (Coakes, 2013, p. 133).

<table><caption>Table 9. KMO and Bartlett's Test</caption><tr><td>Kaiser-Meyer-Olkin Measure of Sampling Adequacy</td><td>.655</td></tr><tr><td>Approx. Chi-Square</td><td>512.542</td></tr><tr><td>Bartlett's Test of Sphericity</td><td></td></tr><tr><td>df</td><td>171</td></tr><tr><td>Sig.</td><td>.000</td></tr></table>

Communalities reported in Table 10 varied from 0.474 to 0.787. According to Burns & Burns (2008), “Communalities show how much of the variances in each variable have been accounted for the extracted factors” (p. 455). For example, over 78.70% of the variance in “Channels of Communication Open” is accounted for, while only 47.4% of the variance in “Sex” is accounted for. Next, Table 11 displays the cumulative percentages and the total variance explained. Moreover, Figure 2 shows the Scree plot and that six factors can be extracted because they have eigenvalues greater than one (1), that is, six factors extracted means that 62.879% of the variance would be explained. According to Burns & Burns (2008, p. 456), the aforementioned is consistent with Kaiser's Rule.

<table><caption>Table 10. Communalities</caption><thead><tr><th></th><th>Initial</th><th>Extraction</th></tr></thead><tbody><tr><td>Sex</td><td>1.000</td><td>.474</td></tr><tr><td>Wide Recruitment System</td><td>1.000</td><td>.549</td></tr><tr><td>Use Fair Selection System</td><td>1.000</td><td>.561</td></tr><tr><td>Trainings Relevant to Work</td><td>1.000</td><td>.629</td></tr><tr><td>Training Program Meet Goals & Expectations</td><td>1.000</td><td>.671</td></tr><tr><td>Based On Job Status Salary is Fair</td><td>1.000</td><td>.575</td></tr><tr><td>Satisfied with Compensation Program at Hospital</td><td>1.000</td><td>.665</td></tr><tr><td>Appraisal is Continuous Every 6 Mon</td><td>1.000</td><td>.599</td></tr></tbody></table>
<table><tr><td>Satisfied with Performance Feedback</td><td>1.000</td><td>.617</td></tr><tr><td>Evaluation of Performance Highlights Strengths & Improves Weaknesses</td><td>1.000</td><td>.553</td></tr><tr><td>Promotes Atmosphere of Teamwork</td><td>1.000</td><td>.633</td></tr><tr><td>Provides Feedback & Support to Motivate</td><td>1.000</td><td>.763</td></tr><tr><td>Channels of Communication Open</td><td>1.000</td><td>.787</td></tr><tr><td>Job Gives Opportunities to Learn</td><td>1.000</td><td>.484</td></tr><tr><td>Feel Not Part of the Hospital</td><td>1.000</td><td>.755</td></tr><tr><td>Consider Leaving due to Exhaustion and Stress</td><td>1.000</td><td>.764</td></tr><tr><td>My Prospects are not Good If Staying</td><td>1.000</td><td>.787</td></tr><tr><td>Searching for Work Opportunity Outside Hospital</td><td>1.000</td><td>.554</td></tr><tr><td>Marital Status</td><td>1.000</td><td>.528</td></tr></table>

Extraction Method: Principal Component Analysis.

<table><caption>Table 11. Total Variance Explained</caption><thead><tr><th rowspan="2">Component</th><th colspan="3">Initial Eigenvalues</th><th colspan="3">Extraction Sums of Squared Loadings</th><th>Rotation Sums of Squared Loadings<sup>a</sup></th></tr><tr><th>Total</th><th>% of Variance</th><th>Cumulative %</th><th>Total</th><th>% of Variance</th><th>Cumulative %</th><th>Total</th></tr></thead><tbody><tr><td>1</td><td>3.367</td><td>17.723</td><td>17.723</td><td>3.367</td><td>17.723</td><td>17.723</td><td>2.789</td></tr><tr><td>2</td><td>2.335</td><td>12.289</td><td>30.013</td><td>2.335</td><td>12.289</td><td>30.013</td><td>2.818</td></tr><tr><td>3</td><td>1.997</td><td>10.512</td><td>40.524</td><td>1.997</td><td>10.512</td><td>40.524</td><td>1.744</td></tr><tr><td>4</td><td>1.592</td><td>8.379</td><td>48.904</td><td>1.592</td><td>8.379</td><td>48.904</td><td>1.886</td></tr><tr><td>5</td><td>1.431</td><td>7.532</td><td>56.436</td><td>1.431</td><td>7.532</td><td>56.436</td><td>1.566</td></tr><tr><td>6</td><td>1.224</td><td>6.444</td><td>62.879</td><td>1.224</td><td>6.444</td><td>62.879</td><td>1.532</td></tr><tr><td>7</td><td>.949</td><td>4.996</td><td>67.875</td><td></td><td></td><td></td><td></td></tr><tr><td>8</td><td>.850</td><td>4.476</td><td>72.351</td><td></td><td></td><td></td><td></td></tr><tr><td>9</td><td>.822</td><td>4.328</td><td>76.679</td><td></td><td></td><td></td><td></td></tr><tr><td>10</td><td>.738</td><td>3.884</td><td>80.563</td><td></td><td></td><td></td><td></td></tr><tr><td>11</td><td>.659</td><td>3.466</td><td>84.029</td><td></td><td></td><td></td><td></td></tr><tr><td>12</td><td>.590</td><td>3.104</td><td>87.133</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>13</td><td>.531</td><td>2.794</td><td>89.927</td></tr><tr><td>14</td><td>.445</td><td>2.342</td><td>92.269</td></tr><tr><td>15</td><td>.394</td><td>2.074</td><td>94.343</td></tr><tr><td>16</td><td>.351</td><td>1.845</td><td>96.188</td></tr><tr><td>17</td><td>.264</td><td>1.390</td><td>97.578</td></tr><tr><td>18</td><td>.252</td><td>1.327</td><td>98.904</td></tr><tr><td>19</td><td>.208</td><td>1.096</td><td>100.000</td></tr></table>

Extraction Method: Principal Component Analysis.

a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.

<figure><img src="image_2.png" /><figcaption>Figure 2. Scree Plot</figcaption></figure>

Initially Varimax rotation is carried out, where “the factor axes are kept at right angles to each other. This rotation is regularly chosen. Ordinarily, rotation reduces the number of complex variables and improves interpretation” (Hejase et al., 2014, p. 1573). However, the rotated solution still included several complex variables. These items must be interpreted with caution and to lessen the intensity of the observed ambiguous structure, oblique rotation (Direct Oblimin) is chosen (Coakes, 2013, p. 137).

#### 4.3.2.3 PCA using Oblimin Rotation

Oblimin rotation leads to more interpretable solution. Two matrices result. The first is the “Pattern” matrix (Table 12) and the second is the “Structure” matrix (Table 13). Loadings differences are clearly seen and separated in the pattern matrix, a fact that leads to choose this matrix for interpretation. Actually the loadings represent the unique relationship between the factor and the variable. In addition, the Pattern matrix has fewer complex variables and simpler structure. The Factor correlation matrix indicates the relationship between factors. All factors are goodly related.
<table><caption>Table 12. Pattern Matrixª</caption><thead><tr><th></th><th colspan="6">Component</th></tr><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Sex</td><td></td><td></td><td></td><td></td><td>.469</td><td></td></tr><tr><td>Wide Recruitment System</td><td></td><td></td><td></td><td></td><td>.647</td><td></td></tr><tr><td>Use Fair Selection System</td><td></td><td></td><td></td><td></td><td>.713</td><td></td></tr><tr><td>Trainings Relevant to Work</td><td></td><td></td><td></td><td></td><td></td><td>.772</td></tr><tr><td>Training Program Meet Goals &amp; Expectations</td><td></td><td></td><td></td><td></td><td></td><td>.718</td></tr><tr><td>Based On Job Status Salary is Fair</td><td></td><td></td><td>.676</td><td></td><td></td><td></td></tr><tr><td>Satisfied with Compensation Program at Hospital</td><td></td><td></td><td>.764</td><td></td><td></td><td></td></tr><tr><td>Appraisal is Continuous Every 6 Mon</td><td></td><td></td><td></td><td>.724</td><td></td><td></td></tr><tr><td>Satisfied with Performance Feedback</td><td></td><td></td><td></td><td>.771</td><td></td><td></td></tr><tr><td>Evaluation of Performance Highlights Strengths &amp; Improves Weaknesses</td><td></td><td></td><td></td><td>.628</td><td></td><td></td></tr><tr><td>Promotes Atmosphere of Teamwork</td><td></td><td>.744</td><td></td><td></td><td></td><td></td></tr><tr><td>Provides Feedback &amp; Support to Motivate</td><td></td><td>.871</td><td></td><td></td><td></td><td></td></tr><tr><td>Channels of Communication Open</td><td></td><td>.877</td><td></td><td></td><td></td><td></td></tr><tr><td>Job Gives Opportunities to Learn</td><td></td><td>.552</td><td></td><td></td><td></td><td></td></tr><tr><td>Feel Not Part of the Hospital</td><td></td><td></td><td>.782</td><td></td><td></td><td></td></tr><tr><td>Consider Leaving due to Exhaustion and Stress</td><td></td><td></td><td>.834</td><td></td><td></td><td></td></tr><tr><td>My Prospects are not Good If Staying</td><td></td><td></td><td>.850</td><td></td><td></td><td></td></tr><tr><td>Searching for Work Opportunity Outside Hospital</td><td></td><td></td><td>.656</td><td></td><td></td><td></td></tr><tr><td>Marital Status</td><td></td><td></td><td></td><td></td><td></td><td>-.465</td></tr><tr><td colspan="7">Extraction Method: Principal Component Analysis.</td></tr><tr><td colspan="7">Rotation Method: Oblimin with Kaiser Normalization.</td></tr><tr><td colspan="7">a. Rotation converged in 34 iterations.</td></tr></tbody></table>
<table><caption>Table 13. Structure Matrix</caption><thead><tr><th></th><th colspan="6">Component</th></tr><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Sex</td><td></td><td></td><td></td><td></td><td>.503</td><td></td></tr><tr><td>Wide Recruitment System</td><td></td><td></td><td></td><td></td><td>.628</td><td></td></tr><tr><td>Use Fair Selection System</td><td></td><td></td><td></td><td></td><td>.722</td><td></td></tr><tr><td>Trainings Relevant to Work</td><td></td><td></td><td></td><td></td><td></td><td>.776</td></tr><tr><td>Training Program Meet Goals &amp; Expectations</td><td></td><td></td><td></td><td></td><td></td><td>.723</td></tr><tr><td>Based On Job Status Salary is Fair</td><td></td><td></td><td>.684</td><td></td><td></td><td></td></tr><tr><td>Satisfied with Compensation Program at Hospital</td><td></td><td></td><td>.762</td><td></td><td></td><td></td></tr><tr><td>Appraisal is Continuous Every 6 Mon</td><td></td><td></td><td></td><td>.717</td><td></td><td></td></tr><tr><td>Satisfied with Performance Feedback</td><td></td><td></td><td></td><td>.767</td><td></td><td></td></tr><tr><td>Evaluation of Performance Highlights Strengths &amp; Improves Weaknesses</td><td></td><td></td><td></td><td>.658</td><td></td><td></td></tr><tr><td>Promotes Atmosphere of Teamwork</td><td></td><td></td><td>.760</td><td></td><td></td><td></td></tr><tr><td>Provides Feedback &amp; Support to Motivate</td><td></td><td></td><td>.856</td><td></td><td></td><td></td></tr><tr><td>Channels of Communication Open</td><td></td><td></td><td>.875</td><td></td><td></td><td></td></tr><tr><td>Job Gives Opportunities to Learn</td><td></td><td></td><td>.596</td><td></td><td></td><td></td></tr><tr><td>Feel Not Part of the Hospital</td><td></td><td></td><td>.793</td><td></td><td></td><td></td></tr><tr><td>Consider Leaving due to Exhaustion and Stress</td><td></td><td></td><td>.816</td><td></td><td></td><td></td></tr><tr><td>My Prospects are not Good If Staying</td><td></td><td></td><td>.860</td><td></td><td></td><td></td></tr><tr><td>Searching for Work Opportunity Outside Hospital</td><td></td><td></td><td>.662</td><td></td><td></td><td></td></tr><tr><td>Marital Status</td><td></td><td></td><td></td><td></td><td>.474</td><td></td></tr><tr><td colspan="7">Extraction Method: Principal Component Analysis.</td></tr><tr><td colspan="7">Rotation Method: Oblimin with Kaiser Normalization.</td></tr></tbody></table>

### 4.3.2.4 Interpretation of Factors

The next step determines the number of factors to interpret and their assigned labels (Younis et al., 2021). Applying Kaiser's Rule and the Scree-test, six factors were deemed important. Following Oblimin rotation, factor 1 was loaded on 4 elements (see Table 14) that reflected “Supervision” and accounted for 17.723% of the variance (see Table 9). Factor 2 was loaded on 4 items (accounted for 12.289% of the variance). It was labeled “Intention to Leave”. The third factor with 2 items was labeled
"Compensation" and accounted for 10.512% of the variance. Remaining factors follow suit. All factors are depicted in Table 13.

<table><caption>Table 14. Interpretation of Factors/Components</caption><thead><tr><th>Rotation Sum of Squared Loadings<br>(Varimax) / % of Variance</th><th>Component</th></tr></thead><tbody><tr><td>17.723%</td><td><strong>Supervision [4 elements]</strong><ul><li>Promotes Atmosphere of Teamwork</li><li>Provides Feedback & Support to Motivate</li><li>Channels of Communication Open</li><li>Job Gives Opportunities to Learn</li></ul></td></tr><tr><td>12.289%</td><td><strong>Intention to Leave [4 elements]</strong><ul><li>Feel Not Part of the Hospital</li><li>Consider Leaving due to Exhaustion and Stress</li><li>My Prospects are not Good If Staying</li><li>Searching for Work Opportunity Outside Hospital</li></ul></td></tr><tr><td>10.512%</td><td><strong>Compensation [2 elements]</strong><ul><li>Based On Job Status Salary is Fair</li><li>Satisfied with Compensation Program at Hospital</li></ul></td></tr><tr><td>8.379%</td><td><strong>Appraisal System [3 elements]</strong><ul><li>Appraisal is Continuous Every 6 Mon</li><li>Satisfied with Performance Feedback</li><li>Evaluation of Performance Highlights Strengths & Improves Weaknesses</li></ul></td></tr><tr><td>7.532%</td><td><strong>Recruiting System [3 elements]</strong><ul><li>Sex</li><li>Wide Recruitment System</li><li>Use Fair Selection System</li></ul></td></tr><tr><td>6.444%</td><td><strong>Training Program [2 elements]</strong><ul><li>Trainings Relevant to Work</li><li>Training Program Meet Goals & Expectations</li></ul></td></tr><tr><td>62.879%</td><td></td></tr></tbody></table>
### 4.3.2.5 Generation of Weighted Factors

Based on Tables 13 and 14, the resultant factors/components were weighted based on the Factor analysis resultant weights for the 19 items-scales and a new transformed (computed) weighted factor is obtained. Exhibit 2 herein depicts the details of the computations performed.

#### Exhibit 2: Weighted factors computations using SPSS v. 25

**Supervision [4 elements]**

Supervision = SUM(SupervisAtt1*.760,SupervisAtt2*.856,SupervisAtt3*.875,CareerAdv1*.596).

**Intention to Leave [4 elements]**

IntentionLeave = SUM(TurnoverInt1*.793,TurnoverInt2*.816,TurnoverInt3*.860,TurnoverInt4*.662).

**Compensation [2 elements]**

Compensation = SUM(SalaryBenefits1*.684,SalaryBenefits2*.762).

**Appraisal System [3 elements]**

AppraisalSyst = SUM(AppraisalSyst1*.717,AppraisalSyst2*.767,AppraisalSyst3*.658).

**Recruiting System [3 elements]**

RecruitSyst = SUM(Gender*.503,RECSELECT2*.628,RECSELECT3*.722).

**Training Program [2 elements]**

TrainProg = SUM(Training3*.684,Training4*.723).

The weighted factors actually depict components of the suggested model for this research.

### 4.3.3 Regression Analysis

A number of elements were regressed against the "Intention to Leave" factor (dependent variable). A backward stepwise analysis (Criteria: Probability-of-F-to-enter ≤ .050, Probability-of-F-to-remove ≥ .100) was used to find out the individual contribution of each predictor (independent variables). However, it was necessary to study the dependent "Intention to Leave" variable as it represents the weighted sum of four statements all leading to signify that the nurses next step is to leave the hospital. These four statements are:

* Feel Not Part of the Hospital

* Consider Leaving due to Exhaustion and Stress

* My Prospects are not Good If Staying

* Searching for Work Opportunity Outside Hospital

The first intent to study the regression model with weighted sum dependent variable didn't provide statistically significant regression model, most probably due to the multicollinearity of the four statements, a fact that led to test each statement versus the resultant explanatory weighted variables in Exhibit 2. Indeed, the variable "Searching for Work Opportunity Outside Hospital" is highly related to the variable "My Prospects are not Good If Staying" causing that the first be rejected when regressing any one of the three statements against the others. This led to isolate one statement namely "I Feel I Am Not Part of the Hospital" which empirically regressed well with the explanatory variables including the other two statements namely "Consider Leaving due to Exhaustion and Stress" and "My Prospects are not Good If Staying", therefore leading to Model 1 presented next.

#### 4.3.3.1 Model 1

This regression is performed based on the aforementioned resultant factors. Therefore, as this paper aims to assess the impact of HRM practices mainly in relation to job satisfaction on nurses' turnover intention, the following regression variables are defined:
**Dependent variable:** I Feel I Am Not Part of the Hospital

**Independent variables:** Supervision [4 elements], Compensation [2 elements], AppraisalSyst [3 elements], RecruitSyst [3 elements], TrainProg [2 elements], “Consider Leaving due to Exhaustion and Stress” and “My Prospects are not Good If Staying”.

<table><caption>Table 15. Model Summary<sup>b</sup></caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">R</th><th rowspan="2">R Square</th><th colspan="2">Adjusted R Std. Error of</th><th colspan="5">Change Statistics</th><th rowspan="2">Durbin-Watson</th></tr><tr><th>Square</th><th>the Estimate</th><th>R Square Change</th><th>F Change</th><th>df1</th><th>df2</th><th>Sig. F Change</th></tr></thead><tbody><tr><td>1</td><td>.715<sup>a</sup></td><td>.511</td><td>.479</td><td>.974</td><td>.511</td><td>16.195</td><td>6</td><td>93</td><td>.000</td><td>2.157</td></tr></tbody></table>

Note. a. Predictors: (Constant), Consider Leaving due to Exhaustion and Stress, RecruitSyst, AppraisalSyst, TrainProg, Supervision, My Prospects are not Good If Staying; b. Dependent Variable: Feel Not Part of the Hospital.

<table><caption>Table 16. ANOVA<sup>a</sup></caption><thead><tr><th>Model</th><th></th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>Sig.</th></tr></thead><tbody><tr><td></td><td>Regression</td><td>92.235</td><td>6</td><td>15.372</td><td>16.195</td><td>.000<sup>b</sup></td></tr><tr><td>1</td><td>Residual</td><td>88.275</td><td>93</td><td>.949</td><td></td><td></td></tr><tr><td></td><td>Total</td><td>180.510</td><td>99</td><td></td><td></td><td></td></tr></tbody></table>

Note. a. Dependent Variable: Feel Not Part of the Hospital; b. Predictors: (Constant), Consider Leaving due to Exhaustion and Stress, RecruitSyst, AppraisalSyst, TrainProg, Supervision, My Prospects are not Good If Staying.

<table><caption>Table 17. Coefficients<sup>a</sup></caption><thead><tr><th rowspan="2">Model</th><th colspan="2">Unstandardized Coefficients</th><th>Standardized Coefficients</th><th rowspan="2">t</th><th rowspan="2">Sig.</th><th colspan="2">Collinearity Statistics</th></tr><tr><th>B</th><th>Std. Error</th><th>Beta</th><th>Tolerance</th><th>VIF</th></tr></thead><tbody><tr><td>(Constant)</td><td>-.278</td><td>.736</td><td></td><td>-.378</td><td>.706</td><td></td><td></td></tr><tr><td>RecruitSyst</td><td>.174</td><td>.086</td><td>.153</td><td>2.026</td><td>.046</td><td>.925</td><td>1.081</td></tr><tr><td>TrainProg</td><td>-.179</td><td>.082</td><td>-.160</td><td>-2.172</td><td>.032</td><td>.964</td><td>1.037</td></tr><tr><td>AppraisalSyst</td><td>.111</td><td>.063</td><td>.134</td><td>1.773</td><td>.079</td><td>.920</td><td>1.086</td></tr><tr><td>1 Supervision</td><td>-.076</td><td>.041</td><td>-.145</td><td>-1.872</td><td>.064</td><td>.871</td><td>1.148</td></tr><tr><td>My Prospects are not Good If Staying</td><td>.322</td><td>.120</td><td>.263</td><td>2.679</td><td>.009</td><td>.546</td><td>1.830</td></tr><tr><td>Consider Leaving due to Exhaustion and Stress</td><td>.703</td><td>.135</td><td>.496</td><td>5.196</td><td>.000</td><td>.578</td><td>1.730</td></tr></tbody></table>

Note. a. Dependent Variable: Feel Not Part of the Hospital.
<figure><img src="image_3.png" /><figcaption>Figure 3. Histogram and Normal P-P Plot of Regression Standardized Residuals</figcaption></figure>

Model 1 also shows that the Variance Inflation Factor (VIF) (the reciprocal of Tolerance), is always (≥ 1). Values of VIF [all indep. var. have values spread between 1.307 and 1.830] show that these do not exceed 2 indicating no multicollinearity presence (see Table 17). Therefore, there is no correlation or bidirectional relationship among the predictor variables, and all the predictor variables are suitable to form a causal relationship using regression. As for the Durbin-Watson (D-W) statistic, a value of 2.0 means that there is no autocorrelation detected in the sample. Here D-W is 2.157 which is approximately 2. Moreover, the Histogram and the Normal P-P plots show an acceptable fit between the regression line and the given data. As shown in Figure 3.

Furthermore, the model shows that four (4) out of five (5) explanatory variables were statistically valid as predictors excluding compensation. Therefore, the resultant regression model is:

"Feel Not Part of the Hospital" = 0.153RecruitSyst - 0.160TrainProg + .134AppraisalSyst - .145 Supervision + .263 My Prospects are not Good If Staying +.496 Consider Leaving due to Exhaustion and Stress.

As depicted in Table 17 the predictors RecruitSyst, AppraisalSyst, "My Prospects are not Good If Staying", and "Consider Leaving due to Exhaustion and Stress" were statistically significant within 95% confidence, while TrainProg and Supervision were statistically significant within 90% confidence.

Results from Table 15 show that the predictors (independent variables) have a positive strong correlation with the dep. var., R=.715. The coefficient of determination (R²) value is "the measure of how much of the variability in the outcome is accounted for by the variability of the predictors" (Field, 2005, p. 154). However, knowing that AdjR² is insensitive to the number of variables or the number of respondents, one can tell that the nurses satisfaction with the HR practices "RecruitSyst, TrainProg, AppraisalSyst and Supervision" in addition to the nurses' state of exhaustion and stress and not having good prospects by staying in the hospital" account for 51.1% (R² = .511) of variability in "Feel Not Part of the Hospital" which represents an intention to leave. Adjusted R² is .479 (less than the R² by .032). This reduction means that if "the model were derived from the population rather than a sample it would have accounted for approximately 3.2% less variance in the outcome" (Hejase et al., 2014, p. 1578). Moreover, Table 16 shows that the F ratio is adequate (F = 16.195, p<.000), which means the model has significantly improved ability to predict the outcome variable.
Moreover, the nurses' dissatisfaction expressed as "Feel Not Part of the Hospital" is significantly predicted by the extent nurses are not satisfied with the HR practices of the hospital in the areas of recruitment and selection, training, performance appraisal and supervision impact manifested in "Promotion of an atmosphere of teamwork, having adequate feedback and motivational support, open communication, and having the opportunities to learn". In addition to the feeling of exhaustion, stress and the conviction that there are no prospects in the hospital.

More in detail, the more the nurses believe the HR function is not giving enough attention to recruitment and selection system evidenced by the fact that only 35% and 37% of the nurses (70% females) agree that their hospitals are using a "Wide Recruitment System" and "Use Fair Selection System", respectively the more they are convinced that they are not part of the hospital. Consequently, there is a weak positive and statistically significant relationship between RecruitSyst and Intention to Leave (Beta = .153, Sig. = .046 < .5%). This is in agreement with Mosadeghrad (2013), Al Salibi (2012), Albougami et al. (2020) and Abdulaziz, Alsufyani, Alforihidi et al. (2020) who contend that the hospital has to have fair HR policies in place to satisfy nurses. Moreover, the predictor variable TrainProg representing nurses attitude towards "Trainings Relevant to Work" (53% agree) and "Training Program Meet Goals & Expectations" (29% agree) is also characterized with a weak negative and statistically significant relationship with Intention to Leave (Beta = -.160, Sig. = .032 < .5%). The negative sign is highly related to the nurses' opinion and disagreement (62% disagree) that training received does not match their goals and expectations. In fact, researchers reported that nurses' dissatisfaction with training is a potential factor to boost their intention to leave as manifested in the variable "Feel Not Part of the Hospital" (Mosadeghrad, 2013; Hannawi & Al Salmi, 2014; Amarneh et al., 2021).

Having the appropriate performance appraisal system is a strong step towards better employees' engagement. In this case the predictor variable "AppraisalSyst" is representing four statements as follows, "Appraisal is Continuous Every 6 Mon" (44% of nurses agree), "Satisfied with Performance Feedback" (42% of nurses agree) and "Evaluation of Performance Highlights Strengths & Improves Weaknesses" (47% agree), therefore, less than half the nurses agree that there are appropriate performance appraisal systems in their hospitals, a fact manifested in a low positive and statistically significant relationship between predictor and intention to leave (Beta = .163, Sig. = .079 < .10%). Therefore, if nurses are more satisfied will lead to lower their intention to leave. These results are congruent with findings by Dieleman & Harnmeijer (2006), Al Salibi (2012), and Omar et al. (2018).

The next predictor is "Supervision" representing "Promotion of an atmosphere of teamwork", "having adequate feedback and motivational support", "open communication", and "having the opportunities to learn". Nurses' agreement on these four dimensions are 44%, 47%, 45%, and 50%, respectively. The last dimension is actually classified in the questionnaire under career development. The coefficient for this predictor is Beta = -.145, Sig. = .064 < .10%, which means the relationship is weak negative and statistically significant. Many researchers agree that supervision is a critical factor to retain health care employees especially nurses. In this case, nurses' intention to leave will be mitigated if the hospital supervision system is more adequate, otherwise nurses' turnover will be high (Perry et al., 2018; Omar et al., 2018; Najera, 2008; Zhao & Zhou, 2008).

The last two predictors are critical due to the nurses' personal level convictions namely, "My Prospects are not Good If Staying" (66% of nurses agree) and "Consider Leaving due to Exhaustion and Stress" (76% of nurses agree). The first predictor is weak positive and statistically significant (Beta = .263, Sig. = .009 < .1%), however it is a serious predictor because it may encompass all sources related to HR,
organizational and economic factors; and, the second is moderate positive and statistically significant (Beta = .496, Sig. = .000 < 1%). These two predictors are clearly stated and lead to high intention to leave. Several researchers mentioned that stress, exhaustion and losing hope due imbalances of quality of life are serious factors which increase the turnover of nurses (de Oliveira et al., 2017; Hasselhorn et al., 2005; Albougami, Almazan, Cruz et al., 2020; Gebregziabher et al., 2020).

#### 4.3.3.2 Resultant Research Model 1

The final research model (see Figure 4) actually represents the influence of internal (nurses' convictions) as well as the external factors (organizational HR policies and leadership) (Zhang & Goel, 2011; Hammarlund, Nilsson, & Gummesson, 2015) on nurses' satisfaction leading to intention to leave which actually shows if the HR experience has achieved its objectives in the nurses' retention process. However, this final model stresses the statistical significance of selected items all which have particular significance for this research. Figure 4 depicts the resultant model whereby the dependent variable or the Intention to Leave (Feel Not Part of the Hospital) Factor is a non-weighted component of the four items making it after Factor analysis. The influencing predictors are the unique items (weighted factors) having statistical significance and leading to the nurses' dissatisfaction and hence turnover. Moreover, when compared to the proposed model earlier, one observes that certain factors are not statistically significant as discussed earlier, and some other were inter-mixed with the weighted factors.
<figure><img src="image_4.png" /><figcaption>Figure 4. Resultant Research Model</figcaption></figure>

## 4.4 Hypotheses Testing Results

Table 18 depicts the full analysis of the hypothesis based on the resultant regression model representing the final research model. As a matter of fact, four HR factors namely, RecruitSyst, TrainProg, AppraisalSyst, and Supervision showed that these influence the nurses’ intention to leave, one factor was semi verified because one of the two elements was added to the Supervision factor which in turn was verified. Moreover, two HR factors were not verified because one was excluded [Compensation] in regression analysis and the other was excluded [Promotion & Recognition] and not classified under factor analysis. In summary, four hypotheses were fully verified by accepting the alternative hypotheses, [H1a, H2a, H5a, H6a], one was semi verified [H7a], and two Null hypotheses were verified, [H3a, H4a].
<table><caption>Table 18. Verified Hypotheses</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>H1. Recruitment & Selection</strong><ul><li>H1<sub>o</sub>: There is no relationship between nurses' satisfaction due to recruitment & selection process and nurses' turnover intention.</li><li>H1<sub>a</sub>: There is a relationship between nurses' satisfaction due to recruitment & selection process and nurses' turnover intention.</li></ul></td><td><strong>Rejected</strong></td></tr><tr><td><strong>H2. Training Program</strong><ul><li>H2<sub>o</sub>: There is no relationship between nurses' satisfaction due to training programs and nurses' turnover intention.</li><li>H2<sub>a</sub>: There is a relationship between nurses' satisfaction due to training programs and nurses' turnover intention.</li></ul></td><td><strong>Verified</strong></td></tr><tr><td><strong>H3. Salary & Compensation</strong><ul><li>H3<sub>o</sub>: There is no relationship between nurses' satisfaction due to salary & compensation & nurses' turnover intention.</li><li>H3<sub>a</sub>: There is a relationship between nurses' satisfaction due to salary & compensation & nurses' turnover intention.</li></ul></td><td><strong>Rejected</strong><br><strong>Verified</strong><br><strong>Rejected</strong></td></tr><tr><td><strong>H4. Promotion & Recognition</strong><ul><li>H4<sub>o</sub>: There is no relationship between nurses' satisfaction due to promotion & recognition practices & nurses' turnover intention.</li><li>H4<sub>a</sub>: There is a relationship between nurses' satisfaction due to promotion & recognition practices & nurses' turnover intention.</li></ul></td><td><strong>Verified</strong><br><strong>Rejected</strong></td></tr><tr><td><strong>H5. Appraisal System</strong><ul><li>H5<sub>o</sub>: There is no relationship between nurses' satisfaction due to appraisal system & nurses' turnover intention.</li><li>H5<sub>a</sub>: There is a relationship between nurses' satisfaction due to appraisal system & nurses' turnover intention.</li></ul></td><td><strong>Rejected</strong></td></tr></tbody></table>
<table><tr><td>nurses' turnover intention.</td><td>Verified</td></tr><tr><td><strong>H6. Supervisor Attitude</strong><ul><li>Promotes an atmosphere of teamwork [Weighted in Supervision]</li><li>Provides nurses with continuous feedback & support to keep them motivated [Weighted in Supervision]</li><li>Channels of communication are open between nurses and supervisor [Weighted in Supervision]</li></ul><strong>H6<sub>0</sub>:</strong> There is no relationship between nurses' satisfaction due to supervisor attitude & nurses' turnover intention.<br><strong>H6<sub>a</sub>:</strong> There is a relationship between nurses' satisfaction due to supervisor attitude & nurses' turnover intention.</td><td>Rejected<br>Verified</td></tr><tr><td><strong>H7. Career Advancement</strong><ul><li>Job gives the nurse opportunity to learn [Weighted in Supervision]</li><li>Aware of advancement opportunities existing in Hospital [Not included in any factor]</li></ul><strong>H7<sub>0</sub>:</strong> There is no relationship between nurses' satisfaction due to career advancement opportunities at hospital & nurses' turnover intention.<br><strong>H7<sub>a</sub>:</strong> There is a relationship between nurses' satisfaction due to career advancement opportunities at hospital & nurses' turnover intention.</td><td>Rejected<br>Semi-Verified</td></tr></table>

# 5. Conclusion & Recommendations

## 5.1 Conclusion

Findings of this research assure the fact that planned and strategized HR activities are fundamental to foment and strengthen the bonds with nurses in Lebanese hospitals. Results have shown that nurses' conviction that their place of work does not offer any prospects for the future coupled with their burnout, exhaustion and stress are very strong motivational forces to strengthen their intention to leave and therefore causing hospital turnover rates increase. Moreover, this paper found that nurses are sensitive, conscientious and highly determined to leave when fundamental HR pillars are not adequately applied namely recruitment and selection policies which are transparent and clear; well-developed training which fit the nurses' goals and expectations beside the job relevance; salaries that are commensurate with their efforts and heavy schedules; opportunities to grow and progress in their careers as nurses; a fair, unbiased and relevant performance appraisal system, and finally an empathetic management that understand, appreciate, support and act as coaching mentor. The aforementioned have been ratified by international researchers (Perry et al., 2018; Omar et al., 2018; Mosadeghrad, 2013; El-Jardali, Alameddine, Jamal et al., 2013; Yürümezoğlu et al., 2019; Al-Ahmadi, 2009; Albougami et al., 2020; Abdulaziz, Alsufyani, Alforihidi et al., 2020 among many others). In fact, this paper shows that the grand majority of nurses are not satisfied and therefore scoring all HR dimensions with less than 50% agreement that these were practiced adequately. Therefore, the aforementioned inefficiency creates dissatisfaction and demotivation among nurses. In addition, the highest percentage of nurses who quit their jobs are the younger ones, simply because they lack the ability to take full responsibility, to work independently and to deal with pressure and stressful situations at work. Notwithstanding their expectations which are more sophisticated and demand more
flexibility, better involvement in decision making and clearer career advancement in their jobs which match other researchers' work (Najera, 2008; Zhao & Zhou, 2008).

The hospitals' services are a combined effort of nurses, doctors and other support staff to improve the health and well-being of patients. In fact, nurses have the highest level of direct patient contact among employees. Motivating this workforce will surely have a positive impact on the bottom line of healthcare organizations.

In summary, and based on the results, nurses are more likely to feel dissatisfied and thus at high risk for turnover because they suffer from: a stressful environment, a non-commensurate salary versus exerted efforts, the absence of respect, an overload work, a weak rewarding system and compensation program, an ineffective training programs and inflexible schedules.

In order to keep a skilled nurse, an institution should strive for enhancing the existing internal systems and policies at workplace, provide assistance to their staff, reduce stress at working place as much as possible and finally keep the staff inspired. Keeping the nurses inspired boosts their performance and quality of the service they provide to patients.

The study offers a much current and new insight on nurses' turnover intent in Lebanese hospitals. The results are similar to many other results that have been reported in the Arab region and the international community of hospitals. However, Lebanese hospitals' behaviour towards the different HR pillars, especially to what pertains the wellbeing of nurses, in particular, must change to flip the turnover rates to more acceptable levels and which do not cause shortages. Also, this research insinuates that specialized medical administrators still lack the expertise to deal with concepts like HR and to safeguard the integrated nature of the different recommended dimensions. Worth mentioning that the findings cannot be generalized for several reasons including the fact that this is a case of one zone of Lebanon' hospitals, few respondents, and particular to one managerial function. Consequently, future research may close the aforementioned gaps in number of organizations involved, sample size and multi-territorial coverage. Researchers are invited to duplicate the research process involving the various healthcare institutions. Notwithstanding, that cross-country research is also recommended.

## 5.2 Recommendations

The researchers suggest several changing factors as follows:

- Fitting HR strategies to the hospital strategy. The administration of the hospital should pay more attention to its responsibilities towards its employees, in particular, nurses. For such an endeavour, the top administration must be more creative and innovative in its review of policies and strategies dealing with the Human Resource function. For example, involving all the healthcare staff, including nurses, in a strategic review for enhancement of all HR related activities to pay more attention to the employees/workers wellbeing including remuneration upgrades, richer benefits, more engagement activities, realistic career planning, more flexibility, better and more effective reward and incentive system, and develop continuous training programs that enable better response to today needs. It goes without saying that job security, compensation and health and safety are highly important in this context (Marom & Lussier, 2020). In fact, Murphy (2020) proposed 18 different outlooks that enforce engagement that must be seriously considered to upgrade the societal dimension of an organization, as it supports the positive assertiveness between employer and employees. Examples of such outlooks include proactivity, leadership, problem solving, assertiveness, ambition, optimism, and self-efficacy. Consequently, if these outlooks are nourished constantly “leads to employee trust and builds ownership, positive forces which strengthen the leadership efforts and help to build resilience with full employee support” (Hejase, 2020a, p. 3).
- The hospital should diversify its programs that are concerned with its responsibilities towards its local community and try to develop them permanently. That is, job opportunities, internships, and training & development in terms of creating awareness about hospital-related jobs especially nursing in order to foster more interest among the young students looking forward for healthcare service careers (Younis et al., 2021b).

- The administration must be more empathetic to the nurses' needs. According to Drucker (1986), "Work implies not only that somebody is supposed to do the job, but also accountability, a deadline and, finally, the measurement of results—that is, feedback from results on the work and on the planning process itself." Moreover, Hejase (2020b) contends that "one must include the measurement of all aspects of an organization's resources being physical and human in order to accurately and thoroughly assist the control of its activities" (p. 1). Actually, Zak (2013) stressed the human side of management of any institution by repeating Drucker's words, "It is the relationship with people, the development of mutual confidence, the identification of people, and the creation of a community".

- The administration need to offer several immediate improvements to boost nurses' motivation level and thus minimizing turnover rate:

* ✓ Allocate appropriate time and resources to select the right person for the right position.

* ✓ Align the nurses' purposes with that of the hospital especially that clear expectations promote longer periods of sustained effort to achieve desired outcomes.

* ✓ Create positive environment where nurses feel that they are valued members of the hospital.

* ✓ Encourage feedback that shapes performance, builds confidence, and sustained effort.

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