abdullah alshareef - qut ghaleb s alshareef the… · abdullah alshareef mba (wilkes university),...
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Identifying!Factors!Influencing!Saudi!Arabian!
Nurses’!Turnover!
Abdullah Alshareef MBA (Wilkes University), BSc (King Abdul-Aziz University)
IF49 Doctor of Philosophy
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
Faculty of Health School of Public Health and Social Work
Queensland University of Technology (QUT) Kelvin Grove Campus, Australia
2019
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Keywords!
Nursing staff, turnover, personnel management, work environment, structural equation
modelling
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Abstract!
Background
The Saudi healthcare system relies on contracted expatriate nurses to provide most of the direct
patient health care. For nurses from other countries, Saudi Arabia can be a challenging place
to work due to a range of factors including personal, social and organisational variables. There
is a high turnover of expatriate nurses and this has been long-standing problem for the Saudi
Arabian healthcare system. High staff turnover increases the cost of healthcare while, at the
same time, decreasing its quality. In 2006, the World Health Organisation drew attention to the
problems associated with chronic shortage of nurses, whereby need is outstripping the supply
of local nursing staff. In meeting the health demands of a rapidly growing population that
require increasingly complex medical services, there is an obvious need for skilled nurses.
Although the Saudi Government has been trying to attract Saudis to nursing, the numbers
remain low, the quality of training is low, and they have high attrition. Many newly qualified
nurses, both expatriate and Saudi, leave their job in Saudi Arabia after obtaining minimal
postgraduate experience.
Purpose
The purpose of this study is to identify the factors that contribute to turnover of hospital nurse
in Saudi Arabia, and identify strategies to reduce turnover and improve retention among
hospital nurses in Saudi Arabia. To date, most of research into nursing turnover in Saudi Arabia
has focused on job satisfaction rather than intention leave (Alasmari & Douglas, 2012; Al-
Dossary, Vail & Macfarlane, 2012; Alonazi & Omar, 2013), few studies have comprehensively
assessed all the relevant factors, including work environment, cultural, social, and socio-
demographics, related to intention to leave.
Design
The study used a mixed-methods cross-sectional survey design. 502 nurses from the Western
Region of Saudi Arabia, working in hospitals in Jeddah city and the holy city of Mecca,
completed the survey. Data was collected using a survey comprising five sections (anticipated
turnover intention, personal factors, work environment factors, social factors, organisational
factors, and open-ended questions). Confirmatory factor analysis was used to test relationships
within and between the six concepts: the Distributive Justice Index (DJI), the Organisational
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Commitment Questionnaire (OCQ), the Job Satisfaction Scale (JSS), the Social Support Scale
(SSS), the Sob Autonomy Scale (JAS), and Job Stress Questionnaire (LSQ). IBM® SPSS®
Statistics V23.0 predictive analytics software and NVivo 11 qualitative data analysis software
were used for analysis. Structural equation modelling was used to examine the relationships
among the study variables. Confirmatory factor analysis was used to create and validate
measurement models for variables.
Findings
The quantitative findings indicated that several independent variables were found to be
significant predictors of anticipated turnover: discrimination, workload, job satisfaction,
opportunity for promotion, social support immediate supervisor, organisational commitment,
and autonomy. Filipino nurses were more likely to intend to leave their current position than
other expatriates, including Malaysian, Pakistani, or Indian, nurses or local Saudi nurses. Many
expatriates identified unequal pay structures as an important contributing factor to intention to
leave; they felt the national salary remuneration for nurses should be based on competency and
delivery of care. The qualitative data supported this and added critical insight into what factors
influence nurses to stay their job in Saudi, such as social, work environment, organisational,
personal, and policy. Intention to stay was influenced by the level of support nurses received
to complete their study in Saudi Arabia, and social support of friends and family, as well as the
ability to adapt to the Saudi culture.
Conclusion
This study provides the most comprehensive information available, to date, about factors that
influence nurses desire to leave their current job and provides evidence for better health
workforce planning in Saudi Arabia. This study strongly indicates that the main factor related
to turnover is unfair, unequal salaries for different nationalities in Saudi Arabia. To avoid the
significant health service implications of continued nursing turnover, the Saudi Ministry of
Health decision makers must understand the factors that affect the ongoing high turnover of,
both local Saudi and expatriate, nurses. Military and Ministry of Health hospitals in Saudi
Arabia need to work very closely with Saudi universities and public and private nurses’
colleges if they are meet the targets set out in Vision 2030 for a mostly Saudi health workforce
delivering direct patient care. Although the Saudi government wants to reduce its reliance on
expatriate nurses, at present, the number of locally trained Saudis remain low. As well, turnover
of expatriate remains high many expatriate nurses increasing healthcare cost and impacting the
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quality of the service delivery. The findings of the study will help inform the design nurse
education programs and potentially encourage expatriate nurses to remain. The Saudi
government can use the findings to reconsider policy factors that affect non-Saudi nurses, for
example, recent increases in the cost of visa fees for family entry may deter many nurses from
committing to work in Saudi for longer periods. Producing the right conditions through
strengthening these factors provides a platform for increasing retention and maintaining a
quality nursing workforce into the future. This study makes a significant contribution to
extending existing literature on nursing turnover in Saudi Arabia by presenting new
information on the factors that function as barriers to maintaining a strong nursing workforce
and preventing turnover. The study provides a unique insight into the way factors interact,
which can then be further drawn on for future research or policy and program development in
the healthcare system.
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Table!of!Contents!
Identifying Factors Influencing Saudi Arabian Nurses’ Turnover ........................................... i!
Keywords ............................................................................................................................. ii!
Abstract ............................................................................................................................... iii!
Table of Contents .................................................................................................................vi!
List of Figures .................................................................................................................... xii!
List of Tables ...................................................................................................................... xiv!
Abbreviations ..................................................................................................................... xvi!
Declaration of authorship ................................................................................................. xviii!
Dedication .......................................................................................................................... xix!
Acknowledgements ............................................................................................................. xx!
Associated publications - .................................................................................................... xxi!
1. CHAPTER ONE: INTRODUCTION ................................................................................. 1!
1.1 Introductory Statement and Background....................................................................... 1!
1.2 Research Problem ........................................................................................................ 5!
1.3 Significance of the Study ............................................................................................. 7!
1.4 Objectives .................................................................................................................... 9!
1.5 Structure of Thesis ..................................................................................................... 10!
2. CHAPTER TWO: REVIEW OF LITERATURE ............................................................. 12!
2.1 Saudi Arabia Health System and Nurse Workforce .................................................... 12!
2.2 Literature Review of nursing in Saudi Arabia ............................................................. 14!
2.3 Turnover .................................................................................................................... 15!
2.4 Job Satisfaction .......................................................................................................... 18!
2.5 Gaps in the Literature ................................................................................................. 25!
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2.6 An Overview of the Intention to Leave Among Nurses Globally ................................ 26!
2.6.1 Environmental factors ......................................................................................... 26!
2.6.2 Organisational factors .......................................................................................... 30!
2.6.3 Personal factors ................................................................................................... 33!
2.7 Conceptual Framework .............................................................................................. 36!
3. CHAPTER THREE: RESEARCH PROGRAM AND DESIGN ....................................... 40!
3.1 Methodology ............................................................................................................. 40!
3.1.2 Rational for mixed methods and strengths and challenges .................................... 41!
3.2 Quantitative Study ..................................................................................................... 43!
3.2.1 Sample ................................................................................................................ 43!
3.3 Ethical considerations ................................................................................................ 43!
3.4 Survey questions – measurement and scales, items ..................................................... 46!
3.4.1 Structure of the survey......................................................................................... 46!
3.4.2 Survey questions –scales, items ........................................................................... 47!
3.4.3 Demographics ..................................................................................................... 50!
3.3.4 The anticipated turnover scale ............................................................................. 50!
3.3.5 Intent to stay........................................................................................................ 51!
3.3.6 Social support scale ............................................................................................. 52!
3.3.7 Job satisfaction scale ........................................................................................... 54!
3.3.8 Organisational commitment questionnaire ........................................................... 54!
3.3.9 Distributive justice index ..................................................................................... 55!
3.3.10 Job autonomy scale ........................................................................................... 56!
3.3.11 Social factors ..................................................................................................... 59!
3.4. Pilot study for survey validation ................................................................................ 60!
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3.4.1 Reliability and validity ........................................................................................ 61!
3.5. Data Analysis ............................................................................................................ 63!
3. 5.1 Descriptive analysis ............................................................................................ 63!
3. 5.2 Structural equation modelling ............................................................................. 63!
3. 5.3 Confirmatory factor analysis .............................................................................. 64!
3. 5.4 Multiple regression ............................................................................................. 65!
3.6 Qualitative Dimension of the Study ............................................................................ 66!
3.6.1 Data collection .................................................................................................... 66!
3.6.2 Data analysis ....................................................................................................... 66!
3.6.3 Research rigour ................................................................................................... 68!
4. CHAPTER FOUR: QUANTITATIVE RESULTS ........................................................... 69!
4.1 Introduction ............................................................................................................... 69!
4.2 Sample ....................................................................................................................... 69!
4.3 Descriptive Analysis .................................................................................................. 71!
4.3.1 The Anticipated turnover results .......................................................................... 72!
4.3.2 Influence of personal factors on anticipated turnover ........................................... 76!
4.3.3 Intent to stay results............................................................................................. 80!
4.3.4 Organisational factor results ................................................................................ 81!
4.3.5 Environmental factor results ................................................................................ 82!
4.3.6 Social factor results ............................................................................................. 84!
5. CHAPTER FIVE: FACTOR ANALYSIS ........................................................................ 86!
Introduction ..................................................................................................................... 86!
5.1. Factor Analysis ......................................................................................................... 86!
5.1.1 Factor analysis for organisational factors ............................................................. 87!
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5.1.2 Factor analysis for environmental factors ............................................................ 88!
5.1.3 Factor analysis for social factors .......................................................................... 89!
5.2. Confirmatory Factor Analysis ................................................................................... 91!
5.2.1 Measurement model for anticipated turnover ....................................................... 91!
5.2.2 Measurement model for intent to stay .................................................................. 95!
5.2.3 Measurement model for organisational factors..................................................... 96!
5.2.4 Measurement model for work environment factors .............................................. 99!
5.2.5 Measurement model for social factors ............................................................... 104!
5.3 Covariance structure model ...................................................................................... 107!
5.3.1 Hypotheses Testing ........................................................................................... 108!
5.3.2 H1: Anticipated turnover of nurses is influenced by organisational factors (social
support immediate supervisor, organisational commitment, and autonomy). .............. 109!
5.3.3 H2: Anticipated turnover of nurses is influenced by work environmental factors
(workload, opportunity for promotion, job satisfaction, and distributive justice). ........ 111!
5.3.4 H3: Anticipated turnover of nurses is influenced by social factors (Gender-Mixing,
Perception of nursing, and discrimination social support spouse) ............................... 112!
5.4 The Multiple Regression Analysis ............................................................................ 114!
5.4.1 Organisational factor results .............................................................................. 114!
5.4.2 Work environmental factor results ..................................................................... 116!
5.4.3 Social factor results ........................................................................................... 119!
6. CHAPTER SIX: QUALITATIVE DIMENSION FINDINGS ........................................ 121!
6.1 Introduction ............................................................................................................. 121!
6.2 Are There Additional Factors That Affect Your Decision to Leave Your Current Job? ...................................................................................................................................... 122!
6.2.1 Social factors ..................................................................................................... 125!
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6.2.2 Organisational factors ........................................................................................ 126!
6.2.3 Work environmental factors .............................................................................. 129!
6.2.4 Personal factors ................................................................................................. 131!
6.2.5 Policy factors .................................................................................................... 133!
6.3. What strategies would prevent turnover and encourage nurses to stay? .................... 135!
6.3.1 Social factors ..................................................................................................... 136!
6.3.2 Organisational factors ........................................................................................ 138!
6.3.3 Work environment factors ................................................................................. 140!
6.3.4 Personal factors ................................................................................................. 143!
6.3.4 Policy factors .................................................................................................... 144!
6.4. Other additional comments from respondents .......................................................... 145!
6.5 Summary ................................................................................................................. 149!
7. CHAPTER SEVEN: DISCUSSION ............................................................................... 151!
7.1 Summary of the Study .............................................................................................. 151!
7.2 Organisational Factor ............................................................................................... 151!
7.3 Work Environment Factor ........................................................................................ 154!
7.4 Social Factor ............................................................................................................ 157!
7.5 Personal Factor ........................................................................................................ 160!
7.6 Policy Factor ............................................................................................................ 162!
8. CHAPTER EIGHT: CONCLUSION ............................................................................. 165!
8.1. Introduction ............................................................................................................ 165!
8.2. Limitation of the Study ........................................................................................... 165!
8.3. Practice and policy Implication ............................................................................... 166!
8.4 Suggestions for Further Research ............................................................................. 168!
8.5. Chapter Summary ................................................................................................... 169!
References ......................................................................................................................... 171!
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Appendices ........................................................................................................................ 185!
Appendix A: Personal characteristics - categories ...................................................... 185!
Appendix B: Frequency Distribution Anticipated Turnover items .............................. 186!
Appendix C: Operational definitions for measurement scales ..................................... 206!
Appendix D: Examples of responses to the open-ended questions .............................. 207!
Appendix E: Permissions to Use Copyright Protected Materials (Turnover Intention
Scale) ......................................................................................................................... 211!
Appendix F: Ethical Approval .................................................................................... 213!
Appendix G: Survey questionnaire for nurses............................................................. 214!
Appendix H: Institutional Review Board (IRB) approval – Jeddah ............................. 232!
Appendix I: NIH – Research unit completion ............................................................. 232!
Appendix J: Conference Presentations ....................................................................... 233!
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List!of!Figures!
Figure 1. Nurses per 1000 population, 2017 (WHO Statistics Report, 2017; MOH, KSA,
2017; OECD, 2017). .............................................................................................................. 3!
Figure 2. Total nursing in health system in Saudi Arabia by nationality – 2015 ...................... 6!
Figure 3. Change in demographics (in millions) ..................................................................... 8!
Figure 4. Top 10 causes of death in Saudi Arabia and percent change, 2005 to 2016, all ages
(adapted from Institute for Health Metrics and Evaluation, 2016). ......................................... 8!
Figure 5. Thesis outline ....................................................................................................... 11!
Figure 6. Geography of Saudi Arabia ................................................................................... 12!
Figure 7. Theoretical model from current literature .............................................................. 36!
Figure 8. Theoretical model for the thesis ............................................................................ 38!
Figure 9 Education and Religion of respondents (n=502) ..................................................... 71!
Figure 10. Anticipated turnover results by ethnicity ............................................................. 73!
Figure 11. Anticipated turnover results by education level ................................................... 73!
Figure 12. Anticipated turnover results by religion............................................................... 74!
Figure 13. Anticipated turnover results by flexible work schedules ...................................... 74!
Figure 14. Anticipated turnover results by age ..................................................................... 75!
Figure 15 Anticipated turnover results by leave for family matters ....................................... 76!
Figure 16. Measurement model for anticipated turnover ...................................................... 92!
Figure 17. Measurement model for anticipated turnover (revised model) ............................. 94!
Figure 18. Measurement model for intent to stay ................................................................. 95!
Figure 19. Measurement model for organisational factors (revised model) ........................... 98!
Figure 20. Measurement model for work environment factors............................................ 100!
Figure 21. Measurement Model for work environment factors (revised model) .................. 103!
Figure 22. Measurement model for social factors ............................................................... 104!
Figure 23. Measurement model for social factors (revised model) ...................................... 106!
Figure 24. Conceptual model ............................................................................................. 109!
Figure 25. Results of the structural equation modelling of the effect of organisational factors
on anticipated turnover ...................................................................................................... 110!
Figure 26. Results of the structural equation modelling of the effect of work environmental
factors on anticipated turnover ........................................................................................... 111!
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Figure 27. Results of the structural equation modelling of the effect of social factors on
anticipated turnover ........................................................................................................... 113!
Figure 28. Word cloud for the first question ....................................................................... 123!
Figure 29. Word cloud for the second open-ended question ............................................... 135!
Figure 30: Themes that emerged from the analysis............................................................. 148!
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List!of!Tables!
Table 1. Summary current published evidence of nursing turnover and job satisfaction in
Saudi Arabia ........................................................................................................................ 24!
Table 2. Details of the scale questions .................................................................................. 48!
Table 3. Reliability of scale as reported in previous studies .................................................. 58!
Table 4. Characteristics of respondents of the pilot study (n=41).......................................... 61!
Table 5. Demographics of the nurse respondents (n = 502) .................................................. 70!
Table 6. Personal factors using independent sample t-tests and ANOVA ............................. 79!
Table 7. Kaiser-Meyer-Olkin and Bartlett’s test ................................................................... 86!
Table 8. Reliability analysis of the all items of organisational factors ................................... 88!
Table 9. Reliability analysis of the all items of environmental factor .................................... 89!
Table 10. Reliability analysis of the all items of social factor ............................................... 90!
Table 11. Parameter estimates and regression weights for anticipated turnover .................... 93!
Table 12. Model fitness indices for intent to stay ................................................................. 94!
Table 13. Model fitness indices for intent to stay ................................................................. 95!
Table 14. Parameter estimates and regression weights for organisational factors .................. 97!
Table 15. Model fitness indices for organisational factors .................................................... 99!
Table 16. Parameter estimates and regression weights for work environmental factor ........ 102!
Table 17. Model fitness indices for work environment factors ........................................... 103!
Table 18. Parameter estimates and regression weights for social factors ............................. 105!
Table 19. Model fitness indices for social factors ............................................................... 105!
Table 20. Model fitness values criteria ............................................................................... 108!
Table 21. Regression statistic of the organisational factors on anticipated turnover ............ 111!
Table 22. Regression statistic of the environmental factors on anticipated turnover ............ 112!
Table 23. Regression statistic of the social factors on anticipated turnover ......................... 114!
Table 24. Model summary ................................................................................................. 114!
Table 25. ANOVA ............................................................................................................. 115!
Table 26. Coefficients ........................................................................................................ 116!
Table 27. Model summary ................................................................................................. 117!
Table 28. ANOVA ............................................................................................................. 117!
Table 29. Coefficients ........................................................................................................ 118!
Table 30. Model summary ................................................................................................. 119!
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Table 31. ANOVA ............................................................................................................. 119!
Table 32. Coefficients ........................................................................................................ 120!
Table 33. Examples of responses to Question 1 .................................................................. 124!
Table 34. Social factors influencing nursing intention to leave or stay, excerpts related to
Question 1 ......................................................................................................................... 125!
Table 35. Organisational factors influencing nursing intention to leave or stay, with example
excerpts related to Question 1 ............................................................................................ 127!
Table 36. Environmental factors influencing nursing intention to leave or stay, with examples
from Question 1 ................................................................................................................. 129!
Table 37. Personal factors influencing nursing intention to leave or stay, with example
excerpts related to Question 1 ............................................................................................ 132!
Table 38. Policy factors influencing nursing intention to leave or stay, with example excerpts
.......................................................................................................................................... 134!
Table 39. Examples of responses to Question 2 .................................................................. 136!
Table 40. Social factors influencing nursing intention to leave or stay, example excerpts
Question 2 ......................................................................................................................... 137!
Table 41. Organisational factors influencing nursing intention to leave or stay, with example
excerpts related to Question 2 ............................................................................................ 139!
Table 42. Work Environment factors influencing nursing intention to leave or stay, with
example excerpts Question 2 ............................................................................................. 141!
Table 43. Personal factors influencing nursing intention to leave or stay, with example
excerpts related to Question 2 ............................................................................................ 143!
Table 44. Policy factors influencing nursing intention to leave or stay, with example excerpts
related to Question 2 .......................................................................................................... 144!
Table 45. Example of responses to the open-ended Question 3........................................... 146!
!
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Abbreviations!
AGFI : Adjusted Goodness-of-Fit Index
AMOS : Analysis of Moment Structures
CR : Critical Ratio
CFA : Confirmatory Factor Analysis
CFI : Comparative Fit Index
CMIN : Model Chi-square
CMIN/df : Relative Chi-square
CSM : Covariance Structure Model
Just : Distributive justice
d : Measurement Error
DF : Degree of Freedom
e : Error Term (Disturbance)
EDUC : Education Level
GEND : Gender
GM : Gender-mixing
GFI : Goodness-of-Fit Index
GOF : Goodness of Fit
H : Hypothesis
IFI : Incremental-Fit Index
INC : International Nursing Council
IRB : Institutional Review Board
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JS : Job Satisfaction
MOH : Ministry of Health
MI : Modification Indices
NFI : Normed-Fit Index
NNFI : Normed-Fit Index
OECD : Organization for Economic Cooperation and Development
Nurs : Perception of nursing
Prom : Opportunity for promotion
P : Significance Level
RFI : Relative-Fit Index
RMSEA : Root Mean Square Error of Approximation
RW : Regression Weight
GCC : Gulf Cooperation Council
SE : Standard Error
SEM : Structural Equation Modelling
SPSS : Statistical Package for the Social Sciences
SRW : Standardised Regression Weight
URW : Unstandardised Regression Weight
WL : Workload
WHO : World Health Organisation
χ2 : Chi-Square
χ2/df : Chi-square / Degree of Freedom
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Declaration!of!authorship!!
The work contained in this thesis has not been previously submitted to meet requirements for the
award of a degree or diploma at any educational institutions. To the best of my knowledge and
belief, the thesis contains no material previously published or written by any individual, except
where due reference is provided.
Signature: Abdullah Ghaleb Alshareef May 2019
QUT Verified Signature
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Dedication!
Thanks to my God for giving me the strength and the courage to accomplish writing this thesis. I
gratefully dedicate this thesis to my family, friends, and colleagues. First, I would like to dedicate
this thesis to the soul of my father who is always in my heart. Second, I dedicate this thesis to my
mother who gives me unconditional love and an endless support; she has been so generous,
sacrificing, and dedicated. Third, I dedicate this achievement to my brothers, Mohammad and
Talal, and my sisters, Fowzih, Norah, and Asma, for their endless encouragement, and their love
and prayers.
Fourth, I would like to dedicate this thesis to my beloved wife, Alanood, and my lovely son,
Mohammad, who have supported and motivated me to undertake the PhD program and were with
me during my PhD journey. Without their constant support and munificent care, this work could
not have been completed. Additionally, I would like to dedicate this thesis to my wife’s brothers,
Naif, Ahamd, and Talal, and her sisters, Wadha and Nouf, for their continuous support and
encouragement.
Finally, I would like to dedicate this achievement to my colleagues and friends at Queensland
University of Technology (QUT), in particular Hani, John, and Sultan. They have been always
available and generously gave me courage, support, and help. For that, I am very grateful.
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Acknowledgements!
I would like to thank the many people at QUT, Australia, who supported me throughout my PhD
journey. I would like to start by thanking QUT for accepting my candidature into the doctoral
program and giving me this opportunity to complete my studies. Also, I would like to thank Jeddah
University, which provided me with a scholarship to study for a PhD in nursing turnover.
I would like to acknowledge the support offered by my supervisory team, Dr Jennifer Mays, Dr
Kaeleen Dingle, and Dr Darren Wraith. My utmost gratitude is to my principal supervisor Dr
Jennifer Mays for her very helpful encouragement, feedback, and support throughout my study. I
cannot think of the most appropriate way to express my deep appreciation to her. She gave me a
lot of inspiration, motivation, and encouragement. Without her help and support this study would
not have been possible.
Moreover, I extend my appreciation and thanks to associate supervisor Dr Kaeleen Dingle for her
kind efforts, insightful guidance, and relevant feedback. She has been a great motivation during my
research journey. Also, my special thanks go to my associate supervisor Dr Darren Wraith for
assisting me in the statistical analysis. Dr Darren was especially helpful with his expertise and
pinpointing suggestions on statistical data analysis throughout my study. I would not have been
able to finish this work without his help.
I am also indebted to QUT’s Health Research Services for their guidance and encouragement. They
always offered me with support throughout all stages of my study. Their efforts and time are highly
appreciated. I would also like to thank professional editor Ms Leanne McKnoulty for her
appreciated services in proofreading and editing the thesis according to the Australian standards
for editing practice. Lastly, a special thanks goes to the dedicated nurses who participated in my
dissertation research study. Their cooperation and participation has been very much appreciated
and pivotal to the success of this project.
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Associated!publications!C!!
Conference Presentations
Alshareef, A, (16-17 August 2018) Identifying factors influencing Saudi Arabian nurses’
turnover. A qualitative research. Paper was presented at the Pan IHBI Inspires Postgraduate
Student Conference 2018, in Brisbane, Queensland.
Alshareef, A, (29-30 November 2018) Identifying factors influencing Saudi Arabian nurses’
turnover. Paper was presented at the Pan Pacific 9th International Congress on Innovations in
Nursing, in Perth, Western Australia.
1.!CHAPTER!ONE:!INTRODUCTION!!
Nursing turnover has a considerable impact on the Saudi Arabian healthcare system. Increased
turnover can lead to shortages of healthcare providers, with serious impact on the quality of
care delivered in hospitals. Foreign nurses make up a significant proportion of the nursing
workforce in Saudi Arabia, yet little is known about the factors that influence their intention to
leave Saudi Arabia. Chapter 1, the introductory chapter, provides a justification for the study
and outlines the overall structure of the thesis. The first section of the introduction (1.1)
provides a background to this study and gives a brief overview of the recent history of the
shortage of nurses within the health workforce. Section 1.2 states the problem being
investigated; section 1.3 discusses the significance of the study; section 1.4 states the objective
of the study, and the final section summaries the thesis structure and briefly clarifies the
purpose of each chapter.
1.1!Introductory!Statement!and!Background!A global shortage of health professionals currently exists within the health workforce, in
particular within the nursing profession (Almalki, Fitzgerald, & Clark, 2011). Given that there
are a range of definitions for the concept of nursing, the following definition will be used in
the study: nursing “encompasses autonomous and collaborative care of individuals of all ages,
families, groups, and communities, sick or well, and in all settings” (World Health
Organisation, 2009, p. 14). Modern conceptions of nursing view nursing as a part of allied
health practice. Nursing is inclusive of health promotion, wellbeing and prevention of illness,
and the care of people who are ill, disabled or require palliative, care (Royal College of
Nursing, 2014). There are a range of impacts arising from the shortages of nurses with the most
significant factor being increased healthcare costs, and the associated consequences of
decreased quality of patient care, increased patient mortality, and increased staffing costs
(Jones, 2004; McHugh et al., 2016). Globally, the retention of nurses is an important issue for
healthcare providers, as staff shortages can have compounding effects of increased levels of
dissatisfaction across all staff and higher workloads among existing staff (Abualrub, 2007;
Adano, 2008; Aiken, Sloane, Cimiotti, Clarke, Flynn, Seago, & Smith, 2010). Indeed, there is
evidence that turnover among nurses is a major cause of the nursing shortage (Cox, Willis, &
Coustasse, 2014). In Saudi Arabia, there is increasing concern about the high numbers of nurses
leaving to work in developed countries, and this has been linked to the nursing shortage in the
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Saudi health system (Falatah & Salem, 2018). Understanding the reasons for this turnover can
contribute to improving the country’s healthcare system
Given the high costs associated with recruitment and training, labour shortages impact both
healthcare and an organisation’s “bottom line” in terms of the provision of quality patient care
and, possibly, attracting the best staff (Buerhaus et al., 2007). In hospital settings, nurses are
the main caregivers and nurturers; they fulfil this role to a greater capacity than the physicians,
because they work exclusively in patients’ wards (Hoffman, Tasota, Scharfenberg, Zullo, &
Donahoe, 2003). As such, high patient satisfaction rates with hospital visits can largely be
credited to the quality of the nursing care received, as opposed to care from other providers
(Aiken, Sermeus, Van den Heede, Sloane, Busse, McKee, & Tishelman, 2012). Additionally,
these shortages affect worker and patient satisfaction levels, presenting a systemic challenge
for healthcare worldwide. Several reports, for example Kuehn (2007), have predicted that
during the period 2002–2020, the demand for nurses would rise by as much as 40%. In contrast,
the supply of nurses was expected to show only a marginal growth of 6%. On that basis, both
globally and within Saudi Arabia, the future deficit of nursing staff represents a clear detriment
to the healthcare industry, because of cost, productivity, and healthcare support.
The shortage of health workers is among the factors that have increased the cost of Saudi
healthcare (Falatah & Salem, 2018). Staff reductions have also contributed to significant
human resource management and public health issues. The 2015 report from the Saudi Ministry
of Health (MOH) stated that the Kingdom of Saudi Arabia (KSA) was one of the largest
importers of foreign nurses among the Gulf Cooperation Council (GCC) countries, with the
main supply coming from Asian countries (Khoja et al., 2017). Almost 70% of nurses working
in Saudi Arabia were non-Saudi nurses on short-term contracts (MOH, 2015); the majority
come from Asia, mostly from the Philippines (Aiken, Buchan, Sochalski, Nichols, & Powell,
2004). A majority of foreign nurses leave the KSA once they have gained sufficient training
and experience in order to be employable in other parts of the world, especially high-income
countries in Europe, the United States of America, Canada or Australia and New Zealand. If
the majority of expatriate nurses are only willing to stay in Saudi for short contacts, this means
the training and professional development of expatriate nurses is costly and problematic for
Saudi health providers (Almalki et al., 2011). Also, healthcare providers in Saudi Arabia are
increasingly having significant problems attracting adequately qualified and experienced
nurses to replace staff lost through attrition (Li & Jones, 2013).
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Patients and healthcare providers, in general, might be expected to benefit from having nurses
who are qualified and experienced (Currie, & Hill, 2012). Moreover, Saudi Arabia’s healthcare
system has fewer nurses per capita than other GCC countries (Alkhamis, Hassan, & Cosgrove,
2014). Saudi Arabia has a nurse/population ratio half that of other high-income countries in the
Organisation for Economic Cooperation and Development (OECD) see Figure 1; KSA has 5.4
nurses per 1,000 compared to OECD average of 9.66.
Figure 1. Nurses per 1000 population, 2017 (WHO Statistics Report, 2017; MOH, KSA, 2017; OECD, 2017).
From an economic perspective, the demand for healthcare labour is derived from the demand
for healthcare, largely reflecting the health and age of the population, as well as the
improvements in disease management, diagnosis and treatment, and the cost of healthcare
technologies. The KSA has a relatively young and fast-growing population, more than two-
thirds (67.6%) of population are aged between 15 and 64 years of age. Compared to many
OECD countries, Saudi Arabia is experiencing rapid population growth of 2.8%, with 23.07
births per 1,000 (MOH, 2013), which also adds to demand on the healthcare system. The United
Nations projects that the Saudi population will grow from 27.5 million in 2010 to an estimated
39.8 million by 2025, and 54.7 million by 2050 (Almalki et al., 2011). This population growth
will lead to increasing demands on the delivery, and quality, of healthcare. Therefore, an
adequate supply of nurses and allied healthcare providers is essential. Population growth places
considerable pressure on Saudi leaders to deliver and manage the country economic and
infrastructure growth, the rising expectations for healthcare and education, and government
policies that address these issues over many years. Consequently, there is an obvious urgent
12.811.6 11.6 11.3
10.5 9.9
7.9
5.4
Germany USA Australia Japan France Candad UK KSA
Nurses&(per&1000)&2017
OECD=&9.66/1000
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need for greater numbers of trained local Saudi nurses and other health workers to meet the
increased demand on services and local employment opportunities. This task is daunting given
that only 30% (101,298) of the nurse workforce are native Saudis are employed by was either
the public and private health providers.
Migration and emigration are among the many factors that influence healthcare workforce
demand and supply. Currently, due to a depleted nursing workforce, Saudi Arabia has become
one of the largest importers of foreign nurses, with the main supply coming from the
Philippines (Lorenzo, Galvez, Icamina, & Javier, 2007). To complicate matters further, the
majority of foreign nurses do not speak Arabic (Mitchell, 2009). This situation creates
communication problems between nurses and patients. Also, their short employment periods
often aggravate retention and turnover problems, which in turn exacerbates the ongoing
continuity of care problem. Almalki et al. (2011) reported that the majority of foreign nurses
leave the KSA after they have gained sufficient training and experience to work in other parts
of the world, predominantly developed countries. Thus, nurse retention is a difficult balance
that needs to be addressed across the world and, especially in the KSA. It is important to note
that in Saudi Arabia, the challenge is not new. Even as far back as 1985, Al-Swilim pointed
out the need for adequate nursing workforce forecasting to ensure stability into the future.
The dilemma of the migration of health professionals from developing to developed countries
is threatening the ability of developing countries to provide quality healthcare and achieve
health system improvements (Connell, 2010; International Nursing Council, 2005; Lamadah
& Sayed, 2014). Historically, nurses and other health professionals have embraced their ability
to use their qualifications across national and global boundaries to access higher pay rates, and
a better quality of life and working conditions. Nevertheless, experts in the field, such as Brush,
Sochalski, and Berger (2004), have suggested that the primary reason for such a large migration
rate of nurses is due, primarily, to the low salary being offered in poorer countries. However,
the supply of physicians or nurses from other countries may not solve the lack of qualified
physicians or nurses in any country. For example, the United States’ continued reliance on
nurses from other countries has the potential to negatively impact upon the provision of
adequate healthcare of lower socio-economic countries and, at the same time, the United States
fails to address its own nurse shortage (Aiken, 2007; Lamadah & Sayed, 2014).
Hence, it is apparent that training and providing an experienced healthcare workforce can be
problematic. In 1915, Saudi Arabia established the MOH to oversee the complex scope of
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healthcare needs. Other factors specific to Saudi Arabia also pressure the health system, the
KSA and the MOH have the fundamental mission to provide healthcare for all pilgrims visiting
the holy cities of Saudi Arabia during the season of Hajj (Al-Harbi, 2000). Hajj, an act of
worship, is one of the five pillars of Islam. Muslims from around the world make a pilgrimage
to Mecca, during the twelfth and last month of the Islamic calendar (8 to 13 Dhu al-Hijjah).
In 2005, more than three million people travelled as pilgrims to KSA from outside. The
government provides free health services to all pilgrims and, consequently, there is significant
costs associated with responding to the needs of people attending Hajj (Almalki et al., 2011).
In 2010, the MOH provided 21 hospitals, 3,408 beds, and a total increased workforce of 18,000
during Hajj (MOH, 2012). To provide the health workers needed for these facilities, the MOH
transfers health workers to the Jeddah, the capital of the Western Region of Saudi Arabia, and
the holy cities such as Mecca and Medina; creating shortages of nurses, physicians, and other
healthcare personnel in other Saudi regions and cities (WHO, 2013; Al-Swilim, 1985). With
such an increase in demand, health services elsewhere are reduced to meet the health needs of
this season and existing workforce shortages in other professions are worsened. Outside the
Holy Season, there is a continued strain on health services to cope with the increasing
population growth, ageing populations, along with the greater burden of more frequent, chronic
and non-communicable diseases. Consequently, there is an obvious need for trained nurses and
physicians to meet the demand for increased medical services (Aldossary, While, & Barriball,
2008; Jannadi, Alshammari, Khan, & Hussain, 2008). The situation in Saudi Arabia demands
research that can build an evidence-base that both identifies the health needs of the population,
provides an in-depth analysis of the health workforce and job satisfaction, to deliver quality
health outcomes and good health policy that stems the current nursing turnover. The following
sections present an outline of the research problem, the study objectives, and an explanation of
the significance of the current study.
1.2!Research!Problem!In Saudi Arabia, there is increasing concern about nurses who leave the profession, because of
the link to staff and skills shortage in the Saudi health system. The high turnover of nurses is
clearly a problem in the Saudi healthcare system (Falatah, and Salem, 2018), because it
increases the cost of healthcare while, at the same time, decreasing its quality (Atencio, Cohen,
& Gorenberg, 2003; Hayes et al., 2012). Minimising the rate of this turnover is a key priority.
First, a majority of foreign nurses leave the KSA once they have gained sufficient training and
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experience, in order to work in other parts of the world. This makes it difficult for healthcare
providers to hire skilled nurses, which will have a knock-on effect on the existing workforce,
as demands on them are sure to be increased due to the escalating population growth and longer
lifespans. Yet, there is an absence of systematic data to establish the particular number of nurses
who left their job, due to lack of a tracking system. Intention to leave among nurses appears to
be impacted upon by customs and cultural barriers. The nurses’ lack of understanding of the
Saudi culture, customs, and religion can prevent them from staying in their jobs. Further, little
is identified about the social factors that influence nurses’ intention to leave the Saudi health
system to work in developed countries. In 2015, the MOH estimated that approximately 70%
of nurses working in the Saudi health system were from countries outside of the KSA (Figure
2) (MOH, KSA, 2015).
Figure 2. Total nursing in health system in Saudi Arabia by nationality – 2015
The lack of experienced domestic Saudi nurses is a contributory factor in the heavy dependence
on foreign nurses. Additionally, the older generation of Saudi nurses tend to leave their clinical
posts in favour of administrative roles (Alotaibi, Paliadelis, & Valenzuela, 2016). The inability
to educate nurses is another factor in the lack of available nurses in Saudi Arabia, because the
education system acts as a pipeline for the supply of nurses. To address these issues, the
government of Saudi Arabia offered policies to reduce the number of the foreign nurses and
increase the number of native Saudi nurses. Once the Saudi Government applied these policies,
there was a noticeable increase in private medical institutes that provided diplomas in nursing
(Ministry of Economy & Planning, KSA, 2017). Unfortunately, new entrants into medical
institutes are not increasing at a rate sufficient enough to meet the demand, and simultaneously
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foreign nurses are leaving. Moreover, a significant proportion of recently graduated Saudi
nurses, from private medical institutes, have been unable to secure suitable employment due to
inadequate training (Okaz News, 2017, Sabq, 2017). The projection is that the contractor
workforce will probably remain in Saudi Arabia for an additional ten years. Furthermore, one
of the Saudi Vision 2030 objectives is to reduce Saudi Arabia’s dependence on foreign nurses
and provide citizens with the knowledge and skills to meet the future needs of the labour market
(Saudi National Transform Program, 2017). In this context, these current changes in the KSA
may result in decreased loyalty among contract (foreign) nurses. However, no study has yet
assessed of the impact of these factors. It may also become increasingly more difficult
particularly for foreign nurses to be able to stay longer in Saudi Arabia.
It is very important for healthcare providers to grasp the need to retain nurses in the workforce
for longer periods. Previous studies have indicated that more than half of nurses in Saudi public
hospitals have signalled an intention to leave (Al-Ahmadi, 2013; Saeed, 1995). Further, there
is little published data about the factors that influence nurses’ intention to leave their job in
Saudi Arabia. It is crucial to understand why nurses choose to leave or work for other healthcare
providers, in terms of how their value proposition is met. An understanding of the reasons for
the high turnover could help to enhance the organisational capacity for the delivery of nursing
services (Hayes et al., 2012), by improving economic efficiencies, workforce capacity and
productivity, and reducing nurse shortages and staffing costs. A gap in the literature and the
study objectives associated with nurses’ intention to leave has been identified and is outlined
in the following sections.
1.3!Significance!of!the!Study!All over the world, rising healthcare costs place increased financial pressures on the healthcare
sector. It is vital that the organisations concerned develop efficient funding allocation strategies
to ensure the development and retention of a quality workforce. The high proportion of nurses
leaving also negatively impacts on patient outcomes and safety as well as nurses’ wellbeing.
As shown in Figure 3, as of May 2015, almost 31 million people were living in Saudi Arabia
and the population is expected to increase to almost 37 million by 2025, with a doubling in the
population above the age of 60 years, which is going to significantly increase the demand and
cost for healthcare services (MOH, KSA, 2015). In meeting the health demands of a rapidly
growing population that requires increased medical services, there is an obvious need for
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skilled nurses. Identification and analysis of risk factors can contribute to the development of
solutions by preventing turnover and encouraging nurses to stay in their jobs.
Figure 3. Change in demographics (in millions)
This workforce should have the capacity to meet the growing demands on the healthcare system
in Saudi Arabia, arising from an ageing population, and frequent, chronic and non-
communicable diseases as shown in Figure 4 below (Institute for Health Metrics and
Evaluation, 2016). Moreover, Saudi Arabia has among the highest rates of adult obesity in the
world at 33%, just slightly behind the United States at 36.5%, and Australia at 28.3%. It is
important to note that in the health workforce setting, the change in burden of disease, increase
population growth, and the ageing of the population have a strong impact on demand for
healthcare services and the need for nurses to meet the demand.
Figure 4. Top 10 causes of death in Saudi Arabia and percent change, 2005 to 2016, all ages (adapted from Institute for Health Metrics and Evaluation, 2016).
9 | P a g e
The current study will identify and classify factors associated with nurse turnover, in order to
offer guidelines for change that prevents or reduces turnover. The study could also benefit
current healthcare providers by improving their understanding of how turnover affects overall
administrative and healthcare costs, and of the reasons for nurses’ departure. The study’s
recommendations contribute to informing policymakers with strategies to reduce the current
rate of nursing turnover.
The importance of this study arises out of classifying why a rising number of nurses are leaving
their jobs in Saudi Arabia. The need for such a study arises because Saudi Arabia is one of
largest importers of foreign nurses and these nurses leave Saudi Arabia once they have gained
sufficient training and experience to work in other parts of the world, often in developed
countries. Importantly, there is little published data on what factors influence nurses to leave
their current job and what factors encourage them to stay in Saudi Arabia. The current study
desired to address this knowledge gap, mainly in regard to improving the understanding of
what factors possibly influence nurses to leave their job in Saudi Arabia.
1.4!Objectives!To reduce the rate of turnover among nurses in Saudi Arabia, the overarching goal of this study
is to identify, more precisely, the factors contributing to that turnover. To that end and to
facilitate an evidence-based approach to better staff management, the study will pursue the
following objectives:
•! To identify factors that influence nurse turnover in government hospitals in Saudi
Arabia,
•! To identify factors contributing to turnover from the perspective of hospital nurses in
Saudi Arabia, and
•! To identify strategies to reduce turnover and improve retention among hospital nurses
in Saudi Arabia.
To meet these objectives, the following research questions will be addressed:
1.! What are the organisational factors that affect nurse turnover in hospitals in Saudi
Arabia?
2.! What are the work environment factors that affect nurse turnover in hospitals in Saudi
Arabia?
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3.! What are the social, cultural, and political factors that affect nurse turnover in
hospitals in Saudi Arabia?
4.! What are the personal factors that affect nurse turnover in hospitals in Saudi Arabia?
The research questions were answered using quantitative (survey) and qualitative dimension
(open-ended questions) research designs, based on the study’s framework (see Chapter 2,
section 2.7). As discussed in more detail later, the framework proposes that the intention of
nurses to leave their job in Saudi Arabia are determined by a combination of associated factors
(social, work environment, organisational, personal, and policy factors) existing within Saudi
Arabia.
1.5!Structure!of!Thesis!This thesis is divided into eight chapters. Chapter 1 includes a background of the study,
research problem, significance of the study, research questions, and the purpose and objectives.
Chapter 2 begins by laying out a comprehensive review of the literature on nursing turnover
within Saudi Arabia. Additionally, it gives an overview of nurses’ intention to leave globally
and identifies gaps in the research area. As well, this chapter presents the theoretical framework
for the current study.
Chapter 3 presents research design, data collection methods, data analysis, sample size, pilot
study, and outlines ethical aspects of the study. Chapter 4 provides the quantitative (descriptive
analysis) and open-ended results, respectively. It presents an analysis of the survey and open-
ended data and expands on factors that drive nurses to leave, stay, or encourage them to stay in
Saudi Arabia. Chapters 5 presents the findings of hypotheses tested using structural equation
modelling and regression analysis. Chapter 6 provides the qualitative (open-ended questions)
results, respectively and discuss the significant results of the study by integrating the survey
and open-ended questions findings with respect to a variety of factors (social, organisational,
work environment, personal, and policy) that affect nurse’s intention to leave Saudi Arabia.
Chapter 7 presents the discussions of the research findings. Chapter 8 provides the conclusion
of the research, acknowledges the research limitations, highlights future policy and practical
implications, and provides suggestions for further research. Figure 5 below shows the thesis
outline.
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Figure 5. Thesis outline
2.!CHAPTER!TWO:!REVIEW!OF!LITERATURE!!
Chapter 2 presents an analysis of the extant literature on nursing turnover in Saudi Arabia and
internationally. The review focused on gaining an understanding of the factors that influence
nurses to leave their current job. This review also formed the background for this research and
highlighted the need for continued study in this area. Chapter 2 is presented in seven sections.
Section 2.1 gives a brief overview of the Saudi Arabia health system and nurse workforce.
Section 2.2 begins by laying out a comprehensive review of the literature on nursing turnover
within Saudi Arabia. Section 2.3 provides existing knowledge about nurse turnover in Saudi
Arabia, while Section 2.4 offers existing knowledge about job satisfaction among nurses in
Saudi Arabia. Section 2.4 identifies a knowledge gap in the files of the study. Section 2.5 gives
an overview of nurses’ intention to leave globally. The final section, Section 2.6, presents a
discussion on the theoretical framework for the current study.
2.1!Saudi!Arabia!Health!System!and!Nurse!Workforce!The KSA is a Middle Eastern nation that lies at the furthermost part of south-western Asia.
Saudi Arabia has five geographic regions: the western, central, northern, southern, and eastern
regions. The western region includes the holy cities of Makkah (Mecca), Madinah (Medina),
and Jeddah city. The capital city, Riyadh, is located in Central Saudi Arabia (General Authority
for Statistics, 2016). Figure 6 illustrates the western region cities of Makkah and Jeddah were
selected for this study because they differ in regard to dimensions that ensure a breadth of
possible nurse’s characteristics. The area has a large population and many international nurses
and Saudi nurses work in these cities. A detailed report about sample selection and size is
presented in the next chapter.
Figure 6. Geography of Saudi Arabia
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Saudi Arabia has 13 administrative regions and 20 health regions. Saudi Arabia’s population
was estimated to be 31 million, as of May 2015 (General Authority of Statistics, 2015).
According to the MOH, life expectancy at birth stands at 74 years in Saudi Arabia, almost six
years below the average life expectancy of the 34 (Organisation for Economic Cooperation and
Development [OECD]) countries. The share of gross domestic product (GDP) allocated to
health spending in Saudi Arabia was 3.5% in 2015, compared with an OECD average of 8.9%.
The total health spending per capita (purchasing power parity dollars) in 2015 was 2,320
compared with an OECD average of USD 3,453 (IHME, 2015). The annual population growth
rate is 1.87. The current infant mortality rate is 7.4 deaths per 1,000 live births, and the maternal
mortality ratio is 12 deaths per 100,000 live births (General Authority of Statistics, 2015; MOH,
2015). Taken together, these indicators suggest that a growing population and disease
management will affect healthcare service in the Saudi health system. In Saudi Arabia, the
citizens’ needs are changing, and the population is growing and living longer. This also
includes people with health conditions. Keeping nurses in their job for a longer period will
enable employers to maintain a consistent supply, meet demands, and avoid shortages of nurses
in the Saudi health system. Consequently, it is vital to identify factors that affect nurses’
decisions to leave their current job, and strategies that could prevent turnover and encourage
nurses to stay.
Health is considered a state subject. In Article 27 of Chapter 5 of the Saudi law, the government
guarantees the right to healthcare for citizens and their families in cases of emergencies,
sickness, disability, and old age. Article 31 states the government is responsible for public
health in the KSA and will provide healthcare services for every citizen (The Embassy of the
KSA, 2016). The health system of Saudi Arabia consists of three providers: the MOH, other
governmental sectors, and the private sector. The MOH is the major provider of health services,
administering health services to 60% of the population and is also responsible for the
supervision of healthcare. According to the MOH, in 2015 there were 13.1 MOH hospital beds,
0.7 primary healthcare centres, and 54.7 nurses including midwives (approximately 71% of the
nurses were female) per 10,000 people (MOH, 2015).
The distribution of health services to Saudi’s population is concentrated through both the public
(MOH and Military Hospitals) and private sectors. In the public sector, MOH provides basic
primary healthcare and secondary and tertiary services and is responsible for the development,
planning, guidance, monitoring, evaluation, and implementation of plans to ensure the
provision of healthcare services.
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The Saudi government’s MOH is the main organisation for protecting the health of all people
in the KSA. The government of Saudi Arabia financially supports the MOH by assigning a
percentage of the total governmental budget through five development plans (Ministry of
Economy & Planning in Saudi Arabia, 2017). During the last decades, spending on healthcare
in Saudi has grown fast with an increase in the number of hospitals. In regard to the hospitals,
there are 274 MOH hospitals and 41,297 MOH hospital beds (MOH, 2015). The health system
of Saudi consists of 20 health regions that are managed by general directors who are
accountable to the MOH, which is an important human service for those who are unable to help
themselves financially or who lack health provisions (Mufti, 2000).
One of the Saudi Vision 2030 objectives is to increase the private sector share of spending
through alternative financing methods and service provision (Saudi National Transform
Program 2017). Since 2017, the private sector in Saudi Arabia has been on the rise and is
growing fast. Approximately 20% of healthcare services in Saudi Arabia are provided by the
private sector and 20% by government agencies, such as the Ministry of Defence and Aviation,
National Guard, Ministry of Interior, and Ministry of Education. These agencies provide health
services to their employees and their families. All universities in Saudi Arabia provide free
healthcare services for their students and employees through their medical colleges, or
hospitals’ curative and medical education and training programs. The latest available statistics
from 2015 indicate that there are 43 other governmental hospitals, 11,449 other governmental
hospitals beds, and 35,119 nurses including midwives (MOH, 2015). In the private sector, there
are 145 private hospitals, 16,648.3 hospital beds and 41,985 nurses (including midwives)
(MOH, 2015). This means that as there has been a noticeable increase healthcare service in
Saudi Arabia, there will be general increases in nurses’ supply. In order to meet the demand
for quality healthcare in a growing population, the political, health, and education systems need
to ensure an adequate health workforce supply. This study will provide insight into the issues
of nurses in Saudi Arabia.
2.2!Literature!Review!of!nursing!in!Saudi!Arabia!!Retention of nurses is a highly complex issue for local and global governments and healthcare
system in need of improvement. Further research investigation provides one way to prevent
poor healthcare outcomes. A comprehensive review of the literature on nursing turnover,
initially within Saudi Arabia, was conducted, to gain a better understanding of the key factors
involved. The electronic databases PubMed, EBSCOhost, Academic Search Premier,
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ProQuest, USF University Library, and Google Scholar were searched using the terms ‘nursing
turnover’ or ‘nursing retention’ or ‘nursing job satisfaction’ or ‘nurse shortage’. Reputable
websites were searched, including those of the Australian Nursing Journal, American Journal
of Nursing, World Health Organisation, American Association of Colleges of Nursing,
American Health Care Association, Saudi Nurses Association, and the Saudi MOH. The search
was limited to books, a research thesis, government documents, and research articles, as well
as newspaper articles relevant to the research topic. The literature search for concepts began
by classifying various related terms, for example ‘nursing turnover’. In addition to these
concepts, the examination included searches under appropriate synonyms, narrower terms, and
related terms, such as ‘nursing shortage’, ‘nursing retention’ and ‘nursing job satisfaction’.
Although globally there was a vast amount of literature on nursing turnover, this was not the
case for studies related to Saudi Arabia. Research studies specifically from and in relation to
Saudi Arabia were extremely limited and there were few studies that provided insights into the
issue of nursing turnover. In the interests of clarity, this literature review is divided into two
sections. The first relates to existing knowledge about nurse turnover in Saudi Arabia and
existing knowledge about job satisfaction among nurses in Saudi Arabia as well as its
relationship with turnover. The second section of the review seeks to identify the main reasons
for the high turnover.
2.3!Turnover!
Global research studies, such as De Gieter, Hofmans, and Pepermans (2011), have suggested
that the Price-Mueller model offers the most coherent and relevant explanation of employee
turnover. In classifying factors that produce variations in turnover, the model incorporates
economic, organisational, psychological, and sociological arguments (Gurney, Mueller, &
Price, 1997). Drawing on Hayes et al. (2012), two key measures of turnover, external and
internal, assist in providing a definition to underpin this study. External turnover refers to the
number of nurses who leave their current organisation for whatever reasons; and internal
turnover refers to the number who change their current role within the organisation.
In an earlier definition, Hayes et al. (2006) described turnover as “the process whereby nursing
staff leave or transfer within the hospital environment” (p. 238). Therefore, departures or
transfers can negatively impact scheduling and continuity care and impact patient outcomes
and safety.
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There is limited research examining the causes of nurse turnover in Saudi Arabia. The early
work of Saeed (1995) spanning three hospitals in Riyadh, Saudi Arabia, identified perceived
work overload as one of the most important factors affecting intention or decision to leave.
Saeed (1995) further found that job stress and limited flexibility in schedules were among the
work-related variables that strongly influenced intention to leave. The research findings in
Saeed indicated that there was consensus that job stress and workload had increased throughout
their nursing employment. This pressure led some nurses to feel that they needed to leave or to
look for another position that would offer greater scheduling flexibility and predictability. In
this study, Saeed (1995) assessed the influence of sociodemographic and work-related factors
on nurses’ intention to leave. The author found that marital status did not significantly alter
nurses’ intention to stay or leave. However, somewhat contradictorily, more than half of those
surveyed reported that the most important reason for their desire to leave their job was family
reasons. His conclusions might have been far more persuasive if the author had assessed family
reasons.
A study by Almalki et al. (2012) in the south of Saudi Arabia also addressed the links between
primary health care (PHC) nurse retention and work life quality. Their data found support for
the impact of work design variables, such as workload and time constraints on task completion.
The modern nurse also faces long working hours, characterised by multiple shifts, making it
more difficult for nurses to benefit sufficiently from personal time outside the workplace.
Almalki et al. in revealing a strong link between turnover intention and marital status among
Saudi PHC nurses in the Jazan region, similarly aligns with the findings of other research. This
is significant because a nurse who has never married may have less family responsibilities, and
therefore does not have to consider transferring family members when moving to another
organisation, in another location. However, the study would have been more relevant if the
researchers had asked participants about the amount of effort required to satisfy job
requirements. In the current study, a number of questions will be used to collect information
about workload, including job difficulty, hazards, resource inadequacy, and workplace conflict.
In the Alonazi and Omar’s (2013) study, of 245 nurses in the paediatric department at Prince
Sultan Military Medical City, between 2006 and 2010, nurses tended to leave their jobs for
family reasons (39.7%). However, as the authors failed to clarify what was meant by “family
reasons”, the precise nature of this issue requires further investigation. In this respect, the
concept of network theory is important when examining the personal and social factors that
influence nurses to leave their jobs in Saudi Arabia.
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Alsaqri (2014) derived additional factors regarding intention to leave in Saudi Arabia. Alsaqri
indicated that intention to leave was significantly associated with emotional exhaustion and
personal accomplishment. These findings suggest that fewer nurses would consider leaving
their positions if healthcare organisations provided better working environments and greater
flexibility (Lephalala, 2008). More evidence can be drawn from a more recent study from Saudi
Arabia that identified factors influencing the termination of Filipino nurses in public hospitals
(Aljohani, & Alomarib, 2018). The authors indicated that nurses were influenced in their
intention to leave by their heavy workload and low patient-staff ratios.
In returning to Al-Ahmadi (2013), the author explored literature on the Saudi Arabian turnover
predictors and concluded that personal factors, such as marital status and years of experience
needed to be included in the study. In the study, Al-Ahmadi (2013) investigated turnover
intention among 5423 Saudi Arabian nurses at 80 Saudi MOH hospital sites. The study
instrument consisted of 36 questions in four parts: work environment (13 items), organisational
factors (19 items), expectation of finding alternative employment (three items), and anticipated
turnover (one item). The study found that marital status played no significant role in intention
to leave, and that nurses with only a high school education had the lowest anticipated turnover
rates. The study also indicated that turnover is highest among nurses on lower salaries,
indicating that improved salaries may be a factor in reducing turnover rates. In contrast,
Alonazi and Omar’s (2013) analysis revealed that family circumstances significantly affected
turnover. These studies point to the relevance of looking at personal factors, such as marital
status and education level, which may influence nurse turnover in Saudi Arabia.
These studies might have been much more interesting if they had focused on social factors.
However, more research is needed to investigate social factors in-depth. In order to reduce the
rate of turnover, it is very important to identify exactly which social factors contribute to this
phenomenon. The social factors such as, support of family and friends, and the integration
capacity of nurses into a new culture and work system need to be considered.
Work experience has been identified as strongly influencing nurse turnover. For example, in a
study of the role of satisfaction in anticipated nurse turnover at an academic medical institution
in Saudi Arabia, Zaghloul, Al-Hassaini and Al-Bassam (2008) found that intention to leave
associated with demographic factors such as years of experience was higher among nurses with
less than ten years of experience. While 17% of these nurses indicated an intention to leave, a
majority could not say whether or not they intended to leave. Job satisfaction was assessed as
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moderate. Almalki et al. (2012) then later also reported that turnover intention among PHC
nurses in the Jazan region decreased with years of experience. Similarly, Al-Ahmadi (2013)
found that nurses who had been in their job for a longer time had higher rates of anticipated
turnover than nurses with less than one year of experience. In respect of internal turnover,
Alotaibi, Paliadelis, and Valenzuela (2016) found that the older generation of Saudi nurses left
clinical work to transfer to administrative roles. One limitation of this explanation is that
authors do not explain why and what factors influenced these nurses’ decision to transfer to
administrative roles, which is an important consideration.
Since 2016, no research on nursing intention to leave in Saudi Arabia has been as
comprehensive. The literature review identified what is known about nursing intention to leave
in terms of (a) the work environment factors that influence nurses to leave, (b) the
organisational factors that influence nurses to leave, and (c) personal factors that affect nurses
to leave their job in Saudi Arabia. In the current study, the main contribution rests on the fact
that there is the first study in Saudi Arabia to investigate the social, policy, work environment,
organisational, and personal factors that affect nurses’ intention to leave.
2.4!Job!Satisfaction Much of the current literature on nursing has devoted particular attention to job satisfaction. In
attempting to understand job satisfaction among Saudi Arabian nurses, it seems important to
classify the relevant factors and to clarify the terms used in the literature. While a number of
definitions of job satisfaction focused on the individual’s feelings of satisfaction in relation to
their job, others emphasised the relationships between person, work, and managers or co-
workers. The most widely referenced study of job satisfaction is an early study by Locke
(1976), who defined job satisfaction as “a pleasurable or positive emotional state resulting from
the appraisal of one’s job or job experiences” (p. 1304). Since that time, conceptualising the
term job satisfaction has been advanced, with some researchers pointing to workforce
conditions and task devolvement as important factors for maintaining job satisfaction. For
example, Zangaro and Soeken (2007) suggested that job satisfaction is an integral component
of proper health workforce conditions. Van Ham et al. (2006, p. 174) defined the concept as
“satisfaction with different aspects of one’s job”. From another perspective, Smith (1996)
asserted that greater nurse satisfaction is associated with greater patient satisfaction.
Although there remains a breadth of studies and conceptualisations of job satisfaction globally,
few studies to date have specifically focused on job satisfaction among nurses in Saudi Arabia.
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One of these rare studies, conducted in the north of Saudi Arabia by El-Gilany and Al-Wehady
(2001), investigated factors that would increase levels of fulfilment among female Saudi
nurses. An analysis of the data from 233 respondents indicated that almost 90% were satisfied
with their workplace. However, any work patterns that differed from a one-shift duty tended to
create problems for them, in terms of their social and family obligations. The authors
pinpointed the significance of customs and traditions as an impacting factor on job satisfaction.
In Saudi Arabia, there is a custom that forbids women to drive cars in public areas. Foreign
nurses working in the country face a dual challenge when attempting to deliver quality
healthcare for their patients. First, they are working within a healthcare environment that differs
from their country of origin, and, second, the particular cultures, values, and customs of their
adopted homeland have an impact on how they operate in the workplace. Such difficulties are
not unexpected when a nurse moves from one country to another. What is important to
understand is that within every society, there are different factors that influence nurses,
especially in terms of turnover. However, the study provides limited insight for the Saudi
context, because the researchers did not disclose the source of the instrument used to collect
data, nor did they report the scale’s reliability and validity.
Another factor often studied in combination with job satisfaction is organisational
commitment. Meyer and Allen (1997) defined organisational commitment as “a psychological
state that a) characterises the employee's relationship with the organisation, and b) has
implications for the decision to continue membership in the organisation” (Meyer and Allen,
1997, p. 67). Addressing the relationship between job satisfaction, organisational commitment
and demographic variables, in a study of public hospital nurses in Saudi Arabia, Al-Aameri
(2000) confirmed that job satisfaction was associated with organisational commitment. This
descriptive quantitative study was conducted with 290 participants in Riyadh city. The
instrument consisted of 38 questions in three sections: job satisfaction (18 items),
organisational commitment (15 items), and personal information (five items). However, other
common facets of job satisfaction, such as communication and supervision, were not included.
This omission compromises the usefulness of the study, as the majority of foreign nurses do
not speak Arabic, leading to communication problems between nurses, managers and patients.
A recent study by Al-Yami, Galdas, and Watson (2018) examined how nurse mangers’
leadership styles and nurses’ organisational commitment relate in Saudi Arabia. The results
indicated that transformational leadership was the strongest contributor to organisational
commitment. This outcome is similar to an earlier study by Abualrub and Alghamdi (2012)
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used a descriptive correlational design to study nurse managers in six public hospitals in the
western region of Saudi Arabia. The aim was to examine the impact of nurse managers’
leadership styles on job satisfaction among Saudi nurses. The study attracted 308 respondents,
representing a response rate of 51.3%. The instrument consisted of 94 questions in four parts:
job satisfaction (36 items) organisational commitment (45 items), intention to stay at work (five
items) and demographics (eight items). The findings indicated that nurses were more satisfied
with leaders who used transformational leadership styles. However, the results may be biased,
because the study used a convenience sample from only six public hospitals, and the collected
data depended on participants’ recall. Other researchers, Alotaibi, Paliadelis, and Valenzuela
(2016), used a quantitative, non-experimental, descriptive design to examine job satisfaction
among 271 Saudi Arabian nurses at seven hospital sites, each of which represented a different
region of Saudi Arabia. The findings indicated that, overall, Saudi nurses experienced only
moderate job satisfaction.
The role of work experience as a component of job satisfaction has also been investigated by a
number of researchers. For example, Al-Ahmadi’s (2002) descriptive study of nurses at nine
MOH hospitals in Riyadh City assessed the magnitude and determinants of job satisfaction.
The study attracted 366 respondents, representing a response rate of 73%. The instrument
consisted of 25 items related to facets of job satisfaction, including skill utilisation, job
advancement, pay, colleagues, recognition, autonomy, job security, work conditions, and
technical supervision, along with a global measure of job satisfaction. The findings indicated
that nurses’ job satisfaction was positively associated with their number of years of experience.
The other principle determining factors of job satisfaction were work conditions, skill
utilisation, pay, and advancement. The magnitude of job satisfaction was found to be moderate.
However, the study failed to include vital facets of job satisfaction, such as communication.
Another factor impacting job satisfaction relates to nurses feeling that they are inferior to
doctors and administrators. This sense of inferiority in the work environment appears to relate
to a lack of proper recognition for work performed, and improving such recognition seems
likely to have a positive effect on motivation. For example, Mitchell (2009) reported that the
top two sources of job satisfaction for nurses in public hospitals in Saudi Arabia were
recognition of their efforts and the work itself. In another study in Saudi Arabia, Zaghloul, Al-
Hassaini, and Al-Bassam (2008) used a cross-sectional descriptive design and an ordinal
regression model to assess satisfaction among nurses at King Fahd University Hospital, in
Khobar, in relation to anticipated nurse turnover. The study attracted 499 respondents,
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representing a response rate of 55.3%. The sampling design was not specified. The study
instrument was developed by the researchers, consisting of 26 items in two parts: demographic
information (11 items) and job satisfaction (15 items). The findings indicated that nurses were
dissatisfied with the benefits offered by the hospital and the lack of recognition of their
achievements. The researchers developed a self-administered survey, but the credibility of the
study was questionable, because the validity and reliability of the survey instrument was not
confirmed.
Mitchell (2009) investigated the relationship between demographic factors, work environment
factors, job satisfaction, and burnout among foreign nurses in 25 hospitals in the Makkah region
of Saudi Arabia. The study attracted 453 respondents, representing a response rate of 48%; one
nurse from each hospital participated in a focus group interview. The instrument consisted of
33 questions in four parts: job satisfaction (12 items), burnout (three items), work environment
(four items), and demographics (14 items). The findings of this quantitative study confirmed
an association between personal and work environment factors, and hospital characteristics, in
respect of job satisfaction. Based on Herzberg’s (1966) theory of motivation, the qualitative
data identified a number of factors that included company policy and administration,
relationships with supervisors and peers, work conditions, recognition, and salary. The study
would have been more useful if validity of the study was not limited to reliability of the
instruments used. One of the limitations for this study is the study included hospitals with
accreditations.
According to Mitchell (2009), nurse managers reported that foreign-trained nurses were
sceptical and concerned about the repercussions of participating in the study, despite assurances
of the hospital administration’s support. This issue may have impacted on the results of the
study, which was otherwise methodologically rigorous, incorporating qualitative and
quantitative techniques to provide both inferential data and depth of detail.
The literature points to the notion that nurses develop a sense of accomplishment when their
work is recognised as worthy by a doctor, and their need to contribute and share while making
educated decisions can build their self-worth (Hayes et al., 2012). However, the status and
disposition of doctors was seen to cause nurses to feel unimportant, less valued, and therefore
inferior during interactions with doctors. The status and general approach of doctors is distinct
from transformational leadership which promotes empowerment and capacity to contribute
perspectives. One study by Al Juhani and Kishk (2006) used a descriptive cross-sectional
design to evaluate levels of job satisfaction among physicians and nurses in 32 primary
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healthcare centres in Saudi Arabia. Conducted at Al-Madinah Al-Munawwara city, the study
attracted 445 respondents (105 physicians and 340 nurses), representing a response rate of
85.4%. The sampling design was not specified. An instrument of 30 items to assess job
satisfaction addressed seven facets: work load (five items), financial reward (three items),
appreciation reward (four items), work environment (five items), personal satisfaction (five
items), and patient care satisfaction (four items). The results indicated that expectations not
being met regarding workload, professional opportunities, and appreciation/reward were
sources of dissatisfaction for a majority of nurses. However, the included dimensions of
leadership behaviour and relationship with supervisors may have led to misinterpretation of the
results, as the sample comprised physicians and nurses who were themselves supervisors.
Al-Ahmadi (2009) used a descriptive, quantitative design to identify factors influencing the
performance of hospital nurses in 15 randomly selected MOH hospitals in the Riyadh region
of Saudi Arabia. The study attracted 933 respondents, representing a response rate of 50%. The
instrument consisted of 56 questions in four parts: job satisfaction (25 items), organisational
commitment (15 items), performance (nine items), and demographics (seven items). The
findings indicated that nurse performance was seen as strongly linked with job satisfaction and
organisational commitment. Moreover, job performance was found to depend on personal
factors, such as gender, years of experience, marital status, nationality, and level of education.
The findings might have been far more interesting if all measures used were not based on self-
report. One concern would be that Saudi regions may differ in terms of the factors that influence
nurses’ job satisfaction.
Alasmari and Douglas (2012) used an exploratory, cross-sectional survey design to observe
nurses working in critical care areas at King Abdulaziz University Hospital, Jeddah. The aim
was to examine the relationship between registered nurses’ job satisfaction and their intention
to leave critical care nursing. The study involved 182 respondents, representing a response rate
of 95.7%. The instrument consisted of 50 questions in three parts: job satisfaction (38 items),
intention to leave (five items), and demographic questions (seven items). The findings
indicated that overall job satisfaction was high and overall intention to leave was moderate. No
statistically significant association was found between personal factors and overall job
satisfaction scores. However, because the study was confined to nurses working in critical care
at King Abdulaziz University Hospital, the uniqueness of the study, its context, and the specific
population make it difficult to generalise to other contexts and populations. It would therefore
be useful to replicate this study with a larger sample of nurses from both public and private
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hospitals. Moreover, the study would have been more relevant if the authors had not relied on
self-report measures alone for both intentions to leave and job satisfaction.
In a later Saudi study, Al-Dossary, Vail, and Macfarlane (2012) focused on job satisfaction
among nurses in a university teaching hospital, Dammam University, in the eastern region of
Saudi Arabia. The aim of the study was to measure nurses’ job satisfaction and to identify
influencing factors. The quantitative, cross sectional study attracted 217 respondents,
representing a response rate of 87.5%. A 36-items instrument was organised into nine facets of
job satisfaction: pay (four items), promotion (four items), appreciation reward (four items),
fringe benefits (four items), supervision (four items), contingent rewards (four items), co-
workers (four items), communication (four items), and nature of the work (four items). The
findings indicated that nurses were dissatisfied with pay, fringe benefits, contingent rewards,
and operating conditions. In contrast, nurses were pleased with supervision and nature of the
work. One concern is the cross-sectional design, which limited cause-and-effect inferences
despite the high response rate. In addition, limiting the study to a single university teaching
hospital constitutes a selection bias that reduces the reliability and validity of the results. The
researchers recommended that future research should employ a mixed-methods approach, to
elicit a better understanding of the issues.
In summary, the scoping of the literature and studies to date provides an overview of existing
knowledge about nurse turnover and job satisfaction, as well as identifying the main reasons
for high turnover. It discussed factors and concepts of job satisfaction, such as the feelings and
attitudes of nurses towards their job. Personal, organisational, and work environment factors
all play a part in the level of turnover. Table 1 presents a summary of recent studies whose
findings may be useful for this study.
Table 1. Summary current published evidence of nursing turnover and job satisfaction in Saudi Arabia
Turnover or job satisfaction studies
Key finding
Saeed (1995) Intention to leave was significantly related to work environment factors, including salary and workload. Al-Aameri (2000) A strong positive correlation was found between organisational commitment and job satisfaction. El-Gilany & Al-Wehady (2001) Nurses were satisfied with their workplace; the majority preferred one-shift work patterns due to social and family
obligations. Al-Ahmadi (2002) Overall, moderate job satisfaction was found; the most important factor being recognition, positively correlated with
years of experience. Al Juhani & Kishk (2006) Workload, lack of professional opportunities, and lack of appreciation/reward were sources of dissatisfaction for a
majority of nurses. Zaghloul, Al-Hassaini, & Al-Bassam (2008)
Nurses were dissatisfied with the hospital benefits offered and recognition of their achievement.
Mitchell (2009) There was satisfaction with recognition, salary, and work conditions. There was dissatisfaction with company policy and administration, relationships with supervisors, and personal life.
Al-Ahmadi (2009) Job performance was positively correlated with job satisfaction, organisational commitment, and personal factors. Almalki et al. (2012) Intention to leave was significantly related to quality of work life. Abualrub & Alghamdi (2012) Saudi nurses were moderately satisfied in their jobs. In addition, nurses were satisfied to a greater degree with leaders
who demonstrated transformational leadership styles. Alasmari & Douglas (2012) Personal factors, including experience, age, and family, were associated with intention to leave. Al-Dossary, Vail, & Macfarlane (2012)
Nurses indicated satisfaction with supervision and nature of the work. While they were dissatisfied with pay, fringe benefits, contingent rewards, and operating conditions.
Al-Ahmadi (2013) Pay and equity, management, recognition, hours and shifts, job satisfaction, and organisational commitment were found to be significant predictors of intention to leave.
Alonazi & Omar (2013) There was a strong positive correlation between length of employment (turnover) and personal factors. Alotaibi, Paliadelis, & Valenzuela (2016)
Saudi nurses would be more satisfied with their job if they had greater access to education opportunities.
Al-Yami, Galdas, & Watson (2018)
Nurse managers and nursing staff concurred in their perception of different leadership behaviours.
Aljohani & Alomarib (2018) Nurses were influenced in their intention to leave by their low salary and high nurse-to-patient ratios.
2.5$Gaps$in$the$Literature$The high turnover of nurses is an identified, critical problem in the Saudi healthcare system,
due to the impact on long-term cost of healthcare and quality of healthcare provision. There
have been few Saudi Arabian studies on the causes of nurse turnover, which potentially results
in a limited understanding of such factors. Identifying exactly which factors contribute to this
phenomenon is very important. A number of studies have evaluated job satisfaction among
nurses in Saudi Arabia and investigated contributory factors (Al-Dossary, Vail, & Macfarlane,
2012; Al-Ahmadi, 2002; Al Juhani & Kishk, 2006; El-Gilany & Al-Wehady, 2001; Mitchell,
2009). Other studies have investigated nurses’ job satisfaction as it relates to turnover (e.g., Al
Asmri & Douglas; 2012; Zaghloul, Al-Hassaini, & Al-Bassam, 2008) or the relationship
between job satisfaction and organisational commitment (Abualrub & Alghamdi, 2012; Al-
Aameri, 2000; Al-Ahmadi, 2009). The general finding is that intention to leave is high;
however, no study has specifically identified the reasons for this high turnover, focusing instead
on job satisfaction. Despite the increasing number of both foreign nurses and Saudi nurses who
leave their job to work in administrative area or in developed countries, there is little published
data about the factors that might encourage them to remain in their current employment.
Since then, in Saudi, no research on nurses’ intention to leave has been as comprehensive. The
current study proposes to address these gaps in the literature, particularly in relation to
identifying social, policy, and organisational and work environmental factors. Generally,
different societies have different factors that influence nursing retention, such as workplace
and non-workplace support. The Saudi cultural context and society has its own unique set of
characteristics that shape the lifestyle of its population, namely customs, traditions, values, and
beliefs. Foreign nurses can also face challenges and difficulties due to religion, culture, and
customs, particularly when working with Saudi patients. Moreover, nurses’ varying cultures,
values, traditions, religion, and language can create difficulties when these characteristics differ
from the patients’ characteristics. Little is known about social factors that prevent and
encourage nurses’ intention to leave. Implementing these values is very important because they
can assist nurses to feel more comfortable with their working environment, which will
encourage them to stay in their current employment.
The study provides new insights into factors that possibly influence nurses’ intention to leave
their job in Saudi Arabia. The study is expected to inform the primary healthcare work
environment through advancing knowledge about the topic and which, in turn, may reduce the
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rate of turnover among nurses in Saudi Arabia. The study could also benefit current healthcare
providers by improving their understanding of how turnover affects overall administrative and
healthcare costs, and of the reasons for nurses’ departure. The study’s recommendations
contribute to informing policymakers with strategies to prevent turnover and reduce the current
rate of nursing turnover.
2.6$An$Overview$of$the$Intention$to$Leave$Among$Nurses$Globally$$In this section, the researcher presents an overview of nurses’ intention to leave globally.
Research on nursing turnover has distributed the influencing factors into three groups: work
environmnet factors (job satisfaction, workload, opportunity for promotion, and distributive
justice); organisational factors (social support from immediate supervisor, social support from
co-workers, autonomy, and organisational commitment), and personal factors (age, gender,
level of education, ethnicity, nationality, religion, and kinship-responsive employer).
2.6.1 Environmental factors
To date, several research studies have linked nurses’ intention to leave with the work they
undertake and the nature of the work system factors, such as job demand (workload),
distributive justice, patient load, and opportunity for promotion (Bogaert et al., 2013; Bungay,
Wolff, & MacDonald, 2016; Estryn-Behar et al., 2007; Frijters et al., 2007; Rijk et al., 2009;
Zeytinoglu et al., 2006; Zurmehly, Martin, & Fitzpatrick, 2009). In addressing the relationship
between job satisfaction, distributive justice, and workload variables correlated to the retention
of Taiwanese nurses, Chen, Chu, Wang, and Lin (2008) confirmed that the factors workload,
job satisfaction, and distributive justice are associated with the intent to stay. A later study by
Chin et al. (2017), also undertaken in Taiwan, explored how nurse retention was associated
with workplace justice. The authors found that nurses with low workplace justice had a higher
intention to leave their current employment.
In an investigation into job demand (workload), Unruh and Zhang (2013) evaluated the
relationship between the work environment factors job demand (workload) and nurses’
turnover intention. Their study showed that high job demands was almost as strong a risk factor
for nurses’ turnover intention as low job control. Moreover, when nurses had a heavy workload,
they also had low job satisfaction. Thus, a low job satisfaction level tends to reflect that nurses
are assigned too many tasks to complete, are working under time pressures, have strict and
inflexible working schedules, and have unpredictable working hours (Brewer et al., 2009;
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Hayes et al., 2012). In another investigation into the causes of nurses’ intention to leave,
Kovner, Brewer, Greene, and Fairchild (2009) identified that work setting characteristics are
linked to the intention to leave and job satisfaction, namely, heavy workloads, high patient
loads, and undesirable working hours. Similarly, when Hayward et al. (2016) examined the
environmental and personal factors that influenced nurses’ decision to leave, the authors found
that nurses’ decisions to leave were influenced by increased workload demands and high
patient acuity.
The factors interacting with workload, time pressures, and work difficulties, have been the
subject of attention in the literature. In the nursing field, the concept of job demand is an
important driving factor of work-related stress. Job demand (heavy workload) can lead to stress
(particularly psychological) in the nursing workplace, such as when the nurse-to-patient ratio
is high or when work is too physically demanding. (In the 1970s, Karasek (1979) defined
psychological demand as “psychological stressors present in the work environment”, p. 35,
which helped provide a frame of reference for studying nursing turnover and intention to leave).
Moreover, in the health field, several more studies (e.g., Roelen, Koopmans, & Groothoff,
2008) have shown that registered nurses perceive higher job demand as leading to less job
satisfaction.
In extending this further, many scholars have focused on workload, time pressures, interaction
with stressors and work difficulties, which can be seen in earlier studies by Karasek and
Theorell (1990). In recent decades, the theory of job demand–control has gained attention and
been successfully applied to the turnover area (see Chiu, Chung, Wu, & Ho, 2009 for an
example). The theory assumes that job strain outcomes arise from the interaction between two
dimensions of the work system: job demand and control. The key problem with this theory is
the difficulty of defining job demand. Others (e.g., Soderfeldt et al., 1996) have criticised the
model because it does not capture the difficulties of working with patients in hospitals. Thus,
it is necessary to consider that, in regard to healthcare providers, there are different concepts
of job demands, including emotional, psychological, and physical demands. Much earlier
studies such as
Today’s working environment for the nursing workforce is comprised on challenges such as
long working hours, which are characterised by multiple shifts. Thus, as healthcare providers
in the current climate require nurses to work longer hours, it is more difficult for nurses to have
effective personal time or time outside of the profession (Lobburi, 2012). Further, healthcare
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organisations frequently experience scheduling issues which can affect nursing experience and
performance. For example, nurses during instances where nurses are scheduled to perform on
unpredictable shifts, there can be difficulties in accomplishing all task and duties. A
combination of these factors pressures nurses into believing that they need to leave or look for
other positions that will provide greater flexibility and reliability in their schedules. Therefore,
it would appear that if healthcare organisations provided better working environments and
greater flexibility with their nursing schedules, fewer nurses would consider leaving their
positions (Brewer, Kovner, Yingrengreung, & Djukic, 2012).
These findings also connect with a study of turnover exploring how turnover is influenced by
work environment factors, such as control over practice and opportunities for advancement and
promotion. Beecroft, Dorey and Wenten’s (2008) study of the Children’s Hospital in Los
Angeles aimed to identify the factors that contributed to nursing turnover and to compare the
turnover intent of new nurses with actual turnover rates. The findings indicated that nurses are
more likely to have turnover intent if they did not get a choice of which ward to work in. An
earlier study conducted by Shields and Ward (2001) in England, found that dissatisfaction with
promotion and training opportunities have a stronger influence than workload or pay.
Turnover intention has also been found to be influenced by work environment factors such as
low pay, limited leave allowance and workload and as mentioned above the work schedule.
Dawson, Stasa, Roche, Homer and Duffield’s (2014) study of Australian hospitals also aimed
to determine the factors contributing to nursing turnover and identify strategies to improve
retention. The study used a qualitative design to reveal nurses’ perception of turnover in
Australian hospitals. The findings indicated that limited career opportunities, high nurse-to-
patient ratios, and negative staff attitudes are seen as factors strongly related to nursing
turnover.
Moreover, when Tei-Tominaga (2013) investigated female nurses’ opinions of work-related
variables and their potential to have a direct impact on intention to leave and decision to resign,
the study found that negative work environment issues, such as an insufficient amount of
permitted rest time and inadequate break facilities, were seen as factors strongly related to
nurses’ decision to resign, while cumulative fatigue was seen as a factor strongly related to
nurses’ intention to leave. Another study conducted by McHugh et al. (2016) aimed to
determine the association between nurse staffing, nurse work environment, and in-hospital
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cardiac arrest survival. Result from the study indicated that the care environment had a
significant effect on intention to leave.
Mark and Smith (2012), examining the relationship between job characteristics and the ability
to cope in predicting levels of anxiety in nurses working in the south of England, found that
job demand was positively associated with anxiety. Chiu et al. (2009) also found that “nurses
in high job demand and low-control work situations [revealed] the highest turnover intention”
(p. 258). Therefore, the job stress–strain model is helpful in clarifying the impact of job
demands, skills discretion, and social support on nurses’ intentions to stay or leave.
When hospitals provided flexible scheduling, autonomy, and professional growth for nurses
(Upenieks, 2001), lower turnover rates and higher levels of job satisfaction resulted (Lephalala,
2008). In a study by Shader, Broome, Broome, West and Nash (2001), the relationship between
job satisfaction, age, stress, work schedule, and turnover were examined. The study assessed
241 nurses and nurse supervisors within a 12 unit 908 university hospital in the South-eastern
United States of America. S. The results were congruent with the theory that the most likely
causes of nurse burnout were job stress, work dissatisfaction, and weekend overtime. However,
a range of factors appeared to predict the turnover of various age groups. Overall, the study
identified that changes to work schedules could enhance the job satisfaction levels of nurses.
In the current study, it is very important to examine factors that predict anticipated turnover for
nurses of different groups.
Cartledge (2001) studied the variables related to turnover in the intensive care unit (ICU)
environment, where there were high levels of work-related stress activities, such as shift work.
The study involved 11 interviews with ICU nurses who had left their positions and who had
faced negative experiences at work. The study found that four factors negatively impacted the
nurses: high stress levels, the ability to improve their professional knowledge and skills, the
recognition and respect by others of their capabilities, and the effect of shift work on their
personal and family lives.
Healthcare organisations also give nurses a workload that is often difficult to manage, with
those nurses seldom being adequately remunerated and compensated for such schedules
(Lephalal, 2008; Zangaro & Soeken, 2007). Validating this finding is the work of Hayes et al.
(2006) and Janssen, De Jonge, and Bakker (1999), who identified that being satisfied with their
remuneration meant that the nurses had a lower level of intention to leave. This result contrasted
with the intention to stay of nurses who received less remuneration. Congruent results were
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found in a Finnish study on job satisfaction and nurses’ retention (Flinkman et al., 2008). The
authors found that job demand, dissatisfaction with their salary, shift work, and an uncertain
work status influenced nurses’ intention to leave their job.
Much of the studies indicate a western developed country lens to nursing intention. Studies on
distributive justice identified that nurses who think they are fairly rewarded for their own
investment and those of comparable others in the hospitals are consequently more likely to stay
in their job (Chin et al., 2017; Chen, Chu, Wang & Lin, 2008; Kim, 1996). This means that a
nurse who perceives that he or she performs better at his or her job than other nurses and who
is subsequently rewarded would be an example of high distributive justice. Distributive justice
is generally understood to mean “the degree to which rewards and punishments are related to
performance inputs into the organisation” (Kim, 1996. p. 29). Cowin’s (2002) study showed
that Australian nurses were unhappy with their salaries, while Lephalala (2008) identified that
such unhappiness was often related to the fact that their additional nursing qualifications were
not considered in their remuneration package. However, both studies showed that satisfaction
with their salaries was more prevalent with experienced nurses. In Saudi Arabia however, the
situation is more complicated because of divergent factors such as culture and the lack of
standardisation of salaries in the healthcare system. When nurses work in Saudi Arabian
military hospitals, they receive a higher salary than the remuneration received by nurses who
work at the MOH or in the private sector (Saeed, 1995). Consequently, improvements to
nurses’ salaries can be seen as a relevant factor in reducing the turnover rates for nurses.
2.6.2 Organisational factors
A number of studies (e.g., Beecroft, Dorey, & Wenten, 2008; Duffield et al., 2011) have linked
nurse retention to organisational variables such as job control (autonomy, supervisor support,
and colleague support) and organisational commitment. Job control (decision latitude) was
described as the employee having the discretion or ability to determine or control their own
tasks or activities using whichever skills they consider necessary (Karasek & Theorell, 1990).
Thus, the effect of job control on job satisfaction is usually related to the concept of autonomy,
while there is a strong association between autonomy and job satisfaction. Additionally,
Khowaja, Merchant and Hirani (2005) observed that nurses’ job satisfaction level was often
influenced by their level of autonomy and their excess workload.
Another factor impacting on health workforce retention relates to the nurses’ feeling that they
are inferior to doctors and administrators. This sense of inferiority in the work environment
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appears to be related to receiving the proper recognition for work done; such recognition would
also have the positive result of helping to keep motivation high. For example, Sammons (2009)
reported that when hospitals seek to “employ supportive managers and give employees more
autonomy over decisions that affect their job experience [there is a] lower incidence of nurses
leaving that organisation” (p. 25).
A study of turnover explored how turnover is influenced by structural factors, such as social
support from supervisors, social support from co-workers, and decision-making authority.
Bruyneel, Thoelen, Adriaenssens and Sermeus’ (2017) study in hospitals in Flanders, the
Dutch-speaking part of Belgium, aimed to examine the association between personal factors,
structural factors, and turnover intention. The study used a cross-sectional multicentre survey.
The findings indicated that the association between social support from supervisors and
turnover intention was only significant for female nurses, among whom turnover intention was
higher. Similarly, Tei-Tominaga (2013) earlier found that support from supervisors has an
important influence on the intention to leave among female nurses in Japan. Likewise, Cohen,
Stuenkel, and Nguyen (2009) indicated poor supervisor support as a statistically significant
reason for why nurses reported leaving their job in Northern California.
Nurses receive a sense of accomplishment in their role when their work is recognised as worthy
by a doctor. Their need to contribute and share, while making educated decisions, can also
build their self-worth (Hayes et al., 2012). However, one of the key themes across studies is
professional standing and the treatment of nurses by doctors and senior staff, which, if negative,
can lead to devaluating the worth of the role and person. This means that doctors should
embrace working as a team and collaborate with nurses. The Australian study by Cowin,
Johnson, Craven and Marsh (2008) also addressed how the multidimensional aspects of nurses’
job satisfaction and self-concept were linked to nurses’ retention plans. The authors found that
self-concept had a stronger association with nurses’ intention plans than job satisfaction.
Because organisational factors, such as organisational commitment, can vary in their
correlation with turnover, it is important to clarify their meaning. For example, in an earlier
study, Meyer and Allen (1997) define commitment as “a psychological state that (a)
characterises the employee's relationship with the organisation, and (b) has implications for the
decision to continue membership in the organisation” (p. 67). A recent study by Brewer, Chao,
Colder, Kovner and Chacko (2015) found the relationship between organisational commitment
and turnover was minimal. Using a longitudinal panel design testing a model linking major
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turnover variables, the study found that organisational commitment did not have a significant
effect on turnover, while organisational commitment had a significant positive direct effect on
intent to stay. Therefore, it is imperative that the current research examine the organisational
commitment and anticipated turnover among nurses in Saudi health system, to better
understand reasons of high turnover. In addition, organisational commitment for both Saudi
nurses and non-Saudi nurses is of paramount importance for supervisors and leaders in
hospitals, due to the crucial role it plays in nurses’ turnover.
In examining how the relationship between job satisfaction, organisational commitment, and
demographic variables correlated to the retention of Chinese student nurses, Wu and Norman
(2006) confirmed that organisational commitment is associated with job satisfaction. Similarly,
Brewer et al. (2015), examining the correlation among these variables for nurses in 51
metropolitan areas and nine non-metropolitan, rural areas in 34 states and the District of
Columbia, highlighted that organisational commitment was an important driving factor of job
satisfaction. Lum et al. (1998) identified that organisational commitment was seen as a factor
strongly related to intention to leave, while job satisfaction was seen as a factor weakly linked
to intention to leave.
Boyle et al. (1999) investigated the effects (both direct and indirect) on nurses’ intention to
stay by focussing on three issues in relation to nurses: managers’ power, influence, and
leadership style. They showed that a manager’s authority and impact over work coordination
was seen as a factor strongly related to nurses’ intention to stay. A similar study by Castle
(2005) studied the relationship between the turnover of caregivers and the turnover of nursing
homes’ top management. They highlighted a relationship between top management and nurses’
intention to leave. For example, when top management turnover increased by 10%, it was
expected that the turnover rate for registered nurses would increase by about a 30%.
A literature review by Wagner (2007) posited that nurse turnover is related to multiple factors,
such as organisational commitment, job satisfaction, demographics, and job-related factors and
argued that organisational commitment is a robust, indirect predictor in nursing turnover
studies. Ahn, Lee, Kim, and Jeong (2015) used a cross-sectional survey design to investigate
organisational commitment, job satisfaction, and intention to leave among male nurses (150
respondents) in South Korea. The instrument consisted of four parts: organisational
commitment, job satisfaction, turnover intention, and demographic questions. The findings
indicated that the most influential factor on turnover was organisational commitment. A
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statistically significant association was found between organisational commitment and job
satisfaction. Furthermore, the authors suggested that to reduce turnover intention in nursing, it
is very important to increase organisational commitment as well as job satisfaction.
2.6.3 Personal factors
Numerous studies (Delobelle et al., 2011; Ma, Yang, Lee, & Chang, 2009; Tschannen et al.,
2010) have linked nurses’ intention to leave with personal factors, such as level of education,
level of experience, marital status, age, gender, and nationality. Investigating the variables that
contribute to nurses’ intention to leave their job, Borkowski et al. (2007) found that the lower
the nurses’ qualification, the more likely they were to have an intention to leave their
profession, while having a master’s degree or above appeared to influence their intention to
stay. In contrast, this outcome is contrary to that the findings of research by Delobelle et al.
(2011) who identified that younger nurses and those with more education were more likely to
consider leaving their job. Another study conducted by Brewer et al. (2009) in the United States
found that nurses tend to have a plan to leave if they have higher levels of education. Another
study conducted by Stewart et al. (2011), in Canada, found that nurses are more likely to plan
to leave their nursing positions if they had higher education. In contrast, Chan et al. (2009)
found no statistically significant association between personal factors and nurses’ intention to
leave, including nurse’s educational level. These studies would have been more useful if they
had asked participants about opportunities for specialist training or completion of their
education. Indeed, in the current study, the factors of personal factors, especially education
level of non-Saudi nurses, will be different from that of Saudi nurses who have the opportunity
to complete education in Saudi Arabia. Thus, it is important to examine both Saudi nurses and
non-Saudi nurses from different perspectives.
Work experience is an important component of job satisfaction and plays a key factor in
intention to leave. For example, Delobelle et al. (2011) found a negative link between work
experience and intention to leave among nurses. Lum et al. (1998) identified that nurses who
had been at their job for a long time had a greater level of job satisfaction and thus were not
inclined to leave their job. Moreover, a number of studies (e.g., Hayes et al., 2012; Wieck,
Dols, & Landrum, 2010; Shader et al., 2001) found that the older generation of nurses had a
greater level of job satisfaction than the younger generation; similarly, intention to leave was
higher in the younger generation. In contrast, Chan and Morrison (2002) reported no significant
difference in the age factor for those who intended to stay or leave. However, marital status has
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been found to be a factor strongly related to job satisfaction and intention to leave. For example,
Price and Mueller (1981) determined that marital status (that is, being married) was an
important predictor for nurses’ intention to stay. This outcome is contrary to that of McCarthy,
Tyrrell and Lehane (2007), in Ireland, who found that almost 60% of nurses who expressed
intent to leave were single.
In regard to family obligation, McCarthy, Tyrrell and Lehane (2007) found that the most
significant predictors of intent to leave, by registered nurses at 10 hospitals in Ireland, were job
satisfaction and family responsibilities. Authors indicated that nurses who had no family
obligations were more likely to leave than nurses who had family obligations. The study would
have been more convincing if the authors had included the Irish kinship system. It is important
to note that within every society, there are different kinship systems that may influence nurses,
especially in terms of intention to leave.
Many studies have examined the relationship between gender and intention to leave.
Supporting this, one study by Borkowski et al. (2007), also undertaken in Florida, explored
issues relating to the retention of the existing nursing workforce. The authors found that nurses
who were male and had less than a master’s degree were more likely to leave than were nurses
who were female and had a master’s degree or higher. In contrast, this outcome is contrary to
that of Boumans et al. (2008) who found that there was a significant association between gender
and intention to retire early. The authors found that that female nurses were more likely to retire
early than male nurses. It seems possible these results are due to the fact that women may have
more responsibilities to people in their personal situation than men do. Moreover, according to
multiple role theory, men and women have different combinations of work and family roles,
which have been indicated in previous studies (Rajapaksa & Rothstein, 2009; Whittock &
Leonard, 2003). In their useful study of factors that influence the decisions of male and female
nurses to leave nursing in the United States, Rajapaksa and Rothstein (2009) concluded that
nurses frequently must work unusual hours to meet their work obligation, which can impose
strains on females’ family obligations.
In regard to ethnicity (race), the majority of nurses who work in Saudi Arabia are highly
diverse. Nurses work with doctors, immediate supervisors, nurses, and patients from several
cultureless, and nurses may have different perspectives about their job. They may feel different
and isolated from their immediate supervisor and society. Moreover, discrimination may also
be perceived by both Saudi and non-Saudi nurses from immediate supervisors, top
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management, and patients. Previous studies have suggested that nurses who are different from
their immediate supervisor in racial or ethical background tend to be less inclined to stay with
the hospital (Dreachslin, Hunt, & Sprainer, 2000). One study conducted by Milliken and
Martins (1996) indicated that diverse groups have lower member satisfaction and high
turnover; this means nurses who differ from their immediate supervisor member. Supporting
this, an earlier study by Greenhaus, Parasuraman and Wormley (1990), undertaken in United
States, explored the relationships between ethnicity, job performance evaluation, and career
outcomes for culturally diverse managers. The authors found culture to be a factor in nursing
intention, whereby the revealed that nurses with a cultural background, during performance
appraisals, were less likely to be promoted and in turn were more likely to be less satisficed in
their careers.
Because the majority of nurses are non-Saudi, it is very important to examine cultural and
ethnic differences in the determinants of social support. In addition, it is very important to
identify if diversity and social factors may influence nurses to leave the Saudi health system.
The current study provides recommendations for the Saudi health system, because a
discrimination-free society and work environment could prevent nurses from leaving and
encourage them to stay.
However, there are personal factors that influence nurses’ characteristics, which prevent
nurses’ intention to leave, such as age, gender, education level, religion, family obligation,
nationality, and flexible work schedules. The current study provides a greater understanding of
how personal factors may influence both Saudi and non-Saudi nurses’ intention to leave their
job. To conclude this section, the literature identifies numerous factors that may influence
nurses to leave their job. These include work environment factors, organisational factors, and
personal factors as shown in Figure 7.
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Figure 7. Theoretical model from current literature
2.7$Conceptual$Framework$The theoretical framework for this thesis emerged from the insights gained from the wider body
of literature assessed for the thesis. To date, much of the previous literature has been focussed
at an individual level, developing models to predict the employment decisions of individuals
(Gurney, Mueller, & Price, 1997; Prescott, 1986). Many studies have drawn on the Price-
Mueller theory (1982), as it offers the most useful framework for explaining high levels of
employees coming and going. Although published over three decades ago, the model still has
currency today as it suggests that family responsibility, generalised training and
professionalism have an indirect impact on turnover in terms of intention to stay. These are the
key factors all studies refer back to as an initial scoping for their study. Another early study
indicated that pay, promotional opportunities, distributive justice, integration, instrumental
Actual'turnover
Work'environment'factorsJob$satisfaction,$workload,$opportunity$for$promotion,$
distributive$justice
Organizational'factorsSocial$support$(Immediate$
supervisor,$Social$support(Co=workers),$Autonomy,$
organizational$commitment
Personal'factorsAge,$gender,$marital$status,$level$of$education,$work$
experience,$family$obligation,$nationality
Nurse'turnover'intent Social'factors
?
37 | P a g e
communication, routinisation and participation have indirect impacts on turnover in terms of
job satisfaction (Gurney, Mueller, & Price, 1997). A number of factors related to work
environment, as well as organisational and personal factors, have also been identified as
significant in nurse turnover on an international scale, in both older and more recent studies
(Hayes et al., 2012; Lucas et al., 1993). The question of whether these factors apply to nurses
in Saudi Arabia has received limited attention.
This study attempts to address this shortcoming. The current study will construct a theoretical
framework for use in the Saudi Arabian context, drawing on previous research (e.g., Beecroft
et al., 2008; Tourangeau & Cranely, 2006; Mueller & Price, 2004), to analyse the personal,
organisational, and environmental factors that influence intended or actual turnover (see Figure
8 below). Price (2004) suggested that the two new kinship factors proposed, organisational
responsiveness to kinship consideration and the existence of careers for both spouses should
be added to the model. In the current study, social factors (gender-mixing, perception of
nursing, discrimination, and social support-spouse) will be added to the model. However, the
evidence for social factors’ positive impact on anticipated turnover is of such a magnitude as
to require its inclusion in this study. Moreover, this study will include factors related to work
setting that the research literature indicates are of interest to nurses’ employment. The personal
factors will include marital status, nationality, work experience, education level, gender, age,
and family obligations. Given these insights, four organisational factors have been identified
for examination, including organisational commitment, social support (immediate supervisor),
social support (co-workers), and autonomy, while the environmental variables will include
workload, job satisfaction, opportunity for promotion, and distributive justice.
The researcher adapted the model to focus on variables (work environment factors,
organisational factors, social, cultural, political and personal factors) that may influence the
two outcomes (actual turnover and intention to leave) (See Figure 8).
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Figure 8. Theoretical model for the thesis
In the light of present literature and below-mentioned theoretical framework, the current
research has four hypotheses. As mentioned earlier, work environment factors (job satisfaction,
workload, opportunity for promotion, distributive justice) were found important to nurse’s
retention in the literature (Beecroft et al., 2008; Kovner et al., 2007; Tourangeau & Cranely,
2006; Mueller & Price, 2004). The first hypothesis assumes that anticipated turnover of nurses
is influenced by environmental factors (job satisfaction, workload, opportunity for promotion,
and distributive justice).
Organisational factors such as social support from immediate supervisor, social support from
co-workers, autonomy, and organisational commitment are variables that had significant
relationship with anticipated turnover (Hayes et al., 2012; Beecroft et al., 2008). The second
hypothesis assumes that anticipated turnover of nurses is influenced by organisational factors
(social support from immediate supervisor, social support from co-workers, autonomy, and
organisational commitment).
Actual'turnover
Work'environment'factorsJob$satisfaction,$workload,$opportunity$for$promotion,$
distributive$justice
Organizational'factorsSocial$support$(Immediate$
supervisor,$Social$support(Co=workers),$Autonomy,$
organizational$commitment
Personal'factorsAge,$gender,$level$of$education,$
religion,$family$obligation,$nationality,$flexible$work$
schedules
Nurse'turnover'intent Social'factorsSocail$support(Spouse),$Kinship$responsive$employer,$Gender=Mixing,$Perception$of$nursing,$
Discrimnation
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Nurses’ decision to stay or leave hospitals is affected by personal factors such as, age, gender,
level of education, religion, family obligation, nationality, and flexible work schedules. For
example, family obligation is the existence of responsibility toward relatives living in the
community. Based on knowledge of the Saudi Arabia kinship system, the study concentrated
on parents, brothers, sisters, and children as the relatives to whom the people would have the
strongest obligation. Furthermore, other relatives such as grandparents, aunts, and uncles
would seem to be very important to people in Saudi Arabian society. The third hypothesis
assumes that anticipated turnover of nurses is influenced by personal factors, such as age,
gender, level of education, religion, family obligation, nationality, and flexible work schedules.
Finally, in this current research, social factors in respect of both Saudi and non-Saudi nurses
considered in relation to nurses’ intention to leave include local support of their spouse, family,
friends, relatives, their quality of life, living conditions, as well as their perceived skill to adopt
to Saudi culture. The last hypothesis assumes that anticipated turnover of nurses is influenced
by social factors (gender-mixing, perception of nursing, discrimination and, social support
from spouse).
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3.$CHAPTER$THREE:$RESEARCH$PROGRAM$AND$DESIGN$
This current study has two aims, the first of which is to identify the factors that contribute to
turnover of hospital nurses in Saudi Arabia. The second aim is to identify strategies to reduce
turnover and improve retention among hospital nurses in Saudi Arabia. Chapter 3 describes the
methodology used and clarifies the choice of the quantitative and qualitative research designs
to address the research questions. The analysis focused on gaining an understanding of the
factors that influence nurses to leave their current job. The chapter is presented in five sections.
The first section (3.1) gives a brief overview of the mixed-method approach. The second
section (3.2) lays out the sample size of the study and presents a brief overview of
instrumentation and details the collection, as well as analysis. The third section (3.3) and the
fourth section (3.4) present detailed discussions of the pilot study for survey validation and
research quality standards, by establishing the validity of the survey used. The fifth section
(3.5) details the statistical analysis, structural equation modelling, and confirmatory factor
analysis (CFA). The final section (3.6) details the data collection, data analysis, and research
rigor for qualitative dimension of the study.
3.1$Methodology$The conceptual framework informs the methodological design of the current study, which used
a mixed-methods approach to gain a better understanding of the cause of turnover. A mixed-
methods approach is best suited to address the research questions. The term mixed-methods
has been variously defined by researchers across a number of disciplines. Greene (2007)
characterises it as multiple ways of seeing and hearing (p. 20). A definition that will inform the
study’s purposes. Tashakkori and Teddlie (1998) more standard definition, where mixed-
methods refers to the combined use of qualitative and quantitative tools when collecting
information (p. ix) is also relevant to the approach taken by this research. A further definition
of the mixed-methods is given by Creswell and Plano Clark (2011) who describe it as an
approach to study in which the researcher gathers both quantitative (closed-ended) and
qualitative ( open-ended) replies to research questions and combines all of resulting data in
order to understand a research problem. The advantage of obtaining qualitative data (open-
ended questions) is to corroborate the quantitative data and ensure credibility in the information
provided by the nurses. Another advantage of the qualitative dimension (open-ended questions)
is that it allows nurses to identify any specific information that would be important to the
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research and was not covered by the survey tools. In the current study, sets of three questions
(open-ended) are asked of nurses to enhance the understanding of the nurses’ perspectives of
the factors contributing to nursing turnover. The qualitative dimension sought responses to the
open-ended questions to supplement the quantitative data and add rich, contextual insights to
the findings. The qualitative dimension is analysed using NVivo 11 analysis software for
exploring themes.
In addition to the quantitative instruments used in the current study, three open-ended questions
were used as well. The qualitative dimension will allow the participants to express their stories
in their own words, which is a correct way of gaining the confidence of the participant to be
open about their thoughts. Open-ended question is a valid tool by which to identify those
factors that influence the study nurses’ intention to leave their job (Broussard, 2006).
As noted above, the current study will use a mixed methods design to identify and describe the
factors that influence nurses’ intention to leave their job in hospitals in Saudi Arabia. To obtain
a comprehensive understanding of the research topic, a mix of quantitative and qualitative
methods will be adopted. Both types of methods will be used due to the many unknown factors
that might influence the study nurses’ intention to leave their job. No previous research has
specifically identified the social factors that influence nurses’ intention to leave Saudi hospitals.
3.1.2 Rational for mixed methods and strengths and challenges
In this study, the purposeful integration of a qualitative aspect (three-open ended questions)
with the quantitative survey (closed-ended questions) helped to refine the mixed method of
program of inquiry. The benefit for this study was in the way mixed methods could yield some
additional deeper insights for the allied health and nursing sector around nursing intention.
Creswell and Plano Clark (2011) suggested that the qualitative aspect can augment quantitative
outcomes of the study, as it reflects an embedded research design (rather than exploratory
sequential or parallel mixed method type) to help explain the findings (QUANT-qual) (Shorten
& Smith, 2017). Embedded research design involves a smaller aspect of qualitative embedded
in the larger quantitative study. Such an approach of using a qualitative aspect is highly relevant
for this study in giving voice to the study participants that otherwise is not captured in a
quantitative survey. It also helped capture any similarities and contradictions between
quantitative and qualitative aspects. Integrating a qualitative aspect helped establish and
maintain research rigour and enhance data analysis (Creswell & Plano Clark, 2011). While
other studies use singular methods such as explicitly quantitative only or qualitative only, in
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the health sector there has been a substantial increase in the use of integrated mixed methods,
particularly in nursing. This is because the quantitative positivist (deductive) side helps in the
design of procedures to test an a priori hypothesis and generate generalizable findings.
Although qualitative uses interpretative, inductive approach, it compliments quantitative by
yielding a thicker deeper understanding of the research problem. Further, qualitative generates
insights that can inform health policy makers and program designers.
The researcher was able to gain a broader view of the research environment and explore the
phenomena from diverse perspectives. For example, in this study, the cross-sectional survey
relating to the topic of nursing turnover allowed for quantitative data to be collected (Shorten
& Smith, 2017). This approach helped the researcher to make sense of demographics, work
environment factors, organisational factors and social factors influencing turnover intention.
The three qualitative open ended questions allowed for data to be collected on deeper insights
into these factors and capture any other relevant factors that influenced decision-making intent
(Shorten & Smith, 2017).
The challenges of using the integrated approach centred on the potential for adding complexity
to the study design and over complicating research methods implementation. The researcher
initially started with a larger, more time-intensive mixed methods approach. However
following valuable feedback from a milestone panel session, the researcher scaled down the
research design for manageability of the study and meet doctoral timeframes. Drawing on
Halcomb and Hickman (2015) and Shorten and Smith (2017) helped the researcher in achieving
a modified, yet simultaneously rigorous study inclusive of the qualitative aspect. From the
redesign of the qualitative aspect, the outcome was three open-ended questions positioned at
the end of closed-question survey. This outcome functioned to maintain the integrity of the
overall mixed method study within a bounded timeframe. The researcher rapidly became
oriented to using the different approaches to data collection, data analysis (quantitative and
qualitative technology), data synthesis, and integration found in quantitative and qualitative
approaches (Shorten & Smith, 2017).
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3.2$Quantitative$Study$$
3.2.1 Sample
A quantitative survey was used in the study, the survey included a number of open-ended
questions to collect information on factors that influence the intent to leave, and their
perceptions of the factors affecting turnover. The study was conducted in the Saudi Arabian
cities of Jeddah and Mecca and the study population included all nurses working in two
governmental-owned hospitals in the Jeddah and Mecca regions, one hospital was a military
hospital and the other was a public hospital. They had a combined total of 900 functioning
beds, and are among the highest ranked hospitals managed by the MOH in KSA (Arab News,
2015). Approximately 20% of all healthcare services are provided by the government, and
hospitals are run by organisations including the Ministry of Defence and Aviation, the National
Guard, and the Ministry of Interior. Military hospitals provide healthcare to employees and
their families. Staffing from these hospitals was possibly representative of the whole country
(Security Forces Hospital Program, 2017; MOH, 2015). Both hospitals employ nurses from
across Saudi Arabia and international nurses from many different countries.
A total of 1,200 nurses worked at the government hospitals in the Jeddah and Mecca (MOH,
2015). To be included in the study respondents needed to be: employed as nurses staff at one
of the identified government hospital in Jeddah and Mecca; be 20 years of age or more; both
Saudi nationals and foreign nurses and men and women could complete the survey. 502 (41.8%
response rate) nurses returned their completed survey to the researcher. Nurses could complete
the survey either as a paper-based or online questionnaire, half (n=253, 50.4%) choose to
complete the paper-based survey. To determine the required sample size for this research, the
sample size calculation was used, assuming a confidence level of 95% and margin of error at
5%.
3.3$Ethical$considerations$Permission to conduct the study was obtained from the Hospitals Institutional Review Board
(IRB) and the Human Research Ethics Committee at the Queensland University of Technology
(N0: 1700000331), see Appendix F. The recruitment of potential participants was negotiated
with the relevant authorities or boards within the hospitals. An introductory letter was sent to
a representative from the hospitals’ management bureaus explaining the study’s intent and
seeking permission to conduct research on their premises. Upon obtaining formal approval, the
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researcher negotiated with the heads of the nursing departments of each hospital to distribute
the survey and associated documents, such as the information sheet. Participants were informed
that their privacy and safety would be ensured and respected. Participant private details or
personal information without permission from the hospital and the individual involved in the
survey. One of hospitals was not able participate in the current study due to the following
reasons:
•! They are unable to provide a primary investigator from the nursing department, and
•! They presently have other internal nursing research studies underway on similar topics
(please see Appendix H).
In the survey questionnaires for nurses, in particular questions about personal factors (section
one), the study did not include personal questions about marital status, number of dependent
children, and how long the individual had worked in these hospitals, in order to meet the
requirements of the national statement on Ethical Conduct in Human Research. According to
ethical committees, it could be possible for responses to these questions to enable identification
of an individual. For these reasons, these questions have been removed from the current study.
An anonymous electronic survey (Key Survey) was made available online for nursing staff to
access if there were literacy or language issues. QUT’s Key Survey is an official web-based
survey creation and management system, which is provided to all QUT staff and postgraduate
students. The survey was in English with an exact translation into Saudi Arabian (see attached
questionnaire). They survey was translated to the Saudi Arabian language, but the final versions
(English and translated final copies) was the same as the attached version (see Appendix Gd.
The survey was translated into Arabic using a translation and back-translation technique to
ensure the same meaning between the two versions. It was then sent to two experts in Saudi
who have knowledge about the topic and one expert in Australia (academic) for review. The
survey was then revised based on experts’ reviews (Cha, Kim, & Erlen, 2007).
Involvement
Participants completed an anonymous survey with Likert scale answers (strongly agree to
strongly disagree) that was designed to take approximately 20 to 25 minutes for nursing staff
to complete, either on paper or electronically. Participation in this project was entirely
voluntary. If nurses agreed to participate, they did not have to complete any question(s) they
were uncomfortable answering. The decision to participate or not participate would in no way
impact upon any nurse’s current or future relationship with QUT or employment organisation.
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If nurses did agree to participate, they could withdraw from the project at any time without
comment or penalty. However, because the survey was anonymous, it was impossible to
withdraw once responses had been submitted. The principle researcher conducted two site
visits to each of the three identified hospitals.
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Researcher’s field visits
After receiving permission to enter the hospital, the researcher provided department
representatives with an overview of the project, including the study’s aims, information and
participation, and background information on ethical requirements, including the nature of
participant involvement.
During the follow-up visit, the researcher held information session with nursing teams (for
example, during team meetings) to outline the aims and scope of the project and distribute
hard-copy surveys. Instructions were provided regarding the return of surveys using a self-
addressed stamped envelope. An option of completing the same survey online was also offered
and instructions for completing an electronic survey were provided. At the end of the meeting,
the researcher distributed the information sheet and survey material with a self-addressed
stamped envelope. The researcher also left hard copies of the survey in strategic places to
maximise uptake.
3.4$Survey$questions$–$measurement$and$scales,$items$
3.4.1 Structure of the survey
The study was a cross-sectional survey (see Appendix G for the full survey), part of which
relates to the topic of nurse turnover. The survey was comprised of six sections. The section
one collected demographic and work-related details including: age, gender, education, years of
nursing experience, religion, type of work schedules, and family obligations. The section two
contains a number of scales and individual questions that assessed work environment factors,
such as job satisfaction, workload, opportunity for promotion, and distributive justice. The
third and fourth sections deals with organisational factors (social support from immediate
supervisor, social support from co-workers, autonomy, and organisational commitment) and
social factors (gender-mixing, perception of nursing, discrimination, and social support from
spouse). The section five collects data using the Anticipated Turnover Scale developed by
Hinshaw and Atwood in 1978 to study turnover intention among nurses. The section six
included three general open-ended questions to address any issues not covered by the closed
questions and to gain insights into factors contributing to nurse turnover from the nurse’s
perspective.
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3.4.2 Survey questions –scales, items The study measured four exogenous variables (work environment factors, social factors,
organisational factors and personal factors) and two endogenous variables (intent to leave and
intent to stay). The first exogenous variable, work environment factors, includes job
satisfaction (seven items), workload (12 items), opportunity for promotion (three items), and
distributive justice (six items). Job satisfaction was measured by seven indicators, using a
global measure of job satisfaction on a five-point Likert scale, ranging from strongly disagree
to strongly agree (Porter & Lawler, 1968). The second exogenous variable (organisational
factors) includes organisational commitment (six items), autonomy (six items), social support
from co-workers (four items), and social support from immediate supervisor (four items).
Organisational commitment was measured by six indicators on a five-point Likert scale,
ranging from strongly disagree to strongly agree (Kim et al., 1996). In this section, responses
related to supervisor and colleague support were measured using a four-point scale, ranging
from strongly disagree to strongly agree.
The third exogenous variable (social factors) includes gender-mixing (five items), perception
of nursing (four items), discrimination (three items), and social support from spouse (four
items). Social support from spouse was measured by four indicators on a five-point Likert
scale, ranging from strongly disagree to strongly agree (Kim et al., 1996). The final exogenous
variable (personal factors) includes demographic and work-related information about the
participant’s age, gender, education, experience, and family obligations.
The first endogenous variable, anticipated turnover scale, was measured by 12 indicators on a
five-point Likert scale, ranging from strongly disagree to strongly agree (Hinshaw & Atwood,
1982). The second endogenous variable, intent to stay, was measured by four indicators on a
five-point scale, ranging from strongly disagree to strongly agree (Kim et al., 1996). Table 2
provides details of the items or indicators used to create the scales.
Table 2. Details of the scale questions
Domain Scales (abbreviated variable name used for CFA)
Individual items in the scale W
ork
envi
ronm
ent Job satisfaction (JS)
7 items: JS1 ‘I find real enjoyment in my job’; JS2 ‘I consider my job rather unpleasant; JS3 ‘I am often bored with my job; JS4 ‘I am fairly well satisfied with my job’; JS5 ‘I definitely dislike my job’; JS6 ‘Each day on my job seems like it will never end’; JS7 ‘Most days I am enthusiastic about my job’.
Workload (WL) 12 items: WL1 ‘I have enough time to get everything done in my job’; WL2 ‘My workload is unacceptable’; WL3 ‘I have to work very hard in my job’; WL4, ‘I have to work very fast in my job’; WL5 ‘My workload is predominantly physical’; WL6 ‘My workload is predominantly mental’; WL7 ‘I can perform all my tasks without help from others’; WL8 ‘I often have headaches from my work’; WL9 ‘I often feel muscle pain from my work’; WL10 ‘My work does not cause any pain’; WL11 ‘My work requires skills that I don’t have’; WL12 ‘I am very confident about my work’.
Opportunity for promotion (Prom)
5 items: Prom1 ‘Promotions are regular with my hospital’; Prom2 ‘There is a very good chance to advance in my career in my hospital’; Prom3 “The practice of beginning at the bottom and working up is widespread with my hospital’; Prom4 ‘The practice of internal promotion is not widespread with my hospital.’; Prom5 ‘It is difficult to get promoted in my hospital’.
Distributive justice (Just) 6 items: Just1 ‘Promotions by my employer are almost totally based on seniority’; Just2, ‘Raises by my employer heavily depend on who you know’; Just3, ‘The hiring of new employees by my employer is strictly determined by job-related ability’; Just4, ‘The employees who do well for my employer are those who contribute the most to its success’; Just5, ‘One sure way to get fired by my employer is to fail to do your work in a competent manner’; Just6 ‘Very competent employees are well rewarded by my employer’.
Soci
al F
acto
rs Gender-mixing (GM) 5 items: GM1 ‘I feel uncomfortable dealing with opposite sex’; GM2 ‘I feel uncomfortable dealing with nurses
from the opposite sex’; GM3 ‘I feel uncomfortable dealing with patient from the opposite sex’; GM4 ‘I feel uncomfortable dealing with physicians from the opposite sex’; GM5 ‘My families reject gender-mixing’.
Perception of nursing (Nurs)
4 items: Nurs1 ‘Nursing is a respected profession’; Nurs2 ‘Nursing is caring profession’; Nurs3, ‘Nursing as a profession is less highly regarded than being a doctor’; Nurs4 ‘In general, society has an accurate image of nurses, such as their roles and responsibilities’
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Domain Scales (abbreviated variable name used for CFA)
Individual items in the scale
Discrimination (Racis) 4 items: Racis1 ‘I would prefer to work in a country where there is no racism’; Racis2 ‘I experience discrimination because of my race’; Racism ‘I experience discrimination because of my gender’; Racis4 ‘My spouse is helpful to me in getting my job done’
Social support from spouse (Spou)
4 items: Spou1 ‘My spouse is not willing to listen to my job-related problems’; Spou2 ‘My spouse does not show a lot of concern for me on my job’; Spou3 ‘My spouse can be relied on when things get tough on my job’; Spou4 ‘My spouse is helpful to me in getting my job done’.
Org
anis
atio
nal F
acto
r Social support from immediate supervisor (Superv)
4 items: Superv1 ‘My immediate supervisor is willing to listen to my job-related problems.’; Superv2 ‘My immediate supervisor shows a lot of concern for me on my job’; Superv3, ‘My immediate supervisor cannot be relied on when things get tough on my job’; Superv4 ‘My immediate supervisor really does not care about my well-being’.
Social support from co-workers (Worke)
4 items: Worke1 ‘I am very friendly with one or more of my co-workers’; Worke2 ‘I regularly do things outside of work with one or more of my co-workers’; Worke3 ‘I rarely discuss important personal problems with my co-workers’; Worke4 ‘I know almost nothing about my co-workers as persons’.
Organisational commitment (Commit)
6 items: Commit1 ‘I think that my present hospital is a great organisation to work for’; Commit2 ‘My present hospital inspires the very best in me in the way of job performance’; Commit3 ‘I am glad that I chose this present hospital to work for over others I was considering at the time I joined’; Commit4, I am not proud to tell others I work for my present hospital’; Commit5, ‘I really do not care about the fate of my present hospital’; Commit6 ‘My present hospital is not the best of all possible places to work for me’.
Autonomy (Auton) 6 items: Auton1 ‘I am able to choose the way to go about my job’; Auton2 ‘I am able to choose the way to go about my job’; Auton3 ‘Generally, I can control the time at which I start working for the day’; Auton4 ‘My job is such that I cannot decide when to do particular work activities’; Auton5 ‘I have no control over the sequencing of my work activities’; Auton6 ‘Generally, I do not have any control over time at which I stop working for the day’.
3.4.3 Demographics Six personal factors and personal characteristics of participants were measured, including
gender, age, ethnicity, nationality, religion, education, and kinship responsive employer (see
Appendix A). Participants identified their age in years on the questionnaire, this was later
grouped in using eight categories of 5-year intervals: 20–25 years, 26–30, 31–35, 36–40, 41–
45, 46–50, 51–55, and 56–60.
Participants could identify their ethnic origins or identity from six categories: Arab, Asian,
Indian, African, Caucasian, and other. Education level was also collected and grouped under
eight categories for the purpose of regression analysis, including associate degree, diploma,
bachelor’s degree, postgraduate certificate, master’s degree, doctorate, no degree, and other.
Participants could choose religion from seven categories, this was later reduced to five
categories: Muslim, Christian, Hindus, Buddhist, and other. For the regression test, the Hindus,
Buddhists, and other groups were omitted as no one selected these religions.
There were four work environment variables (job satisfaction, workload, opportunity for
promotion and distributive justice), four social variables (gender-mixing, perception of
nursing, discrimination, and social support from spouse), and four organisational variables
(social support from immediate supervisor, social support from co-workers, organisational
commitment, and autonomy) measured at the scale/interval level as shown in Table 2.
3.3.4 The anticipated turnover scale The anticipated turnover scale (ATS) was developed by Hinshaw and Atwood in 1984.
Hinshaw (2000, p. 2) defined anticipated turnover as “the degree to which a staff member
thinks or is of the opinion that s(he) will voluntarily terminate her or his present position”. The
purpose of the ATS study is to identify the impact of Saudi nurses and non-Saudi nurses’
characteristics and social, environmental, and organisational factors have on anticipated or
actual turnover. According to Bolima (2015) and Barlow and Zangaro (2010), the ATS is one
of the most commonly used scales to measure turnover intention among nurses. This
instrument was designed to measure nurses’ perceptions or opinions toward them voluntarily
leaving their present job. The scale included 12 items and each item is scored according to
whether the scoring key is negative or positive, including six positive items and six negative
items.
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In Hinshaw and Atwood’s original study in 1984, to test the reliability and validity, a total of
1597 nurses in Arizona, United States, were assessed. A coefficient alpha of 0.84 was reported
for internal consistency, which indicates that the instrument was valid and reliable. Principal
components factor analysis and predictive modelling techniques were used to estimate
construct validity. ATS reliability was assessed in several studies. The reliability and validity
of the ATS was tested by Bolima (2015) using 183 registered professional nurses in northern
New Jersey, United States. Based on this study, the reliability estimate of the ATS using
Cronbach’s alpha was 0.85. In 2010, Barlow and Zangaro confirmed that the reliability
coefficient for the ATS developed by Hinshaw and Atwood (1984) was 0.89, and Hart (2005)
confirmed the Cronbach’s alpha score was 0.94. Furthermore, the ATS was tested in 2012 by
surveying all of the primary health care (PHC) nurses (N = 508) located in the Jazan region,
Saudi Arabia (Almalki et al., 2012). Based on their study, the reliability estimate of the ATS
using Cronbach’s alpha was 0.90. In this current study, the ATS was chosen because of its
demonstrated reliability and validity. Table 3 shows the reliability values of the ATS in
previous studies and current study.
3.3.5 Intent to stay
Intent to stay is generally understood to mean “the extent to which employees plan to continue
membership with their employers” (Price, 2001, p. 608). The intent to stay scale (Price &
Mueller, 1990) assessed nurses’ intent to stay in an organisation by measuring four items on a
five-point Likert Scale, with options from strongly disagree to strongly agree. Samples of
questions included in the questionnaire were “I would like to leave my present hospital,” “I
plan to leave my present hospital as soon as possible,” “I plan to stay with my present hospital
as long as possible”, and “Under no circumstances will I voluntarily leave my present hospital.”
Price reported a high reliability of 0.89 (Cronbach’s Alpha) with a mean and standard deviation
of 3.38 and 1.3 respectively.
The reliability and validity of this instrument was tested by Kovner et al. (2007) using 3,266
registered professional nurses in 35 states and the District of Columbia, in the United States.
Based on this study, the reliability estimate of intent to stay using Cronbach’s alpha was higher
than 0.8. The mean and standard deviation are 3.4 and 1.0 respectively. Kim et al. (1996)
indicated that discriminant and convergent validity of the measures were evaluated by the
exploratory factor analysis. The purpose of their study was to estimate a causal model of intent
to stay in an organisation. The sample was 244 male physicians at a U.S. Air Force hospital.
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Kim et al. (1993) validated the variability and stability of the intent to stay scale using the CFA
test. In their study to test the reliability and validity of the intent to stay scale, the sample (N =
2468 employees) indicated that the four-item tapping intent to stay had loadings varying from
0.725 to 0.892 with an alpha of 0.895. Four items were rated on a five-Likert scale indicating
one (strongly disagree) to five (strongly agree). Reverse scoring was used for the negative item.
Similar to other researchers, Brewer et al. (2012) also verified the validity and reliability of the
instrument. Importantly, the intent to stay four items have been used before in a healthcare
setting (Kovner et al., 2007; Kovner et al., 2009), which justifies its use in the current study.
Table 3 shows the reliability values of the intent to stay in previous studies and current study.
3.3.6 Social support scale
The term social support is used here to refer to “work-related assistance”. Kim et al. (1993)
stated the social support scale includes three subgroups: spouse, immediate supervisor, and co-
workers. The social support scale (adopted from Kim et al., 1993) assesses social support. Kim
provided written permission to use this instrument in this research. For the purpose of this
research, three sources of social support, which represent different dimensions, will be
measured: support from spouse, support from immediate supervisor, and support from co-
workers. Four items will be used to measure each one of these sources of support. Five
responses were provided for each of the questionnaire items. The scores for the responses range
from five (strongly agree) to one (strongly disagree) for the positive items and for the negative
items the scores are reversed.
Support from spouse
Kim (1993, p. 45) defined spousal support as “work-related assistance from an employee’s
spouse” and measured it using four items introduced by Price and Mueller (1990) on a five-
point Likert scale with options from strongly disagree to strongly agree. The score on spousal
support will be gathered by asking nurses four questions adopted from Kim et al. (1996).
Samples of questions included in the questionnaire are “My spouse is not willing to listen to
my job-related problems,” “My spouse does not show a lot of concern for me on my job,” “My
spouse can be relied on when things get tough on my job,” and “My spouse is helpful to me in
getting my job done.” Kim et al. (1996) stated that the discriminant and convergent validity of
the measures were evaluated by the exploratory factor analysis. The coefficient alpha for four
items was 0.95, with a mean and standard deviation of 2.17 and 1.816 respectively.
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Spousal support items will be used because based on Saudi culture, living together outside of
marriage is not allowed in Saudi Arabia. It is very impotent to measure spousal support for
both Saudi and non-Saudi nurses. The four items adopted from Kim are used for this current
study (Price, 2001).
Samples of questions included in the questionnaire are: “My spouse is not willing to listen to
my job-related problems,” “My spouse does not show a lot of concern for me on my job,” “My
spouse can be relied on when things get tough on my job,” and “My spouse is helpful to me in
getting my job done.”
Support from your immediate supervisor
Kim (1993, p. 45) defined supervisor support as “the work-related assistance an employee
receives from his or her immediate supervisor”. The four-item survey of social support from
immediate supervisor used by Price and Mueller (1990) used a five-point Likert format ranging
from strongly agree to strongly disagree. According to Price and Mueller (1990), the obtained
Cronbach’s alpha was 0.83, with a mean and standard deviation of 3.52 and 0.86 respectively.
Kim (1996) administered the four items to a sample of 2,468 employees. The Cronbach’s alpha
for the four-item scale was 0.84. According to Kim, CFA indicated that the scales had
acceptable discriminant validity.
Support from co-workers
Co-worker support can broadly be defined as “the work-related assistance from his or her co-
worker inside the workplace and was measured using four items developed by Kim (1993, p.
116). In Kim’s study, he used two items for co-workers and another two items were used for
friends outside of work. According to Kim, the two items that related to co-worker support
inside the workplace had strong loadings (0.729 and 0.824). In contrast, the two items that
related to friends’ support outside of work had weak loadings (0.482 and 0.462). However, the
four items that related to support from co-workers were adopted from Price and were the guide
for this study (Price, 2001). Table 3 shows the reliability values of the social support scale in
previous studies and the current study.
Samples of questions included in the questionnaire are “I am very friendly with one or more of
my co-workers,” “I regularly do things outside of work with one or more of my co-workers,”
“I rarely discuss important personal problems with my co-workers,” and “I know almost
nothing about my co-workers as people.”
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3.3.7 Job satisfaction scale The job satisfaction scale (JSS) was originally developed by Brayfield and Rothe (1951). It
consists of 18 items that were designed to measure how people feel about different jobs. The
authors’ measure was a standard questionnaire measure, with the widely used Likert response
pattern of agree–disagree items (Brayfield & Rothe, 1951). Price and Mueller (1981) have used
a shortened version of the Brayfield and Rothe scale for many years, with excellent
psychometric results. It has been assumed that this measure provides a global assessment of
an employee’s affective response to a job instead of measuring only certain dimensions or
facets of job (Kim, 1993, p. 108). However, Price and Mueller (1981), in their study on the
causal model of turnover for nurses, listed an updated version of the job satisfaction scale that
included seven items on a five-point Likert scale. They used job satisfaction as a subscale of
intention to leave, with the remaining three determinants as intention to stay, opportunity, and
general training. In Price and Mueller’s study to test the reliability and validity of the
instrument, the sample was 1,091 registered nurses in seven hospitals in the United States.
From their data, the reliability estimate for a score using Cronbach’s alpha was 0.87. Job
satisfaction was one of nine variables subjected to factor analysis for both convergent and
discriminant validity. This instrument has been used for many years in nursing turnover
research. For the purpose of this research, the seven-item version was the guide for this study
and Cronbach’s alpha was used to measure the reliability of this instrument. Kim (1993)
mentioned that the JSS had strong reliability scores and the overall Cronbach’s alpha was
0.869. However, Table 3 shows the reliability values of the JSS in previous studies and the
current study.
3.3.8 Organisational commitment questionnaire
Studies of commitment have a long history. Porter, Steers, Mowday and Boulian (1974)
developed an index called the organisational commitment questionnaire (OCQ). In particular,
commitment was categorised by three factors:
•! A strong belief in and an acceptance of the organisation’s goals and values
•! A willingness to exert considerable effort on behalf of the organisation
•! A strong desire to maintain membership in the organisation (Price, 1997, p 337)
However, Curry, Wakefield, Price and Mueller (1986), in their study on the causal ordering of
the job satisfaction and organisational commitment, listed an updated version of the OCQ that
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included nine items on a five-point Likert scale. Kim (1993) later published a shortened version
of the scale, which included six items from the 15-item scale (Porter et al., 1974). For the
purpose of this research, the six-item version was the guide for this study.
A shortened version of the scale reliability has been assessed in several studies. The reliability
and validity of this scale was tested by Curry, Wakefield, Price and Mueller (1986) using 508
registered nurses in a general hospital in a western state in the United States. Based on this
study, the reliability estimate of the OCQ using Cronbach’s alpha was 0.898, with a mean and
standard deviation of 31.79 and 5.62 respectively. Moreover, this scale was tested in 1993 by
Kim who reported that the two items had weak loading. However, the four remaining items
had factor loading from 0.664 to 0.841, with a reliability of 0.834.
This scale was adopted by Kim el al. (1996) to look at the variations in care intent among
physicians in a U.S. Air Force hospital. The tool was also tested on 244 physicians in a larger
study of all military medical personnel in Wilford Hall Medical Centre, Lackland Air Force
Base, San Antonio. Organisational commitment was studied regarding intent to stay. Their
study reported that organisational commitment was more important than job satisfaction as a
predictor of intent to stay. A coefficient alpha of 0.82 was reported for internal consistency,
which indicates that the instrument was valid and reliable. Table 3 shows the reliability values
of the OCQ in previous studies and the current study.
3.3.9 Distributive justice index
Several instruments have been developed to measure distributive justice. The distributive
justice index (DJI) is one of the most reliable and popular instruments and has been used
previously in health settings in different countries (Brewer et al., 2012; Kovner et al., 2007;
Seo & Price, 2004). It has been suggested that distributive justice decreases turnover due to its
positive impact on job satisfaction and organisational commitment.
Curry, Wakefield, Price and Mueller (1986), in their study on the causal ordering of the job
satisfaction and organisational commitment, used a DJI that included four items on a five-point
Likert scale. Based on their study, the reliability estimate of the DJI using Cronbach’s alpha
was 0.838, with a mean and standard deviation of 12.50 and 3.28 respectively. Price and
Mueller (1986) developed a DJI which consists of four items. Previous studies mostly defined
distributive justice as “the degree to which rewards and punishments related to performance
inputs into the organization” (Kim, 1993.p. 123).
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It has been suggested that when nurses who contribute more to the hospital receive more
rewards or when nurses who contribute little to hospitals receive few rewards, distributive
justice is high (Price, 2001). The distributive justice scale is a four-item questionnaire adopted
by Kim et al. (1996) to estimate a cause of intent to stay in an organisation. In their research,
distributive justice was viewed as a determinant of the physicians’ intent to stay in the US Air
Force. The five responses were scored from one to five with strongly agree scored as five. Each
item is scored according to whether the scoring key is negative or positive, including two
positive items and two negative items. The study reported a high-reliability rating of 0.85 with
the mean and standard deviation of 2.45 and 0.99 respectively. Brewer et al. (2012) reported in
prior testing that the Cronbach’s alpha for DJI was 0.93, with a mean and standard deviation
of 11.42 and 3.52 respectively. Due to the intention of this study to look at the factors that
contribute to nursing turnover, the six-item distributive justice scale index questionnaire was
selected. Table 3 shows the reliability values of the DJI in previous studies and the current
study.
3.3.10 Job autonomy scale
The job autonomy scale was originally developed by Breaugh (1985, 1989), and Breaugh and
Becker (1987). It consists of nine items that were designed to measure work autonomy. The
authors’ measure was a standard questionnaire measure. The purpose of their study was to
develop a scale of work autonomy. In their original study to measure a scale of work autonomy,
Breaugh and Becker’s (1987) sample (n = 9421) included three studies with employees and
three studies with students. From their data, the reliability estimate for scores using Cronbach’s
alpha was 0.92. They assessed reliability using alpha and test-retest coefficients. Job autonomy
can be broadly defined as “the extent to which an employee exercises control over his/her jobs”
(1996. p. 29).
Kim (1996) published a shortened version of the scale, which included six items from the nine-
item scale (Breaugh, 1985). Kim’s questionnaire has six instead of four items under the heading
of job autonomy. Two items in the questionnaire were designed to measure another theoretical
construct. Based on Kim’s study, the reliability estimate of the job autonomy scale using
Cronbach’s alpha was 0.819, with a mean and standard deviation of 3.012 and 1.105
respectively. The scale included six items, with each item scored according to whether the
scoring key was negative or positive, including three positive items and three negative items
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(Price, 2001). For the purpose of this research, the six-item version was the guide for this study.
Table 3 shows the reliability values of the job autonomy in previous studies and current study.
Table 3. Reliability of scale as reported in previous studies
Previous studies The current study Scale Authors N of
Items
Reliability (Cronbach’s Alpha)
Mean, SD Cronbach's Alpha
Mean (SD) Number of items deleted items to
improve reliability
Cronbach's Alpha Standardised Items
The anticipated turnover scale
Hinshaw & Atwood (1984)
12 0.84 .619 35.72 (5.008) 0 .619
Barlow & Zangaro (2010)
12 89.
Almalki et al., 2012 12 0.90
Intent to stay Price & Mueller, 1990 4 0.895 3.383 (1.028) .776 13.09 (3.161) 0 .770
Kim (1996) 4 0.89 3.4 (1) Brewer et al., 2012 4 0.89 13.72 (0.69)
Organisational commitment questionnaire
Curry et al (1986) 6
0.898 31.79 (5.62) .833 21.17 (4.30) 0 .834
Kim (1993) 6 .834
Brewer et al., 2012 6 .86 22.96 (4.58)
Distributive justice index
Curry et al (1986) 4 0.838 12.50 (3.28) .604 13.14 (2.74) 2 item (5,2) .623
Kim (1993) 4 0.85 2.45 (.99)
Kim (1996) 6 0.85 2.45 (.99)
Job autonomy scale
Breaugh & Becker (1987)
6 0.92. .745 19.29 (3.88) 0 .748
Kim (1993) 6 0.819 3.012(1.105)
3.3.11 Social factors Social factors (gender-mixing, perceptions of nursing and discrimination) were measured using
a scale developed specifically for this research. Based on the Saudi setting, from the literature,
the researcher summarised the social factors that influenced nursing turnover and intention to
leave into gender-mixing (five items), perceptions of nursing (four items), and discrimination
(four items). The purpose of developing this instrument was to identify the impact of Saudi
nurses and non-Saudi nurses’ characteristics and social factors on anticipated or intent to stay.
Saudi Arabia has several characteristics different to those of other societies and this is
important to consider. For example, in relation to gender-mixing, the gender variable is an
important factor because, based on cultural values, female nurses do not deal with male patients
and female patients do not accept treatment from male nurses. The cultural values underpinning
this gender variable are based on Saudi traditions, which dictate female work settings should
not include gender-mixed environments, unless the females and males are related, or the
situation is an emergency situation. In this current study, five items measuring gender-mixing
were rated on a five-point scale, from strongly disagree to strongly agree. Samples of questions
included in the questionnaire are: “I feel uncomfortable dealing with opposite sex,” “I feel
uncomfortable dealing with nurses from the opposite sex,” “I feel uncomfortable dealing with
patient from the opposite sex,” “I feel uncomfortable dealing with physicians from the opposite
sex,” and “My families reject gender-mixing.”
Thus, the cultural beliefs and customs of a country are generally seen as a factor that may be
related to health workforce retention. In this current study, four items measuring the perception
of nursing were rated on a five-point scale, from strongly disagree to strongly agree. Samples
of questions included in the questionnaire are: “Nursing is a respected profession,” “Nursing
is caring profession,” “Nursing as a profession is less highly regarded than being a doctor,” and
“In general, society has an accurate image of nurses, such as their roles and responsibilities”.
Regarding the cultural value, the concept measured one main aspect of social factors;
specifically, the views that nurses have about the Saudi Arabia in relation to racism and
discrimination. For example, one factor that may impact nurses’ decision to leave, even though
they are satisfied with their job, may be linked to racism and cultural values (Saeed, 1995).
Samples of questions included in the questionnaire are: “I experience discrimination because
of my race,” “I experience discrimination because of my gender” and “I experience
discrimination because of my religion”.
3.4.$Pilot$study$for$survey$validation$To ensure the appropriateness of the instrument for this current study, the questionnaire was
contextualised for the local setting of nursing in Mecca and Jeddah. The pilot study was
conducted in 2017 with the cooperation of nurses in one governmental hospital in Jeddah city
(Connelly, 2008). Additionally, a pilot study helps to “determine initial data for the primary
outcomes measure, in order to perform a sample size calculation for a larger trial” (Lancaster,
Dodd, & Williamson, 2004, p. 308). The purpose of this pilot study was to test the survey
questionnaire for suitability, clarity of the response, the average time taken for participants
answering the questionnaires, content validity, and reliability. Several methods can be used to
identify the required minimum sample size for a pilot study. Nieswiadomy (2012) suggested
that a sample of 10 would be sufficient for a pilot study. Other researchers (e.g. Connelly, 2008;
Hertzog, 2008) have suggested that a sample size of 10 percent of the final study size should
be sufficient for pilot studies.
The pilot study was conducted among 41 nurses of the target population in order to assess the
validity of survey measures. Participants were informed regarding the purpose of the polity
study and given the work environment, organisational, personal, and social factors
questionnaires as well as the ATS. Nurses who agreed to participate in the pilot study
completed the questionnaire and returned it to the research. Following recommendations from
nurses, a number of minor changes to the social and personal factors and three open-ended
questions were achieved.
The majority were female (90%), and 59% were Muslim and 39% were Christian as shown in
Table 4. The majority of participants had a bachelor’s degree (63.4%) or nursing diploma
(17.1%). However, no complains about structure of the questions or the difficulty of the
language were informed by the participants.
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Table 4. Characteristics of respondents of the pilot study (n=41)
Number (%)
Gender Male 4 (9.8%) Female 37 (90.2%) Ethnicity Arab 5(12.2%) Asian 31(75.6%) Caucasian 3(7.3%) Other 2(4.9%) Education Associate degree 2(4.9%) Diploma 7(17.1%) Bachelor’s degree 26(63.4%) Postgraduate certificate 3(7.3%) Master’s degree (postgraduate) 3(7.3%) Age 20–25 years 4(9.8%) 26–30 years 12(29.3%) 31–35 years 4(9.8%) 36–40 years 6(14.6%) 41–45 years 5(12.2%) 46–50 years 7(17.1%) 51–55 years 2(4.9%) 56–60 years 1(2.4%)
Religion Muslim 25(58.5%) Christian 16(39.0%) Other 1(2.4%)
3.4.1 Reliability and validity Reliability analysis was conducted and the minimum acceptance of Cronbach’s alpha for a
scale was 0.7, as suggested by a number of researchers (Nunnally & Bernstein, 1994; Cortina,
1993). Other researchers suggested that in the social and health science research the minimum
acceptance of Cronbach’s alpha for a scale should be 6.0 (De Vellis, 2016; Chandra & Fisher,
2009). Cronbach’s alpha tends to be a high estimate of reliability (Trochim & Donnelly, 2008).
In the current study, the internal consistency reliability analysis was achieved for 41 nurses
employed from one of the study hospitals in Jeddah. The alpha coefficient of the intention to
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leave and each group of factors potentially influencing the intention to leave (organisational,
work environment, social, and personal factors) were calculated separately for nurses.
Based on the sample of 41 nurses for the pilot study, results indicated that the minimum and
maximum alpha coefficients were 0.619 for the anticipated turnover scale, 0.833 for
organisational commitment, 0.745 for job autonomy scale, and 0.750 for workload,
respectively. In the pilot study, validity of the surveys was recognized using content and face
validity. More details about the factor analysis are presented in Chapter Five (Section 5.1).
Validity is the accurate measurement of the concept that the research tests. For example, in this
current study, the researcher is measuring the social, personal, organisational, and
environmental factors that influence nurses’ decisions to leave their jobs. If this study’s
measurement model is not good enough to measure the factors that may influence nurses to
leave, as defined in literature, this measurement will not be valid. There are four main types of
validity: face validity, content validity, construct validity, and criterion validity.
Face validity is the reasonability of the measurement. If a measurement looks valid to everyone,
it has face validity. Trochim (year) suggested that a researcher can improve the quality of face
validity assessment considerably by making it more systematic. In this case, if the researcher
is trying to assess the face validity of his instrument, it would be more convincing if he sent
the test to a carefully selected sample of experts on his topic for testing and they all reported
back that they thought his measure appears to be a good measure of his topic. In this current
study, face validity was checked to ensure the questionnaire content matched the research
question, which is: “What are the social, cultural, political and personal factors that affect nurse
turnover in hospitals in Saudi Arabia.” Moreover, experts in questionnaire design and one
academic were consulted. Therefore, two expert questionnaire designers who had more
knowledge about the topic of nursing turnover and intention and one manager from the nursing
department of a hospital who was employed for the study in Saudi Arabia were requested their
opinions on a range of attributes of questions. This involved the average time taken for
participants answering the questionnaires and whether they understood what each question
meant. Minor comments on the questionnaire from experts were discussed and considered.
Because the average time taken to respond to the questionnaire was a reasonable 20 to 25
minutes, no items had to be deleted, added, or changed.
Content validity is about the concepts the research uses to describe and measure in the study.
This means that the research needs to use the right concepts, coming from the literature, to find
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a measurement that is valid for the phenomenon. In the current study, an extensive review of
the literature was conducted to classify the factors that may influence both Saudi nurses and
non-Saudi nurses to leave their job in Saudi Arabia. The review mostly resulted in identification
of organisational, work environment, social, and personal factors. Construct validity is the most
important validity in terms of research, because it is about how the researcher conceptualises
the study. In the current study, a pilot study was conducted to examine construct validity and
the reliability of the instrument. The construct validity was also assessed in the main survey,
with a sample of 502 nurses. The construct validity of the survey was examined using
confirmatory factor analysis. More details about the CFA are presented in Section 5.2.
3.5.$Data$Analysis$$
3. 5.1 Descriptive analysis Data was analysed using IBM® Statistical Package for Social Science (SPSS®) V23.0.
Descriptive analysis was used to elicit the general features of the dataset. For ease of
understanding, the variables are distributed and displayed in sets of tables. The data collocation
was gathered from an electronic survey (Key Survey) and a paper survey. QUT’s Key Survey
is a web-based survey creation and management system, which is provided to all QUT staff
and postgraduate students. Key Survey automatically presented the scores for each and all
surveys completed by the participants. Online surveys have many advantages, including saving
time, especially on data analysis. When using Key Survey, the researchers’ data can be
downloaded to the Statistical Package for Social Science program. The quantitative data were
entered into IBM® SPSS® V23.0, while the qualitative data (open ended questions) were
entered into NVivo (Computer-Assisted Qualitative Data Analysis Software). Upon inspection,
the researcher identified any incomplete surveys and any incomplete surveys were excluded
from the data set used for analysis. The data was also checked for missing values.
In this current study, the independent sample t-test, analysis of one-way variance, structural
equation modelling, CFA, and a standard multiple regression were conducted to test
hypotheses. The data analysis plan related to the study research hypotheses was as follows:
3. 5.2 Structural equation modelling Structural equation modelling (SEM), a powerful multivariate analysis technique, will be used
to examine relationships between the study variables (Hoyle, 1995). The researcher will use
the rule of thumb for SEM to estimate the necessary sample size. According to the rule of
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thumb, sample size can be calculated by multiplying the number of unknown parameters from
the number of known parameters with 5–20. Based on the literature review, a minimum of
100–200 samples are required for SEM (Kline, 2011; Boomsma & Hoogland, 2001), although
these figures may increase, depending on the complexity of the model. In any event, the
researcher will develop an SEM model based on the theoretical framework, which will then be
validated. Model validation will entail two parts: validating the developed measurement model
and validating the covariance structure model (Weston & Gore, 2006).
3. 5.3 Confirmatory factor analysis Confirmatory factor analysis is used to create and validate measurement models for variables.
Confirmatory factor analysis is useful when examining the relationship between a latent
construct and its indicators. According to DeCoster (1998), CFA is based on a theoretical
understanding that defines the variation and covariation between observed variables, latent
variables, and measurement errors. One advantage of CFA is that the researcher can identify
the fit of the measurement model before estimating the SEM models. Another benefit is that
CFA enables the researcher to create relationships between the variables. Moreover, it is a
useful method to measure whether a pre-specified factor model provides a good fit to the data
(Reid, Courtney, Anderson, & Hurst, 2015).
Lei and Wu (2007) identified several advantages of CFA. It allows an indicator to load on
multiple factors and, also, enables residual or errors to correlate. Kline (2011) and Wan (2002)
suggested three stages in the CFA analysis. In the first stage, factor loading are examined.
Factor loading, having critical ratio value equal to +1.96 or higher, and -1.96 or lower, are
considered to be statistically significant at the .05 level. Next, the indicators with insignificant
factor loading or low standardised regression coefficients will be removed from the model for
better model fit. In the second stage of CFA, overall model fit is evaluated to understand how
well the study’s measurement model fits the data. Goodness of fit statistics produced by AMOS
software will be used to judge whether the measurement models fit the data. First, the goodness
of fit of this current study models will be evaluated looking at the chi-square fit index. To
conclude that current study specified models fit well the data, the probability value of the chi-
square test should not be smaller than 0.05. This study also evaluates goodness of fit by looking
at Chi-square value (χ 2) and degree of freedom (df). The ratio of Chi-square value divided by
degrees of freedom is used to indicate the overall model fit. This ratio has to be smaller than
four to claim that the study’s model fits reasonably well. On the other hand, the other goodness
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of fit parameters, including goodness-of-fit index (GFI) and adjusted GFI (AGFI) scores,
should be higher than 0.9. In addition, root mean squared error of approximation (RMSEA)
should be lower than 0.05 (Hooper, Coughlan, & Mullen, 2008). Besides, the Tucker Lewis
Index (TLI), normed fit index (NFI) is used if the GFI and AGFI fail to provide satisfactory
results. If the study results in TLI and TFI values equal or greater than 0.90, it is a well fitted
model.
In next stage, modification index (MI) is used to obtain a better-fitting model. The
measurement errors of factor loadings will be correlated with each other to get a better fit. In
the final stage, an improvement test between generic and nested models for combined model
is applied. In the third stage of CFA, possible sources of lack of fit are identified. In order to
obtain a better model fit, MIs are examined. The pair of error terms having the largest MIs are
correlated to each other to reduce the chi-square (Wan, 2002). Correlated error terms suggest
that indicators have a shared common variance not accounted for in the measurement model.
3. 5.4 Multiple regression Multiple regression is a simple extension of bivariate regression, was used to predict the value
of a variable based on the value of two or more other variables (De Vaus, 2002). Multiple linear
regressions were considered to be a possible method to test hypotheses in this current study.
Multiple linear regressions were conducted to predict the value of the variable anticipated
turnover based on the value of organisational, work environment, and social variables. And
multiple linear regressions were used to determine how well these factors were able to predict
anticipated turnover and which factors were significant predictors of the anticipated turnover.
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3.6$Qualitative$Dimension$of$the$Study$An aspect of qualitative research was incorporated into the study to elicit rich textual
information about the nature of turnover and to help to draw out contextual features that are
omitted from solely quantitative studies. Such an approach is necessary for gaining a deeper
understanding of a social phenomenon from the perspective of participants. It enables an
understanding of the social world through the interpretation of its participants, allowing them
to tell their stories in their own words (Silverman, 2010). The qualitative aspect used
interpretation to glean rich insights into the everyday realities of nursing in Saudi Arabia, from
the nurses’ perspective through a written record. In this phase, three open-ended questions
helped to enhance understanding of factors contributing to nursing turnover from the nurses’
perspective. To meet these objectives, the following research questions were investigated.
•! Are there additional factors that affect your decision to leave your current job?
•! What strategies would prevent turnover and encourage nurses to stay?
•! Please provide any additional comments you would like to mention.
3.6.1 Data collection Participants contributed to a quantitative study examining the relationships between nurse
turnover and personal, organisational, and environmental factors. All nurses who opted to
complete a survey for the quantitative study were asked to respond to three open-ended
questions to illuminate the factors that contribute to nursing turnover in Saudi hospitals, from
an individual perspective. They were asked to suggest strategies for attracting nurses and
improving nurse retention. The qualitative dimension of the study classified those factors seen
to contribute to nursing turnover in Saudi hospitals from the nurses’ perspective, informing
strategies to improve retention.
3.6.2 Data analysis The qualitative aspect used thematic analysis to analyse and code the data derived from the
open-ended questions. This helped to reveal the patterns and contextual properties of factors
that point to a reduction of turnover and strategies for retention. Coding and analysis helped to
generate description and interpretation. Inductive reasoning was applied in order to reveal rich
contextual descriptions of empirical real-life nursing experiences, in relation to satisfaction and
turnover. The advantage of using the thematic analysis in the current study was pinpointed by
Braun and Clarke (2006), who noted that it is “a flexible approach that can be used across a
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range of epistemologies and research questions” (p. 28). It also provided a trustworthy way to
develop insights that connected to quantitative findings. Rather than attend to broad
generalisations, the qualitative aspect allowed for deriving rich contextual information
associated with the factors to gain a better sense of what was occurring.
In order to support data analysis following an initial sweep of the qualitative data, the researcher
used NVivo software to refine and conduct further analyses. Recorded responses to the three
open-ended questions were initially entered into an Excel spreadsheet before being imported
into the NVivo tool, which is specifically designed to support qualitative data analysis. Bazeley
and Jackson (2013) suggested that the advantages of NVivo are due to “the flexibility of the
coding system in NVivo means the detail of the text material is readily coded into new emergent
concepts or categories, rather than simply being sorted by the question asked” (p. 199). Once
entered, the data was evaluated using thematic analysis to generate an inductive categorisation
process (Ritchie & Spencer, 2002).
In the first stage, the researcher became familiar with the data, reading and rereading the
participants’ responses. Short phrases or words were then assigned codes that effectively
captured the meaning of the nurses’ expressions (Braun & Clarke, 2006). In the second stage,
initial codes were generated. This involved reading participants’ responses again, line by line,
to identify relevant sentences for labelling. In the coding stage, all data was given equal
attention in order to identify any repeated patterns. Codes were then grouped into categories
and concept maps drawn to identify interconnections between them (Braun & Clarke, 2006).
The final stage involved searching for themes and patterns; at this point, all the data related to
each potential theme was collected. Pattern-coding helped to generate a more in-depth insight
for tentatively exploring connections between context, events, and situations. Essentially, by
moving between themes and concepts, the researcher was able to ascribe some meaning
ascribed to concepts and to re-order, refine, and re-label concepts for a fuller contextual picture.
In doing so, the researcher reduced the data in the qualitative aspect and developed tentative
connections with meanings for a more nuanced understanding of the factors revealed in the
quantitative dimension (Braun & Clarke, 2006).
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3.6.3 Research rigour For the qualitative dimension, trustworthiness and credibility, dependability and
confirmability, and transferability were considered (Liamputtong, 2013). Credibility is akin to
internal validity and is used to determine that the research findings from the qualitative phase
can be trusted. In the current study, the results of the qualitative aspect were believable from
the perspective of the participants. The aim of the qualitative aspect was, not to draw
generalisations, but to better understand the factors associated with the phenomenon of
turnover from the nurses’ perspective, because they are the only ones who could reasonably
judge the credibility of the result (Liamputtong, 2013). According to Trochim and Donnelly
(2008), confirmability ensures that the logic of the study was upheld and reflects an audit trail,
supporting the fact that logical decisions about the study have been made and were not purely
a product of the researcher’s imagination. In the current study, the researcher bolstered the
confirmability by documenting the procedures for checking and rechecking the data throughout
the study. According to Trochim and Donnelly (2008), transferability refers to “the degree to
which the results of qualitative research can be generalised or transferred to other contexts or
settings” (p. 162). That is, transferability is akin to external validity and is used to determine
whether the rich descriptions of the participants can be transferred and replicated in other
studies. In the current study, the transferability would align with the use of the three qualitative
derived questions and interconnection with the factors found in the study.
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4.$CHAPTER$FOUR:$QUANTITATIVE$RESULTS$$
4.1$Introduction$Chapter 4 presents the quantitative results of the study. The chapter will review descriptive
information concerning the factors related to anticipated turnover and intent to stay. A second
purpose is to examine whether the anticipated turnover of nurses is influenced by organisational
factors (autonomy, organisation commitment, social support from immediate supervisors, and
social support from co-workers), social factors (gender-mixing, perception of nursing,
discrimination, and social support from spouse), and environmental factors (job satisfaction,
promotion, distributive justice, and workload).
Chapter 4 consists of five sections. The first section outlines the sample profile of the
respondents. The second section provides descriptive statistics, while the third section
examines the relationship between anticipated turnover and the personal factors of education
level, age religion, gender, nationality, and flexible work schedules. The fourth section presents
the multiple linear regression analysis, describes the reason for using this method, and outlines
whether the anticipated turnover of nurses is influenced by organisational factors. Likewise,
the fourth and fifth sections examine whether the anticipated turnover of nurses is influenced
by social and environmental factors respectively.
4.2$Sample$$Table 5 shows the demographic characteristics of the respondents. The participants were asked
to provide information about their age, gender, ethnicity, and educational qualifications. The
ages of the participants ranged from 20 to 60 years. Ages were categorised in 5-year intervals:
the largest group were aged between 26–30 years (45.2%) and 31–35 (22.9%); only 11% were
aged over 40 years of age. Of the 502 respondents, the majority were women 420 (83.7%), this
is typical for the nursing workforce, which is mostly female workforce. Most nurses were either
Bachelor prepared (68%), which is consistent with the large number of Filipino respondents,
or diploma prepared (23%) less than 10% of respondents had postgraduate qualifications.
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Table 5. Demographics of the nurse respondents (n = 502)
Number (%) Gender Men 82 (16.3%) Women 420 (83.7%) Ethnicity Asian 296 (59.0%) Indian 108 (21.5%) Arab 80 (15.9%) Caucasian 6 (1.2%) African 4 (0.8%) Other 8 (1.6%) Age 20–25 50 (10.0%) 26–30 227 (45.2%) 31–35 115 (22.9%) 36–40 55(11.0%) 41–45 28(5.6%) 46–50 19(3.8%) 51–60 8(1.6%)
Flexible work schedules No 213 (42.4%) Yes 289 (57.6%) Unpaid leave for family matters No 346 (68.9%) Yes 156 (31.1%) Leave for childbirth No 374 (74.5%) Yes 128(25.5%) Paid leave for childbirth No 384(76.5%) Yes 118(23.5%) Leave for childbirth of at least three months No 424(84.5%) Yes 78(15.5%)
The majority of respondents identified as Asians (59.0%), next most common ethnicities
identified were Indian (21.5%), and Arab (15.9%); see Figure 9 for the frequency distribution
religion and highest level of education level of participants. The majority of nurses (68.3%)
held a bachelor’s degree, which was consistent with the large number of Filipino participants.
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Figure 9 Education and Religion of respondents (n=502)
The majority of participants (73%) are Muslim, one of the hospitals in the Holy City of Mecca
and only Muslims can enter the city. Most participants did not receive paid leave for family
matters (68%), approximately 57.6% of participants indicated that they have flexible work
schedules. Few employers offered either paid 3-month maternity leave (15.5%) or leave for
childbirth or unpaid family leave. Most participants (74.5%) reported that their hospital did
not provide leave for childbirth. In regard to paid leave for childbirth, approximately 76.5% of
participants indicated they did not receive paid leave for childbirth.
4.3$Descriptive$Analysis$Descriptive statistics, including mean and standard deviation, were used to analyse personal
characteristics. Data was analysed using IBM® SPSS® V23.0 and descriptive analysis was used
to elicit the general features of the dataset. For ease of understanding, variables are distributed
and displayed in sets of tables. The answers to the survey were compared to determine the
mean scores of the personal variables, and anticipated turnover, and intent to stay. Moreover,
since the researcher used Likert-scale data for most variables, the mode and the most frequent
response were the best measures to use. The average mean scores of the personal factors and
anticipated turnover of the nurses (N=502) were compared, in order to determine difference.
All survey questions used a 5-point Likert scale, mean and standard deviation was reported.
Since the purpose of the current study was to identify factors that influence nurse turnover in
Saudi Arabia, the Likert scale was selected as the scale permits the measurement of nurses’
73%
27%
Religion
Muslim Christian
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opinions and attitudes (Burns, & Grove, 2010). The following sections of this chapter present
the findings for each variable.
4.3.1 The Anticipated turnover results As mention in Chapter three (section 3.3.2), the study has two endogenous variables, which are
anticipated turnover and intent to stay. Operational definitions and measurement instruments
of the study variables are shown in Appendix (A). The anticipated turnover was measured using
the Anticipated Turnover Scale (ATS) using 12 items to identify the impact of Saudi nurses
and non-Saudi nurses’ characteristics, and social, environmental, and organisational factors on
anticipated turnover (Hinshaw & Atwood, 1982).
The frequency distribution of indicators of ATS of nurses is shown in Appendix A: Personal
characteristics - categories. The first item, ATS1, indicates if nurses plan to stay in their
position a while. As Appendix (A) indicates 45.6% (229) of nurses agreed and 33% of nurses
strongly agreed they would be staying in their position a while. Item ATS2 indicates if nurses
are quite sure they will leave their position in the near future. According to the results, 42%
(212) of respondents indicated they would be leaving their position in the near future.
As mentioned above, the ATS is a self-reported instrument that contains 12 items. The main
survey assessed the overall anticipated turnover. For the purpose of the current study, the score
ranged from 1 (strongly disagree) to 5 (strongly agree). The scores were summed and divided
by 12. Based on the mean scores, the nurses were classified into ‘intent to leave’ and ‘intent to
stay’. A response with a mean score of over 3.0 was considered an indication of the intent to
leave, while a lower score indicated a lower intent to leave (Almalki et al., 2012; Armstrong,
2004), 49% of participants indicated that they intended to leave.
The scores for intention to leave were analysed according to personal factors, such as ethnicity,
education level, religion, flexible work schedules, age, gender, and leave for family matters.
Figure 10 shows the distribution of anticipated turnover the ethnicity of participants in regard
to anticipated turnover. Approximately 54% of Asian nurses and 51.2 % of Arab nurses
indicated that they intended to leave, while 63% of Indian nurses reported that they intended
to stay.
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Figure 10. Anticipated turnover results by ethnicity
The education qualifications of the nurses were dominated by associate degree, diploma,
bachelor’s degree, post graduate certificate, master’s degree, doctorate, no degree, and other.
Almost three-quarters (68.3%) of the sample hold a bachelor’s degree, 23% had a diploma,
4.3% had a master’s degree, and 3.6% had a post graduate certificate. The scores for anticipated
turnover were then analysed according to participants’ education level. Approximately 53.6%
of participants who hold bachelor’s degree reported they intended to leave, while the majority
of participants (67.2%) who hold a diploma indicated they intended to stay. Two third of
participants who have a postgraduate masters degree and 54% of participants who have a
postgraduate certificate reported that they intended to leave. Doctorate, no degree, and ‘other’
group were omitted because there were no cases.
Figure 11. Anticipated turnover results by education level
51 54
37
8
Arab Asian Indian Other
P<=value<=<.000
Indication<for<intention<to<leave
32.8
53.6 54
67
Diploma Bachelors Post<GraduateCertificate
Master<(PostGraduate)
Indication<for<intention<to<leave
Indication<for<intention<to<leave
P<=value<=<.000
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Approximately three quarters (73 %) of the respondents reported they are Muslim. For the
religion variable, approximately 49.6% of participants who are Muslim indicated they intended
to leave, while 48% of participants who are Christian indicated they intended to leave. The
Hindus, Buddhist and ‘other’ group were omitted because there were no cases.
Figure 12. Anticipated turnover results by religion
Approximately 57.6% of respondents indicated they have flexible work schedules. With regard
to flexible work schedules, 64% of the participants who do not have flexible work schedules
indicated they intended to leave. In contrast, about 61% of participants who had flexible work
schedules indicated they intended to leave.
Figure 13. Anticipated turnover results by flexible work schedules
49
51
48
52
Indication<for<intention<to<leave Indication<for<intention<to<stay
P<=value<=<0.354
Muslim Christian
61
39
64
36
Indication<for<intention<to<leave Indication<for<intention<to<stay
P<=value<=<.000
Flexible<work<<schedules No<flexible<work<<schedules
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For the age variable, the variable of age was grouped under eight categories: 20–25 years, 26–
30, 31–35, 36–40, 41–45, 46–50, 51–55, and 56–60. The age of respondents ranged from 20 to
60 years, with 45.2% 26–30 years and 22.9% 31–35 years, respectively. The results of age
distribution indicate that a majority of participants (68.1%) were 26–35 years of age. Forty-six
percent of participants aged 20–25 years and 52% of participants aged 26–30 years indicated
that they intended to leave. Moreover, 55% of participants aged 31–35 years and 40% of
participants aged 36–40 years indicated they intended to leave. Participants aged 41–45 years
were more likely to intend to stay, while participants aged 46–50 years were more likely to
intend to leave.
Figure 14. Anticipated turnover results by age
Almost three-quarters (68.9%) of the respondents reported they did not receive paid leave for
family matters. With regard to paid leave for family matters, participants were more likely to
leave if they did not receive paid leave for family matters. Approximately 53% of participants
indicated that they intended to leave if their hospital did not provide paid leave for family
matters. On the other hand, 46% of participants reported a more likely intent to leave, if they
received paid leave for family matters.
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Figure 15 Anticipated turnover results by leave for family matters
4.3.2 Influence of personal factors on anticipated turnover The associations between personal factors and anticipated turnover are presented in Table 6.
Independent t-test and analysis of variance (ANOVA) were used to analyse the relationship
between personal factors and anticipated turnover. Post-hoc Turkey HSD calculator was used
when one-way ANOVA was significant. The purpose of the current study is to determine if
anticipated turnover was influenced by personal variables. Because an independent t-test was
used, the results included the t-statistics value, the degree of freedom (DF) and the significant
value of the test (p-value).
The average mean scores of the gender variable and the anticipated turnover of the nurses were
calculated. The researcher determined whether there was a difference between the mean score
for the intention to leave for female nurses and male nurses.
Influence of gender on anticipated turnover
In regard to nurses’ gender, no significant difference in intention to leave was found based on
gender. As shown in Table 6, there was no significant difference in mean scores comparing the
responses of male nurse (M = 2.9827, SD = 0.442) and female nurses (M = 2.9760, SD = 0.412)
[t (500) = 0.133, p = 0.894]. This result suggests that gender does not have an effect on intention
to leave among nurses in Saudi Arabia.
46
5453
47
Indication<for<intention<to<leave Indication<for<intention<to<stay
P<=value<=<.000
Paid<leave<for<family<matters Unpaid<leave<for<family<matters
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Influence of religion on Anticipated Turnover
In regard to nurses’ religion, no significant difference in intention to leave was found based on
religion. As shown in Table 6, there was no significant difference in mean scores comparing
Muslim nurses (M = 2.970, SD = 0.4132) and Christian nurses (M = 3.027, SD = 0.4554) (t
(498) = -0.928, p = 0.354). This result suggests that religion does not have an effect on intention
to leave among nurses in Saudi Arabia.
Influence of flexible work schedules on anticipated turnover
In regard to flexible work schedules, nurses were divided into two groups according to their
work schedules: Group 1, who had inflexible work schedules; and Group 2, who had flexible
work schedules. The results from the independent sample test showed that the group’s means
are statistically significantly different because the value in the Sig is 0, which is less than (0.05).
The study found that nurses who have inflexible work schedules (M = 3.106, SD = 0.4322)
were more likely to leave compared to those who have flexible work schedules (M = 2.887,
SD = 0.382). The magnitude of the difference in the means (mean difference = .21105, 95%
CI: .13792 to .28417) showed a small effect.
Influence of level of education on anticipated turnover
Because the nurses were divided into five groups according to their level of education, one-
way analysis of variance was used to analyse the effect of level of education on intention to
leave. The results showed a statistically significant difference among groups, assuming that
there was a statistically significant difference at the P is less than 0.05 level in anticipated
turnover scores for the five groups (associate degree, diploma, bachelor’s degree, postgraduate
certificate and master’s degree (postgraduate) (F (4, 497) = 7.47, p = 0.00). The associate
degree (Group 1) (M = 2.938, SD = 0.4159) was significantly different from the master’s
degree (postgraduate) (Group 5) (M = 3.350, SD = 0.6685). In other words, the examination of
the Student-Newman-Keuls (SNK) and the post-hoc Tukey HSD tests indicate that while a
statistically significant difference exists between the means of Groups 1 and 5, no statistically
significant difference exists between either of these groups and Group 2 (diploma) (M = 2.86,
SD = 0.36) and Group 3 (bachelor’s degree) (M = 0.299, SD = 0.39). This result suggests that
level of education was significantly associated with anticipated turnover scored among nurses
in Saudi Arabia.
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Influence of age on anticipated turnover
The age variable has eight categories: 20–25 years, 26–30, 31–35, 36–40, 41–45, 46–50, 51–
55, and 56–60. Table 6 shows the mean anticipated turnover for each of these groups and their
standard deviation, as well as the analysis of variance table. There was no statistically
significant difference between groups as determined by one-way ANOVA (F7, 494 = 0.880, p
= 0.522). The effect size, calculated using eta squared, was 0.012. The mean score for nurses
aged 20–25 years (Group 1: M = 2.9600, SD = 0.3765) was significantly different to nurses
aged 51–55 years (Group 7: M = 2.694400, SD = 0.34427). Nurses aged 26–30 years (Group
2: M = 3.0015, SD = 0.4373) had a higher mean score than other groups. This result suggests
that nurses in Group 2 were more likely to intend to leave, while nurses in Groups 7 (51–55
years) and 8 (56–60 years) were more likely to intend to stay in their current job.
Influence of ethnicity on anticipated turnover
Ethnicity was found to be statistically associated with intention to leave using ANOVA.
Participants were divided into five groups according to their ethnicity. From the multiple
comparisons table, which contains the results of the post-hoc Tukey HSD test, the preferred
test for conducting hoc tests on an ANOVA. A statistically significant difference in intention
to leave was found comparing Asian and Indian nurses (p = 0.008). However, there were no
differences in intention to leave comparing Arab and Indian (p = 0.992), as well as Arab and
Asian (p = 0.158). This indicates that Asian nurses were more likely to intent to leave compared
to Arab and Indian nurses.
Influence of nationality on Anticipated Turnover
The nationality of nurses was found to be statistically associated with intention to leave using
ANOVA. Table 6 reports that the significance value was 0.005, which was below 0.05 and,
therefore, there was a statistically significant difference in intention to leave for the nationality
of participants. There was a statistically significant difference in intention to leave between
Filipino and Indian nurses (p = 0.004). However, there were no differences in intention to leave
between Saudi Arabian and Filipino nurses (p = 0.952), as well as Malaysian, and Pakistani (p
= 0.775). This indicates that Filipino nurses were more likely to intend to leave compared to
Saudi Arabian, Malaysian, Pakistani, and Indian nurses.
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Overall, these results from t-test and the ANOVA indicate that there was a relationship between
intention to leave and the personal factors of education level, ethnicity, nationality, gender, and
flexible work schedules. On the other hand, there was no relationship between the personal
factors of age and religion and intention to leave among nurses in Saudi Arabia.
Table 6. Personal factors using independent sample t-tests and ANOVA
Variable Categories Mean Std. Deviation Test statistic P-Value Gender Male 2.982 0.4426 -0.133 0.894
Female 2.976 0.4127
Religion Muslim 2.970 0.4132 -0.928 0.354.
Christian 3.027 0.4554
schedules Flexible 3.0986 0.4322 5.77 0.00
Not flexible 2.8875 0.3827
Age 20–25 2.960000 0.376552 0.880 0.522 26–30 3.001468 0.437388 31–35 2.989855 0.403165
36–40 2.945455 0.380943 41–45 2.910714 0.507114 46–50 2.969298 0.339310 51–55 2.694444 0.344265 56–60 2.625000 0.176777
Ethnicity Arab 2.9104 .03843 4.180 0.001
Asian 3.0346 .02629
Indian 2.8750 .03248
Caucasian 2.9444 .20146
African 3.3542 .11968
Other 2.7292 .08733
Nationality Saudi 2.9671 .33091 2.683 .003
Filipino 3.0615 .47382
Malaysian 2.9836 .38874
Indian 2.8727 .33670
Pakistani 2.7937 .23661
Jordanian 2.8958 .33100
American 3.1667 .46771
Egyptian 2.8095 .39282
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4.3.3 Intent to stay results The second endogenous variable of the study is Measure of Turnover Intent (Intent to Stay),
which Price and Mueller developed in 1990. The intent to stay scale assesses nurses’ intent to
stay in an organisation by measuring four items on a five-point Likert Scale, with options from
strongly disagree to strongly agree (Price & Mueller, 1990; Kim & Mueller, 1978). Appendix
(A) outlines the results and indicates that 50% (250) of nurses disagreed and strongly disagreed
with the idea of leaving their present hospital while, 22% (100) of nurses agreed and strongly
agreed with the idea of leaving their present hospital. Furthermore, 56% (281) of nurses
disagreed and strongly disagreed with the idea of planning to leave their present hospital as
soon as possible while, 17.6% (86) of nurses, agreed and strongly agreed with the idea of
planning to leave their present hospital as soon as possible. Moreover, 42.3% (212) of the
nurses agreed and strongly agreed with the idea of staying with their present hospital as soon
as possible while, 22% (111) of nurses disagreed and strongly disagreed with the idea of
leaving their present hospital as soon as possible.
An independent sample t-test and ANOVA were used to examine if there was any relationship
between personal factors of gender, religion, age, nationality, ethnicity, flexible work
schedules, and intent to stay, using Intent to Stay developed by Price and Mueller (1990). In
regard to gender, there was no significant association between intent to stay and gender, t (500)
= .438, p = .661. There was no significant difference in scores for female nurses (M = 3.30, SD
= .799), and male nurses (M = 3.26, SD = .789). This result indicates that the factor of gender
does not have an effect on intent to stay.
The factor of religion was not significant associated with intent to stay. There was no significant
difference in scores for Muslim nurses (M = 3.27, SD = .796), and Christian nurses (M = 3.25,
SD = .752), t (498) = .231, p = .818. This result indicates that the factor of religion does not
have an effect to on intent to stay.
ANOVA test was used to examine the influence of nationality on intent to stay scores among
nurses in Saudi Arabia. There was a statistically significant difference at the p value was below
0.05 level in intent to stay scores based on nurses’ nationality [F (491) = 2.483, p = 0.007].
There was a statistically significant difference in intent to stay between Filipino and Indian
nurses (p = 0.032). However, there were no differences in intent to stay between Saudi and
Filipino nurses (p = 0.781), as well as Malaysian, and Egyptian (p = 0.979). This result indicates
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that Saudi, Malaysian, Egyptian, and Indian nurses were more likely to intent to stay compared
to Filipino nurses.
The factor of education level was a significant associated with intent to stay [F (497) = 3.680,
p = 0.006]. Post-hoc Tukey HSD tests indicate that there was a statistically significant
difference exists between nurses who had bachelor’s degree and master’s degree (p = 0.013),
as well as nurses who had diploma degree and master’s degree (p = 0.002). However, there
were no differences in intent to stay between nurses who had associated degree and post
graduate certificate (p = 0.984).
4.3.4 Organisational factor results This section discusses the descriptive information of exogenous variables (autonomy,
organisational commitment, social support from immediate supervisors, and social support
from co-workers) of the study. The job autonomy scale was originally developed by Breaugh
(1985), Breaugh (1989), and Breaugh and Becker (1987). However, Kim (1996) published a
shortened version of the scale, which included six items from the nine-item scale (Breaugh,
1985). For the purpose of this research, the six-item version was the guide for this study.
Appendix B shows the frequency distribution of indicators of job autonomy scale of nurses,
Appendix (A) shows that 49.8% (250) of nurses agreed and strongly agreed that they are able
to choose the way they go about their job, while 17.1% (86) of nurses disagreed and strongly
disagreed that they are able to choose the way they go about their job. Likewise, 53.2% (267)
of nurses agreed and strongly agreed that they are able to modify what their job objectives are,
while 19.1% (91) of nurses disagreed and strongly disagreed that they are able to modify what
their job objectives are. Furthermore, 42.6% (213) of nurses agreed and strongly agreed they
can control the time at which they start working for the day, while 34.7% (174) of nurses
disagreed and strongly disagreed that they can control the time at which they start working for
the day.
Organisational commitment, as previously indicated, was measured using six indicators. Kim
(1993) published a shortened version of the scale, which included six items from the 15-item
scale (Porter et al., 1974). For the purpose of this research, the six-item version was the guide
for this study. As shown by Appendix B, the majority of participants (289 nurses) agreed and
strongly agreed with the statement that their present hospital is a great organisation to work
for. Moreover, the majority of participants (261 nurses) indicated that they agreed and
strongly agreed with the statement that their present hospital inspires the very best in them, in
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the way of job performance. When asked whether they are glad that they chose this present
hospital to work for over others they were considering at the time they joined, 67% (288) of
respondents reported that they agreed and strongly agreed with the statement.
Social support from immediate supervisors, as previously defined the work-related assistance
from immediate supervisors, was measured by four items used by Price and Mueller (1990).
The vast majority of nurses 58% (290) thought that their immediate supervisor is willing to
listen to their job-related problems while 51% (255) nurses disagreed and strongly disagreed
with the statement that their immediate supervisor really does not care about their well-being.
Moreover, 274 nurses felt that their immediate supervisor shows a lot of concern for them on
their job, while 198 nurses disagreed and strongly disagreed with the statement that their
immediate supervisor cannot be relied on when their job-related tasks become very
challenging.
Social support from a co-worker, as previously defined the work-related assistance from friends
inside the hospital, was measured by four items used by Kim (1993). As shown by Appendix
(A), the majority of nurses 91.5% (459) agreed with the statement that they are very friendly
with one or more of their co-workers. Moreover, the majority of nurses 59.7% (300) reported
they regularly socialise, outside of work, with one or more of their co-workers. Additionally,
52% (260) of nurses disagreed and strongly disagreed with the statement that they know almost
nothing about their co-workers as persons.
4.3.5 Environmental factor results This section discusses the descriptive information of exogenous variables (job satisfaction,
opportunity for promotions, distributive justice, and workload) of the study.
Job satisfaction, as previously specified, was measured by seven indicators. Price and Mueller
(1981) published a shortened version of the scale, which included seven items from the 18-
items scale (Brayfield & Rothe, 1951). Price and Mueller (1981) used a shortened version of
the Brayfield and Rothe scale. For the purpose of this research, the seven-item version was the
guide for this study. As shown by Appendix (A), 53% (267) of nurses agreed with the statement
that they find enjoyment in the job, while 56% (283) of nurses disagreed and strongly disagreed
with the statement that they are often bored with their job. Likewise, a large number of nurses
(368) disagreed and strongly disagreed with the statement that they definitely dislike my job.
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Workload defined as the extent to which job demands are high, was measured by 12 indicators.
Kim (1996) published a shortened version of the scale, which included four items from the Job
Stress Questionnaire (LSQ) (Caplan et al., 1975). And other items from Price and Mueller
(1981). As shown by Appendix (A), the majority of nurses (342) agreed with statement that
they have to work very fast in their job. Similarly, 69% (246) of nurses reported that they have
to work very hard in their job. When asked whether their workload is predominantly physical,
294 of respondents reported that they agreed with the statement. Moreover, the majority of
nurses (320) disagreed and strongly disagreed with the statement that their work does not cause
any pain, while 85% (426) of nurses agreed strongly agreed with the statement that they are
very confident about my work.
Opportunity for promotion defined as the extent to which potential vertical, upward
occupational flexibility is allowed within the hospitals (Kim, 1993). It was measured by five
indicators used by Price and Mueller (1986). As displayed by Appendix (A), 58% (290) of
nurses disagreed and strongly disagreed with the statement that promotions are regular within
their hospital. When asked whether there is a very good chance to advance in their career in
their hospital, 212 of respondents reported that they disagreed and strongly disagreed with the
statement. Moreover, 55.6% (279) of nurses agreed with the statement that it is difficult to get
promoted in their hospital, while 40.7% (204) of nurses agreed and strongly agreed with the
statement that the practice of internal promotion is not widespread within their hospital.
Distributive justice is defined as the degree to which rewards and punishment are related to
performance in the hospital. The distributive justice scale is a six-item questionnaire adopted
by Kim et al. (1996) to estimate a cause of intent to stay in an organisation. Kim (1996)
published a shortened version of the scale, which included six items from the DJI (Price &
Mueller, 1986). Appendix (A) shows the frequency distribution of indicators of the distributive
justice scale of nurses. It can be seen from this table that 61.4% (306) of nurses agreed and
strongly agreed with statement – the hiring of new employees by their employer is strictly
determined by job-related ability. Of the participants, 60% (302) of nurses thought that the
nurses who do well for their hospital are those who contribute the most to its success. Over half
of those surveyed agreed with the statement that one sure way to get fired by their hospital is
to fail to do their work in a competent manner. Moreover, 43.7% (219) of participants felt that
promotions by their hospital are almost totally based on seniority, while other considered that
raises by their employer heavily depend on who they know.
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4.3.6 Social factor results This section discusses the descriptive information of exogenous variables (gender-mixing,
perception of nursing, discrimination, and social support from spouse) of the study. These
factors were measured by a scale developed for this research. The purpose of developing this
instrument is to identify the impact of Saudi and non-Saudi nurses’ characteristics and social
factors on anticipated turnover or intent to stay.
The first factor relating to the social actors was gender-mixing. Hospitals in Saudi Arabia have
several characteristics different to those of other societies and this is an important
consideration. For example, in relation to gender-mixing, the gender variable is an important
factor because, based on cultural values, female nurses do not deal with male patients and
female patients do not accept treatment from male nurses. Gender-mixing was measured by
five indicators on 5-point Likers type scale, with responses ranging from 1 (strongly disagree)
to 5 (strongly agree). Appendix (A) shows the frequency distribution of indicators of gender-
mixing of nurses. When the nurses were asked whether they feel uncomfortable dealing with
the opposite sex, 58.6% (294) of nurses disagreed and strongly disagreed with that statement.
Furthermore, 69.3% (348) of nurses disagreed and strongly disagreed with the statement that
they feel uncomfortable dealing with nurses of the opposite sex, while 55.5% (279) of nurses
disagreed and strongly disagreed with the statement that they feel uncomfortable dealing with
patients of the opposite sex.
In this current study, four indicators measuring the perception of nursing were rated on a five-
point scale, from strongly disagree to strongly agree. Appendix (A) shows 89% (448) of nurses
agreed and strongly agreed with the statement that nursing is a respected profession.
Furthermore, the majority of nurses (473) strongly agreed that nursing is a caring profession.
When asked whether society has an accurate image of nurses, such as their roles and
responsibilities, 58.5% of the respondents strongly agreed with that statement. Moreover,
65.8% (330) of nurses agreed with the statement that nursing as a profession is less highly
regarded than being a doctor.
Social factors related to discrimination or racism issues, relating to cultural adaptability and
adjustment in the Saudi Arabia, were examined and measured by four indicators. Appendix (A)
shows 83% (415) of nurses agreed and strongly agreed with the statement that nursing is a
respected profession. However, these nurses said they would prefer to work in a country
without racism. When participants were asked whether they experience discrimination because
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of their cultural background and ethnicity, half of them reported they had experienced racism
at work. In addition, the majority of nurses disagreed and strongly disagreed with the statement
that they experience discrimination because of their religion or gender.
Social support from a spouse, previously defined as the work-related assistance from a nurse’s
spouse, was measured using four items developed by Price and Mueller (1990) on a five-point
Likert scale with options from strongly disagree to strongly agree. Appendix (A) shows that
the majority of nurses (319) disagreed and strongly disagreed with the statement that their
spouse is not willing to listen to any job-related problems. Also, 62.3% (313) of nurses
disagreed or strongly disagreed with the statement that their spouse does not show a lot of
concern for them on their job.
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5.$CHAPTER$FIVE:$FACTOR$ANALYSIS$$
Introduction$Chapter 5 presents the quantitative results of the study and presents a discussion about how the
data was analysed. The four sections of Chapter 5 explain the structure and the specific
analytical techniques. The first section provides factor analysis and reliability analysis, while
the second section details confirmatory factor analysis (CFA). Confirmatory factor analysis
and reliability analysis were used to test the study’s hypothesis, as well as to determine if the
thirty items of work environment factors, seventeen items of social factors, and nineteen items
of organisational factors were consistently measured unifying constructs. The third and fourth
sections present the multiple linear regression analysis, and Structural equation modelling
(SEM), describe the reason for using these methods, and outline whether the anticipated
turnover of nurses is influenced by organisational, work environment, social, personal, and
policy factors respectively. Similarly, SEM using AMOS software was considered to be a
potential method for examining whether the anticipated turnover of nurses is influenced by
organisational, work environment, social, personal, and policy factors.
5.1.$Factor$Analysis$$Although the construct validity and internal reliability were well measured in social, work
environment, and organisational factors scales, other essential factor analysis components such
as, CFA and exploratory factor analysis (EFA), were not studied for these scales in Saudi
Arabia. It would have been more useful if these scales were tested more rigorously in Saudi
sitting. In the current study, EFA was used to explore a construct from data and reduce a large
set of variables into a smaller set of artificial variables, as well as measure the relationship
among construct variabilities. As shown in Table 7, the Kaiser–Meyer–Olkin coefficient for
this dataset was 0·856 and the Bartlett Test of Sphericity was statistically significant (χ2 =
19041.684, d.f. = 3403, P < 0·000). Both of these measures indicated that the data were
appropriate for factor analysis.
Table 7. Kaiser-Meyer-Olkin and Bartlett’s test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .856 Bartlett's Test of Sphericity
Approx. Chi-Square 19041.684 df 3403 Sig. .000
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5.1.1 Factor analysis for organisational factors The results of the descriptive analysis, reliability analysis, and factor analysis with respect to
the indicators of organisational factors (organisational commitment, autonomy, social support
from immediate supervisor, and social support from co-workers) are shown in Table 8. One
factor explained 77.5% of the variance in the organisational commitment, with strong loading
coefficients (.541 to .838) for six indicators with high reliability (Cronbach’s alpha = .833).
One factor clarified 70.15% of the variance in autonomy, with strong loading coefficients (.571
to .722) for six indicators with high reliability (.745). One factor explained 86.3% of the
variance in the social support (immediate supervisor), with strong loading coefficients (.636 to
.832) for four indicators with high reliability (.885).
One factor containing four indicators explained 56.9% of the social support (co-workers), but
with very low reliability (Cronbach’s alpha = .267). When two indicators with the weak loading
coefficient (Worke3 = -.435; Worke4 = -.071) were removed, then the variance explained
improved to 67.8%, with the reliability being higher (.511).
In summaries, all organisational factors (organisational commitment, autonomy, social support
from immediate supervisor, and social support from co-workers) were reliably measured. To
increase the reliability of the study’s measurements, all the factors with the weakest loading
coefficients were removed. Based on the suggestion by Chin (1998), the Cronbach’s alpha
coefficient for all indicators showed in each measurement had to be ≥ .6.
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Table 8. Reliability analysis of the all items of organisational factors
Dimension
Indicators M
SD Factor Loading Coefficient
Average Variance Explained
Cronbach's Alpha
Organisational commitment
Commit1 3.58 .955 .829 77.51 .833 Commit2 3.44 .935 .838 Commit3 3.53 .888 .810 Commit4 3.53 1.05 .587 Commit5 3.67 .934 .541 Commit6 3.41 1.05 .810
Autonomy Auton1 3.37 .887 .699 70.15
.745
Auton2 3.39 .919 .676 Auton3 3.04 1.10 .611 Auton4 3.15 .963 .571 Auton5 3.19 .991 .707 Auton6 3.15 .988 .722
Social support (immediate supervisor)
Superv1 3.47 1.00 .832 86.36
.785 Superv2 3.33 .997 .828
Superv3 3.18 .956 .636 Superv4 3.36 .965 .815
Social support (co-workers)
Worke1 4.23 .704 .748 65.91 (67.81 if Item Worke3, Worke4 deleted)
.267 (.511 if Item Worke3, Worke4 deleted)
Worke2 3.54 .982 .800 Worke3 2.71 .931 -.435 Worke4 3.41 .906 -.071
5.1.2 Factor analysis for environmental factors The results of the descriptive analysis, reliability analysis, and factor analysis in regard to the
items of environmental factors (job satisfaction, workload, opportunity for promotion, and
distributive justice) are shown in Table 9. After removing two indicators with very weak
loading coefficients (JS4 = .475 and JS7 = .466), one factor covering five indicators clarified
54.4% of the variance in the job satisfaction, with strong loading coefficients (.620 to .733) for
five items with acceptable reliability (.705).
After the removal of three indicators with a very weak loading coefficient (WL7 = .109; WL11
= .056; WL12 = -.087), one factor comprising nine indicators clarified 54.3% of the variance
in the workload, with strong loading coefficients (.510 to .734) and high reliability (.821). One
factor clarified 76.7% of the variance in the opportunity for promotion, with strong loading
coefficients (.715 to .848) for five items and high reliability (.776). One factor containing six
items explained 52.8% of the distributive justice, but with very low reliability (Cronbach’s
alpha = .305). When three items with weak loading coefficients (Just1 = -.525; Just2 = .290;
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Just5 = .188) were removed, then the variance clarified improved to 81%, with the reliability
being higher (.612). The seven measurements of job satisfaction, twelve measurements of
workload, five measurements of opportunity for promotion, and six measurements of
distributive justice were reliably measured.
Table 9. Reliability analysis of the all items of environmental factor
Environmental Factors
Indicators M
SD Factor Loading Coefficient
Average Variance Explained
Cronbach's Alpha
Job satisfaction
JS1 3.50 .932 .709 54.484 .705 JS2 3.40 .950 .513 JS3 3.38 .817 .668 JS4 3.139 .968 .475 JS5 3.496 .938 .733 JS6 3.850 .9197 .620 JS7 3.231 .9841 .466
Workload WL1 2.62 .947 .433 54.317
.750 (.821 if Item WL7, WL11, WL12 deleted)
WL2 3.05 1.032 .594 WL3 3.76 .873 .660 WL4 3.81 .816 .717 WL5 3.59 .893 .734 WL6 3.59 .906 .707 WL7 3.161 1.049 .109 WL8 3.13 1.024 .723 WL9 3.58 1.029 .690 WL10 3.677 .943 .510 WL11 2.33 .975 .056 WL12 1.884 .733 -.087
Opportunity for promotion
Prom1 2.42 1.15 .848 76.702
.776 Prom2 2.71 1.09 .818
Prom3 3.01 .987 .756 Prom4 2.80 1.05 .465 Prom5 2.43 1.110 .715
Distributive justice
Just1 2.88 1.185 -.525 52.808 (81.03 if Item Just1, Just2, Just5 deleted)
.305 (.612 if Item Just1, Just2, Just5 deleted)
Just2 2.72 .931 .290 Just3 3.51 .895 .767 Just4 3.51 .877 .743 Just5 3.36 .941 .188 Just6 3.00 1.076 .649
5.1.3 Factor analysis for social factors The results of the descriptive analysis, reliability analysis, and factor analysis in regard to the
indicators of social factors (gender-mixing, perception of nursing, discrimination, and social
support from spouse) are shown in Table 10. One factor clarified 65.9% of the variance in the
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gender-mixing, with very strong loading coefficients (.719 to .862) for five indicators with high
reliability (.866).
In regard to perception of nursing, after the removal of one indicator with a very weak loading
coefficient (Nurs3 =.411), one factor comprising three indicators clarified 75.3% of the
variance in the perception of nursing, with strong loading coefficients (.446 to .888) and
adequate reliability (.632).
In regard to discrimination factors, one factor comprising four indicators clarified 78% of the
variance in the discrimination, but with adequate reliability (.671). When one indicator with a
weak loading coefficient (Racis1=.291) was removed, the reliability increased to .756. In
regard to discrimination factors and social support from spouse, one factor explained 88% of
the variance in the social support (spouse), with strong lading coefficients (.588 to .764) for
four items and adequate reliability (Cronbach’s alpha = .642).
The five indicators of gender-mixing, the four indicators of perception of nursing, the four
indicators of discrimination, and the four indicators of social support from spouse were reliably
measured. To increase the reliability of the study’s measurements, all the indicators with the
weakest loading coefficients (< .6) were removed.
Table 10. Reliability analysis of the all items of social factor
Dimension
Indicators M
SD Factor Loading Coefficient
Average Variance Explained
Cronbach's Alpha
Gender-mixing
GM1 2.48 .953 .815 65.97 .866 GM2 2.26 .814 .862 GM3 2.57 1.051 .805 GM4 2.22 .815 .852 GM5 2.21 .829 .719
Perception of nursing
Nurs1 4.45 .871 .888 75.30
.602 (.632 if Item Nurs3 deleted), (.847 if Item Nurs3, Nurs4 deleted)
Nurs2 4.58 .698 .867 Nurs3 3.72 1.148 .411 Nurs4 3.49 1.195 .446
Discrimination Racis1 4.29 .885 .291 78.26
.671 (.756 if Item Racis1 deleted)
Racis2 3.16 1.214 .820 Racis3 2.56 1.055 .863 Racis4 2.30 .989 .760
Social support (spouse)
Spou1 3.7550 .92736 .764 88.64
.642
Spou2 3.7390 .89444 .763 Spou3 3.35 1.072 .588 Spou4 3.52 1.085 .649
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5.2.$Confirmatory$Factor$Analysis$$ To measure the convergent and discrimination validates, CFA was conducted (Keyser,
Harrington, & Ahn, 2016), while EFA was conducted to explore a constructer from data.
Moreover, factor analysis is adopted to create and validate the measurement models for latent
constructs. As mentioned in Chapter 3 (Section 3.5.3), CFA is useful when examining the
relationship between a latent construct and its indicators. In the current study, CFA was used
to test known scales in the Saudi setting or culture. CFA was used to determine the structure
of the scales of anticipated turnover (12 items), organisational factors (20 items), work
environment factors (30 items), and social factors (17 items). For example, in the current study,
a latent construct, organisational factors, which is reflected by four indicators of autonomy,
organisational commitment, social support from immediate supervisor, and social support from
co-workers. According to DeCoster (1998), CFA is based on a theoretical understanding that
defines the variation and covariation between observed variables, latent variables, and
measurement errors.
The study adopted the following validation stages as mentioned in Section 3.5.3. For CFA, it
is assumed that both the latent and the observed variables are measured as deviations from their
means. Moreover, it is assumed that the common factors and the unique factors are uncorrelated
(Wan, 2002). The first stage of CFA is to develop a generic model for each construct. The
model to be verified must be identified based on the structure of the factors of the EFA. When
the model does not fit well with the dataset, a revision is performed for a generic model by
delating the weak indicators that have load factor loading from the subscale (Bollen, 1989;
Long, 1983). The second stage of CFA is to offer a better model after delating the weak
indicators from each latent construct in the measurement models. In the third stage of CFA,
some indices will be used to revise the model and finally modification indices will be used to
revise the model (Newsom, 2012). However, the measurement models of anticipated turnover,
intent to stay, organisational, environmental, and social factors are separately validated by
confirmatory factor analysis. Analysis of the maximum likelihood estimate for individual
parameters and overall model fit is performed in the following sections.
5.2.1 Measurement model for anticipated turnover The first endogenous (dependent variables are called endogenous variable) of the study is
anticipated turnover. As the figure 16 illustrates, anticipated turnover is measured by 12
indicators.
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Figure 16. Measurement model for anticipated turnover
As shown in Figure 16, one latent variable, twelve observed variables, and twelve error
estimations. The factor loading of the indicators are examined for the appropriateness of
indicators in the generic models. A critical ratio (CR) and p value are used in order to check
whether an indicator significantly affect the latent construct. The regression parameter
estimates of the generic model for anticipated turnover illustrated that the ATS1, ATS2, ATS3,
ATS5, ATS6, ATS8, ATS9, ATS10, ATS11, ATS12, of anticipated turnover items are
statistically significant at the significance level of .01. For more details of these items, see
appendix D.
Table 11 indicates that two items of anticipated turnover (ATS4 “Deciding to stay or leave my
position is not a critical issue for me at this point in time”, ATS7 “I have been in my position
about as long as I want to”) are not statistically significant at the significance level of .01. When
the factor loading is not statistically significant, it determines that this item is not a suitable
measure for the latent construct. According to Bickel (2007), the stronger factor loading means
the stronger influence of that item on the latent construct.
Besides, ATS9, ATS10, ATS12 factors loadings provided insufficient results in terms of the
strengths of the factor loadings. Consequently; those items are removed from the measurement
model of anticipated turnover. As suggested by Anderson and Gerbing (1984), the normal
values must be between -1.96 and +1.96. If the values fall outside of this variety, then the
researcher might try to remove the items causing problems.
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Table 11. Parameter estimates and regression weights for anticipated turnover
Generic Model Revised Model Indicators
Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p
ATS1 .512 1.000 .423 1.000 ATS2 .512 .955 .114 8.353 *** .531 1.199 .163 7.370 *** ATS3 .518 1.034 .123 8.412 *** .545 1.319 .177 7.466 *** ATS4 -.041 -.079 .095 -.831 .406 ATS5 .569 1.163 .130 8.914 *** .616 1.526 .193 7.888 *** ATS6 .594 1.164 .127 9.142 *** .500 1.187 .165 7.174 *** ATS7 -.375 -.655 .098 -6.673 *** ATS8 .543 .951 .110 8.666 *** .404 .848 .112 7.559 *** ATS9 .339 .611 .099 6.165 *** ATS10 .287 .524 .098 5.361 *** ATS11 .097 .162 .084 1.938 .053 ATS12 .763 1.452 .141 10.295 *** .836 1.926 .230 8.385 ***
Goodness-of-fit indices of both the generic model and revised model of anticipated turnover
are shown in Table 12. As the table indicates that, all of the statistics for the generic model of
anticipated turnover does not provide the sufficient indices for a well-fit model. This means
that a revision is very important to improve and get better-fit model.
In the following stage, as suggested by Newsom (2012), modification indices will be used to
check for highly correlated measurement errors amongst indicators of the latent constructs to
obtain a well-fitted model. This path will reduce the chi-square values and, therefore, improve
the overall goodness of fit. Correlated errors are often due to similar item wording or content.
For example, ATS1 “I plan to stay in my position a while” and ATS8 “I am certain I will be
staying here a while”. According to Newsom (2012), the basic for including between
measurement errors can be data driven or theoretical driven. However, in the current study,
modification indices are examined to see highly correlated indicators to revise the generic
model and get better-fit model for anticipated turnover variables. In order to revise the model,
measurement errors were correlated by using modification indices. These modification indices
can help improve the strength of the construct and convergent validity.
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Figure 17. Measurement model for anticipated turnover (revised model)
After revising the model of anticipated turnover, there is a significant improvement in the
model fitness indices of the anticipated turnover. The factor loadings for ATS1, ATS2, ATS3,
ATS5, ATS6, ATS8, and ATS12 are 0.30, 0.56, 0.58, 0.64, 0.48, 0.38, and 0.80, respectively,
with statistical significance at the 0.05 level (Figure 17). The final structural model for the
seven indicators of the anticipated turnover in Saudi Arabia was estimated (Figure 17). As
shown in Table 11 above, ATS12 has the biggest effect, while ATS8 has the smallest effect on
anticipated turnover with standardised weights of .836 and .404 respectively.
Figure 17 shows the possible effects of the indicators on the latent variable of anticipated
turnover. As Table 12 shows, the goodness-of-fit of the structure was statistically significant
(χ2 = 2.944, df = 11, p = 0.00; RMSEA = 0.062; GFI = 0.985; NFI = 0.970; IFI = 0.980; TLI
= 0.953; CFI = 0.980, N = 502). Overall, these result from CFA, indicate that the measurement
model of anticipated turnover is validated with excellent goodness-of-fit indices (Kline, 2011).
Table 12. Model fitness indices for anticipated turnover
Model X2/df CFI TLI RMSEA Hoelter
Generic 11.584< .591< .500< .145< 58 Revised 2.944< .980< .953< .062< 174
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5.2.2 Measurement model for intent to stay Intent to stay is the second endogenous variable. It’s measured by the Intent to Stay Scale of
Price & Mueller (1990) by using four indicators. Intent to stay will be measured by four
indicators, as shown in Figure 18, one latent variable, four observed variables, and four error
estimations. Identification the loading of the Stay1: ‘I would like to leave my present
hospital’ was fixed because it was the strongest loading on the model.
Figure 18. Measurement model for intent to stay
The regression weights estimate of the generic model for Intent to Stay is shown in Table 13,
which shows that four indicators are statistically significant at the significance level of .01
except Stay4. As shown in Figure 18, Stay4 factor loadings (-263) provided insufficient results
in terms of the strengths of the factor loadings. Stay4 was removed because its residual
covariance was outside the normal values range (+1.96 to -1.96). As a result, Stay1, Stay2, and
Stay3 will be kept for the measurement model of intent to stay. Table 13 illustrates the
goodness-of-fit indices of the generic model for intent to stay. After achieving the model fit for
the study variable intent to stay, the next step will be to give brief information on the evaluation
and clarification of the relationship between indicators. As can be seen from the figure above,
Stay1 and Stay2 had the biggest effect with standardised weights of .955 and .933 respectively.
Table 13. Model fitness indices for intent to stay
Model X2/df CFI TLI RMSEA Hoelter
Generic 2.726< .997< .990< .059< 551
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5.2.3 Measurement model for organisational factors Figure 19 depicts the generic measurement model for the exogenous variables of organisational
factors. As the figure illustrates, organisational factors were measured by four, first-order latent
factors with 20 items, including organisational commitment (six items), autonomy (six items),
social support from co-workers (four items), and social support from immediate supervisor
(four items).
Figure 19. Measurement model for organisational factors
The measurement model for organisational factors shows four latent variables, twenty observed
variables, and twenty error estimations. For identification of the loading, Auton2, Commit2,
Super1 and Worke1 were fixed because they had the strongest loading on the model. In Table
14, the regression parameter estimates of the generic model for organisational factors depicted
that the items of organisational commitment, autonomy, and immediate supervisor have a
significant level of .01. In contrast, the regression weight for Support Co-workers in the
prediction of Worke2, Worke3, and Worke4 is not significantly different from zero at the 0.05
level (two-tailed). After using the maximum likelihood as an estimation method, the goodness
of fit for this model’s parameters failed to the range of acceptance level. Model fitness of
indices of both the generic model and revised model for the organisational factors are shown
in Table 14, which shows that none of the statistics for the generic model provided the sufficient
indices for a well-fit model. Thus, a revision will be made to improve and obtain a better-fit
model.
Table 14. Parameter estimates and regression weights for organisational factors
Generic Model Revised Model
Indicator
s
Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p
Auton3 .578 1.000 .638 1.000 Auton4 .332 .502 .080 6.277 *** .239 .328 .075 4.388 *** Auton5 .443 .690 .086 8.021 *** .362 .506 .078 6.473 *** Auton6 .472 .732 .087 8.427 *** .432 .604 .083 7.308 *** Commit3 .826 1.000 .833 1.000 Commit4 .382 .550 .065 8.452 *** .320 .458 .065 7.009 *** Commit5 .327 .417 .058 7.170 *** .256 .324 .058 5.558 *** Commit6 .663 .953 .060 15.935 *** .629 .898 .060 15.043 *** Superv1 .879 1.000 .884 1.000 Superv2 .882 .999 .046 21.637 *** .890 1.002 .047 21.164 *** Superv3 .345 .374 .050 7.530 *** .296 .320 .049 6.476 *** Superv4 .571 .626 .047 13.326 *** .543 .592 .047 12.560 *** Worke1 .283 1.000
Worke2 1.210 5.956 5.000 1.191 .234
Worke3 -.111 -.518 .182 -2.842 .004
Worke4 .011 .049 .166 .297 .766
Auton2 .749 1.081 .095 11.435 *** .712 .931 .104 8.992 *** Auton1 .765 1.066 .092 11.527 *** .683 .863 .096 8.946 *** Commit2 .877 1.118 .048 23.223 *** .890 1.125 .047 24.062 *** Commit1 .853 1.110 .050 22.390 *** .857 1.108 .048 22.970 ***
In the next stage, modification indices are verified for highly correlated indicators to revise the
generic model and achieve a better-fit model. As can be seen in Figure 20, measurement errors
are correlated by using modification indices to revise model. The largest MI values are found
in the first seven pairs of residual covariance.
Because the factor loading of Worke2, Worke3, and Worke4 has a negative value, the
indicators will be removed from the measurement model before revising the model.
Furthermore, because Support Co-workers has only two indicators after removing negative
value, Support Co-workers will remove from the model. There are some straightforward rules
that concern minimum numbers of indicators per factors. According to Kiln (2011), CFA
models with factors that only have two indicators are more prone to problems in the analysis;
it is superior to have at least three or four indicators per factors to prevent such problems (Little
& Nesselroade, 1999; Jackson, 2001).
Figure 19. Measurement model for organisational factors (revised model)
The factor loadings for Auton1, Auton2, Auton3, Auton4, Auton5, and Auton6 are 0.68, 0.71,
0.64, 0.24, 0.36, and 0.43, respectively, with statistical significance at the 0.05 level (Figure
20). After revising the model, there is a significant improvement in the model fitness indices
of the organisational factors. As can be seen from the table below, the goodness of fit of the
structure was statistically significant (χ2 = 3.049, df = 90, p = 0.00; RMSEA = 0.64; GFI =
0.935; NFI = 0.930; IFI = 0.952; TLI = 0.936; CFI = 0.952, N = 502).
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Table 15. Model fitness indices for organisational factors
Model X2/df CFI TLI RMSEA Hoelter
Generic 8.109 .707 .644 .119 74 Revised 3.049 .952 .936 .064 207
After accomplishing the model fit, the evaluation and clarification of the relationship between
indicators will be discussed in the following section. Table 15 indicates the possible effects of
the indicators on the latent variable of organisational factors. As can be seen from the table,
Superv2 and Commit2 had the biggest effect, while Auton4 had the smallest effect on intent to
stay with standardised weights of .890 and .239 respectively. Moreover, since the Support Co-
workers is not important in terms of theory and having only two indicators to identify a
construct has been recognised as problematic, the Support Co-workers will be removed in the
current study. In the previous study by Kim (1996), estimation of LISREL measurement model
indicated that Worke3 “I rarely discuss important personal problems with my co-workers” and
Worke4 “I know almost nothing about my co-workers as persons” for Support Co-workers had
weak loadings. Further, deletion of Worke3 and Worke4 made almost no change to the
reliability of the construct. Overall, CFA was used to investigate construct validity of the
organisational factors and these results from CFA indicate that the measurement model of
organisational factors is validated with excellent goodness-of-fit indices (Little & Nesselroade,
1999).
5.2.4 Measurement model for work environment factors
Figure 20 depicts the generic measurement model for the exogenous variables of work
environment factors. As the figure illustrates, work environment factors were measured using
four first-order latent factors with 30 items. The four first order latent factors of environmental
factors are workload, job satisfaction, promotion, and distributive justice. For the identification,
the loading of the WL1, JS1, Prom1 and Just1 were fixed because they were the strongest
loading on the model.
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Figure 20. Measurement model for work environment factors
The regression parameter estimates of the generic model for environmental factors illustrated
that all of the job satisfaction items and promotion items are statistically significant at the
significance level of .01. Table 16 indicates that three items of workload (WL7, WL11, WL12)
and distributive justice (Just5) are not statistically significant at the significance level of .01.
Moreover, not all of the factor loadings exceeded the predetermined criteria of .30. As a
consequence of the known results, a few items were removed from the measurement model of
environmental factors. Since factor loading of WL12 (-.074), Just2 (-.405), Just3 (-.497), Just4
(-.455), Just6 (-.696) has a negative value, the indicators will be removed from the
measurement model before revising the model.
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Furthermore, since distributive justice has only two indicators after removing, distributive
justice will be removed from the model. Distributive justice index has presented acceptable
measurement in several studies (Brooke, & Price, 1989; Price & Mueller, 1989). In contrast,
the negatively worded Just1 “Promotions by my employer are almost totally based on
seniority” and Just2 “Raises by my employer heavily depend on who you know” had weak
loadings in the previous study (Kim, 1993). Moreover, the result measurement model of
promotion from CFA indicated that Prom4 “The practice of internal promotion is not
widespread with my hospital” and Prom5 “It is difficult to get promoted in my hospital” have
weaker loadings compared to the remaining three items.
Prom4 will be removed from the measurement model with no fundamental reduction in the
reliability. The remaining four items (Prom1, Prom2, Prom3, Prom5) will be adopted for the
scale and they presented factor loadings ranging from .426 to .869 with Alpha of .776.
Table 16. Parameter estimates and regression weights for work environmental factor
Generic Model Revised Model Indicators Standardised
Regression Weight Unstandardised Regression Weight
Standard Error
CV/ p Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p
WL1 .379 1.000 .397 1.000 WL2 .538 1.550 .220 7.053 *** .560 1.537 .190 8.079 *** WL3 .588 1.431 .196 7.292 *** .510 1.184 .170 6.961 *** WL4 .652 1.483 .197 7.549 *** .550 1.179 .164 7.195 *** WL5 .683 1.702 .222 7.657 *** .558 1.315 .182 7.228 *** WL6 .671 1.696 .223 7.615 *** .597 1.440 .194 7.435 *** WL7 .072 .211 .144 1.466 .143 WL8 .684 1.954 .255 7.660 *** .771 2.098 .262 8.021 *** WL9 .638 1.830 .244 7.497 *** .659 1.802 .234 7.691 *** WL10 .440 1.156 .180 6.434 *** .398 .999 .164 6.084 *** WL11 .064 .173 .133 1.300 .194 WL12 -.074 -.151 .101 -1.506 .132 JS1 .732 1.000 .855 1.000
JS2 .350 .497 .073 6.773 ***
JS3 .502 .690 .073 9.488 *** .342 .405 .062 6.571 *** JS4 .447 .622 .073 8.538 *** .481 .576 .064 8.990 *** JS5 .583 .786 .073 10.820 *** .480 .556 .062 8.952 *** JS6 .506 .729 .076 9.560 *** .381 .474 .065 7.309 *** JS7 .388 .464 .062 7.476 *** .411 .424 .054 7.859 *** Prom1 .790 1.000 .751 1.000 Prom2 .819 .984 .055 17.983 *** .869 1.097 .065 16.763 *** Prom3 .738 .803 .049 16.331 *** .745 .851 .054 15.670 *** Prom4 .288 .333 .055 6.038 *** Prom5 .533 .652 .057 11.478 *** .426 .547 .055 9.997 *** Just1 .211 1.000 Just2 -.405 -1.511 .398 -3.801 *** Just3 -.497 -1.780 .450 -3.953 *** Just4 -.455 -1.595 .410 -3.892 *** Just5 .005 .020 .191 .106 .915 Just6 -.696 -2.995 .729 -4.109 ***
Table 17 shows the goodness-of-fit indices of both the generic and revised model for work
environmental factors. It can be seen from the table that none of the model fitness indices for
the generic model provide sufficient indices for a well-fit mode, except Likelihood ratio (x2
/df). As a result of analysis, revision is required to improve and get better-fit model for
environmental factors.
Table 17. Model fitness indices for work environment factors
Model X2/df CFI TLI RMSEA Hoelter
Generic 4.149 .695 .670 .079 136 Revised 2.729 .920 .902 .059 222
In the next stage, highly correlated indicators are examined by using modification indices to
revise the model. Figure 21. Measurement Model for work environment factors (revised
model)shows the revised measurement model for environmental factors. After revising the
model, there is a significant improvement in the model fit indices of the study variable of
environmental factors. As can be seen from the table below, the goodness of fit of the structure
was statistically significant (χ2 = 2.729, df = 139, p = 0.00; RMSEA = 0.59; GFI = 0.930; NFI
= 0.925; IFI = 0.902; TLI = 0.902; CFI = 0.920, N = 502).
Figure 21. Measurement Model for work environment factors (revised model)
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After accomplishing the model fit, the evaluation and clarification of the relationship between
indicators will be discussed in the following section. Table 16 indicates the possible effects of
the indicators on the latent variable of environmental factors. As can be seen from the table 16
that, Prom2 “There is a very good chance to advance in my career in my hospital” had the
biggest effect, while JS3 “I am often bored with my job” had the smallest effect on intent to
stay with standardised weights of .869 and .342 respectively. CFA of the items from the three
attitudinal measures (job satisfaction, workload, promotion) provided evidence of three
empirically distinct constructs that are moderately inter-correlated. Overall, CFA was used to
investigate construct validity of the work environmental factors and these result from CFA,
indicate that the measurement model of work environmental factors is validated with excellent
goodness-of-fit indices.
5.2.5 Measurement model for social factors Figure 22. Measurement model for social factors depicts the generic measurement model for
the exogenous variables of social factors. As the figure illustrates, social factors were measured
using four first-order latent factors with 17 items. The four first-order latent factors of social
factors are gender-mixing, perception of nursing, discrimination, and support from spouse. For
the identification, the loading of GM1, Nurs1, Racis1 and Spou1 were fixed, because they were
the strongest loading on the model.
Figure 22. Measurement model for social factors
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Table 18 indicates the parameter estimates of the generic model for social factors, all indicators
of social factors are statistically significant at the significance level of the .01, except Spou3.
The probability of getting a CR as large as 1.33 in absolute value is .183. In other words, the
regression weight for spouse in the prediction of Spou3 is not significantly different from zero
at the 0.05 level (two-tailed). Thus, Spou3 “My spouse can be relied on when things get tough
on my job” will be removed from the measurement model of social factors.
Table 18. Parameter estimates and regression weights for social factors
Generic Model Revised Model
Indicators SRW URW S.E. CV/ P SRW URW S.E. CV/ p
GM1 .747 1.000 .747 1.000 GM2 .841 .962 .052 18.398 *** .841 .962 .052 18.396 *** GM3 .734 1.083 .068 16.030 *** .734 1.083 .068 16.025 *** GM4 .828 .948 .052 18.143 *** .829 .948 .052 18.144 *** GM5 .636 .740 .054 13.779 *** .636 .740 .054 13.782 *** Nurs1 .961 1.000 .970 1.000 Nurs2 .783 .652 .075 8.735 *** .757 .608 .076 7.953 *** Nurs3 .167 .229 .066 3.449 *** .162 .220 .066 3.319 *** Nurs4 .209 .299 .071 4.227 *** .208 .294 .071 4.127 *** Racis1 .137 1.000 .097 1.000 Racis2 .650 6.503 2.335 2.785 .005 .643 9.133 4.401 2.075 .038 Racis3 .880 7.654 2.742 2.791 .005 .876 10.819 5.399 2.004 .045 Racis4 .643 5.239 1.882 2.783 .005 .650 7.521 3.753 2.004 .045 Spou1 .918 1.000 .920 1.000 Spou2 .875 .919 .090 10.165 *** .873 .915 .092 9.970 *** Spou3 .063 .079 .059 1.330 .183
Spou4 .133 .169 .060 2.795 .005 .127 .162 .060 2.679 .007
Table 19 indicates goodness-of-fit indices of both the generic model and revised model for
social factors. As the table illustrates, all the statistics for the generic model of the social
factors does not provide the sufficient indices for a well-fit model. As a consequence of this
fact, a revision will be made to improve and get a better-fit model.
Table 19. Model fitness indices for social factors
Model X2/df CFI TLI RMSEA Hoelter
Generic 6.488 .807 .772 .105 95 Revised 2.281 .957 .946 .051 274
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In the next stage, highly correlated indicators are examined by using modification indices to
revise the model. Figure 23 shows the revised measurement model for social factors. After
revising the model, there is a significant improvement in the model fit indices of the study
variable of social factors. As can be seen from the table above, the goodness of fit of the
structure was statistically significant (χ2 = 2.281, df = 97, p = 0.00; RMSEA = 0.051; GFI =
0.945; NFI = 0.926; IFI = 0.957; TLI = 0.946; CFI = 0.957, N = 502).
Figure 23. Measurement model for social factors (revised model)
After accomplishing the model fit, the evaluation and clarification of the relationship between
indicators will be discussed in the following section. Table 18 indicates the possible effects of
the indicators on the latent variable of social factors. It can be seen from the table that Nurs1
“Nursing is a respected profession” had the biggest effect, while Racis1 “I would prefer to
work in a country where there is no racism” had the smallest effect on intent to stay with
standardised weights of .970 and .097 respectively.
In order to examine if the four scales are indeed measuring four social factors (distinct
constructs), CFA is used, and the result has supported the four factors model. CFA
measurement model of social factors have indicated that all items loaded together with
appropriate loadings, suggesting evidence to support discriminant-convergent validity of the
scale. As shown in Table 18 above, gender-mixing has factor loading from .636 to .841 with
Alpha of .866; perception of nursing has factor loading from .757 to .970 with Alpha of .602;
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discrimination has factor loading from .643 to .876 with Alpha of .671; and support from
spouse has factor loading from .873 to .920 with Alpha of .642; Overall, these results from
CFA indicate that the measurement model of social factors is validated with excellent
goodness-of-fit indices
After the validation of the measurement model of the latent constructs via CFA by using IBM®
SPSS® AMOS V23.0, CSM will be developed by including exogenous latent variables,
endogenous latent variable, and control variables. CSM will be used to estimate the structural
relationships between the latent study constructs.
5.3$Covariance$structure$model$$As shown in figure 25, SEM using AMOS software was considered to be a potential method
for testing hypotheses (#1, #2, #3). SEM is a more sophisticated method than multiple linear
regressions. Moreover, the main reason for using SEM, in preference to covariance structure
model, in the current study, is because CDM is a powerful tool that combines CFA and SEM
into one method to estimate latent variables from observed variables, as well as the structural
relationships among latent variables. CSM can be used to test multiple hypotheses
simultaneously. Moreover, it can be used to indicate relationships between exogenous
(anticipated turnover), endogenous (organisational, work environment, and social factors) and
control variables even they are latent constructs. The overall model validation can be tested by
CSM. The covariance structure analysis is then performed to determine the relationship
between organisational, environmental, and social factors and anticipated turnover, as shown
in Figure 25. Based on upon the current study conceptual framework, the research hypotheses,
tested here, were as follows:
•! Anticipated turnover of nurses is influenced by organisational factors (social support
immediate supervisor, (social support co-workers, organisational commitment, and
autonomy).
•! Anticipated turnover of nurses is influenced by work environmental factors (workload,
distributive justice, opportunity for promotion, job satisfaction).
•! Anticipated turnover of nurses is influenced by social factors (Gender-Mixing,
Perception of nursing, discrimination, social support spouse).
•! Anticipated turnover of nurses is influenced by personal factors (gender, age,
race/ethnicity, nationality, religion, education).
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The same goodness-of-fit statistics with CFA will be used to test the validity of overall generic
measurement model including exogenous, endogenous, and control variables. In the first step
of CSM, factor loading will be examined. The indicators with insignificant factor loading or
low standardised regression coefficient will be excluded from the model for better model fit
(Wan, 2002). In the second step of CSM, overall model fit is evaluated to better understand the
study’s CSM. Finally, several indicators are used to determine the validity of the study’s model,
as shown in Table 20. When the model of this study does not fit well with the data, further
revisions will be made.
Table 20. Model fitness values criteria
5.3.1 Hypotheses Testing This study tested eight hypotheses. This section will discuss whether and to what extent the
hypotheses are supported by the dataset based on the structure equation modelling analysis
results. Figure 24 shows the research conceptual model, as mentioned in section 2.7.
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Figure 24. Conceptual model
5.3.2 H1: Anticipated turnover of nurses is influenced by organisational factors (social support immediate supervisor, organisational commitment, and autonomy). The third hypothesis supposed that (organisational factors) social support of an immediate
supervisor, organisational commitment, and autonomy affected the anticipated turnover of
nurses in Saudi Arabia. For example, increased social support from an immediate supervisor is
associated with decrease anticipated turnover among nurses in hospitals.
In order for this hypothesis is to be supported, the relationship between these factors and
anticipated turnover of nurses should be statistically significant. From the data in Figure 25, it
is apparent that each single narrow between variables indicated causal relationship between
endogenous and exogenous variables of the study. For example, in the current study, the figure
indicates how nurses’ anticipated turnover variable is affected by organisational and social
factors. The SEM results indicated a good fit between the study’s hypothesised model and the
data (X2/df = 2.98, P = 0.00, RMSEA =0.06, CFI = 0.948, GFI = 0.979, TLI = 0.903). The
total variance in anticipated turnover accounted for the four predictors is 30%. As shown in
Figure 25, social support from immediate supervisor had a significant negative affect on
anticipated turnover. The negative regression coefficient indicates that increased social support
from immediate supervisor will decrease turnover among nurses. A significant relationship (-
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0.15) was found between social support from immediate supervisor and anticipated turnover
(Figure 25).
Figure 25. Results of the structural equation modelling of the effect of organisational factors on anticipated turnover
The SEM results are shown in Table 21. Organisational commitment had a significant negative
effect on anticipated turnover among nurses (β= -0.33, p < .01). This means that nurses with
high levels of commitment have lower level of turnover. This finding was also reported by
Brewer et al. (2015). Autonomy also had a significant negative effect on anticipated turnover
among nurses (β= -0.16, p < .01). The negative regression coefficient indicated that the higher
the autonomy observed among nurses, the lower their levels of turnover.
Gender, religion, and education were considered as control variables in the primary analysis of
anticipated turnover. However, gender and religion were found not to be statistically significant
factors in explaining the variation in anticipated turnover. Education was found to be positively
correlated with anticipated turnover.
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Table 21. Regression statistic of the organisational factors on anticipated turnover
Variable
Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p
Autonomy -.159 -.101 .028 -3.618 *** Organisational commitment -.328 -.188 .028 -6.769 ***
Social support immediate supervisor
-.147 -.079 .024 -3.252 .001
Social support co-worker .085 .072 .031 2.296 .022
Gender -.021 -.023 .041 -.558 .577 Education .133 .082 .023 3.585 *** Religion .030 .032 .039 .811 .418
5.3.3 H2: Anticipated turnover of nurses is influenced by work environmental factors (workload, opportunity for promotion, job satisfaction, and distributive justice). The SEM results indicate a good fit between the study’s hypothesised model and the data
(X2/df = 1.972, P = 0.014, RMSEA =0.044, CFI = 0.973, GFI = 0.985, TLI = 0.950). These
goodness-of-fit statistics show that the study’s model fits very well with the data. The total
variance in anticipated turnover accounting for the four predictors is 29%. As shown in Figure
26, job satisfaction had a significant negative affect on anticipated turnover (β= -0.29, p < .01).
The negative regression coefficient indicates that increased job satisfaction will decrease
turnover among nurses.
Figure 26. Results of the structural equation modelling of the effect of work environmental factors on anticipated turnover
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The SEM results are shown in Table 22. The opportunity for promotion had a significant
negative affect on anticipated turnover among nurses (β= -0.20, p < .01). This means that as
opportunity for promotion increase by unit, the outcomes will decrease. The workload also had
significant negative affect on anticipated turnover among nurses (β= -0.15, p < .01). The
negative regression coefficient indicates that the higher the workload observed among nurses,
the lower their levels of turnover. The results broadly support the work of other studies in this
area linking environmental factors with anticipated turnover (Bogaert et al., 2013; Bungay,
Wolff, & MacDonald, 2016; Chan et al., 2009; Zeytinoglu et al., 2006).
Table 22. Regression statistic of the environmental factors on anticipated turnover
Indicators
Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p
Workload -.153 -.131 .037 -3.507 *** Job satisfaction -.288 -.213 .034 -6.318 *** opportunity for promotion -.201 -.106 .023 -4.672 ***
Distributive justice -.029 -.026 .040 -.646 .518
Gender .027 .030 .042 .717 .473 Education .168 .104 .023 4.471 *** Religion .007 .007 .040 .179 .858
5.3.4 H3: Anticipated turnover of nurses is influenced by social factors (Gender-Mixing, Perception of nursing, and discrimination social support spouse) The SEM results indicate a good fit between the study’s hypothesised model and the data
(X2/df = 1.434, P = 0.014, RMSEA =0.029, CFI = 0.969, GFI = 0.993, TLI = 0.927). The total
variance in anticipated turnover accounted for the four predictors is 13%. As shown in Figure
27, of the three statistically significant predictors of anticipated turnover, discrimination (β=
0.27) exerts the most influence on the variability in anticipated turnover.
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Figure 27. Results of the structural equation modelling of the effect of social factors on anticipated turnover
The discrimination had a significant positive affect on anticipated turnover among nurses (β=
0.27, p < .01), see Table 23. This means that as discrimination decrease by unit, the outcomes
will increase. Gender mixing also had significant positive affect on anticipated turnover among
nurses (β= 0.13, p > .01). However, perception of nursing had a significant negative affect on
anticipated turnover among nurses (β= -0.12, p > .01). The negative regression coefficient
indicates that the higher the perception of nursing observed among nurses, the lower their levels
of turnover.
In summary, this study had three hypotheses examining the relationship between variables. The
SEM findings indicated that several independent variables were found to be significant
predictors of anticipated turnover, including discrimination, workload, job satisfaction,
opportunity for promotion, social support immediate supervisor, organisational commitment,
and autonomy. Other independent variables that entered the SEM model were not found to
significantly predict anticipated turnover, including gender mixing, perception of nursing,
social support spouse, distributive justice, and social support co-worker. In the next chapter
(discussions), the results of analysis and implication of the study will be discussed in depth.
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Table 23. Regression statistic of the social factors on anticipated turnover
Indicators
Standardised Regression Weight
Unstandardised Regression Weight
Standard Error
CV/ p
Gender-mixing .128 .074 .026 2.882 .004 Perception of nursing -.117 -.075 .027 -2.761 .006
Discrimination .274 .155 .024 6.351 *** Social support Spouse -.006 -.004 .026 -.145 .884
Gender .014 .016 .047 .338 .736 Religion .071 .076 .045 1.713 .087
5.4$The$Multiple$Regression$Analysis$The standard multiple regression analysis was considered to be a possible method to test
hypotheses in this current study. It was conducted to examine the ability of the organisational,
work environmental and social factors to predict intention to leave among nurses in Saudi
Arabia. The standard multiple regression analysis was conducted to determine the
organisational, work environmental and social variables that make the best contribution to
clarifying intention to leave. The advantage of using standard multiple regression analysis in
the current study is to determine the overall fit of the model and the relative contribution of
each of the predictors to the total variance were explained as well as determine which factors
are most important in the predication intention to leave (Chatterjee & Hadi, 2015).
5.4.1 Organisational factor results In this section, the research described carries out multiple regressions using IBM® SPSS®
Statistics V23.0, as well as reports the results from this test. This method was conducted to
understand whether the anticipated turnover of nurses is influenced by organisational factors
(autonomy, organisational commitment, social support from immediate supervisors, and social
support from co-workers). Table 24 shows the model summary including the R square and the
standard error of the estimate, which was used to determine how well a regression model fits
the data in this current study.
Table 24. Model summary
Model R R square Adjusted R square Std. error of the estimate
1 556a 0.310 0.314 0.34815
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Overall, 31% of intention to leave among nurses was explained by knowing the scores for the
organisational factors (autonomy, organisational commitment, social support from immediate
supervisors, and social support from co-workers).
Table 25 illustrates the F-ratio in order to examine whether the overall regression model is a
good fit for the data. It can be seen from the data in the table that the independent variables
organisation commitment, autonomy, social support from immediate supervisors and social
support from co-workers statistically significantly predict the dependent variable anticipated
turnover. R for regressions was significantly different from zero, F (4, 497 = 55.738, p < 0.0005
with R2 .314). This means that the regression model is a good fit for the data.
Table 25. ANOVA
Model Sum of squares
df Mean square
F Sig.
Regression Residual
27.024 60.241
4 497
6.756 0.121
55.738 0
Table 26 displays the coefficients, providing the general form of the equation to predict the
anticipated turnover from the autonomy, organisational commitment, social support from
immediate supervisors and social support from co-workers variable as follows:
Predicted anticipated turnover = 3.971- (0.115* autonomy) - (0.188* commitment) - (0.081*
immediate supervisor) + (0.089* co-workers).
The results of the multiple linear regression analysis, as in Table 26, shows all independent
variable coefficients are statistically significantly different from zero at α = .05. Moreover, the
standardised partial regression coefficients indicated that four organisational factors,
organisational commitment (β = -0.324; p = .00), autonomy (β = - 0.178; p = .00), support from
immediate supervisor (β = -0.148; p = .001), and support from co-worker (β = 0.105; p = .005)
were significant predictor of anticipated turnover at α = .05. However, the magnitudes of the
regression coefficients (the regression coefficients range from 0.008 to 0.188) were low,
reflecting a moderately weak model. As it can be seen from the largest beta value in the table
below, the organisational commitment variable makes the strongest unique contribution to
clarifying the anticipated turnover, followed by the autonomy variable.
The unstandardised B1 for the independent variable social support from immediate supervisors
is equal to -0.081. This means that the intention to leave for nurses is influenced by the support
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they receive from their immediate supervisors. This suggests that as support increase by unit,
the outcomes will decrease.
Table 26. Coefficients
Model Unstandardised coefficients
Standardised coefficients
t Sig. Collinearity Statistics Tolerance VIF
B Std. error
Beta
(Constant) Anticipated turnover
3.971 0.148 26.826 .000
Autonomy -0.115 0.028 -0.178 -4.034 .000 .712 1.405 Commitment -0.188 0.028 -0.324 -6.658 .000 .586 1.705 Immediate supervisor
-0.081 0.025 -0.148 -3.257 .001 .676 1.479
Co-workers 0.089 0.032 0.105 2.795 .005 .992 1.009
In summary, a multiple regression was run to predict the anticipated turnover from autonomy,
organisational commitment, social support from immediate supervisors, and social support
from co-workers. These variables statistically significantly predicted anticipated turnover of F
(4, 497) = 55.738, with p less than 0.0005 and R.310. All four variables added statistically
significantly to the predication p < 0.05.
5.4.2 Work environmental factor results In this section, the research described how to carry out multiple regression using SPSS
Statistics V23.0, as well as report the results from this test. As mentioned above, this method
was used to test the research question #2 (Anticipated turnover of nurses is influenced by work
environmental factors “workload, distributive justice, opportunity for promotion, job
satisfaction”). Table 27 shows the model summary including the R square and the standard
error of the estimate, which was used to determine how well a regression model fits the data in
this current study.
As can be seen from the table, the ‘R’ column represents the value of R, the multiple correlation
coefficients. R can be considered to be one measure of the quality of the prediction of the
dependent variables, which in this case is anticipated turnover. A value of 0.526 indicates a
good level of prediction. Table 27 displays the R square, which is called the coefficient of
determination. It also presents the proportion of variance in the dependent variable anticipated
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turnover that can be explained by the independent variables autonomy, organisational
commitment, social support from immediate supervisors and social support from co-workers).
The adjusted R = 27% indicated that a moderate proportion of the variance in the dependent
variable was explained. Overall, 27% of the intention to leave among nurses was explained by
knowing the scores for the work environmental factors (workload, distributive justice,
opportunity for promotion, job satisfaction). However, when the distributive justice factor is
removed, the adjusted R is increased to 34%.
Table 27. Model summary
Model R R square Adjusted R square Std. error of the estimate
1 526a 0.276 0.270 0.35648
Table 28 illustrates the F-ratio in order to examine whether the overall regression model is a
good fit for the data. It can be seen from the data in the table that the independent variables job
satisfaction, opportunity for promotion, distributive justice, and workload statistically
significantly predict the dependent variable anticipated turnover. R for regressions was
significantly different from zero, F (4, 497 = 47.427, p < 0.0000 with R2 .270).
Table 28. ANOVA
Model Sum of squares
df Mean square
F Sig.
Regression Residual
24.107 63.157
4 497
6.027 0.127
47.427 0
Table 29 displays the coefficients, providing the general form of the equation to predict the
anticipated turnover from the job satisfaction, opportunity for promotion, distributive justice
and workload variable as follows:
Predicted anticipated turnover = 4.519- (0.218* job satisfaction) - (0.106* opportunity for
promotion) - (0.036* distributive justice) + (0.142* workload).
As can be seen from the table below, the most important significant predictor of anticipated
turnover is job satisfaction (β = -0.292; p <.001); whereas opportunity for promotion (β = -
0.199; p = .001) and workload (β = -0.164; p = .001) are less important, while the distributive
justice item (β =-0.039, p = .388) is not a significant predictor. The magnitudes of the regression
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coefficients (the regression coefficients range from 0.036 to 0.218) were low, reflecting a
relatively weak model.
The unstandardized B1 for the independent variable distributive justice is equal to -0.036. This
means that the intention to stay for nurses is influenced by distributive justice. This suggests
that when the rewards nurses receive are distributed in proportion to their contribution to the
hospital, nurses will define the situation as fair and that fairness may increase nurses’ intention
to stay in their job. It can be seen from this table that the job satisfaction, opportunity for
promotion, and workload contribute to the model, but distributive justice does not.
Table 29. Coefficients
Model Unstandardised coefficients
Standardised coefficients
t Sig. Collinearity Statistics
Tolerance
VIF
B Std. error Beta (Constant) Anticipated turnover
4.519 0.134 33.754 0
Job satisfaction
-0.218 0.034 -0.292 -6.318 0 .682 1.467
Distributive justice
-0.036 0.041 -0.039 -0.864 .388 .723 1.384
Opportunity for promotion
-0.106 0.23 -0.199 -4.565 0 .764 1.309
Workload -0.142 0.038 -0.164 -3.710 0 .741 1.349
In summary, a multiple regression was run to predict the anticipated turnover from job
satisfaction, opportunity for promotion, distributive justice and workload. Several independent
variables are found to be significant predictors of anticipated turnover, including job
satisfaction, opportunity for promotion and workload. Only independent variable that entered
the regression model was not found to be significant predictors of anticipated turnover is
distributive justice. However, these variables statistically significantly predicted anticipated
turnover of F (4, 497) = 47.427, with p < 0.0000 and R2.276. All three variables added
statistically significantly to the predication p < 0.05.
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5.4.3 Social factor results A multiple regression analysis was used to answer the third research questions related to the
influence of social variables (gender-mixing, perception of nursing, discrimination and social
support from spouse) on anticipated turnover. The purpose is to compare the contribution of
each social variable to the anticipated turnover. Therefore, the beta values, standardised
regression coefficients, will be used.
As can be seen from the table, the ‘R’ column represents the value of R, the multiple correlation
coefficients. R can be considered to be one measure of the quality of the prediction of the
dependent variables, which in this case is anticipated turnover. A value of 0.345 indicates a
good level of prediction. As shown in Table 30, 11% of the anticipated turnover among nurses
is explained by knowing the scores for the four social factors.
Table 30. Model summary
Model R R square Adjusted R square Standard error of the estimate
1 345a 0.119 0.112 0.39324
Table 31 illustrates the F-ratio in order to examine whether the overall regression model is a
good fit for the data. It can be seen from the data in the table that the independent variables
gender-mixing, perception of nursing, discrimination and social support from spouse
statistically significantly predict the dependent variable anticipated turnover F (4, 497 =
16.828, p < 0.0000 with R2 .112).
Table 31. ANOVA
Model Sum of squares
df Mean square
F Sig.
Regression Residual
10.409 76.855
4 497
2.602 0.155
16.828 0
Table 32 displays the coefficients, providing the general form of the equation to predict the
anticipated turnover from the gender-mixing, perception of nursing, discrimination and social
support from spouse variable as follows:
Predicted anticipated turnover = 2.358- (0.073* perception of nursing) + (0.069* gender-
mixing) + (0.155* discrimination) - (0.006* social support from spouse).
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As can be seen from the table below, the largest beta value in this model was 0.274 which is
for discrimination variable, followed by gender-mixing (0.120). This means that discrimination
variable makes the strongest unique contribution to explaining anticipated turnover, when the
variance explained by the other variables in the model is controlled for. The other significant
variable is gender-mixing. Its beta value (0.120) is lower than discrimination variable value,
indicating that it makes less of a contribution. As shown in the Table 32, several independent
variables are found to be significant predictors of anticipated turnover, including
discrimination, gender-mixing, and perception of nursing, while social support from spouse
variable is not found to be significant predictors of anticipated turnover.
Table 32. Coefficients
Model Unstandardised coefficients
Standardised coefficients
t Sig.
B Std. error Beta (Constant) anticipated turnover 2.658 0.174 15.317 .00 Perception of nursing -0.073 0.027 -0.115 -2.681 .008 Gender-mixing 0.069 0.026 0.120 2.687 .007 Discrimination 0.155 0.25 0.274 6.302 .00 Social support from spouse -0.006 0.026 -0.10 -.233 .816
In summary, a multiple regression was run to predict the anticipated turnover from gender-
mixing, perception of nursing, discrimination and social support from spouse and to determine
the best contributor to anticipated turnover. Altogether, approximately 11% of the anticipated
turnover among nurses is explained by knowing the scores for the four social factors. These
variables statistically significantly predicted anticipated turnover of F (4, 497) = 16.828, with
p < 0.0000 with R2 .112. Having presented the quantitative questionnaire data, the next chapter
presents the qualitative aspect of findings from the open-ended questions. The qualitative
aspect allowed for deeper, richer insights into the factors and nurses’ intention to stay in Saudi
Arabia.
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6.$CHAPTER$SIX:$QUALITATIVE$DIMENSION$FINDINGS$$
6.1$Introduction$$This section presents the qualitative dimension findings from each of the open-ended questions
embedded in the questionnaire. The three posed questions were designed to yield richer
contextual insight into the study and add contextual information not otherwise covered in
quantitative research. This section reports on the perception of the nurse respondents towards
their intention to stay and related factors. The factors that affect nurses’ decision to leave their
current job and the strategies that would prevent turnover and encourage nurses to stay are also
presented and discussed. Finally, this section concludes by providing a brief summary of the
qualitative insights.
Three open-ended questions were incorporated in the questionnaire to help nurses identify
particular factors that influence their perspective on the quantitative questions that measured
turnover and intention to stay. Although the three questions does not constitute qualitative
research in itself, the study is clear that aspects of qualitative approach were used to distil
information, not otherwise revealed in surveys. To maintain logical consistency and
trustworthiness of the data from open-ended questions, qualitative techniques such as the data
analysis tool NVivo were used. This is a critical element of the study to uphold credibility of
the data and responses.
The questions posed to the nurses for response were:
Q1. Are there additional factors that affect your decision to leave your current job?
Q2. What strategies would prevent turnover and encourage nurses to stay?
Q3. Please provide any additional comments you would like to mention.
The quantitative survey did not specifically respond to the question of nurses’ intention to leave
Saudi Arabia. Therefore, the above research questions allowed for any additional insights and
factors not otherwise covered in the survey data. An unexpected finding was the reporting of
nursing intention to leave the country. Further discussion on this aspect is detailed in the
findings and the Discussion Chapter.
As noted in previous chapter, thematic analysis was employed to reveal common patterns
emerging from the data (Ritchie & Spencer, 2002). While Question 1 was designed to gather
information relating directly to other factors that affect turnover. Question 2 was designed to
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allow nurses to suggest strategies for attracting nurses into the jobs and retaining them.
Question 3 asked nurses for any additional comments on any other factors that might be
considered of importance to nurses’ retention. The following section of this chapter present the
findings for each open-ended question. For more details about data analysis, please see chapter
three, section 3.6.
6.2$Are$There$Additional$Factors$That$Affect$Your$Decision$to$Leave$Your$Current$Job?$The first research question seeks any additional factors not necessarily covered or explained in
the questionnaire (Borkan, 2004). Kinn and Curzio (2005) supported this approach, by
emphasising the significance of including a qualitative aspect to generate greater understanding
and insight not otherwise considered in either method on its own. The qualitative dimension
allowed for richer description of the context. The result rates for the open-ended questions were
significant. Of the 249 nurses who completed the online survey, 177 (71 %) responded to this
open-ended question, while 24% of the 253 (of the nurses who completed the paper survey
provided comments. Figure 28 is the word cloud of all responses provided, the most commonly
used central words can be identified. In this current study, five main themes emerged from the
participants’ responses to the first questions. Table 33. Examples of responses to Question 1
Table 33 below presents the themes and sub-themes that emerged from the data. These were
social factors (recreational activity, quality of life, perception of nursing, racial and nationality
discrimination, and patient attitude), personal factors (personal matters, family issues, spouse
support, education opportunities, accommodation, and food benefits), organisational factors
(unfair salary for different nationalities, lack of recognition, poor management, poor housing
conditions, favouritism, and lack of appreciation), environmental factors (workload, salary,
promotion, mental and physical exhaustion, understaffing, staffing ratio, and lack of support)
and political factors (no family status visa, independence, and new Saudi regulations). Given
that there were considerable responses to the open-ended questions and associated quantity of
influencing factors on intention to stay, the findings with examples of associated raw data are
presented in table format for ease of organisation and readability. For the three questions, the
discovered factors were categorised according to the questionnaire variables and
demographics.
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Figure 28. Word cloud for the first question
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Table 33. Examples of responses to Question 1
Question Themes Sub-themes Are there additional factors that affect your decision to leave your current job?
Social factors
Perception of nursing Lack of respect Recreational activity Quality of life Racial and nationality discrimination
Organisational factors
Justice, different salaries for different nationalities (pay equity) Favouritism Floating system Lack of recognition Lack of involvement in decision-making Autocratic leadership
Environmental factors Heavy workload Salary The opportunity for promotion Mental and physical exhaustion Understaffing Low patient-staff ratio Schedule and the number of hours of work Issues with shifts, leave and pay Poor staff support
Personal factors Family factors Continuing education Homesickness Family commitments
Policy factors Saudi new regulations Family visa Education privilege for children Hajj privilege Visiting visa
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6.2.1 Social factors
In the first theme (social factors), nurses were asked to respond to factors influencing intention
to leave. The response rate for this question was 71% (n=177), when compared with overall
response (177) to the open-ended questions. Respondents to this question reported negative
responses, such as a lack of appreciation and professional status as reasons behind
dissatisfaction and intention to stay. While a poor perception of nursing was highlighted by the
respondents as an important factor contributing to their decision to leave their job, other factors
such as cultural differences, discrimination, recreational activity, quality of life, and respect in
the workplace were equally emphasised by nurses as important factors influencing their
intention to stay. Tables 34 illustrates and presents a series of selected statements from
respondents to highlight (for further examples, refer to Appendix D response tables).
Table 34. Social factors influencing nursing intention to leave or stay, excerpts related to Question 1
Q1 Participant ID, cultural background and qualification level
Selection of response Sub-theme Dimension
48: a 30-year-old Filipino female with master’s degree
“Poor management of our hospital that never gives value to the effort of their nurses.” “Nurses as modern-day slaves.”
Perception of nursing: Lower status of nurses Lack of respect
Perception of nursing
241: a 27-year-old Filipino female with a bachelor’s degree
“Doctors’ attitude towards nurses, patients’ attitude towards nurses, treating nurses like they are maids, and not attending to nurses’ concerns”.
Perception of nursing: Lower status of nurses Lack of respect
Perception of nursing Social support from immediate supervisor
147: a 29-year-old Indian female with a bachelor’s degree
“There is no value placed on sincerity and skills.”
Perception of nursing: Lower status of nurses Lack of respect
Social support from immediate supervisor
299: a 27-year-old Saudi female with a master’s degree
“If we are respected by doctors and others, we will stay forever. If we are at least called by our names, we would like to stay.” “We love nursing and we choose this career by ourselves but then we find that we get no respect … while we are doing everything.”
Perception of nursing: Lower status of nurses Lack of respect
87: a 32-year-old Filipino female with a bachelor’s degree.
“Overstaying patients refusing to leave the hospital … abusive … patient and family members deciding when to be discharged.”
Cultural differences
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6.2.2 Organisational factors
The second theme is made up of a number of subthemes that relate to comments regarding
hospital policies and procedures. The organisational factors identified that affected nurses’
decision to leave their jobs included justice, unfair salary for different nationalities (pay equity),
favouritism, floating system, lack of recognition, and autocratic leadership.
The main organisational factor that was highlighted was distributive justice, especially in pay
equity. It was found that most of the participants were disappointed with hospital policies and
procedures and reported these as reasons for them to leave their jobs. The nurses respectively
displayed how a salary difference could impact upon their living conditions and their decision
concerning whether to stay or leave the nursing profession. Making the situation more complex
is that each Saudi hospital tends to have different policies for hiring nurses. Therefore, some
nurses feel that they experience discrimination in their salary and benefits when compared with
other nationalities. Table 35 shows the common organisational factors that were highlighted
by nurses.
Q1 Participant ID, cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
89: a 26-year-old Filipino female with a bachelor’s degree
“Long stay patients that are dischargeable but who refused to leave, and the hospital tolerated the decision of the family.” “No strong management to push the patients out from hospital (dischargeable patients).”
Cultural differences
91: a 31-year-old Malaysian female with a diploma of nursing
“There is discrimination between head nurse and staff. Those the head nurse likes will get what they want without any problems.”
Discrimination Social factors
221: a 31-year-old Malaysian female with a diploma of nursing
“Unfair leadership dominant by race”
Discrimination Social factors
227: a 33-year-old Filipino female with a bachelor’s degree
“discrimination from the hospital’s administration”
Discrimination Social factors
42: a 28-year-old Filipino female with a master’s degree
“No extracurricular activities, no gym and no swimming pool”
Recreational activity Quality of life
Social factors
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Table 35. Organisational factors influencing nursing intention to leave or stay, with example excerpts related to Question 1
Question 1 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire dimension and demographics
118: a 34-year year-old Indian female with a master’s degree
“Salary scale and benefits should be same for all nationalities and should only be fixed on the basis of position but not nationality…. Salary enhancement should be standardised. Evaluation ought not to be … [nor] on the basis on ethnicity, race and nationality.”
Justice, lack of pay equity based on different salaries for different nationalities (pay equity)
Distributive justice index
66: a 27-year year-old Filipino female with a bachelor’s degree
“The salary scale based on staff’s nationalities is really biased. Those who have the greatest workload have the lowest salary.”
Justice, lack of pay equity based on different salaries for different nationalities (pay equity)
Distributive justice index The opportunity for promotion
84: a 27-year year-old Jordanian female with a bachelor’s degree
“No fairness between all nationalities.”
Justice, lack of pay equity based on different salaries for different nationalities (pay equity)
Distributive justice index
140: a 40-year year-old Malaysian female with a master’s degree
“If justice and fairness is compromised, I may consider leaving the job.”
Justice, lack of pay equity based on different salaries for different nationalities (pay equity)
Distributive justice index
114: a 28-year-old Filipino female with a bachelor’s degree
“Unfair salary for different nationalities.”
Justice, lack of pay equity based on different salaries for different nationalities (pay equity) Favouritism Floating system Lack of recognition, Autocratic leadership
Distributive justice index
149: a 30-year-old Filipino female with a bachelor’s degree
“Malaysians have a higher salary than Filipinos, but we have same workload!”
Justice, lack of pay equity based on different salaries for different nationalities (pay equity) Favouritism
Distributive justice index
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Floating system Lack of recognition, Autocratic leadership
6: a 26-year-old Filipino female with a bachelor’s degree
“Promotion is based on whom the manager likes the most.”
Favouritism
Distributive justice index
Participant range: 91,149, 186 and 198.
“There is staff favouritism in work.”
Favouritism Organisational commitment
365: a 24-old Saudi male with a bachelor’s degree
“The main factors that influenced his decision to leave his job was not sharing in decision-making.”
Lack of involvement in decision-making
Autonomy
97: a 32-old Filipino female with a bachelor’s degree
“I am a paediatric nurse. I don't like the idea that we are being floated to all of the units, most of the time as a primary nurse. You cannot render quality care to a patient you’re not used to taking care of, such as an adult patient.”
Floating system Compounded by understaffing
Workload
153: a 32-year-old Jordanian female, with a bachelor’s degree
“They float staff to other departments that are not related to their experience, for example paediatric nurses are sent to male medical units; mixing paediatric patient with isolation cases.”
Floating system Compounded by understaffing
Workload
178: a 27-year-old Filipino female with a bachelor’s degree!
“The only reason she is leaving this job is because of floating in unfamiliar departments.”
Floating system Compounded by understaffing
Workload
241: a 29-year-old Filipino female with a bachelor’s degree !
“Due to understaffing in other units, nurses will be floated from their unit, even their unit is in need of staff.”
Floating system Compounded by understaffing
Workload
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6.2.3 Work environmental factors
The factors identified in the environment category that could affect nurses’ decision to leave
their jobs included, workload, salary, the opportunity for promotion, mental and physical
exhaustion, understaffing, staffing ratio, schedule and the number of hours worked, patient
load, and support from the supervisors. Concerns regarding workloads and understaffing were
widespread. Many of the comments made by participants related to high workloads and mental
and physical exhaustion. This may be related to hospitals being understaffed and having a high
patient–nurse ratio or hospital administrations that give nurses a workload that is often difficult
to handle. Some nurses also noted that they also face long working hours, which are
characterised by multiple shifts. Thus, as the administration of the hospital requires them to
work long hours, it is difficult for nurses to have effective personal time. According to them, a
lack of staff, scheduling and the number of hours worked, and the patient load were the main
factors that influence them to leave their job. Table 36 presents a number of selective statements
from the responses to the third theme.
Table 36. Environmental factors influencing nursing intention to leave or stay, with examples from Question 1
Q1 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire dimension and demographics
6: a 26-year-old Filipino female with a bachelor’s degree
“Promotion is based on who the manager likes the most, even though others have worked a lot longer in this hospital.”
The opportunity for promotion
The opportunity for promotion
9: a 26-year-old Filipino female with a bachelor’s degree
“Low salary and no promotion availability.”
Salary The opportunity for promotion
The opportunity for promotion
180: a 31-year-old Indian female with a diploma of nursing
“Promotion … I have been here more than three years and have a total of seven years of experience and I still have not got a promotion while some nurses have been promoted twice. New nurses are coming with less experience but in a high position. Some new nurses don't know anything, even the basics. We are seniors in words only.”
Awards based on staff performance The opportunity for promotion
The opportunity for promotion
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141: a 35-year-old Indian female with a bachelor’s degree
“I have been here for three years, still no consideration for promotion.”
The opportunity for promotion
334: a 32-year-old Jordanian female with a bachelor’s degree
“There is no fairness in promotion and many personal issues affecting on staff by the highest level of directors speciality in nursing and annual leave process of consuming or to take it very difficult which let staff to leave.”
The opportunity for promotion
307: a 27-year-old Filipino female with a bachelor’s degree
“Maybe the staff will leave the hospital unless offered a salary increase. The duty schedule is too much of a burden shift to the staff.”
Workload Salary Schedule and the number of hours of work
Flexible work schedules Workload
185: a 30-year-old Malaysian female with a diploma of nursing
“Almost all people are abusing the emergency department with cold cases and unnecessary problems. The hospital is crowded, [I am] overloaded with work and continuously working, a lot of multitasking. Nurses and doctors also become victim to aggressive people. The above factors make healthcare workers stressed and depressed.”
Workload Mental and physical exhaustion Understaffing Staffing ratio Patient load
Workload
3: a 28-year-old Filipino female with a bachelor’s degree
“The hospital is understaffed, which gives us more workload than the normal ratio of patients.”
Workload Salary Mental and physical exhaustion Understaffing Staffing ratio Patient load
Workload
149: a 30-year-old Filipino female with a bachelor’s degree
“No allowance is given. Annual leave is very short, and you cannot take all your accrued hours compared to other hospitals, even though you deserve it. There is no maternity leave but in other hospitals maternity leave is three months with salary.”
increase the wages, allowed maternity leave, overtime pay,
80: a 28-year-old Filipino female with a bachelor’s degree
“Lack of staff and pressure in the working environment are the factors that I consider for leaving my job.”
Understaffing Staffing ratio Patient load
Workload
437: a 28-year-old Indian male with a bachelor’s degree
“The main factor influencing my decision to leave my current job was workload, physios and mental stress.”
Understaffing Staffing ratio Patient load Mental and physical exhaustion
Workload
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6.2.4 Personal factors
The category called personal factors identified what factors could affect nurses’ decision to
leave their jobs and included family factors, continuing education, homesickness, and family
commitments. Most of the participants expressed their disappointment with the limited
educational opportunities in their jobs. Family plays a significant role in the decision of nurses
to stay or leave their job. Many nurses reported that the family factor was one of the main
factors that influenced them in leaving their job. Table 37 presents a number of selective
statements from the responses to the third theme.
89: a 26-year-old Filipino female with a bachelor’s degree
“Lack of staff, 12 hours duty, a low of days for vacation, irresponsible watchers of the patients and weak patient relation officers, too much workload and not enough staff. We need more staff.”
Understaffing Staffing ratio Patient load Schedule and the number of hours of work
Flexible work schedules Workload
246: a 35-year-old Saudi female with a bachelor’s degree
“The long working hours is the biggest issue for any nurse, not only the mothers especially, during the nightshift.”
Schedule and the number of hours of work
Flexible work schedules
213: a 28-year-old Filipino female with a bachelor’s degree
“The hospital prioritises quantity over quality. Understaffed nurses with a heavy workload will never render quality of care. The voices of the nurses are not being heard.”
Workload Understaffing Staffing ratio Patient load
Workload Autonomy
199: a 36-year-old Malaysian female with a bachelor’s degree
“My supervisor never respects me employees.”
Relationship with supervisor Lack of support
Social support from immediate supervisor
335: a 28-year-old Filipino female with a bachelor’s degree
“I do not feel safe with my immediate supervisor.”
Relationship with supervisor Lack of support
Social support from immediate supervisor
109: a 38-year-old Malaysian female with a bachelor’s degree
“If a supervisor is not fair to the staff, there will be a turnover in staff.”
Relationship with supervisor Lack of support
Social support from immediate supervisor
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Table 37. Personal factors influencing nursing intention to leave or stay, with example excerpts related to Question 1
Q1 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire dimension and demographics
6: a 34-year-old Egyptian female with a bachelor’s degree!
“Lack of support I received from my family.” Family factors Family factors Family commitments
Social support from spouse
113: a 42-year-old Malaysian female with a master’s degree
“Family commitments.” Family commitments
Social support from spouse
164: a 32-year-old Malaysian female with a diploma of nursing
“Homesick and the little excitement in current environment.”
Homesickness Social support from spouse
100:, a 31-year-old Indian female nurse with a diploma of nursing
“Sometimes it’s hard to deal with family circumstances and our job. I cannot take care of my kids, so I’m leaving this job.”
Family commitments
Social support from spouse
40: a 29-year-old Filipino female with a bachelor’s degree
“If I leave my job, the reason will be to stay with my family.”
Family commitments
Social support from spouse
179: a 23-year-old Saudi female with a bachelor’s degree
“If I feel I can’t improve myself or when I decide to start a higher degree, I will leave my current job.”
Limited educational opportunities Continuing education
Level of education
33: a 29-year-old Filipino female with a bachelor’s degree
“Probably personal reasons and continuing education in other country or immigration.”
Limited educational opportunities Continuing education
Level of education
53: a 43-year-old Filipino female with a bachelor’s degree
“I have no intention to leave my current job but I’m hoping that the assignment of a new position will be reflected in your salary and have no racism at all. It should reflect your years of working experience and expertise. I’m also hoping to earn a higher degree – education opportunities.”
Limited educational opportunities Continuing education
Level of education
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6.2.5 Policy factors
The analyses of responses recognized additional policy factors, especially new Saudi
regulations, which may discourage non-Saudi nurses from staying in Saudi Arabia. Non-
Saudi nurses expressed concern and sadness over the decision to apply new fees on the
dependents of foreign workers in Saudi Arabia. Nurses were sceptical about the future of
nursing in Saudi Arabia. They believed that in the near future, the Saudi government will rely
on just Saudi nurses and job opportunities may decline, which will impact non-Saudi nurses.
Furthermore, some nurses also wrote about the difficulties they face due to the high cost of
living in Saudi Arabia. The following example (Table 38) illustrates the categories of
comments nurses made in the context of this theme.
The quotes below represent nurses’ concerns about the future of their immediate family
members, if the employers in Saudi Arabia do not support them in regard to paying the
dependencies fee and the cost of their children’s schooling. Taken together, these results
suggest that the intention to stay for nurses may be influenced by the encouragement and
support they receive from employers and the policymakers in the government of Saudi Arabia.
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Table 38. Policy factors influencing nursing intention to leave or stay, with example excerpts
Q1 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire dimension and demographics
453: a 26-year-old Filipino male with a bachelor’s degree
“The present changes in the kingdom, no more free visit visas for family, the dependent fee for the private sectors.”
Lack of policy standardisation Standard of living Economic costs Educational fees
338: a 29-year-old Filipino female with a bachelor’s degree
“Life in Saudi Arabia costs a lot for dependents and the schooling of our children.”
Lack of policy Standardisation Standard of living Economic costs Educational fees
112: a 37-year-old Filipino female with a bachelor’s degree
“Housing and recreation, no family status visa with my current.”
Lack of policy standardisation Standard of living Economic costs
250: a 27-year-old Filipino female with a bachelor’s degree
“It depends on the superior on how they will nurture their staff to avoid leaving the hospital. As for me; I am depending on my future husband if he will come to work here or I will go to work in his workplace.”
Lack of policy standardisation Standard of living Economic costs
Social support from spouse
246: a 35-year-old Saudi female with a bachelor’s degree
“There is not any consideration for working mothers during the pregnancy period, [or any] maternity leave or leave for sick days for kids. Moreover, there is no day care for the babies to make it easy for the mothers to work.”
Lack of policy standardisation Standard of living Economic costs
358: a 34-year-old Egyptian female with a bachelor’s degree
“Husband, workplace, kids’ needs.” Lack of policy Standard of living Economic costs Educational fees
Social support from spouse
237: a 30-year-old Malaysian female with a diploma of nursing
“If the employer did not allow for pregnancy.” Lack of policy standardisation Standard of living Economic costs
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6.3.$What$strategies$would$prevent$turnover$and$encourage$nurses$to$stay?$Of the 249 nurses who completed the online survey, 183 (71%) responded to this open-ended
question, while only one quarter of nurses (62 of 253) who completed the paper survey
responded. The high response rate for this question in the online questionnaire suggests that
many nurses, while experiencing challenges in the workplace, also had strategies that prevent
turnover and encourage nurses to stay in their jobs. This question was designed to reveal sub-
themes involving strategies according to the influencing factors of social, organisational,
environmental, policy, and personal. Figure 29 shows the world cloud for the second question,
which enables us to identify the most central word on the basis of the frequency of appearance.
Table 39 illustrates and presents a series of selected statements from respondents to highlight
(for further examples, refer to Appendix C response tables).
Figure 29. Word cloud for the second open-ended question
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Table 39. Examples of responses to Question 2
Question 2 Themes Sub-themes What strategies would prevent turnover and encourage nurses to stay?
Social factors
Patient educations and awareness about respecting nursing personnel Provide recreational facility Establishing of gym, and swimming pool Improve housing condition Having place for the nurses to eat their meals
Organisational factors
Fair benefits Incentives and appreciation Eliminate racism/favouritism Allowed maternity leave Leave out pay work as a team Improve hospital management
Environmental factors Low workload Increase the wages Giving an annual bonus Improving staff safety Increase manpower Patient and staff ratio with the usage of Patient's acuity level, Yearly salary increment Enhance nursing leadership Support staff and provide counselling
Personal factors Sponsoring for her higher education, family allowances, transportation allowances, housing allowance, offer of day-care
Policy factors Saudi new regulations Family visa Education privilege for children Hajj privilege Visiting visa
6.3.1 Social factors
In the first theme, social factors, nurses identified strategies consistent with question 1
responses, that is, responding to issues of perception of nursing, recreational activities, cultural
differences, discrimination, recreational activity, quality of life, and respect at the workplace
to prevent turnover and encourage nurses to stay in their current job. While improved housing
conditions and providing recreational facilities were highlighted by the nurses as a significant
factor contributing to preventing turnover and encouraging nurse to stay, other factors, such as
providing a gym and swimming pool, and providing a suitable place for nurses to enjoy their
coffee, lunch or dinner breaks were equally emphasised by nurses as important factors.
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Table 40. Social factors influencing nursing intention to leave or stay, example excerpts Question 2
Q2 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
33: a 29-year-old Filipino female with a bachelor’s degree!
“Physical and recreational activities that will lessen the stress of work issues and concerns, such as team-building, promotions, etc., so that the staff will stay and will be motivated to stay.”
Leisure and recreational activities
!
31: a 31-year-old Filipino female with a master’s degree
“The top management should improve housing conditions, provide recreational facilities, improve supplies and equipment, and provide nursery service to staff with kids.”
Leisure and recreational activities Housing conditions Childcare
!
53: a 43-year-old Filipino female with a bachelor’s degree
“Recreational activities for staff to avoid burnout and free CME courses which include how to cope with stress and manage conflict.”
Leisure and recreational activities Stress management and conflict management training
!
149:, a 30-year-old Filipino female with a bachelor’s degree
“Provide us good accommodation with recreational activities.”
Leisure and recreational activities Housing conditions
180: a 31-year-old Indian female nurse with a diploma of nursing
“Patient educations and awareness about respecting nursing personnel. Having place for the nurses to have their lunch. The availability of other activities to do at the free times like (Gym, pool)."
Leisure and recreational activities
Perception of nursing
299: a female Saudi nurse, with a master’s degree
“Educating the public and others about the real work of nursing and educating them to love nursing and so that nurses feel proud to be in nursing, like in developing countries; having doctors and others show respect, and the colours of clothes colours to be organised and open (for example nurses being allowed to choose between two or three permitted colours) so nurses do not feel like students in high school.”
Leisure and recreational activities Stress management and conflict management training Housing conditions Childcare
Perception of nursing
232: a 45-year-old Malaysian female with a diploma of nursing
“Good transportation and housing.”
Leisure and recreational activities Housing conditions
!
54: a 33-year-old Malaysian female with a diploma of nursing
“Give house and transport allowance for contracted single, female staff who stay outside the compound.”
Leisure and recreational activities Housing conditions Transportation
!
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6.3.2 Organisational factors In the second theme, organisational factors, nurses identified strategies consistent with question
1 responses, that is, responding to issues of unequal pay differentials, discrimination according
to nationality, favouritism, and lack of recognition to prevent turnover and encourage nurses to
stay in their current job. The main organisational factor that was highlighted was distributive
justice, especially in pay equity. While high salaries and promotion in Saudi Arabia were noted
as important factors that encourage nurses to stay in their job, the differences in the earnings
for nurses of different nationalities was the main factor that influenced nurses to leave their
job. Both Saudi and non-Saudi nurses emphasised the difference in nurses’ salaries. The nurses
respectively displayed how a salary difference could influence upon their decision to stay or
leave a job. While fairness in salaries in Saudi Arabia was displayed by the majority of nurses
as a significant factor contributing to preventing turnover and attracting nurses to stay, other
factors such as favouritism were equally emphasised by nurses a significant factor influencing
their intention to leave. Most of the participants suggested that when nurses are happy with
hospital policies and procedures in regard to fair treatment, they will stay in their job. The
themes identified in these responses are summarised in Table 41.
The below examples suggest that there was a mixed response from different nationalities.
Among Asian nurses, there were two contrasting opinions. Some Filipino nurses revealed that
unfair salaries can harshly affect their intention to leave Saudi Arabia because they felt
Malaysian nurses were paid a higher salary in comparison even though they have same
workload; while Malaysian nurses mentioned that unfair benefits can severely affect their
intention to leave Saudi Arabia because they felt that Saudi nurses receive more benefits than
them. These findings suggest that nurses’ intention to stay is affected, because the nurse salaries
in Saudi Arabia are not standardised.
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Table 41. Organisational factors influencing nursing intention to leave or stay, with example excerpts related to Question 2
Q2 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
149: was a 30-year old Filipino female with a bachelor’s degree
“Giving the same benefits and salary to everyone with the same workload regardless of nationalities and giving housing and transportation allowances.”
Justice, lack of pay equity based on different salaries for different nationalities
Distributive justice index
48: was a 30-year-old Filipino female with a master’s degree
“I believe that the most essential factor is to eliminate racism/favouritism totally. After all, we know that people should be promoted or given a position they deserve not just because they look pretty in make-up or are citizens of Saudi Arabia. There should be venues for professional growth that would cater to postgraduate degrees for nurses. Really, our brains just get "rusted" here. A sense of fulfilment and a little recognition is sufficient for me at least to stay.”
Fair treatment policies and practices Anti-discrimination policies Favouritism
Distributive justice index Autonomy
388: a 26-year old Filipino female with a bachelor’s degree
“Involve employees in decisions that affected their jobs and the overall direction of the hospital whenever possible.”
Lack of involvement in decision making
Autonomy
181:, a 27-year-old Malaysian female with a bachelor’s degree
“There should be no difference in the benefits given to Saudi and non-Saudi nurses. For example, if non-Saudi female nurses are pregnant they not provided with confinement leave. The must take annual leave and there is no unpaid leave allowed for us. My experienced is that I only got 20 days for my postnatal leave!”
Justice Adequate allowances and benefits
Distributive justice index
118: a 34-year-old Indian female with a master’s degree
“More protection needs to be given to female staff from other countries when they are at work, and impartiality regarding race, colour, gender and nationality for salary and promotions.”
Protections and safeguards
49: a 37-year-old Malaysian female, with a bachelor’s degree
“There should be no discrimination towards Saudi or non-Saudi staff and fair judgement if anything happens.”
Justice Favouritism
Distributive justice index
202: a 25-year old Filipino female with a bachelor’s degree
“Fair treatment for the staffs, both local and foreigners, seniors or juniors of both genders, or whichever position they have.”
Justice Lack of recognition
Distributive justice index
207: a 25-year old Filipino female with a bachelor’s degree
“Address all nurses concerns and some changes in management, this is the rampant reason I heard why the staff is filing for exit because of the management and sort of favouritism.”
Favouritism
Organisational commitment
111: a 31-year-old Jordanian male with a bachelor’s degree
“Salary should be rearranged because there is big difference according to nationalities.”
Lack of pay equity Distributive justice index
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6.3.3 Work environment factors
In the third theme, work environment factors, nurses identified many strategies that prevent
turnover and encourage nurses to stay in their current job. The majority of respondents
indicated that the main environmental factors include increment of nurses, promotions and
incremental increases in salary increases, additional staff in every unit and a reduced workload.
Most of nurses who responded to this question perceived that hospitals in Saudi Arabia should
offer better employment opportunities and more lucrative salaries in order to attract nurses to
stay in their current job. While providing enough staff, increasing salaries and improving the
opportunities for promotion were highlighted by the nurses as significant factors contributing
to preventing turnover and encouraging nurse to stay, other factors, such as overtime pay for
nurse, maternity leave, and increasing the days of annual leave were equally emphasised by
nurses as important factors. The themes identified in these responses are summarised in Table
42.
The examples propose that the intention to stay for nurses is affected by the support they receive
from management and supervisor. This support should be achieved by improving the working
relationship between nurses, improving teamwork, and providing a happy work environment.
To summarise the result for the theme of environmental factors, intention to stay can be
improved by increasing the salary and opportunities for promotion, as well as providing
overtime pay for nurses, giving maternity leave, and increasing the number of days of annual
leave.
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Table 42. Work Environment factors influencing nursing intention to leave or stay, with example excerpts Question 2
Q2 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
67: a 26-year-old Filipino female with a bachelor’s degree!
“Hospitals should appreciate our sacrifices in leaving our family. Give us the right salary.”
Effective/attractive wages and conditions
The opportunity for promotion Autonomy
53: a 43-year-old Filipino female with a bachelor’s degree
“Competitive salary basing from skills and years of experience. Yearly salary increment. Improving staff safety i.e. to avoid risk of hepatitis.”
Salary The opportunity for promotion
The opportunity for promotion
88: a 31-year-old Indian female nurse with a bachelor’s degree
“Encourage the nursing staff by promoting and providing increments as yearly basis, my hospital should provide maternity leave for female nurses at least eight weeks, which is not practiced at the present. Many nurses left their jobs here when they get pregnant because there was no maternity leave.”
Reward and recognition for qualifications and work Effective/attractive wages and conditions Incremental salary increases
The opportunity for promotion Leave for childbirth Paid leave for childbirth
46, a 40-year-old Malaysian female nurse with a bachelor’s degree-
“All disciplines must work and know their roles and work as a team.”
Lack of teamwork and coordination
Social support from immediate supervisor and co-workers
33, 45, and 80.
“Teamwork should be maintained.” Lack of teamwork and coordination
Social support from immediate supervisor and co-workers
109, a 38-year-old Malaysian female with a bachelor’s degree
“Appreciate the nurses, such as giving a bonus per year or giving a certificate to the best nurse every month so that will identify the best to the unit and the hospital.”
Reward and recognition The opportunity for promotion
388: 26-year-old Filipino female with a bachelor’s degree
“Recognise excellent performance, especially link pay to performance to reduce turnover of employees.”
Reward and recognition Job satisfaction
140: was a 40-year-old Malaysian female with a master’s degree
“Enhance nursing leadership and management skill, to improve job scope related to nursing proficiency, not only multitasking nursing when giving care to patients. The provision of liaison personnel in each unit to promote customer patient relationship satisfaction would be helpful.”
Workload Lack of teamwork and coordination Staffing ratio
Workload
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Q2 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
161: a 28-year-old Filipino female with a bachelor’s degree
“The heads must listen to their staff problems and an immediate solution must be implemented in accordance with staff satisfaction.”
Relationship with supervisor Lack of support
Social support from immediate supervisor Job satisfaction
52: a 26-year-old Filipino female with a bachelor’s degree
“For supervisors to treat their staff fairly, be considerate for the wellness of the staff, give enough or more staff for the census of the patient.”
Equity in treatment of nurses by managers
Workload Social support from immediate supervisor
171: a 48-year-old Malaysian female with a bachelor’s degree
“Management and doctors should listen to staff’s problems and negotiate the best possible way to solve these problems, not just ask staff to follow their rules, giving staff no say at all. The only option for staff is to follow orders.”
Relationship with supervisor Lack of support Recognition
Social support from immediate supervisor Autonomy
185: a 30-year-old Malaysian female with a bachelor’s degree
“Support staff and provide counselling to the staff who have problems with their job or stress; don't just blame them, help them to solve the problem.”
Relationship with supervisor Lack of support Recognition
Social support from immediate supervisor and co-workers
248: a 27-year-old Filipino female with a bachelor’s degree!
“The top management and nursing supervisors should increase manpower and unified accountability and responsibility to all staff, not merely blaming the nurses for any incident.”
Lack of support
Social support from immediate supervisor and co-workers
45: a 54-year-old Malaysian female with a master’s degree
“The boss: Be a good listener; talk professionally, no bias, do not shout and show good example as a model.”
Poor staff support Low morale
Social support from immediate supervisor and co-workers
239: a 44-year-old Filipino Female with a master’s degree!
“Rewarding staff acknowledges the staff’s contribution and empowers them.”
Reward and recognition The opportunity for promotion
299: a female Saudi nurse with a master’s degree
“Nursing assistance is needed and nursing aid to help us in notice work, so we can focus and take care of every single one of our patients.”
Lack of support
Social support from immediate supervisor and co-workers
161, 122 and 171 “Patients acuity must be distributed fairly.” Workload Staffing ratio
Workload
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6.3.4 Personal factors
In the fourth theme, personal factors, the participants identified many strategies that prevent
turnover and encourage nurses to stay in their current job. Most participants consistently wrote
that chance for further education was an important factor that may help them to stay in their
job. The below examples in Table 43 indicate that the intention to stay for nurses is affected
by the support they obtain to complete their study in Saudi Arabia. This support should be
achieved by providing scholarships for nurses to complete their education inside Saudi Arabia.
To summarise the result for the theme of personal factors, intention to stay can be improved by
increasing the opportunities for education, as well as increasing the number of days of annual
leave.
Table 43. Personal factors influencing nursing intention to leave or stay, with example excerpts related to Question 2
Q2 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
118: a 34-year-old Indian Female with a master’s degree!
“Disciplinary committees and separate sections for guidance and counselling Support for on job training and sponsoring for her higher education.”
Limited educational opportunities Continuing education
53: a 43-year-old Filipino female with a bachelor’s degree
“One of factors that would prevent turnover and encourage nurse to stay in their jobs is education opportunities.”
Limited educational opportunities Continuing education
333: a 28-year-old Pakistani female with a diploma of nursing
“Increase leave for maternity and education, professional clinics skills, seminar conference to be attend by the nurses.”
Opportunities for further education Opportunities for professional development Career advancement
Leave for childbirth Paid leave for childbirth
54, 66, 79, 89, and 90.
“Provide more days of annual leave will encourage nurses to stay here.”
Stability in personal and recreational leave
Unpaid leave for family matters
88:, a 31-year-old Indian female with a bachelor’s degree
“Encourage the nursing staff by promoting and providing increments as yearly basis, my hospital should provide maternity leave for female nurses at least eight weeks, which is not practicing at present. Many nurses left their jobs here when they fell pregnant because there is no maternity leave.”
Stability in personal and recreational leave
Leave for childbirth Paid leave for childbirth
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6.3.4 Policy factors
In the last theme, policy factors, the participants identified many strategies that prevent
turnover and encourage nurses to stay in their current job. The main policy factor that was
highlighted by the majority of nurses was the new expatriate dependent levy, to be paid by all
foreign nationals in Saudi Arabia who are sponsoring dependents. The majority of nurses who
responded to this open-ended question expressed a strong disappointment about the fees
charged for dependents (noted in responses to Question 1). When asked what strategies would
prevent turnover and encourage nurses to stay in the current jobs, participants suggested that
visas for the dependents of health workers should be supplied for free, as shown in Table 44.
Since the salaries of nurses in Saudi Arabia are low, it is difficult to pay the application fees
for foreign nurses, whether family member or domestic labour. As well as the low salaries and
the fee applications for foreign workers, the nurses highlighted that nurses’ families have no
option to stay here and that some nurses will have to send their families back home. The
responses suggest that the intention to stay for non-Saudi nurses may be influenced by
providing free family visas with education privileges for children in Saudi Arabia. Taken
together, these results suggest that the government or health providers should consider paying
the fees on behalf of health workers, especially nurses, since Saudi Arabia relies heavily on
foreign nurses.
Table 44. Policy factors influencing nursing intention to leave or stay, with example excerpts related to Question 2
Q2 Participant no., cultural background and qualification level
Example responses Sub-theme Questionnaire Dimension and demographics
453: a 26-year-old Filipino male with a bachelor’s degree!
“Promote programs for the welfare of expats.”
Lack of policy standardisation Standard of living Economic costs Educational fees
78: a 27-year-old Filipino female with a bachelor’s degree
“Please provide visa for family, Hajj privileges for staff, and return our yearly Muharam increment.”
Lack of policy standardisation Standard of living Economic costs
Social support from spouse
40, 101, and 112.
“Giving a visiting visa or providing free visas for nurses’ family members will encourage nurses to stay here.”
Lack of policy standardisation Standard of living Economic costs
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6.4.$Other$additional$comments$from$respondents$The aim of this question was to provide insight into additional factors contributing to nurses’
turnover from the perspective of nurses in hospitals in Saudi Arabia. Out of the 249 nurses
who completed the online survey, 125 (50%) responded to this open-ended question, while
out of the 253 nurses who completed the paper survey, only 30 (12%) responded to this open-
ended question. Five main themes emerged from this question in the current research, these
were organisational, work environment, social, personal, and social factors, as shown in
Table 45.
112: a 37-year-old Filipino female with a bachelor’s degree
“Family visa status.” Lack of policy standardisation Standard of living Economic costs
246: a 35-year-old Saudi female with a bachelor’s degree
“Choose the best shift for nurses and offer a day-care for their children.”
Lack of policy standardisation Standard of living Economic costs Educational fees
60: a 40-year-old Filipino female with a bachelor’s degree
“Visas for the dependents of health workers should be supplied.”
Lack of policy standardisation Standard of living Economic costs Educational fees
Social support from spouse
53: a 43-year-old Filipino female with a bachelor’s degree
“The%availability%of%a%family%visa%with%education%privilege%for%children%and%Hajj%privilege.”%%
Lack%of%policy%%Standard%of%living%Economic%costs%Educational%fees%
%
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Table 45. Example of responses to the open-ended Question 3
Q2 Participant no., cultural background and qualification level
Example responses Theme Sub-theme Dimension
272: a 42-year-old Malaysian female with a bachelor’s degree
“It’s important to have multinational nursing staff in order to have skills mix and competencies in every area to improve safe nursing and safe patents.”
Organisational factors
Poor skills-mix Patient safety concern Poor quality care
53: a 43-year-old Filipino female with a bachelor’s degree!
“Fair treatment with all the staff, no discrimination and racism - this will reduce the nurses' turnover rate.”
Organisational factors
Fair treatment policies and practices Anti-discrimination policies
29: a 27-year-old Indian female with a Diploma’s degree
“Try to avoid discrimination between nationalities and try to pay salary as per experience.”
Organisational factors
Fair treatment policies and practices Anti-discrimination policies
33: a 31-year-old Filipino female with a master’s degree
“Top management to improve in providing opportunities fairly to all staff regardless of Nationalities. To provide increment based on work performance instead of nationality. Top management to be firm in managing and implementing hospital policies not only to nurses”.
Organisational factors
Fair treatment policies and practices
149, 132, 86, 60, 58, 52, and 118.
“Fair salary not based on nationality because we all have same workload, be fair and equal.”
Organisational factors
Fair treatment policies and practices
43: a 28-year-old Filipino female with an associate degree
“Being fair enough is good enough.” Organisational factors
Fair treatment policies and practices
66: a 27 year-old Filipino female with a bachelor’s degree
“Some of the staff in a higher position does not deserve and fit for the position, worst is they are adding more burden to their subordinates.”
Organisational factors
171: a 48-year-old Malaysian female with a bachelor’s degree.
“A workplace is a nice place to work when nurses, doctor, and management work together as a team. Nurses voices must be heard and take into consideration and not just issued ordered to us.”
Organisational factors
Team work Lack of involvement in decision making Poor staff support
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140: a 40-year-old Malaysian female with a master’s degree
“Effective interpersonal communication and working as solid team among all sections of staffs will promote efficient outcomes, increase patients - relative’s satisfaction and improve patient care.”
Organisational factors
Team work communication
241: a 27-year-old Filipino female with a bachelor’s degree
“I hate floating to other department.” Organisational factors
Floating system
Workload
211: a 28-year-old Malaysian female with a diploma of nursing
“We work more than we supposed to do, yet the management still don't appreciate us. We worked extra time, yet there are no confirmations to get extra pay.”
Organisational factors
Feeling undervalued Lack of recognition, Autocratic leadership
45: a 54-year-old Malaysian female with a master’s degree
“Salary is not a main issue but "Happy working environment is the best amount all in Life."
Work environment factors
job satisfaction
161: a 28-year-old Filipino Female with a bachelor’s degree.
“To prevent staff turnover always consider the staff job satisfaction.”
Work environment factors
job satisfaction
176: a 48-year-old Malaysian female with a postgraduate certificate
“Professional, knowledgeable, skills and attitude are importance in our professional nursing.”
Work environment factors
Low morale Feeling undervalued Poor staff attitudes
170: a 59-year-old Malaysian female with a diploma of nursing
“Don’t make staff too much stress because they have enough stress by facing the patient.”
Work environment factors
Stress Workload job satisfaction
Workload Job satisfaction
90: a 33-year-old Malaysian female with a diploma of nursing
“Having extra staff in each department would prevent extra working workload and create happy safety working environment.”
Work environment factors
Heavy workload High nurse-to-patient ratios
115: a 28-year-old Filipino female with a bachelor’s degree
“I think the big factor that affects the nurses to stay in the hospital, is the staffing. Because if there is enough staff in a unit, there would be less burden and workload.”
Work environment factors
Heavy workload High nurse-to-patient ratios Burn out
122: a 29-year-old Filipino female with a master’s degree
“We hope that our concerns will be heard in order to formulate solutions to solve the uprising problem of our unit. Nurses are mentally physically and emotionally exhausted, without any aid from the higher heads, burnouts and staff turnover will be the result.”
Work environment factors
Heavy workload High nurse-to-patient ratios Burn out Poor staff support
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124: a 27-year-old Filipino female with a bachelor’s degree!
“If there will be enough staff and less workload, organisation of work, help like phlebotomy, more patients care assistant for bedridden patients, discipline for watchers or family.”
Work environment factors
Heavy workload High nurse-to-patient ratios Burn out Poor staff support
185: a 30-year-old Malaysian female with a diploma of nursing
“Appropriate patient-to-nurse ratios, to be able to carry out the best and safe care to the patient. This will prevent negligence.”
Work environment factors
Heavy workload High nurse-to-patient ratios Patient safety concerns Poor quality care
146: a 26-year-old Filipino female with a bachelor’s degree
“Improve Housing, recreations and support for nurse's especially when problems related to patients comes; personally, I feel that nurses lack support from superiors when problems as such arises; particularly with expats.”
Social factors Leisure and recreational activities Housing conditions
97: a 32-year-old Filipino female with a bachelor’s degree
“Please treat the nurses with respect and professionalism.”
Social factors Perception of nursing: Lower status of nurses Lack of respect
Perception of nursing
48: a 30 year-old Filipino female with a master’s degree
“Thank you for conducting this study and being interested in why we leave this country, even if your own government is considering eliminating us all by 2020.”
Policy factors Lack of policy standardisation New Saudi policies
226: was a 40-year-old Malaysian female with a diploma of nursing
“Nurses leave sometimes not because of work related but because of the welfare/benefits from the hospital itself.”
Policy factors Lack of policy standardisation
338: a 29-year-old Filipino female with a bachelor’s degree
“The benefits (family) of nurses are very limited especially for the family. Unlike doctors, they are allowed to have family visa. Hospitals can offer family visa to nurses with consideration, such as the length of stay.”
Policy factors Lack of policy standardisation Standard of living Economic costs Educational fees
180: a 31-year-old Indian female with a diploma of nursing
“Vacation plan is very poor; give enough days’ vacation. There are provisions in the system but one cannot get leave because of staff shortage.”
Personal factors Stability in personal and recreational leave
322: a 29-year-old Filipino female with a bachelor’s degree
“Provide paid leave for childbirth without conditions or limitations.”
Personal factors Stability in personal and recreational leave
Leave for childbirth Paid leave for childbirth
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6.5$Summary$$This chapter is presented in the survey and open-ended questions results. It has also described
other significant factors perceived as significant to nurses’ intention to leave their job in Saudi
Arabia. This chapter examines the finding of both SEM and the multiple linear regressions of
the survey data for the important factors driving nurses to leave their job in Saudi Arabia. The
SEM findings indicated that several independent variables were found to be significant
predictors of anticipated turnover, including discrimination, workload, job satisfaction,
opportunity for promotion, social support immediate supervisor, organisational commitment,
and autonomy. Personal factors such as ethnicity, gender, and religion were not found to
influence nurses to leave their job, and these factors did not show an important effect on the
nurses’ intention to leave. In contrast, personal factor, such as the opportunity for further
education, which established important effects on nurses’ intention to leave did show an impact
on nurses’ intention to leave. While social factors such discrimination were found to be
significant in influencing nurses to leave, fair treatment policies and practices were found to be
important in influencing their intention to leave.
This chapter, also, enabled a deeper examination of other factors identified in the open-ended
questions. Because organisational factors (floating system, favouritism), work environment
factors (nurse-to-patient ratios), personal factors (lack of education opportunities, family
factors), and policy factors (lack of policy standardisation, visa regulations) encouraging nurses
to leave their job were greater in number (as noted in this chapter), it is likely that more non-
Saudi nurses may leave Saudi Arabia to gulf countries or developed countries. Several
independent variables were found to be related to anticipated turnover is given in Figure 30.
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Figure 30: Themes that emerged from the analysis
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7.#CHAPTER#SEVEN:#DISCUSSION#
7.1#Summary#of#the#Study#The current research sought to investigate nursing intention to leave and provide strategies to
prevent intention to leave in Saudi Arabia. The overarching goal of this study was to identify
more precisely the factors contributing to nursing turnover. The study applied a survey of five
sections (Anticipated Turnover Intention, organisational, work environment, social, and
personal factors, and open-ended questions) to obtain data from 502 of nurses. The SPSS V23.0
for Windows and NVivo 11 were used for analysis purpose. Furthermore, procedures and tests
conducted in this research to analyse the quantitative data were descriptive statistics, multiple
regression, SEM and CFA. In this study, sets of three questions (open-ended) were asked of
nurses to help to enhance understanding of the nurses’ perspectives of the factors contributing
to nursing turnover. Qualitative data obtained from responses to the open-ended questions were
analysed using the NVivo 11. The research questions explored whether organisational, work
environment, social, and personal factors influenced nurses’ turnover in the Saudi Arabia.
In the next section, the organisational, work environment, social, and personal factors
potentially responsible for influencing nursing to leave their job are discussed by contrasting
and comparing the findings of this current study with previous global research and in Saudi
Arabia. Furthermore, the researcher has recognised the scope for future study and
recommended how studies of this nature could prevent nursing turnover, as well as encourage
nurses to stay in their job. #
7.2#Organisational#Factor##In this study, nurses were invited to complete the survey online or paper version questionnaire.
This survey sought to reveal data that would give an understanding of factors influencing the
intention of nurses to leave their job in Saudi Arabia. The main survey findings indicate that
factors contributing to nursing turnover were related to organisational factors, including social
support, immediate supervisor, organisational commitment, and autonomy, because these
factors contributed to job satisfaction or potential for nursing turnover.
The results of the current study indicate that social support from immediate supervisors can
reduce anticipated turnover among nurses in Saudi Arabia. Another important finding was that
nurses reported that they did not receive encouragement and support from their immediate
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supervisor, especially in making decisions. The findings in this study suggested that nurses
receive a sense of accomplishment in their role when their work is recognised as worthy by an
immediate supervisor. However, the condescending approach taken by many immediate
supervisors can lead nurses to feel unimportant and undervalued. Hence, they feel inferior when
taking orders from immediate supervisors, rather than when being given the opportunity to
offer an opinion. Supporting this, Participant 171, a 48-year-old female Malaysian nurse, with
a bachelor’s degree in nursing, argued that management and doctors should listen to staff’s
problems and negotiate the best possible way to solve these problems, rather than just asking
staff to follow rules and giving staff no say at all. Similar to these responses, Participant 185,
a 30-year-old female Malaysian nurse, said “Support staff and provide counselling to the staff
who have problems with their job or stress; don't just blame them, help them to solve the
problem”. This suggests that nurses who have supportive immediate supervisors are more
likely to stay in their job. Supporting this, an Australian study conducted by Duffield et al.
(2011) found that having an immediate supervisor who is a good manager and leader decreased
nurses’ intent to leave by 20%. As indicated previously, these findings show that immediate
supervisors’ support is important in explaining intention to leave among nurses in Saudi
Arabia.
A recent study has also indicated that nurses encountering poor treatment by immediate
supervisor reported that this experience affected their self-esteem and self-worth at work. This
sense of inferiority in the work environment appears to be related to receiving the proper
autonomy for work done; such autonomy would also have the benefit of making them want to
stay in their job. This was supported by the findings from the open-ended questions. The open-
ended questions gave insight into the processes that contributed to their decision to leave the
nursing profession. The respondents indicated that it was the absence of teamwork or social
supports and poor communication between nurses and supervisors that affected commitment
to the workplace. These findings are consistent with previous studies from Saudi Arabia (Al-
Ahmadi, 2013), as well as other countries (Ahn, Lee, Kim, & Jeong, 2015). While there are
some similarities to other studies, the point of difference for this study in the findings is related
to diversity. This means that immediate supervisors are unable to manage diversity in the
hospitals in Saudi Arabia. A possible explanation for this might be that immediate supervisors
have a hard time because the nursing workforce is so culturally diverse (Milliken & Martins,
1996). In addition to immediate supervisors, further influence is felt due to the fact that the
majority of nurses who are working in Saudi Arabia are foreign nurses. It is important to note
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that supervisors or nurses do not necessarily share common values because they possess
different customs and values (Dreachslin, Hunt, & Sprainer, 2000).
An analysis of the organisational factors in Saudi Arabia indicates that the factors influencing
nurses’ decision to leave their current job were favouritism, the promotion of other staff with
good credentials, and autocratic leadership. This finding is consistent with that of Alotaibi,
Paliadelis and Valenzuela (2016) who found that Saudi-nurses would be more satisfied and
intended to stay if there was a reduction in perceived favouritism in the workplace. The
organisational factors involving perceived favouritism and leadership style of hospital
administrators in Saudi Arabia were found to be a source of tension for respondents, who
suggested that the relationship between a healthcare practitioner and their nurses within their
different specialties should always be based on respect and natural justice. These suggestions
corroborate the findings of much of the previous work in workplace justice (Chin et al., 2017).
The current study suggests that decreased autonomy at work and an unfair performance
appraisal system the important predictors of turnover and dissatisfaction among nurses and
factors influencing them to leave their job in Saudi Arabia.
Another organisational factor that was highlighted, primarily by nurses in the open-ended
section, was the floating of staff nurses to other departments within the hospital. Nurses do not
like the idea that they are expected to work in any of the units (float), most of the time as a
primary nurse. The respondents suggested that a nurse cannot render quality care to a patient
when they are not experienced in taking care of their particular health problem. It appeared that
the floating of staff nurses to other departments was compounded by the lack of staff. For
example, Participant 3, a 28-year-old Filipino female, responded that workload was one of
factors that influenced her decision to leave her current job. She stated that: “The hospital is
understaffed, which gives us more workload than the normal ratio of patients.” Participant 80,
a 28-year-old female Filipino nurse noted that “A lack of staff and pressure in the working
environment are the factors that I consider for leaving my job.” A possible reason that supports
their responses could be that nurses recruited for this study are from governmental hospitals in
Makkah and Jeddah city where the population was very high. This complaint signifies the floating
of staff nurses to other departments as an important predictor of turnover and dissatisfaction
amongst nurses and a factor influencing them to leave their current job.
These factors demonstrate hospital administrators encounter challenges in attempts to
standardise justice systems and frame rules that enable improved working relationship between
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nurses and supervisors. As such, a constraint within the organisational content appears to
influence work environments. These findings suggest that hospital policies that lead to happy
work environments and its poor regulations have played a significant role in influencing both
Saudi and non-Saudi nurses to leave their current job. Therefore, this study revealed that any
strategy aimed at preventing nurses from leaving their jobs must concentrate on addressing the
issues prevailing within the health system in Saudi Arabia. For example, the nurses respectively
displayed how a salary difference could impact upon their living conditions and their decision
concerning whether to staying or leave their current job. It was found that most of the
participants were disappointed with hospital policies and procedures and reported these as
reasons for them to leave their jobs. For example, Participant 118, a 34-year-old Indian female
with a master’s degree in nursing wrote about the unfair salary for different nationalities in
Saudi Arabia, stating that:
Salary scale and benefits should be same for all nationalities and should only be fixed
on the basis of position, but not nationality, and salary enhancement should be
standardised. Evaluation ought to not be biased and ought to not be done on the basis
of ethnicity, race, and nationality.
A possible reason that supports their responses could be that each Saudi hospital tends to have
different policies for hiring nurses and giving a bonus. This study suggests that when the
rewards nurses receive are distributed in proportion to their contribution to the hospital, nurses
will define the situation as fair and that fairness may increase nurses’ intention to stay in their
job (Chin et al., 2017; Chen, Chu, Wang, & Lin, 2008; Kim, 1996). However, results from the
current study also indicate that the provision of liaison personnel in each unit to promote nurse-
patient relationship satisfaction would be helpful. In summary, the findings of the current study
indicate that organisational factors, including social support of immediate supervisor,
organisational commitment, and autonomy are predictors of retention for Saudi Arabia
healthcare nurses and are somewhat similar to those of nurses working in other countries.
7.3#Work#Environment#Factor#The analysis of the main survey indicates that anticipated turnover of nurses is influenced by
workload, opportunity for promotion, and job satisfaction. Workload variables that entered the
SEM model suggest that workload is an important predictor of turnover and dissatisfaction
among nurses and a factor influencing them to leave their current job. These results reflect
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those of Almalki et al. (2012) who also addressed the links between primary healthcare (PHC)
nurse retention and work-life quality in the south of Saudi Arabia. Their data found support for
the impact of work design variables such as workload and time constraints on task completion.
Another study from Saudi Arabia also noted similar results for nurse turnover. Alsaqri (2014)
indicated that intention to leave is significantly associated with emotional exhaustion and
personal accomplishment. In contrast, this outcome is contrary to that of Al-Ahmadi (2013)
who found that job characteristics (workload, complexity, variety, exposure to risk, and
autonomy) were not found to significantly predict anticipated turnover behaviour.
In the current study, the open-ended questions (qualitative) insights went some way to affirm
the connection with the relevance of work environment factors and anticipated turnover. These
results reveal a number of new factors that were not included in the main survey. These include
patient load, staffing ratio, scheduler, and the numbers of hours worked. Concerns regarding
workloads and understaffing were widespread. Many of the comments made by participants
related to high workloads and mental and physical exhaustion. A possible reason that supports
their responses could be the limited number of nurses to take care of many patients in the units.
In Saudi Arabia, hospitals managed by the government are required to admit every patient,
even though there may not be a bed available. This means that the number of patents often
exceeds the number of beds in the unit. Participant 90, a 33-year-old female Malaysian nurse
also provided a few suggestions that could prevent turnover and encourage nurses to stay. She
stated that, “Having extra staff in each department would prevent extra working workload and
create happy safety working environment.”
Supporting this, a study by Bogaert et al. (2013), also undertaken in Belgium, explored the
relationship between nurse practice environment, workload, job outcomes, and nurse-assessed
quality of care. The authors found that heavy work demands increase emotional exhaustion.
This may be related to hospitals being understaffed and having a high patient-nurse ratio or
hospital administrations who give nurses a workload that is often difficult to handle. These
findings confirm previous studies from other countries. For example, one Australian study
conducted by Dawson et al. (2014) found that limited career opportunities, high nurse-to-
patient ratios, and negative staff attitudes are seen as factors strongly related to nursing
turnover. In this study, the association between large and complex workloads can potentially
impact on quality of care and generate increased turnover (job satisfaction). Improving the
work environment is a factor in enhancing staff retention. Although improving the work
environment requires changes at the hospitals level, government policy can influence and
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regulate these changes. This study revealed a way forward through the identification of the
need for mandatory staffing ratios, as enacted in developed countries. This study also suggests
management and immediate supervisors should provide counselling to the nurses who have a
problem with their job or heavy workload. Nurses suggested that support from supervisors and
giving assistance to nurses are important ways to prevent nurse turnover. Participant 299, a
Saudi nurse, said “Nursing assistance is needed and nursing aid to help us in notice work, so
we can focus and take care of every single one of our patients.” The findings of previous studies
have suggested that a competent immediate supervisor can play a significant role in nurse
retention (Duffield et al., 2011).
Meanwhile, opportunity for promotion has a significant impact on anticipated turnover in the
current study, which also showed that. The majority of nurses agreed with the statement that it
is difficult to get promoted in their hospital. For example, Participant 180, a 31-year-old Indian
female nurse with a diploma of nursing, reported that “Promotion is the main factor. I have
been here more than three years and have a total of seven years of experience and I still have
not got a promotion while some staff have been promoted twice.”
This study suggests that for staff to get ahead and progress in an upward trajectory, some form
of management intervention through policies and procedures is required. Research evidence
from different countries similarly revealed that dissatisfaction with promotion had more impact
on intent to leave than salary (Beecroft et al., 2008; Shields & Ward, 2001). These findings
were also reported by Kovner et al. (2009) who found promotional opportunity and autonomy
would have the highest marginal influence on decreasing intent to stay. This outcome of the
current study is contrary to research by Al-Ahmadi (2013) who found that opportunity for
promotion was not found to significantly predict anticipated turnover behaviour. In the open-
ended response questions of what strategies would prevent turnover and encourage nurses to
stay, nurses indicated that promotions should be based on seniority, qualification, experience,
skills, and attitude. A possible reason that supports their responses could be that when nurses
become more knowledgeable and qualified, their job prospects may improve.
In addition to reducing workload providing maternity leave, providing increments on a yearly
basis, and increasing the days of annual leave, nurses also commented about job satisfaction.
As observed from the SEM and multiples regression analysis of main survey data, the
anticipated turnover among nurses in Saudi Arabia to leave their job is linked with work
environment factors, such as job satisfaction. This complaint signifies the increasing the days
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of annual leave and providing increments on a yearly basis as an important predictor of
dissatisfaction among nurses and factors influencing nurses to leave their job in Saudi Arabia.
Both quantitative and qualitative findings reaffirm results from previous studies regarding
work environment factors. For example, a study by Al-Ahmadi (2013) showed that job
satisfaction was found to significantly predict anticipated turnover behaviour. Another study
in Saudi Arabia noted that Saudi nurses would be more satisfied with their jobs if there was a
reduction in workload (Alotaibi, Paliadelis, & Valenzuela, 2016). These findings were also
reported by Alasmari and Douglas (2012) who found that job satisfaction scores were highly
and inversely associated with nurses’ intention to leave. The qualitative aspect gave deeper
meaning to the workload issues and revealed the way policy factors function as barriers to
workload responses.
As already indicated, the healthcare system in Saudi Arabia might have immediate issues of
nursing shortage (Saudi nurses) and high turnover rates, in particular from non-Saudi nurses.
Hospitals’ management and immediate supervisors need to reduce nurses’ intention to leave
and encourage the nursing staff to stay by promoting and providing incremental, annual salary
increases. This study also suggests that high turnover, high workload, lack of opportunity for
promotion, and low job satisfaction might lead to a decrease in quality of care and, thus,
patients’ satisfaction.
7.4#Social#Factor##In this research, social factors, in particular the variable discrimination, were found to be one
of the important predictors influencing both Saudi and non-Saudi nurses to leave their job. This
is because both the main survey and open-ended data collected from this study support the
social factors in discouraging some nurses to stay in their job. In the quantitative results, for
example, when participants were asked whether they have experienced discrimination because
of their cultural background and ethnicity, referring to the interaction between race and racism,
half of the respondents commented that they feel racism in their job. Nurses claimed that they
been discriminated against by the hospital’s administration, the head nurse, and other staff. In
the open-ended questions results, for example, participant 91, a 31-year-old, female Malaysian
nurse, also expressed strong disappointment about the status of discrimination in her hospital,
in particular from her supervisor. When she was asked if there were any other additional factors
that affected her decision to leave her current job, she replied “Yes, because there is
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discrimination between head nurse and staff. Those the head nurse likes will get what they
want without any problems and in an easy way will be granted 100 percent.”
In the current study, the open-ended questions (qualitative) findings are consistent with
quantitative findings in terms of social factors and anticipated turnover. One of the significant
social factors known in this research, in particular from open-ended questions discouraging
nurses from leaving Saudi Arabia, is their insights about the prevalence of discrimination
regarding salary system existing within the Saudi healthcare system. This study proves that the
main factor that is related to turnover is unfair salary for different nationalities in Saudi Arabia.
The nurses respectively displayed how a salary difference could impact upon their living
conditions and their decision concerning whether to stay or leave their current job. However,
these results from the current study suggest that a substantial difference in salaries between
nationalities proves the inability of hospital management and the Saudi government to
standardise salary systems and frame rules that promotes salary equity across nationalities.
This study also highlighted how cultural variances between Saudi and other countries could
discourage non-Saudi nurses from staying in Saudi Arabia. It is well documented that cultural
and traditional values are barriers to optimal healthcare delivery and can cause the intention to
leave with the consequent problem of turnover (Almutairi, McCarthy, & Gardner, 2015). For
example, in Saudi culture, what medical staff must do for patients and what the family of the
patient wants causes many conflicts between nursing staff, the patient’s relations, management,
and the family. Importantly, this highlights that cultural differences exist between people of
different ethnicities. Almutairi and McCarthy (2012) suggested that the impact of society and
the influence of culture are strongly reflected in the health workforce intention to leave. The
Saudi cultural context and society has its own unique set of characteristics that shape the
lifestyle of its population, namely customs, traditions, values, and beliefs. Further, the people’s
beliefs and attitudes are intrinsically linked to Islamic and Arabic tribal traditions. Previous
studies (e.g. Almutairi, McCarthy, & Gardner, 2015; Mitchell, 2009; Saeed, 1995) suggested
that the turnover of the foreign health workforce might be related to the employee’s inability
to adjust to living in a new culture. Without social support it is more difficult for nurses, for
example, to handle different customs and learn a new language (Saeed, 1995; Mitchell, 2009).
In this study, alike results are noted. It was indicated that another factor raised by some nurses
was recreational activities and quality of life. Nurses argued that they should be offered
recreational activities and other activities to enhance their living conditions. The social factor
most frequently mentioned was “no social activities organised”. Consequently, the results of
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this study have practical strategy impacts for the Saudi Health system, because providing
recreational activities such as a free membership for a fitness centre or sport club would
encourage nurses to stay in their job. While providing recreational facilities was highlighted
by the nurses as a significant factor causal to preventing turnover and encouraging nurse to
stay, other factors, such as improve housing conditions and having a comfortable place for
nurses to eat their meals and relax during breaks, were equally emphasised by nurses as
important factors.
Thus, cultural beliefs and customs of a country are generally seen as a factor that is strongly
related to health workforce retention. For example, the findings of this study, especially open-
ended questions, also identified those variables that impact intention to leave of foreign nurses
who work and live in Saudi Arabia. The findings showed that nurses’ perceptions of anticipated
turnover were influenced by the social support they received to integrate into the country as a
whole, or their acceptance of the customs and the culture of the new country, as well as the
support they received to learn how to communicate within that culture.
Additionally, foreign nurses can also face challenges and difficulties due to culture and
customs, in particular when working with Saudi patients, who may have dissimilar kinds of
demands in terms of health and healthcare (Almutairi, & McCarthy, 2012). Also, the nurses’
lack of understanding of the Saudi culture and customs can inhibit healthcare provision. Better
and improved communication between foreign health workers and patients may to lead an
increase in both nurses’ and patients’ satisfaction (Mitchell, 2009).
Moreover, nurses’ varying cultures, values, traditions, and language can create difficulties
when these characteristics differ from the patients’ characteristics. Hence, foreign nurses need
to recognise and respect the culture and customs of their new country. Implementing these
values is very important because they can assist nurses to feel more comfortable with their
working environment, which will help them be more satisfied and stay with their job.
Additionally, anticipated turnover among nurses appears to be impacted upon by social factors.
In the current study, the open-ended questions (qualitative) findings are consistent with
quantitative findings in terms of social factors and anticipated turnover. These results revealed
a number of new factors that were included in the min survey. These included the perception
of nursing and a lack of appreciation. All of these values can cause difficulties for, and put
pressure on, nurses so that they leave their jobs, even though they may be satisfied with their
job.
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Consequently, the results of this study suggest that a sense of inferiority in the work
environment appears to be related to receiving the proper recognition for work done. Such
recognition would also have the positive result of helping to keep motivation high and reducing
nursing turnover. In addition, some of the comments made by participants related to the
public’s lack of support for the nursing profession. The responses of Saudi nurses also had the
same opinion about the perception of nursing. For example, Participant 299, a 27-year-old,
female Saudi nurse, provided a few recommendations that could prevent turnover and
encourage nurses to stay. These were educating the public and others about the real work of
nursing and educating them to love nursing so that nurses feel proud to be in nursing, as is the
case in developing countries. Also, having doctors and others show respect towards nursing
staff, and the colours of clothes to be organised and open (for example nurses being allowed to
choose between two or three permitted colours) so nurses do not feel like they are students
wearing high school-type uniforms.
7.5#Personal#Factor##The T-test and ANOVA were conducted to analyse the relationship between personal factors
and anticipated turnover. The mean score of anticipated turnover was highest (3.0) in the age
group 26–30 years. It decreased to 2.6 and 2.7 in the age groups 56–60 and 51–55 years, and
then increased to 2.9 in the age group 31–35 years. However, the differences in the overall
anticipated turnover by age were not significant. This outcome is contrary to that of Al-Ahmadi
(2013) who indicated that a significant difference in anticipated turnover was found based on
age. Another study in Saudi Arabia by Almalki et al. (2012) showed similar findings. The study
indicated that age was significantly associated with turnover intention.
In regard to the gender variable, there was a significant association between gender and
anticipated turnover. The mean score of men (M = 3.0, SD = 0.442) exceeds the mean score of
women (M = 2.97, SD = 0.412) to a statistically significant degree [t (500) = 0.133, p = 0.894].
This means that male nurses are more likely to leave than female nurses. This is consistent with
a number of previous studies (Al-Ahmadi, 2013; Borkowski et al., 2007; Tourangeau &
Cranely, 2006). In Saudi Arabia, Almalki et al. (2012) found that gender was significantly
associated with turnover intention. Another study conducted by Tourangeau and Cranely
(2006) in Ontario, Canada, found that male nurses were less likely to stay employed than were
female nurses. Moreover, the findings of this study also suggested that male nurses leaving the
nursing profession may be attributed to Saudi culture. In Saudi Arabia, Saudi men are
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responsible for their families. It is well known that Saudi societies have a strong cultural
tradition to value and stress obligations to family members. Considering that, male nurses in
this study, as already indicated, complained about low salary and no promotional opportunities
and they bear large financial obligations to their family. This can lead male nurses to leave
nursing to find a better-paid job.
In regard to education level, the result showed a statistically significant difference among
groups. Assuming that there was a statistically significant difference, the P is less than 0.05
levels in anticipated turnover scores for the five groups (associate degree, diploma, bachelor’s
degree, postgraduate certificate, and master’s degree (postgraduate). The mean score of
anticipated turnover was highest (3.3) in the master’s degree (postgraduate) group. It decreases
to 2.8 and 2.9 in the diploma group and bachelor’s degree group, and then increased to 3.0 in
the associate degree group. However, the differences in the overall anticipated turnover by
education were significant. The findings of the current study found that nurses who have a high
level of education are more likely to leave the profession. This is consistent with previous
studies (Al-Ahmadi, 2013; Delobelle et al., 2011; Borkowski et al., 2007). For example, one
study conducted in Saudi Arabia by Stewart et al. (2011) reported that nurses were more likely
to plan to leave their nursing positions if they had higher education. However, in contrast to
other researchers, Chan et al. (2009) found no statistically significant association between
personal factors and nurses’ intention to stay or leave, including nurses’ educational level.
In regard to flexible work schedules, the study found that nurses who do not have flexible work
schedules (M = 3.106, SD = 0.4322) were more likely to leave compared to those who have
flexible work schedules (M = 2.887, SD = 0.382). These findings are consistent with previous
studies from Saudi Arabia, as well as other countries. For example, one study conducted by
Shader et al. (2001) in the southeast of the United States found that a more flexible work
schedule resulted in low anticipated turnover. Another study conducted by Al-Dossary, Vail,
and Macfarlane (2012), in Saudi Arabia, found that when nurses have a flexible work schedule,
they will be more satisfied and have enough time to spend with family. Therefore, this research
suggests that it would appear that if healthcare organisations provided better working
environments and greater flexibility with their nursing schedules, fewer nurses would consider
leaving their positions.
In the current study, the open-ended questions (qualitative) findings are connected to
quantitative findings in term of personal factors and anticipated turnover. These insights
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revealed the way new factors (not found in the main survey) of family factors, homesickness,
and continuing education interconnected with personal factors and anticipated turnover. From
the insights, family plays an important role in the decision of nurses to stay or leave their job.
Some nurses reported that the family factor was one of the main factors that influenced them
in leaving their job. Family factors may not have received as much attention in other studies
and this highlights, a new insight but also a need for greater research to quantify such a finding.
Moreover, some nurses also highlighted the importance of nurses receiving encouragement
from their family. They reported that the additional factors could affect their decision to leave
current jobs are their unstable and unpredictable family situation.
Most of the participants expressed their disappointment with the limited educational
opportunities in their careers. It was evident that education is an important factor for both Saudi
and non-Saudi nurses to continue working in nursing. The analysis of personal factors is
consistent with previous research that showed nurses intend to leave Saudi Arabia to travel to
developed countries for specialist training, due to limited specialist medical school in Saudi
Arabia. For example, Alotaibi, Paliadelis and Valenzuela (2016) found that a lack of education
opportunities is one of main personal factor among Saudi nurses. These findings from the
current study suggest that the intention to stay for nurses is influenced by the support they
receive to complete their study in Saudi Arabia. This support could be achieved by providing
scholarships for nurses to complete their education inside Saudi Arabia.
In summary, the findings of the main survey in the current study indicate that personal factors,
including education level, age, and flexible work schedules, which are predictors of retention
for Saudi Arabia healthcare nurses, are somewhat similar to those of nurses working in other
settings. In contrast, the findings of open-ended questions, in the current study, indicated that
personal factors, including lack of education opportunities, family factors, and the opportunity
to become a permanent resident, which are predictors of retention for Saudi Arabia healthcare
nurses, are somewhat dissimilar to those of nurses working in other settings.
7.6#Policy#Factor#This research found that policy factors play a fundamental role in determining the intention to
leave among both Saudi and nun-Saudi nurses employed in the Saudi healthcare system.
Regarding policy factors that affect non-Saudi nurses, new Saudi policies related to new fees
(visa regulations) on the dependents of foreign workers in the Saudi Arabia play an important
role in creating barriers to foreign nurses staying. Non-Saudi nurses expressed concern and
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sadness over the decision to apply new fees on the dependents of foreign workers in Saudi
Arabia. For example, when Participant 453, a 26-year-old, Filipino man with a bachelor’s
degree in nursing expressed concern over his future in Saudi Arabia, he explained it was
because of “the present changes in the kingdom, no freer visit visas for family, the dependent
fee for the private sectors.” Similarly, when Participant 112, a 37-year-old Filipino woman,
was asked about the additional factors that affect her decision to leave her current job, she
commented that the “no family status” visa in her current post was the influencing factor. It
appears that non-Saudi nurses were sceptical about the future of nursing in Saudi Arabia. They
believed that in the near future, the Saudi government will rely on just Saudi nurses and job
opportunities may decline, which will impact non-Saudi nurses. This study suggests that the
Saudi health system is unable to rely solely on Saudi nurses, most probably for the next ten
years, because need outstrips capacity in Saudi Arabian universities. Thus, according to the
MOH, the total number of nurses in both the public and private healthcare providers was 101,
298; only 29.1% of the nurses were indigenous Saudis (Almalki et al., 2011).
The above quotes show the worries of nurses about the future of their immediate family
members if Saudi employers do not support them in regard to wages, the annual dependent visa
fees, and the cost of their children’s schooling. Together, it also brings to light their views about
Saudi Arabia and the assistances they observe they could gain from staying in Saudi. Taken
together, these results recommend that the intention to stay for nurses may be influenced by
the motivation and support they have from employers and the policymakers in the government
of Saudi Arabia. Removing the family visa fee may encourage foreign nurses to stay in Saudi
Arabia for a longer period. New Saudi regulations concerning family visas fee need to be
reviewed because it may be expected that the opportunity to move family member would
encourage nurses to remain in Saudi Arabia.
Regarding the policy factors in respect of Saudi nurses, new Saudi policies related to reducing
the number of the foreign nurses and increasing the number of Saudi nurses play a significant
role in creating barriers to Saudi nurses finding employment in Saudi. It is important to
understand that once Saudi Arabia applied these policies, there was a noticeable increase in
private medical institutes that provided diplomas in nursing. In retrospect, some students who
graduated from private medical institutes were unable to find a job in either private or public
hospitals, and so the outcome has been poor. The result from these changes has intended that
Saudi nurses, who have concluded their graduated from these institutes, have not been able to
secure employment in either private or public hospitals within Saudi Arabia. There has been
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some evidence of regulations imposed by the Saudi government, in particular the Ministry of
Heath, towards assistance in the Saudi recruitment of nurses (Okaz News, 2017; Sabq, 2017).
The policy of the Saudi government to reduce the number of the foreign nurses and to increase
the number of qualified Saudi nurses, and its poor regulation have not played an imperative
role in helping Saudi nurses to find employment in either private or public hospitals.
Consequently, this study indicates that any polices aimed at increasing the number of Saudi
nurses must focus on addressing the problems prevailing within the Saudi nursing education
system. This is an important issue for future research.
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8.#CHAPTER#EIGHT:#CONCLUSION#
8.1.#Introduction#The final chapter of this thesis discusses the implications of findings and is presented in four
sections. The first section (8.2) evaluates research limitations and identifies the directions for
further research. The second section (8.3) presents a set of recommendations for reducing
turnover, improving work environments, and improving nurses’ retention. The third section
(8.4) presents a set of suggestions for further research. The final section (8.5) ends with a
general conclusion for the entire research.
8.2.#Limitation#of#the#Study#The generalisability of these results is subject to certain limitations. For instance, the study
included nurses who, at the time, were working in government hospitals in Makkah and Jeddah
city. Nurses working in the private sector could not be involved due to the purpose of the study.
The inclusion of nurses working in the private sector could have given a better insight into
factors that reduce the likelihood of nurses leaving their job. Furthermore, one of main
limitations of this study is that the majority of the respondents were women (83.7%) and
Muslim (73%); thus, the sample may not represent a more diverse nursing workforce.
In respect of Saudi Arabia, the recent announcement of new regulations relating to non-Saudi
nurses (dependant visa regulations) took place during data collection in Saudi Arabia. The
result from open-ended questions in the current study explored the effects of these regulations,
as previously mentioned (policy factors section). However, the findings of the current study
offer an outline that may enable others to better consider the effects of these visa regulation
changes on the Saudi healthcare system. The qualitative dimension revealed some deeper level
insights that were otherwise not available through the quantitative study. For example, it
pointed to the interaction of policy factors, which represents an important area for future
research.
In the survey questionnaires for nurses, in particular questions about personal factors, the study
did not include personal questions about marital status, number of dependent children, and how
long the individual had worked in these hospitals, in order to meet the requirements of the
National Statement on Ethical Conduct in Human Research (2007). According to ethical
committees, it would seem possible that the personal details provided in answering these
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questions may enable the identification of an individual. A final limitation of the study and
findings is that there was limited scope to fully undertake a gendered analysis of the findings.
This constitutes another study and forms part of a potential future research project.
8.3.#Practice#and#policy#Implication##This research has provided insight into issues of nurses in Saudi Arabia, in addition to
providing strategies to prevent nurses leaving their employment and encouraging them to stay
in their current job. The major contribution of this research rests on the fact that there is no
comprehensive study that has investigated social, work environment, organisational, personal,
and policy factors that influence nurses to leave their current job in Saudi Arabia. The use of
mixed-methods (main survey and open-ended questions) enabled a more complete
understanding of the study problems, and supported elicitation of any unknown factors that
may influence intent to leave and nurses’ own perceptions of the factors affecting turnover.
The current study has suggested the following policies to prevent turnover and encourage
nurses to stay.
This study has shown that there are substantial wage differences depending on nurses’
nationality in the Saudi healthcare system. Inequitable salaries between nationalities is a very
strong factor in influencing nurses to leave their job in Saudi Arabia. From the current study,
this proves the inability of hospital management and the Saudi government to standardise the
salary system and frame rules that enable equity in the payment of wages across nationalities.
If the Saudi government wants to prevent turnover and encourage nurses to stay, then this study
indicates the need to standardise the salary system and frame rules that enable equity in wages
based on merit rather than on nationality.
In the current study, work environment factors, including workload, job satisfaction, and the
opportunity for promotion, were significant predictor variables for anticipated turnover.
Moreover, the open-ended questions’ (qualitative) insights show that patient load, staffing
ratio, scheduling, and the numbers of hours worked were strong factors in influencing nurses
to leave their job. Heavy workload appears to be a greater issue for nurses than had been
expected and, thus, it is essential for an increased awareness of the issues of heavy workload.
These findings have significant implications for the understanding of how to prevent turnover
and encourage nurses to remain in employment. Improving the work environment is paramount
to retaining staff. Although improvements in the work environment require changes at the
hospital level, government policy can influence and regulate these changes. This study suggests
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that there is a need to apply mandatory staffing ratios, such as those as enacted in developed
countries, such as Australia and the United States. Therefore, implementation of the Magnet
Recognition Program in the Saudi healthcare system is recommended. This program has proven
its ability to lower nurses’ turnover, nurse burnout, and increase nurses’ job satisfaction
(American Nurses Credentialing Center, 2018; Moss, Mitchell, & Casey, 2017). The program
allows nurses to recognise nursing excellence in other nurses. Moreover, this program has been
associated with creating a more positive hospital work environment. Policymakers in the Saudi
healthcare system should legislate maximum nurse-to-patient ratios to reduce turnover and
heavy workload, as well as ensure safe patient outcomes. Hence, government and leaders in
the health sector in Saudi Arabia could look to providing such a framework to assist nurses to
develop workload-management strategies for their services.
It was evident that education is an important factor for both Saudi and non-Saudi nurses to
continue working in nursing. These findings from the current study suggest that the intention
to stay for nurses is influenced by the support they receive to complete their education in Saudi
Arabia. There is discordance between graduate numbers of Saudi nurses and the demand for
nurses in the Saudi health system. In regard to Saudi nurses, there is a shortage of Saudi nurses
and, as a result, the Saudi healthcare system relies on foreign nurses to meet the needs. One of
the reasons for this discordance is the limited number of good quality nursing schools in Saudi
Arabia. If the Saudi government wants to increase the number of Saudi entering the nursing
profession and at the same time encourage non-Saudi nurses to remain, then the most important
policy would be to increase the capacity of the educational system in Saudi Arabia, in particular
for nurse education. Public and private scholarships could be awarded to nurses or universities
to increase the number of students entering nursing. In regard to foreign nurses, if the Saudi
government wants to slow foreign nurses leaving and enhance the quality of the workforce,
then this study indicates it needs to provide the opportunity for foreign nurses to complete their
education in Saudi Arabia, by increasing funding in higher education. It is viewed that a lack
of continuing education in Saudi Arabia has resulted in many foreign nurses leaving Saudi for
higher education abroad.
This study of a relatively small group of nurses has raised very interesting questions that fit
with what the government of Saudi knows, but training more Saudi nurses is not going to solve
the problem for another ten years. There will be continuing reliance on non-Saudi nurses. The
study also identified that recent changes to Saudi policies around family migration of non-
Saudi nurses (visa regulations) have increased living costs because of increased dependent visa
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costs. This could be a serious problem that will continue in the Saudi health system for at least
in next ten years until enough nurses are trained in Saudi Arabia. This study indicates that
nurses really want to stay and enjoy working in Saudi Arabia, but there are a number of
government policies that make it difficult for them to stay.
New policies for international and foreign nurses’ new policies must be considered. On the
positive side, relying on foreign nurses helps to improve the supply of nurses in Saudi Arabia
and eases the current shortage, although so far it has not been enough to close the gap
completely. On the challenging side, a reliance on foreign nurses can postpone the local supply
changes needed to reduce the gap. In other words, improving the supply of nurses in the Saudi
health system through the importation of foreign nurse reduces the incentive to expand
education capacity. At the same time, a reliance on foreign nurses should not be considered a
long-term solution to the nursing turnover or shortage in Saudi Arabia. Improvements in the
retention of nurses must continue even if relying on foreign nurse eases the current shortage
somewhat.
The findings of this study revealed that cultural differences and leisure and recreational
activities are an important barrier in the leaving of non-Saudi nurses into the Saudi health
system. Therefore, this research suggests that if there is a need to prevent non-Saudi nurses
from leaving then there is a need to provide leisure and recreational activities. The majority of
foreign nurses do not speak Arabic and this situation can create communication problems
between the nurse, patient, and society. There is a need to design professional learning
programs, coupled with education that inform about cultural diversity relevant to healthcare.
These programs are described as the development of the social skills to engage with people
from varied cultures in a respectful way. This is an increasingly significant consideration for
hospitals that have a multicultural community. This would support the attraction and
integration of non-Saudi nurses into the Saudi health system.
8.4#Suggestions#for#Further#Research#This research has also identified some important topics for future research on the impact of
Saudi and non-Saudi nurses leaving their current job in Saudi.
Some areas that require further research include:
•! The effects of new regulations implemented by the Saudi government to increase Saudi
nurses or promote retention of foreign nurses in Saudi Arabia.
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•! Applying the Magnet Recognition Program in Saudi Health system will be associated
with positive work environments.
•! The effects of integration and adaptability of foreign nurses into the Saudi Health
system.
•! Improving the Saudi nursing education system.
•! Longitudinal research to follow whether those nurses with intention to leave do actually
end up leaving and do they move into another organization or leave Saudi Arabia
entirely.
This study proves that any polices aimed at increasing the number of Saudi nurses must focus
on addressing the problems prevailing with the Saudi nursing education system and this is an
important issue for future research.
8.5.#Chapter#Summary#The purpose of this study was to identify, more precisely, the factors contributing to intention
to leave among nurses in Saudi Arabia. This research answered important questions relating to
factors that influence nurses to leave their current job in Saudi Arabia. The answer to the first
research question ‘what are the organisational factors that affect nurse turnover in hospital in
Saudi Arabia?’ this research found that, social support from immediate supervisor,
organisational commitment, and autonomy have a significant impact on anticipated turnover.
Furthermore, an important finding noted among foreign nurses was unfair salary for different
nationalities.
In response to the second research questions ‘what are the work environment factors that affect
nurse turnover in hospital in Saudi Arabia?’ this research indicated that turnover was
significantly associated with workload, opportunity for promotion, and job satisfaction.
Additionally, anticipated turnover among nurses appears to be impacted upon by include
patient load, staffing ratio, scheduler, and the numbers of hours worked.
In response to the third research questions ‘what are the social, cultural, and political factors
that affect nurse turnover in hospital in Saudi Arabia?’ identified a combination of social
factors. These factors differ between Saudi nurses and foreign nurses. In the case of foreign
nurses, social support of family, recreational activities, cultural differences, discrimination, and
quality of life were important factors that influence nurses to leave Saudi Arabia. Additionally,
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an important finding noted among foreign nurses was new Saudi policies related to new fees
(visa regulations) on the dependents of foreign workers in the Saudi Arabia. This was a
significant finding because previous researches did not establish such social factors that would
influence intentions of foreign nurses to leave Saudi Arabia.
In response to the last question ‘what are the personal factors that affect nurse turnover in
hospital in Saudi Arabia?’, this study found that education level, age, and flexible work
schedules, lack of education opportunities, and the opportunity to become a permanent resident
were important factors that influence nurses to leave their job in Saudi Arabia.
In summary, the results of this study indicate that several independent variables were found to
be significant predictors of anticipated turnover, including discrimination, workload, floating
system, job satisfaction, opportunity for promotion, lack of education opportunity, salary
system, favouritism, recreational activity, visa regulations, social support from immediate
supervisor, organisational commitment, and autonomy. This chapter closed with several
policies to optimise the nursing workforces in Saudi Arabia. New policies have been suggested
to discourage turnover and encourage nurses to stay in their job longer. The supply of both
Saudi and foreign nurses could be increased by retaining more nurses in the profession by
improving their working environment. Policies can be adopted that keep both Saudi and foreign
nurses in the workplace longer, such as reducing workload, providing educational
opportunities, and promoting staff. Importantly, the conclusions of this research provide a
broader view of nurses in Saudi Arabia and has generated a theoretical understanding of factors
influencing the intentions of both Saudi and non-Saudi nurses’ to leave their job.
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Appendices#
Appendix A: Personal characteristics - categories Characteristic Measure Category Value label Gender Nominal Male 1
Female 2 Age Ordinal
20–25 1 26–30 2 31–35 3 36–40 4 41–45 5 46–50 6 51–55 7 56–60 8
Ethnicity Nominal Arab 1 Asian 2 Indian 3 Caucasian 4 African 5 Other 6
Nationality Nominal Saudi Filipino Malaysian Indian Jordanian Egyptian Other
1 2 3 4 5 6 7
Religion Nominal Muslim 1 Christian 2 Hindus 3 Buddhist 4 Other 5
Education Nominal Associate degree 1 Diploma 2 Bachelor’s degree 3 Postgraduate certificate 4 Master’s degree (postgraduate) 5 Doctorate 6 No degree 7 Other 8
Kinship responsive employer
Nominal No 0 Yes 1
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Appendix B: Frequency Distribution Anticipated Turnover items
15#1.%I%plan%to%stay%in%my%position%a%while.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 33! 6.6! 6.6! 6.6!
Agree! 229! 45.6! 45.6! 52.2!
Neutral! 136! 27.1! 27.1! 79.3!
Disagree! 77! 15.3! 15.3! 94.6!
Strongly!Disagree! 27! 5.4! 5.4! 100.0!
Total! 502! 100.0! 100.0! 15#2.%I%am%quite%sure%I%will%leave%my%position%in%the%foreseeable%future.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 10! 2.0! 2.0! 2.0!
Disagree! 62! 12.4! 12.4! 14.3!
Neutral! 148! 29.5! 29.5! 43.8!
Agree! 212! 42.2! 42.2! 86.1!
Strongly!Agree! 70! 13.9! 13.9! 100.0!
Total! 502! 100.0! 100.0! 15#3.%I%know%whether%or%not%I%will%be%leaving%this%hospital%within%a%short%
time.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 27! 5.4! 5.4! 5.4!
Disagree! 112! 22.3! 22.3! 27.7!
Neutral! 168! 33.5! 33.5! 61.2!
Agree! 159! 31.7! 31.7! 92.8!
Strongly!Agree! 36! 7.2! 7.2! 100.0!
Total! 502! 100.0! 100.0! 15#4.%Deciding%to%stay%or%leave%my%position%is%not%a%critical%issue%for%me%
at%this%point%in%time.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 30! 6.0! 6.0! 6.0!
Agree! 207! 41.2! 41.2! 47.2!
Neutral! 156! 31.1! 31.1! 78.3!
Disagree! 86! 17.1! 17.1! 95.4!
Strongly!Disagree! 23! 4.6! 4.6! 100.0!
Total! 502! 100.0! 100.0!
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%
15#5.%If%I%got%another%job%offer%tomorrow,%I%would%give%it%serious%
consideration.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 21! 4.2! 4.2! 4.2!
Disagree! 90! 17.9! 17.9! 22.1!
Neutral! 144! 28.7! 28.7! 50.8!
Agree! 187! 37.3! 37.3! 88.0!
Strongly!Agree! 60! 12.0! 12.0! 100.0!
Total! 502! 100.0! 100.0!
15#6.%I%have%no%intentions%of%leaving%my%present%position! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 20! 4.0! 4.0! 4.0!
Agree! 160! 31.9! 31.9! 35.9!
Neutral! 176! 35.1! 35.1! 70.9!
Disagree! 109! 21.7! 21.7! 92.6!
Strongly!Disagree! 37! 7.4! 7.4! 100.0!
Total! 502! 100.0! 100.0!
15#7.%I%have%been%in%my%position%about%as%long%as%I%want%to! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 21! 4.2! 4.2! 4.2!
Disagree! 66! 13.1! 13.1! 17.3!
Neutral! 195! 38.8! 38.8! 56.2!
Agree! 200! 39.8! 39.8! 96.0!
Strongly!Agree! 20! 4.0! 4.0! 100.0!
Total! 502! 100.0! 100.0!
15#8.%I%am%certain%I%will%be%staying%here%a%while! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 8! 1.6! 1.6! 1.6!
Agree! 208! 41.4! 41.4! 43.0!
Neutral! 183! 36.5! 36.5! 79.5!
Disagree! 79! 15.7! 15.7! 95.2!
Strongly!Disagree! 24! 4.8! 4.8! 100.0!
Total! 502! 100.0! 100.0!
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15#9.%I%don't%have%any%specific%idea%how%much%longer%I%will%stay!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 39! 7.8! 7.8! 7.8!
Agree! 234! 46.6! 46.6! 54.4!
Neutral! 146! 29.1! 29.1! 83.5!
Disagree! 70! 13.9! 13.9! 97.4!
Strongly!Disagree! 13! 2.6! 2.6! 100.0!
Total! 502! 100.0! 100.0!
15#10.%I%plan%to%hang%on%to%this%job%a%while! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 16! 3.2! 3.2! 3.2!
Agree! 163! 32.5! 32.5! 35.7!
Neutral! 186! 37.1! 37.1! 72.7!
Disagree! 114! 22.7! 22.7! 95.4!
Strongly!Disagree! 23! 4.6! 4.6! 100.0!
Total! 502! 100.0! 100.0! 15#11.%There%are%big%doubts%in%my%mind%as%to%whether%or%not%I%will%really%
stay%in%this%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 23! 4.6! 4.6! 4.6!
Disagree! 109! 21.7! 21.7! 26.3!
Neutral! 222! 44.2! 44.2! 70.5!
Agree! 144! 28.7! 28.7! 99.2!
Strongly!Agree! 4! .8! .8! 100.0!
Total! 502! 100.0! 100.0!
15#12.%I%plan%to%leave%this%position%shortly.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 30! 6.0! 6.0! 6.0!
Disagree! 184! 36.7! 36.7! 42.6!
Neutral! 186! 37.1! 37.1! 79.7!
Agree! 73! 14.5! 14.5! 94.2!
Strongly!Agree! 29! 5.8! 5.8! 100.0!
Total! 502! 100.0! 100.0!
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Intent to Stay
16#1.%I%would%like%to%leave%my%present%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 33! 6.6! 6.6! 6.6!
Agree! 77! 15.3! 15.3! 21.9!
Neutral! 142! 28.3! 28.3! 50.2!
Disagree! 203! 40.4! 40.4! 90.6!
Strongly!Disagree! 47! 9.4! 9.4! 100.0!
Total! 502! 100.0! 100.0!
16#2.%I%plan%to%leave%my%present%hospital%as%soon%as%possible.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 28! 5.6! 5.6! 5.6!
Agree! 60! 12.0! 12.0! 17.5!
Neutral! 133! 26.5! 26.5! 44.0!
Disagree! 221! 44.0! 44.0! 88.0!
Strongly!Disagree! 60! 12.0! 12.0! 100.0!
Total! 502! 100.0! 100.0!
16#3.%I%plan%to%stay%with%my%present%hospital%as%long%as%possible.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 27! 5.4! 5.4! 5.4!
Disagree! 84! 16.7! 16.7! 22.1!
Neutral! 179! 35.7! 35.7! 57.8!
Agree! 154! 30.7! 30.7! 88.4!
Strongly!Agree! 58! 11.6! 11.6! 100.0!
Total! 502! 100.0! 100.0!
16#4.%Under%no%circumstances%will%I%voluntarily%leave%my%present%hospital.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 29! 5.8! 5.8! 5.8!
Disagree! 134! 26.7! 26.7! 32.5!
Neutral! 215! 42.8! 42.8! 75.3!
Agree! 93! 18.5! 18.5! 93.8!
Strongly!Agree! 31! 6.2! 6.2! 100.0!
Total! 502! 100.0! 100.0!
190 | P a g e
Environment Factors Job satisfaction
3#1.%I%find%enjoyment%in%the%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 19! 3.8! 3.8! 3.8!
Disagree! 39! 7.8! 7.8! 11.6!
Neutral! 177! 35.3! 35.3! 46.8!
Agree! 208! 41.4! 41.4! 88.2!
Strongly!Agree! 59! 11.8! 11.8! 100.0!
Total! 502! 100.0! 100.0!
3#2.%I%consider%my%job%rather%unpleasant.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 19! 3.8! 3.8! 3.8!
Agree! 113! 22.5! 22.5! 26.3!
Neutral! 184! 36.7! 36.7! 62.9!
Disagree! 151! 30.1! 30.1! 93.0!
Strongly!Disagree! 35! 7.0! 7.0! 100.0!
Total! 502! 100.0! 100.0!
3#3.%I%am%often%bored%with%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 14! 2.8! 2.8! 2.8!
Agree! 61! 12.2! 12.2! 14.9!
Neutral! 144! 28.7! 28.7! 43.6!
Disagree! 228! 45.4! 45.4! 89.0!
Strongly!Disagree! 55! 11.0! 11.0! 100.0!
Total! 502! 100.0! 100.0!
3#4.%I%am%fairly%well%satisfied%with%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 22! 4.4! 4.4! 4.4!
Disagree! 58! 11.6! 11.6! 15.9!
Neutral! 163! 32.5! 32.5! 48.4!
Agree! 217! 43.2! 43.2! 91.6!
Strongly!Agree! 42! 8.4! 8.4! 100.0!
Total! 502! 100.0! 100.0!
191 | P a g e
3#5.%I%definitely%dislike%my%job.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 11! 2.2! 2.2! 2.2!
Agree! 33! 6.6! 6.6! 8.8!
Neutral! 90! 17.9! 17.9! 26.7!
Disagree! 254! 50.6! 50.6! 77.3!
Strongly!Disagree! 114! 22.7! 22.7! 100.0!
Total! 502! 100.0! 100.0!
3#6.%Each%day%on%my%job%seems%like%it%will%never%end.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 22! 4.4! 4.4! 4.4!
Agree! 98! 19.5! 19.5! 23.9!
Neutral! 158! 31.5! 31.5! 55.4!
Disagree! 190! 37.8! 37.8! 93.2!
Strongly!Disagree! 34! 6.8! 6.8! 100.0!
Total! 502! 100.0! 100.0!
3#7.%Most%days%I%am%enthusiastic%about%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 8! 1.6! 1.6! 1.6!
Disagree! 54! 10.8! 10.8! 12.4!
Neutral! 210! 41.8! 41.8! 54.2!
Agree! 200! 39.8! 39.8! 94.0!
Strongly!Agree! 30! 6.0! 6.0! 100.0!
Total! 502! 100.0! 100.0! Workload
4#1.%I%have%enough%time%to%get%everything%done%in%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 36! 7.2! 7.2! 7.2!
Agree! 228! 45.4! 45.4! 52.6!
Neutral! 142! 28.3! 28.3! 80.9!
Disagree! 79! 15.7! 15.7! 96.6!
Strongly!Disagree! 17! 3.4! 3.4! 100.0!
Total! 502! 100.0! 100.0!
192 | P a g e
4#2.%My%workload%is%unacceptable.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 17! 3.4! 3.4! 3.4!
Disagree! 159! 31.7! 31.7! 35.1!
Neutral! 153! 30.5! 30.5! 65.5!
Agree! 128! 25.5! 25.5! 91.0!
Strongly!Agree! 45! 9.0! 9.0! 100.0!
Total! 502! 100.0! 100.0!
4#3.%I%have%to%work%very%hard%in%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 6! 1.2! 1.2! 1.2!
Disagree! 40! 8.0! 8.0! 9.2!
Neutral! 110! 21.9! 21.9! 31.1!
Agree! 259! 51.6! 51.6! 82.7!
Strongly!Agree! 87! 17.3! 17.3! 100.0!
Total! 502! 100.0! 100.0!
4#4.%I%have%to%work%very%fast%in%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 4! .8! .8! .8!
Disagree! 31! 6.2! 6.2! 7.0!
Neutral! 107! 21.3! 21.3! 28.3!
Agree! 275! 54.8! 54.8! 83.1!
Strongly!Agree! 85! 16.9! 16.9! 100.0!
Total! 502! 100.0! 100.0!
4#5.%My%workload%is%predominantly%physical.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 5! 1.0! 1.0! 1.0!
Disagree! 56! 11.2! 11.2! 12.2!
Neutral! 147! 29.3! 29.3! 41.4!
Agree! 226! 45.0! 45.0! 86.5!
Strongly!Agree! 68! 13.5! 13.5! 100.0!
Total! 502! 100.0! 100.0!
193 | P a g e
4#6.%My%workload%is%predominantly%mental.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 6! 1.2! 1.2! 1.2!
Disagree! 56! 11.2! 11.2! 12.4!
Neutral! 148! 29.5! 29.5! 41.8!
Agree! 221! 44.0! 44.0! 85.9!
Strongly!Agree! 71! 14.1! 14.1! 100.0!
Total! 502! 100.0! 100.0!
4#7.%I%can%perform%all%my%tasks%without%help%from%others.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 23! 4.6! 4.6! 4.6!
Agree! 124! 24.7! 24.7! 29.3!
Neutral! 153! 30.5! 30.5! 59.8!
Disagree! 153! 30.5! 30.5! 90.2!
Strongly!Disagree! 49! 9.8! 9.8! 100.0!
Total! 502! 100.0! 100.0!
4#8.%I%often%have%headaches%from%my%work.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 14! 2.8! 2.8! 2.8!
Disagree! 138! 27.5! 27.5! 30.3!
Neutral! 172! 34.3! 34.3! 64.5!
Agree! 124! 24.7! 24.7! 89.2!
Strongly!Agree! 54! 10.8! 10.8! 100.0!
Total! 502! 100.0! 100.0!
4#9.%I%often%feel%muscle%pain%from%my%work.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 10! 2.0! 2.0! 2.0!
Disagree! 79! 15.7! 15.7! 17.7!
Neutral! 119! 23.7! 23.7! 41.4!
Agree! 199! 39.6! 39.6! 81.1!
Strongly!Agree! 95! 18.9! 18.9! 100.0!
Total! 502! 100.0! 100.0!
194 | P a g e
4#10.%My%work%does%not%cause%any%pain.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 8! 1.6! 1.6! 1.6!
Agree! 54! 10.8! 10.8! 12.4!
Neutral! 120! 23.9! 23.9! 36.3!
Disagree! 230! 45.8! 45.8! 82.1!
Strongly!Disagree! 90! 17.9! 17.9! 100.0!
Total! 502! 100.0! 100.0!
4#11.%My%work%requires%skills%that%I%don’t%have.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 83! 16.5! 16.5! 16.5!
Disagree! 260! 51.8! 51.8! 68.3!
Neutral! 83! 16.5! 16.5! 84.9!
Agree! 64! 12.7! 12.7! 97.6!
Strongly!Agree! 12! 2.4! 2.4! 100.0!
Total! 502! 100.0! 100.0!
4#12.%I%am%very%confident%about%my%work.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 148! 29.5! 29.5! 29.5!
Agree! 278! 55.4! 55.4! 84.9!
Neutral! 67! 13.3! 13.3! 98.2!
Disagree! 4! .8! .8! 99.0!
Strongly!Disagree! 5! 1.0! 1.0! 100.0!
Total! 502! 100.0! 100.0!
Opportunity for promotion
5#1.%Promotions%are%regular%with%my%hospital.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 127! 25.3! 25.3! 25.3!
Disagree! 163! 32.5! 32.5! 57.8!
Neutral! 101! 20.1! 20.1! 77.9!
Agree! 95! 18.9! 18.9! 96.8!
Strongly!Agree! 16! 3.2! 3.2! 100.0!
Total! 502! 100.0! 100.0!
195 | P a g e
5#2.%There%is%a%very%good%chance%to%advance%in%my%career%in%my%hospital.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 82! 16.3! 16.3! 16.3!
Disagree! 130! 25.9! 25.9! 42.2!
Neutral! 162! 32.3! 32.3! 74.5!
Agree! 110! 21.9! 21.9! 96.4!
Strongly!Agree! 18! 3.6! 3.6! 100.0!
Total! 502! 100.0! 100.0!
5#3.%The%practice%of%beginning%at%the%bottom%and%working%up%is%widespread%with%
my%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 53! 10.6! 10.6! 10.6!
Disagree! 71! 14.1! 14.1! 24.7!
Neutral! 209! 41.6! 41.6! 66.3!
Agree! 157! 31.3! 31.3! 97.6!
Strongly!Agree! 12! 2.4! 2.4! 100.0!
Total! 502! 100.0! 100.0!
5#4.%The%practice%of%internal%promotion%is%not%widespread%with%my%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 53! 10.6! 10.6! 10.6!
Agree! 151! 30.1! 30.1! 40.6!
Neutral! 161! 32.1! 32.1! 72.7!
Disagree! 113! 22.5! 22.5! 95.2!
Strongly!Disagree! 24! 4.8! 4.8! 100.0!
Total! 502! 100.0! 100.0!
5#5.%It%is%difficult%to%get%promoted%in%my%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 115! 22.9! 22.9! 22.9!
Agree! 164! 32.7! 32.7! 55.6!
Neutral! 131! 26.1! 26.1! 81.7!
Disagree! 72! 14.3! 14.3! 96.0!
Strongly!Disagree! 20! 4.0! 4.0! 100.0!
Total! 502! 100.0! 100.0!
196 | P a g e
Distributive Justice
6#1.%Promotions%by%my%employer%are%almost%totally%based%on%seniority.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 56! 11.2! 11.2! 11.2!
Agree! 163! 32.5! 32.5! 43.6!
Neutral! 121! 24.1! 24.1! 67.7!
Disagree! 108! 21.5! 21.5! 89.2!
Strongly!Disagree! 54! 10.8! 10.8! 100.0!
Total! 502! 100.0! 100.0!
6#2.%Raises%by%my%employer%heavily%depend%on%who%you%know.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 45! 9.0! 9.0! 9.0!
Agree! 163! 32.5! 32.5! 41.4!
Neutral! 190! 37.8! 37.8! 79.3!
Disagree! 95! 18.9! 18.9! 98.2!
Strongly!Disagree! 9! 1.8! 1.8! 100.0!
Total! 502! 100.0! 100.0! 6#3.%The%hiring%of%new%employees%by%my%employer%is%strictly%determined%by%job#
related%ability.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 20! 4.0! 4.0! 4.0!
Disagree! 44! 8.8! 8.8! 12.7!
Neutral! 132! 26.3! 26.3! 39.0!
Agree! 272! 54.2! 54.2! 93.2!
Strongly!Agree! 34! 6.8! 6.8! 100.0!
Total! 502! 100.0! 100.0! 6#4.%The%employees%who%do%well%for%my%employer%are%those%who%contribute%the%
most%to%its%success.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 19! 3.8! 3.8! 3.8!
Disagree! 40! 8.0! 8.0! 11.8!
Neutral! 141! 28.1! 28.1! 39.8!
Agree! 269! 53.6! 53.6! 93.4!
Strongly!Agree! 33! 6.6! 6.6! 100.0!
Total! 502! 100.0! 100.0!
197 | P a g e
6#5.%One%sure%way%to%get%fired%by%my%employer%is%to%fail%to%do%your%work%in%a%
competent%manner.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 14! 2.8! 2.8! 2.8!
Disagree! 83! 16.5! 16.5! 19.3!
Neutral! 151! 30.1! 30.1! 49.4!
Agree! 215! 42.8! 42.8! 92.2!
Strongly!Agree! 39! 7.8! 7.8! 100.0!
Total! 502! 100.0! 100.0!
6#6.%Very%competent%employees%are%well%rewarded%by%my%employer.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 60! 12.0! 12.0! 12.0!
Disagree! 92! 18.3! 18.3! 30.3!
Neutral! 153! 30.5! 30.5! 60.8!
Agree! 180! 35.9! 35.9! 96.6!
Strongly!Agree! 17! 3.4! 3.4! 100.0!
Total! 502! 100.0! 100.0!
Social Factors Gender-Mixing
7#1.%I%feel%uncomfortable%dealing%with%opposite%sex.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 57! 11.4! 11.4! 11.4!
Disagree! 237! 47.2! 47.2! 58.6!
Neutral! 134! 26.7! 26.7! 85.3!
Agree! 57! 11.4! 11.4! 96.6!
Strongly!Agree! 17! 3.4! 3.4! 100.0!
Total! 502! 100.0! 100.0!
7#2.%I%feel%uncomfortable%dealing%with%nurses%from%the%opposite%sex.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 68! 13.5! 13.5! 13.5!
Disagree! 280! 55.8! 55.8! 69.3!
Neutral! 116! 23.1! 23.1! 92.4!
Agree! 32! 6.4! 6.4! 98.8!
Strongly!Agree! 6! 1.2! 1.2! 100.0!
Total! 502! 100.0! 100.0!
198 | P a g e
7#3.%I%feel%uncomfortable%dealing%with%patient%from%the%opposite%sex.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 63! 12.5! 12.5! 12.5!
Disagree! 216! 43.0! 43.0! 55.6!
Neutral! 121! 24.1! 24.1! 79.7!
Agree! 77! 15.3! 15.3! 95.0!
Strongly!Agree! 25! 5.0! 5.0! 100.0!
Total! 502! 100.0! 100.0!
7#4.%I%feel%uncomfortable%dealing%with%physicians%from%the%opposite%sex.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 73! 14.5! 14.5! 14.5!
Disagree! 289! 57.6! 57.6! 72.1!
Neutral! 102! 20.3! 20.3! 92.4!
Agree! 32! 6.4! 6.4! 98.8!
Strongly!Agree! 6! 1.2! 1.2! 100.0!
Total! 502! 100.0! 100.0!
7#5.%My%families%reject%gender#mixing.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 82! 16.3! 16.3! 16.3!
Disagree! 276! 55.0! 55.0! 71.3!
Neutral! 109! 21.7! 21.7! 93.0!
Agree! 28! 5.6! 5.6! 98.6!
Strongly!Agree! 7! 1.4! 1.4! 100.0!
Total! 502! 100.0! 100.0!
Discrimination
9#1.%I%would%prefer%to%work%in%a%country%where%there%is%no%racism.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 4! .8! .8! .8!
Disagree! 19! 3.8! 3.8! 4.6!
Neutral! 64! 12.7! 12.7! 17.3!
Agree! 157! 31.3! 31.3! 48.6!
Strongly!Agree! 258! 51.4! 51.4! 100.0!
Total! 502! 100.0! 100.0!
199 | P a g e
9#2.%I%experience%discrimination%because%of%my%race.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 35! 7.0! 7.0! 7.0!
Disagree! 152! 30.3! 30.3! 37.3!
Neutral! 90! 17.9! 17.9! 55.2!
Agree! 147! 29.3! 29.3! 84.5!
Strongly!Agree! 78! 15.5! 15.5! 100.0!
Total! 502! 100.0! 100.0!
9#3.%I%experience%discrimination%because%of%my%gender.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 56! 11.2! 11.2! 11.2!
Disagree! 239! 47.6! 47.6! 58.8!
Neutral! 110! 21.9! 21.9! 80.7!
Agree! 65! 12.9! 12.9! 93.6!
Strongly!Agree! 32! 6.4! 6.4! 100.0!
Total! 502! 100.0! 100.0!
9#4.%I%experience%discrimination%because%of%my%religion.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 87! 17.3! 17.3! 17.3!
Disagree! 260! 51.8! 51.8! 69.1!
Neutral! 92! 18.3! 18.3! 87.5!
Agree! 42! 8.4! 8.4! 95.8!
Strongly!Agree! 21! 4.2! 4.2! 100.0!
Total! 502! 100.0! 100.0! Support from Spouse
10#1.%My%spouse%is%not%willing%to%listen%to%my%job#related%problems.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 9! 1.8! 1.8! 1.8!
Agree! 32! 6.4! 6.4! 8.2!
Neutral! 142! 28.3! 28.3! 36.5!
Disagree! 209! 41.6! 41.6! 78.1!
Strongly!Disagree! 110! 21.9! 21.9! 100.0!
Total! 502! 100.0! 100.0!
200 | P a g e
10#2.%My%spouse%does%not%show%a%lot%of%concern%for%me%on%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 6! 1.2! 1.2! 1.2!
Agree! 32! 6.4! 6.4! 7.6!
Neutral! 151! 30.1! 30.1! 37.6!
Disagree! 211! 42.0! 42.0! 79.7!
Strongly!Disagree! 102! 20.3! 20.3! 100.0!
Total! 502! 100.0! 100.0!
10#3.%My%spouse%can%be%relied%on%when%things%get%tough%on%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 28! 5.6! 5.6! 5.6!
Disagree! 68! 13.5! 13.5! 19.1!
Neutral! 186! 37.1! 37.1! 56.2!
Agree! 141! 28.1! 28.1! 84.3!
Strongly!Agree! 79! 15.7! 15.7! 100.0!
Total! 502! 100.0! 100.0!
10#4.%My%spouse%is%helpful%to%me%in%getting%my%job%done.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 31! 6.2! 6.2! 6.2!
Disagree! 40! 8.0! 8.0! 14.1!
Neutral! 169! 33.7! 33.7! 47.8!
Agree! 163! 32.5! 32.5! 80.3!
Strongly!Agree! 99! 19.7! 19.7! 100.0!
Total! 502! 100.0! 100.0!
Organisational Factors Support from immediate supervisor 11#1.%My%immediate%supervisor%is%willing%to%listen%to%my%job#related%problems.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 28! 5.6! 5.6! 5.6!
Disagree! 52! 10.4! 10.4! 15.9!
Neutral! 132! 26.3! 26.3! 42.2!
Agree! 237! 47.2! 47.2! 89.4!
Strongly!Agree! 53! 10.6! 10.6! 100.0!
Total! 502! 100.0! 100.0!
201 | P a g e
11#2.%My%immediate%supervisor%shows%a%lot%of%concern%for%me%on%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 30! 6.0! 6.0! 6.0!
Disagree! 61! 12.2! 12.2! 18.1!
Neutral! 164! 32.7! 32.7! 50.8!
Agree! 205! 40.8! 40.8! 91.6!
Strongly!Agree! 42! 8.4! 8.4! 100.0!
Total! 502! 100.0! 100.0!
11#3.%My%immediate%supervisor%cannot%be%relied%on%when%things%get%tough%on%
my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 23! 4.6! 4.6! 4.6!
Agree! 90! 17.9! 17.9! 22.5!
Neutral! 191! 38.0! 38.0! 60.6!
Disagree! 166! 33.1! 33.1! 93.6!
Strongly!Disagree! 32! 6.4! 6.4! 100.0!
Total! 502! 100.0! 100.0!
11#4.%My%immediate%supervisor%really%does%not%care%about%my%well#being.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 20! 4.0! 4.0! 4.0!
Agree! 73! 14.5! 14.5! 18.5!
Neutral! 154! 30.7! 30.7! 49.2!
Disagree! 213! 42.4! 42.4! 91.6!
Strongly!Disagree! 42! 8.4! 8.4! 100.0!
Total! 502! 100.0! 100.0!
Support from Co-workers
12#1.%I%am%very%friendly%with%one%or%more%of%my%co#workers.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 3! .6! .6! .6!
Disagree! 11! 2.2! 2.2! 2.8!
Neutral! 29! 5.8! 5.8! 8.6!
Agree! 285! 56.8! 56.8! 65.3!
Strongly!Agree! 174! 34.7! 34.7! 100.0!
Total! 502! 100.0! 100.0!
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12#2.%I%regularly%do%things%outside%of%work%with%one%or%more%of%my%co#workers! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 10! 2.0! 2.0! 2.0!
Disagree! 80! 15.9! 15.9! 17.9!
Neutral! 112! 22.3! 22.3! 40.2!
Agree! 231! 46.0! 46.0! 86.3!
Strongly!Agree! 69! 13.7! 13.7! 100.0!
Total! 502! 100.0! 100.0!
12#3.%I%rarely%discuss%important%personal%problems%with%my%co#workers.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 39! 7.8! 7.8! 7.8!
Agree! 176! 35.1! 35.1! 42.8!
Neutral! 192! 38.2! 38.2! 81.1!
Disagree! 79! 15.7! 15.7! 96.8!
Strongly!Disagree! 16! 3.2! 3.2! 100.0!
Total! 502! 100.0! 100.0!
12#4.%I%know%almost%nothing%about%my%co#workers%as%persons.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 8! 1.6! 1.6! 1.6!
Agree! 78! 15.5! 15.5! 17.1!
Neutral! 156! 31.1! 31.1! 48.2!
Disagree! 217! 43.2! 43.2! 91.4!
Strongly!Disagree! 43! 8.6! 8.6! 100.0!
Total! 502! 100.0! 100.0! Organisational commitment
13#1.%I%think%that%my%present%hospital%is%a%great%organisation%to%work%for.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 17! 3.4! 3.4! 3.4!
Disagree! 41! 8.2! 8.2! 11.6!
Neutral! 155! 30.9! 30.9! 42.4!
Agree! 214! 42.6! 42.6! 85.1!
Strongly!Agree! 75! 14.9! 14.9! 100.0!
Total! 502! 100.0! 100.0!
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13#2.%My%present%hospital%inspires%the%very%best%in%me%in%the%way%of%job%
performance.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 21! 4.2! 4.2! 4.2!
Disagree! 45! 9.0! 9.0! 13.1!
Neutral! 175! 34.9! 34.9! 48.0!
Agree! 212! 42.2! 42.2! 90.2!
Strongly!Agree! 49! 9.8! 9.8! 100.0!
Total! 502! 100.0! 100.0!
13#3.%I%am%glad%that%I%chose%this%present%hospital%to%work%for%over%others%I%was%
considering%at%the%time%I%joined.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 21! 4.2! 4.2! 4.2!
Disagree! 26! 5.2! 5.2! 9.4!
Neutral! 167! 33.3! 33.3! 42.6!
Agree! 242! 48.2! 48.2! 90.8!
Strongly!Agree! 46! 9.2! 9.2! 100.0!
Total! 502! 100.0! 100.0!
13#4.%I%am%not%proud%to%tell%others%I%work%for%my%present%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 25! 5.0! 5.0! 5.0!
Agree! 61! 12.2! 12.2! 17.1!
Neutral! 119! 23.7! 23.7! 40.8!
Disagree! 215! 42.8! 42.8! 83.7!
Strongly!Disagree! 82! 16.3! 16.3! 100.0!
Total! 502! 100.0! 100.0!
13#5.%I%really%do%not%care%about%the%fate%of%my%present%hospital.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 11! 2.2! 2.2! 2.2!
Agree! 52! 10.4! 10.4! 12.5!
Neutral! 104! 20.7! 20.7! 33.3!
Disagree! 256! 51.0! 51.0! 84.3!
Strongly!Disagree! 79! 15.7! 15.7! 100.0!
Total! 502! 100.0! 100.0!
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13#6.%My%present%hospital%is%not%the%best%of%all%possible%places%for%me%to%work.!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 21! 4.2! 4.2! 4.2!
Agree! 80! 15.9! 15.9! 20.1!
Neutral! 144! 28.7! 28.7! 48.8!
Disagree! 182! 36.3! 36.3! 85.1!
Strongly!Disagree! 75! 14.9! 14.9! 100.0!
Total! 502! 100.0! 100.0! Job Autonomy
14#1.%I%am%able%to%choose%the%way%to%go%about%my%job.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 11! 2.2! 2.2! 2.2!
Disagree! 75! 14.9! 14.9! 17.1!
Neutral! 166! 33.1! 33.1! 50.2!
Agree! 219! 43.6! 43.6! 93.8!
Strongly!Agree! 31! 6.2! 6.2! 100.0!
Total! 502! 100.0! 100.0!
14#2.%I%am%able%to%modify%what%my%job%objectives%are.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 14! 2.8! 2.8! 2.8!
Disagree! 77! 15.3! 15.3! 18.1!
Neutral! 144! 28.7! 28.7! 46.8!
Agree! 234! 46.6! 46.6! 93.4!
Strongly!Agree! 33! 6.6! 6.6! 100.0!
Total! 502! 100.0! 100.0!
14#3.%Generally,%I%can%control%the%time%at%which%I%start%working%for%the%day.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Disagree! 47! 9.4! 9.4! 9.4!
Disagree! 127! 25.3! 25.3! 34.7!
Neutral! 115! 22.9! 22.9! 57.6!
Agree! 186! 37.1! 37.1! 94.6!
Strongly!Agree! 27! 5.4! 5.4! 100.0!
Total! 502! 100.0! 100.0!
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14#4.%My%job%is%such%that%I%cannot%decide%when%to%do%particular%work%%activities!
Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 23! 4.6! 4.6! 4.6!
Agree! 107! 21.3! 21.3! 25.9!
Neutral! 166! 33.1! 33.1! 59.0!
Disagree! 182! 36.3! 36.3! 95.2!
Strongly!Disagree! 24! 4.8! 4.8! 100.0!
Total! 502! 100.0! 100.0!
14#5.%I%have%no%control%over%the%sequencing%of%my%work%activities.! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 23! 4.6! 4.6! 4.6!
Agree! 106! 21.1! 21.1! 25.7!
Neutral! 156! 31.1! 31.1! 56.8!
Disagree! 185! 36.9! 36.9! 93.6!
Strongly!Disagree! 32! 6.4! 6.4! 100.0!
Total! 502! 100.0! 100.0!
14#6.%Generally,%I%do%not%have%any%control%over%time%at%which%I%stop%working%for%
the%day! Frequency! Percent! Valid!Percent! Cumulative!Percent!
Valid! Strongly!Agree! 24! 4.8! 4.8! 4.8!
Agree! 117! 23.3! 23.3! 28.1!
Neutral! 138! 27.5! 27.5! 55.6!
Disagree! 201! 40.0! 40.0! 95.6!
Strongly!Disagree! 22! 4.4! 4.4! 100.0!
Total! 502! 100.0! 100.0!
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Appendix C: Operational definitions for measurement scales
Scale Indicators/items Operational Measurement/Scale
Anticipated Turnover (Endogenous Latent)
Anticipated Turnover was measured by 12 indicators.
Anticipated Turnover Scale is a 12-item Likert-type survey about nurses’ opinion or perception of the possibility of voluntarily terminating their present job (Hinshaw & Atwood, 1982) 5-item Likert response: Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree.
Intent to Stay (Endogenous Latent)
Intent to Stay was measured by 4 indicators.
Intent to Stay Scale of 4 items (Adapted from Kim et al., 1996) 5-item Likert response: Strongly Agree to Strongly Disagree.
Perception of nursing (Exogenous Latent)
Perception of nursing will be measured by 4 indicators.
Perception of nursing Scale 5-item Likert response: Strongly Agree to Strongly Disagree.
Social support (Spouse) (Exogenous Latent)
Social support (Spouse) will be measured by 4 indicators.
Social support (Spouse) Scale (Adapted from Kim et al., 1996) 5-item Likert response: Strongly Agree to Strongly Disagree.
Kinship responsive Kinship responsive will be measured by 6 indicators.
Each of the features has a yes or no response (Adapted from Price, 2004)
Gender-Mixing (Exogenous Latent)
Gender-Mixing will be measured by 12 indicators.
Gender-Mixing Scale 5-item Likert response: Strongly Agree to Strongly Disagree.
Workload (Exogenous Latent)
Workload will be measured by 12 indicators.
Workload Scale (Adapted from Kim et al., 1996) 5-item Likert response: Strongly Agree to Strongly Disagree.
Job Satisfaction (Exogenous Latent)
Job Satisfaction will be measured by 7 indicators.
Job Satisfaction Scale (Adapted from Kim et al., 1996) 5-item Likert response: Strongly Agree to Strongly Disagree.
Organisational Commitment (Exogenous Latent)
Organisational Commitment will be measured by 6 indicators.
Organisational Commitment Scale (Adapted from Kim et al., 1996) 5-item Likert response: Strongly Agree to Strongly Disagree.
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Appendix D: Examples of responses to the open-ended questions
Themes Responses Participant identifiers
Social factors
recreational activity, quality of life,
Gym, swimming pool, perception of nursing,
Discrimination, no Recognition from patients and abusive family members,
patients refused to leave, discrimination between head nurse and staff,
the basis on ethnicity race and
Nationality, dominance for those who are native Arab speakers.
Racial and nationality discrimination,
less excitement in current environment, Administration Hospital Discrimination, treating nurses like their maids,
Patient attitude towards nurses, no recreation facilities nor programs for the nurses,
31,42, 48,
49,56,87,89,91,147,166,1
64,227,241,248, 264,266
Personal factors Family factors, continuing education,
take care of my kids, homesick, personal matter, family issues, spouse support, higher degree -
education opportunities, Family commitments
1,7,10,11,33,34,40,50,53,
54,65,67,90,100,101,113,
115,120,121,164,176,226
, 249,250, 253,265
Organisational
factors
Unfair, poor management, poor housing condition, Favouritism,
No fair between all nationality,
Less number of days of vacation,
unfair salary for different nationality, no value for sincerity and skills, Unfair salary (Malaysian has higher salary
compare to us Filipino), Floating system (the float staff to other department that not related to her experience),
The hospital management does not seem to appreciate the nurses. floating in unfamiliar departments,
new staffs are coming with less
6, 19,31,50,56,
66,84,87,89,
97,114,12,138,140,
147,149,150,153,168,178
,180,184,198,211,221,22
5,236,239,245,248
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Themes Responses Participant identifiers
experience but in high position, Unfair leadership dominant by race, floating due to lack of staff, job description,
Lack of recognition, Autocratic Leadership
Environment
factors
Workload, Salary, promotion, Mental and physical exhaustion, understaff, Staffing ratio,
pressure in the working environment,
Support from the superiors,
physical pain, emotional, no compensation, a lot of
multitask, relation with supervisor, work environment stress, annual leave, longer hours of working
3, 9, 38,39,52,53,56, 59,
61,62,65,66,68,71,76,79,
80,87,89,114,115,116,12
2,124,127,144,145,146,1
49,161,178,180,185,186.
198,199,201,207,213,225
,232,233,244, 246,
252,272
Political factors No family status visa, no day-care for the babies to easy the work for mothers,
If the employer did not allow for pregnancy, working mother during pregnancy period, long term tenure is not
secure among expat in Saudi
112, 237, 246,248
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Themes Responses Participant identifiers
Social factors
Social life, Restrictions, a recreation living condition, improve housing condition, provide
recreational facility, physical and recreational activities, eliminate racism/favouritism,
Establishing of gym swimming pool. no discrimination towards Saudi or non-Saudi staff,
Recreational activities for staff to avoid burnout, please provide housing and recreation,
Recreational activities for nurses, protection to be given to female, good accommodation with
recreational activities, the availability of other activities to do at the free
times like (Gym), Patient educations and awareness about respecting nursing personnel, place for
the nurses to have their lunch, better living environment, sort of favouritism
1,2,29,30,31,33,34,42,48,49,
53,56,57,58,89,107,118,149,
179,182,207
Personal factors Paid maternity leave, venues for professional growth, post-graduate degrees for nurses, Education
Opportunities, give maternity leave, Muharam increment, annual leave, vacation 2 time in a year,
many nurses left their jobs here when they pregnant because of no maternity leave, Emergency
leaves, sponsoring for her higher education, family allowances, transportation allowances, housing
allowance, offer a day-care
28,48,53,56,78,79,82,88,112,
118,123,149,198,206,146
Organisational
factors
Increase the wages/ motivation, incentives and appreciation for their job well done, Allowed
maternity leave, Leave out pay, Overtime pay for nurses, fairness to all nationality, supported
hospital management, awards based on staff performance, Be a good leader, fairly judgement if
anything happen, supporting between supervisor and employer, fair treatment of the superior to
their staff, Equality in benefits between Saudis & Expats, Teamwork, Respect staff, Open
communication and respect, Improve hospital management, Enhance nursing leadership, Provision
of
3,5,9,10,11,18,21,18,19,21,2
9,34,45,49,
50,52,60,67,68,78,80,82,84,8
5,91,92,97,109,111,115,116,
117,118,120,121,124,131,13
2,138,140,144,174,149,150,1
53,
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Themes Responses Participant identifiers
liaison personnel in each unit, deal equally with all nationalities based on education and
experience, condusive area,
160,161,171,176,178,181,18
4,185,202,208,215
Environment
factors
increment of nurses, Promotions and salary increment, additional staffs in every unit, Low
workload, Salary satisfaction, improve salary, increase manpower, team building, more days of
leave, work as a team, less workload, Monetary compensation for overtime, More benefits for the
staff, Patient and staff ratio with the usage of patient's acuity level, enough staff, flexible duties,
patient ratio
3,6,9,11,15,16,19,21,25,31,3
2,33,35,39,40,42,46,47,50,51
,53,54,56,59,60,65,71,73,75,
76,77,79,83,86,87,88,90,95,9
9,103,104,107,112,113,114,1
15,120,123,124,125,126,127,
131,133,135.136,141,146,15
4,156,161,168,170,177,180,1
93,203,204
Political factors free visa for family, Family visa with education privilege for children, Hajj privilege, provide
family visa's for health worker dependents, Hajj privileges for staff, referral to home care for long
stay patients, Giving visiting visa
40,53,60,78,89,101,112,
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Appendix E: Permissions to Use Copyright Protected Materials (Turnover Intention Scale)
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Appendix F: Ethical Approval
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Appendix G: Survey questionnaire for nurses
Section A-1: Demographic and profession related information
1.1.What is your gender? (please √ one)
�����Male � Female
1.2.What is your age? (please √ one)
���� 20- 25 � 26-30 � 31-35 �36-40 �41-45 � 46-50 �51-55 � 56-60
1.3.What is your ethnicity/race? � Arab � Asian � Indian �African �Caucasian
�Other, please specify Click here to enter text.
1.4.What is your nationality? ______________________________
1.5.What is your religion? (please √ one)
���� Muslim � Christian � Hindus � Buddhist � Other
If other, please mention Click here to enter text.
1.6.What is your highest nursing education? (please √ one)
� Associate Degree � Diploma � Bachelors � Post Graduate Certificate
� Masters (Post Graduate) � Doctorate � No degree
2.! Kinship responsive employer
Please indicate which of the following features characterizes your present hospital:
2.1.On-site childbirth? (please √ one)
����Yes � No
2.2.Leave for childbirth? (please √ one)
����Yes � No
2.3.Paid leave for childbirth? (please √ one)
����Yes � No
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2.4.Leave for childbirth of at least three months? (please √ one)
�����Yes � No
2.5.Flexible work schedules? (please √ one) �Yes � No
2.6.Unpaid leave for family matters? (please √ one) �Yes � No
Section B: This section contains statement about nursing work environment. Please indicate
how much you disagree or agree with each statement using the scale given below (from 1 to
5). Number (1) indicates that you strongly Disagree with statement, (2) Disagree, (3) neither
agree or nor disagree, (4) Agree with contents, and (5) strongly Agree
WORK ENVIRONMENT SURVEY
Statements Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
3.! Job Satisfaction
3.1. I find real enjoyment in my job. � � � � �
3.2. I consider my job rather unpleasant. � � � � �
3.3. I am often bored with my job. � � � � �
3.4. I am fairly well satisfied with my job. � � � � �
3.5. I definitely dislike my job. � � � � �
3.6. Each day on my job seems like it will never end. � � � � �
3.7. Most days I am enthusiastic about my job. � � � � �
Strongly disagree D
isagree
Ne utral
Agree
Strongly A
gree
4.! Workload
4.1. I have enough time to get everything done in my job.
4.2. My workload is unacceptable
4.3. I have to work very hard in my job
4.4. I have to work very fast in my job
4.5. My workload is predominantly physical
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4.6. My workload is predominantly mental
4.7. I can perform all my tasks without help from others
4.8. I often have headaches from my work
4.9. I often feel muscle pain from my work
4.10. My work does not cause any pain
4.11. My work requires skills that I don’t have
4.12. I am very confident about my work
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
5.! Opportunity for promotion
5.1. Promotions are regular with my hospital.
5.2. There is a very good chance to advance in my career in
my hospital.
5.3. The practice of beginning at the bottom and working up
is widespread with my hospital
5.4. The practice of internal promotion is not widespread
with my hospital.
5.5. It is difficult to get promoted in my hospital
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
6.! Distributive justice
6.1. Promotions by my employer are almost totally based on
seniority
6.2. Raises by my employer heavily depend on who you
know
6.3. The hiring of new employees by my employer is strictly
determined by job-related ability
6.4. The employees who do well for my employer are those
who contribute the most to its success
6.5. One sure way to get fired by my employer is to fail to
do your work in a competent manner.
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6.6 Very competent employees are well rewarded by my
employer
Section C:
SOCIAL FACTORS SURVEY
Statements Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
7.! Gender-Mixing
7.1. I feel uncomfortable dealing with opposite sex � � � � �
7.2. I feel uncomfortable dealing with nurses from the
opposite sex � � � � �
7.3. I feel uncomfortable dealing with patient from the
opposite sex � � � � �
7.4. I feel uncomfortable dealing with physicians from the
opposite sex � � � � �
7.5. My families reject gender-mixing � � � � �
Strongly disagree D
isagree
Ne utral
Agree
Strongly A
gree
8.! Perception of nursing
8.1. Nursing is a respected profession
8.2. Nursing is caring profession
8.3. Nursing as a profession is less highly regarded than
being a doctor.
8.4. In general, society has an accurate image of nurses, such
as their roles and responsibilities.
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
9.! Discrimination
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9.1. I would prefer to work in a country where there is no
racism
9.2. I experience discrimination because of my race
9.3. I experience discrimination because of my gender
9.4. I experience discrimination because of my religion
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
10.!Social support ( Spouse)
10.1.! My spouse is not willing to listen to my job-related
problems
10.2.! My spouse does not show a lot of concern for me on my
job
10.3.! My spouse can be relied on when things get tough on
my job
10.4.! My spouse is helpful to me in getting my job done
SECTION D
ORIGNAZTIONAL SURVEY
Please indicate your agreement or disagreement with each of the following statement about
support in your work.
Statements Strongly disagree D
isagree
Ne utral
Agree
Strongly A
gree
11.!Social Support (Immediate Supervisor
11.1.! My immediate supervisor is willing to listen to my job-
related problems. � � � � �
11.2.! My immediate supervisor shows a lot of concern for me
on my job
� � � � �
11.3.! My immediate supervisor cannot be relied on when
things get tough on my job � � � � �
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11.4.! My immediate supervisor really does not care about my
well-being � � � � �
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
12.!Social Support (co-workers)
12.1.! I am very friendly with one or more of my co-workers
12.2.! I regularly do things outside of work with one or more
of my co-workers
12.3.! I rarely discuss important personal problems with my
co-workers
12.4.! I know almost nothing about my co-workers as persons
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
13.!Organisational Commitment
13.1.! I think that my present hospital is a great organisation to
work for
13.2.! My present hospital inspires the very best in me in the
way of job performance.
13.3.! I am glad that I chose this present hospital to work for
over others I was considering at the time I joined
13.4.! I am not proud to tell others I work for my present
hospital
13.5.! I really do not care about the fate of my present hospital
13.6.! My present hospital is not the best of all possible places
to work for me
Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
14.!Autonomy
14.1.! I am able to choose the way to go about my job
14.2.! I am able to modify what my job objectives are
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14.3.! Generally, I can control the time at which I start
working for the day
14.4.! My job is such that I cannot decide when to do
particular work activities
14.5.! I have no control over the sequencing of my work
activities
14.6.! Generally, I do not have any control over time at which
I stop working for the day
SECTION D
ANTICIPATED TURNOVER
Statements Strongly disagree D
isagree
Neutral
Agree
Strongly A
gree
15.!Anticipated Turnover Scale (ATS)
15.1.! I plan to stay in my position awhile � � � � �
15.2.! I am quite sure I will leave my position in the
foreseeable future � � � � �
15.3.! I know whether or not I will be leaving this hospital
within a short time
� � � � �
15.4.! Deciding to stay or leave my position is not a critical
issue for me at this point in time. � � � � �
15.5.! If I got another job offer tomorrow, I would give it
serious consideration.
15.6.! I have no intentions of leaving my present position
15.7.! I have been in my position about as long as I want to
15.8.! I am certain I will be staying here awhile
15.9.! I don't have any specific idea how much longer I will
stay
15.10.! I plan to hang on to this job awhile
15.11.! There are big doubts in my mind as to whether or not I
will really stay in this hospital.
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15.12.! I plan to leave this position shortly.
1.! Are there additional factors that affect your decision to leave your current job?
2.! What strategies would prevent turnover and encourage nurse to stay?
3.! Please provide any additional comments you would like to mention.
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!
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Appendix H: Institutional Review Board (IRB) approval – Jeddah
Appendix I: NIH – Research unit completion
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Appendix J: Conference Presentations
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