How does job dissatisfaction fuel nurse exit, voice, loyalty, and neglect (EVLN) behaviors? A Cross-sectional Survey

How does job dissatisfaction fuel nurse exit, voice, loyalty, and neglect (EVLN) behaviors? 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A Cross-sectional Survey Jackie Zhanbiao Li, Ming Chen, Yingqian Lao, Wanqin Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5315438/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In organizational behavior, job dissatisfaction has become an essential factor in shaping employees’ responses, including exit, voice, loyalty, and neglect (EVLN) behaviors, impacting workplace dynamics and outcomes. This study is based on a questionnaire survey investigating EVLN behaviors resulting from job dissatisfaction among nurses in public hospitals in Chongqing. The aim is to identify the underlying reasons for nurse shortages driven by job dissatisfaction. To begin with, drawing on Farrell's EVLN behavior framework and utilizing dynamic game theory, this study examines how nurse job dissatisfaction influences exit, voice, loyalty, and neglect behaviors. The findings reveal that nurse job dissatisfaction is positively correlated with exit and neglect behaviors, while negatively correlated with voice and loyalty behaviors. And then, a subsequent heterogeneity analysis shows that the effects of job dissatisfaction on EVLN behaviors are fully heterogeneous in terms of gender and education level, and partially heterogeneous in terms of age and marital status. Additionally, dynamic game theory is applied to study the internal evolution of EVLN behaviors triggered by job dissatisfaction. The results indicate that nurses typically choose loyalty behavior initially; however, if dissatisfaction persists, they shift to voice behavior. If voice behavior is not addressed, nurses either directly opt for exit behavior or transition to exit behavior through neglect. Finally, the study discusses the implications of these findings for medical policymakers, hospital managers, and practitioners. Humanities/Medical humanities Social science/Psychology Nurses’ job dissatisfaction EVLN behaviors Dynamic game theory Heterogeneity analysis Evolution of EVLN behaviors Public hospital Figures Figure 1 Figure 2 Figure 3 1. Introduction Nursing plays a vital role in global medical care and has significantly contributed to the provision of high-quality medical services (Tamata & Mohammadnezhad, 2022). A sufficient number of nurses is essential for strengthening the medical care sector, expanding health care coverage, and achieving the WHO's sustainable development health goals (Alameddine et al., 2017 ). Despite the crucial role of nursing in medical care, one of the major challenges facing the global medical system today is the severe shortage of nurses, which adversely affects the quality of care and the health and well-being of populations worldwide (Yahyaei et al., 2022 ). This shortage impacts over 1 billion people, particularly vulnerable groups such as women and children, who are in urgent need of high-quality medical services (Aluko et al., 2019 ). A primary reason for this shortage is the high turnover rate among nurses due to job dissatisfaction. If left unaddressed, this issue will perpetuate a vicious cycle of continued nursing shortages, declining care quality, and an inability to meet the health needs of global populations effectively (Matsuo et al., 2021 ; Varasteh et al., 2021 ). Therefore, addressing the root causes of the nurse shortage and understanding the underlying reasons for nurse attrition have become critical topics in both academia and practice. Investigating and analyzing nurses' behavioral responses to job dissatisfaction is a key step in addressing this issue. As a model for measuring behavioral responses to job dissatisfaction, EVLN (exit, voice, loyalty, and neglect) has become a key framework for scholars globally to study stakeholder behavior in situations of dissatisfaction. It has been widely applied in various research areas, such as psychological contracts (Kingshott et al., 2020 ; Hu et al., 2023 ), human resource management (Hsiung & Yang, 2012 ; Barry & Wilkinson, 2022 ), and conflict management (Merolla & Harman, 2018 ; Lee & Varon, 2020 ). However, the use of the EVLN model to examine nurses' behavioral responses to job dissatisfaction within the medical system remains underexplored. This study employs a mixed-methods approach to analyze the relationship between nurses' job dissatisfaction and their corresponding EVLN behaviors, as well as the evolution of these behaviors in response to job dissatisfaction. To establish the link between nurses' job dissatisfaction and the EVLN behaviors, we utilized a questionnaire-based approach to collect data on nurses' behavioral responses in tertiary public hospitals in Chongqing’s urban and county districts from January to May 2024. Dynamic game theory was then applied to investigate the relationship between nurses' job dissatisfaction and their EVLN behaviors, and to explore the evolution of these behaviors over time. This study contributes to the existing academic literature on the EVLN model driven by job dissatisfaction in several ways. First, it extends the impact of job dissatisfaction to the EVLN behaviors of hospital nurses, providing a comprehensive theoretical framework for understanding the relationship between nurse job dissatisfaction and EVLN behaviors. This, in turn, strengthens existing research on the influence of EVLN behaviors on hospital management. Second, our study integrates insights from psychology, behavioral economics, and hospital management, offering valuable contributions to expanding the literature in these fields. Finally, dynamic game theory is applied for the first time to study the evolution of EVLN behaviors in response to nurses’ job dissatisfaction, thereby enhancing the behavioral evolution mechanism model in hospital management. Our study is organized into six sections: the second section presents the theoretical foundation and research hypotheses, the third covers the research design, the fourth reports the results, the fifth explores heterogeneity and the evolution of EVLN behaviors, and the sixth provides the conclusion. 2. The theoretical basis and research hypotheses 2.1 The theoretical basis of EVLN model The development of the EVLN model has progressed through three crucial stages. First, the EVL theory, proposed by Hirschman ( 1970 ), suggests that when individuals are dissatisfied with the functioning of a social system, they typically exhibit three behaviors—exit, voice, or loyalty—collectively known as the EVL theory. Hirschman also elaborated on the specific concepts underlying these three behaviors. Second, Kolarska & Aldrich ( 1980 ) and Rusbult & Zembrodt ( 1982 ) further advanced this theory by introducing the EVLN model, explicitly identifying the behavior of "neglect" for the first time. In their research, neglect was characterized by a lack of concern, carelessness, and irresponsible behavior. Finally, Farrell ( 1983 ) refined and expanded the EVLN theory by formally incorporating neglect behavior and organizing the four behaviors—exit, voice, loyalty, and neglect—along two dimensions: constructive-destructive and active-passive. This development resulted in a more comprehensive EVLN model, as illustrated in Fig. 1 below. As illustrated in Fig. 1, the first dimension of the EVLN model is the destructive-constructive dimension. Voice and loyalty behaviors are considered constructive forms of expression, as they reflect efforts to maintain a positive employment relationship and a satisfactory working environment. In contrast, exit and neglect behaviors are classified as destructive forms of expression. The second dimension is the active-passive (or positive-negative) dimension. Exit and voice behaviors are viewed as active forms of expression, indicating a willingness to address job dissatisfaction (Spencer & Spencer, 1993). Conversely, neglect and loyalty behaviors are considered passive and negative forms of expression. According to Farrell, exit refers to the voluntary act of leaving a job, whether by exiting the company or seeking new employment to escape an unsatisfactory position. Voice emphasizes the importance of employee participation in addressing organizational decline and restoring previous performance levels, functioning as a key recovery mechanism that requires collective employee involvement. Loyalty reflects an individual’s patience in awaiting positive changes within the organization, with the belief that the organization’s decisions are ultimately correct, consistent with the findings of Williams et al. ( 2001 ). Neglect, on the other hand, refers to behaviors such as disengagement, absenteeism, and increased error rates that arise from job dissatisfaction, leading to reduced productivity and serving as a warning signal to management. 2.2 Research hypothesis on the relationship between job dissatisfaction and EVLN behaviors 2.2.1 Job dissatisfaction and exit behavior Exit behaviors among employees primarily refer to inter-organizational mobility, intra-organizational mobility, and cognitive activities preceding such mobility (Farrell, 1983 ; Rai et al., 2019). Research has demonstrated that when employees experience job dissatisfaction due to both internal and external factors, they often choose to exit (Houshmand et al., 2012 ; Hörberg et al., 2023 ). According to dynamic game theory, when conflicts of interest arise between employees and organizations and the organization fails to implement effective measures to address employee demands and allocate necessary resources, employees will feel dissatisfaction and disappointment during the interaction. To maximize their benefits, employees may seek to change their environment (Laraki & Mertikopoulos, 2013 ; Ke et al., 2022 ). In such cases, exit behavior serves as an effective means of resolution (Grandey & Cropanzano, 1999 ). Numerous empirical studies have shown that when job satisfaction is low, exiting to protect one’s resources is a rational choice (Tummers et al., 2013 ; Fasbender et al., 2019 ; Chen et al., 2023 ). Therefore, based on the aforementioned theory, when hospital nurses experience job dissatisfaction, they are likely to exhibit exit behavior. From this, we propose hypothesis H1a: H1a: Nurses’ job dissatisfaction is positively related to exit behavior. 2.2.2 Job dissatisfaction and voice behavior Voice behaviors are regarded as a form of constructive communication that allows individuals to express opinions thoughtfully and improve organizational conditions (Van Dyne & LePine, 1998 ; Kim & Rim, 2023 ). Voice is an autonomous behavior that reflects the exercise of individual rights and is typically a result of careful consideration, weighing the potential benefits and drawbacks (Kish-Gephart et al., 2009 ; Rai et al., 2019). Employee-centered voice behaviors involve actively expressing opinions when employees perceive a task as low-risk and efficient and when they believe that voicing their concerns will result in obtaining deserved resources. However, when organizational responses do not support these behaviors, employees may choose to remain silent (Shipton et al., 2024 ). According to dynamic game theory, conflicts between employees and organizations arising from work-related issues lead employees to evaluate the risks and rewards of voice behavior, while organizations respond based on employee demands. When employees perceive that expressing their concerns may lead to potential losses—such as diminished access to valuable personal resources or career opportunities—and when the expected benefits do not outweigh these risks, they often opt for silence (Haurie et al., 2012 ; Başar & Zaccour, 2018 ). Empirical research has shown that voice behavior can have negative consequences for those who engage in it, as they may be perceived as troublemakers, potentially affecting their career advancement and personal resources (Detert & Burris, 2007 ; Kish-Gephart et al., 2009 ). In many workplace contexts, remaining silent is viewed as the safest and most effective strategy for self-preservation (Xu et al., 2015 ). Therefore, when nurses in hospital experience job dissatisfaction, they often avoid voice behavior to protect their careers and opportunities for advancement. Based on this, we propose hypothesis H1b: H1b Nurses' job dissatisfaction is negatively related to voice behavior. 2.2.3 Job dissatisfaction and loyalty behavior Loyalty behaviors refer to the actions of employees who adhere to strong professional ethics and either passively or optimistically expect the organization to improve itself and make necessary adjustments (Rusbult et al., 1988 ; Allen & Grisaffe, 2001 ). Research shows that subordinates are more likely to exhibit loyalty toward supervisors or organizations that prioritize their vital interests and well-being. Employees believe that such supervisors or organizations will act in their best interest, fostering a sense of belonging (Wiedmer, 2010 ; Turkyilmaz et al., 2011 ; Nguyen & Ha, 2023 ). From the perspective of dynamic game theory, when conflicts of interest arise between employees and organizations, employees may feel dissatisfied if their resource investments are not reciprocated. In response, organizations are expected to promptly address employee concerns and dynamically adjust policies to maximize employee satisfaction, thereby increasing organizational loyalty. Conversely, if employees’ resourse investments go unrewarded, they become unwilling to invest further, leading to a decline of organizational loyalty (Williams et al., 2001 ; Rai et al., 2019). Therefore, when nurses in hospital experience job dissatisfaction, they are likely to develop negative emotions and reduce their organizational loyalty to the hospital. Based on this, we propose hypothesis H1c: H1c Nurses' job dissatisfaction is negatively related to loyalty behavior. 2.2.4 Job dissatisfaction and neglect behavior Employee neglect behaviors refer to actions where employees passively worsen the situation by being consistently late, absent, or using work time for personal matters, ultimately leading to a state of disengagement or slacking (Rusbult et al., 1988 ). Neglect behavior is often viewed as a manifestation of stress relief. When employees are dissatisfied with their work, they respond negatively to stressors (Spector & Jex, 1998 ). From the perspective of dynamic game theory, conflicts between employees and organizations, particularly when employees feel that their resource investments are not reciprocated, can result in dissatisfaction. In these situations, the organization should promptly address employee demands during the dynamic game process. Failure to do so can lead employees to feel resource-threatened, resulting in unmet psychological and physical needs and triggering passive reactions, such as neglect behaviors (Haurie et al., 2012 ; Başar & Zaccour, 2018 ; Ke et al., 2022 ). Previous studies have demonstrated that when employee resource investments are frequently unrewarded, dissatisfaction arises, and employees often resort to passive behaviors as a way to relieve stress. For instance, taking extended breaks can be viewed as a stress-related coping mechanism to conserve resources (Halbesleben et al., 2014 ; Rai et al., 2019). Therefore, when nurses in hospital are dissatisfied with their work, they may adopt neglect behaviors as a passive means of self-preservation. Based on this, we propose hypothesis H1d: H1d Nurses’ job dissatisfaction is positively related to neglect behavior. 2.2.5 The evolution of EVLN behaviors affected by nurses’ job dissatisfaction Based on the aforementioned assumptions and analysis, it can be concluded that employee job dissatisfaction leads to the emergence of EVLN behaviors. Specifically, employee job dissatisfaction (including internal and external) is positively correlated with exit and neglect behaviors, while negatively correlated with voice and loyalty behaviors. However, the question remains: how do these EVLN behaviors evolve? According to dynamic game theory (Haurie et al., 2012 ; Başar & Zaccour, 2018 ; Ke et al., 2022 ), when there is an imbalance between resource allocation and compensation between employees and organizations, gaming behavior arises. If employees are dissatisfied with the resource allocation, a rational organization, seeking to minimize losses, will strive to maximize employee satisfaction during the gaming process. This, in turn, enhances employee loyalty and mitigates job dissatisfaction (Rusbult et al., 1988 ). As employees continue to work for an extended period, the original resource allocation from the organization may no longer meet their needs, prompting them to make new compensation demands. When the organization fails to promptly adjust to these demands during the dynamic game process, employees may initially remain silent, hoping for improvement. If no changes occur, they will eventually resort to voice behavior instead of directly exiting (Hirschman, 1970 ; Jone, 2017). Following their expression of concerns, if the organization only minimally addresses the dissatisfaction, employees may become slightly disillusioned, thus may manifest as a decline in work performance or a passive desire to leave when an opportunity arises (note: or lead to the employee leaving the organization directly). During this period, the organization and employees are engaged in a dynamic game. If the employee's dissatisfaction with work is not adequately addressed, this can lead to greater disappointment, making it unlikely that the organization will meet the employee’s resource allocation and compensation needs, significantly increasing the likelihood of employee exit (Farrell, 1983 ). This demonstrates that if an organization fails to respond to voice behavior, employees may first exhibit negative behaviors, which may eventually escalate into exit behavior, or they may transition directly from voice behavior to exiting (Lin et al., 2010 ). In summary, the evolution of EVLN behaviors in the dynamic game between employees and organizations unfolds as follows: Employees initially adopt loyalty behavior in response to job dissatisfaction (both internal and external). If the dissatisfaction persists, they shift to voice behavior. If voice behavior is not addressed, employees may either directly choose exit behavior or transition from neglect to exit behavior. Thus, the evolution of EVLN behaviors among nurses in hospital, driven by job dissatisfaction, follows this progression. Based on this framework, we propose hypotheses H2a, H2b, H2c, and H2d. H2a: Nurses' loyalty behavior has a positive impact on voice behavior. H2b: Nurses' voice behavior has a positive impact on neglect behavior. H2c: Nurses' neglect behavior has a positive impact on exit behavior. H2d: Nurses' voice behavior has a positive impact on exit behavior. Based on the aforementioned analysis, we determined the impact of nurses' job dissatisfaction on the relationships among various EVLN behaviors and developed a theoretical framework depicting the evolution of these behaviors (see Fig. 2). 3. Research design 3.1 Sample selection This study examines the behavioral responses of nurses in tertiary public hospitals in Chongqing's urban and county districts to job dissatisfaction. Adhering to the principles of comprehensiveness, objectivity, fairness, and prudence, data were collected from 1,391 nurses (all holding professional nursing qualifications) in tertiary public hospitals across nine districts of Chongqing's main urban area between January 2024 and May 2024 through an anonymous questionnaire (see supplementary document). With the assistance of friends, classmates, and medical professionals, 1,381 questionnaires were distributed, of which 1,219 were returned, yielding a response rate of 88.27%. Following standard protocols, questionnaires from trainees and interns, as well as those with careless or incomplete responses, were excluded, resulting in 1,132 valid questionnaires, achieving an efficiency rate of 81.97%. The descriptive statistics of the sample are detailed in Table 1. Table 1 Sample Descriptive Statistics Sociometric Variables Project N Percentage (%) Gender (N = 1132) Male 58 5.12 Female 1074 94.88 Age (In Years) (N = 1132) < 20 5 0.44 20—29 431 38.07 30—39 540 47.70 ≥ 40 156 13.78 Marital Status (N = 1132) Unmarried/Divorced 323 28.53 Married 809 71.47 Education Level (N = 1132) Junior College (Diploma) 192 16.96 Bachelor’s Degree 912 80.57 Master’s Degree 28 2.47 PhD 6 0.53 Work Duration (In Years) (N = 1132) < 5 231 20.41 5—10 375 33.13 11—15 316 27.92 16—19 92 8.13 ≥ 20 118 10.42 Title (N = 1132) Nurse 207 18.29 Nurse Practitioner 438 38.69 Supervising Nurse Practitioner 457 40.37 Associate Director of Nurse Practitioners 30 2.65 Ranking (N = 1132) N0 91 8.04 N1 218 19.26 N2 435 38.43 N3 388 34.28 Position (N = 1132) Nurse 1039 91.78 Nurse Manager 81 7.16 Section Nurse Manager 12 1.06 Unit Establishment (N = 1132) Contractual 912 80.57 Personnel Agency 55 4.86 Official Establishment 165 14.58 Salary Level (In Months CNY) (N = 1132) < 4000 136 12.01 4000—7999 820 72.44 ≥ 8000 176 15.55 Section (N = 1132) Internal Medicine 361 31.89 Surgery 286 25.27 Outpatient 138 12.19 Operating Room 82 7.24 Others 265 23.41 Note: N value represents the effective sample size of the project Source : Authors 3.2 Model design and variable description 3.2.1 Variable description 3.2.1.1 Dependent variable (DV) The dependent variables of this study are the EVLN behaviors. We utilized the four-dimensional behavior scale developed by Farrell ( 1983 ) and applied the CRITIC weighting method to measure these behaviors (Diakoulaki et al., 1995 ; Hou et al., 2024). Specifically, we used the CRITIC weighting method to calculate the weight of each indicator (see Table 2) and then formed the dependent variable value for each secondary indicator factor through weighted summation. The indicator values are presented in Table 2. Table 2 EVLN behavioral indicators of nurses in hospital First–level indicators Second–level indicators Sign Weight Exit behavior I often feel like quitting my job. + 31.57% I often feel like quitting my current job. + 26.62% I would accept another new job. + 28.39% I would quit my current job. + 13.42% Voice behavior I often discuss the current situation with my superiors. + 29.63% I often make suggestions to my superiors about certain problems in the organization. + 19.48% I often suggest solutions to problems in the way leaders in my department supervise or direct work. + 25.82% Outside of work, I often ask my colleagues if they need help. + 25.07% Loyalty behavior I often think that my work is the best. + 27.25% I will not leave the organization regardless of whether it is in good times or bad times. + 28.71% I will always do my best to protect the reputation of the organization when others criticize it. + 25.34% I will not leave until the problems in the organization are resolved. + 18.70% Neglect behavior Sometimes, when I don't feel like working, I will call in sick or find other reasons not to work. + 21.65% If I am not in the mood to work, I may be late for work. + 13.57% I will not work at full strength. + 23.28% I will often take breaks during work or not work at all. + 16.31% I may lose motivation for work. + 25.19% Note: A five-point Likert scale was employed for all items (1 = "strongly disagree", 2 = "disagree", 3 = "neutral", 4 = "agree", 5 = "strongly agree"). The questionnaires were anonymous, and respondents were asked to independently ,conscientiously, and responsibly select the EVLN behaviors corresponding to the 17 items. Source : Authors 3.2.1.2 Independent variable (IV) The independent variable of this study is nurses' job dissatisfaction ( NJD ). We utilized the job dissatisfaction scale developed by Sun et al. ( 2023 ) and Turnbach et al.(2024), and applied the combined assignment method to measure nurses' job dissatisfaction. Initially, the APH hierarchical method was employed to calculate the weights of the three-level indicators. Subsequently, the entropy method was used to calculate the weights of the secondary indicators. Finally, the combined assignment method was used to compute the weighted sum of the values of nurses' job dissatisfaction. The values of the indicators are presented in Table 3. Table 3 Job dissatisfaction indicators of nurses in hospital First–level indicators Second–level indicators Third–level indicators Sign Weight Nurse Job Dissatisfaction Internal Job Dissatisfaction Emotional Exhaustion and Burnout + 9.05% Feelings of Ineffectiveness + 6.17% Lack of Autonomy + 12.82% Compassion Fatigue + 12.14% Conflicting Personal Values + 14.76% Other internal reasons + 9.21% External Job Dissatisfaction Insufficient Professional Development Opportunities + 4.36% Poor Work-Life Balance + 7.28% Heavy Workload and Understaffing + 5.87% Inadequate Compensation + 6.12% Lack of Administrative Support + 7.83% Other external reasons + 4.39% Note:The scale utilizes a five-point Likert scale for measurement (1 = "strongly disagree", 2 = "disagree", 3 = "neutral", 4 = "agree", 5 = "strongly agree"). All questionnaires were completed anonymously, and respondents were required to independently, conscientiously, and responsibly select their level of satisfaction for each of the five items. Source : Authors 3.2.1.3 Control variables (CV) Based on the literature, several factors such as working years, professional title, ranking and others impact nurses' behavioral responses, including Exit, Voice, Loyalty, and Neglect (EVLN) behaviors (Blegen, 1993 ; Aiken et al., 2002 ; Tourangeau & Cranley, 2006 ; Laschinger et al., 2009 ; McHugh & Lake, 2010 ; Cowden & Cummings, 2012 ). Drawing on existing research, we selected working years ( WY ), professional title ( Title ), ranking ( Rank ), position ( Posit ), unit establishment ( UE ), salary level ( Salary ), and section ( Sec ) as control variables. The specific definitions of each variable are provided in Table 4. Table 4 Variable definition table Variable Type Variable Name Symbol Measurement method Dependent variable Exit Behavior Exit According to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively. Exit=⅀(three-level indicators affecting Exit×Weight) Voice Behavior Voice According to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively. Voice=⅀(three-level indicators affecting Voice×Weight) Loyalty Behavior Loyalty According to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively. Loyalty=⅀(three-level indicators affecting Loyalty×Weight) Neglect Behavior Neglect According to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively. Neglect=⅀(three-level indicators affecting Neglect×Weight) Independent variable Nurse Job Dissatisfaction NJD The APH hierarchical analysis method was employed to comprehensively calculate the nurse job dissatisfaction scale. NJD = Internal Nurses’ Job Dissatisfaction(INJD)×Weight + External Nurses’ Job Dissatisfaction(ENJD)×Weight Control variables Work Duration (In Years) WY < 5 = 1,5—10 = 2,11—15 = 3,16—19 = 4,≥20 = 5 Title Title Nurse = 1,Nurse Practitioner = 2,Supervising Nurse Practitioner = 3 ,Associate Director of Nurse Practitioners = 4 Ranking Rank N0 = 1,N1 = 2,N2 = 3,N3 = 4 Position Posit Nurse = 1,Nurse Manager = 2,Section Nurse Manager = 3 Unit Establishment UE Contractual = 1,Personnel Agency = 2,Official Establishment = 3 Salary Level Salary < 4000 = 1,4000—7999 = 2,≥8000 = 3 Section Sec Virtual variables: Internal Medicine = 1,Surgery = 2,others = 0 Source : Authors 3.2.2 Model design To test research hypotheses H1a, H1b, H1c, and H1d, we constructed the following regression model (3 − 1): EVLN i t = β1 + β2 NJD i t + β3⅀Control it + β4⅀Sec + α i t ..........................................................( 3 − 1 ) In model (3 − 1), EVLN is the dependent variable (DV), respectively representing the exit, voice, loyalty, and neglect behavior of nurses in hospital; NJD is the independent variable (IV), representing the job dissatisfaction of nurses in hospital; Control is a series of control variables (CV); Sec is the fixed effect, representing the fixed effect of the section; α is the random disturbance term; subscripts i and t represent individual nurses and time, respectively. To test research hypotheses H2a, H2b, H2c, and H2d, the study constructed the following regression model (3 − 2): VNEE i t = β1 + β2 LVNV i t + β3⅀Control it + β4⅀Sec + α i t .........................................................( 3 − 2 ) In model (3 − 1), VNEE is the dependent variable (DV), respectively representing the voice, neglect, exit, and exit behavior of nurses in hospital in light of the order of research hypotheses; LVNV is the independent variable (IV), respectively representing the loyalty, voice, neglect, and voice behavior of nurses in hospital in view of the order of research hypotheses; Control is a series of control variables (CV); Sec is the fixed effect, representing the fixed effect of the section; α is the random disturbance term; subscripts i and t represent individual nurses and time, respectively. 3.3 Statistical and analytical methods This study employed SPSS V29.0, Stata 17.0, SmartPLS 4.0, and SPSSAU software to analyze the valid survey data and perform empirical analyses using quantitative methods. 4. Results 4.1 Descriptive statistics and reliability checks Refer to Table 5 for descriptive statistics and reliability checks. The analysis results in Table 5 show that the measurement reliability of each item exceeds 0.9 (Nunnally,1967; Babbie,1975), indicating that the consistency, stability, and reliability of the scale's test results are very high. In the descriptive statistics, the mean and standard deviation of loyalty behavior and voice behavior are higher than those of neglect behavior and exit behavior. Additionally, the mean and standard deviation of internal nurses’ job dissatisfaction are higher than those of external nurses’ job dissatisfaction (Mean:1.4143 > 0.9772; SD: 0.5936 > 0.4623). The mean values of EVLN behavioral responses to nurses' job dissatisfaction, arranged from highest to lowest, are as follows: loyalty behavior ( Loyalty ) > voice behavior ( Voice ) > neglect behavior ( Neglect ) > exit behavior ( Exit ). From this we can know that when nurses in tertiary public hospitals in Chongqing's urban and county districts experience job dissatisfaction, their initial response is typically one of observation, waiting for the organization to address their legitimate concerns. During this phase, their behavior reflects a form of loyalty. If no improvements are observed over time, they shift to expressing their concerns through voice behavior. If their voice behavior remains unheeded, they resort to neglect, passively reducing their work effort (note: or, alternatively, they may directly choose to exit the organization). In the end, if neglect does not lead to satisfaction, they opt for exit behavior in search of better career opportunities. This sequence of behaviors provides preliminary support for research hypotheses H2a, H2b, H2c, and H2d, and aligns with the findings of Lin et al. ( 2010 ). In the descriptive statistics, the mean of loyalty behavior ( Loyalty ) is 4.4898 with a median of 4.4996 and a standard deviation of 1.3134, showing significant variability in loyalty behavior among the nurses in the sample. The mean of voice behavior ( Voice ) is 4.1465, with a median of 4.0000 and a standard deviation of 1.2676, indicating remarkable variation in voice behavior across the sampled nurses. Conversely, variables such as neglect behavior ( Neglect ), exit behavior ( Exit ), internal nurses’ job dissatisfaction ( INJD ), and external nurses’ job dissatisfaction ( ENJD ) exhibit small standard deviations, presentng little variation in these indicators within the sample. Table 5 Descriptive statistics and reliability checks Variables Obs Mean SD Min Median Max (Cronbach's Alpha) Exit 1132 3.4226 0.2823 0.9999 3.2331 4.9995 0.974 Voice 1132 4.1465 1.2676 3.2638 4.0000 5.0000 0.955 Loyalty 1132 4.4898 1.3134 3.9920 4.4996 5.0000 0.959 Neglect 1132 3.7615 0.4331 1.0000 4.0000 5.0000 0.973 INJD 1132 1.4143 0.5936 0.5472 1.6416 2.7360 0.957 ENJD 1132 0.9772 0.4623 0.4528 0.9056 2.2640 0.923 Source : Authors 4.2 Exploratory factor analysis and correlation analysis When conducting the correlation analysis, an exploratory factor analysis (EFA) (Hotelling, 1933 ) was initially performed to determine whether each question was adequately and scientifically represented within each factor. The results indicated that most of the 29 questions aligned well with the theoretical dimensions, were reasonably and scientifically reflected in the six factor dimensions with a cumulative variance explanation rate of 91.298%, and with only a few cases exhibiting cross-loadings or negative loadings. Subsequently, a Pearson correlation analysis was performed to examine potential collinearity among the variables. The correlation analysis results in Table 6 indicate that nurses' job dissatisfaction ( NJD ) is significantly positively correlated with exit behavior ( Exit ) and neglect behavior ( Neglect ) at the 0.01 level, supporting the hypotheses H1a and H1b. Additionally, nurses' job dissatisfaction ( NJD ) is significantly negatively related to voice behavior ( Voice ) and loyalty behavior ( Loyalty ) at the 0.01 level, supporting the hypotheses H1c and H1d. To further verify the presence of multicollinearity, a variance inflation factor (VIF) test was conducted on all variables. If the Pearson test indicates that the correlation coefficient between variables is greater than 0.8, multicollinearity may be present. Additionally, if the VIF value exceeds 10, multicollinearity is confirmed. The results of the VIF test for this study showed that all variable values ranged between 1.017 and 2.914, which is below the critical value of 10. This confirms that the constructed model does not have multicollinearity problems. Table 6 Correlations between key study variables 6 − 1 Correlations analysis(Exit behavior) Exit NJD WY Title Rank Posit UE Slary Sec Exit 1 NJD 0.1267*** 1 WY 0.0131 -0.0210 1 Title 0.0538* -0.0855*** 0.6582*** 1 Rank 0.0304 -0.0655** 0.7090*** 0.7406*** 1 Posit 0.0639** -0.0442 0.3233*** 0.3518*** 0.2747*** 1 UE -0.0423 0.0348 0.5077*** 0.4263*** 0.3218*** 0.4119*** 1 Slary 0.0642** -0.1155*** 0.3802*** 0.4355*** 0.4364*** 0.3459*** 0.3061*** 1 Sec -0.0274 -0.0016 -0.0320 -0.0257 0.0071 0.0057 0.0162 0.0874*** 1 6 − 2 Correlations analysis(Voice behavior) Voice NJD WY Title Rank Posit UE Slary Sec Voice 1 NJD -0.1694*** 1 WY 0.0745** -0.0210 1 Title 0.0670** -0.0855*** 0.6582*** 1 Rank 0.1072*** -0.0655** 0.7090*** 0.7406*** 1 Posit 0.0296 -0.0442 0.3233*** 0.3518*** 0.2747*** 1 UE -0.0235 0.0348 0.5077*** 0.4263*** 0.3218*** 0.4119*** 1 Slary 0.0354 -0.1155*** 0.3802*** 0.4355*** 0.4364*** 0.3459*** 0.3061*** 1 Sec -0.0030 -0.0016 -0.0320 -0.0257 0.0071 0.0057 0.0162 0.0874*** 1 6 − 3 Correlations analysis(Loyalty behavior) Loyalty NJD WY Title Rank Posit UE Slary Sec Loyalty 1 NJD -0.2937*** 1 WY 0.0363 -0.0210 1 Title 0.0519* -0.0855*** 0.6582*** 1 Rank 0.0682** -0.0655** 0.7090*** 0.7406*** 1 Posit 0.0305 -0.0442 0.3233*** 0.3518*** 0.2747*** 1 UE -0.0309 0.0348 0.5077*** 0.4263*** 0.3218*** 0.4119*** 1 Slary 0.0550* -0.1155*** 0.3802*** 0.4355*** 0.4364*** 0.3459*** 0.3061*** 1 Sec -0.0179 -0.0016 -0.0320 -0.0257 0.0071 0.0057 0.0162 0.0874*** 1 6 − 4 Correlations analysis(Neglect behavior) Neglect NJD WY Title Rank Posit UE Slary Sec Neglect 1 NJD 0.1928*** 1 WY -0.0557* -0.0210 1 Title 0.0051 -0.0855*** 0.6582*** 1 Rank -0.0052 -0.0655** 0.7090*** 0.7406*** 1 Posit -0.0125 -0.0442 0.3233*** 0.3518*** 0.2747*** 1 UE -0.1012*** 0.0348 0.5077*** 0.4263*** 0.3218*** 0.4119*** 1 Slary 0.0115 -0.1155*** 0.3802*** 0.4355*** 0.4364*** 0.3459*** 0.3061*** 1 Sec -0.0438 -0.0016 -0.0320 -0.0257 0.0071 0.0057 0.0162 0.0874*** 1 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 4.3 Benchmark regression Table 7 presents the results of the baseline regression analysis. Column (1) displays the regression results between nurses’ job dissatisfaction (NJD ) and exit behavior ( Exit ) after controlling for department effects and incorporating control variables. Columns (2) and (3) respectively show the regression results between NJD and voice behavior ( Voice ) and between NJD and loyalty behavior ( Loyalty ), also after controlling for department effects and adding control variables. Column (4) presents the regression results between NJD and neglect behavior ( Neglect ), similarly accounting for department effects and control variables. The results indicate that nurses' job dissatisfaction ( NJD ) is significantly positively correlated with exit behavior ( Exit ) and neglect behavior ( Neglect ) at the 0.01 level. This finding suggests that NJD has a substantial positive impact on both Exit and Neglect behaviors, thereby supporting hypotheses H1a and H1b of this study. The primary reason is that hospitals have consistently failed to address the underlying causes of NJD effectively and have not implemented targeted, effective measures to reduce it. This neglect has resulted in increased occurrences of Exit and Neglect behaviors among nurses in hospital. Nurses' job dissatisfaction ( NJD ) is significantly negatively correlated with voice behavior ( Voice ) and loyalty behavior (Loyalty ) at the 0.01 level, indicating that NJD has a substantial negative impact on these behaviors. This finding supports hypotheses H1c and H1d. The primary reason is that hospitals have failed to effectively address nurses' job satisfaction in practice, leading to high levels of NJD , decreased loyalty to the hospital, and a reduction in voice behavior among nurses in hospital. Table 7 Benchmark regression (1) (2) (3) (4) Variables Exit Voice Loyalty Neglect NJD 0.3924*** -0.1483*** -0.1714*** 0.6603*** (4.21) (-3.51) (-6.41) (6.38) WY -0.0032 0.0352 0.0072 -0.0751 (-0.06) (1.57) (0.63) (-1.53) Title 0.1545** -0.0247 -0.0051 0.1582** (2.03) (-0.51) (-0.36) (3.19) Rank -0.0717 0.0523* 0.0225 -0.0224 (-0.86) (1.97) (0.86) (-0.18) Posit 0.3614** 0.0231 0.0147 0.1335 (2.34) (0.76) (0.58) (0.85) UE -0.2146*** -0.0461* -0.0186 -0.2405*** (-3.31) (-1.91) (-1.53) (-3.81) Slary 0.1821** -0.0336 0.0218 0.2426 (2.08) (-0.87) (0.76) (1.39) Constant 2.6641*** 4.2638*** 4.7174*** 2.7572*** (8.23) (46.18) (82.27) (8.35) Sec YES YES YES YES N 1132 1132 1132 1132 R 2 0.04 0.06 0.11 0.07 Adj. R 2 0.03 0.05 0.10 0.06 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 4.4 Robustness checks To further test the reliability of the results, we employed two methods for robustness checks (RC): subsample regressions method (Cifci & Oliver, 2018 ; Cao et al., 2023 ) and remeasurement method (Li & Tian, 2023 ; Zhou et al., 2024 ). 4.4.1 Subsample regressions method Subsample regression is a common method for measuring the robustness of test results (Cifci & Oliver, 2018 ; Cao et al., 2023 ). Given that the nurse manager is a key factor affecting nurses’ job dissatisfaction (Lackey & Antrum, 2024 ), we used subsample regression to test the robustness of our research conclusions, overcome endogeneity, and eliminate the impact of differences in sample hospital characteristics. We reduced the sample by excluding nurse managers and section nurse managers to obtain a subsample. Additionally, to prevent multicollinearity, we removed the control variable Position ( Posit ). The regression results in Table 8 indicate that nurses’ job dissatisfaction ( NJD ) is significantly positively correlated with exit behavior ( Exit ) and neglect behavior ( Neglect ) at the 0.01 level, and significantly negatively related to voice behavior ( Voice ) and loyalty behavior ( Loyalty ) at the 0.01 level. These robustness check results are consistent with the conclusions of this study, demonstrating that our research findings are robust. Table 8 Robust Checks(Subsample regressions method) (1) (2) (3) (4) Variables Exit Voice Loyalty Neglect NJD 0.3768*** -0.1275*** -0.1523*** 0.5404*** (3.93) (-2.99) (-5.77) (5.92) WY 0.0198 0.0168 0.0023 -0.0700 (0.41) (0.97) (0.20) (-1.40) Title 0.1450* -0.0217 -0.0014 0.1658** (1.94) (-0.89) (-0.09) (2.14) Rank -0.0847 0.0545** 0.0133 -0.0311 (-1.23) (2.40) (0.87) (-0.45) UE -0.1905*** -0.0274 -0.0137 -0.2061*** (-2.86) (-1.29) (-0.98) (-3.13) Slary 0.1590* -0.0335 0.0125 0.1414 (1.76) (-1.08) (0.66) (1.52) Costant 3.0082*** 4.2802*** 4.7130*** 2.8224*** (9.18) (44.06) (78.17) (8.55) Sec YES YES YES YES N 1043 1043 1043 1043 R 2 0.04 0.06 0.10 0.07 Adj. R 2 0.02 0.04 0.09 0.05 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 4.4.2 Remeasurement method To further address the endogeneity problem, this study employs the remeasurement method (Li & Tian, 2023 ; Zhou et al., 2024 ) to mitigate its impact on the conclusions. A new nurse job dissatisfaction scale is utilized, incorporating sub-factors that influence nurse job dissatisfaction, such as relationships, support, freedom from fear, and breaks (Lackey & Antrum, 2024 ). This approach allows for a reevaluation of the impact of nurses’ job dissatisfaction ( NJD ) on EVLN behavior. The results in Tables 9 demonstrate that NJD is significantly positively correlated with exit behavior ( Exit ) and neglect behavior ( Neglect ), while being significantly negatively correlated with voice behavior ( Voice ) and loyalty behavior ( Loyalty ). These findings further underscore the robustness of our research results. Table 9 Robust Checks(Remeasurement method) (1) (2) (3) (4) Variables Exit Voice Loyalty Neglect NJD 0.3673*** -0.1362*** -0.1413*** 0.5923*** (4.47) (-3.95) (-6.75) (7.60) WY -0.0039 0.0247 0.0065 -0.0725 (-0.09) (1.53) (0.61) (-1.53) Title 0.1449** -0.0138 -0.0033 0.1520** (2.02) (-0.60) (-0.21) (2.05) Rank -0.0637 0.0418* 0.0121 -0.0132 (-0.97) (1.92) (0.83) (-0.20) Posit 0.3635** 0.0278 0.0119 0.1436 (2.42) (0.59) (0.41) (0.90) UE -0.2022*** -0.0373* -0.0208 -0.2336*** (-3.28) (-1.91) (-1.65) (-3.86) Slary 0.1790** -0.0259 0.0102 0.1514* (2.10) (-0.89) (0.57) (1.73) Constant 2.5888*** 4.2818*** 4.7176*** 2.5647*** (7.89) (45.64) (80.62) (7.72) Sec YES YES YES YES N 1132 1132 1132 1132 R 2 0.05 0.07 0.11 0.09 Adj. R 2 0.03 0.05 0.10 0.08 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 5. Heterogeneity checks and EVLN behaviors evolution study 5.1 Heterogeneity checks Numerous scholars have demonstrated that employees' job dissatisfaction, influenced by personality traits, exhibits variations across socio-demographic variables such as gender, age, marital status, and education level (Metle, 2001 ; Halkos & Bousinakis, 2017 ; Bagheri Hosseinabadi et al., 2019 ). Moreover, the reasons of nurses' job dissatisfaction vary (Pressley & Garside, 2023 ; Lackey & Antrum, 2024 ). Therefore, this study considers gender, age, marital status, education level as heterogeneity indicators to examine the disparities in nurses' job dissatisfaction ( NJD ) and EVLN behaviors. 5.1.1 Gender’s heterogeneity The study explores how nurses' job dissatisfaction ( NJD ) varies by gender due to different personality traits. To investigate this, nurses were divided into male (assigned 1) and female (assigned 0) groups. Table 10 illustrates that compared to the male, NJD in female is associated with increased exit and neglect behaviors ( Exit and Neglext ), while it decreases voice and loyalty behaviors ( Voice and Loyalty ). Table 10 Gender heterogeneity (1) (2) (3) (4) (5) (6) (7) (8) Variables Exit Exit Voice Voice Loyalty Loyalty Neglect Neglect Gender = 1 Gender = 0 Gender = 1 Gender = 0 Gender = 1 Gender = 0 Gender = 1 Gender = 0 NJD 0.1838 0.3763*** 0.2166 -0.1600*** -0.0508 -0.1679*** 0.7110 0.5328*** (0.40) (3.96) (1.62) (-3.70) (-0.52) (-6.33) (1.67) (5.83) WY -0.1301 -0.0005 0.0483 0.0230 -0.0891 0.0083 -0.0675 -0.0654 (-0.33) (-0.01) (0.42) (1.39) (-0.97) (0.77) (-0.20) (-1.33) Title 0.3420 0.1192 -0.0319 -0.0086 -0.0350 0.0008 0.3004 0.1249 (0.67) (1.62) (-0.20) (-0.36) (-0.35) (0.05) (0.59) (1.63) Rank -0.0523 -0.0664 0.1170 0.0423* 0.1535* 0.0081 0.0344 -0.0228 (-0.14) (-0.97) (0.96) (1.89) (1.81) (0.53) (0.09) (-0.33) Posit 0.4740 0.3500** 0.2934* 0.0192 0.2867 0.0058 -0.2300 0.1274 (0.63) (2.22) (1.71) (0.40) (1.42) (0.20) (-0.33) (0.76) UE -0.2230 -0.2065*** -0.2256** -0.0328 -0.1751* -0.0173 -0.1479 -0.2352*** (-0.45) (-3.26) (-2.29) (-1.62) (-1.69) (-1.34) (-0.38) (-3.71) Slary 0.1151 0.2071** -0.2000 -0.0202 -0.0466 0.0142 0.4349 0.1328 (0.23) (2.33) (-1.06) (-0.68) (-0.36) (0.78) (1.01) (1.44) Constant 2.7195* 2.6628*** 4.1505*** 4.2386*** 4.6745*** 4.6896*** 1.9299 2.8497*** (1.95) (7.78) (10.94) (43.98) (22.77) (76.59) (1.60) (8.15) Sec YES YES YES YES YES YES YES YES N 56 1076 56 1076 56 1076 56 1076 R 2 0.26 0.04 0.32 0.07 0.29 0.12 0.37 0.07 Adj. R 2 -0.04 0.03 0.03 0.05 0.00 0.10 0.11 0.05 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 5..1.2 Age’s heterogeneity The study investigates how nurses' age impacts their job dissatisfaction due to varying personality traits. Nurses were categorized into two age groups: under 30 years old (assigned 1) and over 30 years old (including 30 years old, assigned 0). Table 11 reveals that compared to the younger (under 30 years old), nurses’ job dissatisfaction ( NJD ) in the older (over 30 years old) is linked to increased exit and neglect behaviors ( Exit and Neglect ), while it decreases voice behavior ( Voice ). The regression results from columns (5) and (6) of Table 11 reveal significantly negative coefficients of job dissatisfaction ( NJD ) for nurses both over and under 30 years old at the 1% significance level. This suggests that job dissatisfaction ( NJD ) among nurses of different age groups can reduce the likelihood of loyalty behavior (Loyalty ). Subsequent inclusion of the interaction term ( NJD×Age ) did not yield a significant coefficient, indicating no substantial difference in the impact of job dissatisfaction ( NJD ) on loyalty behavior ( Loyalty ) between nurses of different age groups. Table 11 Age heterogeneity (1) (2) (3) (4) (5) (6) (7) (8) (9) Variables Exit Exit Voice Voice Loyalty Loyalty Loyalty Neglect Neglect Age = 1 Age = 0 Age = 1 Age = 0 Age = 1 Age = 0 Total sample Age = 1 Age = 0 NJD 0.1582 0.5398*** -0.0810 -0.1730*** -0.1257*** -0.1848*** -0.1720*** 0.2388 0.7355*** (1.02) (4.71) (-1.21) (-3.38) (-2.99) (-5.76) (-6.43) (1.61) (6.79) NJD×Age 0.0295 (1.23) WY 0.2203* -0.0090 0.0537 0.0289 0.0321 0.0047 0.0129 0.2045* -0.0659 (1.81) (-0.16) (1.06) (1.47) (0.95) (0.37) (1.13) (1.67) (-1.10) Title 0.1964* 0.1381 0.0079 -0.0262 -0.0012 -0.0081 -0.0024 0.0472 0.1929** (1.67) (1.48) (0.18) (-0.93) (-0.04) (-0.43) (-0.15) (0.39) (1.99) Rank -0.1173 -0.0384 0.0458 0.0432 -0.0079 0.0370* 0.0164 0.0014 -0.0353 (-1.19) (-0.40) (1.29) (1.41) (-0.34) (1.78) (1.09) (0.01) (-0.36) Posit -0.0731 0.4218*** -0.2638*** 0.0382 0.0425 0.0052 0.0128 -0.5878 0.2199 (-0.29) (2.60) (-3.69) (0.78) (0.29) (0.18) (0.46) (-1.21) (1.28) UE -0.1565 -0.1990*** 0.0321 -0.0424* 0.0068 -0.0256* -0.0207 0.0248 -0.2696*** (-0.92) (-2.89) (0.68) (-1.93) (0.20) (-1.81) (-1.64) (0.17) (-3.89) Slary 0.2225 0.1464 -0.0709 0.0097 -0.0045 0.0276 0.0125 0.2474* 0.0709 (1.60) (1.31) (-1.38) (0.27) (-0.14) (1.27) (0.70) (1.73) (0.62) Constant 2.9536*** 2.3745*** 4.4299*** 4.2091*** 4.6474*** 4.6443*** 4.6727*** 3.1919*** 2.5070*** (5.64) (5.21) (30.54) (31.81) (27.68) (52.75) (73.43) (4.75) (5.51) Sec YES YES YES YES YES YES YES YES YES N 433 699 433 699 433 699 1132 433 699 R 2 0.05 0.07 0.07 0.09 0.08 0.15 0.11 0.08 0.10 Adj. R 2 0.01 0.04 0.03 0.06 0.04 0.12 0.10 0.04 0.08 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 5.1.3 Marital heterogeneity Nurses' job dissatisfaction ( NJD ) can be influenced by objective environmental factors, such as family circumstances, which vary based on marital status. To assess the impact of marital heterogeneity on the research conclusions, this study categorized NJD in hospital into two groups: the married group, assigned a value of 1, and the unmarried/divorced group, assigned a value of 0. The research results in columns (1) through (4) of Table 12 indicate that, compared to unmarried or divorced nurses, job dissatisfaction ( NJD ) among married nurses can increase the occurrence of exit behavior ( Exit ) and decrease the occurrence of voice behavior ( Voice ). From the regression results in columns (5) and (6) of Table 12, the coefficients for job dissatisfaction ( NJD ) among married nurses and unmarried/divorced nurses are significantly negative at the 1% level, suggesting that job dissatisfaction ( NJD ) in nurses with different marital statuses can reduce the occurrence of loyalty behavior ( Loyalty ). Additionally, after including the interaction term (NJD × Marriage ) in the regression, the coefficient for the interaction term was not significant, indicating no significant difference in the impact of job dissatisfaction ( NJD ) on reducing loyalty behavior ( Loyalty ) between nurses of different marital statuses. The regression results in columns (8) and (9) of Table 12 indicate that job dissatisfaction ( NJD ) among nurses with different marital statuses can promote neglect behavior ( Neglect ). After adding the interaction term (NJD × Marriage ) and performing the regression, the coefficient for the interaction term was not significant, suggesting that job dissatisfaction ( NJD ) among nurses with different marital statuses does not significantly differ in promoting neglect behavior ( Neglect ). Table 12 Marriage heterogeneity (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Variables Exit Exit Voice Voice Loyalty Loyalty Loyalty Neglect Neglect Neglect Marrige = 1 Marrige = 0 Marrige = 1 Marrige = 0 Marrige = 1 Marrige = 0 Total Sample Marrige = 1 Marrige = 0 Total Sample NJD 0.4153*** 0.2905 -0.1459*** -0.1061 -0.1657*** -0.1442*** -0.1389*** 0.5800*** 0.4129** 0.5120*** (3.95) (1.55) (-3.08) (-1.25) (-5.64) (-2.75) (-4.61) (5.68) (2.38) (5.10) NJD× Marrige -0.0289 0.0529 (-1.38) (0.85) WY -0.0171 -0.0654 0.0217 0.0253 0.0109 0.0143 0.0102 -0.0408 -0.2329* -0.0764 (-0.33) (-0.58) (1.15) (0.66) (0.89) (0.55) (0.94) (-0.75) (-1.97) (-1.54) Title 0.1031 0.2510* -0.0115 -0.0230 -0.0048 -0.0157 -0.0016 0.0978 0.2881** 0.1445* (1.21) (1.73) (-0.44) (-0.45) (-0.27) (-0.44) (-0.10) (1.08) (2.01) (1.93) Rank -0.0283 -0.1375 0.0355 0.0758 0.0091 0.0421 0.0146 0.0082 0.0104 -0.0168 (-0.36) (-1.06) (1.38) (1.64) (0.51) (1.46) (0.98) (0.10) (0.08) (-0.25) Posit 0.3693** 0.1909 0.0307 -0.1397 0.0318 -0.1579* 0.0153 0.1852 -0.5464** 0.1232 (2.22) (0.61) (0.61) (-0.93) (1.08) (-1.69) (0.54) (1.06) (-2.19) (0.77) UE -0.1806** -0.2457* -0.0438** -0.0109 -0.0371** 0.0476* -0.0221* -0.2594*** -0.1328 -0.2268*** (-2.54) (-1.90) (-1.96) (-0.22) (-2.54) (1.71) (-1.74) (-3.63) (-0.99) (-3.63) Slary 0.1549 0.2317 0.0067 -0.0946 0.0289 -0.0366 0.0109 0.1146 0.1506 0.1336 (1.47) (1.47) (0.20) (-1.52) (1.48) (-0.96) (0.62) (1.06) (0.91) (1.50) Constant 2.8389*** 2.4629*** 4.2613*** 4.3507*** 4.6975*** 4.7733*** 4.6884*** 2.9941*** 2.7126*** 2.7845*** (7.44) (4.17) (35.23) (23.34) (62.08) (41.30) (77.10) (7.73) (4.91) (8.32) Sec YES YES YES YES YES YES YES YES YES YES N 814 318 814 318 814 318 1132 814 318 1132 R 2 0.06 0.06 0.08 0.07 0.14 0.12 0.11 0.07 0.11 0.07 Adj. R 2 0.03 0.00 0.05 0.02 0.12 0.07 0.10 0.05 0.06 0.05 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 5.1.4 Heterogeneity of education level Different education levels result in varying personality traits among nurses, which subsequently influence their job dissatisfaction. To examine the impact of educational heterogeneity on the study's conclusions, nurses with a bachelor's degree or higher were grouped into one category (Degree = 1), while those with less than a bachelor's degree were grouped into another category (Degree = 0). The regression results in columns (1) and (2) of Table 13 indicate that the coefficient of nurses’ job dissatisfaction ( NJD ) is significantly positive at the 1% level for the bachelor's degree or higher, and significantly positive at the 10% level for nurses with less than a bachelor's degree. This suggests that job dissatisfaction ( NJD ) among nurses with different educational backgrounds promotes exit behavior ( Exit ). Moreover, the impact of nurses’ job dissatisfaction ( NJD ) on promoting exit behavior ( Exit ) is stronger for the bachelor's degree or higher compared to those with less education. The regression results in columns (3) and (4) of Table 13 indicate that nurses with an undergraduate degree or higher experience greater reductions in voice behavior ( Voice ) due to job dissatisfaction (NJD ) compared to those with less education. Additionally, columns (5) and (6) show that the coefficient for job dissatisfaction (NJD) is significantly negative at the 1% level for nurses with an undergraduate degree or higher, and at the 5% level for those with less education. This suggests that nurses’ job dissatisfaction ( NJD ) leads to a reduction in loyalty behavior ( Loyalty ) across different educational backgrounds, with a more pronounced effect for nurses with an undergraduate degree or higher. The regression results in columns (8) and (9) of Table 13 show that the coefficients for job dissatisfaction ( NJD ) are significantly positive at the 1% level for both nurses with a bachelor's degree or higher and those with less than a bachelor's degree. This indicates that job dissatisfaction ( NJD ) across different educational backgrounds can increase the occurrence of neglect behavior ( Neglect ). Furthermore, when the interaction term ( NJD×Degree ) is included in the model, its regression coefficient is significantly positive, suggesting that job dissatisfaction (NJD ) is more likely to promote neglect behavior ( Neglec t) among nurses with a bachelor's degree or higher compared to those with less education. Table 13 Degree heterogeneity (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Variables Exit Exit Voice Voice Loyalty Loyalty Loyalty Neglect Neglect Neglect Degree = 1 Degree = 0 Degree = 1 Degree = 0 Degree = 1 Degree = 0 Total Sample Degree = 1 Degree = 0 Total Sample NJD 0.3737*** 0.4463* -0.1592*** -0.0614 -0.1665*** -0.1254** -0.1473*** 0.5290*** 0.6258*** 0.4443*** (3.66) (1.94) (-3.50) (-0.65) (-5.97) (-2.04) (-4.63) (5.44) (2.82) (4.23) NJD× Degree -0.0164 0.1336** (-0.73) (2.02) WY -0.0304 0.1727 0.0302 -0.0314 0.0058 0.0035 0.0051 -0.0848 0.0944 -0.0612 (-0.57) (1.65) (1.64) (-0.78) (0.47) (0.13) (0.48) (-1.51) (0.90) (-1.27) Title 0.0966 0.1439 -0.0205 0.0896 -0.0066 0.0159 -0.0008 0.0862 0.1847 0.1210 (1.16) (0.78) (-0.78) (1.63) (-0.38) (0.33) (-0.05) (1.00) (0.99) (1.57) Rank -0.0668 -0.1145 0.0451* 0.0138 0.0141 0.0038 0.0133 -0.0328 -0.0629 -0.0245 (-0.91) (-0.71) (1.86) (0.25) (0.87) (0.09) (0.89) (-0.43) (-0.38) (-0.36) Posit 0.3392** 0.6680 0.0340 -0.0360 0.0111 -0.0109 0.0144 0.0982 0.6752 0.1323 (2.07) (1.25) (0.67) (-0.24) (0.36) (-0.13) (0.51) (0.57) (1.31) (0.82) UE -0.1533** -0.4561*** -0.0324 -0.0673 -0.0162 -0.0269 -0.0200 -0.1722** -0.5886*** -0.2268*** (-2.23) (-2.98) (-1.49) (-1.39) (-1.15) (-0.83) (-1.56) (-2.57) (-3.57) (-3.68) Slary 0.1842* 0.0663 -0.0331 0.0008 0.0132 -0.0030 0.0120 0.1433 0.0234 0.1308 (1.91) (0.33) (-1.02) (0.01) (0.68) (-0.07) (0.68) (1.43) (0.11) (1.47) Constant 2.9146*** 1.9131** 4.2973*** 4.1751*** 4.7157*** 4.7116*** 4.7004*** 3.0817*** 1.8168** 2.8124*** (8.24) (2.15) (40.12) (17.22) (70.43) (34.00) (78.72) (8.47) (2.07) (8.41) Sec YES YES YES YES YES YES YES YES YES YES N 910 222 910 222 910 222 1132 910 222 1132 R 2 0.04 0.10 0.08 0.12 0.13 0.11 0.11 0.06 0.16 0.07 Adj. R 2 0.02 0.03 0.06 0.05 0.11 0.04 0.10 0.04 0.09 0.06 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 5.2 Study on the evolution of EVLN behaviors in relation to nurses' job dissatisfaction In terms of the internal evolution of hospital nurses' EVLN behaviors, the data in Table 14 show that loyalty behavior has a positive impact on voice behavior, voice behavior positively influences neglect behavior, neglect behavior positively impacts exit behavior, and voice behavior also has a positive impact on exit behavior. Therefore, hypotheses H2a, H2b, H2c, and H2d are all confirmed. This indicates that the internal evolution of hospital nurses' EVLN behaviors, from loyalty to exit, generally follows the path of "loyalty behavior → voice behavior → neglect behavior → exit behavior." Specifically, the transition of hospital nurses from loyalty to exit behavior can be divided into two main stages: The first step is the transition from loyalty behavior to voice behavior. The findings of this study align with the conclusions of Hirschman ( 1970 ) and Farrell ( 1983 ), indicating a positive correlation between loyalty and voice behavior. A key distinction in this study is that loyalty behavior is not directly linked to exit behavior. When employees experience job dissatisfaction, they initially remain silent, hoping for improvement. If the situation does not improve, they will opt for voice behavior rather than exit. The second step involves the transition from voice behavior to exit behavior. Specifically, the results of the model (3 − 2) analysis show that when voice behavior is not addressed, hospital nurses may choose to exit directly. Furthermore, the results indicate that voice behavior is positively correlated with neglect behavior, which in turn is positively correlated with exit behavior, suggesting that neglect behavior serves as a mediating variable between voice and exit behaviors. When nurses in hospital express dissatisfaction with their work and the organization fails to address these concerns, they may develop negative attitudes toward their job, exhibit a careless work attitude, and eventually seek opportunities to leave. If voice behavior fails, neglect behavior often follows, and this neglect may ultimately lead to exit. This demonstrates that if voice behavior is not addressed, nurses are likely to engage in exit behavior. In summary, after choosing loyalty behavior, if nurses in hospital see no improvement in their dissatisfaction, they will resort to voice behavior. If their voice behavior is still not addressed, they will either choose to exit directly or transition to exit behavior through neglect (see Fig. 3). Table 14 Hypothesis testing results of EVLN behaviors Hypotheses Relationships between variables Standardized path coefficients(β) Standard error(STDEV) T-Value P-Value Results H2a Loyalty→Voice 0.611 0.023 26.965 0.000 (***) Accepted H2b Voice→Neglect 0.005 0.031 3.171 0.024 (**) Accepted H2c Neglect→Exit 0.770 0.020 39.366 0.000 (***) Accepted H2d Voice→Exit 0.065 0.020 3.216 0.001 (***) Accepted Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Authors 6. Conclusion 6.1 Research Conclusion and Discussion Based on the principles of dynamic game theory, this study developed a theoretical model to examine the relationship between nurse job dissatisfaction (NJD) and EVLN behavioral responses in Chongqing's public tertiary hospitals, as well as the evolution of these behaviors. The empirical findings reveal the following: First of all, nurse job dissatisfaction (NJD) is positively correlated with exit behavior and neglect behavior, while it is negatively correlated with voice behavior and loyalty behavior. These results align with the predictions of dynamic game theory (Haurie et al., 2012 ; Laraki & Mertikopoulos, 2013 ; Başar & Zaccour, 2018 ; Ke et al., 2022 ), indicating that the relationship between job dissatisfaction and EVLN behaviors among nurses in Chongqing's public hospitals is consistent with findings from studies on corporate governance. This suggests that a certain level of commonality exists between corporate governance and hospital management, allowing both fields to benefit from the insights of one another's research. And second, the heterogeneity analysis shows that the impact of nurse job dissatisfaction on EVLN behaviors varies across gender, age, marital status, and education level. Specifically, complete heterogeneity is observed in gender and education level, while partial heterogeneity is found in age and marital status. These findings are largely consistent with previous research, indicating that job dissatisfaction, influenced by personality traits, is heterogeneous across socio-demographic variables such as gender, age, marital status, and education level (Metle, 2001 ; Bagheri Hosseinabadi et al., 2019 ; Halkos & Bousinakis, 2017 ), which further leads to differences in the reasons for nurse job dissatisfaction (Pressley & Garside, 2023 ; Lackey & Antrum, 2024 ). Thirdly, according to the evolutionary analysis of EVLN behaviors, nurses in hospital tend to choose voice behavior following loyalty behavior, followed by neglect and exit behaviors. This pattern aligns with the Confucian cultural emphasis on "moderation and stability," which often leads hospital nurses to be reluctant to leave institutions where they have worked for many years. Under the influence of historical inertia and traditional values, nurses in hospital typically wait for a period when dissatisfied with their work. If the situation does not improve, they actively engage in communication with the hospital to remain employed. This phenomenon is more evident in highly competitive market environments and during economic downturns. Only when communication fails or nurses are compelled by circumstances do they opt for exit behavior. 6.2 Managerial Implication The findings of this study have important practical and managerial implications. Our results suggest that medical policymakers, hospital managers, and practitioners should implement proactive measures to prevent nurse job dissatisfaction, as well as reactive measures to address EVLN behaviors once they occur. These efforts are essential to minimizing exit and neglect behaviors, while enhancing nurse satisfaction, voice behavior, and loyalty. Especially, voice behavior holds a central role in addressing job dissatisfaction. Creating effective communication channels is crucial for resolving nurse dissatisfaction. First of all, nursing practitioners should thoroughly understand the root causes of dissatisfaction, both in form and nature, and perhaps conduct regular evaluations to reduce the occurrence of dissatisfaction-related emotions and behaviors. Secondly, hospital managers must establish dedicated channels for reporting nurse dissatisfaction, dismantle administrative barriers to hierarchical reporting, and foster a people-centered, open hospital culture. By creating a supportive work environment through psychological counseling, performance reform, skills training, and opportunities for job promotion, hospitals can reduce dissatisfaction, encourage loyalty among nurses, and improve overall management. These measures will contribute to the high-quality development of hospitals. In the end, medical policymakers should address nurse job dissatisfaction and its relationship with EVLN behaviors through system design and policy frameworks. By conducting in-depth research into the underlying causes and crafting health policies based on real-world conditions, policymakers can promote the efficient allocation of medical resources and support the sustainable development of healthcare systems in the region. 6.3 Theoretical implications This study makes several contributions to the literature. To begin with, it offers a new research avenue for Farrell’s EVLN behavior theory (Pendola, 2024 ), specifically examining the reactions and levels of nurses' job dissatisfaction and providing a medical adaptation of the theoretical model, thereby expanding the scope of theoretical research. Secondly, it deepens the application of dynamic game theory in hospital management and enhances behavioral management theory within hospitals. And then, this study analyzes the relationship between nurses’ job dissatisfaction and EVLN behaviors, refining the theoretical model by exploring the heterogeneity of variables such as gender, age, marital status, and education level. Finally, this study is the first to apply dynamic game theory to the evolution of EVLN behaviors resulting from nurses' job dissatisfaction, which contributes to improving the internal management mechanisms in hospitals. 6.4 Limitations and future directions We must acknowledge the limitations of this study and propose directions for future research. First, the nurses’ job dissatisfaction scale used in this study considered only a few critical factors, while numerous factors contribute to nurse job dissatisfaction (Pressley & Garside, 2023 ; Lackey & Antrum, 2024 ). These additional factors were not accounted for and should be included in future studies to improve the reliability of the conclusions. Second, this study was limited to Chongqing, China, and did not consider data from other provinces or cities. Thus, the generalizability of the findings is uncertain. Future research should expand the dataset to include public hospitals from other regions in China to enhance the universality of the conclusions. Third, this study focused solely on public hospitals and did not include private hospitals. With the ongoing reform of China’s healthcare system, private hospitals are expected to increase in number. Whether the same conclusions apply to private hospitals remains to be explored. Future research should incorporate private hospitals to examine the relationship between nurse’s job dissatisfaction and EVLN behaviors, further enriching hospital management theory and promoting the high-quality development of healthcare system. Fourth, while this study explored the internal pathways of EVLN behaviors caused by nurse job dissatisfaction, it did not investigate the evolution of EVLN behaviors arising from structural differences in nurse dissatisfaction. Future research should address this to better manage and mitigate nurses’ job dissatisfaction, and further decrease the occurrence of nurses shortage phenomenon. Fifth, due to the particularities of China’s healthcare system (Blumenthal & Hsiao, 2005 ; Jakovljevic et al., 2023 ) and objective constraints, public hospital data from other countries were not included. Whether the findings of this study are applicable to countries outside of China remains to be determined. Future research should involve collaboration with international scholars to further contribute to the global development of healthcare management theory. Declarations Author Contribution Jackie Zhanbiao Li analysed data and drafted the manuscript. Ming Chen and Wanqin Hu designed the study and performed the data collection. Yingqian Lao and Ming Chen made substantive intellectual contributions to the conception of the work and the interpretation of the data and revised the manuscript. These authors contributed equally to this work. Data availability Data will be made available on request. Declaration of competing interest Authors have no competing and conflicting interests . Ethics statement Approval was obtained from the ethics committee of Ophthalmic & Dental Center of Putuo District in Shanghai (No. 2023-03). All research was performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Informed consent Informed consent was obtained from all individual participants included in the study. References Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5315438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":373882957,"identity":"544054ce-329f-43ef-9fd8-cc1cccee6351","order_by":0,"name":"Jackie Zhanbiao Li","email":"","orcid":"","institution":"The School of Nursing and Health Studies, Hong Kong Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Jackie","middleName":"Zhanbiao","lastName":"Li","suffix":""},{"id":373882958,"identity":"1dc2a685-857c-411a-bda2-2073b9be1ddc","order_by":1,"name":"Ming Chen","email":"data:image/png;base64,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","orcid":"","institution":"The School of Nursing and Health Studies, Hong Kong Metropolitan University","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Chen","suffix":""},{"id":373882959,"identity":"cdad2c2f-2d15-49d3-9a2d-5c3531a3ad69","order_by":2,"name":"Yingqian Lao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yingqian","middleName":"","lastName":"Lao","suffix":""},{"id":373882960,"identity":"3f5c237e-ec15-41ea-b193-6732bd9b9518","order_by":3,"name":"Wanqin Hu","email":"","orcid":"","institution":"The School of Nursing and Health Studies, Hong Kong Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Wanqin","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-10-23 03:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5315438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5315438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69086345,"identity":"e203f0cf-29ec-46f2-8097-3f34146cdcb1","added_by":"auto","created_at":"2024-11-15 12:44:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9562,"visible":true,"origin":"","legend":"\u003cp\u003eThe EVLN individual response categorization model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e EVLN: Exit, Voice, Loyalty, and Neglect. Source: Farrell (1983)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5315438/v1/6f0e7f2572d234789b6c30ed.png"},{"id":69086348,"identity":"baa470c7-ca44-4ac1-9b57-aca113c79616","added_by":"auto","created_at":"2024-11-15 12:44:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31697,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework on Nurses’ Job Dissatisfaction and EVLN Behaviors\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e H1a: Nurses’ job dissatisfaction is positively related to exit behavior; H1b: Nurses' job dissatisfaction is negatively related to voice behavior; H1c: Nurses' job dissatisfaction is negatively related to loyalty behavior; H1d: Nurses’ job dissatisfaction is positively related to neglect behavior; H2a: Nurses' loyalty behavior has a positive impact on voice behavior; H2b: Nurses' voice behavior has a positive impact on neglect behavior; H2c: Nurses' neglect behavior has a positive impact on exit behavior; H2d: Nurses' voice behavior has a positive impact on exit behavior. (-): negative; (+): positive.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5315438/v1/6412424a05eea5b95d68058e.png"},{"id":69086347,"identity":"a4beceab-935c-4649-b08d-9c0fe8cc0aa9","added_by":"auto","created_at":"2024-11-15 12:44:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40186,"visible":true,"origin":"","legend":"\u003cp\u003eInternal evolution path of EVLN behaviors\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e EVLN: Exit, Voice, Loyalty, and Neglect.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5315438/v1/8e1434a5e50975e0aa392c66.png"},{"id":79930873,"identity":"dbd5a661-b519-4370-b22f-a568bb7f1e36","added_by":"auto","created_at":"2025-04-04 16:01:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3086285,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5315438/v1/532b3fcf-67b6-4879-b165-c0eab5b9f936.pdf"},{"id":69086346,"identity":"a898b18c-c5f3-45ea-86c8-2ed141f19109","added_by":"auto","created_at":"2024-11-15 12:44:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22058,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-5315438/v1/ee3dbdbbaf666dea61d3eb01.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"How does job dissatisfaction fuel nurse exit, voice, loyalty, and neglect (EVLN) behaviors? A Cross-sectional Survey","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNursing plays a vital role in global medical care and has significantly contributed to the provision of high-quality medical services (Tamata \u0026amp; Mohammadnezhad, 2022). A sufficient number of nurses is essential for strengthening the medical care sector, expanding health care coverage, and achieving the WHO's sustainable development health goals (Alameddine et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the crucial role of nursing in medical care, one of the major challenges facing the global medical system today is the severe shortage of nurses, which adversely affects the quality of care and the health and well-being of populations worldwide (Yahyaei et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This shortage impacts over 1\u0026nbsp;billion people, particularly vulnerable groups such as women and children, who are in urgent need of high-quality medical services (Aluko et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A primary reason for this shortage is the high turnover rate among nurses due to job dissatisfaction. If left unaddressed, this issue will perpetuate a vicious cycle of continued nursing shortages, declining care quality, and an inability to meet the health needs of global populations effectively (Matsuo et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Varasteh et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, addressing the root causes of the nurse shortage and understanding the underlying reasons for nurse attrition have become critical topics in both academia and practice. Investigating and analyzing nurses' behavioral responses to job dissatisfaction is a key step in addressing this issue.\u003c/p\u003e \u003cp\u003eAs a model for measuring behavioral responses to job dissatisfaction, EVLN (exit, voice, loyalty, and neglect) has become a key framework for scholars globally to study stakeholder behavior in situations of dissatisfaction. It has been widely applied in various research areas, such as psychological contracts (Kingshott et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), human resource management (Hsiung \u0026amp; Yang, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barry \u0026amp; Wilkinson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and conflict management (Merolla \u0026amp; Harman, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lee \u0026amp; Varon, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the use of the EVLN model to examine nurses' behavioral responses to job dissatisfaction within the medical system remains underexplored. This study employs a mixed-methods approach to analyze the relationship between nurses' job dissatisfaction and their corresponding EVLN behaviors, as well as the evolution of these behaviors in response to job dissatisfaction.\u003c/p\u003e \u003cp\u003eTo establish the link between nurses' job dissatisfaction and the EVLN behaviors, we utilized a questionnaire-based approach to collect data on nurses' behavioral responses in tertiary public hospitals in Chongqing\u0026rsquo;s urban and county districts from January to May 2024. Dynamic game theory was then applied to investigate the relationship between nurses' job dissatisfaction and their EVLN behaviors, and to explore the evolution of these behaviors over time.\u003c/p\u003e \u003cp\u003eThis study contributes to the existing academic literature on the EVLN model driven by job dissatisfaction in several ways. First, it extends the impact of job dissatisfaction to the EVLN behaviors of hospital nurses, providing a comprehensive theoretical framework for understanding the relationship between nurse job dissatisfaction and EVLN behaviors. This, in turn, strengthens existing research on the influence of EVLN behaviors on hospital management. Second, our study integrates insights from psychology, behavioral economics, and hospital management, offering valuable contributions to expanding the literature in these fields. Finally, dynamic game theory is applied for the first time to study the evolution of EVLN behaviors in response to nurses\u0026rsquo; job dissatisfaction, thereby enhancing the behavioral evolution mechanism model in hospital management.\u003c/p\u003e \u003cp\u003eOur study is organized into six sections: the second section presents the theoretical foundation and research hypotheses, the third covers the research design, the fourth reports the results, the fifth explores heterogeneity and the evolution of EVLN behaviors, and the sixth provides the conclusion.\u003c/p\u003e"},{"header":"2. The theoretical basis and research hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The theoretical basis of EVLN model\u003c/h2\u003e \u003cp\u003eThe development of the EVLN model has progressed through three crucial stages. First, the EVL theory, proposed by Hirschman (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), suggests that when individuals are dissatisfied with the functioning of a social system, they typically exhibit three behaviors\u0026mdash;exit, voice, or loyalty\u0026mdash;collectively known as the EVL theory. Hirschman also elaborated on the specific concepts underlying these three behaviors. Second, Kolarska \u0026amp; Aldrich (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Rusbult \u0026amp; Zembrodt (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) further advanced this theory by introducing the EVLN model, explicitly identifying the behavior of \"neglect\" for the first time. In their research, neglect was characterized by a lack of concern, carelessness, and irresponsible behavior. Finally, Farrell (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) refined and expanded the EVLN theory by formally incorporating neglect behavior and organizing the four behaviors\u0026mdash;exit, voice, loyalty, and neglect\u0026mdash;along two dimensions: constructive-destructive and active-passive. This development resulted in a more comprehensive EVLN model, as illustrated in Fig.\u0026nbsp;1 below.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;1, the first dimension of the EVLN model is the destructive-constructive dimension. Voice and loyalty behaviors are considered constructive forms of expression, as they reflect efforts to maintain a positive employment relationship and a satisfactory working environment. In contrast, exit and neglect behaviors are classified as destructive forms of expression. The second dimension is the active-passive (or positive-negative) dimension. Exit and voice behaviors are viewed as active forms of expression, indicating a willingness to address job dissatisfaction (Spencer \u0026amp; Spencer, 1993). Conversely, neglect and loyalty behaviors are considered passive and negative forms of expression. According to Farrell, exit refers to the voluntary act of leaving a job, whether by exiting the company or seeking new employment to escape an unsatisfactory position. Voice emphasizes the importance of employee participation in addressing organizational decline and restoring previous performance levels, functioning as a key recovery mechanism that requires collective employee involvement. Loyalty reflects an individual\u0026rsquo;s patience in awaiting positive changes within the organization, with the belief that the organization\u0026rsquo;s decisions are ultimately correct, consistent with the findings of Williams et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Neglect, on the other hand, refers to behaviors such as disengagement, absenteeism, and increased error rates that arise from job dissatisfaction, leading to reduced productivity and serving as a warning signal to management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research hypothesis on the relationship between job dissatisfaction and EVLN behaviors\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Job dissatisfaction and exit behavior\u003c/h2\u003e \u003cp\u003eExit behaviors among employees primarily refer to inter-organizational mobility, intra-organizational mobility, and cognitive activities preceding such mobility (Farrell, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Rai et al., 2019). Research has demonstrated that when employees experience job dissatisfaction due to both internal and external factors, they often choose to exit (Houshmand et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; H\u0026ouml;rberg et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to dynamic game theory, when conflicts of interest arise between employees and organizations and the organization fails to implement effective measures to address employee demands and allocate necessary resources, employees will feel dissatisfaction and disappointment during the interaction. To maximize their benefits, employees may seek to change their environment (Laraki \u0026amp; Mertikopoulos, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In such cases, exit behavior serves as an effective means of resolution (Grandey \u0026amp; Cropanzano, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Numerous empirical studies have shown that when job satisfaction is low, exiting to protect one\u0026rsquo;s resources is a rational choice (Tummers et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fasbender et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, based on the aforementioned theory, when hospital nurses experience job dissatisfaction, they are likely to exhibit exit behavior. From this, we propose hypothesis H1a:\u003c/p\u003e \u003cp\u003eH1a: Nurses\u0026rsquo; job dissatisfaction is positively related to exit behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Job dissatisfaction and voice behavior\u003c/h2\u003e \u003cp\u003eVoice behaviors are regarded as a form of constructive communication that allows individuals to express opinions thoughtfully and improve organizational conditions (Van Dyne \u0026amp; LePine, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Kim \u0026amp; Rim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Voice is an autonomous behavior that reflects the exercise of individual rights and is typically a result of careful consideration, weighing the potential benefits and drawbacks (Kish-Gephart et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rai et al., 2019). Employee-centered voice behaviors involve actively expressing opinions when employees perceive a task as low-risk and efficient and when they believe that voicing their concerns will result in obtaining deserved resources. However, when organizational responses do not support these behaviors, employees may choose to remain silent (Shipton et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to dynamic game theory, conflicts between employees and organizations arising from work-related issues lead employees to evaluate the risks and rewards of voice behavior, while organizations respond based on employee demands. When employees perceive that expressing their concerns may lead to potential losses\u0026mdash;such as diminished access to valuable personal resources or career opportunities\u0026mdash;and when the expected benefits do not outweigh these risks, they often opt for silence (Haurie et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Başar \u0026amp; Zaccour, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Empirical research has shown that voice behavior can have negative consequences for those who engage in it, as they may be perceived as troublemakers, potentially affecting their career advancement and personal resources (Detert \u0026amp; Burris, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kish-Gephart et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In many workplace contexts, remaining silent is viewed as the safest and most effective strategy for self-preservation (Xu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, when nurses in hospital experience job dissatisfaction, they often avoid voice behavior to protect their careers and opportunities for advancement. Based on this, we propose hypothesis H1b:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1b\u003c/strong\u003e \u003cp\u003eNurses' job dissatisfaction is negatively related to voice behavior.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Job dissatisfaction and loyalty behavior\u003c/h2\u003e \u003cp\u003eLoyalty behaviors refer to the actions of employees who adhere to strong professional ethics and either passively or optimistically expect the organization to improve itself and make necessary adjustments (Rusbult et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Allen \u0026amp; Grisaffe, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Research shows that subordinates are more likely to exhibit loyalty toward supervisors or organizations that prioritize their vital interests and well-being. Employees believe that such supervisors or organizations will act in their best interest, fostering a sense of belonging (Wiedmer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Turkyilmaz et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nguyen \u0026amp; Ha, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From the perspective of dynamic game theory, when conflicts of interest arise between employees and organizations, employees may feel dissatisfied if their resource investments are not reciprocated. In response, organizations are expected to promptly address employee concerns and dynamically adjust policies to maximize employee satisfaction, thereby increasing organizational loyalty. Conversely, if employees\u0026rsquo; resourse investments go unrewarded, they become unwilling to invest further, leading to a decline of organizational loyalty (Williams et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rai et al., 2019). Therefore, when nurses in hospital experience job dissatisfaction, they are likely to develop negative emotions and reduce their organizational loyalty to the hospital. Based on this, we propose hypothesis H1c:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1c\u003c/strong\u003e \u003cp\u003eNurses' job dissatisfaction is negatively related to loyalty behavior.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Job dissatisfaction and neglect behavior\u003c/h2\u003e \u003cp\u003eEmployee neglect behaviors refer to actions where employees passively worsen the situation by being consistently late, absent, or using work time for personal matters, ultimately leading to a state of disengagement or slacking (Rusbult et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Neglect behavior is often viewed as a manifestation of stress relief. When employees are dissatisfied with their work, they respond negatively to stressors (Spector \u0026amp; Jex, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). From the perspective of dynamic game theory, conflicts between employees and organizations, particularly when employees feel that their resource investments are not reciprocated, can result in dissatisfaction. In these situations, the organization should promptly address employee demands during the dynamic game process. Failure to do so can lead employees to feel resource-threatened, resulting in unmet psychological and physical needs and triggering passive reactions, such as neglect behaviors (Haurie et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Başar \u0026amp; Zaccour, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have demonstrated that when employee resource investments are frequently unrewarded, dissatisfaction arises, and employees often resort to passive behaviors as a way to relieve stress. For instance, taking extended breaks can be viewed as a stress-related coping mechanism to conserve resources (Halbesleben et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rai et al., 2019). Therefore, when nurses in hospital are dissatisfied with their work, they may adopt neglect behaviors as a passive means of self-preservation. Based on this, we propose hypothesis H1d:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1d\u003c/strong\u003e \u003cp\u003eNurses\u0026rsquo; job dissatisfaction is positively related to neglect behavior.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 The evolution of EVLN behaviors affected by nurses\u0026rsquo; job dissatisfaction\u003c/h2\u003e \u003cp\u003eBased on the aforementioned assumptions and analysis, it can be concluded that employee job dissatisfaction leads to the emergence of EVLN behaviors. Specifically, employee job dissatisfaction (including internal and external) is positively correlated with exit and neglect behaviors, while negatively correlated with voice and loyalty behaviors. However, the question remains: how do these EVLN behaviors evolve?\u003c/p\u003e \u003cp\u003eAccording to dynamic game theory (Haurie et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Başar \u0026amp; Zaccour, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), when there is an imbalance between resource allocation and compensation between employees and organizations, gaming behavior arises. If employees are dissatisfied with the resource allocation, a rational organization, seeking to minimize losses, will strive to maximize employee satisfaction during the gaming process. This, in turn, enhances employee loyalty and mitigates job dissatisfaction (Rusbult et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs employees continue to work for an extended period, the original resource allocation from the organization may no longer meet their needs, prompting them to make new compensation demands. When the organization fails to promptly adjust to these demands during the dynamic game process, employees may initially remain silent, hoping for improvement. If no changes occur, they will eventually resort to voice behavior instead of directly exiting (Hirschman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Jone, 2017). Following their expression of concerns, if the organization only minimally addresses the dissatisfaction, employees may become slightly disillusioned, thus may manifest as a decline in work performance or a passive desire to leave when an opportunity arises (note: or lead to the employee leaving the organization directly). During this period, the organization and employees are engaged in a dynamic game. If the employee's dissatisfaction with work is not adequately addressed, this can lead to greater disappointment, making it unlikely that the organization will meet the employee\u0026rsquo;s resource allocation and compensation needs, significantly increasing the likelihood of employee exit (Farrell, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This demonstrates that if an organization fails to respond to voice behavior, employees may first exhibit negative behaviors, which may eventually escalate into exit behavior, or they may transition directly from voice behavior to exiting (Lin et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, the evolution of EVLN behaviors in the dynamic game between employees and organizations unfolds as follows: Employees initially adopt loyalty behavior in response to job dissatisfaction (both internal and external). If the dissatisfaction persists, they shift to voice behavior. If voice behavior is not addressed, employees may either directly choose exit behavior or transition from neglect to exit behavior. Thus, the evolution of EVLN behaviors among nurses in hospital, driven by job dissatisfaction, follows this progression. Based on this framework, we propose hypotheses H2a, H2b, H2c, and H2d.\u003c/p\u003e \u003cp\u003eH2a: Nurses' loyalty behavior has a positive impact on voice behavior.\u003c/p\u003e \u003cp\u003eH2b: Nurses' voice behavior has a positive impact on neglect behavior.\u003c/p\u003e \u003cp\u003eH2c: Nurses' neglect behavior has a positive impact on exit behavior.\u003c/p\u003e \u003cp\u003eH2d: Nurses' voice behavior has a positive impact on exit behavior.\u003c/p\u003e \u003cp\u003eBased on the aforementioned analysis, we determined the impact of nurses' job dissatisfaction on the relationships among various EVLN behaviors and developed a theoretical framework depicting the evolution of these behaviors (see Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample selection\u003c/h2\u003e \u003cp\u003eThis study examines the behavioral responses of nurses in tertiary public hospitals in Chongqing's urban and county districts to job dissatisfaction. Adhering to the principles of comprehensiveness, objectivity, fairness, and prudence, data were collected from 1,391 nurses (all holding professional nursing qualifications) in tertiary public hospitals across nine districts of Chongqing's main urban area between January 2024 and May 2024 through an anonymous questionnaire (see supplementary document). With the assistance of friends, classmates, and medical professionals, 1,381 questionnaires were distributed, of which 1,219 were returned, yielding a response rate of 88.27%. Following standard protocols, questionnaires from trainees and interns, as well as those with careless or incomplete responses, were excluded, resulting in 1,132 valid questionnaires, achieving an efficiency rate of 81.97%. The descriptive statistics of the sample are detailed in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Descriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociometric Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e(In Years)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026mdash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026mdash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior College (Diploma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWork Duration (In Years)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026mdash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026mdash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026mdash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse Practitioner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupervising Nurse Practitioner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate Director of Nurse Practitioners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse Manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSection Nurse Manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUnit Establishment\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContractual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersonnel Agency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOfficial Establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSalary Level\u003c/p\u003e \u003cp\u003e(In Months CNY)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4000\u0026mdash;7999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperating Room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: N value represents the effective sample size of the project\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Model design and variable description\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Variable description\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.1 Dependent variable (DV)\u003c/h2\u003e \u003cp\u003eThe dependent variables of this study are the EVLN behaviors. We utilized the four-dimensional behavior scale developed by Farrell (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and applied the CRITIC weighting method to measure these behaviors (Diakoulaki et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Hou et al., 2024). Specifically, we used the CRITIC weighting method to calculate the weight of each indicator (see Table\u0026nbsp;2) and then formed the dependent variable value for each secondary indicator factor through weighted summation. The indicator values are presented in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEVLN behavioral indicators of nurses in hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst\u0026ndash;level indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond\u0026ndash;level indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eExit behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI often feel like quitting my job.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI often feel like quitting my current job.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI would accept another new job.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI would quit my current job.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eVoice behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI often discuss the current situation with my superiors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI often make suggestions to my superiors about certain problems in the organization.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI often suggest solutions to problems in the way leaders in my department supervise or direct work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutside of work, I often ask my colleagues if they need help.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLoyalty behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI often think that my work is the best.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI will not leave the organization regardless of whether it is in good times or bad times.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI will always do my best to protect the reputation of the organization when others criticize it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI will not leave until the problems in the organization are resolved.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNeglect behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSometimes, when I don't feel like working, I will call in sick or find other reasons not to work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf I am not in the mood to work, I may be late for work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI will not work at full strength.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI will often take breaks during work or not work at all.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI may lose motivation for work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: A five-point Likert scale was employed for all items (1 = \"strongly disagree\", 2 = \"disagree\", 3 = \"neutral\", 4 = \"agree\", 5 = \"strongly agree\"). The questionnaires were anonymous, and respondents were asked to independently ,conscientiously, and responsibly select the EVLN behaviors corresponding to the 17 items.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.2 Independent variable (IV)\u003c/h2\u003e \u003cp\u003eThe independent variable of this study is nurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e). We utilized the job dissatisfaction scale developed by Sun et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Turnbach et al.(2024), and applied the combined assignment method to measure nurses' job dissatisfaction. Initially, the APH hierarchical method was employed to calculate the weights of the three-level indicators. Subsequently, the entropy method was used to calculate the weights of the secondary indicators. Finally, the combined assignment method was used to compute the weighted sum of the values of nurses' job dissatisfaction. The values of the indicators are presented in Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJob dissatisfaction indicators of nurses in hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst\u0026ndash;level indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond\u0026ndash;level indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThird\u0026ndash;level indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eNurse Job Dissatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eInternal Job Dissatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional Exhaustion and Burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeelings of Ineffectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLack of Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompassion Fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConflicting Personal Values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther internal reasons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eExternal Job Dissatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsufficient Professional Development Opportunities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor Work-Life Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeavy Workload and Understaffing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInadequate Compensation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLack of Administrative Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther external reasons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote:The scale utilizes a five-point Likert scale for measurement (1 = \"strongly disagree\", 2 = \"disagree\", 3 = \"neutral\", 4 = \"agree\", 5 = \"strongly agree\"). All questionnaires were completed anonymously, and respondents were required to independently, conscientiously, and responsibly select their level of satisfaction for each of the five items.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.3 Control variables (CV)\u003c/h2\u003e \u003cp\u003eBased on the literature, several factors such as working years, professional title, ranking and others impact nurses' behavioral responses, including Exit, Voice, Loyalty, and Neglect (EVLN) behaviors (Blegen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Aiken et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Tourangeau \u0026amp; Cranley, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Laschinger et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; McHugh \u0026amp; Lake, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cowden \u0026amp; Cummings, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Drawing on existing research, we selected working years (\u003cb\u003eWY\u003c/b\u003e), professional title (\u003cb\u003eTitle\u003c/b\u003e), ranking (\u003cb\u003eRank\u003c/b\u003e), position (\u003cb\u003ePosit\u003c/b\u003e), unit establishment (\u003cb\u003eUE\u003c/b\u003e), salary level (\u003cb\u003eSalary\u003c/b\u003e), and section (\u003cb\u003eSec\u003c/b\u003e) as control variables. The specific definitions of each variable are provided in Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable definition table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasurement method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccording to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively.\u003c/p\u003e \u003cp\u003eExit=⅀(three-level indicators affecting Exit\u0026times;Weight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVoice Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccording to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively.\u003c/p\u003e \u003cp\u003eVoice=⅀(three-level indicators affecting Voice\u0026times;Weight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoyalty Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccording to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively.\u003c/p\u003e \u003cp\u003eLoyalty=⅀(three-level indicators affecting Loyalty\u0026times;Weight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeglect Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccording to the CRITIC weighting method of EVLN Scale, it is calculated comprehensively.\u003c/p\u003e \u003cp\u003eNeglect=⅀(three-level indicators affecting Neglect\u0026times;Weight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse Job Dissatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe APH hierarchical analysis method was employed to comprehensively calculate the nurse job dissatisfaction scale.\u003c/p\u003e \u003cp\u003eNJD\u0026thinsp;=\u0026thinsp;Internal Nurses\u0026rsquo; Job Dissatisfaction(INJD)\u0026times;Weight\u0026thinsp;+\u0026thinsp;External Nurses\u0026rsquo; Job Dissatisfaction(ENJD)\u0026times;Weight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork Duration (In Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u0026thinsp;=\u0026thinsp;1,5\u0026mdash;10\u0026thinsp;=\u0026thinsp;2,11\u0026mdash;15\u0026thinsp;=\u0026thinsp;3,16\u0026mdash;19\u0026thinsp;=\u0026thinsp;4,\u0026ge;20\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNurse\u0026thinsp;=\u0026thinsp;1,Nurse Practitioner\u0026thinsp;=\u0026thinsp;2,Supervising Nurse Practitioner\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003cp\u003e,Associate Director of Nurse Practitioners\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0\u0026thinsp;=\u0026thinsp;1,N1\u0026thinsp;=\u0026thinsp;2,N2\u0026thinsp;=\u0026thinsp;3,N3\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNurse\u0026thinsp;=\u0026thinsp;1,Nurse Manager\u0026thinsp;=\u0026thinsp;2,Section Nurse Manager\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit Establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContractual\u0026thinsp;=\u0026thinsp;1,Personnel Agency\u0026thinsp;=\u0026thinsp;2,Official Establishment\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalary Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4000\u0026thinsp;=\u0026thinsp;1,4000\u0026mdash;7999\u0026thinsp;=\u0026thinsp;2,\u0026ge;8000\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVirtual variables: Internal Medicine\u0026thinsp;=\u0026thinsp;1,Surgery\u0026thinsp;=\u0026thinsp;2,others\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Model design\u003c/h2\u003e \u003cp\u003eTo test research hypotheses H1a, H1b, H1c, and H1d, we constructed the following regression model (3\u0026thinsp;\u0026minus;\u0026thinsp;1):\u003c/p\u003e \u003cp\u003e \u003cb\u003eEVLN\u003c/b\u003e \u003csub\u003e \u003cb\u003ei t\u003c/b\u003e \u003c/sub\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;β1\u003c/b\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eβ2 NJD\u003c/b\u003e\u003csub\u003e\u003cb\u003ei t\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eβ3⅀Control\u003c/b\u003e\u003csub\u003e\u003cb\u003eit\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eβ4⅀Sec\u003c/b\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eα\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u003cb\u003et\u003c/b\u003e ..........................................................(\u003cb\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn model (3\u0026thinsp;\u0026minus;\u0026thinsp;1), \u003cb\u003eEVLN\u003c/b\u003e is the dependent variable (DV), respectively representing the exit, voice, loyalty, and neglect behavior of nurses in hospital; \u003cb\u003eNJD\u003c/b\u003e is the independent variable (IV), representing the job dissatisfaction of nurses in hospital; \u003cb\u003eControl\u003c/b\u003e is a series of control variables (CV); \u003cb\u003eSec\u003c/b\u003e is the fixed effect, representing the fixed effect of the section; \u003cb\u003eα\u003c/b\u003e is the random disturbance term; subscripts \u003cb\u003ei\u003c/b\u003e and \u003cb\u003et\u003c/b\u003e represent individual nurses and time, respectively.\u003c/p\u003e \u003cp\u003eTo test research hypotheses H2a, H2b, H2c, and H2d, the study constructed the following regression model (3\u0026thinsp;\u0026minus;\u0026thinsp;2):\u003c/p\u003e \u003cp\u003e \u003cb\u003eVNEE\u003c/b\u003e \u003csub\u003e \u003cb\u003ei t\u003c/b\u003e \u003c/sub\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;β1\u003c/b\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eβ2 LVNV\u003c/b\u003e\u003csub\u003e\u003cb\u003ei t\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eβ3⅀Control\u003c/b\u003e\u003csub\u003e\u003cb\u003eit\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eβ4⅀Sec\u003c/b\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;\u003cb\u003eα\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u003cb\u003et\u003c/b\u003e .........................................................(\u003cb\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn model (3\u0026thinsp;\u0026minus;\u0026thinsp;1), \u003cb\u003eVNEE\u003c/b\u003e is the dependent variable (DV), respectively representing the voice, neglect, exit, and exit behavior of nurses in hospital in light of the order of research hypotheses; \u003cb\u003eLVNV\u003c/b\u003e is the independent variable (IV), respectively representing the loyalty, voice, neglect, and voice behavior of nurses in hospital in view of the order of research hypotheses; \u003cb\u003eControl\u003c/b\u003e is a series of control variables (CV); \u003cb\u003eSec\u003c/b\u003e is the fixed effect, representing the fixed effect of the section; \u003cb\u003eα\u003c/b\u003e is the random disturbance term; subscripts \u003cb\u003ei\u003c/b\u003e and \u003cb\u003et\u003c/b\u003e represent individual nurses and time, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Statistical and analytical methods\u003c/h2\u003e \u003cp\u003eThis study employed SPSS V29.0, Stata 17.0, SmartPLS 4.0, and SPSSAU software to analyze the valid survey data and perform empirical analyses using quantitative methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive statistics and reliability checks\u003c/h2\u003e \u003cp\u003eRefer to Table\u0026nbsp;5 for descriptive statistics and reliability checks. The analysis results in Table\u0026nbsp;5 show that the measurement reliability of each item exceeds 0.9 (Nunnally,1967; Babbie,1975), indicating that the consistency, stability, and reliability of the scale's test results are very high. In the descriptive statistics, the mean and standard deviation of loyalty behavior and voice behavior are higher than those of neglect behavior and exit behavior. Additionally, the mean and standard deviation of internal nurses\u0026rsquo; job dissatisfaction are higher than those of external nurses\u0026rsquo; job dissatisfaction (Mean:1.4143\u0026thinsp;\u0026gt;\u0026thinsp;0.9772; SD: 0.5936\u0026thinsp;\u0026gt;\u0026thinsp;0.4623). The mean values of EVLN behavioral responses to nurses' job dissatisfaction, arranged from highest to lowest, are as follows: loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;voice behavior (\u003cb\u003eVoice\u003c/b\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;exit behavior (\u003cb\u003eExit\u003c/b\u003e). From this we can know that when nurses in tertiary public hospitals in Chongqing's urban and county districts experience job dissatisfaction, their initial response is typically one of observation, waiting for the organization to address their legitimate concerns. During this phase, their behavior reflects a form of loyalty. If no improvements are observed over time, they shift to expressing their concerns through voice behavior. If their voice behavior remains unheeded, they resort to neglect, passively reducing their work effort (note: or, alternatively, they may directly choose to exit the organization). In the end, if neglect does not lead to satisfaction, they opt for exit behavior in search of better career opportunities. This sequence of behaviors provides preliminary support for research hypotheses H2a, H2b, H2c, and H2d, and aligns with the findings of Lin et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the descriptive statistics, the mean of loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e) is 4.4898 with a median of 4.4996 and a standard deviation of 1.3134, showing significant variability in loyalty behavior among the nurses in the sample. The mean of voice behavior (\u003cb\u003eVoice\u003c/b\u003e) is 4.1465, with a median of 4.0000 and a standard deviation of 1.2676, indicating remarkable variation in voice behavior across the sampled nurses. Conversely, variables such as neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e), exit behavior (\u003cb\u003eExit\u003c/b\u003e), internal nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eINJD\u003c/b\u003e), and external nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eENJD\u003c/b\u003e) exhibit small standard deviations, presentng little variation in these indicators within the sample.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and reliability checks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Cronbach's Alpha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Exploratory factor analysis and correlation analysis\u003c/h2\u003e \u003cp\u003eWhen conducting the correlation analysis, an exploratory factor analysis (EFA) (Hotelling, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1933\u003c/span\u003e) was initially performed to determine whether each question was adequately and scientifically represented within each factor. The results indicated that most of the 29 questions aligned well with the theoretical dimensions, were reasonably and scientifically reflected in the six factor dimensions with a cumulative variance explanation rate of 91.298%, and with only a few cases exhibiting cross-loadings or negative loadings.\u003c/p\u003e \u003cp\u003eSubsequently, a Pearson correlation analysis was performed to examine potential collinearity among the variables. The correlation analysis results in Table\u0026nbsp;6 indicate that nurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) is significantly positively correlated with exit behavior (\u003cb\u003eExit\u003c/b\u003e) and neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e) at the 0.01 level, supporting the hypotheses H1a and H1b. Additionally, nurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) is significantly negatively related to voice behavior (\u003cb\u003eVoice\u003c/b\u003e) and loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e) at the 0.01 level, supporting the hypotheses H1c and H1d.\u003c/p\u003e \u003cp\u003eTo further verify the presence of multicollinearity, a variance inflation factor (VIF) test was conducted on all variables. If the Pearson test indicates that the correlation coefficient between variables is greater than 0.8, multicollinearity may be present. Additionally, if the VIF value exceeds 10, multicollinearity is confirmed. The results of the VIF test for this study showed that all variable values ranged between 1.017 and 2.914, which is below the critical value of 10. This confirms that the constructed model does not have multicollinearity problems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between key study variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;1 Correlations analysis(Exit behavior)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1267***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0538*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0855***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0655**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0639**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3518***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2747***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3218***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0642**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3802***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4355***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.0874***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;2 Correlations analysis(Voice behavior)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1694***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0745**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0670**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0855***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1072***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0655**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3518***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2747***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3218***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3802***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4355***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.0874***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;3 Correlations analysis(Loyalty behavior)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2937***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0519*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0855***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0682**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0655**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3518***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2747***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3218***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0550*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3802***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4355***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.0874***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;4 Correlations analysis(Neglect behavior)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1928***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0557*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0855***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0655**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3518***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2747***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3218***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3802***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4355***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.0874***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Benchmark regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;7 presents the results of the baseline regression analysis. Column (1) displays the regression results between nurses\u0026rsquo; job dissatisfaction \u003cb\u003e(NJD\u003c/b\u003e) and exit behavior (\u003cb\u003eExit\u003c/b\u003e) after controlling for department effects and incorporating control variables. Columns (2) and (3) respectively show the regression results between \u003cb\u003eNJD\u003c/b\u003e and voice behavior (\u003cb\u003eVoice\u003c/b\u003e) and between \u003cb\u003eNJD\u003c/b\u003e and loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e), also after controlling for department effects and adding control variables. Column (4) presents the regression results between \u003cb\u003eNJD\u003c/b\u003e and neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e), similarly accounting for department effects and control variables.\u003c/p\u003e \u003cp\u003eThe results indicate that nurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) is significantly positively correlated with exit behavior (\u003cb\u003eExit\u003c/b\u003e) and neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e) at the 0.01 level. This finding suggests that \u003cb\u003eNJD\u003c/b\u003e has a substantial positive impact on both Exit and Neglect behaviors, thereby supporting hypotheses H1a and H1b of this study. The primary reason is that hospitals have consistently failed to address the underlying causes of \u003cb\u003eNJD\u003c/b\u003e effectively and have not implemented targeted, effective measures to reduce it. This neglect has resulted in increased occurrences of Exit and Neglect behaviors among nurses in hospital.\u003c/p\u003e \u003cp\u003eNurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) is significantly negatively correlated with voice behavior (\u003cb\u003eVoice\u003c/b\u003e) and loyalty behavior \u003cb\u003e(Loyalty\u003c/b\u003e) at the 0.01 level, indicating that \u003cb\u003eNJD\u003c/b\u003e has a substantial negative impact on these behaviors. This finding supports hypotheses H1c and H1d. The primary reason is that hospitals have failed to effectively address nurses' job satisfaction in practice, leading to high levels of \u003cb\u003eNJD\u003c/b\u003e, decreased loyalty to the hospital, and a reduction in voice behavior among nurses in hospital.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBenchmark regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3924***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1483***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1714***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6603***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1545**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1582**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0523*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3614**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0461*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2405***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1821**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6641***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2638***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7174***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7572***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(82.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness checks\u003c/h2\u003e \u003cp\u003eTo further test the reliability of the results, we employed two methods for robustness checks (RC): subsample regressions method (Cifci \u0026amp; Oliver, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and remeasurement method (Li \u0026amp; Tian, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Subsample regressions method\u003c/h2\u003e \u003cp\u003eSubsample regression is a common method for measuring the robustness of test results (Cifci \u0026amp; Oliver, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given that the nurse manager is a key factor affecting nurses\u0026rsquo; job dissatisfaction (Lackey \u0026amp; Antrum, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we used subsample regression to test the robustness of our research conclusions, overcome endogeneity, and eliminate the impact of differences in sample hospital characteristics. We reduced the sample by excluding nurse managers and section nurse managers to obtain a subsample. Additionally, to prevent multicollinearity, we removed the control variable Position (\u003cb\u003ePosit\u003c/b\u003e). The regression results in Table\u0026nbsp;8 indicate that nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) is significantly positively correlated with exit behavior (\u003cb\u003eExit\u003c/b\u003e) and neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e) at the 0.01 level, and significantly negatively related to voice behavior (\u003cb\u003eVoice\u003c/b\u003e) and loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e) at the 0.01 level. These robustness check results are consistent with the conclusions of this study, demonstrating that our research findings are robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobust Checks(Subsample regressions method)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3768***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1523***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5404***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1450*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1658**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0545**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1905***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2061***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1590*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCostant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0082***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2802***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7130***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8224***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(44.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(78.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Remeasurement method\u003c/h2\u003e \u003cp\u003eTo further address the endogeneity problem, this study employs the remeasurement method (Li \u0026amp; Tian, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to mitigate its impact on the conclusions. A new nurse job dissatisfaction scale is utilized, incorporating sub-factors that influence nurse job dissatisfaction, such as relationships, support, freedom from fear, and breaks (Lackey \u0026amp; Antrum, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This approach allows for a reevaluation of the impact of nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) on EVLN behavior. The results in Tables\u0026nbsp;9 demonstrate that \u003cb\u003eNJD\u003c/b\u003e is significantly positively correlated with exit behavior (\u003cb\u003eExit\u003c/b\u003e) and neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e), while being significantly negatively correlated with voice behavior (\u003cb\u003eVoice\u003c/b\u003e) and loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e). These findings further underscore the robustness of our research results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobust Checks(Remeasurement method)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3673***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1362***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1413***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5923***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1449**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1520**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0418*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3635**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0373*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2336***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1790**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1514*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5888***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2818***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5647***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(45.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(80.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Heterogeneity checks and EVLN behaviors evolution study","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Heterogeneity checks\u003c/h2\u003e \u003cp\u003eNumerous scholars have demonstrated that employees' job dissatisfaction, influenced by personality traits, exhibits variations across socio-demographic variables such as gender, age, marital status, and education level (Metle, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Halkos \u0026amp; Bousinakis, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bagheri Hosseinabadi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the reasons of nurses' job dissatisfaction vary (Pressley \u0026amp; Garside, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lackey \u0026amp; Antrum, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, this study considers gender, age, marital status, education level as heterogeneity indicators to examine the disparities in nurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) and EVLN behaviors.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Gender\u0026rsquo;s heterogeneity\u003c/h2\u003e \u003cp\u003eThe study explores how nurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) varies by gender due to different personality traits. To investigate this, nurses were divided into male (assigned 1) and female (assigned 0) groups. Table\u0026nbsp;10 illustrates that compared to the male, \u003cb\u003eNJD\u003c/b\u003e in female is associated with increased exit and neglect behaviors (\u003cb\u003eExit\u003c/b\u003e and \u003cb\u003eNeglext\u003c/b\u003e), while it decreases voice and loyalty behaviors (\u003cb\u003eVoice\u003c/b\u003e and \u003cb\u003eLoyalty\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e-0.0508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1679***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5328***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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colname=\"c7\"\u003e \u003cp\u003e-0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.2352***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-3.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2071**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7195*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6628***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1505***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2386***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6745***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6896***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.9299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.8497***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(10.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(43.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(22.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(76.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e5..1.2 Age’s heterogeneity\u003c/h3\u003e\n\u003cp\u003eThe study investigates how nurses' age impacts their job dissatisfaction due to varying personality traits. Nurses were categorized into two age groups: under 30 years old (assigned 1) and over 30 years old (including 30 years old, assigned 0). Table\u0026nbsp;11 reveals that compared to the younger (under 30 years old), nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) in the older (over 30 years old) is linked to increased exit and neglect behaviors (\u003cb\u003eExit\u003c/b\u003e and \u003cb\u003eNeglect\u003c/b\u003e), while it decreases voice behavior (\u003cb\u003eVoice\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe regression results from columns (5) and (6) of Table\u0026nbsp;11 reveal significantly negative coefficients of job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) for nurses both over and under 30 years old at the 1% significance level. This suggests that job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) among nurses of different age groups can reduce the likelihood of loyalty behavior \u003cb\u003e(Loyalty\u003c/b\u003e). Subsequent inclusion of the interaction term (\u003cb\u003eNJD\u0026times;Age\u003c/b\u003e) did not yield a significant coefficient, indicating no substantial difference in the impact of job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) on loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e) between nurses of different age groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAge\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5398***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1730***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1848***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1720***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7355***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.02)\u003c/p\u003e \u003c/td\u003e 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colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2203*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(1.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0370*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.2696***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-3.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2474*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9536***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3745***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6474***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6443***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.6727***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1919***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.5070***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(30.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(31.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(27.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(52.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(73.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(5.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.1.3 Marital heterogeneity\u003c/h2\u003e \u003cp\u003eNurses' job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) can be influenced by objective environmental factors, such as family circumstances, which vary based on marital status. To assess the impact of marital heterogeneity on the research conclusions, this study categorized \u003cb\u003eNJD\u003c/b\u003e in hospital into two groups: the married group, assigned a value of 1, and the unmarried/divorced group, assigned a value of 0.\u003c/p\u003e \u003cp\u003eThe research results in columns (1) through (4) of Table\u0026nbsp;12 indicate that, compared to unmarried or divorced nurses, job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) among married nurses can increase the occurrence of exit behavior (\u003cb\u003eExit\u003c/b\u003e) and decrease the occurrence of voice behavior (\u003cb\u003eVoice\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFrom the regression results in columns (5) and (6) of Table\u0026nbsp;12, the coefficients for job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) among married nurses and unmarried/divorced nurses are significantly negative at the 1% level, suggesting that job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) in nurses with different marital statuses can reduce the occurrence of loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e). Additionally, after including the interaction term \u003cb\u003e(NJD \u0026times; Marriage\u003c/b\u003e) in the regression, the coefficient for the interaction term was not significant, indicating no significant difference in the impact of job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) on reducing loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e) between nurses of different marital statuses.\u003c/p\u003e \u003cp\u003eThe regression results in columns (8) and (9) of Table\u0026nbsp;12 indicate that job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) among nurses with different marital statuses can promote neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e). After adding the interaction term \u003cb\u003e(NJD \u0026times; Marriage\u003c/b\u003e) and performing the regression, the coefficient for the interaction term was not significant, suggesting that job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) among nurses with different marital statuses does not significantly differ in promoting neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarriage heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMarrige\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal Sample\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1657***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1442***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1389***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5800***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4129**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5120***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(5.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNJD\u0026times;\u003c/p\u003e \u003cp\u003eMarrige\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.2329*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2510*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2881**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1445*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3693**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1579*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.5464**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1806**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2457*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0438**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0371**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0476*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0221*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.2594***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.2268***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-3.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8389***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4629***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2613***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3507***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6975***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.7733***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.6884***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.9941***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.7126***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.7845***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(35.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(23.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(62.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(41.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(77.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(7.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(8.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4 Heterogeneity of education level\u003c/h2\u003e \u003cp\u003eDifferent education levels result in varying personality traits among nurses, which subsequently influence their job dissatisfaction. To examine the impact of educational heterogeneity on the study's conclusions, nurses with a bachelor's degree or higher were grouped into one category (Degree\u0026thinsp;=\u0026thinsp;1), while those with less than a bachelor's degree were grouped into another category (Degree\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003eThe regression results in columns (1) and (2) of Table\u0026nbsp;13 indicate that the coefficient of nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) is significantly positive at the 1% level for the bachelor's degree or higher, and significantly positive at the 10% level for nurses with less than a bachelor's degree. This suggests that job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) among nurses with different educational backgrounds promotes exit behavior (\u003cb\u003eExit\u003c/b\u003e). Moreover, the impact of nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) on promoting exit behavior (\u003cb\u003eExit\u003c/b\u003e) is stronger for the bachelor's degree or higher compared to those with less education.\u003c/p\u003e \u003cp\u003eThe regression results in columns (3) and (4) of Table\u0026nbsp;13 indicate that nurses with an undergraduate degree or higher experience greater reductions in voice behavior (\u003cb\u003eVoice\u003c/b\u003e) due to job dissatisfaction \u003cb\u003e(NJD\u003c/b\u003e) compared to those with less education. Additionally, columns (5) and (6) show that the coefficient for job dissatisfaction (NJD) is significantly negative at the 1% level for nurses with an undergraduate degree or higher, and at the 5% level for those with less education. This suggests that nurses\u0026rsquo; job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) leads to a reduction in loyalty behavior (\u003cb\u003eLoyalty\u003c/b\u003e) across different educational backgrounds, with a more pronounced effect for nurses with an undergraduate degree or higher.\u003c/p\u003e \u003cp\u003eThe regression results in columns (8) and (9) of Table\u0026nbsp;13 show that the coefficients for job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) are significantly positive at the 1% level for both nurses with a bachelor's degree or higher and those with less than a bachelor's degree. This indicates that job dissatisfaction (\u003cb\u003eNJD\u003c/b\u003e) across different educational backgrounds can increase the occurrence of neglect behavior (\u003cb\u003eNeglect\u003c/b\u003e). Furthermore, when the interaction term (\u003cb\u003eNJD\u0026times;Degree\u003c/b\u003e) is included in the model, its regression coefficient is significantly positive, suggesting that job dissatisfaction \u003cb\u003e(NJD\u003c/b\u003e) is more likely to promote neglect behavior (\u003cb\u003eNeglec\u003c/b\u003et) among nurses with a bachelor's degree or higher compared to those with less education.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDegree heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNeglect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDegree\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal Sample\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3737***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4463*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1592***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1665***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1254**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1473***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5290***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6258***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4443***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(4.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNJD\u0026times;\u003c/p\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1336**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(2.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0451*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Study on the evolution of EVLN behaviors in relation to nurses' job dissatisfaction\u003c/h2\u003e \u003cp\u003eIn terms of the internal evolution of hospital nurses' EVLN behaviors, the data in Table\u0026nbsp;14 show that loyalty behavior has a positive impact on voice behavior, voice behavior positively influences neglect behavior, neglect behavior positively impacts exit behavior, and voice behavior also has a positive impact on exit behavior. Therefore, hypotheses H2a, H2b, H2c, and H2d are all confirmed. This indicates that the internal evolution of hospital nurses' EVLN behaviors, from loyalty to exit, generally follows the path of \"loyalty behavior \u0026rarr; voice behavior \u0026rarr; neglect behavior \u0026rarr; exit behavior.\"\u003c/p\u003e \u003cp\u003eSpecifically, the transition of hospital nurses from loyalty to exit behavior can be divided into two main stages:\u003c/p\u003e \u003cp\u003eThe first step is the transition from loyalty behavior to voice behavior. The findings of this study align with the conclusions of Hirschman (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) and Farrell (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), indicating a positive correlation between loyalty and voice behavior. A key distinction in this study is that loyalty behavior is not directly linked to exit behavior. When employees experience job dissatisfaction, they initially remain silent, hoping for improvement. If the situation does not improve, they will opt for voice behavior rather than exit.\u003c/p\u003e \u003cp\u003eThe second step involves the transition from voice behavior to exit behavior. Specifically, the results of the model (3\u0026thinsp;\u0026minus;\u0026thinsp;2) analysis show that when voice behavior is not addressed, hospital nurses may choose to exit directly. Furthermore, the results indicate that voice behavior is positively correlated with neglect behavior, which in turn is positively correlated with exit behavior, suggesting that neglect behavior serves as a mediating variable between voice and exit behaviors. When nurses in hospital express dissatisfaction with their work and the organization fails to address these concerns, they may develop negative attitudes toward their job, exhibit a careless work attitude, and eventually seek opportunities to leave. If voice behavior fails, neglect behavior often follows, and this neglect may ultimately lead to exit. This demonstrates that if voice behavior is not addressed, nurses are likely to engage in exit behavior.\u003c/p\u003e \u003cp\u003eIn summary, after choosing loyalty behavior, if nurses in hospital see no improvement in their dissatisfaction, they will resort to voice behavior. If their voice behavior is still not addressed, they will either choose to exit directly or transition to exit behavior through neglect (see Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothesis testing results of EVLN behaviors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationships\u003c/p\u003e \u003cp\u003ebetween variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized path coefficients(β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard error(STDEV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoyalty\u0026rarr;Voice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVoice\u0026rarr;Neglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024 (**)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeglect\u0026rarr;Exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVoice\u0026rarr;Exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001 (***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Research Conclusion and Discussion\u003c/h2\u003e \u003cp\u003eBased on the principles of dynamic game theory, this study developed a theoretical model to examine the relationship between nurse job dissatisfaction (NJD) and EVLN behavioral responses in Chongqing's public tertiary hospitals, as well as the evolution of these behaviors.\u003c/p\u003e \u003cp\u003eThe empirical findings reveal the following: First of all, nurse job dissatisfaction (NJD) is positively correlated with exit behavior and neglect behavior, while it is negatively correlated with voice behavior and loyalty behavior. These results align with the predictions of dynamic game theory (Haurie et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Laraki \u0026amp; Mertikopoulos, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Başar \u0026amp; Zaccour, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), indicating that the relationship between job dissatisfaction and EVLN behaviors among nurses in Chongqing's public hospitals is consistent with findings from studies on corporate governance. This suggests that a certain level of commonality exists between corporate governance and hospital management, allowing both fields to benefit from the insights of one another's research.\u003c/p\u003e \u003cp\u003eAnd second, the heterogeneity analysis shows that the impact of nurse job dissatisfaction on EVLN behaviors varies across gender, age, marital status, and education level. Specifically, complete heterogeneity is observed in gender and education level, while partial heterogeneity is found in age and marital status. These findings are largely consistent with previous research, indicating that job dissatisfaction, influenced by personality traits, is heterogeneous across socio-demographic variables such as gender, age, marital status, and education level (Metle, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bagheri Hosseinabadi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Halkos \u0026amp; Bousinakis, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which further leads to differences in the reasons for nurse job dissatisfaction (Pressley \u0026amp; Garside, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lackey \u0026amp; Antrum, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Thirdly, according to the evolutionary analysis of EVLN behaviors, nurses in hospital tend to choose voice behavior following loyalty behavior, followed by neglect and exit behaviors. This pattern aligns with the Confucian cultural emphasis on \"moderation and stability,\" which often leads hospital nurses to be reluctant to leave institutions where they have worked for many years. Under the influence of historical inertia and traditional values, nurses in hospital typically wait for a period when dissatisfied with their work. If the situation does not improve, they actively engage in communication with the hospital to remain employed. This phenomenon is more evident in highly competitive market environments and during economic downturns. Only when communication fails or nurses are compelled by circumstances do they opt for exit behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Managerial Implication\u003c/h2\u003e \u003cp\u003eThe findings of this study have important practical and managerial implications. Our results suggest that medical policymakers, hospital managers, and practitioners should implement proactive measures to prevent nurse job dissatisfaction, as well as reactive measures to address EVLN behaviors once they occur. These efforts are essential to minimizing exit and neglect behaviors, while enhancing nurse satisfaction, voice behavior, and loyalty. Especially, voice behavior holds a central role in addressing job dissatisfaction. Creating effective communication channels is crucial for resolving nurse dissatisfaction.\u003c/p\u003e \u003cp\u003eFirst of all, nursing practitioners should thoroughly understand the root causes of dissatisfaction, both in form and nature, and perhaps conduct regular evaluations to reduce the occurrence of dissatisfaction-related emotions and behaviors.\u003c/p\u003e \u003cp\u003eSecondly, hospital managers must establish dedicated channels for reporting nurse dissatisfaction, dismantle administrative barriers to hierarchical reporting, and foster a people-centered, open hospital culture. By creating a supportive work environment through psychological counseling, performance reform, skills training, and opportunities for job promotion, hospitals can reduce dissatisfaction, encourage loyalty among nurses, and improve overall management. These measures will contribute to the high-quality development of hospitals.\u003c/p\u003e \u003cp\u003eIn the end, medical policymakers should address nurse job dissatisfaction and its relationship with EVLN behaviors through system design and policy frameworks. By conducting in-depth research into the underlying causes and crafting health policies based on real-world conditions, policymakers can promote the efficient allocation of medical resources and support the sustainable development of healthcare systems in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Theoretical implications\u003c/h2\u003e \u003cp\u003eThis study makes several contributions to the literature.\u003c/p\u003e \u003cp\u003eTo begin with, it offers a new research avenue for Farrell\u0026rsquo;s EVLN behavior theory (Pendola, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), specifically examining the reactions and levels of nurses' job dissatisfaction and providing a medical adaptation of the theoretical model, thereby expanding the scope of theoretical research.\u003c/p\u003e \u003cp\u003eSecondly, it deepens the application of dynamic game theory in hospital management and enhances behavioral management theory within hospitals.\u003c/p\u003e \u003cp\u003eAnd then, this study analyzes the relationship between nurses\u0026rsquo; job dissatisfaction and EVLN behaviors, refining the theoretical model by exploring the heterogeneity of variables such as gender, age, marital status, and education level.\u003c/p\u003e \u003cp\u003eFinally, this study is the first to apply dynamic game theory to the evolution of EVLN behaviors resulting from nurses' job dissatisfaction, which contributes to improving the internal management mechanisms in hospitals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Limitations and future directions\u003c/h2\u003e \u003cp\u003eWe must acknowledge the limitations of this study and propose directions for future research.\u003c/p\u003e \u003cp\u003eFirst, the nurses\u0026rsquo; job dissatisfaction scale used in this study considered only a few critical factors, while numerous factors contribute to nurse job dissatisfaction (Pressley \u0026amp; Garside, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lackey \u0026amp; Antrum, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These additional factors were not accounted for and should be included in future studies to improve the reliability of the conclusions.\u003c/p\u003e \u003cp\u003eSecond, this study was limited to Chongqing, China, and did not consider data from other provinces or cities. Thus, the generalizability of the findings is uncertain. Future research should expand the dataset to include public hospitals from other regions in China to enhance the universality of the conclusions.\u003c/p\u003e \u003cp\u003eThird, this study focused solely on public hospitals and did not include private hospitals. With the ongoing reform of China\u0026rsquo;s healthcare system, private hospitals are expected to increase in number. Whether the same conclusions apply to private hospitals remains to be explored. Future research should incorporate private hospitals to examine the relationship between nurse\u0026rsquo;s job dissatisfaction and EVLN behaviors, further enriching hospital management theory and promoting the high-quality development of healthcare system.\u003c/p\u003e \u003cp\u003eFourth, while this study explored the internal pathways of EVLN behaviors caused by nurse job dissatisfaction, it did not investigate the evolution of EVLN behaviors arising from structural differences in nurse dissatisfaction. Future research should address this to better manage and mitigate nurses\u0026rsquo; job dissatisfaction, and further decrease the occurrence of nurses shortage phenomenon.\u003c/p\u003e \u003cp\u003eFifth, due to the particularities of China\u0026rsquo;s healthcare system (Blumenthal \u0026amp; Hsiao, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jakovljevic et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and objective constraints, public hospital data from other countries were not included. Whether the findings of this study are applicable to countries outside of China remains to be determined. Future research should involve collaboration with international scholars to further contribute to the global development of healthcare management theory.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJackie Zhanbiao Li analysed data and drafted the manuscript. Ming Chen and Wanqin Hu designed the study and performed the data collection. Yingqian Lao and Ming Chen made substantive intellectual contributions to the conception of the work and the interpretation of the data and revised the manuscript. These authors contributed equally to this work.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no competing and conflicting interests\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was obtained from the ethics committee of Ophthalmic \u0026amp; Dental Center of Putuo\u003c/p\u003e\n\u003cp\u003eDistrict in Shanghai (No. 2023-03). All research was performed in accordance with the relevant\u003c/p\u003e\n\u003cp\u003eguidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., \u0026amp; Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. \u003cem\u003eJAMA, 288\u003c/em\u003e(16), 1987-1993.\u003c/li\u003e\n\u003cli\u003eAlameddine, M., Bauer, J. M., Richter, M., \u0026amp; Sousa-Poza, A. (2017). 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(2024).How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology.\u003cem\u003eEnergy Economics,131\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nurses’ job dissatisfaction, EVLN behaviors, Dynamic game theory, Heterogeneity analysis, Evolution of EVLN behaviors, Public hospital","lastPublishedDoi":"10.21203/rs.3.rs-5315438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5315438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn organizational behavior, job dissatisfaction has become an essential factor in shaping employees\u0026rsquo; responses, including exit, voice, loyalty, and neglect (EVLN) behaviors, impacting workplace dynamics and outcomes. This study is based on a questionnaire survey investigating EVLN behaviors resulting from job dissatisfaction among nurses in public hospitals in Chongqing. The aim is to identify the underlying reasons for nurse shortages driven by job dissatisfaction. To begin with, drawing on Farrell's EVLN behavior framework and utilizing dynamic game theory, this study examines how nurse job dissatisfaction influences exit, voice, loyalty, and neglect behaviors. The findings reveal that nurse job dissatisfaction is positively correlated with exit and neglect behaviors, while negatively correlated with voice and loyalty behaviors. And then, a subsequent heterogeneity analysis shows that the effects of job dissatisfaction on EVLN behaviors are fully heterogeneous in terms of gender and education level, and partially heterogeneous in terms of age and marital status. Additionally, dynamic game theory is applied to study the internal evolution of EVLN behaviors triggered by job dissatisfaction. The results indicate that nurses typically choose loyalty behavior initially; however, if dissatisfaction persists, they shift to voice behavior. If voice behavior is not addressed, nurses either directly opt for exit behavior or transition to exit behavior through neglect. Finally, the study discusses the implications of these findings for medical policymakers, hospital managers, and practitioners.\u003c/p\u003e","manuscriptTitle":"How does job dissatisfaction fuel nurse exit, voice, loyalty, and neglect (EVLN) behaviors? A Cross-sectional Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 12:44:21","doi":"10.21203/rs.3.rs-5315438/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c686c25-45ba-4063-8f94-2ec26aeaaa02","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39791704,"name":"Humanities/Medical humanities"},{"id":39791705,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-04-04T15:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 12:44:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5315438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5315438","identity":"rs-5315438","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}