Social Undermining and Coping Mechanisms as Predictors of Turnover Intentions Among Healthcare Workers in Ghana

Social Undermining and Coping Mechanisms as Predictors of Turnover Intentions Among Healthcare Workers in Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Social Undermining and Coping Mechanisms as Predictors of Turnover Intentions Among Healthcare Workers in Ghana Richard Kofi Boateng, Salomey Ofori Appiah, Dorothy Amfo-Antiri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9305225/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The study examined how social undermining influence turnover intentions among healthcare employees in Ghana, while assessing the roles of cutting corners and spiritual intelligence (SI). The study used the Social Information Processing Theory (SIP) to explore how employees interpret and respond to negative social cues within their work environment. The quantitative approach and an explanatory design were used. Again, data was collected through structured questionnaires and analysed using structural equation modeling partial least square. The results revealed that supervisor social undermining had a significant positive effect on turnover intentions, emphasizing the critical role of supervisory behavior in shaping perceptions of fairness and trust. Patient social undermining also significantly predicted turnover intentions, showing the emotional burden of hostile patient interactions. Coworker social undermining did not directly predict turnover but influenced it indirectly through cutting corners, indicating behavioral disengagement as a coping response. Cutting corners fully mediated coworker undermining and partially mediated supervisor and patient undermining, confirming that maladaptive coping bridges social mistreatment and withdrawal. Moreover, spiritual intelligence moderated the relationship between supervisor undermining and turnover intentions, suggesting that employees with higher SI reinterpret negative cues constructively and are less likely to quit. The study contributes to HRM theory by integrating SI into the SIP framework, highlighting how meaning-making and coping processes shape turnover intentions in healthcare settings. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Employee turnover intentions have become one of the most significant challenges facing modern organizations, especially in the healthcare sector (Farasat & Azam, 2022). Turnover intentions refer to employees’ desire to leave their organizations, placing a burden on companies to replace them (Kumar, 2022). The departure of skilled employees not only disrupts workflows but also increases costs related to recruitment, training, and the loss of institutional knowledge. High turnover rates negatively impact organizational effectiveness, customer satisfaction, and employee morale (Tanwar & Prasad, 2016 ). The inability to retain top talent leads to disengaged colleagues and diminished organizational performance (Sui et al., 2023). Fuller and Raman (2019) revealed that organizations failing to manage turnover intentions may lose over $ 50 million annually, coupled with a 15% reduction in market share and growth. Despite efforts to mitigate turnover intentions, it remains a critical issue, particularly in the healthcare sector worldwide. For instance, a 2021 report in Ghana indicated that over 1,200 nurses and 200 midwives left their jobs in search of better work conditions abroad (Ministry of Health, 2022). The frequent turnover of nurses in Ghana’s healthcare sector has resulted in performance setbacks and undermined efforts to achieve Sustainable Development Goal 3.1. Research by Konlan et al. (2023) and Opoku et al. (2022) attributes high turnover intentions to inadequate compensation and poor work environments. However, Aubry and Schapendonk (2023) argue that an often-overlooked driver of negative employee behaviours maybe be social undermining. Social undermining refers to series of behaviors aimed at sabotaging the development of strong interpersonal relationships, work performance, and a positive reputation (Eissa & Wyland, 2016). This phenomenon often manifests in three forms: (1) Supervisor undermining, where managers intentionally weaken the authority or confidence of subordinates (Afshan et al., 2022); (2) Colleague undermining, involving peers who attempt to sabotage each other’s efforts (Sun, 2022); and (3) Client or patient undermining, where customers or patients damage the reputation or success of service providers (Farasat & Azam, 2022). Social undermining behaviors erode trust, collaboration, and administrative efficiency, leading to toxic work environments that diminish morale and creativity, ultimately increasing turnover rates (Yoo & Frankwick, 2013; Farasat & Azam, 2022). According to the Social Information Processing Theory (SIPT) by Salancik and Pfeffer (1978), employees shape their attitudes and behaviors based on social cues from their environment, including interactions with supervisors, colleagues, and clients (Eissa & Wyland, 2015). When negative social interactions, such as social undermining, occur, they significantly alter employee perceptions, increasing turnover intentions and actual turnover, particularly in healthcare (Schwepker & Dimitriou, 2023 ). Social undermining from colleagues, supervisors, or patients depletes crucial resources like social support, self-esteem, and job satisfaction, all of which drive turnover intentions. While several factors contribute to turnover intentions in healthcare (Konlan et al., 2023; Lin et al., 2023), social undermining remains a critical yet underexplored factor (Yusoff et al., 2023). The (SIPT) further suggests that negative relationships at work contribute to toxic environments, heightening the likelihood of employees leaving (Berg et al., 2010). Though this link is theoretically sound, empirical studies exploring the direct impact of all three forms of social undermining on turnover intentions, particularly among healthcare workers like nurses, remain limited. Given that nurses are integral to healthcare systems worldwide, hostile workplace factors threaten their well-being and contribute to higher turnover rates. Hence, hospitals, tasked with providing quality healthcare to a diverse clientele, must address these dynamics to improve their work environment and retain key personnel. While social undermining is a significant factor in turnover intentions, the coping mechanisms employees adopt are equally important. Allport (1936) posits that individuals may adopt specific strategies to cope with undermining behaviors. For instance, when employees face persistent social undermining, they may resort to shortcuts otherwise known as cutting corners as a form of self-preservation. Cutting corners refers to behaviors where employees take shortcuts to meet job expectations without adhering to standard procedures, allowing them to conserve emotional energy in a hostile environment (Kelly, 2018 ). In healthcare, this may lead to compromised patient care or shortcuts in administrative processes (Schwepker & Dimitriou, 2023 ). Employees who engage in cutting corners often do so because they believe their efforts go unrecognised, thus attempting to meet job expectations with minimal exposure to negativity. However, this behavior compromises job quality, increases stress, and eventually leads to turnover intentions. Skrzypińska (2021) posits that while cutting corners prolongs employees’ tenure in challenging environments, it ultimately undermines job satisfaction and performance which may trigger quitting behaviours. Despite this, there is limited empirical evidence exploring how coping mechanisms like cutting corners translate social undermining into turnover intentions, particularly in healthcare settings. Further, scholars have pinpointed that some employees are able to use some positive personal resources to minimize the negative impact of social undermining in their workplace. For instance, Azeem et al. ( 2024 ) believe that religiosity is an important resource to minimize the negative hostile workplace. Similarly, De Clercq and Pereira ( 2024 ) also believe that resilience is an important resource to reduce the effect of some negative workplace treatment. Owing to this, this study believes that spiritual intelligence can be an important resource that can help nurses cope with negative emotions like social undermining behaviours. Even though this assertation maybe true, there is no empirical literature to buttress this claim. Thus, this study would investigate how spiritual intelligence, which gives individuals the ability to connect deeply with one’s inner self and values, fostering purpose, compassion, and meaning in life helps minimise the negative impact of undermining behaviours. This study is significant as it addresses gaps in the existing literature, particularly in the healthcare sector, where research on workplace dynamics like social undermining and turnover intentions remains limited. By examining these issues, the study seeks to enhance understanding of how social undermining contributes to turnover intentions and how coping behaviors, such as cutting corners and spiritual intelligence, exacerbate the issue. The findings are expected to provide actionable insights that can inform organizational policies aimed at creating healthier, more supportive work environments. These insights can guide efforts to develop gender-sensitive retention strategies that foster resilience, collaboration, and productivity in healthcare settings, with broader implications for the sector and society at large. Theoretical and Hypotheses Development This Study is underpinned by the Social Information Processing Theory (SIPT). The Social Information Processing Theory, developed by Salancik and Pfeffer (1978), suggests that individuals' attitudes, behaviors, and perceptions are significantly shaped by their social environment, particularly by the information they receive from others. In the workplace, social information plays a vital role in influencing employees' perceptions of their work environment, job satisfaction, and ultimately their intentions to stay or leave. According to the theory, negative social interactions such as social undermining from supervisors, coworkers, or clients can foster a hostile work environment that may increase turnover intentions rates. In healthcare settings, social undermining can take many forms, including disrespectful behavior, micromanagement, credit-stealing, gossip, or exclusion from decision-making processes (Mahdi et al., 2021). These actions not only erode employees' confidence and self-esteem but also diminish their sense of belonging and trust within the organisation. In the workplace, undermining can stem from various sources, including supervisors, coworkers, and even patients in industries like healthcare. Inferring from social information planning theory, this negative behavior has been found to significantly contribute to employee turnover intentions, as it erodes job satisfaction, trust, and the sense of belonging within an organization (Song & Zhao 2022 ; Zhao et al. 2024 . When supervisors engage in undermining behavior, it often leads to increased turnover intentions because employees feel unsupported, disrespected, and devalued. The power imbalance between supervisors and employees exacerbates this effect, as employees may feel trapped, unable to address the issue, or find other sources of support. Similarly, coworker undermining can foster a toxic work environment, reduce morale and increase turnover intentions (Song & Zhao 2022 ). In teams, this kind of behavior disrupts collaboration and trust, making employees more likely to seek new job opportunities where they feel more valued. Patient undermining, particularly in healthcare settings, adds another layer of stress. Constant negative interactions with patients, especially when paired with a lack of support from supervisors or coworkers, can lead to burnout and increased turnover intentions. Ultimately, the presence of social undermining from any source creates a hostile work environment, prompting employees to leave in search of more positive, supportive workplaces. Social undermining has been found to have several negative effects on several employees and organizational behaviour. Again, Duffy et al. (2006) found social undermining to be a negative predictor of social support and interpersonal relationships. Again, Hershcovis (2011) found social undermining as a predictor of workplace bullying and incivility. Also, Reh et al. (2018) found social undermining as a key driver of employee envy. Similarly, studies such as (Dar et al. 2023; Eissa and Wyland, 2016; Gail Hepburn and Enns, 2013; Quade et al. 2019; Smith and Webster, 2017) found social undermining as a negative significant predictor of employee job performance. Additionally, (Meier and Cho, 2019; Shaheen et al., 2021; Strongman, 2013; Valipour et al. 2023) found social undermining to have a negative impact on employee well-being. Based on these findings, this study proposed H1a, H1b and H1c. H1a: supervisor undermining has a statistically significant effect on turnover intentions H1b: coworker undermining has a statistically significant effect on turnover intentions H1c: patient undermining has a statistically significant effect on turnover intentions Cutting Corners and Spiritual Intelligence as coping behaviors To cope with the psychological and emotional toll of social undermining, nurses may adopt cutting corners as a coping mechanism. In the view of Maslach and Leiter ( 2022 ), cutting corners involves bypassing standard procedures, reducing effort on tasks, or neglecting certain responsibilities to alleviate stress and conserve energy. Campbell ( 2020 ) posits that cutting corners bridges the relationship between social undermining and turnover intentions by offering employees a temporary means of coping. In the short term, this behavior may help employees endure the hostile environment by minimising the direct impact of stressors. For instance, nurses subjected to micromanagement or exclusion might intentionally reduce their engagement with work processes to avoid further conflict or frustration (Grimwood, 2022 ). With time cutting corners may further strain their relationships with colleagues and supervisors, reduce their overall performance, and intensify feelings of dissatisfaction (Schwepker Jr & Dimitriou, 2023 ). This diminished sense of fulfillment and increased detachment from the organisation can ultimately reinforce turnover intentions (Arani & Fayyazi, 2022 ). Thus, cutting corners acts as a double-edged sword. While it provides immediate psychological relief, it perpetuates a cycle of disengagement and dissatisfaction, amplifying the likelihood of employees leaving the organisation. Thus, this study believes that cutting corners can mediate the relationship between social undermining behaviours and turnover intentions of nurses. Indeed, cutting corners have been found to mediate some negative workplace outcomes (Enwereuzor, 2024 : Yan et al. 2021 ). owing to this we propose that- H2 cutting corners significantly mediates the relationship between all three dimensions of social undermining on turnover intentions. Furthermore, individuals' traits, such as Spiritual Intelligence (SI), play a pivotal role in how they respond to social undermining (Skrzypińska, 2021). Those with higher levels of SI demonstrate resilience, emotional regulation, and strong interpersonal skills, all of which help them navigate undermining behaviors in the workplace (Atroszko et al., 2021). McGhee and Grant (2017) assert that employees with elevated SI are proficient at conflict resolution, fostering positive relationships, and promoting a supportive organizational environment. This becomes particularly relevant in instances of social undermining, as positive workplace relationships can buffer the harmful effects of negative behaviors on retention, especially among nurses (Dar et al., 2023). SI not only aids employees, such as nurses, in handling negativity from supervisors, colleagues, and clients (Bayighomog & Arasli, 2022), but it can also turn challenging situations into opportunities for professional growth, potentially encouraging them to remain in their roles longer (Skrzypińska, 2021). SI reshapes how employees view challenges, making it an essential factor in fostering job satisfaction and retention. The Social Information Processing Theory suggests that individuals with high SI perceive social undermining as an opportunity for personal growth rather than a threat (Salancik & Pfeffer, 1978). Moreover, the qualities of resilience, empathy, and compassion nurtured by SI help transform negative interactions into positive outcomes, enhancing employees' loyalty, commitment, and job satisfaction (Allport, 1936). Spiritual intelligence is thus a crucial factor in determining how workers respond to negative workplace behaviors, including social undermining (Giannoukou, 2023; Jin, 2023). It has also been shown to moderate the relationship between several negative workplace behaviors, as evidenced by Hamid et al. (2016), who found SI to buffer the impact of abusive supervision on workplace deviance. Similarly, Mahdi et al. (2021) identified SI as a moderator in the determinants of fraud prevention within organizations. Research by Hasanuddin and Sjahruddin (2017) also found that SI significantly impacts work enthusiasm. This study proposed hypotheses H3: spiritual intelligence has a significant reduces the effect of all dimensions of social undermining on turnover intentions. Conceptual Framework Fig. 1 Research Methods This study used a non-interventional research design. Based on this research design, an explanatory study design was used. This design aided the researchers in ascertaining reasons why undermining behaviors exist in the various Teaching Hospitals in Ghana. By so doing the study was able to ascertain how these behaviours affect turnover intentions. Data was collected from health workers in all 3 Teaching hospitals in Ghana: Okomfo Teaching Hospital, Korle-bu Teaching Hospital, and Cape Coast Teaching Hospital respectively. This study focused on these hospitals due to the number of workloads and duties performed by nurses daily. Again, a report by the Ghana Health Service showed that most nurses who left their job roles in 2022 and 2023 were from these hospitals (Health Service Report, 2023). Thus, focusing on these three health centers intends to give an understanding of why nurses quit their jobs and how the social undermining of all forms affects them. Moreover, these hospitals' status as leading healthcare providers in Ghana ensures that the findings have significant implications for policy and practice, offering actionable insights to enhance worker retention, morale, and overall hospital functioning. The study targeted all nurses in the various Teaching Hospitals in Ghana. This is because based on reports by WHO (2022) and the Ministry of Health report (2022), management in the health sector finds it difficult to retain these classes' staff. In this view, the study used a total of 3100 nurses across these hospitals. Within these numbers, 1200 nurses come from the Okomfo Teaching Hospital, 1000 nurses from the Korle-bu Teaching Hospital, and 900 nurses from Cape Coast Teaching Hospital (Ministry of Health report, 2022). The study used the simple random technique to select nurses for the survey. The simple random sampling was used because nurses used in the study are homogeneous (Etikan & Bala, 2017). This guarantees the necessary representation of all groupings in the institution. Owing to this an equal proportion was used to arrive at the sample size as shown in Table 1 . Thus, a sample size of 341 was used for the study. However, due to non-response rate the sample size was increased by 50% as suggested by (Bartlett et al., 2001). Hence, the sample size was increased to 512. It must be noted that this sample size was set with a 95% confidence interval and a 5% margin of error. Table 1 Sample Size Hospital Population Sample size Korle-bu 1000 110 Komfo Anokye 1200 132 Cape Coast Teaching Hospital 900 99 3100 341 Data collection Data collection was carried out using structured questionnaire. Additionally, to enhance the questionnaires' reliability and validity, feedback was solicited from two organisational behavior professors with extensive academic and research experience. After a thorough review, these experts largely endorsed the questionnaire content but suggested minor adjustments, which were incorporated. For instance, a question on social undermining was modified from focusing on supervisor behavior to coworker interactions. The revised survey was then distributed through the organizations' internal email systems, with assurances of anonymity and confidentiality emphasized to protect participants' rights (Lim, 2002). The data collection was done in three stages, spaced one month apart, to allow for temporal separation between measurements of independent, moderating, and mediating variables, thereby reducing the potential for common method bias (CMB) (Podsakoff et al., 2003). To mitigate the risk of CMB, several measures were adopted, including the staggered data collection schedule and the use of an online survey format to minimize social desirability biases. The study's incorporation of interaction and mediation effects also aimed to reduce the likelihood of response biases, as it is less probable that respondents would be familiar with the underlying theories to skew their answers deliberately. Detailed discussion of CMB considerations is provided in the results section. Measurement The study variables were measured based on empirical studies. Specifically, validated items were adapted from previous studies. In this view, items for social undermining were adapted from the empirical work of (Bluedorn et al., 1999). Again, items for spiritual intelligence and cutting corners were adapted from (Frone et al., 1992 and Rhoades & Eisenberger, 2002) respectively. And finally, items for turnover intentions were adapted from (Afzal et al., 2010). All items were measured on a 5-item Likert scale ranging from least agreement to high agreement. That is all measures are anchored on a five-point Likert-type scale ranging from 1 (“least agreement”) to 5 (“strongly agree”). A total of 512 questionnaires were distributed. Out of which 470 were retrieved for data analysis. Ethics Statement All participants provided written informed consent for the collection of information and the publication of data generated by the study. The target population for this study was nurses who were over 18 years and were deemed as adults per the constitution of Ghana. All participants voluntarily partake in the survey and may withdraw at any time. Considering that the survey content does not involve any sensitive issues and that the data collected are completely anonymous. An introductory letter was sent to all three hospitals to ensure the research meets ethical standards. Furthermore, the research does not pose a risk to the physical or psychological health of participants, and the content of the survey was conducted in a manner that respects and protects the privacy of participants. The constructs are modelled as: CU-Coworker Social Undermining, SU-Supervisor Social Undermining, PU- Patient Social Undermining and To- Turnover Intentions Data analysis The accuracy of the data was verified after a thorough review process. Once the questionnaires had been coded, the responses were entered into the Statistical Package for the Social Sciences (SPSS) version 25, which was employed to organise the dataset, generate descriptive summaries, and compute essential analytical parameters. Subsequent analyses were conducted using SMART PLS version 4.1.0.6, a software widely applied in studies of this nature (Appau et al., 2021). The model assessment followed the procedures recommended by Hair et al. (2020), adhering strictly to the two-stage approach for evaluating structural models. During this evaluation, indicators with factor loadings below 0.40 were removed on the premise that excluding weak measures improves the reliability and validity of the final model. Descriptive Statistics of Variables Table 2 presents the correlation matrix for the study variables. The findings indicate that all six constructs exhibited significant positive correlations with one another, with none of the coefficients exceeding the 0.90 threshold, thereby suggesting the absence of common method bias (Hair et al., 2016). These correlation patterns lend preliminary support to the study’s proposed relationships and offer a sound empirical basis for the subsequent hypothesis testing. Table 2 Mean, standard deviation and correlation coefficient of variables (N = 470 ) Constructs Mean SD 1 2 3 4 5 6 1.Customer Undermining 4.123 0.764 1 2.Cutting Corners 3.976 0.741 0.480* 1 3.Patient Undermining 4.024 0.842 0.544* 0.633* 1 4.Spiritual Intelligence 3.987 0.806 0.504* 0.648* 0.677* 1 5.Supervisor Undermining 4.223 0.825 0.595* 0.686* 0.682* 0.737* 1 6. Turnover 4.211 0.834 0.553* 0.690* 0.534* 0.784* 0.818* 1 Note: **p < 0.01, **p < 0.05 (same in structural path) Test of Reliability, Validity, and Multicollinearity To assess construct reliability, we considered Composite Reliability (CR). The composite reliability (CR) values for all constructs exceeded the minimum acceptable benchmark of 0.70 (see Table 3 ), indicating strong internal consistency. Construct validity was further assessed through convergent and discriminant validity. As shown in Table 3 , the Average Variance Extracted (AVE) values ranged from 0.623 to 0.837, and item loadings fell between 0.704 and 0.914, thereby satisfying the criteria for convergent validity. Additionally, the results in Table 3 showed that multicollinearity was not detected, as all Variance Inflation Factor (VIF) values were below the recommended threshold of 3.3 (Kock, 2015). Table 3 Reliability Analysis, Validity and VIF Cutting Corners Indicator loadings Composite Reliability Average Variance Extracted Variance Inflation Factor 0.817 0.528 2.048 CC2 0.825 CC3 0.685 CC4 0.710 CC6 0.678 Coworker UND 0.728 0.521 1.995 COU3 0.880 COU4 0.704 COU5 0.875 COU6 0.583 PATIENT UND 0.533 0.905 0.658 2.048 PU1 0.871 PU2 0.855 PU3 0.717 PU4 0.745 PU5 0.853 SUPERVISOR UND 0.897 0.639 0.220 SUPU1 0.890 SUPU2 0.889 SUPU3 0.655 SUPU4 0.642 SUPU5 0.0878 Spiritual Intelligence 0.947 0.749 2.000 SI2 0.832 SI3 0.879 SI4 0.879 SI5 0.863 SI6 0.887 SI7 0.833 Turnover Intention 0.874 0.623 1.670 TI1 0.771 TI3 0.853 TI4 0.737 TI5 0.814 TI6 0.813 TI7 0.774 Composite Reliability (CR) > 0.7 and Cronbach’s alpha (α) > 0.7, AVE > 0.5, VIF 0.70 Model Fit Testing Assessing model fit is essential in structural equation modelling, as emphasised by Hair et al. (2021) and Henseler et al. (2015). Accordingly, the present study evaluated the overall fit of the estimated model prior to testing the hypotheses. Following the recommendations of Tomarken and Waller (2005) and Lohmöller and Lohmöller (1989), a chi-square (χ²) p-value greater than 0.05 indicates acceptable model fit; the current analysis produced a value of 0.072, which meets this criterion. The Normed Fit Index (NFI) is regarded as excellent at 0.95 or above (Hooper et al., 2008), while Hu and Bentler (1999) consider values of 0.90 or higher to reflect a good fit. Additionally, Hu and Bentler (1999) and Schuberth et al. (2023) note that the Standardised Root Mean Square Residual (SRMR) should be below 0.08, and the RMS theta should not exceed 0.12. The results obtained in this study meet these recommended thresholds, as reported in Table 3 . Table 3 Model Fit Testing Fit Indices Estimated Model SRMR 0.085 Chi-Square 0.072 NFI 0.940 Rms Theta 0.100 SRMR 0.05; NFI > 0.90; Rms < 0.12 Discriminant Validity (HTMT Criteria) The HTMT approach was employed to overcome the shortcomings of the Fornell lacker. Henseler et al . (2015) criticise Fornell Lacker for its inability to assess the validity of slightly different constructs. As a result, it is unable to deal with validity issues properly (Radomir & Moisescu, 2019). Henseler et al . (2015) recommended the HTMT correlation in assessing validity. The HTMT assesses the mean value of the correlation between and across the individual constructs. Hair et al . (2020) recommends a threshold of 0.85 for HTMT correlation values. The correlation between and across individual constructs must be less than 0.85, particularly when the constructs are similar (Henseler et al ., 2015). The results presented in Table 4 show that all HTMT correlation values are within the 0.85 range. This implies that there is no discriminant validity issue in this study. Table 4 Discriminant Validity (HMT) Constructs 1 2 3 4 5 6 1.Customer Undermining 1 2.Cutting Corners 0.480* 1 3.Patient Undermining 0.544* 0.633* 1 4.Spiritual Intelligence 0.504* 0.648* 0.677* 1 5.Supervisor Undermining 0.595* 0.686* 0.682* 0.737* 1 6.Turnover intention 0.553* 0.690* 0.534* 0.784* 0.818* 1 Source: Field Survey (2025) Structural model Assessment Once all measurement model requirements were met and the results fell within acceptable thresholds, attention shifted to evaluating the structural model. The structural model outlines the proposed causal links between the latent constructs. Before testing the significance of these relationships, several model fit considerations were reviewed. Following the guidance of Hair et al. (2016), the assessment began with checks for potential collinearity among the predictor variables. After confirming the absence of problematic collinearity, the analysis proceeded to examine the model’s predictive strength using the coefficient of determination R 2 , the effect size f 2 , and predictive relevance Q 2 . The findings showed R 2 values of 0.842 for cutting corners and 0.857 for turnover intentions. This means that 84.2 percent of the variability in cutting corners was accounted for collectively by the three dimensions of social undermining, namely supervisor undermining, coworker undermining, and patient undermining. In a similar way, these three dimensions together with cutting corners explained 85.7 percent of the variance in turnover intentions. Using the conventional interpretation of effect sizes, where small is defined as values between 0.0 and 0.15, medium between 0.15 and 0.35, and large above 0.35, the results indicate that excluding supervisor undermining, patient undermining, or spiritual intelligence would produce only minimal changes in turnover intentions. In contrast, coworker undermining had a large effect, and cutting corners had a medium effect on turnover intentions. The predictive relevance values also demonstrated strong predictive capacity, with all three forms of social undermining showing high predictive relevance for cutting corners at 0.838 and for turnover intentions at 0.738. The overall structural model presented in Fig. 2 illustrates the significance and direction of the proposed relationships among the constructs. Table 5 F-Square, R-Squared and Q-Squared Supervisor undermining Effect Size (f 2 ) R-Squared (R 2 ) Predictive Relevance (Q 2 ) 0.015 Coworker undermining 1.139 Patient undermining 1.088 Spiritual intelligence 0.026 Cutting corners 0.117 0.527 0.838*** Turnover intentions 0.847 0.738** Note: *0.02 ≤ f 2 ≤ 0.15 is a weak effect, **0.15 ≤ f 2 ≤ 0.35 is a moderate effect *** f 2 ≥ 0.35 shows a strong effect Note: *0.02 ≤ Q 2 ≤ 0.15 weak effect, **0.15 ≤ Q 2 ≤ 0.35 moderate effect, *** Q 2 ≥ 0.35 strong effect Note: *R-Square > 0.10 Assessment of Path Relationships The results of the structural model presented in Table 6 revealed mixed findings regarding the direct effects of social undermining on turnover intention (TI). Specifically, coworker undermining (CU) showed a positive but statistically insignificant effect on turnover intention (β = 0.060, p = 0.100), leading to the rejection of H1a. In contrast, supervisor undermining (SU) exerted a strong positive and statistically significant effect on turnover intention (β = 0.315, p = 0.000), thereby supporting H1b. Similarly, patient undermining (PU) demonstrated a positive and significant effect on turnover intention (β = 0.303, p = 0.000), resulting in the acceptance of H1c. Regarding the moderating role of spiritual intelligence (SI), the results showed that the interaction between SI and CU on turnover intention was positive but insignificant (β = 0.056, p = 0.135), indicating that SI does not moderate this relationship. Hence, H3a was not supported. On the other hand, the moderating effect of SI on the relationship between SU and turnover intention was negative and statistically significant (β = -0.130, p = 0.021), suggesting that higher SI weakens the effect of supervisor undermining on turnover intention. Thus, H3b was supported. Interestingly, SI’s moderating effect on the relationship between PU and turnover intention was positive but statistically insignificant (β = 0.055, p = 0.327). Thus, H3c was not supported. The mediation analysis provided further insights. First, cutting corners (CC) significantly mediated the relationship between CU and turnover intention (β = 0.017, p = 0.029). Since the direct path of CU → TI was insignificant, but the indirect path via CC was significant, this indicates a case of full mediation. Therefore, H2a was supported. Second, CC mediated the relationship between SU and turnover intention (β = 0.023, p = 0.036). Here, both the direct and indirect paths were significant, suggesting partial mediation. Thus, H2b was supported. Finally, CC also mediated the effect of PU on turnover intention (β = 0.054, p = 0.000), again with both direct and indirect effects significant, indicating partial mediation. Hence, H2c was supported. Table 6 Structural Model Direct Effect Β Sample mean (M) SD T statistics P values 5.0% 95.0% Remarks H1a. CU-> TI 0.060 0.061 0.037 1.645 .100 -0.016 0.128 Not Supported H1b. SU -> TI 0.315 0.315 0.052 6.090 .000 0.217 0.416 Supported H1c. PU -> TI 0.303 0.306 0.048 6.277 .000 0.207 0.396 Supported Moderating Effect H3a. SI x CU -> TI 0.056 0.054 0.037 1.496 0.135 -0.020 0.127 Not Supported H3b. SI x SU -> TI -0.130 0.124 0.056 2.314 0.021 -0.239 -0.023 Supported H3c. SI x PU -> TI 0.055 0.050 0.056 0.980 0.327 -0.058 0.161 Not Supported Mediation Effect H2a.CU-> CC->TI 0.017 0.017 0.008 2.184 .029 0.008 0.068 Supported H2b.SU-> CC->TI 0.023 0.023 0.011 2.093 .036 0.039 0.148 Supported H2c.PU-> CC->TI 0.054 0.053 0.015 3.695 .000 0.044 0.152 Supported Note: CU-Coworker Social Undermining, SU-Supervisor Social Undermining, PU- Patient Social Undermining and TI- Turnover Intentions Simple Slope Analysis Figure 3 illustrates the moderating role of spiritual intelligence (SI) on the relationship between supervisor undermining (SU) and turnover intention. As shown, turnover intention increases with higher levels of supervisor undermining across all employees. However, the slope of the relationship is steeper for those with low SI (–1 SD) compared to employees with high SI (+ 1 SD). This indicates that individuals with lower SI are more likely to translate supervisor mistreatment into stronger intentions to quit, whereas those with higher SI demonstrate a weaker relationship between SU and turnover intention. The significant negative interaction term (β = − 0.130, p = 0.021) confirms that SI buffers the harmful impact of supervisor undermining, thereby reducing the likelihood that employees with higher SI will develop turnover intentions despite experiencing undermining from supervisors. Importance Performance Analysis Figure 3 presents the importance–performance map, which highlights the relative influence of different forms of social undermining on turnover intention. Among the predictors, supervisor undermining emerges as the most critical factor (importance = 0.464) while showing only moderate performance, indicating that supervisor-related mistreatment is the strongest driver of employees’ intention to quit. In comparison, patient undermining (importance = 0.194) and coworker undermining (importance = 0.144) rank considerably lower in importance, though their performance scores remain comparable. This suggests that while peer and patient mistreatment contribute to turnover, their effects are overshadowed by the dominant role of supervisor undermining. These findings imply that organizations should first focus on minimizing supervisor undermining through leadership training, monitoring, and accountability mechanisms. At the same time, peer-support initiatives and structured patient management protocols can help address coworker and patient undermining, thereby reducing additional sources of strain. Collectively, such measures can reinforce one another, lowering turnover intentions and fostering a more supportive and resilient work environment. Discussion of Results The study revealed that supervisor social undermining had a significant positive effect on turnover intentions. This indicates that supervisory behavior is central in shaping employees’ perceptions of fairness, recognition, and trust. That is, employees see supervisors as reflections of the organization’s values and intentions. When supervisors behave unfairly, employees equate it with institutional betrayal, leading to low morale, poor engagement, and eventual turnover. Within Ghana’s hierarchical healthcare context, supervisors represent the organization’s authority and moral climate. Drawing on the Social Information Processing Theory (Salancik & Pfeffer, 1978), employees interpret supervisory actions as meaningful social information that guides their attitudes and decisions. When supervisors engage in hostile or unsupportive behaviors, they convey negative signals that the organization lacks concern and justice. Through this theoretical lens, supervisor undermining serves as a communication of organizational neglect, while supportive leadership communicates care and fairness. Reh et al. (2018) explain that such cues generate psychological insecurity and lead employees to withdraw emotionally. Mahdi et al. (2021) further note that persistent hostility creates emotional fatigue and weakens job commitment. Supportive supervisors, on the other hand, provide positive social cues that foster belonging and motivation. The findings further showed that patient social undermining was a strong determinant of turnover intentions, demonstrating the emotional intensity and relational vulnerability embedded in healthcare work. Patients are the focal point of care delivery and directly shape employees’ sense of purpose and professional fulfillment. When patients engage in verbal hostility, disrespect, or non-cooperation, they disrupt the therapeutic relationship and create emotional exhaustion. Such negative encounters accumulate over time, diminishing morale and increasing the desire to quit. The result emphasizes the emotional weight that patients carry in influencing healthcare staff retention. Within the framework of the Social Information Processing Theory (Salancik & Pfeffer, 1978), employees interpret negative interactions from these salient groups as cues that the work environment is unsafe or unappreciative, prompting cognitive withdrawal and eventual turnover intentions. This finding is consistent with prior HR literature linking mistreatment and toxic social climates with high turnover tendencies (Mahdi et al., 2021; Hershcovis, 2011; Reh et al., 2018). In contrast, coworker social undermining did not directly predict turnover intentions. This outcome suggests that peer hostility, while unpleasant, may not immediately provoke the desire to leave the organization. Coworker mistreatment is often normalized or minimized as part of daily work challenges, particularly in team-based healthcare environments where interdependence is necessary for service delivery (Mahdi et al., 2021). Employees may perceive such behavior as a temporary conflict rather than a systemic problem. From an HRM perspective, this indicates that coworker-related tensions primarily erode teamwork quality, collaboration, and psychological safety rather than directly influencing decisions to quit. However, persistent peer hostility may gradually foster disengagement, reduce cooperation, and impair collective efficiency. This interpretation aligns with the Social Information Processing framework, which suggests that employees process social information differently depending on the perceived source of the threat. When the source is a coworker, the behavior may be viewed as manageable and situational, unless it interferes with job performance, at which point it triggers behavioral withdrawal such as cutting corners. The mediation results confirmed that cutting corners acted as a full mediator in the link between coworker social undermining and turnover intentions. The implication of this finding is that negative workplace interactions do not always produce immediate withdrawal but may first manifest through behavioral disengagement. Employees who feel mistreated by coworkers conserve emotional and mental energy by reducing effort, overlooking procedures, or avoiding collaboration. These behaviors, although initially serving as coping mechanisms, gradually diminish performance, teamwork, and organizational commitment. This suggests that HR managers should pay close attention to subtle behavioral changes as early indicators of deeper relational problems. From the perspective of the Social Information Processing Theory (Salancik & Pfeffer, 1978), such coping behavior reflects how employees interpret and respond to the social cues in their environment. When coworker hostility sends information that the social climate is unsupportive, employees internalize these signals and adapt defensively by cutting corners to reduce stress. With time, this maladaptive coping becomes part of their behavioral pattern, eroding motivation and strengthening intentions to leave. Again, it was found that cutting corners is a partial mediator in the link between supervisor and patient social undermining on turnover intentions. This finding implies that employees do not only decide to leave because of immediate frustration or perceived injustice but also because the resulting strain alters their work behavior in ways that damage their professional standards and long-term satisfaction. When supervisors or patients engage in hostile acts, employees may respond by adopting short-term coping behaviors such as cutting corners, which provide temporary psychological relief from stress or conflict. However, these behaviors erode professional integrity, weaken commitment, and create feelings of guilt or dissatisfaction that eventually translate into turnover intentions. This means that organizations face dual risks, immediate turnover from emotional distress and gradual disengagement from maladaptive coping. From the Social Information Processing Theory (Salancik & Pfeffer, 1978) perspective, this finding illustrates how employees interpret social information from significant others such as supervisors and patients and act upon it. Negative cues from these sources are perceived as powerful signs that the work environment is unjust, disrespectful, or unsafe. Employees cognitively process these signals and, to protect their psychological well-being, modify their behavior by disengaging or cutting corners. While this may momentarily reduce emotional tension, it simultaneously undermines role fulfillment and deepens dissatisfaction. As such, the continuous processing of such negative information reinforces negative attitudes and solidifies the intention to leave. The results revealed that spiritual intelligence (SI) significantly moderates the relationship between supervisor social undermining and turnover intentions. This result implies that employees who possess higher levels of SI demonstrate stronger psychological resilience and are less likely to view supervisory hostility as a justification for leaving their organization. Such employees rely on deeper value systems, moral awareness, and reflective thinking to reinterpret adverse experiences constructively. This enables them to sustain emotional stability and maintain focus on their professional purpose even under negative supervisory conditions. Consequently, SI functions as an internal coping resource that reduces emotional exhaustion, enhances optimism, and preserves commitment. This outcome aligns with previous evidence that SI facilitates self-regulation and positive coping responses in stressful work environments (McGhee & Grant, 2017; Bayighomog & Arasli, 2022). However, the moderating effect of SI was not observed in relationships involving coworkers and patient undermining, suggesting that its buffering capacity depends on the relational source of mistreatment. Employees may perceive peer or patient hostility as situational, transient, and external to the institution, while supervisory mistreatment is interpreted as reflective of the organization’s ethical and relational climate. In this regard, SI is more effective in reframing the meaning of threats that are perceived as systemic or value-based rather than incidental. Viewed through the lens of the Social Information Processing Theory (Salancik & Pfeffer, 1978), this finding demonstrates that employees use their spiritual intelligence as a cognitive filter through which they interpret and respond to social information. When faced with supervisory hostility, individuals with high SI reinterpret negative cues in ways that preserve psychological safety and organizational attachment. They process these cues not as evidence of institutional rejection but as opportunities for growth, empathy, and forgiveness. By reshaping how negative social information is understood, SI disrupts the link between perceived hostility and turnover intentions. In contrast, when mistreatment originates from coworkers or patients, employees are more likely to attribute the behavior to interpersonal dynamics or situational pressures, reducing the relevance of SI in shaping their responses. Therefore, within the SIP framework, SI operates as a meaning-making and resilience mechanism that transforms how employees cognitively and emotionally respond to toxic supervisory relationships, ultimately mitigating the likelihood of turnover. Conclusion The study found that supervisor and patient undermining significantly increased turnover intentions, while coworker undermining influenced turnover only indirectly through cutting corners in the Ghanaian health system. Again, cutting corners emerged as a key mediating pathway, showing how negative treatment translates into maladaptive coping and ultimately withdrawal. Finally, Spiritual intelligence buffered the impact of supervisor undermining but showed no significant effect for coworker or patient undermining, indicating its protective value is context dependent. Together, these findings highlight the central role of social undermining, coping behaviors, and personal resources in shaping employees’ decisions to stay or leave. Implications of the Study This study advances Social Information Processing Theory (SIPT) in the healthcare context by showing that negative social cues from supervisors and patients are central drivers of turnover intentions, while coworker undermining exerts its influence indirectly through cutting corners. The IPMA results further highlight performance gaps in addressing undermining behaviors, emphasizing that SIPT must account more explicitly for the weight of interpersonal interactions in service-intensive environments. The findings extend the theory by revealing that cutting corners functions as a behavioral pathway through which social information is translated into withdrawal intentions, thus broadening SIPT beyond its cognitive lens. The moderation results add perspectives by showing that spiritual intelligence buffered the impact of supervisor undermining on turnover, though its protective effect was weaker than expected, suggesting that personal resources operate unevenly depending on the nature of the social cue and the power dynamics involved. Practically, the results provide actionable insights for managers and policymakers seeking to improve healthcare staff retention. For managers, the IPMA indicates that interventions should prioritize reducing supervisor and patient undermining through peer-support initiatives, structured communication, protective mechanisms, and accountability systems. Cutting corners should be recognized as a maladaptive coping strategy and addressed early through supportive supervision, equitable workload allocation, and performance feedback. While spiritual intelligence emerged as an important resource, its relatively low performance suggests that organizations cannot rely solely on employees’ resilience; instead, they must foster professional environments that reinforce fairness, compassion, and support. At the policy level, systemic reforms are needed to treat undermining as an organizational risk, enforce zero-tolerance approaches to abuse, ensure transparent reporting, and build safeguards that replace maladaptive coping with constructive resilience. Embedding compassion, fairness, and respect into organizational values and accountability structures is therefore critical to sustaining healthcare employees’ commitment and reducing turnover. Declarations Acknowledgement We gratefully acknowledge the support and contributions of colleagues, participants, and institutional resources that made this study possible. Funding Declaration No Funding was obtained Conflicts of Interest All authors declare that they have no conflicts of interest Clinical Trial Number NA Financial support We can declare that we did not receive any financial assistance from any institution Ethical approval and Accordance The study was approved by the University of Cape Coast Ethical Review Committee, and it was conducted in accordance with the ethical standards of the committee, the 1964 Declaration of Helsinki and its later amendments, as well as relevant national research guidelines. As such, an ethical approval ( UCCIRB/CHSL/2025/011 ) was taken from the right authority. Consent to Participate The authors affirm that informed consent was obtained from all participants. Each participant was fully briefed on the study’s purpose, potential risks, and expected benefits before participation. Consent for publication NA Data Availability Statement The data that was used for the study has been attached to the online platform and in the manuscript as appendix I. Competing Interest The authors declare that they have no competing financial or non-financial interests that could have influenced the work reported in this manuscript. Dual-Publication This manuscript is original, has not been published previously, and is not under consideration for publication elsewhere in any form. Authorship All authors meet the authorship criteria, made substantial contributions to the conception, design, analysis, and writing of the manuscript, and approved the final version for submission. Open-access The authors agree to the journal’s open-access policy and, where applicable, consent to publication under the specified Creative Commons licence. Third-party material All third-party materials included in this manuscript have been properly acknowledged, and permission has been obtained where required. References Allen, D. G., & Vardaman, J. M. (2021). Global talent retention: Understanding employee turnover intentions around the world. In Global talent retention: Understanding employee turnover intentions around the world (pp. 1-15). Emerald Publishing Limited. Arani, V. Y., & Fayyazi, M. (2022). Social undermining and organisational attitudes: The moderating role of personality traits. International Journal of Business Governance and Ethics, 16 (3), 355-375. Azeem, M. U., De Clercq, D., & Haq, I. U. (2024). Religiosity as a buffer of the harmful effects of workplace loneliness on negative work rumination and job performance. Journal of Organizational Effectiveness: People and Performance. Campbell, T. I. E. (2020). An exploration into the roles of managerial support and occupational stigma in the employee turnover intentions process amongst non-managerial quick service workers in Guyana (Doctoral dissertation). De Clercq, D., & Pereira, R. (2024). How resilient employees can prevent family ostracism from escalating into diminished work engagement and change-oriented organizational citizenship behavior. International Studies of Management & Organization, 54 (1), 25-47. Enwereuzor, I. K. (2024). Dispositional greed and knowledge sabotage: The roles of cutting corners at work and ethical leadership. Current Psychology, 43 (2), 1325-1339. Grimwood, K. (2022). Workplace mistreatment: A qualitative study of the antecedents of supervisor-employee relationship challenges. Kelly, G. (2018). Ways to minimize unethical behavior by employees (Doctoral dissertation, Northcentral University). Maslach, C., & Leiter, M. P. (2022). The burnout challenge: Managing people’s relationships with their jobs. Harvard University Press. Mulaphong, D. (2023). Social undermining in public sector organizations: Examining its effects on employees’ work attitudes, behaviors, and performance. Public Organization Review, 23 (3), 1229-1248. Polo-Peña, A. I., Frías-Jamilena, D. M., & Fernández-Ruano, M. L. (2021). Influence of gamification on perceived self-efficacy: Gender and age moderator effect. International Journal of Sports Marketing and Sponsorship, 22 (3), 453-476. Rasool, S. F., Wang, M., Zhang, Y., & Samma, M. (2020). Sustainable work performance: The roles of workplace violence and occupational stress. International Journal of Environmental Research and Public Health, 17 (3), 912. Sariani, N. L. P., Mahayasa, I. G. A., Maheswari, A. I. A., Astakoni, I. M. P., & Utami, N. M. S. (2022). Antecedent of organizational citizenship behavior variables: Gender as moderator. Journal of Social Science, 3 (3), 516-533. Schwepker Jr, C. H., & Dimitriou, C. K. (2023). Reducing service sabotage: The influence of supervisor social undermining, job stress, turnover intentions and ethical conflict. Journal of Marketing Theory and Practice, 31 (4), 450-469. Song, Y., & Zhao, Z. (2022). Social undermining and interpersonal rumination among employees: The mediating role of being the subject of envy and the moderating role of social support. International Journal of Environmental Research and Public Health, 19 (14), 8419. Tanwar, K., & Prasad, A. (2016). The effect of employer brand dimensions on job satisfaction: Gender as a moderator. Management Decision, 54 (4), 854-886. Yan, H., Hu, X., & Wu, C. H. (2021). When and why does proactive personality inhibit corner-cutting behaviors: A moderated mediation model of customer orientation and productivity climate. Personality and Individual Differences, 170, 110443. Zhao, S., Ben-Abdallah, R., Khattak, S. A., & Wang, N. (2024). Examining the effects of tyrannical leadership on workplace incivility: Interplay of employee low morale and supportive organizational culture. Current Psychology, 1-12. Additional Declarations No competing interests reported. Supplementary Files AppendixI.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 18 Apr, 2026 Editor invited by journal 17 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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the PLS-SEM\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305225/v1/1eb2d35af52a602deb8627b9.jpg"},{"id":108821511,"identity":"59b8acbb-52a0-480a-8e54-dc603ec4c5bb","added_by":"auto","created_at":"2026-05-08 16:45:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83104,"visible":true,"origin":"","legend":"\u003cp\u003eSlope Analysis of moderating effect of Spiritual Intelligence\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305225/v1/222b8a343586737ef7b43ac9.jpg"},{"id":108821163,"identity":"7c33978f-3545-4969-8a40-ec68563ce7ac","added_by":"auto","created_at":"2026-05-08 16:44:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118609,"visible":true,"origin":"","legend":"\u003cp\u003eIPMA\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305225/v1/eff36475083021e679e10954.jpg"},{"id":108823111,"identity":"a2e8714f-05f6-47f3-baee-8e52e1b4a0a8","added_by":"auto","created_at":"2026-05-08 16:52:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":912312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9305225/v1/909a9a90-0d12-476d-94d8-91a1b7fb5b67.pdf"},{"id":108821528,"identity":"e0861cb4-05f9-4837-b374-155955a16aad","added_by":"auto","created_at":"2026-05-08 16:45:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":275151,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixI.docx","url":"https://assets-eu.researchsquare.com/files/rs-9305225/v1/a2feec1c8da490a9026e56f1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSocial Undermining and Coping Mechanisms as Predictors of Turnover Intentions Among Healthcare Workers in Ghana\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEmployee turnover intentions have become one of the most significant challenges facing modern organizations, especially in the healthcare sector (Farasat \u0026amp; Azam, 2022). Turnover intentions refer to employees’ desire to leave their organizations, placing a burden on companies to replace them (Kumar, 2022). The departure of skilled employees not only disrupts workflows but also increases costs related to recruitment, training, and the loss of institutional knowledge. High turnover rates negatively impact organizational effectiveness, customer satisfaction, and employee morale (Tanwar \u0026amp; Prasad, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The inability to retain top talent leads to disengaged colleagues and diminished organizational performance (Sui et al., 2023). Fuller and Raman (2019) revealed that organizations failing to manage turnover intentions may lose over \u003cspan\u003e$\u003c/span\u003e50\u0026nbsp;million annually, coupled with a 15% reduction in market share and growth. Despite efforts to mitigate turnover intentions, it remains a critical issue, particularly in the healthcare sector worldwide. For instance, a 2021 report in Ghana indicated that over 1,200 nurses and 200 midwives left their jobs in search of better work conditions abroad (Ministry of Health, 2022). The frequent turnover of nurses in Ghana’s healthcare sector has resulted in performance setbacks and undermined efforts to achieve Sustainable Development Goal 3.1. Research by Konlan et al. (2023) and Opoku et al. (2022) attributes high turnover intentions to inadequate compensation and poor work environments. However, Aubry and Schapendonk (2023) argue that an often-overlooked driver of negative employee behaviours maybe be social undermining.\u003c/p\u003e \u003cp\u003eSocial undermining refers to series of behaviors aimed at sabotaging the development of strong interpersonal relationships, work performance, and a positive reputation (Eissa \u0026amp; Wyland, 2016). This phenomenon often manifests in three forms: (1) Supervisor undermining, where managers intentionally weaken the authority or confidence of subordinates (Afshan et al., 2022); (2) Colleague undermining, involving peers who attempt to sabotage each other’s efforts (Sun, 2022); and (3) Client or patient undermining, where customers or patients damage the reputation or success of service providers (Farasat \u0026amp; Azam, 2022). Social undermining behaviors erode trust, collaboration, and administrative efficiency, leading to toxic work environments that diminish morale and creativity, ultimately increasing turnover rates (Yoo \u0026amp; Frankwick, 2013; Farasat \u0026amp; Azam, 2022).\u003c/p\u003e \u003cp\u003eAccording to the Social Information Processing Theory (SIPT) by Salancik and Pfeffer (1978), employees shape their attitudes and behaviors based on social cues from their environment, including interactions with supervisors, colleagues, and clients (Eissa \u0026amp; Wyland, 2015). When negative social interactions, such as social undermining, occur, they significantly alter employee perceptions, increasing turnover intentions and actual turnover, particularly in healthcare (Schwepker \u0026amp; Dimitriou, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Social undermining from colleagues, supervisors, or patients depletes crucial resources like social support, self-esteem, and job satisfaction, all of which drive turnover intentions. While several factors contribute to turnover intentions in healthcare (Konlan et al., 2023; Lin et al., 2023), social undermining remains a critical yet underexplored factor (Yusoff et al., 2023). The (SIPT) further suggests that negative relationships at work contribute to toxic environments, heightening the likelihood of employees leaving (Berg et al., 2010). Though this link is theoretically sound, empirical studies exploring the direct impact of all three forms of social undermining on turnover intentions, particularly among healthcare workers like nurses, remain limited. Given that nurses are integral to healthcare systems worldwide, hostile workplace factors threaten their well-being and contribute to higher turnover rates. Hence, hospitals, tasked with providing quality healthcare to a diverse clientele, must address these dynamics to improve their work environment and retain key personnel.\u003c/p\u003e \u003cp\u003eWhile social undermining is a significant factor in turnover intentions, the coping mechanisms employees adopt are equally important. Allport (1936) posits that individuals may adopt specific strategies to cope with undermining behaviors. For instance, when employees face persistent social undermining, they may resort to shortcuts otherwise known as cutting corners as a form of self-preservation. Cutting corners refers to behaviors where employees take shortcuts to meet job expectations without adhering to standard procedures, allowing them to conserve emotional energy in a hostile environment (Kelly, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In healthcare, this may lead to compromised patient care or shortcuts in administrative processes (Schwepker \u0026amp; Dimitriou, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Employees who engage in cutting corners often do so because they believe their efforts go unrecognised, thus attempting to meet job expectations with minimal exposure to negativity. However, this behavior compromises job quality, increases stress, and eventually leads to turnover intentions. Skrzypińska (2021) posits that while cutting corners prolongs employees’ tenure in challenging environments, it ultimately undermines job satisfaction and performance which may trigger quitting behaviours. Despite this, there is limited empirical evidence exploring how coping mechanisms like cutting corners translate social undermining into turnover intentions, particularly in healthcare settings.\u003c/p\u003e \u003cp\u003eFurther, scholars have pinpointed that some employees are able to use some positive personal resources to minimize the negative impact of social undermining in their workplace. For instance, Azeem et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) believe that religiosity is an important resource to minimize the negative hostile workplace. Similarly, De Clercq and Pereira (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) also believe that resilience is an important resource to reduce the effect of some negative workplace treatment. Owing to this, this study believes that spiritual intelligence can be an important resource that can help nurses cope with negative emotions like social undermining behaviours. Even though this assertation maybe true, there is no empirical literature to buttress this claim. Thus, this study would investigate how spiritual intelligence, which gives individuals the ability to connect deeply with one’s inner self and values, fostering purpose, compassion, and meaning in life helps minimise the negative impact of undermining behaviours.\u003c/p\u003e \u003cp\u003eThis study is significant as it addresses gaps in the existing literature, particularly in the healthcare sector, where research on workplace dynamics like social undermining and turnover intentions remains limited. By examining these issues, the study seeks to enhance understanding of how social undermining contributes to turnover intentions and how coping behaviors, such as cutting corners and spiritual intelligence, exacerbate the issue. The findings are expected to provide actionable insights that can inform organizational policies aimed at creating healthier, more supportive work environments. These insights can guide efforts to develop gender-sensitive retention strategies that foster resilience, collaboration, and productivity in healthcare settings, with broader implications for the sector and society at large.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eTheoretical and Hypotheses Development\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis Study is underpinned by the Social Information Processing Theory (SIPT). The Social Information Processing Theory, developed by Salancik and Pfeffer (1978), suggests that individuals' attitudes, behaviors, and perceptions are significantly shaped by their social environment, particularly by the information they receive from others. In the workplace, social information plays a vital role in influencing employees' perceptions of their work environment, job satisfaction, and ultimately their intentions to stay or leave. According to the theory, negative social interactions such as social undermining from supervisors, coworkers, or clients can foster a hostile work environment that may increase turnover intentions rates. In healthcare settings, social undermining can take many forms, including disrespectful behavior, micromanagement, credit-stealing, gossip, or exclusion from decision-making processes (Mahdi et al., 2021). These actions not only erode employees' confidence and self-esteem but also diminish their sense of belonging and trust within the organisation.\u003c/p\u003e \u003cp\u003eIn the workplace, undermining can stem from various sources, including supervisors, coworkers, and even patients in industries like healthcare. Inferring from social information planning theory, this negative behavior has been found to significantly contribute to employee turnover intentions, as it erodes job satisfaction, trust, and the sense of belonging within an organization (Song \u0026amp; Zhao \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e. When supervisors engage in undermining behavior, it often leads to increased turnover intentions because employees feel unsupported, disrespected, and devalued. The power imbalance between supervisors and employees exacerbates this effect, as employees may feel trapped, unable to address the issue, or find other sources of support. Similarly, coworker undermining can foster a toxic work environment, reduce morale and increase turnover intentions (Song \u0026amp; Zhao \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In teams, this kind of behavior disrupts collaboration and trust, making employees more likely to seek new job opportunities where they feel more valued. Patient undermining, particularly in healthcare settings, adds another layer of stress. Constant negative interactions with patients, especially when paired with a lack of support from supervisors or coworkers, can lead to burnout and increased turnover intentions.\u003c/p\u003e \u003cp\u003eUltimately, the presence of social undermining from any source creates a hostile work environment, prompting employees to leave in search of more positive, supportive workplaces. Social undermining has been found to have several negative effects on several employees and organizational behaviour. Again, Duffy et al. (2006) found social undermining to be a negative predictor of social support and interpersonal relationships. Again, Hershcovis (2011) found social undermining as a predictor of workplace bullying and incivility. Also, Reh et al. (2018) found social undermining as a key driver of employee envy. Similarly, studies such as (Dar et al. 2023; Eissa and Wyland, 2016; Gail Hepburn and Enns, 2013; Quade et al. 2019; Smith and Webster, 2017) found social undermining as a negative significant predictor of employee job performance. Additionally, (Meier and Cho, 2019; Shaheen et al., 2021; Strongman, 2013; Valipour et al. 2023) found social undermining to have a negative impact on employee well-being. Based on these findings, this study proposed H1a, H1b and H1c.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eH1a: supervisor undermining has a statistically significant effect on turnover intentions\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003cp\u003eH1b: coworker undermining has a statistically significant effect on turnover intentions\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section4\"\u003e \u003cp\u003eH1c: patient undermining has a statistically significant effect on turnover intentions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eCutting Corners and Spiritual Intelligence as coping behaviors\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo cope with the psychological and emotional toll of social undermining, nurses may adopt cutting corners as a coping mechanism. In the view of Maslach and Leiter (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), cutting corners involves bypassing standard procedures, reducing effort on tasks, or neglecting certain responsibilities to alleviate stress and conserve energy. Campbell (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) posits that cutting corners bridges the relationship between social undermining and turnover intentions by offering employees a temporary means of coping. In the short term, this behavior may help employees endure the hostile environment by minimising the direct impact of stressors. For instance, nurses subjected to micromanagement or exclusion might intentionally reduce their engagement with work processes to avoid further conflict or frustration (Grimwood, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). With time cutting corners may further strain their relationships with colleagues and supervisors, reduce their overall performance, and intensify feelings of dissatisfaction (Schwepker Jr \u0026amp; Dimitriou, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This diminished sense of fulfillment and increased detachment from the organisation can ultimately reinforce turnover intentions (Arani \u0026amp; Fayyazi, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, cutting corners acts as a double-edged sword. While it provides immediate psychological relief, it perpetuates a cycle of disengagement and dissatisfaction, amplifying the likelihood of employees leaving the organisation. Thus, this study believes that cutting corners can mediate the relationship between social undermining behaviours and turnover intentions of nurses. Indeed, cutting corners have been found to mediate some negative workplace outcomes (Enwereuzor, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e: Yan et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eowing to this we propose that- H2 cutting corners significantly mediates the relationship between all three dimensions of social undermining on turnover intentions.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFurthermore, individuals' traits, such as Spiritual Intelligence (SI), play a pivotal role in how they respond to social undermining (Skrzypińska, 2021). Those with higher levels of SI demonstrate resilience, emotional regulation, and strong interpersonal skills, all of which help them navigate undermining behaviors in the workplace (Atroszko et al., 2021). McGhee and Grant (2017) assert that employees with elevated SI are proficient at conflict resolution, fostering positive relationships, and promoting a supportive organizational environment. This becomes particularly relevant in instances of social undermining, as positive workplace relationships can buffer the harmful effects of negative behaviors on retention, especially among nurses (Dar et al., 2023). SI not only aids employees, such as nurses, in handling negativity from supervisors, colleagues, and clients (Bayighomog \u0026amp; Arasli, 2022), but it can also turn challenging situations into opportunities for professional growth, potentially encouraging them to remain in their roles longer (Skrzypińska, 2021). SI reshapes how employees view challenges, making it an essential factor in fostering job satisfaction and retention. The Social Information Processing Theory suggests that individuals with high SI perceive social undermining as an opportunity for personal growth rather than a threat (Salancik \u0026amp; Pfeffer, 1978). Moreover, the qualities of resilience, empathy, and compassion nurtured by SI help transform negative interactions into positive outcomes, enhancing employees' loyalty, commitment, and job satisfaction (Allport, 1936). Spiritual intelligence is thus a crucial factor in determining how workers respond to negative workplace behaviors, including social undermining (Giannoukou, 2023; Jin, 2023). It has also been shown to moderate the relationship between several negative workplace behaviors, as evidenced by Hamid et al. (2016), who found SI to buffer the impact of abusive supervision on workplace deviance. Similarly, Mahdi et al. (2021) identified SI as a moderator in the determinants of fraud prevention within organizations. Research by Hasanuddin and Sjahruddin (2017) also found that SI significantly impacts work enthusiasm. This study proposed hypotheses \u003cem\u003eH3: spiritual intelligence has a significant reduces the effect of all dimensions of social undermining on turnover intentions.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \n\u003ch3\u003eConceptual Framework\u003c/h3\u003e\n\u003cp\u003eFig. 1\u003c/p\u003e"},{"header":"Research Methods","content":"\u003cp\u003eThis study used a non-interventional research design. Based on this research design, an explanatory study design was used. This design aided the researchers in ascertaining reasons why undermining behaviors exist in the various Teaching Hospitals in Ghana. By so doing the study was able to ascertain how these behaviours affect turnover intentions. Data was collected from health workers in all 3 Teaching hospitals in Ghana: Okomfo Teaching Hospital, Korle-bu Teaching Hospital, and Cape Coast Teaching Hospital respectively. This study focused on these hospitals due to the number of workloads and duties performed by nurses daily. Again, a report by the Ghana Health Service showed that most nurses who left their job roles in 2022 and 2023 were from these hospitals (Health Service Report, 2023). Thus, focusing on these three health centers intends to give an understanding of why nurses quit their jobs and how the social undermining of all forms affects them. Moreover, these hospitals' status as leading healthcare providers in Ghana ensures that the findings have significant implications for policy and practice, offering actionable insights to enhance worker retention, morale, and overall hospital functioning.\u003c/p\u003e\u003cp\u003eThe study targeted all nurses in the various Teaching Hospitals in Ghana. This is because based on reports by WHO (2022) and the Ministry of Health report (2022), management in the health sector finds it difficult to retain these classes' staff. In this view, the study used a total of 3100 nurses across these hospitals. Within these numbers, 1200 nurses come from the Okomfo Teaching Hospital, 1000 nurses from the Korle-bu Teaching Hospital, and 900 nurses from Cape Coast Teaching Hospital (Ministry of Health report, 2022). The study used the simple random technique to select nurses for the survey. The simple random sampling was used because nurses used in the study are homogeneous (Etikan \u0026amp; Bala, 2017). This guarantees the necessary representation of all groupings in the institution. Owing to this an equal proportion was used to arrive at the sample size as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Thus, a sample size of 341 was used for the study. However, due to non-response rate the sample size was increased by 50% as suggested by (Bartlett et al., 2001). Hence, the sample size was increased to 512. It must be noted that this sample size was set with a 95% confidence interval and a 5% margin of error.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eKorle-bu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eKomfo Anokye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCape Coast Teaching Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch3\u003eData collection\u003c/h3\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eData collection was carried out using structured questionnaire. Additionally, to enhance the questionnaires' reliability and validity, feedback was solicited from two organisational behavior professors with extensive academic and research experience. After a thorough review, these experts largely endorsed the questionnaire content but suggested minor adjustments, which were incorporated. For instance, a question on social undermining was modified from focusing on supervisor behavior to coworker interactions. The revised survey was then distributed through the organizations' internal email systems, with assurances of anonymity and confidentiality emphasized to protect participants' rights (Lim, 2002). The data collection was done in three stages, spaced one month apart, to allow for temporal separation between measurements of independent, moderating, and mediating variables, thereby reducing the potential for common method bias (CMB) (Podsakoff et al., 2003). To mitigate the risk of CMB, several measures were adopted, including the staggered data collection schedule and the use of an online survey format to minimize social desirability biases. The study's incorporation of interaction and mediation effects also aimed to reduce the likelihood of response biases, as it is less probable that respondents would be familiar with the underlying theories to skew their answers deliberately. Detailed discussion of CMB considerations is provided in the results section.\u003c/p\u003e\u003ch3\u003eMeasurement\u003c/h3\u003e\u003cp\u003eThe study variables were measured based on empirical studies. Specifically, validated items were adapted from previous studies. In this view, items for social undermining were adapted from the empirical work of (Bluedorn et al., 1999). Again, items for spiritual intelligence and cutting corners were adapted from (Frone et al., 1992 and Rhoades \u0026amp; Eisenberger, 2002) respectively. And finally, items for turnover intentions were adapted from (Afzal et al., 2010). All items were measured on a 5-item Likert scale ranging from least agreement to high agreement. That is all measures are anchored on a five-point Likert-type scale ranging from 1 (“least agreement”) to 5 (“strongly agree”). A total of 512 questionnaires were distributed. Out of which 470 were retrieved for data analysis.\u003c/p\u003e\u003ch2\u003eEthics Statement\u003c/h2\u003e\u003cp\u003e All participants provided written informed consent for the collection of information and the publication of data generated by the study. The target population for this study was nurses who were over 18 years and were deemed as adults per the constitution of Ghana. All participants voluntarily partake in the survey and may withdraw at any time. Considering that the survey content does not involve any sensitive issues and that the data collected are completely anonymous. An introductory letter was sent to all three hospitals to ensure the research meets ethical standards. Furthermore, the research does not pose a risk to the physical or psychological health of participants, and the content of the survey was conducted in a manner that respects and protects the privacy of participants. The constructs are modelled as: \u003cem\u003eCU-Coworker Social Undermining, SU-Supervisor Social Undermining, PU- Patient Social Undermining and To- Turnover Intentions\u003c/em\u003e\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe accuracy of the data was verified after a thorough review process. Once the questionnaires had been coded, the responses were entered into the Statistical Package for the Social Sciences (SPSS) version 25, which was employed to organise the dataset, generate descriptive summaries, and compute essential analytical parameters. Subsequent analyses were conducted using SMART PLS version 4.1.0.6, a software widely applied in studies of this nature (Appau et al., 2021). The model assessment followed the procedures recommended by Hair et al. (2020), adhering strictly to the two-stage approach for evaluating structural models. During this evaluation, indicators with factor loadings below 0.40 were removed on the premise that excluding weak measures improves the reliability and validity of the final model.\u003c/p\u003e\u003ch2\u003eDescriptive Statistics of Variables\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the correlation matrix for the study variables. The findings indicate that all six constructs exhibited significant positive correlations with one another, with none of the coefficients exceeding the 0.90 threshold, thereby suggesting the absence of common method bias (Hair et al., 2016). These correlation patterns lend preliminary support to the study’s proposed relationships and offer a sound empirical basis for the subsequent hypothesis testing.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMean, standard deviation and correlation coefficient of variables (N = 470\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e1.Customer Undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2.Cutting Corners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.480*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e3.Patient Undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.544*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.633*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e4.Spiritual Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.504*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.648*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.677*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e5.Supervisor Undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.595*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.686*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.682*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.737*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e6. Turnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.553*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.690*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.534*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.784*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.818*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: **p \u0026lt; 0.01, **p \u0026lt; 0.05 (same in structural path)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eTest of Reliability, Validity, and Multicollinearity\u003c/h2\u003e\u003cp\u003eTo assess construct reliability, we considered Composite Reliability (CR). The composite reliability (CR) values for all constructs exceeded the minimum acceptable benchmark of 0.70 (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating strong internal consistency. Construct validity was further assessed through convergent and discriminant validity. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the Average Variance Extracted (AVE) values ranged from 0.623 to 0.837, and item loadings fell between 0.704 and 0.914, thereby satisfying the criteria for convergent validity. Additionally, the results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e showed that multicollinearity was not detected, as all Variance Inflation Factor (VIF) values were below the recommended threshold of 3.3 (Kock, 2015).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability Analysis, Validity and VIF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eCutting Corners\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eIndicator loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAverage Variance Extracted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e2.048\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCoworker UND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCOU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCOU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCOU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCOU6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePATIENT UND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSUPERVISOR UND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSUPU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSUPU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSUPU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSUPU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSUPU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.0878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSpiritual Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTurnover Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTI7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eComposite Reliability (CR) \u0026gt; 0.7 and Cronbach’s alpha (α) \u0026gt; 0.7, AVE \u0026gt; 0.5, VIF \u0026lt; 3.3, Item loadings \u0026gt; 0.70\u003c/p\u003e\u003ch2\u003eModel Fit Testing\u003c/h2\u003e\u003cp\u003eAssessing model fit is essential in structural equation modelling, as emphasised by Hair et al. (2021) and Henseler et al. (2015). Accordingly, the present study evaluated the overall fit of the estimated model prior to testing the hypotheses. Following the recommendations of Tomarken and Waller (2005) and Lohmöller and Lohmöller (1989), a chi-square (χ²) p-value greater than 0.05 indicates acceptable model fit; the current analysis produced a value of 0.072, which meets this criterion. The Normed Fit Index (NFI) is regarded as excellent at 0.95 or above (Hooper et al., 2008), while Hu and Bentler (1999) consider values of 0.90 or higher to reflect a good fit. Additionally, Hu and Bentler (1999) and Schuberth et al. (2023) note that the Standardised Root Mean Square Residual (SRMR) should be below 0.08, and the RMS theta should not exceed 0.12. The results obtained in this study meet these recommended thresholds, as reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Testing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEstimated Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRms Theta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eSRMR \u0026lt; 0.08; chi-square \u0026gt; 0.05; NFI \u0026gt; 0.90; Rms \u0026lt; 0.12\u003c/p\u003e\u003ch2\u003eDiscriminant Validity (HTMT Criteria)\u003c/h2\u003e\u003cp\u003eThe HTMT approach was employed to overcome the shortcomings of the Fornell lacker. Henseler \u003cem\u003eet al\u003c/em\u003e. (2015) criticise Fornell Lacker for its inability to assess the validity of slightly different constructs. As a result, it is unable to deal with validity issues properly (Radomir \u0026amp; Moisescu, 2019). Henseler \u003cem\u003eet al\u003c/em\u003e. (2015) recommended the HTMT correlation in assessing validity. The HTMT assesses the mean value of the correlation between and across the individual constructs. Hair \u003cem\u003eet al\u003c/em\u003e. (2020) recommends a threshold of 0.85 for HTMT correlation values. The correlation between and across individual constructs must be less than 0.85, particularly when the constructs are similar (Henseler \u003cem\u003eet al\u003c/em\u003e., 2015). The results presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e show that all HTMT correlation values are within the 0.85 range. This implies that there is no discriminant validity issue in this study.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab5\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity (HMT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.Customer Undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.Cutting Corners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.480*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.Patient Undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.544*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.633*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.Spiritual Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.504*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.648*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.677*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.Supervisor Undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.595*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.686*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.682*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.737*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.Turnover intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.553*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.690*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.534*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.784*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.818*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: Field Survey (2025)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eStructural model Assessment\u003c/h2\u003e\u003cp\u003eOnce all measurement model requirements were met and the results fell within acceptable thresholds, attention shifted to evaluating the structural model. The structural model outlines the proposed causal links between the latent constructs. Before testing the significance of these relationships, several model fit considerations were reviewed. Following the guidance of Hair et al. (2016), the assessment began with checks for potential collinearity among the predictor variables. After confirming the absence of problematic collinearity, the analysis proceeded to examine the model’s predictive strength using the coefficient of determination R\u003csup\u003e2\u003c/sup\u003e, the effect size f\u003csup\u003e2\u003c/sup\u003e, and predictive relevance Q\u003csup\u003e2\u003c/sup\u003e. The findings showed R\u003csup\u003e2\u003c/sup\u003e values of 0.842 for cutting corners and 0.857 for turnover intentions. This means that 84.2 percent of the variability in cutting corners was accounted for collectively by the three dimensions of social undermining, namely supervisor undermining, coworker undermining, and patient undermining. In a similar way, these three dimensions together with cutting corners explained 85.7 percent of the variance in turnover intentions. Using the conventional interpretation of effect sizes, where small is defined as values between 0.0 and 0.15, medium between 0.15 and 0.35, and large above 0.35, the results indicate that excluding supervisor undermining, patient undermining, or spiritual intelligence would produce only minimal changes in turnover intentions. In contrast, coworker undermining had a large effect, and cutting corners had a medium effect on turnover intentions. The predictive relevance values also demonstrated strong predictive capacity, with all three forms of social undermining showing high predictive relevance for cutting corners at 0.838 and for turnover intentions at 0.738. The overall structural model presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the significance and direction of the proposed relationships among the constructs.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eF-Square, R-Squared and Q-Squared\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eSupervisor undermining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEffect Size (f\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eR-Squared (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePredictive Relevance (Q\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCoworker undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePatient undermining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSpiritual intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCutting corners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.838***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTurnover intentions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.738**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: *0.02 ≤ f\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e≤ 0.15 is a weak effect, **0.15 ≤ f\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e≤ 0.35 is a moderate effect *** f\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e≥ 0.35 shows a strong effect\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eNote: *0.02 ≤ Q\u003csup\u003e2\u003c/sup\u003e≤ 0.15 weak effect, **0.15 ≤ Q\u003csup\u003e2\u003c/sup\u003e≤ 0.35 moderate effect, *** Q\u003csup\u003e2\u003c/sup\u003e ≥ 0.35 strong effect\u003c/p\u003e\u003cp\u003eNote: *R-Square \u0026gt; 0.10\u003c/p\u003e\u003ch2\u003eAssessment of Path Relationships\u003c/h2\u003e\u003cp\u003eThe results of the structural model presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e revealed mixed findings regarding the direct effects of social undermining on turnover intention (TI). Specifically, coworker undermining (CU) showed a positive but statistically insignificant effect on turnover intention (β = 0.060, p = 0.100), leading to the rejection of H1a. In contrast, supervisor undermining (SU) exerted a strong positive and statistically significant effect on turnover intention (β = 0.315, p = 0.000), thereby supporting H1b. Similarly, patient undermining (PU) demonstrated a positive and significant effect on turnover intention (β = 0.303, p = 0.000), resulting in the acceptance of H1c.\u003c/p\u003e\u003cp\u003eRegarding the moderating role of spiritual intelligence (SI), the results showed that the interaction between SI and CU on turnover intention was positive but insignificant (β = 0.056, p = 0.135), indicating that SI does not moderate this relationship. Hence, H3a was not supported. On the other hand, the moderating effect of SI on the relationship between SU and turnover intention was negative and statistically significant (β = -0.130, p = 0.021), suggesting that higher SI weakens the effect of supervisor undermining on turnover intention. Thus, H3b was supported. Interestingly, SI’s moderating effect on the relationship between PU and turnover intention was positive but statistically insignificant (β = 0.055, p = 0.327). Thus, H3c was not supported.\u003c/p\u003e\u003cp\u003eThe mediation analysis provided further insights. First, cutting corners (CC) significantly mediated the relationship between CU and turnover intention (β = 0.017, p = 0.029). Since the direct path of CU → TI was insignificant, but the indirect path via CC was significant, this indicates a case of full mediation. Therefore, H2a was supported. Second, CC mediated the relationship between SU and turnover intention (β = 0.023, p = 0.036). Here, both the direct and indirect paths were significant, suggesting partial mediation. Thus, H2b was supported. Finally, CC also mediated the effect of PU on turnover intention (β = 0.054, p = 0.000), again with both direct and indirect effects significant, indicating partial mediation. Hence, H2c was supported.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab7\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"25\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eΒ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003eSample mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\"\u003e \u003cp\u003eT statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\"\u003e \u003cp\u003e95.0%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eH1a. CU-\u0026gt; TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eNot Supported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eH1b. SU -\u0026gt; TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\"\u003e \u003cp\u003e6.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eH1c. PU -\u0026gt; TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\"\u003e \u003cp\u003e6.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"24\"\u003e \u003cp\u003eModerating Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eH3a. SI x CU -\u0026gt; TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e1.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eNot\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eH3b. SI x SU -\u0026gt; TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eH3c. SI x PU -\u0026gt; TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eNot\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"24\"\u003e \u003cp\u003eMediation Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eH2a.CU-\u0026gt; CC-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eH2b.SU-\u0026gt; CC-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eH2c.PU-\u0026gt; CC-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e3.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eSupported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eNote: CU-Coworker Social Undermining, SU-Supervisor Social Undermining, PU- Patient Social Undermining and TI- Turnover Intentions\u003c/p\u003e\u003ch2\u003eSimple Slope Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the moderating role of spiritual intelligence (SI) on the relationship between supervisor undermining (SU) and turnover intention. As shown, turnover intention increases with higher levels of supervisor undermining across all employees. However, the slope of the relationship is steeper for those with low SI (–1 SD) compared to employees with high SI (+ 1 SD). This indicates that individuals with lower SI are more likely to translate supervisor mistreatment into stronger intentions to quit, whereas those with higher SI demonstrate a weaker relationship between SU and turnover intention. The significant negative interaction term (β = − 0.130, p = 0.021) confirms that SI buffers the harmful impact of supervisor undermining, thereby reducing the likelihood that employees with higher SI will develop turnover intentions despite experiencing undermining from supervisors.\u003c/p\u003e\u003ch2\u003eImportance Performance Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the importance–performance map, which highlights the relative influence of different forms of social undermining on turnover intention. Among the predictors, supervisor undermining emerges as the most critical factor (importance = 0.464) while showing only moderate performance, indicating that supervisor-related mistreatment is the strongest driver of employees’ intention to quit. In comparison, patient undermining (importance = 0.194) and coworker undermining (importance = 0.144) rank considerably lower in importance, though their performance scores remain comparable. This suggests that while peer and patient mistreatment contribute to turnover, their effects are overshadowed by the dominant role of supervisor undermining. These findings imply that organizations should first focus on minimizing supervisor undermining through leadership training, monitoring, and accountability mechanisms. At the same time, peer-support initiatives and structured patient management protocols can help address coworker and patient undermining, thereby reducing additional sources of strain. Collectively, such measures can reinforce one another, lowering turnover intentions and fostering a more supportive and resilient work environment.\u003c/p\u003e"},{"header":"Discussion of Results","content":"\u003cp\u003eThe study revealed that supervisor social undermining had a significant positive effect on turnover intentions. This indicates that supervisory behavior is central in shaping employees’ perceptions of fairness, recognition, and trust. That is, employees see supervisors as reflections of the organization’s values and intentions. When supervisors behave unfairly, employees equate it with institutional betrayal, leading to low morale, poor engagement, and eventual turnover. Within Ghana’s hierarchical healthcare context, supervisors represent the organization’s authority and moral climate. Drawing on the Social Information Processing Theory (Salancik \u0026amp; Pfeffer, 1978), employees interpret supervisory actions as meaningful social information that guides their attitudes and decisions. When supervisors engage in hostile or unsupportive behaviors, they convey negative signals that the organization lacks concern and justice. Through this theoretical lens, supervisor undermining serves as a communication of organizational neglect, while supportive leadership communicates care and fairness. Reh et al. (2018) explain that such cues generate psychological insecurity and lead employees to withdraw emotionally. Mahdi et al. (2021) further note that persistent hostility creates emotional fatigue and weakens job commitment. Supportive supervisors, on the other hand, provide positive social cues that foster belonging and motivation.\u003c/p\u003e\u003cp\u003eThe findings further showed that patient social undermining was a strong determinant of turnover intentions, demonstrating the emotional intensity and relational vulnerability embedded in healthcare work. Patients are the focal point of care delivery and directly shape employees’ sense of purpose and professional fulfillment. When patients engage in verbal hostility, disrespect, or non-cooperation, they disrupt the therapeutic relationship and create emotional exhaustion. Such negative encounters accumulate over time, diminishing morale and increasing the desire to quit. The result emphasizes the emotional weight that patients carry in influencing healthcare staff retention. Within the framework of the Social Information Processing Theory (Salancik \u0026amp; Pfeffer, 1978), employees interpret negative interactions from these salient groups as cues that the work environment is unsafe or unappreciative, prompting cognitive withdrawal and eventual turnover intentions. This finding is consistent with prior HR literature linking mistreatment and toxic social climates with high turnover tendencies (Mahdi et al., 2021; Hershcovis, 2011; Reh et al., 2018).\u003c/p\u003e\u003cp\u003eIn contrast, coworker social undermining did not directly predict turnover intentions. This outcome suggests that peer hostility, while unpleasant, may not immediately provoke the desire to leave the organization. Coworker mistreatment is often normalized or minimized as part of daily work challenges, particularly in team-based healthcare environments where interdependence is necessary for service delivery (Mahdi et al., 2021). Employees may perceive such behavior as a temporary conflict rather than a systemic problem. From an HRM perspective, this indicates that coworker-related tensions primarily erode teamwork quality, collaboration, and psychological safety rather than directly influencing decisions to quit. However, persistent peer hostility may gradually foster disengagement, reduce cooperation, and impair collective efficiency. This interpretation aligns with the Social Information Processing framework, which suggests that employees process social information differently depending on the perceived source of the threat. When the source is a coworker, the behavior may be viewed as manageable and situational, unless it interferes with job performance, at which point it triggers behavioral withdrawal such as cutting corners.\u003c/p\u003e\u003cp\u003eThe mediation results confirmed that cutting corners acted as a full mediator in the link between coworker social undermining and turnover intentions. The implication of this finding is that negative workplace interactions do not always produce immediate withdrawal but may first manifest through behavioral disengagement. Employees who feel mistreated by coworkers conserve emotional and mental energy by reducing effort, overlooking procedures, or avoiding collaboration. These behaviors, although initially serving as coping mechanisms, gradually diminish performance, teamwork, and organizational commitment. This suggests that HR managers should pay close attention to subtle behavioral changes as early indicators of deeper relational problems. From the perspective of the Social Information Processing Theory (Salancik \u0026amp; Pfeffer, 1978), such coping behavior reflects how employees interpret and respond to the social cues in their environment. When coworker hostility sends information that the social climate is unsupportive, employees internalize these signals and adapt defensively by cutting corners to reduce stress. With time, this maladaptive coping becomes part of their behavioral pattern, eroding motivation and strengthening intentions to leave.\u003c/p\u003e\u003cp\u003eAgain, it was found that cutting corners is a partial mediator in the link between supervisor and patient social undermining on turnover intentions. This finding implies that employees do not only decide to leave because of immediate frustration or perceived injustice but also because the resulting strain alters their work behavior in ways that damage their professional standards and long-term satisfaction. When supervisors or patients engage in hostile acts, employees may respond by adopting short-term coping behaviors such as cutting corners, which provide temporary psychological relief from stress or conflict. However, these behaviors erode professional integrity, weaken commitment, and create feelings of guilt or dissatisfaction that eventually translate into turnover intentions. This means that organizations face dual risks, immediate turnover from emotional distress and gradual disengagement from maladaptive coping. From the Social Information Processing Theory (Salancik \u0026amp; Pfeffer, 1978) perspective, this finding illustrates how employees interpret social information from significant others such as supervisors and patients and act upon it. Negative cues from these sources are perceived as powerful signs that the work environment is unjust, disrespectful, or unsafe. Employees cognitively process these signals and, to protect their psychological well-being, modify their behavior by disengaging or cutting corners. While this may momentarily reduce emotional tension, it simultaneously undermines role fulfillment and deepens dissatisfaction. As such, the continuous processing of such negative information reinforces negative attitudes and solidifies the intention to leave.\u003c/p\u003e\u003cp\u003eThe results revealed that spiritual intelligence (SI) significantly moderates the relationship between supervisor social undermining and turnover intentions. This result implies that employees who possess higher levels of SI demonstrate stronger psychological resilience and are less likely to view supervisory hostility as a justification for leaving their organization. Such employees rely on deeper value systems, moral awareness, and reflective thinking to reinterpret adverse experiences constructively. This enables them to sustain emotional stability and maintain focus on their professional purpose even under negative supervisory conditions. Consequently, SI functions as an internal coping resource that reduces emotional exhaustion, enhances optimism, and preserves commitment. This outcome aligns with previous evidence that SI facilitates self-regulation and positive coping responses in stressful work environments (McGhee \u0026amp; Grant, 2017; Bayighomog \u0026amp; Arasli, 2022). However, the moderating effect of SI was not observed in relationships involving coworkers and patient undermining, suggesting that its buffering capacity depends on the relational source of mistreatment. Employees may perceive peer or patient hostility as situational, transient, and external to the institution, while supervisory mistreatment is interpreted as reflective of the organization’s ethical and relational climate. In this regard, SI is more effective in reframing the meaning of threats that are perceived as systemic or value-based rather than incidental.\u003c/p\u003e\u003cp\u003eViewed through the lens of the Social Information Processing Theory (Salancik \u0026amp; Pfeffer, 1978), this finding demonstrates that employees use their spiritual intelligence as a cognitive filter through which they interpret and respond to social information. When faced with supervisory hostility, individuals with high SI reinterpret negative cues in ways that preserve psychological safety and organizational attachment. They process these cues not as evidence of institutional rejection but as opportunities for growth, empathy, and forgiveness. By reshaping how negative social information is understood, SI disrupts the link between perceived hostility and turnover intentions. In contrast, when mistreatment originates from coworkers or patients, employees are more likely to attribute the behavior to interpersonal dynamics or situational pressures, reducing the relevance of SI in shaping their responses. Therefore, within the SIP framework, SI operates as a meaning-making and resilience mechanism that transforms how employees cognitively and emotionally respond to toxic supervisory relationships, ultimately mitigating the likelihood of turnover.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe study found that supervisor and patient undermining significantly increased turnover intentions, while coworker undermining influenced turnover only indirectly through cutting corners in the Ghanaian health system. Again, cutting corners emerged as a key mediating pathway, showing how negative treatment translates into maladaptive coping and ultimately withdrawal. Finally, Spiritual intelligence buffered the impact of supervisor undermining but showed no significant effect for coworker or patient undermining, indicating its protective value is context dependent. Together, these findings highlight the central role of social undermining, coping behaviors, and personal resources in shaping employees\u0026rsquo; decisions to stay or leave.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eImplications of the Study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study advances Social Information Processing Theory (SIPT) in the healthcare context by showing that negative social cues from supervisors and patients are central drivers of turnover intentions, while coworker undermining exerts its influence indirectly through cutting corners. The IPMA results further highlight performance gaps in addressing undermining behaviors, emphasizing that SIPT must account more explicitly for the weight of interpersonal interactions in service-intensive environments. The findings extend the theory by revealing that cutting corners functions as a behavioral pathway through which social information is translated into withdrawal intentions, thus broadening SIPT beyond its cognitive lens. The moderation results add perspectives by showing that spiritual intelligence buffered the impact of supervisor undermining on turnover, though its protective effect was weaker than expected, suggesting that personal resources operate unevenly depending on the nature of the social cue and the power dynamics involved.\u003c/p\u003e \u003cp\u003ePractically, the results provide actionable insights for managers and policymakers seeking to improve healthcare staff retention. For managers, the IPMA indicates that interventions should prioritize reducing supervisor and patient undermining through peer-support initiatives, structured communication, protective mechanisms, and accountability systems. Cutting corners should be recognized as a maladaptive coping strategy and addressed early through supportive supervision, equitable workload allocation, and performance feedback. While spiritual intelligence emerged as an important resource, its relatively low performance suggests that organizations cannot rely solely on employees\u0026rsquo; resilience; instead, they must foster professional environments that reinforce fairness, compassion, and support. At the policy level, systemic reforms are needed to treat undermining as an organizational risk, enforce zero-tolerance approaches to abuse, ensure transparent reporting, and build safeguards that replace maladaptive coping with constructive resilience. Embedding compassion, fairness, and respect into organizational values and accountability structures is therefore critical to sustaining healthcare employees\u0026rsquo; commitment and reducing turnover.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the support and contributions of colleagues, participants, and institutional resources that made this study possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo Funding was obtained\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe can declare that we did not receive any financial assistance from any institution\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and Accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the University of Cape Coast Ethical Review Committee, and it was conducted in accordance with the ethical standards of the committee, the 1964 Declaration of Helsinki and its later amendments, as well as relevant national research guidelines. As such, an ethical approval (\u003cstrong\u003e\u003cem\u003eUCCIRB/CHSL/2025/011\u003c/em\u003e\u003c/strong\u003e) was taken from the right authority.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that informed consent was obtained from all participants. Each participant was fully briefed on the study\u0026rsquo;s purpose, potential risks, and expected benefits before participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that was used for the study has been attached to the online platform and in the manuscript as appendix I.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial or non-financial interests that could have influenced the work reported in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual-Publication\u003cbr\u003e\u003c/strong\u003eThis manuscript is original, has not been published previously, and is not under consideration for publication elsewhere in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All authors meet the authorship criteria, made substantial contributions to the conception, design, analysis, and writing of the manuscript, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen-access\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors agree to the journal\u0026rsquo;s open-access policy and, where applicable, consent to publication under the specified Creative Commons licence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThird-party material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll third-party materials included in this manuscript have been properly acknowledged, and permission has been obtained where required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, D. G., \u0026amp; Vardaman, J. M. (2021). Global talent retention: Understanding employee turnover intentions around the world. In \u003cem\u003eGlobal talent retention: Understanding employee turnover intentions around the world\u003c/em\u003e (pp. 1-15). Emerald Publishing Limited.\u003c/li\u003e\n\u003cli\u003eArani, V. Y., \u0026amp; Fayyazi, M. (2022). Social undermining and organisational attitudes: The moderating role of personality traits. \u003cem\u003eInternational Journal of Business Governance and Ethics, 16\u003c/em\u003e(3), 355-375.\u003c/li\u003e\n\u003cli\u003eAzeem, M. U., De Clercq, D., \u0026amp; Haq, I. U. (2024). Religiosity as a buffer of the harmful effects of workplace loneliness on negative work rumination and job performance. \u003cem\u003eJournal of Organizational Effectiveness: People and Performance.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eCampbell, T. I. E. (2020). \u003cem\u003eAn exploration into the roles of managerial support and occupational stigma in the employee turnover intentions process amongst non-managerial quick service workers in Guyana\u003c/em\u003e (Doctoral dissertation).\u003c/li\u003e\n\u003cli\u003eDe Clercq, D., \u0026amp; Pereira, R. (2024). How resilient employees can prevent family ostracism from escalating into diminished work engagement and change-oriented organizational citizenship behavior. \u003cem\u003eInternational Studies of Management \u0026amp; Organization, 54\u003c/em\u003e(1), 25-47.\u003c/li\u003e\n\u003cli\u003eEnwereuzor, I. K. (2024). Dispositional greed and knowledge sabotage: The roles of cutting corners at work and ethical leadership. \u003cem\u003eCurrent Psychology, 43\u003c/em\u003e(2), 1325-1339.\u003c/li\u003e\n\u003cli\u003eGrimwood, K. (2022). \u003cem\u003eWorkplace mistreatment: A qualitative study of the antecedents of supervisor-employee relationship challenges.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eKelly, G. (2018). \u003cem\u003eWays to minimize unethical behavior by employees\u003c/em\u003e (Doctoral dissertation, Northcentral University).\u003c/li\u003e\n\u003cli\u003eMaslach, C., \u0026amp; Leiter, M. P. (2022). \u003cem\u003eThe burnout challenge: Managing people\u0026rsquo;s relationships with their jobs.\u003c/em\u003e Harvard University Press.\u003c/li\u003e\n\u003cli\u003eMulaphong, D. (2023). Social undermining in public sector organizations: Examining its effects on employees\u0026rsquo; work attitudes, behaviors, and performance. \u003cem\u003ePublic Organization Review, 23\u003c/em\u003e(3), 1229-1248.\u003c/li\u003e\n\u003cli\u003ePolo-Pe\u0026ntilde;a, A. I., Fr\u0026iacute;as-Jamilena, D. M., \u0026amp; Fern\u0026aacute;ndez-Ruano, M. L. (2021). Influence of gamification on perceived self-efficacy: Gender and age moderator effect. \u003cem\u003eInternational Journal of Sports Marketing and Sponsorship, 22\u003c/em\u003e(3), 453-476.\u003c/li\u003e\n\u003cli\u003eRasool, S. F., Wang, M., Zhang, Y., \u0026amp; Samma, M. (2020). Sustainable work performance: The roles of workplace violence and occupational stress. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 17\u003c/em\u003e(3), 912.\u003c/li\u003e\n\u003cli\u003eSariani, N. L. P., Mahayasa, I. G. A., Maheswari, A. I. A., Astakoni, I. M. P., \u0026amp; Utami, N. M. S. (2022). Antecedent of organizational citizenship behavior variables: Gender as moderator. \u003cem\u003eJournal of Social Science, 3\u003c/em\u003e(3), 516-533.\u003c/li\u003e\n\u003cli\u003eSchwepker Jr, C. H., \u0026amp; Dimitriou, C. K. (2023). Reducing service sabotage: The influence of supervisor social undermining, job stress, turnover intentions and ethical conflict. \u003cem\u003eJournal of Marketing Theory and Practice, 31\u003c/em\u003e(4), 450-469.\u003c/li\u003e\n\u003cli\u003eSong, Y., \u0026amp; Zhao, Z. (2022). Social undermining and interpersonal rumination among employees: The mediating role of being the subject of envy and the moderating role of social support. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 19\u003c/em\u003e(14), 8419.\u003c/li\u003e\n\u003cli\u003eTanwar, K., \u0026amp; Prasad, A. (2016). The effect of employer brand dimensions on job satisfaction: Gender as a moderator. \u003cem\u003eManagement Decision, 54\u003c/em\u003e(4), 854-886.\u003c/li\u003e\n\u003cli\u003eYan, H., Hu, X., \u0026amp; Wu, C. H. (2021). When and why does proactive personality inhibit corner-cutting behaviors: A moderated mediation model of customer orientation and productivity climate. \u003cem\u003ePersonality and Individual Differences, 170,\u003c/em\u003e 110443.\u003c/li\u003e\n\u003cli\u003eZhao, S., Ben-Abdallah, R., Khattak, S. A., \u0026amp; Wang, N. (2024). Examining the effects of tyrannical leadership on workplace incivility: Interplay of employee low morale and supportive organizational culture. \u003cem\u003eCurrent Psychology,\u003c/em\u003e 1-12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9305225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9305225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study examined how social undermining influence turnover intentions among healthcare employees in Ghana, while assessing the roles of cutting corners and spiritual intelligence (SI). The study used the Social Information Processing Theory (SIP) to explore how employees interpret and respond to negative social cues within their work environment. The quantitative approach and an explanatory design were used. Again, data was collected through structured questionnaires and analysed using structural equation modeling partial least square. The results revealed that supervisor social undermining had a significant positive effect on turnover intentions, emphasizing the critical role of supervisory behavior in shaping perceptions of fairness and trust. Patient social undermining also significantly predicted turnover intentions, showing the emotional burden of hostile patient interactions. Coworker social undermining did not directly predict turnover but influenced it indirectly through cutting corners, indicating behavioral disengagement as a coping response. Cutting corners fully mediated coworker undermining and partially mediated supervisor and patient undermining, confirming that maladaptive coping bridges social mistreatment and withdrawal. Moreover, spiritual intelligence moderated the relationship between supervisor undermining and turnover intentions, suggesting that employees with higher SI reinterpret negative cues constructively and are less likely to quit. The study contributes to HRM theory by integrating SI into the SIP framework, highlighting how meaning-making and coping processes shape turnover intentions in healthcare settings.\u003c/p\u003e","manuscriptTitle":"Social Undermining and Coping Mechanisms as Predictors of Turnover Intentions Among Healthcare Workers in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 16:28:15","doi":"10.21203/rs.3.rs-9305225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"153273576958216581883249301852033119742","date":"2026-05-05T10:14:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T10:23:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-19T02:19:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-17T10:14:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T14:43:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-09T13:40:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"851e58e1-8dc4-4af0-99d9-da69e9ff847a","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"153273576958216581883249301852033119742","date":"2026-05-05T10:14:01+00:00","index":22,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T16:28:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 16:28:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9305225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9305225","identity":"rs-9305225","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}