1. Introduction
To ensure the integrity and competence of nursing practice, nurses are expected to follow the profession’s core moral principles, beliefs, and duties (Zaghini et al., 2017). While nurses realize that they should follow prearranged work ethics, dilemmas, and erroneous situations at the workplace that compel them to ignore the code of ethics (Caruso et al., 2015). Thus, despite the importance of observing moral principles, deviant workplace behavior is common in the medical field worldwide (Jahantigh et al., 2016). Wiernik & Ones (2018) described deviant workplace behavior as unethical behavior that deviates from organizational goals. Very little is known about the factors that cause dedicated nurses to disregard social and organizational norms (Fida et al., 2018). 
Based on the general aggression model (GAM), people display aggressiveness because of personal and situational factors, internal moods, and the consequences of the appraisal and decision-making process. When people are affected by personal and situational circumstances, this creates internal moods contributing to erratic behavior. It has been discovered that nurses are susceptible to conflict and strong sentiments of anger, as well as negative affectivity (Fornés-Vives et al., 2019). Although personality variations such as trait anger and negative affectivity have been linked to deviant workplace behavior (Yang & Diefendorff, 2009), limited literature exists on this topic from the perspective of nurses (Hershcovis et al., 2007; Yang & Diefendorff, 2009; Zhao & Xia, 2018). Therefore, the current work examines the impact of trait anger and negative affectivity as personal factors on deviant workplace behavior among staff nurses in Malaysia’s public healthcare system with the mediating role of moral disengagement.

Literature review 
Trait anger is an emotional response that people may exhibit when confronted with others’ unfavorable behavior (Wang et al., 2018). It serves various functions depending on the context and cultural framework in which it occurs. Trait anger has been extensively studied and debated in the literature due to its development complexity and functional and affective meanings (Anderson & Bushman, 2002; Gresham et al., 2016). People with high-trait anger tend to show severe anger and hostility in response to provocations and may exhibit violent or aggressive behavior. Trait anger is also linked to deviant workplace behavior (Spielberger et al., 1990). 
Negative affectivity refers to a person’s perception of painful or distressing emotions (Neuman & Baron, 1998), such as anger, disgust, worry, guilt, and fear, as well as the experience of negative emotions and a negative self-concept (Watson & Clark, 1984). It is often related to neuroticism, which involves emotional instability (Fornés-Vives et al., 2019) and a tendency to perceive everyday circumstances as threatening. People with high levels of negative affectivity or neuroticism may engage in poor coping strategies, leading to stress and potentially negative outcomes in the workplace, such as low productivity, absenteeism, low organizational performance, and employee theft. Negative affectivity is an important individual difference that may contribute to deviant workplace behavior (Neuman & Baron, 1998).
Organizational aggressiveness refers to behaviors intended to harm the organization, and workplace deviance refers to actions that stray from organizational values and negatively impact the organization and its members (Rogojan, 2009). These behaviors may include aggression, antisocial behavior, counterproductive behavior, delinquency, deviance, retaliation, and revenge. There is no universally accepted definition or terminology for workplace deviance, but these terms are often used to describe deviant behavior in the workplace.
Spector (1978) may have been the first to advocate for this approach in describing organizational aggressiveness as activities meant to hurt the organization. However, no commonly acknowledged definition or terminology has been established regarding workplace deviance. Different labels have been used about this behavior in studying deviant conduct based on numerous theoretical perspectives (Tuna et al., 2018). Some terms commonly used to describe deviant work behavior include organizational misbehavior, non-compliant behavior, antisocial behavior, dysfunctional workplace behavior, employee vice, organizational retaliation behavior, and organization-motivated aggression (Rogojan, 2009). More recently, terms such as aggression (Neuman & Baron, 1998), antisocial (Giacalone & Greenberg, 1997) and counterproductive have been used. Although each term differs in form and scope, they share comparable outcomes and traits (Rogojan, 2009), where they deviate from organizational values and negatively impact the organization and its members (Giacalone & Greenberg, 1997; Spector & Fox, 2005).
Moral disengagement refers to a person’s ability to justify immoral actions and avoid feeling remorse, guilt, regret, or shame. It allows people to behave in ways that deviate from moral standards without experiencing psychological discomfort. Research has shown that moral disengagement may contribute to callous-unemotional traits, angry rumination, irritability, and aggressiveness, leading to deviant workplace behavior (Caprara et al., 2014; Kokkinos et al., 2016; Wang et al., 2017).
Trait anger is described as a dispositional trait in which a person experiences frequent anger with varying intensities (such as mild irritability or intense anger) and is often accompanied by associated negative emotions such as jealousy, resentment, hatred, and disgust (Buss, 1961; Siegman & Smith, 1994). In provoking situations, people with a high degree of trait anger tend to exhibit aggravation, irritation, rage, and physiological arousal (Spielberger & Rickman, 1990). Employees with high-trait anger have a higher rate of deviant reactions in the workplace than those with low-trait anger. 
Employees with a high degree of negative affectivity tend to view themselves as the victims of their colleagues’ aggression, particularly if they work in a “low-status” job. Regardless of whether these aggression allegations are genuine, negative affectivity in a workplace can damage the organization (Aquino et al., 1999). The tension created by this negative atmosphere will affect teamwork, cooperation, and productivity. Additionally, negative affectivity may influence work satisfaction and contribute to depression (Brief et al., 1988). Over time, this will develop into deviant workplace behavior such as absenteeism, poor job performance, and employee theft (Chen et al., 2013). 
A survey by Wang et al. (2018) concluded that anger rumination and aggressiveness are associated with moral disengagement. This is consistent with the previous reports, which claimed that an increase in anger and hostility leads to an increase in aggression (Anderson & Bushman, 2002; Archer, 2004; Berkowitz, 1990). Furthermore, it was reported that physical or verbal aggression is connected to moral disengagement (Bandura et al., 1996; Paciello et al., 2008). These studies indicate that anger, hostility, and moral disengagement contribute to aggressive actions. 
Negative information perceived by people with strong negative affectivity hinders the application of the dominant processing style, causing them to experience moral disengagement as a secondary cognitive strategy (Isbell et al., 2013). Consequently, their internal moral norms are deactivated, and they show signs of irritability and aggressiveness (Wang et al., 2017). While working in a fast-paced and stressful environment, negative emotional states experienced by employees can result in tension, moral disengagement, and inappropriate behavior in the workplace (Fida et al., 2015; Zhao & Xia, 2018). 
Several studies have found that moral disengagement promotes unethical and deviant behavior at work (Bandura et al., 1996; Detert et al., 2008; Duffy et al., 2012; Schweitzer & Hsee, 2002; Shalvi et al., 2011). Previously, moral disengagement has been shown to act as the mediator in studying the effects of envy on social undermining (Dufy et al., 2012), self-monitoring on unethical decision-making (Ogunfowora et al., 2022), psychopathy on unethical decision-making (Stevens et al., 2014), authenticity on unethical behavior (Knoll et al., 2016), and resource depletion on social undermining (Lee et al., 2016). Valle et al. (2011) state that moral disengagement mediates the relationship between abusive supervision and organizational deviance. Consequently, moral disengagement is important for detecting deviant behavior and workplace ethics (Trevino et al. 2006).
Based on the generalized additive model (GAM), this study proposed moral disengagement as a mediator between trait anger, negative affectivity, and deviant workplace behavior (Anderson & Bushman, 2018; DeWall et al., 2011). Furthermore, since moral disengagement is a fundamental driver of deviant workplace conduct, it is logical to assume that it will regulate the association between negative affectivity and deviant workplace behavior. Although Zhao et al. (2018) proved that the association between nurses’ negative emotional states and their knowledge-hiding practices is slightly mediated by moral disengagement, no similar study has been done in Malaysian public-sector nursing. Based on the review of related literature, this study proposed 7 hypotheses (Table 1). 

Figure 1 illustrates the formulated framework in this research.

2. Materials and Methods
Design, setting, and sample
It was a cross-sectional correlational study. The study population for this research is defined as nurses in government hospitals in Malaysia. The accessible population comprised all nurses in the government hospitals of 4 regions (northern, southern, east coast, and central) in Malaysia. The sample size for a given population of the target respondents (n=52331) is 382, based on Krejcie and Morgan (1970), and recruited by proportionate stratified random sampling. In line with Sekaran and Bougie (2016), the researcher included the sample from each stratum. After determining each stratum’s percentage, each stratum’s proportion was also determined.
The staff nurses from general hospitals were invited to participate in a multicentre survey. The main investigator contacted the heads of the nursing departments of the particular hospitals and convinced them to support the present study. The head nurses informed all their staff nurses to respond to the survey within two weeks if they wished. Initially, more than 400 surveys were distributed to the prospective respondents to compensate for incomplete answers and other technical issues. Finally, 387 surveys were identified as suitable for further development.

Study instruments
In addition to a demographic questionnaire, four validated survey instruments were used: negative affect scale, deviant workplace behavior scale, trait anger scale, and moral disengagement scale. 
The negative affect scale (Watson, Clark & Tellegen, 1988) is a part of the brief measures of positive and negative affect schedule (PANAS). This scale has 20 items (10 for positive and 10 for negative affect). Questions 2, 4, 6, 7, 8, 11, 13, 15, 18, and 20 relate to negative affect, including distress, upset, ashamed, guilt, embarrassment, irritability, fear, hostility, and anger. It is scored on a 5-point Likert scale (1=very slightly or not at all to 5=extremely). The total score of the negative scale (used in this study) is obtained by calculating the sum of the 10 negative items. The total score ranges from 10 to 50; a lower score indicates less negative affect. For this study, after going through the content validity process with experts, they suggested dropping one item because when it was translated into Malay, it had the same meaning. Therefore, the questionnaire was modified, but the original question from the real authors was still kept. The internal consistency for the PANAS ranges from 0.86 to 0.90 for positive affect and 0.84 to 0.87 for negative affect. The test-retest reliability for the PANAS is reported as 0.79 for positive affect and 0.81 for negative affect (Watson et al., 1988). 
The deviant workplace behavior scale (Bennet & Robinson, 2000) is a 19-item scale to measure deviant behavior in the workplace. On this scale, 12 items are related to organizational deviance and 7 to interpersonal deviance. The responses range from 1 (never) to 7 (daily), and higher scores indicate more deviant behavior at work. The Cronbach α reliability for organizational deviance is 0.81, and interpersonal deviance is reported as 0.78 (Bennet & Robinson, 2000).
Trait anger scale (TAS) (Spielberger, 1999) is a 10-item, Likert-type scale (1=almost never to 4=almost always). The respondents report how angry they generally felt. The total score ranges from 10 to 40, with higher scores indicating more anger. The internal consistency reliability of this scale ranges from 0.81 to 0.91, with the highest reliabilities for college students (Spielberger et al., 2014). The TAS correlates positively with various anger and hostility measures and distinguishes high and low-anger groups (Spielberger, 1999).
The moral disengagement scale is a valid and reliable scale constructed by Fida et al. (2015). It has 24 items scored on a 5-point Likert scale (from 1==completely disagree to 5=completely agree). The total score ranges from 24 to 120, with higher scores indicating more moral disengagement. The Cronbach reliability coefficient is 0.89. 
The questionnaires distributed to the respondents were bilingual (English and Malay versions), and they could complete either version as they wished. The experts confirmed the content validity of all translated questionnaires, and their Cronbach α coefficients ranged from 0.75 to 0.83. 

Data analysis
Our hypotheses were tested using partial least squares (PLS) modeling (Ringle et al.l, 2005). The measurement and structural models were used to interpret the PLS model (Henseler et al., 2009). The quality of the measurement model was assessed by determining the construct reliability (composite reliability), two different validities (convergent validity through average variance extracted [AVE] and discriminant validity based heterotrait-monotrait ratio criterion), and collinearity testing (variance inflation factor). The structural model then concentrated on the causality between proposed exogenous and endogenous constructs. The performance of the structural model was evaluated based on the significance of the path coefficients and R2 values (Hair et al., 2017).

3. Results
Descriptive statistics
This section describes the demographic profile of the respondents.
According to Table 2, the age of 36.4% of the nurses was between 30 and 39 years, most of them were female (86.2%), married (70.8%), and the majority of nurses (49.9%) had work experience of more than 10 years.

Inferential statistics
We used PLS modeling with the well-known of SmartPLS software, version 3.2.8 (Ringle et al.,2005) as the primary method to analyze the measurement and structural models since it does not require strict assumptions of normality and is preferable for any samples size (Chin et al., 2003). To determine the reliability and validity of each construct applied for this study, several examinations were conducted at the measurement stage by determining their factor loadings, composite reliability (CR), and average variance extracted (AVE). The acceptable value for CR should be at least 0.70, whereas the recommended value for the AVE should be more than half of the total variation, approximately 0.50. Since all the indicators shown in Table 3 exhibited loadings ranging from 0.637 to 0.958 (which exceeds the minimum acceptable value of 0.5) (Hair et al., 2010; Afthanorhan et al., 2021b), the data were kept. Similarly, all latent constructs show good convergent validity since the AVE values range from 0.662 to 0.667. Meanwhile, the latent variable CR values (ranging from 0.903 to 0.968) are higher than Hair et al.’s (2010) 0.7 criterion, implying strong homogeneity. Finally, the measurement model used in the study (Table 3) is reliable and has appropriate convergence. 

The heterotrait-monotrait (HTMT) ratio criterion was applied to examine the discriminant validity of the measurement model. This method has recently become a method of choice for the composite construct when establishing discriminant validity (Afthanorhan et al., 2021a). The HTMT values in Table 4 do not exceed 0.90 (ranging from 0.223 to 0.507); thus, the constructs are truly unique and distinct from each other, as clearly stated by Gold, Malhotra and Segars (2001).

Before hypothesis testing, collinearity issues were inspected to avoid the detrimental effect of bias in estimating the parameter estimates of path coefficients. To do so, the authors combined the effects of the exogenous constructs (trait anger and negative affectivity) on moral disengagement and deviant workplace behavior to construct the variance inflation factor (VIF). After that, the VIFs were calculated using the effects of the aforementioned predictor factors. All VIFs and tolerance values for each construct (trait anger=1.289; negative affectivity=1.292; and moral disengagement=1.072) are less than the Hair et al. (2017) threshold value of 5. Thus, collinearity is not considered a serious concern among the predictors in our model.
The R2 values for moral disengagement and deviant workplace behavior were 0.068 and 0.298, respectively. Our measurement model (Figure 2) implies that trait anger and negative affectivity account for only 6.8% of the variance in moral disengagement and 29.8% of the variance in deviant workplace behavior.

Structural model results
The bootstrapping results (Table 5) suggest that the trait anger plays a significant role in deviant workplace behavior.

The results show that trait anger influences deviant workplace construct. As a result, H1 is supoted. Negative affectivity, on the other hand, does not influence deviant workplace behavior. Thus, H2 is not supported. Moral disengagement is positively and significantly associated with trait anger and negative affectivity, supporting H3 and H4. Surprisingly, moral disengagement and deviant workplace behavior have a good relation. As a result, H5 is supported. All of the formulated hypotheses on direct relationships are supported except for H2.
To test the mediation model, the authors bootstrapped the indirect impact, as suggested by Preacher and Hayes (2004, 2008). Thus, there is significant mediation if the confidence intervals (lower and upper limit) do not straddle a value of 0, negative affectivity (moral disengagement (deviant workplace behavior (β=0.063, P<0.01), and trait anger (moral disengagement (deviant workplace behavior (β=0.059, P<0.01) are all significant, as indicated in Table 5. The bias-adjusted 95% confidence intervals do not show any intervals straddling a 0, validating our findings. As a result, H6 and H7 are also supported. 

4. Discussion
The current study investigated the mediating role of moral disengagement in the effect of trait anger and negative affectivity on deviant workplace behavior among Malaysian government hospital nurses. Our statistical analysis yielded mixed results. 
Negative affectivity, in particular, had no effect on workplace misbehavior in any way. The characteristics of the nurses in our sample could explain this surprising conclusion. Most participants had more than 6 years of nursing experience in their respective institutions. Throughout their career, it is plausible that they have developed coping mechanisms to manage their negative affectivity due to their familiarity with the human resource policies of their employing hospitals, leading to emotions of acceptance. In addition, it may have been satisfying for nurses to consider the profession from their perspective as an inherently authentic role (Fida et al., 2018). Consequently, nurses’ negative affectivity does not influence them to show deviant workplace behaviors. Our result differs from previous studies, advocating that individuals’ negative affectivity will have a direct impact on deviant workplace behavior (Chen et al., 2013; Alias et al., 2012; Zhang et al., 2019).
On the other hand, it was shown in our study that trait anger affects nurses’ deviant workplace behavior. When dealing with daily conflicts in challenging medical settings, employees with an angry temperament are more likely to demonstrate deviant reaction. This finding is consistent with the extensive works on this topic (Ansari et al., 2013; Eatough et al., 2016; Jiang et al., 2019; Kozako et al., 2013; Santos & Eger, 2014; Zhou et al., 2018).
In line with the work of others, this study found that moral disengagement acts as a mediator in the relationship between trait anger, negative affectivity, and deviant workplace conduct (Fida et al., 2015 & 2018; Caprara et al., 2014). Staff nurses with strong trait anger and negative affectivity might engage in moral disengagement, leading to workplace misbehavior. In another study, moral disengagement was discovered to be a mediator between anger and physical aggression, as well as anger and verbal aggression (Rubio-Garay et al., 2016). The result is also synchrony with the study conducted by Zhao and Xia (2018), who reported that nurses’ negative emotional states were positively linked to their knowledge-hiding practices, with a slight mediating role of moral disengagement. In a more recent investigation on the impact of individual differences on deviant workplace behavior, moral disengagement was discovered to be an underlying mechanism (Newman et al., 2020). 
This study has several limitations that need to be addressed. Firstly, the study’s cross-sectional nature limits our capacity to make causal findings. Since the survey simultaneously examined exogenous and endogenous variables, bias might emerge because multiple variables being considered simultaneously could potentially introduce bias in the results. Therefore, future longitudinal research should cross-validate the existing findings and add to the evidence for a causal link between trait anger, negative affectivity, moral disengagement, and deviant workplace behavior. Secondly, the data were collected from staff nurses working at Peninsular Malaysia’s major government hospitals. Future research should include nurses working in private hospitals to improve the findings’ generalizability. Next, seeing as this study only examined two qualities, future studies should consider additional personality qualities when predicting deviant workplace behavior, such as shyness. Finally, in addition to moral disengagement, other attitudinal categories mediating the link between trait anger, negative affectivity, and deviant workplace behavior should be investigated, such as job alienation, work-family conflict, work ethics, and workplace spirituality.

5. Conclusion
This study discovered that, through moral disengagement, trait anger and negative affectivity influence deviant workplace behavior among staff nurses. The present study provides new insights into factors that contribute to deviant workplace behavior in Malaysia’s public healthcare industry which is useful for any empirical research related to nursing issues.
The outcomes of this study have theoretical and practical implications. Theoretically, the current study offers new insight that enriches knowledge about moral disengagement and inappropriate workplace behavior in healthcare settings, specifically Malaysia’s public healthcare industry. Moreover, our findings support the application of Bushman’s general aggression theory (Bushman& Anderson, 2002) and Bandura’s moral disengagement theory (1986). 
Practically, since this study has established that anger may raise the risk of deviant workplace behavior among nurses, hospital managers should offer support to help their staff regulate their emotions and responses. Training or seminar programs might be useful to instill awareness of the negative impacts of moral disengagement and deviant workplace behavior among staff nurses.

Ethical Considerations
Compliance with ethical guidelines
This research was approved by the Medical Research and Ethics Committee of the University Malaysia Terengganu (Code: NMRR-20-1104-52987 IIR). Written approval was received from the Malaysian Ministry of Health to conduct the study. The necessary permissions were also obtained from University Malaysia Terengganu before sampling. Informed written consent was obtained from all the subjects, and they were assured of the confidentiality of their information. 

Funding
This research received no grant from public, commercial, or not-for-profit funding agencies.

Authors' contributions
Conceptualisation, theoretical basis and framework, compiling abstract, introduction, discussion, and conclusion sections: Maslina Mansor; Methodology and literature review: Rashidah Mohd Ibrahim; Statistical analysis: Asyraf Afthanorhan; Data collection and logistics: Ahmad Munir and Mohd Salleh; Final approval: All authors.

Conflict of interest
The authors declared no conflict of interest.

Acknowledgments
 The authors thank all the nurses who participated in this study. 


References
Afthanorhan, A., Ghazali, P. L. & Rashid, N., 2021. Discriminant validity: A comparison of CBSEM and consistent PLS using fornell & larcker and HTMT approaches. Journal of Physics, 1874(1), pp. 1-7. [DOI:10.1088/1742-6596/1874/1/012085]
Afthanorhan, A., et al., 2021. Gain more insight from common latent factor in structural equation modeling. Journal of Physics: Conference Series, 1793(1), pp. 1-10. [DOI:10.1088/1742-6596/1793/1/012030]
Alias, M., Rasdi, R. M. & Said, A. M.A., 2012. The Impact of negative affectivity, job satisfaction and interpersonal justice on workplace deviance in the private organizations. Pertanika Journal of Social Sciences & Humanities, 20(3), pp. 829-46. [Link]
Anderson, C. A. & Bushman, B. J., 2018. Media violence and the general aggression model. Journal of Social Issues, 74(2), pp. 386-413. [DOI:10.1111/josi.12275]
Archer, J., 2004. Sex differences in aggression in real-world settings: A meta-analytic review. Review of General Psychology, 8(4), pp. 291-322. [DOI:10.1037/1089-2680.8.4.291]
Anderson, C. A. & Bushman, B. J., 2002. Human aggression. Annual Review of Psychology, 53, pp. 27-51. [DOI:10.1146/annurev.psych.53.100901.135231] [PMID]
Ansari, M. E., Maleki, S. & Mazreah S., 2013. Analysis of factors affected on employees’ counterproductive work behavior: The moderating role of job burnout and engagement. Journal of American Science, 9(1), pp. 350-9. [Link]
Bandura, A., 1986. Social foundation of thoughts and action: A social cognitive theory. In: D. Marks (Ed), The health psychology reader. New Jersey: Prentice-Hall. [Link]
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C., 1996. Mechanisms of moral disengagement in the exercise of moral agency. Journal of Personality and Social Psychology, 71(2), 364. [DOI:10.1037/0022-3514.71.2.364]
Shahid, B. & Ahmad, I., 2016. The impact of organizational learning on organizational corruption and the deviant workplace behavior: The case study of public sector organizations in Pakistan. International Journal of Business & Management, 11( 2), pp. 50-67. [Link]
Bennett, R. J. & Robinson, S. L., 2000. Development of a measure of workplace deviance. Journal of Applied Psychology, 85(3), pp. 349-60. [DOI:10.1037/0021-9010.85.3.349] [PMID]
Beu, D. S. & Buckley, M. R., 2004. This is war: How the politically astute achieve crimes of obedience through the use of moral disengagement. The Leadership Quarterly, 15(4), pp. 551-68. [DOI:10.1016/j.leaqua.2004.05.007]
Berkowitz, L., 1990. On the formation and regulation of anger and aggression: A cognitive-neoassociationistic analysis. American Psychologist, 45(4), pp. 494-503. [DOI:10.1037/0003-066X.45.4.494]
Bies, R. J., Tripp, T. M., & Kramer, R. M., 1997. [At the breaking point]. In Glacalone, R. A., Greenberg, J (Eds). Antisocial behavior in organizations, (pp. 18-36). Thousand Oaks: Sage Publications. [Link]
Brief, A. P., 1988. Should negative affectivity remain an unmeasured variable in the study of job stress? Journal of Applied Psychology, 73(2), pp. 193-8. [DOI:10.1037/0021-9010.73.2.193] [PMID]
Bushman, B. J. & Anderson, C. A, 2002. Violent video games and hostile expectations: A test of the general aggression model. Personality and Social Psychology Bulletin, 28(12), pp. 1679-86. [DOI:10.1177/014616702237649]
Buss, A. H., 1961. The psychology of aggression. , New Jersey: John Wiley & Sons. [DOI:10.1037/11160-000]
Caprara, G. V., et al., 2014. The contribution of moral disengagement in mediating individual tendencies toward aggression and violence. Developmental Psychology, 50(1), pp. 71-85. [DOI:10.1037/a0034488] [PMID]
Chen, C. C., Chen, M. Y. C. & Lin, Y. C., 2013. Negative affectivity and workplace deviance: The moderating role of ethical climate. The International Journal of Human Resource Management, 24(15), pp. 2894-910. [DOI:10.1080/09585192.2012.753550]
Chin, W. W., Marcolin, B. L. & Newsted, P. R., 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), pp. 189-217. [DOI:10.1287/isre.14.2.189.16018]
Detert, J. R., Trevin ˜o, L. K. & Sweitzer, V. L., 2008. Moral disengagement in ethical decision making: A study of antecedents and outcomes. Journal of Applied Psychology, 93(2), pp. 374–91. [DOI:10.1037/0021-9010.93.2.374] [PMID]
DeWall, C. N., Anderson, C. A. & Bushman, B. J., 2011. The general aggression model: Theoretical extensions to violence. Psychology of Violence, 1(3), pp. 245-58. [DOI:10.1037/a0023842]
Duffy, R. D. & Dik, B. J., 2012. Research on work as a calling: Introduction to the special issue. Journal of Career Assessment, 20(3), pp. 239-41. [DOI:10.1177/1069072711434409]
Eatough, E. M., et al., 2016. You want me to do what? Two daily diary studies of illegitimate tasks and employee well‐being. Journal of Organizational Behavior, 37(1), pp. 108-27. [DOI:10.1002/job.2032]
Fida, R., et al., 2018. ‘First, do no harm’: The role of negative emotions and moral disengagement in understanding the relationship between workplace aggression and misbehavior. Frontiers in Psychology, 9, pp. 671. [DOI:10.3389/fpsyg.2018.00671] [PMID] [PMCID]
Fida, R., et al., 2015. An integrative approach to understanding counterproductive work behavior: The roles of stressors, negative emotions, and moral disengagement. Journal of Business Ethics, 130, pp. 131-44. [Link]
Fornés-Vives, J., et al., 2019. The role of neuroticism in predicting psychological harassment in nursing: A longitudinal study. International Journal of Environmental Research and Public Health, 16(5), pp. 889. [DOI:10.3390/ijerph16050889] [PMID] [PMCID]
Fox, S., & Spector, P. E., 1999. A model of work frustration–aggression. Journal of Organizational Behavior, 20(6), 915-931. [DOI:10.1002/(SICI)1099-1379(199911)20:6<915::AID-JOB918>3.0.CO;2-6]
Gresham, D., Melvin, G. A. & Gullone, E., 2016. The role of anger in the relationship between internalising symptoms and aggression in adolescents. Journal of Child and Family Studies, 25(9), pp. 2674-82. [DOI:10.1007/s10826-016-0435-4]
Giacalone, R. A. & Greenberg, J., 1997. Antisocial behavior in organizations. Newcastle: Sage. [Link]
Gold A. H., Malhotra A. & Segars, A. H., 2001. Knowledge Management: An Organizational Capabilities Perspective. Journal of Management Information Systems, 18 (1), pp. 185-214. [DOI:10.1080/07421222.2001.11045669]
Hair J. F., et al., 2010. Multivariate data analysis: A global perspective (Vol. 7). London: Pearson. [Link] 
Hair, J. F., et al., 2017. Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), pp. 616-32. [Link]
Henseler, J., Ringle, C. M. & Sinkovics, R. R., 2009. The use of partial least squares path modeling in international marketing. In: R. R. Sinkovics., & P. N. Ghauri (Eds.), New challenges to international marketing. Bingley: Emerald Group Publishing Limited. [Link]
Hershcovis, M. S., et al., 2007. Predicting workplace aggression: A meta-analysis. Journal of Applied Psychology, 92(1), pp. 228-38. [DOI:10.1037/0021-9010.92.1.228] [PMID]
Hogan, J., & Hogan, R., 1989. How to measure employee reliability. Journal of Applied Psychology, 74(2), 273. [DOI:10.1037/0021-9010.74.2.273]
Hollinger, R. C., 1986. Acts against the workplace: Social bonding and employee deviance. Deviant Behavior, 7(1), 53-75. [DOI:10.1080/01639625.1986.9967695]
Isbell, L. M., Lair, E. C. & Rovenpor, D. R., 2013. Affect‐as‐information about processing styles: A cognitive malleability approach. Social and Personality Psychology Compass, 7(2), pp. 93-114. [DOI:10.1111/spc3.12010]
Jahantigh, M., Zar,e S. & Shahrakipour, M., 2016. The survey of the relationship between ethical climate and ethical behavior in nurses. Der Pharma Chemica, 8(3), pp. 189-93. [Link]
Jiang, Q., et al., 2019. The differing roles of cognitive empathy and affective empathy in the relationship between trait anger and aggressive behavior: A Chinese College Students Survey. Journal of Interpersonal Violence, 36(19–20), pp. NP10937–57.[DOI:10.1177/0886260519879229] [PMID]
Knoll, M., et al., 2016. Examining the moral grey zone: The role of moral disengagement, authenticity, and situational strength in predicting unethical managerial behavior. Journal of Applied Social Psychology, 46(1), pp. 65-78. [DOI:10.1111/jasp.12353]
Kokkinos, C. M., Voulgaridou, I. & Markos, A., 2016. Personality and relational aggression: Moral disengagement and friendship quality as mediators. Personality and Individual Differences, 95, pp. 74-9. [DOI:10.1016/j.paid.2016.02.028]
Kozako, I. N. ‘Ain M. F., Safin, S. Z. & Rahim, A. R. A., 2013. The relationship of big five personality traits on counterproductive work behavior among hotel employees: An exploratory study. Procedia Economics and Finance, 7, pp. 181-7. [DOI:10.1016/S2212-5671(13)00233-5]
Krejcie, R. V. & Morgan, D. W., 1970. Determining sample size for research activities. Educational and Psychological Measurement, 30(3), pp. 607-10. [DOI:10.1177/001316447003000308]
Lee, K., et al., 2016. Why victims of undermining at work become perpetrators of undermining: An integrative model. Journal of Applied Psychology, 101(6), pp. 915-24. [DOI:10.1037/apl0000092] [PMID]
Neuman, J. H. & Baron, R. A., 1998. Workplace violence and workplace aggression: Evidence concerning specific forms, potential causes, and preferred targets. Journal of Management, 24(3), pp. 391-419. [DOI:10.1177/014920639802400305]
Newman, A., Le, H., North-Samardzic, A., & Cohen, M., 2020. Moral disengagement at work: A review and research agenda. Journal of Business Ethics, 167, 535-570. [DOI: 10.1007/s10551-019-04173-0]
Ogunfowora, B. T., et al., 2022. A meta-analytic investigation of the antecedents, theoretical correlates, and consequences of moral disengagement at work. Journal of Applied Psychology, 107(5), pp. 746-75. [DOI:10.1037/apl0000912] [PMID]
Paciello, M., et al., 2008. Stability and change of moral disengagement and its impact on aggression and violence in late adolescence. Child Development, 79(5), pp. 1288-309. [DOI:10.1111/j.1467-8624.2008.01189.x] [PMID]
Preacher, K. J. & Hayes, A. F., 2004. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, pp. 717-31. [Link] 
Preacher, K. J. & Hayes, A. F., 2008. Contemporary approaches to assessing mediation in communication research. In A. F. Hayes, M. D. Slater, & L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage. [DOI:10.4135/9781452272054.n2]
Rubio‐Garay, F., Carrasco, M. A. & Amor, P. J., 2016. Aggression, anger and hostility: Evaluation of moral disengagement as a mediational process. Scandinavian Journal of Psychology, 57(2), pp. 129-35. [DOI:10.1111/sjop.12270] [PMID]
Robinson, S. L., & Bennett, R. J., 1995. A typology of deviant workplace behaviors: A multidimensional scaling study. Academy of Management Journal, 38(2), 555-572. [DOI: 10.5465/256693]
Ringle, C. M., Wende, S. & Will, A., 2005. Smart PLS 2.0 M3. Hamburg: University of Hamburg. [Link]
Rogojan, P. T., 2009. Deviant workplace behavior in organizations: Antecedents, influences, and remedies. Vienna: University of Wien. [Link]
Rodell, J. B. & Judge, T. A., 2009. Can “good” stressors spark “bad” behaviors? The mediating role of emotions in links of challenge and hindrance stressors with citizenship and counterproductive behaviors. Journal of Applied Psychology, 94(6), pp. 1438-51. [DOI:10.1037/a0016752] [PMID]
Santos, A. & Eger, A., 2014. Gender differences and predictors of workplace deviance behaviour: The role of job stress, job satisfaction and personality on interpersonal and organisational deviance. International Journal of Management Practice, 7(1), pp. 19-38. [DOI:10.1504/IJMP.2014.060541]
Schweitzer, M. E. & Hsee, C. K., 2002. Stretching the truth: Elastic justification and motivated communication of uncertain information. Journal of Risk and Uncertainty, 25(2), pp. 185-201. [DOI:10.1023/A:1020647814263]
Shalvi, S., et al., 2011. Justified ethicality: Observing desired counterfactuals modifies ethical perceptions and behavior. Organizational Behavior and Human Decision Processes, 115(2), pp. 181-90. [DOI:10.1016/j.obhdp.2011.02.001]
Spielberger, C. D. & Rickman, R. L., 1990. Assessment of state and trait anxiety. In: Sartorius, N., Andreoli, V.M., Cassano, G., Eisenberg, L. and Kielholz, P., Eds., Anxiety: Psychobio- logical and Clinical Perspectives, Hemisphere/Taylor & Fran- cis, WA, 69-84. [Link]
Sekaran, U. & Bougie, R., 2016. Research methods for business: A skill-building approach. New Jersey: Wiley. [Link]
Skarlicki, D. P., & Folger, R., 1997. Retaliation in the workplace: The roles of distributive, procedural, and interactional justice. Journal of Applied Psychology, 82(3), 434. [DOI: /10.1037/0021-9010.82.3.434]
Spielberger, C. D., et al., 2014. Measuring anxiety and anger with the state-trait anxiety inventory (STAI) and the state-trait anger expression inventory (STAXI). In: M. E. Maruish (Ed.), The use of psychological testing for treatment planning and outcomes assessment (pp. 993-1021). Oxfordshire: Taylor & Francis. [Link]
Spector, P. E., 1978. Organizational frustration: A model and review of the literature. Personnel Psychology, 31(4), pp. 815-29. [DOI:10.1111/j.1744-6570.1978.tb02125.x]
Spector, P. E. & Fox, S., 2005. The stressor-emotion model of counterproductive work behavior. In: S. Fox & P. E. Spector (Eds.), Counterproductive work behavior: Investigations of actors and targets (pp. 151–174). Washington, D.C.: American Psychological Association. [Link] 
Tanrikulu, I. & Campbell, M. A., 2015. Sibling bullying perpetration: Associations with gender, grade, peer perpetration, trait anger, and moral disengagement. Journal of Interpersonal Violence, 30(6), pp. 1010-24. [Lik] 
Steven, A., et al., 2014. Patient safety in nursing education: contexts, tensions and feeling safe to learn. Nurse Education Today, 34(2), 277-284. [DOI:10.1016/j.nedt.2013.04.025]
Trevin ˜o, L. K., Weaver, G. R. & Reynolds, S., 2006. Behavioral ethics in organizations: A review. Journal of Management, 32(6), pp. 951-90. [DOI:10.1177/0149206306294258]
Tuna, R., Bacaksız, F. E., & Seren, A. K. H., 2018. The effects of organizational identification and organizational cynicism on employee performance among nurses. International Journal of Caring Sciences, 11(3), 1707-1714. [Link]
Wang, X., et al., 2017. Moral disengagement as mediator and moderator of the relation between empathy and aggression among chinese male juvenile delinquents. Child Psychiatry & Human Development, 48(2), pp. 316-26. [Link]
Wang, Y., et al., 2018. Hostile attribution bias mediates the relationship between structural variations in the left middle frontal gyrus and trait angry rumination. Frontiers in Psychology, 9, pp. 526. [DOI:10.3389/fpsyg.2018.00526] [PMID] [PMCID]
Watson, D. & Clark, L. A., 1984. Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96(3), pp. 465-90. [DOI:10.1037/0033-2909.96.3.465] [PMID]
Watson, D., Clark, L. A.,& Tellegan, A., 1988. Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), pp. 106-70. [DOI:10.1037/0022-3514.54.6.1063] [PMID]
Wiernik B. M. & Ones D. S., 2018. Ethical employee behaviors in the consensus taxonomy of counterproductive work behaviors. International Journal of Selection and Assessment, 26(1), pp. 36-48. [DOI:10.1111/ijsa.12199]
Yang, J. & Diefendorff, J. M., 2009. The relations of daily counterproductive workplace behavior with emotions, situational antecedents, and personality moderators: A diary study in Hong Kong. Personnel Psychology, 62(2), pp. 259-95. [DOI:10.1111/j.1744-6570.2009.01138.x]
Zaghini, F., et al., 2017. What is behind counterproductive work behaviors in the nursing profession? A systematic review. Journal of Clinical Research & Bioethics, 7(4), pp. 1000277 [DOI:10.4172/2155-9627.1000277]
Zhang, Y., Crant, J. M. & Weng, Q., 2019. Role stressors and counterproductive work behavior: The role of negative affect and proactive personality. International Journal of Selection and Assessment, 27(3), pp. 267-79. [Link]
Zhao, H. & Xia, Q., 2018. Nurses’ negative affective states, moral disengagement, and knowledge hiding: The moderating role of ethical leadership. Journal of Nursing Management, 27(2), pp. 357-70. [DOI:10.1111/jonm.12675] [PMID]
Zhao, X., et al., 2018. Loneliness and depression symptoms among the elderly in nursing homes: A moderated mediation model of resilience and social support. Psychiatry Research, 268, 143-151. [DOI:10.1016/j.psychres.2018.07.011]
Zhou, Z. E., Eatough, E. M. & Wald, D. R., 2018. Feeling insulted? Examining end-of-work anger as a mediator in the relationship between daily illegitimate tasks and next-day CWB. Journal of Organizational Behavior, 39(8), pp. 911-21. [DOI:10.1002/job.2266]