Background
In Malaysia, the number of errors and accidents in both government and private hospitals has increased in recent years, despite efforts by the administrators of these health centers to improve treatment safety. Official figures from Health Ministry show that between 2016 and 2018, the number of incidents related to wrong operations, unintentional foreign body injuries (URFOs), transfusion and medication errors, and patient falls almost doubled in both public and private hospitals (Malaysian Ministry of Health, 2020). During the three-year period, wrong operations increased from six to 11 cases, while URFOs rose from 27 to 32 cases. Transfusion errors increased from 40 to 47 cases, while medication errors increased from 3,104 cases to 3,741 cases. Patient falls experienced the highest increase, with adult patient falls increasing from 2,374 to 3,547 cases and pediatric falls increasing from 441 to 696 cases (Zainuddin, 2019).
In addition, the incidents are due to ineffective communication, lack of teamwork, high workload, staff fatigue, non-compliance with procedures, and the use of shortcuts (Zainuddin, 2019). Other newspaper clippings indicate that the Ministry of Health receives an average of 7,000 complaints per year relating to various aspects such as services and facilities. Complaints from the public include poor services or communication skills of doctors and nurses, long waiting time before treatment, and inadequate facilities (Shazwani, 2017). The statistics displayed deficiencies and are not in line with the charter set by the Malaysian Ministry of Health itself; “Ensuring Every Customer Is Satisfied with The Services Provided,” So it is obvious that deviant workplace behavior has taken place. Therefore, our study aimed to determine the factors that lead to deviant behavior in the workplace among nurses in a government hospital in Malaysia. Are our nurses bad and not capable?
This study focuses on two variables that lead to the occurrence of deviant workplace behavior by taking into account moral disengagement as an intermediary between the two. This topic is very important because nurses are the people who are responsible for managing us while we are sick. In addition, nurses manage most of the ongoing care and treatment in hospitals—the importance of their role in managing the health of patients and assisting doctors in their daily affairs. Many studies are still being conducted related to the factors driving deviant workplace behavior in nurses, and this shows how important this study continues to be done.
Deviant behavior in the workplace is a form of unethical behavior that runs counter to organizational goals (Wiernik & Ones, 2018) and is a significant problem in many industries, especially in the medical industry (Liang et al., 2018). In developed and emerging countries, such as the United States and the United Kingdom (Zaghini et al., 2016), deviant workplace behavior has become frequent (Sarwar, Naseer, et al., 2020). According to Sarwar, Irshad, et al. (2020), the epidemic has expanded to Asian countries as well.
Inpatient care has long been a source of unethical activity for nurses worldwide (Zaghini et al., 2016). Nursing ethics are an essential element of the job (Zolkefli, 2021), yet nurses often find it difficult to follow the moral obligations that arise out of nowhere (Jahantigh et al., 2016). A dedicated nurse deviates from the standard due to a lack of understanding of the fundamentals (Fida et al., 2016). According to Caruso et al. (2016), it is becoming increasingly challenging for nurses worldwide to work with integrity despite the complex ethical choices and demands.
Nurses have been found to have more conflictual and severe sentiments of anger (Das & Avci, 2015) and emotional instability (negative affectivity) in stressful work environments (Fornés-Vives et al., 2019). Anger and negative affectivity, for example, have been found to play a substantial impact in the emergence of deviant conduct in the workplace (Yang & Diefendorff, 2009). The price of workplace anger and negative affectivity can be devastating. Indeed, for many, this issue immediately reminds us of the headlines of mass shootings at work with horrific victims. Such tragedy comes at the ultimate price, from death, serious injury, and trauma to reduced productivity, property damage, and endless confusion. They represent the most extreme, costly, and attention-grabbing symptom of anger, the actual violence. Fortunately, this is also the least common form of anger. Most angry feelings do not lead to violence. However, mismanagement can still be very costly. However, while it is difficult to calculate the monetary value of direct and indirect costs to an individual or organization, it is possible to list the essential cost-effectiveness of poorly managed anger. Individual anger and negative feelings cause powerful emotional and physical functions. If angry and negative feelings continue to nestle in a person’s soul, it will damage the individual’s body itself. Anger can hurt your career, and if anger is chronic, it can lead to long-term health problems.
Nurses’ deviant behavior in the workplace can contribute to poor patient outcomes and, if not stopped, addressed, or managed, can establish a culture of deviant behavior that can quickly spread throughout the organization, putting hospital patients at risk (Low et al., 2021; Sili et al., 2014; Stimpfel et al., 2012; Wong et al., 2010; Zhao & Xia, 2019). As a result, more research is needed to uncover other unknown factors linked to nurses’ deviant workplace behavior (Fida et al., 2016). Unfortunately, despite the growing amount of research on deviant workplace behavior among employees, many studies have overlooked the usefulness of deviant workplace behavior among hospital nurses in Malaysia (Low et al., 2021; Roopa et al., 2016).
Underpinning theory of General Aggression Model
The General Aggression Model is relatively relevant for the current study, and it was recognized as a new theory in explaining the role of cognitive, emotional, and arousal states that function to determine whether an employee will engage in deviant workplace behavior (Anderson & Bushman, 2002; DeWall et al., 2011). In addition, individual differences such as trait anger and negative affectivity directly affect individual cognition and affect the state of arousal. Finally, this theory seeks to explain individuals will determine the appropriate response to the events by assessing what can and should be done. This includes considering the consequences of their actions by considering a final decision of whether or not to engage in deviant workplace behavior. Thus, the findings will provide some support for this theory. The research framework of the study can be seen in Figure 1.
Trait Anger and Deviant Workplace Behavior
Trait anger refers to individual differences who experience frequent and intense anger. Individuals with an emotional state of anger, such as subjective thoughts of irritation, frustration, hatred, and associated physiological arousal, are more prone to react in difficult or irritating situations (Spielberger, 1988; Spielberger et al., 1999). Adnan (2012) added that characteristic anger could lead to confrontational behavior and dysfunctional outcomes.
Empirical evidence shows that trait anger has also been associated with deviant behavior in the workplace (Howald et al., 2018; Piecuch & Wojciechowska, 2019; Runge et al., 2020; Zhang et al., 2019; Zhou et al., 2018). Previous research by Spector and Fox (2005) suggests that deviant behavior in the workplace is related to individual differences in the propensity to feel and express anger. Individuals with a solid trait for anger, particularly an angry temperament, report an increased prevalence of deviant behavior. Similarly, the prevalence would be higher among employees who believe that they can and are allowed to harm the organization without being detected or punished under the aforementioned deviant work conditions. Since trait anger influences deviant behavior in the workplace, it can be hypothesized that: H1: There is a positive relationship between trait anger and deviant behavior in the workplace.
Negative Affectivity and Deviant Workplace Behavior
Personality traits have been associated with a range of workplace deviant behaviors (Zhang et al., 2019). Accordingly, previous researchers have used the stressor-emotion model to examine the regulatory role of personality traits in the relationships between stressors and negative affectivity and between negative affectivity and deviant behavior in the workplace (Yang & Diefendorff, 2009). Negative affectivity can be harmful regardless of where or when it is experienced or displayed; it can be particularly detrimental in the workplace. Lan et al. (2021) reported significant correlations of negative affect with job satisfaction and depression. One of the reasons negative affect has a more significant destructive potential in the workplace is that people generally work closely together in close quarters. This provides more opportunities for collaboration, innovation, and cooperation. However, it can also be an opportunity to fuel and spread negative feelings. According to early research, people with high levels of negativity, especially if they are “low,” are more prone to regard themselves as victims of hostility toward their coworkers. It is easy to understand how the combination of aggression (perceived or actual) and high levels of negativity can make a workplace toxic. High levels of negativity are associated with anomalies such as absenteeism, employee theft, lower productivity, and a decline in organizational performance (Koopman et al., 2021). Based on the above discussion, the following hypotheses are suggested: H2: There is a positive relationship between an individual’s negative affectivity on deviant workplace behavior.
Methods
Study Design
The present study used a quantitative analysis with a cross-sectional approach.
Participants
In this study, the participants are staff nurses from a Malaysian government hospital, as stated earlier in our research problem. Therefore, it was expected that almost 70,000 nurses in Malaysia were recorded from the Department of Statistics Malaysia (DOSM) (2018). The research setting is conducted in six government hospitals from four states in Malaysia representing each Malaysia zone, such as Kelantan, Kedah, Selangor, and Johor.
To determine the sample size, Hair Jr et al. (2017) and Westland (2010) approaches were used as both of them can accommodate the minimum requirement for a quantitative method such as Structural Equation Modeling (SEM). Using the Hair Jr et al. (2017) approach, the acceptable sample size ranged between 190 and 380, assessed based on the number of measurement items (38 items). Other than that, the Westland (2010) approach suggested that the minimum sample was 296 samples using 0.20 of anticipated effect size and 0.80 of statistical power. Thus, we printed out 450 questionnaires to ensure the minimum samples could be obtained. We also created respondent criteria to ensure the participants involved in this project were reliable and valid for further investigations. Thus, we mapped out the sampling frame and wrote to the managers of government sectors to obtain permissions.
Instruments
According to Golafshani (2003), the study’s credibility in quantitative research is based mainly on developing the instrument used to measure the underlying concept. Similarly, the measurement of a variable is developed to gain consistency, adequacy, accuracy, precision, uniformity, and comparison in assessing and explaining a particular concept. Therefore, the measurement approach includes testifying and confirming the manifest variable that serves as representations of the constructs (Hair et al., 2019). This study measured three variables: Deviant Workplace Behavior, Negative Affectivity, and Trait Anger, measured on an individual level. According to the rule of thumb, the number of an item or variable should be at least three items per construct (Hair et al., 2019). A 5-point Likert scale was used to collect responses (1 = strongly disagree to 5 = strongly agree). Each latent construct was treated as a reflective construct and lower-model component. To reduce standard method variance, specific procedural steps were done using Common Method Variance (CMV). According to Afthanorhan and Aimran (2020), CMV can be assessed using three different approaches: Harman Single Factor, Common Latent Factor, and Marker variable. Among CMV approaches, the Harman Single-Factor was recognized as an established method as it can be accommodated to all statistical methods, including PLS-SEM. In this study, the Harman Single-Factor results were 38.9% which met the acceptable limit of 50%.
The instrument was approved by three experts in the particular field for establishing the criterion and content validities under pre-testing. To check the validity of the developed instrument, a small-scale pilot study of the questionnaire was performed using a convenience sampling technique. According to Hair Jr et al. (2017), Afthanorhan and Aimran (2020), and Afthanorhan et al. (2021), the minimum sample for a pilot study is at least 50 samples that is usually tested using Exploratory Factor Analysis (EFA) and Cronbach Alpha methods. The result from the EFA method is considered valid when the sample is above 50.
The results from the pilot study suggested that all factors applied in the current study were reliable, and some of the measurement items were dropped from further investigations due to poor loadings. At last, 38 items were considered valid and maintained for fieldwork (Table 1). On the first page of the questionnaire, a cover page explaining the study’s goal and significance to the respondents and profession. The respondents were also provided written instructions and assurances that their personal information, responses, and identities would be treated with confidentiality.
Data Collection
The researchers divided the population into regions. Then, with the assistance of enumerators assigned for this task, the survey questionnaires were distributed proportionately to the number of staff nurses in each hospital, using a simple random sampling technique recognized as one of the probability samplings. A total of 450 surveys were disseminated. Out of them, 387 surveys were used for further analysis after removing the questionnaire with incomplete responses and lining cases. The data collection process took approximately two weeks to be completed, from March 2020 to June 2020.
Data Analysis
All constructs were assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM) method. PLS-SEM was recognized as an alternative to Covariance Based Structural Equation Modeling (CB-SEM) if the assumptions cannot be fulfilled or the proposed model is developed based on lack of evidence (exploratory). In addition, PLS-SEM can be useful for testing the relationships among each construct in a conceptual model. SmartPLS 3.2.8 was used to perform the PLS-SEM method. SmartPLS 3.2.8 provides various options to generate the outer and inner models to calculate the latent variable scores for the latent variables applied in the research model.
Notwithstanding, the PLS-SEM has received criticism from many applied scholars regarding its consistency and biasedness, such as Rönkkö and Evermann (2013), McIntosh et al. (2014), Aimran et al. (2017), Afthanorhan and Aimran (2020), and Evermann and Tate (2016). Consistent PLS and PLS predictions have been introduced to address these criticisms, but their development remains incomplete. But since this study is in exploratory nature where the proposed model is not recognizable for this setting, the PLS-SEM is suitable for hypothesis testing. Moreover, it does not require a stringent assumption such as normality issue and large sample size, which is particularly useful for the present study. Another reason for applying PLS-PM for this study is that the items used for assessing the prospective construct are mostly developed by ourselves and verified by experts in the particular field.
Ethical Considerations
The ethical approval of the study was obtained from Universiti Malaysia Terengganu and Universiti Sultan Zainal Abidin (UniSZA). In addition, ethical clearance was obtained from the authorities of the selected nurses in Malaysia. Each participant signed informed consent prior to data collection.
Results
As shown in Table 2, the majority of the respondents who participated in this study were female, constituting 336 (86.2%), while the remaining 51, representing 13.2%, were male. Regarding marital status, married people occupied the most significant number, 274 respondents representing 70.8%. In contrast, single and divorcees responses 94 and 19 represented 24.2 % and 5%, respectively. Regarding the age group, 111 respondents representing 28.7% of the participants, were between 20-29 years, followed by the age group between 30-39 years with 141 respondents, which accounted for 36.4% of the total samples. Also, the age group between 40-49 years occupied 116 respondents, representing 30% of the sample, and 50 and above occupied 19 respondents, 4.9%. Regarding working experience in the sector, 47 (12.1%) respondents work less than a year. While the majority of the respondents were from 10 and above years, with almost 193 participants representing 49.9%.
| Characteristics | Items | n | % |
|---|---|---|---|
| Gender | Male | 51 | 13.2 |
| Female | 336 | 86.2 | |
| Marital Status | Married | 274 | 70.8 |
| Single | 94 | 24.2 | |
| Divorce | 19 | 5 | |
| Age | 20-29 | 111 | 28.7 |
| 30-39 | 141 | 36.4 | |
| 40-49 | 116 | 30 | |
| 50 and above | 19 | 4.9 | |
| Working Experience | Less than a year | 47 | 12.1 |
| 1 to 5 years | 74 | 19.1 | |
| 6 to 9 years | 73 | 18.9 | |
| Ten and above years | 193 | 49.9 |
Convergence validity was determined by factor loadings, Composite Reliability (CR), and Average Variance Extracted (AVE). As shown in Table 3, the loadings of all indicators ranged from 0.631 to 0.849, which is higher than the minimum cut-off value of 0.5 proposed by Hair et al. (2010) and Abd Majid et al. (2019). All latent constructs showed sufficient convergent validity. The values of the latent variable CR (ranging from 0.903 to 0.968) were higher than the value of 0.70 proposed by Hair et al. (2019) and Dalila et al. (2020), indicating considerable homogeneity.
| Constructs | Question items | Loadings | AVE | CR |
|---|---|---|---|---|
| Trait Anger | TA9 | 0.701 | 0.631 | 0.903 |
| TA8 | 0.849 | |||
| TA7 | 0.839 | |||
| TA6 | 0.745 | |||
| TA5 | 0.847 | |||
| TA4 | 0.752 | |||
| TA3 | 0.832 | |||
| TA2 | 0.818 | |||
| TA1 | 0.748 | |||
| Negative Affectivity | NA9 | 0.755 | 0.662 | 0.968 |
| NA7 | 0.798 | |||
| NA6 | 0.845 | |||
| NA5 | 0.824 | |||
| NA4 | 0.839 | |||
| NA3 | 0.805 | |||
| NA2 | 0.827 | |||
| Deviant Workplace Behavior | DWB6 | 0.79 | 0.651 | 0.939 |
| DWB5 | 0.834 | |||
| DWB4 | 0.827 | |||
| DWB17 | 0.775 | |||
| DWB16 | 0.808 |
Note: TA = trait anger; NA = negative affectivity; DWB = deviant workplace behavior
The HeterotTait-MonoTrait (HTMT) ratio of correlations between study variables was used to test the measurement model’s discriminant validity. The results presented in Table 4 show that the values ranged from 0.295 to 0.499. Therefore, it can be assumed that discriminant validity demonstrated as the HTMT values in this research did not exceed 0.90 as recommended by Hair et al. (2010).
| DWB | NA | TA | |
|---|---|---|---|
| Deviant Workplace Behavior | |||
| Negative Affectivity | 0.295 | ||
| Trait Anger | 0.393 | 0.499 |
Note: TA = trait anger; NA = negative affectivity; DWB = deviant workplace behavior
Collinearity concerns were identified prior to hypothesis testing to prevent bias in calculating the path coefficients. We first calculated the variance inflation factor (VIF) by combining the impacts of the predictor variables (trait anger and negative affectivity) on moral disengagement and deviant workplace conduct. The VIFs were then estimated based on the impacts of the two predictor factors described above. Because all VIFs and tolerance values were below the threshold value 5 (Hair et al., 2021), collinearity was not a significant concern among the predictors in our model, as shown in Table 5.
| Model 1 | Variance Inflation Factor (VIF) |
|---|---|
| Trait Anger | 1.289 |
| Negative Affectivity | 1.292 |
From our measurement model (see Figure 2), the R2 values for deviant workplace behavior were 0.298, suggesting that only 29.8% of the variance in deviant workplace behavior could be explained by trait anger and negative affectivity.
Following the validation of the measurement model, a path analysis was used to assess the two hypotheses. The direction of the beta value, the significance level of the t-value, and the confidence intervals of the lower level (LL) and the upper level (UL), which do not span a zero, were all assessed in order to evaluate the structure, as recommended by Hair et al. (2019). The findings of bootstrapping (see Table 6) show that trait anger was linked to deviant workplace behavior (Beta Estimate = 0.245; t = 5.764, LL = 0.176, UL= 0.313, p = 0.00), indicating that H1 was supported. On the other hand, negative affectivity was unrelated to deviant workplace conduct (Beta Estimate = 0.074; p = 0.064), indicating that H2 was unsubstantiated.
| Relationship | H | Path | Beta value | t-statistics | p-value | LL | UL | Decision |
|---|---|---|---|---|---|---|---|---|
| Direct | H1 | TA -> DWB | 0.245 | 5.764 | 0.000 | 0.176 | 0.313 | Supported |
| H2 | NA-> DWB | 0.074 | 1.524 | 0.064 | -0.007 | 0.153 | Unsupported |
Note: ** p < 0.01; Bootstrapping (n = 500); TA = trait anger; NA = negative affectivity; DWB = deviant workplace behavior
PLS-Predict is a holdout sample-based technique developed by Hair et al. (2010) that employs the PLS-Predict with a 10-fold procedure to verify predictive relevance and provide case-level predictions on an item or construct level. If all item differences (PLS-LM) are lower, solid predictive power is demonstrated; if all are higher, predictive relevance is not demonstrated. If the majority is lower, moderate predictive strength is demonstrated; and if the minority is lower, weak predictive power is demonstrated. Table 7 shows that, with the exception of DWB4, all of the errors in the PLS model were lower than those in the LM, indicating that our model had strong prediction power.
| PLS RMSE | LM RMSE | PLS-LM | Q²_predict | |
|---|---|---|---|---|
| DWB4 | 1.094 | 1.093 | 0.001 | 0.093 |
| DWB5 | 1.216 | 1.22 | -0.004 | 0.104 |
| DWB6 | 1.231 | 1.26 | -0.029 | 0.037 |
| DWB16 | 1.052 | 1.055 | -0.003 | 0.067 |
| DWB17 | 1.034 | 1.045 | -0.011 | 0.074 |
Note: DWB = deviant workplace behavior
Discussion
This research makes contributions to both the theory and practice of management. Although scholars have recognized that deviance can be socially constructed, a dearth of research has specifically addressed this issue, especially from the perspective of public sector employees. Further, this study aimed to contribute to the workplace deviance literature by empirically examining how individual-based variables interact to explain workplace deviance.
This study brings together research on antecedents of deviant workplace behavior, namely trait anger and negative affectivity underpinned by the General Aggression Model (GAM). Previous studies on antecedents of deviant workplace behavior have used complex models to explain the relationship between variables (Ambrose et al., 2013; Rahim et al., 2012). The General Aggression Model (GAM) outlines a framework by which an employee’s frustration provokes deviant workplace behavior. This theory suggests that situational factors stimulate frustration and are followed by emotions often associated with frustration “ranging from minor annoyance to rage” (Spector, 1997). However, a theoretical model of aggression has proven useful in exploring and explaining the many causes of frustration and moderating processes with a wide variety of harm-doing behaviors.
With regard to the managerial contribution, this study contributes to the practice of management. First, on the implication, it benefits the superiors and managers. It is also expected that the result from this study will assist policymakers and practitioners in reducing the occurrences of workplace deviance issues related to individual differences. By understanding the factors that influence deviant workplace behavior, the government would also be in a better position to plan and implement complementary and integrated public administration policies and practices to enhance the effectiveness of the employees in the health sector, thus reducing the existence of deviant workplace behavior.
Second, the result of the study is also expected to assist government sectors in using the information from the study to implement training and development related to this issue. It will also contribute to all fields, which in turn enhance the theoretical and academic integration of several branches (organization behavior and human resource management). The ultimate objective is to reduce deviant workplace behavior, which contributes tangible and intangible costs to the victims and organization. Conducting research in this area also improved the research centers, stakeholders, and students with essential knowledge that are equally important. The result of this research can be used by legislators in designing policies, programs, planning, and strategies in human resource management practices for the entire nation.
Another critical point is that most nurses are women, ranging from 30 to 39 years. Thus, this figure is in line with the findings of Batz and Tay (2018), who found gender differences in subjective well-being. The result shows that women have higher positive affectivity than men. Furthermore, the discrete emotion “happy” or “happiness” is frequently used to operationalize positive affectivity in large, nationally representative populations. Batz and Tay (2018) discovered that happiness discrepancies between men and women differ by age: young women (18-44) are happier than young men, middle-aged women and men (45-54) are happy, and elderly women (55+) are less happy than older men. As a result, this remark illustrates that our nurses, regardless of their age or gender, are content and capable of managing their emotions.
Limitations
This study has its limitations. First, the study’s cross-sectional design limits our ability to draw causal conclusions. Because the study looked at both exogenous and endogenous variables at the same time, it could be biased. As a result, further longitudinal research could confirm the current findings and add to the evidence of causation in the relationship between trait anger, negative affectivity, and workplace deviance. Second, our data came from nurses in Peninsular Malaysia’s leading government hospitals. To strengthen the generalizability of the findings, future studies should expand the sample to include nurses working in private hospitals. Finally, this study only looked at two criteria. Other personality qualities such as shyness, openness, conscientiousness, extraversion, and agreeableness should be considered in future studies when predicting deviant conduct in the workplace.
Implications of the Study
Deviant work behaviors have become major concerns in organizations and disrupt the normal activities of the organization and/or its employees and create obstacles for efficient operations. Deviant work behaviors continue to emerge as a major concern in many industrial sectors, particularly in the healthcare industries of both developed and developing countries. The main objective of this study was to empirically analyze the occurrence of deviant workplace behavior and its related factors among nurses in Malaysia. Adopting the General Aggression Model, this study examined whether trait anger and negative affectivity affect nurses’ deviant workplace behaviors using cross-section survey methods with a proportionate sampling technique (N = 387) with nurses working in government hospitals in Peninsular Malaysia. The study hypotheses were tested using the PLS-SEM.
Our statistical results were inconsistent. Specifically, negative affectivity has little impact on deviant workplace behavior. The characteristics of the nurses in our sample may have contributed to this unexpected finding. The respondents in this study had an average of five years of experience as a nurse in their respective facilities. Therefore, negative affectivity may not influence deviant behavior in the workplace.
However, our study’s findings have consequences for both theory and practice. The study has contributed to the literature on the antecedents of deviant workplace behavior on a theoretical level, which revealed that the trait anger, negative affectivity, and deviant behavior in the workplace are indirectly related. Our findings confirm the applicability of Bushman’s general aggression theory (Anderson & Bushman, 2002) in the context of public health in Malaysia.
From a practical perspective, the findings indicate that because characteristic anger could increase the likelihood of deviant behavior in the workplace, hospital administrators should promote regulatory mechanisms within their nursing staff and provide adequate support. This could be done through an anger management event. In addition, employers may need to teach nurses to regulate the trait objectively and correctly to avoid deviant behavior; nurses with positive traits will encourage their colleagues to be more engaged.
Managers and administrators in the healthcare industry can also help by supporting conferences or seminars to raise awareness of undesired social habits that appear harmless but can cause workplace disruption. In addition, employees should be helped to develop resilient attitudes and cope with stressful situations utilizing cognitive-behavioral techniques in workshops like this. Previous research has shown that such training methods can help individuals improve their positive thinking, resilience, and self-efficacy, supporting this conclusion (Luthans et al., 2006).
Conclusion
The study findings revealed that the Malaysian nurses had positive affectivity, which was related to positive emotions like happiness, work interest, and attentiveness. On the other hand, the nurses were suffering from high levels of emotional tiredness. In addition, trait anger was found to influence deviant behavior in the workplace positively. Individuals with a high trait anger level reported more frequent and intense everyday anger in various provocative situations, more anger in ongoing personally provocative situations, a greater tendency to respond to provocation with physical and verbal antagonism, and a lower likelihood of dealing with it constructively. As a result, employees with high levels of anger are likely to respond to a wider range of deviant work behavior.