According to some psychoanalytic views, noncompliance is not indicative of true passive-aggressive behavior, which may instead be defined as the manifestation of emotions that have been repressed based on a self-imposed need for acceptance. From Wikipedia, the free encyclopedia. Indirect resistance to the demands of others. For The Radio Dept.
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Main article: Passive-aggressive personality disorder. Main article: Workplace conflict.
Antisocial personality disorder Counterproductive work behavior Gossip Guilt trip Let the Wookiee win Malicious compliance Mind games Neglect Nonviolent resistance Oppositional defiant disorder Passivity Psychological manipulation Relational aggression Silent treatment Qahr and Ashti , Iranian "silent treatment" Social undermining Work to rule. Oxford Dictionaries English. Retrieved Defence mechanisms.
Delusional projection Denial Distortion Extreme projection Splitting. Compartmentalization Defensive pessimism Exaggeration Minimisation Postponement of affect.
Evaluate how each of the following personality types deals with interpersonal conflic:
Narcissistic defences Censorship psychoanalysis. Psychological manipulation. Rewarding : pleasant positive reinforcement. Aversive : unpleasant positive punishment. Climate of fear Traumatic bonding. Categories : Human behavior Protest tactics Psychological abuse Workplace bullying Abnormal psychology Borderline personality disorder Motivation Defence mechanisms Bullying Abuse Aggression Symptoms and signs: Cognition, perception, emotional state and behaviour.
Hidden categories: Articles with short description All articles with unsourced statements Articles with unsourced statements from February The traditional LC model Goodman, assumes that every observation is an exclusive member of one latent unobservable class and that local independence exists between the manifest variables.
LC analysis only assumes nominally distributed LC dimensions and binary or polytomous observations Rist et al. An important difference from traditional cluster methods like K -means clustering is that LC analysis is based on a statistical model that can be tested Magidson and Vermunt, As a consequence, determining the number of latent classes is less arbitrary than when using traditional cluster methods. In fact, LC analysis offers robust, empirically supported tests to determine the optimal number of classes. The starting-point for a LC model is homogeneity, that is, every respondent resides in the same single group.
This baseline model is a one-LCC model. In a LCC model, clusters of respondents with similar response patterns are subsequently added. A n-cluster model may then result in latent classes that differ in function of the nature and the frequency of reported social stressors. The metric of this single latent variable is typically nominal. This would fit the assumption that aggressive behavior manifests itself in different shapes and doses. Instead of increasing the number of LCCs only, the number of latent variables factors may be increased as well, addressing, in our case, the degree to which these three measures are representing either one or, rather, multiple factors.
The idea of defining a LC model with several latent variables started with Goodman , Haberman , and Hagenaars , who proposed restricted 4-class LC models yielding models with two latent variables. Magidson and Vermunt labeled this type of LC models as LCF models because of the natural analogy to standard factor analysis.
Like with traditional confirmatory factor models, a priori knowledge about the relationship between items and latent variables is needed Vermunt and Magidson, Moreover, with traditional measurement models, the discrete latent variable must adequately explain the initial relationship between the indicators. In an LC model, every subject is assigned to only one cluster or class based upon the modal assignment rule that classifies a subject to the class with the highest classification probability.
These membership probabilities are being calculated upon the estimated parameters of the measurement model Magidson and Vermunt, Evaluation of fit of LC models is not straightforward. Firstly, the model fit needs to be evaluated. Secondly, the local fit has to be assessed and finally, the quality of the classification has to be scrutinized. After selecting a specific model, it is assessed whether it fits to the data. A model that does not fit to the data has a significant squared log-likelihood L 2. However, for very sparse tables such as the ones we have, Langeheine et al.
In addition to statistical fit measures, it is also important to inspect local fit and the quality of the classification. To evaluate local fit or misfit and its origin one may use bivariate residuals BVR. BVR show how much association between each pair of indicators remains, using the 1-cluster model as a reference. Ideally, the value should be lower than 3. Finally, the quality of the classification is assessed. Here R 2 , entropy R 2 , and the total rate of classification errors, due to adjacent erroneous classifications, are indicators of mis classification.
Given the large sample size in our study, both BIC and L 2 may lose their power to select the most appropriate model Paas, The proper use of these statistical fit measures has only been illustrated for samples with a maximum of respondents, leaving big data in the rain Paas, Because the evaluation of fit and the comparison of fit between the different measurement models are central to evaluate our first research hypothesis, we randomly selected six mutually exclusive subsamples from the overall sample to investigate which of the models had the best fit to the data.
Thereafter, we applied this model to the entire sample and studied the relationships between the constructs and their criterion validity. To test the first hypothesis, we inspected the BIC across the six samples that were randomly selected see first part of Table 2. The second part of Table 2 portrays the BIC of the final model and the p-value of the bootstrapping procedure of the L 2. Hence, we reject Hypothesis 1 and conclude that the items measuring interpersonal conflicts, aggression and workplace bullying, respectively, do not fit into one single and unified concept.
TABLE 2. Evaluation of fit: Bayesian information criterion across different competing measurement models and samples. In addition, the BIC showed that any of the distinguished multi-dimensional factor models had a lower BIC than the single variable latent model. The most plausible model among these alternative measurement models was a two-LCF model Model 2a— CA-WB wherein bullying represents one factor with different classes, and wherein conflicts and aggression represent a second factor again with different latent classes.
This model portrayed the lowest BIC in 5 out of 6 subsamples. The two-factor model with the lowest BIC across the six samples distinguished four latent classes for each factor. As one of the classes in the conflicts-aggression factor was relatively small 3. Apart from the previous model, this final model had the lowest BIC and its bootstrapped L 2 was also non-significant in 5 out of 6 samples, indicating that this measurement model statistically fits well to the data. The precise meaning of the latent classes of the two factors can be derived from the conditional probabilities.
However, for the sake of economic expression the full table has 8 columns and 41 rows covering conditional probabilities and is portrayed in the Annex we limit us to Figure 1 where we have plotted the conditional average scores of the items across the LCFs. The dashed lines represent the classes of the conflicts-aggression factor whereas the full lines represent the classes of the bullying factor. The percentages in Figure 1 correspond to the size of the latent classes. Conditional means plot.
In the first class, neither conflicts nor exposure to aggression were reported. The bullying factor consisted of four latent classes. In the second class, there was a slightly higher, yet still low, frequency of reported exposure to the bullying items. The two factors i. Figure 2 sheds more light on the relationship between both factors via a bi-plot. This tendency also seems to exist for bullying at work.
This implies that the strength of the relationship between the two factors decreased as conflict, aggression, and bullying were reported more frequently. Bi-plot: relationship between conflict-aggression and workplace bullying and their indicators. The findings presented in Table 3 allow us to further elaborate on the relationship between the two factors. However, when both behaviors become more frequent occasional and more often , their overlap decreased. To test the second hypothesis stating that conflicts, aggression, and bullying are similarly detrimental to those exposed, we disentangled the effect size of both factors in a multi-variate analysis of variance.
The partial eta 2 resulting from the multi-variate analysis of variance helps to understand the predictive value of the two factors in explaining the various criterion variables. After assessing the effect sizes of both factors, we further discerned the mean differences of the latent classes of both factors on the criterion variables. A Tukey pair-wise comparison procedure yielded that all pair-wise comparisons were significantly different from each other for all criterion variables. Table 5 enlists the specific z -scores of the criterion variables, for each class separately.
However, being classified as a target of bullying seems to be responsible for a further deterioration of job satisfaction etcetera as the z-values increase on average, with 0. Hence, targets of bullying report significantly more detrimental outcomes than respondents who are most strongly exposed to interpersonal conflicts and aggression. Construct proliferation is a major problem in the organizational sciences, and research on conflict, aggression, and bullying might not be immune to that problem Aquino and Thau, ; Hershcovis, ; Tepper and Henle, ; Hershcovis and Reich, However, existing research may not be fine-grained enough to distinguish clearly between the different concepts Tepper and Henle, We examined a set of competing LC models using a large heterogeneous sample of Belgian workers.
While comparing LC models, we found our data to not support an approach where labels could be used interchangeably i. From a statistical point of view, a two-factor model fitted the data best—with one factor comprising both conflict and aggression and another one comprising bullying. A three-factor model only provided the second-best solution. Hence, it appears that bullying is not only perceived differently than aggression and conflict but also seems to have a unique impact on employees, which seems to be especially detrimental for those employees that are highly targeted.
Although more difficult to differentiate between the three types of social stressors for lower intensity levels, we found that when reported more frequently, interpersonal conflict, aggression and bullying cannot easily be construed as the same underlying phenomenon. Moreover, the targets of severe bullying reported by far the lowest levels of well-being and the highest levels of strain among all identified classes of respondents.
Hence, being a target of severe bullying seems to constitute a discrete experience associated with particularly low levels of well-being and particularly elevated levels of work-related strain. Introducing a separate factor for aggression did not improve model fit. Aggression was primarily reported in situations where interpersonal conflicts exist between subordinates and superiors or between peers.
This might imply that interpersonal conflicts might encompass workplace aggression. Note that we do not claim that all conflicts involve aggression, as we also found some instances where conflicts did exist without any trace of aggression, particularly at low levels of conflict. Even though we found a two-factor solution providing the best fit to the data, the overlap between these two factors appeared to be quite large, with the correlation between the two being almost 0. The scores on the selected criterion variables for those experiencing some involvement in interpersonal conflicts or for those who were occasionally bullied were also quite similar.
Hence, it seems that both phenomena are not that different when exposure is low. The cross-tabulation table see Table 2 further demonstrates that not all conflicts involve bullying, but that targets of bullying tend to experience some degree of interpersonal conflict. Existing research may help explain the interrelations between conflict and bullying.
Einarsen proposed a model wherein these two processes are strongly interrelated. His model starts with an escalating conflict which may provoke aggression, which, in turn, may result in bullying. The first class was typified by not reporting any conflicts and no aggressive behavior. The second class was typified by increasing level of conflicts but hardly any aggressive behavior. Compared to the previous class, the last class consisted of employees reporting increasing levels of conflicts and also aggressive behaviors.
Nevertheless, the class that is missing in the current results is the one that describes a stage of conflict escalation wherein the conflicting parties go so far that they envisage total annihilation. According to Einarsen and colleagues , it is unlikely to find such highly escalated interpersonal conflicts while at work.
A reason for why we did not find this class may be that such highly escalated conflicts are most likely to be stopped by management. Furthermore, in general, such intense overt aggressive behaviors will not be tolerated in working life. Such types of behaviors, as they are illegal, may even warrant dismissal Welzijnswet, , which explains why this class may be rare in working life.
However, to the extent that parties engage in subtle, covert, and difficult-to-detect wrong-doing, such behavior may persist for long, as often the case with bullying. For example, dispute-related bullying describes a form of bullying that develops out of grievances and involves social control reactions to perceived wrong-doing Einarsen, ; also see Felson and Tedeschi, Einarsen et al. Hence, a certain critical level of interpersonal conflicts may give rise to a process of escalating bullying. If dispute-related bullying would be the only way for bullying to emerge, our empirically tested cluster model wherein conflicts, aggression and bullying are indicators of the same phenomenon should have had the best fit.
That this is not the case is potentially due to the existence of predatory bullying, which describes a form of workplace bullying that gradually evolves in the absence of an escalated conflict. Previously, scholars already distinguished between different phases in this process.
Later on, more direct negative social behaviors seem to appear Einarsen et al. Targets are isolated and avoided, humiliated in public by being made a laughing-stock, and so on. In this phase both physical and psychological means of violence may be used Einarsen et al. Our results may coincide with earlier empirical research using a LC approach that seems to underwrite such a description of the process of bullying — note though that such a process cannot be modeled with cross-sectional data.
The increases in conditional probabilities between the different levels of bullying classes do however, point to such a process. Finally, similar to other studies, the target of bullying class yielded the highest conditional probability to report the most frequent exposure to all types of negative behaviors. Our re-analysis of the extensive data used originally by Notelaers et al.
Although clearly advancing prior research, our study is not without limitations. Our data is constraint by the fact that conflict and aggression were only measured with two items each. Although the global and the local fit were sufficient, measuring latent variables with only two indicators is sub-optimal because it is generally known that it takes three indicators to identify a factor using covariance modeling.
Another possible limitation is that the number of items to measure each construct may be imbalanced.
Specifically, one may question whether the established two-factor solution may be a consequence of the fact that we used more items to measure bullying than conflict and aggression. Researchers should replicate our findings by using more established and more extensive conflict and aggression measures. Finally, it may be that respondents are more familiar with conflict and aggression than with bullying.
Although research has shown that such a bias did not play in the Scandinavian countries Nielsen, , we cannot rule out that this may be a factor that influences the prevalence in other countries. Due to the cross-sectional nature of our data, we cannot investigate possible escalation processes, which theoretically, are assumed to be central to the understanding of both escalated conflicts and to workplace bullying. The lack of longitudinal data also defers conclusions on the cause and effect relationships between the social stressors and the included outcome variables.
Furthermore, we were unable to assess the validity of the identified classes by means of any objective measure. This would also help to develop a more profound understanding of the nomological network of social stressors at work. With respect to the nomological network, we urge scholars to not only use alternative measures and methods to measure conflicts, aggression and bullying but also to further investigate the convergent and divergent validity of these three concepts. While the target perspective is the dominant one in risk management research on interpersonal conflicts and workplace bullying, it limits research as conflict, aggression, and bullying involve at least two parties, and sometimes also bystanders Branch et al.
An interesting but under investigated question is whether offenders and bystanders construe negative social workplace behaviors in a similar way as targets do. Recent research findings indeed have shown that targets and witnesses react differently toward the display of negative social workplace behavior Nielsen and Einarsen, Future research also is needed to clarify the nomological network of conflict, aggression, and bullying Hershcovis, ; Tepper and Henle, The current study together with that of Baillien et al.
To further establish construct validity and ascertain that one is not using different labels for the same construct, we need, however, more than valid statistical methods and existing data. Campbell and Fiske bring the importance of discriminant and convergent validity to our attention when presenting the multi-trait multi-method matrix. Recently Baillien et al. They also perceived bullying to be a longer-lasting process that was governed by an intent to harm.
Future research aiming to advance research on the construct validity of the three concepts could operationalize these characteristics together with the characteristics that are typical for aggression and conflicts. This would allow an in-depth study of the convergent and discriminant validity.
Our study findings provide some guidance for managers and policy makers who seek to prevent and manage workplace conflict, aggression, and bullying. Our findings suggest that companies, at a minimum, would need to raise awareness for the potentially grave implications of conflict, aggression, and bullying and would need to design policies to help prevent those social stressors at work from occurring in the first place. Employees who are occasionally bullied should receive individual counseling to help them cope with the situation. In cases of severe bullying, however, counseling will not suffice.
Thus, companies need to develop and enforce legal procedures to help protect targets of bullying Hoel and Einarsen, ; Yamada, Managers also need to be aware that they may do more harm than good when confusing incidents of conflicts or aggression with examples of workplace bullying, and vice versa. The challenge for managers thus is to learn to tell apart workplace conflicts from bullying incidents, as both kinds of social stressors afford different kinds of interventions Hoel and Einarsen, In this article, we have addressed a rather straightforward research question that is relevant for both theorists and practitioners dealing with social stressors at work: Are interpersonal conflicts, aggression and bullying at work different or overlapping phenomena, at least as experienced and perceived by those exposed, and, are their outcomes similar or different?
Our findings from a LC analysis speak against using these labels interchangeably. While interpersonal conflicts and aggression are strongly intertwined, workplace bullying is construed as a distinct concept. The results of the present study show that severe forms of workplace bullying are not seen by the targets as being merely another kind of interpersonal conflicts at work or as a case of aggression, but that they rather constitute a distinct phenomenon associated with even more severe outcomes. Furthermore, the large majority of employees who experience conflicts did not report any exposure to bullying.
Our findings have important implications for practitioners and researchers. We call for more attention for this topic in order to prevent unnecessary human suffering at work and to enable sustainable employability van der Heijden, No review and approval from an ethical board was required according to the relevant institutional and national guidelines. For a study that does not comprise of any health related an informed consent was neither needed according to the institutional and national standards of Belgium in However, respondents were informed about the goal of the project, that they were added to the benchmark and the use of the latter for scientific research.
They had after completing the questionnaire that in total had items over 14 pages, to either put it sealed envelope and to put in a sealed urn or sent it back to the Health and Safety Executive or to the Directorate Research of Working Conditions of the Belgian Federal Department of Labor.
This Federal Research Institute treated the data and made reports for the HSEs in order to ensure their further commitment to future studies on psychosocial factors. Person data was made anonymous which is also in line with the current European General Data Protection Act. GN has collected and analyzed the data.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Amason, A. Distinguishing the effects of functional and dysfunctional conflict on strategic decision making: resolving a paradox for top management teams. Google Scholar. Anderson, C. Human aggression. Aquino, K. CrossRef Full Text. Ayoko, O. Workplace conflict, bullying, and counterproductive behaviors. Baillien, E. Towards a conceptual and empirical differentiation between workplace bullying and interpersonal conflict. Work Organ. Aggression among university employees. Sex differences in covert aggression among adults. Bowling, N. Branch, S. Redman and A. Brodbeck, F.
The dissemination of critical, unshared information in decision-making groups: the effects of pre-discussion dissent. Buss, A. The Aggression Questionnaire. Campbell, D. Convergent and discriminant validation by the multitrait-multimethod matrix. Cosier, R. Agreement and thinking alike: ingredients for poor decisions. De Dreu, C. The virtue and vice of workplace conflict: food for pessimistic thought. Ziekmakende conflicten en pesterijen op het werk [Sickening conflicts and workplace bullying]. Gedrag Organ.
Schabracq, J. Winnubst, and C. Cooper Chichester: Wiley. Conflict at work and individual well-being. Conflict Manag. Einarsen, S.
The nature and causes of bullying at work. International Journal of Manpower 20, 16— Harassment and bullying at work: a review of the Scandinavian approach. Violent Behav. The nature, causes and consequences of bullying at work: the Norwegian experience. Pistes 7, 1— Measuring exposure to bullying and harassment at work: validity, factor structure and psychometric properties of the negative acts questionnaire-revised. Work Stress 23, 24— Developments in Theory, Research and Practice , eds S.
Einarsen, H. Hoel, D. Zapf, and C. Mobbing og Harde Personkonflikter. Unhealthy Interaction at Work. Bergen: Sigma Forlag. Felson, R. Aggression and violence: Social interactionist perspectives. Washington, D. C: American Psychological Association. Frone, M. Interpersonal conflict at work and psychological outcomes: testing a model among young workers.
Health Psychol. Glasl, F. Bern: Verlag Paul Haupt. Goodman, L. Exploratory latent structure models using boht identifiable and unidentifiable models. Biometrika 61, — Guenter, H. When task conflict becomes personal: the impact of perceived team performance. Small Group Res. Haberman, S. Hagenaars, J. Newbury Park, CA: Sage. Newbury Park: Sage. Hershcovis, M. Langan-Fox, C.