Out of sight, out of mind? Investigating the impact of emotional intelligence on counterproductive work behavior in remote work

Abstract

Purpose
The purpose of this research is to investigate the relationship between emotional intelligence (EI) and counterproductive work behavior (CWB) in remote work (RW).

Design/methodology/approach
In total, 489 currently employed people were surveyed in April 2022 – 2 years after the start of the pandemic and 1 year before “the great return” to the workplace.

Findings
Results reveal that EI is negatively associated with CWB and that RW moderates this relationship. The more people work remotely, the more likely they are to adopt CWB. However, this tendency is lower for people with higher EI.

Originality/value
This research contributes to the literature on CWB by highlighting the role EI plays in the specific context of RW. In terms of practical implications, it sheds lights on the importance of carefully designing human resource management policies towards RW.

Full text

Out of sight, out of mind? Investigating the impact of emotional intelligence
on counterproductive work behavior in remote work
Abstract
Purpose: The purpose of this research is to investigate the relationship between Emotional
Intelligence and counterproductive work behavior in remote work.
Design/Methodology/Approach: 489 currently employed people were surveyed in April 2022
– 2 years after the start of the pandemic and 1 year before “the great return” to the workplace.
Findings: Results reveal that emotional intelligence is negatively associated with
Counterproductive Work Behavior, and that remote work moderates this relationship. The more
people work remotely, the more likely they are to adopt counterproductive work behavior.
However, this tendency is lower for people with higher emotional intelligence.
Originality: This research contributes to the literature on counterproductive work behavior by
highlighting the role emotional intelligence plays in the specific context of remote work. In
terms of practical implications, it sheds lights on the importance of carefully designing HRM
policies towards remote work.
Keywords
Emotional intelligence; Counterproductive work behavior; Remote Work
Introduction
Counterproductive work behavior (CWB) is a central issue for organizations: it can harm an
organization and its stakeholders, it can deteriorate relationships between employees and
increase turnover, and it affects key organizational outcomes such as productivity, customer
satisfaction and profit (Carpenter et al., 2021; Marcus et al., 2016). What makes it even more
challenging is that even “good” employees might adopt CWB (Moore and Gino, 2013).
Research has therefore analyzed the role of multiple contextual and personal factors related to

CWB (Carpenter et al., 2021; O’Boyle et al., 2011). Studies have examined counterproductive
work behavior (CWB) in relationship with emotional intelligence (EI) (Dirican and Erdil, 2020;
Greenidge et al., 2014), showing that people with higher EI tend to engage less in CWB (Miao
et al., 2017).
In this study, we are interested in investigating the conjoint role of EI and the frequency of
remote work (RW) on CWB. Working away from the office is not new, but Covid-19 has
however created a major shift in ways of working by forcing people to work remotely (Zhang
et al., 2021), causing long-lasting changes in terms of work practices (Angelucci et al., 2020;
Gupta, 2022). Research has investigated the impact of Covid-19 on RW by focusing on factors
influencing productivity (Galanti et al., 2021; Morikawa, 2023; Straus et al., 2023), challenges
faced by managers (Errichiello and Pianese, 2016), and collaboration (Yang et al., 2022).
Research has investigated the impact of remote work on performance and some insights have
documented increased performance, measured through self-reports (Feng and Savani, 2020;
Morikawa, 2023), direct and indirect measures of productivity (Bao et al., 2022; Sherman,
2020). Although there are to date not enough data obtained post-pandemic to draw conclusions
on the impact of post-pandemic remote work on productivity, recent findings tend to suggest
that flexible forms of remote work seem to benefit workers’ productivity (Angelici and Profeta,
2024).
Research has also analyzed the impact of post-covid RW on CWB (Becker et al., 2022). Yet,
the combination of RW and EI has not been studied in a post-pandemic context to understand
their impact on CWB. On the one hand, RW might decrease the relationship between EI and
CWB as interpersonal contacts are reduced – and with them the “opportunity” to engage into
personal deviance. On the other hand, RW might increase the relationship between EI and CWB
because employees need to be more intentional with all types of connections and interactions
and thus have access to less resources (or less easily), such as colleagues, to vent their

frustrations. As RW is at the center of the discussions about the future of work (Itam and
Warrier, 2024; Nyathani, 2023; Ugar, 2023), it is of great importance to get a better
understanding of the behavior and work ethics in RW.
The paper is structured as follows: we first review literature on CWB, EI and RW. We then
present our study, its model and measures as well as its results, and consequently discuss its
contributions as well as its managerial implications.
Counterproductive work behavior and emotional intelligence in RW
Emotional intelligence and CWB
CWB can be defined as “any volitional acts by employees that potentially violate the legitimate
interests of, or do harm to, an organization or its stakeholders” (Marcus et al., 2016, p. 204).
Bennett and Robinson (2000) conceptualized CWB as constituted of two dimensions: behavior
directed towards the organization as a whole (organizational deviance) and behavior directed
towards other employees in the organization (interpersonal deviance). Several types of
behaviors have traditionally been considered as CWB, such as theft, destruction of property,
misuse of information, time and resources, unsafe behavior, poor attendance and quality work,
and inappropriate actions (Gruys and Sackett, 2003). At the organizational level, CWB has been
associated with lower productivity, customer dissatisfaction and losses (Carpenter et al., 2021;
Marcus et al., 2016). At the individual level, CWB is associated with lower job satisfaction,
and organizational commitment (Robinson & Bennett, 1995), as well as higher mental health
risks (Lim et al., 2008), increased turnover intentions and absenteeism (Spector et al., 2006).
EI can be presented as the faculty to perceive, use, understand and manage one’s own and
other's emotions (Mayer et al., 2016). It has gained widespread scholarly interest in the last
decades, especially regarding its impact on work behaviors (O’Boyle Jr et al., 2011; Van Rooy

and Viswesvaran, 2004). Studies have found that people scoring high in EI were less likely to
adopt CWB (Miao et al., 2017). These results can be explained because individuals scoring
high in EI are more likely to experience positive affect and less likely to experience negative
affect (Kafetsios and Zampetakis, 2008). Finally, emotionally intelligent people have a better
capacity to regulate their negative emotions (Peña-Sarrionandia et al., 2015). For instance,
emotionally intelligent individuals can persuade themselves they are capable of overcoming
difficult situations (self-efficacy) or engage in more efficient problem-solving. It causes them
to feel less negative affect, and therefore they are less inclined to engage in CWB. In the specific
context of Covid-19, Sadovyy et al. (2021) studied the moderating effect of EI in the
relationship between stress and CWB. Their results show that the association between stress
and CWB was significant at low levels of EI but not significant at high levels of EI.
H1: EI is related negatively to CWB
RW and CWB
RW can take many different shapes and forms. Many terms and definitions of RW can be found
in literature, using various labels such as distributed work, telework, flexible work
arrangements, etc. (see Allen et al., 2015 for a review). Leonardi et al., (2024) identify four
types of distance that characterize RW: psychological distance, temporal distance,
technological distance, and structural distance. Psychological distance can be presented as the
cognitive, affective, or social separation between oneself and another person (Leonardi et al.,
2024). Research focusing on psychological distance notably study challenges in communication
or isolation (Wang et al., 2021). Temporal distance can be presented as the separation across
time because of time zones, work schedules, and other forms of asynchronicity (Leonardi et al.,
2024). Most research on temporal distance focuses on geographically distributed teams and the

opportunities and challenges in terms of coordination and collaboration (O’Leary and
Mortensen, 2010). Technological distance can be defined as the distance that comes from
technological artefacts themselves (Leonardi et al., 2024), and their characteristics in terms of
media richness (Daft and Lengel, 1986) and synchronicity (Dennis et al., 2008). Research
focusing on technological distance notably focuses on the accessibility of cues in remote
interactions (Audrin and Audrin, 2024). Structural distance refers to the distance from the
organization, its structure and demands (Leonardi et al., 2024). Research notably investigates
how RW is associated with so-called alternative work arrangements, such as gig work (Spreitzer
et al., 2017). It is important to realize that these different forms of distance can vary depending
on the situation. Technological artefacts, organizational structures and cultures, and
employment relationships have a strong impact on RW. People can experience more or less
psychological distance (depending on their individual characteristics), temporal distance (in the
case of virtual teams, distributed work or depending on the characteristics of the flexible
arrangements for example), technological distance (depending on their organization’s
technological artefacts), or structural distance (depending on their relationship to the
organization, for example in the case of gig workers).
RW can influence employees differently when such work results from an agreement with the
employer rather than when it is imposed and occurs over a very long period (such as during the
pandemic). When considering CWB in the context of RW, several issues can arise from three
specific aspects of RW: lack of direct interpersonal interaction, flexibility of the workday, and
lack of direct oversight (Anderson et al., 2015; Golden et al., 2008). In these conditions, and
without close monitoring, employees might be tempted to work more slowly than they normally
would or engage in other activities such as taking care of their family, shopping, or cyberloafing
(Çolak and Çetin, 2021). Even if the nature of CWB might differ when working from home

versus in the office, remote workers may still engage in multiple damaging ways for the
organization (Holland et al., 2016). For example, remote CWB can consist of different forms
of abuse (for example cursing at or ignoring people), production deviance (for example
avoiding calls), misuse of time (for example doing household chores, playing video games or
watching movies), concealing bad behavior (for example blaming technology for missing a
deadline) (Holland et al., 2016).
Many studies have supported the relationship between need frustration and CWB (Van den
Broeck et al., 2014). According to self-determination theory, frustration of the three universal
needs of autonomy, competence, and relatedness can lead to maladaptive outcomes (Deci et al.,
2017). On one hand, when working from home, employees might feel more autonomous given
that there is less supervisor monitoring, leading them to engage less in CWB (e.g., Jensen &
Raver, 2012). One the other hand, RW can frustrate the needs for relatedness as employees have
less face-to-face interactions with their coworkers and supervisors and thus feel isolated (Wang
et al., 2021), which might hinder their need for relatedness. As RW can frustrate employees’
needs, and as frustration of these needs can lead to CWB (Van den Broeck et al., 2014), we
expect employees who work remotely more often to be more likely to engage in CWB.
H2: RW frequency is related positively to CWB
The interaction between EI, RW and CWB
In working remotely, employees do not enjoy the same environment than in the office. They
have less opportunities to engage in small talk or even receive support from colleagues. When
experiencing daily hassles, they do not have access to the same repertoire of emotional
regulation strategies than they might have when working on site as their interactions are

mediated through digital technology (Wadley et al., 2020). Raišienė et al. (2020) have shown
that employees who had to work remotely reported various issues during the pandemic.
Emotionally intelligent individuals might be more capable to overcome the emotional burden
created by issues happening in RW. On the contrary, individuals who lack EI might be more
affected by the issues they are facing in RW and less likely to be able to regulate their emotions.
H3: EI moderates the relationship between RW frequency and CWB, such that the relationship
between RW frequency and CWB will be weaker for people with higher EI
Methods
Participants
510 participants located in the United States of America were recruited on MTurk
(compensation = 1,50$) in April 2022. This is a little bit more than 2 years after the mandatory
state-imposed remote work (i.e. March 15-17, 2020) and about 1 year before the call from many
companies to bring people back to the office (i.e. around 2023). MTurk represents a valuable
option for data collection as it is an efficient and reliable tool (Mortensen and Hughes, 2018).
However, the demographics of MTurkers need to be considered (Aguinis et a., 2021), especially
when studying workplace behaviors. Indeed, an estimated 40% of MTurk workers consider
themselves primary as gig workers (Brawley & Pury, 2016), which might raise questions about
their relationship to their “employer” and their behaviors at work (Cropanzano et al., 2023).
However, in the case of that study, we specifically selected participants who were employed
(i.e., in traditional employment relationships) through the “qualification requirements” option,
following Cheung et al. (2017) recommendations to increase the quality of the sample and its
adequation with our research question.

The participants provided their written informed consent to participate in this study and could
stop their participation at any moment. As no formal ethic committee exists in any of the
authors' institution, no formal ethic approval number has been issued. The authors have had the
data collection protocol validated by their teams ahead of launching the study. Of these
participants, 7 did not answer at least three of the ten validity items correctly and 13 did not
finish the experiment and, as such, were excluded from further analyses. The remaining sample
consisted of 489 participants (49.5% male), most of whom were aged between 25 and 45 years
old (63.5%) and were classified as full-time workers (90%).
Measures
Emotional intelligence
EI was measured using the TeiqueSF (Petrides and Furnham, 2006). This questionnaire
distinguishes between four types of EI: Emotionality (8 items), Self-Control (6 items), Well-
being (6 items) and Sociability (6 items). In addition to the 26 items mentioned above, four
remaining items were further included as they contribute to the global trait EI score
(Zampetakis, 2011). Participants could answer on a 7-point Likert scale ranging from 1
(strongly disagree) to 7 (strongly agree), we computed the total score of EI by averaging the 30
items on the scale (alpha = .94).
Counterproductive work behavior
CWB was measured using the questionnaire of Bennett and Robinson (2000) – see appendix.
The questionnaire consists of 19 items describing both interpersonal and organizational CWB.
Participants could answer on a 7-point Likert scale ranging from 1 (never) to 7 (daily) to
indicate how often they had engaged in such behaviors (alpha = .98). The total score was used
in this study.

RW frequency
RW frequency was measured with a single-item question: How frequently are you working
remotely? Participants could answer on a 5-point Likert scale ranging from 1 (never) to 5
(always). This is in line with recent research on RW assessing RW frequency with one item
(van Zoonen et al., 2021; van Zoonen and Sivunen, 2022).
Data analyses
Data analyses consisted of two steps: descriptive statistics and multivariate hierarchical linear
regressions. To test our hypotheses, we performed hierarchical regression analysis. The steps
involved are the followings: 1) we introduced age, gender as control variables as well as EI as
a predictor (model 1), 2) we added the frequency of RW as a predictor (model 2) and 3) we
finally added the interaction between EI and the frequency of RW as a predictor (model 3).
Plots of the residuals against the predicted scores of the dependent variables showed no major
sign of heteroscedasticity. Residuals followed a normal distribution and were centered around
zero. No multicollinearity between predictors was found (we computed the VIF score for all
the independent variables in each model, and no values were above 3.07). Finally, the Cook’s
distance of all cases was examined and showed no undue influence of any case on the
parameters. As such, the statistical assumptions of multiple linear regressions were met.
Results
Descriptive statistics
Descriptive statistics and intercorrelations between variables can be found in Table I).
Respondents further show relatively high frequencies of RW: 21.4 % (N = 109) reported

working remotely half of the time, 31.6% (N = 161) reported working remotely most of the
time, and 22.2% (N = 113) reported always working remotely.
Table I
Descriptive statistics
(Source: Authors’ own work)
Mean
Median
SD
1.
2.
3.
4.
5.
1.EI
4.37
4.09
0.769
—
2.CWB
3.81
4.46
1.968
-0.633***
—
3.Remote work
3.43
4
1.239
-0.236***
0.303***
—
4.Age
0.246***
-0.253***
-0.232***
—
5.Gender
0.013
-0.093*
-0.048
-0.011
—
Note. * p < .05, ** p < .01, *** p < .001; gender variable is coded 1 for male and 2 for female.
Regression analysis
To test our hypotheses, we conducted a multiple linear regression analysis on CWB and
introduced age, gender, frequency of RW, EI, and their interaction as predictors. Gender and
age were introduced as control variables as previous literature suggests that these variables may
explain CWB were significant predictors of CWB (Hjalmarsson and Dåderman, 2022; Ng et
al., 2016).
Table II : Regression coefficients predicting CWB
(Source: Authors’ own work)
Model 1
95% Confidence Interval
Predictor
Estimate
SE
Lower
Upper
t
p
Intercept
4.074
0.4790
3.133
5.01553
8.51
< .001
Remote
work
0.400
0.0691
0.265
0.53609
5.80
< .001
Age
-
0.312
0.0696
-0.448
-0.17481
-4.48
< .001

Table II : Regression coefficients predicting CWB
(Source: Authors’ own work)
Model 1
95% Confidence Interval
Predictor
Estimate
SE
Lower
Upper
t
p
Gender
-
0.324
0.1667
-0.651
0.00395
-1.94
0.053
Model 2
95% Confidence Interval
Predictor
Estimate
SE
Lower
Upper
t
p
Intercept
10.406
0.5513
9.323
11.4896
18.88
< .001
Remote
work
0.227
0.0567
0.116
0.3386
4.00
< .001
Age
-0.125
0.0573
-0.238
-0.0127
-2.19
0.029
Gender
-0.311
0.1345
-0.575
-0.0466
-2.31
0.021
EI
-1.473
0.0915
-1.653
-1.2931
-16.10
< .001
Model 3
95% Confidence Interval
Predictor
Estimate
SE
Lower
Upper
t
p
Intercept
8.005
1.0924
5.858
10.15126
7.33
< .001
Remote
work
0.986
0.3037
0.389
1.58240
3.25
0.001
Age
-
0.110
0.0573
-
0.223
0.00271
-
1.92
0.056
Gender
-
0.319
0.1338
-
0.582
-0.05617
-
2.38
0.017
EI
-
0.965
0.2197
-
1.396
-0.53295
-
4.39
< .001
EI* Remote
work
-
0.167
0.0657
-
0.296
-0.03789
-
2.54
0.011
Results are depicted in Table II. Our model significantly predicted CWB (F(3,487) = 119, p <
.001, Adj. R2 =.420). Results reveal that EI (b = -1.015, 95%CI = [-1.45. -0.57], t = -4.51, p
<.001), RW (b = .977, 95%CI = [0.376, 1.57], t = 3.20, p =.001), significantly predicted CWB.

Results further show a significant effect in the moderation of EI on the link between RW
frequency and CWB (b = -0.163, 95%CI = [-0.294, -0.03], t = -2.46, p = .014).
Figure I
Graphical representation of the interaction between remote work and EI on CWB
(Source: Authors’ own work)
These results reveal that the frequency of RW is positively related to CWB, suggesting that the
more employees work remotely, the more they will show CWB. Interestingly, our results further
suggest that EI negatively predicts CWB, revealing that the more participants show EI, the less
they tend to perform deviant behaviors. Moreover, our results show a significant interaction
between the frequency of RW and EI highlighting that the impact of RW is moderated by EI.
As shown in Figure I, the relationship between EI and CWB is stronger for employees working
remotely.
0.0
2.5
5.0
7.5
234567
Emotional Intelligence
CWB
Frequency
of remote
work
+ 1 SD
Mean
−1 SD

Discussion
The purpose of this paper was to investigate the role of EI and RW on CWB. Results show that
EI is negatively related to CWB: the higher employees score on EI, the less likely they engage
in CWB. Results also reveal that RW is positively related to CWB: the more often employees
work remotely, the more likely they are to engage in CWB. These results can be put in line with
research showing the negative outcomes of RW, such as higher stress (Molino et al., 2020) and
isolation (Wang et al., 2021), as well as stress and job dissatisfaction De Clercq et al., 2019;
Penney & Spector, 2005), all of which are antecedents of CWB. Finally, our findings show that
the frequency of RW moderates the relationship between EI and CWB, such that the
relationship between EI and CWB is stronger with higher RW frequency. This shows the
importance of EI in RW, to mitigate the risk of developing CWB.
Theoretical implications
This study makes three clear contributions. First, it builds on previous research on EI and CWB
by investigating the specific context of RW. Existing studies have shown that people with high
EI are less likely to adopt CWB (Miao et al., 2017), given their tendency to experience positive
affects rather than negative ones (Kafetsios and Zampetakis, 2008) and their ability to regulate
their negative emotions (Peña-Sarrionandia et al., 2015), as well as their ability to feel to
rationalize other people decision making and perceive that they are being treated fairly (Mustafa
et al., 2023). This study thus highlights the importance of EI in CWB, even in RW.
Second, this study sheds light on the impact of late pandemic stage RW on CWB. These results
can be put in line with research showing the negative outcomes of RW, such as higher stress
(Molino et al., 2020) and isolation (Wang et al., 2021), as well as stress and job dissatisfaction
(De Clercq et al., 2019; Penney & Spector, 2005), all of which are antecedents of CWB. Since
this study was conducted in April 2022, while RW was still mandatory for most people, the

way people perceive RW can differ between pre-pandemic, during pandemic, and post-
pandemic. It suggests that frequent RW might have implications in terms of employees’ CWB
in a late pandemic stage during which RW still was the norm. One avenue that would be worth
investigating is the lack of autonomy resulting from RW when it is mandatory and not chosen.
Finally, our findings show that the frequency of RW moderates the relationship between EI and
CWB, such that the relationship between EI and CWB is stronger with higher RW frequency.
In that respect, CWB can be understood as a consequence of problems related to the way RW
is implemented in organizations and its frequency (Carillo et al., 2021). This can signal a deeper
problem for organizations (Robinson and Bennett, 1995) – especially regarding RW policies
and how employees perceive them as fair or not (Beauregard, 2014).
Practical implications
From a managerial perspective, these results suggests that organizations should treat CWB as
a symptom of, and not as the main problem with RW. By considering CWB as a symptom of
forced RW, organizations and policy makers can better determine how to create boundaries
concerning RW frequency in a post-pandemic context. More specifically, three ranges of
implications can be identified based on our findings, focusing on organization policies,
organizational support, and flexible work arrangements.
First, the context of the study (that of April 2022, in the United States of America), suggests
that participants were still “forced” to work remotely when surveyed, and that their
organizations were – to an extent – rushed to implement RW for everyone at the beginning of
the pandemic. In this respect, it is highly likely that the rules and policies governing RW have
not been clearly defined beforehand. This can lead individuals to misinterpret the organization's
expectations or their expected contribution, and, consequentially, engage in CWB. In this

respect, a first recommendation is to clarify the rules when implementing flexible working, to
avoid any misunderstandings and frustrations.
Second, and in line with studies on individual characteristics and CWB, another implication
would be to provide remote workers with increased (and tailored) organizational support.
Literature has already highlighted the moderating impact of perceived organizational support
(POS) on CWB (Palmer et al., 2017), as well as its importance in the context of RW (Daniels
et al., 2022). In that respect, it seems relevant for organizations to provide adequate support for
their remote employees, tailored to their needs in RW.
Finally, organizations should consider the frequency of RW when developing their RW
policies, as the relationship between EI and CWB is stronger with higher frequency of RW. In
that respect, flexible work arrangements might appear as a more balanced option that might
mitigate some of the risks associated with RW and create a fairer environment (Beauregard,
2014).
Limitations
This study has several limitations, linked to both its context and its measures. Data have been
collected in the USA in April of 2022, about two years after the start of the pandemic. More
specifically, April 2022 is a little bit more than 2 years after the mandatory state-imposed
remote work (i.e. March 15-17, 2020) and 1 year before the call from many companies to bring
people back to the office (i.e. around 2023). In that respect, the results of this study refer to a
specific “late pandemic stage” and as such need to be considered in a different manner than
studies that have been led “pre-pandemic”, or that will be led in later periods (post-2023 when
companies’ remote work policies might change again). Additional research in this area is
therefore needed to grasp a nuanced understanding of the phenomenon.

The participants surveyed recruited and compensated through MTurk, and even though MTurk
represents a valuable option for data collection (Goodman et al., 2013), its representativity,
especially when studying workplace behaviors can be questioned. While compensation is a
common practice in online data collection platforms, it introduces a potential bias in terms of
the self-selection of individuals motivated by monetary reward. Moreover, since a fair share of
MTurkers consider themselves primary as gig workers (Brawley & Pury, 2016), the results
might not present the perspective of “traditional” employment perspectives only. However, to
address this limitation, we required participants to be employed.
Also, CWB has been measured with the scale of Robinson and Bennett (1995). Recently, some
voices have called for the development of specific scales that target CWB in RW (Holland et
al., 2016). Further studies could thus compare our results with results obtained using RW-
specific scales. Moreover, there is much debate around EI and its measures. According to
Ashkanasy & Daus (2005), EI can be considered either as an ability, a constellation of
personality traits, or as a set of self-reported competences. In this study, EI is considered as a
constellation of personality traits. It would be interesting to confront these results with measures
of EI that come from other streams. Moreover, recent calls have been made to develop
contextualized measures of EI in digital contexts (Audrin and Audrin, 2024). This type of
measures might thus be more adequate for the context of RW. Finally, the cross-sectional nature
of the data should be addressed: RW and CWB have been measured at a given time, with no
overview of how CWB could have evolved over time. Further studies could tackle this issue
with longitudinal approaches to better understand the development and evolution of CWB over
time.
Conclusion

The purpose of this research was to investigate the role of EI and RW frequency on CWB.
Results show that EI is negatively associated with CWB, and that RW is positively associated
with CWB. They further highlight that RW moderates the relationship between EI and CWB.
This research contributes to research on CWB by focusing jointly on a contextual (RW) and a
personal (EI) factor to better understand their impact on CWB and showing how RW might
participate to CWB and suggests that individual performance predictors in RW still need to be
investigated. It proposes that RW policies should take the factor of frequency into account.
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Appendix: Items used to measure CWB (from Bennett and Robinson, 2000)

During the last year, to which extent have you engaged in each of the behaviors below:
Made fun of someone at work
Said something hurtful to someone at work
Made an ethnic, religious, or racial remark at work
Cursed at someone at work
Played a mean prank on someone at work
Acted rudely toward someone at work
Publicly embarrassed someone at work
Taken property from work without permission
Spent too much time fantasizing or daydreaming instead of working
Falsified a receipt to get reimbursed for more money than you spent on business expenses
Taken an additional or longer break than is acceptable at your workplace
Come in late to work without permission
Littered your work environment
Neglected to follow your boss's instructions
Intentionally worked slower than you could have worked
Discussed confidential company information with an unauthorized person
Used an illegal drug or consumed alcohol on the job
Put little effort into your work
Dragged out work in order to get overtime