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Cyberaggression in Work-Related Email: Nomological Network and Links to Victims’ Counterproductive Work Behavior

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Abstract

Due to the ubiquitous nature of email communication, the use of the medium as a tool for aggression (termed cyberaggression) creates unique challenges for organizations. However, little is known about cyberaggression’s relation to other forms of workplace mistreatment or the extent to which it predicts victims’ work-related behavior. Two studies presented here enhance understanding of the cyberaggression construct by examining its nomological network, potential outcomes, and mediating mechanisms. Study 1 examines cyberaggression’s relationships with verbal aggression, workplace incivility, relationship conflict, and abusive supervision. Results suggest that cyberaggression is strongly related but empirically distinct from these other forms of workplace mistreatment. Study 2 then employs a three-wave survey to (1) link cyberaggression to victims’ counterproductive work behavior (CWB) through the proposed mechanisms of rumination and negative emotion, and (2) examine cyberaggression’s incremental prediction of these outcomes beyond face-to-face aggression and cyber incivility. Results suggest that cyberaggression has an indirect effect on victims’ CWB targeted at the organization (CWB-O), through serial mediators of rumination and negative emotion, respectively, and an indirect effect on CWB targeted at individuals (CWB-I) through rumination only. After controlling for face-to-face aggression and cyber incivility, supervisor-enacted cyberaggression no longer predicted CWB-O or CWB-I, but coworker-enacted cyberaggression continued to predict CWB through rumination.

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Notes

  1. In the current research, we limited the source of cyberaggression to coworkers and supervisors. The decision to do so was mainly a practical one. These sources are more likely to be common across occupations, whereas cyberaggression from subordinates or customers would not be relevant to non-supervisory or non-service-oriented jobs, respectively.

  2. Despite these data transformation procedures, item 10 on the aggression scales (i.e., “threatening to me”) continued to result in empty cell warnings during the analyses. Upon further inspection of the data, this item was consistently endorsed by the fewest number of respondents across sources and modes of aggression (only 10 to 13 respondents gave a response other than “never” on these items). Item 10 was therefore removed from all the aggression scales (both coworker-enacted and supervisor-enacted, email and face-to-face aggression) for the item-level analyses (CFAs) only. Because this item is consistent with Weatherbee’s original definition and measure, however, it was retained in the scale-level analyses (hypothesis tests).

    An anonymous reviewer also pointed out that the first two items of the aggression scales overlap conceptually with the concept of incivility. We therefore ran the CFAs with these items 1 and 2 removed from the email and face-to-face aggression scales. Those results are also reported in Table 4. The removal of these two items resulted in a slightly stronger correlation between email and face-to-face aggression (from coworkers) and a slightly lower correlation between cyberaggression and cyber incivility (from both sources). However, the overall results of the CFA analyses were quite similar after dropping these two items. We therefore proceeded with hypothesis testing using the 10-item scales for both cyberaggression and face-to-face aggression.

  3. Because of a severe positive skew in the cyberaggression predictor variables, we transformed the data by taking the reciprocal of each score (Howell 2013), which resulted in distributions that remained skewed but to a much lesser extent (skewness statistics ranging from −0.55 to −2.58). As a result of these transformations, the sign of coefficients associated with these variables must be interpreted in the opposite direction. To reduce confusion in interpretation, we therefore multiplied the coefficients by −1 and report those coefficients.

    As another way of addressing the skewness issues, we also re-ran these analyses using dichotomous cyberaggression variables, where all non-zero values on each mistreatment score were recoded as 1, and all scores of 1 (indicating the participant had chosen “never” for all items on that scale) were recoded as 0. The pattern of significant and non-significant effects was identical to the reported results. Details of these additional analyses are available from the first author.

  4. It is worth noting that very few participants experienced all three forms of mistreatment from the same source, preventing a comparison of the relative strengths of their effects. Indeed, only 16.8% of our sample experienced all three types of mistreatment from supervisors, and only 17.6% of our sample experienced all three types of mistreatment from coworkers. Only 10.9% experienced all three types from both sources.

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Authors and Affiliations

  1. School of Leadership and Human Resource Development, Louisiana State University, Baton Rouge, LA, USA

    Erin M. Richard

  2. Center for Creative Leadership, Greensboro, NC, USA

    Stephen F. Young

  3. Florida Institute of Technology, School of Psychology, Melbourne, FL, USA

    Julianna J. Walsh

  4. Quinnipiac University, Department of Psychology, Hamden, CT, USA

    Gary W. Giumetti

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Table 7 Cyberaggression Items (Weatherbee 2007) and Factor Loadings from Study 1 and Study 2
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Richard, E.M., Young, S.F., Walsh, J.J. et al. Cyberaggression in Work-Related Email: Nomological Network and Links to Victims’ Counterproductive Work Behavior. Occup Health Sci 4, 161–190 (2020). https://doi.org/10.1007/s41542-020-00056-3

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