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Revisiting the Inhibitory Effect of General Mental Ability on Counterproductive Work Behavior: The Case for GMA-Personality Interaction

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Abstract

Counterproductive work behavior (CWB) is an important component of job performance that is known to be related to critical personal and organizational consequences. Thus, both researchers and practitioners are interested in better understanding CWB’s primary drivers. Despite its popularity, the theoretical inhibitory effect of GMA on CWB, which predicts that employees with higher GMA will show lower CWB, has seen weak and inconsistent empirical support. Here, we propose that a reason for this divide between theory and empirical studies can be explained by a more appropriate interpretation of the inhibitory effect as conditional, in that the strength of the GMA-CWB relationship is dependent on other critical individual differences. We suggest that the meta-trait stability–which subsumes conscientiousness, agreeableness, and emotional stability, the three personality traits shown to be consistently positively related to CWB–is critical for revealing the GMA-CWB relation in empirical studies. Specifically, we hypothesize that the inhibitory effect is dependent on the meta-trait stability such that the expected negative GMA-CWB relationship is strongest for those with low levels of stability but is not apparent for those with high levels of stability. Results supported the conditional inhibitory hypothesis across two large samples. Implications for theory and practice are discussed.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. The second meta-trait plasticity encompasses the remaining Big Five traits of openness and extraversion and concerns the creation and exploration of new goals. Importantly, the two meta-traits do not reflect opposing ends of the same dimension. As noted by DeYoung (2015), “The opposite of stability is not plasticity but instability, and the opposite of plasticity is not stability but rigidity” (p. 47). See DeYoung et al., (2002, 2010) for further reading on possible neurobiological substrates of the Big Five traits, including the relationship between plasticity and dopaminergic functioning.

  2. A 10-item verbal measure was administered. However, to due to an error in administration for one of the items, only 9-items were used to score verbal GMA.

  3. To evaluate whether intelligence type may have affected findings, we also estimated separate models for an interaction between stability and verbal intelligence and an interaction between stability and fluid intelligence (i.e., spatial measure). Results for fluid and verbal intelligence were not notably different from each other, nor were they different from results using the average of both intelligence types presented in-text.

  4. We also ran a z-test to compare the proportion of “never” endorsement in Sample 1 (7.30%) and Sample 2 (9.36%) and found no significant difference, z = -1.92, p = .059.

  5. In our first dataset (78% White, 6% Black, 5% Asian, 5% Hispanic) we found no significant observed differences in CWB between race categories, F(3,1314) = 0.56, p = .640. However, predicting CWB from GMA scores alone resulted in a significant race effect, F(3,1314) = 4.72, p = .002, such that Black respondents had higher predicted CWB, t(1176) = 3.77, p < .001, with a moderately large effect size, d = .44. Notably, in this sample GMA alone did not significantly predict CWB (see Table 5). However, all other models utilizing stability (stability alone, joint “main effects” of GMA and stability, or interacting GMA and stability) showed no significant differences in predicted CWB between race categories. In our second dataset (74% White, 12% Black, 5% Asian, 5% Hispanic), we found significant observed differences in CWB, F(3,1177) = 6.99, p < .001, such that Asian respondents showed lower CWB than White respondents, t(968) = -2.55, p = .011, d = -.33, whereas Black respondents showed higher CWB than White respondents, t(1056) = 3.40, p < .001, d = .30. Using GMA alone to predict CWB resulted in a significant effect of race on predicted values of CWB, F(3,1177) = 21.84, p < .001. However, when using predicted CWB (via GMA), the Black-White effect size was exaggerated at d = .65 for predicted CWB, compared to d = .30 for observed CWB (see above). The Asian-White effect for predicted CWB was similar to the observed CWB finding at d = -.36. Utilizing stability as a lone predictor showed no significant race differences in predicted CWB. However, the effect of race category returned when considering joint prediction models. Using the “main effects” of GMA and stability resulted in a significant race effect, F(3,1177) = 5.49, p < .001, with significant and similar adverse effects for Black respondents, d = .31, and Hispanic respondents, d = .29. Nearly identical results were found when utilizing the interaction between GMA and Stability with effect sizes of .29 for Black respondents and .30 for Hispanic respondents.

  6. The only exception was that in Sample 2, Class 4 showed lower Agreeableness than Class 2.

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Correspondence to Alexandra M. Harris-Watson.

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We have no conflicts of interest to disclose. This article is based on the dissertation completed by Harris (2020). The work of Alexandra M. Harris-Watson was supported in part by the National Science Foundation Research Fellowship Program (DGE-1443117), and the work of Nathan T. Carter was supported in part by the National Science Foundation (SES1561070). Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the view of the National Science Foundation.

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Appendices

Appendix 1

CWB Subdimensions Predicted by Lower-order Traits

Below, we provide results for both CWB-I and CWB-O predicted by conscientiousness, agreeableness, and emotional stability. Table 13 shows results of moderated multiple regression models in Sample 1, and Table 14 shows corresponding results of simple slopes tests. Table 15 shows results of moderated multiple regression models in Sample 2, and Table 16 shows corresponding results of simple slopes tests. Consistent with other exploratory analyses described in the main manuscript, all p-values reported here are two-tailed. Further, JN alpha and p-values for simple slopes tests are Bonferroni corrected (Bauer & Curran, 2005).

Conscientiousness

In Sample 1, there was a significant interactive influence of conscientiousness and GMA on CWB-I (b = 0.07, p = 0.014) but not CWB-O. Conscientiousness accounted for 41.2% of the variability in the relationship between GMA and CWB-I (R2 = 0.058). The effect of GMA on CWB-I was significant outside the JN 95% CI [0.12, 13.34] such that the effect was negative at low levels of conscientiousness.

In Sample 2, conscientiousness and GMA showed a significant interactive effect on CWB-I (b = 0.15, p < 0.001) and CWB-O (b = 0.10, p < 0.001). Conscientiousness accounted for 38.0% of the variability in the relationship between GMA and CWB-I (R2 = 0.258) and 37.7% of the variability in the relationship between GMA and CWB-O (R2 = 0.307). The effect of GMA on CWB-I was significant outside the JN 95% CI [0.71, 1.74] and the effect of GMA on CWB-I was significant outside the JN 95% CI [0.52, 2.68]. Thus, the effect of GMA on both CWB-I and CWB-O was significant and negative at low levels of conscientiousness in Sample 2.

Agreeableness

In Sample 1, there was a significant interactive influence of agreeableness and GMA on CWB-O (b = 0.07, p = 0.008) but not on CWB-I. Agreeableness accounted for 97.4% of the variability in the relationship between GMA and CWB-O (R2 = 0.134). The effect of GMA on CWB-O was significant outside the JN 95% CI [-4.17, 0.59] such that the effect was positive at high levels of agreeableness.

In Sample 2, agreeableness and GMA showed a significant interactive effect on CWB-I (b = 0.11, p < 0.001) and CWB-O (b = 0.11, p < 0.001). Agreeableness accounted for 17.1% of the variability in the relationship between GMA and CWB-I (R2 = 0.135) and 25.7% of the variability in the relationship between GMA and CWB-O (R2 = 0.108). The effect of GMA on CWB-I was significant outside the JN 95% CI [1.21, 4.26] and the effect of GMA on CWB-O was significant outside the JN 95% CI [0.79, 4.18]. Thus, the effect of GMA on both CWB-I and CWB-O was negative at low levels of agreeableness. However, the effect of GMA on CWB-I was also negative at moderately high levels of agreeableness and non-significant beyond 1.21 SD above mean agreeableness.

Emotional Stability

In Sample 1, there was a significant interactive influence of emotional stability on CWB-I (b = 0.06, p = 0.003) and CWB-O (b = 0.07, p = 0.003). However, when quadratic controls were included in the model (Cortina, 1993), the interactive influence on CWB-I was no longer significant and, therefore, the JN 95% CI and corresponding simple slopes tests are not reported. Emotional stability accounted 95.7% of the variability in the relationship between GMA and CWB-O (R2 = 0.059). The effect of GMA on CWB-O was significant outside the JN 95% CI [-1.43, 1.78] such that the effect was negative at very low levels of stability and positive at very high levels of stability.

In Sample 2, emotional stability and GMA also showed a significant interactive effect on CWB-I (b = 0.15, p < 0.001) and CWB-O (b = 0.12, p < 0.001). Emotional stability accounted for 30.6% of the variability in the relationship between GMA and CWB-I (R2 = 0.151) and 33.1% of the variability in the relationship between GMA and CWB-O (R2 = 0.172). The effect of GMA on CWB-I was significant outside the JN 95% CI [0.89, 2.23] and the effect of GMA on CWB-O was significant outside the JN 95% CI [0.68, 3.00] such that the effect of GMA on both CWB-I and CWB-O was significant at low levels of emotional stability.

Summary

Overall, in Sample 1, results for CWB-I were consistent with the form hypothesized for conscientiousness and emotional stability. However, results for CWB-O were not consistent with the form hypothesized in Sample 1. Although results suggest a negative effect of GMA on CWB-O at very low emotional stability, results also show a positive effect of GMA on CWB-O at high levels of agreeableness and very high levels of emotional stability. Importantly, these differences in subdimension and trait-level results were not replicated in Sample 2. Rather, results in Sample 2 were generally consistent across all three traits and both CWB subdimensions. Thus, Sample 1 differences reported here should be interpreted with caution.

Table 13

Table 14

Table 15

Table 16

Table 13 Results of regression analyses for CWB-I and CWB-O predicted by lower-order traits and GMA in Sample 1
Table 14 Results of simple slope tests for the effect of GMA on CWB-I and CWB-O at low, average, and high levels of lower-order traits in Sample 1
Table 15 Results of regression analyses for CWB-I and CWB-O predicted by lower-order traits and GMA in Sample 2
Table 16 Results of simple slope tests for the effect of GMA on CWB-I and CWB-O at low, average, and high levels of lower-order traits in Sample 2

Appendix 2

Simulated Selection Analyses

To evaluate the practical impact of results, we conducted simulation analyses in which we compared CWB among “applicants” selected using a stability-GMA interaction model and model with only stability included as a predictor. We compared the interaction model to a stability-only model because stability-related traits (i.e., conscientiousness, agreeableness, and emotional stability) have been shown to negatively predict CWB and are used in selection systems. In contrast, prior research does not consistently support a GMA-CWB relationship, and we are not aware of any selection systems that use GMA to predict CWB.

First, we simulated applicant pools by randomly selecting people from our existing samples. We selected 1,000 samples of 100 people each from each of the samples, as well as from the combined samples. Next, we used coefficient estimates from the stability-only and interaction model to predict CWB and identified applicants to “screen out” (i.e., highest 10% and 20% in CWB). Notably, we cross-validated parameters by using coefficients estimated from each sample to predict CWB in both samples separately, as well as the combined samples. We then compared the observed average and maximum CWB scores of applicants that were identified by the interaction versus stability-only model. A model was considered to have “won” if observed CWB was more than 0.10 SD above that predicted by the other model. The number of “wins” for each model are shown in Tables 17 and 18 for average and maximum CWB, respectively. Results show that the interaction model was better than the stability-only model at predicting high CWB in 10 of 12 comparisons for average CWB and all (12 of 12) comparisons for maximum CWB. That is, the interaction model more accurately “screened out” applicants with the highest CWB in nearly all (22 of 24) comparisons.

Table 17

Table 18

Table 17 Comparison of interaction and stability-only model “wins” in simulated selection comparison for average CWB
Table 18 Comparison of interaction and stability-only model “wins” in simulated selection comparison for maximum CWB

Appendix 3

Latent Profile Analyses

As one reviewer noted, another potential way of examining the veracity of a moderation hypothesis is to estimate latent profiles among the variables of interest. Therefore, we estimated a latent profile model using the ‘tidyLPA’ package (Rosenberg et al., 2018) in R. We utilized the IRT-derived scores for each of the three lower-order stability traits (i.e., conscientiousness, agreeableness, and emotional stability), along with the two subdimensions of CWB (CWB-I and CWB-O). To rule out any effects driven by the lower-order plasticity traits (i.e., extraversion and openness), we also included IRT scores for these two FFM dimensions.

Considering the Bayesian Information Criterion (BIC; smallest value criteria), Entropy (highest value criteria), smallest class size (in proportions, all classes equal to or greater than 0.10), and overall interpretability, we concluded that in both samples a 4-class solution was best. Statistics for model evaluation are shown in Table 19. As shown in Figure 7, the class with the highest level of CWBs (i.e., Class 3 in Sample 1, and Class 2 in Sample 2) also showed the lowest levels of conscientiousness, agreeableness, and emotional stability,Footnote 6 as well as the lowest GMA compared to other classes. Thus, individuals with very high CWB (11% of Sample 1; and 19% of Sample 2) were also low in both stability and GMA. The class with the lowest level of CWB (i.e., Class 2 in Sample 1, and Class 1 in Sample 2) had high levels of stability but average levels of GMA. That is, GMA was not a distinguishing feature for those in classes with low CWB (20% in both samples), whereas these classes were high in stability. These results are fully consistent with our hypothesized interaction; individuals with low GMA and low stability have high CWB while individuals with high stability (but average GMA) have low CWB. Individuals with levels of CWB closer to average (i.e., the remaining classes: Classes 1 and 4 in Sample 1; Classes 3 and 4 in Sample 2) generally showed average levels of other traits. Notably, there were no clear systematic differences in openness and extraversion across classes and samples. Thus, we believe that LPA shows the same effects that are more explicitly tested in our moderated multiple regression analysis.

Table 19

Figure 7

Table 19 Fit statistics for latent profile model evaluations
Fig. 7
figure 7

Mean plots for each in latent profile class in Sample 1 (top) and Sample 2 (bottom)

Data Transparency Appendix

Sample 1 data reported in this manuscript have been previously published. Findings from the data collection have been reported in separate manuscripts. MS 1 (published) focuses on general mental ability (GMA), openness to experience, and creative achievement. MS 2 (published) focuses on GMA only and gender of participants. MS 2 (current manuscript) focuses on GMA, the Five Factor Model traits comprising meta-trait stability (conscientiousness, agreeableness, and emotional stability), and counterproductive work behavior. The table below displays which data variables appear in each study, as well as the current status of each study. Sample 2 data reported in this manuscript have not been previously published.

Table 20

Table 20 Table of manuscripts connected to data collection

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Harris-Watson, A.M., Miller, J.D. & Carter, N.T. Revisiting the Inhibitory Effect of General Mental Ability on Counterproductive Work Behavior: The Case for GMA-Personality Interaction. J Bus Psychol (2024). https://doi.org/10.1007/s10869-024-09948-5

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