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Political skill and emotional cue learning
Personality and Individual Differences 49 (2010) 396–401
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Political skill and emotional cue learning Tassilo Momm a, Gerhard Blickle a,*, Yongmei Liu b a b
University of Bonn, Germany College of Business, Illinois State University, 250 College of Business Building, Campus Box 5580, Normal, IL 61790-5580, USA
a r t i c l e
i n f o
Article history: Received 25 November 2009 Received in revised form 2 April 2010 Accepted 10 April 2010 Available online 6 May 2010
a b s t r a c t We seek to further validate the political skill inventory (PSI) by tapping into the core of political skill, the ability to quickly identify and learn about relevant social cues. In three quasi-experimental studies in which brief training on emotion recognition was given, politically skilled individuals consistently demonstrated greater improvement in emotion recognition accuracy after training. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Political skill Emotion recognition Training
1. Introduction Effective interpersonal interactions require interpersonal skills, among which is one’s political skill. Political skill refers to ‘‘the ability to effectively understand others at work and to use such knowledge to influence others to act in ways that enhance one’s personal and/or organizational objectives” (Ferris et al., 2005). Key to political skill is one’s ability to develop accurate understanding of, and react quickly to, social cues germane to adaptation to the immediate situation, without which social adaptation, interpersonal influence, and effective networking, all considered essential components of political skill, become less likely. The construct of political skill has generated a great deal of research interest, and its measure, political skill inventory (PSI; Ferris et al., 2005), was extensively validated. However, a key argument, that those politically skilled are more effective in learning relevant cues in social situations, is yet to be explicitly tested. The current research is the first study to investigate this research question. Most past research validating PSI has been correlational in nature. Recently, Borsboom, Mellenbergh, and Van Heerden (2004) critiqued the over reliance on the correlational approach for construct validation. They pointed to the necessity for validation tests to be directed at the processes that convey the effect of the measured attribute (i.e., causality). Responding to their call, we use a quasi-experimental design to examine the relationship between
* Corresponding author. Address: Arbeits-, Organisations- und Wirtschaftspsychologie, Institut fuer Psychologie, Universitaet Bonn, Kaiser-Karl-Ring 9, 53111 Bonn, Germany. Tel.: +49 228 734375; fax: +49 228 734670. E-mail address: [email protected] (G. Blickle). 0191-8869/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2010.04.006
political skill and an outcome variable that it theoretically predicts (i.e., emotional cue learning). 2. Theoretical background and hypothesis development To be politically skilled, one has to be sensitive to social cues (Ferris et al., 2005). The reading, processing, and making sense of social cues serve as the foundation for those politically skilled to understand situational demands, based upon which they modify and adapt their interpersonal influence strategies. Being sensitive to social cues means both the ability to accurately label social cues, and equally importantly, the ability to quickly gear their attention toward important cues, and to filter out less useful cues. Furthermore, once cues are processed, politically skilled individuals also should be quick at gauging their utilization of such cues based on feedback indicating accuracy of their social understanding. Given the dynamic nature of social interactions and the amount of cues available for processing in a given encounter, such adaptation in cue utilization has to occur relatively rapidly. An important type of social cue prevalent in social interactions is emotional expressive cues. Emotional expressions provide useful information for the perceiver to understand others’ feeling states, attitudes, and intentions (Van Kleef, 2009). The ability to recognize emotions differs across individuals and situations (e.g., O’Sullivan, 1982; Petrides & Furnham, 2003; Prkachin, Casey, & Prkachin, 2009; Rosenthal, Hall, DiMatteo, Rogers, & Archer, 1979; Suzuki, Hoshino, & Shigemasu, 2010). Better emotion recognition ability is associated with greater social adjustment, more effective interpersonal coordination, and improved workplace effectiveness (e.g., Elfenbein, Foo, White, Tan, & Aik, 2007; Nowicki & Duke, 1994).
T. Momm et al. / Personality and Individual Differences 49 (2010) 396–401
Among all social cues available, emotional expressive cues may not be the only ones that politically skilled individuals rely on for social understanding. In fact, because emotional expressions are oftentimes guided by display norms (Hochschild, 1983) and are more controlled and deliberate than other nonverbal cues (Ekman & Friesen, 1969), emotional expressive cues oftentimes are not valid indicators of one’s internal states. Given politically skilled individuals’ sensitivity to cue utilization, they should rely on a combination of various types of social cues rather than on emotional cues alone, suggesting that there might not be a significant main effect of political skill on emotion recognition. However, politically skilled individuals should be able to quickly gear their attention toward and learn about emotional expressive cues when situations demand. Thus, in the current research we investigate whether politically skilled individuals are quicker at improving their emotion recognition accuracy after a training session during which they are given performance feedback. During the training, no instructions were given regarding what particular set of cues to use to identify emotions. Rather, participants were given feedback regarding whether they accurately labelled a face or voice tone as an emotional cue. Thus, the success of training depends on whether the participants can learn to filter in and/or filter out cues that would improve their performance. We predict that individuals high (as opposed to low) in political skill will be able to learn more quickly about which emotional expressive cues to focus on, resulting in greater improvement in emotion recognition after training. Hypothesis: Individuals high in political skill will show greater improvement on emotion recognition accuracy after a brief training session than those low in political skill.
397
sad, angry, and fearful) and 8 voice items of children of the same emotions. The voices and the faces were drawn from pictures and voices of children of the diagnostic analysis of nonverbal accuracy (DANVA2, Nowicki, 2009). Face and voice items were presented in blocs. The items were presented to the participants and they were told to select the appropriate emotion. Participants in the warming-up stage received no feedback as to whether their judgement was correct or not. After the warming-up stage the pre-test was conducted. The pre-test consisted of the DANVA2 version for adults (Nowicki, 2009). It includes an adult faces and an adult paralanguage scale. Each scale contains 24 emotional expressions, using photos of faces and audiotapes of voices, respectively. In each subtest, there are 6 items each for the emotion of anger, fear, happiness, and sadness. The DANVA score represents the degree to which participants’ responses diverge from the correct emotions. The 24-item scales range between 0 and 24 points, with higher score indicating lower accuracy. During the training stage, training group participants received feedback on 12 facial expression items. After each emotion judgment, a feedback screen appeared with the picture and the correct label of the emotion displayed. The photos used in the training stage were drawn from the children’s version of DANVA2 (Nowicki, 2009). In the post-test stage, the participants were presented again with the DANVA2 adult photos and voices as in the pre-test stage. The sequences of the two DANVA2 subtests were randomized. When the participants finished the post-test DANVA2 they received feedback on their DANVA2 scores in the pre-test and the post-test. 3.2. Sample and procedure
We present three studies. In Study 1, we explored whether politically skilled individuals will show greater improvement than those less politically skilled in emotion recognition accuracy after training on identifying facial expressions of emotion. In Study 2, we sought replication of the effect of political skill on emotional cue learning through training in vocal expressions of emotion. In Study 3, we constructively replicated Studies 1 and 2 by additionally controlling for the effects of two theoretically relevant personality variables (i.e., extraversion, self-monitoring), as well as data sources. 3. Study 1 3.1. Method Study 1 was conducted to demonstrate that employees’ political skill predicts the emotional cue learning via faces.1 Prior to the training, we measured individual differences on political skill. The design was based on a single treatment group and compared two different dependent variables. One variable is hypothesized to be affected by the training which is called the experimental variable, and the other variable is hypothesized to be unaffected by the training which is called the control variable; Hawthorne and testing effects can be ruled out if the training leads to changes in the experimental but not in the control variable (Frese, Beimel, & Schoenborn, 2003). The study started with a warming-up stage consisting of 8 pictures of children with facial expressions of 4 emotions (happy, 1 Before Study 1, we conducted a pilot study to demonstrate that the feedback-onfaces training improved the emotion recognition via faces but not on the emotion recognition via voices, thus establishing the independence of cues from face versus voice as means for emotion recognition, a precondition for the use of the nonequivalent dependent variable design (Cook & Campbell, 1979, p. 118). Detailed information on the pilot study is available from the corresponding author.
Participants were 218 German employees (67% response rate) from a broad variety of jobs. The participants were randomly selected for either Study 1 or Study 2. One-hundred and seven subjects participated in Study 1. The sample included 52 males and 55 females with a mean age of 35.7, and an average work experience of 12.4 years. The participants reported an average of 38 working hours per week. All participants received a personal code to login a website. First the participants completed a questionnaire on political skill at work. They were then asked to login after three days using the same code to participate in an emotional skill training. 3.3. Measures Political skill. The German translation (Blickle et al., 2008) of the 18-item political skill inventory (PSI) (Ferris et al., 2005) was used to assess political skill. A 7-point Likert-type scale was used. A sample item is ‘‘I understand people very well.” DANVA2. The DANVA children items were used for feedback training on emotion recognition and the DANVA adult items were used as measures of emotion recognition via faces and voices. 3.4. Data analysis and results The data were analyzed with univariate analyses of covariance (ANCOVA). A hierarchical mode of analysis (Cohen, Cohen, West, & Aiken, 2003) was used. The random factor variable (pre–posttest sequences) was entered first, followed by the pre-test variable, and lastly, the political skill variable. If political skill predicted the dependent variable after having controlled for the random factor variable and the pre-test score, political skill predicted improvement of emotion recognition accuracy after training.
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T. Momm et al. / Personality and Individual Differences 49 (2010) 396–401
Table 1 reports the means, standard deviations, correlations, and coefficient a reliability estimates of the variables in Study 1. As in previous studies (e.g., Nowicki, 2009) Cronbach Alphas of the DANVA scales were low, however, the test–retest reliabilities were good (.66 6 r 6 .69). As expected, the mean number of errors in the faces scale dropped significantly from the pre-test (M = 5.36) to the post-test (M = 4.38) (t = 5.43, df = 106, p < .001), while the number of errors in the voices scale did not change significantly (M = 7.64 versus 7.44, t = 1.01, df = 106, p < .30). Thus, the internal referencing strategy was successful because the feedback-on-emotion-from-face training demonstrated an impact on the experimental variable (emotion recognition via faces) but not on the control variable (emotion recognition via voice). The analyses showed that political skill predicted change in the emotion recognition via faces (Table 2) but not via voices (Table 3). The t-value of political skill in Table 2 is negative, indicating the higher the scores on political skill the more effective individuals were able to learn from feedback-on-emotion-via-face training. Thus, these findings support the prediction that the political skill predicts emotional cues learning, indicated by objective performance improvement in emotion recognition. 4. Study 2 This study was conducted to replicate the effect of political skill on emotional cue learning using training on emotion recognition via voice. This study also used the non-equivalent dependent variable design. In this study, emotion recognition via voices was the experimental variable, and emotion recognition via faces was the control variable.
Table 3 Analysis of covariance of emotion recognition via voices in the post-test (Study 1). Predictors
Dependent variable: emotion recognition via Voices in the post-test F
Step 1 Step 2 Step 3
Randomized grouping factor Emotion recognition via voices in the pre-test Political skill
t
df
DR 2
R2
9.22**
3 1
.00 .47
.00 .47
.14
1
.03
.50
.14
*p < .05. ** p < .01.
with a mean age of 35.1, and an average work experience of 11.7 years. The participants reported an average of 38.8 working hours per week. Study 2 used the same procedure for data collection as was used in Study 1, albeit the training materials were from the children DANVA Paralanguage scale. All participants were presented 12 vocal expression items. After each item, a feedback screen appeared with the correct label of the emotion expressed. After receiving the feedback on a specific voice item participants could, as often as they liked, repeat the same voice item before going to the next. In the post-test stage, the participants were presented the DANVA2 adult tests as they had been in the pre-test stage. The sequences of the two DANVA subtests were randomized. When the participants finished the post-test DANVA they received a feedback on their DANVA scores in the pre-test and the post-test. The same measures as in Study 1 were used.
4.2. Data analysis and results 4.1. Sample and procedure Participants were recruited via the same procedure that was used for Study 1. The sample included 59 males and 52 females
Table 1 Descriptive statistics, correlations, and reliabilities for variables (Study 1). Variables
M
SD
1
2
1. DANVA facial – pre-test 2. DANVA facial – posttest 3. DANVA voices – pretest 4. DANVA voices – posttest 5. Political skill
5.36 4.38
2.42 2.37
(.39) .69**
3
(.47)
7.64
2.54
.26*
.27**
7.44
2.54
*
.20
4.98
.64
.13
.21
*
.02
4
5
(.40) .66**
(.36)
.02
.03
(.87)
Note: N = 107; Cronbach Alphas in the diagonal. p < .05. p < .01.
*
Same data analysis procedure as used in Study 1 was used. Table 4 reports the means, standard deviations, correlations, and coefficient a reliability estimates of the variables. As expected, the mean number of errors in the voices scale dropped significantly from the pre-test (M = 7.34) to the post-test (M = 6.89) (t = 2.14, df = 111, p < .05), while the number of errors in the faces scale did not change significantly (M = 4.73 versus 4.81, t = .34, df = 111, p < .70). Thus, the internal referencing strategy was again successful. The analyses of covariance showed that political skill predicted change in emotion recognition via voices (Table 5), but no change in emotion recognition via faces (Table 6). The t-value of political skill in Table 5 is negative, indicating the higher the scores on political skill, the more effective individuals were in learning from feedback-on-emotion-via-voices training. Thus, the findings replicate the results from Study 1 to indicate that the political skill predicts effectiveness in emotional cue learning.
**
Table 2 Analysis of covariance of emotion recognition via faces in the post-test (Study 1). Predictors
Dependent variable: emotion recognition via Faces in the post-test F
Step 1 Step 2 Step 3 *
p < .05. ** p < .01.
Randomized grouping factor Emotion recognition via faces in the pre-test Political skill
Table 4 Descriptive statistics, correlations, and reliabilities for variables (Study 2).
t
df
.12 9.85** 1.84*
DR 2
R2
3 1
.00 .48
.00 .48
1
.06
.54
Variables
M
SD
1
1. DANVA facial – pre-test 2. DANVA facial – posttest 3. DANVA voices – pretest 4. DANVA voices – posttest 5. Political skill
4.73 4.81
2.24 2.34
(.41) .53**
7.34
2.61
*
.19
3
4
5
(.43) .21*
6.89
2.94
.15
.32
4.91
.78
.27**
.02
Note: N = 111; Cronbach Alphas in the diagonal. p < .05. ** p < .01. *
2
**
(.41) .70**
(.53)
.23*
.01
(.89)
T. Momm et al. / Personality and Individual Differences 49 (2010) 396–401 Table 5 Analysis of covariance of emotion recognition via voices in the post-test (Study 2). Predictors
Dependent variable: emotion recognition via Voices in the post-test (DANVA adults) F
Step 1 Step 2 Step 3 * **
Randomized grouping factor Emotion recognition via voices in the pre-test Political skill
t
df
DR 2
R2
10.7**
3 1
.00 .54
.00 .54
1
.03
.57
.19
1.93*
p < .05. p < .01.
399
itively associated with emotional cue learning even after controlling for peer ratings of extraversion and self-monitoring. 5.1. Method Participants provided an online self-rating of political skill, extraversion, and self-monitoring in random order. They also sent a link to a peer at work who was asked to provide another rating of the above variables. Self- and other-ratings were linked by a randomly generated common code. One week after the peer ratings were provided, the participants received an online link for the feedback training on emotion recognition via faces. 5.2. Sample and procedure
Table 6 Analysis of covariance of emotion recognition via faces in the post-test (Study 2). Predictors
F Step 1 Step 2 Step 3
Randomized grouping factor Emotion recognition via faces in the pre-test Political skill
t
df
DR 2
R2
Participants were 146 (41% response rate) German employeedyads from a broad variety of jobs. The sample included 62 males and 84 females with a mean age of 37.1 and an average work experience of 14.5 years. The participants reported an average of 38 working hours per week.
6.49**
3 1
.00 .31
.00 .31
5.3. Measures
1.32
1
.00
.31
Dependent variable: emotion recognition via Faces in the post-test (DANVA adults)
.17
*
p < .05. ** p < .01.
5. Study 3 The purpose of this study was to constructively replicate the effect of political skill on emotional cue learning using training on emotion recognition via faces. We did so by controlling two theoretically relevant personality variables, extraversion and self-monitoring, to further exclude the possibility that the observed significant effect of political skill on emotional cue learning was due to other sources of influence. We also controlled for data sources from which personality variables and political skill were assessed. We focused on the face training in this study because Studies 1 and 2 showed that emotions are recognizable more easily via faces than via voices. Among personality variables, the highest correlation of self-reports of political skill is with self-reports of extraversion (r .50, Ferris et al., 2008). To demonstrate the specificity of the effect of political skill on emotional cue learning, extraversion was controlled. Rosenthal et al. (1979) had found that extraversion did not correlate with emotion recognition via faces and pictures. Thus, we expected that self-reports of political skill still predict emotional cue learning via faces after controlling for self-report extraversion. Self-monitoring is another personality variable that has conceptual overlap with political skill (Ferris et al., 2005, 2008). Previous research has shown that self-monitoring positively associates with emotion recognition (Gangestad & Snyder, 2000). However, the core of self-monitoring relates to status-oriented impression management motives (Gangestad & Snyder, 2000, p. 547). Because the focus of PSI is on social skills and not on motives, the emotional cue learning should be more strongly associated with political skill than with self-monitoring. Thus, we expect that self-reports of political skill still predict emotional cue learning via faces after controlling for self-report self-monitoring. In Studies 1 and 2 we had solely relied on self-ratings of political skill. Whereas self-ratings can incorporate less observable information such as motives, intentions, feelings, and past behavior, other-ratings are tied to observation of targets’ behaviors or performance resulting from these behaviors (Hogan, 1991). We therefore expected that peer ratings of political skill should be pos-
Political skill. Self-ratings of political skill were measured as in Study 1. For the other-ratings, all items were converted into the third person perspective (e.g., ‘‘This person always seems to understand people well”). Extraversion. Extraversion was measured with the German version of the NEO-FFI (Borkenau & Ostendorf, 1993). For the otherratings, all items were converted into the third person perspective. Self-Monitoring. A German adaptation of the self-monitoring scale by Nowack and Kammer (1987) was used. For the other-ratings, all items were converted into the third person perspective. DANVA2. Same measures were used as in Study 1. 5.4. Data analysis and results The means, correlations, and reliabilities of the variables are shown in Table 7. Self-ratings of political skill, and other-ratings of extraversion and political skill correlated negatively with DANVA faces post-training scores. We analyzed the data with a hierarchical regression analysis (Cohen et al., 2003). The dependent variables were the DANVA faces post-training scores. First, we entered DANVA faces pre-training scores. Second, we entered self-ratings of political skill. In replication of Study 1 self-ratings of political skill predicted emotional cue learning. The higher self-ratings of political skill the lower assessment errors in the DANVA faces post-training (b = .11, p < .05) (cf. Table 8). Third, we additionally controlled for self-ratings of extraversion and self-monitoring. Neither variable predicts emotional cue learning; in addition, the effect of political skill remained significant (b = .13, p < .05). Finally, we entered the other-ratings of extraversion, self-monitoring, and political skill. The significant effect of self-rating of political skill remained (b = .10, p < .05). The strongest predictor of emotional cue learning from the faces training was the other-rating of political skill (b = .24, p < .01), being nearly twice as strong as other-ratings of self-monitoring (b = .13, p < .01). 5.5. Discussion The findings of Study 3 replicated findings form Study 1: Selfratings of political skill predicted success in emotional cue learning via faces. In addition, although self-ratings of political skill associate with self-ratings of extraversion and have functional communalities with self-ratings of self-monitoring in emotional
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Table 7 Descriptive statistics, correlations, and reliabilities for variables (Study 3). Variables
M
SD
1
2
3
4
5
6
7
8
1. 2. 3. 4. 5. 6. 7. 8.
5.76 4.08 3.53 3.49 .38 .39 4.93 5.01
2.75 2.68 .58 .51 .15 .17 .73 .75
(.55) .81** .16 .24** .05 .01 .05 .07
(.62) .13 .24** .10 .13 .15 .29**
(.81) .45** .32** .16 .45** .08
(.78) .19* .25** .23** .41**
(.59) .29** .20* .16*
(.70) .04 .16
(.88) .16*
(.88)
DANVA facial – pre-test DANVA facial – post-test Extraversion – self-rating Extraversion – other-rating Self-monitoring – self-rating Self-monitoring – other-rating Political skill – self-rating Political skill – other-rating
Note: N = 146; Cronbach Alphas in the diagonal. * p < .05. ** p < .01. p < .05 (one-tailed).
Table 8 Hierarchical regression of emotion recognition via voices in the post-test (Study 3). Predictors
Criterion: emotion recognition via Faces in the posttest Std. betas
DR 2
**
Step 1
Emotion recognition via faces in the pre-test
.81
Step 2
Emotion recognition via faces in the pre-test Political skill – self-rating
.80** .11*
Step 3
Emotion recognition via faces in the pre-test Extraversion – self-rating Self-monitoring – self-rating Political skill – self-rating
.81** .07 .06 .13*
Step 4
Emotion recognition via faces in the pre-test Extraversion – self-rating Self-monitoring – self-rating Political skill – self-rating Extraversion – other-rating Self-monitoring – other-rating Political Skill – other-rating
.82** .04 .00 .10* .10 .13** .24**
.65**
.01*
.00
.06** * **
p 6 .05. p < .01.
cue recognition, political skill remained a significant predictor of emotional cue learning after controlling for these variables, underscoring the specificity of the effects of political skill. Finally, peer ratings of political skill, which result from targets’ past observable behaviors and social performance, predicted targets’ emotional cue learning even more strongly. Note that in Study 1 political skill was unrelated to the DANVA pre- and post-test scores (Table 2), and in Study 2 political skill positively correlated with the DANVA pre-test scores but not with the post-test scores (Table 5). Further, in Study 3 both self-ratings and peer ratings of political skill correlated negatively with DANVA post-test scores, but not with the pre-test scores. The inconsistent pattern of correlations between political skill and DANVA scores provide partial support to our earlier argument that political skill may not have a significant main effect on emotion recognition, a possibility that can be further explored in future research.
tional research (e.g., Blickle et al., 2008; Ferris et al., 2005). However, what lies at the core of political skill (i.e., the ability to pay attention to and understand social cues) has remained as mere conceptual speculation. The current research adds to the existing evidence of construct validity of the PSI by asking this essential question of whether politically skilled individuals are indeed better at grasping relevant social cues than those less politically skilled. This is a critical piece of information because it provides crucial information pertinent to construct validity of the PSI, which is the ability of a construct to predict what it is supposed to predict (i.e., causality) (cf., Borsboom et al., 2004). To answer this question, we used a quasi-experimental design that is well suited for the improved assessment of causality. The results of the present research indicate that politically skilled individuals, although not necessarily better than others in emotion recognition via faces or voices in pre-tests, demonstrated greater improvement on emotion recognition accuracy after brief training sessions, in which they were given feedback on the correct labeling of emotional expressions. Thus, the current research provided direct evidence supporting the claim that politically skilled individuals are more socially perceptive and adaptive. One limitation of the current research is that it used only one training method. A comparison of different training methods, such as micro expression training tool (METT), or subtle expression training tool (SETT) (cf., Ekman, 2003), will help to further assess the interaction effect between the specific training and the dispositional influence (i.e., political skill). In sum, the current research looked into the attending to and learning of emotional expressive cues of individuals with various degrees of political skill. It was concluded that politically skilled individuals were quicker at learning about these cues than others. The findings provided construct validity support for the existing conceptualization of political skill. We hope future research will further refine the usefulness of the political skill construct in personal and social situations. Acknowledgement The authors thank Rainer Banse, Gerald Ferris, and Steve Nowicki for their insightful comments on an earlier version of this manuscript.
6. General discussion
References
In this research, we described three studies intended to further validate PSI. We used a quasi-experimental design to investigate a key element of political skill, the ability to quickly and effectively learn about social cues. The validity of the PSI has been extensively demonstrated through establishing its nomological net via correla-
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Political skill and emotional cue learning Tassilo Momm a, Gerhard Blickle a,*, Yongmei Liu b a b
University of Bonn, Germany College of Business, Illinois State University, 250 College of Business Building, Campus Box 5580, Normal, IL 61790-5580, USA
a r t i c l e
i n f o
Article history: Received 25 November 2009 Received in revised form 2 April 2010 Accepted 10 April 2010 Available online 6 May 2010
a b s t r a c t We seek to further validate the political skill inventory (PSI) by tapping into the core of political skill, the ability to quickly identify and learn about relevant social cues. In three quasi-experimental studies in which brief training on emotion recognition was given, politically skilled individuals consistently demonstrated greater improvement in emotion recognition accuracy after training. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Political skill Emotion recognition Training
1. Introduction Effective interpersonal interactions require interpersonal skills, among which is one’s political skill. Political skill refers to ‘‘the ability to effectively understand others at work and to use such knowledge to influence others to act in ways that enhance one’s personal and/or organizational objectives” (Ferris et al., 2005). Key to political skill is one’s ability to develop accurate understanding of, and react quickly to, social cues germane to adaptation to the immediate situation, without which social adaptation, interpersonal influence, and effective networking, all considered essential components of political skill, become less likely. The construct of political skill has generated a great deal of research interest, and its measure, political skill inventory (PSI; Ferris et al., 2005), was extensively validated. However, a key argument, that those politically skilled are more effective in learning relevant cues in social situations, is yet to be explicitly tested. The current research is the first study to investigate this research question. Most past research validating PSI has been correlational in nature. Recently, Borsboom, Mellenbergh, and Van Heerden (2004) critiqued the over reliance on the correlational approach for construct validation. They pointed to the necessity for validation tests to be directed at the processes that convey the effect of the measured attribute (i.e., causality). Responding to their call, we use a quasi-experimental design to examine the relationship between
* Corresponding author. Address: Arbeits-, Organisations- und Wirtschaftspsychologie, Institut fuer Psychologie, Universitaet Bonn, Kaiser-Karl-Ring 9, 53111 Bonn, Germany. Tel.: +49 228 734375; fax: +49 228 734670. E-mail address: [email protected] (G. Blickle). 0191-8869/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2010.04.006
political skill and an outcome variable that it theoretically predicts (i.e., emotional cue learning). 2. Theoretical background and hypothesis development To be politically skilled, one has to be sensitive to social cues (Ferris et al., 2005). The reading, processing, and making sense of social cues serve as the foundation for those politically skilled to understand situational demands, based upon which they modify and adapt their interpersonal influence strategies. Being sensitive to social cues means both the ability to accurately label social cues, and equally importantly, the ability to quickly gear their attention toward important cues, and to filter out less useful cues. Furthermore, once cues are processed, politically skilled individuals also should be quick at gauging their utilization of such cues based on feedback indicating accuracy of their social understanding. Given the dynamic nature of social interactions and the amount of cues available for processing in a given encounter, such adaptation in cue utilization has to occur relatively rapidly. An important type of social cue prevalent in social interactions is emotional expressive cues. Emotional expressions provide useful information for the perceiver to understand others’ feeling states, attitudes, and intentions (Van Kleef, 2009). The ability to recognize emotions differs across individuals and situations (e.g., O’Sullivan, 1982; Petrides & Furnham, 2003; Prkachin, Casey, & Prkachin, 2009; Rosenthal, Hall, DiMatteo, Rogers, & Archer, 1979; Suzuki, Hoshino, & Shigemasu, 2010). Better emotion recognition ability is associated with greater social adjustment, more effective interpersonal coordination, and improved workplace effectiveness (e.g., Elfenbein, Foo, White, Tan, & Aik, 2007; Nowicki & Duke, 1994).
T. Momm et al. / Personality and Individual Differences 49 (2010) 396–401
Among all social cues available, emotional expressive cues may not be the only ones that politically skilled individuals rely on for social understanding. In fact, because emotional expressions are oftentimes guided by display norms (Hochschild, 1983) and are more controlled and deliberate than other nonverbal cues (Ekman & Friesen, 1969), emotional expressive cues oftentimes are not valid indicators of one’s internal states. Given politically skilled individuals’ sensitivity to cue utilization, they should rely on a combination of various types of social cues rather than on emotional cues alone, suggesting that there might not be a significant main effect of political skill on emotion recognition. However, politically skilled individuals should be able to quickly gear their attention toward and learn about emotional expressive cues when situations demand. Thus, in the current research we investigate whether politically skilled individuals are quicker at improving their emotion recognition accuracy after a training session during which they are given performance feedback. During the training, no instructions were given regarding what particular set of cues to use to identify emotions. Rather, participants were given feedback regarding whether they accurately labelled a face or voice tone as an emotional cue. Thus, the success of training depends on whether the participants can learn to filter in and/or filter out cues that would improve their performance. We predict that individuals high (as opposed to low) in political skill will be able to learn more quickly about which emotional expressive cues to focus on, resulting in greater improvement in emotion recognition after training. Hypothesis: Individuals high in political skill will show greater improvement on emotion recognition accuracy after a brief training session than those low in political skill.
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sad, angry, and fearful) and 8 voice items of children of the same emotions. The voices and the faces were drawn from pictures and voices of children of the diagnostic analysis of nonverbal accuracy (DANVA2, Nowicki, 2009). Face and voice items were presented in blocs. The items were presented to the participants and they were told to select the appropriate emotion. Participants in the warming-up stage received no feedback as to whether their judgement was correct or not. After the warming-up stage the pre-test was conducted. The pre-test consisted of the DANVA2 version for adults (Nowicki, 2009). It includes an adult faces and an adult paralanguage scale. Each scale contains 24 emotional expressions, using photos of faces and audiotapes of voices, respectively. In each subtest, there are 6 items each for the emotion of anger, fear, happiness, and sadness. The DANVA score represents the degree to which participants’ responses diverge from the correct emotions. The 24-item scales range between 0 and 24 points, with higher score indicating lower accuracy. During the training stage, training group participants received feedback on 12 facial expression items. After each emotion judgment, a feedback screen appeared with the picture and the correct label of the emotion displayed. The photos used in the training stage were drawn from the children’s version of DANVA2 (Nowicki, 2009). In the post-test stage, the participants were presented again with the DANVA2 adult photos and voices as in the pre-test stage. The sequences of the two DANVA2 subtests were randomized. When the participants finished the post-test DANVA2 they received feedback on their DANVA2 scores in the pre-test and the post-test. 3.2. Sample and procedure
We present three studies. In Study 1, we explored whether politically skilled individuals will show greater improvement than those less politically skilled in emotion recognition accuracy after training on identifying facial expressions of emotion. In Study 2, we sought replication of the effect of political skill on emotional cue learning through training in vocal expressions of emotion. In Study 3, we constructively replicated Studies 1 and 2 by additionally controlling for the effects of two theoretically relevant personality variables (i.e., extraversion, self-monitoring), as well as data sources. 3. Study 1 3.1. Method Study 1 was conducted to demonstrate that employees’ political skill predicts the emotional cue learning via faces.1 Prior to the training, we measured individual differences on political skill. The design was based on a single treatment group and compared two different dependent variables. One variable is hypothesized to be affected by the training which is called the experimental variable, and the other variable is hypothesized to be unaffected by the training which is called the control variable; Hawthorne and testing effects can be ruled out if the training leads to changes in the experimental but not in the control variable (Frese, Beimel, & Schoenborn, 2003). The study started with a warming-up stage consisting of 8 pictures of children with facial expressions of 4 emotions (happy, 1 Before Study 1, we conducted a pilot study to demonstrate that the feedback-onfaces training improved the emotion recognition via faces but not on the emotion recognition via voices, thus establishing the independence of cues from face versus voice as means for emotion recognition, a precondition for the use of the nonequivalent dependent variable design (Cook & Campbell, 1979, p. 118). Detailed information on the pilot study is available from the corresponding author.
Participants were 218 German employees (67% response rate) from a broad variety of jobs. The participants were randomly selected for either Study 1 or Study 2. One-hundred and seven subjects participated in Study 1. The sample included 52 males and 55 females with a mean age of 35.7, and an average work experience of 12.4 years. The participants reported an average of 38 working hours per week. All participants received a personal code to login a website. First the participants completed a questionnaire on political skill at work. They were then asked to login after three days using the same code to participate in an emotional skill training. 3.3. Measures Political skill. The German translation (Blickle et al., 2008) of the 18-item political skill inventory (PSI) (Ferris et al., 2005) was used to assess political skill. A 7-point Likert-type scale was used. A sample item is ‘‘I understand people very well.” DANVA2. The DANVA children items were used for feedback training on emotion recognition and the DANVA adult items were used as measures of emotion recognition via faces and voices. 3.4. Data analysis and results The data were analyzed with univariate analyses of covariance (ANCOVA). A hierarchical mode of analysis (Cohen, Cohen, West, & Aiken, 2003) was used. The random factor variable (pre–posttest sequences) was entered first, followed by the pre-test variable, and lastly, the political skill variable. If political skill predicted the dependent variable after having controlled for the random factor variable and the pre-test score, political skill predicted improvement of emotion recognition accuracy after training.
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Table 1 reports the means, standard deviations, correlations, and coefficient a reliability estimates of the variables in Study 1. As in previous studies (e.g., Nowicki, 2009) Cronbach Alphas of the DANVA scales were low, however, the test–retest reliabilities were good (.66 6 r 6 .69). As expected, the mean number of errors in the faces scale dropped significantly from the pre-test (M = 5.36) to the post-test (M = 4.38) (t = 5.43, df = 106, p < .001), while the number of errors in the voices scale did not change significantly (M = 7.64 versus 7.44, t = 1.01, df = 106, p < .30). Thus, the internal referencing strategy was successful because the feedback-on-emotion-from-face training demonstrated an impact on the experimental variable (emotion recognition via faces) but not on the control variable (emotion recognition via voice). The analyses showed that political skill predicted change in the emotion recognition via faces (Table 2) but not via voices (Table 3). The t-value of political skill in Table 2 is negative, indicating the higher the scores on political skill the more effective individuals were able to learn from feedback-on-emotion-via-face training. Thus, these findings support the prediction that the political skill predicts emotional cues learning, indicated by objective performance improvement in emotion recognition. 4. Study 2 This study was conducted to replicate the effect of political skill on emotional cue learning using training on emotion recognition via voice. This study also used the non-equivalent dependent variable design. In this study, emotion recognition via voices was the experimental variable, and emotion recognition via faces was the control variable.
Table 3 Analysis of covariance of emotion recognition via voices in the post-test (Study 1). Predictors
Dependent variable: emotion recognition via Voices in the post-test F
Step 1 Step 2 Step 3
Randomized grouping factor Emotion recognition via voices in the pre-test Political skill
t
df
DR 2
R2
9.22**
3 1
.00 .47
.00 .47
.14
1
.03
.50
.14
*p < .05. ** p < .01.
with a mean age of 35.1, and an average work experience of 11.7 years. The participants reported an average of 38.8 working hours per week. Study 2 used the same procedure for data collection as was used in Study 1, albeit the training materials were from the children DANVA Paralanguage scale. All participants were presented 12 vocal expression items. After each item, a feedback screen appeared with the correct label of the emotion expressed. After receiving the feedback on a specific voice item participants could, as often as they liked, repeat the same voice item before going to the next. In the post-test stage, the participants were presented the DANVA2 adult tests as they had been in the pre-test stage. The sequences of the two DANVA subtests were randomized. When the participants finished the post-test DANVA they received a feedback on their DANVA scores in the pre-test and the post-test. The same measures as in Study 1 were used.
4.2. Data analysis and results 4.1. Sample and procedure Participants were recruited via the same procedure that was used for Study 1. The sample included 59 males and 52 females
Table 1 Descriptive statistics, correlations, and reliabilities for variables (Study 1). Variables
M
SD
1
2
1. DANVA facial – pre-test 2. DANVA facial – posttest 3. DANVA voices – pretest 4. DANVA voices – posttest 5. Political skill
5.36 4.38
2.42 2.37
(.39) .69**
3
(.47)
7.64
2.54
.26*
.27**
7.44
2.54
*
.20
4.98
.64
.13
.21
*
.02
4
5
(.40) .66**
(.36)
.02
.03
(.87)
Note: N = 107; Cronbach Alphas in the diagonal. p < .05. p < .01.
*
Same data analysis procedure as used in Study 1 was used. Table 4 reports the means, standard deviations, correlations, and coefficient a reliability estimates of the variables. As expected, the mean number of errors in the voices scale dropped significantly from the pre-test (M = 7.34) to the post-test (M = 6.89) (t = 2.14, df = 111, p < .05), while the number of errors in the faces scale did not change significantly (M = 4.73 versus 4.81, t = .34, df = 111, p < .70). Thus, the internal referencing strategy was again successful. The analyses of covariance showed that political skill predicted change in emotion recognition via voices (Table 5), but no change in emotion recognition via faces (Table 6). The t-value of political skill in Table 5 is negative, indicating the higher the scores on political skill, the more effective individuals were in learning from feedback-on-emotion-via-voices training. Thus, the findings replicate the results from Study 1 to indicate that the political skill predicts effectiveness in emotional cue learning.
**
Table 2 Analysis of covariance of emotion recognition via faces in the post-test (Study 1). Predictors
Dependent variable: emotion recognition via Faces in the post-test F
Step 1 Step 2 Step 3 *
p < .05. ** p < .01.
Randomized grouping factor Emotion recognition via faces in the pre-test Political skill
Table 4 Descriptive statistics, correlations, and reliabilities for variables (Study 2).
t
df
.12 9.85** 1.84*
DR 2
R2
3 1
.00 .48
.00 .48
1
.06
.54
Variables
M
SD
1
1. DANVA facial – pre-test 2. DANVA facial – posttest 3. DANVA voices – pretest 4. DANVA voices – posttest 5. Political skill
4.73 4.81
2.24 2.34
(.41) .53**
7.34
2.61
*
.19
3
4
5
(.43) .21*
6.89
2.94
.15
.32
4.91
.78
.27**
.02
Note: N = 111; Cronbach Alphas in the diagonal. p < .05. ** p < .01. *
2
**
(.41) .70**
(.53)
.23*
.01
(.89)
T. Momm et al. / Personality and Individual Differences 49 (2010) 396–401 Table 5 Analysis of covariance of emotion recognition via voices in the post-test (Study 2). Predictors
Dependent variable: emotion recognition via Voices in the post-test (DANVA adults) F
Step 1 Step 2 Step 3 * **
Randomized grouping factor Emotion recognition via voices in the pre-test Political skill
t
df
DR 2
R2
10.7**
3 1
.00 .54
.00 .54
1
.03
.57
.19
1.93*
p < .05. p < .01.
399
itively associated with emotional cue learning even after controlling for peer ratings of extraversion and self-monitoring. 5.1. Method Participants provided an online self-rating of political skill, extraversion, and self-monitoring in random order. They also sent a link to a peer at work who was asked to provide another rating of the above variables. Self- and other-ratings were linked by a randomly generated common code. One week after the peer ratings were provided, the participants received an online link for the feedback training on emotion recognition via faces. 5.2. Sample and procedure
Table 6 Analysis of covariance of emotion recognition via faces in the post-test (Study 2). Predictors
F Step 1 Step 2 Step 3
Randomized grouping factor Emotion recognition via faces in the pre-test Political skill
t
df
DR 2
R2
Participants were 146 (41% response rate) German employeedyads from a broad variety of jobs. The sample included 62 males and 84 females with a mean age of 37.1 and an average work experience of 14.5 years. The participants reported an average of 38 working hours per week.
6.49**
3 1
.00 .31
.00 .31
5.3. Measures
1.32
1
.00
.31
Dependent variable: emotion recognition via Faces in the post-test (DANVA adults)
.17
*
p < .05. ** p < .01.
5. Study 3 The purpose of this study was to constructively replicate the effect of political skill on emotional cue learning using training on emotion recognition via faces. We did so by controlling two theoretically relevant personality variables, extraversion and self-monitoring, to further exclude the possibility that the observed significant effect of political skill on emotional cue learning was due to other sources of influence. We also controlled for data sources from which personality variables and political skill were assessed. We focused on the face training in this study because Studies 1 and 2 showed that emotions are recognizable more easily via faces than via voices. Among personality variables, the highest correlation of self-reports of political skill is with self-reports of extraversion (r .50, Ferris et al., 2008). To demonstrate the specificity of the effect of political skill on emotional cue learning, extraversion was controlled. Rosenthal et al. (1979) had found that extraversion did not correlate with emotion recognition via faces and pictures. Thus, we expected that self-reports of political skill still predict emotional cue learning via faces after controlling for self-report extraversion. Self-monitoring is another personality variable that has conceptual overlap with political skill (Ferris et al., 2005, 2008). Previous research has shown that self-monitoring positively associates with emotion recognition (Gangestad & Snyder, 2000). However, the core of self-monitoring relates to status-oriented impression management motives (Gangestad & Snyder, 2000, p. 547). Because the focus of PSI is on social skills and not on motives, the emotional cue learning should be more strongly associated with political skill than with self-monitoring. Thus, we expect that self-reports of political skill still predict emotional cue learning via faces after controlling for self-report self-monitoring. In Studies 1 and 2 we had solely relied on self-ratings of political skill. Whereas self-ratings can incorporate less observable information such as motives, intentions, feelings, and past behavior, other-ratings are tied to observation of targets’ behaviors or performance resulting from these behaviors (Hogan, 1991). We therefore expected that peer ratings of political skill should be pos-
Political skill. Self-ratings of political skill were measured as in Study 1. For the other-ratings, all items were converted into the third person perspective (e.g., ‘‘This person always seems to understand people well”). Extraversion. Extraversion was measured with the German version of the NEO-FFI (Borkenau & Ostendorf, 1993). For the otherratings, all items were converted into the third person perspective. Self-Monitoring. A German adaptation of the self-monitoring scale by Nowack and Kammer (1987) was used. For the other-ratings, all items were converted into the third person perspective. DANVA2. Same measures were used as in Study 1. 5.4. Data analysis and results The means, correlations, and reliabilities of the variables are shown in Table 7. Self-ratings of political skill, and other-ratings of extraversion and political skill correlated negatively with DANVA faces post-training scores. We analyzed the data with a hierarchical regression analysis (Cohen et al., 2003). The dependent variables were the DANVA faces post-training scores. First, we entered DANVA faces pre-training scores. Second, we entered self-ratings of political skill. In replication of Study 1 self-ratings of political skill predicted emotional cue learning. The higher self-ratings of political skill the lower assessment errors in the DANVA faces post-training (b = .11, p < .05) (cf. Table 8). Third, we additionally controlled for self-ratings of extraversion and self-monitoring. Neither variable predicts emotional cue learning; in addition, the effect of political skill remained significant (b = .13, p < .05). Finally, we entered the other-ratings of extraversion, self-monitoring, and political skill. The significant effect of self-rating of political skill remained (b = .10, p < .05). The strongest predictor of emotional cue learning from the faces training was the other-rating of political skill (b = .24, p < .01), being nearly twice as strong as other-ratings of self-monitoring (b = .13, p < .01). 5.5. Discussion The findings of Study 3 replicated findings form Study 1: Selfratings of political skill predicted success in emotional cue learning via faces. In addition, although self-ratings of political skill associate with self-ratings of extraversion and have functional communalities with self-ratings of self-monitoring in emotional
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Table 7 Descriptive statistics, correlations, and reliabilities for variables (Study 3). Variables
M
SD
1
2
3
4
5
6
7
8
1. 2. 3. 4. 5. 6. 7. 8.
5.76 4.08 3.53 3.49 .38 .39 4.93 5.01
2.75 2.68 .58 .51 .15 .17 .73 .75
(.55) .81** .16 .24** .05 .01 .05 .07
(.62) .13 .24** .10 .13 .15 .29**
(.81) .45** .32** .16 .45** .08
(.78) .19* .25** .23** .41**
(.59) .29** .20* .16*
(.70) .04 .16
(.88) .16*
(.88)
DANVA facial – pre-test DANVA facial – post-test Extraversion – self-rating Extraversion – other-rating Self-monitoring – self-rating Self-monitoring – other-rating Political skill – self-rating Political skill – other-rating
Note: N = 146; Cronbach Alphas in the diagonal. * p < .05. ** p < .01. p < .05 (one-tailed).
Table 8 Hierarchical regression of emotion recognition via voices in the post-test (Study 3). Predictors
Criterion: emotion recognition via Faces in the posttest Std. betas
DR 2
**
Step 1
Emotion recognition via faces in the pre-test
.81
Step 2
Emotion recognition via faces in the pre-test Political skill – self-rating
.80** .11*
Step 3
Emotion recognition via faces in the pre-test Extraversion – self-rating Self-monitoring – self-rating Political skill – self-rating
.81** .07 .06 .13*
Step 4
Emotion recognition via faces in the pre-test Extraversion – self-rating Self-monitoring – self-rating Political skill – self-rating Extraversion – other-rating Self-monitoring – other-rating Political Skill – other-rating
.82** .04 .00 .10* .10 .13** .24**
.65**
.01*
.00
.06** * **
p 6 .05. p < .01.
cue recognition, political skill remained a significant predictor of emotional cue learning after controlling for these variables, underscoring the specificity of the effects of political skill. Finally, peer ratings of political skill, which result from targets’ past observable behaviors and social performance, predicted targets’ emotional cue learning even more strongly. Note that in Study 1 political skill was unrelated to the DANVA pre- and post-test scores (Table 2), and in Study 2 political skill positively correlated with the DANVA pre-test scores but not with the post-test scores (Table 5). Further, in Study 3 both self-ratings and peer ratings of political skill correlated negatively with DANVA post-test scores, but not with the pre-test scores. The inconsistent pattern of correlations between political skill and DANVA scores provide partial support to our earlier argument that political skill may not have a significant main effect on emotion recognition, a possibility that can be further explored in future research.
tional research (e.g., Blickle et al., 2008; Ferris et al., 2005). However, what lies at the core of political skill (i.e., the ability to pay attention to and understand social cues) has remained as mere conceptual speculation. The current research adds to the existing evidence of construct validity of the PSI by asking this essential question of whether politically skilled individuals are indeed better at grasping relevant social cues than those less politically skilled. This is a critical piece of information because it provides crucial information pertinent to construct validity of the PSI, which is the ability of a construct to predict what it is supposed to predict (i.e., causality) (cf., Borsboom et al., 2004). To answer this question, we used a quasi-experimental design that is well suited for the improved assessment of causality. The results of the present research indicate that politically skilled individuals, although not necessarily better than others in emotion recognition via faces or voices in pre-tests, demonstrated greater improvement on emotion recognition accuracy after brief training sessions, in which they were given feedback on the correct labeling of emotional expressions. Thus, the current research provided direct evidence supporting the claim that politically skilled individuals are more socially perceptive and adaptive. One limitation of the current research is that it used only one training method. A comparison of different training methods, such as micro expression training tool (METT), or subtle expression training tool (SETT) (cf., Ekman, 2003), will help to further assess the interaction effect between the specific training and the dispositional influence (i.e., political skill). In sum, the current research looked into the attending to and learning of emotional expressive cues of individuals with various degrees of political skill. It was concluded that politically skilled individuals were quicker at learning about these cues than others. The findings provided construct validity support for the existing conceptualization of political skill. We hope future research will further refine the usefulness of the political skill construct in personal and social situations. Acknowledgement The authors thank Rainer Banse, Gerald Ferris, and Steve Nowicki for their insightful comments on an earlier version of this manuscript.
6. General discussion
References
In this research, we described three studies intended to further validate PSI. We used a quasi-experimental design to investigate a key element of political skill, the ability to quickly and effectively learn about social cues. The validity of the PSI has been extensively demonstrated through establishing its nomological net via correla-
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