How Individuals High on Personal Initiative Use
Proactive Behaviour to Regulate Negative Affects
Barbara Johanna Bakcsy 10417559 30-06-2015 Academic year: 2014/2015 Supervisor: Inge Wolsink MSc Proactive behaviour and emotion regulation Bachelor thesis BA
Statement of originality
This document is written by Barbara Johanna Bakcsy who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
Literature documented widespread benefits of proactive behavior in the workplace. For example an active approach helps performing under demanding conditions. Therefore, we focus on the effects of proactive behavior on affective demand: moreover, how proactivity influences the experience of affect. Further, in fleshing out the antecedents of proactive behaviors, authors identify the importance of individual characteristics. We propose that especially employees who are high on personal initiative will be more successful in discarding negative affective states. We also propose that they will do so by using proactive behavior. These hypotheses were tested in a mixed design experiment (N=112), where affect was manipulated (negative vs. control) and measured before and after a proactive task, whereas proactive trait was measured one week before the experiment. Regarding the first hypothesis, trait-proactive people experienced and regulated more negative affects than passive people. Our second hypothesis was supported in the neutral condition only. When exposed to negative mood manipulation, we found that both trait-proactive and passive people engaged in proactive strategies to eliminate their negative feelings, whereas in neutral circumstances, only trait-proactive people did.
Table of contents
1 Introduction ... 5
2 Theoretical Framework ... 7
2.1 The benefits of reduced negative affects at the workplace ... 7
2.2 The Role of Personal Initiatives and Action Orientation in Negative Affect Regulation ... 8
2.3 How Proactive Behavior Guides the Relationship of Personal Initiative & Negative Affect Regulation ... 10
2.4 Research Design ... 11
3 Methodology ... 12
3.1 Design & Sample ... 12
3.2 Procedure ... 12
3.3 Affect Measures, Manipulation & Dependent Variable ... 13
3.4 Proactivity Measures & Control ... 15
3.5 Correlations ... 18
4 Results ... 19
4.1 Affect Manipulation Check ... 19
4.2 Results ... 21
4.2.1 Remarks on the Control Variable Action State ... 21
4.2.2 Correlations by condition ... 22
4.2.3 PROCESS Analysis ... 25
4.2.4 Repeated Measures ANOVA comparing trait-proactive and trait-passive people ... 28
5 Discussion ... 31
5.1 Summary of results ... 31
5.2 Interpretations ... 32
5.3 Limitations & Future Research ... 34
5.4 Practical Implications ... 35
6 Conclusion ... 36
References ... 38
1 Introduction
“Have courage and be kind”
In the 2015 screenplay, Cinderella pursues a virtuous life despite the continuous acts of mistreatment. The young girl refuses to drown in her sorrow; she fulfills her stepsisters’ wishes while taking chances in life as well. We might ask ourselves “how can she cope despite the hardships?” or “how does she keep on going?”. The development of Cinderella’s story can be better understood by exploring her character. In fact, there is supporting evidence that although high demands may complicate reaching our goals, individuals that take action as opposed to passively letting things happen to them, have a tendency to discard negative feelings (Frese, 2001). In the fairy tale, Cinderella was not simply rescued by a prince with help of magic. She waited patiently and when opportunity rose she was resourceful and imaginative: she was determined to redesign her mother’s old dress for the ball and she unlocked the door creatively to escape and reveal her identity. Who would have thought that attending the ball for a magical experience would charge her life full of misery for the better. We believe that her active character and persistence led to her happy ending.
Although when we face hardships not all opportunities are life-changing, literature tells us that proactive strategies can be a key to both change a situation (Crant, 2000) as well as produce desirable emotions (Grant, 2013). As we define here, proactivity in the workplace is not a limited set of actions, rather a process that employees apply in any set of actions through anticipating, planning, with the aim of improving circumstances (Grant, 2008). Existing research provides support for the various forms and benefits of proactivity, such as creativity, innovation, and change. As we live in an increasingly knowledge-based society, however, it is important to understand the underlying mechanisms so that managers can cultivate proactivity among their employees (Grant & Ashford, 2008). This study contributes to understand the
facets of proactivity and how they may enhance a working environment by studying both antecedents and consequences of proactive behavior in an experimental design.
In this paper we distinguish between trait personal initiative and proactive behavior. The first concept refers to having an active and self-starting approach to work goals and being persistent in overcoming barriers and setbacks (Frese, Fay, Hilburger, Leng, & Tag, 1997; Frese, Kring, Soose, & Zempel, 1996). The second, proactive behavior is the “anticipatory action that employees take to impact themselves and/or their environments” (Grant & Ashford, 2008). Thus although the concepts overlap, we regard trait personal initiative as a tendency, an approach, while proactive behavior as an act. So far research has not made this distinction and measured both trait and act using cross-sectional (questionnaire) designs. In this paper, therefore, we aim to measure both but separately, we use a questionnaire for trait personal initiative and an actual behavioral measure for proactive behavior.
Furthermore, we also add to literature by connecting each of our two concepts, trait personal initiative and proactive behavior, to negative affect regulation. Despite the different conceptualizations and interpretations of proactivity (Grant & Ashford, 2008), ‘action’ is a resurfacing key word. Under demanding conditions, some individuals take matters to the hand, while others choke under pressure and are less successful adjusting to the circumstances (Koole, Jostmann & Baumann, 2012). Thus, they behave either in an action- or state-oriented manner. We borrow ideas from action-state theory to highlight that showing initiatives and taking actions not only relate but together they can jointly facilitate how people adapt to stressful situations (Koole & Jostmann, 2004). Therefore, we expect to find a positive relation between proactivity and negative affect regulation.
Although previously found relationships between action orientation and personal initiative suggest that having a proactive approach to life helps under demanding conditions, as stated above, it is still unknown how personal initiative would lead to reduced negative affects when
those affects are unrelated to the behavior. For example, we can imagine an employee being angry as he has too many deadlines at work. However, instead of being passive, he takes a proactive approach and enrolls to a gym to exercise regularly. The new energetic lifestyle, in return would be an unexpected solution to the problem as it would minimize anger and associated stress levels. So the question is whether proactive behavior serves as a subconscious affect regulation strategy for unrelated moods. In other words; whether proactive behavior in and of itself, makes people feel better or less negative. So we propose that having a proactive approach, an active nature, would allow people to engage in proactive behavior when they face negative moods and this behavior in return could help them discard those negative feelings.
By distinguishing between trait personal initiatives and proactive behavior we believe we can both understand the mechanisms of proactivity and learn how these concepts relate to less negative affects. Thus the central question is whether people high on personal initiative use proactive behavior to discard negative affects. In order to verify this statement, we test the research question within a mixed design experiment applying mood manipulation.
2 Theoretical Framework
2.1 The benefits of reduced negative affects at the workplace
As indicated above, proactivity seeks improvement. For example, if negative employee affective states adversely affect overall organizational performance they should be regulated. In general, emotion regulation encompasses the strategies that people use to decrease, maintain, or increase their emotions (Gross, 2007). In this study, however, we will be referring to affect since affect is the most general term, including both mood and the most
emotion regulation theory. Despite the confusion regarding the terminology, authors stress the importance of negative affect regulation as negative affects shift employees’ focus of attention away from job performance, (George & Brief, 1996) and reduce the amount of attentional commitment to the task (Smallwood, Fitzgerald, Miles & Phillips, 2009). Therefore, it is central to work performance to efficiently regulate emotions and moods; as it has a wide range of impact ranging from mental and physical health (Gross & Muñoz, 1995; Sapolsky, 2007) to social functioning (Eisenberg, Farbes, Guthrie, & Reiser, 2000).
2.2 The Role of Personal Initiatives and Action Orientation in Negative Affect Regulation
One way to efficiently regulate emotions or moods, might be an action approach to problems. Presumably, people who take a self-starting and persistent approach to work, might also take such an approach when it comes to their internal affective state. People who are high on personal initiative, by definition are persistent in overcoming barriers and setbacks (Frese, Fay, Hilburger, Leng, & Tag, 1997; Frese, Kring, Soose, & Zempel, 1996). So firstly, personal initiative as an active approach that allows the individual to deal with job difficulties more actively as opposed to passively ‘letting things happen’ may facilitate negative affect regulation. In support of this though, Frese and Fay (2001) found that personal initiatives allow individuals to successfully regulate their emotions that arise when barriers appear repeatedly, whereas helpless people, who are low on personal initiative will give up quickly when barriers emerge. Thus, according to theory on emotion regulation, an active nature helps employees to make changes in their environment and perhaps also deal with their undesired affective states.
Secondly, when we experience negative affect, we can be creative. As creativity is a catalyst for change (Koole & Jostman, 2004), employees high on trait personal initiative employ creative ways of dealing with barriers (Frese, 2001). Emotion regulation theory also
suggests that employees do not react passively to emotional demands, they also actively apply a range of creative tactics to regulate their emotions, (Brotheridge & Grandey, 2002). Some engage in surface acting, whereby they modify and control emotional expressions. While other employees control their internal emotions by the means of deep-acting (Brotheridge & Grandey, 2002). Thus there are various creative ways to reduce negative affect states and trait personal initiative might be an important perquisite of success.
Thirdly, we can deal with negative feelings by being rather action- than state-oriented. In terms of the action-state theory, action orientation promotes decisiveness and initiative. On the other end, state orientation is change preventing and characterized by indecisiveness and hesitation (Koole & Coenen, 2007). Kuhl (1982) measured action-state as a personality variable and since then research found a variety of benefits of action orientation, such as academic success (Jostmann & Koole, 2010) on a personal level and higher performance on a group level (Johnston, Reed, Lawrence & Onken, 2007). However, most importantly under demanding conditions action-oriented individuals are more efficient in affect regulation in comparison to state-oriented individuals (Jostmann & Koole, 2010). Given this finding and the interrelatedness of the two traits, action orientation and personal initiative, we believe that action-state theory also supports our speculation that people high on personal initiative will also cope with more efficiently with unpleasant affect states. In light of the discussion above we propose:
H1: People high on personal initiative are more successful at negative emotion regulation
2.3 How Proactive Behavior Guides the Relationship of Personal Initiative & Negative Affect Regulation
However, personal initiative is just one of the multiple conceptualizations and measurements of proactivity (Crant, 2000). On the trait-end of the continuum, proactivity can be measured by having a proactive personality: some people repeatedly take action to impact their environments while others do not (Bateman & Crant, 1993). This concept is functionally equivalent to the aforementioned personal initiative (Tornau & Frese, 2013) and since the two are strongly related (r≥.7), they basically measure the same and conceptualize general
proactive behavior (Tornau & Frese, 2013). Past research measured both with personal initiative scale (Freese, 2001). On the other end of the continuum, proactivity can be measured on a proactive behavior scale. For example, taking charge entails voluntary and constructive efforts to elicit functional change (Morrison & Phelps, 1999, p. 403), or voice is a promotive behavior making innovative suggestions for change (LePine & Van Dyne, 2001, p. 326). Thus, these two concepts, taking charge and voice, are related to specific behaviors (Tornau & Frese, 2013). Since research emphasized personal initiative, taking charge and voice to be the most important proactivity constructs, in this study we measure all three of them. We use personal initiative scale (Frese, 2001) to measure trait personal initiative and measure taking charge and voice with an actual behavioral measure.
Crant (2000) also believes personal initiative may be an important antecedent of proactive behavior. Past research did not measure these constructs separately but we combine the use of a personal initiative scale with a behavioral measure and test participants’ proactive behavior under neutral vs. negative circumstances. This is especially important because the more demanding environments get, the more valuable proactive behavior is (Aspinwall & Taylor, 1997). As previous work shows negative mood as an antecedent can trigger proactive
behavior by signaling that the present situation needs change (Carver & Scheier, 2000). However, to date literature is limited on the consequences of proactive behavior on affective states.
To summarize we believe that people high on personal initiative are more likely to engage in proactive behavior (Crant, 2000). Secondly, proactive behavior, with the aim to elicit change on the self, will help to discard negative affects. To put it simply, both active and passive employees may experience negative moods. If they are trait-proactive, that is high on trait personal initiative, we suppose they are more likely to engage in proactive behavior in order to distract themselves or gain a sense of control. Thus they do not engage in proactive behavior to target and regulate those specific negative affects, but because they were proactive, they could achieve regulation. In line with these thoughts, we propose:
H2: People high on personal initiative are more likely to engage in proactive behavior to
regulate negative affects
2.4 Research Design
In the present study we aimed to understand the relationship between trait personal initiative and negative affect regulation and how that is guided by proactive behavior. Furthermore, to ensure validity, we controlled for action-state. The research strategy combined the use of surveys and an experiment involving negative mood manipulation. We expected that trait personal initiative would predict negative affect regulation and that this relationship is guided (mediated) by proactive behavior. Thus we also assume that personal initiative is positively related to proactive behavior, proactive behavior is positively related to negative affect regulation, and the proposed mediation model would be the strongest for negative mood condition.
3 Methodology
3.1 Design & Sample
Data was collected by a small team of Bachelor and Master students studying at the
University of Amsterdam. In exchange of 10 euros, 112 Dutch-speaking students in the Netherlands with similar educational background were tested. The final sample consisted of 67% female, 33% male, with average age of years 22 (SD= 2.9).
A between-subjects 2 (neutral/negative affects) x 2 (high/low on trait personal initiative)
design was adopted. Neutral affect as the control, negative affect as the experimental condition were manipulated via images and music. Participants were randomly allocated to each condition and tested in duos to create the necessary social environment that proactive behavior requires (Grant & Ashford, 2008).
3.2 Procedure
Recruitment started with lecture promotions whereby participants were asked to fill out an online survey (Qualtrics.com) as a pre-test measurement. They were also requested to share a second questionnaire with a friend or partner who knows them for more than a year. In these two surveys the independent variable trait personal initiative was measured. Once pre-tests were completed, the respondents could volunteer for the experiment. Participants in the laboratory arrived in duos, were told their answers would be confidential and they can op-out anytime. Then the two participants signed the informed consent and were installed in two separate closed cubicles. To mask the real purpose of the study, participants played a short WMC game (working memory capacity). Then the real testing began by measuring participants’ baseline negative and positive affects (A1), so they filled out a digital survey
(Frese, 1996). This was followed by a five-minute affect manipulation, where depending on the condition (negative versus control) participants were presented with images accompanied with adequate musical background (Marchewka, Żurawski, Jednoróg, & Grabowska, 2014). Once the slideshow ended, another affect measure was taken to check if the manipulation was successful (A2). This was followed by a proactivity task allowing us to measure proactive behavior. In a role-play with a fictive leader participants had to choose the best candidate to be the dean of their faculty. Following the task, participants filled out the affect survey for the last time (A3). In the negative condition, where participants were exposed to negative affect, proactive people were expected to experience less negative affect and more positive affects (Carver & Scheier, 2000). To control for action-state orientation, participants were asked to complete an exit survey (Kuhl, 1982). Lastly, they were debriefed, paid and dismissed.
3.3 Affect Measures, Manipulation & Dependent Variable
Affect Measures
As mentioned above, affect was measured on 3 occasions: before the manipulation (A1), right after manipulation (A2) and after the proactivity task (A3). In the 20-item survey (Hess & Blairy, 2001), of which 9 were capturing negative affects an example item was: ‘I feel sad’. Participants used a slider from 1% to 100%. A high percentage on the relevant negative affects reflects participants experiencing negative feelings. The average Crohnbach’s Alpha for negative affects over all measures was α=.889.
Because positive and negative affects are independent of another (Zautra, Potter, Reich, 1997), previous research often reports both negative and positive affects. Following this rule of thumb, we could also double-check for the mood manipulation. So we measured 8 positive affects, an example being: ‘I feel happy’. With the use of the slider, a high percentage
reflected participants having positive feelings. The average Crohnbach’s Alpha for positive affects over all measures was α=.934.
Mood Manipulation
The difference between the first (A1) and second measure (A2) of positive and negative affect was used as a manipulation check. To study the effect of mood on our proposed model, we used two stimuli, visual and auditory, as their combination is more effective in evoking emotions than the two stimuli separately (Baumgartner, Esslen & Jäncke, 2006). This was necessary as we expected our mediation model to be the strongest in the negative mood condition. Therefore, we selected 40 pictures from the NAPS (Marchewka, Żurawski, Jednoróg, & Grabowska, 2014) and IAPS (Anderson, Siegel & Barrett, 2011) databases. For the negative condition, the selected 20 pictures had a valence of -1 SD from the mean, indicating severe negativity. Whereas the 20 pictures of the neutral condition, had an average valence, ranging from -0.5 and +0.5. On a scale of 1 denoting no arousal to 10 meaning extreme arousal, our pictures were all rated above 4 by the NAPS and IAPS databases. During the slideshow, for each participant the order, the timing and the pauses between the pictures were randomly shuffled. We also matched the pictures with instrumental music according to moderate to high arousal levels (Baumgartner, Esslen & Jäncke, 2006). For the neutral condition we selected John Adams’ ‘Common tones in simple’ (Västfjäll, 2002). Whereas for the negative condition we chose a segment of Clint Mansell’s ‘Requiem for a dream’ based on uniform agreement on its negative affecting, yet activating effect. The overall negative stimuli was expected to be rather activating than deactivating.
Dependent Variable Negative Affect Regulation
The dependent variable, negative affect regulation was computed by creating a variable
that captured the difference between negative affects at A2 (manipulation check) and A3 (after the proactivity task). A high score reflected successful regulation of negative affect.
3.4 Proactivity Measures & Control
Independent Variable Personal Initiative
Personal initiative was measured as a facet of personality trait in the two pre-tests with an 8-item survey respectively (Frese, 1996). Questions were behavior-related in both self-reported and spouse-self-reported questionnaires. Example items were: ‘I take the initiative immediately if others do not’ and ‘My friends takes the initiative immediately if others do not’, respectively. Measurement scales ranged from (1) not applicable to (7) applicable. The friend- or spouse-rated survey would increase validity since it contained the same set of questions. The results of both tests were aggregated into one variable measuring (trait) personal initiative. There was one item counterbalanced, which was recoded for analyses. A high score indicated being high on trait personal initiative / being trait-proactive. Aggregating the results of both conditions, the reliability of Personal Initiative (Trait) was satisfactory for both equally weighted self-rated (α = .76) and partner-rated (α = .79) components.
Proactivity Task & Mediator Proactive behavior
During the proactivity task we measured the situation-specific constructs of proactive
behavior: taking charge and voice (Tornau & Frese, 2013). In addition, since proactive behavior is self-initiated, we designed the experiment allowing participants to be proactive or remain passive (Wolsink, Den Hartog, Belschak & Sligte, forthcoming). Participants being
under the impression, they were communicating with one another during the proactivity task, they could initiate (take charge), decide not to initiate but challenge (voice), or neither initiate nor challenge. Therefore, at the allocation of the roles, both participants were appointed to be followers under the impression that their partner was the leader. Their joint task was to choose the next (hypothetical) dean for the faculty out of three candidates. Seated in different rooms with a laptop each, the participants believed they were communicating with each other. However, the communication was a simulation, answers were pre-generated, as both participants were followers. Upon receiving the 9 statements on candidates A,B & C participants could talk to the leaders. Based on the information they were given, by definition proactive participants were expected to identify improvements take charge and thus opt for option B (Crant, 2000). If they decided not to initiate, the computer, that is the leader, would argue in favor of A. The follower now had to answer if they agreed or they would prefer another dean and therefore challenge the leader’s opinion. Proactive people were expected to challenge the status quo, and argue in favor of candidate B (Crant, 2000).
To create a variable measuring proactive behavior, firstly two raters rated initiative; whereby a 0 score was given to participants who didn’t engage in communication, 1 for those who started a conversation and a 2 for those who stated their candidate preference. The two ratings were then correlated (r=.819, p=.000) to create an Initiative variable. Secondly, Challenge was also rated by two raters: a 0 score was given to the inactive participants, 1 for those who shared opinions, tried to communicate and a 2 for participants who opted for their preferred candidate and resisted the leader’s pick. The ratings of the two raters were correlated (r=.923, p=.000) to create a Challenge variable. Initiative and Challenge were then equally-weighted to formulate the Proactive Behavior variable. Hence, the reliability of Proactive Behavior was calculated for its two components separately, yielding a substantial agreement for initiative (κ = .66) and an almost perfect agreement for challenge (κ = .81).
Figure 1 depicts how we expected proactive behavior to guide the relationship between the independent variable trait personal initiative and dependent variable negative affect regulation. We expected this relationship to hold the strongest in the negative condition, whereby participants were exposed to negative stimuli.
Figure 1 Conceptual Model with Measures & Expectations
Action State (Control)
Finally, the control variable Action State was measured with a 12-item exit survey. We used a nominal scale, thus participants were either action- or state-oriented. An example item was ‘When I embark on a major project’: ‘I often think too long about where to begin’ or ‘I have no problem to deciding where to begin’. There were 4 items counterbalanced. Aggregating the results of both conditions, the reliability of Action State was acceptable (α = .63).
3.5 Correlations
Before analyzing the data by conditions, we wanted to get a general overview how the variables Negative Affect Regulation, Proactive Behavior, Personal Initiative and Action State related to each other. Most importantly, as a proof of the validity of the proactivity task, we found a positive correlation between personal initiative and proactive behavior, r= .337, p = .000 (Table 1). Furthermore, when considering both conditions trait personal initiative (r=.246, p=.009) as well as proactive behavior (r=.220, p=.020) was positively related to negative affect regulation. Finally, Action State correlated with the independent variable Personal Initiative, r=.320, p=.001. However, since we expected the mood condition to influence these variables, we now proceed analyzing the data separately for the two mood conditions (negative vs. control).
Table 1 Descriptive Statistics and Correlations (Reliabilities on diagonal)
M SD 1 2 3 4
1. Negative Affect Regulation: Δ(A2 – A3) 16.85 13.39 -
2. Mediator: Proactive Behavior 4.37 2.131 .220* (.66 & .81)
3. Personal Initiative (Trait) 4.99 .64 .246** .337** (.76 & .79)
4. Control: Action State 20.79 2.59 .070 .156 .320** (0.63)
Note. N=112. * p<.05. ** p<.01.
4 Results
We wanted to test whether people high on initiative use proactive strategies in order to discard negative affect states. To do that, data were analyzed at the group level using a series of 2 (condition: neutral vs. negative) X 2 (affect) analyses of variance (ANOVAs), with changes in positive and negative mood/affective states (A1, A2 and A3) as repeated measures variables Sample sizes for both negative and neutral conditions were equal. Furthermore, the ANOVA showed no significant difference between the means of the variables Age, Gender, Personal Initiative, Proactive Behavior and Action State across the two conditions. However, as expected, the dependent variable Negative Affect Regulation was significantly different for the two groups (See Table 2).
Table 2 Descriptive Statistics for Age, Gender and Main Variables for Negative & Control Condition
Negative condition N=56 Control condition N=56 µ SD µ SD F p Age 21.82 2.622 22.29 3.161 .716 .399 Gender - - - - .040 .843
DV Negative Affect Regulation 16.85 13.394 2.78 8.038 45.404 .000
Proactive Behavior 4.70 2.008 4.05 2.219 2.584 .111
Personal Initiative 5.07 .572 4.92 .697 1.492 .225
Control: Action State 20.86 2.497 20.73 2.711 .069 .793
Note. N=112. * p<.05. ** p<.01.
4.1 Affect Manipulation Check
Without checking whether the mood manipulation worked, we could not investigate whether people indeed become more proactive because of negative mood condition, or due to other reasons. In order to argue that trait-proactive people become more proactive as a means
to regulate negative moods, the manipulation was necessary to find the causality between negative mood and proactive behavior. In other words, only a successful negative mood manipulation could reveal whether proactive behavior is indeed a regulation strategy. We therefore measured (changes in) both negative and positive and affects by conditions to be certain that the manipulation worked (Table 3). A repeated measures ANOVA confirmed that the affect manipulation was effective. After the manipulation, participants in the negative condition showed a larger increase in negative affects F(1)=67.55, p=.000, partial η2=.380
and decrease in positive affects (F(1)= 47.05, p=.000, partial η2=.300) (µneg = 19.52, SD
=16.14, µpos=-18.74, SD=16.71), than participants in the neutral condition (µneg =-0.65,
SD=8.77, µpos=-1.29 , SD=9.12). Since the mood manipulation was successful, we could proceed exploring each condition.
Table 3 Means for Positive and Negative Affects by Condition & Changes in Affects
A1 A2 A3 ΔA1A2 ΔA2A3 µ SD µ SD µ SD Μ SD Μ SD Negative condition Negative Affects 22.07 2.01 41.59 2.76 24.75 2.08 19.52** 16.14 -16.85** 13.39 Positive Affects 69.17 1.72 50.43 2.45 64.13 1.72 -18.74** 16.71 13.70** 14.95 Control condition Negative Affects 24.06 1.98 23.41 1.79 20.63 1.72 -.65 8.77 -2.77 8.04 Positive Affects 65.24 2.17 63.95 2.24 66.25 1.95 -1.29 9.12 2.29 8.65 Differences between conditions F η2 F η2 Negative Affects 67.55** .380 45.404** .292 Positive Affects 47.05** .300 24.415** .182
4.2 Results
4.2.1 Remarks on the Control Variable Action State
Since we controlled for action state, it was noteworthy to mention its role in the model under the two conditions. Firstly, opposite to our predictions, we did not find a relationship between action state and the dependent variable negative affect regulation in any condition. In the negative condition, people high on action-orientation would be also high on personal initiative (r =.392, p =.003). In the control condition, action state and personal initiative was
no longer related (r =.264, p =.051), however, this could be due to the fact that action state
orientation was measured in the exit survey.
Figure 2 The Difference between Action and State
Oriented Individuals’ Changes in Negative Affects in the Control Condition
Figure 3 The Difference between Action and State
Oriented Individuals’ Changes in Negative Affects in the Negative Condition
Furthermore, the repeated measures ANOVA revealed that in both conditions action- and state-oriented individuals reacted the same way in terms of experiencing negative affects (see Figures 2 & 3). However, only in the negative condition, state-oriented individuals did experience more negative affects: at A1 (F=7.137, p=.010), at A2 (F=4.694, p=.035) and at A3 (F=6.978, p=.011) overall. Thus, experiencing negative affects was the same in both
conditions, but only in the negative condition we found a significant difference between action- and state-oriented people. These findings indicate that action-orientation is contingent on conditions, rather than a fixed trait. Therefore, in the remainder of this paper, we continued to control for action-state, however, we did not elaborate on its effects.
4.2.2 Correlations by condition
Given that negative affect regulation is the dependent variable, statistically we expected a bigger effect in the negative condition. The aforementioned mood manipulation was successful, so we could explore whether the experienced negative affects indeed trigger people high on personal initiative to make attempts to discard those negative affects (H1). We
also predicted that proactive behavior would mediate the relationship of high trait personal initiative and successful negative affect regulation (H2). In other words, we expected full
mediation of proactive behavior in the relationship between personal initiative and negative mood regulation, especially in the negative mood condition. Therefore, we now investigate each condition separately. Table 4 and 5 report the correlations between the dependent variable Negative Affect Regulation, Proactive Behavior, Personal Initiative under the two conditions: control and negative.
Table 4 Descriptive Statistics and Correlations in the Control Condition
M SD 1 2 3
1. Negative Affect Regulation: ΔNegative Affects 2.78 8.04
2. Mediator: Proactive Behavior 4.05 2.22 .322*
3. Personal Initiative (Trait) 4.92 .70 .068 .497**
4. Control: Action State 20.73 2.71 .130 .214 .264
In the control condition, we didn’t expect a relationship between personal initiative and negative affect regulation, but we did expect that trait proactive people would behave more proactively and that proactive behavior would make people feel better. Zero order correlations revealed that in the control condition, people high on personal initiative indeed engaged in more proactive behavior (r =.497, p =.000) and that proactive behavior predicted negative
affect regulation (r =.322, p=.015), whereas personal initiative and negative affect regulation
were unrelated (r =.068, p=.621). It thus seemed that in this particular condition, our mediation model may apply.
Nonetheless, in the negative condition, we found very different results. We expected that the trait-proactive group would successfully regulate their negative affects through proactive behavior. However, as Table 5 shows, personal initiative was unrelated to proactive behavior (r =.094, p=.492) and proactive behavior was unrelated to negative affect regulation (r =.074,
p=.587) . This suggests that proactive behavior did not help the participants of the negative
mood condition in discarding their negative feelings. Furthermore, it means that in this mood
Table 5 Descriptive Statistics and Correlations in the Negative Condition
M SD 1 2 3
1. Negative Affect Regulation: ΔNegative Affects 16.85 13.39
2. Mediator: Proactive Behavior 4.67 2.01 .074
3. Personal Initiative (Trait) 5.07 .57 .353** .094
4. Control: Action State 20.86 2.497 .030 .082 .392*
condition, either trait-passive people became very proactive, or trait-proactive people became very passive (see Figure 4). We only found that, as depicted in Figure 5, trait personal initiative predicted negative affect regulation (r =.353, p=.008). These results may mean that in the negative condition our mediation model may not hold. This is very surprising since we expected the strongest effect of our mediation model in this group. To confirm these initial findings, we continued by running a mediation analysis in both conditions.
Figure 4 Relationship of Trait Personal Initiative & Proactive Behavior by Condition
Figure 5 Relationship of Trait Personal Initiative & Negative Affect Regulation by Condition
4.2.3 PROCESS Analysis
So far it seems that depending on the condition, proactive behavior may not necessarily mediate the relationship of the independent variable personal initiative and the dependent variable negative affect regulation. To explore the mediation role of proactive behavior, we ran a PROCESS analysis for both conditions separately (Hayes, 2012). We controlled for action-state orientation, however as the correlations already revealed, it did not significantly relate to the dependent variable negative affect regulation in any of the conditions.
Control condition
For the people in the control condition results are reported in Table 6. Our first hypothesis predicted that being trait-proactive predicts negative affect regulation. In this condition, we didn’t find support for H1, as the direct effect of personal initiative on negative affect
regulation was not significant (β= -1.6182, t=-.9092, p=.3675). However, we also expected that proactive behavior would mediate the relationship of trait personal initiative and negative affect regulation. In fact, the direct effect of proactive behavior on negative affect regulation was significant (β= 1.3500, t=2.4418, p=.0181), thus proactive behavior decreased negative affect. Furthermore, the indirect effect of personal initiative through proactive behavior on negative affect regulation was significant, as the bootstrap values at 95% CI did not contain zero (.5016, 4.2606). Therefore, in the control condition, in support of H2, proactive behavior
mediates the relationship of personal initiative and negative affect regulation. Furthermore, since trait personal initiative did not affect negative affect regulation after proactive behavior has been controlled, this is a complete mediation. This finding was consistent to our predictions the effect sizes are visualized in Figure 6.
Table 6 Results of Process analysis on the Role of Proactive Behavior Mediating between Personal Initiative and Negative Affect
Regulation in the Control Condition
Negative Affect Regulation (DV) Main effects Indirect effect
Coefficient SE p Coefficient SE p BootLLCI BootULCI
Control: Action State .2615 .4089 .5253 .3592 .4259 .4028 -.4953 1.2138
Personal Initiative (IV) -1.6182 1.7798 .3675 -1.6182 1.7798 .3675 -5.1913 1.9549
Proactive Behavior (Med) 1.3500 .5529 .0181 2.0522 .9641 .5016 4.2606
Mediation effect .4339 1.6420 .7926 -2.8610 3.7289
Adjusted R2 .2586 .1210
Note. Dependent variable is Negative Affect Regulation, N=55 *p<.05, **p<.01, ***p<0.001
Negative mood condition
In contrast, for the people in negative mood condition results are reported in Table 7. Confirming Hypothesis 1, we found a direct effect of personal initiative on negative affect regulation (β = 9.3594, t =.2.8550, p =.0062). The direct effect of proactive behavior on negative affect regulation was not significant (β= .3150, t=.3654, p=.7163). However, whether proactive behavior changes negative affects, might be different for trait-proactive versus trait-passive people, which is why we expected a mediation effect of proactive behavior between trait personal initiative and negative affect regulation. Thus, the second hypothesis predicted that the link between personal initiative and negative affect regulation is mediated by proactive behavior. For H2, results do not support that proactive behavior would
have a significant indirect effect on negative affect regulation as the bootstrap values at 95% CI contained zero (-.5107, 1.6380). This indicates that no mediation is present in this condition; we visualized effect sizes in Figure 7. Because we expected the strongest effect of our mediation model in this group and we still were not sure whether trait-passive people became proactive or trait-proactive people became passive, we continued our analysis with running repeated measures ANOVA.
Table 7 Results of Process analysis on the Role of Proactive Behavior Mediating between Personal Initiative and
Negative Affect Regulation in the Negative Condition
Negative Affect Regulation (DV) Main effects Indirect effect
Coefficient SE p Coefficient SE p BootLLCI BootULCI
Control: Action State -.6983 .7506 .3565 -.6848 .7435 .3612 -2.1761 .8065
Personal Initiative (IV) 9.3594 3.2783 .0062 9.3594 3.2783 .0062 2.7811 15.9378
Proactive Behavior (Med) .3150 .8621 .7163 .0806 .5028 -.5107 1.6380
Mediation effect 9.4400 3.2440 .0053 2.9333 15.9467
Adjusted R2 .0112 .1408
Note. Dependent variable is Negative Affect Regulation, N=55 *p<.05, **p<.01, ***p<0.001
Figure 7 Effects Model in Negative Condition
4.2.4 Repeated Measures ANOVA comparing trait-proactive and trait-passive people
As Table 8 shows we divided our sample by condition and by participants being high on personal initiative (trait-proactive) and low on personal initiative (trait-passive). As proactive
behavior did not mediate the relationship between trait personal initiative and negative affect regulation in the negative condition, we decided to investigate the differences in trait-proactive and trait-passive peoples’ affect changes. As discussed, due to our model, we focused on the changes in negative affects and the negative affect regulation but to stay consistent with former research and to double-check for similar patterns, we also reported changes in positive affects.
Table 8 (Changes in) Positive & Negative Affects by High & Low Personal Initiative
A1 A2 A3 ΔA1A2 ΔA2A3 µ SD Μ SD µ SD µ SD µ SD Negative condition Low on Personal Initiative N=28 Negative Affects 24.63 17.52 38.20 19.52 26.13 15.63 13.58** 12.86 -12.07** 10.38 Positive Affects 64.71 13.13 53.56 16.38 61.76 12.77 -11.15** 9.26 8.20** 11.20 High on Personal Initiative N=28 Negative Affects 19.52 11.92 44.98 21.47 23.37 15.67 25.46** 17.08 -21.62** 14.50 Positive Affects 73.63 11.10 47.30 19.95 66.50 12.87 -26.33** 19.07 19.20** 16.35 Control condition Low on Personal Initiative N=25 Negative Affects 23.63 12.17 23.37 12.94 21.71 12.64 -.26 7.53 -1.66 8.64 Positive Affects 65.93 15.74 63.42 16.79 65.39 14.91 -2.51 7.66 1.97 8.18 High on Personal Initiative N=31 Negative Affects 24.42 16.86 23.44 14.03 19.76 13.21 -.97 9.77 -3.68** 7.54 Positive Affects 64.68 16.92 64.38 16.99 66.94 14.59 -.30 10.17 2.55 9.14
Negative mood condition
So far for the negative conditions, both the correlations and PROCESS results showed that
proactive behavior did not predict negative affect regulation. We only found that trait personal initiative related to negative affect regulation. Indeed, after the proactivity task, trait-proactive people felt significantly more positive affects (F(1) 8.628, p=.005, partial η2 =.138) and less
negative affects (F(1)= 8.021, p=.006, partial η2 =.129) compared to people low on trait personal initiative. Yet interestingly, people high on personal initiative reacted more strongly to the negative mood manipulation both in terms of decreases in positive affects (F(1) = 14.342, p =.000, partial η2 =.210) and increases in negative affects (F(1) = 8.656, p =.005
partial η2 =.138). Furthermore, Table 8 also indicates that trait-passive people of this condition experienced significantly less negative affects after the proactivity task at A3 compared to A2. Simply put, for some reason passive people were triggered by the negative moods to become proactive.
So Table 6 shows two very important findings. Firstly, both proactive and trait-passive people successfully regulated their negative affects, however, trait-proactive people were more sensitive to the negative mood manipulation and they regulated more negative affects compared to trait-passive people (Figure 6). Secondly, and most importantly, since suddenly passive people also became proactive, the predicted relationship between high trait personal initiative and proactive behavior disappeared. These results explain why our mediation model did not apply in this particular condition.
Figure 6 The Difference between High and Low PI
Individuals’ Changes in Negative Affects in the Negative Condition
Figure 7 The Difference between High and Low PI
Individuals’ Changes in Negative Affects in the Control Condition
Control condition
In the control condition, depicted in Figure 7, after the proactivity task there was no difference between trait-proactive and passive people in terms of regulating negative affects (F(1)= .878, p=.353, partial η2 =.016). However, people high on trait personal initiative felt less (more) negative (positive) affects at A3 compared to A2 measurements. These test results support our PROCESS analysis and also explain why we found complete mediation in this condition.
5 Discussion
5.1 Summary of results
In the negative mood condition we expected that trait-proactive people would apply proactive behavior to regulate their negative affects. We also expected that trait-passive people would not engage in proactive behavior and they would also not be able to regulate their negative affects. In relation to Hypothesis 1, both trait-proactive and trait-passive people could regulate their negative affects. However, being high on personal initiative resulted in more negative affect regulation, thus confirming H1. Furthermore, contradicting Hypothesis 2, in the negative condition both trait-proactive and trait-passive people engaged in proactive behavior. Hence, proactive behavior did not guide the relationship between trait personal initiative and negative affect regulation.
Meanwhile in the control condition since there was no negative mood manipulation, we expected less negative affect regulation as there were less negative moods to regulate. We only expected that trait-proactive people would still behave more proactively compared to the trait-passive people. We found that trait-proactive people indeed used more proactive behavior. These individuals also felt better after completing the proactivity task. However, being high on personal initiative by itself was not enough to regulate their negative affects,
they also needed to engage in proactive behavior. In other words, Hypothesis 1 did not hold: trait-proactive people were not better in reducing negative affects in the control condition. Yet, in support for our second hypothesis, we found a full mediation in the control condition: trait-proactive people were engaging in more proactive behavior and as a result they felt better after the task.
5.2 Interpretations
The main question of this paper was whether people high on initiative use proactive strategies in order to discard negative affect states. Since we expected our model to have the biggest effect in the negative mood condition, we first address the unexpected outcomes in this particular condition. Perhaps the most surprising finding is that both people high and low on trait personal initiative engaged in proactive behavior. One could argue that subconsciously they were attempting to regulate the negative affects. In our sample both trait-proactive and trait-passive people suddenly became trait-proactive, hence the predicted relationship between trait personal initiative and proactive behavior disappeared. Crant (2000), summarizing the findings of proactivity literature, described personal initiative as action-oriented and persistent in the face of obstacles and proactive. However, trait-passive people also behaved proactively. Previous work shows that in creative tasks, such as brainstorming, where persistence is required negative moods may result in better creative performance (De Dreu, Baas & Nijstad, 2008). For example Higgins (2006) argued that some
activating mood states, such anger, associate with promotion focus, which relates to creativity (Friedman & Förster, 2001). This would suggest that although trait-proactive people are self-starting by nature, trait-passive people may become proactive as a means to discard their negative feelings. Thus our finding provides further evidence that people may engage in proactive behavior in anticipation of its mood-lifting consequences (Andrade, 2005).
Another interesting issue is whether people high on trait personal initiative were better at negative affect regulation. The answer is probably yes and no. On one hand, trait-proactive people regulated more negative affects than passive people. Nonetheless, the trait-proactive people were also more sensitive to the manipulation –both in terms of suffering decrease in positive affects and increase in negative affects. In other words, they have regulated more negative affects but they had a larger variance in affects during the experiment. So the question is whether trait-proactive people are simply more sensitive or they are sensitive and therefore regulate better. In support of the second idea, literature on emotion regulation found that personality traits, such as attentiveness, sociability and constructiveness, influence emotion regulations (Calkins, 1994; Pulkkinen, 1988).
Furthermore, individual differences in emotion thresholds translate to individual differences in rates of experiencing each emotion. This in turn may lead to individual differences in personality traits (Izard, Libero, Putnam & Haynes, 1993). Following this logic, it might be that the different emotional threshold divides people into active and passive by trait. Thus, future research should attempt to measure emotional thresholds and study how they predict proactive behavior, In that way, we could also understand better why our model did not hold in the negative mood condition.
However, in the control condition, we found support that people high on trait personal initiative would engage in proactive strategies to reduce negative affects. Since there was no negative mood manipulation, only the trait-proactive people engaged in proactive behavior. This was not surprising since showing initiative generally is important to ensure high levels of proactive behavior (Sonnentag, 2003). Parker, Bindl, and Strauss (2010) also found that people engage in proactive behavior when they feel capable and energized to do so. Perhaps being high on trait personal initiative means always being energized. In addition, despite the lack of mood manipulation, for the trait-proactive people who chose to be proactive, their
behavior led to less negative affects. This suggests that proactive behavior makes people feel better. In fact, proactivity has been linked to personal achievements that made an impact and therefore resulted in a sense of accomplishment (Bateman & Crant, 1993). Notwithstanding, as we explained trait-proactive people might have become more proactive based on past experience knowing that such behavior is mood-lifting (Andrade, 2005).
Finally, we would like to address the issue of action-state orientation. Action theory helped to explain the concept of personal initiative (Frese, 1996) and it also predicted negative affect regulation (Koole & Jostmann, 2004). Nonetheless, only in the negative mood condition, we found that trait-proactive people are also high on action-orientation. The most likely explanation is that the measure of action-state orientation might have been influenced by the mood manipulation. Perhaps similarly to previous experimental research studying individuals under demanding conditions (Kazén & Kuhl 2008,) we could have used action orientation as a mediator/moderator. Further limitations and ideas for future research are presented below, however, we believe the findings of this paper greatly contribute to understand the mechanisms underlying proactivity.
5.3 Limitations & Future Research
From a methodological point of view Frese (2001) noted that within an experimental design it is difficult to study self-starting goals, because experiments, by their very nature, restrict behaviors to non-self-started actions. Nevertheless, the participation was in duos and the experiment allowed for some socialization making the atmosphere more natural. Furthermore, people were not required or expected to be proactive, the role play during the proactivity task gave them to option to behave passively. Also on a positive note, the sample was adequate in terms of size and similar background. The successful mood manipulation also makes a point that our results are valid. Finally, when creating our measurements,
inter-rater reliabilities and correlations were appropriate. Therefore, the design and the execution of the study could have confirmed our idea that people high on trait personal initiative would use proactive behavior to reduce their negative feelings. And it partially did.
Confirming current proactivity literature, we found that indeed trait-proactive people have a general tendency to engage in proactive behavior. Furthermore, also in line with current theory, we found in both conditions that being trait-proactive facilitates negative affect regulation. In fact, a new finding is that under neutral circumstances trait-proactive people felt better due to engaging in proactive behavior. What we found surprising was that in the negative condition, trait-proactivity meant experiencing a greater variability in both positive and negative affects. Still in the negative condition, we did not anticipate that trait-passive people would become proactive and therefore they would also successfully discard negative affects. We tried to present some possible explanations for this finding, however, it is still worthwhile for future research to examine what triggers passive people to become suddenly proactive when exposed to negative feelings. In addition, our basic prediction was that trait would predict affects but perhaps literature would benefit from studying how affects shape personality traits. Researchers could explore what emotional environment may result in a more proactive nature. Alternatively, we could also benefit from a study using the same model in combination with a within-subject design. That would also shed more light on the influence of affects on trait and the antecedents of proactive behavior. Until then, we can apply the initial findings of this paper in practice.
5.4 Practical Implications
In a working environment our results may mean the following. Firstly, generally managers can expect more proactive behavior from active employees. Passive employees will only
behave more proactively when they experience negative feelings. Negative energies at the workplace seems to trigger proactive behavior for both trait-proactive and passive employees, however, managers can also elicit proactive behavior by providing clarity. Applying standards reduces the variability in proactive behaviors resulting from individual traits and values (House, Shane, & Herold, 1996). When employees are accountable, they will also plan and act in advance. Furthermore, giving employees autonomy, as a sense of confidence also triggers proactive behaviors (Grant & Ashford, 2008).
6 Conclusion
In the introduction we referred to Cinderella, a brave girl who overcomes hardships by initiative and goal-orientedness. We believed that her happy ending was partly determined by her character. To extend our knowledge on proactivity, we wanted to see whether people high on initiative use proactive strategies in order to discard negative affect states. We explained how having an active self-starting approach could help people deal with negative feelings. However, we emphasized that having a trait-proactive nature is not the same as taking actions and engaging in proactive behavior. What we proposed was that proactive behavior, as a subconscious affect regulation strategy, would be the link between an active nature and successful reduction of negative affective states.
The most important finding was that when exposed to negative moods both trait-proactive and trait-passive people became proactive. Under neutral circumstances only people high on trait personal initiative use proactive behavior and they also feel better afterwards. However, unpleasant circumstances “switched on” proactivity among passive people, which is a new piece of information in the proactivity literature. Lastly, our study also contributed to emotion regulation theory by revealing that having an active nature means showing a greater
variability in both positive and negative feelings. These findings can be useful to keep in mind for managers who aim to benefit from workplace proactivity and ensure steady organizational performance.
References
Ashford, S. J., & Cummings, L. L. (1985). Proactive feedback seeking: The instrumental use of the information environment. Journal of Occupational Psychology, 58: pp. 67–79.
Anderson, E., Siegel, E. H., & Barrett, L. F (2011). What you feel influences what you see: The role of affective feelings in resolving binocular rivalry. Journal of Experimental Social Psychology, 47, 856-860.
Andrade, E. B. (2005). Behavioral Consequences of Affect: Combining Evaluative and Regulatory Mechanisms. Journal of Consumer Research 32 (3), pp. 355-362
Aspinwall, L. G., & Taylor, S. E. (1997). A stitch in time: Self-regulation and proactive coping. Psychological Bulletin, 121 pp. 417–436.
Baas, M., de Dreu, C. K. W., & Nijstad, B. A. (2008). A meta-analysis of 25 years of mood-creativity research: Hedonic tone, activation, or regulatory focus?
Psychological Bulletin, 134 (6), pp.779-806.
Bateman, T. S., & Crant, J. M. (1993). The proactive component of organizational behavior. Journal of Organizational Behavior, 14 pp. 103–118.
Baumgartner, T., Esslen, M. & Jäncke, L. (2006). From emotion perception to emotion experience: Emotions evoked by pictures and classical music. International Journal of
Brans, K., Koval, P., Verduyn, P., Lim, Y.L., & Kuppens, P. (2013). The regulation of negative and positive affect in daily life. Emotion 13: pp. 926–939. doi: 10.1037/a0032400
Brotheridge, C. M.; Grandey, A. A. (2002). "Emotional labor and burnout: Comparing two perspectives of people work" (PDF). Journal of Vocational Behavior (Aop.psy.unibe.ch). 60, pp. 17-39.
Calkins, S. D. (1994). Origins and outcomes of individual di erences in emotion regulation. In N. A. Fox, The development of emotion regulation: biological and behavioral
considerations (pp. 53±72). Chicago: The University of Chicago Press (Monographs of the Society for Research in Child Development, 59, Serial No. 240).
Carver, C. S., Sutton, S. K., & Scheier, M. F. (2000). Action, emotion, and personality: Emerging conceptual integration. Personality and Social
Psychology Bulletin, 26(6), pp. 741-751.
Crant, J. M. (1996). The proactive personality scale as a predictor of entrepreneurial intentions. Journal of Small Business Management, 34(3) pp. 42–49.
Crant, J. M. (2000). "Proactive Behavior in Organizations". Journal of Management 26 (3): 435. doi:10.1177/014920630002600304
impact of proactive personality. Journal of Organizational Behavior, 21 pp. 63– 75.
De Dreu, C. K. W., Baas, M., & Nijstad, B. A. (2008). Hedonic Tone and Activation Level in the Mood– Creativity Link: Toward a Dual Pathway to Creativity Model, Journal of
Personality and Social Psychology, 94 (5), pp. 739-756.
Eisenberg, N., Fabes, R. A., Guthrie, I. K., & Reiser, M. (2000). Dispositional emotionality and regulation: Their role in predicting quality of social
functioning. Journal of Personality and Social Psychology, 78, pp. 136–157. doi:10.1037/0022-3514.78.1.136
Fay, D., & Frese, M. (2001). The concept of personal initiative: An overview of validity studies. Human performance, 14, pp. 97-124.
Frese, M., & Fay, D. (2001). Personal initiative (PI): An active performance concept for work in the 21st century. In B.M. Staw & R.M. Sutton (Eds.), Research in
Organizational Behavior (Vol. 23, pp. 133-187). Amsterdam: Elsevier
Science.
Frese, M., Fay, D., Hilburger, T., Leng, K., & Tag, A. (1997). The concept of personal initiative: Operationalization, reliability and validity in two German samples. Journal of Organizational and Occupational Psychology, 70, pp. 139- 161.
Differences between East and West Germany. The Academy of Management
Journal, 39(1), pp. 37-63.
Friedman, R. S., & Fo ̈rster, J. (2001). The effects of promotion and prevention cues on creativity. Journal of Personality and Social Psychology, 81, pp. 1001–1013.
Gasper, K. (2003). When necessity is the mother of invention: Mood and problem solving. Journal of Experimental Social Psychology, 39, pp. 248– 262.
George, J. M., & Brief, A. P. (1996). Motivational agendas in the workplace: The effects of feelings on focus of attention and work motivation. Research in
Organizational Behavior, 18, pp. 75–109.
Grant, A.M. (2013). Rocking the Boat but Keeping It Steady: The Role of Emotion Regulation in Employee Voice Academy of Management Journal pp. 1703- 1723.
Grant, A. M., & Ashford, S. J. (2008). The dynamics of proactivity at work. Research in Organizational Behavior, 28 pp. 3-34.
Gross, J. J. (Ed.). (2007). Handbook of emotion regulation. New York, NY: Guilford.
Gross, J. J., & Muñoz, R. F. (1995). Emotion regulation and mental health. Clinical
Psychology: Science and Practice, 2, pp.151–164. doi:10.1111/j .1468-
Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling [White paper]. Retrieved from http://www.afhayes.com/
Hess, U., & Blairy, S. (2001). Facial mimicry and emotional contagion to dynamic emotional facial expressions and their influence on decoding accuracy.
International Journal of Psychophysiology, 40(2), pp. 129-141.
Higgins, E. T. (2006). Value from hedonic experience and engagement. Psychological
Review, 113, pp. 439–460.
Hirt, E. R., Levine, G. M., McDonald, H. E., Melton, R. J., & Martin, L. L. (1997). The role of mood in quantitative and qualitative aspects of performance: Single or multiple
mechanisms? Journal of Personality and Social Psychology, 33, pp. 602–629.
House, R. J., Shane, S. A., & Herold, D. M. (1996). Rumors of the death of dispositional research are
vastly exaggerated. Academy of Management Review, 21,pp. 203–224.
Izard, C. E., Libero, D. Z., Putnam, P., & Haynes, O. M. (1993). Stability of emotion experiences and their relations to traits of personality. Journal of Personality and Social
Psychology, 64, pp. 847-860.
Johnston, M., Reed, K., Lawrence, K., & Onken, M. (2007). "The Link between Communication and Financial Performance in Simulated Organizational
Teams." Journal of Managerial Issues 19 (4) pp. 536-555.
Jostmann, N. B. , & Koole, S. L. (2010). Dealing with demands: The role of action versus state orientation. In R. Hoyle (Ed.), Handbook of personality and self- regulation (pp. 332-352). Oxford, UK : Wiley-Blackwell.
Kazén, M., Kaschel, R., & Kuhl, J. (2008). Individual differences in intention initiation under demanding conditions: Interactive effects of state vs. action orientation and enactment difficulty. Journal of Research in Personality, 42, pp. 693–715.
doi:10.1016/j.jrp.2007.09.005.
Kirkman, B. L., & Rosen, B. 1999. Beyond self-management: Antecedents and
consequences of team empowerment. Academy of Management Journal, 42 pp. 58–74.
Koole, S. L., & Coenen, L. H. M. (2007). Implicit self and affect regulation: Effects
of action orientation and subliminal self priming in an affective priming task. Self and Identity, 6, pp. 118-136.
Koole, S., Jostmann, N., & Baumann, N. (2012). Do Demanding Conditions Help or Hurt Self-Regulation? Social and Personality Psychology Compass, 6(4), pp. 328- 346.
Koole, S. L., & Jostmann, N. B. (2004). Getting a grip on your feelings: Effects of action orientation and external demands on intuitive affect regulation. Journal of Personality and Social Psychology , 87 , pp. 974-990.
