The Relationship Between Emotions Throughout Competition and
Performance Satisfaction
Sebastian Robert Laurel Hilbrand Buurma 10876707
Universiteit van Amsterdam
Abstract
The aim of the study was to examine the relationship between five discrete emotions based on both valence and activation level measured prior and during the game in relationship to performance satisfaction after the game. The current field study in which 98 female volleyball players participated in actual competition, was one of the first to incorporate the exploration of time-wise developments of emotions during a game. It was expected that active emotions regardless of valence would positively relate to performance satisfaction, while the opposite was expected for passive emotions. However, the results of the current study revealed emotions’ valence in general seemed to be a main predictor of performance satisfaction, finding both negative relationships for negative emotions and performance satisfaction and positive relationships for positive emotions and performance satisfaction. The exploration of the time-wise developments of emotions throughout a game revealed negative-passive emotions just prior to competition, and both negative emotions and positive emotions during the game held significant relationships with performance satisfaction. The results of the current study, their implications and possible limitations were discussed.
Introduction
Emotions have received a lot of attention and interest in the field of sport psychology due to their effects on performance. When considering emotions have a motivational nature due to the fact they are reactions to the status of one's ongoing goals (DeSteno, Petty, Wegener & Rucker, 2000; Nicholls, Polman, & Levy, 2012), generate a state of readiness to meet the challenges of the environment (Friesen, Lane, Devonport, Stellars, Stanely & Beedie, 2013), and coordinate physical, behavioral and psychological responses (Allen, Jones & Sheffield, 2009; Lerner & Keltner, 2000), it should be clear that emotions influence important subcomponents of sport performance (McCarthy, 2011). Despite being extensively studied, past research yielded a lot of ambiguous results regarding the emotion-performance relationship. Whereas past research mainly focused on either discrete emotions or the valence of emotion, the current research examines the emotion-performance relationship from another perspective by classifying emotions on a combination of valence and degree of arousal accompanying it. Besides considering emotions from another possibly more comprehensive perspective in their relationship to performance, the current research also includes the in-game measurement of emotions to provide more insight into dynamics occurring during a game.
Emotions
Emotions are a natural and inherent reaction on those events that hold relevance to the goals, motives and concerns of an individual (Vallerand & Blanchard, 2000). Deci (1980) defined emotion as a reaction to an actual or imagined event, which involves physical and cognitive changes, is experienced subjectively, is expressed physically or through action tendencies, and may be the mediator or energizer of subsequent behaviors. As mentioned before, past research yielded ambiguous results regarding the emotion-performance relationship, finding both positive relationships for positive (Nicholls et al., 2012; Totterdell, 2000, Vast, Young & Thomas, 2010) and negative emotions (Hanin, 2007; Robazza & Bortoli, 2007; Robazza, Bortoli & Nougier, 1998;
Woodman, Davis, Hardy, Callow, Glasscock & Yiull-Proctor, 2009), as well as negative relationships for positive (Carver, 2003; Hanin, 2007) and negative emotions (Nicholls et al., 2012; Robazza et al., 1998; Woodman & Hardy, 2003). Although methodological and sport specific related issues can account for these different results (Atkinson & Nevill, 2001; Craft, Magyar, Becker & Feltz, 2003; Totterdell, 2000), we believe it seems more plausible neither positive or negative emotions do necessarily relate to a similar relationship to performance (Hanin, 2007; Nicholls et al., 2012), and therefore might be subject more complex dynamics.
When looking at emotions, the Circumplex Model of Affect (Russell, 1980) conceptualizes emotions as a two-dimensional construct, incorporating not only valence, but also arousal as a dimension of the emotional experience. From this point of view, emotions can thus be seen as the product of a complex interaction between those two dimensions (Posner, Russell & Peterson, 2005). The way in which emotions influence our behavior thus does not only rely on the different effects of positive and negative emotions, but also by the extent in which emotions can either promote (activating emotions) or inhibit (passive emotions) arousal states (Mouratidis, Vansteenkiste, Lens & Auweele, 2009). Research has shown that high arousal intensifies the emotional experience (Barsade, 2002; Vast et al., 2010), thereby possibly strengthening the effects. In this relationship, it seems higher experienced intensities of emotions motivate individuals to invest more resources to the current task (Woodman et al, 2009) in one's anticipation to reach desired outcomes, whereas low activation represents the absence of need for further action (McCarthy, 2011). We thus reason that not the valence of an emotion, but the functionality of activating moods in stimulating action tendencies and the use of resources might be more relevant in the emotion-performance relationship.
Emotions' activation level
Activating as opposed to deactivating emotions relate to increased attention, approach processes and the efficient integration of information (De Dreu, Baas & Nijstad, 2008), motivation and the investment of resources (Woodman et al., 2009), involvement (Barsade, 2002), efficient
problem-solving strategies (De Dreu et al., 2008; McCarthy, 2011) and the guiding of attention, relevant focus and attention (Vast et al., 2010). Low levels of cognitive activation will primarily lead to inactivity or inhibiting behaviors and might therefore have a negative influence on one's performance, while high levels of activation might enhance cognitive flexibility and persistence and thereby provide more possibilities to reach good performance levels (Nijstad, De Dreu, Rietzschel & Baas, 2010). Despite emotions' valence though, we believe in both cases emotions with high activation levels might represent one's motivation to pursue desired goals (Baumeister, Zell & Tice, 2007), affective involvement (Barsade, 2002), a state of readiness (Brooks, 2014), the appraisal that the situation is under human control (Lerner & Keltner, 2000), and the expectation challenges can be reached (McCarthy, 2011). On the other hand, we believe passive emotions are quite the opposite and on top of that might be detrimental for motivation and the efficient processing of task-relevant information (Pekrun & Perry, 2014), thereby unlikely to stimulate superior performance.
Performance Satisfaction
Because there are a lot of factors at play in influencing the outcome of a sport match, we believe the subjective measurement of performance might be more appropriate than reflecting performance to objective outcomes of a match. Environmental factors, match conditions and variable skill levels might influence performance outcomes (Nicholls et al., 2012) but not necessarily represent individual performance levels. The inclusion of performance satisfaction as a measure of performance in the current design might provide a more sensitive and comprehensive insight into individual personal levels. In this light, more of an understanding can be achieved to what extent emotion relates to these individual performance levels as rated by the athletes themselves in contrast to match outcomes. On top of that, it is believed advanced players are more aware of the domain-specific factors of which they know are relevant in rating performance (Totterdell, 2000).
level and performance satisfaction?
Negative emotions and activation level
Negative emotions arise when one's goals are frustrated or interfered with (Van Kleef, 2009), or when evaluations of one's progress seem negative (Jones, Lane, Bray, Uphill & Catlin, 2005). We theorize that negative emotions high in activation level, thereby stimulating approach processes, stimulate effort and one's action tendencies (Woodman et al., 2009) to overcome discrepancies in one´s desired and current state (Carver, 2003). It seems with these negative-active emotions one's appraisal of the emotion at hand (Craft et al., 2003; McCarthy, 2011; Robazza & Bortoli, 2007) and the desire to decrease the negative discrepancy (Carver, 2003), may be the motivator to engage in behaviors and to direct resources to prevent from not reaching one's goal (Vast et al., 2010). Indeed, past research already showed negative-active emotions (e.g. anger) related to positive outcomes in performance domains (De Dreu et al., 2008; Robazza et al., 1998; Robazza & Bortoli, 2007; Vast et al., 2010; Woodman et al., 2009).
On the other hand, negative emotions that are more passive or even deactivating, are associated with negative evaluations about one's capability in removing or altering the threat (Eysenck et al, 2007; Robazza & Bortoli, 2007). Following these expectations, avoidance processes might be promoted (Carver, 2003; Fredrickson & Losada, 2005), while attention is being directed inwards towards task-irrelevant cues instead of being used on the task at hand (Robazza & Bortoli, 2007; Vast et al, 2010; Woodman et al, 2009), sometimes even leading to disengaging from the task at hand (Gaudreau & Blondin, 2004). When considering the importance in optimal sport performance of one's ability to utilize relevant cues (Eccles, 2004), beliefs regarding their own ability in the face of adversity (Myers, Feltz & Short, 2004) and the use of resources for the task at hand (Woodman & Hardy, 2003). Past research already shown negative-passive emotions (e.g. anxiety) compromise the efficient functioning of a goal-directed system (Eysenck, Derakshan, Santos & Calvo, 2007).
Hypothesis 1A: We expect that a negative-passive emotion (e.g., anxiety, dejection) has a
negative relationship to performance satisfaction. Conversely, we expect that a negative-active emotion (e.g., anger) will have a positive relationship to performance satisfaction.
Positive emotions and activation level
Positive emotions arise following positive evaluations of the self in the current situation, or when good prospects are in sight (Van Kleef, 2009; McCarthy, 2011). Research has shown several positive emotions tend to relate with improved visual attention, reaction time and visual processing which then enables a wider array of thought and action possibilities (Fredrickson & Brannigan, 2005; Moll, Jordet & Pepping, 2010), flexibility and efficient processing of information (McCarthy, 2011), positive appraisals and optimism (Brooks, 2014), positive expectations to reach challenges (Jones et al., 2005) and the automatic execution of physical movements in well-learned tasks (Vast et al., 2010). However, when considering activation level, it is believed positive-passive emotion (e.g. happiness) may suggest a state that desires no immediate need to change anything at the ongoing situation (Woodman et al., 2009), drives the need for anticipation of more favorable outcomes (McCarthy, 2011) or to signal superiority (Moll et al., 2010). Positive-active emotions on the other hand, are more likely to motivate one's urge and the positive expectation to reach favorable outcomes in performance (Jones et al., 2005; McCarthy, 2011;, while strengthening the already existing possible positive effects of positive emotions.
Hypothesis 1B: We expect that a positive- passive emotion (e.g., happiness) will have a
negative relationship with performance satisfaction, whereas a positive-active emotion (e.g., excitement) will have a positive relationship with performance satisfaction.
Emotions during a game
Besides examining the relationship between valence and activation level of emotions and performance satisfaction, the current research will investigate the effects of emotions during
competition. Most research on emotions and performance concerned the measurement of pre-competition emotions (Cerin & Barnet, 2007; Totterdell, 2000). When considering emotions can be described as short-lived experiences (Fredrickson & Branigan, 2005) which for a percentage of around 80 percent end within the first half hour after the experienced emotion (Verduyn, Van Mechelen & Tuerlinckx, 2011), the measurement of only pre-competition emotions might seem odd. Even from a methodological point of view, the measurement of emotions of sports short in duration seem to yield the most success in studying performance (Totterdell, 2000), which seems logical when considering the huge amount of environmental and personal factors which can influence the initially felt emotions prior to a game. When considering the initial activation levels of the emotion provoking stimuli can lead to retention of the emotion (Ritchie, Skowronski, Hartnett, Wells & Walker, 2009; Verduyn et al., 2011), while also the personal importance can lead to longer emotional episodes (Verduyn et al., 2011), the use of emotions classified by valence and activation level, also might provide interesting insights. By measuring emotions during the game, we hope to obtain more of an insight into the course of emotions during the game and their relationship with performance.
Research question 2: How do different time-points relate to performance after the game?
Method
Participants
Participants were 98 Dutch adult female volleyball players between the ages of 16 and 55 (M= 25.90, SD= 8.47), with average competitive experience ranging from 3 to 37 years (M= 14.41,
SD= 6.52). The participants came from 11 teams, which performed in the promotion, first, second
and fifth division in the Netherlands.
Materials
Questionnaire (SEQ; Jones, Lane, Bray, Uphill & Catlin, 2005; short version, Turner, Jones,
Sheffield, Slater, Barker & Bell, 2013) with a modified visual analogue response scale. The length of this scale was set at 15 centimeters. The emotions measured were anxiety, dejection, excitement,
anger, and happiness (each measured with one item, i.e., anxious, dejected, excited, angry, happy).
Participants were asked to mark on the response scale (ranging from not at all to very much) for each emotion in which intensity this was present at the moment of measurement.
Performance satisfaction. Two single questionnaire items were used to measure subjective
performance satisfaction. Specifically, athletes indicated the degree of satisfaction with their own performance (''Regarding the current game, how satisfied are you with your own performance?'') and their satisfaction with their team's performance ('' Regarding the current game, how satisfied are you with your team's performance?''). Both items were rated on a 7-point Likert-type scale ranging from 1 = not satisfied at all to 7 = very satisfied.
Demographic Information. The questionnaire package used in the research included a
section containing questions to obtain demographic information about the participants. These questions regarded the name, age, player's number, years of experience, years of involvement with the current team, position, experience with higher level competitions in volleyball, frequency being in the starting line-up of the team, and satisfaction with own and team performance during current season of the participant.
Procedure
After having obtained approval from the appropriate institutional ethics review board, head-coaches of potential teams for our study were emailed, after which contact through either phone calls or email was established to explain the study. When the coaches approved to participate in the study, two moments of measurement were planned for an information meeting prior to competition for the volleyball team so that they could complete informed consent forms and demographic questionnaires, and for the day of competition where measurement of emotion and performance satisfaction would take place. Participants participated voluntarily, no rewards were given.
As displayed in Figure 1, on the day of competition emotions were measured at multiple time-points. As soon as the volleyball players left the field during the game, forms containing the measurement of emotion were handed out immediately. Performance satisfaction was measured immediately after the final set.
Figure 1. Schematic overview of the moments of measurement of emotions and performance
satisfaction
Analysis
All statistical analyses were processed using the IBM SPSS statistics (Version 24.0; IBM Corporation,NY, USA).
Results
Of the 98 original participants, four cases were excluded from the current analysis due to the absence of both performance satisfaction items. When participants missed an emotion or performance satisfaction item, these cases were marked as missing and subsequently excluded from the analysis pairwise. Descriptive statistics for emotions across the different time-points and the performance satisfaction variables are displayed in Table 1.
Both emotion and performance satisfaction were considered continuous in the current research since the variables represent an underlying continuous spectrum and are measured in a way which captures a continuous measure (Allen & Seaman, 2007), thus to analyze the relationships between emotions and performance satisfaction, multiple regression analyses were conducted.
Pre-Game During the game Post-Game
1 hour prior to game Just before the game Set 1 Set 2 Set 3 Directly after the game
Emotions Emotions Emotions Emotions Emotions Performance Satisfaction
Table 1
Descriptive Statistics of Means, Standard Deviation and Response Range for Emotions and Performance Satisfaction
Mean Overall Pre-Game 1 Pre-Game 2 Set 1 Set 2 Set 3
Variable M SD N M SD N M SD N M SD N M SD N M SD N Anxiety 3.32 2.11 91 3.58 3.17 89 4.44 4.13 60 2.91 2.13 87 2.38 2.19 83 2.52 2.26 89 Dejection 3.48 2.25 91 2.71 2.50 87 2.49 2.40 56 3.11 2.36 87 3.86 3.35 86 3.68 3.17 88 Anger 3.18 2.16 94 1.42 1.74 87 1.51 1.82 58 3.37 3.05 89 4.18 3.86 90 3.40 2.75 90 Excitement 11.23 1.84 93 11.60 1.77 90 12.12 2.16 60 11.79 2.32 89 11.11 2.85 88 10.43 2.96 91 Happiness 10.41 1.94 92 10.94 1.96 90 11.43 2.48 59 10.85 2.99 89 9.89 3.80 90 9.69 3.25 92 Satisfaction with own performance 4.80 1.43 92 Satisfaction with teams’ performance 5.63 1.02 92
Before running the analysis, several assumptions were evaluated. Evaluation of the normal P-P Plot of regression standardized residual and the scattterplot of standardized residuals against standardized predicted values revealed that the assumptions of normality, linearity and homoscedasticity of residuals were met in all emotion and performance satisfaction variables. Collinearity statistics revealed multicollinearity was not of any concern among the current set of predictors. Specifically, emotion variables did not exceed the critical Mahalanobis distance value of
X2 = 20.52 for df = 5 (at α = .001; Allen & Bennet, 2010). Boxplots revealed a total of 77 outliers on data points, which were subsequently omitted from the analysis.
To answer research question one, averages of emotions (i.e., anxiety, dejection, anger, excitement and happiness) were calculated across all-time points as predictors of satisfaction with own and teams’ performance. Multiple regression analysis revealed that averaged emotions accounted for 17.8% of the variance in personal performance satisfaction (adjusted R2 = .13, F(5, 83) = 3.60, p < .01) and 23.4% of the variance in satisfaction with teams' performance (adjusted R2 = .19, F(5, 82) = 5.01, p < .001).
Regarding the first hypothesis it was expected that negative-active emotions would positively relate to performance satisfaction, while negative-passive emotions would negatively relate to performance satisfaction. However, as can be seen in Table 2, results revealed that only dejection and anger predicted performance satisfaction. More specific, in line with our hypothesis volleyball players who reported higher levels of dejection were less satisfied with personal performance, while in contrast with our expectations volleyball players who reported more anger were less satisfied with teams' performance.
Regarding the second hypothesis it was expected that positive-active emotions would positively relate to performance satisfaction, while positive-passive emotions would negatively relate to performance satisfaction. In contrast with this hypothesis, as can be seen in Table 2, results indicated that neither of these emotions predicted performance satisfaction.
Table 2
Summary of Regression Analysis for Emotions Predicting Performance Satisfaction
Performance Satisfaction
Satisfaction with own performance a Satisfaction with teams' performance b
Emotions β 95% CI β 95% CI Anxiety .07 [-.12, .22] .07 [-.08, .15] Dejection -.33* [-.40, -.02] -.09 [-.18, .09] Excitement .19 [-.13, .42] .20 [-.08, .30] Anger .24 [-.03, .35] -.33* [-.28, -.03] Happiness .17 [-.16, .40] .00 [-.19, .20]
Note. aN Anxiety and Dejection (N = 90), Excitement and Happiness (N = 91), Angry (N = 92). bN Anxiety and Dejection (N = 88), Angry (N = 91), Excitement (N = 90), Happiness (N = 89)
* p < .05.
To answer the second research question, multiple regression analyses were conducted with the distinct emotions (i.e., anxiety, dejection, anger, excitement and happiness) across the different time-points as predictors of satisfaction with own and teams' performance. An overview of the results is presented in Table 3.
Results revealed that anxiety accounted for 4.6% of the variance in personal performance satisfaction (adjusted R2 = -.05, F(5, 48) = 0.46, p = .803) and 11.9% of the variance in satisfaction with teams' performance (adjusted R2 = .03, F(5, 48) = 1.30, p = .282). It was expected that anxiety as a negative-passive emotion would negatively predict performance satisfaction. However, as can be seen in Table 3, this was only the case for anxiety measured just prior to the game (Pre-Game 2) and only with satisfaction with teams’ performance.
Dejection accounted for 19.4% of the variance in personal performance satisfaction (adjusted R2 = .11, F(5, 47) = 2.26, p = .064) and 44.0% of the variance in satisfaction with team's performance (adjusted R2 = .38, F(5, 47) = 7.50, p < .001). It was expected a negative-passive emotion like dejection would negatively predict performance satisfaction. As can be seen in Table 3, results revealed that dejection predicted satisfaction with own performance positively at Pre-Game 1, although correlations revealed this was probably a suppression effect (see Appendix for correlation table), while a trend at Pre-Game 2 was observed. In relation to satisfaction with teams' performance, results indicated that dejection at Pre-Game 1, Pre-Game 2, Set 1 and Set 2 all predicted performance satisfaction. In line with our hypothesis, higher levels of dejection measured at Pre-Game 2 and Set 2 negatively predicted satisfaction with teams' performance, while in contrast with our hypothesis higher levels of dejection at Pre-Game 1 and Set 1 positively predicted performance satisfaction. However, correlations revealed this might be due to suppression effects.
Anger accounted for 7.6% of the variance in personal performance satisfaction (adjusted R2 = -.02, F(5, 49) = 0.81, p = .548) and 35.7% of the variance in satisfaction with team's performance (adjusted R2 = .29, F(5, 49) = 5.45, p < .001). In contrast with the expectation that a negative-active emotion like anger would positively predict performance satisfaction, results revealed higher levels of anger at Set 2 negatively predicted satisfaction with teams' performance, while a trend was observed with more anger at Set 1 and satisfaction with own performance. No positive relationships or trends with performance satisfaction were observed.
Excitement accounted for a 23.0% of the variance in personal performance satisfaction (adjusted R2 = .16, F(5, 51) = 3.05, p < .05) and 16.9% of the variance in satisfaction with team's performance (adjusted R2 = .09, F(5, 51) = 2.07, p = .084). It was hypothesized that a positive-active emotion like excitement, would positively predict performance satisfaction. The results revealed that higher levels of excitement measured at Pre-Game 2 negatively predicted satisfaction with own performance, however, correlations revealed this was probably due to suppression effects. Despite the absence of significant predictive values, in line with the hypothesis, trends were
observed with excitement measured at Pre-Game 1, Set 1 and Set 3 and satisfaction with own performance, and excitement measured at Set 2 and Set 3 and satisfaction with teams' performance.
Finally, happiness accounted for 18,4% of the variance in personal performance satisfaction (adjusted R2 = .10, F(5, 51) = 2.31, p = .058) and 30.2% of the variance in satisfaction with team's performance (adjusted R2 = .23, F(5, 51) = 4.41, p < .01). It was hypothesized that a positive-passive emotion like happiness would negatively predict performance satisfaction. In contrast with this hypothesis, the results revealed that higher levels of measured happiness at Set 1 and Set 3 positively predicted satisfaction with own performance, while happiness at Set 2 positively predicted satisfaction with teams' performance.
Table 3
Summary of Regression Analysis for Emotions Predicting Performance Satisfaction at Multiple Time-Points
Performance Satisfaction
Satisfaction with own performance a
Satisfaction with teams’ performance b Emotions β 95% CI β 95% CI Anxiety Pre-Game 1 .12 [-.16, .27] .31 [-.05, .25] Pre-Game 2 -.22 [-.22, .07] -.46* [-.21, -.01] Set 1 -.06 [-.27, .20] .03 [-.15, .18] Set 2 .02 [-.26, .28] -.21 [-.28, .09] Set 3 -.10 [-.29, .16] .07 [-.12, .18] Dejection Pre-Game 1 .52* [.06, .53] .40* [.03, .30] Pre-Game 2 -.43 [-.55, .03] -.90** [-.55, -.21]
Set 1 -.16 [-.34, .15] .65** [.13, .42] Set 2 -.07 [-.19, .12] -.59** [-.27, -.09] Set 3 -.11 [-.20, .10] .11 [-.05, .12] Anger Pre-Game 1 -.18 [-.46, .17] .25 [-.04, .33] Pre-Game 2 .31 [-.10, .58] -.25 [-.34, .07] Set 1 -.34 [-.33, .01] .07 [-.08, .12] Set 2 .03 [-.12, .14] -.66** [-.25, -.10] Set 3 .04 [-.15, .19] .18 [-.03, .17] Excitement Pre-Game 1 .31 [-.03, .53] .03 [-.19, .22] Pre-Game 2 -.53** [-.60, -.11] -.08 [-.22, .14] Set 1 .30 [-.01, .38] -.04 [-.16, .13] Set 2 .07 [-.12, 18] .31 [.00, .22] Set 3 .27 [-.02, .27] .19 [-.04, .17] Happiness Pre-Game 1 .02 [-.26, .28] .02 [-.17, .19] Pre-Game 2 -.39 [-.48, .04] -.13 [-.22, .12] Set 1 .45** [.06, .37] .06 [-.08, .12] Set 2 .09 [-.07, .14] .56** [.08, .22] Set 3 .32* [.01, .27] .02 [-.08, .09]
Note. aN ranging from 82 to 91, except for pre-game two with N ranging from 55 to 58. bN ranging from 80 to 89, except for pre-game two with N ranging from 54 to 58.
* p < .05. ** p < .01.
The current research included the exploration of the time-wise development of emotions and performance satisfaction. Results revealed that in relationship to satisfaction with own performance, higher levels of happiness at Set 1 and Set 3 revealed to predict performance satisfaction positively. In relationship to satisfaction with teams' performance, results revealed that negative-passive emotions at Pre-Game 2 and (anxiety and dejection) negatively predicted performance satisfaction.
At Set 2, both negative-active (anger), negative-passive (dejection) and positive-passive (happiness) emotions predicted satisfaction with teams' performance. Several trends were observed, with excitement mainly recurring as a trend at Set 2 and Set 3 for satisfaction with teams' performance, and at Pre-Game 1, Set 1 and Set 3 for satisfaction with own performance.
Discussion
The aim of the current study was to investigate the relationship between discrete emotions throughout the game and performance satisfaction after the game. It was expected that active emotions regardless of valence would positively predict performance, while the opposite was expected for passive emotions. The results of the current research contrasted partially with these hypothesized relationships by finding that despite activation level, negative emotions in general predicted performance satisfaction negatively, while finding the opposite for positive emotions. The exploration of time-wise development of emotions and performance satisfaction in the current study revealed the existence of specific points before and during a game that seem to be predictive in their relationship to performance satisfaction.
Negative emotions in general thus seem to be debilitative to performance satisfaction, regardless of activation level. Interestingly, the current study revealed that a negative-passive emotion like dejection predicted one to evaluate one’s own performance as poorly, while a negative-active emotion like anger predicted one to evaluate teams’ performance as poorly. It thus might be suggested that a negative-passive emotion is associated more with a focus turned inwards, while a negative-active emotion might be directed at the team. Indeed, past research already identified dejection as an emotion associated with negative self-schema’s (Lane, Terry, Devonport, Friesen & Totterdell, 2017). It thus can be reasoned those experiencing higher levels of dejection might blame themselves for negative outcomes or performance. In the case of negative-active emotions, research has shown that anger is likely to surface when negative consequences are perceived to be under the control of others (Allen, Jones & Sheffield, 2009). It might thus be
suggested that in the case of anger the athletes blame negative performance outcomes on their team instead of themselves.
Despite being a negative-passive emotion, the same results weren’t obtained for the emotion of anxiety. As research has shown, it might be that not always the emotion of anxiety per se, but rather our own personal functionality or relevance that might be of importance in the emotion-performance relationship (Hanin, 2010). Models like the Individual Zone of Optimal Functioning (Hanin, 2000) account for these differences in the functionality of emotions, by classifying emotions on their facilitative or debilitative character on a subjective level. Indeed, past research has already shown that the personal appraisal of emotions as either facilitative or debilitative was associated with experiencing anxiety as either performance enhancing or performance impairing (Franklin, Smith & Holmes, 2015; Neil, Hanton, Mellalieu & Fletcher, 2011).
This personal functionality might also be applicable to the emotion of a negative-active emotion like anger, which was theorized to be beneficial to performance when one’s action tendencies were stimulated in order to decrease an undesired discrepancy in current and desired state. A possible explanation for the lack of support for this theorized relationship, might be that past as research has shown that when directed inwards, anger results in more debilitative moods (Robazza & Bortoli, 2007) and concentration disruption (McCarthy, Allen & Jones, 2013), which could subsequently be debilitative to performance. On the other hand, research has also shown that experienced anger for individuals in specific individualized ranges might be beneficial for performance (Ruiz & Hanin, 2004). Future research could further investigate the importance of the personal relevance or functionality. Again, as for anxiety, the personal functionality of the emotion wasn’t accounted for in this research.
According to the results of the current study, positive emotions in general did not seem to specifically predict performance. This is in contrast with a wide range of literature reporting positive relationships between positive emotions and performance (Nicholls et al., 2012; Totterdell, 2000; Vast et al., 2010). A possible explanation is that although there is an extending field of
research on positive emotions and performance related behaviors, the mechanisms at play are mostly indirect (McCarthy, 2011). It thus seems there is a certain complexity in positive emotions that make their relationship to performance much more complicated than it seems at first sight.
However, when considering the time-wise developments included in this study, happiness experienced during the game did seem to be a good predictor of satisfaction with performance. This might be in line with the suggestion that positive emotions indirectly relate to performance, when considering that positive evaluations of the current game might raise the awareness that performance states are as desired, while also enabling performance facilitative behaviors like creativity, problem-solving and decision making (McCarthy, 2011). Despite being non-significant, subjective measures of a positive-active emotion as excitement experienced during the game also seemed to relate to satisfaction with both own and teams’ performance, thereby possibly highlighting the beliefs in preferable outcomes of the game. Altogether the experience of both active and passive positive emotions during competition might be of importance in relation to desired performance.
On the contrary, the experience of negative-passive emotions just prior to the game seems to negatively affect one’s satisfaction with teams’ performance. These results suggest that these negative feelings have an impact on satisfaction with performance, despite not even a second has been played in the game. It might be that the negative beliefs about one's capability in overcoming the negative discrepancy (Eysenck et al, 2007), possible avoidance processes (Carver, 2003), and the attention to task-irrelevant cues (Vast et al, 2010) that can accompany negative emotions, due to the short-term character of an emotion (Fredrickson & Branigan, 2005) has immediate effect on performance. However, some caution is warranted when considering the ambiguous results regarding negative emotions and the importance of individualized relevance.
Theoretical implications
The current study was one of the first to investigate the dynamics of discrete emotions throughout a game in relationship to performance satisfaction on a personal and on a team level. In
contrast, previous work mostly measured emotions only prior (Cerin & Barnet, 2007; Totterdell, 2000) or after competition (Vast et al, 2010), with the disadvantage that participants’ emotions might be highly influenced by the outcome of a match, thereby eluding the actual dynamics of emotions leading up to performance states. The current study thus shed a novel insight into the predictive value of emotions in general and throughout a game on performance in an actual context of competition. In line with previous work, the current study highlights the negative relationship between negative-passive emotions prior to a game and performance (Nicholls et al., 2012). Moreover, the predictive value of positive and negative emotions during the game adds to the existing literature by providing insight into developments during the game.
Practical implications
From an applied perspective, the current research shows promising results regarding discrete emotions and performance. Put simply, the results of this study suggest the absence of negative emotions prior and during the game, and the presence of positive emotions during the game, should make it more likely satisfaction with performance is obtained. When considering that there are several strategies to reappraise negative emotions into positive emotions (Brooks, 2014) and the fact that skills like imagery can regulate one’s emotions (Holmes & Mathews, 2010; Post, Muncle & Simpson, 2012) this could have interesting opportunities for both athletes and coaches in actively influencing one’s emotions. From an individual point of view, athletes could invest in these techniques to actively counterbalance either negative emotions prior and during the game, or enhancing positive emotions during a game to increase the chance of desired performance.
On a more collective level, coaches and trainers on the other hand could create an environment that supports positive emotions, or try forms of warming-up that are likely to stimulate positive affect. When considering that through the process of emotional contagion a collective emotion of a sports team as a whole can be achieved, which has shown to be a relevant determinant in future team performance (Barsade, 2002, Myers et al, 2004; Myers, Payment, & Feltz, 2004),
coaches could stimulate their team in a fashion that stimulates the positive emotions that increase the chance of preferable performance. Even players within a team can actively try to empower positive emotions to teammates.
Limitations of the study
There are several limitations to the current research that need to be considered in interpreting the results. First, the current research relied on self-reported indications of performance. Although these self-reported measures possibly give more of an insight of individual performance, these measures might also be more sensitive to the value participants give to certain scores. Future research could make response scales more specific, by possibly conceptualizing elements of personal performance in the response items. Second, the current population was quite specific and might therefore be applicable to only a small range of athletes. Future research could thus focus on examining a wider range of sports, competitive levels and gender. Third, the current study was a longitudinal field study, thereby any causal explanations can’t be made. Future studies could try to actively manipulate emotions to examine possibly causal relationships in the emotion-performance relationship. Finally, as mentioned before, a final recommendation could be that despite measuring emotions, personal functionality could be assessed in the future to provide a more nuanced view on how emotions relate to performance.
Conclusion
Altogether, the current study examined five discrete emotions based on valence and activation level in relationship to performance satisfaction throughout a game. Herein emotions’ valence seemed to be of greater importance than activation level by suggesting that negative emotions in general resulted in less satisfactory performance. The exploration of the effects of emotions at specific times prior and during the game revealed novel insights into the dynamics of emotion and performance during the game. Specifically, negative emotions prior and during a game
predicted satisfaction with performance negatively, while positive emotions during the game related to satisfaction with performance positively. Besides having theoretical significance, the current research has applied significance when considering relationships of emotions have been determined as a whole and across the game, which could improve the practice of coaches and trainers.
References
Allen, P., & Bennett, K. (2010). PASW statistics by SPSS: A practical guide, version 18.0. Melbourne, Australia: Cengage Learning Australia.
Allen, M. S., Jones, M. V., & Sheffield, D. (2009). Attribution, emotion, and collective ffficacy in sports teams. Group Dynamics: Theory, Research and Practice, 13, 205-217.
Allen, M. S., Jones, M. V., & Sheffield, D. (2009). Causal attribution and emotion in the days following competition. Journal of Sports Sciences, 27, 461-468.
Allen, I. E., & Seaman, C. A. (2007). Likert scales and data analyses. Quality progress, 40(7), 64. Atkinson, G., & Nevill, A. M. (2001). Selected issues in the design and analysis of sport
performance research. Journal of Sports Sciences, 19, 811-827.
Barsade, S. (2002). The ripple effect: Emotional Contagion and its influence on group behavior.
Administrative Science Quarterly, 47, 644-675.
Baumeister, R.F., Zell, A.L., & Tice, D.M. (2007). How emotions facilitate and impair self-regulation. In J.J. Gross (Ed.), Handbook of emotion regulation (pp.408-426). New York: The Guilford Press.
Brooks, A. W. (2014). Get excited: Reappraising pre-performance anxiety as excitement. Journal of
Experimental Psychology: General, 143, 1144-1158.
Carver, C. (2003). Pleasure as a sign you can attend to something else: Placing positive feelings within a general model of affect. Cognition & Emotion, 17, 241-261.
Cerin, E., & Barnett, A. (2006). A processual analysis of basic emotions and sources of concerns as they are lived before and after a competition. Psychology of Sport and Exercise, 7, 287–307. Craft, L. L., Magyar, T. M., Becker, B. J., & Feltz, D. L. (2003). The relationship between the
Competitive State Anxiety Inventory-2 and sport performance: A meta-analysis. Journal of
Sport and Exercise Psychology, 25, 44-65.
Deci, E. L. (1980). The psychology of self-determination. Free Press.
mood-creativity link: toward a dual pathway to mood-creativity model. Journal of Personality and Social
Psychology, 94, 739.
DeSteno, D., Petty, R. E., Wegener, D. T., & Rucker, D. D. (2000). Beyond valence in the perception of likelihood: the role of emotion specificity. Journal of personality and social
psychology, 78, 397.
Eccles, D. W. (2004). Why an expert team is more than a team of experts: A social-cognitive conceptualization of team coordination and communication in sport. Journal of Sport and
Exercise Psychology, 26, 542-560.
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7, 336-353.
Franklin, Z. C., Smith, N. C., & Holmes, P. S. (2015). Anxiety symptom interpretation and performance expectations in high-anxious, low-anxious, defensive high-anxious and repressor individuals. Personality and Individual Differences, 77, 27-32.
Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19, 313–332.
Fredrickson, B. L., & Losada, M. F. (2005). Positive affect and the complex dynamics of human flourishing. American psychologist, 60, 678.
Friesen, A. P., Lane, A. M., Devonport, T. J., Sellars, C.N., Stanley, D. N., & Beedie, C. J. (2013). Emotion in sport: Considering interpersonal regulation strategies. International Review of Sport
and Exercise Psychology, 6, 139-154.
Gaudreau, P., & Blondin, J. P. (2004). Different athletes cope differently during a sport competition: A cluster analysis of coping. Personality and Individual Differences, 36, 1865-1877.
Hanin, Y. L. (2000a). Appendix A: Introduction to the IZOF-Based Individualized Emotion Profiling Forms. In Y. L. Hanin (Ed.), Emotions in sport (pp. 301-302). Champaign, IL: Human Kinetics.
Hanin, Y. L. (2000b). Appendix B: IZOF-Based emotion-profiling: step-wise procedures and forms. In Y. L. Hanin (Ed.), Emotions in sport (pp. 303-313). Champaign, IL: Human Kinetics.
Hanin, Y. L. (2000c). Appendix C: Practical example of IZOF profiles. In Y. L. Hanin (Ed.),
Emotions in sport (pp. 315-316). Champaign, IL: Human Kinetics.
Hanin, Y. L. (2007). Emotions in sport: Current issues and perspectives. Handbook of Sport
Psychology, 3, 31-58.
Hanin, Y. L. (2010). “Coping with anxiety in sport,” in Coping in Sport: Theory, Methods, and
Related Constructs, ed. A. R. Nicholls (Hauppauge, NY: Nova Science), 159–175.
Holmes, E. A., & Mathews, A. (2010). Mental imagery in emotion and emotional disorders.
Clinical Psychology Review, 30, 349–362.
Jones, M. V, Lane, A. M., Bray, S. R., Uphill, M., & Catlin, J. (2005). Development and validation of the Sport Emotion Questionnaire. Journal of Sport & Exercise Psychology, 27, 407–431. Lane, A. M., Terry, P. C., Devonport, T. J., Friesen, A. P., & Totterdell, P. A. (2017). A Test and
extension of Lane and Terry’s (2000) conceptual model of mood-performance relationships using a large internet sample. Frontiers in Psychology, 8.
Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific influences on judgement and choice. Cognition & Emotion, 14, 473-493.
McCarthy, P. J. (2011). Positive emotion in sport performance: current status and future directions.
International Review of Sport and Exercise Psychology, 4, 50-69.
McCarthy, P. J., Allen, M. S., & Jones, M. V. (2013). Emotions, cognitive interference, and concentration disruption in youth sport. Journal of Sports Sciences, 31, 505-515.
Moll, T., Jordet, G., & Pepping, G. (2010). Emotional contagion in soccer penalty shootouts: Celebration of individual success is associated with ultimate team success. Journal of Sport
Sciences, 28, 983-992.
negative affect: Achievement goals and discrete emotions in the elementary physical education classroom. Psychology of Sport and Exercise, 10, 336-343.
Myers, N. D., Feltz, D. L., & Short, S. E. (2004). Collective efficacy and team performance: A longitudinal study of collegiate football teams. Group Dynamics: Theory, Research, and
Practice, 8, 126–138.
Myers, N. D., Payment, C. A., & Feltz, D. L. (2004). Reciprocal relationships between collective efficacy and team performance in women’s ice hockey. Group Dynamics: Theory, Research,
and Practice, 8, 182–195.
Neil, R., Hanton, S., Mellalieu, S. D., & Fletcher, D. (2011). Competition stress and emotions in sport performers: The role of further appraisals. Psychology of Sport and Exercise, 12, 460- 470. Nicholls, A. R., Polman, R. C., & Levy, A. R. (2012). A path analysis of stress appraisals,
emotions, coping, and performance satisfaction among athletes. Psychology of Sport and
Exercise, 13, 263-270.
Nijstad, B. A., De Dreu, C. K., Rietzschel, E. F., & Baas, M. (2010). The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence. European Review of Social
Psychology, 21, 34-77.
Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. International
Handbook of Emotions in Education, 120-141.
Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development
and Psychopathology, 17, 715-734.
Post, P., Muncle, S., & Simpson, D. (2012). The Effects of Imagery Training on Swimming Performance: An Applied Investigation. Journal of Applied Sport Psychology, 24, 323-337. Ritchie, T., Skowronski, J. J., Hartnett, J., Wells, B., & Walker, W. R. (2009). The fading affect
bias in the context of emotion activation level, mood, and personal theories of emotion change.
Robazza, C., Bortoli, L., & Nougier, V. (1998). Performance-related emotions in skilled athletes: Hedonic tone and functional impact. Perceptual and Motor skills, 87, 547-564.
Robazza, C., & Bortoli, L. (2007). Perceived impact of anger and anxiety on sporting performance in rugby players. Psychology of Sport and Exercise, 8, 875-896.
Ruiz, M. C., & Hanin, Y. L. (2004). Idiosyncratic description of anger states in skilled Spanish karate athletes: an application of the IZOF model. Revista de Psicología del Deporte, 13, 75-93. Russell, J. A. (1980). A Circumplex Model of Affect. Journal of Personality and Social
Psychology, 39, 1161-1178
Totterdell, P. (2000). Catching moods and hitting runs: Mood linkage and subjective performance in professional sports teams. Journal of Applied Psychology, 83, 848–859.
Turner, M. J., Jones, M. V, Sheffield, D., Slater, M. J., Barker, J. B., & Bell, J. J. (2013). Who thrives under pressure? Predicting the performance of elite academy cricketers using the cardiovascular indicators of challenge and threat states. Journal of Sport & Exercise
Psychology, 35, 387–97.
Vallerand, R. J., & Blanchard, C. M. (2000). The study of emotion in sport and exercise: historical,
definitional and conceptual perspectives. In Y. L. Hanin (Ed.), Emotions in sport (3-37).
Champaign, IL: Human Kinetics.
Van Kleef, G. A. (2009). How emotions regulate social life: The emotions as social information (EASI) model. Current Directions in Psychological Science, 18, 184–188.
Vast, R. L., Young, R. L., & Thomas, P. R. (2010). Emotions in sport: Perceived effects on attention, concentration, and performance. Australian Psychologist, 45, 132-140.
Verduyn, P., Van Mechelen, I., & Tuerlinckx, F. (2011). The relation between event processing and the duration of emotional experience. Emotion, 11, 20-28.
Woodman, T., Davis, P. A., Hardy, L., Callow, N., Glasscock, I., & Yuill-Proctor, J. (2009). Emotions and sport performance: An exploration of happiness, hope, and anger. Journal of
Woodman, T., & Hardy, L. (2003). The relative impact of cognitive anxiety and self-confidence upon sport performance: A meta-analysis. Journal of Sports Sciences, 21, 443-457.
Appendix
Summary of Correlations for Emotions, Timepoints and Performance Satisfaction
Emotion
Satisfaction with own performance
Satisfaction with team’s performance Anxiety Overall -.120 -.15 Pre-Game 1 -.10 -.10 Pre-Game 2 -.18 -.27* Set 1 -.11 -.10 Set 2 -.09 -.16 Set 3 -.13 -.06 Dejection Overall -.29** -.34** Pre-Game 1 .04 -.14 Pre-Game 2 -.23* -.37** Set 1 -.26** -.06 Set 2 -.21* -.41** Set 3 -.16 -.25** Anger Overall -.10 -.44** Pre-Game 1 -.07 -.13 Pre-Game 2 .00 -.28* Set 1 -.19* -.21* Set 2 -.04 -.54** Set 3 -.03 -.11 Excitement Overall .34** .35** Pre-Game 1 .20* .10 Pre-Game 2 -.03 .09 Set 1 .24* .09
Set 2 .22* .38** Set 3 .26** .32** Happiness Overall .32** .37** Pre-Game 1 .05 .11 Pre-Game 2 .06 .10 Set 1 .28** .06 Set 2 .13 .54** Set 3 .22* .15 * p < .05. ** p < .01.
