JOURNAL OF

Journal of Sport & Exercise Psychology, 2014, 36, 131-145 http://dx.doi.org/10.1123/jsep.2012-0271 © 2014 Human Kinetics, Inc.

SPORT EXERCISE PSYCHOLOGY

Official Journal of NASPSPA

www.JSEP-Journal.com ORIGINAL RESEARCH

Examining Correlates of Game-to-Game Variation in Volleyball Players’ Achievement Goal Pursuit and Underlying Autonomous and Controlling Reasons Maarten Vansteenkiste,1 Athanasios Mouratidis,2,3 Thomas Van Riet,1 and Willy Lens2,4 1University

of Gent; 2University of Leuven; 3Hacettepe University; 4University of the Free State

In the current study we aimed to examine the antecedents and outcomes associated with the variability in competitive volleyball players’ (N = 67; Mage = 19.45; SD = 5.13) situational achievement goal pursuit and its underlying autonomous and controlling reasons. Players were followed during six consecutive games and data were analyzed through multilevel modeling. Players’ dominant contextual goal pursuit reported at the onset of the study related to their situational (i.e., game-specific) goal pursuit. Further, variation in gameto-game mastery-approach goal pursuit, as compared with the pursuit of other achievement goals, related to variation in prosocial behavior. Finally, autonomous reasons underlying situational mastery-approach goal pursuit related positively to games-specific prosocial behavior, enjoyment, and performance satisfaction. The discussion emphasizes the necessity to study players’ game-to-game motivational dynamics and the reasons underlying players’ achievement goal pursuit. Keywords: achievement goals, autonomous and controlled motivation, self-determination theory, moral behavior

All players would likely agree that their motivation can shift considerably from game to game. Although sport psychologists have intensively studied between-player differences in motivation (e.g., Boardley & Jackson, 2012), they have—rather surprisingly—paid less attention to the variability in players’ motivational dynamics from game to game and the correlates of such withinplayer variability (but see Gagné, Ryan, & Bargmann, 2003). Therefore, we examined the game-to-game fluctuation of volleyball players’ motivation and how such variation related to variation in game-specific outcomes. Different aspects of athletes’ game-specific motivation were addressed, thereby drawing upon two wellvalidated motivational frameworks, the achievement goal approach (AGA; Elliot, 2005) and self-determination theory (SDT; Deci & Ryan, 2008). Specifically, we studied (a) which type of achievement goals athletes preferred pursuing during each game (i.e., the direction of achievement goal pursuit) and (b) the underlying reasons for pursuing these game-specific goals (i.e., the regulation Note. The first two authors equally contributed to this project and can both be considered as leading authors on this project. Maarten Vansteenkiste is with the University of Gent, Belgium. Athanasios Mouratidis is with the University of Leuven, Belgium, and with Hacettepe University, Turkey. Thomas Van Riet is with the University of Gent, Belgium. Willy Lens is with the University of Leuven, Belgium, and with the University of the Free State, Bloemfontein, South Africa.

of achievement goal pursuit; Vansteenkiste, Lens, Elliot, Soenens, & Mouratidis, 2014). The separate treatment of the type of achievement goals or standards players set for themselves and the way they regulate these standards fits with previous theoretical claims (Elliot & Thrash, 2001) and recent empirical work (e.g., Gaudreau, 2012; Vansteenkiste, Mouratidis, & Lens, 2010). Practically, it also maps onto players’ game-specific motivational dynamics as players’ type of pursued achievement goal may not only be game specific, but also their reasons for doing so. For instance, although a player may in general be oriented toward mastering her skills, she may during a crucial game prefer focusing primarily on outperforming her opponents. Concomitantly, when competing with others, she may feel pressured during some games (i.e., controlled regulation), while feeling challenged during other games (i.e., autonomous regulation). Assuming that athletes focus their attention and efforts on attaining a specific goal during a particular game (Treasure et al., 2001), we studied the variation in athletes’ dominant goal pursuit (Van Yperen, 2006). A critical question is then whether athletes’ situational (i.e., game-specific) goal pursuit can be predicted by their contextual (i.e., domain-specific) goal pursuit, assessed at the outset of the study. Moreover, we investigated whether the game-to-game variation in athletes’ dominant achievement goal pursuit and its underlying reasons covaries with their game-to-game functioning. Extending past work (e.g., Vansteenkiste, Mouratidis, et al., 2010),

131

132  Vansteenkiste et al.

we focused on athletes’ prosocial and antisocial behavior, game enjoyment, and performance satisfaction.

Achievement Goal Approach: Direction of Achievement Goal Pursuits In the current study, we relied on the 2 × 2 model (Elliot & McGregor, 2001; Conroy, Elliot, & Hofer, 2003), thereby distinguishing four different achievement goals, depending on the type of standard or aim that players pursue (i.e., definition) and whether these standards are construed as a positive outcome to be approached or a negative outcome to be avoided (i.e., valence). Specifically, mastery-approach goals (MAp) focus on mastering the requirements of the task and doing as well as one possibly can, whereas mastery-avoidance goals (MAv) refer to avoiding to fall short of meeting task requirements or one’s potential. Performance-approach goals (PAp) focus on outperforming others, whereas performanceavoidance goals (PAv) refer to avoiding performing worse than others. The achievement goal framework has strongly influenced the sport literature, with dozens of studies examining the correlates of achievement goals. Either relying on the dichotomous (e.g., Balaguer, Duda, & Crespo, 1999), trichotomous (e.g., Cury, Da Fonseca, Rufo, Peres, & Sarrazin, 2003) or the 2 × 2 framework (e.g., Barkoukis, Lazuras, Tsorbatzoudis, & Rodafinos, 2011), these studies showed MAp (but not PAp goals) to relate positively to effort expenditure, self-talk, and enjoyment among tennis players (van de Pol & Kavussanu, 2011), positive emotions and perceived performance among golfers (Dewar & Kavussanu, 2011), and moral functioning in contact sports (Kavussanu & Ntoumanis, 2003). Rather strikingly, the vast majority of these studies focused on between-person differences in achievement goals, with the exception of the study by Gernigon, D’Arripe-Longueville, Delignières, and Ninot (2004), which showed that that the goal pursuit of two judo athletes considerably fluctuated during a single practice period. Such a within-person approach allows for a more dynamic consideration of athletes’ motivational states, which can be very transient from game to game or, even within a certain situation. A first set of critical questions arising from such within-person approach pertains to whether there exists substantial variance in athletes’ game-to-game achievement goal pursuit and whether it can be predicted by their contextual achievement goal pursuit (Harwood, 2002; Swain & Harwood, 1996). Equally important is that the direct comparison of previous studies is considerably compromised because researchers have used different questionnaires (e.g., Barkoukis, Ntoumanis, & Nikitaras, 2007; Conroy et al., 2003; Duda & Nicholls, 1992 Ommundsen, 2004; Roberts, Treasure, & Balague, 1998), which not only varied in the number of assessed achievement goals but also in their operationalization. Indeed, some items exclusively focus on the type of standard that athletes pursue, while other items tap into additional aspects,

including ego-validation concerns, effort expenditure, and affective reactions. Although the assessment of such additional aspects is justified in case one adopts a rather broad definition of achievement goals, differences in the operationalization of achievement goals across questionnaires has been found to impact on the obtained relations (Hulleman, Schrager, Bodmann, & Harackiewicz, 2010). For instance, PAp goals related negatively to achievement in case an ego-validating (or appearance-evaluation) aspect was part of the operationalization (e.g., “to show how good I am”), whereas this relation was positive in case the items refer only to the outcome of outperforming others as such (e.g., “trying to do better than others”; Hulleman et al., 2010). Such thought-provoking findings urge achievement goal researchers to rethink the core and peripheral features of achievement goals. Elliot and collaborators pointed out the need for conceptual refinement of the achievement goal construct already more than a decade ago (Elliot & Thrash, 2001). Specifically, they suggested that the core feature of the achievement goal construct involves the way competence and success is defined, that is, based on interpersonal, intrapersonal, or task-based criteria. Further, they recommended that noncompetence-related features should be removed from both the definition and operationalization of achievement goals and that these features need to be assessed and studied separately (Elliot, 2005; Elliot & Murayama, 2008; Vansteenkiste, 2014). We equally favor the studying of reasons underlying athletes’ achievement goals as distinct constructs from the very same achievement goals. This approach allows for a more systematic study of a diversity of reasons underlying achievement goal pursuit, which may yield differential relations with outcomes. In fact, achievement goals and their underlying reasons can be conceived as, respectively, the “what” and “why” of achievement goals (Vansteenkiste, Lens, Elliot, et al., 2012).

Self-Determination Theory: Regulation of Achievement Goal Pursuits A family of reasons that has been proposed to energize the pursuit of achievement goals refers to the reasons delineated within the SDT framework (Deci & Ryan, 2000). Specifically, Vansteenkiste, Mouratidis, et al. (2010) suggested that athletes can have more controlling or pressuring or more volitional and autonomous reasons for focusing on outperforming their opponents during a competitive game. The pressure to beat other athletes can originate from external pressures, such as bonuses that are made contingent upon the outcome of game, demanding coach expectations, or threatening sanctions; alternatively, the pressure can reside in internal forces, such as the avoidance of feelings of guilt and shame or the tendency to demonstrate one’s value and importance as a player. Although competitive situations and the pursuit of performance-approach goal may come with pressure (Deci et al., 1981; Vansteenkiste & Deci, 2003), this is not necessarily the case. Indeed, the goal of outperforming

Game-to-Game Achievement Goal Pursuit   133

others may be perceived as a challenge or may be seen as personally valuable and meaningful, such that athletes would experience a greater sense of volition during their competitive striving. Although initial work in this area was limited to the study of autonomous and controlling reasons underlying the pursuit of PAp goals among soccer players (Vansteenkiste, Mouratidis, et al., 2010), learners (Vansteenkiste, Smeets, Soenens, Lens, Matos, & Deci, 2010) and workers (Gillet, Lafrenière, Vallerand, Huart, & Fouquereau, in press), more recent work has extend this logic to the study of the reasons underlying MAp goals of learners (Gaudreau, 2012). Indeed, athletes, like learners, may feel pressured or autonomous to master a technique. Previous studies, then, have shown that autonomous, as compared with controlled regulation, of PAp goals is associated with more desirable outcomes, including higher well-being, less antisocial play (Vansteenkiste, Mouratidis, et al., 2010), better concentration and persistence (Vansteenkiste, Smeets, et al., 2010), and greater goal-attainment (Gillet et al., in press). Further, Gaudreau (2012) showed that university students’ MAp and PAp goal pursuit related to less anxiety and more satisfaction when these goals were pursued for relative more autonomous than controlling reasons; when controlling reasons dominated over autonomous reasons, these same achievement goals related to more anxiety and less satisfaction. What remains unclear from past work is whether the type of pursued achievement goal and the underlying autonomous and controlling reasons fluctuate across time and if these fluctuations are associated with game-related outcomes. In this respect, Gagné et al. (2003) showed that there exists considerable day-to-day variation in athletes’ autonomous and controlling reasons during a four-week practice period which covaried with their daily well-being. Building on this line of research, we examined to what extent autonomous and controlling reasons underlying volleyball players’ achievement goals vary from game to game (rather than from practice to practice) and whether fluctuations of both situational goal pursuit and its underlying reason are linked with athletes’ moral behavior, game enjoyment, and performance satisfaction. Previous studies have shown that autonomous participation in sports predicts more prosocial behavior (e.g., Ntoumanis & Standage, 2009), greater well-being (e.g., Mouratidis, Lens, & Vansteenkiste, 2010), and better rated performance (e.g., Assor, Vansteenkiste, & Kaplan, 2009), whereas the controlled participation related to antisocial behavior (e.g., Hodge & Lonsdale, 2011).

Present Study The present study was set up among volleyball players, which were followed for six consecutive games. We used the dominant achievement goal approach (Van Yperen, 2006), which requires participants to prioritize certain achievement goals above others. Although this approach has hardly been used in the sport domain (see Van Yperen & Renkema, 2008, Study 3, for an exception), we opted

for this approach for two reasons. First, we reasoned that players orient their attention and efforts toward a specific achievement goal during a particular game and wanted to examine the variability versus stability of such game-specific achievement goal pursuit. Second, asking players to indicate their dominant situational achievement goal yielded the practical advantage that the assessment of the type of reasons (i.e., autonomous and controlling) underlying achievement goals could be limited to their dominant achievement goal rather than asking participants to rate reasons underlying any kind of achievement goal, even goals that were not salient to them during the game. Given these assessments, we pursued the following four research questions. First, in light of the paucity of work examining within-player variability in situational achievement goals and underlying autonomous and controlling reasons, we first inspected the variability in these motivational dynamics across the six games. Second, we examined whether athletes’ situational dominant goal pursuit could be predicted by their more contextual achievement goal pursuit as assessed at the outset of the study. We expected a positive, yet imperfect, association between players’ contextual and situational goal pursuit, as other factors (e.g., coaching style, ranking of the opponent) may impact on players’ game-specific goals. Third, we examined whether, after controlling for the outcome of the game (i.e., loss vs. victory), situational achievement goal pursuit would relate to game-specific outcomes. In light of previous findings (Kavussanu & Ntoumanis, 2003; Schantz & Conroy, 2009), we expected that adopting a dominant MAp goal during a specific game, relative to the adoption of other achievement goals, would relate to enjoyment and performance satisfaction. Further, MAp goal pursuit was expected to relate to more prosocial and less antisocial behavior as MAp goals coincide with the view that participation in sports should foster learning of prosocial skills, such as cooperation (Roberts, 2001). Fourth, we addressed the “why” of situational achievement goal pursuit. Specifically, consistent with past work (Vansteenkiste, Mouratidis, et al., 2010), we expected that controlling goal pursuit would relate to antisocial behavior as pressure to attain an achievement standard may lead players to objectify their opponents (Haslam, 2006), that is, to perceive them as obstacles that need to be removed, which would lower the threshold to aggress them. This might be especially the case if players adopt a dominant performance goal, because such a goal pursuit is more likely to be outcome rather than process oriented. In contrast, we expected that autonomous situational goal pursuit would relate to prosocial behavior, game enjoyment, and performance satisfaction. This is because when functioning autonomously, athletes would display a greater receptivity for ongoing events, especially if they adopt a dominant mastery goal. This enhanced openness and process focus would allow them to get more fully immersed in the activity, thereby deriving more enjoyment and satisfaction from their performance. In

134  Vansteenkiste et al.

addition, their openness may render them more concerned with others, as reflected by more prosocial behaviors toward their own teammates and their opponents.

Method Participants and Procedure Participants were 67 volleyball athletes (61.8% males; Mage = 19.45; SD = 5.13) from different urban areas of Flanders (Belgium). The athletes had been playing volleyball for approximately 10.66 years (SD = 5.13), belonged to the same team for 4.78 years (SD = 5.74), and trained for approximately 13.89 (SD = 7.63) hours per week. The players played volleyball in seven different volleyball teams, which all played in national competitions in Belgium. Two of these teams played at the second national level, another two at the third level, and the remaining three at the fifth level. About half of the players (53.7%) attended a sport school where they got a special training with the aim of becoming a professional volleyball player. Athletes attending the volleyball school were younger, Mage = 15.83; SD = 1.11; t (65) = -9.59, p < .01; had been part of their team for a shorter period of time, M = 1.83; SD = 0.81; t (65) = –5.41, p < .01; and trained more hours a week, (Mage = 20.56; SD = 2.95; t (65) = 24.23, p < .01, compared with the volleyball players who were not attending the top-sport school (respectively, Mage = 23.65; SD = 4.74; M = 8.19; SD = 7.01; M = 6.15; SD = 1.61). A research assistant contacted the coaches of eight different teams, which were all willing to participate in the study. Although the questionnaires of the eight teams were collected, the completed questionnaires of one team got lost at some point during the process of data collection, such that a total of seven teams participated. Seven different measurement moments were planned. The first assessment took place after a training with the aim of assessing global measures (i.e., between-person measures including background characteristics and dominant goal orientation) and six other assessments took place after six consecutive games with the aim of collecting game-specific measures (i.e., within-person measures including game-specific achievement goal pursuit, reasons, and game-related outcomes). The six games during which participants participated took place in the middle of the volleyball season. Consistent with Gaudreau, Nicholls, and Levy (2010), we considered a competitive game as a meaningful time-bounded unit with a clear demarcation of a beginning and ending, which make them useful for analyses. Athletes filled out the questionnaires in the dressing room. During the first measurement, the research assistant explained to the participants the general scope of the study and informed them about consecutive measurement moments. He assured participants about the confidentiality of their responses, that the participation was voluntary, and that they could withdraw from the study during any point in time. The study was approved by the Research Ethics Board of the host university.

Measures Contextual Achievement Goal Pursuit.  Using the

method introduced by Van Yperen (2006), we assessed at the outset of the study (i.e., a week before the first game took place) athletes’ general achievement goal pursuit in the volleyball domain, thereby selecting one item per achievement goal of the AGQ-S (Conroy et al., 2003). Specifically, athletes indicated their dominant achievement goal in volleyball by answering six forcedchoice items in which each type of achievement goal (i.e., MAp, MAv, Pap, and PAv) was contrasted in a pairwise manner with the other three achievement goals. A sample item reads, “In volleyball my goal is usually (A) to perform as well as I can (MAp) or (B) avoid performing less well than I can (PAv).” To classify athletes in one of the four achievement goal categories, the athletes needed to consistently select one goal above the other three achievement goals. The majority of the athletes (n = 56; 83.6%) indicated MAp goals as their dominant goal orientation followed by six athletes with a dominant PAp (9.0%), two with a dominant MAv (3.0%), and only one with a dominant PAv (1.4%) achievement goal orientation. Two athletes (3.0%) did not choose one achievement goal consistently above the three other achievement goals, suggesting they lacked a dominant goal orientation. In light of the small number of athletes with such an orientation, they were excluded when examining Research Questions 1 and 2. Similar to studies in the educational domain (e.g., Van Yperen, 2006), participants with a dominant MAp goal orientation outnumbered participants with other dominant achievement goals, χ2 (3, N = 66) = 130.51, p < .01). Situational Achievement Goal Pursuit.  We used

the same items as those used to measure athletes’ dominant contextual goal pursuit to assess their dominant situational goal pursuit; that is, a MAp goal (“During this game my goal was to perform as well as I can”), a PAp goal (“to outperform my opponent”), a PAv goal (“avoid performing worse than my opponent”), and a MAv goal (“avoid performing worse than I really could”). Yet, rather than assessing them with respect to volleyball in general, athletes rank-ordered the four achievement goals with the past specific game in mind. Specifically, athletes rank ordered the four goals in terms of importance from 1 (most important) to 4 (least important) to obtain a measure of dominant goal pursuit during a specific game.

Reasons Underlying Dominant Goal Pursuit.  Having chosen their dominant game-specific achievement goal, the athletes were instructed to rate their reasons for pursuing their dominant achievement goal. Specifically, after the stem “Why did you indicate this goal as the most important?,” volleyball players rated, in line with SDT and past research (Vansteenkiste, Mouratidis, et al., 2010), their intrinsic (e.g., “Because I liked to pursue this goal”; two items), identified (e.g., “Because I found this a personally important goal”; two items), introjected (e.g., “Because I had to prove myself”; two items), and

Game-to-Game Achievement Goal Pursuit   135

external reasons (e.g., “Because I feel obliged by others [trainer, team members, parents, friends] for doing so”; two items). The assessment of the reasons underlying achievement goals was limited to the dominant goal to avoid overburdening athletes with “why” questions for achievement goals they may only weakly endorse during a particular game and because we assumed that athletes had a dominant goal in mind during the game. Consistent with prior research, we created an autonomous and controlled regulation score by averaging, respectively, the intrinsic and identified and the introjected and external regulation items. The reliability of these scales after accounting for the nested structure of the data (Nezlek, 2007) was λ = .85 and = 81. Game-Specific Prosocial and Antisocial Behavior. 

We slightly adapted the Prosocial and Antisocial Behavior in Sport Scale (Kavussanu & Boardley, 2009) to the volleyball context to assess players’ (un)sportspersonship behaviors in each game. We used four sets of three items that were gauged on a 5-point scale ranging from 1 (Not at all true of me) to 5 (Very true of me), tapping into players’ (a) prosocial behavior toward their teammates (e.g., “Encouraged a teammate”; reliability, λ = .67); (b) prosocial behavior toward their opponents (e.g., “Admitted a touched ball”; λ = .46); (c) their antisocial behavior against their own teammates (e.g., “Criticized a teammate”; λ = .81) and (d) toward their opponents (e.g., “Intentionally distracted an opponent”; λ = .85). Because of the low reliability of prosocial behavior toward opponents, this measure was excluded from all analyses. Game-Specific Enjoyment and Performance Satisfaction.  A visual analog scale was used to assess

participants’ game-related enjoyment and performance satisfaction. Specifically, participants responded to the following items: (1) “To what extent did you enjoy this game?”; (2) “To what extent are you satisfied with the outcome of the game?”; “To what extent are you satisfied with your personal performance?”. The latter two items were collapsed to create an index of performance satisfaction (λ = .58). For each of these items, we made use of a visual analog scale, such that participants indicated their level of agreement on a continuum of which only the anchor points were labeled (i.e., “No enjoyment” versus “A lot of enjoyment”; “Not satisfied” versus “Very much satisfied”). The item tapping into game-related enjoyment was taken from the Intrinsic Motivation Inventory (McAuley, Duncan, & Tammen, 1989), whereas the performance satisfaction items were especially devised for the current study.

Results Plan of Analyses Given the nested structure of the data, as day-to-day measures (which represent Level 1 or within-athlete variance) were nested into individuals (which represent Level 2 or between-athlete differences), we set up a

series of multilevel models to examine our research questions. Those models addressing each of the research questions can be found in Table 1. Specifically, the first model enabled us to detect, through inspection of the intraclass correlation coefficient (ICC), the degree of variance lying at the intrapersonal and between-athlete level (Research Question 1). We assumed that this probability would follow a Bernoulli distribution such that the proportion of the participants reporting dominant goal pursuit for a given goal would fall between .01 and .99. Preliminary analyses showed that testing simultaneously all four dominant situational goals within a single model yielded no converging solution, most likely due to the few observations in particular situational goals (see below). Therefore, we opted for testing four separate models, in each of which only one particular situational goal was entered as an outcome. To address Research Question 2, we tested in each of these models to what extent the gameto-game situational goals are predicted by the respective contextual goal (lying at the interpersonal level). Given that contextual dominant goals were binary coded, they were entered uncentered. To address Research Question 3, we used as a reference group the games during which players endorsed MAp dominant goals. We used β1j, β2j, β3j to estimate within a single multivariate model the differences in the reported outcome due to, respectively, endorsement of PAp, PAv, and MAv situational goals relative to MAp goals, while we controlled for the outcome of the game (all variables uncentered). Regarding Research Question 4, autonomous and controlling reasons underlying the pursuit of a particular situational goal (both groupmean centered), along with the outcome of the game (uncentered) were entered as predictors at the intrapersonal level. In the latter two models, we controlled for between-athletes differences in gender (uncentered), age, and hours of training (grand mean centered). In all models the random errors (i.e., the ujs) were fixed unless they were found to significantly vary from person to person.

Preliminary Analyses Descriptive statistics and bivariate correlations between the game-to-game measures (aggregated across the six measurement moments) are presented in Table 2. A MANOVA showed significant gender differences in the game-specific variables, Wilks’s Λ = .830, F(10, 339) = 6.94, p < .01, multivariate η2 = .17, and the follow-up ANOVA with a Bonferroni-adjusted alpha level revealed that these differences concerned antisocial behavior against both one’s own teammates, F(1, 348) = 35.82, p < .01, η2 = .09, and the opponent, F(1, 348) = 20.16, p < .01, η2 = .06. Specifically, females scored lower in antisocial behavior against teammates (M = 2.25, SD = 0.70) and the opponents (M = 1.75, SD = 0.72) than males (respectively, M = 2.71, SD = 0.69; and M = 2.11, SD = 0.75). In addition, preliminary analyses showed that age and hours of training were significantly associated with situational dominant goal pursuit and game-related

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Table 1  The Multilevel Models Aiming to Address the Four Research Questions (RQs) of the Study RQ 1: What is the game-to-game variance in players’ achievement goal and their underlying autonomous and controlling reasons?   Intrapersonal level: ηij = β0j + rij   Interpersonal level: β0j = γ00 + u0j RQ 2: Does the respective contextual achievement goal predict endorsement of situational (i.e., game-specific) achievement goals?   Intrapersonal level: ηij = β0j + rij   Interpersonal level: β0j = γ00 + γ01 [Contextual achievement goal] + u0j RQ 3: Do situational achievement goals predict game-to-game variation in outcomes?   Intrapersonal level: Yij = β0j + β1j [PAp] + β2j [PAv] + β3j [MAv] + β4j [Game outcome] + rij   Interpersonal level: β00 = γ00 + γ01 [Gender] + γ02 [Age] + γ03 [Hours of training] + u0j β1j = γ10 + u1j β2j = γ20 + u2j β3j = γ30 + u3j β4j = γ40 + u4j RQ 4: Do autonomous and controlling reasons underlying the pursuit of situational achievement goals predict game-to-game variation in outcomes?   Intrapersonal level: Yij = β0j + β1j [Autonomous] + β2j [Controlling] + β3j [Game outcome] + rij   Interpersonal level: β0j = γ00 + γ01 [Gender] + γ02 [Age] + γ03 [Hours of training] + u0j β1j = γ10 + u1j β2j = γ20 + u2j β3j = γ30 + u3j Note. η = log[φ/(1 – φ)].

outcomes. Specifically, age was associated positively with aggregated scores of situational MAp goal pursuit (r = .30, p < 05) and negatively with situational PAv goal pursuit (r = –.25, p < .05), while it also related positively to performance satisfaction (r = .29, p < .05) and antisocial behavior against opponents (r = .29, p < .05). Hours of training was associated positively with the aggregated score of situational MAv goal pursuit (r = .29, p < .05) and negatively with performance satisfaction (r = –.34, p < .01). Consequently, gender, age, and hours of training were included as covariates in all the subsequent analyses.

Main Analyses Research Question 1: Variability of Situational Goal Pursuit and Underlying Reasons.  We first examined

the degree of variability in volleyball players’ situational achievement goal pursuit and the autonomous and controlling reasons underlying its pursuit. Inspection of the intercept coefficient in the unconditional models suggested that the probability of reporting situational MAp goal pursuit as the most dominant goal across the six games was 70.77%, while the probabilities for dominant MAv, PAp, and PAv goal pursuit were, respectively 10.26%, 5.26%, and 13.78%. The ICC as computed for logistic models (see Snijders & Bosker, 1999), was ρ = .23 for situational MAp goal pursuit, suggesting considerable within-person variability (i.e., 77.2%). The respective within-person variability for situational MAv, PAp, and PAv goal pursuit was, respectively, 79.7%, 99.9%, and

84.5%. These findings suggest that volleyball players’ dominant goal varied considerably across the six games. The ICC of the unconditional models for autonomous and controlling reasons underlying dominant, situational goal pursuit revealed that 49.7% and 49.2% of variance in autonomous and controlling reasons lie at the withinperson level. Research Question 2: Predicting Situational Goal Pursuit From Contextual Goal Pursuit.  We estimated the

odds that volleyball players adopting a specific contextual goal pursuit would report pursuing the same goal during a specific game, controlled for gender, age, and hours of training. Therefore, the probability of adopting a specific dominant game-specific goal was entered as a binary outcome (0 = situational, nondominant goal pursuit; 1 = situational, dominant goal pursuit) and estimated as a function of the respective dominant, contextual goal pursuit (0 = contextual, nondominant goal pursuit; 1 = contextual, dominant goal pursuit) at the interpersonal level. The hierarchical generalized linear model (population average) for the situational MAp goal pursuit showed that contextual MAp goal pursuit—as assessed at the outset of the study—was a significant predictor γ01 (contextual MAp goal pursuit) = 0.92, SE = 0.41, p < .05, with the odds being 2.52 (95% CI [1.11, 5.69]), or yielding a probability of 71.6%. This finding indicates that volleyball players with a dominant MAp goal for their sport in general had a sizeable probability of adopting a different-than-MAp dominant goal during a particular game (i.e., 28.4%).

137

2

— –.07 –.12* –.15** .09 –.10 .11* .10 –.12* –.11*

1 — –.41** –.27** –.46** .23** –.01 .16** –.05 –.04 .08 .09

–.10 .06 .07 –.01 .00

–.17** .06

— –.08

3

*p < .05. **p < .01. ‡Represent percentages of endorsing the observed situational goal.

Variables Dominant Situational Goal 1 Mastery-approach goals 2 Mastery-avoidance goals 3 Performance-approach goals 4 Performance-avoidance goals Underlying Reasons 5 Autonomous motivation 6 Controlled motivation Game-Specific Outcomes 7 Prosocial team behavior 8 Antisocial team behavior 9 Antisocial opponent behavior 10 Game enjoyment 11 Performance satisfaction –.06 –.07 –.08 .01 –.02

–.07 –.11*

—

4

.24** –.11* –.02 .33** .26**

— .17**

5

.02 .09 .03 –.13* –.15**

—

6

— .05 .18** .25** .24**

7

— .28** –.22** –.21**

8

— .12** .17**

9

— .79**

10

—

11

4.01 2.53 1.98 5.91 5.04

4.03 3.02

0.71‡ 0.11‡ 0.05‡ 0.13‡

M

Table 2  Means, Standards Deviations, and Bivariate Correlations Between Game-Specific Measured Variables for Athletes With a Mastery-Approach Dominant Goal (N = 56; Number of Observations = 290)

0.62 0.73 0.75 2.67 2.80

0.62 0.74

0.46 0.31 0.22 0.34

SD

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Accordingly, the model for the PAp goal showed that contextual PAp goal pursuit significantly predicted situational PAp goal pursuit, γ01 (contextual PAp goal pursuit) = 1.30, SE = 0.47, p < .01, with the odds and the probability being 3.65 (95% CI [1.43, 9.30]) and 78.5%, respectively. In contrast, the model for situational MAv goal pursuit showed that contextual MAv goal pursuit was a nonsignificant predictor of situational MAv goal pursuit, γ01 (MAv dominant orientation) = 1.13, SE = 0.98, p = .25, ns. As for the model for situational PAv goal pursuit, no estimate could be made because the only person with a dominant PAv goal pursuit at the contextual level never reported adopting a similar situational goal during the six games. Collectively, these analyses indicated some degree of discrepancy between volleyball players’ dominant goal pursuit at the contextual and situational level, although the null findings need to be interpreted with caution given the low number of individuals adopting certain achievement goals. Research Question 3: Correlates of Situational Achievement Goal Pursuit.  Next, we examined

whether game-to-game variation in situational goal pursuit would relate to game-to-game variation in the studied correlates after accounting for the outcome of the game. All the outcomes were included within a single multivariate model in which they were regressed on the outcome of the game (uncentered; 0 = loss; 1 = victory) and the MAp, MAv, PAp, and PAv situational goal pursuit indicators (all uncentered; 0 = non-dominant goal pursuit; 1 = dominant goal pursuit) at the intrapersonal level and on gender (uncentered; 0 = males; 1 = females), age and hours of training (both grand centered) at the interpersonal level. Because the four situational goal measures were dummy coded, we used the situational MAp goal pursuit as the reference group, as this goal was chosen most frequently (n = 216; 74.0%). As a result, the intercept of the slopes of the situational MAv, PAp, and PAv goals represent the mean differences from the situational MAp goal group. In the interest of computation stability of the tested model, all the slopes were fixed, whereas the three covariates (i.e., gender, age, and hours of training) were entered only in the intercept (that is, they were included in the dominant MAp goal reference group as this group was the biggest one). In addition, preliminary analyses showed that due to the high intercorrelations between enjoyment and performance satisfaction, the model would only converge if these two variables would be aggregated. Thus, we decided to create an average score for these two variables. The results of this multivariate multilevel model are presented in Table 3. As can be noticed, only a few differences emerged. Specifically, players with situational MAp as compared with PAp, PAv, or MAv goals reported, on average, more prosocial behavior toward the teammates, but they did not differ with respect to the other outcomes. As these analyses revealed also, players’ reported more prosocial toward and less antisocial behavior against the teammates as well as more game enjoyment and perfor-

mance satisfaction when they had won compared with when they lost the game. Research Question 4: Regulation of Situational Achievement Goal Pursuit.  Finally, we examined

within a single multivariate multilevel model whether autonomous and controlling reasons underlying situational goal pursuit would relate to the studied correlates. Because of the small number of observations for dominant MAv (n = 27), PAp (n = 11), and PAv (n = 38) goal pursuit across the six games, we limited this test to instances where players had picked a dominant MAp goal (n = 214). Doing so allowed us to answer the underexplored issue of whether the reasons underlying MAp goal pursuit would matter in predicting players’ psychosocial functioning during a particular game. Moreover, examining the role of autonomous and controlling reasons across achievement goals would considerably blur the conclusions that can be drawn as it would be unclear whether the reasons matter for any or specific types of adopted situational achievement goals. Preliminary analyses showed that the slopes of the within-player predictors did not significantly vary. Therefore, they were fixed for the interest of model parsimony. Similarly to the model we used to address Research Question 3, we aggregated enjoyment and performance satisfaction because of their high intercorrelations. The results of this multivariate multilevel model are presented in Table 4. After controlling for the outcome of the game, autonomous motives underlying dominant MAp goal pursuit related positively to prosocial behavior toward teammates and the aggregated score of game enjoyment and performance satisfaction. Further, victory was a positive predictor of prosocial behavior toward one’s teammates and game enjoyment and performance satisfaction. At the interpersonal level, females reported less antisocial behavior against their teammates.

Discussion The achievement goal approach (APA; Elliot, 2005) and self-determination theory (SDT; Deci & Ryan, 2000) have been quite influential in the sport literature over the past two decades (Hagger & Chatzisarantis, 2007; Williams, Hardy, & Mutrie, 2008). Whereas previous studies have linked concepts derived from both frameworks, the restriction of the definition of achievement goals to aims per se (Elliot, 2005) opened the door for a more systematic study of the interplay between both frameworks as attention can be paid to the diversity of reasons underlying athletes’ achievement goals (Vansteenkiste et al., 2014). The present study extends the limited body of work on the intersection between the AGA and SDT by taking a more dynamic stance, that is, by examining whether these two critical features of players’ motivated behavior vary across a series of competitive games and by linking this variability to variability in the studied outcomes. A number of interesting findings emerged.

139

7.19%

(0.11) (0.01) (0.01)

(0.14) (0.02) (0.01)

5.34%

49.70%

Within-Person Variance

0.25

0.21**

Variance Components

Intercorrelations .04 — .01 –.27**

–0.50** 0.00 0.00

Antisocial team behavior 2.81 (0.09) 0.03 (0.11) –0.14 (0.10) 0.17 (0.07) –0.24** (0.08)

Moral Correlates

–0.31 0.04* 0.01

2.82%

36.60%

0.21

0.30**

.10** .08* — .09

(0.17) (0.02) (0.02)

Antisocial opponent behavior 2.01 (0.11) –0.04 (0.10) –0.17 (0.10) 0.13 (0.10) 0.08 (0.09)

0.15 –0.02 –0.03

37.66%

80.70%

3.34

0.34

.07 .01 .12 —

(0.26) (0.04) (0.02)

Affective Correlates Game enjoyment and satisfaction 3.96 (0.20) –0.17 (0.63) 0.21 (0.27) –0.64 (0.34) 3.30** (0.25)

Note. Intercorrelations at the lower and upper diagonal refer respectively at the within- and between-person level. MAp = mastery-approach: PAp = performance-approach; PAv = performance-avoidance; MAv = mastery-avoidance. Achievement goals coefficients represent differences from the MAp dominant goal (reference group). *p < .05. **p < .01.

71.10%

0.26

0.11**

— .00 .00 .25**

% variance explained

ej

Level 1

0.07 0.02 0.02

Prosocial team behavior 3.98 (0.09) –0.35* (0.14) –0.19* (0.09) –0.28* (0.10) 0.16** (0.05)

% variance

r0j

β01 β02 β03

β00 β10 β20 β30 β40

Intercept

Random Effects

Prosocial team behavior Antisocial team behavior Antisocial opponent behavior Enjoyment and satisfaction

MAp goal pursuit PAp goal pursuit PAv goal pursuit MAv goal pursuit Victory vs. Loss Interpersonal predictors  Gender  Age   Hours of training

Fixed Effects

Table 3  Game-to-game Moral Behavior and Affective Experiences as a Function of Gender Age, and Hours of Training (Interpersonal Level) and Outcome of the Game and Game-related Dominant Achievement Goal Involvement (Intrapersonal Level)

140 14.55%

% variance explained

(0.09) (0.08) (0.07)

(0.12) (0.02) (0.01) (0.14) (0.07) (0.12)

(0.14) (0.02) (0.01)

5.02%

51.01%

Within-Person Variance

Variance Components 0.19** 0.27

–.29**

.01

—

.04

Intercorrelations

–0.13 0.11 –0.22†

–0.69** –0.02 –0.01

Antisocial team behavior 2.81 (0.11)

Moral Correlates

Note. Intercorrelations at the lower and upper diagonal refer respectively at the within- and between-person level. †p = .06. *p < .05. **p < .01.

72.83%

% variance

Intercept Level 1

0.11** 0.23

Enjoyment and satisfaction

r0j ej

.01 .19*

Antisocial opponent behavior

Random Effects

— .01

0.36** –0.07 0.20**

β10 β20 β30

Antisocial team behavior

0.03 0.03 0.02†

β01 β02 β03

β00

Prosocial team behavior 3.93 (0.09)

Prosocial team behavior

Intercept Interpersonal predictors  Gender  Age   Hours of training Intrapersonal predictors   Autonomous reasons   Controlling reasons   Victory vs. loss

Fixed Effects

–0.07 0.04 0.06

–0.34 0.03 0.01

—

31.86%

0.34** 0.17

.07

—

.08*

.10**

(0.09) (0.07) (0.08)

(0.20) (0.03) (0.02)

Antisocial opponent behavior 2.07 (0.12)

0.25 3.11

—

.12

.01

.07

35.21%

79.88%

1.32** 0.17 3.03**

0.34 0.04 0.00

(0.35) (0.31) (0.29)

(0.30) (0.04) (0.02)

Affective Correlates Game enjoyment and satisfaction 4.00 (0.18)

Table 4  Game-related Moral Behavior and Affective Experiences as a Function of Gender, Age, and Hours of Training (Interpersonal Level) and Outcome of the Game and Autonomous and Controlling Reasons Underlying Situational Mastery-Approach Goal Pursuit (Intrapersonal Level) Among Athletes With a Dominant Mastery-Approach Goal Orientation (N = 56)

Game-to-Game Achievement Goal Pursuit   141

First, extending recent research showing considerable variability in athletes’ goal pursuit across training and competitive circumstances (van de Pol, Kavussanu, & Ring, 2012), the present findings indicate that volleyball players’ achievement goal pursuit also varies across a series of competitive games. Thus, athletes may adopt different dominant achievement goals not only during training versus competitive contexts, but also within the very same context. Likely, athletes’ dominant achievement goals may also switch on different moments during the competitive game itself depending on the way the game unfolds and athletes’ own performance (Van Yperen & Renkema, 2008), an issue that deserves greater attention in future research (see Gernigon et al., 2004). Apart from players’ achievement aims themselves, also the reasons underlying their achievement strivings varied from game to game. Apparently, the same athletes felt challenged to master the requirements of the task during some games, thereby displaying a more autonomous form of regulation, whereas they felt pressured during their mastery goal pursuit during other games, thereby displaying a controlled form of regulation. Second, consistent with the observed variation in volleyball players’ situational achievement goal pursuit, we found that contextual achievement goal pursuit, which reflects the typical type of achievement goals they pursue in their sport, was only modestly related to their game-specific goal pursuit. This especially appeared to be the case for athletes reporting approach goals as their dominant game-related achievement goals; that is, those who adopt in general a dominant MAp and a dominant PAp goal tend to choose, respectively, a dominant MAp and PAp achievement goal during a specific competitive game. Yet, the association is not perfect, suggesting that even players reporting dominant MAp goal pursuit in general may shift to avoidance or competitive game-specific dominant goals, presumably depending on the circumstances of the game at hand. Both contextual avoidance goals were unrelated to athletes’ situational, that is, game-specific goals. Such findings may indicate a greater instability for avoidance goals across games and indirectly indicate that such athletes may more easily shift their focus away from avoiding failure to attain success and vice versa. Yet, we hasten to not overinterpret these findings as only a limited number of athletes reported MAv or a PAv goal pursuit in the present research. In addition, it should be highlighted that the dominant goal approach used to map out players’ contextual and situational achievement goals is categorical in nature, which hampers the probability of finding convergences between the contextual and situational level. In spite of this limitation, we believe the present findings further confirm the claim that athletes’ goal pursuit can best be conceived as a dynamic process that fluctuates across time, likely depending on both athletes’ more dispositional achievement goal characteristics but also prevailing contextual features or their interaction (see Barron & Harackiewicz, 2001). The study of the antecedents of athletes’ situational achieve-

ment goal pursuit certainly deserves more attention in future research. Third, as for the relation between game-to-game goal pursuit and moral and affective correlates, it was found that volleyball players reported more prosocial behavior toward their teammates during games in which they adopted a MAp goal compared with games during which they adopted a different goal. Other outcomes were not found to be predicted by the type of pursued dominant achievement goal. Although similar null findings have been reported in the literature (e.g., Kavussanu, Morris, & Ring, 2009; Ntoumanis, Thogersen-Ntoumani, & Smith, 2009), we hasten to point out a number of differences between the current study and previous work. These include the use of continuous rather than categorical (i.e., dominant vs. nondominant) measures of achievement goals (e.g., Barkoukis et al., 2011), the sampling of a rarely examined group of athletes from a motivational perspective (i.e., volleyball players), and the different unit of analysis (i.e., within person vs. between persons). Clearly, more game-to-game studies in more diverse athlete samples are needed before any definite conclusions can be drawn regarding the association between game-specific achievement goal pursuit and game-specific outcomes. Fourth, regarding the reasons underlying volleyball players’ dominant MAp goal pursuit, autonomous reasons emerged as the most reliable predictor of both moral and affective correlates. In games in which volleyball players adopted a dominant MAp goal because they felt challenged or saw the personal significance of that goal, they enjoyed the game more, ended up being more satisfied with their performance, and reported engaging in more prosocial behaviors toward their own teammates. Thus, if volleyball players managed to regulate their mastery strivings in a more autonomous way, they had energy available to engage in proactive moral behavior (e.g., encouraging teammates). Presumably, this is the case because an autonomous regulation goes along with a greater receptivity for incoming experiences, both intra- and interpersonally. This receptivity may promote a greater absorption in the game itself leading one to derive a greater sense of enjoyment from the game, but may simultaneously promote a greater connection with teammates whom one tries to support. Thus, an autonomous regulation allows one “to open up,” whereas a controlled regulation likely leads on to be more concerned with one’s own play and performance. Two further observations can be made. First, when the present findings are considered in conjunction with the findings reported by Vansteenkiste, Mouratidis, et al. (2010) among soccer players, it appears that a goal complex (Elliot & Murayama, 2008), consisting of a specific achievement goal and a specific underlying reason, relates to moral outcomes. Specifically, for prosocial behavior to occur, it seems that players need to pursue MAp goals for autonomous reasons (as shown in the current study), while antisocial behavior is predicted by the combined presence of pursuing PAp goals for controlling reasons

142  Vansteenkiste et al.

(as shown by Vansteenkiste, Mouratidis et al., 2010). Or said differently, the adoption of a MAp goal offsets the negative effect associated with controlling reasons. Second, the positive relation of autonomous regulation to prosocial outcomes was limited to within-team behavior and did not radiate to prosocial behavior toward the opponent team. Because of its low reliability, the potential beneficial relation of autonomous regulation of situational MAp goal pursuit to this outcome could not be examined. Yet, it will be interesting to examine whether autonomously regulating one’s achievement strivings not only yields a more constructive and helpful attitude toward one’s own team, but also to the opponent, which may reflect a more altruistic form of prosocial behavior. Finally, it should be noted that the effects of achievement aims and underlying reasons emerged on top of the effects of the outcome. After winning the game, volleyball players reported being more satisfied with their performance and having enjoyed the game more, a finding that replicates past experimental work (e.g., Standage, Duda, & Pensgaard, 2005; Vansteenkiste & Deci, 2003). Interestingly, during games won by athletes, they reported having engaged in more prosocial and less antisocial behavior toward their own teammates.

Theoretical Reflections A critical yet pertinent question that arises from this study is whether the current approach, which considers both the “what” and “why” of achievement goals, unnecessarily complicates the achievement goal literature or helps to further our understanding of the motivational dynamics observed in sport settings. Over the past two decades, the number of studied achievement goals in the sport literature has been gradually expanded, increasing from two (e.g., Nicholls, 1984) to three (e.g., Kavussanu et al., 2009), four (e.g., Conroy, et al. 2003) and even six (Elliot, Murayama, & Pekrun, 2011). Does the consideration of the reasons underlying each of these achievement goals undermine the obtained predictive validity for achievement goals in past work (e.g., Adie, Duda, & Ntoumanis, 2008, 2010)? No, it does not in our view, as the reasons are not meant to replace the achievement goals themselves. Does the consideration of these reasons represent a threat to the parsimony of the achievement goal model? Although that might be the case in the eyes of some scholars or practitioners, we believe that a systematic consideration of the reasons underlying achievement goals represents a very much needed next step in the development of the framework as well as its practical use. That is because achievement goals indicate the direction of their achievement goal pursuit, whereas the reasons underlying their achievement aspirations reflect the way achievement goal pursuit gets regulated. Importantly, this approach can only be taken on the precondition that achievement goal theorists embrace Elliot and colleagues’ (Elliot & Thrash, 2001) more narrow definition of achievement goals in which the reasons are removed from the very heart of the goal concept.

Thus, whereas players’ ego is by definition implicated when they adopt performance goals and players’ interest is central to their mastery goal striving according to the dichotomous achievement goal perspective (Nicholls, 1984), this is no longer the case within Elliot’s view. The core aspect to differentiate different forms of achievement goals involves the type of standard or reference points used to define competence and success, with competence being defined based on interpersonal criteria or task-based requirements in the case of performance and mastery goals, respectively. As a result, players can feel pressured by their coach to beat others or to master a new technique in volleyball, or they could be more volitional in pursuing any of these two standards. Stripping the reasons underlying achievement goals from the achievement goals themselves then helps to gain more exact insight in the factors relating to athletes’ psychosocial functioning; that is, are the achievement goals themselves, the underlying reasons or a particular combination of goals and reasons, constituting a motivational cocktail or goal complex (Elliot & Murayama, 2008), critical to predict outcomes? Thus, the consideration of underlying reasons might help to refine certain conclusions drawn. Whereas past work shows that PAp goal pursuit does not yield negative outcomes when it is autonomously regulated (e.g., Gaudreau, 2012), the present findings suggest that MAp goal pursuit among athletes may not yield desirable outcomes when it is controlled regulated. In practice, this implies that coaches and sport psychologists would do well to consider not only the type of achievement goals they promote, but also the type of regulation underlying achievement goal pursuit.

Limitations and Future Research The current research contains several limitations. First, we cannot claim causality as the data are correlation in nature. Indeed, although we conceived achievement goal involvement and underlying reasons as predictors of game-specific outcomes, it is also possible that athletes’ ongoing experiences may have led them to prioritize certain achievement goals and to provide a retrospective justification for the reasons underlying their goal involvement. To somewhat alleviate this issue, it would have been more desirable to assess athletes’ preferred achievement goals and underlying reasons prior or during the competitive game and their experiences after the game. Indeed, we may only have assessed postgame achievement states in the current study rather than have tapped into ongoing game-specific achievement goal dynamics. We only made use of postgame assessments to not overburden athletes. Of course, experimental work in which both athletes’ achievement goals themselves and their underlying reasons would be manipulated would shed a more definite light on the direction of the observed effects. Second, we sampled a small number of athletes from a single sport discipline (i.e., volleyball), which leaves unanswered the question whether the present findings would generalize to other sport disciplines.

Game-to-Game Achievement Goal Pursuit   143

Third, as the majority of the athletes picked a MAp goal as their dominant goal during a specific game, we could not test the role of autonomous and controlling reasons underlying the other achievement goals in the prediction of game-related correlates. It is possible that particular combinations of achievement goals and reasons work in tandem to predict outcomes. Fourth, an additional issue concerns the relatively few instances in which athletes picked PAp goals as their dominant achievement goal (see also Van Yperen & Renkema, 2008). This is a noteworthy finding given that achievement goals were assessed in a competitive context—a volleyball league game. The infrequent endorsement of PAp goals in educational settings has been portrayed as a weakness of this construct (Brophy, 2005) and the current game-to-game findings casts perhaps further light on their true prevalence in achievement settings like sport games where competition is even more salient. Nevertheless, first, given that we assessed athletes’ dominant goal, it is unknown to what degree athletes with a dominant MAp goal involvement also endorsed—yet, to lesser extent—PAp goals, as the pursuit of multiple goals is a viable possibility (Barron & Harackiewicz, 2001). Second, the low incidence of performance goals should not prevent researchers from examining its effects, as even lowly occurring phenomena (e.g., burnout, depression) may yield implications for one’s psychosocial functioning. Finally, an in-depth analysis with interviews in tandem with quantitative research would further inform us about the reasons why athletes change their game-togame goal pursuit or their underlying reasons. Likely, both are affected by particular characteristics of the game (e.g., winning a crucial versus less crucial game), the opponent team (e.g., the best vs. the worst team in the league), the coach’s particular behavior (e.g., autonomous supportive vs. controlling style of coaching during the game), the general atmosphere in the club (e.g., mastery or performance oriented), recent team history (e.g., a series of victories or losses in a row), or players’ past experiences (e.g., a substitute is given the opportunity to play as a “starter” in one particular game). Among these reasons we should also consider the possibility that method effects are at play, as being asked to indicate a dominant goal during a particular game may provoke some introspection about the dominant goal adopted in previous games, leading one to either choose the same or deviate from the previously chosen dominant goal.

Conclusion Although athletes’ motivation undergoes multiple changes during the course of a season, sport psychologists have paid little attention to these within-person dynamics. The present study reveals that both the type of achievement goals volleyball players pursue during a particular game as well as their reasons for their goal adoption vary substantially across a series of six games.

This variation related to their psychosocial functioning, with especially a more volitional or autonomous regulation of MAp goal pursuit yielding game-specific benefits, including prosocial behavior, game enjoyment, and performance satisfaction.

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