Goal Orientations and Performance: Differential Relationships Across Levels of Analysis and as a Function of Task Demands

Transcription

1 Journal of Applied Psychology 2009 American Psychological Association 2009, Vol. 94, No. 3, /09/$12.00 DOI: /a Goal Orientations and Performance: Differential Relationships Across Levels of Analysis and as a Function of Task Demands Gillian Yeo and Shayne Loft University of Western Australia Tania Xiao University of Queensland Christian Kiewitz University of Dayton Goal orientation and self-regulation theories were integrated to develop a multilevel framework aimed at addressing controversies regarding the magnitude and direction of goal orientation effects on performance. In Study 1, goal orientations were measured repeatedly whilst individuals performed an air traffic control task. In Study 2, goal orientations and exam performance were measured across 3 time points while undergraduates completed a course. Mastery-approach orientation was positively related to performance at the intraindividual level, but not at the interindividual level, and its effect was not moderated by task demands. Performanceapproach positively predicted performance at the interindividual level, and at the intraindividual level, the direction of its effect switched as a function of task demands. Performance-avoid negatively predicted performance at the interindividual level but did not emerge as an intraindividual predictor. Mastery-avoid did not relate to performance at either level of analysis. This consistent pattern across 2 studies suggests that levels of analysis and task demands can determine the magnitude and direction of goal orientation effects on performance and highlights avenues for theory development. Keywords: goal orientation, performance, intraindividual and interindividual levels of analysis, task complexity, task practice There is considerable research regarding goal orientations and performance (e.g., Dweck, 1986; Payne, Satoris, & Beaubien, 2007), yet controversies exist regarding the magnitude and direction of these effects. For example, although theory suggests that mastery-approach orientation should be positively related to performance, research has often indicated a null relationship (e.g., Cury, Elliot, Da Fonseca, & Moller, 2006; Elliot & Murayama, 2008). In addition, some researchers have argued that both theory and empirical evidence are mixed in relation to the direction of performance-approach orientation effects on performance (e.g., Davis, Mero, & Goodman, 2007; Payne et al., 2007). These inconsistencies may be resolved by addressing the call to examine the dynamic nature of goal orientations (DeShon & Gillespie, 2005). These constructs are conceptualized as malleable (Dweck, 1986), yet most research has measured them at a single point in time. Between 1998 and 2008, 27 studies were published in the Journal of Applied Psychology with the keywords goal orientation or achievement goals. None of these took repeated measurements of goal orientations. We integrated goal orientation and selfregulation theories to predict differential effects of goal orientations on performance across the intraindividual and interindividual levels of analysis and as a function of task demands. We tested these predictions by Gillian Yeo, Business School, University of Western Australia, Perth, Australia; Shayne Loft, School of Psychology, University of Western Australia, Perth, Australia; Tania Xiao, School of Psychology, University of Queensland, Brisbane, Australia; Christian Kiewitz, Management/Marketing Department, University of Dayton. Correspondence concerning this article should be addressed to Gillian Yeo, Business School, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia. gyeo@biz.uwa.edu.au collecting repeated measurements of goal orientations and performance from undergraduate students while they (a) practiced an air traffic control (ATC) task (Study 1) and (b) completed assessments for a university course (Study 2). Conceptualization of Goal Orientations Dispositional goal orientations refer to stable patterns of cognition and action that result from the chronic pursuit of achievement goals in different situations over time, whereas domain-specific orientations reflect a similar pattern in a specific domain (DeShon & Gillespie, 2005). We focus on domain-specific orientations because our aims relate to the malleable nature of goal orientations. A dominant conceptualization of goal orientation reflects a three-factor model (Payne et al., 2007; Vandewalle, 1997). Mastery-approach reflects a desire to develop knowledge, skills, and task mastery; performance-approach reflects a desire to demonstrate competence on performance indicators relative to others; and performance-avoid reflects a desire to avoid the demonstration of incompetence on performance indicators relative to others (Elliot & McGregor, 2001). 1 The mastery performance distinction reflects differences in the definition of competence (Elliot & McGregor, 2001): Mastery-approach is concerned with developing task-related competence, whereas the two performance orientations are concerned with demonstrations of perfor- 1 Consistent with recent conceptualizations (Elliot & Murayama, 2008), we focus on normative comparisons (see also Smither & Houston, 1992; Van Yperen, 2006). This contrasts with operationalizations of related aspects, such as avoiding poor performance or demonstrating good performance per se (Elliot & Church, 1997) or aiming not to look foolish (Vandewalle, 1997). 710

2 GOAL ORIENTATION AND PERFORMANCE 711 mance-related competence. The approach avoid distinction relates to performance discrepancies (Carver & Scheier, 1998): The two approach orientations relate to discrepancy reduction, that is, a desire to move toward a goal, whereas performance-avoid relates to discrepancy amplification, that is, a desire to move away from a goal. We argue below that these distinctions, in combination with levels of analysis and task demands, have implications for the expected magnitude and direction of goal orientation effects on performance. Relationships Among Goal Orientations and Performance In this section, we predict that mastery-approach orientation will relate to performance at the intraindividual level, whereas the two performance orientations will relate to performance at both levels of analysis. We further predict that the intraindividual relationships will be moderated by task practice. Mastery-Approach Orientation and Performance at the Intraindividual Level Controversy exists regarding the magnitude of mastery-approach orientation effects on performance. Theoretically, this construct is viewed as adaptive (Dweck, 1986; Elliot & McGregor, 2001) because it is associated with beneficial approach-related processes (e.g., effort, persistence, task absorption; Elliot, Shell, Bouas Henry, & Maier, 2005). Consequently, it is expected to predict a range of positive outcomes (Elliot et al., 2005). Research at the interindividual level has consistently shown that mastery-approach is positively related to constructs such as self-efficacy and self-set goals (Payne et al., 2007). In contrast, a number of studies have demonstrated null relationships between mastery-approach and performance (e.g., Cury et al., 2006; Elliot & McGregor, 2001; Payne et al., 2007). To reconcile this discrepancy, researchers have argued that mastery-approach orientation may not relate to performance because performance success requires a focus on external evaluations (e.g., what is required to outperform others) that is beyond the task and intrapersonal focus associated with this orientation (Cury et al., 2006; Elliot et al., 2005). We agree that this argument applies to the interindividual level but do not believe it applies to the intraindividual level. Mastery-approach is conceptualized as a malleable construct (Dweck, 1986), characterized by a desire to improve knowledge and skill (Elliot & McGregor, 2001), and the associated approach-related processes are expected to influence changes within individuals (Kanfer & Ackerman, 1989). Furthermore, knowledge and skill typically translate into performance benefits (Anderson, 1982; Kanfer & Ackerman, 1989). Therefore, changes in this orientation within an individual should relate to changes in performance. As an individual increases his or her mastery-approach orientation, his or her performance should improve. In contrast, this desire to improve can be realized without performing better than others, that is, selfimprovement does not necessarily equate to a performance advantage relative to others. Therefore, individuals with high mastery-approach orientation may not necessarily outperform their counterparts. Hypothesis 1: At the intraindividual level, there is a positive relationship between mastery-approach and performance. Performance Orientations and Performance at Both Levels of Analysis Performance-Approach Orientation A second controversy concerns the directionality of performanceapproach orientation effects on performance. Positive, negative, and null relationships have been predicted, and findings have also been mixed (e.g., Davis et al., 2007; Payne et al., 2007; Seijts, Latham, Tasa, & Latham, 2004). Performance-approach subsumes negative and positive elements. Its external focus may be maladaptive for some processes and outcomes (Elliot et al., 2005). Predominantly, however, these negative elements are expected to be outweighed by the benefits of approach-related processes (e.g., effort, persistence; Elliot & McGregor, 2001; Vandewalle, 1997). Most research that has included a pure measure of performance-approach has demonstrated a positive relationship with performance (e.g., Church, Elliot, & Gable, 2001; Urdan, 2004; Wolters, 2004). We expected to replicate this effect at the interindividual level because performance-approach is characterized by a desire to outperform others and its external focus highlights what is required to achieve this aim (Elliot et al., 2005). We also expected this effect to emerge at the intraindividual level because goal orientations are viewed as malleable (Dweck, 1986) and the approach-related processes associated with performance-approach should influence changes within individuals (Kanfer & Ackerman, 1989). Hypothesis 2a: At the intraindividual level, there is a positive relationship between performance-approach and performance. Hypothesis 2b: At the interindividual level, there is a positive relationship between performance-approach and performance. Performance-Avoid Orientation This construct is viewed as maladaptive due to its association with avoid-related processes (e.g., distraction, effort withdrawal or minimization; Elliot et al., 2005). Research supports the notion that performance-avoid is negatively related to performance (e.g., Elliot, McGregor, & Gable, 1999; Pajares & Valiante, 2001; Zusho, Pintrich, & Cortina, 2005). This effect should replicate at the interindividual level because performance-avoid is characterized by an external focus on interpersonal standards. As noted, however, the avoid-related processes are expected to thwart the aim of avoiding normative failure (Elliot et al., 2005), and consequently, individuals with high performance-avoid orientation are expected to perform worse than their counterparts. We also expected this negative effect to emerge at the intraindividual level because goal orientations are viewed as malleable (Dweck, 1986) and the avoid-related processes associated with performance-avoid should influence changes within individuals (Kanfer & Ackerman, 1989). Hypothesis 3a: At the intraindividual level, there is a negative relationship between performance-avoid and performance. Hypothesis 3b: At the interindividual level, there is a negative relationship between performance-avoid and performance.

3 712 YEO, LOFT, XIAO, AND KIEWITZ Goal Orientations, Task Practice, and Performance at the Intraindividual Level We now generate predictions regarding interactions between goal orientations and task practice at the intraindividual level. A key contribution here is that we address the controversy regarding the directionality of performance-approach orientation effects on performance. We argue that the positive and negative elements of performanceapproach should be evident when examining its intraindividual-level relationship with performance as a function of task practice. Task practice reflects variability in the demands placed on cognitive resource capacities (Kanfer & Ackerman, 1989). In tasks with consistent mapping between stimuli and responses, performance early in practice is expected to impose a greater cognitive demand compared with later in practice because practice reflects a shift from controlled to automatic processing (e.g., Fitts & Posner, 1967). We specify task practice as a moderator because the self-regulatory foci that characterize goal orientations are expected to influence performance as a function of resource demands. We conceptualize these interactions as intraindividual level (i.e., where task practice moderates intraindividual relationships) as opposed to cross-level (i.e., where task practice moderates interindividual relationships) because the proposed underlying mechanisms refer to intraindividual (i.e., allocation of resources within an individual) as opposed to interindividual (e.g., differences in total resource capacity between individuals) processes. We now present a theoretical framework relating to self-regulation and resource allocation that we use to generate our predictions. Information-processing theories are useful for considering the consequences of intraindividual changes in motivational constructs (e.g. Kahneman, 1973; Kanfer & Ackerman, 1989). Attention is conceptualized as a limited capacity of cognitive resources that can be allocated to on-task, off-task, and self-regulatory activities. This process is described as dynamic, in that allocations vary within individuals. Self-regulatory activities include self-monitoring, selfevaluation, and self-reactions (Kanfer, 1990). Self-regulation is expected to be detrimental in cognitively demanding situations because it consumes limited resources (Muraven & Baumeister, 2000). In less demanding situations, more resources are available for self-regulation, and such activity is posited to be beneficial because it aids the maintenance of high standards (Kanfer & Ackerman, 1989; Zimmerman & Kitsantas, 1997). More recent theorizing delineates between performance- versus task-focused self-regulation (e.g., a focus on performance indicators vs. knowledge and skills). It is performance-focused selfregulation that is thought to be detrimental in cognitively demanding situations because such a focus should consume resources required for acquiring knowledge and skill (Seijts et al., 2004; Zimmerman & Kitsantas, 1999). We further argue that performance-focused self-regulation that has an interpersonal component (e.g., evaluating one s current standard relative to normative standards) should be particularly detrimental under these conditions because both the subject matter (e.g., performance score) and referent (i.e., other people) shift attention from core task activities. In contrast, task-focused self-regulation that has an intrapersonal component (e.g., evaluating one s current standard relative to prior personal standards) should be particularly beneficial under high task demands because both the subject matter (e.g., knowledge) and referent (i.e., the self) direct attention toward task activities. The self-regulatory foci that characterize goal orientations align with the distinctions described above. Mastery orientations are characterized by a focus on task outcomes and intrapersonal standards, whereas performance orientations are characterized by a focus on performance outcomes and interpersonal standards. These parallels suggest the following predictions. First, given that a self-referenced task focus is expected to be particularly beneficial under high cognitive demands, the positive relationship between mastery-approach and performance should be strongest early in practice. Second, a focus on performance outcomes and normative standards is expected to be detrimental under high demands but beneficial under low demands. The latter argument implicitly applies to approach-oriented self-regulation because it is regulation focused on goal striving as opposed to goal avoidance that is expected to facilitate the maintenance of high standards (Kanfer & Ackerman, 1989). Therefore, the intraindividual relationship between performance-approach and performance should be negative early in practice and positive later in practice, whereas the negative intraindividual relationship between performance-avoid and performance should be strongest early in practice. The goal-setting literature provides indirect empirical support for our arguments. Research suggests that learning goals are effective in complex tasks but less effective in simpler tasks, whereas performance goals are maladaptive in complex tasks but beneficial in simpler tasks (e.g., Earley, Connolly, & Ekegren, 1989; Seijts et al., 2004; Winters & Latham, 1996). An induced performance orientation has also been shown to be beneficial for performance relative to an induced learning orientation on a low-complexity task (Steele-Johnson, Beauregard, Hoover, & Schmidt, 2000). Specifically related to task practice, research has shown that performance can be enhanced if individuals are assigned a learning goal followed by a performance goal over practice (Zimmerman & Kitsantas, 1997, 1999). It should be noted that these studies analyzed data at the interindividual level. Hypothesis 4a: At the intraindividual level, the positive relationship between mastery-approach and performance weakens over practice. Hypothesis 4b: At the intraindividual level, the relationship between performance-approach and performance is negative early in practice but becomes positive over time. Hypothesis 4c: At the intraindividual level, the negative relationship between performance-avoid and performance weakens over practice. 2 2 One may speculate whether the intraindividual main effect hypotheses (Hypotheses 1, 2a, and 3a) are superseded by these interaction hypotheses. This depends on the rate at which these effects change over practice, which is difficult to predict a priori. Past research has indicated that performance in our ATC task reflects a classic learning curve (Yeo & Neal, 2004, 2006). This suggests that this task allows us to sample observations over a meaningful range of the task practice continuum and thus is an appropriate context for testing whether the goal orientations exert positive (mastery-approach and performance-approach) versus negative (performance-avoid) main effects collapsed across time points, as theory implies (Elliot & McGregor, 2001), and whether these effects change throughout (i.e., interact with) task practice. These issues are discussed further in the General Discussion.

4 GOAL ORIENTATION AND PERFORMANCE 713 Participants and Procedure Study 1 Method One hundred three undergraduate students (41 female, 62 male) from the University of Queensland, Brisbane, Australia, participated for course credit (mean age years; SD 4.03). The 2-hr experiment was conducted in a laboratory that held 6 participants. Following experimenter instructions, the participants performed a familiarization trial and twenty-nine 2-min trials. Goal orientation items were presented prior to each trial. Experimental Task An ATC task characterized by consistent information processing was selected from ATC-lab (Loft, Hill, Neal, Humphreys, & Yeo, 2004). During each trial, participants were presented four aircraft pairs, each converging on a common point. The pairs were distributed in four quadrants and labeled with matching letters (A, B, C, and D). Aircraft call signs and velocities were displayed next to each aircraft. The trial number and timer were displayed at the top left-hand corner. Participants judged whether the paths of each aircraft pair would conflict or not. A conflict occurred if aircraft passed within five kilometers of each other. Participants were instructed to register their responses, as quickly and accurately as possible, by clicking the appropriate button in a box displayed onscreen. After participants made their decision, they observed the aircraft continue on their flight paths. At the end of each trial, the word correct or incorrect appeared in addition to the performance score. Participants were presented with 60 conflicts and 60 nonconflicts. Trial complexity was relatively constant over practice. Three factors can influence the complexity of conflict detection: the minimum distance of aircraft, the angle of intersection, and the time to minimum separation (Neal & Kwantes, 2008). These parameters were counterbalanced across five blocks of six trials and randomized within blocks. Skilled performance typically increases at a diminishing rate with practice on consistent informationprocessing tasks with stable complexity (Newell & Rosenbloom, 1981). If our Study 1 design represented a typical skill-acquisition context, we should have replicated this finding. Measures Domain-specific goal orientations. Before each trial, participants goal orientation levels were assessed with Horvath, Scheu, and DeShon s (2004) classroom goal orientation measure, which has demonstrated validity and reliability (Horvath, Herleman, & McKie, 2006). The measure comprises five items for each goal orientation. To minimize fatigue, we selected two items for each dimension. The highest loading items (excluding those not appropriate for the task domain) were chosen and tailored to our domain. For example, we adapted the performance-avoid orientation item In this class, I try to hide from others that they are better than me to I want to hide from others that they are better than me at this task. The other performance-avoid item was I aim to avoid discovering that others are better than me at this task. The performance-approach items were It is important to me to perform better in this task than others and I want others to recognize that I am one of the best at this task. The mastery-approach items were The opportunity to extend the range of my abilities during this task is important to me and The opportunity to learn new things during this task is important to me. All items were preceded with the phrase At the moment (cf. Watson, Clark, & Tellegen, 1988). Participants responded on a 7-point scale (1 strongly disagree, 7 strongly agree). The two item ratings for each scale were averaged within each trial to calculate mastery-approach, performance-approach, and performance-avoid orientation scores for each participant on each trial. Internal consistency estimates were calculated for each pair of raw scores for each trial. For performance-avoid, they ranged from.81 (Trial 1) to.97 (Trials 17 and 22). For performance-approach, they ranged from.86 (Trial 30) to.95 (Trial 18). For mastery-approach, they ranged from.94 (Trial 30) to.99 (Trial 4). Participants overall goal orientation levels were calculated by averaging mastery-approach, performance-approach, and performance-avoid across all trials. Task practice. Task practice was modeled with orthogonal linear and quadratic growth trajectory parameters (Fisher & Yates, 1974). Task performance. The total number of points obtained in each trial was used as a measure of task performance. This score was the sum of the scores for each of the four aircraft pairs. Correct decisions made within 30, 60, 90, and 120 s gained 40, 30, 20, and 10 points, respectively. Incorrect decisions resulted in a deduction of 25 points. Scores could thus range from 100 to 160 per trial. The score from the familiarization trial was used as the past performance measure for Trial 1. Results Results from the power-in-two-level designs program (PINT; Bosker, Snijders, & Guldemond, 2003) indicated that our sample of 103 was sufficient to detect small effect sizes at a level of 80% confidence or better with.05 (assuming standard errors less than or equal to.08). Descriptive statistics are displayed in Tables 1 and 2. Hypotheses were tested using hierarchical linear modeling (Raudenbush & Bryk, 2002). The intraclass correlation coefficients (ICC1s) 3 indicated that 14%, 15%, and 12% of the variance in mastery-approach, performance-approach, and performance-avoid orientations, respectively, existed at the intraindividual level. The ICC1 for task performance was.32, indicating that 68% of the variability in task performance scores existed at the intraindividual level. All predictor variables were grand-mean centered, except the orthogonal polynomial contrasts. Hypotheses 1, 2a, and 3a predict that all three goal orientations relate to performance at the intraindividual level; whereas Hypotheses 2b and 3b predict that the two performance orientations relate to performance at the interindividual level. We specified task performance as the dependent variable; the linear and quadratic trajectories, past performance, and the three goal orientations as 3 The ICC1s were determined by using the intercept and Level 1 variance components to calculate the ratio of Level 1 variance to total variability (i.e., Level 1 variance component/intercept variance component Level 1 variance component).

5 714 YEO, LOFT, XIAO, AND KIEWITZ Table 1 Study 1: Descriptive Statistics and Intercorrelations Among the Variables at the Intraindividual Level (N 3,090) Variable M (SD) Task practice (8.66) 2. Mastery-approach orientation 4.22 (1.79) Performance-approach orientation 3.14 (1.63) Performance-avoid orientation 2.19 (1.38) Past task performance (72.56) Task performance (72.41) p.05. p.01. p.001. Level 1 predictors, and average levels of the goal orientations as Level 2 predictors. As seen in Step 1 of Table 3, the linear, t(102) 15.22, p.001, and quadratic, t(102) 6.66, p.001, trajectories were significant. This finding reflects a classic skill-acquisition curve (e.g., Fitts & Posner, 1967). The trajectories accounted for 39.4% of the intraindividual variability in task performance (25.99% total). Past performance accounted for an additional 0.34% of the intraindividual variability in task performance (0.22% total) and 4.85% of the interindividual variance (1.65% total). As seen in Step 2 of Table 3, 4 analyses supported Hypotheses 2b and 3b. Performance-approach, t(99) 2.32, p.05, and performance-avoid, t(99) 2.05, p.05, orientations predicted task performance at the interindividual level. Individuals with higher average levels of performance-approach or lower levels of performance-avoid outperformed their counterparts. The average goal orientations accounted for an additional 1.27% (0.43% total) of the interindividual variance. As seen in Step 3 of Table 3, results supported Hypothesis 1. Mastery-approach orientation predicted task performance at the intraindividual level, t(102) 2.04, p.05, indicating that as mastery-approach orientation increased, performance increased. Figure 1 shows how positive and null effects can exist at two levels of analysis. The dashed line represents the null relationship between mastery-approach and performance at the interindividual level, while the solid lines represent the positive intraindividual relationships (for a similar representation regarding self-efficacy, see Vancouver, Thompson, & Williams, 2001). Hypotheses 2a and 3a were not supported; neither performance-approach, t(102) 1.44, ns, nor performance-avoid, t(102) 0.83, ns, predicted performance at the intraindividual level. The Level 1 goal orientation variables accounted for an additional 0.63% of the intraindividual (0.42% total) and 10.14% of the interindividual (3.45% total) variability. Hypotheses 4a, 4b, and 4c predict that the intraindividual relationships between goal orientations and task performance change as a function of practice. We specified the three Level 1 interaction terms as Level 1 predictors, while controlling for the average goal orientations as predictors of the linear trajectory slope (see Step 4 in Table 3). 5 Hypothesis 4a was not supported, as the Level 1 interaction between mastery-approach and the linear trajectory was not significant, t(102) 0.13, ns. In support of Hypothesis 4b, the Level 1 interaction between performance-approach and the linear trajectory was significant, t(102) 2.05, p.05. The intraindividual relationship between performance-approach and task performance was negative at the beginning of practice and positive by the end of practice (see Figure 2). Figure 3 shows how both positive and negative effects can exist at two levels of analysis and over time. The dashed lines represent the stability of the positive interindividual relationship between performanceapproach and performance across practice, while the solid lines represent the shift from a negative to a positive relationship over practice at the intraindividual level. Hypothesis 4c was not supported, as the Level 1 interaction between performance-avoid and the linear trajectory was not significant, t(102) 1.44, ns. These interactions accounted for an additional 0.47% of the intraindividual (0.31% total) and 0.44% of the interindividual (0.15% total) variability in task performance. Summary Our hypotheses received moderate support. Mastery-approach orientation was positively related to performance at the intraindividual level (Hypothesis 1), whereas performance-approach and performance-avoid were related to performance at the interindividual level (positively and negatively, respectively; Hypotheses 2b and 3b). The nonsignificant intraindividual main effect of performance-approach (Hypothesis 2a) was qualified in that this relationship shifted from negative to positive throughout task practice (Hypothesis 4b). The intraindividual effect of masteryapproach did not change over practice (Hypothesis 4a) and the effect of performance-avoid did not emerge at the intraindividual level (Hypotheses 3a and 4c). 4 We entered the average goal orientations prior to the Level 1 goal orientation variables. This allowed us to determine how much variance the Level 1 variables explained beyond the interindividual effects (which have been established in the literature). It is not advisable to test these effects in the opposite order because, to ensure an unbiased estimate of the intraindividual relationships, it is important to control for the average levels of those variables at Level 2 (Raudenbush & Bryk, 2002). An alternative would be to group-mean center the Level 1 predictors; however, our approach is preferable because group-mean centering is associated with problems in estimating effect sizes (Snijders & Bosker, 1994). 5 The Level 1 mastery-approach effect became nonsignificant in Step 4. Nicholls (1984) suggested that mastery-approach may only be activated if the task mastery is viewed as meaningful. Regardless of the extent to which individuals aimed to develop competence in this task, the mastery-approach effect may have been diminished if participants did not view the learning opportunities as worthwhile. In a more meaningful context, fluctuations in mastery-approach may remain important for performance after controlling for the Level 1 interactions. This notion is supported in Study 2.

6 GOAL ORIENTATION AND PERFORMANCE 715 Table 2 Study 1: Descriptive Statistics and Intercorrelations Among the Variables at the Interindividual Level (N 103) Variable M (SD) Trial 1 mastery-approach 4.35 (1.65) 2. Trial 15 mastery-approach 4.17 (1.81) Trial 29 mastery-approach 4.09 (1.96) Average mastery-approach 4.22 (1.66) Trial 1 performance-approach 3.15 (1.53) Trial 15 performance-approach 3.24 (1.65) Trial 29 performance-approach 3.21 (1.78) Average performance-approach 3.14 (1.51) Trial 1 performance-avoid 2.34 (1.30) Trial 15 performance-avoid 2.16 (1.36) Trial 29 performance-avoid 2.25 (1.55) Average performance-avoid 2.19 (1.29) Trial 1 task performance (59.36) Trial 15 task performance (67.36) Trial 29 task performance (55.59) Average task performance (42.45) p.10. p.05. p.01. p.001. Study 2 We addressed three issues in Study 2 to replicate and extend our Study 1 findings. The first issue is theoretical and concerns the structure of goal orientation. Recent research has conceptualized a four-factor model of goal orientation, which includes mastery-avoid orientation (Elliot & McGregor, 2001). A complete test of our propositions regarding the mastery performance and approach avoid distinctions should include all four combinations of these dimensions. The second two issues concern the generalizability of our results. In Study 1, the null results pertained to intraindividual relationships that may have resulted from the low intraindividual variability in the goal orientations (12% 15%). A greater proportion of intraindividual variability may be captured over a longer time period or in a more meaningful task context, in which factors thought to shape goal orientations, such as feedback (Dweck, 1986), may be more influential. Furthermore, given the importance of replications with theoretically relevant extensions (Hubbard, Vetter, & Little, 1998; Rosenthal & Rosnow, 1984; Tsang & Kai-Man, 1999), we sought to establish whether our findings would replicate in a different population and setting and with a different operationalization of task demands. In Study 2, we examined the effects of all four goal orientations in a classroom context in which task (exam) complexity increased over three time points throughout a 16-week semester. Mastery-avoid orientation reflects the positive elements associated with a task and intrapersonal view of competence but also involves a focus on avoiding the loss of mastery (Elliot & McGregor, 2001). Individuals with high mastery-avoid orientation are expected to focus on not allowing their skills to deteriorate, not forgetting, and avoiding mistakes (Elliot, 1999). This focus is expected to prompt maladaptive avoid-related processes (e.g., distraction, effort withdrawal or minimization) and negative associations with outcomes (Elliot & McGregor, 2001; Linnenbrink & Pintrich, 2002). Research, albeit limited, supports these claims by demonstrating negative relationships with self-efficacy (Van Yperen, 2006) and intrinsic motivation (Cury et al., 2006; although, for a null result, see Elliot & Murayama, 2008), plus a positive relationship with anxiety (Elliot & McGregor, 2001). In contrast, research has demonstrated null relationships with performance (Cury et al., 2006; Elliot & Murayama, 2008). Cury et al. (2006) suggested that mastery-avoid may not relate to performance because its definition of competence precludes a focus on external factors. We expected its negative effect on performance to emerge at the intraindividual level as opposed to the interindividual level. Mastery-avoid is conceptualized as a malleable construct characterized by a focus on intrapersonal standards (Dweck, 1986; Elliot & McGregor, 2001), and the associated self-regulatory processes should influence changes within individuals (Kanfer & Ackerman, 1989). However, the avoid-oriented nature of these processes was expected to thwart the aim of avoiding the loss of competence. Therefore, a detrimental effect of this orientation should have been evident within individuals. As an individual increases his or her mastery-avoid orientation, his or her performance should decrease. In contrast, this intrapersonal aim is independent of any desires relating to interpersonal standards, that is, a loss of personal competence does not

7 716 YEO, LOFT, XIAO, AND KIEWITZ Table 3 Study 1: The Effects of Goal Orientations on Task Performance From Two Levels of Analysis Step 1 Step 2 Step 3 Step 4 Coefficient SE df t test Coefficient SE df t test Coefficient SE df t test Coefficient SE df t test Variable Intercept ( 00 ) Linear trajectory ( 10 ) Quadratic trajectory ( 20 ) Past performance ( 30 ) Average MAP ( 01 ) Average PAP ( 02 ) Average PAV ( 03 ) MAP ( 40 ) PAP ( 50 ) PAV ( 60 ) Linear Average MAP ( 11 ) Linear Average PAP ( 12 ) Linear Average PAV ( 13 ) Linear MAP ( 70 ) Linear PAP ( 80 ) Linear PAV ( 90 ) Note. MAP mastery-approach; PAP performance-approach; PAV performance-avoid. p.05. p.001. necessarily equate to a performance disadvantage relative to others. Therefore, highly mastery-avoid-oriented individuals may not necessarily perform worse than their counterparts. Hypothesis 5: At the intraindividual level, there is a negative relationship between mastery-avoid and performance. Task complexity should moderate goal orientation effects at the intraindividual level because it reflects variability in demands placed on cognitive resource capacity. Complex tasks should place a heavier demand on capacity, relative to simple tasks, because they require handling a greater number of inputs and their interactions (Wood, 1986). Our previous theoretical integration (e.g., Kanfer & Ackerman, 1989; Zimmerman & Kitsantas, 1997) allowed us to generate predictions regarding task complexity and goal orientations. In relation to mastery orientations, these arguments suggested that a self-referenced task focus should be particularly beneficial under high cognitive demands because this is when a task focus is most necessary. However, this argument implicitly applies to approachrelated regulation because it is this type of regulation that is expected to be adaptive (Elliot & McGregor, 2001). In contrast, an avoid-oriented task focus should be particularly detrimental under high demands because the associated maladaptive processes should undermine the benefits of a task focus. Therefore, the positive effect of mastery-approach and the negative effect of mastery-avoid at the intraindividual level should be strongest for high-complexity tasks. In relation to performance orientations, our arguments suggested that a normatively referenced performance focus is detrimental under high demands because it shifts attention away from task activities when they are most needed but is beneficial under low demands because it aids in the maintenance of high standards. Again, it is approach-related as opposed to avoid-related regulation that is expected to be beneficial under low task demands (Kanfer & Ackerman, 1989). Therefore, the intraindividual effect of performance-approach should be positive for low-complexity tasks and negative for high-complexity tasks, whereas the negative intraindividual effect of performance-avoid should be strongest for high-complexity tasks. At the intraindividual level, then, the following hypotheses should apply: Hypothesis 6a: The positive relationship between masteryapproach and performance is strongest for tasks with high complexity. Hypothesis 6b: The negative relationship between masteryavoid and performance is strongest for tasks with high complexity. Hypothesis 6c: The relationship between performanceapproach and performance is positive for low-complexity, but negative for high-complexity, tasks. Hypothesis 6d: The negative relationship between performance-avoid and performance is strongest for tasks with high complexity.

8 GOAL ORIENTATION AND PERFORMANCE 717 Intra-individual level Inter-individual level Worse Better Low High Figure 1. Study 1: A visual representation of the relationship between mastery-approach orientation and task performance at two levels of analysis. Method Participants and Procedure One hundred sixteen undergraduate students (45 female, 71 male; mean age years, SD 1.00) from the University of Dayton, Dayton, OH, participated for course credit as part of a 16-week organizational behavior course. Data were collected at five time Figure 2. Study 1: The intraindividual interaction between performanceapproach orientation and task practice points: demographics in Week 1, performance on a multiple-choice quiz in Week 4, and goal orientations before each of three exams (Weeks 5, 10, and 16). Measures Domain-specific goal orientations. Mastery-approach and performance-approach orientations were measured with Elliot and McGregor s (2001) three-item classroom-specific scales. Example items include I want to learn as much as possible from this class and It is important for me to do well compared to others in this class, respectively. Mastery-avoid and performance-avoid were measured with 2 four-item scales. These items were based on Elliot and McGregor s (2001) scales but modified to ensure that the items did not include an affective component and that the performance-avoid items were explicitly normative (for a similar approach, see Cury et al., 2006). For example, the mastery-avoid item I worry that I may not learn all that I possibly can in this class was changed to I want to avoid failing to learn all that I possibly can in this class. Similarly, the performance-avoid item I just want to avoid doing poorly in this class was changed to I just want to avoid performing poorly in this class compared to other students. Previous research with 197 undergraduate university students showed that the four-factor model provides a good fit to the data, 2 (71, N 197) , p.01; comparative fit index.98; Tucker-Lewis index.97; goodnessof-fit index.93; root-mean-square error of approximation.06; all items displayed strong loadings on their respective factors (.69.93; Sorbello & Yeo, 2006). One hundred forty-one students were from the University of Queensland, Brisbane, Australia (72 male, 64 female, 5 unspecified; mean age years, SD 12.33), and 56 were from De La Salle University, Manila, the Philippines (30 male,

9 718 YEO, LOFT, XIAO, AND KIEWITZ Worse Better Intra-individual level Inter-individual level (early in practice) Inter-individual level (late in practice) Low High Figure 3. Study 1: A visual representation of the relationship between performance-approach orientation and task performance at two levels of analysis. 26 female; mean age years, SD 2.57). An invariance test indicated that the four-factor models did not differ across samples, 2 (20) 25.7, p.05. Items were measured on a 7-point scale ranging from 1 (strongly disagree) to7(strongly agree). Reliabilities for mastery-approach ranged from.84 (Time 1) to.91 (Time 3). For mastery-avoid, they ranged from.86 (Time 2) to.89 (Time 1). For performance-approach, they ranged from.88 (Time 1) to.94 (Time 3). For performance-avoid, they ranged from.88 (Time 1) to.90 (Time 2). Overall goal orientations were calculated by averaging the scores for each orientation across all three time points. Exam performance. The percentage score obtained by individuals on each exam was used as the performance measure for each of the three time points. To control for past performance in the analyses, we used the percentage score from the quiz preceding Exam 1 as a past performance measure for that exam. The quiz consisted of 10 multiple-choice items, which were based on the content of one of the chapters assessed in Exam 1 but did not overlap with the questions presented in Exam 1. Task complexity. We operationalized component and coordinative task complexity (Wallace, Little, & Shull, 2008; Wood, 1986). 6 Component complexity refers to the number and similarity of inputs required to be processed during task completion: The more dissimilar the inputs, the higher the component complexity. Coordinative complexity refers to the nature of relationships among inputs and outputs: When the relationship between inputs and outputs is clear, coordinative complexity is low, but when the relationship is less clear or nonlinear, coordinative complexity is high. Exams increased in component and coordinative complexity across the three time points. Exam output refers to answer quality, whereas the inputs refer to the questions and actions required. Exams with high component complexity should have a larger number of dissimilar questions than those with low component complexity. The number of examinable textbook chapters and the number of questions increased across exams. Exams 1 3 covered five, six, and seven chapters, respectively. Exams 1 and 2 included 48 multiple-choice questions, and Exam 3 included 68. Exams 1 3 included 12, 15, and 20 written-response questions (i.e., fill-inthe-blank, short-answer, and essay questions), respectively. Exams with high as opposed to low coordinative complexity should include more questions requiring a complex integration of knowledge and skill. Accordingly, the number of difficult questions increased across exams. The textbook question bank (Kreitner & Kinicki, 2004) was used to select difficult versus easy multiple-choice questions. The number of difficult multiple-choice questions increased from 29 questions in Exam 1 to 48 in Exam 2 to 50 in Exam 3. Bloom s taxonomy (Bloom, 1956) was used to ensure that the cognitive skill requirements of the written-response questions in- 6 Wood (1986) also conceptualized dynamic complexity, which refers to nonstatic relationships between task inputs and outputs (i.e., changes in the means end relationship). It was not appropriate to model dynamic complexity within a given performance episode in this context due to the static nature of written exams. For an example of how to model dynamic complexity within a dynamic task, see Wallace et al. (2008).

10 GOAL ORIENTATION AND PERFORMANCE 719 creased across exams. This taxonomy categorizes cognitive skill requirements as follows: (a) knowledge, (b) comprehension, (c) application, (d) analysis, (e) synthesis, and (f) evaluation. We assigned each question a score of 1 or 2 (for the first and last three categories, respectively). The weighted sums of written-response questions for Exams 1 3 were 13, 16, and 25, respectively. Exam 3 included more difficult written-response questions than Exams 1 or 2. For example, some of the short-answer questions in Exam 3 required analysis, synthesis, and/or evaluation (e.g., Is avoiding a bad conflict management style? Tell me when it is and when it is not ), whereas the short-answer questions in Exams 1 and 2 required only knowledge, comprehension, and/or application (e.g., The contingency approach can be defined as... ). Similarly, even though both Exams 1 and 2 included an essay question that relied on analysis, synthesis, and/or evaluation, Exam 3 included two of these types of questions (e.g., Why is LMX theory important for all of you who will soon start a new career? ). Information-processing theories propose that complex tasks are associated with lower performance than simple tasks due to the demands placed on cognitive resource capacity (e.g., Kanfer & Ackerman, 1989; Wood, 1986). Research supports this notion (Steele-Johnson et al., 2000; Wallace et al., 2008). If our manipulation of complexity was successful in Study 2, we should have replicated this finding. Results Results from PINT (Bosker et al., 2003) indicated that our sample of 116 was sufficient to detect small effect sizes at a level of 80% confidence or better, with.05 (assuming standard errors less than or equal to.08). Descriptive statistics are displayed in Tables 4 and 5. The ICC1s indicated that 36%, 39%, 26%, and 29% of the variance in mastery-approach, mastery-avoid, performance-approach, and performance-avoid existed at the intraindividual level. Thus, intraindividual variability was higher than in Study 1. The ICC1 for exam performance indicated that 74% of variability existed at the intraindividual level. All predictors were grand-mean centered except exam complexity, which was coded from 1 (least complex) to 1 (most complex). Hypotheses 1, 2a, 3a, and 5 predict that the four goal orientations relate to performance at the intraindividual level, whereas Hypotheses 2b and 3b predict that the two performance orientations relate to performance at the interindividual level. We specified exam performance as the dependent variable; exam complexity, past exam performance, and the Level 1 goal orientations as Level 1 predictors; and the average goal orientation variables as Level 2 predictors. As can be seen in Step 1 of Table 6, exam complexity was negatively related to exam performance, t(115) 14.17, p.001. This finding supports the notion that exam complexity increased across the three time points. Exam complexity accounted for 38.89% of the intraindividual variability in exam performance (28.67% total). Past performance accounted for an additional 27.19% of the interindividual variance (9.75% total). Hypotheses 2a and 3a were supported. As seen in Step 2 of Table 6, performance-approach, t(111) 2.34, p.05, and performance-avoid, t(111) 2.26, p.05, orientations predicted exam performance at the interindividual level. Individuals who reported high performance-approach or low performanceavoid orientation on average outperformed their counterparts. The average goal orientation variables accounted for an additional 7.34% of the interindividual variance (2.63% total). Hypothesis 1 was supported. As indicated in Step 3 of Table 6, mastery-approach orientation was positively related to performance at the intraindividual level of analysis, t(336) 2.05, p.05. An increase in mastery-approach orientation within an individual was associated with an increase in exam performance. Hypotheses 2a, 3, and 5 were not supported because performanceapproach, t(336) 0.16, ns; performance-avoid, t(336) 0.05, ns; and mastery-avoid, t(336) 0.31, ns, were not significant predictors of exam performance at the intraindividual level. The Level 1 goal orientation variables accounted for an additional 2.96% (0.70% total) of the interindividual variability. Hypotheses 6a, 6b, 6c, and 6d predict that the intraindividual relationships between goal orientations and exam performance change as a function of task complexity. We specified the four Level 1 interaction terms as Level 1 predictors, while controlling for average goal orientations as predictors of the exam complexity slope (see Step 4 of Table 6). In support of Hypothesis 4a, the Level 1 interaction between performance-approach and exam complexity was significant, t(328) 2.02, p.05. The relationship between performanceapproach orientation and exam performance was positive for the least complex exam but negative for the most complex exam (see Figure 4). Hypotheses 6a, 6b, and 6d were not supported, as the Level 1 interactions involving mastery-approach, t(328) 1.21, ns; mastery-avoid, t(328) 0.94, ns; and performance-avoid, t(328) 0.41, ns, were not significant. These interactions accounted for an additional 0.11% of the intraindividual (0.05% total) and 2.94% of the interindividual (0.67% total) variability. Summary This pattern of significant versus nonsignificant results is consistent with Study 1. We replicated support for Hypotheses 1, 2b, and 3c, in addition to the moderation hypothesis involving performance-approach orientation (Hypothesis 6b), which, consistent with Study 1, qualified the nonsignificant main effect (Hypothesis 2a). Also consistent with Study 1, neither the intraindividual interaction associated with mastery-approach (Hypothesis 6a) nor the intraindividual relationships associated with performanceavoid (Hypotheses 3a and 6b) were significant. The effects concerning mastery-avoid (Hypotheses 5 and 6d) were not significant. General Discussion We integrated goal orientation and self-regulation theories to generate predictions regarding goal orientation effects on performance across levels of analysis and as a function of task demands. Both studies demonstrated (a) main effects of mastery-approach orientation at the intraindividual level, (b) main effects of the performance orientations at the interindividual level, and (c) that the direction of the intraindividual effect of performance-approach switched as a function of task demands. Unexpectedly, however, the intraindividual effect of mastery-approach was not moderated by task demands, and the intraindividual effects of the avoid orientations were not significant. Addressing Controversies Regarding the Effects of Approach Goal Orientations on Performance We demonstrated positive relationships between masteryapproach and performance at the intraindividual level. This finding