Goal orientation dispositions and performance trajectories: The roles of supplementary and complementary situational inducements

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Organizational Behavior and Human Decision Processes 106 (2008) 21–38 www.elsevier.com/locate/obhdp

Goal orientation dispositions and performance trajectories: The roles of supplementary and complementary situational inducements Gilad Chen a,*, John E. Mathieu b a

Robert H. Smith School of Business, University of Maryland, 4514 Van Munching Hall, College Park, MD 20742-1815, USA b University of Connecticut, 2100 Hillside Road, Unit 1041MG Storrs, CT 06269-1041, USA Received 15 September 2005 Available online 4 March 2008 Accepted by Dave Harrison

Abstract Integrating goal orientation theory with interactionist approaches, this experimental study (N = 104) tested the unique and interactive effects of individual differences in goal orientations and situational goal orientation inducements on performance trajectories during skill acquisition. Results indicated that learning goal orientation predicted performance trajectories more positively when coupled with one situational inducement that captures a complementary feature (a performance, as opposed to a learning, goal frame), and when jointed with a situational inducement that captures a supplementary feature (self-referenced vs. normative-based performance feedback). There was also a complementary-like interaction between the two situational inducements, such that a learning goal frame led to more positive performance trajectories when coupled with normative, as opposed to self-referent, feedback. Implications for the motivation and skill acquisition literatures are discussed. Ó 2008 Elsevier Inc. All rights reserved. Keywords: Goal orientation; Self-regulation; Attribute-treatment interaction; Performance change; Learning

Over much of the second half of the 20th century, there was an ongoing and lively debate in applied psychology about whether performance criteria are dynamic or static (e.g., Ackerman, 1987; Barrett, Caldwell, & Alexander, 1985; Ghiselli & Hare, 1960; Humphreys, 1960; Henry & Hulin, 1987; Keil & Cortina, 2001; Murphy, 1989). This debate has been largely resolved, as recent theories and empirical research have illustrated that individuals can display systematically different performance patterns, or trajectories, over time (e.g., Hofmann, Jacobs, & Baratta, 1993; Hofmann, Jacobs, & Gerras, 1992; Ployhart & Hakel, 1998). Furthermore, several studies have demonstrated relationships between various individual differences and different performance patterns over time, reinforcing * Corresponding author. Fax: +1 301 314 8787 (G. Chen), +1 860 486 6415 (J.E. Mathieu). E-mail addresses: [email protected] (G. Chen), [email protected] (J.E. Mathieu).

0749-5978/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.obhdp.2007.11.001

the notion that performance trajectories capture systematic, meaningful phenomena (e.g., Eyring, Johnson, & Francis, 1993; Ployhart & Hakel, 1998; Thoresen, Bradley, Bliese, & Thoresen, 2004; Yeo & Neal, 2004). The conclusion that individual differences can be related to different performance trajectories over time portends a significant advancement for understanding behavior in settings where individual performance change is particularly likely, such as training, socialization, and organizational development. While recent advancements have shed new light on the age-old question of dynamic criteria, many issues remain to be addressed. The present research addresses the question of whether situational factors, in addition to individual differences, impact performance trajectories directly or moderate the relationships between individual differences and performance patterns over time. Understanding individual differences in performance trajectories can help shed light on how and why individuals

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inter-individual differences in intra-individual change patterns exist—i.e., individuals differ systematically in whether, and the rate at which, their task or job performance changes over time. For instance, Hofmann et al. (1992, 1993) illustrated that there are systematic differences in the performance trajectories of clusters of professional athletes and sales personnel. The negatively accelerated form of performance trajectories during skill acquisition or transitional stages of work reflects progression from early (declarative) to later (procedural) stages of learning (Ackerman, 1987; Anderson, 1983; Murphy, 1989). During the declarative phase of learning, performance requires a substantial amount of cognitive resources as individuals rely on general principles when learning to manage and apply rules and strategies in a new task context. With practice, individuals reach a procedural phase of learning, which requires fewer cognitive resources given that they have compiled greater task knowledge and learned when and how to apply which task strategies. For simple and very consistent tasks, individuals may eventually reach a level at which performance on a task becomes almost entirely automatic, without requiring much cognitive resources (e.g., driving a car on a familiar route). Kanfer and Ackerman’s (1989) resource allocation model proposed that performance during skill acquisition is a function of both cognitive ability and self-regulatory processes. The specific self-regulatory processes considered by Kanfer and Ackerman include self-monitoring (attending to one’s behavior on a task), self-evaluation (comparison of one’s performance against a desired goal state), and self-reactions (affective and cognitive evaluations of one’s self, such as self-satisfaction and self-efficacy). Given tasks become increasingly less cognitively

differ in how quickly and effectively they learn new skills or adapt to new circumstances. From a practical standpoint, such understanding can help develop more effective and efficient training and development programs, which facilitate quicker learning and adaptation rates. For instance, faster learning rates during training may translate into less time employees take away from their jobs, as well as spending less on resources devoted towards training. However, better understanding of the individual and situational factors that promote more positive performance trajectories (i.e., quicker rates of performance improvement) is needed before reaping such theoretical and practical benefits. Accordingly, building on an interactionist, aptitude-by-treatment interaction (ATI) framework, we examine the unique and combined influences of goal orientation dispositions and situational inducements on performance trajectories in a skill acquisition context. The theoretical model tested in this study, which we delineate below, is summarized in Fig. 1. Nature of performance trajectories The skill acquisition literature indicates that task performance levels improve over time in a negatively accelerated fashion, as individuals learn to master new knowledge and skill sets (Kanfer & Ackerman, 1989). Similarly, in work settings, Murphy (1989) proposed that job performance levels are more likely to change during transitional, relative to maintenance, phases of employment (e.g., during newcomer socialization or following organizational change), given that transitional phases require employees to learn new skills or to adapt existing ones in a new task environment (see also Chen, 2005; Thoresen et al., 2004). There is also evidence that INDIVIDUAL DIFFERENCES Learning Goal Orientation

Performance Goal Orientation

H1 (+) PERFORMANCE TRAJECTORY

H2 (-)

H5 (+) H7 (-)

•

H6 (+) H8 (-)

H3 & 4 (+)

Goal Frame

Feedback Referent

Performance (1) vs. Learning (2)

•

Normative (1) vs. Self (2)

SITUATIONAL INDUCEMENTS

Fig. 1. Summary of hypotheses.

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taxing across skill acquisition phases, cognitive ability becomes less predictive of performance as one progresses from the declarative to the procedural phases of learning. Self-regulatory processes, however, detract from task performance during early phases of skill acquisition, because they require allocation of additional cognitive resources beyond the large resources individuals already devote to learning to perform the task. However, as the task becomes better-learned (or proceduralized), self-regulatory processes become more beneficial for task performance. Extending Kanfer and Ackerman’s work, Yeo and Neal (2004) have proposed that, so long as task performance does not become automatic, effort devoted to executing a task becomes increasingly more predictive of task performance as individuals learn the task, given ‘‘trying hard will not help if the individual does not know how to perform the task” (p. 232). Eyring et al. (1993) investigated the influence of cognitive ability and self-efficacy on the performance trajectories of individuals working on a laboratory air traffic control simulation. They found that both ability and self-efficacy related positively to differences in performance rates over time, and self-efficacy also predicted differences in asymptotic performance. Yeo and Neal (2004) conducted a laboratory investigation using a similar task to that employed by Eyring et al. (1993), and found that the relationship between effort and performance became more positive over time during the skill acquisition process. In addition, general mental ability strengthened the extent to which effort became more predictive of performance over time. These two studies provide evidence for the roles of ability and self-regulation processes in predicting performance trajectories during the skill acquisition process. What is less clear, however, is whether and how individual differences interact with environmental stimuli to influence performance trajectories. This is a particularly important question to consider when examining the role of motivation in the prediction of performance trajectories, because motivational processes are believed to be influenced by both person and situational variables (Kanfer, Chen, & Pritchard, 2008). In the present study, we focus primarily on the unique and combined roles of individual differences in goal orientation and related situational stimuli, which are hypothesized to influence the self-regulation of effort and thereby the rate and direction at which individual performance increases during skill acquisition. Our study considers performance trajectories during repeated trials on a novel task which requires individuals to allocate cognitive effort to learn and improve their performance on the task over time. We designed the study such that individuals would likely transition between declarative to procedural phases of learning, but would not be able to reach an automatic (‘‘effortfree”) stage of learning. In line with the skill acquisition literature (e.g., Ackerman, 1987; Kanfer & Ackerman,

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1989; Yeo & Neal, 2004), we expected performance trajectories to reflect a negatively accelerated learning curve, or a quadratic effect over time as participants move from practice through the last performance stages. Although we acknowledge that ability plays a significant role during the skill acquisition process, we only control for it in this research, because our focus was on the roles of individual differences in, and situational inducements of, goal orientations in accounting for individual differences in performance trajectories. Goal orientation and performance trajectories The notion of goal orientation was developed initially in the educational literature to explain individual differences in students’ learning (e.g., Dweck & Leggett, 1988). In general, goal orientations refer to how individuals approach performance engagements. The two most basic goal orientation dimensions are learning and performance (Button, Mathieu, & Zajac, 1996; DeShon & Gillespie, 2005; Payne, Youngcourt, & Beaubien, 2007). Individuals who are high on learning goal-orientation strive to understand something new or to increase their level of competence in a given activity. In particular, learning-oriented individuals believe their ability is malleable, use personal standards to evaluate their level of task mastery, and thus are generally less anxious and more efficacious when learning a new task, and allocate greater effort towards task accomplishment, especially while learning new information or skills. In contrast, individuals who are high on performance goal-orientation seek to gain favorable judgments of, and demonstrate, their competence via task performance. Performance-oriented individuals believe their ability is relatively fixed, prefer normative standards to evaluate their level of task mastery, and therefore they tend to be more anxious and less efficacious when learning new skills. Button et al. (1996) provided clear evidence that learning and performance goal orientations are distinct constructs, rather than two points along a single continuum. In a recent meta-analysis, Payne et al. (2007) found an average correlation of only .15 between measures of performance and learning goal orientations. Research by VandeWalle (1997) and VandeWalle et al. (2001) later differentiated two aspects of performance goal orientation—performance-prove (a desire to gain favorable judgment of one’s competence) and performance-avoid (a desire to avoid unfavorable judgment of one’s competence). We concentrate on the performanceprove dimension, because our focus was on how individuals approach a task differently, rather than whether they seek to avoid a task.1 The goal orientation 1 Herein, our use of the term performance goal orientation refers to performance-prove goal orientation, unless specified otherwise. We consider the conceptual distinction between performance-prove and performance-avoid dimensions further in the Discussion section.

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literature has also focused on both individual differences in learning and performance goal orientations and on environmental stimuli that trigger goal orientation states (Button et al., 1996; DeShon & Gillespie, 2005; Kozlowski & Bell, 2006; Kozlowski et al., 2001). As such, we consider below the potential influences of goal orientation dispositions, goal orientation-related interventions, and the interactions between them on performance trajectories. Goal orientation dispositions and performance trajectories Button et al. (1996) argued that ‘‘goal orientation may have an important impact on self-regulatory processes that influence job performance over time” (p. 41). In a learning context, learning goal orientation is believed to trigger adaptive responses, whereas performance goal orientation is believed to lead to maladaptive responses (Kozlowski et al., 2001). According to Fisher and Ford (1998), learning goal orientation is associated with allocation of more task-related effort, whereas performance goal orientation tends to divert effort away from the task. Specifically, individuals with a high learning goal orientation approach situations with a desire to develop mastery skills, explore a task domain, and try out new strategies. For these people, failure is diagnostic and provides valuable feedback for developing their competencies. Not surprisingly, individuals with high levels of learning goal orientation have been found to have higher self-efficacy, develop more effective learning strategies, and allocate more effort towards task performance (Fisher & Ford, 1998; Payne et al., 2007). Since learning goal orientation is expected to positively relate to self-regulation processes such as effort individuals devote to performing a task, and since self-regulation becomes increasingly predictive of performance with practice, we predict that individuals with higher levels of learning goal orientation would improve their performance at faster rates than those with low levels of learning goal orientation. Providing limited evidence for this expectation, Yeo and Neal (2004) found that learning goal orientation related positively, albeit non-significantly, to both effort and the rate at which individuals improve their performance over time in a skill acquisition context. Although performance goal orientation is not merely the mirror image of learning goal orientation (Button et al., 1996), the opposite pattern of relationships with performance trajectories should be evident. To the extent that individuals possess a high performance orientation, they try to score well and demonstrate their competence. As argued by Kanfer and Ackerman (1989), effort directed at demonstrating competence can be detrimental to task performance during early phases of skill acquisition, before individuals compile sufficient knowledge and skill levels. Further, individuals

high on performance goal orientation are worried about ego management, and are less motivated to improve their performance on a task (e.g., Fisher & Ford, 1998; Yeo & Neal, 2004). There is also evidence that performance goal orientation is positively related to anxiety in a learning context (Chen, Gully, Whiteman, & Kilcullen, 2000; Payne et al., 2007). Hence, we argue that concern for demonstrating one’s skill, rather than learning or developing skills, can come at the expense of learning subtle aspects of a task domain and developing appropriate performance strategies. Consequently, we would anticipate that individuals with high levels of performance goal orientation would be less likely to devote effort to learning a task, and hence less likely to improve their performance over time. Indeed, Yeo and Neal (2004) found evidence that performance goal orientation negatively predicted performance trajectories during skill acquisition (albeit its negative relationship with effort was non-significant). Therefore, we advanced the following hypotheses: H1: Learning goal orientation positively relates to performance trajectories, such that individuals higher on learning goal orientation improve their performance at faster rates than those lower on learning goal orientation. H2: Performance goal orientation negatively relates to performance trajectories, such that individuals lower on performance goal orientation improve their performance at faster rates than those higher on performance goal orientation. Situational inducements of goal orientation and performance trajectories Researchers (e.g., Kozlowski & Bell, 2006; Kozlowski et al., 2001; Gist & Stevens, 1998; Martocchio, 1994; Stevens & Gist, 1997) have developed various interventions directed at triggering goal orientation states, or affect more directly goal orientation mindsets and behavioral manifestations, such as goal frame (i.e., framing the task as either an opportunity to develop new skills or as an opportunity to assess one’s ability) and feedback frame (i.e., encouraging individuals to compare their current level of performance to either internal/self or external/normative standards or referents). Framing goals in terms of opportunity to develop new skills and encouraging the use of personal standards to evaluate one’s performance feedback are both consistent with a learning goal orientation, whereas framing goals in terms of an opportunity to assess one’s ability and encouraging the use of normative standards to evaluate one’s performance are both consistent with a performance goal orientation. Although there is consensus that both the disposition and situational stimuli approaches to the study of goal orientation are viable (e.g., DeShon & Gillespie, 2005;

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Payne et al., 2007), very little research has examined both approaches simultaneously (Kozlowski et al., 2001 is a notable exception). According to Kozlowski et al. (2001, p. 6), ‘‘results for goal orientation-based training interventions have tended to be consistent with the findings of trait-based research.” Martocchio (1994) found that inducement of a learning, as opposed performance, goal state reduced anxiety during training. Stevens and Gist (1997) found further that training interventions directed at enhancing learning goal states, relative to performance goal states, increased effort and reduced negative affect. Kozlowski and Bell (2006) likewise found beneficial effects for learning goal inducements on self-regulatory processes. In addition, Kozlowski et al. (2001) found that a training intervention that increased learning goal state, as opposed performance goal state, increased trainee self-efficacy and enhanced the development of more coherent knowledge structure. Importantly, Kozlowski et al. found unique influences for goal orientation interventions and dispositions—e.g., learning goal orientation disposition and the learning goal orientation intervention uniquely and positively influenced trainees’ self-efficacy. Although we are not aware of research to date that has tested the influences of goal orientation interventions on performance trajectories during skill acquisition, we anticipate that these influences would parallel relationships involving dispositional goal orientation, given that previous studies have shown goal orientation dispositions and interventions lead to similar motivational and selfregulation processes. In other words, based on goal orientation theory and prior research it appears that interventions that induce a learning goal orientation would increase the regulation of effort toward learning a task (relative to off-task effort), whereas interventions that induce a performance goal orientation would reduce task-related effort and increase off-task effort. Accordingly, we advance the following hypotheses: H3: Goal framing significantly relates to performance trajectories. Specifically, learning goal frame relates positively to performance trajectories, whereas performance goal frame relates negatively to performance trajectories. H4: Feedback referents significantly relates to performance trajectories. Specifically, self-referenced feedback relates positively to performance trajectories, whereas normative-referenced feedback relates negatively to performance trajectories. Goal orientation dispositions  situational inducements interactions An important contribution of the present research is the examination of potential attribute-by-treatment

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interaction (ATI) effects (Cronbach, 1957). ATIs can enhance the fidelity and effectiveness of organizational applications (e.g., training and development, socialization), by allowing practitioners to know how to employ different kinds of interventions to employees with different attributes (e.g., Gully, Payne, Koles, & Whiteman, 2002). Indeed, there is evidence in the educational literature that individual differences often interact with situational treatments to affect learning (e.g., Snow, 1986). However, in the field of I–O Psychology, ATI studies on the topic of skill acquisition are still the exception, rather than the rule (Gully & Chen, in press). Applying the ATI framework to the current investigation suggests that situational inducements may interact with individuals’ goal orientations as related to performance trajectories (cf., Button et al., 1996; Farr, Hofmann, & Ringenbach, 1993; Mathieu & Martineau, 1997). Indeed, Button et al. (1996) argued that ‘‘future research will need to investigate the situational aspects of goal orientation and how situational cues . . . interact with individuals’ dispositions (pp. 41–42).” Unfortunately, only few studies have examined plausible ATIs involving goal orientation, and none have considered them as related to performance trajectories (Gully & Chen, in press). Towler and Dipboye (2001) found that goal orientation interacted with trainer expressiveness and lecture organization to influence learning performance, such that individuals with low learning orientation did not respond to trainer expressiveness and lecture organization, whereas individuals with high learning orientation learned the least from trainers’ who were disorganized and inexpressive. Heimbeck, Frese, Sonnentag, and Keith (2003) found that instructions that encouraged errors facilitated learning among learning oriented individuals, but not among performance-oriented individuals. However, these two studies considered a limited number of situational factors, did not explicitly manipulate goal orientation, and did not model performance trajectories. The literature on P–E fit has focused on the confluence of person–situation effects, and hence may help guide hypotheses regarding plausible ATIs between goal orientation dispositions and interventions. According to Cable and Edwards (2004) and Muchinsky and Monahan (1987), there are two forms of P–E fit interactions—supplementary and complementary. In line with the notion of value congruence, supplementary P–E fit occurs when situations provide individuals with certain aspects that they already possess or value. In contrast, operating on the principle of need fulfillment, complementary P–E fit occurs when a situation provides an individual with something s/he needs or desires but does not possess. However, P–E fit concerns the absolute distance between a person score and an environment score whereas, in contrast, ATIs only focus on the interaction between a person score and environmental score, or on

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whether situations impact relationships between dispositions and outcomes (cf. Harrison, 2007). Thus, although we borrow from the notions of supplementary and complementary fit, our focus is on whether situational inducements which trigger psychological states or mindsets congruent with (i.e., complementary inducements) or incongruent with (i.e., supplementary inducements) dispositional goal orientations serve to attenuate or to accentuate relationships between goal orientations and performance trajectories. To understand how certain combinations involving goal orientation disposition and interventions impact performance trajectories, it is first important to consider how the interaction between dispositions and interventions affect the regulation of effort during skill acquisition. To the extent that such interactions enhance adaptive self-regulatory processes (e.g., allocation of task-related effort) during skill acquisition, they are likely to increase the rate of performance improvement over time (cf. Kanfer & Ackerman, 1989; Yeo & Neal, 2004). Following this guiding principle, we next summarize theoretical arguments that would suggest two competing hypotheses concerning the interaction between both goal orientation dispositions and situational inducements. We provide competing hypotheses, because no research to date considered ATIs between goal orientation dispositions and situational inducements, and since, as we summarize below, prior goal orientation theory research suggest conflicting effects are plausible. Supplementary inducements hypothesis Following supplementary P–E fit logic, it is possible that the proposed effects of learning and performance goal orientation dispositions become stronger when coupled with goal orientation interventions of similar nature. In the motivation literature, congruence between a personal disposition and environmental characteristics has been discussed in terms of ‘‘situational affordance” that intensifies the impact of dispositions on behavior (Kanfer et al., 2008; Kanfer & Heggestad, 1997). For instance, Chen and Kanfer (2006) proposed that individual differences in achievement orientation more positively influence self-regulation and performance in team environments that challenge and empower team members. In other words, congruence between goal orientation dispositions and situational stimuli can intensify the main effect of each other, to result in more pronounced effects on the allocation of effort during skill acquisition. In contrast, training interventions that supplement individuals with a goal orientation characteristic opposite to the level they possess (e.g., high learning oriented individual coupled with performance goal frame or normative feedback frame interventions) may interfere with how individuals prefer to (or naturally) allocate effort when learning a task, and hence reduce

the positive influence of learning goal orientation, or the negative influence of performance goal orientation, on performance trajectories. Although their study focused on interaction between learning and performance goal orientation dispositions, Yeo and Neal (2004) found some evidence in support of a supplementary interaction. Specifically, they found that individuals improved their performance to the greatest extent when they were high on learning goal orientation and low on performance goal orientation, and improved to the least extent when they were high on both learning and performance goal orientations. Consequently, Yeo and Neal speculated that ‘‘the induction of a high performance orientation may have negative effects in a training context, particularly for individuals with a high learning orientation” (p. 243). By extension, their finding support that notion that learning goal orientation individuals learn more quickly when training interventions induce learning, as opposed to performance goal orientation. Thus, the basic tenet of the supplementary inducement hypothesis is that the effects of learning and performance goal orientations on performance trajectories are accentuated or enhanced by situational cues that are consistent with each predisposition. Accordingly, we advance the following hypotheses: H5: The relationship between learning goal orientation and performance trajectories is more positive when coupled with: (A) learning (vs. performance) goal framing; and (B) self-referent (vs. normative-referent) feedback. H6: The relationship between performance goal orientation and performance trajectories is more negative when coupled with: (A) performance (vs. learning) goal framing; and (B) normative-referent (vs. self-referent) feedback. Complementary inducements hypothesis In contrast to the tenet of supplementary P–E fit, and in line with the complementary P–E fit notion, some authors have proclaimed the benefits of a more balanced approach toward ideal combinations of individual and situational goal orientations. For example, Button et al. (1996) proposed, ‘‘a balance of both orientations is adaptive in most work settings” (p. 41). In a similar vein, Farr et al. (1993) have argued that learning goal orientation may be especially beneficial in work contexts that emphasize meeting performance goals. In addition, Kanfer and Ackerman’s (1989) resource allocation model suggests that, while on-task effort is important throughout the skill acquisition process, attention to performance goals may be detrimental in early phases, but beneficial in later phases of skill acquisition. That is, a balanced combination of a learning goal orientation disposition with performance goal orientation intervention (or vice versa) may result in steeper performance

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trajectories as a result of greater differences in performance between early to later stages of skill acquisition. More specifically, highly learning goal orientated individuals exposed to performance goal interventions, or highly performance orientated individuals exposed to learning goal interventions, may struggle during early skill acquisition phases as they attempt to balance effort devoted to task learning and demonstration of skill, but such dual emphasis may eventually be very beneficial during later stages of skill acquisition. Thus, the logic of the compensatory inducement hypothesis is that successful individuals need to not only develop useful skills and to learn task-related material, they must also be motivated to excel at performing the task. Hence, we submit that an over-emphasis on learning —simply for learning sake—may be suboptimal. Indeed, in contrast to Yeo and Neal’s (2004) findings, a study by Pintrich (2000) found that a high learning goal orientation is more adaptive when combined with high, as opposed to low, performance goal orientation. Although Pintrich, like Yeo and Neal, focused on goal orientation dispositions and not on interventions, their findings are consistent with Kanfer and Ackerman’s (1989) assertion that performance goals may become beneficial once a task is well-learned. Furthermore, Kozlowski and Bell’s (2006) study has found that a balance between performance goal and learning goal situational inducements produced the most beneficial effects on selfregulatory processes in training. All this converges on an alternative position for hypotheses H5 and H6, which suggests that performances trajectories will be optimized when there is a balance between situational cues and individual predispositions. Consequently, we advance the alternative complementary inducement hypothesis, which suggests that the tendencies of learning and performance goal orientations are enhanced by interventions that elicit situations that compensate or complement these pre-dispositions. Specifically: H7: The relationship between learning goal orientation and performance trajectories is more positive when coupled with: (A) performance (vs. learning) goal framing; and (B) normative-referent (vs. self-referent) feedback. H8: The relationship between performance goal orientation and performance trajectories is more positive when coupled with: (A) learning (vs. performance) goal framing; and (B) self-referent (vs. normative-referent) feedback. For completeness we also test, in a more exploratory fashion, interactions between dispositional goal orientations, as well as between the goal frame and feedback frame conditions, to examine the potential generalizability of the supplementary inducement hypotheses (H6–H7) or the complementary inducement hypotheses (H8–H9).

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Method Sample and design One hundred and four undergraduates were recruited from introductory psychology courses at a large northeastern university and received extra credit toward their course grade for participation. Forty-nine percent of the participants were male and their average age was 20 years (SD = 2.3). The overall design of the study was 2  2  8 mixed model, with the goal frame (learning vs. performance) and feedback referent (self vs. normative) constituting between-person factors, and the eight performance blocks as a within-person repeated measure. Participants were randomly assigned to the four between-person cells. Task The task in this study was a computer-based logic game (‘‘Money Trail”, Tomic, 1988). The object of the game was for participants to gather information about the sequential exchange of counterfeit money and to determine who initiated this trail of currency. At the beginning of each trial, six blank cells were presented on the screen; each cell can be ‘‘clicked on” to reveal exchange information. The selected cell shows a pair of letters, such as A ? B, which indicates that person A passed the counterfeit money to person B. Previously selected cells become blank as other cells are opened. Ultimately, the participant must determine who the counterfeiter is (i.e., who started the trail) by viewing and remembering money movement. For example, if a participant clicked on six screens he or she might uncover the following information: D!G A!B G!F B!D E!C C!A This would mean that the money trail was: E ? C ? A ? B ? D ? G ? F and implies that E started the trail and is, therefore, the counterfeiter. Participants may offer a guess as to who is the counterfeiter at any time, may reopen any number of cells, and continue guessing until they are correct. The maximum points (i.e., 10 per trial) are earned by minimizing the number of times each cell is opened and wrong guesses (i.e., opening each cell once and guessing correctly). Pilot work suggested that individuals tend to exhibit performance trajectories akin to classic learning curves over time, which has been detected in longitudinal studies of performance change in both training/learning (e.g., Eyring et al., 1993; Yeo & Neal, 2004) and onthe-job (e.g., Chen, 2005; Hofmann et al., 1993; Ployhart & Hakel, 1998) settings. Moreover, since participants viewed different information across trials, it was unlikely that high performance in this study could be achieved without exertion of cognitive effort.

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Procedure and manipulations The experimental sessions were conducted in groups ranging from 10 to 15 individuals, with each participant assigned to an individual computer work station. At the beginning of each 2-h experimental session, participants were asked to complete measures of cognitive ability and learning and performance goal orientations. Once the survey was completed, the experimental task was explained to participants and they were asked to complete a 4-trial practice block. Next, participants performed the eight experimental performance blocks, each consisting of four trials. Individuals summed and transcribed their scores over the four trials in each performance block, and compared them against the self or normative referent, per their condition. This method ensured that participants were attending to their relative scores. Participants were assigned randomly into one goal frame condition and one feedback frame condition in a balanced design. The goal frame manipulation involved instructing participants prior to the first of eight performance blocks that the purpose of the performance task was either (a) to reveal how smart they are (performance goal frame), or (b) to allow them to improve their skills (learning goal frame). This goal frame manipulation was conceptually-based, and highly consistent with that used by Kozlowski et al. (2001), which was shown to elicit the intended goal orientation states. Specifically, those in the performance goal frame were told: ‘‘Research has demonstrated that your performance on this task is an accurate representation of your ability on these kinds of tasks. While performing this task, you should work hard and concentrate on scoring as well as you can.” In contrast, those in the learning goal frame conditions were told: ‘‘Research has demonstrated that the performance on this task sharpens the mind and learning to do it well could help academic studies. While performing this task, you will probably make a bunch of mistakes, get a little confused, maybe feel a little dumb at times—but eventually you will learn some useful things.” The feedback frame manipulation involved asking participants to compare their performance score after each of the eight performance blocks to either normative-referenced feedback (a specific normative performance score after each block, which was based on average participants’ score for the corresponding block

from pilot testing) or to self-referenced feedback (their performance on the previous performance block). To ensure that participants actually adhered to the two feedback frames, after each of the eight performance blocks we asked them to list their current performance score, and then to calculate a difference score between their current score and either the normative performance score (in the normative-referenced condition) or their own score from the previous performance block (in the self-referenced feedback condition). A review of the participants’ forms indicated that all had followed our instructions accurately. To ensure the validity of the goal frame manipulation, we collected pilot data from 36 undergraduates in the same university as the study’s sample. Participants were randomly assigned to either the learning goal frame or performance goal frame condition, along with one of the two feedback conditions, after which they completed a 4-item measure of learning goal state (a = .60; e.g., ‘‘I’m eager to get started trying to figure this task out”), and a 4-item measure of performance goal state (a = .63; e.g., ‘‘I wonder how my scores will compare to others”) from Button et al. (1996). Their responses where made on 7-point scales, where 1 = ‘strongly disagree’ and 7 = ‘strongly agree’. Results indicated that those in the learning goal frame condition scored significantly higher (F(1,34) = 6.02, p < .05) on the learning goal state measure (M = 5.57; SD = 1.15) than those in the performance goal frame condition (M = 4.49; SD = 1.68). In contrast, those in the performance goal frame condition scored significantly higher (F(1,34) = 6.51, p < .05) on the performance goal state measure (M = 5.87; SD = 1.53) than those in the learning goal frame condition (M = 4.79; SD = 0.95). The feedback condition had no significant influences on either the learning state measure (F(1,34) = .58, ns) or the performance state measure (F(1,34) = .04, ns), nor were there any goal by feedback interactions for either state measure (Learning: F(1,34) = .03, ns; Performance: F(1,34) = .02, ns). These results support the validity and effectiveness of the goal frame manipulation. Measures Goal orientations Individuals’ learning goal orientation was assessed with an 8-item scale (a = .75) developed by Button et al. (1996). Responses were based on a seven-point Likert-type scale that ranged from (1) ‘strongly disagree’ to (7) ‘strongly agree.’ Strong agreement with these items suggests a desire to perform challenging activities, learn new skills, and develop alternative strategies when working on a difficult task (i.e., a strong learning goal orientation). Individuals’ performance goal orientation was assessed with an 8-item scale developed by Button et al., although we dropped one item deemed to con-

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found slightly performance-avoid goal orientation (a = .72).2 These items were rated on the same agreement scale with higher values indicating a stronger desire to perform well relative to others and to obtain favorable judgments of one’s competencies (i.e., a strong performance goal orientation). Performance Recall that participants performed eight performance blocks, each including four trials. Performance was indexed as the total game score summed across the four trials in each of the eight performance blocks; performance scores could range between 0 and 40 pts in each block. Covariates Given previous research has suggested both general and specific aspects of cognitive ability can explain individual differences in performance (e.g., Ackerman, 1987; Yeo & Neal, 2004), we included measures of each type as covariates. General cognitive ability was measured using self-reported overall Scholastic Aptitude Test (SAT). Gully et al. (2002) reported that self-reported SAT scores correlate very highly (r = .95) with actual scores retrieved from university records. Based on our review of the experimental task we decided that working memory was the most important specific aspect of cognitive ability for performing well. Further, the software designer claimed that this task ‘‘exercises two aspects of your memory: (1) memory size—how much you can remember, and (2) agility—manipulating what is already in memory” (Tomic, 1988, p. 11). Accordingly, we administered a 19-item Visual Number Span Test

2

Although Button et al.’s measure is not a direct measure of performance-prove goal orientation, it is more consistent with the performance-prove than the performance-avoid dimension of goal orientation (see Day, Radosevich, & Chasteen, 2003; Payne et al., 2007). To further examine the content adequacy of the eight performance goal orientation items, the authors independently sorted the items into those that appear to be pure measures of performanceprove goal orientation, and those that may confound performanceprove with performance-avoid goal orientation. We reached 100% consensus regarding three items that were pure measures of performance-prove goal orientation, and three items that may slightly confound performance-prove with performance-avoid goal orientation. Of the three possibly confounding items, one item (‘‘I am happiest at work when I perform tasks on which I know that I won’t make any errors”) had the lowest commonality among the eight items, and detracted from scale reliability (coefficient alpha increased from .71 to .72 when dropping this item; dropping any other item resulted in reduction of scale reliability to below .70 values). Thus, we dropped this one item from all analyses reported herein. Note that analyses with the full (8-item) performance goal orientation scale, a 5-item version of the scale (in which all three possibly confounding items were dropped), and a 3-item version of the scale (in which only the most pure performance-prove goal orientation items were included) yielded practically identical results. The additional results are available upon request from the authors.

29

(a = .73; Ekstrom, French, Harman, & Dermen, 1976) to capture specific cognitive ability, namely aspects of working memory capacity (cf. Ackerman, Beier, & Boyle, 2005). For each item, an experimenter flashed a series of between four and ten letters on an overhead projector for 15 s. Participants then wrote down the letters they saw in sequence. One point was awarded for each correctly recalled sequence, and 0 for each incorrect recall; the overall score was the average score across the 19 items (ranging from a low of ‘0’ to a high of ‘1’). Analyses Following the growth modeling analytic framework delineated by Bliese and Ployhart (2002), data were analyzed using random coefficient modeling with the Nonlinear and Linear Mixed Effects (NLME) program for S-PLUS and R (Pinheiro & Bates, 2000). We coded time, or the eight performance blocks, as 0, 1, 2, . . . and 7, respectively. As such, analyses of performance intercepts captured initial performance, or performance on the first performance block (Bliese & Ployhart, 2002). Because our hypotheses tests involved interaction terms, we entered the main (or linear) effects in initial models, and then the interaction terms in subsequent models (see Aiken & West, 1991). Additional analyses in which variables were grand-mean centered yielded highly similar results.

Results Table 1 reports descriptive statistics and correlations among the variables. Close inspection of the performance score means across the eight performance blocks indicated that participants generally improved their performance across blocks/time, but did so more pronouncedly during earlier than later performance blocks. Also, in general, performance scores across the eight blocks exhibited a ‘‘simplex pattern” (i.e., correlations between performance scores tended to decrease as the number of intervening performance blocks increased; Humphreys, 1960), which is often found in studies of performance change. Of all the individual difference and situational (manipulated) variables, only general and specific ability significantly predicted performance scores. However, these between-person results obscure the potential differences in performance trajectories, which are the basis of our hypotheses. Nature of performance trajectories Following Bliese and Ployhart (2002), we first examined the intercept-only model of performance, which evidenced an ICC (1) value for performance of .56. This indicates that 56% of the total performance variance was

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Table 1 Descriptive statistic and correlations Variable

Mean

SD

1

2

3

4

5

6

7

8

9

10

11

12

13

14

1. General ability 2. Specific ability 3. LGO 4. PGO 5. Goal frame a 6. Feedback frame b 7. T1 Performance 8. T2 Performance 9. T3 Performance 10. T4 Performance 11. T5 Performance 12. T6 Performance 13. T7 Performance 14. T8 Performance

1076.26 0.63 5.57 5.63 1.50 1.50 26.32 29.00 29.09 29.79 29.46 30.50 30.85 30.51

119.11 0.16 0.56 0.66 0.50 0.50 7.95 6.94 8.75 7.38 8.17 7.99 7.68 7.64

— .24* .02 .07 .09 .01 .24* .34* .23* .35* .33* .31* .31* .30*

— .17 .02 .07 .02 .38* .35* .42* .41* .27* .34* .26* .28*

— .02 .07 .05 .13 .06 .11 .14 .07 .03 .06 .07

— -.12 .04 .08 .03 .05 .03 .11 .14 .18 .01

— .00 .07 .05 .22* .13 .13 .13 .14 .05

— .07 .09 .01 .05 .13 .05 .08 .07

— .63* .52* .57* .50* .56* .47* .41*

— .64* .65* .54* .62* .46* .48*

— .68* .52* .62* .55* .61*

— .63* .65* .66* .61*

— .65* .67* .52*

— .72* .60*

— .54*

—

*

Note. N = 104; p < .05; LGO, learning goal orientation; PGO, performance goal orientation; T, time/block. a 1 = Performance goal frame, 2 = learning goal frame. b 1 = Normative-referenced feedback, 2 = self-referenced feedback.

between-individuals, whereas 44% resided within individuals over time. We then examined the overall performance trajectory trends, which were set to be fixed across the 104 participants. In a first model, results illustrated that, as expected, performance exhibited a significant positive linear trend (p = 0.52, p < .05), indicating that, on average, participants scored 0.52 points higher in each subsequent performance block. A subsequent model illustrated the presence of a significant negative quadratic trend (p =  0.11, p < .05), indicating that, on average, performance improved by 0.11 fewer points in each subsequent block. These results represent a classic overall learning, or negatively accelerated, curve, and parallel those of previous related lab and field research on performance trajectories (e.g., Chen, 2005; Eyring et al., 1993; Ployhart & Hakel, 1998; Yeo & Neal, 2004). We next tested whether individuals’ intercepts and their linear and quadratic trends varied significantly across people, by contrasting models in which each of these parameters was set to be fixed across participants to models in which the parameters were allowed to randomly differ across participants. As outlined in Bliese and Ployhart (2002), these models are nested, and can be compared statistically using the Likelihood Ratio (LR), derived from models’ sum of squares error. Although the intercepts (LR = 451.48, p < .05) and the linear trend (LR = 13.22, p < .05) evidenced significant between-individual variability, the quadratic trend did not (LR = 7.12, ns). In addition, the ICC(2) values for the intercept, linear slope, and quadratic slope terms were .80, .40, and .12, respectively, indicating that the quadratic slope varied substantially less reliably between individuals than did the linear slope. Consequently, we will focus on the linear, but not quadratic, trend as the criterion for testing the performance trajectories hypotheses. Notably, we include a constant quadratic term in the latter analysis to maintain the nature of the perfor-

mance trajectories. Finally, additional tests indicated significant autocorrelations among adjacent performance scores (average r = .12, LR = 4.56, p < .05), but no significant heteroscadisticy in errors across performance blocks (LR = 0.34, ns). Therefore, subsequent models used to test the hypotheses allowed errors to auto-correlate, and included random intercepts, a random linear growth parameter (i.e., ‘‘Time”), and a fixed quadratic growth parameter (i.e., ‘‘Time2”). Hypothesis tests Tests of initial performance Before testing for our hypotheses, which concern performance trajectories, we tested the effects of the variables on the performance intercept, or on individuals’ initial levels of performance. As shown in Table 2, we tested two models of the performance intercepts—one in which the main (or linear) cross-level effects of Level 2 predictors (i.e., individual differences in ability and goal orientations and goal orientation interventions) were entered as predictors of the Level 1 random performance intercepts (Model#1), and another in which the interactions among goal orientation dispositions and interventions were entered as additional predictors of the random performance intercepts (Model#2). This strategy essentially parallels what ones does in traditional moderated regression analyses, only using time sensitive criteria. In both models, the within-person (Level 1) linear (Time) and quadratic (Time2) performance trends were set to be fixed across individuals. As shown in Table 2, in the first model (Model#1), the linear cross-level influences of general ability (b = 0.02, p < .05) and specific ability (b = 12.98, p < .05) on initial performance were statistically significant, although the goal orientation dispositions and interventions were not. Model#1 accounted for 28% of

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Table 2 Random coefficient modeling analyses of initial performance Model

Parameter estimate

SE

0.02* 12.98* 0.18 0.42 1.81 0.94

0.00 3.49 0.96 0.81 1.08 1.06

p2j ðTime2ij Þ

þ rij 1: Performanceij ¼ p0j þ p1j ðTimeij Þ þ p0j ¼ b000 þ b001 ðGAÞ þ b002 ðSAÞ þ b003 ðLGOÞ þ b004 ðPGOÞþ b005 ðGoalÞ þ b006 ðFeedbackÞ þ u0j p1j ¼ b100 p2j ¼ b200 General ability (GA; b001) Specific ability (SA; b002) Learning goal orientation (LGO; b003) Performance goal orientation (PGO; b004) Goal frame condition (Goal; b005)a Feedback frame condition (Feedback; b006)b

2: Performanceij ¼ p0j þ p1j ðTimeij Þ þ p2j ðTime2ij Þ þ rij p0j ¼ b000 þ b001 ðGAÞ þ b002 ðSAÞ þ b003 ðLGOÞ þ b004 ðPGOÞþ b005 ðGoalÞ þ b006 ðFeedbackÞ þ b007 ðLGO  PGOÞ þ b008 ðGoal  FeedbackÞþ b009 ðLGO  GoalÞ þ b010 ðLGO  FeedbackÞ þ b011 ðPGO  GoalÞ þ b012 ðPGO  FeedbackÞ þ u0j p1j ¼ b100 p2j ¼ b200 LGO  PGO (b007) 1.19 Goal  Feedback (b008) 1.99 LGO  Goal (b009) 2.26 0.35 LGO  Feedback (b010) PGO  Goal (b011) 0.05 PGO  Feedback (b012) 1.58

1.34 2.19 1.97 1.99 1.70 1.75

Note. N = 104 individuals and 832 performance observations; *p < .05; Unstandardized estimates are reported; SE, standard error. a 1 = Performance goal frame, 2 = learning goal frame. b 1 = Normative-referenced feedback, 2 = self-referenced feedback.

the between-person variance in initial performance scores. In Model#2, none of the interactions among goal orientation dispositions and interventions yielded significant influences, and the model did not account for any additional variance. These results indicated that initial performance was largely a function of general and specific ability. Additional analyses (not shown in Table 2) did not reveal any interaction effects involving the two ability measures. Tests of performance trajectories To test the hypotheses, we analyzed two additional models which built on the first two models (see Table 3, Models #3 and #4), in which the linear within-person performance trajectories were allowed to vary freely across individuals. Note that participants’ performance trajectories were operationalized as individual differences in the performance-linear time trend slope (i.e., individual differences in the p1j term). Therefore, in essence, the effects shown in Model#3 represent twoway cross-level interactions between the Level 2 predictors and the Level 1 linear time factor. Similarly, the two-way interactions that we test in Model#4 are, in effect, three-way interactions represented by the two substantive variables combined with the linear time factor. Analyses of Model#3 indicated that individual difference in general and specific ability did not predict performance trajectories. In addition, Hypotheses 1 and

2, concerning learning and performance goal orientations, were not supported—neither goal orientation significantly predicted performance trajectories (although their non-significant effects were in the anticipated directions: positive for learning goal orientation, and negative for performance goal orientation). Likewise, Hypotheses 3 and 4 were not supported, as the manipulated situational cues in terms of the goal and feedback frame conditions did not significantly predict performance trajectories. Model#3 accounted for none of the between-person variance in performance trajectories. In Model#4 analyses, we entered the interaction terms among goal orientation dispositions and interventions as additional predictors of the linear performance trajectories (see Table 3). Consistent with the supplementary inducement hypothesis (H5B) learning orientation positively interacted with the feedback framing intervention as related to performance trajectories (b = .86, p < .05). In contrast, consistent with the complementary inducement hypothesis (H7A) learning orientation negatively interacted with the goal framing intervention as related to performance trajectories (b =  .70, p < .05). No other interactions involving the learning or performance goal orientations were evident, failing to support the remaining H5A, H7B, H6A, H6B, H8A, and H8B. However, we also found a significant negative goal framing by feedback referent interaction as related to performance trajectories, which

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Table 3 Random coefficient modeling analyses of performance trajectories Model

Parameter estimate

SE

p2j ðTime2ij Þ

þ rij 3: Performanceij ¼ p0j þ p1j ðTimeij Þ þ p0j ¼ b000 þ b001 ðGAÞ þ b002 ðSAÞ þ b003 ðLGOÞ þ b004 ðPGOÞ þ b005 ðGoalÞþ b006 ðFeedbackÞ þ b007 ðLGO  PGOÞ þ b008 ðGoal  FeedbackÞ þ b009 ðLGO  GoalÞþ b010 ðLGO  FeedbackÞ þ b011 ðPGO  GoalÞ þ b012 ðPGO  FeedbackÞ þ u0j p1j ¼ b100 þ b101 ðGAÞ þ b102 ðSAÞ þ b103 ðLGOÞ þ b104 ðPGOÞ þ b105 ðGoalÞ þ b106 ðFeedbackÞ þ u1j p2j ¼ b200 General ability (GA; b101) 0.00 Specific ability (SA; b102) 0.87 Learning goal orientation (H1; LGO; b103) 0.22 Performance goal orientation (H2; PGO; b104) 0.08 0.02 Goal frame condition (H3; Goal; b105)a Feedback frame condition (H4; Feedback; b106)b 0.07

0.00 0.64 0.18 0.15 0.20 0.19

4: Performanceij ¼ p0j þ p1j ðTimeij Þ þ p2j ðTime2ij Þ þ rij p0j ¼ b000 þ b001 ðGAÞ þ b002 ðSAÞ þ b003 ðLGOÞ þ b004 ðPGOÞ þ b005 ðGoalÞþ b006 ðFeedbackÞ þ b007 ðLGO  PGOÞ þ b008 ðGoal  FeedbackÞ þ b009 ðLGO  b010 ðLGO  FeedbackÞ þ b011 ðPGO  GoalÞ þ b012 ðPGO  FeedbackÞ þ u0j p1j ¼ b100 þ b101 ðGAÞ þ b102 ðSAÞ þ b103 ðLGOÞ þ b104 ðPGOÞ þ b105 ðGoalÞþ b106 ðFeedbackÞ þ b107 ðLGO  PGOÞ þ b108 ðGoal  FeedbackÞ þ b109 ðLGO  b110 ðLGO  FeedbackÞ þ b111 ðPGO  GoalÞ þ b112 ðPGO  FeedbackÞ þ u1j p2j ¼ b200 LGO  PGO (b107) Goal  Feedback (b108) LGO  Goal (H5A; H7A; b109) LGO  Feedback (H5B; H7B; b110) PGO  Goal (H6A; H8A; b111) PGO  Feedback (H6B; H8B; b112)

0.23 0.37 0.34 0.34 0.29 0.30

GoalÞþ

GoalÞþ

0.19 0.72* 0.70* 0.86* 0.08 0.04

Note. N = 104 individuals and 832 performance observations; p < .05; Unstandardized estimates are reported; SE, standard error; H, hypothesis. a 1 = Performance goal frame, 2 = learning goal frame. b 1 = Normative-referenced feedback, 2 = self-referenced feedback.

was in line with our complementary inducement hypotheses (b =  .72, p < .05). Model#4 accounted for 29% of the between-person variance in performance trajectories. Additional analyses (not shown in Table 3) found that the ability measures did not interact with any of the manipulations or goal orientation dispositions to influence performance trajectories. To help facilitate the interpretation of the three detected interaction effects on performance trajectories, we plotted the interactions using Aiken and West’s (1991) procedures (see Figs. 2–4). Because the effects portrayed in Figs. 2–4 involve three-way interactions between the linear slope (i.e., time) and two different goal orientation variables, the figures compare the influence of the specific independent variable on performance trajectories at different levels of the specific situational manipulation. Fig. 2 depicts the complementary goal frame X learning goal orientation interaction effect (H7A) effect on performance trajectories. Results indicated that learning goal orientation more positively predicted the performance trajectories in the performance goal frame condition (upper figure), as opposed to learning goal frame condition (lower figure). Follow-up tests conducted separately on data within each goal frame condition indicated that learning goal orientation positively predicted performance trajectories in the perfor-

mance goal frame condition (b = .59, p < .05), but not in the learning goal frame condition (b =  .11, ns). Although it seems that this interaction is driven by a negative influence of learning goal orientation on performance at the first performance block, specific tests did not indicate significant effects for learning goal orientation on the performance intercepts in either goal frame condition. Also, the relationship between initial performance and the performance trajectory across individuals and conditions was weak, r =  .09, ns. Thus, it does not appear that initial performance levels alone are responsible for this effect; rather, it emerges when the entire range of the predictors are considered over time. Fig. 3 shows the supplementary learning goal orientation X feedback frame interaction (H5B) on performance trajectories. In line with the figure, results from specific tests conducted separately using data within each feedback frame condition indicated that learning goal orientation positively and significantly predicted performance trajectories in the self-referent feedback condition (b = .65, p < .05; lower figure), but did not have a significant effect in the normative-referent feedback condition (b =  .09, ns; upper figure). We again did not detect any significant effects for learning goal orientation on the performance intercepts in either of these two specific conditions.

G. Chen, J.E. Mathieu / Organizational Behavior and Human Decision Processes 106 (2008) 21–38 Performance Goal Frame

Normative Referent Feedback

36

36

Low learning orientation

Low learning orientation

High learning orientation

32

Performance

Performance

High learning orientation

28

32

28

24

24 1

2

3

4

5

6

7

1

8

2

3

Performance Block

4

5

6

7

8

7

8

Performance Block

Learning Goal Frame

Self Referent Feedback

36

36 Low learning orientation

Low learning orientation

High learning orientation

High learning orientation

32

Performance

Performance

33

28

24

32

28

24 1

2

3 4 5 6 Performance Block

7

8

1

2

3 4 5 6 Performance Block

Fig. 2. Goal frame condition  learning goal orientation interaction effect on performance trajectory.

Fig. 3. Feedback frame condition  learning goal orientation interaction effect on performance trajectory.

Finally, Fig. 4 depicts the goal frame  feedback frame interaction effect on performance trajectories. As shown, the learning, relative to performance, goal frame condition positively predicted performance trajectories in the normative referent feedback condition, and negatively in the self-referent feedback condition. Although specific tests indicated the effects of the goal frame manipulation under normative-referent (b = .36, ns) and self-referent (b =  .40, ns) feedback conditions were non-significant, the interaction effect was in line with the complementary inducement hypothesis, as it suggests that the more positive performance trajectories occurred under conditions that balanced aspects of both learning and performance goal orientations. Here, again, no significant effects were detected for the goal frame manipulation on performance intercepts in the two specific conditions.

ments of goal orientations combine to promote performance trajectories during skill acquisition. Results provided mixed support for the hypotheses, highlighting the complexities inherent in explaining individual differences in performance over time.

Discussion The main purpose of this experiment was to examine how individual differences in and situational induce-

Theoretical implications Results indicated that neither disposition nor situational inducements of learning and performance goal orientation significantly predicted performance trajectories. With regard to the learning goal orientation disposition, results were consistent with Yeo and Neal (2004), who also found a positive yet non-significant relationship with performance trajectories. However, our results failed to replicate their findings regarding the negative relationship between performance goal orientation and performance trajectories (although we also detected a negative, albeit non-significant, relationship). One possible explanation for these inconsistent and largely nonsupportive findings is the relatively low reliability of the goal orientation measures coupled with limited variability in the measures (both were lower relative to Yeo

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Normative Referent Feedback 36

Performance goal frame

Performance

Learning goal frame 32

28

24 1

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3 4 5 6 Performance Block

7

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Self Referent Feedback 36 Performance goal frame

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24 1

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3 4 5 6 Performance Block

Fig. 4. Goal frame condition  feedback frame condition interaction effect on performance trajectory.

and Neal’s study, which employed the same measures). It is also possible that the generic (context-free) nature of the goal orientation measures prevented us from finding stronger effects (cf. VandeWalle et al., 2001). However, it is worth noting that the goal orientation manipulations, which were context-specific, also did not exert any direct influences on performance in this study. Instead, we found three interaction effects involving combinations of learning goal orientation and the goal orientation interventions. The fact that significant interactions were evident in this work suggests that the failure to obtain significant linear effects on the performance intercept and trajectory cannot be simply attributable to low statistical power. Since product terms have notoriously lower power than their linear components (Aiken & West, 1991), explanations for the non-significant linear effects are likely to be more substantively based. The conflicting results pertaining to supplementary and complementary ATIs involving goal frame and feedback frame are puzzling, as they do not provide a clear picture of how situational inducements of goal orientation interact with learning goal orientation to influ-

ence performance trajectories. One possible explanation is that, for high learning-oriented participants, our selfreferent feedback condition might have triggered the self-regulation processes described by Kanfer and Ackerman (1989)—i.e., self-monitoring, self-evaluation, and self-reactions—which can debilitate performance during early stages of skill acquisition, before benefiting performance during later stages of skill acquisition. Asking individuals in the self-referent feedback condition to actively compare current to prior performance levels might have also led them to be concerned about performing, as opposed to learning, the task, similarly to those in a performance goal mindset (cf., Fisher & Ford, 1998). Indeed, comparison of Figs. 2 and 3 illustrates that these two conditions yielded almost identical patterns, such that individuals high on learning orientation performed somewhat worse initially, but caught up and eventually outperformed those low in learning orientation by the last performance block. Although learning goal orientation did not significantly relate to performance on the first block in the performance goal frame and self-referent feedback conditions, high learning oriented individuals clearly experienced greater change in their performance between the first and final performance blocks. These results suggest that these two ATIs were most potent in influencing self-regulation processes and allocation of effort to the task, which become more beneficial to task performance once the task becomes better-learned (Kanfer & Ackerman, 1989; Yeo & Neal, 2004). Thus, although we did not measure self-regulation processes in this research, the pattern of performance trajectories we detected are suggestive of ATI effects on self-regulation during skill acquisition, given the empirical work of Kanfer and Ackerman (1989) and Yeo and Neal (2004). However, extending Yeo and Neal and Kanfer and Ackerman, our findings suggest that learning goal orientation is more likely to relate to self-regulation processes during skill acquisition under two specific conditions of goal orientation interventions, namely the inducements of a performance goal frame and a self-referenced feedback. That is, goal orientation interventions serve as situational boundaries for the likely effects of individual differences in learning goal orientation on self-regulation during skill acquisition. Interestingly, our results also suggested that participants exposed to a learning goal frame condition benefited more from a complementary inducement condition, namely a normative-referent feedback condition. These results are consistent with Kozlowski and Bell’s (2006) finding, and suggest that goal orientation interventions that balance aspects of learning and performance orientations may generate steeper positive performance trajectories during skill acquisition, perhaps as mediated by self-regulatory processes. It may be that the most effective intervention might be one that induces a

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learning goal orientation during the declarative stage of learning, and switches to inducing a performance goal orientation during the procedural stage of learning. Such time-varying intervention may be most effective at ensuring effective allocation of individuals’ effort during different stages of skill acquisition. The efficacy of such nuanced interventions and whether and how such interventions interact with goal orientation dispositions, however, remain to be explored. Such an approach would be consistent with the positions advanced by Kanfer and Ackerman’s (1989) resource allocation theory. Although not the focus of our study, we also found interesting results pertaining to both general and specific ability measures. Specifically, both measures predicted the performance intercept and performance on each separate performance block, but not the performance trajectory. In other words, ability predicted overall performance level, but not the rate at which performance improved over time. Interestingly, other studies on different tasks also yielded inconsistent findings pertaining to the relationship between ability and performance trajectories. Eyring et al. (1993) found that ability positively predicted performance trajectories in a more complex learning context. However, in a similar context, Yeo and Neal (2004) found that, although ability facilitated the within-person relationship between effort and performance, it did not directly influence performance trajectories (or the relationship between practice and performance). Ployhart and Hakel (1998) also failed to detect a relationship between ability and performance trajectory in their study of new salespersons’ performance trajectories. It is possible that our null findings with respect to ability and performance trajectories are a function of the simple task, coupled with the rapid transition from declarative to procedural stage of skill acquisition by most participants (cf. Kanfer & Ackerman, 1989; Yeo & Neal, 2004). Indeed, inspection of the eight performance means in Table 1 suggests that, on average, participants experienced the largest performance improvement between the first and second blocks. More generally, the theoretical interactionist framework we delineated can serve as guide for more ATI research that attempts to understand how training interventions might interact with trainee characteristics to influence learning process. Based on our findings, we believe that time should play a larger role in such future research. Mitchell and James (2001), among others, have argued that understanding better the underlying temporal dynamics of performance-related phenomena offers a powerful avenue for advancing our understanding of organizational behavior. Whereas research is developing along these lines (Ployhart & Hakel, 1998; Thoresen et al., 2004; Yeo & Neal, 2004), relatively few studies have examined how substantive variables interact with

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each other, and with time, as related to performance trajectories. Practical implications Practically speaking, our findings highlight the importance of designing training programs that include well-aligned design features and fit individuals’ attributes (see also Kozlowski, Toney, et al., 2001). Specifically, while employees’ learning orientation needs to be supplemented by incorporating self-referenced feedback, a high learning orientation might also be beneficial in complementary situations that emphasize performance goals (i.e., programs which encourage employees to perform well and not merely acquire new knowledge or skill sets). Further, our study suggests that managers and practitioners need to strike a balance between encouraging learning and encouraging performance, as too much emphasis on one aspect versus the other may be detrimental to employees’ rate of performance improvement. Indeed, organizations and employees both benefit from combined high levels of learning and performance, which may explain why a balance of the two may be most beneficial to employee development, especially once employees reach higher mastery level on work tasks. Although our results highlight the need for both supplementary and complementary situational cues, as related to individuals’ dispositions, they are also indicative of a more dynamic underlying phenomenon that depends on individuals’ stage of development or skill acquisition. What we are suggesting is that there may well be a three-way ATI-type of relationship where the optimal combination of individual differences and situational cues varies as a function of time. For example, perhaps highly learning oriented individuals benefit from inducement of a learning state during skill acquisition, but require emphasis on performance at later stages if they are to exhibit high performance. In short, simplistic admonishments to ‘‘build in learning goals” or to ‘‘adopt a flexible approach that meets trainees’ preferences” may not be enough to ensure optimal learning or training effectiveness. Rather, the ideal approach may involve a complex process that balances competing learning and performance demands, both across participants and across time as the learning process unfolds. Certainly more research is needed in order to understand better the nature of such a balance. Limitations, boundary conditions, and future research Our study is the first to test the unique influences of different goal orientation dispositions and situational cues, as well as ATI effects among them, on performance trajectories. However, this study was not without limitations. First, while the controlled lab settings enhanced

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the internal validity of our study, it has also resulted in more limited external validity. However, the forms of the performance trajectories we obtained (in terms of the rate, direction, and differences in within-individual performance change) were consistent with those found in studies conducted in both laboratory and field settings, indicating the psychological fidelity of our performance task was sufficiently high. Of course, though, more research is needed to confirm our findings in different settings to test the generalizability of our results. Another limitation is that the short time frame and context of this study might have led to an under-estimation of the true main and interaction effects of goal orientation and situational variables on performance trajectories. In actual work settings, situational cues might be acute and persistent over time, and therefore person-by-situation interactions could be more powerful. Thus, the artificial setting in which we tested our hypotheses might have attenuated the prevalence of situational cues in work organizations. Indeed, although we obtained evidence of complicated ATI effects, it is also true that many of our hypotheses were not supported. In addition to the limited reliability and variance of our goal orientation measures, the contrived setting of this study may explain some of our null findings. Thus, research is needed to examine whether study setting and the prevalence of situational cues serve as critical boundary conditions for the relationships examined in this study. Researchers should also consider different strategies of inducing goal orientation states or mindsets than the ones considered in this research. Furthermore, it is possible that our findings may be limited to instances where participants quickly reach high levels of task mastery, as compared to when tasks are more complex. In line with Kanfer and Ackerman (1989), a mixture of performance and learning orientation may be more beneficial when tasks are more easily learned, but that such mixture may be less beneficial for performance improvements on more complex tasks. Further, a mixture of learning and performance orientation may only be beneficial once a task has been welllearned, irrespective of task complexity levels. While our initial findings are important in showing that too much of a learning orientation and focus may be harmful in some situations, clearly more research is needed to uncover the ideal mixture of learning and performance orientations and focus across different levels of task complexity and mastery. Some might argue that the performance goal orientation measure we employed (i.e., Button et al., 1996) was a major limitation in this study, because it may confound the performance-prove and performance-avoid goal orientation dimensions or fail to sufficiently capture the performance-avoid goal orientation dimension (cf. VandeWalle, 1997). As we reported in the Method section, we did not detect any different results even when

stripping performance goal orientation items that might confound (however slightly) performance-avoid goal orientation. In addition, Day et al. (2003) found that Button et al.’s performance goal orientation measure aligns more strongly with VandeWalle’s (1997) performance-prove measure than his performance-avoid measure (see also Payne et al., 2007). Furthermore, researchers have often failed to recognize that Button et al. (1996) developed their performance goal orientation measure with the intention to capture an approach or achievement-oriented motivational tendency, as opposed to an anxiety or avoidance-oriented motivational tendency. In concert with Kanfer and Heggestad (1997; see also Kanfer, 2002) motivational trait framework, we would argue that Button et al.’s learning goal orientation and performance goal orientation (which we believe largely captures VandeWalle’s (1997) performance-prove goal orientation) align with the mastery and competitive excellence aspects of achievement motivation, respectively, which generally direct and intensify one’s effort toward task accomplishments. In contrast, VandeWalle’s performance-avoid goal orientation dimension aligns with anxiety or avoidance motivation, which can lead individuals to withdraw effort from task engagements (cf. Kanfer, 2002; Kanfer & Heggestad, 1997). In fact, we would go so far as to say that the performance-avoid orientation is far more consistent with the construct of fear of failure (Atkinson, 1957) than it is a performance orientation. In other words, performance-avoid may capture quite a different type of motivational construct than do learning or performance-prove goal orientations. To our knowledge, we have not seen a hierarchical confirmatory factor analysis that has mapped measures of the first-order performance-prove and performance-avoid dimensions to higher-order motivational constructs, such as approach motivation and fear of failure or avoidance motivation (cf. DeShon & Gillespie, 2005). Thus, we posit that Button et al.’s performance goal orientation measure aligns better with performance-prove goal orientation than it does with a performance-avoid goal orientation, and that the performance-prove and performance-avoid goal orientation dimensions likely align with different motivational tendencies. This clearly represents an interesting avenue for future research. Given our posited distinction outlined above, the question is: do performance-prove and performanceavoid goal orientations differentially influence self-regulation processes and performance trajectories? The answer, we believe, depends on individuals’ level of task mastery (or one’s skill acquisition phase). When the task is novel (i.e., in early phases of skill acquisition), individuals high on either performance-prove or performanceavoid goal orientations may similarly withdraw effort away from the task, because they are unlikely to demonstrate competence on a task and are fearful of failing. In

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contrast, when the task becomes better-learned (i.e., in later skill acquisition phases), we believe individuals high on performance-prove goal orientation will likely devote effort towards task accomplishment (because they may view a well-learned task as an opportunity to demonstrate competence), whereas those high on performance-avoid goal orientation may still shy away from performing the task. Thus, we suggest that longitudinal studies in which self-regulation processes and performance are examined at different phases of skill acquisition may reveal both similarity (at early phases of skill acquisition) and differences (at later stages of skill acquisition) in the influences of the performanceprove and performance-avoid dimensions. Finally, our initial findings also suggest the need to consider a broader mixture of individual differences in motivational tendencies, additional motivating aspects of situations, and, perhaps most importantly, ATI effects among greater array of individual differences and situational characteristics. Even though we are just now beginning to scratch the surface of that line of research, initial ATI research has been promising (e.g., Gully et al., 2002; Heimbeck et al., 2003; Towler & Dipboye, 2001). Developing a better understanding of when, or under which situations, different individual differences promote or debilitate performance improvements in various applied settings can greatly enhance our understanding of the factors contributing to better employee learning, development and adaptation.

Acknowledgments We greatly appreciate comments provided by Ruth Kanfer on earlier version of the manuscript, and the guidance provided by David Harrison and the three anonymous reviewers.

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