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Beyond objectivity: The performance impact of the perceived ability to learnand solve problems

Michael J. Tews a,⁎, John W. Michel b, Raymond A. Noe c

a School of Hospitality Management, 222 Mateer Building, The Pennsylvania State University, University Park, PA 16802, USAb Department of Management, College of Business & Economics, Towson University, 8000 York Road, Towson, MD 21252, USAc Fisher College of Business, Department of Management and Human Resources, 700 Fisher Hall, The Ohio State University, 2100 Neil Avenue, Columbus, OH 43210, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 6 July 2010Available online 9 November 2010

The purpose of this research was to develop and provide initial validation evidence forthe performance impact of a measure of an individual's perceived ability to learn and solveproblems (PALS). Building on the self-efficacy literature and the importance of learning andproblem solving, the fundamental premise of this research was that PALS would significantlyexplain employee performance. In addition to demonstrating that PALS represented a distinctconstruct, PALS was a significant predictor of performance for managerial and entry-levelemployees in two different organizational contexts. Moreover, PALS explained additionalvariance in performance beyond general mental ability, personality, and similar constructsrelated to learning and problem solving.

© 2010 Elsevier Inc. All rights reserved.

Keywords:Perceived abilityLearningProblem solvingIndividual differencesPerformance

Nowmore than ever, learning and problem solving are critical for employee success on the job. Today's organizations must beagile and quickly adapt to changing customer demands for new and different products and services. As a result, employees need tocreatively solve problems; deal with uncertain and unpredictable work situations; learn new tasks, technology, and procedures;and demonstrate adaptability (Pulakos, Arad, Donovan, & Plamondon, 2000). Molloy and Noe (2010) underscore this point byasserting that employees must “learn for a living.” That is, employees must constantly refine and add to their skill sets throughouttheir careers. Given the importance of learning and problem solving for employee performance, it is important to examine howindividual differences related to these processes impact on-the-job performance success.

In the extant literature, one of the most consistent findings is that general mental ability (GMA) is a key predictor of employeejob performance (Hunter, 1986; Hunter & Hunter, 1984; Ree & Earles, 1992). For example, Hunter and Hunter's (1984) meta-analysis estimated GMAperformance validities of .58 for professional managerial jobs, .56 for highly complex technical jobs, .51 formoderately complex jobs, .40 for semi-skilled jobs, and .23 for completely unskilled jobs. Given these findings, Schmidt and Hunter(1998) argued that GMA should be the primary basis on which to select employees. GMA is important because learning andproblem solving are central to performance success, and GMA is a primary individual difference that influences learning andproblem solving (Gottfredson, 2002).

Notwithstanding the importance and validity of GMA, additional constructs related to learning and problem solving may havevalue in predicting performance. In particular, an individual's perceptions of his or her ability to learn and solve problems may beof particular relevance. Such perceptions may motivate employees to engage in learning and problem-solving efforts, andtherefore have a positive impact on employee performance. In their critical review of the staffing literature, Cascio and Aguinis(2008) argued that the current staffing model has reached a ceiling in its ability to predict performance. Thus, while GMA incombination with other predictors (such as personality) do in fact reliably predict performance, a significant proportion ofvariance is still yet unexplained, suggesting the need to examine additional predictors.

Journal of Vocational Behavior 79 (2011) 484–495

⁎ Corresponding author.E-mail addresses: mjt17@psu.edu (M.J. Tews), jmichel@towson.edu (J.W. Michel), noe@cob.ohio-state.edu (R.A. Noe).

0001-8791/$ – see front matter © 2010 Elsevier Inc. All rights reserved.doi:10.1016/j.jvb.2010.11.005

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As one step forward toward addressing this need, this research focuses on the development and validation of a measure of anindividual's perceived ability to learn and solve problems (PALS). We begin by discussing the conceptual foundation for PALS. Wethen report the results of three studies, which together provide initial construct validity evidence for PALS. Study 1 focuses oninitial scale development. Studies 2 and 3 establish the discriminant and predictive validity of the PALS construct. This researchcontributes to the literature by demonstrating that PALS is a distinct construct and by showing that PALS explains unique variancein performance beyond other predictors including GMA, the Big Five personality dimensions, generalized self-efficacy, andlearning orientation.

1. The perceived ability to learn and solve problems

We define PALS as confidence in one's ability to effectively learn, acquire new knowledge and skills, and solve problems on thejob. As highlighted above, we focus on learning and problem solving, given the importance of these processes in facilitatingindividual performance success. Schmidt (2002) articulates that higher levels of job knowledge lead to higher levels ofperformance. Moreover, beyond job knowledge itself, successful performance requires direct problem solving on the job. WhereasGMA reflects an individual's actual ability to learn and solve problems, PALS reflects an individual's confidence in doing so.

The importance of confidence in one's abilities lies in vast body of research on self-efficacy. Banudra (1986, p. 391) defined self-efficacy as “peoples' judgments of their capabilities to organize and execute courses of action required to attain designated types ofperformances.” That is, self-efficacy reflects a person's confidence in his or her capabilities to perform a task. Wood and Bandura(1989, p. 408) contended that self-efficacy beliefs relate to individuals' perceived capabilities “to mobilize the motivation,cognitive resources, and courses of action to meet given situational demands.” They further articulated that self-efficacy has apositive influence on performance because these beliefs impact “the challenges that are undertaken, the amount of effortexpended in an endeavor, the level of perseverance in the face of difficulties, whether thinking patterns take self-aiding or self-impeding forms, and vulnerability to stress and depression” (p. 408).

One way of conceptualizing self-efficacy is as a belief that one can succeed across contexts, which is known as generalized self-efficacy (GSE). Specifically, GSE refers to the extent to which a person has an enduring belief that he or she is capable ofaccomplishment irrespective of the situation or task demands (Chen, Gully, & Eden, 2001; Judge, Erez, & Bono, 1998; Judge, Locke,& Durham, 1997). In other words, GSE refers to one's overall confidence to succeed in various achievement situations. Judge andBono's (2001) meta-analysis provides support for the relationship between GSE and performance by demonstrating a weightedaverage correlation of .23 between GSE and job performance across 81 studies.

More commonly, self-efficacy has been conceptualized as an individual's confidence to succeed in a particular performancedomain (Banudra, 1986), which is often referred to as task-specific self-efficacy (TSSE) (Gist & Mitchell, 1992; Stajkovic & Luthans,1998). Bandura argued that self-efficacy beliefs are not necessarily constant across performance domains. Thus, according toBandura, self-efficacy beliefs should be examined in a domain-specific context if the goal is to maximize performance in thatparticular domain. Stajkovic and Luthans's (1998) meta-analysis provides support for the relationship between TSSE andperformance. Specifically, they demonstrated a weighted average correlation of .38 between TSSE and work-related performanceacross 114 studies. Consistent with Bandura's argument that TSSE is more relevant than GSE, Stajkovic and Luthans's averagecorrelation is larger than that estimated in Judge and Bono's (2001) research.

Interestingly, Judge, Jackson, Shaw, Scott, and Rich's (2007) meta-analysis on the impact of self-efficacy on work-relatedperformance calls into question the importance of TSSE relative to other individual differences. Specifically, they demonstratedthat after controlling for key individual differences – GMA, agreeableness, conscientiousness, emotional stability, extraversion,openness to experience, and previous experience – TSSE did not significantly predict performance. Furthermore, the inclusion ofTSSE after the other individual differences did not result in a significant ΔR2 in the prediction of performance. Several of the otherindividual differences were, however, significantly related to TSSE (i.e., GMA, conscientiousness, emotional stability, extraversion,and previous experience) and to performance (i.e., GMA, conscientiousness, and previous experience). Thus, while Stajkovic andLuthans' (1998) findings demonstrate that TSSE does have a positive performance impact, Judge et al.'s findings suggest that TSSEmay be less important once more distal individual differences are considered.

In the context of employee job performance, PALS reflects a mid-range level of specificity between GSE and TSSE. While GSErelates to confidence across all achievement domains, PALS is a form of TSSE that is narrowly focused on learning problem solving.PALS is more general, however, than TSSE beliefs that are isomorphically linked to specific performance criteria. We would like tohighlight that we are in agreement that TSSE beliefs that are narrowly linked to specific criteria may best predict performance in anarrowly defined context (Chen, Gully, & Eden, 2004; Kanfer & Heggestad, 1997). However, it is our contention that PALS will bevaluable for predicting performance across a number of job contexts, given the importance of learning and problem solving inevery job. Just as GMA generalizes in predicting performance by influencing one's ability to learn and solve problems acrosscontexts, self-efficacy beliefs related to an individual's ability to learn and solve problems may also have a generalizable influenceon performance.

It should be emphasized thatmuch research has focused on the role of self-efficacy related to learning in training, development,and other learning contexts. Colquitt, LePine, and Noe's (2000) meta-analysis on training motivation demonstrated that self-efficacy related to learning is important in formal training contexts in facilitating knowledge and skill acquisition. FollowingColquitt et al.'s (2002) work, research has continued to focus on self-efficacy related to learning. For example, Lee and Klein (2002)demonstrated that self-efficacy related to academic performance was related to subsequent examination performance among asample of MBA students. Maurer, Weiss, and Barbeite (2003) demonstrated that self-efficacy for development was related to

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participation in development activities among a large sample of adults. Moreover, Maurer,Wrenn, Pierce, Tross, and Collins (2003)found that self-efficacy for learning and development was related to more favorable learning-oriented attitudes amongundergraduate students.

The current research examines the relationship between PALS and job performance, rather than PALS in relation to specificlearning and problem-solving tasks in a formal learning environment. Examining the PALS–job performance relationship isimportant because research suggests that themajority of learning and problem solving occurs informally on the jobwith the intentof improving job performance (Flynn, Eddy, & Tannenbaum, 2006; Tannenbaum, 1997). Informal learning occurs on an as neededbasis involving knowledge and skill acquisition needed for effective performance. Informal learning encompasses intent to learn,experience and action, feedback, and self-reflection (Marsick &Watkins, 1999). Examining the PALS–job performance relationshipis warranted because in today's workplace, job performance requires individuals to adjust or adapt to new conditions andunexpected job requirements (Pulakos et al., 2000).

In addition to self-efficacy beliefs, PALS is thought to be related to, yet distinct from, two other constructs. The first of which isopenness to experience, one of the Big Five personality dimensions. Openness to experience reflects characteristics such asimaginativeness, artistic sensitivity, curiosity, broad-mindedness, intelligence, and creativity (Barrick & Mount, 1991; Costa &McCrae, 1992). To an extent, these characteristics may reflect one's proclivity to learn and solve problems. For example, anindividual who is curious, broad-minded, and intelligent may have a desire and the ability to learn new information. Similarly,an individual who is imaginative and creative may be better at solving complex problems. PALS is distinct from opennessto experience, however, in that PALS focuses narrowly on competence related to learning and problem solving. Opennessto experience is broader, representing “receptivity to many varieties of experience and a fluid and permeable structure ofconsciousness” (McCrae, 1994, p. 251).

PALS is also argued to be related to, but distinct from, an individual's learning orientation. Kozlowski, Gully, Brown, Salas,Smith, and Nason (2001) characterize learning orientation as “an adaptive response to novel or challenging achievementsituations” (p. 4). Individuals with a learning orientation are attracted to challenging situations and believe that effort directedtoward exploration and learning will yield self-improvement. A learning orientation can be contrasted with a performanceorientation, which reflects the extent to which individuals seek out “easy situations that ensure positive evaluations of theircapabilities” (p. 4). Peoplewith a performance orientation prefer to gain favorable judgments of their competence, whereas peoplewith a learning orientation prefer to focus on finding ways to improve their competence (Dweck & Leggett, 1988). Both PALS andlearning orientation are similar in that they focus on learning and problem solving. PALS, however, reflects confidence regardinglearning and problem solving, whereas learning orientation reflects individuals' goals in such situations. Just as self-efficacy andlearning orientation are distinct constructs (Chen, Gully, Whiteman, & Kilcullen, 2000), PALS is argued to be distinct from learningorientation.

In the following sections, we detail three studies that provide preliminary construct validity evidence for PALS. The first studydescribes the initial scale development efforts. The second study provides evidence for the discriminant and predictive validity ofPALS relative to related constructs with a sample of managerial employees. The third study provides further evidence for thepredictive validity of PALS with a sample of entry-level employees.

2. Study 1

The purpose of study 1 was to develop a measure to assess the PALS construct. We sought to develop a newmeasure to assessPALS because extant self-efficacy measures related to learning tend to be too context specific. For example, Martocchio's (1994)measure of self-efficacy used in research on computer-based training included items such as “Using microcomputers is probablysomething I will be good at.” Maurer and Tarulli's (1994) measure of self-efficacy for development, which was used in Maurer,Weiss, et al. (2003), included items such as “If I were to participate in a development activity (workshop, course, etc.), my successin that activity would be at least comparable to most others.” The self-efficacy measure used in Lee and Klein (2002) askedparticipants to indicate how confident they were of attaining different test scores. Notwithstanding the validity of these andsimilar scales, we sought to develop a measure of one's perceived ability to learn and solve problems that was more general andthus potentially applicable across a variety of contexts.

An initial list of eight items was generated to operationalize the PALS construct. Items were based on the terms learning andproblem solving, synonyms for these terms, and closely related processes. For example, the items reflect an individual's confidenceto be trained, learn new things, retain information, find solutions to problems, and reason. Next, the first two authors reviewed theitems based on rules outlined by Edwards, Thomas, Rosenfeld, and Booth-Kewley (1997). Specifically, the items were reviewedand refined to ensure that they were not (1) too ambiguous or vague or (2) double-barreled but were (3) appropriately wordedand (4) simple and specific. Two reverse-coded items were removed from the scale (e.g., “I have difficultly learning new things”)because research has demonstrated that reverse-coded items may compromise a scale's factor structure (Williams, Ford, &Nguyen, 2002). The final six-scale items are presented in the Appendix. Following the development of the scale items, data werecollected and analyzed to assess the factor structure of the measure.

2.1. Methods

The sample for the factor analysis was 163 undergraduate students at a large, public university located in the Midwestern US.The respondents participated on a voluntary and anonymous basis. The average age was 22 years, 52% were males, and 88% were

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Caucasian. The respondents completed a survey during class time that assessed PALS, along with other items not used for researchpurposes. The students indicated the extent to which each of the six items from the PALS accurately described themselves on a5-point scale with anchors ranging from (1) very inaccurate to (5) very accurate. A principle components analysis (PCA) withvarimax rotation was performed (Schwab, 1980) to determine the factor structure for the measure. PCA is most appropriate inearly stages of scale development because it does not impose an assumption that a hypothetical model underlies the data (Ford,MacCallum, & Tait, 1986; Kelloway, 1995).

2.2. Results

In evaluating the results from the PCA, two decision rules were employed to determine the number of components in themodel: (1) eigenvalues over 1.0 and (2) components that were discrete from other factors as indicated in the scree plot. Thisanalysis yielded a single factor that accounted for 50.4% of the variance. In addition, the factor loadings for the individual itemsranged from .50 to .82. The internal consistency reliability estimate for this scale was acceptable (α=.83). These results suggestthat the PALS construct is best represented by a single factor.

3. Study 2

The first purpose of study 2 was to establish the discriminant validity of PALS. Namely, the purpose was to show that PALS isdistinct from GSE, openness to experience, and learning orientation. Although both PALS and GSE assess confidence, PALS isspecific to learning and problem solving. PALS is distinct from openness to experience because it is more narrowly focused onlearning and problem solving, whereas openness reflects broader intellectual curiosity and creativity. Finally, compared tolearning orientation, PALS reflects confidence in one's ability to learn and solve problems, whereas learning orientation refersspecifically to individuals' goals in such situations.

Hypothesis 1. PALS will be distinct from GSE, openness to experience, and learning orientation.

The fundamental premise of this research is that PALSwill have a positive impact on job performance. Thus, the second purposeof study 2 was to provide evidence for the predictive validity of PALS. The positive PALS–job performance relationship is expectedfor two reasons. First, the ability to learn and solve problems is critical for successful performance. Second, confidence in one'sabilities directs an individual to try to perform effectively in a work context.

Hypothesis 2. PALS will be positively related to job performance.

Beyond examining the PALS–job performance relationship, we investigate the predictive validity of PALS relative to otherrelated individual differences. Specifically, the predictive validity of PALS will be examined relative to GMA, GSE, openness toexperience, and learning orientation. That is, this researchwill examinewhether PALS is a better predictor of job performance thaneach of these individual differences. Research questions, as opposed to directional hypotheses, are presented since it is notaltogether clear whether PALS will be a stronger predictor, given the limited research in this area.

Question 1. Will PALS be a stronger predictor of performance than GMA, GSE, openness to experience, and learning orientation?

3.1. Methods

Two hundred and sixty-five managers from a national restaurant chain served as the basis for study 2. The data on theindividual differences were obtained through online survey administration, and the performance data were obtained fromorganizational records. The sample includedmanagers from the approximately 110 restaurants company-wide. The 265managersin the sample represented approximately 65% of the managers in the organization. Approximately 75% of the respondents weremales, and approximately 75% were Caucasian. The average age was 39 years, and the average tenure was 6 years. Along with thePALS scale, the full scales for GSE, openness to experience, and learning orientation are presented in the Appendix.

3.1.1. PALSThe six-item scale from study 1 was used to assess PALS. The internal consistency reliability estimate was again .83.

3.1.2. GSEGSE was measured with eight items from Judge, Locke, Durham, and Kluger (1998). A sample item included “I can handle the

situations that life brings.” The five-point scale ranged from 1=very inaccurate to 5=very accurate. The internal consistencyreliability estimate was .81.

3.1.3. Openness to experienceOpenness to experiencewas assessedwith the four-itemmeasure from theMini-IPIP (Donnellan, Oswald, Baird & Lucas, 2006).

A sample item included “I have a vivid imagination.” The five-point scale ranged from 1=very inaccurate to 5=very accurate. The

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internal consistency reliability estimate was .65. While the internal consistency estimate for this scale is relatively low, it is similarto that reported in Donnellan et al.'s (2006) validation study.

3.1.4. Learning orientationLearning orientation was measured with VandeWalle's (1997) five-item measure. A sample item included “I like challenging

work assignments I can learn a lot from.” The five-point scale ranged from 1=very inaccurate to 5=very accurate. The internalconsistency reliability estimate was .76.

3.1.5. GMAGMA was assessed with the Wonderlic QuickTest (WPT-Q) (Wonderlic, Inc., 2004a). This online, 8-min timed assessment

includes a series of 30 verbal, numeric, and logic problems. An individual's resulting score reflects a projected score on the fullWonderlic Personnel Test, ranging from 0 to 50. It has been demonstrated thatWPT-Q scores correlate .93with scores from the fullWonderlic assessment (Wonderlic, Inc., 2004b).

3.1.6. PerformanceThe measure of managerial performance was based on the twelve dimensions from the organization's performance appraisal.

The items relate to goal achievement focused on sales, profitability, employee retention, health and safety, and customersatisfaction. A manager's superior rated him or her on each performance item with a five-point scale with anchors ranging from1=unsatisfactory to 5=exceptional. The internal consistency reliability estimate for the measure was .77.

3.2. Results

Table 1 provides the descriptive statistics and correlations among the study variables.To assess the discriminant validity of PALS, GSE, openness to experience, and learning orientation, confirmatory factor analysis

(CFA) was performed using Mplus 5.21 (Muthen & Muthen, 2007) with the sample covariance matrix as input and a maximumlikelihood solution. Although the model possessed a statistically significant Chi-squared statistic, χ2 (224, n=265)=404.61,pb .01, the individual fit indices provided adequate support for the four-factor model (Hu & Bentler, 1999). Specifically, theComparative Fit Index (CFI) was .93; the Tucker-Lewis Index (TLI) was .92; the root–mean–square error of approximation(RMSEA) was .06 (90% confidence interval ranged from .05 to .06); and the standardized root–mean–square residual (SRMR) was.05. Because the constructs were highly correlated, discriminant validity was further established by comparing two models forevery pair of latent variables. In onemodel, itemswere fit to their respective hypothesized latent variable (e.g., PALS or GSE), and inthe secondmodel, all itemswere fit to a single latent variable. Pairwise Chi-squared difference tests were conducted to ensure thatthe two-factormodels fit the data better than each single-factormodel. In each case, the two-factormodels fit the data significantlybetter, providing additional evidence for the discriminant validity of these constructs (Bagozzi & Phillips, 1982). Overall, theseresults suggest that PALS is distinct fromGSE, openness to experience, and learning orientation. Thus, Hypothesis 1 was supported.

Table 2 refers to the following paragraph.To serve as the basis for assessing the predictive validity of PALS, performance was regressed on the independent variables in a

two-stage hierarchical multiple regression. Performance was regressed on GMA, GSE, openness to experience, and learningorientation in stage 1, with the inclusion of PALS in stage 2. For the independent variables, the variance inflation factors (VIFs)ranged from 1.07 for GMA to 2.35 for PALS. Given that the VIFs were less than 10, substantial multicollinearity was not present,which might have otherwise biased the coefficients (Cohen, Cohen, West, & Aiken, 2003). The R2 was .04 and in stage 1 and .06 instage 2. The ΔR2 of .02, although modest, was significant after the inclusion of PALS [F=5.43 (pb .05)]. GMA was a significantpredictor in stage 1 (β=.10, pb .05), but a non-significant predictor in stage 2 (β=.09, pN .05) (Table 2).

Hypothesis 2, which proposed that PALS would be positively related to job performance, was supported. The beta was .22 formanagerial performance (pb .05).

The research question asked whether PALS would be a stronger predictor of performance than GMA, GSE, openness toexperience, and learning orientation, respectively. PALS was deemed to be a stronger predictor of performance than all four of theother individual differences, as none of the four was a significant predictor in stage 2, whereas PALS was. As noted above, the betafor GMA in stage 2 was .09 (pN .05). Further, the beta for GSE was .05 (pN .05),−.04 for openness to experience (pN .05), and−.04

Table 1Descriptive statistics and correlations among study variables for study 1.

M SD 1 2 3 4 5 6

1. Performance 3.08 .39 –

2. PALS 4.24 .47 .21** –

3. GMA 25.01 4.47 .12* .13* –

4. GSE 4.43 .45 .15** .66** .06 –

5. Openness 3.87 .39 .11* .60** .16** .53** –

6. Learning orientation 4.12 .48 .11* .65** −.03 .63** .57** –

Note. n=265. Significance levels reflect one-tailed tests. *pb .05, **pb .01.

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for learning orientation (pN .05). A non-significant beta coefficient indicates that an effect size is not significantly different thanzero. Therefore, if one coefficient is significant and one is not, the significant coefficient is a stronger predictor. Had the GSE,openness to experience, and learning orientation coefficients been significant, follow-up tests to assess significant differences withPALS would have been conducted utilizing the formula outlined by Paternoster, Brame, Mazerolle, and Piquero (1998) for testingthe equality of regression coefficients.

3.2.1. Post hoc analysisAs detailed above, once PALS was included in the regression equation, the impact of GMA on performance became non-

significant, suggesting that PALSmightmediate the GMA–performance relationship. To fully examine this proposition, amediatinganalysis was conducted using the bootstrap approach detailed by Preacher and Hayes (2004). This approach is similar to Baron andKenny's (1986) approach in that regression coefficients and standard errors are provided for each step required for assessingmediating relationships (x→m,m→y controlling for x, x→y controlling form) (Kenny, Kasher, & Bolger, 1998). However, unlikethe Baron and Kenny approach, the bootstrap approach reduces Type I error, increases power, and does not require that normalityassumptions be met for variables and sampling distributions (Mooney & Duval, 1993). Using k number of resamples, the bootstrapapproach yields a sampling distribution of the indirect effect with confidence intervals. The bounds of the 95% confidence intervalindicate whether the population indirect effect is significantly different from zero (Preacher & Hayes, 2004). MacKinnon,Lockwood, and Williams (2004) advocate the use of confidence intervals to interpret tests of indirect effects because confidenceintervals attained from resampling (i.e., bootstrapping) techniques provide both an estimation of the size and variability of theindirect effect.

The relationships between GMA and PALS (b=.01, pb .05) and PALS and performance after controlling for GMA (b=.16,pb .01) were both significant. Furthermore, after controlling for PALS, the relationship between GMA and performance was non-significant (b=.01, pN .05). However, the bootstrapped indirect effect (k=5,000 resamples) was non-significant, as the 95%confidence interval generated from the sampling distribution included zero (LL=.00, UL=.01). The inclusion of zero indicatesthat the indirect effect in the population is not significantly different from zero, thus providing no evidence of mediation (Preacher& Hayes, 2004). Thus, the results do not provide support for PALS mediating the GMA–performance relationship.

4. Study 3

The purpose of study 3 was to assess the generalizability of the predictive validity of PALS in another employment context. Forthis study, the predictive validity of PALS was examined for entry-level service employees. While learning and problem solvingmay be more relevant in higher level positions, they are critical in entry-level positions as well. Schmidt (2002) contends thatknowledge and skill requirements in entry-level jobs aremuch greater than typically realized. Thus, it is proposed that PALSwill bea relevant predictor of performance for entry-level employees.

Hypothesis 1. PALS will be positively related to job performance.

Study 3 also examines the predictive validity of PALS relative to GMA and the complete set of the Big Five personalitydimensions (agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience). Meta-analyses ofthe impact of the Big Five personality dimensions on job performance and training performance demonstrate the value of theseattributes. Conscientiousness is the strongest predictor of performance across jobs (Hurtz & Donovan, 2000) and of motivation tolearn in training (Colquitt et al., 2000). Emotional stability is significantly, althoughmodestly, related to performance across jobs aswell (Hurtz & Donovan, 2000). Each of the Big Five has been shown to be significantly related to performance for jobs involvinginterpersonal interactions, such as those in the present study (Mount, Barrick, & Stewart, 1998). Conscientiousness has thestrongest influence, followed by agreeableness, emotional stability, extraversion, and openness to experience. The followingresearch question will be answered.

Table 2Regression of performance on PALS, GMA, GSE, openness, and learning orientation.

Stage 1 Stage 2

Predictor β β

GMA .10* .09GSE .12 .05Openness .01 −.04Learning orientation .03 −.04PALS – .22*

R2 .04 .06F 2.36* 3.00*ΔR2 – .02FΔ – 5.43*

Note. n=265. Significance levels reflect one-tailed tests. *pb .05, **pb .01.

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Question 1. Will PALS be a stronger predictor of performance than GMA and the Big Five personality dimensions?

4.1. Methods

The sample for study 3 consisted of 133 servers from 17 restaurants in one region of a different national restaurant than thatused in study 2. The research team visited each restaurant for 1 day and administered surveys to all servers scheduled to workduring that time. To obtain the performance ratings for the servers, two managers were provided with performance evaluationsfor each participating employee. These evaluations were completed within 1 week's time, on average, and then returned directlyto the researchers. The 133 employees represented approximately one-third of the total servers in the 17 restaurants. Sixty-sevenpercent of the respondents were female, and 90% were Caucasian. The average age was 25 years, and the average tenure was 25months.

4.1.1. PALSThe internal consistency reliability estimate for the six-item scale was .80.

4.1.2. GMAGMA was measured using the written Wonderlic Personnel Test, Form A (Wonderlic, 2001). The assessment consists of 50

items and was administered under the standard 12-min, timed protocol.

4.1.3. PersonalityThe Big Five were measured with the Mini-IPIP (Donnellan et al., 2006). Four items were used to measure each dimension.

Sample items included the following: “I sympathize with others' feelings” (agreeableness), “I like order” (conscientiousness), “Iam relaxed most of the time” (emotional stability), “I am the life of the party” (extraversion), and “I have a vivid imagination”(openness to experience). The employees indicated the extent to which each statement generally described themselves withresponse choices ranging from 1=very inaccurate to 5=very accurate. The internal consistency estimates were .75, .73, .67, .70,and .63 for agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience, respectively. Whilethe internal consistency estimates for emotional stability and openness to experience are relatively low, they are similar to thosereported in Donnellan et al.'s (2006) validation study.

4.1.4. PerformanceThe three-item measure of performance was based on Williams and Anderson's (1991) in-role performance scale. A sample

item was “This employee performs assigned tasks efficiently.” For each employee, two managers indicated the extent to whicheach performance item generally described the employee with response choices ranging from 1=strongly disagree to 5=stronglyagree. The overall performance score for each employee was first created based on each manager's ratings and then averagedacross the two managers to yield a single score. The median rwg,, an estimate of interrater agreement (James, 1982), for theperformance ratings was .80. The internal consistency reliability estimate for the measure was .86.

4.2. Results

Table 3 provides the descriptive statistics and correlations among the study variables.Performancewas regressed on the independent variables in a two-stage hierarchical multiple regression, similar to the analytic

strategy used in study 2. Performance was regressed on GMA and the personality dimensions in the first stage and PALS entered inthe second stage. The VIFs ranged from 1.15 for emotional stability to 1.68 for PALS. The R2 was .11 in stage 1 and .15 in stage 2. TheΔR2 of .04 was significant after the inclusion of PALS [F=5.10 (pb .05)]. GMA and conscientiousness were both significantpredictors in stage 1. The beta for GMAwas .16 (pb .05), and the conscientiousness beta was .21 (pb .05). However, theywere non-significant predictors in stage 2. With the inclusion of PALS, the beta for GMAwas .12 (pN .05), and the conscientiousness beta was.11 (pN .05) (Table 4).

Table 3Descriptive statistics and correlations among study variables for study 3.

M SD 1 2 3 4 5 6 7 8

1. Performance 4.01 .53 –

2. PALS 4.09 .54 .21** –

3. GMA 24.41 .72 .17* .09 –

4. Agreeableness 4.10 .71 .12 .24** −.01 –

5. Conscientiousness 3.98 .71 .20* .45** −.14 .28** –

6. Emotional stability 3.71 .74 .07 .24** .09 .18* .30** –

7. Extraversion 3.85 .75 −.18* .31** −.22** .20* .08 −.02 – .8. Openness to experience 3.91 .71 −.05 .38** .10 .19* .03 .04 .33** –

Note. n=133. Significance levels reflect one-tailed tests. *pb .05, **pb .01.

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Hypothesis 1, which proposed that PALS would be positively related to job performance, was supported. The beta was .24employee performance (pb .05).

The research question asked whether PALS would be a stronger predictor of performance than GMA and the five individualpersonality dimensions. PALS was a stronger predictor than GMA, agreeableness, emotional stability, conscientiousness, andopenness to experience. PALS was a significant predictor of performance in stage 2, while GMA, agreeableness, conscientiousness,emotional stability, and openness to experience were not. As noted above, the beta for GMA in stage 2 was .12 (pN .05), and theconscientiousness beta in stage 2 was .11(pN .05). Further, the agreeableness beta was .11 (pN .05), the emotional stability betawas −.05 (pN .05), and the openness to experience beta was −.09 (pN .05). The effect size was extraversion was significant (β=−.23, pb .01). To determine whether the effect size for extraversion was significantly different than the effect size for PALS, a two-tailed t-test for the equality of the absolute value of regression coefficients was examined (Paternoster et al., 1998). The effect sizesfor PALS and extraversion were not significantly different (t=.12, pN .05). It should be noted that extraversion was negativelyrelated to performance, running counter to previous research (Mount et al., 1998).

4.2.1. Post hoc analysisSimilar to study 2, follow-up tests of mediation were conducted to assess the extent to which PALS mediated the GMA–

performance and conscientiousness–performance relationships, as both GMA and conscientiousness became non-significant withthe inclusion of PALS in the regression equation. Again, the mediating relationships were examined using Preacher and Hayes'(2004) bootstrap approach.

The results do not provide evidence for PALS mediating either the GMA–performance and conscientiousness–performancerelationships. The relationship between GMA and PALS (b=.01, pN .05) was non-significant, thus precluding assessing the m→ycontrolling for x and x→y controlling for m relationships. In addition, the bootstrapped indirect effect (k=5000 resamples) wasnon-significant, as the 95% confidence interval generated from the sampling distribution did include zero (LL=.00, UL=.01). Therelationship between conscientiousness and PALS (b=.34, pb .01) was significant, but the relationship between PALS andperformance was non-significant after controlling for conscientiousness (b=.15, pN .05), thus precluding assessing the x→ycontrolling for m relationship. The bootstrapped indirect effect (k=5000 resamples) was non-significant, as the 95% confidenceinterval included zero (LL=−.01, UL=.12).

5. Discussion

The purpose of this research was to develop a measure of an individual's self-efficacy towards learning and problem solving—the perceived ability to learn and solve problems (PALS) and examine its relationshipwith job performance. This effort contributesto the staffing literature by addressing the need to develop new predictors that can help us more fully explain the performancecriterion (Cascio & Aguinis, 2008). Our results demonstrate that PALS is a significant predictor of performance for both managersand entry-level employees. Furthermore, PALS accounted for additional explanation in performance beyond other individualdifferences that have traditionally been used in studies of predictors of performance. In the managerial sample, PALS explainedadditional variance beyond GMA, GSE, openness to experience, and learning orientation. In the entry-level employee sample, PALSexplained additional variance beyond the Big Five personality dimensions—agreeableness, conscientiousness, emotional stability,extraversion, and openness to experience. These findings are noteworthy, following that task-specific self-efficacy (TSSE) beliefsdid not significantly predict performance when other key individual differences were considered in Judge et al.'s (2007) research.In the present study, however, PALS was a significant predictor and did explain additional variance in performance. On the whole,the results of this study support our perspective that using a predictor that focuses specifically on confidence relating to learningand problem solving is important given the relevance of learning and problem solving in job performance contexts.

Another goal of this studywas to demonstrate that PALS is distinct from related constructs such as GSE, openness to experience,and learning orientation. Although GSE, openness to experience, and learning orientation relate to confidence and learning and

Table 4Regression of performance on PALS, GMA, and personality.

Stage 1 Stage 2

Predictor β β

GMA .16* .12Agreeableness .11 .11Conscientiousness .21* .11Emotional stability −.03 −.05Extraversion −.18* −.23**Openness to experience −.03 −.09PALS – .24*

R2 .11 .15F 2.66** 3.09**ΔR2 – .04FΔ – 5.10*

Note. n=133. Significance levels reflect one-tailed tests. *pb .05, **pb .01.

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problem solving to varying degrees, PALS is specifically and narrowly focused on this content domain. The confirmatory factoranalyses demonstrated that PALS is in fact distinct, although related to, GSE, openness to experience, and learning orientation.Furthermore, PALS was a stronger predictor of performance than these similar constructs, as demonstrated in the sample ofmanagers.

Previous research has shown that GMA and conscientiousness are two primary individual differences that consistently predictperformance (Hunter & Hunter, 1984; Hurtz & Donovan, 2000). The importance of PALS for predicting performance was furthersupported by the findings that GMA and conscientiousness became non-significant when PALS was included as a predictor ofperformance. In the managerial sample, GMA became non-significant with the inclusion of PALS. (Conscientiousness was notassessedwith this sample.) In the entry-level employee sample, GMA and conscientiousness both became non-significant with theinclusion of PALS. GMA and conscientiousness may have become non-significant with the inclusion of PALS because PALS mayhave been a mediator in the GMA–performance and conscientiousness–performance relationships.

However, follow-up tests of mediation suggest that PALS does not mediate these relationships. One reason why a mediatingrelationship was not found in the GMA–performance relationship is that PALS was not strongly related to GMA. The modestassociation between these constructsmay be because GMA is relatively fixed, whereas individuals' perceptions of their abilities aremore malleable and subject to situational influences and experience over time. Banudra (1986) suggests that verbal persuasionfrom others, mastery experiences, and positive feedback can increase an individual's confidence relating to executing a specifictask, thus making self-efficacy less dependent on a fixed ability. A possible reason PALS did not mediate the conscientiousness–performance relationship is that they are both motivational constructs, having no casual relationship. While no support formediation was found, PALS may have reduced the impact of conscientiousness because PALS focuses more narrowly on learningand problem solving, whereas conscientiousness is a more general personality trait. Research is needed to replicate our results indifferent samples and settings to further establish the predictive validity of PALS relative to GMA and conscientiousness, given thewell-established importance of these individual differences in a performance context.

In both samples, the overall variance explained by PALS and the other individual differences was modest. One explanation forthis finding is possible range restriction in the independent variables because this study used a concurrent validation design(Pedhazur & Schmelkin, 1991). That is, data on the predictors and criteria were collected from job incumbents. The organizationsdid not use the measures employed in this study to select current job incumbents. However, their selection protocols could havefocused on similar constructs, thus creating range restriction. A second explanation is that situational characteristics were notexamined, such as organizational climate. Tett and Burnett (2003) argue that situational characteristics can amplify or minimizethe extent to which individual differences are expressed and therefore predictive of performance. In the context of PALS, futureresearch should examine its interactive effects with training climate (Tracey & Tews, 2005). The PALS–job performancerelationship may be stronger in a work environment that supports ongoing learning and problem solving compared to anunsupportive environment.

5.1. Implications for practice

One practical implication is that PALS could be used to make better selection decisions. Moreover, since GMA became non-significant in both samples once PALS was used to predict performance, PALS could potentially be used as a substitute forstandardized GMA assessments. The often discussed challenge of GMA assessments is that practitioners may be resistant to usingthem due to the apparent lack of face validity of the numeric, verbal, and logical problems that typify these assessments (Chan,1997). Situational judgment tests have been suggested as one viable alternative to GMA assessments, given their relationshipwith performance and GMA (McDaniel, Morgeson, Finnegan, Campion, and Braverman, 2001). Our results suggest that PALS mayalso potentially be a viable alternative to explicit GMA assessments. Additional research is necessary, however, before firmrecommendations can be made for using PALS as a substitute for a standardized GMA assessment given the preliminary nature ofthe findings from this study.

In addition to using PALS for selection purposes, managers should focus onmeans to further promote individuals' confidence intheir ability to learn and solve problems. Just as self-efficacy in general is affected by environmental factors in addition todispositional influences (Gist & Mitchell, 1992), PALS in particular also may be influenced by environmental factors. For example,self-efficacy beliefs may be enhanced through persuasion, feedback, modeling, andmastery experiences (Gist &Mitchell, 1992). Assuch, managers should encourage employees' learning and problem-solving efforts, provide effective feedback on the process,appropriately model learning and problem solving, and provide opportunities for individuals to succeed in learning and problem-solving efforts.

5.2. Study limitations and future research opportunities

The findings from this research should be interpreted in the context of two primary limitations. The first limitation is that PALSwas only examined using a limited set of performance criteria, i.e., dimensions of task performance. Additional work shouldexamine the impact of PALS on contextual dimensions of performance, such as helping behavior. For example, those higher in PALSmay be more willing and able to train coworkers and otherwise aid them in their on-the-job efforts. The second study limitation isthat only two samples in one industry were used. As such, it is worthwhile to examine the PALS–performance relationship usingother types of employees (e.g., knowledge workers) to enhance the generalizability of the current results.

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Several additional opportunities for further research are worth pursuing. One, it is important to examine the mediatingmechanisms through which PALS influences performance. For example, what specific learning behaviors do those higher in PALSengage in and how do these behaviors, in turn, impact employee performance? Two, it is worth examining the temporal stability ofPALS. That is, to what extent are individuals' perceptions of themselves regarding learning and problem solving relatively stableover time, or do they substantially fluctuate? The degree of temporal stability will help determine whether the application of PALSshould be more in a selection context or via managerial interventions once employees are on the job. Three, research shouldexamine the extent to which PALS has disparate impact. That is, to what extent are there subgroup differences within protectedclasses? In particular, are there racial/ethnic differences in PALS that have been controversial with respect to GMA (Roth, Bevier,Bobko, Switzer, & Tyler, 2006)? Such an assessment was not possible in the present context due to the lack of diversity in thesamples.

The prediction of employee performance is one of the primary goals of research applied to the workplace. The findings fromthis study, although preliminary, suggest that PALS may be an important addition to the set of individual differences used topredict performance. As learning and problem solving continue to become more central in today's organizations, researchingpredictors such as PALS should continue to be a focus of concerted attention.

Appendix. PALS, GSE, openness to experience, and learning orientation scales

PALS

1. I can readily find solutions to problems.2. I can quickly be trained to do almost anything.3. I have excellent reasoning ability.4. I retain information with little effort.5. I quickly learn new things.6. I quickly understand new situations.

GSE

1. I am strong enough to overcome life's struggles.2. At root, I am a weak person (reverse coded).3. I can handle the situations that life brings.4. I usually feel that I am an unsuccessful person (reverse coded).5. I often feel that there is nothing that I can do well (reverse coded).6. I feel competent to deal effectively with the real world.7. I often feel like a failure (reverse coded).8. I usually feel that I can handle the typical problems that come up in life.

Openness to experience

1. I have a vivid imagination.2. I am not interested in abstract ideas (reverse coded).3. I have difficulty understanding abstract ideas (reverse coded).4. I do not have a good imagination (reverse coded).

Learning orientation

1. I like challenging work assignments I can learn a lot from.2. I often look for opportunities to develop new knowledge and skills.3. I enjoy challenging and difficult tasks where I'll learn new skills.4. It's important for me to take risks to develop my work abilities.5. I prefer to work in situations that require a high level of ability and talent.

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