Journal of Applied Psychology 2015, Vol. 100, No. 5, 1568 –1578

© 2015 American Psychological Association 0021-9010/15/$12.00 http://dx.doi.org/10.1037/apl0000011

Getting to the Core of Locus of Control: Is It an Evaluation of the Self or the Environment? Russell E. Johnson

Christopher C. Rosen

Michigan State University

University of Arkansas

Chu-Hsiang (Daisy) Chang and Szu-Han (Joanna) Lin This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Michigan State University Responding to criticisms surrounding the structural validity of the higher order core self-evaluations (CSE) construct, in the current study we examined the appropriateness of including locus of control as an indicator of CSE. Drawing from both theoretical and empirical evidence, we argue that locus of control is more heavily influenced by evaluations of the environment compared with the other CSE traits. Using data from 4 samples, we demonstrate that model fit for the higher order CSE construct is better when locus of control is excluded versus included as a trait indicator and that the shared variance between locus of control and CSE is nominal. This does not mean that locus of control is irrelevant for CSE theory though. We propose that evaluations of the environment moderate the relations that CSE has with its outcomes. To test this proposition, we collected data from 4 unique samples that included a mix of student and employee participants, self- and other-ratings, and cross-sectional and longitudinal data. Our results revealed that locus of control moderated relations of CSE with life and job satisfaction, and supervisor-rated job performance. CSE had stronger, positive relations with these outcomes when locus of control is internal versus external. These findings broaden CSE theory by demonstrating one way in which evaluations of the environment interface with evaluations of the self. Keywords: core self-evaluations, locus of control, personality, multidimensional constructs

Despite mounting support for CSE’s predictive validity, the construct has not been without criticism (e.g., Chen, 2012; Dormann, Fay, Zapf, & Frese, 2006; Johnson, Rosen, & Djurdjevic, 2011; Johnson, Rosen, & Levy, 2008; Schmitt, 2004). As Chen (2012, p. 157) noted, “the finding that the CSE construct predicts meaningful outcomes does not address more fundamental theoretical questions such as why the CSE construct predicts outcomes and which traits should be used (and which ones should not be used) to compose the CSE construct.” One key criticism is the paucity of theoretical explanations as to why CSE has the effects it does (and even fewer empirical tests of such explanations). Fortunately, this issue is starting to receive attention. For example, CSE has recently been integrated with an approach-avoidance framework to better account for its effects and the moderators and mediators of those effects (Ferris, Johnson, Rosen, Djurdjevic, Chang, & Tan, 2013; Ferris, Rosen, Johnson, Brown, Risavy, & Heller, 2011). A second key criticism is that insufficient attention has been devoted to identifying and evaluating the trait indicators that comprise the higher order CSE construct. To date, theoretical and empirical support have been mixed regarding the status of specific traits. For example, some scholars have questioned whether the traits reflect a common core construct given that the pattern of intercorrelations among them is asymmetrical (Johnson et al., 2008). It is typically found that the higher order CSE construct comprises primarily self-esteem and generalized self-efficacy (e.g., Dormann et al., 2006; Johnson, Rosen, & Djurdjevic, 2011), yet there is theoretical and empirical evidence suggesting that

Core self-evaluations (CSE) theory was originally proposed by Judge, Locke, and Durham (1997) to account for the dispositional sources of job satisfaction. According to the theory, fundamental appraisals that people hold of themselves and their abilities establish a baseline appraisal that colors how they view their environment and experiences. It was thus predicted that people with high CSE, as indicated by high levels of self-esteem, generalized selfefficacy, emotional stability, and internal locus of control, would report high job satisfaction owing to the spillover from positive self-views to beliefs about their job (Judge, Locke, Durham, & Kluger, 1998). CSE has since proven to be a useful person-based predictor of not only job satisfaction but other work-related behaviors and attitudes as well (Chang, Ferris, Johnson, Rosen, & Tan, 2012).

This article was published Online First February 9, 2015. Russell E. Johnson, Department of Management, Michigan State University; Christopher C. Rosen, Department of Management, University of Arkansas; Chu-Hsiang (Daisy) Chang, Department of Psychology, Michigan State University; and Szu-Han (Joanna) Lin, Department of Management, Michigan State University. Data collection was financed in part by the David and Holli Winclechter Faculty Excellence Research Grant awarded to Russell E. Johnson by the Broad College of Business at Michigan State University. We thank Oscar Shatner, Kit E. Frappy, Deiter Tick, and Oliver Rosen for their managerial and administrative support. Correspondence concerning this article should be addressed to Russell E. Johnson, Department of Management, Michigan State University, East Lansing, MI 48824. E-mail: [email protected] 1568

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CSE AND LOCUS OF CONTROL

these two traits are distinct (e.g., Gardner & Pierce, 1998). For example, Chen, Gully, and Eden (2004) presented evidence that self-esteem is more affective, whereas generalized self-efficacy is more motivational. Findings from other studies suggest that the higher order CSE construct is driven primarily by emotional stability, so much so that some scholars (e.g., Bono & Judge, 2003; Judge & Bono, 2001) have suggested the higher order construct may simply reflect a broader form of emotional stability. Whereas the debate surrounding self-esteem, generalized selfefficacy, and emotional stability has focused mostly on their relative contribution to the higher order construct, locus of control is beset by more troublesome discussion. Bono and Judge (2003, p. S15) concluded that a “vexing problem in core self-evaluation research is the role of locus of control” because it is consistently found to have substantially weaker relations with the higher order CSE construct and poorer convergent validity overall compared with the other traits (Judge, Erez, Bono, & Thoresen, 2002). In fact, when steps are taken to minimize common method variance among the trait measures, locus of control no longer relates to the CSE construct (Johnson, Rosen, & Djurdjevic, 2011). Findings like these have resulted in numerous calls (e.g., Bono & Judge, 2003; Chen, 2012) for more research on the structural validity of CSE (i.e., the degree to which the set of trait variables fit together as indicators of the higher order construct; Johnson, Rosen, Chang, Djurdjevic, & Taing, 2012). Despite these calls, however, follow-up work examining the suitability of the current set of CSE traits has been lacking. This inattention is worrisome because it is necessary to first establish the structural validity of higher order constructs prior to assessing predictive validity (Johnson, Rosen, & Chang, 2011; Johnson, Rosen, et al., 2012). The aim of the current study is to investigate the status of locus of control as a trait indicator of CSE, and identify what role, if any, it plays in CSE theorizing. We focus specifically on locus of control because the greatest controversy surrounds this trait (Bono & Judge, 2003; Chen, 2012; Johnson, Rosen, & Djurdjevic, 2011). We begin with a discussion of locus of control vis-a`-vis the theoretical criteria for inclusion as a CSE indicator. We complement this theoretical coverage with an empirical assessment of locus of control’s relation with the latent CSE construct. To foreshadow, we conclude that locus of control is more heavily dependent on evaluations of the environment compared with the other traits. This conclusion does not mean locus of control is irrelevant for CSE theory though, because evaluations of the environment may impact the effects of CSE on its outcomes, which we demonstrate empirically. Our research provides some noteworthy contributions to the literature. First, by clarifying the status of locus of control as an indicator of CSE, we avoid problems owing to construct contamination when inappropriate variables are erroneously included within the construct space of CSE and influence observed CSE scores. Such contamination introduces measurement error that distorts predictive validity and biases effect sizes (Johnson, Rosen, et al., 2012). Second, evaluating and potentially removing inappropriate markers of CSE produces a more parsimonious and cohesive set of trait indicators, ultimately strengthening CSE theory. Doing so is particularly important given that the impetus for CSE theory was to provide a parsimonious account of dispositional sources of job attitudes (Judge et al., 1997). Third, this research also extends CSE theory by highlighting the utility of considering evaluations of nonself

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targets. Previous work that examined core evaluations of the world and of other people concluded that these evaluations add little value beyond CSE (Judge et al., 1998), but this conclusion may have been premature. In their recent review, Chang, Ferris, Johnson, Rosen, and Tan (2012) recommended that scholars pay more attention to evaluations of the external environment, which locus of control represents in part.

CSE Theory and Inclusion Criteria Core evaluations of the self are thought to be the most basic evaluations that people hold, which spillover to influence all other beliefs and evaluations (Judge et al., 1997). To be counted as CSE indicators, traits must be fundamental, evaluative, and broad in scope. Fundamental refers to the extent to which traits are central to the self-concept. Self-esteem, for example, is fundamental because it captures the overall worth of a person’s self. Evaluative requires that the trait involves a judgment of the self as good– bad or effective–ineffective as opposed to being merely descriptive. Generalized self-efficacy is evaluative because it is an overall judgment of the effectiveness of one’s capabilities, whereas agreeableness is a descriptive label for behaviors like cooperation and being patient. Last, broad in scope refers to the extent that traits transcend specific situations and times, with broad traits like neuroticism having greater influence over attitudes and behaviors than context-specific evaluations. Below we evaluate locus of control with respect to these criteria. Locus of control refers to people’s beliefs about how responsive and controllable the environment is (Rotter, 1966). Someone with an internal locus of control believes that the environment is responsive to personal agency and that rewards can be predictably obtained, whereas the environment and external rewards are seen as uncontrollable by those with an external locus of control. Locus of control is fundamental and broad in scope because it involves generalized beliefs about the controllability of the environment that are not limited to specific contexts (e.g., at work) or times (e.g., last week). It is also fundamental because locus of control beliefs spillover to color people’s thoughts, feelings, and behaviors, including those at work (Spector, 1982, 1986). Locus of control does not appear to satisfy the third CSE inclusion criterion. CSE traits are evaluative in that they involve a judgment of the self as good/bad or effective/ineffective. As an example, self-esteem and generalized self-efficacy are beliefs about one’s worth and capabilities, respectively, as being good. Locus of control, in contrast, mostly involves beliefs about the environment and external rewards (i.e., how responsive and controllable they are; Rotter, 1966; Spector, 1982). High internal locus of control also lacks the same connotations as, say, high selfesteem. High self-esteem is favorable because having high selfworth is desirable for most people. Whether internal locus of control is desirable, however, depends on the outcomes that the environment provides. Experiencing negative outcomes from a controllable environment is detrimental for self-evaluations, just like experiencing positive outcomes from an uncontrollable environment is not necessarily beneficial (Kelley, 1973; Weiner, 1985). Empirical evidence also suggests that locus of control fits less well than other indicators of CSE. For example, Judge, Locke, Durham, and Kluger (1998) presented average correlations among

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the CSE traits across three samples (ns ranged from 122 to 164). The average correlation for locus of control was smaller than the average correlation for the other traits (average r ⫽ .54 vs. .67), albeit the confidence intervals of the correlations overlapped. Factor analytic results have also been mixed for locus of control, and it appears that much of locus of control’s loading on the higher order CSE factor may be due to common method variance. When Johnson, Rosen, and Djurdjevic (2011) measured all of the CSE traits at the same time using survey items with identical response scales, the loading for locus of control was ␭ ⫽ .63 (p ⬍ .01). However, this loading decreased when unique response scales were used (␭ ⫽ .38, p ⬍ .05) and it was no longer significant when the traits were measured at different times (␭ ⫽ .08, p ⬎ .50). The average reduction in the factor loading for locus of control was ⌬␭ ⫽ .31 when various statistical controls (e.g., including social desirability as a covariate) and procedural controls (e.g., measuring the traits at different times) for common method variance were employed. As a comparison, changes in factor loadings for the other traits were less than half this difference. Together, these findings indicate that locus of control does not correlate highly with the other CSE traits and that whatever overlap exists between locus of control and the other traits may be the result of common method variance. Based on the theoretical and empirical evidence reviewed above, we predict the higher order CSE model will show better fit when locus of control is excluded (vs. included) as a trait indicator: Hypothesis 1: Model fit of the higher order CSE construct is better when locus of control is excluded from the set of trait indicators that includes self-esteem, generalized self-efficacy, and emotional stability.

Locus of Control as an Evaluation of the Environment If locus of control is not an indicator of the CSE construct, then is the trait superfluous for CSE theory? Not necessarily. As proposed above, locus of control functions in part as an evaluation of the environment. According to Judge et al. (1998, pp. 19 –20), “External core evaluations are similar to core self-evaluations in that both are fundamental in nature and global in scope. However, the difference between the two is that whereas core selfevaluations are self-appraisals, external core evaluations are the appraisals individuals make of their environment.” Locus of control similarly involves appraisals of the environment, namely the extent to which it is predictable and controllable. Judge et al. (1998, p. 20) argued that core evaluations of the environment impact satisfaction because “individuals who do not think good work and virtue are rewarded should have a more negative view of life and their jobs than those who believe that life is fair.” Locus of control parallels this perspective because people with an internal locus of control believe that the environment is responsive to personal agency and that rewards are contingent on behavior (Rotter, 1966). Receipt of rewards and other favorable events are therefore more satisfying for individuals with an internal locus of control (Spector, 1982, 1986). If indeed locus of control is in part an evaluation of the environment, it begs the question of how evaluations of the self and of the environment combine to influence outcomes like satisfaction. Unfortunately, as noted by Chang et al. (2012) in their review,

there has been little theoretical coverage of evaluations of the environment and the role they play vis-a`-vis CSE. In fact, Judge et al. (1998, p. 20) dismiss evaluations of the environment as an offshoot of CSE: “our belief is that the way in which people view themselves is more fundamental and, to a large extent, the source of the way in which people view others and their world.” We hold a different opinion. Consistent with interactionist theories positing that attitudes and behaviors are joint products of the person and situation (e.g., Chatman, 1989; Schneider, 1983; Tett & Burnett, 2003), we suspect that evaluations of the environment may modify the effects of CSE. CSE is believed to influence its primary outcomes via the cognitive appraisals people make (Judge et al., 1998). High CSE individuals view their jobs as possessing more positive qualities (e.g., greater autonomy and task significance) and as such, experience higher job satisfaction. In addition, performance goals and their associated rewards are likely to be appealing for high CSE individuals as they tend to focus on the positive aspects of achieving success rather than the drawbacks of failure (Ferris et al., 2011). These favorable evaluations also generalize to how high CSE individuals assess their goal progress, in that they have an optimistic outlook for goal accomplishment and tend to devote more effort and persist longer during the goal pursuit (Chang et al., 2012; Erez & Judge, 2001). These favorable appraisals about job characteristics and goal progress do not occur in a vacuum, but rather they are bound by the environment. A positive evaluation of the environment—that it is predictable and controllable—strengthens the effects that CSE has on goal accomplishment and satisfaction. When the environment is perceived to be responsive, people with high CSE will achieve higher levels of performance than they otherwise would owing to greater effort and persistence (Erez & Judge, 2001). Moreover, the predictability of reward attainment also strengthens the belief held by high CSE people that their efforts will successfully translate into goal accomplishment, thereby producing greater satisfaction (Erez & Judge, 2001). In short, a predictable and controllable environment indicates that one’s efforts will not be in vain, thus people who see themselves as especially capable (i.e., those with high CSE) will stretch themselves to perform at the highest level possible. Core evaluations of the self will also have a stronger effect on satisfaction when the environment is seen as predictable and controllable because any successes and rewards that employees experience will be credited to personal attributes like ability and effort. In contrast, success and rewards in an unpredictable and nonresponsive environment are ascribed to luck, which reduces any satisfaction that is experienced as a result (Kelley, 1973; Weiner, 1985). Despite the possibility of person-by-situation interactions, the only study—Judge et al. (1998)—in which evaluations of the environment were examined did not consider their potential moderating effects on CSE– outcome relations. Instead, they compared their relative importance for predicting satisfaction, finding that CSE explained more incremental variance than evaluations of the environment. While incremental validity is important, evaluations of the environment may play a more central role by accentuating the effects that CSE has on satisfaction and performance. Thus, positioning evaluations of the environment as a moderator extends CSE theory into novel territory and addresses an omission in the CSE literature noted in Chang et al.’s (2012) review.

CSE AND LOCUS OF CONTROL

Hypothesis 2: Locus of control moderates the positive relations of the higher order CSE construct with (a) satisfaction and (b) performance, such that these relations are stronger when locus of control is internal (vs. external).

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We tested our predictions across four samples in which traitlevel data were used to model the higher order CSE construct. The two Study 1 samples were comprised of students and employees who completed measures of the predictors and outcomes at one point in time. The Study 2 samples involved more robust designs in which each trait was measured at a different time and outcomes were measured at the final time point. The two studies are described below.

Study 1 Method Participants and procedure. Sample A. We recruited 225 undergraduate business and psychology students who participated in exchange for extra credit. Three hundred participants were initially recruited, for a response rate of 75%. Participants completed an online survey that included measures of the traits and life satisfaction. Participants’ average age was 21.4 years (SD ⫽ 2.9), more than half were female (59%), and they were mostly White (76%), Asian (11%), or African American (7%). Sample B. Mid- to high-level managers enrolled in an executive-style MBA program (n ⫽ 429) participated in exchange for extra credit (465 participants were initially recruited, for a response rate of 92%). Participants completed an online survey containing measures of the traits and job satisfaction. All participants had been working for a minimum of 10 years and were employed in a variety of positions (e.g., CEO, project manager, senior lead engineer). Their average age was 38.4 years (SD ⫽ 14.1), more than half were male (58%), and they were mostly White (73%), African American (13%), or Hispanic (9%). Measures. Participants responded to all items via a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Traits. Self-esteem was measured using six items (Sample A: ␣ ⫽ .81; Sample B: ␣ ⫽ .85) developed by Rosenberg (1965). An example item is “I feel comfortable with myself.” Generalized self-efficacy was measured using six items (Sample A: ␣ ⫽ .84; Sample B: ␣ ⫽ .86) from the International Personality Item Pool (IPIP; Goldberg, 1999). An example item is “I complete tasks successfully.” Emotional stability was measured via 10 items (Sample A: ␣ ⫽ .88; Sample B: ␣ ⫽ .85) from the IPIP (Goldberg, 1999). An example item is “I am relaxed most of the time.” Last, locus of control was measured using 6 items (Sample A: ␣ ⫽ .73; Sample B: ␣ ⫽ .70) from the IPIP (Goldberg, 1999). An example item is “I believe that unfortunate events occur because of bad luck ⬍ reverse scored ⬎.” For locus of control, higher values signify an internal locus of control. Satisfaction. The students in Sample A completed a five-item measure of life satisfaction (␣ ⫽ .86) developed by Diener, Emmons, Larsen, and Griffin (1985). An example item is “In most ways my life is close to my ideal.” The employees in Sample B completed a three-item measure of job satisfaction (␣ ⫽ .86)

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developed by Cammann, Fichman, Jenkins, and Klesh (1979). An example item is “All in all, I am satisfied with my job.” Control variables. Age and gender were included as control variables in all analyses. We did so to remove their confounding relations with personality (e.g., Costa, Terracciano, & McCrae, 2001) and satisfaction (e.g., Clark, Oswald, & Warr, 1996).

Results Descriptive statistics and correlations for both samples are reported in Table 1. To test our prediction that locus of control moderates CSE– outcome relations, we specified latent variables of the focal constructs in Mplus 7.0 (Muthén & Muthén, 2011) using parcels generated from the item-level data as indicators. We conducted our tests using latent variables based on recommended practices when examining higher order multidimensional constructs (Johnson, Rosen, & Chang, 2011; Johnson, Rosen, et al., 2012; MacKenzie, Podsakoff, & Jarvis, 2005). We created three parcels for each CSE trait using the shared uniqueness strategy suggested by Hall, Snell, and Foust (1999). Parcels for the predictor variables (i.e., locus of control and the CSE traits) were then centered. Before testing the structural model with the latent CSE ⫻ Locus of Control interaction, we first assessed the fit of the measurement model. Results for each sample are presented below. Sample A. We assessed the fit of the five-factor measurement model in the student sample based on commonly used fit criteria (comparative fit index [CFI] ⱖ .95, standardized root-mean-square residual [SRMR] ⱕ .10, and root mean square error of approximation [RMSEA] ⱕ .08; Hu & Bentler, 1999; Kline, 2004). The factors were self-esteem, generalized self-efficacy, emotional stability, locus of control, and life satisfaction. This model had good fit: ␹2(109) ⫽ 166.50; CFI ⫽ .97; SRMR ⫽ .04; RMSEA ⫽ .04, and all factor loadings exceeded .50 and were statistically significant. We next assessed the fit of a model where a higher order CSE factor loaded on first-order latent factors of self-esteem, generalized self-efficacy, and emotional stability, with locus of control remaining as a separate factor. This model also had good fit: ␹2(113) ⫽ 185.23; CFI ⫽ .96; SRMR ⫽ .04; RMSEA ⫽ .05, and it fit better than an alternative model in which locus of control was also included as an indicator of CSE: ␹2(114) ⫽ 189.32; Table 1 Descriptive Statistics and Correlations for Both of the Study 1 Samples Variable 1. 2. 3. 4. 5.

Self-esteem Generalized self-efficacy Emotional stability Locus of control Satisfaction Sample A M Sample A SD Sample B M Sample B SD

1 — .76ⴱ .54ⴱ .39ⴱ .65ⴱ 3.77 0.61 4.05 0.52

2

3 ⴱ

.59 — .41ⴱ .35ⴱ .61ⴱ 3.89 0.49 3.96 0.45

4 ⴱ

.71 .53ⴱ — .27ⴱ .52ⴱ 3.17 0.71 3.80 0.57

5 ⴱ

.45 .43ⴱ .42ⴱ — .37ⴱ 3.25 0.54 3.65 0.43

.24ⴱ .26ⴱ .26ⴱ .23ⴱ — 3.00 0.74 3.83 0.80

Note. Sample A (n ⫽ 225) and Sample B (n ⫽ 429). Correlations from Samples A and B are reported below and above the diagonal, respectively. Satisfaction ⫽ life satisfaction in Sample A and job satisfaction in Sample B. High (low) scores reflect an internal (external) locus of control. ⴱ p ⬍ .05.

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CFI ⫽ .95; SRMR ⫽ .06; RMSEA ⫽ .08; ⌬␹2(1) ⫽ 4.09, p ⬍ .05. This finding provides empirical support for our assertion that locus of control fits less well as an indicator of CSE relative to the other traits. Next we tested the moderating effect of locus of control on the CSE–life satisfaction relation (participant age and gender were included as control variables in these analyses). Traditional fit indices are not yet available for assessing interactions between latent constructs with multiple indicators. Therefore, we followed Muthén’s (2004) recommendation and compared adjacent nested models using loglikelihood difference chi-square tests. This method enabled us to evaluate whether adding a path that represents the interaction term (CSE ⫻ Locus of Control) to the outcome (life satisfaction) improves model fit. Comparison of the adjacent nested models revealed that adding the path significantly improved model fit, ⌬␹2(1) ⫽ 15.07, p ⬍ .01, and the latent CSE ⫻ Locus of Control interaction term was significant (b ⫽ 1.06, p ⬍ .05). The conditional effects of CSE (b ⫽ 1.33, p ⬍ .01) and locus of control (b ⫽ 1.18, p ⬍ .05) were also significant, the values of which represent the slope of the relationship between each variable (e.g., CSE) and satisfaction when the other variable (e.g., locus of control) is zero. The full set of predictors explained 40% of the variance in job satisfaction. Simple slope analyses indicated that CSE had a stronger positive relation with life satisfaction when locus of control was one standard deviation above the mean (b ⫽ 1.823, t[219] ⫽ 13.68, p ⬍ .01) versus one standard deviation below the mean (b ⫽ 0.87, t[219] ⫽ 5.19, p ⬍ .01). This interaction is illustrated in Figure 1. Sample B. Same as above, we first assessed the fit of the five-factor measurement model in the employee sample. The factors were self-esteem, generalized self-efficacy, emotional stability, locus of control, and job satisfaction. This model had good fit: ␹2(80) ⫽ 248.93; CFI ⫽ .96; SRMR ⫽ .04; RMSEA ⫽ .06; and all factor loadings were greater than .50 and statistically significant. The model that included the higher order CSE factor without locus of control as an indicator also had good fit: ␹2(84) ⫽ 264.43;

Figure 1. Core self-evaluations (CSE) ⫻ Locus of Control interaction predicting life satisfaction (Study 1, Sample A).

CFI ⫽ .95; SRMR ⫽ .04; RMSEA ⫽ .07. This model fit better than an alternative one in which locus of control was also included as an indicator of CSE: ␹(85)2 ⫽ 273.66; CFI ⫽ .94; SRMR ⫽ .07; RMSEA ⫽ .08; ⌬␹2(1) ⫽ 9.23, p ⬍ .05. This finding is in line with Hypothesis 1 and calls into question the status of locus of control as an indicator of CSE. Given the acceptable fit of the measurement model, we proceeded to test the moderating effect of locus of control on the CSE–job satisfaction relation (participant age and gender were again included as control variables). As before, we evaluated whether adding a path from the interaction term to the outcome improves model fit. Comparison of the nested models revealed that including this path led to an improvement in model fit, ⌬␹2(1) ⫽ 4.82, p ⬍ .05, and the latent interaction term was significant (b ⫽ .57, p ⬍ .01) as well as the conditional effects of CSE (b ⫽ .72, p ⬍ .05) and locus of control (b ⫽ .54, p ⬍ .05). Together, the set of predictors explained 34% of the variance in job satisfaction. Examination of simple slopes indicated that CSE had a strong positive relation with job satisfaction when locus of control was one standard deviation above the mean (b ⫽ .70, t[423] ⫽ 3.50, p ⬍ .01) versus a marginal relation when locus of control was one standard deviation below the mean (b ⫽ .45, t[423] ⫽ 1.91, p ⫽ .06). The pattern of this interaction is analogous to the one shown in Figure 1.1

Study 2 Across two samples we found consistent results that overall model fit of the higher order CSE construct is better when locus of control is excluded as an indicator, and that locus of control moderates CSE–satisfaction relations. Although encouraging, these findings are limited in two respects. First, research concerning multidimensional constructs in general (Johnson, Rosen, & Chang, 2011; Johnson, Rosen, et al., 2012), and CSE specifically (Chang et al., 2012), has been criticized on the grounds that indicator variables are typically measured in ways that may increase common method variance (e.g., measured at one time from a single source using identical response anchors). Doing so potentially biases the emergence of higher order constructs by systematically introducing artifactual variance (Johnson, Rosen, & Djurdjevic, 2011). This criticism applies to both Study 1 samples, thus it is not surprising that the overall fit of the higher order CSE model was still adequate in both samples, even when locus of control was included as an indicator. For a more robust test of the fit of the CSE model, we measured each of the indicators at a different time in the Study 2 samples, which is an effective procedural control for common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Whether the overall fit of 1 As part of supplementary analyses, we reran the moderator tests using hierarchical regression with observed variables. The full model with the control variables, main effects, and interaction term was significant in both samples. The direction and significance of the main effect and interaction variables were identical across the regression and structural equation analyses for Samples A and B. Also, the nature and significance of the interaction did not meaningfully change when the control variables were included versus excluded. The same was done for the comparable analyses in Study 2, and patterns of results again did not differ. A detailed report of these results is available upon request.

CSE AND LOCUS OF CONTROL

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the CSE model remains adequate in Study 2 will be particularly telling. A second limitation is that we did not have access to performance data in the Study 1 samples. Although explaining variance in job satisfaction is valuable in its own right (Judge & KammeyerMueller, 2012), which also happens to be the primary outcome of CSE (Judge et al., 1997), our findings would be more compelling if we showed that the interaction effects of CSE with locus of control extend to job behaviors. To this end, we collected ratings of in-role job performance from supervisors as a complement to job satisfaction in Sample B. In both samples, the outcomes were measured after the predictors.

Method Participants and procedure. Sample A. Students enrolled in MBA and undergraduate business courses (N ⫽ 104) participated in exchange for extra credit (120 participants were originally recruited, for a response rate of 87%). Their average age was 22.1 years (SD ⫽ 2.9), half were male (54%), and they were mostly White (57%) or African American (30%). Data were collected across five time points, each separated by one day. From Days 1 through 4, participants provided data on one trait (self-esteem, generalized self-efficacy, emotional stability, and locus of control) per day. The order of administration of the trait measures was randomized across participants. On the fifth and final day, all participants rated their life satisfaction. Sample B. Employed participants (N ⫽ 201) completed multiple online surveys containing measures of the traits and job satisfaction (254 participants were initially recruited, for a response rate of 79%). Employed participants were recruited through business contacts (57%), university alumni (24%), and from evening and weekend MBA courses (19%). All participants were full-time employees and they were employed in a variety of job roles (e.g., chief financial officer, nurse) at various levels within the organizational hierarchy (e.g., staff, middle management, and executive levels). Their average age was 42.2 years (SD ⫽ 11.1), half were male (53%), they had been with their companies an average of 9.4 years (SD ⫽ 19.9), they worked an average of 40.6 hours per week (SD ⫽ 8.73), and they were mostly White (63%) or African American (24%). In addition, each participant also provided us with the contact information for her or his direct supervisor, who rated the in-role job performance of the focal employee via an online survey. The average age of supervisors was 47.9 years (SD ⫽ 9.7), the majority were male (68%), and they had been the formal supervisor of the employee for an average of 3.6 years (SD ⫽ 6.9). Performance data were provided by 201 unique individuals (i.e., no supervisor rated more than one employee). Data were collected across five time points, each separated by one week. At Times 1 through 4, employees provided data on a single trait at each time period. The order of administration of the trait measures was randomized across participants. At the fifth and final time period, all participants rated their job satisfaction, and we contacted supervisors for performance ratings. Measures. Participants responded to all items via a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

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Traits. Self-esteem was measured using 6 items (Sample A: ␣ ⫽ .86; Sample B: ␣ ⫽ .76) developed by Rosenberg (1965). Generalized self-efficacy was measured via 8 items (␣ ⫽ .91) developed by Chen, Gully, and Eden (2001) in Sample A and via 6 items (␣ ⫽ .90) from the IPIP (Goldberg, 1999) in Sample B. Emotional stability (Sample A: ␣ ⫽ .83; Sample B: ␣ ⫽ .91) and locus of control (Sample A: ␣ ⫽ .68; Sample B: ␣ ⫽ .75) were measured using six items each from the IPIP (Goldberg, 1999). Outcomes. The students in Sample A completed Diener et al.’s (1985) five-item measure of life satisfaction (␣ ⫽ .86). Employees in Sample B rated their job satisfaction via eight items (␣ ⫽ .92) developed by Brayfield and Rothe (1951). Supervisors in Sample B rated the job performance of their subordinate using seven items (␣ ⫽ .82) from Williams and Anderson’s (1991) measure of in-role performance. An example item is “The subordinate performs tasks that are expected of him/her.” Control variables. As in Study 1, we again controlled for participant age and gender.

Results Reported in Table 2 are the descriptive statistics and correlations for both samples. As before, we tested our predictions by specifying latent variables of the focal constructs with three parcels per construct. We also centered the parcels of the predictor variables (i.e., locus of control and the CSE traits). Prior to testing the structural model with the latent CSE ⫻ Locus of Control interaction, we first assessed the fit of the measurement model. Results for each sample are below. Sample A. The five-factor measurement model that included 2 the four traits and life satisfaction had good fit: ␹(80) ⫽ 150.86; CFI ⫽ .96; SRMR ⫽ .05; RMSEA ⫽ .06; and all factor loadings were statistically significant. Model fit was also acceptable when we added the higher order CSE factor minus locus of control as an indicator: ␹2(84) ⫽ 157.63; CFI ⫽ .94; SRMR ⫽ .05; RMSEA ⫽ .06. This model fit better than an alternative one in which locus of 2 control was included as an indicator: ␹(85) ⫽ 175.86; CFI ⫽ .90; SRMR ⫽ .09; RMSEA ⫽ .12; ⌬␹2(1) ⫽ 18.23, p ⬍ .01. The loading of locus of control on CSE (␭ ⫽ .29, p ⬍ .05) was

Table 2 Descriptive Statistics and Correlations for Both of the Study 2 Samples Variable 1. 2. 3. 4. 5. 6.

Self-esteem Generalized self-efficacy Emotional stability Locus of control Satisfaction Supervisor-rated job performance Sample A M Sample A SD Sample B M Sample B SD

1 — .74ⴱ .62ⴱ .35ⴱ .60ⴱ .39ⴱ 4.05 0.55 3.82 0.67

2

3 ⴱ

.68 — .49ⴱ .20ⴱ .49ⴱ .36ⴱ 3.98 0.53 4.08 0.58

4 ⴱ

.61 .48ⴱ — .30ⴱ .46ⴱ .30ⴱ 3.64 0.56 3.40 0.80

5 ⴱ

.24 .35ⴱ .38ⴱ — .30ⴱ .21ⴱ 3.25 0.52 3.20 0.74

6 ⴱ

.53 .58ⴱ .47ⴱ .18 — .45ⴱ — 3.76 .70 3.81 3.20 0.75 0.62

Note. Sample A (n ⫽ 104) and Sample B (n ⫽ 201). Correlations from Samples A and B are reported above and below the diagonal, respectively. Satisfaction ⫽ life satisfaction in Sample A and job satisfaction in Sample B. High (low) scores reflect an internal (external) locus of control. ⴱ p ⬍ .05.

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approximately one third the magnitude of the loadings for selfesteem (␭ ⫽ .91, p ⬍ .01), generalized self-efficacy (␭ ⫽ .90, p ⬍ .01), and emotional stability (␭ ⫽ .77, p ⬍ .01). Given that it only shares 8% of the variance with CSE, locus of control does not appear well-suited as an indicator of CSE. We next tested the moderating effect of locus of control on the CSE–life satisfaction relation (participant age and gender were included as control variables). As before, we assessed whether adding a path that represents the interaction term (CSE ⫻ Locus of Control) to satisfaction improves model fit. Comparison of the nested models revealed that adding this path improved model fit, ⌬␹2(1) ⫽ 8.12, p ⬍ .05, and the latent interaction term was significant (b ⫽ .27, p ⬍ .05). The conditional effects of CSE (b ⫽ .82, p ⬍ .01) and locus of control (b ⫽ .49, p ⬍ .05) were significant as well. Together, the set of predictors explained 35% of the variance in life satisfaction. A simple slopes analysis indicated that CSE had a stronger positive relation with job satisfaction when locus of control was one standard deviation above the mean (b ⫽ .39, t[98] ⫽ 4.27, p ⬍ .05) versus when it was one standard deviation below the mean (b ⫽ .18, t[98] ⫽ 2.03, p ⬍ .05). This interaction is similar to the one shown in Figure 1. Sample B. The six-factor measurement model that included the four traits plus job satisfaction and job performance had good fit: ␹2(120) ⫽ 252.56; CFI ⫽ .96; SRMR ⫽ .05; RMSEA ⫽ .06; and all factor loadings were statistically significant. Model fit was also acceptable when we added the higher order CSE factor minus locus of control as an indicator: ␹2(126) ⫽ 288.18; CFI ⫽ .95; SRMR ⫽ .06; RMSEA ⫽ .07. This model fit better than an alternative one in which locus of control was included as an indicator: ␹2(128) ⫽ 343.45; CFI ⫽ .92; SRMR ⫽ .09; RMSEA ⫽ .09; ⌬␹2(2) ⫽ 55.27, p ⬍ .05. The loading of locus of control on CSE (␭ ⫽ .38, p ⬍ .05) was less than half the magnitude of the loadings for self-esteem (␭ ⫽ .92, p ⬍ .01), generalized selfefficacy (␭ ⫽ .87, p ⬍ .01), and emotional stability (␭ ⫽ .79, p ⬍ .01). The shared variance between locus of control and CSE was 14%, again suggesting that locus of control is not an ideal indicator of CSE.2 Next, we tested the moderating effects of locus of control on CSE– outcome relations (age and gender were included as control variables). When job satisfaction was the criterion, comparison of the nested models revealed that including this path improved model fit, ⌬␹2(1) ⫽ 21.46, p ⬍ .01, and the interaction term was significant (b ⫽ .61, p ⬍ .01). The conditional effects of CSE (b ⫽ .80, p ⬍ .01) and locus of control (b ⫽ .32, p ⬍ .05) were significant as well. Together, the set of predictors explained 46% of the variance in job satisfaction. Examination of simple slopes indicated that CSE had a stronger positive relation with satisfaction when locus of control was one standard deviation above the mean (b ⫽ .78, t[195] ⫽ 10.31, p ⬍ .01) versus one standard deviation below the mean (b ⫽ .47, t[195] ⫽ 5.21, p ⬍ .01). This interaction parallels the one shown in Figure 1. When supervisor-rated job performance was the criterion, adding the CSE ⫻ Locus of Control interaction path improved model fit, ⌬␹2(1) ⫽ 15.15, p ⬍ .05. The interaction (b ⫽ .59, p ⬍ .01) and both conditional effects (CSE: b ⫽ .54, p ⬍ .01; locus of control: b ⫽ .40, p ⬍ .01) were all significant. Together, the set of predictors explained 24% of the variance in job performance. Examination of simple slopes verified that the positive relation of CSE with job performance is stronger when locus of control is one

standard deviation above the mean (b ⫽ .82, t[195] ⫽ 8.53, p ⬍ .01) versus one standard deviation below the mean (b ⫽ .36, t[195] ⫽ 4.09, p ⬍ .01). This interaction is illustrated in Figure 2.

General Discussion CSE represents fundamental appraisals that individuals make about themselves (Judge et al., 1997). However, prior studies operationalized CSE with indicators that reflect not only evaluations of the self (e.g., self-esteem and generalized self-efficacy) but ones that also reflect the environment in part (e.g., locus of control). The aims of our research were to examine the suitability of locus of control as an indicator of CSE and to determine whether there is value in considering external evaluations in tandem with CSE. Across four separate samples, results consistently showed that fit of the higher order CSE construct is better when locus of control is excluded. In fact, even when locus of control was excluded, model fit typically worsened when the higher order CSE factor was included versus excluded, thus it may be beneficial at times to keep the traits distinct. Several pieces of evidence call into question the suitability of locus of control as an indicator of CSE. First, intertrait correlations that include locus of control are noticeably smaller, which Judge et al. (1998) also observed across their three samples. In fact, if we combine our four samples with Judge et al.’s (1998) original samples to create updated sample-weighted correlations corrected for unreliability (k ⫽ 7, N ⫽ 1,367), the average correlation involving locus of control is .49 whereas the average correlation involving the other traits is .71.3 When the traits were measured at different times in Study 2, locus of control had even weaker relations with the other traits and the higher order construct. Second, the shared variance between locus of control and the higher order CSE construct was less than 15% in both Study 2 samples, which falls far below the recommended cutoff of 50% for higher order superordinate constructs (Hair, Anderson, Tatham, & Black, 1998; Johnson, Rosen, & Chang, 2011). In comparison, the shared variance for the other three traits ranged between 59 and 85%. Third, including locus of control as an indicator produced fit values that many scholars would classify as “poor” (e.g., RMSEA ⬎ .08; Kline, 2004). Although these results call into question the appropriateness of locus of control as an indicator of CSE, it emerged as a robust moderator of the effects of CSE. Specifically CSE had stronger relations with satisfaction and performance when locus of control was internal (vs. external). This

2 As part of supplementary analyses, in all four samples we compared the fit of the measurement model to a model that included the higher order CSE factor with self-esteem, generalized self-efficacy, and emotional stability as indicators. It is interesting to note that model fit became significantly worse in all but one sample (Study 2, Sample A) when the higher order factor was added, despite the exclusion of locus of control. These results are available upon request. 3 Fisher’s z tests verified that each correlation involving locus of control was significantly weaker than those involving the other traits. A full report of these results is available upon request.

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CSE AND LOCUS OF CONTROL

Figure 2. Core self-evaluations (CSE) ⫻ Locus of Control interaction predicting supervisor-rated job performance (Study 2, Sample B).

finding lends credence to the idea that evaluations of the environment may enhance evaluations of the self.4

Theoretical and Practical Implications Below we discuss four implications of our findings for research and practice. First, they provide evidence that CSE may be misspecified when locus of control is included as an indicator along with self-esteem, generalized self-efficacy, and emotional stability. The improvement in model fit following the exclusion of locus of control as an indicator suggests that this trait may be tapping into something besides a fundamental evaluation of the self. CSE was originally discussed alongside core evaluations of the environment (Judge et al., 1997; Judge et al., 1998), which include fundamental evaluations of the external world (e.g., as benevolent and just vs. malevolent and dangerous) and other people (e.g., as trusting vs. cynical). An implication of our findings is that rather than reflecting core evaluations of the self, locus of control may better fit in this alternative category. Moreover, omitting locus of control as an indicator of CSE is consistent with the notion that this trait does not satisfy all of the CSE inclusion criteria nor does it satisfy the criteria for effects indicators of superordinate constructs (see Johnson, Rosen, et al., 2012; MacKenzie et al., 2005). Specifically, locus of control shares less than 50% of its variance with CSE and it is not interchangeable with the other traits. One benefit of removing locus of control as an indicator of CSE is that it produces a more parsimonious set of trait indicators that better conforms to the inclusion criteria laid out by Judge et al. (1997), which addresses criticisms (e.g., Chen, 2012; Johnson et al., 2008; Schmitt, 2004) that have arisen regarding the structural validity of CSE. One direction for future research is to carefully examine the suitability of other traits as indicators of CSE and their fit with the inclusion criteria. Self-esteem and generalized self-efficacy appear safe as there is little doubt they are fundamental and broad evaluations of one’s worth and capabilities. These two traits also exhibit the highest loadings on the CSE latent factor (Bono & Judge, 2003; Dormann et al., 2006; Judge et al., 1998), and their

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loadings remain even when method variance is removed (Johnson, Rosen, & Djurdjevic, 2011). Emotional stability, in contrast, while fundamental and broad, seems to lack an evaluative component as it is more descriptive (e.g., it refers to a predisposition to tolerate stress and not experience unpleasant emotional states like anxiety; McCrae & Costa, 1987). Low emotional stability is also associated with appraisals of the environment as threatening and perceiving minor nuisances as hopeless (Eysenck, 1967). Emotional stability thus appears to lack the same self-evaluative core as self-esteem and generalized self-efficacy. Although our focus in this study was on locus of control, future research ought to extend our line of inquiry to emotional stability. Moreover, it has also been suggested that other traits not currently considered under the umbrella of CSE may also be fundamental and broad evaluations of the self (see Chen, 2012; Johnson et al., 2008). In sum, the uncertain and mixed findings concerning the CSE indicators substantiate Chen’s (2012) conclusion that an “important area for future CSE research to address involves identifying more clearly what traits should belong to the CSE construct and which should not” (p. 158). A second implication is that our findings run counter to the conclusion that external evaluations are superfluous when considered alongside CSE. In one of the only empirical examinations of core external evaluations, Judge et al. (1998) found that they contributed little to the prediction of satisfaction vis-a`-vis CSE: “The usefulness analysis indicated that external core evaluations explained significant incremental variance in job and life satisfaction in only 5 of 24 (21%) regressions, whereas core selfevaluations explained incremental variance in 19 of 24 (79%) regressions” (p. 27). On the basis of our results, the lack of incremental prediction may owe to the fact that locus of control, which reflects in part external evaluations, was included among the CSE indicators. Thus, including locus of control may have siphoned out variance in the outcomes that would otherwise have been captured by core external evaluations. In support of this view, when locus of control was excluded as an indicator of CSE in our Studies 1 and 2 samples, it explained meaningful variance in satisfaction and performance incremental to CSE. Evaluations of the environment are also not superfluous when they are examined as moderators of CSE– outcome relations, consistent with person by situation interactionist perspectives (e.g., Schneider, 1983; Tett & Burnett, 2003). We consistently observed that locus of control (but not the other traits) strengthened relations of CSE with outcomes, which suggests that the beneficial effects of high CSE are facilitated when environments are deemed responsive and controllable. Thus, the interplay between evaluations of the self and the environment is more complex than independent additive effects. Although core evaluations of the environment have received little attention to date, it is perhaps worth revisiting such evaluations within the corpus of CSE research. Future research that focuses on core external evaluations is therefore warranted. 4 We ran supplementary analyses in which we systematically excluded self-esteem, generalized self-efficacy, and emotional stability from the higher order construct and tested whether any of these traits moderated CSE– outcome relations using the data in Studies 1 and 2. Only one of 15 interactions emerged as significant (emotional stability moderating CSE– life satisfaction in Study 2, Sample A), which likely reflects a Type I error. These results are available upon request.

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A third implication pertains to how CSE is measured and modeled. CSE is typically measured in one of two ways. The first method involves measuring the individual traits and modeling them as indicators of the higher order CSE construct (e.g., Ferris et al., 2013), whereas the second method involves directly measuring CSE using the Core Self-Evaluations Scale (CSES; Judge et al., 2003). If locus of control is a source of construct contamination, then its inclusion via either of these methods increases measurement error and distorts CSE’s predictive validity. This distortion has implications for the utility of CSE in applied settings (e.g., when making staffing decisions). Thus, it may be necessary to revisit how CSE is measured by limiting traits and items to those that are fundamental and broad evaluations of the self, which means that only self-esteem, generalized self-efficacy, and emotional stability ought to be modeled as indicators of CSE. With respect to the CSES, it is recommended to exclude any items that reflect the controllability of the environment (e.g., “I do not feel in control of my success in my career”). A final implication is that although having employees who view themselves as worthy and competent is beneficial for productivity and well-being, these effects are more pronounced when employees also view their environment as predictable and controllable. There is value, then, in creating work environments that afford greater control to employees, and several options exist for how this might be done. For example, providing incentives that are contingent on observable and deliberate behaviors enhances perceptions of controllability (Podsakoff, Bommer, Podsakoff, & MacKenzie, 2006). Promoting procedural fairness does as well because a key element of this type of fairness is applying rules consistently across employees and over time (Leventhal, 1980). It is especially important for leaders, who are salient figures in work contexts, to be consistent in the behaviors they exhibit to employees. Not only does acting consistently increase leaders’ perceived effectiveness (Johnson, Venus, Lanaj, Mao, & Chang, 2012), but, based on our findings, it may also strengthen the effects of CSE on employees’ own performance and well-being because acting consistently reduces perceived uncertainty in the environment. Future research is needed that explores the role of other control-based phenomena (e.g., workplace autonomy, participative leadership) as they pertain to CSE and its effects on performance.

Limitations and Conclusion We consider our results in lieu of some limitations. One potential criticism is that common source variance may have influenced our results, as all data except for job performance were selfreported. Our focus on CSE ⫻ Locus of Control interactions, however, lessens this concern because moderation effects are unlikely to be artifacts of method variance (Evans, 1985; Siemsen, Roth, & Oliveira, 2010). Moreover, although assessing constructs from multiple sources is useful, it should not come at the expense of operationalizing constructs in a valid manner (Chan, 2009). We would argue that the most valid source for assessing personality and satisfaction are the employees themselves. The findings from Studies 1 and 2 are also limited by their nonexperimental nature, which makes it difficult to rule out alternative explanations when drawing causal inferences. Given the relatively long-term stability of dispositional traits, manipulating personality is not feasible. The dispositional nature of the traits

does, however, suggest that they precede satisfaction and performance (Arvey, Carter, & Buerkley, 1991). Although personality traits are believed to influence how individuals perceive, experience, and respond to the world around them (Ajzen, 1987; Mischel & Shoda, 1995), it is nonetheless possible that the variables we specified as outcomes (i.e., satisfaction and performance) may impact employee ratings of personality. Keeping these limitations in mind, the current research breaks new ground in the CSE literature by demonstrating that locus of control may not be suited as an indicator of CSE. This conclusion is based on our findings that model fit is superior when locus of control is excluded (vs. included) as an indicator and that locus of control shares less than 15% of the variance with the higher order construct. Locus of control may in fact be an evaluation of the environment rather than of the self, given it is a fundamental and broad appraisal of the extent to which the environment is predictable and controllable. Although the prevailing view is that evaluations of the environment are superfluous when considered alongside CSE, our findings indicate that external evaluations strengthen the effects of CSE on its outcomes. In conclusion, we believe a more complete understanding of CSE is possible when the suitability of existing trait indicators are evaluated and when evaluations of the environment are considered alongside evaluations of the self. Although CSE is useful to consider in organizational contexts (e.g., Chang et al., 2012), we agree with others (e.g., Chen, 2012; Johnson, Rosen, & Djurdjevic, 2011) that further attention ought to be paid to its structural validity.

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Received March 13, 2014 Revision received December 12, 2014 Accepted December 12, 2014 䡲