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The Core Self-Evaluation Scale: Psychometric Properties of the German Version in a Representative Sample a

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Markus Zenger , Annett Körner , Günter W. Maier , Andreas Hinz , Yve Stöbel-Richter , d

Elmar Brähler & Anja Hilbert

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Department of Medical Psychology and Medical Sociology, University of Leipzig, Germany

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Counselling Psychology Program, McGill University, Montreal, Canada

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Department of Work and Organizational Psychology, Bielefeld University, Germany

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Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany e

Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany Published online: 22 Dec 2014.

To cite this article: Markus Zenger, Annett Körner, Günter W. Maier, Andreas Hinz, Yve Stöbel-Richter, Elmar Brähler & Anja Hilbert (2015) The Core Self-Evaluation Scale: Psychometric Properties of the German Version in a Representative Sample, Journal of Personality Assessment, 97:3, 310-318, DOI: 10.1080/00223891.2014.989367 To link to this article: http://dx.doi.org/10.1080/00223891.2014.989367

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Journal of Personality Assessment, 97(3), 310–318, 2015 Copyright Ó Taylor & Francis Group, LLC ISSN: 0022-3891 print / 1532-7752 online DOI: 10.1080/00223891.2014.989367

The Core Self-Evaluation Scale: Psychometric Properties of the German Version in a Representative Sample € € MARKUS ZENGER,1 ANNETT KO€ RNER,2 GU€ NTER W. MAIER,3 ANDREAS HINZ,1 YVE STOBEL -RICHTER,1 ELMAR BRAHLER ,4 AND 5 ANJA HILBERT 1

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Department of Medical Psychology and Medical Sociology, University of Leipzig, Germany 2 Counselling Psychology Program, McGill University, Montreal, Canada 3 Department of Work and Organizational Psychology, Bielefeld University, Germany 4 Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany 5 Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany The Core Self-Evaluation Scale (CSES) is an economical self-reporting instrument that assesses fundamental evaluations of self-worthiness and capabilities. The broad aims of this study were to test the CSES’s psychometric properties. The study is based on a representative survey of the German general population. Confirmatory factor analyses were conducted for different models with 1, 2, and 4 latent factors. The CSES was found to be reliable and valid, as it correlated as expected with measures of depression, anxiety, quality of life, self-report health status, and pain. A 2-factor model with 2 related factors (r D –.62) showed the best model fit. Furthermore, the CSES was measurement invariant across gender and age. In general, males had higher values of positive self-evaluations and lower negative self-evaluations than females. It is concluded that the CSES is a useful tool for assessing resource-oriented personality constructs.

During the past few decades, psychological research and therapeutic interventions have highlighted the importance of resources and capabilities of human beings instead of focusing solely on their deficits. The assessment and inclusion of these psychological resources can make an important contribution to describing personality in a more holistic manner that consists not only of deficits, but also of strengths (Seligman & Csikszentmihalyi, 2000; Simonton & Baumeister, 2005). Uncovering the resources individuals already have is also important in psychosocial interventions, because these resources can be used effectively to achieve goals and to promote changes in the desired direction (Jerusalem, 1993; Lopez, Snyder, & Rasmussen, 2003). Although the inclusion of assessment tools for human strengths and virtues along with the examination and evaluation of deficits of individuals is not completely new (Fernandez-Rıos & Novo, 2012), it has to some extent been neglected in the field of clinical psychology for a long time. Therefore, the existence of reliable and wellvalidated standardized measures that assess psychological resources is mandatory. Regarding the expanding literature on assessment tools of individual resources, there are several well-known questionnaires operationalizing such constructs, including self-efficacy (Schwarzer & Jerusalem, 1995), optimism (Scheier & Carver, 1985), locus of control (Levenson, 1981), self-esteem (Rosenberg, 1965), sense of coherence (Antonovsky, 1993), and resilience (Wagnild & Young, 1993). On the one hand, the conceptualizations of these constructs seem to differ

Received September 27, 2013; Revised October 7, 2014. Address correspondence to Markus Zenger, Department of Medical Psychology and Medical Sociology, University of Leipzig, Philipp-Rosenthal-Str. 55, 04103 Leipzig, Germany; Email: [email protected]

substantially; on the other hand, there is much empirical evidence that they partially overlap and share a considerable amount of empirical variance (Jovanovic & Gavrilov-Jerkovic, 2013; Judge, Erez, Bono, & Thoresen, 2002; Kr€oninger-Jungaberle & Grevenstein, 2013). Consequently, Judge and colleagues (Judge, Erez, Bono, & Thoresen, 2003; Judge, Locke, & Durham, 1997) suggested the measurement of a higher order latent personality trait, called core self-evaluations, that includes several facets of the constructs of self-esteem, self-efficacy, locus of control, and neuroticism. The measurement of a common core of several personality aspects, which includes fundamental appraisals of one’s own self-worthiness and capabilities, “would allow the scattered literature on these specific traits to achieve a greater unity and integration than has been possible in the past” (Judge et al., 2002, p. 707). For this purpose, they developed the 12-item Core Self-Evaluation Scale (CSES). Despite being relatively new, the CSES has become a widely used instrument, and there is evidence of its utility in assessing several outcomes. The CSES was found to be a significant predictor of job and life satisfaction (Bono & Judge 2003; Chang, Lance Ferris, Johnson, Rosen, & Tan, 2012; Heilmann & Jonas, 2010; Hirschi & Herrmann, 2012; Stumpp, Muck, H€ ulsheger, Judge, & Maier, 2010), happiness and positive affectivity (Gardner & Pierce, 2010; Rey, Extremera, & Duran, 2012; Stumpp et al., 2010), positive aspects of career decision making (Di Fabio, Palazzeschi, & Bar-On, 2012; Koumoundourou, Kounenou, & Siavara, 2012), and lower perceived stress levels (Brunborg, 2008; Luria & Torjman, 2009), better health functioning (Hilbert, Braehler, Haeuser, & Zenger, 2014; Tsaousis, Nikolaou, Serdaris, & Judge, 2007; Yagil, Luria, & Gal, 2008), and higher levels of life balance (Grisslich, Proske, & K€orndle, 2012). These results document that the CSES is not only relevant in the field of organizational

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THE CORE SELF-EVALUATION SCALE: PSYCHOMETRIC PROPERTIES psychology, but also in health psychology, clinical psychology, and quality of life research. However, validity aspects of the CSES with regard to specific psychopathological symptoms (e.g., anxiety, depression), quality of life, the experience of physical symptoms (e.g., pain), and work-related outcomes (e.g., duration of unemployment) have not been tested within a representative sample of the general population, and thus need further consideration. Because of the importance of CSE for many research questions, the CSES has been translated into several other languages including Spanish (Judge, Van Vianen, & De Pater, 2004), Dutch (Judge et al., 2004), Italian (Di Fabio & Busoni, 2009), Korean (Holt & Jung, 2008), Persian (Karatepe, 2011), Greek (Tsaousis et al., 2007), Japanese (Piccolo, Judge, Takahashi, Watanabe, & Locke, 2005), and German (e.g., Stumpp et al., 2010). Results on the validity of scores of the German versions have been reported in several studies (Albrecht, Paulus, Dilchert, Deller, & Ones, 2013; Heilmann & Jonas, 2010; Stumpp, H€ ulsheger, Muck, & Maier, 2009; Stumpp et al., 2010). Past research often used the CSES in samples of students and young adults (e.g., Di Fabio et al., 2012; Grisslich et al., 2012; Hirschi & Herrmann, 2012; Judge et al., 2003; Koumoundourou et al., 2012; Luria & Torjman, 2009). However, psychometric evaluation of the CSES, assessed in a representative sample of the general population, is still lacking. Furthermore, differential aspects of the level of core self-evaluations with regard to gender, age, education, and healthrelated indicators have previously not been sufficiently considered. Moreover, the questionnaire has been shown to be unidimensional in several studies (e.g., Judge et al., 2003; Judge et al., 2004; Stumpp et al., 2010), indicating a higher order latent factor, but the results are to some extent ambiguous. Judge et al. (2003) stated that a unidimensional model of the original English version of the CSES fits the data better than a model with four latent variables (one each for the constructs stated earlier), but the degrees of freedom in this model indicated that six pairs of error terms were allowed to correlate. Apart from the fact that this is sometimes common practice, without stating explicitly which error terms correlated in this model and why they were freed, this procedure leads to an increased model fit that might be artificial. The same procedure was used by Stumpp et al. (2010), also confirming the unidimensionality of the German version of the CSES. In the study reported by Judge et al. (2004), the unidimensionality of the Spanish and Dutch versions of the CSES were confirmed, but degrees of freedom in the model tested indicated that only four pairs of error terms were allowed to correlate with each other. Aside from the unknown reason for relaxing the constraints, this modus operandi is inconsistent, and the dimensionality of the CSES needs to be verified. Additionally, Ferris et al. (2011) focused on the partially contradictory conceptualization of the CSE personality construct as an indicator of high approach temperament and low avoidance temperament simultaneously, which are usually seen as separated constructs (Ferris et al., 2011). A high approach temperament describes individuals who seek positive outcomes and have a higher sensitivity to positive achievements, whereas a high avoidance temperament describes individuals who avoid negative outcomes and have a higher sensitivity to negative results (Higgins, 1997).

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Although this fundamental distinction in individual temperament is not sufficient to describe all facets of personality, it has been shown to be a useful conceptual lens to study fundamental differences in human beings (Elliot & Trash, 2002). The relatively stable approach and avoidant temperaments are empirically linked to personality traits (e.g., extraversion, neuroticism), general affectivity (e.g., positive vs. negative emotionality), and motivational systems (behavioral activation system vs. behavioral inhibition system; Elliot & Trash, 2002). Ferris et al. (2011) examined the relationship of CSE and both avoidance and approach temperament and concluded that CSE loaded on both factors. In this study, the associations were examined with the CSES as a unidimensional measure. Separate relationships with positively and negatively worded items of the CSES and approach and avoidant temperaments were not assessed. Therefore, the aims of this study are (a) to examine the dimensionality of the CSES using a latent trait model approach with regard to three competing factor models with one, two, and four factors; (b) to test whether the appropriate model holds for gender and different age groups (invariance test across gender and age); (c) to investigate the construct validity of the CSES scoring with respect to health-related criteria; and (d) to test for potential differences between groups with regard to several socioeconomic variables (gender, age, living in partnership, education). To our knowledge, this is the first study that investigates the psychometric properties of the CSES in a representative sample of the general population.

METHOD Participants and Procedures This study is based on a representative survey of the German general population in 2012. Data were collected by the Independent Service for Surveys, Methods and Analyses (USUMA, Berlin), using the random-route technique. Comparing this sample with information from the Federal Statistical Office, it can be assumed to be fairly representative of the general population concerning age and gender, with a slightly higher proportion of women represented in the study sample (DESTATIS, 2013). Participants were interviewed face to face in their homes by trained interviewers. The first attempt to contact participants was made for 4,480 addresses, of which 4,436 were valid. Out of the initial sample, the final study sample consisted of 2,508 men and women (participation rate D 56.5% of valid addresses). The percentage of women was 53.4% (n D 1,339). Further characteristics of the study sample are given in Table 1. Instruments Core Self-Evaluation Scale. The German version (Stumpp et al., 2010) of the CSES (Judge et al., 2003) was used. The CSES is a short and validated 12-item instrument that covers four central aspects of self-evaluations: selfesteem, locus of control, neuroticism, and self-efficacy. Example items that cover these domains but do not exclusively represent them are “Overall, I’m satisfied with myself” (selfesteem); “Sometimes, I do not feel in control of my work” (locus of control); “There are times when things look pretty bleak and hopeless to me” (neuroticism); and “When I try, I

312 generally succeed” (self-efficacy). Participants indicate their agreement with the statements on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Six items of the CSES are negatively worded and are reverse-coded before calculating the mean score of the total scale. Internal consistency in three samples of German employees (N between 118 and 199) ranged between .81 and .86, and retest reliability over a period of 2 months was .82 (Stumpp et al., 2010).

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Patient Health Questionnaire–9. The Patient Health Questionnaire–9 (PHQ–9; Kroenke, Spitzer, & Williams, 2001) is a short depression screener that asks for the ratings of the nine DSM–IV criteria of depression from 0 (not at all) to 3 (nearly every day) during the last 2 weeks. A sum is calculated with higher scores indicating higher severity of symptoms. Cronbach a was .86 in this study. General Anxiety Disorder–2. The General Anxiety Disorder–2 (GAD–2; Kroenke, Spitzer, Williams, Monahan, & L€ owe, 2007) is a two-item screening instrument for anxiety disorders and evaluates how often the subjects have been bothered by the two core symptoms (excessive anxiety and worry and difficulty in controlling them) of generalized anxiety disorder during the last 2 weeks. Answers ranged from 0 (not at all) to 3 (almost every day) and a sum is calculated. Cronbach a was .75 in this study. Self-reported health status. The Visual Analogue Scale (VAS) of the EuroQol 5–Dimension (EQ5–D; Brooks, Rabin, & de Charro, 2003) was used for the assessment of the subjectively rated health status. Subjects were asked to rate their current general health status on a VAS ranging from 0 (worst imaginable health status) to 100 (best imaginable health status). Quality of life. The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Core 30 (EORTC QLQ–C30) of Aaronson et al. (1993) was used to assess health-related quality of life (HRQOL). The multidimensional 30-item instrument covers several aspects of HRQOL and includes five functioning scales, a global health status/QOL scale, three symptom scales, and six single items. Items of the functioning scales are assessed on a 4-point Likert scale with higher scores representing higher functioning. The responses of the global health status/QOL scale range from 1 (very poor) to 7 (excellent). Internal consistencies (Cronbach a) of the subscales in this study were as follows: .82 (physical functioning), .90 (role functioning), .84 (emotional functioning), .72 (cognitive functioning), .86 (social functioning), and .86 (global health status/QOL). Pain. Participants were asked to indicate how much they had been affected by back pain and headaches during the last 7 days. Answer options ranged from 1 (not at all) to 5 (very much). Duration of unemployment. Participants were asked for the cumulative time of unemployment (in months) they experienced during their life.

ZENGER ET AL. TABLE 1.—Sociodemographic characteristics of the study population.

Age, years M SD Age range Age groups  29 years N % 30–39 years N % 40–49 years N % 50–59 years N % 60–69 years N %  70 years N % Relationship status Married/living together N % Married/separated N % Unmarried N % Divorced N % Widowed N % Living in partnership Yes N % No N % Education  8 years N % 9–11 years N %  12 years N % School student N % Employment status Education/training N % Working N %

Totala

Menb

Womenc

49.35 17.99 14–91

49.29 18.00 14–91

49.41 17.99 14–90

461 18.4

222 19.0

239 17.8

343 13.7

158 13.5

185 13.8

396 15.8

177 15.1

219 16.4

499 19.9

227 19.4

272 20.3

415 16.5

200 17.1

215 16.1

394 15.7

185 15.8

209 15.6

1,253 50.0

633 54.1

620 46.3

36 1.4

15 1.3

21 1.6

644 25.7

340 29.1

304 22.7

296 11.8

110 9.4

186 13.9

279 11.1

71 6.1

208 15.5

1,460 58.2

737 63.0

616 46.0

1,048 41.8

432 37.0

723 54.0

977 39.0

452 38.7

525 39.2

1,039 41.4

448 38.3

591 44.1

427 17.0

240 20.5

187 14.0

65 2.6

29 2.5

36 2.7

200 8.0

98 8.4

102 7.6

1,293 51.6

648 55.5

645 48.2

(Continued on next page)

THE CORE SELF-EVALUATION SCALE: PSYCHOMETRIC PROPERTIES TABLE 1.—Sociodemographic characteristics of the study population. (Continued)

Unemployed/working < 15 hr per week N % Housewife/man N % Retired N %

Totala

Menb

Womenc

159 6.3

59 5.0

100 7.5

130 5.2

11 0.9

119 8.9

726 28.9

353 30.2

373 27.8

N D 2,508. bN D 1,169. cN D 1,339.

a

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Body Mass Index. The Body Mass Index (BMI) of the participants was calculated by taking the self-reported weight in kilograms, divided by the squared height in meters. Data Analyses The factorial structure of the CSES was tested using confirmatory factor analysis (CFA), calculated with AMOS 20. All models were tested using covariance matrices, and each model was estimated with the maximum likelihood method approach. All models were compared to each other on the basis of the following model fit indexes: the minimum discrepancy divided by its degrees of freedom (CMIN/DF), the comparative fit index (CFI), standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), the Tucker–Lewis Index (TLI), and the Bayesian Information Criterion (BIC). For a good model fit, the ratio CMIN/DF should be as small as possible (Schermelleh-Engel, Moosbrugger, & M€ uller, 2003); values of TLI and CFI close to .95 or higher are indicative of a good or at least acceptable (> .90) model fit. Furthermore, RMSEA should be .08, and SRMR should be .05 or smaller. The BIC is a descriptive indicator of poor fit and allows comparisons between two models; the model with the lower BIC should be preferred (Schermelleh-Engel et al., 2003). As the aim of this study was to examine the factorial structure of the original 12-item version of the CSES, other possibilities to continue with the proposed unidimensionality (e.g., deleting items) were not pursued. Additional analyses were conducted to test the invariance of the model across gender and age using multigroup CFA. After testing the factorial structure in each subgroup, measurement invariance was tested in three steps using first the configural model (no constraints), followed by a metric invariant model (with unstandardized item loadings constrained to be equal across groups), and a scalar invariant model (with unstandardized item loadings and unstandardized item intercepts

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simultaneously constrained to be equal across groups). Based on the hierarchy of these nested and increasingly restrictive models, the models were then compared to each other. Because the x2 statistic has often been criticized for its sensitivity to sample size, we focused mainly on the differences DCFI and DRMSEA. Values smaller than .01 indicate the invariance of the models (Cheung & Rensvold, 2002). To avoid the potential problem of selecting a marker variable that is possibly not invariant, the variance of each latent variable was fixed to 1.0 (and the mean was fixed to 0.0) for scaling purposes (Little, Slegers, & Card, 2006). Remaining statistical analyses were conducted with IBM SPSS version 20.

RESULTS Factorial Structure of the CSES Several factor models were tested with CFAs. Fit indexes are given in Table 2. With the intention to replicate the results of previous studies, the following four models were tested. The first model (one-factor model) is a unidimensional model of the CSES with no error terms allowed to correlate. In the second model (one-factor model with correlated error terms), six pairs of error terms were allowed to correlate, according to the results of Judge et al. (2003) and Stumpp et al. (2010). This model is based on the assumption that the CSES reflects a higher order latent trait that is unidimensional. The same procedure as described earlier was used for the two four-factor models, with all four latent variables correlating with each other. Thus, the factor models with correlated error terms resulted in models with six fewer degrees of freedom compared to the models without correlated error terms. The rationale for testing these models is to test whether the empirical data of the CSES are better reflected by the underlying constructs on which the construction of this scale is based. In sum, none of these tested models showed a good model fit, but some differences emerged. The four-factor model fitted the data better than the one-factor model, and of course, introducing error correlations raises the model fit indexes in general. Due to the unsatisfactory results of the confirmatory analyses mentioned earlier, an exploratory factor analysis (EFA) was employed after a random split of the study sample. Both subsamples did not differ significantly with regard to gender and age. A principal component analysis with varimax rotation was conducted with Subsample 1 (n D 1,270). Results indicated a two-factor solution using the Kaiser Guttman criterion with eigenvalues of 5.35 and 1.62, accounting for 45% and 13% of the variance,

TABLE 2.—Summary of fit indexes of different factor models. Model One-factor model One-factor model (with correlated error terms) Four-factor model Four-factor model (with correlated error terms) Two-factor model

x 2 (df)

CMIN/DF

CFI

SRMR

RMSEA [CI]

TLI

BIC

2,655.200 (54) 1,301.976 (48) 1,424.343 (48) 1,198.609 (42) 341.482 (53)

49.170 27.125 29.674 28.538 6.443

.780 .894 .884 .902 .947

.090 .077 .074 .068 .041

.139 (.134, .143) .102 (.097, .107) .107 (.102, .112) .105 (.100, .110) .066 (.060, .073)

.731 .854 .840 .846 .934

2,843.054 1,536.793 1,659.160 1,480.390 519.513

Note. df D degrees of freedom; CMIN/DF D minimum discrepancy, divided by its degrees of freedom; CFI D comparative fit index; SRMR D standardized root mean square residual; RMSEA [CI] D root mean square error of approximation [confidence interval]; TLI D Tucker–Lewis Index; BIC D Bayesian Information Criterion.

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ZENGER ET AL.

respectively. Factor loadings ranged between .67 and .79 on the first factor (including Items 1, 3, 5, 7, 9, and 11) and between .67 and .77 on the second factor (including Items 2, 4, 6, 8, 10, and 12). Additionally, Velicer’s minimum average partial (MAP) test (Velicer, 1976) and Horn’s parallel analysis (PA; Horn, 1965) were employed to determine the number of factors in the CSES. The MAP test is based on the averaged partial correlations of the variables under study after extracting the effect of the factors successively (in order to their eigenvalue). PA focuses on extracting eigenvalues from random data sets that have the same number of variables and cases compared to the original raw data. Only those factors should be retained in the real data whose eigenvalues are greater than those of the random data (O’Connor, 2000). The results of both analyses supported the assumption of a two-factor structure of the CSES (Table 3). Subsequently, the fifth theoretical model that was tested using Subsample 2 (n D 1,238) and is based on the results of the EFA and on the general theory of approach and avoidance temperament (Ferris et al., 2011). A CFA was performed, resulting in a two-factor model with both latent variables correlated with each other and without correlated error terms. In this model, the positively worded items (Items 1, 3, 5, 7, 9, 11) loaded onto one factor, and the negatively worded (Items 2, 4, 6, 8, 10, 12) loaded onto the other factor. This model showed the best fit indexes of all tested models, indicating a good or at least acceptable model fit (with the exception of CFI, which is very close to the value of .95). Thus, empirical data support the assumption of a bidimensional factor structure of the CSES, with two latent factors that correlate (r D –.61). Standardized factor loadings of the latent variables on the related items varied between .59 and .74 for positive CSE and between .61 and .73 for negative CSE (all ps < .001). Furthermore, the invariance of the two-factor model was tested for the whole sample (N D 2,508) across gender and six age groups. Unstandardized factor loadings and intercepts are shown in Table 4 and results of the measurement invariances tests are shown in Table 5. As the indexes of DCFI and DRMSEA indicate, this model can be assumed to be scalar invariant across males TABLE 3.—Results of minimum average partial test and parallel analysis. MAP test Factors 0 1 2 3 4 5 6 7 8 9 10 11 12

Average squared partial correlations .1658 .0390 .0241 .0350 .0509 .0711 .0973 .1346 .1921 .2842 .5084 1

PA Eigenvalues Raw data 5.344855 1.615685 .808705 .622728 .575344 .520184 .493963 .464942 .423040 .406468 .392412 .331675

and females. Regarding the multigroup analyses with several age groups, metric invariance could be confirmed but, due to DCFI D .017, scalar invariance could not be confirmed completely across all age groups. Following the procedure described in Gregorich (2006), the constraint of equal intercepts was freed for Item 1 and Item 3, and the model was reestimated for partial scalar invariance. As shown in Table 5, partial invariance across all age groups could be confirmed. The Dx2 statistic indicated significant differences in most cases of the invariance tests, but due to its sensitiveness to sample size we focused on differences in values for RMSEA and CFI (Schermelleh-Engel et al., 2003). Additionally, because of the general trend that participants  60 years had lower intercepts in the factor loadings of the positive CSE, the full scalar invariance model was tested again only for those subjects < 60 years. According to DCFI (.008) and DRMSEA (.001), scalar invariance could be confirmed for participants younger than 60 years.

Reliability and Correlation of the Subscales Internal consistency coefficients (Cronbach a) were .85 for the positive CSE subscale, .84 for the negative CSE, and .88 for the CSES total score. The Pearson correlation coefficient between positive and negative CSE subscales was r D –.53 in the total sample, r D –.54 in males, and r D –.50 in females. The correlations of the subscales to the total score in the whole sample were r D .86 for the positive CSE and r D .89 for the negative CSE. Validity of the CSES Scores As indicators of convergent validity of the CSES subscale scores and total score, Pearson correlation coefficients with other health-related variables as well as the cumulative duration of experienced unemployment were calculated. Results are presented in Table 6. In general, the total CSES score has the strongest relationship to measures of health status, anxiety, depression, HRQOL, and pain. Furthermore, the negative CSE showed the same or higher associations with negatively worded constructs (e.g., anxiety and depression) compared to the positive CSE, which had significantly higher correlations to VAS and the duration of unemployment. The correlations of the total score and the negative CSE with the BMI are, despite their significance, close to zero.

Random dataa 1.200947 1.148686 1.112190 1.080248 1.052851 1.027060 1.003701 .980371 .956738 .934232 .905785 .876898

Note. MAP D Velicer’s minimum average partial test; PA D parallel analysis. a Eigenvalues corresponding to the 95th percentile of the distribution of random data eigenvalues, which are based on 1,000 random data sets.

Influence of Sociodemographic Variables Several t tests (for gender and partnership status) and analyses of variance (for six age groups and three different educational levels; see Table 1) were conducted to test for mean differences in the subscales and total score of the CSES. Mean differences according to several sociodemographic variables were relatively small in magnitude but statistically significant with two exceptions: There were no significant age differences across groups for negative CSE, and there were no significant differences on positive CSE for partnership status (yes, no). Beside this, males had higher values in positive CSE (d D .20), lower values in negative CSE (d D .17), and thus, higher total scores than females (d D .21). Participants living

THE CORE SELF-EVALUATION SCALE: PSYCHOMETRIC PROPERTIES

315

TABLE 4.—Unstandardized factor loadings and item intercepts tested for measurement invariance (standard error in parentheses). Gender

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Item no.

Men

Unstandardized factor loadings 1 .65 (.03) 2 .77 (.03) 3 .68 (.02) 4 .74 (.03) 5 .61 (.02) 6 .65 (.03) 7 .68 (.03) 8 .60 (.02) 9 .66 (.03) 10 .70 (.03) 11 .71 (.03) 12 .84 (.03) Unstandardized item intercepts 1 3.65 (.03) 2 2.01 (.03) 3 3.81 (.03) 4 1.94 (.03) 5 3.96 (.03) 6 1.86 (.03) 7 4.03 (.03) 8 1.59 (.03) 9 3.80 (.03) 10 1.91 (.03) 11 4.00 (.03) 12 2.11 (.03)

Age groups Women

 29

30–39

40–49

50–59

60–69

 70

.56 (.03) .73 (.03) .60 (.02) .75 (.03) .59 (.02) .69 (.03) .67 (.02) .64 (.02) .60 (.03) .64 (.03) .70 (.02) .78 (.03)

.57 (.04) .86 (.05) .62 (.04) .82 (.05) .59 (.04) .73 (.05) .69 (.04) .67 (.04) .64 (.05) .71 (.05) .72 (.04) .85 (.05)

.63 (.04) .79 (.06) .64 (.04) .78 (.05) .47 (.04) .74 (.05) .59 (.04) .59 (.04) .64 (.05) .67 (.05) .66 (.05) .77 (.05)

.64 (.05) .79 (.05) .60 (.04) .67 (.05) .60 (.04) .66 (.05) .63 (.04) .64 (.04) .57 (.05) .69 (.05) .64 (.04) .83 (.05)

.63 (.04) .78 (.05) .62 (.04) .82 (.04) .59 (.03) .66 (.04) .71 (.04) .66 (.04) .72 (.04) .75 (.05) .75 (.04) .84 (.05)

.53 (.05) .61 (.05) .62 (.04) .58 (.05) .62 (.04) .63 (.05) .65 (.04) .59 (.04) .64 (.04) .56 (.05) .65 (.04) .79 (.05)

.56 (.05) .66 (.05) .69 (.04) .78 (.05) .66 (.04) .60 (.05) .69 (.05) .60 (.04) .51 (.05) .56 (.05) .76 (.05) .74 (.05)

3.50 (.03) 2.23 (.03) 3.66 (.02) 2.14 (.03) 3.80 (.02) 1.91 (.03) 3.93 (.03) 1.77 (.03) 3.74 (.03) 1.97 (.03) 3.83 (.03) 2.22 (.03)

3.79 (.04) 2.11 (.06) 3.88 (.04) 2.12 (.05) 3.94 (.04) 1.98 (.05) 4.02 (.04) 1.73 (.04) 3.77 (.05) 2.01 (.05) 3.93 (.04) 2.07 (.05)

3.74 (.05) 2.06 (.06) 3.88 (.05) 2.00 (.06) 4.06 (.04) 1.87 (.06) 4.11 (.05) 1.56 (.04) 3.86 (.05) 1.90 (.06) 4.04 (.05) 2.02 (.06)

3.62 (.05) 2.17 (.06) 3.77 (.04) 2.09 (.05) 3.95 (.04) 1.97 (.05) 4.03 (.04) 1.72 (.05) 3.79 (.05) 2.04 (.06) 3.95 (.05) 2.19 (.06)

3.45 (.04) 2.14 (.05) 3.75 (.04) 2.04 (.05) 3.90 (.04) 1.81 (.05) 3.94 (.04) 1.68 (.04) 3.81 (.05) 1.97 (.05) 3.95 (.04) 2.19 (.05)

3.51 (.05) 2.00 (.05) 3.64 (.04) 1.99 (.05) 3.79 (.04) 1.84 (.05) 4.01 (.04) 1.68 (.04) 3.75 (.05) 1.80 (.05) 3.89 (.04) 2.16 (.05)

3.34 (.05) 2.27 (.06) 3.43 (.05) 2.05 (.05) 3.61 (.05) 1.88 (.05) 3.77 (.05) 1.74 (.05) 3.63 (.05) 1.91 (.05) 3.69 (.05) 2.35 (.06)

with a partner had lower negative CSE (d D .16) and higher total scores (d D .13). Regarding the influence of age, mainly the oldest age group had lower positive CSE (d D .44) and total scores (d D .26) compared to the youngest age group. Concerning the influence of education, particularly the subgroup with  8 years of education had the lowest mean scores in positive CSE (d D .55, compared to the subgroup with  12 years of education) and total score (d D .42) as well as the highest negative CSE score (d D .21).

Normative values, stratified by gender and age, and percentile rank scores are available from the corresponding author on request.

DISCUSSION The aim of this study was to investigate psychometric properties of the CSES derived from a representative sample of the general population. With regard to the dimensionality, the

TABLE 5.—Tests for invariance across gender and age groups. N Gender Men Women Multigroup analysis Configural model Metric model Scalar model Age  29 years 30–39 years 40–49 years 50–59 years 60–69 years  70 years Multigroup analysis Configural model Metric model Scalar model Partial scalar model

1,169 1,339

x 2 (df)

Dp

427.447 (53) 384.217 (53) 811.670 (106) 831.834 (118) 900.702 (130)

461 343 396 499 415 394

Dx 2

20.164 68.868

.064 < .001

187.062 (53) 144.123 (53) 202.223 (53) 210.605 (53) 127.654 (53) 200.395 (53) 1,072.072 (318) 1,176.218 (378) 1,430.589 (438) 1,341.060 (418)

104.146 254.370 164.841

< .001 < .001 < .001

CMIN/DF

CFI

8.065 7.249

.936 .944

7.657 7.049 6.928

.940 .939 .934

3.529 2.719 3.816 3.974 2.409 3.781

.943 .948 .920 .941 .955 .906

3.371 3.112 3.266 3.133

.937 .933 .916 .923

DCFI

RMSEA

DRMSEA

.078 .068 .001 .005

.052 .049 .049

.003 .80). This is in line with the results of earlier studies (Judge et al., 2003; Judge et al., 2004; Stumpp et al., 2010) and gives further support for the CSES’s psychometric quality. Moreover, the CSES scores were shown to have substantial correlations with other psychological constructs, pain, self-reported health status, and the time of experienced unemployment, indicating the construct validity of the scores of this questionnaire with regard to self-reported data. When compared with each other, the negative core self-evaluation subscale showed stronger relationships to measures of anxiety, depression, and the positive core self-evaluation scale had stronger correlations with the self-reported health status and times of unemployment, but in most cases the overall mean score showed the strongest associations. Prospective studies should address aspects of discriminant validity of the CSES scores, as this was not within the scope of this study. Despite the strenghts of this study with regard to the size and the heterogeneity of the sample, some limitations need to be mentioned. Even if the response rate of this study (56.5%) was comparable to those of other representative surveys of the German general population, 43.5% of individuals addressed were nonresponders. Due to its cross-sectional design, causal inferences concerning the influence of core self-evaluations are not possible. Additionally, taking the criticism of Johnson, Rosen, and Levy (2008) and Chang et al. (2012) into account, the relatively new construct of core self-evaluation as a higher order latent personality trait needs to be further studied and defined, in particular the underlying mechanisms that lead to a higher or lower core self-evaluation in individuals. In summary, the psychometric properties of the CSES were found to be very good, and CFAs lead to the identification of a

THE CORE SELF-EVALUATION SCALE: PSYCHOMETRIC PROPERTIES bidimensional structure of the questionnaire with two correlated latent factors. The CSES is well suited for research and screening purposes and is associated with other health-related variables such as anxiety, depression, quality of life, and pain. Thus, it is a useful tool not only in the field of personnel psychology, but also for clinical and health psychology.

ACKNOWLEDGMENT We are grateful to Eva-Maria Gonser for her support of this article.

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REFERENCES Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., . . . de Haes, J. C. (1993). The European Organisation for Research and Treatment of Cancer QLQ–C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85, 365–376. doi:10.1093/jnci/85.5.365 Albrecht, A. G., Paulus, F. M., Dilchert, S., Deller, J., & Ones, D. S. (2013). Construct- and criterion-related validity of the German Core Self-Evaluations Scale. Journal of Personnel Psychology, 12, 85–91. doi:10.1027/ 1866-5888/a000070 Antonovsky, A. (1993). The structure and properties of the Sense of Coherence Scale. Social Science & Medicine, 36, 725–733. doi:10.1016/02779536(93)90033-Z Baumgartner, H., & Steenkamp, J.-B. E. M. (1998). Multi-group latent variable models for varying number of items and factors with cross-national and longitudinal applications. Marketing Letters, 9, 21–35. Bono, J. E., & Judge, T. A. (2003). Core self-evaluations: A review of the trait and its role in job satisfaction and job performance. European Journal of Personality, 17, S5–S18. doi:10.1002/per.481 Brooks, R., Rabin, R., & de Charro, F. (2003). The measurement and valuation of health status using EQ-5D: A European perspective. Dordrecht, The Netherlands: Kluwer Academic. Brunborg, G. S. (2008). Core self-evaluations: A predictor variable for job stress. European Psychologist, 13, 96–105. doi:10.1027/1016-9040.13.2.96 Byrne, B. M., Shavelson, R. J., & Muthen, B. (1989). Testing for equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466. Chang, C. H., Lance Ferris, D., Johnson, R. E., Rosen, C. C., & Tan, J. A. (2012). Core self-evaluations: A review and evaluation of the literature. Journal of Management, 38, 81–128. doi:10.1177/0149206311419661 Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. DESTATIS. (2013). Population statistics. Federal Statistical Office. Retrieved from https://www.destatis.de/DE/ZahlenFakten/GesellschaftStaat/Bevoel kerung/Bevoelkerungsstand/Tabellen/AltersgruppenFamilienstand.html Di Fabio, A., & Busoni, L. (2009). Proprieta psicometriche della versione italiana della Core Self-Evaluation Scale (CSES) con studenti di scuola secondaria [Psychometric properties of the Italian version of the Core SelfEvaluation Scale (CSES) with high school students]. Counseling Giornale Italiano di Ricerca e Applicazioni, 2, 73–83. Di Fabio, A., Palazzeschi, L., & Bar-On, R. (2012). The role of personality traits, core self-evaluation, and emotional intelligence in career decisionmaking difficulties. Journal of Employment Counseling, 49, 118–129. doi:10.1002/j.2161-1920.2012.00012.x Elliot, A. J., & Trash, T. M. (2002). Approach-avoidance motivation in personality: Approach and avoidance temperaments and goals. Journal of Personality and Social Psychology, 82, 804–818. doi:10.1037//00223514.82.5.804 Fernandez-Rıos, L., & Novo, M. (2012). Positive psychology: Zeitgeist (or spirit of the times) or ignorance (or disinformation) of history? International Journal of Clinical and Health Psychology, 12, 333–344.

317

Ferris, D. L., Rosen, C. R., Johnson, R. E., Brown, D. J., Risavy, S. D., & Heller, D. (2011). Approach or avoidance (or both?): Integrating core selfevaluations within an approach/avoidance framework. Personnel Psychology, 64, 137–161. doi:10.1111/j.1744-6570.2010.01204.x Gable, S. L., Reis, H. T., & Elliot, A. J. (2003). Evidence for bivariate systems: An empirical test of appetition and aversion across domains. Journal of Research in Personality, 37, 349–372. doi:10.1016/S0092-6566(02) 00580-9 Gardner, D. G., & Pierce, J. L. (2010). The core self-evaluation scale: Further construct validation evidence. Educational and Psychological Measurement, 70, 291–304. doi:10.1177/0013164409344505 Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44, 78– 94. doi:10.1097/01.mlr.0000245454.12228.8f Grisslich, P., Proske, A., & K€orndle, A. (2012). Beyond work and life: What role does time for oneself play in work-life balance? Zeitschrift f€ ur Gesundheitspsychologie, 20, 166–177. doi:10.1026/0943-8149/a000076 Heilmann, T., & Jonas, K. (2010). Validation of a German language core selfevaluation scale. Social Behavior and Personality, 38, 209–226. doi:10.2224/sbp.2010.38.2.209 Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 1280–1300. doi:10.1037/0003-066X.52.12.1280 Hilbert, A., Braehler, E., Haeuser, W., & Zenger, M. (2014). Weight bias internalization, core self-evaluation, and health in overweight and obese persons. Obesity, 22, 79–85. doi:10.1002/oby.20561 Hirschi, A., & Herrmann, A. (2012). Vocational identity achievement as a mediator of presence of calling and life satisfaction. Journal of Career Assessment, 20, 309–321. doi:10.1177/1069072711436158 Holt, D. T., & Jung, H. H. (2008). Development of a Korean version of a Core Self-Evaluation Scale. Psychological Reports, 103, 415–425. doi:10.2466/ PR0.103.2.415-425 Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185. Jerusalem, M. (1993). Personal resources, environmental constraints, and adaptational processes: The predictive power of a theoretical stress model. Personality and Individual Differences, 14, 15–24. doi:10.1016/0191-8869 (93)90170-8 Johnson, R. E., Rosen, C. C., & Levy, P. E. (2008). Getting to the core of core self-evaluation: A review and recommendations. Journal of Organizational Behavior, 29, 391–413. doi:10.1002/job.514 Jovanovic, V., & Gavrilov-Jerkovic, V. (2013). Dimensionality and validity of the Serbian version of the Life Orientation Test–Revised in a sample of youths. Journal of Happiness Studies, 14, 771–782. doi:10.1007/s10902012-9354-2 Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2002). Are measures of self-esteem, neuroticism, locus of control, and generalized self-efficacy indicators of a common core construct? Journal of Personality and Social Psychology, 83, 693–710. doi:10.1037//0022-3514.83.3.693 Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2003). The core selfevaluation scale: development of a measure. Personnel Psychology, 56, 303–331. doi:10.1111/j.1744-6570.2003.tb00152.x Judge, T. A., Locke, E. A., & Durham, C. C. (1997). The dispositional causes of job satisfaction: A core evaluations approach. Research in Organizational Behavior, 19, 151–188. Judge, T. A., Van Vianen, A. E. M., & De Pater, I. E. (2004). Emotional stability, core self-evaluations, and job outcomes: A review of the evidence and agenda for future research. Human Performance, 17, 325–346. doi:10.1207/ s15327043hup1703_4 Karatepe, O. M. (2011). Core self-evaluations, exhaustion, and job outcomes: A study of frontline hotel employees in Iran. Tourism and Hospitality Research, 11, 248–257. doi:10.1177/1467358411418898 Koumoundourou, G. A., Kounenou, K., & Siavara, E. (2012). Core self-evaluations, career decision self-efficacy, and vocational identity among Greek adolescents. Journal of Career Development, 39, 269–286. doi:10.1177/ 0894845310397361

Downloaded by [ECU Libraries] at 03:39 23 April 2015

318 Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ–9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16, 606–613. doi:10.1046/j.1525-1497.2001.016009606.x Kroenke, K., Spitzer, R. L., Williams, J. B. W., Monahan, P. O., & L€owe, B. (2007). Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146, 317–325. doi:10.7326/0003-4819-146-5-200703060-00002 Kr€ oninger-Jungaberle, H., & Grevenstein, D. (2013). Development of salutogenetic factors in mental health: Antonovsky’s Sense of Coherence and Bandura’s self-efficacy related to Derogatis’ Symptom Checklist (SCL–90– R). Health and Quality of Life Outcomes, 11, 80. doi:10.1186/1477-752511-80 Levenson, H. (1981). Differentiating between internality, powerful others, and chance. In H. M. Lefcourt (Ed.), Research with the locus of control construct (Vol. 1, pp. 15–63). New York, NY: Academic. Lindwall, M., Barkoukis, V., Grano, C., Lucidi, F., Raudsepp, L., Liukkonen, J., & Thøgersen-Ntoumani, C. (2012). Method effects: The problem with negatively versus positively keyed items. Journal of Personality Assessment, 94, 196–204. doi:10.1080/00223891.2011.645936 Little, T. D., Slegers, D. W., & Card, N. A. (2006). A non-arbitrary method of identifying and scaling latent variables in SEM and MACS models. Structural Equation Modeling, 13, 59–72. doi:10.1207/ s15328007sem1301_3 Lopez, S. J., Snyder, C. R., & Rasmussen, H. N. (2003). Striking a vital balance: Developing a complementary focus on human weakness and strength through positive psychological assessment. In S. J. Lopez & C. R. Snyder (Eds.), Positive psychological assessment: A handbook of models and measures (pp. 3–20). Washington, DC: American Psychological Association. Luria, G., & Torjman, A. (2009). Resources and coping with stressful events. Journal of Organizational Behavior, 30, 685–707. doi:10.1002/job.551 Marsh, H. W., Scalas, L. F., & Nagengast, B. (2010). Longitudinal tests of competing factor structures for the Rosenberg Self-Esteem Scale: Traits, ephemeral artefacts, and stable response styles. Psychological Assessment, 22, 366–381. doi:10.1037/a0019225 O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32, 396–402. doi:10.3758/ BF03200807 Piccolo, R. F., Judge, T. A., Takahashi, K., Watanabe, N., & Locke, E. A. (2005). Core self- evaluations in Japan: Relative effects on job satisfaction, life satisfaction, and happiness. Journal of Organizational Behavior, 26, 965–984. doi:10.1002/job.358 Quilty, L. C., Oakman, J. M., & Risko, E. (2006). Correlates of the Rosenberg Self-Esteem Scale method effects. Structural Equation Modeling, 13, 99– 117. doi:10.1207/s15328007sem1301_5

ZENGER ET AL. Rey, L., Extremera, N., & Duran, A. (2012). Core self-evaluations, meta-mood experience, and happiness: Tests of direct and moderating effects. Personality and Individual Differences, 53, 207–212. doi:10.1016/j.paid.2012.03.009 Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Scheier, M. F., & Carver, C. S. (1985). Optimism, coping, and health— Assessment and implications of generalized outcome expectancies. Health Psychology, 4, 219–247. Schermelleh-Engel, K., Moosbrugger, H., & M€uller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8, 23–74. Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35–37). Windsor, England: NFER-Nelson. Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55, 5–14. doi:10.1037/0003066X.55.1.5 Simonton, D. K., & Baumeister, R. F. (2005). Positive psychology at the summit. Review of General Psychology, 9, 99–102. doi:10.1037/10892680.9.2.99 Steinmetz, H., Schmidt, P., Tina-Booh, A., Wieczorek, S., & Schwartz, S. H. (2009). Testing measurement invariance using multigroup CFA: Differences between educational groups in human values measurement. Quality & Quantity, 43, 599–616. doi:10.1007/s11135-007-9143-x Stumpp, T., H€ulsheger, U. R., Muck, P. M., & Maier, G. W. (2009). Expanding the link between core self-evaluations and affective job attitudes. European Journal of Work and Organizational Psychology, 18, 148–166. doi:10.1080/13594320802281110 Stumpp, T., Muck, P. M., H€ulsheger, U. R., Judge, T. A., & Maier, G. W. (2010). Core self-evaluations in Germany: Validation of a German measure and its relationships with career success. Applied Psychology, 59, 674–700. doi:10.1111/j.1464-0597.2010.00422.x Tsaousis, I., Nikolaou, I., Serdaris, N., & Judge, T. A. (2007). Do the core selfevaluations moderate the relationship between subjective well-being and physical and psychological health? Personality and Individual Differences, 42, 1441–1452. doi:10.1016/j.paid.2006.10.025 Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327. Wagnild, G. M., & Young, H. M. (1993). Development and psychometric evaluation of the Resilience Scale. Journal of Nursing Measurement, 1, 165–178. Yagil, D., Luria, G., & Gal, I. (2008). Stressors and resources in customer service roles. Exploring the relationship between core self-evaluations and burnout. International Journal of Service Industry Management, 19, 575– 595. doi:10.1108/09564230810903479