THE EFFECTS OF ORGANIZATIONAL IDENTIFICATION 1057 maximem erass truc correlation dimerent moor thus.a The second panel of Table 3presents the tests of the correlations onal ide s repored ar of-fit statistic showed that the correlati (0aD=17.96.p01 nable to a 35.95%C[32.39 ing two indices:the O-s i 95%C44.54 f Table 3 present the 003)The whether a statistically signif ficant level of variability exists in 200 =.64.95%C[55.72 o in observed effec The fourth set of resul in Table 3 reports the tests of the g The that the Compared ith the tatistic.the Fis known to he mes were signif ded (Bo aling of 9).However.the F should on 2002 the meta-analytic 95C36. 09 ince (p omes on deling (MASEM:Vi ran One 1995)to tes and in-role performance was ed n.t n and its out mes.we com obiective data .19.95%CI [.03.34D).The relation Lipsey and Wilson (2001). highe wh en-rated n (45.9s Results Relations Between Organizational Identification 103.p1).In additio we also examin and Outcomes performace yarCB-n OCB- that they were ot signficantly differe) vailable correlati The fifth panel of Table 3 shows how the organizational T-LI ding on d nd antly moderate the (02)-209.0 gani7a dence interval did o %C[37,.45.Hou ver.the showed arge ce:the 158713.P)This indicates that the We used MASEM (Viswesvaran &On 1995)to test how naybas mod nizational identification and outcomes. MASEM analysis.we first constructed the matrix of meta-that the average true correlation is different from zero; thus, a maximum of 2.5% are larger than the upper bound of the interval and fewer than 2.5% are smaller than the lower bound. A credi￾bility interval indicates whether the correlations reported are gen￾eralizable to other samples. For example, an 80% credibility in￾terval excluding zero indicates that at least 80% of the correlations reported are different from zero. In addition, we conducted homo￾geneity analysis (Lipsey & Wilson, 2001), which tests whether it is reasonable to assume that all effect sizes are estimating the same population mean, using two indices: the Q-statistic, which indi￾cates the level of variance across study results relative to the sampling error variance (Hedges & Olkin, 1985), and the I-squared (I 2 ) statistic, another indicator of homogeneity statistic (Higgins, Thompson, Deeks, & Altman, 2003). The Q-statistic indicates whether a statistically significant level of variability exists in correlations across studies, by calculating the categorical model results for the between-groups goodness-of-fit statistic (QB) and the within-group goodness-of-fit statistic (QW; Field, 2001; Hedges & Olkin, 1985; Lipsey & Wilson, 2001). The I 2 indicates the ratio of true heterogeneity to total variation in observed effect sizes, which ranges from 0% to 100% with higher values indicat￾ing greater heterogeneity of effect sizes and higher likelihood of moderators. Compared with the Q-statistic, the I 2 is known to be less affected by the scaling of the measures or the number of the studies included (Borenstein, Hedges, Higgins, & Rothstein, 2009). However, the I 2 should also be interpreted with caution because it depends on the size of individual studies (Higgins & Thompson, 2002). Finally, we created a correlation matrix using the meta-analytic correlations and conducted fixed-effect meta-analytic structural equation modeling (MASEM; Viswesvaran & Ones, 1995) to test our hypothesized path models (in Figures 1 and 2). In addition, to test the moderating effect of national culture on the relations between organizational identification and its outcomes, we con￾ducted a random-effects weighted least square (WLS) regression analysis using the meta-regression SPSS syntax developed by Lipsey and Wilson (2001). Results Relations Between Organizational Identification and Outcomes Table 3 presents the analysis of the relations between organiza￾tional identification and its attitudinal/behavioral outcomes using the available correlations. The top panel of Table 3 reports the meta-analysis results using all available independent correlations (k 114; N 36,526). The average corrected correlation between organiza￾tional identification and all attitudinal/behavioral outcomes across all studies was positive ˆ .41), and the 95% confi￾dence interval did not include zero (95% CI [.37, .45]). How￾ever, the corrected correlation showed large variance; the ho￾mogeneity of effect size tests was significant across the analyses (QW 1587.13, p  .01). This indicates that the use of a random effects model is justifiable. It also indicates that moderators may be present for the associations between orga￾nizational identification and outcomes. The second panel of Table 3 presents the tests of the correlations between organizational identification and outcomes by outcome type: attitudinal outcomes and behavioral outcomes. The between￾groups goodness-of-fit statistic QB showed that the correlations between organizational identification and outcomes were signifi￾cantly different across outcome types (QB(1) 17.96, p  .01). Specifically, the results revealed that the size of the correlation between organizational identification and behavioral outcomes ˆ .35, 95% CI [.32, .39]) was smaller than that between organizational identification and attitudinal outcomes ˆ .49, 95% CI [.44, .54]). The third and fourth panels of Table 3 present the organizational identification correlations with detailed outcomes within each out￾come type. The third set of results in Table 3 shows that organi￾zational identification was significantly and positively related to job involvement ˆ .50, 95% CI [.39, .62]), job satisfaction ˆ .45, 95% CI [.40, .50]), and affective organizational commitment ˆ .64, 95% CI [.55, .72]). The fourth set of results in Table 3 reports the tests of the relations between organizational identification and behavioral out￾comes. The QB indicates that the correlations between organiza￾tional identification and behavioral outcomes were significantly different across performance types (QB(1) 19.64, p  .01). Specifically, the correlation between organizational identification and in-role performance was significant and positive ˆ .27, 95% CI [.20, .34]) and was smaller than the correlation between organizational identification and extra-role performance ˆ .42, 95% CI [.36, .48]). We further classified both performance out￾comes based on the rater of performance. The relation between organizational identification and in-role performance was rela￾tively high when in-role performance was self-rated ˆ .33, 95% CI [.23, .38]) and low when in-role performance was rated based on objective data ˆ .19, 95% CI [.03, .34]). The relation between organizational identification and extra-role performance was higher when extra-role performance was measured using self-rated measures ˆ .48, 95% CI [.43, .52]) than when it was measured using other-rated measures ˆ .29, 95% CI [.18, .39]) and the difference between the two was statistically significant (QB(1) 10.38, p  .01). In addition, we also examined whether the relation between organizational identification and extra-role performance differed by target—OCB-I and OCB-O—and the results showed that they were not significantly different (QB(1) 2.02, ns). The fifth panel of Table 3 shows how the organizational identification– outcome correlations change depending on data structure—the study design used to collect the data. The results indicated that data structure did not significantly moderate the relations between organizational identification and its outcomes (QB(2) 2.09, ns). Positioning of Organizational Identification in General Attitude–Behavior Relations We used MASEM (Viswesvaran & Ones, 1995) to test how organizational identification functions in the nomological net￾work of attitudinal and behavioral variables. To conduct the MASEM analysis, we first constructed the matrix of meta￾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. THE EFFECTS OF ORGANIZATIONAL IDENTIFICATION 1057