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Table 3: Correlations (Part II) 4.5 CORRELATION ANALYSIS Corporate Reputation �� One way to proceed would have been to construct an index for each of the categories mentioned in section 4.2, e.g., by estimating a MIMIC model. Since MIMIC models are linear models, high correlations between variables pose a potential threat. Bravais-Pearson coefficients for all pairs of variables are shown in Table 2 and Table 3, where we integrated not only the 21 independent items, but also six endogenous items, to measure sympathy and competence. We return to them in section 4.6. The variance inflation factors (VIF) given in Table 4 {in parentheses} show values between 1.230 und 2.815 and are far below the common threshold 10 72. But we must consider that correlations between (formative) indicators forming different constructs should be as low as possible. For this reason we applied a principal 72 See Reinartz et al. (2003), p. 18. �� sbr 56 (1/2004) 61
M. Schwaiger Table 4: Results of the Principal Component Analysis component analysis to “bundle” correlated items to possible index constructs. Principal component analysis on the complete data set revealed four factors with an eigenvalue > 1, which explain 63.95% of the original information 73. Factor 1 contains attributes which denote premium supplier, among others quality, service and customer orientation. For reasons of simplicity, we name this factor quality. Factor 2 contains many attributes that are predominant in Fortune and ManagerMagazin rankings. These attributes are strongly tied to the performance 73 Factor structures remain stable, even when using other rotation algorithms. Calculating reliability indicators would require reflective items, so we omitted to state Cronbach’s α. 62 sbr 56 (1/2004)
Page 1 and 2: Schmalenbach Business Review ◆ Vo
Page 3 and 4: M. Schwaiger 2 CLARIFICATION AND IM
Page 5 and 6: M. Schwaiger But setting corporate
Page 7 and 8: M. Schwaiger 3.1 FORTUNE AMAC Since
Page 9 and 10: M. Schwaiger cases around 0.9 and a
Page 11 and 12: M. Schwaiger 3. Financial Performan
Page 13 and 14: M. Schwaiger 4.2 DESK RESEARCH AND
Page 15: M. Schwaiger Table 2: Correlations
Page 19 and 20: M. Schwaiger and average variance e
Page 21 and 22: M. Schwaiger On the other hand, com
Page 23 and 24: M. Schwaiger example, if a company
Page 25 and 26: M. Schwaiger Fryxell, Gerald E./Wan

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