coopetition as a small business strategy.pdf

# Analysis and results assessing the coopetition scale

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ANALYSIS AND RESULTS Assessing the Coopetition Scale The proposed coopet1tlon scale was evaluated using a three-step approach. First, exploratory factor analysis (EF A) was employed for classifying the items to reflect the underlying structure of the construct. Second, confirmatory factor analysis (CF A) was employed to determine the acceptability of the data. Finally, the reliability of the scales measuring each of the identified factors or dimensions was examined. Exploratory factor analysis is a useful preliminary technique for scale construction, and is used to purify items. Before the analysis, Kaiser Meyer Olkin (KMO) and Bartlett's Test of Sphericity were used to determine if the correlation matrix is appropriate for factoring. A KMO of .92, exceeding the .60 level suggested by Gursoy and Gavcar (2003), and a Bartlett's Test of Sphericity with p<.001 together indicate that factor reduction by exploratory factor analysis can be applied to the data (see Table 2). Principal axis factoring, rather than components analysis, was used as a variable reduction method. Principal axis analysis seeks the least number of factors which can account for the common variance of a set of variables. Principal component analysis should not be used if a researcher wishes to obtain parameters reflecting latent constructs or factors (Widaman, 1993). Items with a> .50 extraction value were eliminated (see Table 3). The final results produced an 11- item scale. In the new scale, mutual benefit is represented by three items (MB 1, MB2, MB7), trust by three items (Tl, T2, T4) and commitment by five items (COM!, COM2, COM3, COM4, COM5).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Journal of Small Business Strategy Vol. 18, No. 1Spring/Summer2007 Table l - Results for KMO and Bartlett's Test 'ser-Me er-Olkin Measure of Sam . 915 6538.557 190 Si. .000 Table 3 - Results for Factor Reduction Items Extraction MBl . 825 MB2 . 767 MB3 .233 MB4 .417 MB5 . 322 MB6 .402 MB7 .686 MB8 .393 Tl .696 T2 .694 Confinnatory factor analysis using LISREL 8.2 (Joreskog & Sorbom, 1998) was performed to assess the unidimensionality of the proposed dimensions. The overall chi- square statistic was (41)=316.08, p=.000. However, the analysis was conducted with a large sample, and chi-square values will be higher as sample size increases (e.g., see Kelloway, 1998). Widely used fit indices exceeding .90, and .87 when adjusted for degrees of freedom (AGFI), indicate an adequate fit [Goodness-of-Fit Index (GFI) =.92; Comparative Fit Index (CFI) =.96; Normed Fit Index (NFI) =.95; Non-Normed Fit Index (NNFI) =.95]. The Root Mean Square Error of Approximation (RMSEA=.10) was at a level supported by Steiger (1990) and the Root Mean square Residual (RMR=.03) was lower than the recommendedlevelof.05. 47 Items Extraction T3 .346 T4 .712 T5 .279 T6 . 288 COMl .668 COM2 .712 COM3 .675 COM4 .373 COM5 .747 COM6 .675 The results of the CF A are summarized in Table 4. Items loaded on their respective dimensions and all maximum likelihood estimates (MLE' s) were >. 70 and highly significant (p's<.001) (the lowest t-value was 26.50 for COM5). Also in Table 4, composite reliability coefficients for the
• Fall '19
• partner, Firm, Journal of Small Business Strategy

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