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The indicator sets measuring GCA and GPA seemed to include correlated sub-clusters of indicators. The average correlation of an item with its prespecified underlying factor (item-factor correlation) was 0.555. Many items possessed very low correlations with the factors they were supposed to measure. In fact, two of the indicators loaded more highly on factors which they were not selected to measure, than on those they were selected for. The values of the standard score coefficient alpha for the sets of measures of GCA, GPA, and SPA were quite high (GCA: 0.829, GPA: 0.847, SPA: 0.763) given the observed degree of misspecification. However, this may partly be the result of the large number of indicators in each set since the formula used for the computation of coefficient alpha is influenced by the number of indicators that are included. Through a close examination of indicators of poor fit and through an iterative process of progressive respecification and reexamination of results (but without deleting any indicator), the intermediate model (Model 2) was developed. The results of analysis using ITAN indicated a measurement model which satisfied the criteria for unidimensionality quite well.
The item-factor correlations generated by ITAN indicated that every item in the measurement model loaded much more highly on its own factor than on any other factor. The average correlation of an item with its underlying factor is 0.824. Within-construct similarity coefficients were found to be much higher than between-construct coefficients. The values of coefficient alpha of the factors in the adjusted model were 0.872, 0.849, 0.918, 0.735, 0.796, and 0.819 respectively for GCA1, GCA2, GPA1, GPA2, GPA3, and SPA. The goodness-of-fit indices generated by LISREL are presented in Table 2. The indices presented in Table 2 point to a much improved fit, although the chi-square statistic still indicated a lack of fit. While it would have been possible to obtain a better fit of the model through the correlation of error terms, due to theoretical and substantive reasons, such a step was avoided. One of the problems of the chi-square statistic, which is used to test the overall goodness-of-fit of structural equation models, is that it is influenced by sample size. 678), due to a bias in the statistic, it is particularly difficult for a model to be accepted.
To overcome this bias, Bentler and Bonett (1980) have proposed an alternative method involving the analysis of "incremental fit." This method is based on the study of improved fit (represented by lower chi-square values) as a model is respecified. The results of incremental fit analysis are presented in Table 3. The first model (Model 0) is the most restricted model in which all the 40 indicators are specified as observable variables of one unobservable construct. 0.05) indicating that model respecification significantly improves the fit between the specified model and the data. The values of the Non-normed Fit Index and the Normed Fit Index indicates that the final model explains a large portion of the variance unexplained by the first model. This study has to be viewed as an initial attempt at identifying the proper structure for the CO construct. Our results may be product category specific in that the only product category tested in this study was automobiles. Further replications using other product categories and different source and consuming countries should provide definitive evidence regarding country and product images. Baughn, C. Christopher and Attila Yaprak (1991), "Mapping The Country-Of-Origin Literature: Recent Developments and Emerging Research Avenues," forthcoming in Product and Country Images (tentative title), Papadopoulos, N. and N. A. Heslop, eds. Cavusgil, Tamer and John R. Nevin (1981), "State-of-the-Art in International Marketing: An Assessment," in Review of Marketing, Enis B. and K. Roering eds. Gerbing, David W. and John E. Hunter (1988), ITAN: A Statistical Package for Item Analysis with Correlational Data including Multiple Groups Confirmatory Factor Analysis, Portland State University, Portland, OR. Heslop, L. A. , J. Liefeld and M. Wall (1987), "An Experimental Study of the Impact of Country-Of-Origin Information," Paper presented at the Annual Conference of the Administrative Sciences Association of Canada, Toronto, Ontario. Joreskog, Karl G. and Dag Sorbom (1989), LISREL 7: User's Reference Guide, First Edition, Moresville, IN: Scientific Software. Long, J. Scott (1983), Confirmatory Factor Analysis, Beverly Hills, CA: SAGE Publications.
