Confirmatory Factor Analysis of a Country-Of-Origin Scale: Initial Results

ABSTRACT - The globalization of markets since the 80's has enhanced the urgency of research on the impact of country-of-origin image (CO) in cross-national consumer behavior. A rich harvest of such studies has consequently been generated. Prior weaknesses in such studies, such as poor handling of a complex construct, single cue studies and lack of methodological rigor, are being addressed. This study is in the genre of such research where the nature and dimensionality of the CO construct is examined using confirmatory factor analysis. Findings suggest that the theorized structure needs modification.


R. Mohan Pisharodi and Ravi Parameswaran (1992) ,"Confirmatory Factor Analysis of a Country-Of-Origin Scale: Initial Results", in NA - Advances in Consumer Research Volume 19, eds. John F. Sherry, Jr. and Brian Sternthal, Provo, UT : Association for Consumer Research, Pages: 706-714.

Advances in Consumer Research Volume 19, 1992      Pages 706-714


R. Mohan Pisharodi, Oakland University

Ravi Parameswaran, Oakland University


The globalization of markets since the 80's has enhanced the urgency of research on the impact of country-of-origin image (CO) in cross-national consumer behavior. A rich harvest of such studies has consequently been generated. Prior weaknesses in such studies, such as poor handling of a complex construct, single cue studies and lack of methodological rigor, are being addressed. This study is in the genre of such research where the nature and dimensionality of the CO construct is examined using confirmatory factor analysis. Findings suggest that the theorized structure needs modification.


Critical reviews on country-of-origin (CO) studies have identified several major inadequacies which have thus far prevented a definitive identification of the precise nature of the impact of CO in influencing purchase behavior. Among the factors which have been suggested as contributing to this slow progress are included complexity of the construct, the presence of other cues, and methodological issues (Bilkey and Nes 1982; Hong and Wyer 1989; Baughn and Yaprak 1991). Baughn and Yaprak (1991), for example, called for the increased use of multiple cues (such as CO, brand name and price instead of just CO); use of both intrinsic (taste, design, and performance) and extrinsic (price, brand name, warranties, CO) cues; use of "real" products, "typical" users, and "true" characteristic content; in addition to the strengthening of methodological rigor. Hong and Wyer (1989) exhorted their colleagues to include cognitive mechanisms which mediate CO effects on product evaluations, both at the theoretical and empirical levels. Ozsomer and Cavusgil (1991), after a thoroughly exhaustive review of CO effects on product evaluations called for more research in the area. They concluded " although much has been said and done on the relationship between CO and product familiarity, conflicting results call for more research. " A basic question that still needs focusing on is conceptual and methodological. How should the CO construct be operationalized into a methodologically rigorous CO scale? How complex is the construct? In other words, what is its dimensionality?

The focus of this study is a rigorous examination of the nature of the country-of-origin construct and a confirmation of the scales that have been developed to measure it. We do this by subjecting a scale developed to measure country image (Parameswaran and Yaprak 1987) to the confirmatory factor model whose "ability to test specific structures suggested by substantive theory gives it a major advantage over the exploratory factor model (Long 1983)." Thus, it is hoped that we may begin to unravel the complexity of the construct through methodological rigor.


The country-of-origin construct has been a gradually evolving construct which was conceived from the idea that people attached stereotypical "made-in" perceptions to products from specific countries and which influenced purchase and consumption behaviors in multinational markets (Rierson 1967; Nagashima 1970, 1977; Schooler 1971; Anderson and Cunningham 1972; Lillis and Narayana 1974). A single "made-in" indicant of country-of-origin, serving as an extrinsic cue (Thorelli, Lin, and Ye 1989), led to the hypothesis that all factors impinging on the marketing environment had a potential of impacting consumption behavior (Nagashima 1970). A plethora of empirical studies determined that several environmental factors had negligible to minimal effect on product perceptions leading to the current theory on the determinants of country image. The gradual evolution of the country-of-origin image construct and its attendant scales is unlike scale development in marketing and consumer research in the 1980's in that psychometric rigor was not a prime consideration in its evolution (for an example of a recent scale development, see: Shimp and Sharma 1987). Subjecting the CO construct to a rigorous evaluation, the purpose of this paper, is therefore a valuable exercise.

Contemporary Theory Underlying the Country-Of-Origin Construct

As mentioned above, the impact of CO information on consumer purchase behavior has inspired a large body of literature (for excellent reviews, see Cavusgil and Nevin 1981; Bilkey and Nes 1982; and Baughn and Yaprak 1991). Nagashima (1970) has been credited with first defining country image as " the picture, the representation, the stereotype that businessmen and consumers attach to products of a specific country. This image is created by such variables as representative products, national characteristics, economic and political background, history and tradition. " Subsequent empirical researches (refer to summaries in Ozsomer and Cavusgil 1991; and Baughn and Yaprak 1991) have led to our contemporary understanding of the phenomenon. The impact of the CO cue on consumption behavior has been related to producing country characteristics. For example, it has been demonstrated that consumers' willingness to purchase products is related to the economic, political, and cultural characteristics of the product's country-of-origin. Papadopoulos, Heslop and Bamossy (1989) summarized this notion by stating that the perceptions of sourcing countries are impacted by cognition about, and affect and conative orientation toward, that country's peoples. CO effects have also been related to perceptions about the overall product offerings of a particular sourcing country. Papadopoulos, et. al. (1989) noted that a consumer's image of a people with whom he/she is not familiar may well be formed upon the basis of knowledge about that people's capacity for producing quality products in general -- and that perception impacted his/her evaluation of specific products from that country. As an example, they showed a high level of affect toward the Japanese people and specific Japanese products even when few non-Japanese consumers were familiar with Japan and its peoples. Yaprak and Parameswaran (1986) called the former component the general country attribute (GCA) and the latter the general product attribute (GPA). Bearing Bilkey and Nes's (1982) criticism of single cue studies and their drawbacks, Yaprak and Parameswaran (1986) hypothesized that purchase intentions and behavior are impacted by CO effects (GCA and GPA) as well as by specific product attributes (including product-, marketing-, and firm-goodwill related attributes [SPA]).

The relative importance of country and general product image (GCA, GPA) cues in shaping purchase behavior is not entirely clear based on past research. Johansson, Douglas, and Nonaka (1985) claimed that the CO effect, as a result of single cue studies, may be overstated and that its effects may be contaminated by the effect of other cues. Heslop, Liefeld and Wall (1987) stated that the CO effect gained in strength with product complexity, increased risk, and decreased purchase frequency. Bilkey and Nes (1982) reported that a negative CO image was not overcome by a well known brand name.


The Measuring Instrument

An extensive literature search was conducted in the design of the questionnaire that was used in this study. The scales which comprise this questionnaire are modifications of the scales used by Yaprak and collaborators (Yaprak 1978; Yaprak and Parameswaran 1986; Parameswaran and Yaprak 1987). Parts of that scale have been used in a recent psychometric evaluation of the CETSCALE (Netemeyer, Durvasula, and Lichtenstein 1991). A market country's consumers' attitudes towards a source country (in this case, Germany) were measured using twelve statements that are commonly found in the country-of-origin literature. Their attitudes toward the general nature of products from that country were measured using eighteen statements -- these statements again being those that are commonly cited in the CO literature. Their perceptions in regard to specific product related cues were measured using 10 relevant automobile attributes. The specific make of automobile that the survey respondents were evaluating was the German car, the Volkswagen Jetta. The above predictor variables -- GCAs, GPAs, and SPAs -- were measured on a 10-point scale where '1' represented that the statement was 'not at all appropriate' while '10' represented that it was 'most appropriate.' The dependent variable, their likelihood of purchasing a Volkswagen Jetta, was operationalized as a 10 point likelihood scale where '1' represented 'not at all likely' and 10 represented 'extremely likely' to purchase that make of car. A list of indicators is presented in Table 1.

Sampling and Data Collection Procedures

Data were gathered from the adult population of a large midwestern metropolitan area which is highly heterogenous in terms of ethnic composition. These people represented the then current and potential users of automobiles -- both domestic and foreign. In order to adequately capture the ethnic flavor of the metropolitan area, relevant ethnic associations were contacted, membership lists acquired and representative samples selected therefrom. Blank questionnaires were hand delivered to the selected respondents and completed questionnaires were collected within the next two weeks. In order to achieve a high response rate, the president/committee members of these associations were approached and requested to encourage their members to participate. A total of 678 completed and useable questionnaires were returned from the 1025 that were originally placed, a 66% response rate.


A covariance matrix based on the data collected from the 678 respondents was analyzed in order to find out whether the scale developed for the measurement of the CO (GCA, GPA, and SPA) consisted of three sets of unidimensional and reliable measures of these three constructs. In order to assess the unidimensionality of the CO scale used in this research, confirmatory factor analysis (CFA) was employed. CFA has been recognized to be a superior approach for the analysis of unidimensionality vis-a-vis other often-used approaches such as item-total correlations, exploratory factor analysis (EFA), and coefficient alpha (Gerbing and Anderson 1988; Anderson, Gerbing, and Hunter 1987). Unlike EFA, CFA is a hypothesis testing procedure in which an a priori model is tested for goodness-of-fit against a data set (Bagozzi 1983). Thus, it can be used to determine whether the collected data supports the a priori categorization of the indicators into (unidimensional) measures of the three CO constructs.

A second objective of the statistical analysis presented in this paper was to respecify the measurement model (if necessary) through an understanding of the dimensionality of the three concepts of interest. CFA also possesses diagnostic capabilities which assist in the identification of model misspecification (Bagozzi 1983). It can be used in a more exploratory sense by progressively respecifying and relaxing constraints (as dictated by theory) beginning with a more restricted initial model.

The process of analysis was based on the updated paradigm for scale development proposed by Gerbing and Anderson (1988). The analysis consisted of four steps: (1) the specification of the a priori model, (2) the examination of model misspecification, (3) the theory-guided respecification of the a priori model resulting in the adjusted model, and (4) comparison of the a priori model with the adjusted model. The refined measurement model resulting from the analysis is expected to be more useful than current measurement models used for the study of county-of-origin effects.



CFA was applied to the collected data using two complementary approaches: (a) the full information method wherein all the parameters of the measurement model are estimated simultaneously, and (b) a limited information method which uses only the covariances of the variables in any one equation of the model to estimate the parameters in the equation (Anderson and Gerbing 1982). The former approach is more attractive from a theoretical perspective since it explicitly recognizes all the relationships in a measurement model in a single analysis. It also generates more efficient statistics in large samples and provides more indicators of fit. The latter approach estimates the parameters of each factor separately and independently. In the presence of model misspecification this is a strength since poor specification of one factor does not affect the estimation of parameters in another (Anderson and Gerbing 1982).

Maximum likelihood estimation procedures available in the statistical package LISREL 7 (Joreskog and Sorbom 1989) were used for the application of the full information method. The covariances in the input data set were tested against the covariances expected as the result of the a priori specified model. When the parameters of the model are estimated through the method of maximum likelihood (an option in LISREL), the differences between the relationships reflected in the data and those specified by theory are tested using a chi-square statistic. The chi-square statistic tests the overall fit of the model to data, smaller values of the statistic typically representing better fit. The measurement model was also evaluated using other indicators of global goodness-of-fit such as the Adjusted Goodness of Fit Index (AGFI) and Root Mean Square Residual (RMSR) and measures of fit of the internal structure of the model (such as squared multiple correlations, modification indices, and standardized residuals).

Oblique centroid multiple groups factor analysis (MGRP) available in the statistical package ITAN (Gerbing and Hunter 1988) was used for the application of the limited information method. The unidimensionality of the measurement model was assessed through the evaluation of internal and external consistencies (two statistical criteria for unidimensionality) using the procedure recommended by Hunter and Gerbing (1982) and Anderson and Gerbing (1982). The output of MGRP analysis of a correlation matrix was evaluated (a) for internal consistency through the examination of item-factor loadings and the pattern of inter-item correlations, and (b) for external consistency by examining similarity coefficients. Further, measure reliability was assessed by evaluating the values of standard score coefficient alpha computed by ITAN.

The process of model respecification described in this paper was strongly driven by theory. In other words, model respecification based purely on data (and possessing no theoretical rationale) was avoided. Thus, while improvement in fit was sought through respecification, the objective of this process was to develop an improved measurement model which was theoretically defensible and which could be useful for the measurement of CO effects and not to just to develop a perfect-fitting model.

According to Anderson and Gerbing (1988) there are four basic ways to respecify indicators: by relating the indicator to a different factor, by deleting the indicator from the model, by relating the indicator to multiple factors, or by using correlated measurement errors. They recommend the use of the first two ways since "these approaches preserve the potential to have unidimensional measurement, without obscuring the meaning of the estimated underlying constructs." Keeping the objectives of this research in sight, improvement of fit through the linking of indicators to multiple factors or the correlation of error terms was consciously avoided. Although the correlation of error terms would certainly have improved fit, such a procedure would have made the resulting model less elegant from a theoretical perspective without any significant gain in its substantive interpretability (Bagozzi 1983, Gerbing and Anderson 1984). Improvement in fit was achieved in this research through the respecification of factors and through the deletion of poor indicators.

Model 1: The Initial Model

The initial model (Figure 1) consisted of three constructs (unobservable variables): GCA, GPA, and SPA, and 40 indicators (observable variables). GCA was measured using 12 indicators, GPA by 18 indicators, and SPA through 10 indicators. The indicators of goodness-of-fit obtained through the analysis of the model using LISREL 7 indicated a very poor fit (Table 2). The poor fit was also reflected in large standardized residuals and modification indices. Among the within-factor residuals the largest had the value of 17.78, and 44.9 percent exceeded 2.58, the cut-off point for good fit (Bagozzi and Yi 1988). Among the within factor modification indices, the largest had a value of 229.26 and 52.08 percent exceeded the value of 3.84, a cut-off point recommended by Bagozzi and Yi (1988). While the composite reliability indicated by the total coefficient of determination was found to be high (0.999), 22 out of the 40 items possessed squared multiple correlations below 0.5, indicating low individual item reliabilities.

Similarly, the inter-item correlations generated by ITAN indicated the three constructs were being measured using indicators which lacked internal consistency. 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. It is also important to recognize that a high value of coefficient alpha does not reflect unidimensionality (Gerbing and Anderson 1988). In fact, a high alpha in the relative absence of unidimensionality is not an indication of measure reliability since unidimensionality is a prerequisite for reliability.

Model 2: The Intermediate Model

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 indicators of GCA were reclustered into two groups, one consisting of indicators (C1 to C8) which measured general attributes of the country and its people (GCA1) and the other consisting of indicators (C9 to C12) which measured interaction of the country being evaluated with the respondent's country or other countries (GCA2).





Similarly, GPA was reclustered into two groups, one of which (GPA1) included all the indicators (P1, P2, P4, P5, P7, P9, P13, P14) which measured negative product attributes (poor service, poor quality, etc.), and the other (GPA2) included all the indicators (P3, P6, P8, P10, P11, P12, P15, P16, P17, P18) which measured positive attributes. The presence of two such dimensions was very clear from the results of the analysis of Model 1, although the polarity of the responses to negative attributes had been reversed prior to statistical analysis through appropriate data transformation so that higher values reflected superior evaluation. No change was made to the specification of SPA.

The respecification of GCA and GPA in Model 2 resulted in a considerable drop in chi-square and the other indicators of goodness-of-fit (Table 2) vis-a-vis Model 1 indicating a much improved fit of the model to the data, although the chi-square by itself indicated a lack of fit. This finding was also reflected in the standardized residuals and modification indices generated by LISREL and the inter-item correlations and the item-factor loadings computed by ITAN. The average item-factor loading increased to 0.696. The improvement in the measurement model was also reflected in the values of standard score coefficient alpha (GCA1: 0.853, GCA2: 0.803, GPA1:0.934, GPA2: 0.871).

Model 3: The Adjusted Model

An examination of the results of LISREL and ITAN analysis of the intermediate model indicated that many of the indicators of the intermediate model still possessed correlated errors and contributed to the relative lack of unidimensionality of the measures of the five unobservable variables in the intermediate model. Hence, through an iterative process, 16 indicators were dropped from the measurement model. During the iterative process, it was also found that GPA2 needed further subdivision into two dimensions: GPA2 and GPA3, the former consisting of indicators (P6, P8, P12, P17) which reflected distributional and promotional aspects of the marketing mix and the latter consisting of indicators (P11, P16, P18) which reflected product image. The list of indicators selected after assessment of unidimensionality and model respecification are presented with the adjusted measurement model in Figure 2.

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. The total coefficient of determination is 1.000 and all but four of the squared multiple correlations exceed the value of 0.5. Among the within-factor residuals the largest had a value of 6.57 (absolute), and 31.58 percent exceeded a cut off point of 2.58 and among the within factor modification indices, the largest had a value of 30.16 and 34.21 percent exceeded the cut-off point of 3.84. Although a major proportion of the standardized residuals and modification indices possessed values larger than their recommended cut-off points, they were much smaller in magnitude than those generated during the analysis of Model 1.

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. When the sample size is large (as in this research n=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. The drop in chi-square values with each model modification is noticeable and statistically significant (p=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.


Over three decades of pre-occupation with the CO construct and its impact on consumer perceptions have led to a reasonably widely held belief that CO effects are created by a market country's people's cognition (level of economic development, nature of political climate, etc.), affect (toward a country's people) and conation (desired level of interaction with a country's people) regarding the source country. CO effects are also related to perceptions about the overall product offerings of the particular source country. In addition, CO effects are affected by the perceptions of the specific product being evaluated. Scales, such as the one used in this paper, are developed which incorporate three constructs that include the above generalizations. The results of our confirmatory factor analysis indicated that such a structure may need modification. A five factor model produced a significantly better fit which was still substantively in line with the theory of the CO construct. The general country attributes, including cognition, affect and conation are in effect two dimensions -- conation impacting perceptions significantly different from cognition and affect. Similarly, the GPA construct splits into three components--negative attributes behaving differently than positive ones. Positive attributes relating to promotional/distributional image constituted the second GPA dimension and product image the third. This six construct model improved fit substantially more than the original three factor 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.


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R. Mohan Pisharodi, Oakland University
Ravi Parameswaran, Oakland University


NA - Advances in Consumer Research Volume 19 | 1992

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