Country of Origin Effect on Consumer Willingness to ...

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A Procedural Guide to Write a Research Paper

2 Country of Origin Effect on Consumer Willingness to Buy Foreign Product Dr. S.S. Bhakar, Dr. Saloni Mehra, Dr. Yahomandira Kharade, Shikha Priyadarshani, Prerna Bisht & Pratiksha Pathak

ABSTRACT Country of Origin has been understood differently during different phases of development in manufacturing facilities across countries. Initially, all the processes related to manufacturing of products used to be completed in single country and the products were known as ‘made in’. Thus Country of Origin was synonym with ‘made in’. The other factors that affect evaluation of the effect Country of Origin on product evaluation and selection is availability of such products freely in the local market and the customs duties levied in the range that will not make such products out of pocket for average income consumer. Indian consumers have experienced this change increasingly in last two decades. After years of govt. controlled market forces, Indian consumers have access to almost any product manufactured anywhere in the world. Thus it has become imperative to look at country of Origin effect on willingness to buy foreign products. The current study has evaluated the effect of country of origin image on willingness to buy. The two variables of the study evaluating country of origin and willingness to buy were put through confirmatory factor analysis (CFA) and finally the


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path analysis was carried out using structural equation modeling (SEM) approach on AMOS 18. The effect of COO was found to be significant on willingness to buy. Key Words: Country of Origin, Willingness to buy, Country Image, Brand Image, Purchase Intention. Introduction Country of Origin (COO) has been defined as "the place, region, or country to which the brand is perceived to belong by its target customers," (Thakor and Kohli 1996). This definition has expanded the understanding of country of origin to include the perceptions that the brand creates in consumers' minds. This perception may be different from the actual production or design facilities. The perceived location from which a product comes from may create positive and negative images, based on consumers' perceptions of that location (Thakor and Kohli 1996). Roth and Romeo (1992) described Country of Origin as consumer’s overall evaluation of goods manufactured in a particular country, based on their prior perception of that country’s strengths and weaknesses in design, manufacturing and marketing. Huber and Mcann (1992) reported that when customers are not able to evaluate quality of products by any other way COO influences consumers’ product evaluation through the country image of the origin. Similar view is shared by Elliot and Cameron (1984) where they argued that Country-of-origin can be an indicator of quality when it is difficult to assess quality by other objective means. Country of Origin affects evaluation of products manufactured in different countries in three different ways. Country of Origin may provide signal for product quality and performance, thereby creating ‘halo effect’ which in turn effects the beliefs about attributes of products (Erickson, Johansson and Chao, 1984). Secondly, Country of Origin may be evaluated as an attribute of the product similar to other product attributes (Hong and Wyer, 1989). Thirdly, Country of Origin may create ‘stereo typical image’ of the products based on the image of the country of origin, which acts as stimuli in decision making about the product (Huddleston, 1995; Akaria Nagashima, 1970). Kumar and Barker (1987) found COO as an important factor in buying decision and concluded that ‘Country of origin is a relevant variable in the marketing mix’. Roth and Romeo (1992) posited that COO bears a significant influence on consumer perception and decision making. However, there are a number of studies that found that customer perceptions tend to be product specific rather than country specific (Neffenegger, White, and Harmet, 1980; Schooler, 1961).

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Hirschman (1985), described “consumer willingness to buy product links to his/her personal memories of that country of origin and feelings of ‘pride’ associated with the purchasing of the product from certain countries”. Kealy and Turner (1993); Teal and Loonus (2002) describe consumer willingness to buy lies at the heart of consumer buying intentions. They feel that researchers have measured consumers buying intentions for asking questions or bidding games. Chamber, Chamber and Whitchead (1998) examined that “most methods are focused on consumer willingness to pay for particular change in product attributes rather than to buy the services as products themselves”. Mahmud and Mamun (2011) reported that products from developed countries were perceived to be of higher value than the products from developing countries in case of higher value products such as automobiles and refrigerators. The consumers may buy products manufactured in their own country not because they are superior in quality or have higher visibility; it may be because they are proud of their country and would like to buy made in own country even if these products are costlier to the products manufactured outside own country, (Kumar and Barkar, 1987). The image of country of origin was found to have negative cause and effect relationship with purchase intention in case of fridge and the negative relationship became stronger when evaluated with brand image as another independent variable Bhakar et al. (2013), indicating that good country of origin image may not be linked to high purchase intention. Methodology The study was causal in nature and survey method was used to collect data for the study. Three fridge brands with different country of origin (Godrej-India, LG – Korea and Electrolux – USA) were selected to evaluate the effect of country of origin effect on willingness to purchase. The study was carried out at Gwalior city in Madhya Pradesh, India. The since the complete list of customers at Gwalior was not possible to get, judgmental (non probability) sampling technique was used to identify sample elements. The sample included proportionate representation from different demographics such as age, gender, educational qualification, income etc. The sample size for the study was 150 respondents. As the maximum no of items were 12 in willingness to purchase variable the sample size adhered to the thumb rule of at least ten items per item in the scale. The data was collected on two separate self designed, structured questionnaires for COO image and willingness to purchase. The


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questionnaire for COO image contained 11 statements and the questionnaire for willingness to purchase contained 12 statements. The data was collected on a Likert type 5 point scale with 1 indicating minimum agreement with the statement in the questionnaires and 5 indicated maximum agreement with the statement. The data collected using the two questionnaires were tested for reliability using Cronbach’s Alpha coefficient of reliability. Both the questionnaires were found to be highly reliable as the Cronbach’s Alpha coefficient for both was higher than 0.7. Exploratory factor analysis was applied on both the questionnaires to identify underlying factors. The data collected using the two variables were used to test the factor structures using confirmatory factor analysis on AMOS 18. The goodness of fit was tested for both the data sets using CFA and was found to be high. The data from both the questionnaires was tested for construct reliability, discriminant validity and construct validity. The model using COO image along with the factors as independent variable and willingness to buy along with its factors as dependent variable was path analyzed using SEM on AMOS 18. The model was found had good fit indicating appropriate assumption in developing the model structure. The effect of brand names on willingness to buy was evaluated using one way ANOVA. Results Reliability Country of Origin

Internal consistency reliability of ‘willingness to buy’ measure was established through computation of Cronbach’s Alpha using PASW18 software. The results of Cronbach’s Alpha computations are placed below in table 1. Table 1: Showing Reliability of Country of Origin Reliability Statistics Cronbach's Alpha N of Items .803 10

The measure is considered reliable if the Cronbach’s Alpha values are greater than 0.7. As can be seen from the table 1 above the Cronbach’s Alpha for the willing to buy measure was found to be 0.803. Therefore, the measure can be considered to have high internal consistency reliability. Exploratory Factor Analysis – Country of Origin The measure was put through EFA process using principle Component Analysis as the method of extraction and varimax rotation with Kaiser

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Normalization as the method for refining the extracted factors. The data used for EFA was tested for its suitability using KMO and Bartlett’s tests. The results of KMO and Bartlett’s tests are placed in Table 2 below: Table 2: Showing KMO and Bartlett test Results – Country of Origin KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square Df Sig.

.805 372.163 45 .000

Kaiser-Meyer-Olkin Measure of Sampling Adequacy measures whether the sample was large enough to consider data as normally distributed. KMO values greater than 0.5 indicate that the sample was large enough consider the data normally distributed. The KMO value for willingness to buy measure was found to be 0.805 as indicated in the table above. Therefore, the data used for factor analysis of willingness to buy measure was suitable for factor analysis. Bartlett’s test of sphericity tests the hypothesis that the item-to-item correlation matrix is an identity matrix. The hypothesis was tested using Chi-square tests. The hypothesis is rejected as the Chi-square value is significant at .05 level of significance (p=.000). Therefore, the item-to-item correlation matrix is not an identity matrix and thus, the data is suitable for factor analysis. Individual items were also tested for KMO and all the values of KMO for individual items were found to be above 0.657 as read from the diagonal of the Anti-image correlation table as part of EFA results. The KMO values of individual items also need to be greater than .5 for suitability of data for EFA. Table 3: Showing Factor Analysis of ‘Country of Origin measure’ Factor Name Coo1Quality

Eigen Variance Items Converged Factor Value explained Loads 2.914 29,140 2.Country of origin is important for selecting brands .742 I look for made in mark while buying expensive items .725 3.I look at country of Origin to ensure highest quality .701 buy 4. I use country of Origin information for deciding .648 quality for new purchases 6. Country of Origin determines technical .641 sophistication of the product – 2.087 20.847 10. To purchase a product that is acceptable to .822

Coo2 Product Acceptance

my family and my friends, I look for the product’s country of origin


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product’s country of origin 11. I look for country of origin information to .678 choose the best product available in a product class. 5. I refuse to buy a product without knowing .566 its country of origin 9. I consider country of origin to determine .513 the quality of a product 7. While buying a new product the country of .511 origin is the first piece of information that I consider Principle Component Analysis with varimax rotation and Kaiser Normalization converged on two factors after three iterations.

Fig 1: Final Model of Country of Origin Measure

Testing of the Conceptual model: Image Congruence The conceptual model above was tested through goodness of fit tests. The global goodness of fit index Chi-square for the model is 25.242 significant at 0.153. Indicating that Chi-square is not significant and therefore the conceptual model has good fit with the data. In addition to the Chi-square goodness of fit index there are three sets of goodness of fit indices and one set of badness of fit index. All the goodness of fit indices are above 0.9 indicating further that the model very well

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fits to the data. The badness of fit index Root Mean Square error approximation RMSEA should be lower than 0.05. The table-4 below indicates that the RMSEA value is 0.047, showing that the model has good fit. Table 4: Showing CFA results of Country of Origin Measure df p-value Criterion 1<