Country of Origin as a Stereotype: The Effects of Product Knowledge on Product Evaluation and Purchase Intention
The University of Texas at Austin Theoretical Constructs: Independent and Dependent Variables Experimental Design, Stimulus Development, and Sampling
Recent development of Internet significantly affects business markets, consumer attitudes, and behaviors. It is true that consumers around the world are facing a great number of choices for product and services from foreign countries. It must be a great chance for product experts to select various kinds of products confidently because they are able to compare and evaluate products. However, it will be more difficult for product novices to evaluate and choose products. With the development of Internet, country-of-origin (COO) studies have been revisited by many scholars (Brodowsky, Tan and Meilich 2004; Chao, Wührer, and Werani 2005). Also, there has been extensive research on how product knowledge affects the perception of COO. Generally, previous studies show that product experts tend to judge product quality based on the attributes function of products because they have extensive knowledge so that they are able to take advantage of their existing knowledge in their memory. As opposed to, product novices who have shallow depth of product knowledge tend to rely on peripheral cues, such as brand and COO that are basically unrelated to the performance of the product. The purpose of this research is to identify the effects of COO and product knowledge on product evaluation and purchase intention in online situations. This research will contribute by identifying the structural relationship between these variables in an online situation. Also, this study will encompass both objective and subjective knowledge dimensions so that it will enable us to understand the whole essence of the product knowledge concept.Therefore, this study will provide refined and solid explanation about the effects of COO and product knowledge on product evaluation and purchase intention in an online situation. COO can be defined as the image that consumers associate with "the picture, the reputation, the stereotypes that businesses and consumers attach to products of a specific country"(Nagashima 1970). In the past, COO was a pre-determined product characteristic. It was true that the COO was clearly showed up on a label reflecting the country from which a product had been imported. Most marketing studies have studied on assessing how consumers use country information as a cue for product quality (Brodowsky, Tan and Meilich 2004). Also, most of the previous research of COO demonstrated that the consumers’ perception of products from developing countries are unfairly evaluated because of stereotypical judgment of consumers against these developing countries. However, the emergence of global markets has complicated the COO effects. Now, it became more challenging for consumers to judge the origin of product because there are so many factors, such as design of product, manufacturing of product, and shipping locations that affect COO. Researchers have studied that COO effects has become elusive as more and more images that each country has are involved. For example, a SONY laptop can be designed in Japan, but manufactured and assembled in a different country. Previous studies have shown that consumers use product-country images as a cognitive shortcut when evaluating products, especially when other information is scarce. COO has a significant impact on product evaluations when consumers are less motivated to process available information, for example in a low involvement situation (Verlegh, Steenkamp, and Meulenberg 2005). Elaboration Likelihood Model (ELM) holds that the influence of product-country images depends on the level of consumers’ involvement with the ads (Petty and Cacioppo 1986). ELM has two types of information process modes; peripheral and central processing modes. Peripheral processing mode refers to low-effort processing where consumers do not have motivation to form a relevant attitude toward product evaluation and purchase intention. As opposed to, systematic or central processing mode elaborates, entails effortful scrutinizing of information, and devotes more cognitive efforts to the processing of product information that is presented in the form of advertising claims. Consumers are susceptible to rely on COO in a low involvement situation where they process information through peripheral routes. Therefore, consumers in the low involvement situation are more likely to rely on COO cues as a stereotypical cue than those in the high involvement. As the level of involvement increases, it is expected that consumers are less likely to be influenced by COO, but focus on elaborating product relevant information provided in the ads. Thus, the COO effects on product evaluation and purchase intention are different depending on individual consumers’ involvement level (Lee, Yun, and Lee 2005). However, the effect of COO is quite controversial because it is even much complicated to generalize simply based on the level of involvement. It was found that COO may influence product judgments even when consumers do not intended to use COO for their product judgments (Liu and Johnson 2005). Country specific stereotype can be spontaneously activated by the mere presence of COO cues in the environment, and they may influence product evaluations and purchase intention even when consumers, especially product experts, do not want their judgment to be affected by COO information. COO may affect product evaluation and purchase intention even when specific product information exists and consumers do not intend to use COO as judgment criteria (Liu and Johnson 2005). It is asserted that country specific images are automatically associated when consumers are exposed to COO information. In other words, country stereotypes are activated spontaneously when consumers are exposed to COO cues without conscious efforts to use them. Country stereotype seems to be unavoidable and it can be inhibited only when consumers access attribute based information through a controlled cognitive process. Given the previous research results, COO is one of the important factors that affect consumers’ product evaluation and purchase intention. Also, even though consumers tend to judge product quality based on attribute information rather than COO cues, it is possible that they might be automatically affected by COO to the extent that how much they can control the effect of COO consciously. Therefore, two hypotheses are suggested. H1: Consumers with favorable perception of COO will have positive product evaluation. H2: Consumers with favorable perception of COO will have positive purchase intention. The concept of product knowledge has been studied extensively in various fields of social sciences. Knowing a person or an object means increased knowledge structure, thereby, affecting consumer information processing activities in several ways (Alba and Hutchinson 1987). Product knowledge can be conceptually defined as overall product knowledge that includes information about functional attributes of product and about brand differences on attributes (Biswas and Sherrell 1993). Consumer knowledge about products has been studied under various labels, such as experience, expertise, and familiarity. Alba and Hutchinson (1987) suggest that consumer knowledge has two different components: familiarity and expertise. Familiarity is defined as the number of product-related experiences accumulated by consumers, and expertise is the ability to perform product-related tasks successfully. In most product knowledge studies, product familiarity has been used very frequently as a surrogate indicator because this construct includes both an objective knowledge (amount of knowledge possessed) and a subjective knowledge (self-reports of product knowledge) in the operationalization (Alba and Hutchinson 1987). In terms of measurement of product knowledge, objective knowledge is measured by some testing procedures with the supervision of impartial third party, whereas subjective knowledge can be measured based on self-evaluation. Even though both measures have their own validity, there are subtle differences between these two dimensions (Park and Lessig 1981). True knowledge of product can be measured by objective knowledge measures. On the other hand, subjective measures are more relevant to define consumer strategies and heuristics, because subjective measures are based on what consumers think they know. It is interesting to notice that although two measures generally correlate (Cordell 1997), their sources of knowledge retrieval are quite different because objective knowledge heavily resort on the stored information in memory, whereas subjective knowledge relies more on product-related experience (Park et al., 1994). Rudell (1979) found that higher levels of objective knowledge were related to greater use of newly acquired information, whereas subjective knowledge was positively related to dependence on previous knowledge. Brucks (1985) also found out the relationship between these two dimensions of product knowledge in his research. It was found that objective knowledge is positively correlated to the number of product attributes examined. Conversely, subjective knowledge is negatively related to amount of search. This is because subjective knowledge is based on self-judgment so that consumers who are confident on their subjective knowledge are less likely to search information. Park et al. (1994) also indirectly supports this notion that subjective knowledge is more influential in product evaluations, because product experience cue that drive subjective knowledge tends to more salient than product information cues. Although familiarity is expected to contribute to improve ability to use a product, it is neither a necessary nor sufficient condition for product expertise (Cordell 1997). Product expertise, as an another dimension of product knowledge can be defined as the ability to perform product-related tasks. Consumer expertise can be dichotomized into two different ends of continuum. One end is the repetition-based expertise, typically a physical form of expertise. The other end of continuum is knowledge-based expertise that can be improved primarily based on exploration and learning process. Even though there are differences between product familiarity and expertise, it is true that the term of product expertise has been used interchangeably with familiarity and experience when referring product knowledge (Rao and Monroe 1988). There have been extensive studies regarding product knowledge in relation to COO. COO information salience varies according to the information-processing strategies used by consumers in product evaluation. Consumers who have low product knowledge are more likely to use COO as indicators of quality to a great extend than experts, due to their inability to analyze intrinsic cues, such as physical product attributes (Rao and Monroe 1989; Maheswaran 1994). In other words, novices’ expectation of purchasing intention will be positively influenced by COO when COO is favorable. Also, novices’ expectation of purchasing intention will be negatively affected by COO, when COO is unfavorable (Biswas and Sherrell 1993; Chiou, 2003; Chai, Wuher, and Werani 2005). As opposed to consumers who have high product knowledge are able to perform product-related tasks successfully and have extensive prior knowledge about product types, usage, and purchase information. Additionally, product experts are more likely to rely on attribute-based information rather than stereotypical information (Chiou, 2003; Chai, Wuher, and Werani 2005). Thus, it has been empirically supported that product experts are less likely to be affected by COO cue when they need to evaluate product quality. Given the arguments above, it is anticipated that product experts who have high objective and subjective knowledge are less likely to rely on COO cues when they need to evaluate product quality and purchase intention because COO is not attribute related information. As opposed to, product novices who have low objective and subjective knowledge are more likely to evaluate product quality and purchase intention based on COO cues because they are not only unable to process attribute relevant information, but also do not have motivation to process information. Thus, COO effect will be different depending on the levels of objective and subjective knowledge. Therefore, four hypotheses are suggested. H3-1: Consumers with high objective knowledge are less likely to be affected by COO cues on the product evaluation. H3-2: Consumers with high subjective knowledge are less likely to be affected by COO cues on the product evaluation. H4-1: Consumers with high objective knowledge are less likely to be affected by COO cues on the purchase intention. H4-2: Consumers with high subjective knowledge are less likely to be affected by COO cues on the purchase intention. Purchase intention can be conceptually defined as "and individual’s conscious plan to make an effort to purchase a brand"(Spears and Singh 2004). According to Fishbein and Ajzen’s (1975), attitudes affect behavior through behavioral intention. In this study, it is assumed that consumers who have certain predisposition toward a product, their attitude will affect product purchase intention. Therefore, one hypothesis is suggested. H5: Consumers with favorable product evaluation are more likely to have positive purchase intention.
Theoretical Constructs: Independent and Dependent Variables One of independent variable is COO. Nagashima (1970) conceptually defined the COO as the image that consumers associate with " the picture, the reputation, the stereotypes that businesses and consumers attach to products of a specific country."The COO cues used in this study were operationalized as ‘Manufactured and shipped from Japan’ and ‘Manufactured and shipped from South Korea.’ The perception of each country was measured with three items, ‘the country that made this laptop is likely to make high quality laptop’, ‘The country that made this laptop is likely to be technologically superior’, and ‘The country that made this laptop have a good reputation.’ Items used in the study of Maheswaran (1994) were slightly modified. This construct was measured with using 7 point Likert scales (1: Strongly Disagree ~ 7: Strongly Agree). Another independent variable is product knowledge that consists of objective knowledge and subjective knowledge. According to Biswas and Sherrell (1993), product knowledge can be conceptually defined as information about functional attributes of product and about brand differences on attributes. As one of subdimensions of product knowledge, Objective knowledge can be defined as the amount of knowledge that consumers possessed and subjective knowledge also can be conceptualized as self-reported product knowledge (Alba and Hutchinson 1987). Given the complexity of manipulating and standardizing product knowledge of laptop, subjects’ existing product knowledge was measured. In terms of measuring objective knowledge, 13 items regarding the specific attribute information about laptop computer were asked with multiple-choice format. Simultaneously, five items regarding subjective knowledge were asked to create subjective knowledge index using 7 point Likert scales. Subjective knowledge items are as follows: ‘I know pretty much about laptop’, ‘I do not feel very knowledgeable about laptop’, ‘Among my circle of friends, I am one of the experts on laptop’, ‘Compared to most other people, I know less about laptop’, and ‘When it comes to laptop, I really don’t know a lot.’ Subsequently, these indices were averaged out to create product knowledge index. Objective knowledge and subjective knowledge items were borrowed from the study of Byung Kwan Lee (2004). Meanwhile, product evaluation and purchase intention were used as dependent variables. Product evaluation can be defined as "consumers’ judgment and choices among alternatives based on marketer-provided cues and on other sources of information about product characteristics"(Cordell 1997). Product evaluation is measured with five items, such as ‘Bad/Good’, ‘Unfavorable/ Favorable’, ‘High quality/ Low quality’, ‘Dislikable/ Likable’, and ‘Not at all useful/ Very useful’ (Maheswaran 1994; Lee 2005). Also, purchase intention can be conceptualized as "individual’s conscious plan to make an effort to purchase a brand"(Spears and Singh 2004). This concept was measured with three items, such as ‘Never buy it/Definitely buy it’, ‘Definitely do not intend to buy/ Definitely intend to buy’, and ‘Very low purchase interest/ Very high purchase interest’ (Spears and Singh 2004). Both product evaluation and purchase intention items were measured with 7 point semantic differential scales. Experimental Design, Stimulus Development, and Sampling Undergraduate students (n= 64) were randomly assigned to view one of two experimental online ads. The study used a 2 (South Korea vs. Japan) × 2 (Low product knowledge vs. High product knowledge) full-factorial, between subjects design. Two different online ads were devised to manipulate the different levels of COO. The format of the stimuli ads was standardized except for the manipulated variables of interest. Each ad contains fictitious laptop product name (PASOCON) at the center of the page with a large picture of the same laptop. Also considering product evaluation and purchase intention in an online situation, stimuli ads simulate a real online environment by using Amazon.com logo and menu bar. Online stimuli ads were created and subjects were asked to directly respond to questionnaires after carefully observing them. Laptop product was selected because characteristics of technology-based products are not usually affected by specific cultural factors. Second, the purchasing rate of laptop is increasing ever before. In 2005 the worldwide number of mobile PCs-in-use are projected to reach nearly 230 million units -up from 31 million mobile PCs ten years ago. (Computer Industry Almanac 2005). Third, the variation of knowledge is relatively wider than other consumer products given the diverse attributes of laptop. Regarding technical data and product details, 12 attributes related information based on existing laptop ad were used; see Figure 1.
Figure 1. Stimulus Ad Sample
In terms of country selection, Japan and South Korea were selected as COO because COO effect should be associated with a country image, thereby, existing countries were selected for this research. Also, consumers’ perception of these two countries, as manufacturers of laptop, is significantly different (Lee, Yun, and Lee 2005). Thus, Japan is used for high image country condition, whereas South Korea was used for low image country condition. Considering young and educated consumers are more likely to prefer online buying, the sample population for this study is university undergraduate students who major advertising at UT at Austin. Simple random sampling and convenience sampling were used among those who registered for fall semester of 2005. The data for this research was collected via survey website from the 4th to the 11th of November, 2005 that consists of Japan and South Korea webpages. These websites begin with an informed consent form, advertising stimuli, and main questionnaire (https://webspace.utexas.edu/jkl379/consent%20form.htm?uniq=-uufr3u). To induce their participation, extra credit was given to students who complete the entire survey. It is expected that the product experience for laptop affect product evaluation and purchase intention. Thus, product experience was measure with an item, "How much have you used laptop computer in the past year?"Product experience, as a covariate, significantly affects purchase intention, F (1, 56) = 10.32, p < .00, but there was no significant effect on product evaluation, F (1, 56) = 2.03, p = .16. However, product experience is conceptually regarded as one of important factors that affect product evaluation and purchase intention. Thus, product experience was used as a covariate throughout all subsequent analysis.
Independent T-test was used to assess the manipulation of each condition. Participants in the Japan condition (m = 4.8) reported significantly higher country image than those in the South Korea condition (m = 4.0), t (60) = -2.7, p < .00. Consistent with our manipulations, participants correctly perceived all conditions. Twelve items except objective knowledge items were put into factor analysis to reconfirm the validity of product evaluation, purchase intention, country image, and subjective knowledge items; see Table 1. Principal component method and direct oblimin rotation were used to find out relatedness and direction between factors. Additionally, a few items were deleted because they were loaded across factors. Four factors were identified and named as ‘product evaluation’, ‘country image’, ‘subjective knowledge’, and ‘purchase intention.’ Three items of product evaluation (a = .91) and country image (a = .90) turned out to be reliable respectively. Also, three items of subjective knowledge (a = .81) and purchase intention were reliable respectively (a = .89). Subjective knowledge has a significant correlation with product evaluation (r = .63, p < .01) and purchase intention (r = .42, p < .01). Also, country image has significant correlations with product evaluation (r = .30, p < .05) and purchase intention (r = .42, p < .01). It was also found that product evaluation is significantly correlated with purchase intention (r = .76, p < .00). Interestingly, objective knowledge shows high correlation only with subjective knowledge (r = .28, p < .05).
Table 1. Factor Analysis
COO and objective and subjective knowledge were used as independent variables. Then, median was used to split low and high knowledge level condition for both objective and subjective dimensions. In the meantime, product evaluation and purchase intention were used as dependent variables. Items of each factor were averaged out to create indices and these indices are used to test five hypotheses. ANCOVA test examined the effects of COO and product knowledge on product evaluation and purchase intention. Additionally, the mediating role of product evaluation is identified using Baron and Kenny’s mediating test methods (1986) and path analysis. Hypothesis 1 Hypothesis 1 predicts that consumers with favorable perception of COO will have positive product evaluation; see Table 2. As expected, there is a significant simple main effect of COO on product evaluation. Subjects in the Japan condition (m= 5.74) more favorably evaluated the laptop than those in the South Korea condition (m= 4.05), F (1, 35) = 23.61, p < .00. Given the significant main effect of COO, hypothesis 1 is accepted. As consumers have favorable perception of COO, they are more likely to evaluate the product positively.
Table 2. ANCOVA for Hypotheses Testing
Hypothesis 2 Hypothesis 2 anticipates that consumers with favorable perception of COO will have positive purchase intention; see Table 3. As expected, there is a significant simple main effect of COO on purchase intention. Subjects in the Japan condition (m = 4.09) show higher purchase intention than those in the South Korea condition (m = 2.33), F (1, 35) = 18.52, p < .00. Given the significant main effect of COO on purchase intention, hypothesis 2 is accepted. As consumers have favorable perception of COO, they are more likely to buy the product.
Table 3. ANCOVA for Hypotheses
Testing
Hypothesis 3 Hypothesis 3-1 and 3-2 propose that consumers with high objective and subjective knowledge are less likely to be affected by COO cues on the product evaluation respectively. It was found that objective and subjective knowledge have marginally significant interaction effects with COO on the product evaluation, F (1, 35) = 2.06, p < .16, F (1, 35) = 1.83, p < .19; see Table 2, Figure 2. Thus, H3-1 and H3-2 are marginally accepted. There is no significant interaction effect of objective knowledge within the South Korea condition, t (23) = .29, p = .77. However, interaction results are driven by the simple main effect of objective knowledge within the Japan condition, where the low objective knowledge (m = 5.99) leads to higher product evaluation than the high objective knowledge (m = 5.50), t (25) = -1.97, p < .05. There are significant simple main effects of COO within low objective knowledge, where the Japan condition (m = 5.99) leads to significantly higher product evaluation than the South Korea condition (m = 3.80), t (24) = -4.26, p < .00. Also, within the high objective knowledge, the Japan condition (m = 5.50) shows significantly higher product evaluation than the South Korea condition (m = 4.29), t (24) = -5.52, p < .00. This results indicates that consumers who have low objective knowledge are more likely to positively evaluate Japanese laptop than South Korean laptop because their product evaluation is based on peripheral cues, such as COO. As a result, it is supported that consumers who have high objective product knowledge are less likely to be affected by COO cues on the product evaluation. In terms of subjective knowledge, interaction results are driven by the simple main effect of subjective knowledge within the South Korea condition, where high subjective knowledge (m = 4.81) leads to higher product evaluation than low subjective knowledge (m = 3.28), t (25) = -2.30, p < .03. Also, there is a significant interaction effect of subjective knowledge within the Japan condition, where high subjective knowledge (m = 6.05) shows higher product evaluation than the South Korea condition (m = 5.44), t (26) = -2.43, p < .02. There are significant main effects of COO within both low and high subjective knowledge conditions. Within the low subjective knowledge, Japan condition (m = 5.44) shows significantly higher product evaluation than South Korea condition (m = 3.28), t (28) = -3.59, p < .00. Also, within the high subjective knowledge, Japan condition (m = 6.05) shows significantly higher product evaluation than South Korea condition (m = 4.81), t (23) = -4.19, p < .00. This results supports the idea that consumers who have high subjective knowledge are more rely on COO cues in terms of product evaluation, as opposed to the results of objective knowledge interaction effect, where consumers with high product knowledge are less likely to rely on COO cues. Detailed explanation will be discussed in the next chapter.
Hypothesis 4 Hypothesis 4-1 and 4-2 propose that consumers with high objective and subjective knowledge are less likely to be affected by COO cues on the purchase intention respectively. It is found that there is no significant interaction effects between high objective and subjective knowledge and COO respectively, F (1, 35) = .08, p = .78, F (1, 35) = .01, p = .91; see Table 3. Thus, H4-1 and H4-2 are rejected.
Hypothesis 5 Hypothesis 5 proposes that consumers with favorable product evaluation are more likely to have positive purchase intention. High product evaluation condition (m = 4.09) shows greater purchase intention than low product evaluation condition (m = 2.14), F (1, 55) = 29.91, p < .00. This result supports the idea that positive product evaluation is significantly related to purchase intention. Thus, hypothesis 5 is accepted.
Additional Analysis: Mediating Role of Product Evaluation One of our research purposes is to identify the mediating effects of product evaluation between COO and purchase intention, as well as product knowledge and purchase intention. Product knowledge index, as a measured variable, is created by averaging out standardized Z score of objective and subjective knowledge due to their different measurement scale. To demonstrate mediation of product evaluation, four relationships must hold (Baron and Kenny 1986). First, COO must have a significant positive effect on product evaluation. It is found that COO has a significant effect on product evaluation, b = .30, t (60) = 4.68, p < .00, R² = .09; see Table 4. Next, COO must have a significant effect on purchase intention. We find that there is a significant positive effect on purchase intention, b = .42, t (60) = 1.28, p < .00, R² = .18 Third, it must be established that product evaluation significantly affects purchase intention. The estimated regression model shows that there is a significant positive effect of product evaluation on purchase intention, b = .76, t (60) = 8.94, p < .00, R² = .57. Finally, when the COO and product evaluation are both included in the regression model, the formerly significant effect of COO becomes insignificant or reduced. It is shown that the effect of COO is significant, but reduced, b = .21, t (59) = 2.53, p < .01, R² = .21, while the effect of product evaluation on purchase intention remains significant, b = .69, t (59) = 8.15, p < .00, R² = .61. This result means that COO not only has a direct effect on purchase intention, but also has an indirect effect through product evaluation. Thus, it is concluded that product evaluation partially mediates the effect of COO on purchase intention. It is also found that product evaluation fully mediates the effect of product knowledge on purchase intention. There are significant effects of product knowledge on product evaluation, b = .46, t (60) = 3.98, p < .00, R² = .21 and purchase intention, b = .29, t (60) = 2.35, p < .02, R² = .08. When product knowledge and product evaluation are both included in the regression model, formerly significant effect of product knowledge on purchase intention becomes insignificant, b = -.07, t (59) = -.73, p = .47, R² = .58, while the effect of product evaluation on purchase intention remains significant, b = .78, t (59) = 8.26, p < .00, R² = .58. This result indicates that product evaluation fully mediates the effect of product knowledge on purchase intention.
Table 4. Mediating Effects of Product
Evaluation To further examine the proposed mediation relationship among COO, product knowledge, product evaluation, and purchase intention, as well as to understand full pictures of the underlying process, a path analysis using AMOS 4 was performed. The proposed model testified by the regression analysis was reexamined. Also, proposed model was compared to the hypothetical model in which COO and product knowledge have causal effects on both product evaluation and purchase intention; see Figure 3, 4. Path coefficient and fit indices of proposed model showed much better fit. The path coefficient in Figure 3 showed that the direct relationship from product knowledge to purchase intention was not significant. Thus, it was found that there was no direct effect of product knowledge on purchase intention. Meanwhile, COO effect flowed through product evaluation and purchase intention. The direct path from COO to purchase intention remained significant, which means that COO has a direct effect on purchase intention, as well as indirect effects through product evaluation. Taken together, the results support the mediating role of product evaluation.
Figure 3. Hypothetical Model
Figure 4. Proposed Model
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