A paradox of price–quality and market efficiency: a comparative study of the US and China markets

Intern. J. of Research in Marketing 19 (2002) 349 – 365 www.elsevier.com/locate/ijresmar

A paradox of price–quality and market efficiency: a comparative study of the US and China markets Kevin Zheng Zhou a,*, Chenting Su b, Yeqing Bao c a

School of Business, The University of Hong Kong, Hong Kong, China b University of Victoria, Canada c University of Alabama, Huntsville, AL, USA

Received 28 August 2001; received in revised form 14 January 2002; accepted 4 April 2002

Abstract The price – quality schema rests on an assumption that price is credible information about product quality. However, the credibility of price information varies across different markets. In an inefficient market, consumers would believe in the price – quality relationship to a lesser extent because price information is less credible. Paradoxically, in such a market, sometimes consumers have to rely more on price to infer quality because other product information is less available. With a cross-national perspective, this study investigated the influences of market efficiency and consumer risk aversion on the price – quality schema between the China and the US markets. We found that due to the inefficient market environment, Chinese consumers possess a weaker price – quality schema than American consumers. Chinese consumers are more risk averse than their American counterparts. However, in China, risk-averse consumers are more likely to use price to infer product quality. Implications for global marketing are discussed, and directions for future research are suggested. D 2002 Elsevier Science B.V. All rights reserved. Keywords: China; Price – quality schema; Market efficiency; Risk aversion; Measurement invariance

1. Introduction The importance of the price – quality relationship has kindled an enduring research interest (Dodds, Monroe, & Grewal, 1991; Lichtenstein & Burton, 1989; Lichtenstein, Ridgway, & Netemeyer, 1993; Rao & Monroe, 1989; Teas & Agarwal, 2000; Zeithaml, 1988). It is believed that consumers may assume a positive relationship between price and product quality and rely on this ‘‘price reliance * Corresponding author. Tel.: +852-2859-1011; fax: +852-2858-5614. E-mail address: [email protected] (K.Z. Zhou).

schema’’ (Peterson & Wilson, 1985) or ‘‘price – quality schema’’ (Lichtenstein et al., 1993) as a shortcut to make purchase decisions. This belief rests on an assumption that price is credible information about product quality in a market. However, few studies in the price – quality research have scrutinized this assumption in different national market conditions, especially in an inefficient market. A question of interest is that given the different marketing conditions across countries, is the price –quality schema invariant or robust across markets? This issue has important managerial implications for international business operations because marketing strategies should be adapted based on local market

0167-8116/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 11 6 ( 0 2 ) 0 0 0 9 6 - 4

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conditions, cultural factors, and consumer behaviors (Dawar & Parker, 1994; McGowan & Sternquist, 1998). Research has indicated that the price –quality product strategies can significantly improve firm performance if they match the home-country environments. For example, Brouthers, Werner, and Matulich (2000) find that a superior-value product strategy (high quality/low price) performs better in Japan, a premium product strategy (high quality/high price) works better in the European Union, and an economy product strategy (low quality/low price) is more appropriate in the US market. As the economy globalizes, marketing in big emerging markets is vital to Western multinational companies. However, most former studies have mainly focused on price –quality issues in developed markets (e.g., Brouthers et al., 2000; Dawar & Parker, 1994; Lichtenstein & Burton, 1989; McGowan & Sternquist, 1998). The purpose of this study, therefore, is to extend our understanding of the price –quality schema in an inefficient market. In particular, we examine to what extent consumers in an inefficient market believe in the price – quality relationship and how environmental factors (e.g., market efficiency) and psychological factors (e.g., risk aversion) impact their beliefs in a price – quality relationship. We argue that in an inefficient market such as markets in less-developed countries, consumers would believe in the price –quality relationship to a lesser extent because price information is less credible. Paradoxically, sometimes consumers in such a market have to rely more on price to infer quality because other product information is less available or less reliable. To investigate this paradox, we compare market efficiency and consumer price – quality schema between the US and China (PRC), as these two countries represent two typical different economies (a developed economy vs. a less-developed or transitional economy) (Luo & Peng, 1999). To further uncover the effects of market efficiency on consumers’ price – quality schema, we also investigate the relationships between price –quality schema and risk aversion, one important psychological factor predicting consumer information search behavior, in these two markets. Methodologically, cross-cultural studies have long been criticized as lacking cross-national measurement validity (e.g., Durvasula, Andrews, Lysonski, & Nete-

meyer, 1993; Mullen, 1995; Steenkamp & Baumgartner, 1998). To make any meaningful comparisons across cultures, the applicability of models developed in one nation must be assessed in other countries (Steenkamp & Baumgartner, 1998). For this purpose, the cross-national applicability of construct measures in this study is assessed through multi-model comparison procedures proposed by Durvasula et al. (1993) and Steenkamp and Baumgartner (1998).

2. Price– quality schema Price – quality schema is defined as ‘‘the generalized belief across product categories that the level of the price cue is related positively to the quality level of the product’’ (Lichtenstein et al., 1993, p. 236). It reflects a consumer’s propensity to use price to make a judgment of a product’s overall excellence or superiority (i.e., perceived quality) (Zeithaml, 1988). As such, the price – quality schema focuses on consumer’s belief of the relationship between price and perceived quality but not the objective quality (cf. Lichtenstein & Burton, 1989, p. 429). The price – quality schema plays an important role in consumer decision making. It affects the judgments of perceived quality, influences perceived value and purchase intentions, and determines information search and other aspects of consumer decision-making processes (John, Scott, & Bettman, 1986; Lichtenstein et al., 1993; Peterson & Wilson, 1985; Zeithaml, 1988). As Tellis and Gaeth (1990) note, if a consumer is used to inferring the product quality from the price, he/she actually prefers paying higher prices and behaves as ‘‘price seeking’’. Studies have shown that consumers tend to perceive price as positively related to product quality and some consumers have a higher propensity than others to use price as a general indicator of quality, regardless of the product type (e.g., Lichtenstein & Burton, 1989; Peterson & Wilson, 1985; Rao & Monroe, 1989). In particular, Peterson and Wilson (1985) found that consumers could be classified into groups—‘‘schematics’’ and ‘‘aschematics’’—based on their belief of the price – quality relationship. Schematics generally perceive a stronger relationship between price and quality than aschematics. Lichtenstein and Burton (1989) also confirmed the existence

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of a price –quality schema and further categorized consumers into four segments on the basis of their price – quality perceptions. Researchers have suggested that the price – quality schema forms through consumer learning and generalizing process (Peterson & Wilson, 1985). Consumers develop knowledge about the price –quality relationships for different product categories when abstracting information over various consumption experiences. However, few studies have investigated the factors that underlie the formation of price –quality schema or explain ‘‘why do price – quality schemas differ?’’ (Lichtenstein & Burton, 1989), especially from a cross-national perspective. Recently, there has been some cross-national research on the price – quality schema and price-related issues. For example, Dawar and Parker (1994) found that there are few differences in the use of price to signify quality for a highly homogeneous segment of consumers across Western industrialized countries and Japan. McGowan and Sternquist (1998) compared Japanese and US young consumers in terms of their price – quality schema, prestige sensitivity, and value consciousness. Results suggested that price-inference behaviors may be market-universal for a similar segment of Japanese and US consumers. These studies, however, have exclusively focused on comparing consumer price – quality perceptions across developed markets, where market conditions are comparatively homogeneous and efficient. As the first attempt to study the price – quality schema in a less-developed market, Veeck and Burns (1995) successfully grouped urban Chinese consumers into schematics and aschematics based on their price – quality perceptions. This finding contrasts with that of the US studies, which have documented the existence of three or four distinct schematic groups (e.g., Lichtenstein & Burton, 1989). However, Veeck and Burns focused mainly on classifying Chinese consumers into different groups. Little effort has been paid to the market conditions which affect the credibility of market information and thus the schematic differences between Chinese and American consumers. As a consequence, a set of research issues remain unanswered, that is: why do Chinese consumers differ from American consumers in their beliefs in price – quality relationship? How is the price –quality schema affected in an inefficient market like China? As Veeck

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and Burns note, these issues provide challenges for future research on the price –quality relationship. In the next section, we try to explore these questions by examining how market efficiency affects the formation of a price – quality schema and how consumer risk aversion influences a price –quality schema in different market environments, particularly, China and the US.

3. Research hypotheses 3.1. Market efficiency and price – quality schema A market is efficient if all relevant and ascertainable information is widely available to participants (Gabriel & Marsden, 1990). Ideally, in an efficient market, all the information changes are reflected in price changes (Rosen, 1974). Market efficiency may result from perfect competition and intensive communication and some product market may be more efficient than others. As Chernev and Carpenter (2001, p. 350) note, In the case of personal computers, differences between products are often perceived as minimal, which forces manufacturers to offer comparable packages. For other products, such as wine, for which value is more subjective and consumers are less knowledgeable, much less pressure exists to offer the products at value parity with competitors. In this example, the personal computer market is more efficient than the wine market, as the competition and communication are more intense and ascertainable information is widely available and understandable to consumers. In the marketing literature, studies on market efficiency have mainly focused on the relationship between market efficiency and objective product quality (e.g., Hjorth-Anderson, 1984; Ratchford, Agrawal, Grimm, & Srinivasan, 1996). However, how consumers’ perceptions of market efficiency affect their beliefs, values, and behaviors remains unexplored (Chernev & Carpenter, 2001). Here, we extend the current research by examining how one’s perception of market efficiency impacts the formation of his/her price – quality schema.

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As discussed earlier, a price – quality schema forms through consumer learning and generalizing processes about the price – quality relationship (Peterson & Wilson, 1985). In a highly efficient market, brands offer value parity, so that any additional value offered by a product is equal to its marginal price (Rosen, 1974). Because all the information is available to consumers, they may learn through experience that, in this market, equal-priced products offer comparable values and a higher price indicates a higher quality. Over time, consumers form a general belief of a positive price – quality relationship, i.e., ‘‘you get what you pay for’’. In a less-efficient market, low-quality, high-priced products are able to survive, because the competition is not intense and/or product quality information is not available or difficult to assess (Lichtenstein & Burton, 1989). Consumers may find that some necessary information is not available, the searching cost is too high, or the product information is ambiguous to evaluate (Chernev & Carpenter, 2001). From the experience, consumers could gradually learn the inefficiency of the market and that price is not a good indicator of product quality. This suggests that consumer price – quality schema will be affected by their perceptions of market efficiency. Thus, we hypothesize: H1 . The more consumers perceive the market as efficient, the stronger their price –quality schemas. As an emerging market, the China market is not as efficient as the US market. In the latter, the intense competition makes abundant and comparable goods available to every consumer. American companies also tend to use various sale and promotion campaigns in order to attract consumers. In contrast, market competition in China is relatively low, Chinese consumers are more concerned with the availability and/ or the quality instead of the price of a satisfactory product (Batra, 1997). Further, in the US, detailed product information is available from a number of sources such as Consumer Reports and the Internet. While in China, where the communication level is low, detailed product information is difficult to acquire and reliable information source may not be available to most consumers (Ho, 2001). As in the transition from a planned central economy to a market economy, market regulation and legislation by the government and business self-regu-

lation in China are much less refined compared with those in the US (Batra, 1997). In China, deceptive advertising (exaggerated and fraudulent advertising), trademark violation (counterfeit goods with poor quality but always under the names of popular brands), and unethical business practices (e.g., selling poor-quality products at very high prices) are malpractices that have aroused particular concerns to the public (Ho, 2001; Ho & Sin, 1988). The above discussion suggests that the US market is more efficient than the China market. Therefore, price is a relatively credible indicator of product quality in the US. Over time, American consumers will come to understand the efficient nature of the market. Accordingly, they may rely more on price cue to indicate product quality as a heuristic way to reduce information-searching costs and facilitate decision making. In contrast, the less-intensive competition, low level of communication, and weak regulations lead to a less fair pricing system in China. Some products are overpriced (such as counterfeit products), while others are underpriced (e.g., plagiarism products). Because ascertainable information is unavailable, Chinese consumers will perceive the market as less efficient and thus, tend to doubt the credibility of price in indicating product quality. Therefore, we predict the following. H2 . The China market is perceived less efficient than the US market. H3 . Chinese consumers possess a weaker price – quality schema than American consumers. H1 – H3 investigate the impact of market efficiency on the formation of price – quality schema. In the following hypothesis development, we further assess how market efficiency moderates the relationship between risk aversion and price – quality schema. 3.2. Risk aversion and price – quality schema In cross-cultural settings, risk aversion refers to one’s general tendency to avoid uncertainty (Hofstede, 1980). It reflects ‘‘the extent to which people feel threatened by ambiguous situations and have created beliefs and institutions that try to avoid these’’ (Hofstede & Bond, 1984, p. 419). This concept captures

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the cultural pattern of seeking stability, predictability, and low stress rather than volatility and new experiences. Risk-averse people are inclined to feel threatened by uncertain situations (Hofstede, 1991). Past research suggests that Americans value risk seeking, while Chinese tend to be risk averse (Hofstede, 1980). China is a typical collectivistic society in which people tend to act as a group. To sustain withingroup harmony, people are expected to abide by certain behavioral codes. Risk-taking behavior is regarded as a challenge to the group’s interest and existence and is thereby often discouraged (Triandis, 1995). In contrast, the US is a typical individualistic society in which people tend to view themselves as a unique set of internal attributes such as motivations, traits, and values (Markus & Kitayama, 1991). Individuals are willing to and encouraged to make their own decisions and are responsible for their decisions and behaviors. Exploring uncertainty is regarded as one’s merit in life and thus is fostered (Triandis, 1995). These arguments suggest the Chinese to be more risk averse than the Americans. Since this prediction has been tested in former studies, it is treated here as a replication hypothesis. H4 . Chinese consumers are more risk averse than American consumers. Risk aversion affects consumers’ decision making in various ways (e.g., Rao & Bergen, 1992; Shimp & Bearden, 1982). Risk-averse consumers feel threatened by ambiguous and uncertain purchasing situations. They tend to search for more information regarding quality from information sources such as Consumer Reports before making a decision (Shimp & Bearden, 1982). If such information is easy to get, risk-averse consumers can rely on the information to decrease their perceived risk. If such information is not available or not credible, risk-averse consumers have to rely more on extrinsic cues such as price, brand, or store image to infer product quality (Zeithaml, 1988, p. 9). In the US market, the intense competition makes comparable goods available to every consumer. Product information is abundant due to various sales and promotional campaigns and efficient mass communication. Objective product quality information is also easily assessable from external sources such as Consumer Reports. In addition, due to the extensive

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regulations, American companies are less likely to provide deceptive information (such as deceptive advertising) than Chinese companies. As a result, for American consumers, product information is not only easy to get but credible as well. To reduce their risk perceptions, risk-averse consumers can conduct a thorough research and analysis of product information provided in a marketing communication campaign or other third parties (Grewal, Gotlieb, & Marmorstein, 1994). With intrinsic cues or other objective information, consumers are less likely to use price to infer product quality (Dodds et al., 1991; Zeithaml, 1988). Hence, risk-averse consumers in the US will rely less on price to evaluate a product. H5a . In the US market, the more risk averse the consumers, the weaker their price – quality schema. As a transitional economy, the China market is less efficient and characterized by lack of coherent business regulations and legislation, less-intense competition, and low levels of mass communication (Batra, 1997; Luo & Peng, 1999; Nee, 1992). Deceptive advertising, counterfeit products, and unethical business practices are prevalent in the market (Fan & Xiao, 1998). Chinese consumers have to be very cautious about the information in the ad and have to distinguish counterfeit products by themselves (Ho, 2001). However, due to the low level of mass communication, it is difficult for Chinese consumers to obtain sufficient product information to judge the product quality and other objective information sources such as Consumer Reports are not available to most consumers. When lacking information, especially when credible information is unavailable, risk-averse consumers have to infer product quality from available cues. Extrinsic cues such as price, brand, and store information thus will be used more heavily (Zeithaml, 1988). However, the retailing system is fragmented and the distribution channel is long in China. Western countries usually have 50 –80% of sales occurring through large chains, but such large or national retailing chains are nearly absent in China (Batra, 1997). As Batra (p. 101) notes, ‘‘the retail sector in China now consists of about 9.2 million very small (one- and two-person) retail shops . . .’’ Most stores cannot guarantee the quality of the products it sells. As a result, consumers can hardly depend on store

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information to evaluate a product. Further, trademark violation is prevalent in China and counterfeit products can be seen everywhere (Fan & Xiao, 1998; Ho, 2001). These products, on one hand, cause reputation damages for name, and high-quality brands (Batra, 1997), on the other hand, make consumers rely less on brand to assess quality. Because extrinsic cues such as brand and store image are less dependable, price, as a relatively unambiguous cue, will have a greater effect on product quality judgment (Dodds et al., 1991). In sum, because objective quality information is not available and intrinsic product information is less credible in China, risk-averse consumers have to rely more on extrinsic cues to evaluate a product to reduce perceived risks. Further, they are forced to use price as an indicator of quality, because brand and store information is even less dependable. Hence, we predict, H5b . In the China market, the more risk averse the consumers, the stronger their price – quality schema.

4. Methods Measurement reliability and validity across nations have long been a serious concern for cross-national studies. In particular, the equivalence of samples, constructs, and measurement across countries must be empirically assessed to ensure valid comparisons (Dawar & Parker, 1994; Mullen, 1995; Steenkamp & Baumgartner, 1998). 4.1. Survey design A survey study was conducted to collect data in both the United States and China. Singh (1995) suggested that it is necessary to assess the scale validity before cross-national data collection. Therefore, a pretest was conducted among 40 college students in the US to test the measures for this study. Some items were deleted according to the pretest, and some were modified to enhance clarity. To measure theoretical constructs cross-nationally, translation equivalence must be considered (Mullen, 1995). Following Mullen’s suggestion, an original survey was designed in English, and was then trans-

lated to Chinese by a bilingual speaker. The Chinese version of questionnaire was then back-translated to English by another bilingual person. Discrepancies in the translation were carefully inspected and corrected to ensure equivalence in the questionnaire. The English and Chinese versions of the measurement items are presented in Appendix A. 4.2. Sampling Young consumers are especially studied because: (1) young consumers are recognized as a specialized global market segment for a variety of goods and services (Moschis & Moore, 1979), and (2) in China, compared with the older generations, the young have more appetites for, and consuming experience with, Western products, and are more likely to be the potential consumers for Western companies (Anderson & He, 1998). A matched sample of respondents is required in a cross-national study (Dawar & Parker, 1994). For this reason, college students were recruited as subjects in both China and the US. The samples are not nationally representative, but constitute comparable populations (Durvasula et al., 1993, p. 628) and are an important component of the target population. Altogether, 222 completed questionnaires were obtained, in which 106 from a large state university in the US and 116 from a large state university in China. The demographic characteristics of the sample were similar in these two data sets. For example, all the Chinese subjects were undergraduate students, 49.1% were junior, and aged between 18 and 25 years old with a mean of 20.4. All the American subjects were also undergraduate students, 72.9% were junior, and aged from 19 to 27 with a mean of 21.1. Dawar and Parker (1994) also note that economic factor is another important matching criteria for a matched sample. Thus, financial concern, one key economic variable, was included to check this concern. Given that this study is conducted in a crossnational context, a measure indicating subjective financial concern would be more appropriate than a measure of objective financial status. The measure of financial concern (see Appendix A) is adapted from Mandrik, Fern, and Bao (1999) and results indicate no significant difference (MEANus = 4.506, MEANchina = 4.417, Dv2(1) = 2.684, p>0.10, multi-model

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comparison used, see Section 5 for more methodological details). Thus, we concluded that the samples used in this study are matched and comparable. 4.3. Measures Price – quality schema was measured with four seven-point Likert scale items (see Appendix A) adapted from Lichtenstein et al. (1993). The measure of perceptions of market efficiency was specifically designed for this study. The initial measure included a number of items pertaining to market competition and information availability (cf. Chernev & Carpenter, 2001), such as ‘‘there are a lot of alternative brands available even for a specific brand,’’ and ‘‘I can easily collect plenty of information about the price and quality of a product.’’ Based on the pretest, three items were retained in the final questionnaire. As discussed earlier, in cross-national settings, risk aversion refers to one’s general tendency to avoid uncertainty (Hofstede, 1980). Although Hofstede et al. developed a set of measurements for uncertainty avoidance, their measures are more related to people’s behavior in an organizational context, thus are not directly applicable to the consumer decision-making context. In order to reflect consumers’ general uncertainty avoidance pertaining to purchase, the measurement of risk aversion developed by Raju (1980) was adapted. Based on pretest, three items were used in the final survey. 4.4. Measurement invariance assessment Measurement invariance refers to whether or not the measurement operations yield measures of the same attribute under different cultures or countries (Mullen, 1995). If a measure is not cross-nationally invariant, conclusions based on that scale are ‘‘at best ambiguous and at worst erroneous’’ (Steenkamp & Baumgartner, 1998, p. 78). For example, if we compare price –quality schemas between American and Chinese consumers without assessing the measurement invariance, then we will not be able to tell whether the difference (if any) comes from consumers’ price perceptions or just from their semantic interpretations of the questionnaire.

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To make valid cross-national comparisons, it is necessary to establish configural invariance, metric invariance, and scalar invariance for measurement instruments (Mullen, 1995). First, the measurement should be configurally invariant (i.e., the same pattern of factor loadings across different cultural groups). Second, the measurement should be metrically equivalent for at least two items (i.e., equal loadings for the two items across cultural groups). Third, at least two items should be scalar invariant (i.e., equal intercepts for those two items across cultural groups) (cf., Steenkamp & Baumgartner, 1998). These three steps are accumulative, and the former step is the necessary condition for the latter steps. Based on the multigroup comparison procedures proposed by Steenkamp and Baumgartner (1998), the measurement instrument for each construct is examined, and cross-cultural invariance is assessed using AMOS 4.0 with maximum likelihood as the estimation method (Arbuckle & Wothke, 1999). For illustration purposes, the measurement invariance test for price –quality schema is illustrated in Fig. 1. 4.4.1. Price – quality schema The first step was to test the equality of covariances and means of the four indicators of this measure across two groups: China and the US (see Fig. 1a). The test results were: v2(14) = 58.087, p < 0.001; comparative fit index (CFI) = 0.983, Tucker – Lewis index (TLI) = 0.975; root mean square error of approximation (RMSEA) = 0.120, Akaike information criterion (AIC) = 86.087. The significance of chi-square and the relatively large RMSEA and AIC indicated the lack of invariance of covariance matrices and mean vectors across the two samples. Thus, we turned to the test of the configural invariance, which means that indicators should load on the factor in a similar pattern across countries (see Fig. 1b). This model was the baseline model against which further models could be compared. The results (v2(4) = 9.481, p = 0.050; CFI = 0.971, TLI = 0.912; RMSEA = 0.079, AIC = 41.481) indicated a good fit of the model. All factor loadings were highly significant in both countries ( p < 0.001), and standardized factor loadings ranged between 0.45 and 0.82. Therefore, the measure of

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Fig. 1. An example of measurement invariance test.

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price – quality schema was configurally invariant across the US and China samples. Next, we tested the metric invariance which means that the matrix of factor loadings was invariant across countries (see Fig. 1c). The results (v2(7) = 11.195, p = 0.130; CFI = 0.978, TLI = 0.962; RMSEA = 0.052, AIC = 37.195) showed that the increase in chi-square was not significant (Dv2(3) = 1.714, p = 0.634) and TLI, RMSEA, and AIC actually improved. Thus, the price – quality schema was also fully metric invariant across the US and China samples. Finally, the scalar invariance was tested. The intercepts of the four indicators were set to be invar-

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iant across countries (see Fig. 1d). Such a model resulted in an acceptable fit (v2 (11) = 20.962, p = 0.034; CFI = 0.996, TLI = 0.993; RMSEA = 0.064, AIC = 54.962). The increase of chi-square over the full configural invariance model was not significant (Dv2(7) = 11.482, p = 0.119); CFI, TLI, RMSEA, and AIC also exhibited a good fit. Thus, full scalar invariance was supported. The previous procedure was run for measures of market efficiency perception, risk aversion, and financial concern. Note that if the full metric or scalar invariance model was not adequate, then constraints on certain indicators (as revealed by the Modification

Table 1 Assessment of measurement invariance Model

v2

df

p-value

CFI

TLI

RMSEA

AIC

Relaxed constrainta

Price – quality schema Equality of covariances and means Configural invariance Full metric invariance Final partial metric invariance Initial full scalar invariance Final partial scalar invariance

58.087 9.481 11.195 – 20.962 –

14 4 7 – 11 –

0.000 0.050 0.130 – 0.034 –

0.983 0.971 0.978 – 0.996 –

0.975 0.912 0.962 – 0.993 –

0.120 0.079 0.052 – 0.064 –

86.087 41.481 37.195 – 54.962 –

– – – – – –

Perception of market efficiency Equality of covariances and means Configural invariance Full metric invariance Final partial metric invariance Initial full scalar invariance Final partial scalar invariance

207.260 0 5.250 – 80.978 13.303

9 0 2 – 5 4

0.000 – 0.072 – 0.000 0.008

0.895 1.000 0.986 – 0.944 0.974

0.860 – 0.958 – 0.896 0.942

0.316 – 0.086 – 0.250 0.089

225.260 24.000 25.250 – 106.978 32.303

– – – – – ME3

Risk aversion Equality of covariances and means Configural invariance Full metric invariance Final partial metric Invariance Initial full scalar invariance Final partial scalar invariance

72.802 0 2.301 – 66.135 14.121

9 0 2 – 5 4

0.000 – 0.317 – 0.000 0.007

0.962 1.000 0.997 – 0.964 0.994

0.950 – 0.990 – 0.914 0.982

0.180 – 0.026 – 0.236 0.107

90.802 24.000 22.301 – 92.135 42.121

– – – – – RISK3

Financial concern Equality of covariances and means Configural invariance Full metric invariance Final partial metric invariance Initial full scalar invariance Final partial scalar invariance

41.025 0 3.535 – 32.540 11.389

9 0 2 – 5 4

0.000 – 0.171 – 0.000 0.023

0.979 1.000 0.980 – 0.982 0.995

0.971 – 0.940 – 0.956 0.985

0.127 – 0.059 – 0.158 0.092

59.025 24.000 23.535 – 58.540 39.389

– – – – – FS1

a

See Appendix A for details on the scale items.

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nationally invariant and their means could be compared meaningfully (Steenkamp & Baumgartner, 1998).

Index [MI]) were relaxed to get an acceptable partial invariance model (Steenkamp & Baumgartner, 1998). The details of construct invariance assessment are reported in Table 1. Table 1 shows that the measure of price –quality schema is configurally invariant, fully metric invariant, and fully scalar invariant. Measures of market efficiency perception, risk aversion, and financial concern are also configurally invariant and fully metric invariant, but not fully scalar invariant. Nevertheless, all the partial scalar invariance models are acceptable. Therefore, these measures are cross-

4.5. Measurement model analysis Measurement reliability and validity is assessed by estimating a three-factor confirmatory measurement model at both the national level (i.e., China and the US) and the multigroup level (Anderson & Gerbing, 1988; Durvasula et al., 1993). In the model, each item was set to load only on its own factor, and the factors

Table 2 Across-construct measurement validity assessmenta American sample Construct Price – quality schema PQ1 PQ2 PQ3 PQ4 Perception of market efficiency ME1 ME2 ME3 Risk aversion RISK1 RISK2 RISK3

Chinese sample Factor loading

Composite reliability

Construct

0.822

Price – quality schema PQ1 PQ2 PQ3 PQ4 Perception of market efficiency ME1 ME2 ME3 Risk aversion RISK1 RISK2 RISK3

1.000b 0.700 0.925 0.848 0.856 0.646 1.000b 1.215 0.671 0.617 1.000b 0.772

Factor loading

Composite reliability 0.600

1.000b 0.698 0.723 0.788 0.754 0.556 1.000b 0.790 0.652 1.050 1.000b 0.899

Goodness-of-fit statistics American sample: Chinese sample: Multigroup level:

v2 = 50.429, df = 32, p = 0.020; GFI = 0.919, CFI = 0.948, IFI = 0.950; RMSEA = 0.074 v2 = 48.010, df = 32, p = 0.034; GFI = 0.924, CFI = 0.914, IFI = 0.920; RMSEA = 0.066 v2 = 98.442, df = 64, p = 0.004; GFI = 0.922, CFI = 0.936, IFI = 0.939; RMSEA = 0.049

U Matrixc

US 1

China 2

3

1

1.000 0.156

1.000

3.414 1.358

4.957 0.876

Price – quality schema Perception of efficiency Risk aversion

1.000 0.152 0.023

1.000 0.066

1.000

1.000 0.249d 0.280d

Meanc S.D.

5.024 1.043

5.079 0.854

4.164 1.109

4.698 0.889

a

All factor loadings statistically significant at p < 0.01. Fixed parameter. c Measures reversed and averaged by the number of scale items. d p < 0.05 (two-tailed). b

2

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were allowed to correlate. The results of measurement analysis are exhibited in Table 2, which also reports the means and standard deviations of the three measures. To make the comparison more intuitive, construct means are averaged and reverse coded. All three models provided an acceptable fit to the data (American sample: v2(32) = 50.429, p = 0.020; GFI = 0.919, CFI = 0.948, IFI = 0.950; RMSEA = 0.074; Chinese sample: v2(32) = 48.010, p = 0.034; GFI = 0.924, CFI = 0.914, IFI = 0.920; RMSEA = 0.066; multigroup level: v2(64) = 98.442, p = 0.004; GFI = 0.922, CFI = 0.936, IFI = 0.939; RMSEA = 0.049). Although the v2 tests were significant, the goodness-of-fit measures were all satisfactory and the RMSEAs were below the 0.08 rule of thumb (Bollen, 1989). These results indicate the unidimensionality of the measures (Anderson & Gerbing, 1988). Further, all factor loadings were statistically significant

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( p < 0.01) and the composite reliabilities of each construct all exceeded the usual 0.60 benchmark (Bagozzi & Yi, 1988). Thus, these measures demonstrate adequate convergent validity and reliability. Moreover, all the cross-construct correlations were significantly smaller than A1.00A ( p < 0.01), signifying the discriminant validity of these measures (Phillips, 1981). Overall, these results show that the measures in this study possess adequate reliability and validity.

5. Results We tested H2, H3, and H4 with structural equation method, which is advantageous to the traditional ANOVA/MANOVA approach by incorporating errors in construct measurement (Bagozzi & Yi, 1989;

Table 3 Results of hypotheses testing H2, H3, and H4

Mean

Chi-square

US

China

DMean

Dv2

v2 (67) = 196.046 v2 (65) = 187.686

97.604a 89.244a

v2 (65) = 105.227 v2 (65) = 110.348

6.784a 11.905a

5.079

3.414

1.665

v2 (64) = 98.442 v2 (64) = 98.442

5.024 4.164

4.698 4.957

0.326a 0.793a

v2 (64) = 98.442 v2 (64) = 98.442

H1, H5a and H5b

Endogenous latent variables — standardized coefficients (t-value)

Independent latent variables

Price – quality schema (US)

H1: Perceptions of market efficiency (ME) H5a, H5b: Risk aversion (RA) R-square U correlation between ME and RA Goodness-of-fit measures

a

p < 0.01. p < 0.10 (one-tailed). c p < 0.05 (one-tailed). b

Price – quality schema (China)

0.155b (1.385)

0.300c (2.111)

0.033 (

0.327c (2.020)

0.265)

0.024 0.066 v2 = 50.429, df = 32, p = 0.020; GFI = 0.919, CFI = 0.948, IFI = 0.950; RMSEA = 0.074

M1)

M2 (constrained)

Overall test H2: Perceptions of market efficiency H3: Price – quality schema H4: Risk aversion

a

(M2

M1 (unconstrained)

0.166 0.156 v2 = 48.010, df = 32, p = 0.034; GFI = 0.924, CFI = 0.914, IFI = 0.920; RMSEA = 0.066

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Durvasula et al., 1993). As Durvasula et al. (1993) suggest, two models were first compared to assess the overall mean differences (price –quality schema, market efficiency perception, and risk aversion) across two data sets (similar to MANOVA). One model freely estimated all means across countries (M1, unconstrained model), and the other assumed that construct means were equal across countries (M2, constrained model). The v2 difference between M1 (v2(64) = 98.442) and M2 (v2(67) = 196.046) was highly significant (Dv2(3) = 97.604, p < 0.001), indicating that the construct means are significantly different across countries. Then, individual construct mean tests were performed with similar techniques (similar to ANOVA). The results are reported in Table 3. H2 deals with consumer perceptions of market efficiency. As predicted, the China market (l = 3.414) is perceived less efficient than the US market (l = 5.079). A comparison between M1 and M2 (Dv2(1) = 89.244, p < 0.001) indicates that the difference is highly significant. These findings support H2. H3 argues that Chinese consumers possess a weaker price – quality schema than American consumers. The results in Table 3 show that the mean value of Chinese sample (4.698) is significantly less than that of American sample (5.024, Dv2(1) = 6.784, p < 0.01), lending support to H3. H4 deals with the extent of risk aversion between American and Chinese consumers. Table 3 shows that the change of chi-square is highly significant (Dv2(1) = 11.905, p < 0.001), indicating that Chinese consumers (l = 4.957) are more risk averse than American consumers (l = 4.164). Thus, H4 is supported. H1, H5a, and H5b focus on the relationships between market efficiency, risk aversion, and price – quality schema. As suggested by Steenkamp and Baumgartner (1998, p. 82), the structural equation model was applied to test this relationship across countries so that the potential lack of error variance – invariance does not create a problem. In the model, price – quality schema is the dependent variable and perceptions of market efficiency and risk aversion are the two independent variables. Results (see Table 3) show that the model fit the data satisfactorily (American sample: v2(32) = 50.429,

p = 0.020; GFI = 0.919, CFI = 0.948, IFI = 0.950; RMSEA = 0.074; Chinese sample: v2(32) = 48.010, p = 0.034; GFI = 0.924, CFI = 0.914, IFI = 0.920; RMSEA = 0.066). H1 predicts that consumer perception of market efficiency is positively related to their price – quality schema. Table 3 shows that for American consumers, price – quality schema is positively related to market efficiency perception (b = 0.155, p < 0.10, marginally significant); for Chinese consumers, their perceptions of market efficiency positively affect their price – quality schema (b = 0.300, p < 0.05). These results lend support to H1. H5a and H5b states that the relationship between risk aversion and price – quality schema is negative in the US (H5a) but positive in China (H5b). Contrary to H5a, the results show that risk aversion is not significantly related to price – quality for American consumers (b = 0.033, p>0.10). Thus, H5a is not supported. For the Chinese sample, risk aversion is positively linked to price – quality schema (b = 0.327, p < 0.05), supporting H5b.1 5.1. Post-analysis Some post-analyses were run to further examine the relationships of price – quality schema, market efficiency, and risk aversion. More specifically, which factor is more important in influencing the formation of price – quality schema, risk aversion or market

1

When developing H5b, we did a qualitative analysis to show why price has a greater effect on product quality judgment than either brand or store image. We also indirectly tested this reasoning with proxy measures of brand and store-image effects. In the survey, there is one question pertaining to consumer’s general perception of store (Store: ‘‘It is difficult for me to find a satisfactory store to go shopping’’, reversed scale) and one item of consumer’s perception of name brand (Brand: ‘‘The well-known national brands are best for me’’). Both are seven-point Likert scales. Though these two items do not focus on the store/brand – quality relationship, we could use them as proxy indicators. A comparison between PQ, Store, and Brand shows that in China, PQ (m = 4.70) is greater than Brand (m = 3.84, p < 0.001), Brand greater than Store (m = 3.24, p < 0.01), and PQ greater than Store ( p < 0.001). These results indirectly suggest that in China, the price information is perceived as more credible than either brand or store information, and store information is the least dependable.

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efficiency? Further, do market efficiency and risk aversion have similar effects on price – quality schema across these two countries? To answer these two questions, a series of model comparison tests were run between unconstrained models (where b’s are freely estimated) and constrained models (where b’s are constrained to be equal). For question 1, the results show that in the US, the impact of market efficiency upon price – quality schema is slightly stronger than that of risk aversion (Dv2(1) = 2.740, p < 0.10). However, in China, there is no significant difference between the impacts of market efficiency and risk aversion (Dv2(1) = 0.182, p>0.10). For question 2, the results indicate that risk aversion has a stronger impact on price –quality schema in China than in the US (Dv2(1) = 3.156, p < 0.10), while market efficiency has a similar effect on price – quality schema in these two countries (Dv2(1) = 0.002, p>0.10).

6. Discussion and conclusions The major objective of this research is to understand consumer’s price –quality schema in an inefficient market. Our findings generally support the hypothesized relationships. Basically, we found that market efficiency positively affects the formation of price – quality schemas. In an inefficient market, consumers believe the positive relationship between price and quality to a less extent, but paradoxically, riskaverse consumers have to rely more on price to infer product quality. We compared market efficiency and price – quality schema in the China and US market. Our findings show that market efficiency has a positive impact on consumer beliefs of price – quality relationship. As Peterson and Wilson (1985) suggest, price – quality schema develops through consumer experience and generalizing process. If, over time, consumers get to know the efficiency (or inefficiency) of the market through experience, they will (more or less) use price to indicate product quality. Our findings also indicate that the China market is perceived much less efficient than the US market. At the macro-level, market and management literatures have long recognized the inefficiency of a

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transitional economy such as China (e.g., Nee, 1992; Luo & Peng, 1999). Our findings further suggest that at the individual level, market efficiency will be reflected into consumer perceptions of market conditions. Since Chinese consumers perceive the market as less efficient than American consumers, and market efficiency perceptions affect consumer price –quality schemas, Chinese consumers possess a weaker price – quality schema than their American counterparts. These results echo the recent call of Chernev and Carpenter (2001) for more research on how consumer understanding of market efficiency affects their beliefs and decision makings. To further uncover the effect of inefficient market conditions on consumer price –quality schema, the relationship between risk aversion and price – quality schema was investigated from a crossnational perspective. Previous research has already indicated that risk-averse consumers tend to search for more product information in purchase decisions (e.g., Shimp & Bearden, 1982). When reliable product information is unavailable, these consumers are inclined to rely on extrinsic cues such as price and brand to infer product quality (Dodds et al., 1991; Zeithaml, 1988). We found that in China, where the economy is in a transitional process from a central planned one to a market one, ascertainable and credible in product information is hardly available; therefore, risk-averse consumers are forced to rely more on price to infer product quality. These findings support the proposition of Dodds et al. in an inefficient market. The hypothesis that consumer risk aversion is negatively related to their price – quality schema in the US market is not supported. In other words, in the US, more risk-averse and less risk-averse consumers may use price to infer product quality at similar levels. One possible explanation may be that in the US market, product information is so abundant that information search and analysis becomes a burden to many consumers. That is, information overload increases the time and cost of information search. As a result, consumers may rely on their price – quality schema to conserve the efforts and shortcut the decision making, even if they are risk averse. In sum, our findings disclose an interesting paradox of price – quality schema in an inefficient mar-

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ket. That is, in a transitional economy, Chinese consumers in general doubt the credibility of price as an indicator of product quality (H1 – H3). However, still due to the market environment, riskaverse consumers are forced to rely more on price to infer price quality (H4, H5a, and H5b). An intuitive explanation of the paradox may be that Chinese consumers believe ‘‘a high price does not mean high quality, but a high-quality product must have a high price.’’ Our findings also provide some implications for companies seeking opportunities in China. In China, consumers in general are less confident about product –quality relationship due to the poor market environment. However, sometimes, they (highly risk-averse consumers) have to rely on price to infer product quality. Thus, enhancing consumers’ confidence with detailed product information, company information, and a high price might be a way to go. Other market cues such as store reputation and brand name should be provided to assure the product quality and prevent the impairment of counterfeit products. Further, because market efficiency impacts consumers’ use of price to evaluate a product, and the market conditions vary widely across different areas within China (Ho, 2001), companies may also need to adapt their strategies across different areas and markets, even within China. 6.1. Research implications This research advances our understanding of price – quality schema in an inefficient market and provides some preliminary explanations of why consumers differ in price – quality schema. It appears that both market environmental factors (e.g., market efficiency) and consumer psychological characteristics (e.g., risk aversion) have impacts on the formation of price – quality schema in an inefficient market, suggesting the importance of considering both market environmental and individual psychological factors in cross-national studies. Methodologically, we made efforts to establish cross-national measurement equivalence before making comparisons. In line with researchers’ recommendation (Bagozzi & Yi, 1989; Durvasula et al., 1993;

Steenkamp & Baumgartner, 1998), this study assessed and verified the cross-national invariance of the focal construct measures; mean comparison and association analysis were also conducted with structural equation methods. As such, this research contributes to the cross-national consumer research. Future researchers could directly apply the measures of price – quality schema, market efficiency perception, risk aversion, and financial concern in studies involving American and Chinese consumers. However, we must caution that one should not overgeneralize the results of this study and we encourage future research to overcome the limitations of this paper. First, the sample is comprised of students; therefore, samples from other segments are needed to generalize the findings to other populations. Second, this study was conducted in two countries, China and the US. Obviously, research conducted in other countries is necessary to test the robustness of the findings. Further, because our study was cross-sectional, we cannot determine the causal links of the model with certainty. Experimental research is needed to test whether our theoretically based causal model will be fully supported. To further understand the paradox identified, it is worthwhile to cross market efficiency, risk aversion, and other factors such as price and brand in a complete factorial design, and then test why consumers may rely more on price to indicate quality. Is it because they want to reduce high levels of risk perceptions? Or maybe it depends on consumer product knowledge and/or purchase involvement? Finally, it would be interesting to examine whether consumers’ price – quality schema changes in a different country where the market efficiency level differs. This will provide more insights for us to understand important questions such as the persistency of the price – quality schema and whether the price – quality schema is country-specific or consumer-specific.

Acknowledgements We would like to express our sincere gratitude to the three anonymous reviewers and the editor for their invaluable suggestions on previous versions of the article. We also thank Kent Nakamoto for his comments and suggestions.

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