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: kevinzhou@business.hku.hk (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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K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
351
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
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
353
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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K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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.
355
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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K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
Fig. 1. An example of measurement invariance test.
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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-
357
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.
358
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
3
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
359
( 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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K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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.
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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
361
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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K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
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.
K.Z. Zhou et al. / Intern. J. of Research in Marketing 19 (2002) 349–365
363
Appendix A . Measurement appendix
All the items are anchored from ‘‘1’’ (strongly
agree) to ‘‘7’’ (strongly disagree).
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