BRAND EQUITY IN INTERNET BUSINESS: AN EXPLORATORY STUDY
Kai-Lung Hui, Yah-Ting Gwee
Department of Information Systems
National University of Singapore
Singapore
[email protected], [email protected]
Patrick Y.K. Chau
School of Business
The University of Hong Kong
Hong Kong
[email protected]
November 2003
Abstract
Previous research suggests that brand equity is shaped by various marketing mix elements, such
as store image or distribution intensity. However, because the Internet has no physical boundary,
some of these traditional marketing variables may not be applicable in shaping the brand equity
of online firms. Drawing on theories in economics, marketing and psychology, we develop a
model of brand equity formation that pertains to Internet businesses. Inside our model, we
explore the significance of several Web-based marketing variables that could affect consumer
perceptions of product quality and brand knowledge, both of which are relevant components of
brand equity. By performing structural equation and regression analyses on two sets of survey
data, we find that product/technology innovation and Website quality contribute to improving an
online firm’s brand equity. The effect of advertising intensity on perceived quality is mixed, but
advertising always improves the brand knowledge of consumers. These results shed important
strategic implications for online firms who are building up new brands on the Internet.
Keywords: value-added features, product/technology innovation, Website quality, advertising
intensity, perceived quality, brand knowledge, brand equity
We gratefully acknowledge the helpful comments from Bob Zmud, Bernard Tan, three
anonymous reviewers and the seminar participants at the 2002 International Conference on
Information Systems.
1
I. INTRODUCTION
The widespread acceptance of the Internet and electronic commerce has given rise to popular
brand names such as Amazon, eBay, Hotmail and Yahoo. Although the Internet is frequently
characterized as an efficient channel that reduces search and transaction costs, it also intensifies
product and price competitions due to the use of intelligent tools, such as comparison-shopping
agents or interactive decision aids (Haubl and Trifts 2000). To differentiate from competitors and
appeal to online consumers, other than continuously improving products and setting appropriate
prices, an online firm often needs to initiate innovative practices, employ advanced technologies,
or offer high-quality services that create extra value to consumers (Willcocks and Plant 2001).
These efforts may help form unique and favorable brand impressions.
A brand is a name and/or symbol like logo, trademark and package design that uniquely
identifies products or services of a seller, and differentiates them from those of its competitors
(Aaker 1991; Keller 1998; Kotler 1994). Brand is valuable because it influences consumer
preferences. A good brand can signal product superiority to consumers, which may subsequently
lead to favorable consumer attitude that, in turn, brings in better sales and financial performance
for the firm (Aaker and Jacobson 2001; Erdem and Swait 1998). Therefore, a brand may enhance
the perceptual value of its products to consumers. Such an extra value (cf. values provided by
intrinsic product attributes) due to the brand is commonly called brand equity (Farquhar 1989).
For commercial firms, the importance of building and cultivating brand equity is best epitomized
by the frequent sales and acquisitions of reputable brands, which typically involve substantial
monetary premiums (Keller 1998).
Keller (1993) suggests that a brand is built by the creation of firm-associated mental
structure in consumers’ memory, which helps consumers organize their knowledge in a way that
aids their brand selection strategies and decisions. For instance, being the first online bookstore
that carries a wide catalog, Amazon has received substantial recognition in online book retailing.
Despite the fierce competition posed by hundreds of other Internet bookstores, Amazon has been
persistently the leader in the online book retail industry, with an overwhelming market share lead
over all online competitors.1 Similarly, owing to its innovative business concept, Priceline.com
1
See Karen J. Bannan “Book Battle,” Mediaweek, vol. 10, no. 9, February 2000, pp. 72-76; Jim Milliot “Chain
sales rose 4.8% in first quarter, to $1.7 billion,” Publishers Weekly, vol. 249, no. 21, May 2002, pp. 9; and the
company profiles in Hoovers Online (http://www.hoovers.com). It is instructive to observe that the leading brick-
2
has gained much attention and recognition among consumers, which helps create a reputable
brand for its Internet travel-booking service.
Clearly, brand may continue to play an important role in electronic commerce (Willcocks
and Plant 2001). Prior research has identified various marketing mix elements (such as pricing,
promotion, store image, advertising and distribution intensity) that may affect consumer memory
perception of brands (Keller 1993; Yoo et al. 2000). However, because Internet stores have no
physical boundary and are constantly available, some of these marketing mix elements may not
be relevant. For example, store image needs to be redefined for online firms because the “stores”
are now replaced by a group of related Webpages. Similarly, in online retailing, distribution
intensity is not relevant because the Internet is ubiquitous – consumers can access online stores
anytime from anywhere, and most online stores deliver products to buyers in other geographical
regions. Therefore, the brand equity of an online firm is possibly shaped by a different set of
marketing factors pertaining more to Website characteristics and technological attributes. It is
important for firms to recognize and study the emerging factors that help construct their brand
equity in the online context.
In this research, we explore factors that affect consumer perception of online firms and
test a model of brand equity formation that applies to Web-based industries. Specifically, using
brand knowledge and perceived quality as separate components of brand equity, we investigate
whether Web-based marketing efforts could affect consumers’ brand perceptions. The selected
marketing mix factors (value-added features, product/technology innovation, Website quality
and advertising intensity) are generic across business sectors, and they form the basic building
blocks of many business-to-consumer (B2C) electronic storefronts.
Hence they have wide
applicability and should be of interest to Website managers who are eager to establish unique
brand identities among Internet consumers.
We conducted two studies to validate the model and test the theoretical hypotheses about
the studied Web-based factors. In the first study, we surveyed a group of university students
about their perception of various Websites’ attributes, and used the data to refine measurement
items and test the hypotheses. Then, we repeated the survey with another sample of working
students, and further examined the detailed properties/contributions of several constructs. The
and-mortar bookstore, Barnes and Noble, only has a market share of around 40 percent in the conventional book
retail industry, whereas Amazon alone captures close to 80 percent of the online market.
3
studied Websites (online firms) were carefully chosen from three service industries such that
they do not have any physical retail outlets. This was to avoid possible confounds due to existing
brick-and-mortar operation or a priori brand advantage. We selected Websites from three
industries and tested the hypotheses using two groups of subjects with different demographics to
ensure external validity and generalizability of our findings.
Our results strongly suggest the significance of several Web-based marketing factors in
shaping brand equity (through enhancing brand knowledge and perceived quality). Both Website
quality and product/technology innovation are positive attributes that improve brand equities of
Internet businesses. The effect of advertising intensity is mixed, whereas that of value-added
features is not statistically significant. The proposed model explains substantial variations of the
studied firms’ brand equities. This confirms our conjecture that Website- or technology-related
characteristics are important for understanding online firms’ brand positions.
The rest of this article is organized as follows. Section 2 sketches the theoretical model
of brand equity, brand knowledge and perceived quality, and explains the rationales behind the
testing of the selected Web-based marketing factors. Sections 3 and 4 highlight the objectives of
the two studies, and present the research method and empirical results. Section 5 discusses the
findings and implications, and concludes the paper.
II. THEORY
The extant literature has defined brand equity by multiple indicators or dimensions. Specifically,
Aaker (1991, 1996) posits that brand equity is shaped by perceived quality, brand awareness,
association, loyalty and objective indicators such as proprietary assets (patents, trademarks, etc.)
or market share. Based on associative network memory models in cognitive psychology, Keller
(1993) suggests that brand knowledge is key to conceptualizing and managing customer-based
brand equity. From an information economics perspective, Erdem and Swait (1998) propose that
brand equity can be characterized by clarity and credibility of brand signals. Other research has
also measured brand equity using financial worth (Simon and Sullivan 1993) or consumer utility
that is not accountable by physical attributes of the studied products (Kamakura and Russell
1993; Park and Srinivasan 1994; Swait et al. 1993).
It is obvious that multiple dimensions can be used to conceptualize and measure brand
equity. These can be broadly classified as either attitudinal or behavioral (Cobb-Walgren et al.
4
1995). Generally, behavioral indicators such as actual brand choice, loyalty or market share may
reflect the salience of a brand’s equity, but they are not readily observable at the consumer level
in most online environments. Instead, we focus on two attitudinal variables, brand knowledge
and perceived quality, that are well conceived to be important in shaping customer-based brand
equity (Aaker 1996; Keller 1993; Yoo et al. 2000), and that can be directly probed by consumer
surveys. In subsequent discussions, we first conceptualize the relationship between brand equity,
brand knowledge and perceived quality, and then explain why certain Web-based marketing
variables should affect brand equity of Internet businesses.
Brand Equity, Brand Knowledge and Perceived Quality
When consumers are exposed to a particular brand, either through communications or direct
experience with its products, they absorb certain information related to the brand and store such
information in their memory. Such storage of information leads to the accumulation of brand
knowledge. Keller (1993, pp.8) suggests that customer-based brand equity is formed when
consumers’ brand knowledge generates differential effect on their response to the marketing of
the brand. In this perspective, brand knowledge is conceptualized as nodes in consumer memory
that are connected by associative links of various strengths to other memory nodes. The extent
of information retrieval from memory depends on how strong the nodes are connected to each
other and whether they are accessible by external cues (Raaijmakers and Shiffrin 1981; Ratcliff
and McKoon 1988). High brand knowledge implies that the brand nodes are more salient and
easily retrievable from memory, which directly increases the probability of consumers choosing
the brand. Essentially, a brand adds value to its products if consumers have positive knowledge
about it. This view of brand knowledge as a defining characteristic of brand equity is rooted in
memory research in cognitive psychology (Keller 1993), and its basic principle is consistent with
empirical findings in memory and decision-making studies (Alba et al. 1991).
Economic human capital model also supports brand knowledge as a relevant dimension
in characterizing brand equity (Ratchford 2001). The human capital model regards consumers as
production units, who combine input goods, time and knowledge to produce household activities.
The unique feature of this model lies in maximization of consumer utility over activities instead
of goods. Because knowledge facilitates activity production, higher knowledge on a particular
5
brand implies a lower price of activities associated with the brand.2 Such a lower price, in turn,
directly increases demand for the activities and indirectly raises demand for goods that carry the
brand (as compared to rival brands that help produce similar activities, but that are less well
known by the consumers). Therefore, a brand adds value to its products when consumers are
familiar with it and perceive that they could use the products to efficiently generate activities.
This perspective may help explain why consumers occasionally make decisions based on brand
familiarity, and why knowledge could inhibit switching behavior (Alba and Hutchinson 1987;
Jacoby et al. 1977; Ratchford 2001).
To conclude, the cognitive psychological view of brand knowledge focuses on memory
organization and salience of brand nodes, while the human capital model stresses the importance
of brand knowledge in efficient production of consumer activities. Both theories provide a solid
foundation for the instrumental role of consumer knowledge in defining brand equity.
Besides brand knowledge, it is useful to observe that every product carries an intrinsic
level of quality, which could be perceived differently because of its brand.3 Perceived quality is
defined as consumer judgment of a product’s overall excellence or superiority (Zeithaml 1988,
pp. 3). This definition highlights two characteristics of perceived quality. First, it is subject to
consumer judgment and is distinct from objective quality. Second, it is conceived based on
overall product excellence, and therefore it is a high-level abstraction that is more general than
specific product attributes. Essentially, perceived quality is one form of overall evaluation that is
analogous to attitude (Olshavsky 1985). This conceptualization is coherent with common quality
definitions, and it was widely applied in consumer research (see, e.g., Aaker 1991; Erdem and
Swait 1998; Holbrook and Corfman 1985; Yoo et al. 2000).
Perceived quality affects consumer response, such as product choice or store patronage.
Indeed, consumer perception of quality is often more superior than objective quality in predicting
preference or behavior (Howard 1977; Jacoby and Olson 1985). Ceteris paribus, high perceived
quality differentiates a product from competing offers (Erdem and Swait 1998; Yoo et al. 2000).
This might translate into favorable reactions, such as purchasing or having a higher intention to
choose a particular brand (Cronin et al. 2000; Zeithaml 1988). Further, high perceived quality
2
The price here represents the marginal cost of producing the activities, which may include time cost, knowledge
contribution and the price of (input) goods that carry the brand (Ratchford 2001).
3
For ease of presentation, in subsequent discussion, the word “product” includes tangible products, intangible
products (such as digital information), and offline or online services.
6
may act as an internal cue that inspires the superiority of a brand to consumers, and hence may
raise consumer utility. These imply that perceived quality could also be used to conceptualize
brand equity. Finally, perceived quality may relate to brand associations in consumer memory
(Keller 1993). The brand nodes of low quality products are often less frequently activated; prior
experiments have found quality to affect the association and recall of memory nodes that are
reflective of family-brands (Janiszewski and Van Osselaer 2000). Hence perceived quality may
interact with brand knowledge. The shaded parts of Figure 1 sketch the conceptual framework of
brand equity that we adopt in this study.
<Insert Figure 1 here>
Exogenous Web-Based Factors
Given that brand knowledge and perceived quality shape brand equity, it is important for online
firms to devise an appropriate mix of marketing elements that could (perceptually) enhance their
brands and product qualities. Since many Internet firms provide pure online services, traditional
marketing mix elements may not be directly applicable. Instead, online firms need to appreciate
new strategic variables that could differentiate them from competitors. We group the selected
Web-based variables into three dimensions: product, place and promotion.4
Product: value-added features and product/technology innovation
Because no physical salesperson is located in the “store” to offer assistance, many online firms
provide peripheral tools or services to consumers during navigation, purchase or consumption of
their products. We collectively refer such peripheral tools or services as “value-added features”.
Common examples include provision of help information, instant product support, alert service,
personalized update, and many others. These features help improve consumer experience with
online firms, but they are typically not positioned as part of the firms’ products. Rather, they add
value through enhancing products to consumers.
4
We excluded the price dimension, which is the other component in the traditional 4P classification of marketing
mix instruments (Kotler 1994; Van Waterschoot and Van den Bulte 1992), because our studied firms offer free
services to consumers. As mentioned earlier, we restricted our scope to pure online service firms because their
brand equities are not susceptible to influences from brick-and-mortar operations. For instance, it is difficult to
estimate whether the brand equity of Barnes and Noble comes from its large chain of retail stores or its Web
storefront.
7
Prior research in electronic commerce has paid little attention to the effect of value-added
features on consumer perception. The literature on service marketing provides insight into this
area of study. Generally, consumers are found to appreciate extra service or special treatment by
service personnel (Bitner et al. 1990; Ostrom and Iacobucci 1995). Bowen et al. (1989) suggest
that augmented product, which includes customer services, could become a source of product
advantage for service-oriented firms. Further, for postal service, Goodman et al. (1995) find that
consumer perception of peripheral services could spillover to their perception of a firm’s core
services. These imply that peripheral services directly affect consumers’ evaluation and quality
perception of products.
The benefits of peripheral services could potentially be extended to e-businesses. Prior
research has found information technology an effective means for providing support and valueadded services to consumers (Haubl and Trifts 2000; Meuter et al. 2000; Nault and Dexter 1995).
An online firm could achieve synergies by complementing its products with high-quality valueadded features, which may enhance consumers’ quality perception of the products (Willcocks
and Plant 2001). Therefore, we hypothesize:
H1: The quality of value-added features is positively related to perceived quality; better
value-added features lead to a more favorable quality perception.
Besides adding value through peripheral features, an online firm could also improve its
corporate image by continuously investing in product or technological innovations. In this study,
a firm is said to achieve product/technology innovation if it is quick in launching new products
or deploying state-of-the-art technologies.5 Real life examples may include Priceline.com, which
pioneered the “name your own price” practice in its online travel-booking service; and eBay,
which introduced the proxy bidding technology and feedback mechanism in its Internet auction
business.
Our investigation of product/technology innovation is motivated by earlier research on
market pioneer and innovator advantage. In general, studies have found that early movers enjoy
initial and sustain continuous market share advantages. Although such advantages are partly due
5
We consider both product and technology innovation in a single construct because online firms, especially those
providing Web-based services, often incorporate new technologies into their “products” and advertise them together.
For consumers, the two types of innovations may appear inseparable.
8
to supply-side efficiency,6 Schmalensee (1982) proposes that an early mover with satisfactory
product quality could create a perceptual reference point for consumers to make judgments. This
implies that a firm that continuously promotes innovative products or technologies may be able
to “preempt” a particular market by positioning itself as the de facto standard with ideal quality.
Equivalently, innovation activities may improve consumers’ perceived familiarity and quality of
a firm’s products.
In a similar vein, Robinson and Fornell (1985) suggest that being an innovator or product
leader gives consumers a favorable image and higher familiarity on a firm’s products. Carpenter
and Nakamoto (1989) demonstrate that an early mover could shift consumer preference toward
its products because of naïve learning. Consumers may assign disproportional attribute weights
to an early brand, and the brand may become prototypical to them. These discussions lead to the
following hypotheses.
H2a: The degree of product/technology innovation is positively related to perceived
quality; faster innovation leads to a more favorable quality perception.
H2b: The degree of product/technology innovation is positively related to brand
knowledge; faster innovation leads to higher brand knowledge.
Place – Website quality
In the Internet context, a Website is equivalent to a firm’s retail store.7 We define Website
quality as the overall excellence or superiority of a firm’s Website. This includes features such
as user interface, design layout and system performance.8 With the increasing exchange and
marketing activities over the Internet, designing and maintaining a good Website is becoming
important for an online firm to capture higher traffic and sales (Lohse and Spiller 1998).
In conventional retailing, research has found that consumers sometimes evaluate products
based on store information (Dodds et al. 1991). Drawing on theories related to inference,
6
Lieberman and Montgomery (1988) provide a detailed discussion on advantages enjoyed by early movers. For
empirical evidence, see, for example, Dowling and McGee (1994), Henard and Szymanski (2001), Isobe et al.
(2000), Robinson (1988, 1990) and Robinson and Fornell (1985).
7
Many pure-play online firms do not maintain physical outlets, and consumers can patronize them only through
their corporate Websites. This is particularly the case for firms that offer online services. By nature, some online
services, such as search engine or information portal, cannot be provided in a physical setting.
8
For more discussion on Website design and performance assessment, see McKinney et al. (2002), Palmer
(2002) and Szymanski and Hise (2000).
9
schema formation and affordance, Baker et al. (2002) posit that consumers respond to design,
social and ambient environment cues when evaluating stores and products. Their empirical
results confirm that store design consistently affects consumer judgment of merchandise quality.
Generally, a Website can be conceptualized in architectural terms that encompass major
design and performance dimensions (Kim et al. 2002). Various characteristics of Websites, such
as functional excellence or system interface, may assume the role of service speed, store layout
and ambience in conventional retail stores. Recent research has found that Website quality
affects consumer satisfaction of and loyalty toward online firms (McKinney et al. 2002; Palmer
2002; Srinivasan et al. 2002; Szymanski and Hise 2000). A careful manipulation of Webpages
can influence consumer preferences (Mandel and Johnson 2002). Following the theory posited
by Baker et al. (2002), Website quality may serve as an extrinsic cue that helps consumers infer
the quality of a particular firm’s products. Our next hypothesis is posited as:
H3: Website quality is positively related to perceived quality; a higher quality Website
leads to a more favorable quality perception.
Promotion – advertising intensity
Advertising is perhaps one of the most popular means for promoting brands and products to
consumers. We define advertising intensity as the extent to which consumers are exposed to
advertisements of a particular brand across all marketing channels. On the Internet, the majority
of firms increase advertising intensity through pop-up browser windows, banner advertisements
or mass emails. Compared to traditional media such as TV or newspaper, advertising over the
Internet has a global reach and higher interactivity, and it requires less resource input (i.e., lower
expense) from advertisers.
It is widely held that advertising contributes to the establishment of brand equity (Aaker
and Biel 1993; Cobb-Walgren et al. 1995). Advertising affects consumer behavior through both
cognitive and affective responses (Alba et al. 1991; Vakratsas and Ambler 1999). In general, an
advertisement conveys brand and/or product information to consumers. Theories in information
economics hypothesize that, for search goods, advertising may help consumers identify relevant
product attributes and assess objective product quality; for experience goods, the fact that a firm
10
advertises may signal to consumers the superiority of its products (Nelson 1974).9, 10 Empirical
research has repeatedly found advertising to affect perceived product quality. A reasonable level
of advertising expense or repetition may lead to favorable quality evaluations (Kirmani 1997;
Kirmani and Wright 1989).
Although advertising may confer favorable quality signals, previous research has also
found consumers to be skeptical of claims about experience attributes in advertisements (Ford et
al. 1990). Kirmani and Wright (1989) conjecture that consumers might undermine a particular
advertisement when its repetition appears to be excessive, or when advertising cost does not
seem to be of concern to the advertiser.11 Their study (and, subsequently, Kirmani 1997) further
demonstrates that advertising expense and perceived quality exhibit an inverted-U relationship.
That is, an unusually high expenditure actually dampens quality perceptions. Further, in an
extensive review of the advertising literature, Vakratsas and Ambler (1999) conclude that returns
to advertising are diminishing, and consumers’ affective responses may lead to wear-out of
heavily repeated advertisements (Alba et al. 1991).
For online service firms, their products are generally more experiential in nature.12
Because advertising on the Internet generally incurs lower costs, when an online firm advertises
heavily, the two “negative” images, desperation and no pain, put forward by Kirmani and Wright
(1989) may counteract the positive effects suggested by the information economics perspective.
It is difficult to envisage the net effect of advertising intensity on perceived product quality. As
such, we pose our hypothesis in the following exploratory form:
H4a: Advertising intensity is related to perceived quality.
9
In the information economics perspective, the value of advertising stems from the predisposition that consumers
lack price or quality information, and they engage in different nature and extents of search activities. For more
discussion, see Stigler (1961) and Nelson (1970, 1974).
10
In his discussion of experience goods, Nelson (1974) posits that a heavily advertised brand is likely to be more
efficient, and it may offer a lower price per unit of utility to consumers. Therefore, higher advertising intensity may
(indirectly) suggest to consumers that a brand is a better buy.
11
Kirmani and Wright (1989) coin them as “desperation” and “no pain” effects. The underlying thought is that no
pain advertising dampens the quality signal proposed by the information economics literature, whereas desperation
gives a reverse signal that the firm may actually lack confidence in its products.
12
For instance, consumers can hardly know the performance of a search engine before they actually conduct some
searches; the connection speed, functionality and user-friendliness of a browser-based email service is generally not
obvious to consumers prior to usage.
11
Finally, advertising directly confers brand and product information to consumers, and it
serves to increase the awareness of and knowledge on a particular brand (Keller 1987; Nelson
1974; Stigler 1961; Vakratsas and Ambler 1999). Research in memory and consumer knowledge
suggests that repeatedly showing an advertisement enhances a brand’s salience in consumers’
memory (Alba et al. 1991; Alba and Hutchinson 1987). Therefore, our last hypothesis is:
H4b: Advertising intensity is positively related to brand knowledge; more exposure to
advertisements leads to higher brand knowledge.
Figure 1 depicts the full research model and the hypothesized relationships. We conducted two
studies using different samples of subjects to validate and test the model.
III. STUDY ONE
The objective of this study is to establish the measurements of the various constructs modeled in
Figure 1 and test the hypotheses about their relationships. Previous brand valuation research has
employed survey-based, expert judgment and financial methods to measure brand equity.13 We
adopt a self-report survey approach because our hypotheses are formulated at the consumer level.
That is, we use the subjective preference that consumers attach to the tested brands as a surrogate
measure of brand equity. The expert judgment approach is not appropriate because it involves
arbitrary measures and nonstandard brand dimensions (Aaker and Jacobson 2001). We do not
use financial measures because many online firms are not publicly traded; even for those that are
listed, their share prices are probably too volatile for affirming their actual market values.14
Subjects
We solicited subjects from a pool of undergraduate and postgraduate students who enrolled in a
telecommunications course in a large university. All participants were volunteers, but they were
given monetary incentives to complete the survey. We received altogether 402 responses and,
after dropping 10 incomplete responses, we have a total of 392 valid data points.
13
For an in-depth discussion on the strengths and weaknesses of the various valuation methods, see Simon and
Sullivan (1993) and Aaker and Jacobson (2001).
14
The rapid boom and crash of many dot.com’s share prices are sufficient to show the inaptness of the financial
approach to measure brand values of online firms.
12
Stimuli
To ensure that the subjects are aware of the studied brands, we restricted our stimuli to brands
that provide two types of Web-based services, search engine and browser-based email, which are
popular among most Internet users. To identify popular brands within the local context, we
invited another 30 undergraduate students from the same university to suggest three popular
brands in each service category. Frequency counts suggest that AltaVista, Google and Yahoo are
the most popular search engines among the subjects, whereas Hotmail, Lycos Mail and Yahoo
Mail are the most popular browser-based email service providers.15 In our survey, we measured
the seven latent constructs for each of the above brands. We deliberately chose multiple brands
and industries to induce higher variations in the subjects’ responses. These variations are useful
for identifying the structural model and testing the research hypotheses.
Measures
We used multiple items to measure each of the studied constructs. The survey instruments of
advertising intensity, perceived quality, brand knowledge and brand equity were adapted and
modified based on items that were used in previous research (Aaker 1996; Brucks 1985; CobbWalgren 1995; Park et al. 1994; Yoo et al. 2000).16 By following similar styles and focusing on
the conceptual meanings of the constructs, we constructed new items for value-added features,
Website quality and product/technology innovation. Altogether, our initial survey contains 50
items that measure the seven constructs. All items were framed using 7-point Likert scale.
The items were then customized for each of the selected brands, which resulted in six
versions of survey. Except brand name and service category, all items were identically phrased
across the versions. We further conducted two rounds of pilot studies to assess and purify the
items. In each round, 24 upper-division undergraduate students (who did not participate in the
main survey or the brand selection task) from the same university were invited to complete the
pilot survey. Feedbacks were then collected regarding the layout, structure and clarity of the
15
AltaVista, Google and Yahoo were named 70 times (out of 90) in the search engine category, while Hotmail,
Lycos Mail and Yahoo Mail were named 72 times (out of 90) in the browser-based email category. These figures
indicate that the identified brands are popular and representative.
16
We used subjective measures instead of objective measures for brand knowledge. Prior research has found that
subjective and objective knowledge are highly correlated, and subjective knowledge also affects information search
and consumer behavior (Brucks 1985; Park et al. 1994). Subjective knowledge includes consumers’ self-confidence
in their knowledge which, according to the human capital model (Ratchford 2001), is useful for predicting brand
preferences.
13
items. We also performed preliminary reliability analyses to detect problematic items. After the
two rounds of pilot studies, we discarded, rephrased and added some items to arrive at the final
survey, which contains a total of 48 items, with at least six items per construct.17
Procedure
We organized the survey into four sections. The first section contains items that measure the
studied marketing factors, namely value-added features, product/technology innovation, Website
quality and advertising intensity. The second section consists of items that measure perceived
quality and brand knowledge. The third section contains items for measuring brand equity. In
the last section, we ask for demographic data about the subjects. Except for demographics, the
question orders are randomized within each respective section. We organized the questions into
distinct, ordered tiers to minimize possible interference between the marketing mix elements and
the dependent constructs.
The six versions of the survey were then randomly distributed to the subjects. 64 subjects
indicated that they had not tried the assigned brand before filling in the survey. Because brand
name may play an unusually large influence in these cases (especially on perceived quality; see
Dodds et al. 1991 and Rao and Monroe 1989), we discard these 64 data points and focus on the
remaining 328 responses. Except Lycos Mail, the 328 responses are well distributed across the
studied brands – 59 for AltaVista, 66 for Yahoo, 68 for Google, 56 for Hotmail, 20 for Lycos
Mail and 59 for Yahoo Mail. Among the 328 subjects, 45.7 percent of them are female, and the
majority of them are between 18 to 25 years old. Most of the subjects have extensive Internet
experience – 65.2 percent of them spend more than 12 hours per week to surf the Internet.18
Data Analysis
We performed confirmatory factor analysis (CFA) to assess the measurements of the variables
and test the hypotheses.19 The two-step recommendation by Anderson and Gerbing (1988) was
17
For brevity, we only report 27 retained items in Table 1, after the measurement model was re-specified (see
below). The full set of 48 items is available from the authors upon request.
18
While students may not represent the majority of e-business consumers, the use of student subjects is considered
acceptable here, because they are frequent users of both search engine and browser-based email services. In Study
Two, we extend the analysis to a sample of working adults and use brands from another online industry as stimuli to
further assess the proposed hypotheses.
19
LISREL 8.51 was used in all analyses.
14
closely followed. First, we examined whether the measurement model tallies with the subjects’
responses and, if necessary, re-specified the model to ensure that the constructs are adequately
measured. We then tested the hypotheses by estimating a structural model using the validated
items. Note that when examining the CFA model fits and performing model comparisons, we
used a more conservative significance level of 0.01 for all χ2 statistics. This is because the χ2
statistic is sensitive to sample size, and it would probably reject a model when the sample size is
larger than 200, irrespective of true model fit (Hair et al. 1998).
Measurement model
We begin by estimating a 7-construct measurement model that includes all possible correlations
between the constructs (this is sometimes called a “saturated” model; see Anderson and Gerbing
1988). Based on this model, we assess the conceptual distinctiveness of the seven constructs by
performing 21 (=7×6/2) pair-wise comparisons using χ2 difference tests. All the χ2 differences,
obtained by subtracting the overall χ2 of the 7-construct model (the hypothetical measurement
model) from the respective 6-construct models (obtained by merging two constructs at a time),
are significant with p-values substantially less than 0.01. This implies that the 48 items measure
seven distinct constructs.
The initial measurement model revealed a modest fit to the data, with a goodness-of-fit
index (GFI) of 0.70. To detect problematic items, we examined the standardized residual matrix
of the CFA model (Anderson and Gerbing 1988; Hair et al. 1998). Hair et al. (1998) suggest that
a measurement model should have less than five percent of standardized residuals exceeding
2.58. Based on this criterion, we performed successive iterations of estimations and dropped 21
items that either loaded considerably onto other constructs, or did not correlate well with other
items of the own-constructs. The final measurement model consists of 27 items that measure the
seven latent constructs. Each construct is measured by a minimum of three items. Table 1
presents the retained items and their standardized loadings on the respective constructs.
<Insert Table 1 here>
The re-specified measurement model has a χ2 of 433.28 (p < 0.01), GFI of 0.91, root
mean squared residual (RMSR) of 0.05, adjusted goodness-of-fit index (AGFI) of 0.89, nonnormed fit index (NNFI) of 0.97, and parsimonious goodness-of-fit index (PGFI) of 0.73. These
values are considered acceptable (Hair et al. 1998; Sharma 1996).
15
To assess convergent validity, we computed the composite reliability and Cronbach alpha
of the retained items, and the average variance extracted (AVE) per construct. The results of
these computations are reported in Table 1 (under the corresponding constructs). Except valueadded features, all values exceed commonly recommended thresholds (Fornell and Larker 1981;
Nunnally 1978). The variance extracted for value-added features is marginal, but it is close to the
recommended threshold (0.49 vs. 0.50). All items have standardized loadings exceeding 0.50,
and the loadings are statistically significant.
We examined discriminant validity of the final (re-specified) model by inspecting interitem and construct correlations (Fornell and Larcker 1981). The variance extracted for each
construct is larger than its shared variance with the other latent constructs. This indicates high
correlations between items that measure the same constructs, but not across different constructs.
Table 2 reports the shared variance among the constructs.
<Insert Table 2 here>
Structural model
The conceptual model can be represented by the following structural equations,
η1   0
η  =  β
 2   12
η 3   β13
0
0
β 23
ξ 
0 η1  γ 11 γ 21 γ 31 γ 41   1  ζ 1 
ξ
0 η 2  +  0 γ 22 0 γ 42   2  + ζ 2  ,
ξ 
0 η 3   0
0
0
0   3  ζ 3 
ξ 4 
(1)
where η’s represent the endogenous constructs (perceived quality, brand knowledge and brand
equity), ξ’s represent the exogenous constructs (the four Web-based marketing constructs) and
ζ’s denote random errors. β’s and γ’s are path coefficients that are empirically estimated from
the structural model. The relationships between the variables and parameters are graphically
depicted in Figure 1.
The measurement corresponding to the exogenous and endogenous constructs can be
represented by the following equations (in matrix notations).
x = Λ xξ + δ
y = Λ yη + ε ,
(2)
where x and y denote items that measure the exogenous and endogenous constructs respectively,
Λx and Λy are parameter matrices containing the standardized loadings, and δ and ε are random
16
measurement errors. The estimated values in Λx and Λy and their statistical significance in the
final model are similar to those reported in Table 1.
To determine the best-fit structural model, we followed Anderson and Gerbing’s (1988,
Figure 1) decision-tree framework to compare alternative specifications. Figure 2 illustrates the
sequential χ2 difference tests (SCDTs) that we conducted. Note that we present only the tested
branches in Figure 2.
<Insert Figure 2 here>
Essentially, the hypothesized model was compared to a series of competing models. We
omitted the path between perceived quality and brand knowledge, which is the only relationship
between the two components of brand equity, to derive the next most likely constrained model,
Mc. We constructed the next most likely unconstrained models, Mu1 to Mu4, by adding a direct
path from each marketing variable to brand equity. This could test for possible direct effects of
the marketing variables on brand equity. By performing all the tests that are shown in Figure 2,
the proposed structural model, Mt, which is equivalent to equation (1), was eventually accepted.
Table 3 reports a summary of all the tests in the SCDTs. The final structure model has a χ2 of
439.29 (p < 0.01), GFI of 0.91, RMSR of 0.06, AGFI of 0.89, NNFI of 0.97, and PGFI of 0.74.
These values are similar to those reported for the measurement model.
<Insert Table 3 here>
The explanatory power of the structural model is evaluated by inspecting the equationby-equation R2 of the endogenous constructs. Overall, the model can account for 88.24 and 17.79
percent of variations of perceived quality and brand knowledge respectively. 57.77 percent of
variations in brand equity could be explained by the proposed factors. This greatly exceeds 10
percent, which indicates substantial explanatory power of our research model (Falk and Miller
1992). Three of the proposed Web-based marketing constructs are relevant for online firms, and
they contribute to the studied firms’ brand equities.
Table 4 reports all hypothesis testing results. The final structural model supports four of
the six hypotheses. Specifically, product/technology innovation affects brand knowledge (H2b:
γ22 = 0.14, p < 0.05), Website quality affects perceived quality (H3: γ31 = 0.88, p < 0.01), and
advertising intensity influences both perceived quality (H4a: γ41 = -0.13, p < 0.01) and brand
knowledge (H4b: γ42 = 0.15, p < 0.01). We found no relationship between value-added features
(H1), product/technology innovation (H2a) and perceived quality. The model is consistent with
17
our conceptual framework of brand equity – both brand knowledge (β23 = 0.26, p < 0.01) and
perceived quality (β13 = 0.63, p < 0.01) are positive components of brand equity, and they are
positive correlated with each other (β12 = 0.29, p < 0.01).
<Insert Table 4 here>
IV. STUDY TWO
Study One validated the measures of the latent constructs and tested their relationships. To
assess the robustness of the above results, we invited another group of working postgraduate
students to fill in the survey, and at the same time investigated the sensitivity of the results with
respect to the conceptualization and measurement of two constructs, brand knowledge and
Website quality, that are both extensively studied in the extant literature.
Specifically, our measure of brand knowledge (see Table 1) is ex ante consistent with the
basic principal of the human capital model, which concerns the level of knowledge that a person
possesses about the studied brand. Another popular view of brand knowledge that was advanced
by Keller (1993) focuses on two sub-dimensions, brand awareness and association. We included
additional items that measure these two sub-dimensions in the current survey.20 Similarly, our
measure of Website quality focuses on the subjects’ impression on the overall excellence of the
selected Websites. McKinney et al. (2002) separate Website quality into two sub-dimensions,
information quality and system quality, and suggest that both of them could influence consumer
behavior. We followed their characterization of information and system quality, and added new
items to measure each of these two quality dimensions.21 The hypotheses about all the Webbased factors were then re-assessed using the new items. Table 5 lists the new items that we
added in this round.
<Insert Table 5 here>
20
The measures of brand awareness and associations were adapted from Aaker (1996), Droge (1989), Edell and
Keller (1989), Keller (1991), Maheswaran (1994), Putrevu and Lord (1994), Sujan and Bettman (1989) and Yoo et
al. (2000).
21
The items for information and system quality were adapted from Dabholkar et al. (1996) and McKinney et al.
(2002).
18
We followed the same procedures as in Study One, and collected 92 usable responses.22
The subjects were part-time postgraduate students in a large university who have enrolled in two
evening courses on management. The average age of the subjects was 27, and 32.6 percent of
them are female. Most have extensive online experience; 76.1 percent of them spend more than
12 hours per week to surf the Internet. We selected two online recruitment services, JobsDB
(http://www.jobsdb.com) and JobStreet (http://www.jobstreet.com), as stimuli to assess the
generalizability of our findings. This is because recruitment services are relevant to working
students – all subjects have previous experience in using at least one of the two services.
Because the sample size of Study Two is relatively small, it is not feasible to estimate a
CFA model that includes all the path coefficients and measurement errors. Instead, we averaged
the measurement items for each individual construct, and then performed ordinary least squares
regressions to examine the relationships between the constructs. Before testing the hypotheses,
we first verified the measures of brand knowledge and Website quality that were used in Study
One. A regression of brand knowledge on brand awareness and brand association produces:
Brand knowledge = -0.10 + 0.51 × Brand awareness + 0.48 × Brand association.
(0.12)
(0.14)
Both independent variables have positive and significant coefficients (p < 0.01), and R2 is 0.52,
which means that they could explain 52 percent of the variations in brand knowledge.
Similarly, both information and system quality contribute to Website quality:
Website quality = 0.22 + 0.47 × Information quality + 0.44 × System quality.
(0.12)
(0.13)
The two quality dimensions are positive and significant (p < 0.01) with R2 = 0.59. Hence our
measures are consistent with previous wisdoms on brand knowledge and Website quality (Keller
1993; McKinney et al. 2002), which further strengthens the confidence in Study One’s findings.
We estimated the model in Figure 1 with the new sample (with old and new measures of
brand knowledge and Website quality) using least squares regressions. The results are reported
in Table 6. Among all the significant relationships, except the effect of advertising intensity on
perceived quality (which had a negative coefficient in Study One), the rest of the coefficients
22
Before the survey was administered, we conducted one round of pilot study with 31 working subjects to check
the clarity and content validity of the items. Several items were reworded after the pilot study.
19
have the same signs as those in Study One. Hypotheses H2b, H3 and H4b are supported, but not
H1 and H2a. The result of H4a is opposite to that in Study One. These indicate that most of the
findings can be generalized across different subjects and online service industries. They are also
insensitive to the measures that we used for brand knowledge and Website quality.
V. OVERALL DISCUSSION AND CONCLUSIONS
Consistent with prior brand equity theory (e.g., Aaker 1996; Erdem and Swait 1998; Keller
1993), both perceived quality and brand knowledge are positive components of online firms’
brand equity. Collectively, they explain 58 and 43 percent of the variations in brand equity in
Study One and Two respectively. These signify their importance for online firms who are keen
on building brand reputations. Many possible factors, including individual-, firm- and marketlevel characteristics, could influence the formation of quality and knowledge perceptions. In the
following paragraphs, we discuss the findings pertaining to four firm-level Web-based marketing
variables that an online firm could manipulate to influence consumer perceptions. Future works
should extend this domain of knowledge by exploring the significance of individual and market
characteristics in an online setting.
The Effects of Web-based Marketing Variables
Contrary to our prediction, the quality of value-added features does not have significant effect on
perceived quality (H1). That is, providing good peripheral features to consumers does not seem
to raise their perception of product quality. One candidate explanation is that the subjects could
have failed to separate value-added features from Website quality (there is high shared variance
between these two constructs; see Table 2), and the results could emerge because of collinearity.
We have considered this possibility, and we purposely explained to the subjects in Study Two
about the differences between value-added features, Website quality and perceived quality before
the survey. Further, we added an alternative measure of Website quality (in terms of information
and system quality) in Study Two, but the insignificant result of value-added features persists.
Hence we believe the collinearity explanation may not be well grounded.
Several other explanations might account for this finding. First, by nature, value-added
features are useful to consumers who are less familiar with the online services. Given the ample
Internet experience of the subjects, they might have neglected the add-on features provided by
20
the studied brands. This is particularly plausible if those value-added features are designed to
provide on-line help or support for users to simply enjoy the basic functions, which is likely the
case for search engine, browser-based email or online recruitment services. Empirical research
in technology adoption/acceptance has shown that perceived ease of use may not affect adoption
attitude and/or behavior for experienced users (see, e.g., Szajna 1996; Taylor and Todd 1995).
Therefore, future works should examine the benefits of peripheral features to inexperienced Web
users and/or more complicated online products. For instance, due to the variety and complexity
of bank transactions, online banking could be a good testing ground for value-added features.
Second, it is also possible for consumers to have pre-defined expectations on the products
offered by the Websites, and whether such expectations are fulfilled might have dominated their
perceptions of product quality. Empirical evidence has shown that consumer satisfaction toward
self-service technologies might be driven by service functionality (Meuter et al. 2000). That is,
people may care more about the presence and proper functionality of basic services rather than
add-on peripherals. This is more likely for online services that serve well-defined goals or task
objectives with little room for service extension. Note that in physical environments, a point-ofsales agent would typically approach and offer assistance to consumers, through which she may
create unexpected services that the consumers appreciate (Bitner et al. 1990). It is difficult to
deliver such surprises in a pre-programmed online environment. Hence it is possible for valueadded features to affect the quality perception of consumers only for more ambiguous products
and, even with such products, more salient add-on features might be required to induce changes
in consumers’ quality perceptions.
In any case, online firms may wish to review the relevance of value-added features that
they provide to their target customers. For browser-based email, search engine and recruitment
services, it appears that the provision of peripheral services or tools is a giveaway to experienced
Internet users, and these features might not have noticeable impact on strengthening brand
equity.
Product/technology innovation has a positive influence on brand knowledge (H2b), but it
does not affect perceived quality (H2a). Apparently, being innovative helps an online firm impart
more information and product knowledge to consumers, but it may not shift the firm’s products
into better positions along the quality spectrum (cf. Schmalensee 1982). This could be again due
21
to the fact that our subjects are familiar with the studied Web-based services. Any quality signal
brought forward by product/technology innovations (e.g., new indexing scheme, secure email
facilities, etc.) might have been internalized within the industries, which make the innovation
“transparent” to the subjects. Nevertheless, it is possible for more recent innovations, practices
or technologies to exert a high influence on consumers’ quality perceptions. Future work should
investigate whether the age of an innovation matters in an online environment.
The finding that product/technology innovation increases brand knowledge is interesting
and useful for consumer researchers and Website managers. It implies that being an innovator
may help an online firm suppress consumer learning about the attribute and actual quality of its
products. As demonstrated by Van Osselaer and Alba (2000), such a blocking effect is pervasive
in experimental tasks involving consumer judgment, and it may grant the firm additional brand
advantage because consumers may ascertain the excellence of its products based on their initial
assessment and disregard later information that is valuable. Given that perception of innovation
helps raise consumer knowledge of a brand, even if their products are inferior, Website managers
may still want to emphasize their pioneering activities and innovations, and devise appropriate
promotional campaigns to block subsequent learning by consumers.
As hypothesized, Website quality has a significant positive influence on perceived quality
(H3); this positive effect is stable across different Website quality measures. A well designed
Website may help an online firm enhance its product quality to consumers. Therefore, other than
focusing on product excellence, an online firm should also devote resources into constructing an
appealing Website, which directly serves as a storefront for consumers. McKinney et al. (2002)
provide a framework to guide the design of a good Website, which consists of dimensions such
as relevancy, reliability and interactivity. A casual observation on the Internet suggests that most
firms are indeed aware of the importance of having good Websites; many companies profess in
designing and maintaining Websites for corporate clients. It is however instructive to emphasize
that our stimuli consist of brands which offer purely online services. Hence the subjects may
need more time to interact with the Websites in order to “consume” the services, which might
have amplified the importance of Website quality. It would be interesting for future work to
explore whether this finding continues to hold for online firms which sell physical products (e.g.,
books, electronics, etc.).
22
Advertising intensity reinforces the brand knowledge of consumers (H4b), and it lowers
(raises) perceived quality in Study One (Two) (H4a). Consistent with theories in information
economics (Nelson 1974; Stigler 1961), advertising confers brand information and knowledge to
consumers, and its effect persists in online settings. Our results on H4a are mixed – advertising
intensity lowers perceived quality of search engine and Web-based email services. This implies
that the wear-out effect proposed by psychological research may indeed be pervasive in certain
online industries (Alba et al. 1991). Further, this finding is consistent with the “desperation” and
“no pain” explanation advanced by Kirmani and Wright (1989), and it suggests that the extent of
advertising might have been excessive for the studied service providers.
Currently, many online firms use banner advertisements, pop-up browser windows and
mass emails to promote their products. Market forecasts have suggested that advertising expense
on the Internet is growing at astronomical rates (Shamdasani et al. 2001), and it is widely held
that Internet advertisements incur lower costs (Dou et al. 2002). However, if an online firm
over-promotes its brand or products, the excessive advertisements may bring inconvenience and
frustration to consumers,23 which may ultimately lower the perceived quality of its products. In
fact, taking into account the indirect paths, Study One suggests that advertising intensity has a
net negative impact on brand equity. Therefore, online firms need to manipulate the nature and
extent of advertising with care, and they should avoid over-selling their brands or products.
By contrast, Study Two reveals that advertising intensity raises perceived quality of the
recruitment services. This could be due to lower advertising expenses, and advertising may also
give more assurance to job seekers about the reliability and quality of the service providers.
These results collectively imply that advertising is an interesting variable for future studies – it is
good in that it always raises consumers’ brand knowledge; but it is bad for some firms as it could
reduce perceived quality.
To conclude, three of the four Web-based marketing variables are found to be significant,
and they jointly explain sizeable variations in perceived quality and brand knowledge. Through
the later two constructs, they contribute to shaping the brand equities of the studied online firms.
It is instructive to observe that the Internet has continually sparked new business practices and
23
See, for example, http://www.internetnews.com/IAR/article.php/1456271.
23
marketing models. Our studied marketing mix elements are by no means exhaustive, especially
when much variation in brand knowledge is still not accounted for. It would be interesting to see
if other Internet-specific variables or practices could help change consumer perceptions, which
might ultimately be translated into brand equity. Further, another good extension to this study is
to explore the significance and generalizability of our model across different product categories
and online industries. We have tested the hypotheses on three online service industries; it would
be interesting to see if these results hold for retailers that sell tangible goods on the Internet.
24
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30
Value-Added
Features
(ξ1)
γ11 (H1)
Product/
Technology
Innovation
(ξ2)
γ21 (H2a)
β13
γ31 (H3)
γ22 (H2b)
Brand
Equity
(η3)
β12
Website
Quality
(ξ3)
γ41 (H4a)
Advertising
Intensity
(ξ4)
Perceived
Quality
(η1)
Brand
Knowledge
(η2)
β23
γ42 (H4b)
Figure 1. Research Model
31
n.s.
Mt – Ms
Mc – Mt
s.
Mt – Mu1
or
Mt – Mu2
n.s.
or
Mt – Mu3
or
Mt – Mu4
Accept Mt
s. – significant
n.s. – non-significant
Ms – Measurement (saturated) model;
Mt – Hypothesized structural model;
Mc – Next most likely constrained model with respect to Mt (Omit PQ → BK);
Mu1 – Next most likely unconstrained model with respect to Mt (Add VA → BE);
Mu2 – Next most likely unconstrained model with respect to Mt (Add AI → BE);
Mu3 – Next most likely unconstrained model with respect to Mt (Add WQ → BE);
Mu4 – Next most likely unconstrained model with respect to Mt (Add PI → BE).
VA – Value-added features; AI – Advertising intensity; WQ – Website quality;
PI – Product/technology innovation; PQ – Perceived quality; BK – Brand knowledge; BE – Brand equity
Figure 2. Sequential Chi-square Difference Tests
32
Table 1. Items and Measurement Properties
Item1
Standardized
loading2
Value-added features (VA)
(composite reliability = 0.74; Cronbach alpha = 0.73; AVE = 0.49)3
The quality of the value-added features offered by X is impressive.
The value-added features provided by X are desirable.
X provides low standard value-added features.4
0.78 (15.18)
0.70 (13.30)
0.60 (10.96)
Product/technology innovation (PI)
(composite reliability = 0.89; Cronbach alpha = 0.89; AVE = 0.67)
When improving products or technologies, X has been fast.
X is fast in advancing its products or technologies.
It appears that X is in the forefront in terms of new product or technology development.
X has been fast in enhancing its products or technologies.
0.79 (16.61)
0.84 (18.28)
0.74 (15.09)
0.89 (20.04)
Website quality (WQ)
(composite reliability = 0.88; Cronbach alpha = 0.88; AVE = 0.64)
The Website quality of X is well maintained.
I am impressed with the Website quality of X.
X maintains a high standard of Website quality.
The Website quality of X is superior.
0.77 (16.20)
0.82 (17.64)
0.81 (17.38)
0.80 (16.93)
Advertising intensity (AI)
(composite reliability = 0.89; Cronbach alpha = 0.89; AVE = 0.63)
X is intensively advertised on any media.
I find it easy to come across an advertisement for X on any media.
Advertisements for X can be seen frequently on any media.
Advertisements of X are common on any media.
It is rare to see an advertisement for X on any media.4
0.72 (14.59)
0.76 (15.75)
0.90 (20.46)
0.87 (19.14)
0.70 (14.02)
Perceived quality (PQ)
(composite reliability = 0.80; Cronbach alpha = 0.80; AVE = 0.57)
X is of high quality.
X appears to be very reliable.
The quality of X is poor.4
0.85 (18.29)
0.71 (14.19)
0.70 (14.03)
Brand knowledge (BK)
(composite reliability = 0.90; Cronbach alpha = 0.90; AVE = 0.75)
I know much about X.
I have strong knowledge of X.
I have very good understanding on X.
0.81 (17.16)
0.92 (20.67)
0.86 (18.92)
Brand equity (BE)
(composite reliability = 0.89; Cronbach alpha = 0.88; AVE = 0.61)
X is better than any other Y service with same quality.
0.73 (14.88)
Compared to another Y service with identical quality, X is of a higher value to me.
0.90 (20.28)
In the presence of a Y service that is just as good, it is more worthwhile to use X.
0.85 (18.76)
If there is a Y service that is not different from X in any way, I will give X a higher rating.
0.74 (15.33)
It makes sense to use X instead of any other Y service even if they are the same.
0.66 (13.05)
1
X = one of the studied brands in the corresponding category; Y = search engine or Web-based email.
2
t-statistics in parentheses. All t-statistics are significant with p < 0.01.
3
AVE = average variance extracted.
4
Data are reverse-coded.
33
Table 2. Shared Variance between the Latent Constructs
Construct
VA
AI
WQ
PL
PQ
BK
BE
VA
0.49
AI
0.00
0.63
WQ
0.45
0.01
0.64
PI
0.24
0.05
0.31
0.67
PQ
0.38
0.00
0.61
0.28
0.57
BK
0.07
0.03
0.09
0.10
0.11
0.75
BE
0.20
0.00
0.34
0.17
0.38
0.18
0.61
Comparison
Mt / M s
Mc / M t
Mt / Mu1
Mt / Mu2
Mt / Mu3
Mt / Mu4
+
s. = significant (p ≤ 0.01); n.s. = non-significant (p > 0.01).
Hypothesis
H1 (γ11)
H2a (γ21)
H2b (γ22)
H3 (γ31)
H4a (γ41)
H4b (γ42)
+
Table 3. Summary of the SCDTs
χ2
χ2
df Outcome+ Conclusion
ST
ND
difference
1
2
439.29
435.27
4.03
4
n.s.
–
450.51
439.29
11.22
1
s.
–
439.29
437.71
1.58
1
n.s.
Accept Mt
439.29
437.15
2.14
1
n.s.
Accept Mt
439.29
436.06
3.23
1
n.s.
Accept Mt
439.29
438.98
0.32
1
n.s.
Accept Mt
Table 4. Hypotheses Testing
Path
Standard
Path
t-statistic
coefficient
error
0.03
0.10
0.30
VA → PQ
0.06
0.05
1.02
PI → PQ
0.14
0.08
1.75
PI → BK
0.88
0.11
8.37
WQ → PQ
-0.13
0.04
-3.22
AI → PQ
0.15
0.06
2.39
AI → BK
Conclusion+
Not supported
Not supported
Supported
Supported
Supported
Supported
Except H4a: AI → PQ, all hypotheses are evaluated using one-tailed tests. The t-statistic of γ22 is
significant at p < 0.05. All other significant t-statistics have p < 0.01.
34
Table 5. New Measurement Items
Item+
Information Quality (composite reliability = 0.81)
The website of X contains relevant information.
I can understand the information from the website of X.
Information provided by the website of X is reliable.
The website of X covers a broad scope of information.
The information obtained from the website of X is adequate.
Most of the information from the website of X is useful.
System Quality (composite reliability = 0.75)
It is easy to find what I need from the website of X.
The hyperlinks found in the website of X are good.
The accessibility of the website of X is good.
The website of X provides me with an interactive experience.
The website of X is user-friendly.
I find it easy to navigate the website of X.
It is entertaining to surf the website of X.
Brand Awareness (composite reliability = 0.61)
It is easy to recognize X.
Some characteristics of X come to my mind easily.
I am aware of X.
I can recall how the logo of X looks like.
Brand Association (composite reliability = 0.91)
I have a favorable opinion of X.
I like X very much.
I have a pleasant experience with X.
The decision to use X is wise.
X has been a good choice.
X has a personality.
My choice of X is based on its unique features.
I have a clear image of the type of person who would use X.
There are features that can describe X.
Some features of X are beneficial.
X provides superior features.
X has several beneficial features.
The features offered by X are useful.
+
X = one of the studied brands.
35
Hypothesis
/ parameters
H1 (γ11)
H2a (γ21)
H2b (γ22)
H3 (γ31)
H4a (γ41)
H4b (γ42)
β12
β13
β23
Table 6. Results of Study Two+
With Study One
With new BK and
Path
measures
WQ measures++
-0.01 (0.11)
0.02 (0.10)
VA → PQ
0.13 (0.13)
0.06 (0.12)
PI → PQ
***
0.30 *** (0.09)
0.35 (0.15)
PI → BK
0.80 *** (0.15)
0.57 *** (0.13)
WQ → PQ
0.11 * (0.06)
0.14 ** (0.06)
AI → PQ
0.08 * (0.05)
0.22 *** (0.08)
AI → BK
0.05 (0.13)
0.29 *** (0.08)
PQ → BK
0.15 ** (0.09)
0.45 *** (0.09)
PQ → BE
0.39 *** (0.08)
0.92 *** (0.11)
BK → BE
+
Standard errors in parentheses. Except H4a: AI → PQ, all hypotheses are evaluated
using one-tailed tests. *** p < 0.01; ** p < 0.05; * p < 0.10.
++
A composite measure of brand awareness and association was used in place of brand
knowledge; website quality was measured by information and system quality.
36