TOWARD AN INTEGRATIVE FRAMEWORK FOR ONLINE CONSUMER BEHAVIOR
RESEARCH: A META-ANALYSIS APPROACH
Abstract
The recent failure of a large number of e-tail companies
epitomizes the challenges of operating through virtual channels and underscores
the need to better understand key drivers of online consumer behavior. The
objective of this study is to provide a comprehensive review of the extant
information systems literature related to online consumer behavior and
integrate the literature in order to enhance our knowledge of consumer behavior
in electronic markets and provide clear direction for future research. This
paper introduces a framework that integrates research findings across studies
to develop a coherent and comprehensive picture of the online consumer behavior
research conducted in the IS field. The integrative framework proposes system
quality, information quality, service quality, and vendor and channel
characteristics as key factors that impact online consumer behavior, achieving
their effects by altering the perceptions of usefulness, ease of use, trust,
and shopping enjoyment. [PUBLICATION
INTRODUCTION
The Internet offers immense opportunities for companies to reach
a wide base of consumers and efficiently market their products through an
electronic channel. According to the Boston Consulting Group, online retailing
will reach $168 billion by 2005 (Mark, 2001). Such estimates substantiate that
the Internet has emerged as a powerful alternative channel for selling products
and services. However, the recent failure of a large number of e-tail companies
exemplifies the challenges of operating through virtual channels and also
highlights the need to better understand key factors that drive consumer
behavior in online market channels. The infusion of the Internet technology
into customer-supplier interactions requires a reconsideration of existing
theories and frameworks regarding consumer behavior. Explicit attention should
focus on understanding the factors that can explain a consumer’s interaction
with the technology, their purchase behavior in electronic channels, and their
preference to transact with an electronic vendor on a repeat basis.
Although online consumer behavior has been the subject of
considerable research in the last few years, there is a paucity of research
that attempts to integrate research findings across studies. Online consumer
behavior research is a young and dynamic academic domain that is characterized
by a diverse set of variables studied from multiple theoretical perspectives.
Researchers have relied on the Technology Acceptance Model (Davis, 1989; Davis
et al., 1989), the Theory of Reasoned Action (Fishbein&Ajzen, 1975), the
Theory of Planned Behavior (Ajzen, 1991), Innovation Diffusion Theory (Rogers,
1983, 1995), and Flow Theory (Csikszentmihalyi, 1988) in investigating
consumers’ adoption and use of electronic commerce. Studies have examined
various aspects of consumer behavior such as Web site use, future use,
purchase, future purchase, unplanned purchase, channel preference, and
satisfaction. In terms of explanatory factors that drive such behavior,
researchers have explored the role of attributes of the Web site, attributes of
the vendor, consumer characteristics, individual perceptions, and the social
context (Agarwal&Karahanna, 2000; Agarwal&Venkatesh, 2002; Gefen&
Straub, 1997, 2000; Jarvenpaa et al., 2000; Koufaris, 2002; Limayem et al.,
2000; Moon & Kim, 2001; Torkzadeh&Dhillon, 2002). While these studies
individually provide meaningful insights, a single study does not resolve a
major issue (Hunter & Schmidt, 1990). By integrating research findings
across multiple studies, we can accumulate knowledge, develop a comprehensive
understanding of the phenomena, and identify remaining research issues.
The objective of this study is to provide a comprehensive review
of the extant information systems (IS) literature related to online consumer
behavior and rigorously integrate the literature in order to enhance our
knowledge of consumer behavior in electronic markets and provide clear
directions for future research. To that end, we not only review and analyze
studies that have been published in the major IS journals, but also propose an
integrative framework that describes the relationships between key variables
that predict and determine consumer behavior in electronic channels. Such an
approach should provide insights on factors that need to be carefully
considered by companies starting or operating electronic businesses, as well as
researchers developing and testing models to further understand online consumer
behavior.
STUDY APPROACH
A set of sampling criteria was initially determined in order to
identify the studies that formed the foundation for our research endeavor.
First, we decided to include only those studies that have been published in
major journals within the IS domain. Second, only studies published between
1995 and 2002 were included for further consideration. Third, we limited our
focus to those electronic commerce studies that were conducted at the
individual level unit of analysis. Hence, consumers or users of Web
technologies were the main subjects in these studies. Fourth, for a study to be
included, it had to be based on empirical (quantitative) analysis. This allowed
us to focus on empirically tested constructs and relationships rather than
those that have only been conceptualized.
Based on the stated criteria, we conducted a thorough search of
the following major IS journals: Communications of the ACM; Decision Sciences;
Decision Support Systems; IEEE Transactions on Systems, Man, and Cybernetics;
Information Systems Research; Information Technology and Management; Information
and Management; International Journal of Electronic Commerce; Journal of End
User Computing; Journal of Management Information Systems; and MIS Quarterly.
These journals were considered to be mainstream IS journals that are
appropriate outlets for research on online consumer behavior. Studies were
located via computer searches of large bibliographic databases (UMI-Proquest
and ScienceDirect) and by manually scanning the journals. Upon completion, a
total of 42 nonredundant papers were identified for inclusion.
As shown in Table 1, the most popular outlets for online
consumer behavior research were Information Systems Research (11 articles),
International Journal of Electronic Commerce (11 articles), and Information and
Management (nine articles). Two recent special issues on e-commerce metrics
were the main sources of the Information Systems Research articles. While the
number of articles published each year was increasing over time, most articles
were published in 2000 and thereafter (seven articles before 2000, seven
articles in 2000, seven articles in 2001, and 21 articles in 2002).
Two researchers read each of the papers and independently coded
and tabulated the following items in independent tables: methodology, sample
size, sample source, independent and dependent variables, task, theory basis,
and study findings. The coders then met to compare the tables and resolve the
discrepant cases in order to reach a consensus in their categorization and
tabulation as shown in Table 2. The overall inter-rater agreement between the
two coders for the categorization of study methodology, sample source, theory
basis, and task was 94%. Analysis showed that the most common research method
is survey (23 studies), followed by laboratory experiments (15 studies),
combined approaches (three studies), and secondary data analysis (one study).
Half of the studies used consumers and the other half used student (including
undergraduate and graduate) subjects as the source of samples. A total number
of 27,202 individuals participated in the studies that were included in the
final set. Laboratory experiment-based studies either used actual Web sites
(Web site for books, airline tickets, legal services, automotives, car rental,
etc.) or resorted to simulated replicas of actual Web sites.
Books were the most popular product type used in the studies.
Other product types included CDs, airline tickets, used laptop computers,
videos, and flowers. In terms of virtual products, legal services, e-banking
services, financial products, and news services were employed by the studies.
Subjects were typically asked to respond to the instrument based on their
immediate prior experience or their general impression regarding behavior in an
online environment. The tasks ranged from rating Web site attributes that may
influence their behavior to making purchases for a specific product.
REVIEW OF STUDY FINDINGS
Our review of the 42 studies focused on understanding the
interrelationships between the study variables. We first present our review of
the study findings organized around three related but distinct categories of
the dependent variables of online consumer behavior research: Web use, online
purchase, and post-purchase. The Web use category included variables such as
current Web site use, future intention to use a Web site, and satisfaction with
the use of the Web or Internet-based services. However, if the underlying
purpose of use was to “purchase”, that behavior was classified in the second
category called online purchase. Post-purchase behaviors such as future
purchase and satisfaction with purchase were classified in the third category.
Following the review, we present the results of our quantitative analysis
conducted for the theoretic models and variable relationships commonly found across
studies. Table 3 summarizes the list of study variables for the dependent
variables of online consumer behavior research.
Studies on Web Use
The Internet has evolved to become a technology that serves
multiple needs. Users can access various types of services (such as news,
e-banking, information search, etc.). Studies that evaluated use behavior
focused on actual use or willingness to use these services. Some studies
assessed use of the Internet in general, without contextualizing use for a
specific service. The predictors of the use behavior can be segmented into user
characteristics, user perceptions, and the social context of the user (Table
3).
User Characteristics
Two dominant aspects within user characteristics that have been
subjected to empirical analysis are demographic variables and psychographic
variables. The demographic variables investigated by studies as predictors of
Internet use included race, gender, generation, and culture. The findings
supported the notion that the white population used the Internet more than
minorities, males were marginally heavier users than females, and subjects
younger than 19 years of age displayed a much higher usage behavior (Kraut et
al., 1999). Culture (subjects in the U.S. and Hong Kong) not only impacted the
use behavior but also influenced the underlying purpose of the use (Chau et
al., 2002). The subjects in the U.S. were found to be more oriented toward
using the Internet for commerce and entertainment, while subjects in Hong Kong
primarily used the Internet for hobbies and social communication. In terms of
psychographics, researchers have found that personal innovativeness,
playfulness, and computer skill were distal determinants of use, achieving
their effects through ease of use and usefulness (Agarwal&Karahanna, 2000;
Agarwal& Prasad, 1998; Kraut et al., 1999; Moon & Kim, 2001).
User Perceptions
User perceptions were widely used as the main variables of
interest in a variety of studies. User perceptions regarding lack of data
security, instability of the system, information content and accuracy,
responsiveness, download delay, navigation, interactivity, system design
quality, ease of use, and usefulness were found to be significant predictors of
use behavior (Agarwal&Venkatesh, 2002; Han & Noh, 2000; Liao &
Cheung, 2002; Liu & Arnett, 2000; Moon & Kim, 2001; Palmer, 2002). In
addition, it was found that the difference between expectation and perceived
performance regarding Web information quality and service quality significantly
explained Web customer satisfaction (McKinney et al., 2002). Factors such as
control, curiosity, heightened enjoyment, focused immersion and temporal
dissociation collectively proposed as cognitive absorption were also found to
influence perceptions such as ease of use and usefulness, which subsequently
impacted use (Agarwal&Karahanna, 2000).
Social Context
A limited number of studies has investigated the impact of
social context on Web use behavior. Use of the Internet by other family
members, external influence (articles, reviews, and promotion of the Web site),
and interpersonal influence (relatives and colleagues) were identified as
significant predictors of Web use (Agarwal&Venkatesh, 2002; Kruat et al.,
1999; Parthasarathy&Bhattacherjee, 1998).
Studies on Online Purchase
The studies within this category focused on identifying factors
that impacted the intention to purchase or the actual purchase behavior. The
variables used as predictors of purchase behavior are categorized into consumer
characteristics, consumer perceptions, technology attributes, and social
context (Table 3).
Consumer Characteristics
Studies found that the higher a person’s income, education, and
age, the more likely he or she was to buy online (Bellman et al., 1999; Liao
& Cheung, 2001). Gender was found to significantly impact perceptions
toward shopping through the Web. Women view shopping as a social activity and
were found to be less technology oriented compared to men (Slyke et al., 2002).
However, researchers have cautioned that demographic variables alone explain a
very low percentage of variance in the purchase decision (Bellman et al.,
1999). An interesting result that emerged was that consumers that are more
likely to buy online have a “wired lifestyle”. Such consumers have used the
Internet for a long time, received a large number of emails everyday, believed
the Internet improves productivity at work, and used the Internet for most of
their other activities such as reading news and searching for information
(Bellman et al., 1999). Other consumer characteristics, such as personal
innovativeness, discretionary time, search for product information, Web skill,
Internet self-efficacy, email use, and prior Web use were also found to be
predictors of willingness to purchase (Agarwal& Prasad, 1998; Liao &
Cheung, 2001; Limayem et al., 2000; Ramasawami et al., 2001). The impact of
those variables on intention to purchase may be mediated through factors such
as ease of use, shopping enjoyment, and perceived control (Koufaris, 2002;
Limayem et al., 2000).
Consumer Perceptions
Consumer perceptions constituted an important category that
influenced purchase related behavior. However, it was also one of the
categories that showed a high level of diversity in terms of study variables.
Perceived consequences and perceived risk were found to predict purchase
behavior (Grazioli&Jarvenpaa 2000; Liao & Cheung, 2001; Limayem et al.,
2000). Perceived control and involvement with the product were also found to
significantly impact shopping behavior. Consumer perceptions about different
types of quality attributes of the Web site and the vendor were also evaluated.
Perceptual variables from the Technology Acceptance Model (TAM) (Davis, 1989)
and Service Quality (SERVQUAL) (Parasuraman et al., 1988) were examined. The
TAM variables of perceived usefulness and ease of use were found to be
distinguishing factors between bidders and non-bidders in an online auction
context (Stafford & Stern, 2002). The SERVQUAL construct consists of the
five sub-dimensions of tangibles, reliability, responsiveness, assurance, and
empathy (Pitt et al., 1995), but were often used in a disaggregated fashion
resulting in mixed findings. Vendor quality was found to influence willingness
to shop online (Liao & Cheung, 2001), and information or content quality
was also a predictor of purchase behavior (Jarvenpaa& Todd, 1997;
Ranganathan&Ganapathy, 2002).
Technology Attributes
Factors included in this category related to the actual
functionalities and attributes of the Web site rather than the perceptions of
the attributes. Paper-based catalogs were found to generate higher levels of
consumer involvement as compared to Web-based catalogs (Griffith et al., 2001).
No difference was found in money spent or number of products purchased among
different interface designs, including catalog interface designs, bundle-based
interface designs, and virtual reality-based stores (Westland & Au, 1998).
Other attributes of the technology such as comparative shopping, assurance
mechanisms, Web page loading speed, value-added search mechanisms, shopping
carts, feedback mechanisms, and chat channels were found to significantly
influence intentions to shop and actual purchase behavior (Grazioli&Jarvenpaa,
2000; Koufaris, 2002; Liang & Lai, 2002; Limayem et al., 2000).
Social Context
Studies in psychology and sociology have presented a wealth of
knowledge about how individuals are influenced by the social structures in
which they live. Limayem et al. (2000) found that media and family influences
significantly affected intentions to purchase while friends’ influence did not
make a difference.
Studies on Post-Purchase
The primary dependent variables within this category were
satisfaction with purchase, channel preference, switching, attrition, and
re-visitation. The predictor variables are grouped into consumer
characteristics, consumer perceptions, technology attributes, and vendor and
channel characteristics (Table 3).
Consumer Characteristics
Chen and Hitt (2002) provided the only study that investigated
the role of user characteristics in determining two types of post-purchase
behavior (switching and attrition). The study found that age and education
impacted attrition negatively, and that females showed a higher propensity to
become inactive users. However, they concluded that demographics overall did
not explain much variance.
Consumer Perceptions
In the context of consumer perceptions, researchers found that
perceptions regarding data security, inconvenient use, stability of the system,
satisfaction with previous purchase, usefulness, ease of use, Web site quality,
time saving, empathy, assurance, and shopping enjoyment were significant
predictors of channel satisfaction, intention to return, switching, and
attrition (Chen &Hitt, 2002; Devaraj et al., 2002; Han & Noh, 2000;
Koufaris, 2002; Koufaris et al., 2002).
Technology Attributes
The studies evaluating the role of attributes of technology on
post-purchase behavior have yet to identify a significant predictor. No
difference was found in Web satisfaction when the download time of the Web page
was varied between 0 and 15 seconds (Otto et al., 2000). Chen and Hitt (2002)
found no significant relationship between personalization enabled through the
Web site and switching behavior.
Vendor and Channel Characteristics
In the context of vendor characteristics, it was found that the
breadth of offerings was negatively related to switching behavior, while a
greater minimum deposit required to join an online broker reduced attrition
rate (Chen &Hitt, 2002). In terms of channel characteristics, price
differentials between online and offline channels were found to be a
significant predictor of channel satisfaction and subsequent channel preference
(Devaraj et al., 2002).
Quantitative Analysis of the Theoretic Models and Study Variable
Relationships
The dominant theoretical model used in online consumer research
was the Technology Acceptance Model (16% of the studies) (Davis, 1989),
followed by the Theory of Planned Behavior (12%) (Ajzen, 1991), and Innovation
Diffusion Theory (7%) (Rogers, 1983, 1995). Other theoretic models or paradigms
included Transaction Cost Economics (5%) (Williamson, 1979, 1985), Flow Theory
(5%) (Csikszentmihalyi, 1988), SERVQUAL (5%) (Parasuraman et al., 1988), and
Involvement Theory (5%) (Reeves &Nass, 1996). These theories were used
independently or in combination with each other. An effort to examine the
predictive power of different theoretical models proved to be extremely
difficult because studies combined variables from different theories and used
different dependent variables, thus making the comparison task problematic. An
exception is the study conducted by Devaraj et al. (2002), which compared three
alternative models and found that TAM explained the most variance in electronic
commerce channel satisfaction (76%), followed by Transaction Cost Analysis
(72%) and SERVQUAL (56%).
A meta-analysis of the interrelationships between the study
variables was conducted by aggregating the correlation coefficients reported by
individual studies. Since path coefficients are influenced by other variables
present in the model, methods that rely on correlations are deemed more
desirable (Hunter & Schmidt, 1990). Our review of the 42 studies identified
only 17 studies with the correlation table reported in the paper. A subsequent
review further showed that there were only eight relationships examined more
than once across studies. Table 4 summarizes the analysis results of these
common relationships. A weighted average of the correlation coefficients,
instead of the simple average across studies, was computed for each
relationship to correct for sampling error as recommended by Hunter and Schmidt
(1990).
The most commonly studied relationship was found between ease of
use and usefulness, with the weighted correlation average of .55. The highest
correlation was found between information (or content) quality and system
quality at r = .70, and the lowest correlation was between playfulness and
system use at r = .26. Attitude was correlated with willingness to buy at r =
.60. Intention to use was most correlated with usefulness (r = .67), followed
by enjoyment (r = .59), ease of use (r = .51), and perceived control (r = .45).
INTEGRATIVE FRAMEWORK
Figure 1 presents an integrative framework for online consumer
behavior research. The framework builds upon prior research and integrates
research findings across studies to develop a coherent and comprehensive
understanding of the online consumer behavior research conducted in the IS
field. The framework is also grounded on several theoretic perspectives
developed outside of the online consumer behavior research such as IS success
model (DeLone& McLean 1992; Seddon 1997), SERVQUAL (Pitt et al., 1995), and
TAM (Davis, 1989; Davis et al., 1989). Each element of the framework and
relationships between them are further described below.
Dependent Variables
Consistent with our review of the online consumer behavior
research, the framework groups dependent variables into three categories: Web
use, online purchase, and post-purchase. Studies have examined these behaviors
independently or in combination with each other. However, an interesting aspect
that has not been explicitly addressed in literature is the relationship
between these behaviors. The framework proposes significant links between Web
use and online purchase, between online purchase and post-purchase, and between
post-purchase and use. First, frequent use of the system is likely to lead to
online purchasing. Companies on the Internet try to increase traffic and make
their Web sites “sticky,” so that users can spend more time on the Web. Liang
and Lai (2002) report that consumers are more likely to shop at well-designed
Web sites. As the customer has to interact with the system to execute an online
purchase, the use of the Web site is a crucial precursor to online purchase.
Second, online purchase is likely to become a repeated pattern of behavior if
customers are satisfied with their purchase. Online purchasing offers the
opportunity to assess the quality of product and vendor service, as well as to
experience the convenience of online transactions. Thus, the experience from
the purchase becomes a determinant of post-purchase decision variables such as
channel preference, switching, attrition, and re-visitation. Finally,
post-purchase is likely to influence the level of Web use. Customers need to
resolve post-purchase issues, receive technical support, and check product
updates through the use of the Web. Further, as they are satisfied with the
purchase, they will continue using the system to repeat the purchase.
Mediating Perceptual Variables
Extending the prescriptions of the TAM, which theorizes
usefulness and ease of use as fundamental mediating perceptions through which
external factors influence usage behavior (Davis et al., 1989), the framework conceptualizes
usefulness, ease of use, trust, and shopping enjoyment as perceptual variables
that mediate the effects of system quality, information quality, service
quality, and vendor and channel characteristics. Studies based on Flow Theory
have found shopping enjoyment as a mediator between various predictor variables
and intention to return (Koufaris 2002; Koufaris et al., 2002). Trust-related
literature emphasizes trust as a key mediating variable (Bhattacherjee, 2002;
Gefen et al., 2003; Grazioli&Jarvenpaa, 2000; McKnight et al., 2002).
Relationships between the mediating variables have also been found. The
relationship between usefulness and ease of use is well established (Davis et
al, 1989; Venkatesh& Davis, 2000). Gefen et al. (2003) found that ease of
use, trust, and usefulness are related. However, the relationship between
shopping enjoyment and usefulness, while implicitly referred to, has not been
empirically examined.
Predictor Variables
Based on our review of the studies and the theoretical perspectives
presented earlier, we propose that many variables used as predictors of online
consumer behavior can be classified into system quality, information quality,
and service quality. Other factors such as vendor and channel characteristics,
consumer demographics and traits, and the social context of the consumer were
also addressed in the studies and are included in the integrative framework.
System Quality
System quality captures the user perceptions regarding the
effectiveness of system attributes. The infusion of technology in the
interaction between the consumer and the vendor increases the importance of the
technology-enabled interface with which the consumers have to interact.
Navigation, interface layout, download speed, digital seals, and value-added
mechanisms are some factors that constitute the notion of system quality (Han
& Noh, 2000; Liao & Cheung, 2001; Liu & Arnett, 2000; Westland
& Au, 1998).
Koufaris (2002) has found that value-added search mechanisms
play a significant role in shaping consumers’ intention to return to the Web
site and shopping enjoyment partially mediates the effect. Anecdotal evidence
suggests that high performing companies actively pursue enhancements in Web
site features and services that facilitate the consumer purchase experience
(Zbar, 2000). Schubert and Selz (1997) structure an extensive list of Web site
features into three phases common to purchase transactions (information phase,
agreement phase, and settlement phase). TAM suggests that system features affect
use through the perceptions of ease of use and usefulness (Davis et al., 1989).
Therefore, the framework proposes that system quality influences online
consumer behavior by altering consumer perceptions of ease of use, usefulness,
trust, and shopping enjoyment.
Information Quality
Information quality captures the perceptions of the consumer
regarding the characteristics of the Web site content such as accuracy,
comprehensiveness, reliability, relevance and usefulness. Agarwal and Venkatesh
(2002) found that content was equally important across industries (books,
airline, car rental, and automotive) and tasks (customer and investor).
Although studies suggested that information quality was an important
determination of use and user satisfaction, its impact on purchase or
post-purchase behavior was found to be rather moderate. For example, Palmer
(2002) and McKinney et al. (2002) found that information quality impacted use,
while Ranganathan and Ganapathy (2002) concluded that content was the least
important discriminator between subjects with low intent and high intent to
purchase. A possible reason for such findings could be the underlying task or
product. For example, content may be a dominant factor in the context of Web
sites that provide information-based services (news, search, legal counseling,
article delivery, etc.), while its role in purchasing physical products may be
moderate. Liu and Arnett (2000) found a high correlation between information
quality and learning capability (r = .72, p < .001). Overall, prior research
findings suggest that information quality is an important predictor of online
consumer behavior, and its effect may be mediated by user perceptions of
usefulness and ease of use.
Service Quality
Service quality measures the perceptions of the consumers
regarding their service experience. The peculiar nature of the technology in
question (the Web site) and the context (online consumer behavior) creates
complexity in application of service quality in electronic channels. This issue
is also prevalent in the context of other information systems as pointed out by
Seddon (1997) that the system and the IS department are two different entities.
Thus, a distinction needs to be made regarding who is providing the service. If
the service is being provided by the Web site, the elements of service quality
such as tangibility, reliability and responsiveness will tend to overlap with
system quality. However, if the vendor provides the service, service quality
should emerge as a distinct factor. This may be one of the reasons for
contradictory findings in the studies on dimensions of SERVQUAL. Consequently,
we recommend that researchers make a clear distinction regarding the context
and apply SERVQUAL with caution. Most prior studies operationalized SERVQUAL as
a set of service functions of a Web site. In our framework, we conceptualize
SERVQUAL as vendor’s effectiveness in providing customer service, rather than
Web site’s effectiveness in providing service functions. When a vendor’s
service quality changes, it is likely to change user perceptions of trust and
usefulness, thereby changing the users’ intentions to buy online. Thus, the
framework proposes that the service quality of the vendor influences online
consumer behavior through its effects on trust and usefulness perceptions.
Vendor and Channel Characteristics
Vendor characteristics such as vendor competence, size,
reputation, and participation costs have shown consistent results across
different studies (Chen &Hitt, 2002; Jarvenpaa et al., 2000). Vendor
characteristics such as size and reputation enhance consumer perceptions
regarding trust or the integrity of the vendor. Thus, brand issues seem to be
as prevalent, if not stronger, in an online context as they are in an offline
channel. These results raise concerns regarding the assertions that the
Internet provides a level playing field for the companies. It is argued that
electronic markets may be more efficient than offline markets (Devaraj et al.,
2002). The main arguments presented in favor of such an assertion is that the
Internet reduces search costs and makes the delivery processes more efficient,
thus resulting in low prices for products. Empirical results show that lower
prices play an important role in channel choice decisions (Devaraj et al.,
2002; Liang & Huang, 1998; Liao & Cheung, 2001). Furthermore, prior
research found that price differentials between online and offline channels
(Devaraj et al., 2002) and participation costs (Chen &Hitt, 2002)
influenced online behavior. The framework proposes that these characteristics
of the vendor and channel impact online consumer behavior by enhancing vendor
trust and perceived usefulness of the channel.
Consumer Demographics and Personal Traits
Factors that constitute demographics and personal traits have
either been modeled as facilitating factors of certain types of perceptions or
as factors that moderate the relationships between the independent and
dependent variables. Three important findings have emerged concerning
demographics. First, women have been found to be more conservative customers
with respect to electronic channels (Slyke et al., 2002). Multiple arguments
have been presented for these results. For example, women view shopping as a
social activity, and show conservatism toward trying a new technology. Second,
lifestyle has been suggested as an important variable. Researchers have found
that a wired lifestyle (Bellman et al., 1999) and a Net-oriented life style
(Kim et al., 2000) play a significant role in determining consumer behavior. Finally,
studies have concluded that demographics overall do not explain much variance
in behavior (Bellman et al., 1999; Chen &Hitt, 2002). Personal traits such
as personal innovativeness, Web skills, Internet computer self-efficacy, and
affinity with a computer have consistently been found to be significant
variables across the studies.
Social Context Variables
Social context consists of the external influences (mass media,
advertising, and marketing-related stimuli) and interpersonal influences
(word-of-mouth, friends, relatives and other sources involving a consumer’s
social network) (Agarwal&Venkatesh, 2002; Limayem et al., 2000;
Parthasarathy&Bhattacherjee, 1998). Results were, for the most part,
supportive of the significant effects of social context variables on the use of
the Internet and online shopping intention. Outside of the online consumer
behavior context, Venkatesh and Davis (2000) showed that social norm affected
intention to use partially via perceived usefulness. Consistently, the framework
includes social context variables as determinants of both perceptual variables
and consumer behavior.
DISCUSSION
Online consumer behavior is a complex phenomenon. By taking a
meta-analytic approach and integrating previous findings across studies, this study
seeks to overcome the inherent limitation of a single study and provide a
comprehensive overview of the current status of knowledge within the domain of
online consumer behavior research in the IS field. Furthermore, this study
offers an integrative framework that is comprehensive enough to facilitate an
understanding of how key variables fit together and detailed enough to allow
investigation into sub-domains of online consumer behavior. The framework
proposes that system quality, information quality, service quality, and vendor
and channel characteristics are central variables that predict and determine
online consumer behavior, achieving their effects primarily by altering the
perceptions of ease of use, usefulness, trust, and shopping enjoyment. In addition,
the framework includes individual difference variables and social context
variables to further account for their potential influence on mediating
perceptual variables and online consumer behavior variables.
Several major avenues for future research emerge from the
analysis presented in this paper. First, the relationships between Web site
use, online purchase, and post-purchase have not been explicitly investigated.
Research projects that focus on this issue can provide meaningful insights into
what factors contribute toward converting users to customers and customers to
repeat customers. An interesting extension in this regard could also be to
investigate the role of services (such as buy online and subsequently pick up
at or return to the physical store) that leverage both virtual and physical
channels in determining purchase behavior, and also the linkage between that
purchase experience and post-purchase satisfaction.
Second, the integrative model proposes some new relationships
between predicting and mediating variables and between mediating and dependent
variables that have not been subjected to empirical examination, and thus
provide areas toward which future research can be directed. For example, future
research is needed to systematically vary the system quality factors such as
navigation efficiency, interactivity, value-added mechanisms, and assurance
mechanisms, and trace their effects on ease of use, usefulness, trust, shopping
enjoyment, and consumer behaviorvariables. While there are studies that
examined the effects of system quality on consumer behavior (e.g., Koufaris,
2002; Liang & Lai, 2002; Limayem et al., 2000), and the effects of
perceptual variables on consumer behavior (e.g., Agarwal&Karahanna, 2000;
Agarwal& Prasad, 1998; Chen et al., 2002; Devaraj et al., 2002;
Grazioli&Jarvenpaa, 2000; Koufaris, 2000; Stafford & Stern, 2002), only
one study (Koufaris, 2000) examined the linkages between system quality factors
and online consumer behavior via the perceptual variables.
Finally, future research can examine whether task and product
characteristics play a role in determining online consumer behavior. For
example, task type can include information search, purchase, and online service
use, while task complexity can range from programmed (routine, structured) task
to non-programmed (novel, unstructured) task. Based on this categorization, a
matrix for different types of tasks can be developed and subsequently used to
identify important variables within a specific task context.
The business-to-consumer (B2C) segment within electronic
commerce is under enormous pressure due to the frequent failures of a large
number of e-tailers. By identifying specific variables that impact Web use,
online purchase, and post-purchase behaviors of customers, this study provides
meaningful guidance to managers seeking to wisely use limited resources to
improve online transactions. Given that system quality, information quality,
service quality, and vendor and channel characteristics are important drivers
of consumer perceptions and subsequent online behaviors, companies should focus
on providing consumers with a well-designed Web site, accurate and useful
content, high-quality service, and low cost advantage.
Some limitations should be noted. The results and analysis
presented in the study were limited to the pool of journals that satisfied our
selection criteria. The scope of the study did not include other areas such as
marketing and human computer interaction that have also examined online
consumer behavior. In this study, our intention was to understand online
consumer behavior from an IS perspective. It should also be noted that the
number of studies included in the quantitative meta-analysis performed for the
common relationships was rather small. Although we included all the variable
relationships examined by more than one study, due to the lack of the
correlation table and incomparable sets of variables, there were only eight
relationships based on a limited number of studies. Thus, the results of the
meta-analysis should be interpreted with caution and should be expanded by
future research.
CONCLUSION
The main objective of the present study was to integrate past
research that investigated online consumer behavior. Through a rigorous search
of several mainstream IS journals, we identified 42 papers. Our review of those
papers revealed that the studies could be grouped into three categories based
on the dependent variables of the online consumer behavior: Web use, online
purchase, and post-purchase. Within each category, we evaluated the factors
that were examined as predictors of the behavior. Further, building upon the
results of the studies, we developed an integrative framework to provide a
holistic perspective on online consumer behavior. This integrative framework is
offered as a conceptual map to organize seemingly disparate findings across
studies and develop a more coherent and comprehensive understanding of the
dynamics involved in online consumer behavior. The framework also serves as a
unified source of variables and their interrelationships, stimulating future
research on online consumer behavior by drawing attention to the variables and
linkages that need further investigation.
ACKNOWLEDGMENTS
The authors wish to acknowledge the constructive editorial
comments of Martha W. Thomas and Joyce D. Jackson on this paper.
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