International Journal of Electronic Business Management, Vol. 6, No. 4, pp. 213-226 (2008)
213
A STUDY OF THE EFFECT OF RISK-REDUCTION
STRATEGIES ON PURCHASE INTENTIONS IN ONLINE
SHOPPING
Kuo-Kuang Chu and Chi-Hua Li*
Graduate Institute of Marketing and Distribution Management
National Kaohsiung First University of Science and Technology
Kaohsiung(811), Taiwan
ABSTRACT
The rise of online shopping has gradually changed consumer behavior. Not only does it
offer convenient shopping with a variety of products, but also allows quick price
comparisons and fast access to product information. Though it has developed rapidly in
recent years, it is still perceived immature due to risks. This study is to explore the
differences between the perceived risks and risk reduction strategies by different product
types, as well as the effects of online shopping experiences and consumer innovation on
perceived risks. And it also examines insecure factors formed by perceived risks in online
shopping and consumers’ risk mitigation plans, and eventually determines if risk reduction
strategies encourage consumers’ purchase intention. The study finds that experience goods
possess a higher perceived risk than search goods does, and therefore requires a more
effective risk reduction strategy. Abundant online shopping experiences are more helpful in
handling perceived risks of shopping. Innovative characters are capable in taking more
risks. Perceived risks are positively associated with risk reduction strategies. Finally, risk
reduction strategies increase consumers’ purchase intention.
Keywords: Online Shopping, Perceived Risks, Risk Reduction Strategies, Purchase
Intention
1. INTRODUCTION
In recent years, retail shops have changed their
business models with the Internet development.
Many retailers extend their real shops to the virtual
shops. Due to the low setup costs, online shops have
become a vital channel for startup companies or retail
shops to operate. Therefore, the growth of online
retail shops promotes a new buy-sell business model.
Traditional retail shops, like Wal-Mart, JCPenney and
Gap, set up online shops to gain more market share
[32]. Even though online sales increase rapidly, but
some consumers are still unable to accept online
shopping; because they are unable to “touch” the
product and to interact with the sales representative.
Besides, the Internet itself is full of uncertainties to
make people halt in hesitation. Consumers who shop
online will probably face double risks like
disappointment when receiving the goods and
difficulties of returning the goods [7].
Consumers perceive more risks in online shops
than in a traditional retail environment [2][38][60]. In
addition to the incomplete online shopping
environment and security issues, the insufficient
*
Corresponding author: [email protected]
understanding of most enterprises about how to
manage an online shop is also the main reason [9].
Therefore, perceived risks play a strong deciding
factor in the online shopping situation.
Traditionally, many marketing scholars
acknowledge that perceived risks influence
purchasing behavior [48]. Consumers develop risk
control processes and employ risk reduction
strategies to reduce the perceived risk until it is below
his or her level of acceptable risk. When facing
various choices, consumers perceive uncertainties
and risks and therefore feel anxious. At this point,
they look for risk reduction strategies to reduce all
perceived risks (psychological/social/functional and
economic loss). Purchasing decisions are eventually
made when consumers find the information they
want.
Online transactions in Taiwan have increased
by an average of 60% every year since 2000.
According to a study by the Institute for Information
Industry, the market size of online B2C business in
Taiwan reached NT$34.72 billion in 2004 and NT$60
billion dollars in 2006. It is estimated to reach NT$90
billion during 2006 and 2007. The online turnover
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International Journal of Electronic Business Management, Vol. 6, No. 4 (2008)
only contributes 1.2% to the total retailing sales,
whereas in the U.S. online retail accounts for 6% of
the total retail market. With the online population of
925 million people, it is safe to say the B2C
e-commerce in Taiwan will grow strongly in the
future. Therefore, how to reduce the online shopping
risk and enhance the consumers’ purchase intention is
a recent important topic.
Online shopping is still risky due to the
immature online shopping environment in Taiwan.
Some people are still less willing to use the Internet
to shop. To make online retailers realize the types of
perceived risks concerned by consumers and the
importance of risk reducing strategies, the main
objectives of this research focus on antecedent of
perceived risks (product type, purchase experience
and customers’ innovation) and assess the influence
of risk reduction strategies on purchase intention.
To conclude, this study hopes to achieve the
following goals through online and field surveys:
First, we want to understand the differences between
the perceived risks and risk reduction strategies by
different product types. Second, we want to examine
the effects of online shopping experiences and
consumer innovation on perceived risks. Third, we
want to study if online risk reduction strategies
influence purchase intentions. Finally, we want to
thoroughly understand consumers’ thoughts on online
shopping risks and the factors that influence their
purchase intentions, and therefore to provide
theoretical and practical contents in the research of
online shopping.
2. LITERATURE REVIEW
2.1 Perceived Risks
Perceived risks usually play an important role
in the purchase decision-making process, regardless
of the nature of the purchase occasions (planned vs.
impulse). Every purchase contains some degree of
risk. Bauer [5] is the first to bring up the idea of
perceived risk: “consumers perceive uncertainty in
contemplating a particular purchase intention. The
outcome may make consumers unhappy and
regretful.” He considers that consumers’ behavior is
risk-taking. Consumers may not be able to clearly
state their purchase intentions or have never thought
about the word “risk” in their subconsciousness.
Instead, the risk perceived subconsciously may have
affected consumers’ behavior.
Taylor [61] integrate the previous research
findings to outline a structure for risk-taking in
consumer behavior, stating the uncertainty of the
environment generates perceived risks during
decision-making process, and the risks perceived
vary by different levels of self-esteem. Before
making purchase decisions, consumers look for risk
reduction strategies to mitigate uncertainties and
adverse outcomes of risks until they are below the
level of acceptable risk. The development of
perceived risks to purchase intentions in this study is
based on the “process for risk-taking in consumer
behavior” by Taylor [61] to derive the relevant
variables and topics.
By adding the proposed dimension of time risk
by Roselius [52], Peter and Tarpey [48] examine
perceived risks in six dimensions, and they are:
financial risk is defined as a net financial loss to a
consumer through reasons like lack of warranty and
high maintenance fees; performance risk is defined as
the loss incurred when the product chosen might not
perform as desired; psychological risk is defined as
the loss incurred when the product chosen does not
fulfill the consumer’s self-image or perceptions of
self; physical risk is defined as the loss incurred when
the product chosen may physically harm the
consumer; social risk is defined as the loss incurred
when the product chosen is not appreciated by the
consumer’s family and friends and therefore the
value is minimized; and time risk is defined as the
loss incurred when it requires more time and energy
to acquire the product and becomes inconvenient.
Since then, related researches on perceived risks have
employed these six dimensions [24][59][60].
Since the rise of the Internet in 1990s, many
scholars have applied perceived risks to the research
of consumers in virtual channels. However,
employing the traditional dimensions of perceived
risks is inadequate to interpret the new risks of online
shopping. Jarvenpaa and Todd [29] are the first to
conceptualize
a
multi-dimensional
construct
encompassing economic, social, performance,
personal (including security), and privacy risks. Their
definitions are: economic risk is defined as the loss
incurred when a consumer has made a simple
decision to purchase a product that can not be
replaced or refunded, or a consumer has paid for the
product but fail to receive it; performance risk is
defined as the experience of anxiety arising from
anticipated reactions such as worry of unsatisfactory
performance from the purchased product or service
when some consumers can not touch or test the
desired product in personal; personal risk is defined
as a possible harm to the consumer in purchase
behavior; social risk describes instances where a
consumer’s online shopping behavior or decisions are
not accepted by the society (e.g., families and
colleagues) or is considered to make impulsive
decisions; and finally, privacy risk is defined as the
risk of revealing personal information as consumers
shop, and most of the revelations are about consumer
purchase information.
In the research field of online shopping,
privacy and security risks are the most influential in
consumers' perceptions of present and future online
shopping [65]. Many of the views from Internet users
K. K. Chu and C. H. Li: A Study of the Effect of Risk-reduction Strategies
as well as businesses are found to be similar, with
concerns about government policy, security and
privacy [31]. While studying the relationship between
perceived risks and purchase intentions, it is found
that privacy and security risks are two well-perceived
risks under the situation of online shopping [40].
Salisbury [53] applies the technology acceptance
model (TAM) to study decision-making factors in the
online shopping environment, and the study finds the
security of the web site is influential in consumers’
perception of online shopping. Liebermann and
Stashevsky [35] also find in their research that both
piracy and security are two main risk factors in the
online shopping environment. The contents of the
two risks are as followed: privacy risk describes
instances where personal information is revealed
without the person’s consent (e.g., one’s email, age
and sex); and security risk is defined as the fear from
consumers that their credit card and other financial
information will be revealed.
Based on the definitions and dimensions of the
perceived risks explained above, the following
dimensions of the perceived risks in online shopping
are chosen: personal performance, security, financial,
psychological, time and social risks. Amongst them,
security risk is chosen as it offers a broader coverage
than privacy risk does. Consumers normally have to
enter their login information in the front page of the
online shopping web site. Therefore, the online
shopping site not only protects consumers’ private
information but also needs to block itself from attacks
from hackers.
2.2 Antecedent of Perceived Risks: Product Type,
Shopping Experience, and Customers’ Innovation
2.2.1 Product Type of Internet Shopping
There are various products with different
categories online. The most common product
categorization is the classification of search goods
and experience goods. Search goods are products or
services with features and characteristics easily
evaluated before purchase, such as furniture, apparels
and shoes. Whereas experience goods are products or
services used or experienced before purchase or
where the product characteristics can be ascertained
upon consumption, such as cosmetics and
communication products [42][43]. Rao and Ruekert
[50] think “information asymmetry” is a common
phenomenon occurring in experience goods, because
product characteristics, such as quality, are difficult to
be observed before consumers make purchases.
These characteristics can be discovered upon
consumption. To conclude, search goods tend to have
lower intangible characteristics and have more
advantage in online shopping, as they can be
evaluated with external information without actual
check [49]. Experience goods, on the other hand, rely
215
on actual check and therefore possess a higher
perceived risk online.
Product types are categorized into search goods
and experience goods in this study. The chosen
subjects of search goods are apparels, furniture,
sports goods, souvenirs and flight tickets. The chosen
subjects of experience goods are computers/computer
peripherals, beauty care, books and magazines and
communication products. This study is then to further
examine how influential product types are to
perceived risks and to understand how relevant
product types are with risk reduction strategies.
2.2.2 Internet Shopping Experience
When consumers acquire experiences in
purchasing a specific product, they get an easier
access to familiar product information [10]. To online
users, previous shopping experiences also seem to
influence their future purchase intentions. Consumers
provide meaningful psychological reviews in the post
purchase evaluations. These experiences will
continue to affect their future decision-making
processes [54]. Decision-making is a cycle of
feedback activities. Besides, another research points
out that experiences of online purchasing also affects
the purchase decisions [36]. In the online shopping
environment, consumers use their own experiences to
evaluate product information, purchase payments,
services, risks, privacy and warranty [37][47]. Many
people think the previous shopping experiences
encourage consumers to shop online [34][23].
Customers that never shop online adopt a higher level
of risk aversion strategy than repeat customers do
[60]. Comparing with inexperienced consumers,
experienced online shopping consumers make more
purchases [25]. As consumers acquire more online
shopping experiences, they develop confidence that
facilitates more ambitious buying [55]. Most of the
previous shopping experiences have to satisfactory
and positive to encourage future online shopping. If
consumers have negative experiences in the past, they
will probably reduce the use of the Internet shopping
in the future [41].
2.2.3 Innovative Customers
Consumers who frequently shop online are
called innovators. This kind of consumers is more
willing to accept new ideas and try new products.
Most of the consumers are young, highly educated
and willing to take risk at their own. The salient value
of the innovator is adventure [51]. The research by
Darian [19] says in-home shoppers are more
innovative.
The research finds consumers that accept
online shopping are the Internet users and innovative
consumers in certain fields [12]. These online users
often spend a lot of time on the Internet activities,
such as information search. The innovative
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consumers in certain fields often purchase certain
products on the Internet. Donthu and Gilliland [20]
use the innovation of certain fields and common
personal characters to measure the degree of
consumer innovation. Innovative consumers not only
have more positive attitudes towards online shopping,
but also are more creative and intellectual than those
who do not purchase products or services online [25].
Potential online shoppers show highly adventurous
spirit, have a positive attitude to environmental
changes and frequently use the Internet [56].
2.3 Risk Reduction Strategies
In a purchase decision-making process, risk
handling process is often employed on the desired
object, with which consumers try to reduce the
perceived risks and increase certainty in the
pre-purchase stage. Consumers develop risk handling
processes to reduce the perceived risk until it is
below his or her level of acceptable risk, so that they
will have the intention to purchase the product and
the service [15][58].
Before consumers make purchases, they
measure the outcome of the particular purchase
behavior. More positive preferences yield higher
possibility of purchase [22]. The factors that affect
purchase intentions are attribute levels, price, and
cues such as manufacturer brand and online retail
brand and reviews from the online third-party.
Consumers usually reduce uncertainties through the
well-known manufacturer brand and retail brand [64].
Purchase factors are different in online shopping and
non-Internet
environments.
Online
shopping
environment is full of many uncertainties, so
consumers tend to search many informational cues
related to the product to lower its perceived risks.
Informational cues are categorized into intrinsic cues
and extrinsic cues [45][46]. Intrinsic cues involve the
physical composition of the product; whereas
extrinsic cues are external to the product itself
[28][66]. Price, brand, retailer, advertisements and
warranty are classified as extrinsic cues [11].
Consumers use the following strategies to
reduce risks: advertisements, word of mouth, brand,
store loyalty, the relation between price and quality,
and 100% Money Back Satisfaction Guarantee
[3][17][4][52]. Roselius [52] proposes 11 risk
relievers and finds that buyers are more concerned
with brand loyalty and major brand image, and the
followings are the 11 risk reduction strategies: (1)
endorsement; (2) brand loyalty; (3) major brand
image; (4) private testing; (5) store image; (6) free
sample; (7) money-back guarantee; (8) government
testing; (9) shopping; (10) expensive model; (11)
word of mouth. The research of risk reduction
strategies used for in-home shopping finds that,
previously satisfactory shopping experiences,
money-back guarantee and manufacturer’s reputation
clearly classified the degree of perceived risks
[21][2].
Consumers rely on reference group appeal for
certain behavior guidelines [6]. As celebrities and
experts possess professional characteristics, they
influence consumers’ feelings, purchase behavior and
attitude. For online shopping, the most popular risk
reduction strategy is the reference group appeal,
followed by manufacturer’s reputation and brand
image [60]. Money-back guarantee and free trial
periods are also successful risk-reduction strategies,
in terms of absolute risk reduction [2][62].
The points of views in each risk reduction
strategy do not agree with each other, because there
are different types of virtual shops and products. This
research focuses on the Internet shopping
environment, and the chosen dimensions are
standardized with the previous findings and concerns
of the recent Internet users [3][52][60]. The following
10 dimensions are chosen to examine risk reduction
strategies cared by the Internet shoppers in Taiwan:
reference group, brand loyalty, online retailer’s
reputation, brand image, money-back guarantee,
government testing, word of mouth, shopping, free
samples and expensive products.
2.4 Purchase Intention
The theory of planned behavior thinks
behavioral intention influences behavior and
stimulates actions [22][1]. Purchase intention is part
of behavioral intentions, and behavioral intentions are
cognitive plan to perform a definite action or possible
behavior on an object [8]. Under many
circumstances, one’s behavior can not be accurately
predicted from his or her attitude. People’s attitudes
toward objects and things have very little to do with
their actual behavior. Besides that, attitudes can
hardly predict behavior due to possible lack of
behavioral intentions. Therefore, attitudes influence
actual behavior through behavioral intentions
[22][1][8].
Darden and Dorsch [18] think consumers have
many choices in their shopping, and perceived risks
play a role in influencing the shopping mode. The
research finds perceived risks hinder the use of the
Internet and commercial transactions. Through
understanding those factors, online retailers or
service providers can develop appropriate methods to
help consumers reduce their perceived risks [30]. A
low level of perceived risk is also expected to
promote purchase intentions and reactions to actual
sales [39]. In-home shopping scholars among
researchers find that the lack of pre-purchase
inspection of the product quality influences
consumers’ purchase intentions with a high level of
perceived risk [16][21][57]. When consumers shop
online and the perceived risks are too high for them
to take or bear, they will take one step further to find
K. K. Chu and C. H. Li: A Study of the Effect of Risk-reduction Strategies
related risk reduction strategies. This research
examines if risk reduction strategies encourage
consumers’ purchase intentions.
3. HYPOTHESES
Nelson [43] defines the two types of goods in
this research as follows: search goods and experience
goods. Search goods tend to have lower intangible
characteristics. They have more advantage in online
shopping, as they can be evaluated with external
information without actual check [49]. However,
experience goods are the opposite of the search
goods. They rely on actual inspection and therefore
have a higher level of perceived risk. It is assumed
that:
H1a: Experience goods are significantly different
to search goods in different perceived risks.
As perceived risks and risk reduction strategies
are influenced by product types, this research further
examines if experience goods need more risk
reduction strategies than search goods do. Hawes and
Lumpkin [26] find that consumers are aware of a
high level of financial and social risks in their
shopping for apparels. Foucault and Scheufele [23]
find that friends’ recommendations are an important
influential factor. Then and DeLong [62] consider
brand identity is an important factor when shopping
for apparels. Lee and Huddleston [33] say experience
goods require more risk reduction strategies in the
virtual channel than in the physical channel. The risk
reduction strategies like “money-back guarantee”,
“retailer’s reputation”, and “brand image” are used
more often with experience goods than search goods.
Therefore, it was assumed that:
H1b: Experience goods are significantly different
to search goods in different risk reduction
strategies.
Previous shopping experiences influence
consumers’ acceptance of online shopping
[23][34][33]. Customers that never shop online adopt
a higher level of risk aversion strategy than repeat
customers do [60]. Comparing with consumers lack
of online shopping experiences, experienced
consumers are likely to make more purchases [25].
As consumers acquire more online shopping
experiences, they develop confidence that facilitates
more ambitious buying [55]. Most of the previous
shopping experiences have to be satisfactory and
positive to encourage future online shopping.
H2a: Frequent online shoppers adopt a lower
level of perceived risks.
H2b: Consumers who spend more money online
adopt a lower level of perceived risks.
H2c: Consumers who have previously positive
online shopping experience adopt a lower
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level of perceived risks.
Other than previous experiences, the
characteristics of consumers are also another
important factor. Consumers who frequently shop
online are innovators. Innovative consumers are more
willing to accept new ideas and try new products and
new types of transactions. Most of them are young,
highly educated and capable of handling financial
risks on their own. The salient value of the innovators
is adventure [51]. They are also more capable of
taking the risk of making purchase decisions.
H2d: Innovative consumers adopt a lower level
of perceived risks.
According to Roselius [52], a consumer who
has a strong intention to purchase a product and adopt
a low level of risk aversion will intend to find risk
relievers. When consumers want to visit specific
retail shops (e.g., online retail shop), they practice
risk management and handle uncertainties [26].
Ingene and Hughes [27] propose a model of the
three-stage risk management process in consumer
decision-making: risk perception, risk reduction and
risk management. If the internal risk is perceived, risk
reduction will be executed. In other words,
consumers will start collecting information or rely on
certain guarantees. When consumers perceive a high
level of risk, they think risk reduction strategies are
important.
H3: When consumers perceive a high level of
risk, they highly rely on effective risk
reduction strategies.
Previous studies conclude that consumers use
the following strategies to reduce risks:
advertisements, word of mouth, brand, store loyalty,
the relation between product price and quality, and
100% Money Back Satisfaction Guarantee
[3][17][4][52]. In a purchase decision-making
process, risk handling process is often employed on
the desired object, with which consumers try to
reduce the perceived risks and increase certainty in
the pre-purchase stage. Consumers develop risk
handling processes to reduce the perceived risk until
it is below his or her level of acceptable risk, so that
they will have the intention to purchase the product
and the service [15][58]. In the conclusion, many
researches find that consumers who have none or
little online shopping experiences are influenced by
the reputation of the retailer in the decision making
process. References from people who have had good
experiences of transactions with the seller reduce the
sense of insecurity and stimulate purchase intention.
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International Journal of Electronic Business Management, Vol. 6, No. 4 (2008)
H4: Relying on effective risk reduction strategies
will promote high purchase intention.
Previous studies focus on parts of online
shopping for examination, and so this study outlines a
complete research structure of online shopping
factors based on the past reviews and the hypotheses.
Further, we combine the important antecedent factors
that influence perceived risks: product type, purchase
experience and customers’ innovation; and the
importance of risk reduction strategies. The effect of
risk reduction strategies on online shopping is also
examined. The research structure is displayed in
Figure 1:
Figure 1: Research structure
4. STUDY METHOD
4.1 Questionnaire Design
According to research structure, this research
mainly investigates the relations between 5 variables:
product type, online purchase experience, customers’
innovation, perceived risks, risk reduction strategies
and purchase intention. In the survey, product types
are measured in nominal scale and others are
measured by 5-point Likert-scale.
To increase the reliability and validity of the
questionnaire, the scale of the variables in each
dimension of the structure are identified in literature
reviews to develop the questionnaire with Cronbach’s
α value as the standard for the scale items. The target
audience in the pretest is the people who have
shopped online for 3 years or more, or the
professionals who are working in online shopping
companies. Altogether, there are 35 people. After
evaluating the appropriate meaning of each scale item
and correcting twice, the research questionnaire is
released to common consumers to carry out the pilot
test. 78 questionnaires are released. According to the
result of effective sample in this research, Cronbach’s
α value in the pilot test has reached the standard
threshold of reliability (α>0.7) [44].
4.2 Data Collection
The main target audience is the consumers with
online shopping experience. According to the
statistics of online usage conducted by Taiwan
Network Information Center in 2004, most online
users are between the ages of 16-25 (above 78%).
ACNielsen online shopping investigation in 2004
also discovers that 87% of the respondents are
between the ages 15-34. Therefore, questionnaires are
distributed for sampling in universities nation wide.
In field work, northern and southern universities are
selected for sampling. To avoid possible bias created
by the single source of sampling, online survey
method is also used in this study. However, the
authenticity of online surveys is often suspected.
Comfrey and Lee [13] also point out in their study
that the effective online sample size has to reach at
least 250. Comley [14] also says that distributing
questionnaires online is still proper; as the system
reminds the respondents to fill in every necessary
answer and therefore to reduce incomplete and
inappropriate responses. Besides, surveys done by the
double entries of the same IPs will be removed to
reduce the probability of repetition from the same
person. Finally, the discussion forums of the
well-known online shopping web sites, such as
Yahoo Bid, eBay, Pchome Ruten Market, Yam and
Payeasy are chosen for the questionnaire.
The questionnaire’s grand total are 600 (the
field survey is 300; the online survey is 300), and the
received sample size is 550 with the effective return
rate of 91.7%. The final effective size is 478 after
removing the respondents with no online shopping
experience and invalid surveys. The field work
consists of 227 and the online 251, out of all the
surveys.
5. RESULTS
5.1 Sample Characteristics
Based on the analysis of the information
collected, colleges and universities are selected to
distribute field surveys. To avoid possible bias in the
demographic information, online surveys are then
conducted.
The profiles of these two sampling structures
are described as follows: the field work samples
consist of 65.6% women and 34.4% men, and 97.8%
of the samples range from 15-30 years of age. In
occupations, 93.4% of the samples are students and
K. K. Chu and C. H. Li: A Study of the Effect of Risk-reduction Strategies
6.6% of the samples are teaching and administrative
staffs. Spending less than one hour daily on online
shops takes up to 69.2%. The online samples consist
of 63.3% women and 36.7% men, and 97.6% of the
samples range from 15-30 years of age. In
occupations, 59.8% of the samples are students,
21.9% of the samples are service industry, 6.8% of
the samples are financial industry, 6.4% of the
samples are information industry and 5.2% of the
samples are manufacturing industry. Approximately
67% report spending less than one hour daily on
online shops. Thus we see the majority of online
shoppers are students.
In total, the most popular product types
purchased online by the respondents are in the order
as follows: apparels (27.6%), beauty care products
(27.2%), books and magazines (13.6%) and computer
and computer peripherals (11.7%), and each purchase
cost from NT$200 to NT$1,499.
5.2 Reliability and Validity
Before starting to examine the hypotheses,
reliability and validity of the research have to be
evaluated. This research utilizes Cronbach’s α value
to measure the consistency of each question item of
the same dimension. The higher the value, the
stronger the relationship between the items of the
dimension is; also the higher the consistency between
the items of the dimension is. According to Nunnally
[44], Cronbach’s α value of 0.7 and above represents
the consistency in each dimension and therefore
means high reliability. In the research field,
Cronbach’s α value of 0.6 and above confirms the
reliability of the scale. Table 1 is the reliability of
dimensions in this research. In general, the scale used
in this research is reliable.
This survey questions are based on the theories
of Taylor [61] and are modified according to other
scholars’ studies. To provide respondents a clear and
simple language and question method, the survey has
been discussed with the professionals and scholars
and is carried out in a pretest. It is also concluded that
the survey in this research holds a certain degree of
content validity. To examine if the construct validity
meets the previous design, exploratory factor analysis
is employed to examine if the factor loading is above
0.5 in each dimension and if each factor can be
correctly classified. The result finds the factor
loadings of six dimensions of perceived risks are
above 0.5 and are correctly classified. Therefore, the
result confirms good construct validity.
5.3 Factor Analysis
There are 19 items about perceived risks. In
order to simplify the variables, exploratory factor
analysis is employed to analyze the items to comprise
different dimensions. This research adopts principal
component analysis with varimax rotation, hoping to
219
classify the items into each factor and maximize the
total variation. KMO and Bartlett’s test of sphericity
are used examine if the perceived risk variables are
suitable for exploratory factor analysis. The result
shows the value of KMO is 0.864, and the number of
Bartlett’s test of sphericity is significant
( χ =4389.292，p<0.05). It is concluded the analysis
of perceived risks can be accomplished. The numbers
of factor extracted are chosen with the standard
setting of the eigenvalue larger than one. There are
six factors extracted in the end, and the total
accumulated explained variation is 65.132%.
2
Table 1: Reliability of the scales
Number
Cronbach’s
Dimensions
of Items
α Value
Perceived Risks
19
0.869
Performance Risks
3
0.736
Psychological Risks
3
0.611
Financial Risks
4
0.679
Time Risks
3
0.625
Security Risks
3
0.824
Social Risks
3
0.886
Risk Reduction
10
0.779
Strategies
Purchase Intention
3
0.835
Innovative Customers
6
0.797
Appropriate names are given to the factors
based on the collections of the different items. Factor
1 is named transaction security factor as it is
involved with the protection of privacy of name,
credit card number, password and amount of money.
Factor 2 is named external psychological factor, as it
is involved with risks caused by others’ perceptions
and opinions of self. Factor 3 is named product
performance factor, as it is involved with risks caused
by poor performance and value of the chosen product
upon consumption. Factor 4 is named financial loss
factor, as it is involved with risks caused by a net
financial loss to an online consumer through reasons
like high maintenance fees. Factor 5 is named
time-consuming factor as it is involved with a waste
of time through online search time, customer service
and product delivery. Factor 6 is named internal
psychological factor as it is involved with risks
caused by compatibility issue between the
consumer’s style and personality, skepticism towards
the seller and expectation insecurity of the product.
5.4 Empirical Findings
Product types are categorized into search goods
and experience goods by independent-sample t-test in
this research. Search goods, such as apparels,
furniture, sports goods, souvenirs and flight tickets
sum up a sample size of 288. Experience goods, such
as computers/computer peripherals, beauty care,
220
International Journal of Electronic Business Management, Vol. 6, No. 4 (2008)
books and magazines and communication products,
sum up a sample size of 190. Table 2 describes the
result of product types on perceived risks. It is
concluded that search goods and experience goods
are not significantly different on different dimensions
of perceived risks. Therefore, the statement in H1a is
not supported. It means consumers perceive the same
level of risk in both search goods and experience
goods when they shop online. Everyone is worried
about these risks. In the further analysis, consumers
perceive a slightly higher level of risk on experience
goods than on search goods (except time-consuming
factor).
Table 2: T-test of product types on perceived risks
Mean of
Factors
Mean of Experience Goods Mean Difference
Search Goods
Product Performance
5.524
5.590
-0.065
Internal Psychological
5.861
5.926
-0.065
Financial Loss
8.882
9.126
-0.244
Time-consuming
6.434
6.368
0.066
Transaction Security
6.139
6.147
-0.008
External Psychological
7.875
8.211
-0.336
Note: search goods (n=288), experience goods (n=190)
Table 3: T-test of product types on risk reduction strategies
Mean of
Factors
Mean of Experience Goods Mean Difference
Search Goods
Reference Group
3.302
3.505
-0.203
Brand Loyalty
4.028
4.200
-0.172
Brand Image
4.115
4.316
-0.201
Retailer’s Reputation
3.865
4.068
-0.204
Money-back Guarantee
3.840
4.032
-0.191
Government Testing
3.899
4.016
-0.116
Free Samples
3.788
3.826
-0.038
Word of Mouth
3.840
4.005
-0.165
Shopping
4.191
4.268
-0.077
Expensive Products
3.021
3.105
-0.084
Note 1: search goods (n=288), experience goods (n=190)
Note 2: *p<0.05, **p<0.01
Product types are categorized into search goods
(n=288) and experience goods (n=190) by
independent-sample t-test to examine if these two
product types are significantly different in risk
reduction strategies. Table 3 tells that the means in
experience goods are larger than those means in
search goods, which means experience goods possess
a higher level of risk than search goods do. It is then
safe to say experience goods require more effective
risk reduction strategies. The result of the
investigation says the six effective risk reduction
strategies including reference group, brand loyalty,
brand image, retailer’s reputation, money-back
guarantee, word of mouth significantly different are
needed the most when buying experience goods
online. Other risk reduction strategies such as
government testing, free sample, shopping and
expensive products are not significant. To conclude,
the statement in H1b is partially support.
H2a to H2d are examined with multiple
regression analysis to verify if these hypotheses are
supported. The result of the multiple regression
analysis is shown in Table 4. The value of multiple
t
-0.437
-0.469
-1.169
0.420
-0.045
-1.412
t
-2.445*
-2.616**
-1.649*
-2.998**
-2.799**
-1.572
-0.466
-2.345*
-1.148
-0.996
regression analysis has reached a significant level
(F=18.690). The dependent variable of perceived
risks, and the independent variables, such as
frequency of shopping online, amount of money
spent, positive comments from the previous online
shopping experience and characteristics of innovative
consumers, are used to predict the effect of perceived
risks (R2=0.136). To further check collinarity in the
multiple regression model, VIF value and tolerance
are analyzed. The indices of tolerance and VIF are
reciprocal. If VIF<10, it means there is no collinarity.
This multiple regression model is within the standard.
From the result, previously positive shopping
experiment affects the most (β=-1.796), and other
affecting variables are frequency of shopping online
(β=-1.086) and consumers’ innovation (β=-0.824).
It is also safe to say the more positive the previous
online shopping experience, the less risky consumers
perceive. In other words, the consumer has had a
friendly interaction with the buyer, and the product
and the service provided are satisfying. The sense of
trust has increased to lower the level of perceived
K. K. Chu and C. H. Li: A Study of the Effect of Risk-reduction Strategies
risks. The second most affecting factor is frequency
of shopping online. Consumers who shop online
frequently perceive a lower level of risk, as their
knowledge on transactions and products grows after
several transactions. They become more capable to
handle the related perceived risks. When consumers
are highly innovative, they also perceive a lower
level of risk as they are adventure, highly acceptable
of new things and confident to take risks. The
variable that is non-significant is the amount of
money spent. Though the amount of money spent has
a negative relationship with the perceived risks, it
does not really significant affect the perceived risks.
It is suspected that consumers are not impressed
about the amount of money spent when they evaluate
risks, because they are more concerned with the
interaction with the seller and the level of satisfaction
of the product and the service provided. In the
conclusion, the hypotheses in H2a, H2c and H2d are
supported; whereas H2b is not.
The relationship between consumers’ perceived
risks and risk reduction strategies are analyzed with
simple regression analysis. The statistical result is
shown in Table 5. The regression analysis of the
relationship between perceived risks and risk
reduction strategies has reached a significant level
(F=49.351). We see that the riskier the consumer
perceive, the more they will look for effective risk
reduction strategies (R2=0.094). This analysis is
undertaken with the intent to predict the level of risk
that consumer perceive in the online shopping
situation, and to further invent effective methods to
help reduce risk. The effect of perceived risks on risk
reduction has also reached a significant level (β
221
=0.197). It shows that consumers perceive risks in
online shopping, and they look for the related risk
reduction strategies and hope to make it below his or
her level of acceptable risk. Therefore, it is concluded
that the statement in H3 is supported.
This research further takes the six extracted
factors from the exploratory factor analysis to
examine risk reduction strategies through multiple
regression analysis. The research analysis is shown in
Table 6. The value of multiple regression analysis has
reached a significant level (F=12.675). Risk
reduction strategies are influenced by six perceived
risk factors (R2=0.139). The influencing factors that
range from the most to the least are as follows:
product performance (β=1.175), transaction security
( β =0.908), time-consuming ( β =0.732), internal
psychological ( β =0.514) and financial loss ( β
=0.186). The results show that consumers are most
concerned if the product chosen does not perform as
expected or it is not worth the value at all. The
second most concerned factor is transaction security,
because consumers are concerned about protection of
privacy of name, credit card number, password and
etc., when they shop online. They look for strategies
to reduce risks and uncertainties of risks. A
non-significant factor is external psychological factor.
However, it is suspected that consumers do not look
for risk reduction strategies because of other people’s
perceptions and opinions. Most of the strategies are
caused by internal psychological factor, and external
psychological factor has little influence on
stimulating consumers to look for risk reduction
strategies.
Table 4: Multiple regression of previous shopping experience and consumers’ innovation on perceived risks
β Coefficients
Independent Variables
t
Tolerance
VIF
Unstandardized Standardized
(Constant)
67.538
20.464**
Frequency of Shopping Online
-1.086
-0.182
-3.498**
0.674
1.485
Amount of Money Spent
-0.277
-0.057
-1.103
0.690
1.449
Positive Comments from the
-1.796
-0.144
-3.214**
0.907
1.103
Previous Experience
Consumers’ Innovation
-0.824
-0.373
-8.155**
0.864
1.158
R
R2
Adjusted R2
F
Model Fit
0.369
0.136
0.129
18.690**
Note: *p<0.05, **p<0.01
Table 5: Simple regression of perceived risks on risk reduction strategies
β Coefficients
Independent Variables
t
Tolerance
Unstandardized
Standardized
(Constant)
46.497
39.825**
Perceived Risks
0.197
0.028
7.205**
1.000
R
R2
Adjusted R2
F
Model Fit
0.306
0.094
0.092
49.351**
Note: *p<0.05, **p<0.01
VIF
1.000
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International Journal of Electronic Business Management, Vol. 6, No. 4 (2008)
Table 6: Multiple regression of six perceived risk factors on risk reduction strategies
β Coefficients
Independent Variables
t
Tolerance
Unstandardized
Standardized
(Constant)
38.427
192.176**
Transaction Security
0.908
0.194
4.536**
1.000
External Psychological
0.090
0.019
0.451
1.000
Product Performance
1.175
0.251
5.870**
1.000
Financial Loss
0.186
0.172
2.436*
1.000
Time-consuming
0.732
0.156
3.655**
1.000
Internal Psychological
0.514
0.110
2.567*
1.000
R
R2
Adjusted R2
F
Model Fit
0.373
0.139
0.128
12.675**
Note: *p<0.05，**p<0.01
Table 7: Simple regression risk reduction strategies on purchase intention
β Coefficients
Independent Variables
t
Tolerance
Unstandardized
Standardized
(Constant)
6275
9.155**
Risk Reduction Strategies
0.103
0.258
5.816**
1.000
R
R2
Adjusted R2
F
Model Fit
0.258
0.066
0.064
33.822**
Note: *p<0.05，**p<0.01
VIF
1.000
1.000
1.000
1.000
1.000
1.000
VIF
1.000
Finally, risk reduction strategies have a
positive impact on purchase intentions, based on the
hypothesis in H4. The statistical result is shown in
Table 7. This research adopts simple linear regression
analysis, with the independent variable of risk
reduction strategies and the dependent variables of
purchase intention. The regression model of risk
reduction strategies on purchase intentions has
reached a significant level (F=33.822). The intent is
to predict if purchase intention will be stimulated
after consumers try to look for risk reduction methods
(R2=0.066). The influence of risk reduction strategies
on purchase intention has also reached a significant
level (β=0.103). It shows consumers have purchase
intentions after finding necessary risk reduction
strategies. Therefore, the statement in H4 is
supported.
perceive similar risks regardless of the product type
while shopping online. The reason why H2b is not
supported is further analyzed. According to the
previous hypothesis, numerous shopping experiences
are implied if consumers spend more money online.
A negative relation with the perceived risks is
expected. In the design of the questionnaire, the item
said “the total amount of money spent online in the
past year,” and it is non-significant in the result. It is
then said the amount of money that consumer spent
does not influence the perceived risks much. Instead,
it is the quality of the product purchased or the
satisfaction gained while interacting with the seller. It
is conjectured that consumers are less sensitive the
risk of accumulated expenses. It is suggested to focus
on the single purchase for further analysis in the
future.
6. DISCUSSION
7. LIMITATION AND FUTURE
RESEARCH
In this research, all hypotheses are supported
except H1a and H2b. H1a is not supported as the
mean shown in Table 2 indicates experience goods
possess a higher level of perceived risk than search
goods do. That is a result of different product types,
but both products possess the same level of perceived
risks in the environment of online shopping.
Consumers evaluate and examine these products
based on risk factors such as product performance,
internal
psychological,
financial
loss,
time-consuming, transaction security, and external
psychological.
Therefore,
the
results
are
non-significant. It is concluded that consumers
There are several limitations to this study.
Future studies should focus on the limitations to
make the relevant researches better, and the
limitations are described below:
1.
The sample is undercoveraged
While the sample of the students applies to the
subject of this study, but in general, it is a slightly
inadequate representative sample. As the sample
coverage is students, it is impossible to extend the
research to other occupations. Therefore there is a
K. K. Chu and C. H. Li: A Study of the Effect of Risk-reduction Strategies
lack of a large-scale and diverse sample structure;
and the degree of generalization is limited.
2.
Lack of opinions on the research variables
from consumers who do not shop online
As the variables in this study are involved with
experiences, the target group is the consumers with
online shopping experience. This study does not
examine the reactions of the research variable from
consumers with no online shopping experience,
because the conditions of online shopping experience
are pre-selected. It is possible that consumers with no
online shopping experience have different
perceptions of perceived risks, and risk reduction
strategies could have been different. This is can be
discussed in the future.
In the research process and findings, it is found
that other related variables should be further
explored, and some research suggestions have been
recommended below:
3.
Add stimulated online purchase intention
factors
In this research, risk reduction strategies
influence purchase intentions. Most of the study
discusses the variables of online risk reduction
strategies. However, some positive promotions can be
added to stimulate consumers’ purchase intentions.
Such as discounts, free samples and free delivery
services provided by online sellers, may increase the
influence of risk reduction strategies on purchase
intentions and can be further analysed which of the
strategies has a greater influence.
4.
Discuss other personalities
This study only discusses if consumers are
innovative. The relationship between the value and
perceived risks is evaluated. Future studies should
include other personalities, such as introversion and
conservative, and extrovert and energetic, to further
understand opinions on perceived risks from
consumers of different personality and the diversity
of employing risk reduction strategies; or to adopt
cluster analysis to segment consumers of different
personalities and exercise the influence of risk
reduction strategies on purchase intentions.
5.
Analyze the comparison of both virtual
channel and physical channel
Online shopping is a concept of the virtual
channel, and there are uncertainties and perceived
risks present in the purchase behavior of the physical
channel. Future studies should compare both virtual
and physical channels. It is possible to further explore
how perceived risks and risk reduction strategies
differ between virtual and physical channels. The
coverage of the subjects could be extended to
increase the research value.
223
8. CONCLUSIONS
According to the results of the survey from 478
experienced online shoppers, previous research
findings are confirmed. However, situations and
opinions differ due to different research coverage and
fields. To address the online shopping situation in
Taiwan, the findings in this research concludes the
results in the following five points:
1.
Experience goods need more effective risk
reduction strategies than search goods do
Experience goods are a product where product
characteristics can be ascertained upon consumption.
Due to the lack of the “product touch” and interaction
with the sales representative in the online shopping
environment, the experience good in this study such
as computers/computer peripherals, beauty care
products, books and magazines and communication
products perceive a higher level of risk than the
search goods. The content of books and magazines
can only be judged after reading; and the
effectiveness of beauty care products can also be
judged after applying. Therefore, experience goods
require related risk reduction strategies. According to
the mean value, brand image is the risk reduction
strategy that consumers care the most and followed
by brand loyalty, retailer’s reputation, money back
guarantee, word of mouth and the last is reference
group.
2.
People with abundant online shopping
experiences are more capable in handling
perceived risks of shopping
Experiences are an accumulated practice from
similar situations or flows that have happened many
times. Consumers have many online shopping
purchases; buy various products, interact with the
buyers for many times and eventually get satisfying
or disappointing results. However, they will transfer
the previous results to the next expectation of online
shopping. They are also more capable of taking more
risk. They know which online seller to deal with to
avoid risks of money, product, performance and time,
as well as which web site protects privacy
information well. Therefore, a consumer with a nice
experience curve is not only capable of handling
shopping risks but also saving time and energy.
3.
People of innovative characteristics are more
capable in taking risks.
Innovative consumers are interested in
experiencing new things and technology and trying
new products. They like to make some changes and
are more capable of accepting online shopping. Part
of the reasons is that they use the Internet every day,
and therefore they gain some knowledge on the
Internet and self-confidence in computers. They
224
International Journal of Electronic Business Management, Vol. 6, No. 4 (2008)
know the basic information of online shopping and
understand the types of perceived risks encountered
online. However, they proceed to online shopping
because of their characteristics of adventure and
curiosity. Thus, the research finds that consumers’
innovation has a negative relationship with perceived
risks; and innovative consumers perceived a low
level of risk.
The relationship between perceived risks and
risk reduction strategies.
When consumers intend to purchase products
or services, risks are perceived because of the
uncertainty in the shopping environment. When
consumers are aware of the seriousness of perceived
risks, they look for related risk reduction strategies.
Among perceived risks, product performance,
transaction security, time-consuming, internal
psychological and financial loss are the most
concerned by consumers. Depending on the degree of
the risks as threats, they look for strategies such as
sellers of good reputation, good brand image
endorsed by celebrities to lower their perceived risks.
6.
7.
4.
8.
9.
10.
11.
5.
Risk
reduction
strategies
stimulate
consumers’ purchase intention
Consumers look for risk reduction strategies to
make him or her more comfortable with purchase, or
to reduce the perceived risk until it is below his or her
level of acceptable risk. In other words, consumers
become clearer about their purchase object and know
how solve the uncertainties throughout the
transaction process. Therefore, finding key risk
reduction strategies for both experience and search
goods actually help stimulate consumers’ purchase
intentions.
12.
13.
14.
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ABOUT THE AUTHORS
Kuo-Kuang Chu is an associate professor of the
Department of Marketing and Distribution
Management at National Kaohsiung First University
of Science and Technology, where he teaches courses
in distribution management, marketing price theory
and quantitative decision-marketing modeling. He is
the acting chair of Graduate Institute of Business
Management. His research interests are in the
distribution management and the pricing theory. He
was the chairman of Department of Marketing and
Distribution Management from 1999 to 2002. He
received his Ph. D. from National Taiwan University,
Taiwan.
Chi-Hua Li is a doctoral candidate of the Doctoral
Program in Management at National Kaohsiung First
University of Science and Technology. He is a
lecturer of Chia Nan University of Pharmacy &
Science, where he teaches courses in marketing
management and marketing research. His research
interests are in the marketing management and the
distribution management.
(Received November 2007; revised March 2008;
accepted May 2008)