A Study of Consumer Trust in Internet Shopping And the Moderating Effect of Risk Aversion in Mainland China
A Study of Consumer Trust in Internet Shopping And the Moderating Effect of Risk Aversion in Mainland China
https://drive.google.com/file/d/0B8-l_lMvjXWjOHotYWE5aF9mSEk/view?usp=sharing
Abstract
Having many advantages that traditional shopping channels lack of, Internet shopping is
now enjoying its prevalence and rapid development in Mainland China. Famous online
shopping websites including Taobao.com and Amazon.cn attract millions of transactions
as well as new users every day. In many previous researches, focus has been found in the
relationship between consumer trust and its antecedents. Researchers have also
established that online purchase intentions are the product of consumer trust.
The objective of this study is to reexamine some factors affecting consumer trust in
Mainland China as well as to investigate the effect of risk aversion as a moderator on the
relationship between trust and purchase intention.
This paper provided evidence that trust in Internet shopping is built on high service
quality as well as website quality. Size of online retailers is found to be negatively
related to trust. Notably, risk aversion moderates negatively on the effect of trust toward
consumer purchase intention. Implications and suggestions for further research are also
provided in the study.
Table of Contents
1. Introduction......................................................................1
1.1 Statement of the problem..........................................................................1
1.2 Objectives of the study............................................................1
2. Literature review................................................................3
2.1 The concept of trust in Internet shopping........................................3
2.2 Factors impacting trust ..........................................................5
2.3 Risk aversion....................................................................6
2.4 Outcomes of trust................................................................7
3. Research model and hypotheses..............................................8
3.1 Model........................................................................................8
3.2 Statements of Hypotheses.................................................................8
4. Methodology....................................................................11
4.1 Sampling and Data Collection...................................................11
4.1.1 Sampling Method..........................................................11
4.1.2 Sample Size...............................................................12
4.2 Questionnaire Design..........................................................12
4.3 Measurements..................................................................13
5. Findings and Analysis .........................................................14
5.1 Primary data analysis and descriptive statistics.......................................14
5.2 Reliability Analysis.......................................................................15
5.3 T-Test.......................................................................................16
5.4 Regression Analysis.......................................................................17
6. Discussions and Implications.................................................22
6.1 Trust in Internet shopping...............................................................24
6.2 Moderating effect of risk aversion......................................................24
6.3 Theoretical implications.................................................................26
6.4 Managerial implications.................................................................27
7. Limitations and Future Research............................................29
8. Conclusion .....................................................................30
References ........................................................................32
Appendix ...........................................................................36
List of Figures
Figure 1 Conceptual Framework............................................................8
Figure 2 Conceptual Model: Direct Effects on Trust.....................................17
Figure 3 Conceptual Model: Direct Effect of Trust on Purchase Intention............19
Figure 4 Conceptual Model: Moderating Effect of Risk Aversion......................20
Figure 5 Conceptual Explanation: Negative Moderating Effect of Risk Aversion......25
List of Tables
Table 1 Demographic Profile of Respondents...............................................15
Table 2 Reliability Analysis...................................................................16
Table 3 Multiple Regressions: Direct Effects on Trust.....................................19
Table 4 Hierarchical Multiple Regressions: Moderating Effect of Risk Aversion......21
Table 5 Hypotheses Tests.......................................................................22
Table 6 Measurements...........................................................................37
1
1
1. Introduction
1.1. Statement of the problem
The adoption of the Internet as a way to purchase goods and services has seen an
increasing trend over the past two decades globally. Compared to traditional shopping,
the Internet not only facilitates transactions between buyers and sellers from anywhere
at any time, but also provides a wide range of product choices and a platform for
exchanging ideas for customers with low costs. To achieve the success of electronic
commerce, companies place great emphasis on attracting customers continuously and
building long-term relationship with customers on the web.
However, people still remain reluctant to make purchases on the Internet due to the
lack of trust toward businesses in the new electronic environment. Past researches have
indicated that consumers’ lack of trust constituted a key barrier to the use of Internet
shopping as well as long-term commitment to the relationship building. Gefen,
Karahanna, and Straub (2003) identified lack of consumer trust in Internet vendors as a
major factor inhibiting online purchases. Trust plays an essential role for facilitating
online transactions between consumers and electronic retailers and realizing the
development of e-commerce to consumers in the long run (Sonja, 2002).
1.2. Objectives of study
Although the Internet offers enormous advantages which seem to attract massive
interest of customers, recent survey showed that the penetration rate of online
purchasing stayed relatively low, especially in China. According to the most recent
2
“Statistical Report of China Internet Network Development”, the popularity of online
shopping among all Internet users has just reached 26% by the end of June 2009.
Past studies have demonstrated, with empirical evidence, the important role of
consumer trust in Internet shopping (Gefen & Straub, 2004) and argued that the most
significant long-term barrier to the success of the Internet as a commercial medium in
mass markets is a lack of consumer trust in the Internet (Jarvenpaa, Tractinsky & Vitale,
2000; Hoffman, Novak & Peralta, 1999).
However, previous studies that only focus on trust of consumers provide a limited
view of the phenomenon and may hinder a comprehensive understanding of the
consumer purchasing behavior in the e-commerce context in China. In part, this stems
from the cultural values that shape the consumer characteristics and influence the
relationship between trust and consumer purchase intention on the web. For instance,
Bao, Zhou and Su (2003) observed that risk aversion as one of the cultural dimensions
affects consumers’ decision-making. As trust indicates the level of consumers’
perceived risk in the Internet shopping, different degrees of their risk aversion may
have non-ignorable effects on the online buying behavior.
In order to bridge the gap, the objectives of this paper, therefore, are to first
investigate factors affecting consumers’ trust towards Internet shopping in Mainland
China and how it influences their purchase intention. A second objective is to further
examine whether risk aversion as an important consumer psychological attribute places
a moderating effect on the relationship between trust and purchase intention of
3
consumers in Mainland China.
It is expected that consumer trust will be positively related to four antecedents and
risk aversion will negatively moderate the effect of trust on consumer buying intention.
The results will not only provide insights for future research in the new area of risk
aversion as a moderator, but also offer practical implications for building consumer
trust online as well as raise purchase intentions even from customers with high risk
aversion.
2. Literature Review
2.1. The concept of trust in Internet shopping
Before a review of previous literatures on trust in the context of Internet shopping, we
first need to have a look at general definitions of trust in various disciplines.
The concept “trust” is defined as the willingness of a party to be vulnerable to the
actions of another party based on the expectation that the other will perform a
particular action important to the trustor, irrespective of the ability to monitor or
control that other party (Mayer, Davis and Schoorman, 1995). This definition is widely
recognized and commonly cited in other researchers’ work. Later Doney and Cannon
(1997) defined trust as the perceived credibility and benevolence of a target of trust.
According to another two researchers, Lewis and Weigert (1985), trust is further
identified as “the understanding of a risky course of action on the confident
expectation that all persons involved in the action will act competently and dutifully”
(p.971). And precisely, Bhattacharya, Devinney and Pillutla (1998) and Boon and
4
Holmes (1991) conceived trust as predictability and reliance upon another person
under uncertain and risky circumstances.
In the electronic commerce context, a large number of researchers have proposed
both conceptual and empirical studies of trust. Some researchers viewed trust as a
general belief that another party can be trusted (Gefen 2000; Hosmer 1995; Moorman,
Zaltman & Deshpande 1992). One of the most popular studies on electronic commerce
trust is the one conducted by Mayer, Davis and Schoorman (1995) who viewed trust as
a trustor’s intention to take a risk and proposed the trustor’s perceptions about a
trustee’s characteristics as the main predictors of trust. Another commonly cited study
is the conceptual model of McKnight, Cummings and Chervany (1998) where the
researchers defined trust as trusting beliefs and trusting intention only in uncertain and
risky situations and the approach was widely tested by later studies. For example,
Schlosser, White and Lloyd (2006) adopted the model and viewed trust as a way to
reduce uncertainty and complexity in website consumers. Among other studies is the
one that identified trust as a buyer’s perception of appropriate conditions being in place
to facilitate transaction success with online sellers (Pavlou and Gefen, 2004). As
pointed out by Sonja (2002), trust plays a crucial role in the development of electronic
business and some relevant factors in the emergence of trust problems in on
transactions should be therefore analyzed.
In this paper, the definition of trust in the Internet shopping context proposed by
Rousseau (1998) will be employed. According to the author, trust is a psychological
5
condition comprising the intention to accept vulnerability based on positive
expectations of another party’s intention or behavior, in interdependent and risky
environment (Rousseau, 1998).
2.2. Factors impacting trust in Internet shopping
In the electronic commerce context, there are several factors considered as predictors
of trust in online vendors as proposed by previous researchers. They are reputation,
size, perceived service quality and perceived website quality.
Customer’s perceptions of a company’s profile include reputation as well as and
affect trust in the process of Internet shopping. Jarvenpaa and Tractinsky (1999)
considered size and reputation to be the predictors of trust. For example, the authors
believed that larger companies were more likely to be around longer and larger and
more reputable ones might be more trusted by customers (Jarvenpaa & Tractinsky,
1999). Other researchers also viewed reputation and size as important factors forming
consumer trust (Grazioli & Jarvenpaa, 2000; Pavlou, 2003; Kim, Xu & Koh, 2004;
Koufaris & Hampton-Sosa, 2004).
Customers’ perceptions of a company’s service quality affect trust in online
shopping (Daignault, 2001). It seems to be the most significant factor of maintaining
trust and building e-retailer – customer relationships, according to Kim and Tadisina
(2007). As Anderson and Fornell (1994) observed in their study, a high level of service
quality is likely to cause a high level of customer satisfaction which will lead to a
customer’s positive experience and understanding of the company. In this way,
6
customer’s trusting beliefs as well as trusting attitude are confirmed (Kim & Tadisina,
2007). The perceived service quality includes warranties (Grazioli & Jarvenpaa, 2000),
guarantees (Pennington, Wilcox & Grover, 2003-2004), and customized services and
delivery performance (Doney & Cannon, 1997), as well as the general concept of
company’s service quality (Gefen, 2002; Kim, Xu, & Koh, 2004).
Perceived website quality also plays an important role in determining consumer trust
in online shopping (McKnight, Choudhury, & Kacmar, 2002; Araujo, 2003; Kim, Xu,
& Koh, 2004). Websites that are perceived easy to use and of good quality are more
like to build a high level trust in consumers (Wakefield, Stocks, & Wilder, 2004; Want
& Benbasat, 2005)
2.3. Risk aversion
The term risk aversion is defined as “the extent to which people feel threatened by a
ambiguous situations, and have created beliefs and institutions that try to avoid these”
(Hofstede & Bond, 1984, p419). People with high risk aversion tend to feel threatened
by risky and ambiguous situations (Hofstede, 1991). Bao, Zhou and Su (2003)
examined the effects of risk aversion, one of the most important cultural dimensions,
on consumer decision-making and compared two consumer decision-making styles
under cultural differences between United States and China. In order to maintain the
within-group harmony in China as a typically collectivistic society, people are
expected to behave as a group (Bao, Zhou, & Su, 2003), and risk-taking behavior is
often discouraged (Tse, 1996).
7
Another similar study on risk aversion affecting consumers’ decision making is
carried out by Shimp and Bearden (1982) who found that highly risk-averse customers
are likely to search for more information regarding product quality when they make
purchasing decisions. On the other hand, as suggested by Seenkamp, Hofstede and
Wedel (1999), people with low risk aversion feel less threatened by ambiguous and
novel circumstances and tend to feel excited by the purchase of new and innovative
products.
According to the rationale proposed by Raju (1980), the optimum stimulation level,
defined as a property that characterized a person in terms of his general response to
environmental stimuli, is positively related to both risk-taking behavior and switching
behavior. Based on this rationale, Ranaweera, Bansal and McDougall (2008) examined
the effect of risk aversion as one of consumer characteristics on the purchase intention
on the Internet. However, the research focused on the effect of risk aversion on the
relationship between website satisfaction of consumers and their purchase intention
(Bao, Zhou, & Su, 2003). In this paper, the emphasis will be on examining the possible
impact of risk aversion on the relationship between consumers’ trust and the behavioral
intentions.
2.4. Outcomes of trust in Internet shopping
Consumers’ purchase intention is one of the common behavioral dimensions resulting
from their trust in Internet shopping (Boulding, Kalra, Staelin, & Zeithaml, 1993).
Previous research on the relationship between consumer trust and purchase intention
8
by McKnight and Chervany (2002) found out that when customers hold high level of
trust they are more willing to depend on the Internet vendor and make online
purchases.
3. Research Model and Hypotheses
3.1. Model
Based on the literature review, a conceptual model has been designed to study the
effects of perceived company’s reputation, size, perceived service quality and website
quality on consumer trust in Internet shopping and also the effect of risk aversion on
the relationship between consumer trust and purchase intention as a moderator.
Figure 1 Conceptual Framework
3.2. Statement of hypotheses
3.2.1 Factors impacting consumer trust in Internet shopping
Reputation
The perceived reputation is defined as the extent to which consumers believe a selling
(-)
Reputation
Size
Perceived Service
Quality
Perceived Website
Quality
Trust in
Internet Shopping
Purchase
Intention
Risk
Aversion
(+)
(+)
9
company is honest and concerned about its customers (Doney & Cannon, 1997). A
company with a good reputation indicated that long-term investments of resources,
effort, and attention to customer relationship building have been taken into great
concern by that company. Consumers tend to favor companies with a good reputation
in the electronic commerce as they perceive lower risk and uncertainty and know
where to seek for help from the public if something really goes wrong. Therefore this
paper hypothesizes that:
H1: The perceived company’s reputation is positively related to consumers’ trust in
Internet shopping.
Size
Similar to reputation, customers’ perceived size of a company plays an important role
in forming their trust toward Internet shopping. Large size is a signal to buyers that the
company is successful and capable to compensate its customers even there’s
transaction failure (Jarvenpaa, Tractinsky, & Vitale, 2000). In addition, companies
with large size are believed to have more resources which enhance the level of trust in
consumers. Therefore, this paper hypothesized that:
H2: The perceived company’s size is positively to consumers’ trust in Internet
shopping.
Perceived Service Quality
The perceived service quality is related to gaining consumer trust and building
long-term customer relationship by providing high-quality services (Grefen, 2002; Kim,
Xu, & Koh, 2004), including guarantees (Grefen, Karahanna, & Straub, 2003),
warranties (Grazioli & Jarvenpaa, 2000; Pennington, Wilcox, & Grover, 2003-2004),
10
and customized services (Doney & Cannon, 1997). A high level of perceived service
quality enables customers to have more trust in the Internet vendor and make
commitment to the relationship with the company. This paper therefore proposes the
hypothesis that:
H3: The perceived service quality is positively to consumers’trust in Internet shopping.
Perceived Site Quality
A high level of perceived site quality implicates that customers find it easy and
convenient to find the information they need and make transaction on the particular
website. People tend to hold a high level of trust in the online shopping when they
perceive easy use as well as high quality of the website. This paper therefore proposes
the hypothesis that:
H4: The perceived website quality is positively related to consumers’ trust in Internet
shopping.
3.2.2. Trust in Internet Shopping
Trust, defined as the consumers’ willingness to be vulnerable to the actions of an
Internet vendor in an online shopping transaction (Lee & Turban, 2001), is the basis of
customers participating in electronic commerce. Trust helps to reduce customers’
perceived complexity and uncertainty in the online context thus a heightened level of
trust encourages consumers’ activities on the Internet. Hence, it is hypothesized in this
paper that:
H5: Consumers’trust in Internet shopping is positively related to purchase intention.
3.2.3. Risk Aversion
11
Risk aversion is the extent to which customers feel threatened due to ambiguous and
risky environment (Hofstede & Bond, 1984). Faced with low level of trust and high
perceived risk inherited in the Internet transaction context, customers with low risk
aversion may still have relatively strong propensity to participate in online shopping
than those with high risk aversion since they are more open to new opportunities and
shopping styles. However on the other hand, customers who are highly risk-aversion
may still remain reluctant to purchase online though they hold a certain level of trust
toward the Internet vendor. Therefore it is hypothesized that:
H6: Risk aversion will negatively moderate the effect of consumers’ trust in Internet
shopping on the purchase intention.
4. Methodology
4.1. Sampling and Data Collection
4.1.1. Sampling Method
Since the study focuses on factors affecting online trust and how risk aversion
moderates the intensity of consumer trust toward purchase intention, the target
population are individuals from Mainland China who have some knowledge of Internet
shopping. Therefore the target sample should be Internet shoppers who have made
purchases on the Internet at least once. In this research, two sampling methods were
adopted. Simple random sampling was used to collect data using mall-intercept
interviews in Shenzhen. Several places were chosen, including two shopping malls
with one called Maoye Department Store in Huaqiangbei Business center and another
called Wanxiang City, as well as a book center in the city. Every 10th person leaving the
shopping mall will be selected and asked to fill in the paper-form questionnaires and
12
provide opinions related to the constructs of the study. This method is preferred
because it avoids bias in selecting sample unit and is more efficient for the respondent
to come to the interviewer than for the interviewer to go to the respondent (Malhotra,
2007). However in order to overcome the possible bias that people visiting shopping
center tend to be more used to and satisfied with physical shopping, an online survey
on www.my3q.com was created and convenience sampling was used. In order to
assure the reliability as well as representativeness of the result, this method was taken
to draw samples from different cities in Mainland China and different age groups of
people by distributing through social networks for a period of one month.
4.1.2. Sample Size
As the ratio of observations to independent variable affects whether the result of
multiple regressions can be generalized, an observations-to-independent variable ratio
of 40 is considered reasonable (Tabachnick & Fidell, 2001). In this research, given the
number of variables being 6, the sample size of 240 is calculated to meet the statistical
requirement (6*40=240). At last a total number of 259 questionnaires were distributed.
142 of them were obtained from simple random sampling while 117 were from
convenience sampling through Internet.
4.2. Questionnaire Design
The questionnaire was designed in Chinese and consisted of three major parts. After
the first part of asking basic online shopping behavior to screen out those who haven’t
tried Internet shopping, the second part of the questionnaire was divided into four
13
sections measuring factors of forming customer trust (reputation, size, perceived
service quality, and perceived site quality), trust, risk aversion as well as purchase
intention. A Five-point Likert Scale is used in the questionnaire ranging from 1 being
strongly disagree to 5 being strongly agree. And the last part of the questionnaire was
for demographic data collection including respondents’ age, gender, occupation,
educational level as well as average monthly income.
4.3. Measurements
Each variable from the model will be measured with several items derived from
previous research and Table 1 (refer to Appendix A) summarized all the items and
sources of those items.
Reputation. Customers’ perceived reputation of a company is measured by two items
that one is adopted from Park & Kim (2003) and the other one from Teltzrow, Gunther
& Pohle (2003).
Size. Customers’ perceived size of a company is measured by two items adopted from
Doney & Cannon (1997).
Perceived Service Quality. Customers’ perceived service quality is measured by five
item adopted from several previous researches, two from Doney & Cannon (1997), two
from Grefen, Karahanna, & Straub (2003), and one from Grefen (2002).
Perceived Site Quality. It is measured by five items adopted from McKnight,
Choudhury & Kacmar (2002).
14
Trust. Trust towards Internet shopping is measured by six items where four items are
adopted from McKnight, Choudhury & Kacmar (2002) and two of them from Ribbink,
Riel, Liljander & Streukens (2004).
Risk Aversion. The risk aversion scale is based on the original scale developed by Raju
(1980) which was used subsequently by Keaveney and Parthasarathy (2001) and Bao,
Zhou & Su (2003). It is measured by three items.
Purchase Intention. Purchase Intention is measured by three items adopted from
McKnight, Choudhury & Kacmar (2002).
5. Findings and Analysis
The program of SPSS (The Statistical Package for the Social Science) was used to
analyze the data. All statistics were run at 95% confidence level.
5.1. Primary data analysis and descriptive statistics
Among a total number of 259 respondents, 231 of them had online shopping
experience while 28 didn’t. Therefore the total sample number is 231.
Of 231 usable samples, 44.2% were male while 55.8% were female. 92.6% of the
respondents were aged between 19 and 35 and 98.7% of them held a university degree.
The percentages of students and clerical workers were 58.4% and 13.4% which made
up nearly 72% of total respondents, followed by professionals of 11.3%.
15
Demographic
Characteristics
Total
Numbers
Percentage
(%)
Gender
Male 102 44.2
Female 129 55.8
Age
19 – 25 156 67.5
26 – 35 58 25.1
36 – 45 14 6.1
46 or above 3 1.3
Education
Secondary School 3 1.3
Tertiary School 228 98.7
Occupation
Student 135 58.4
Clerical worker 31 13.4
Managerial level 7 3.0
Professional 26 11.3
Self-employed 6 2.6
Others 26 11.3
Income (in RMB)
Below 2000 137 59.3
2000 – 2999 25 10.8
3000 – 3999 27 11.7
4000 – 4999 21 9.1
5000 – 5999 20 8.7
Missing 1 0.4
Table 1 Demographic Profile of Respondents
5.2. Reliability Analysis
After the reverse coded item was recoded, Cronbach’s alpha was used to evaluate the
validity as well as the internal reliability of each construct (Cronbach, 1951). For the
scales of Reputation, Perceived Service Quality, Perceived Website Quality, Trust, and
16
Purchase Intention, the reported Cronbach’s alpha values ranged from 0.817 to 0.872
which were all higher than the acceptable reliability of 0.7 recommended by Mayer
and Davis (1999). And Risk Aversion also had a satisfactory reliability level of 0.735.
However, the alpha value of Size was only 0.610 and should not be accepted.
Therefore the “alpha if item deleted” and “corrected item-total correlation” were
considered. The new Cronbach’s alpha value of Size rose to 0.696 after the originally
reverse coded item was deleted and the mean scores for each variable were then
calculated for running subsequent statistics. The Table summarized the detailed
reliability value for each construct.
Variables Items Cronbach’s
Alphas
Reputation 3 0.817
Size 2 0.696
Perceived Service Quality 5 0.838
Perceived Website Quality 4 0.869
Trust 6 0.851
Risk Aversion 3 0.735
Purchase Intension 3 0.872
Table 2 Reliability Analysis
5.3 T-Test
Since two sampling methods were used in the research, a total number of 231 samples
were consisted of two groups, 121 from simple random sampling and 110 from
convenience sampling. In order to show that there’s no significant difference between
these two samples, a t-test analysis was conducted in SPSS. As shown in Table below,
two sampling methods did not have significant difference (p value < 0.05) in the
17
mean scores for all variables at 95% confidence level. In other words, the data
collected by two sampling methods did not give rise to different results.
5.4. Regression Analysis
Multiple regressions were used to find out both the direct effects of four individual
variables on consumer trust in Internet shopping and the moderating effect of risk
aversion on the relationship between trust and purchase intention.
5.4.1 Direct Effects of Reputation, Size, Perceived Service Quality, and
Perceived Website Quality on Trust.
To simplify the understanding of model, Reputation, Size, Perceived Service Quality,
and Perceived Website Quality were regarded as four independent variables while
Trust in Internet shopping was regarded as a dependent variable. Table summarized the
steps and results of running multiple regressions.
Independent Variables
Dependent Variable
Figure 2 Conceptual Model: Direct Effects on Trust
As shown in Table, there were two stages to test the direct effects among variables.
Firstly, control variables including gender, age, occupation as well as income were
Reputation
Size
Perceived Service
Quality
Perceived Website
Quality
Trust in
Internet Shopping
18
tested in applying regression. For the second step, trust was regressed on reputation,
size, perceived service quality and perceived website quality and the result was
significant with p value being 0.000 and change in R square being 0.250. However,
while size, perceived service quality and perceived website quality were powerful to
explain the variance in Trust with p value less than 0.05, the regression of trust on
reputation was not significant. The unstandardized coefficients B for the three
significant variables were -0.167, 0.351 and 0.340 respectively. That is, while
perceived service quality and website quality both had a positive effect directly on
consumer trust in Internet shopping, size of the website had a negative effect on
consumer Trust which was opposite to the hypothesis. Refer to Appendix for the
complete SPSS output.
Unstandardized
Coefficient
B
R
Square
F Change in
R Square
Model 1 Constant 3.180*** 0.038 2.217 0.038
Gender 0.203
Age 0.086
Occupation 0.046
Income 0.004
Model 2 Constant 1.306 0.288 11.102*** 0.250***
Gender 0.122
Age 0.053
Occupation 0.030
Income 0.022
Reputation -0.036
Size -0.167***
Service
Quality
0.351***
Website 0.340***
19
Quality
**p≤0.05; ***p≤0.01
Table 3 Multiple Regressions: Direct Effect of Reputation, Size, Perceived Service
Quality and Perceived Website Quality on Trust in Internet Shopping.
5.4.2. Relationship between Trust and Consumer Purchase Intention
At the first two stages of applying hierarchical multiple regression to test the
moderating effect of risk aversion, the direct effect of trust on purchase intention was
evaluated as summarized in Table. Control variables of gender, age, occupation and
income were included in the first stage and were not significant to explain the
regression line. Subsequently purchase intention was regressed on trust and the
significant result indicated that consumer trust in Internet shopping positively posed a
direct effect on purchase intention although the level of power was not very strong
with R square change being 0.087 (b=0.291, p=0.000).
Figure 3 Conceptual Model: Direct Effect of Trust on Purchase Intention
5.4.3. Moderating Effect of Risk Aversion on the Relationship between Trust and
Consumer Purchase Intention
By applying hierarchical multiple regression, the moderating effect of risk aversion on
the relationship between trust and purchase intention could be tested as suggested by
Nunally and Bernstein (1994). The moderated multiple regression procedures were
taken after the first two steps of including control variables and the independent
variable carried out in 5.4.2.
Trust in
Internet Shopping
Purchase
Intention
(+)
20
Moderator
Figure 4 Conceptual Model: Moderating Effect of Risk Aversion
Stage 3 in Table showed that under the control of consumer trust in Internet
shopping as well as other control variables including gender, age, occupation and
income, the variable of risk aversion was also significant (p=0.000) to explain the
variance of the dependent variable purchase intention and the unstandardized
coefficients B for trust and risk aversion were 0.271 and 0.365 respectively. R square
change in this stage was 0.117. In Stage 4, the interaction between trust and risk
aversion of consumers was significant (p=0.001) to explain the variance in Purchase
Intention however the change in R square was not strong again being at 0.037. The
result indicated that different levels of consumers’ risk aversion could slightly
moderate the relationship between their trust in Internet shopping and the ultimate
purchase intention in the online context. And the coefficient B of -0.252 showed that
the direction was negative, confirming a weakening effect. The complete SPSS output
was included in Appendix.
(-)
Trust in
Internet Shopping
Purchase
Intention
Risk
Aversion
(+)
21
Unstandardized
Coefficient
B
R
Square
F Change in
R Square
Model 1 Constant 4.205*** 0.016 0.983 0.016
Gender -0.018
Age 0.005
Occupation 0.073
Income -0.049
Model 2 Constant 3.281 0.104 5.157*** 0.087***
Gender -0.077
Age -0.020
Occupation 0.059
Income -0.050
Trust 0.291***
Model 3 Constant 1.780 0.221 10.488*** 0.117***
Gender -0.060
Age -0.036
Occupation 0.029
Income -0.003
Trust 0.271***
Risk
Aversion
0.365***
Model 4 Constant -1.984 0.258 10.962*** 0.037***
Gender -0.053
Age -0.045
Occupation 0.044
Income -0.002
Trust 1.402***
Risk
Aversion
1.201***
Interaction
(Trust*Risk
Aversion)
-0.252***
**p≤0.05; ***p≤0.01
Table 4 Hierarchical Multiple Regression: Moderating effect of Risk Aversion on the
relationship between Trust in Online Shopping and Consumer Purchase Intention.
22
To summarize the findings, Table shows the results of hypotheses test in direct
effects of variables as well as moderating effect on the relationship.
Hypothesis Test Result
Hypothesis 1: Direct effect of reputation on trust Not Significant
Hypothesis 2: Direct effect of size on trust Rejected
Hypothesis 3: Direct effect of perceived service quality on trust Supported
Hypothesis 4: Direct effect of perceived site quality on trust Supported
Hypothesis 5: Direct effect of trust on purchase intention Supported
Hypothesis 6: Moderating effect of risk aversion Supported
Table 5 Hypotheses Tests
6. Discussions and Implications
In response to the research objectives, the results offer strong support for the direct
effects of perceived service quality and perceived website quality in building consumer
trust in online shopping as well as moderating effect of risk aversion on trust toward
purchase intention. However the finding shows that there is no significant relationship
between reputation of an online shopping website and consumer trust while size poses
a negative direct effect on trust which is opposite to hypothesis.
6.1. Trust in Internet Shopping
There are several factors affecting consumer trust in Internet shopping directly and
subsequently affecting purchase intention. According to the findings of this study,
consumers’ trust toward online shopping is positively influenced by service quality and
website quality perceived by customers. There is nothing surprising that people tend to
attach great importance to the quality of service they get in making online purchases as
well as the technical design and security of the shopping website.
23
Although the perceived website size is significant in affecting consumer trust, the
effect is negative and opposite of the prediction. It was posited that large scale of
business ensured a high level of confidence in consumer therefore triggered high trust
in the online environment. However this appears not to be the case. The finding
suggests that the larger the website perceived by customers, the less trust is found in
making purchases on this website. A possible explanation of this contradiction may be
that the large scale of an online website does not convince consumers in Mainland
China that the store is trustworthy. For those stores with a wide range of businesses or
product categories, consumers may believe that they have lower ability to provide
high-quality service, or they become large players in the industry by making a fast
profit and forgetting customers’ interests. Consumers may also find themselves hard to
trust large-scale stores because they are possible to overemphasize on highly profitable
customers and neglect not-so-important clients’ needs. Contrarily, consumers tend to
find online vendors with small scale more trustworthy for they can focus on individual
customers and respond to their different needs more effectively. Those small stores are
believed to keep customers’ interests in mind and provide expected service and values.
Therefore the size of the website is deemed reversely related to consumer trust in
Internet shopping.
Moreover, the proposed positive relationship between reputation and consumer trust
is not supported by data. High reputation of the website does not contribute effectively
to building up consumer trust in the online environment. There are several possible
reasons to explain the finding. Firstly, consumers are becoming more and more rational
24
nowadays. Therefore they rely on not only reputation of sellers but also all-round
information during making purchases. Moreover, as Chinese people have witnessed
accidents from those most esteemed brands one after another in the past few years, for
example, milk powder from San Lu, chicken containing poisonous chemicals from
KFC and disqualified automobiles from Toyota, they have lost much confidence in
relying on the so-called reputation of a company. This explains the insignificance of
relationship between reputation of a website and consumer trust in Internet shopping.
In this study, many respondents mentioned they had online shopping experience on
Taobao (淘寶) and gave a mean of 3.58 out of 5 in grading the website reputation.
However their high trust could not be explained by high level of perceived reputation
since they might think the company’s reputation just does not confirm the performance
of every vendor on the website.
Examining the relative importance of the three trust-building antecedents identified
in this study, service quality and website quality were found to have the most effect on
trust with their unstandardized coefficients being 0.351 and 0.340 respectively. And
size of an online store has a less strong negative effect on trust with its figure at -0.167.
6.2. Moderating effect of risk aversion
As the finding revealed, consumers’ trust in Internet shopping is positive related to
purchase intention yet the impact is negatively moderated by consumers’ risk aversion.
People with low risk aversion feel less threatened by ambiguous and uncertain
situations. Reflected in online consumptions, high risk-taking consumers tend to accept
25
more the purchase of new products as well as innovative buying methods while low
risk-taking consumers are easier to find it risky to try out unfamiliar things.
As the result shows, the role of trust in consumer purchase intention varies under
different levels of risk aversion. For consumers of high risk aversion, the effect of trust
on purchase intention is lower. And for those of low risk aversion, the effect of trust on
purchase intention is higher. Figure shows the interaction effect between consumer
trust and consumers’ risk aversion. The slope of the lines represents the effect of
consumer trust on purchase intention under two risk aversion levels respectively. For
people who are of low risk aversion, i.e. high risk-taking, trust positively affects
purchase intention to a larger extent. For people who are of high risk aversion, i.e. low
risk taking, trust still positively affects purchase intention, yet to a smaller extent. In
other words, the role of consumer trust in purchase intention is reduced with people of
high risk aversion.
Figure 5 Conceptual Explanation: Negative Moderating Effect of Risk Aversion
Purchase
Intention
High
Risk Aversion
Low
Risk Aversion
Consumer Trust Low/High
26
As a result, though consumer trust has positive impact on purchase intention, the
effects differ among customers of low risk aversion and high risk aversion. High risk
aversion will weaken the relationship between trust and purchase intention.
6.3. Theoretical implications
The findings of this study generate several implications. Firstly, consistent with prior
researches the antecedents of trust such as perceived service quality and website
quality have been identified and analyzed in the model. And service quality was found
to have more significant effect on trust. However, contrary to prediction and previous
studies, reputation appeared to not be a significant determinant of consumer trust in an
Internet-based store. Moreover, size of an online store was found to have a negative
effect on trust which prompts interesting questions regarding the relationship between
these variables in future studies. Maybe the measure needs to be reexamined and other
confounding factors are recommended to be included in the model.
Another contribution of this study emerges from the inclusion of risk aversion as an
important consumer characteristic in the online shopping context. The finding suggests
that different risk-taking levels of consumers have different interactive effects on their
buying intentions which originally determined only by trust. The study extends
previous research in the area of trust consequences in online shopping by exploring the
role of risk-taking characteristic in the context. It also extends the study conducted by
Bao, Zhou & Su (2003) in which only the effect of risk-aversion on consumers’ general
decision making was examined.
27
6.4. Managerial implications
There are several practical implications generated from this study. Firstly, the
importance of trust in future purchase intention indicates that trust is a criterion
consumers use to evaluate sellers on the Internet. Therefore it is crucial for online
vendors to attract potential customers to make purchases by increasing their trust. And
the study provides insights into how trust is built by identifying three antecedents of
consumer trust. Since perceived service quality and perceived website quality are key
determinants of trust in positive direction, online retailers should pay efforts to impress
potential customers with these two areas in their operation. For instance, some
value-added services could be provided to customers apart from just selling the product,
such as secure and fast delivery, reliable payment methods, certificate of quality
assurance, and after-sale service etc. Indicators of website quality may include easiness
to use, clear contact information as well as attractive interface design. In other words, a
user-friendly website is believed to be more reliable and professional in the eyes of
customers. The results suggest that retailers should manage customers’ perceptions of
service quality and website quality through focusing on both customer-oriented and
superior services and comprehensive website design and maintenance.
Since reputation is not a factor used by consumers to assess online vendors
according to the findings, it is suggested that retailers should not spend a lot of
resources in advertising blindly. Deeper thoughts should be given into how customers
perceive brands and publicity so that more effective techniques could be incorporated
in company’s marketing communications to enhance firm image.
28
Secondly, the study suggests that people’s different risk-taking attitudes affect their
trust and purchase intention relationship. Therefore it becomes crucial for retailers to
understand not only the determinants of potential consumers’ trust but also their
risk-taking personality.
As discussed earlier, risk aversion reflects one’s general tendency to avoid
uncertainty (Hofstede, 1980). People with low risk aversion tend to feel confident
about their choices therefore enjoy shopping around on the Internet. In order to attract
customers who are relatively risk-taking, online vendors could secure their trust level
by offering superior service and leading over website quality because the effect of trust
on purchase intention is higher with this group of people. More importantly, as
risk-taking consumers are also more likely to switch to other retailers on the Internet,
continuous reassurance as well as quick responses to customers’ enquiries should be
provided to build long-term trust. On the other hand, highly risk-averse people are
more likely to be reluctant in taking actions and search for detailed information in
making a decision. To cope with these highly risk-averse customers, however, retailers
are suggested to act differently. Faced with uncertainty and perceived risks, these
people usually have a desire to look for alternatives unless they have sufficient
knowledge or experience. Merely emphasizing on building trust through
above-mentioned methods will not work for them as effectively as for risk-taking
people. Alternative techniques, such as providing trial experience, and making use of
word-of-mouth of former customers, could be adopted to increase risk-averse
customers’ purchase intention in the unknown environment. Online retailers could also
29
offer clear guidance on useful information to facilitate their decision making, such as
security policies (Grewal, Munger, Iyer & Levy, 2003), and trusted third party
verification (Yousafzai, Pallister, & Foxall, 2005).
7. Limitations and Future Research
There are several limitations on the research findings. Firstly, there’s a problem of
generalizability of study results caused by the sampling methods. Though simple
random sampling was used as a part of data collection, it was not purely random
because the places chosen were all high traffic areas due to constraints on time and cost
(Prendergast, Ho and Phau 2002). And convenience sampling might also have bias
problem in the selection of samples. People to whom the online survey was sent tend to
be frequent users of Internet thus much possibly also the frequent users of online
shopping. In addition, the usable sample size was 231 which were 9 less than the
expected amount calculated according to the previous research. Therefore they might
not be representative of the whole population in Mainland China and a larger sample
size may be considered in future research.
Secondly, reliabilities of most scales in the study ranged between 0.7 and 0.9 except
for one at 0.696, a little bit lower than the accepted level. Therefore future research
may need to reexamine and redefine the measure in order to achieve a higher reliability
level.
Thirdly, others antecedent factors that may impact trust should be found out in future
research in order to obtain a deeper insight into the field, such as previous experience
30
in Internet shopping and word-of-mouth influence from peers. Also, reasons for lack of
direct link between reputation and trust as well as negative effect of website size on
trust should be examined.
Finally, this research is the first one that includes consumers’ risk aversion
characteristic in analyzing trust in Internet shopping. Although the current study has
provided insights into the negative moderating effect of risk aversion on consumer’s
purchase intention, further support is still needed in the future research. Deeper
investigations into this particular area as well as other factors related to risk aversion
are recommended so as to reach a more comprehensive understanding of consumers
trust in the online shopping environment.
8. Conclusion
The main objective of this study is to investigate factors affecting consumers’ trust
towards Internet shopping in Mainland China and how it influences their purchase
intention. And it also examines the moderating effect of risk aversion on the
relationship between trust and purchase intention of consumers in Mainland China.
The results prove the proposed positive direct effect of perceived service quality and
perceived website quality on consumer trust. Perceived website size, however, appears
to be reversely related to consumer trust toward online shopping which is opposite to
expectation. And the fourth factor, reputation, shows no significant effect in
determining consumer trust. Crucial in affecting future purchase intentions, trust
toward Internet shopping however is moderated by the interactive effect of consumers’
31
risk aversion in a negative direction.
The research offers for online retailers not only insight into aspects in which efforts
should be made to build up trust in Internet shoppers, but also directions to enhance
future purchase intentions by taking risk aversion into consideration.
https://drive.google.com/file/d/0B8-l_lMvjXWjOHotYWE5aF9mSEk/view?usp=sharing
Abstract
Having many advantages that traditional shopping channels lack of, Internet shopping is
now enjoying its prevalence and rapid development in Mainland China. Famous online
shopping websites including Taobao.com and Amazon.cn attract millions of transactions
as well as new users every day. In many previous researches, focus has been found in the
relationship between consumer trust and its antecedents. Researchers have also
established that online purchase intentions are the product of consumer trust.
The objective of this study is to reexamine some factors affecting consumer trust in
Mainland China as well as to investigate the effect of risk aversion as a moderator on the
relationship between trust and purchase intention.
This paper provided evidence that trust in Internet shopping is built on high service
quality as well as website quality. Size of online retailers is found to be negatively
related to trust. Notably, risk aversion moderates negatively on the effect of trust toward
consumer purchase intention. Implications and suggestions for further research are also
provided in the study.
Table of Contents
1. Introduction......................................................................1
1.1 Statement of the problem..........................................................................1
1.2 Objectives of the study............................................................1
2. Literature review................................................................3
2.1 The concept of trust in Internet shopping........................................3
2.2 Factors impacting trust ..........................................................5
2.3 Risk aversion....................................................................6
2.4 Outcomes of trust................................................................7
3. Research model and hypotheses..............................................8
3.1 Model........................................................................................8
3.2 Statements of Hypotheses.................................................................8
4. Methodology....................................................................11
4.1 Sampling and Data Collection...................................................11
4.1.1 Sampling Method..........................................................11
4.1.2 Sample Size...............................................................12
4.2 Questionnaire Design..........................................................12
4.3 Measurements..................................................................13
5. Findings and Analysis .........................................................14
5.1 Primary data analysis and descriptive statistics.......................................14
5.2 Reliability Analysis.......................................................................15
5.3 T-Test.......................................................................................16
5.4 Regression Analysis.......................................................................17
6. Discussions and Implications.................................................22
6.1 Trust in Internet shopping...............................................................24
6.2 Moderating effect of risk aversion......................................................24
6.3 Theoretical implications.................................................................26
6.4 Managerial implications.................................................................27
7. Limitations and Future Research............................................29
8. Conclusion .....................................................................30
References ........................................................................32
Appendix ...........................................................................36
List of Figures
Figure 1 Conceptual Framework............................................................8
Figure 2 Conceptual Model: Direct Effects on Trust.....................................17
Figure 3 Conceptual Model: Direct Effect of Trust on Purchase Intention............19
Figure 4 Conceptual Model: Moderating Effect of Risk Aversion......................20
Figure 5 Conceptual Explanation: Negative Moderating Effect of Risk Aversion......25
List of Tables
Table 1 Demographic Profile of Respondents...............................................15
Table 2 Reliability Analysis...................................................................16
Table 3 Multiple Regressions: Direct Effects on Trust.....................................19
Table 4 Hierarchical Multiple Regressions: Moderating Effect of Risk Aversion......21
Table 5 Hypotheses Tests.......................................................................22
Table 6 Measurements...........................................................................37
1
1
1. Introduction
1.1. Statement of the problem
The adoption of the Internet as a way to purchase goods and services has seen an
increasing trend over the past two decades globally. Compared to traditional shopping,
the Internet not only facilitates transactions between buyers and sellers from anywhere
at any time, but also provides a wide range of product choices and a platform for
exchanging ideas for customers with low costs. To achieve the success of electronic
commerce, companies place great emphasis on attracting customers continuously and
building long-term relationship with customers on the web.
However, people still remain reluctant to make purchases on the Internet due to the
lack of trust toward businesses in the new electronic environment. Past researches have
indicated that consumers’ lack of trust constituted a key barrier to the use of Internet
shopping as well as long-term commitment to the relationship building. Gefen,
Karahanna, and Straub (2003) identified lack of consumer trust in Internet vendors as a
major factor inhibiting online purchases. Trust plays an essential role for facilitating
online transactions between consumers and electronic retailers and realizing the
development of e-commerce to consumers in the long run (Sonja, 2002).
1.2. Objectives of study
Although the Internet offers enormous advantages which seem to attract massive
interest of customers, recent survey showed that the penetration rate of online
purchasing stayed relatively low, especially in China. According to the most recent
2
“Statistical Report of China Internet Network Development”, the popularity of online
shopping among all Internet users has just reached 26% by the end of June 2009.
Past studies have demonstrated, with empirical evidence, the important role of
consumer trust in Internet shopping (Gefen & Straub, 2004) and argued that the most
significant long-term barrier to the success of the Internet as a commercial medium in
mass markets is a lack of consumer trust in the Internet (Jarvenpaa, Tractinsky & Vitale,
2000; Hoffman, Novak & Peralta, 1999).
However, previous studies that only focus on trust of consumers provide a limited
view of the phenomenon and may hinder a comprehensive understanding of the
consumer purchasing behavior in the e-commerce context in China. In part, this stems
from the cultural values that shape the consumer characteristics and influence the
relationship between trust and consumer purchase intention on the web. For instance,
Bao, Zhou and Su (2003) observed that risk aversion as one of the cultural dimensions
affects consumers’ decision-making. As trust indicates the level of consumers’
perceived risk in the Internet shopping, different degrees of their risk aversion may
have non-ignorable effects on the online buying behavior.
In order to bridge the gap, the objectives of this paper, therefore, are to first
investigate factors affecting consumers’ trust towards Internet shopping in Mainland
China and how it influences their purchase intention. A second objective is to further
examine whether risk aversion as an important consumer psychological attribute places
a moderating effect on the relationship between trust and purchase intention of
3
consumers in Mainland China.
It is expected that consumer trust will be positively related to four antecedents and
risk aversion will negatively moderate the effect of trust on consumer buying intention.
The results will not only provide insights for future research in the new area of risk
aversion as a moderator, but also offer practical implications for building consumer
trust online as well as raise purchase intentions even from customers with high risk
aversion.
2. Literature Review
2.1. The concept of trust in Internet shopping
Before a review of previous literatures on trust in the context of Internet shopping, we
first need to have a look at general definitions of trust in various disciplines.
The concept “trust” is defined as the willingness of a party to be vulnerable to the
actions of another party based on the expectation that the other will perform a
particular action important to the trustor, irrespective of the ability to monitor or
control that other party (Mayer, Davis and Schoorman, 1995). This definition is widely
recognized and commonly cited in other researchers’ work. Later Doney and Cannon
(1997) defined trust as the perceived credibility and benevolence of a target of trust.
According to another two researchers, Lewis and Weigert (1985), trust is further
identified as “the understanding of a risky course of action on the confident
expectation that all persons involved in the action will act competently and dutifully”
(p.971). And precisely, Bhattacharya, Devinney and Pillutla (1998) and Boon and
4
Holmes (1991) conceived trust as predictability and reliance upon another person
under uncertain and risky circumstances.
In the electronic commerce context, a large number of researchers have proposed
both conceptual and empirical studies of trust. Some researchers viewed trust as a
general belief that another party can be trusted (Gefen 2000; Hosmer 1995; Moorman,
Zaltman & Deshpande 1992). One of the most popular studies on electronic commerce
trust is the one conducted by Mayer, Davis and Schoorman (1995) who viewed trust as
a trustor’s intention to take a risk and proposed the trustor’s perceptions about a
trustee’s characteristics as the main predictors of trust. Another commonly cited study
is the conceptual model of McKnight, Cummings and Chervany (1998) where the
researchers defined trust as trusting beliefs and trusting intention only in uncertain and
risky situations and the approach was widely tested by later studies. For example,
Schlosser, White and Lloyd (2006) adopted the model and viewed trust as a way to
reduce uncertainty and complexity in website consumers. Among other studies is the
one that identified trust as a buyer’s perception of appropriate conditions being in place
to facilitate transaction success with online sellers (Pavlou and Gefen, 2004). As
pointed out by Sonja (2002), trust plays a crucial role in the development of electronic
business and some relevant factors in the emergence of trust problems in on
transactions should be therefore analyzed.
In this paper, the definition of trust in the Internet shopping context proposed by
Rousseau (1998) will be employed. According to the author, trust is a psychological
5
condition comprising the intention to accept vulnerability based on positive
expectations of another party’s intention or behavior, in interdependent and risky
environment (Rousseau, 1998).
2.2. Factors impacting trust in Internet shopping
In the electronic commerce context, there are several factors considered as predictors
of trust in online vendors as proposed by previous researchers. They are reputation,
size, perceived service quality and perceived website quality.
Customer’s perceptions of a company’s profile include reputation as well as and
affect trust in the process of Internet shopping. Jarvenpaa and Tractinsky (1999)
considered size and reputation to be the predictors of trust. For example, the authors
believed that larger companies were more likely to be around longer and larger and
more reputable ones might be more trusted by customers (Jarvenpaa & Tractinsky,
1999). Other researchers also viewed reputation and size as important factors forming
consumer trust (Grazioli & Jarvenpaa, 2000; Pavlou, 2003; Kim, Xu & Koh, 2004;
Koufaris & Hampton-Sosa, 2004).
Customers’ perceptions of a company’s service quality affect trust in online
shopping (Daignault, 2001). It seems to be the most significant factor of maintaining
trust and building e-retailer – customer relationships, according to Kim and Tadisina
(2007). As Anderson and Fornell (1994) observed in their study, a high level of service
quality is likely to cause a high level of customer satisfaction which will lead to a
customer’s positive experience and understanding of the company. In this way,
6
customer’s trusting beliefs as well as trusting attitude are confirmed (Kim & Tadisina,
2007). The perceived service quality includes warranties (Grazioli & Jarvenpaa, 2000),
guarantees (Pennington, Wilcox & Grover, 2003-2004), and customized services and
delivery performance (Doney & Cannon, 1997), as well as the general concept of
company’s service quality (Gefen, 2002; Kim, Xu, & Koh, 2004).
Perceived website quality also plays an important role in determining consumer trust
in online shopping (McKnight, Choudhury, & Kacmar, 2002; Araujo, 2003; Kim, Xu,
& Koh, 2004). Websites that are perceived easy to use and of good quality are more
like to build a high level trust in consumers (Wakefield, Stocks, & Wilder, 2004; Want
& Benbasat, 2005)
2.3. Risk aversion
The term risk aversion is defined as “the extent to which people feel threatened by a
ambiguous situations, and have created beliefs and institutions that try to avoid these”
(Hofstede & Bond, 1984, p419). People with high risk aversion tend to feel threatened
by risky and ambiguous situations (Hofstede, 1991). Bao, Zhou and Su (2003)
examined the effects of risk aversion, one of the most important cultural dimensions,
on consumer decision-making and compared two consumer decision-making styles
under cultural differences between United States and China. In order to maintain the
within-group harmony in China as a typically collectivistic society, people are
expected to behave as a group (Bao, Zhou, & Su, 2003), and risk-taking behavior is
often discouraged (Tse, 1996).
7
Another similar study on risk aversion affecting consumers’ decision making is
carried out by Shimp and Bearden (1982) who found that highly risk-averse customers
are likely to search for more information regarding product quality when they make
purchasing decisions. On the other hand, as suggested by Seenkamp, Hofstede and
Wedel (1999), people with low risk aversion feel less threatened by ambiguous and
novel circumstances and tend to feel excited by the purchase of new and innovative
products.
According to the rationale proposed by Raju (1980), the optimum stimulation level,
defined as a property that characterized a person in terms of his general response to
environmental stimuli, is positively related to both risk-taking behavior and switching
behavior. Based on this rationale, Ranaweera, Bansal and McDougall (2008) examined
the effect of risk aversion as one of consumer characteristics on the purchase intention
on the Internet. However, the research focused on the effect of risk aversion on the
relationship between website satisfaction of consumers and their purchase intention
(Bao, Zhou, & Su, 2003). In this paper, the emphasis will be on examining the possible
impact of risk aversion on the relationship between consumers’ trust and the behavioral
intentions.
2.4. Outcomes of trust in Internet shopping
Consumers’ purchase intention is one of the common behavioral dimensions resulting
from their trust in Internet shopping (Boulding, Kalra, Staelin, & Zeithaml, 1993).
Previous research on the relationship between consumer trust and purchase intention
8
by McKnight and Chervany (2002) found out that when customers hold high level of
trust they are more willing to depend on the Internet vendor and make online
purchases.
3. Research Model and Hypotheses
3.1. Model
Based on the literature review, a conceptual model has been designed to study the
effects of perceived company’s reputation, size, perceived service quality and website
quality on consumer trust in Internet shopping and also the effect of risk aversion on
the relationship between consumer trust and purchase intention as a moderator.
Figure 1 Conceptual Framework
3.2. Statement of hypotheses
3.2.1 Factors impacting consumer trust in Internet shopping
Reputation
The perceived reputation is defined as the extent to which consumers believe a selling
(-)
Reputation
Size
Perceived Service
Quality
Perceived Website
Quality
Trust in
Internet Shopping
Purchase
Intention
Risk
Aversion
(+)
(+)
9
company is honest and concerned about its customers (Doney & Cannon, 1997). A
company with a good reputation indicated that long-term investments of resources,
effort, and attention to customer relationship building have been taken into great
concern by that company. Consumers tend to favor companies with a good reputation
in the electronic commerce as they perceive lower risk and uncertainty and know
where to seek for help from the public if something really goes wrong. Therefore this
paper hypothesizes that:
H1: The perceived company’s reputation is positively related to consumers’ trust in
Internet shopping.
Size
Similar to reputation, customers’ perceived size of a company plays an important role
in forming their trust toward Internet shopping. Large size is a signal to buyers that the
company is successful and capable to compensate its customers even there’s
transaction failure (Jarvenpaa, Tractinsky, & Vitale, 2000). In addition, companies
with large size are believed to have more resources which enhance the level of trust in
consumers. Therefore, this paper hypothesized that:
H2: The perceived company’s size is positively to consumers’ trust in Internet
shopping.
Perceived Service Quality
The perceived service quality is related to gaining consumer trust and building
long-term customer relationship by providing high-quality services (Grefen, 2002; Kim,
Xu, & Koh, 2004), including guarantees (Grefen, Karahanna, & Straub, 2003),
warranties (Grazioli & Jarvenpaa, 2000; Pennington, Wilcox, & Grover, 2003-2004),
10
and customized services (Doney & Cannon, 1997). A high level of perceived service
quality enables customers to have more trust in the Internet vendor and make
commitment to the relationship with the company. This paper therefore proposes the
hypothesis that:
H3: The perceived service quality is positively to consumers’trust in Internet shopping.
Perceived Site Quality
A high level of perceived site quality implicates that customers find it easy and
convenient to find the information they need and make transaction on the particular
website. People tend to hold a high level of trust in the online shopping when they
perceive easy use as well as high quality of the website. This paper therefore proposes
the hypothesis that:
H4: The perceived website quality is positively related to consumers’ trust in Internet
shopping.
3.2.2. Trust in Internet Shopping
Trust, defined as the consumers’ willingness to be vulnerable to the actions of an
Internet vendor in an online shopping transaction (Lee & Turban, 2001), is the basis of
customers participating in electronic commerce. Trust helps to reduce customers’
perceived complexity and uncertainty in the online context thus a heightened level of
trust encourages consumers’ activities on the Internet. Hence, it is hypothesized in this
paper that:
H5: Consumers’trust in Internet shopping is positively related to purchase intention.
3.2.3. Risk Aversion
11
Risk aversion is the extent to which customers feel threatened due to ambiguous and
risky environment (Hofstede & Bond, 1984). Faced with low level of trust and high
perceived risk inherited in the Internet transaction context, customers with low risk
aversion may still have relatively strong propensity to participate in online shopping
than those with high risk aversion since they are more open to new opportunities and
shopping styles. However on the other hand, customers who are highly risk-aversion
may still remain reluctant to purchase online though they hold a certain level of trust
toward the Internet vendor. Therefore it is hypothesized that:
H6: Risk aversion will negatively moderate the effect of consumers’ trust in Internet
shopping on the purchase intention.
4. Methodology
4.1. Sampling and Data Collection
4.1.1. Sampling Method
Since the study focuses on factors affecting online trust and how risk aversion
moderates the intensity of consumer trust toward purchase intention, the target
population are individuals from Mainland China who have some knowledge of Internet
shopping. Therefore the target sample should be Internet shoppers who have made
purchases on the Internet at least once. In this research, two sampling methods were
adopted. Simple random sampling was used to collect data using mall-intercept
interviews in Shenzhen. Several places were chosen, including two shopping malls
with one called Maoye Department Store in Huaqiangbei Business center and another
called Wanxiang City, as well as a book center in the city. Every 10th person leaving the
shopping mall will be selected and asked to fill in the paper-form questionnaires and
12
provide opinions related to the constructs of the study. This method is preferred
because it avoids bias in selecting sample unit and is more efficient for the respondent
to come to the interviewer than for the interviewer to go to the respondent (Malhotra,
2007). However in order to overcome the possible bias that people visiting shopping
center tend to be more used to and satisfied with physical shopping, an online survey
on www.my3q.com was created and convenience sampling was used. In order to
assure the reliability as well as representativeness of the result, this method was taken
to draw samples from different cities in Mainland China and different age groups of
people by distributing through social networks for a period of one month.
4.1.2. Sample Size
As the ratio of observations to independent variable affects whether the result of
multiple regressions can be generalized, an observations-to-independent variable ratio
of 40 is considered reasonable (Tabachnick & Fidell, 2001). In this research, given the
number of variables being 6, the sample size of 240 is calculated to meet the statistical
requirement (6*40=240). At last a total number of 259 questionnaires were distributed.
142 of them were obtained from simple random sampling while 117 were from
convenience sampling through Internet.
4.2. Questionnaire Design
The questionnaire was designed in Chinese and consisted of three major parts. After
the first part of asking basic online shopping behavior to screen out those who haven’t
tried Internet shopping, the second part of the questionnaire was divided into four
13
sections measuring factors of forming customer trust (reputation, size, perceived
service quality, and perceived site quality), trust, risk aversion as well as purchase
intention. A Five-point Likert Scale is used in the questionnaire ranging from 1 being
strongly disagree to 5 being strongly agree. And the last part of the questionnaire was
for demographic data collection including respondents’ age, gender, occupation,
educational level as well as average monthly income.
4.3. Measurements
Each variable from the model will be measured with several items derived from
previous research and Table 1 (refer to Appendix A) summarized all the items and
sources of those items.
Reputation. Customers’ perceived reputation of a company is measured by two items
that one is adopted from Park & Kim (2003) and the other one from Teltzrow, Gunther
& Pohle (2003).
Size. Customers’ perceived size of a company is measured by two items adopted from
Doney & Cannon (1997).
Perceived Service Quality. Customers’ perceived service quality is measured by five
item adopted from several previous researches, two from Doney & Cannon (1997), two
from Grefen, Karahanna, & Straub (2003), and one from Grefen (2002).
Perceived Site Quality. It is measured by five items adopted from McKnight,
Choudhury & Kacmar (2002).
14
Trust. Trust towards Internet shopping is measured by six items where four items are
adopted from McKnight, Choudhury & Kacmar (2002) and two of them from Ribbink,
Riel, Liljander & Streukens (2004).
Risk Aversion. The risk aversion scale is based on the original scale developed by Raju
(1980) which was used subsequently by Keaveney and Parthasarathy (2001) and Bao,
Zhou & Su (2003). It is measured by three items.
Purchase Intention. Purchase Intention is measured by three items adopted from
McKnight, Choudhury & Kacmar (2002).
5. Findings and Analysis
The program of SPSS (The Statistical Package for the Social Science) was used to
analyze the data. All statistics were run at 95% confidence level.
5.1. Primary data analysis and descriptive statistics
Among a total number of 259 respondents, 231 of them had online shopping
experience while 28 didn’t. Therefore the total sample number is 231.
Of 231 usable samples, 44.2% were male while 55.8% were female. 92.6% of the
respondents were aged between 19 and 35 and 98.7% of them held a university degree.
The percentages of students and clerical workers were 58.4% and 13.4% which made
up nearly 72% of total respondents, followed by professionals of 11.3%.
15
Demographic
Characteristics
Total
Numbers
Percentage
(%)
Gender
Male 102 44.2
Female 129 55.8
Age
19 – 25 156 67.5
26 – 35 58 25.1
36 – 45 14 6.1
46 or above 3 1.3
Education
Secondary School 3 1.3
Tertiary School 228 98.7
Occupation
Student 135 58.4
Clerical worker 31 13.4
Managerial level 7 3.0
Professional 26 11.3
Self-employed 6 2.6
Others 26 11.3
Income (in RMB)
Below 2000 137 59.3
2000 – 2999 25 10.8
3000 – 3999 27 11.7
4000 – 4999 21 9.1
5000 – 5999 20 8.7
Missing 1 0.4
Table 1 Demographic Profile of Respondents
5.2. Reliability Analysis
After the reverse coded item was recoded, Cronbach’s alpha was used to evaluate the
validity as well as the internal reliability of each construct (Cronbach, 1951). For the
scales of Reputation, Perceived Service Quality, Perceived Website Quality, Trust, and
16
Purchase Intention, the reported Cronbach’s alpha values ranged from 0.817 to 0.872
which were all higher than the acceptable reliability of 0.7 recommended by Mayer
and Davis (1999). And Risk Aversion also had a satisfactory reliability level of 0.735.
However, the alpha value of Size was only 0.610 and should not be accepted.
Therefore the “alpha if item deleted” and “corrected item-total correlation” were
considered. The new Cronbach’s alpha value of Size rose to 0.696 after the originally
reverse coded item was deleted and the mean scores for each variable were then
calculated for running subsequent statistics. The Table summarized the detailed
reliability value for each construct.
Variables Items Cronbach’s
Alphas
Reputation 3 0.817
Size 2 0.696
Perceived Service Quality 5 0.838
Perceived Website Quality 4 0.869
Trust 6 0.851
Risk Aversion 3 0.735
Purchase Intension 3 0.872
Table 2 Reliability Analysis
5.3 T-Test
Since two sampling methods were used in the research, a total number of 231 samples
were consisted of two groups, 121 from simple random sampling and 110 from
convenience sampling. In order to show that there’s no significant difference between
these two samples, a t-test analysis was conducted in SPSS. As shown in Table below,
two sampling methods did not have significant difference (p value < 0.05) in the
17
mean scores for all variables at 95% confidence level. In other words, the data
collected by two sampling methods did not give rise to different results.
5.4. Regression Analysis
Multiple regressions were used to find out both the direct effects of four individual
variables on consumer trust in Internet shopping and the moderating effect of risk
aversion on the relationship between trust and purchase intention.
5.4.1 Direct Effects of Reputation, Size, Perceived Service Quality, and
Perceived Website Quality on Trust.
To simplify the understanding of model, Reputation, Size, Perceived Service Quality,
and Perceived Website Quality were regarded as four independent variables while
Trust in Internet shopping was regarded as a dependent variable. Table summarized the
steps and results of running multiple regressions.
Independent Variables
Dependent Variable
Figure 2 Conceptual Model: Direct Effects on Trust
As shown in Table, there were two stages to test the direct effects among variables.
Firstly, control variables including gender, age, occupation as well as income were
Reputation
Size
Perceived Service
Quality
Perceived Website
Quality
Trust in
Internet Shopping
18
tested in applying regression. For the second step, trust was regressed on reputation,
size, perceived service quality and perceived website quality and the result was
significant with p value being 0.000 and change in R square being 0.250. However,
while size, perceived service quality and perceived website quality were powerful to
explain the variance in Trust with p value less than 0.05, the regression of trust on
reputation was not significant. The unstandardized coefficients B for the three
significant variables were -0.167, 0.351 and 0.340 respectively. That is, while
perceived service quality and website quality both had a positive effect directly on
consumer trust in Internet shopping, size of the website had a negative effect on
consumer Trust which was opposite to the hypothesis. Refer to Appendix for the
complete SPSS output.
Unstandardized
Coefficient
B
R
Square
F Change in
R Square
Model 1 Constant 3.180*** 0.038 2.217 0.038
Gender 0.203
Age 0.086
Occupation 0.046
Income 0.004
Model 2 Constant 1.306 0.288 11.102*** 0.250***
Gender 0.122
Age 0.053
Occupation 0.030
Income 0.022
Reputation -0.036
Size -0.167***
Service
Quality
0.351***
Website 0.340***
19
Quality
**p≤0.05; ***p≤0.01
Table 3 Multiple Regressions: Direct Effect of Reputation, Size, Perceived Service
Quality and Perceived Website Quality on Trust in Internet Shopping.
5.4.2. Relationship between Trust and Consumer Purchase Intention
At the first two stages of applying hierarchical multiple regression to test the
moderating effect of risk aversion, the direct effect of trust on purchase intention was
evaluated as summarized in Table. Control variables of gender, age, occupation and
income were included in the first stage and were not significant to explain the
regression line. Subsequently purchase intention was regressed on trust and the
significant result indicated that consumer trust in Internet shopping positively posed a
direct effect on purchase intention although the level of power was not very strong
with R square change being 0.087 (b=0.291, p=0.000).
Figure 3 Conceptual Model: Direct Effect of Trust on Purchase Intention
5.4.3. Moderating Effect of Risk Aversion on the Relationship between Trust and
Consumer Purchase Intention
By applying hierarchical multiple regression, the moderating effect of risk aversion on
the relationship between trust and purchase intention could be tested as suggested by
Nunally and Bernstein (1994). The moderated multiple regression procedures were
taken after the first two steps of including control variables and the independent
variable carried out in 5.4.2.
Trust in
Internet Shopping
Purchase
Intention
(+)
20
Moderator
Figure 4 Conceptual Model: Moderating Effect of Risk Aversion
Stage 3 in Table showed that under the control of consumer trust in Internet
shopping as well as other control variables including gender, age, occupation and
income, the variable of risk aversion was also significant (p=0.000) to explain the
variance of the dependent variable purchase intention and the unstandardized
coefficients B for trust and risk aversion were 0.271 and 0.365 respectively. R square
change in this stage was 0.117. In Stage 4, the interaction between trust and risk
aversion of consumers was significant (p=0.001) to explain the variance in Purchase
Intention however the change in R square was not strong again being at 0.037. The
result indicated that different levels of consumers’ risk aversion could slightly
moderate the relationship between their trust in Internet shopping and the ultimate
purchase intention in the online context. And the coefficient B of -0.252 showed that
the direction was negative, confirming a weakening effect. The complete SPSS output
was included in Appendix.
(-)
Trust in
Internet Shopping
Purchase
Intention
Risk
Aversion
(+)
21
Unstandardized
Coefficient
B
R
Square
F Change in
R Square
Model 1 Constant 4.205*** 0.016 0.983 0.016
Gender -0.018
Age 0.005
Occupation 0.073
Income -0.049
Model 2 Constant 3.281 0.104 5.157*** 0.087***
Gender -0.077
Age -0.020
Occupation 0.059
Income -0.050
Trust 0.291***
Model 3 Constant 1.780 0.221 10.488*** 0.117***
Gender -0.060
Age -0.036
Occupation 0.029
Income -0.003
Trust 0.271***
Risk
Aversion
0.365***
Model 4 Constant -1.984 0.258 10.962*** 0.037***
Gender -0.053
Age -0.045
Occupation 0.044
Income -0.002
Trust 1.402***
Risk
Aversion
1.201***
Interaction
(Trust*Risk
Aversion)
-0.252***
**p≤0.05; ***p≤0.01
Table 4 Hierarchical Multiple Regression: Moderating effect of Risk Aversion on the
relationship between Trust in Online Shopping and Consumer Purchase Intention.
22
To summarize the findings, Table shows the results of hypotheses test in direct
effects of variables as well as moderating effect on the relationship.
Hypothesis Test Result
Hypothesis 1: Direct effect of reputation on trust Not Significant
Hypothesis 2: Direct effect of size on trust Rejected
Hypothesis 3: Direct effect of perceived service quality on trust Supported
Hypothesis 4: Direct effect of perceived site quality on trust Supported
Hypothesis 5: Direct effect of trust on purchase intention Supported
Hypothesis 6: Moderating effect of risk aversion Supported
Table 5 Hypotheses Tests
6. Discussions and Implications
In response to the research objectives, the results offer strong support for the direct
effects of perceived service quality and perceived website quality in building consumer
trust in online shopping as well as moderating effect of risk aversion on trust toward
purchase intention. However the finding shows that there is no significant relationship
between reputation of an online shopping website and consumer trust while size poses
a negative direct effect on trust which is opposite to hypothesis.
6.1. Trust in Internet Shopping
There are several factors affecting consumer trust in Internet shopping directly and
subsequently affecting purchase intention. According to the findings of this study,
consumers’ trust toward online shopping is positively influenced by service quality and
website quality perceived by customers. There is nothing surprising that people tend to
attach great importance to the quality of service they get in making online purchases as
well as the technical design and security of the shopping website.
23
Although the perceived website size is significant in affecting consumer trust, the
effect is negative and opposite of the prediction. It was posited that large scale of
business ensured a high level of confidence in consumer therefore triggered high trust
in the online environment. However this appears not to be the case. The finding
suggests that the larger the website perceived by customers, the less trust is found in
making purchases on this website. A possible explanation of this contradiction may be
that the large scale of an online website does not convince consumers in Mainland
China that the store is trustworthy. For those stores with a wide range of businesses or
product categories, consumers may believe that they have lower ability to provide
high-quality service, or they become large players in the industry by making a fast
profit and forgetting customers’ interests. Consumers may also find themselves hard to
trust large-scale stores because they are possible to overemphasize on highly profitable
customers and neglect not-so-important clients’ needs. Contrarily, consumers tend to
find online vendors with small scale more trustworthy for they can focus on individual
customers and respond to their different needs more effectively. Those small stores are
believed to keep customers’ interests in mind and provide expected service and values.
Therefore the size of the website is deemed reversely related to consumer trust in
Internet shopping.
Moreover, the proposed positive relationship between reputation and consumer trust
is not supported by data. High reputation of the website does not contribute effectively
to building up consumer trust in the online environment. There are several possible
reasons to explain the finding. Firstly, consumers are becoming more and more rational
24
nowadays. Therefore they rely on not only reputation of sellers but also all-round
information during making purchases. Moreover, as Chinese people have witnessed
accidents from those most esteemed brands one after another in the past few years, for
example, milk powder from San Lu, chicken containing poisonous chemicals from
KFC and disqualified automobiles from Toyota, they have lost much confidence in
relying on the so-called reputation of a company. This explains the insignificance of
relationship between reputation of a website and consumer trust in Internet shopping.
In this study, many respondents mentioned they had online shopping experience on
Taobao (淘寶) and gave a mean of 3.58 out of 5 in grading the website reputation.
However their high trust could not be explained by high level of perceived reputation
since they might think the company’s reputation just does not confirm the performance
of every vendor on the website.
Examining the relative importance of the three trust-building antecedents identified
in this study, service quality and website quality were found to have the most effect on
trust with their unstandardized coefficients being 0.351 and 0.340 respectively. And
size of an online store has a less strong negative effect on trust with its figure at -0.167.
6.2. Moderating effect of risk aversion
As the finding revealed, consumers’ trust in Internet shopping is positive related to
purchase intention yet the impact is negatively moderated by consumers’ risk aversion.
People with low risk aversion feel less threatened by ambiguous and uncertain
situations. Reflected in online consumptions, high risk-taking consumers tend to accept
25
more the purchase of new products as well as innovative buying methods while low
risk-taking consumers are easier to find it risky to try out unfamiliar things.
As the result shows, the role of trust in consumer purchase intention varies under
different levels of risk aversion. For consumers of high risk aversion, the effect of trust
on purchase intention is lower. And for those of low risk aversion, the effect of trust on
purchase intention is higher. Figure shows the interaction effect between consumer
trust and consumers’ risk aversion. The slope of the lines represents the effect of
consumer trust on purchase intention under two risk aversion levels respectively. For
people who are of low risk aversion, i.e. high risk-taking, trust positively affects
purchase intention to a larger extent. For people who are of high risk aversion, i.e. low
risk taking, trust still positively affects purchase intention, yet to a smaller extent. In
other words, the role of consumer trust in purchase intention is reduced with people of
high risk aversion.
Figure 5 Conceptual Explanation: Negative Moderating Effect of Risk Aversion
Purchase
Intention
High
Risk Aversion
Low
Risk Aversion
Consumer Trust Low/High
26
As a result, though consumer trust has positive impact on purchase intention, the
effects differ among customers of low risk aversion and high risk aversion. High risk
aversion will weaken the relationship between trust and purchase intention.
6.3. Theoretical implications
The findings of this study generate several implications. Firstly, consistent with prior
researches the antecedents of trust such as perceived service quality and website
quality have been identified and analyzed in the model. And service quality was found
to have more significant effect on trust. However, contrary to prediction and previous
studies, reputation appeared to not be a significant determinant of consumer trust in an
Internet-based store. Moreover, size of an online store was found to have a negative
effect on trust which prompts interesting questions regarding the relationship between
these variables in future studies. Maybe the measure needs to be reexamined and other
confounding factors are recommended to be included in the model.
Another contribution of this study emerges from the inclusion of risk aversion as an
important consumer characteristic in the online shopping context. The finding suggests
that different risk-taking levels of consumers have different interactive effects on their
buying intentions which originally determined only by trust. The study extends
previous research in the area of trust consequences in online shopping by exploring the
role of risk-taking characteristic in the context. It also extends the study conducted by
Bao, Zhou & Su (2003) in which only the effect of risk-aversion on consumers’ general
decision making was examined.
27
6.4. Managerial implications
There are several practical implications generated from this study. Firstly, the
importance of trust in future purchase intention indicates that trust is a criterion
consumers use to evaluate sellers on the Internet. Therefore it is crucial for online
vendors to attract potential customers to make purchases by increasing their trust. And
the study provides insights into how trust is built by identifying three antecedents of
consumer trust. Since perceived service quality and perceived website quality are key
determinants of trust in positive direction, online retailers should pay efforts to impress
potential customers with these two areas in their operation. For instance, some
value-added services could be provided to customers apart from just selling the product,
such as secure and fast delivery, reliable payment methods, certificate of quality
assurance, and after-sale service etc. Indicators of website quality may include easiness
to use, clear contact information as well as attractive interface design. In other words, a
user-friendly website is believed to be more reliable and professional in the eyes of
customers. The results suggest that retailers should manage customers’ perceptions of
service quality and website quality through focusing on both customer-oriented and
superior services and comprehensive website design and maintenance.
Since reputation is not a factor used by consumers to assess online vendors
according to the findings, it is suggested that retailers should not spend a lot of
resources in advertising blindly. Deeper thoughts should be given into how customers
perceive brands and publicity so that more effective techniques could be incorporated
in company’s marketing communications to enhance firm image.
28
Secondly, the study suggests that people’s different risk-taking attitudes affect their
trust and purchase intention relationship. Therefore it becomes crucial for retailers to
understand not only the determinants of potential consumers’ trust but also their
risk-taking personality.
As discussed earlier, risk aversion reflects one’s general tendency to avoid
uncertainty (Hofstede, 1980). People with low risk aversion tend to feel confident
about their choices therefore enjoy shopping around on the Internet. In order to attract
customers who are relatively risk-taking, online vendors could secure their trust level
by offering superior service and leading over website quality because the effect of trust
on purchase intention is higher with this group of people. More importantly, as
risk-taking consumers are also more likely to switch to other retailers on the Internet,
continuous reassurance as well as quick responses to customers’ enquiries should be
provided to build long-term trust. On the other hand, highly risk-averse people are
more likely to be reluctant in taking actions and search for detailed information in
making a decision. To cope with these highly risk-averse customers, however, retailers
are suggested to act differently. Faced with uncertainty and perceived risks, these
people usually have a desire to look for alternatives unless they have sufficient
knowledge or experience. Merely emphasizing on building trust through
above-mentioned methods will not work for them as effectively as for risk-taking
people. Alternative techniques, such as providing trial experience, and making use of
word-of-mouth of former customers, could be adopted to increase risk-averse
customers’ purchase intention in the unknown environment. Online retailers could also
29
offer clear guidance on useful information to facilitate their decision making, such as
security policies (Grewal, Munger, Iyer & Levy, 2003), and trusted third party
verification (Yousafzai, Pallister, & Foxall, 2005).
7. Limitations and Future Research
There are several limitations on the research findings. Firstly, there’s a problem of
generalizability of study results caused by the sampling methods. Though simple
random sampling was used as a part of data collection, it was not purely random
because the places chosen were all high traffic areas due to constraints on time and cost
(Prendergast, Ho and Phau 2002). And convenience sampling might also have bias
problem in the selection of samples. People to whom the online survey was sent tend to
be frequent users of Internet thus much possibly also the frequent users of online
shopping. In addition, the usable sample size was 231 which were 9 less than the
expected amount calculated according to the previous research. Therefore they might
not be representative of the whole population in Mainland China and a larger sample
size may be considered in future research.
Secondly, reliabilities of most scales in the study ranged between 0.7 and 0.9 except
for one at 0.696, a little bit lower than the accepted level. Therefore future research
may need to reexamine and redefine the measure in order to achieve a higher reliability
level.
Thirdly, others antecedent factors that may impact trust should be found out in future
research in order to obtain a deeper insight into the field, such as previous experience
30
in Internet shopping and word-of-mouth influence from peers. Also, reasons for lack of
direct link between reputation and trust as well as negative effect of website size on
trust should be examined.
Finally, this research is the first one that includes consumers’ risk aversion
characteristic in analyzing trust in Internet shopping. Although the current study has
provided insights into the negative moderating effect of risk aversion on consumer’s
purchase intention, further support is still needed in the future research. Deeper
investigations into this particular area as well as other factors related to risk aversion
are recommended so as to reach a more comprehensive understanding of consumers
trust in the online shopping environment.
8. Conclusion
The main objective of this study is to investigate factors affecting consumers’ trust
towards Internet shopping in Mainland China and how it influences their purchase
intention. And it also examines the moderating effect of risk aversion on the
relationship between trust and purchase intention of consumers in Mainland China.
The results prove the proposed positive direct effect of perceived service quality and
perceived website quality on consumer trust. Perceived website size, however, appears
to be reversely related to consumer trust toward online shopping which is opposite to
expectation. And the fourth factor, reputation, shows no significant effect in
determining consumer trust. Crucial in affecting future purchase intentions, trust
toward Internet shopping however is moderated by the interactive effect of consumers’
31
risk aversion in a negative direction.
The research offers for online retailers not only insight into aspects in which efforts
should be made to build up trust in Internet shoppers, but also directions to enhance
future purchase intentions by taking risk aversion into consideration.
Comments
Post a Comment