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International Journal of Marketing Studies; Vol. 4, No. 5; 2012

ISSN 1918-719X E-ISSN 1918-7203
Published by Canadian Center of Science and Education

An Analysis of Factors Affecting on

Online Shopping Behavior of Consumers
Mohammad Hossein Moshref Javadi1 , Hossein Rezaei Dolatabadi1 , Mojtaba Nourbakhsh1 , Amir Poursaeedi1 &
Ahmad Reza Asadollahi1

Department of Management, University of Isfahan, Isfahan, Iran

first

Correspondence: Mojtaba Nourbakhsh, Department of Management, University of Isfahan, PO Box 81589-43641,

Isfahan, Iran. Tel: 98-913-169-1865. E-mail: Mojtaba2020@gmail.com

Received: June 29, 2012 Accepted: August 6, 2012 Online Published: September 10, 2012 doi:10.5539/
ijms.v4n5p81 URL: http://dx.doi.org/10.5539/ijms.v4n5p81

Abstract

The purpose of this study is to analyze factors affecting on online shopping behavior of consumers that might be one of
the most important issues of e-commerce and marketing field. However, there is very limited knowledge about online
consumer behavior because it is a complicated socio-technical phenomenon and involves too many factors. One of the
objectives of this study is covering the shortcomings of previous studies that didn't examine main factors that influence
on online shopping behavior. This goal has been followed by using a model examining the impact of perceived risks,
infrastructural variables and return policy on attitude towards online shopping behavior and subjective norms, perceived
behavioral control, domain specific innovativeness and attitude on online shopping behavior as the hypotheses of study.
To investigate these hypotheses 200 questionnaires dispersed among online stores of Iran. Respondents to the
questionnaire were consumers of online stores in Iran which randomly selected. Finally regression analysis was used
on data in order to test hypothesizes of study. This study can be considered as an applied research from purpose
perspective and descriptive-survey with regard to the nature and method (type of correlation).

The study identified that financial risks and non-delivery risk negative attitude towards online shopping.
Results also indicated that domain specific innovativeness and subjective norms positively affect online shopping
behavior. Furthermore, attitude towards online shopping positively affects online shopping behavior of
consumers.

Keywords: online shopping, shopping behavior, consumer attitudes, perceived feelings, B2C e-commerce
1. Introduction

In the business to consumer (B2C) e-commerce cycle activity, consumers use Internet for many reasons and purposes
such as: Searching for product features, prices or reviews, selecting products and services through Internet, placing the
order, making payments, or any other means which is then followed by delivery of the required products through
Internet, or other means and last is sales service through Internet or other mean (Sinha, 2010). Over the past few
decades, the Internet has developed into a vast global market place for the exchange of goods and services. In many
developed countries, the Internet has been adopted as an important medium, offering a wide assortment of products
with 24 hour availability and wide area coverage. In some other countries, such as Iran, however business-to-consumer
electronic commerce has been much below than anticipated proportion of total retail business due to its certain
limitations (Sylke, Belanger, and Comunale, 2002). Also, E-commerce has become an irreplaceable marketing channel
in business transactions. Online stores and services are important sales channels in B2C transactions. Studying online
shopping behavior of consumers has been one of the most important research agendas in e-commerce during the past
decade (Chen, 2009). The research of online consumer behavior has been practiced in multiple disciplines including
information systems, marketing, management science, psychology and social psychology, etc. (Hoffman and Novak,
1996; Koufaris, 2002; Gefen et al., 2003; Pavlou, 2003, 2006; Cheung et al., 2005; Zhou et al., 2007).

Online shopping behavior (also called online buying behavior and Internet shopping/buying behavior) refers to the
process of purchasing products or services via the Internet. The process consists of five steps similar to those
associated with traditional shopping behavior (Liang and Lai, 2000). In the typical online shopping process, when
potential consumers recognize a need for some merchandise or service, they go to the Internet and search

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for need-related information. However, rather than searching actively, at times potential consumers are attracted
by information about products or services associated with the felt need. They then evaluate alternatives and
choose the one that best fits their criteria for meeting the felt need. Finally, a transaction is conducted and post-
sales services provided. Online shopping attitude refers to consumers' psychological state in terms of making
purchases on the Internet (Li and Zhang, 2002).
Although many studies examined various factors affecting on online shopping behavior independently, most of
them isolated a few major factors, usually between three and six factors (Chen, 2009). The drawback of ignoring
some factors is that the compound effects from the interactions among the factors included in the research and
those not included are often ignored and missing, which thus leads to the findings to be lack of generalizability.

This study attempts to develop such a research to study the effects of several major factors which are identified
by prior studies on online shopping behavior. The research questions are thus stated as followed.
1) How do perceived Risks (Financial risks, product risk, convenience risk and non-delivery risk) impact attitude
toward online shopping?
2) How do infrastructural variables and easy and convenient return policy impact attitude toward online shopping?

3) How do subjective norms, Perceived behavioral control and domain specific innovativeness impact online
consumer behavior?
4) How does the attitude impact online consumer behavior?
The finding of this research offer a more comprehensive understanding of online consumer behavior by identifying
the compound effects of various external behavioral beliefs, attitude, intentions and perceived risks, social
influence, etc. specifically, the findings provides in-depth insight into what factors drive online consumers most,
how they work and what are their implications for consumers and e-commerce vendors. The findings also further
confirm some previous research results and help clarify and explain the inconsistent conclusions from prior
studies in the area. In general, this study enriches our knowledge of online shopping behavior from the behavioral
perspective.
To meet the objectives of the research, first, we begin with a review of the literature on online shopping and
factors affecting consumers' shopping behavior. This is followed by an outline of the methodological approach
and the results of the study are reported. Finally, the conclusion and managerial implications are discussed.
2. Theoretical Background
Various studies have used some known theories to explain the online shopping behavior. Prior research has
shown that there are many factors that affect online consumer behavior, but a complete coverage of all potential
factors in one research model is almost impossible. Most studies focused on a few major factors. For example,
Koufaris (2002) tested factors which come from information systems (technology acceptance model), marketing
(Consumer Behavior), and psychology (Flow and Environmental Psychology) in one model; Pavlou (2003) studied
interrelationships between consumer acceptance of e-commerce and trust, risk, perceived usefulness, and
perceived ease of use. Pavlou and Fygenson (2006) examined consumer's adoption of e-commerce with the
extended theory of planned behavior (TPB) (Ajzen, 1991). In their research model, consumer behavior was
separately in terms of getting information behavior and purchasing behavior, both of which were examined by
trust and perceived risk, consumer influence, social, personal online skills, and technology-oriented factors
including perceived usefulness, perceived ease of use and web site features. Also, previous researches have
revealed that online buying behavior is affected by demographics, channel knowledge, perceived channel utility
and shopping orientations (eg, Li, Cheng, and Russell, 1999; Weiss, 2001). Results indicate that compared with
brick-and-mortar shoppers, online consumers tend to be older (Bellman et al., 1999; Donthu and Garcia, 1999;
Weiss, 2001), better educated (Bellman et al., 1999; Li et al ., 1999; Swinwyard and Smith, 2003), have higher
income (Bellman et al., 1999; Li et al., 1999; Donthu and Garcia, 1999; Swinwyard and Smith, 2003), and more
technologically savvy (Li et al. ., 1999; Swinyard and Smith, 2003). Men are more likely to purchase products and/
or services from the Internet than women (Garbarino and Strahilevitz, 2004; Korgaonkar and Wolin, 1999; Slyke
et al., 2002). Reasons for shopping online have been cited for time efficiency, avoidance of crowds, and 24 hour
shopping availability (Karayanni, 2003).

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3. Literature Review

3.1 Online Shopping and Online Stores

Compared to physical stores, online stores have many advantages: They are convenient and time saving and no
more traveling and waiting in lines is needed. They are open in all time and they are accessible anytime and
anywhere. These stores provide consumers with free and rich information about products and services. They also
have some online tools to help consumers compare and make purchase decisions among various products and
services. Hoffman and Novak (1996) indicated that interactivity is the key distinguishing feature between marketing
communication on the Internet and traditional mass media. Today online consumers have more control and
bargaining power than consumers of physical stores because the Internet offers more interactivity between
consumers and product/service providers as well as greater availability of information about products and services.
Geissler and Zinkhan (1998) claim that the Internet shifted the balance of power in favor of consumers as it became
very easy for them to make shopping comparisons and evaluate alternatives without being pressured by salespeople.
Online stores reduce transaction costs and have advantage for both consumers and vendors.

However, online stores also have disadvantages compare to brick-and-mortar stores. In online stores customers
can't have any sense about the product they see in the internet (seeing, touching, tasting, smelling, and hearing)
as they search for and purchase products. In online stores, consumers may develop low trust and perceive elevated
risk highly because of the lack of face-to-face communication. Although this difficulty can be reduced by using
certain software tools such as the online recommendation agent (Häubl and Murray, 2003; Xiao and Benbasat,
2007) and the online negotiation agent (Huang and Sycara, 2002; Huang and Lin, 2007).
3.2 Perceived Risks

Perceived risk refers to the nature and amount of risk perceived by a consumer in contemplating a particular
purchase decision (Cox and Rich, 1964). Before purchasing a product, a consumer considers the various risks
associated with the purchase. The different types of risks are referred to as perceived or anticipated risks.
Research suggests that consumers generally prefer to use electronic commerce for purchasing products that do
not require physical (Peterson et al., 1997). The higher the perceived experience risk, the consumer may shift to
brick-and-mortar retailer for the purchase of the product. Whereas, the lower the perceived risk, the higher the
propensity for online shopping (Tan, 1999). Risks perceived or real, exist due to technology failure (eg, effective in
the system) or human error (eg, data entry mistakes). The most frequently cited risks associated with online
shopping include financial risk (eg, is my credit card information safe?), product risk (eg, is the product the same
quality as viewed on the screen?), convenience (eg, Will I understand how to order and return the merchandise?),
and non-delivery risk (eg, What if the product is not delivered?) The level of uncertainty surrounding the online
purchasing process influences consumers' perceptions of the perceived risks (Bhatnagar et al. , 2000).

3.3 Attitude

Since the mid-1970s, the study of consumer's attitudes has been associated with consumer purchasing behavior
research. to the model of attitude change and behavior (eg, Fishbein and Ajzen, 1975), consumer attitudes are
affected by intention. When this intention is applied to online shopping behavior, the research can examine the
outcome of the purchase transaction. Attitude is a multi-dimensional construct. One such dimension is the
acceptance of the Internet as a shopping channel (Jahng, Jain, and Ramamurthy, 2001). Previous research has
revealed attitude towards online shopping is a significant predictor of making online purchases (Yang et al., 2007)
and purchasing behavior (George, 2004; Yang et al., 2007).
3.4 Perceived Behavioral Control

Ajzen and Madden (1986) extended the TRA into the Theory of Planned Behavior (TPB) by adding a new construct
"perceived behavioral control" as a determinant of both intention and behavior. Perceived behavioral control refers
to consumers' perceptions of their ability to perform a given behavior. TPB allows the prediction of behaviors over
which people do not have complete volitional control. Perceived behavioral control reflects opinions of internal
constraints (self-efficacy) as well as external constraints on behavior, like availability of resources. It has been found
that the Planned Behavioral Control (PBC) directly affects online shopping behavior (George, 2004) and has a
strong relationship with actual Internet purchasing (Khalifa and Limayem, 2003).
3.5 Domain Specific Innovativeness
Domain Specific Innovativeness (DSI) is "the degree to which an individual is relatively earlier in applying an
innovation than other members of his system" (Rogers and Shoemaker 1971, p. 27). For the most part, people

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like continuity in their daily lives, including in their shopping routine. While the Internet and online shopping
offers consumers a wide breadth and depth of product offerings, it also requires them to go outside their normal
shopping routine. Online shoppers need to learn new technology skills in order to search, evaluate and acquire
products. Consumers who prefer brick-and-mortar shopping over other retail channels do not perceive the online
shopping as a convenience (Kaufman-Scarborough and Lindquist, 2002). Research has revealed that online
shopping innovativeness is a function of attitude towards the online environment and individual characteristics
(Midgley and Dowling, 1978; Eastlick, 1993; Sylke, Belanger, and Comunale, 2004; Lassar et al., 2005 ) .
Innovative consumers are more inclined to try new activities (Robinson, Marshall and Stamps, 2004; Rogers,
1995). Adoption of online shopping is depiction of individual's innovative characteristic (Eastlick, 1993). Adopting
a new technology is a function of one's attitude towards it (Moore and Benbasat, 1991). It is expected that
person's domain specific innovativeness has a propensity to shop online.
3.6 Subjective Norms
In order to be successful retailers must understand consumers' purchasing behaviors. This is particularly true
for online retailers. A comprehensive understanding must be made of the website's design and support in order
to match its consumers' information gathering and purchasing behaviors. The visual stimuli and communication
through text and sound can positively or negatively affect consumers' online desires and actions (Vijayasarathy
and Jones, 2000).
The Theory of Reasoned Action (TRA) (Azjen and Fishbein, 1980) has successfully been used to explain human
behavior. The theory proposes that human behavior is preceded by intentions, which are formed on consumers'
attitude towards the behavior and on perceived subjective norms. Attitude reflects the individual's favorable or
unfavorable feeling towards performing a behavior. Subjective norms capture the consumers' perceptions of the
influence of significant others (eg, family, peers, authority figures, and media). It is related to intention because
people often act based on their perception of what others think they should be doing.
Subjective norms tend to be more influential during early stages of innovation implementation when users have
limited direct experience from which to develop attitudes (Taylor and Todd, 1995). It is during this stage of
attitudinal development that online retailers can influence shoppers' propensity for purchasing behaviors (Yu
and Wu, 2007).
4. Background of Research
Barnes and Guo (2011) in a study "Purchase behavior in virtual worlds: An empirical investigation in Second
Life" developed and tested a conceptual model of purchase behavior in virtual worlds using a combination of
existing and new constructs. They examined a kind of shopping behavior that consumers noticeable amount of
money for shopping from internet. Factors of their model were external motivators like perceived value, instinct
motivators like perceived happiness, social factors and consumers' habits. The result of study indicated that
one's habits, external and instinct motivators have great effect on shaping online shopping behavior of them.

Herna'ndez et al. (2011) in a study "Age, gender and income: do they really moderate online shopping
behavior?" Analyzed whether individuals' economic characteristics – age, gender and income – influence their
online shopping behavior. The individuals analyzed are experienced e-shoppers ie individuals who often make
purchases on the internet. The results of their research show that economic variables moderate neither the
influence of previous use of the internet nor the perceptions of e-commerce; in short, they do not condition the
behavior of the experienced e-shopper.
Chen (2009) in his dissertation entitled "Online consumer behavior: an empirical study based on theory of
planned behavior " extends theory of planned behavior (TPB) by including ten important antecedents as external
beliefs to online consumer behavior. The results of data analysis confirm perceived ease of use (PEOU) and
trust are essential antecedents in determining online consumer behavior through behavioral attitude and
perceived behavioral control. The findings also indicate that cost reduction helps the consumer create positive
attitude towards purchase. Further, the findings show the effects of two constructs of flow –concentration and
telepresence, on consumers' attitude. Concentration is positively related to purchase attitude, but telepresence
likely attitude due to the consumers' possible nervousness or concern about uncertainty in the online environment.

Demangeot and Broderick (2007) in a research entitled "Conceptualizing consumer behavior in online shopping
environments", seek to adopt a holistic approach to consider how consumers perceive online shopping
environments. The conceptual model proposes that consumers perceive these environments in terms of their
sense-making and exploratory potential, and it considers the influence of these on user involvement with the web

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site, shopping value and intention to revisit Findings indicate that sense-making and exploratory potential are
distinct constructs; exploratory potential mediates the relationship between sense-making potential and
involvement. Furthermore, involvement is essential in producing shopping value and intention to revisit.
Ying (2006) in his study "Essay on modeling consumer behavior in online shopping environments" examined
online purchase behavior across multiple shopping sessions. Shopping cart abandonment is the bane of many
e-commerce websites. He evaluated abandoned shopping carts in an online grocery shopping setting.
specifically, he developed a joint model for the cart, order, and purchase quantity decisions. The interdependence
between the three decisions is captured by the correlations between the error terms. Empirical analysis shows
that not all abandoned shopping carts result in lost sales. Customers routine pick up abandoned carts and
complete the final orders. Among the factors that propel customers to continue with aborted shopping are the
time of shopping, time elapsed since the previous visit, the number of items left in the abandoned cart, and
promotion intensity. The study offers marketers important managerial implications on how to mitigate the
shopping cart abandonment problem.
Khalifa and Limayem (2003) in a research entitled "Drivers of internet shopping" applied well-established
behavioral theories to explain Internet consumer behavior. Then, they conducted a longitudinal survey study to
identify key factors influencing purchasing on the Web and to examine their relative importance. The results
indicate that the intentions of Internet consumers are significantly affected by the perceived consequences of
online shopping, the consumers' attitudes towards it, and social influence.
Kim and Park (2003) in a study "Identifying key factors affecting consumer purchase behavior in an online
shopping context characteristics" evaluated the relationship between various of online shopping and consumer
purchase behavior. The results of the online survey with 602 Korean customers of online bookstores indicate
that information quality, user interface quality and security perceptions affect information satisfaction and
relational benefit that in turn, are significant related to each consumers' site commitment and actual purchase behavior.
5. Conceptual Model
The model which used in this article was developed to examine the online shopping behaviors of Iranian
consumers. This model examines (1) the relationship between perceived risks, return policy, service,
infrastructural variables and attitudes towards online shopping and (2) the influence of an individual's domain
specific innovativeness (DSI), attitude, subjective norm and planned behavior (PBC) towards online shopping.

Figure 1. Conceptual model

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6. Methodology
6.1 Overview

The purpose of this study is analyzing factors affecting online shopping behavior of consumers in Iran. This goal has
been followed by examining the effect of perceived perceptions, service and infrastructural variables and return policy
on attitude towards online shopping, subjective impact on perceived behavioral control and attitude towards online
shopping behavior as the hypotheses of the study.
Independent variables in this study are as follows:
• Financial risks
Product risks
• Convenience risk

• Non-delivery risk
• Infrastructural variables

• Return policy
• Attitude
• Subjective norms
• Perceived behavioral control

• Domain specific innovativeness

Dependent variables are:
• Attitude

Online shopping behavior For

testing hypotheses of the study, regression analysis was used. Using this method requires some prerequisites that are:

first). There should not be correlation between errors, means errors should be independent.
2). Errors should have normal distribution.
In order to check independence between errors, Durbin-Watson test was used. The DW value should be between 1.5
and 2.5 to reject correlation between errors. For all hypothesis of this study two above conditions were tested and for
all of the hypotheses they were satisfied. By the way, for all hypotheses, Durbin-Watson values were between 1.5 and
2.5 and means errors are independent.
6.2 Sampling and Measurement
To test the main hypothesis of this research, we conducted a questionnaire (See Appendix A). This questionnaire that
adopted and combined from many similar researches, used to collect required data in order to support or reject
hypotheses (See table 1). The questionnaires dispersed among the 5 big online stores of Iran, randomly. This
questionnaire assessed all variables of conceptual model via 51 questions. The reliability of questions was tested by
Cronbach's Alpha and result showed that all of them were above 0.7 offering good reliability of the questionnaire which
was conducted by investigating previous researches and experts' suggestions. All questions were rated on a seven-
point Likert scale from ''strongly disagree'' (1) to ''strongly agree'' (7). At first, an online store was randomly selected
and 30 questionnaires were sent for the consumers that had shopping from that online store. By analyzing the data
achieved by these consumers, deviation of society identified by using Cochran's formula for unlimited society, the
formula turned as the quantity of sample.

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Table 1. Adoption of questions details

Source Variable Question no. Section no.
Forsythe et al. (2006), Online shopping behavior 1 to 17 Section 1
Karayanni (2003)
Swinyard & Smith (2003)
Liang & Huang (1998)

Swinyard & Smith (2003) Financial Risk 1 to 3 Section 2

Forsythe et al. (2006)

Swinyard & Smith (2003) Product Risk 4 to 6

Forsythe et al. (2006)

Swinyard & Smith (2003) Convince Risk 7 to 12

Forsythe et al. (2006)

Forsythe et al. (2006) Non-delivery Risk 13 and 14

Lewis (2006) Return Policy 15 to 17

Lewis (2006) Infrastructural Variables 18 to 20

George (2004) Attitude 1 and 2 Section 3

George (2004) Subjective Norms 3 to 6

Swinyard & Smith (2003)

George (2004) Perceived Behavioral Control 7 to 10

Forsythe et al. (2006)

George (2004) Domain Specific Innovativeness 11 to 14

Lassar et al. (2005)

6.3 Data Collection

200 electronic questionnaires were randomly sent among consumers of 5 big online stores in Iran by email.
There were two main groups among these consumers: consumers that purchase and consumers who do not purchase. We sent
questionnaires for both groups. Finally 107 questionnaires were usable for analyzing.
Respondents were 44.9% female and 55.1% male (see Table 2). Also, this study was done from February, 2012 to June, 2012.

Table 2. Demographic profile of participants (n=107)

Variable Frequency Percentage
Age
Less than 21 2 1.9
21-29 30-39 70 65.4
40 and above 31 29
4 3.7
Gender
Yeast 59 55.1
Women 48 44.9
Education
Lower than high school diploma 7

High school diploma and Associates degree 13 7

Bachelor's degree 32 12

Master's degree or higher 55 30 51

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7. Data Analysis and Results In

order to test the study's hypotheses, a series of regressions were used to analyze the relationships between the predictors
(independent variables) and the dependent variables. The hypotheses and results are as follows: Hypothesis 1: the risk of losing

money and financial details will have negative effect on attitude towards online shopping.

Hypothesis 2: The product risk will have negative effect on attitude towards online shopping.

Hypothesis 3: Having a friendly website and good service to better help the customers for shopping, will have positive influence on
attitude towards shopping online Hypothesis 4: Fear of non-delivery of order will have negative influence on attitude towards

shopping online.

Among perceived risks, effects of financial risks and non-delivery risk on attitude were significant at the 0.05 level. Thus, H1 and H4
are fully supported (See Table 3 And 6). But H2 and H3 were not significant at the 0.05 level and are not supported. (See Tables 4
and 5).

Table 3. Regressions of determinants of attitude towards online shopping (2 items, ÿ = 0.720)

Predictor items ÿ B t-value -0.181 p-value
Financial Risk 3 0.882 -2.128 0.036**
Notes: ** Denotes significance at the 0.05 level

Table 4. Regressions of determinants of attitude towards online shopping (2 items, ÿ = 0.720)

Predictor items ÿ REMOVE t-value p-value
Product Risk 3 0.819 -0.080 -0.734 0.465**
Notes: ** Denotes significance at the 0.05 level

Table 5. Regressions of determinants of attitude towards online shopping (2 items, ÿ = 0.720)

Predictor items ÿ B t-value -0.148 p-value
Convenience Risk 6 0.795 -1.287 0.201**
Notes: ** Denotes significance at the 0.05 level

Table 6. Regressions of determinants of attitude towards online shopping (2 items, ÿ = 0.720)

Predictor items ÿ REMOVE t-value p-value
Non-delivery Risk 2 0.719 -0.227 -2.546 0.012**
Notes: ** Denotes significance at the 0.05 level

Hypothesis 5: After sales service, cyber laws and low shipping fees or free delivery will have positive influence on attitude towards
online shopping.

Hypothesis 6: Convenient product return policy will have positive effect on attitude towards shopping online.

Effect of infrastructural variables and return policy on attitude was not significant at the 0.05 level and is not supported, so H5 and H6
are rejected (See Table 7 and 8).

Table 7. Regressions of determinants of attitude towards online shopping (2 items, ÿ = 0.720)

Predictor items ÿ REMOVE t-value p-value
Return Policy 3 0.897 -0.140 -1.563 0.121**
Notes: ** Denotes significance at the 0.05 level

Table 8. Regressions of determinants of attitude towards online shopping (2 items, ÿ = 0.720) p-value 0.565**
Predictor items ÿ REMOVE t-value
Infrastructural variables 3 0.765 -0.072 -0.557
Notes: ** Denotes significance at the 0.05 level

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Hypothesis 7: Early adopter of technology and products will have positive effect on online shopping behavior.

Hypothesis 8: Family members, friends and peers' online experience and suggestions will positively influence online buying behavior.

Hypothesis 9: Attitude of consumers towards online shopping significantly affects their online shopping behavior.

Effect of domain specific innovativeness, subjective norms and attitude towards online shopping on online shopping behavior were
significant at the 0.05 level. Thus, H7, H8 and H9 are fully supported (see Table 9, 10 and 11).

Table 9. Regressions of determinants of online shopping behavior (17 items, ÿ = 0.867)

Predictor items ÿ 2 B t-value 0.215 p-value
Attitude 0.720 3,620 0.000**
Notes: ** Denotes significance at the 0.05 level

Table 10. Regressions of determinants of online shopping behavior (17 items, ÿ = 0.867)
Predictor items ÿ REMOVE t-value p-value
Subjective norms 4 Notes: ** Denotes 0.762 0.164 2.318 0.022**
significance at the 0.05 level

Table 11. Regressions of determinants of online shopping behavior (17 items, ÿ = 0.867)
Predictor items ÿ B t-value 0.307 p-value
Domain specific innovativeness 4 Notes: ** 0.715 5.065 0.000**
Denotes significance at the 0.05 level

Hypothesis 10: lack of facility to shop online will have negative effect on online shopping behavior.

Effect of perceived behavioral control on online shopping behavior is not significant at the 0.05 level and is not supported, so H10 is
rejected (See Table 12).

Table 12. Regressions of determinants of online shopping behavior (17 items, ÿ = 0.867)
Predictor items ÿ REMOVE t-value p-value
Perceived behavioral control 4 Notes: ** 0.787 0.081 1.114 0.268**
Denotes significance at the 0.05 level

8. Discussion

The results showed that H1 and H4 are significant supported. Thus, fear of losing money and financial details has negative effect on
attitude towards online shopping. Also the Fear of non-delivery of order will have negative influence on attitude towards shopping
online. That is, the higher the risk of losing money and probability of disclosing credit card information, the lower attitude towards online
shopping. This finding is compatible with findings of the Forsythe and Shi (2003) and Biswas and Biswas (2004). In these studies,
financial risk is an important factor for not shopping online. Also the higher the probability of non-delivery of order, the lower attitude
towards online shopping. It indicates that the non-delivery risk is a significant factor for affecting attitude and therefore behavior towards
shopping online. People do not tend to shop online because they are not sure whether the ordered merchandise will be delivered or not
and lack of seriousness and efforts towards building trust by the retailers makes it a significant reason.

Results of testing the hypotheses H2 and H3 indicate that effect of product risks and convenience risk on attitude towards online
shopping is not significant. Our findings are consistent with the findings from the previous study from Sinha (2010). This is also in
contrast with the findings of the existing studies (eg Forsythe and Shi, 2003; Biswas and Biswas, 2004) where product and convenience
risk are significant risk factors for not shopping online. The possible reason of this insignificance in Iranian context appears to be the
indifference and unwillingness of these shoppers towards online medium.

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Results of testing the hypotheses H5 and H6 indicated that infrastructural variables and return policy were not
significant at the 0.05 level and are not supported. That is, the regression results with after sales service, cyber
laws and shipping fees as independent variable and attitude towards online shopping as dependent variable does
not show significant influence of these service and infrastructural factors on consumers' attitude towards online
shopping and regression results on return policy doesn't show significant influence on attitude towards online
shopping.
Regression analysis on hypothesis H7 showed significant influence of domain specific innovativeness (DSI) on
online shopping behavior. That is the higher DSI, the higher effect on online shopping behavior. Results also
supported the hypothesis H8 and H9. Accepting the hypothesis H8 means that the opinion of friends and peers
will more likely influence consumers' online buying behavior. Also the mean response for subjective norm items is
more than 4 means responses were positively inclined towards making an online purchase if they get friends
opinion on merchandise or when they see them shopping online. This finding is in sync with some studies
Jarvelainen (2007) and Khalifa and Limayem (2003) where friends, relatives and media (Subjective Norm) has
been an important factor influencing the online shopping behavior but not with others like Wang et.al (2007 ) where
it subjective norm has not been significant. The possible reason of this is Iranian collective society. People like to
go to market places together and value opinions of others.
Supporting the hypothesis H9 indicates that the attitude of consumers towards online shopping significantly affects
their online shopping behavior. Thus when a consumer has a significant attitude towards online shopping, this will
have effect on his/her shopping behavior.
Results of testing the hypotheses H10 indicated that effect of PBC on online shopping is not significant. Our
findings are consistent with the findings from the previous studies (Sinha, 2010).
9. Conclusion

In this study we examined some factors affecting on online shopping behavior of consumers. A conceptual model
was used in order to assess the effects of variables on each other using regression analysis. Results of hypotheses
testing indicate that financial risk and non-delivery risk has negative effect on attitude towards online shopping
behavior. That is, e-retailers should make their website safer and assure customers for delivery of their products.
has Positive effect attitude towards online shopping on online shopping behavior of consumers indicates that
considering attitude variables make a significant contribution in online shopping. Also, subjective norms have
positive effect on shopping behavior. This means the more people suggest e-buying to each other, the more this
buying method will be popular among people. This makes necessary the use of word of mouth marketing for
retailers. Domain specific innovativeness has positive effect on online shopping behavior. This means that
marketing specialists should target this society in their marketing strategy formulation for better effectiveness of
their marketing program.
Every research has limitations. Limitations of this study are:
1) As we discussed before, there are many factors affecting on online shopping behavior. But in this study because
of time constraints we didn't examine all factors influencing on online shopping behavior.
2) Because of using questionnaire as data gathering tools, the answers may not answer the questions exactly
according to what they think and behave.
3) In this research because of time and cost constraints we examined factors affecting on online shopping behavior
of consumers in Iran. It is obvious that is other countries people have different characteristics and behaviors.
Then result of this study may have lack of generalizability to other countries.
4) Statistical society of this study was online stores selling electronic goods. So development of statistical society
to other stores with different products and services decreases the limitation of study.
5) The methodology of this study for analyzing the data may not be able to assess fully the online shopping
behavior of consumers based on discussed variables.

For future studies, the authors suggest

that: 1) In this research four factors have only been tested on online shopping behavior. Researchers can examine
other factors affect on online shopping behavior with extensive researches.
2) By extending this study to other countries, we can reach to better results and understanding about online
shopping behavior of people.

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3) We used only 5 online stores for our statistical society. By improving better sites like Amazon.com we can
reach to better and more reliable society for research.
4) Including cultural factors in model could show better dimension of online shoppers.
10. Managerial Implications
Based on the results and findings, this study relevant the following implications. First, based on the results and findings of this
study, retail companies should start taking measures to eliminate risk factor and build trust in this form of retail. The retail
managers should sway consumers through different platforms like social networking sites, ads, promotions, online only
discounts etc. to let people cross the threshold and start buying because Iranian consumers are still comfortable with brick
and mortar format as they appreciate friendly approach of salesman and social element of shopping, which has been found
as important element in shopping (Tauber, 1972). In addition, they need to make website user-friendly and less interesting. It
should encourage online consumers to spend time exploring the site and comparing prices online, provide detail product
information and member discounts The results also suggest that after-sales operations like, dispute settlement and delivery,
should be carried out promptly and quickly so that consumer would build faith in the system. During the process of purchasing,
online agents can help customers and simplify the purchasing procedure to give a feeling of friendliness of salesman or
demonstrate how to purchase with clear text, images or examples.

Second, because of perceived lack of secured transaction (financial risk), retailers should introduce a mechanism that would
improve safety and privacy to motivate people to buy online. Customers should not be worry about losing their financial details
and their credit card information. Using SSL protocol for payment pages will secure the web page from disclosure of credit
card information.
Third, the impact of subjective norms on online shopping behavior proposes that online retailers should use word-of-mouth
marketing to for getting their website known to consumers. This method could be one of the most effective method among
other tools and methods of advertising.
Finally, based on the study's results that consumers were worried and uncertain about delivery of their orders (non-delivery
risk), online retailers should provide the insurance for shoppers that they ordered items and make sure that the products will
definitely be delivered to them. They can achieve this goal by providing certain certificate from authorities and governmental
organizations that allow them to sell goods from internet and assure customers that this online retailer is rendering the job
legally, so customers will buy from them with more confident and will not be worry about the delivery of their orders anymore.

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Appendix A

QUESTIONNAIRE
Section-1: In this section you will be asked about attributes of online shopping. Please indicate the number that
best indicates the degree to which you agree or disagree with each of the following statements. 1 means Strongly
Disagree" and 7 being "Strongly Agree".
1. I shop online as I can shop in privacy of home
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2. I shop online as I do not have to leave home for shopping

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3. I shop online as I can shop whenever I want

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4. I shop online as I can then save myself from chaos of traffic

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5. I shop online as I can save myself from market crowd

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6. I shop online as I can get detailed product information online

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7. I shop online as I get general selection of products online

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8. Online shopping gives facility of easy price comparison (Hence, price advantage)
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9. I shop online as I get user/expert reviews on the product

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10. I shop online as there is no embarrassment if I do not buy

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11. I shop online as I can take as much time as I want to decide

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12. I use online shopping for buying products which are otherwise not easily available in the nearby market or are
unique (new)
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13. Online shopping makes my shopping easy

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14. Online shopping gives me better control on my expenses

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15. I find online shopping compatible with my life-style

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16. Using Internet for shopping requires a lot of mental effort

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17. Online shopping procedure is cumbersome and frustrating

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Section-2: In this section you will be asked about perceived feelings, shopping habits and shipping of online
shopping. Please indicate the number best indicates the degree to which you agree or disagree with each of the
following statements. 1 means "Strongly Disagree" and 7 being "Strongly Agree".

Perceived Risks:

1. I feel that my credit-card details may be compromised and misused if I shop online
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2. I might get overcharged if I shop online as the retailer has my credit-card info
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3. I feel that my personal information given for transaction to the retailer may be compromised to 3rd party
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Product Risks:

4. I might not get what I ordered through online shopping

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5. I might receive malfunctioning merchandise

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6. It is hard to judge the quality of merchandise over the Internet

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Convenience Risk:

7. Finding the right product online is difficult

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8. If I shop online I cannot wait until the product arrives

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9. I cannot get to examine the product when I shop online

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10. I feel that it will be difficult to negotiate when I shop online

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11. It is not easy to cancel orders when shop online

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12. I will have problem in returning product bought online (Will have to send the product back through some shipper
and wait to see if the retailer accepts it without any hassle)
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Non-delivery Risk:
13. I might not receive the product ordered online
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14. I do not shop online because of non-availability of reliable & well-equipped shipper
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Return Policy:
1. I do not purchase online if there is no free return shipment service available
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2. I purchase online only when I can return the product without any frills or strings attached
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3. I do not purchase online if there is no money back guarantee

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Cyber laws:
4. I would shop online without any fear if there are strict cyber-laws in place to nab and punish frauds and hackers

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Shipping charge:
5. I don't like being charged for shipping when I shop online
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After Sales Service:

6. Getting good after sales service is time taking and difficult for online purchases
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Section-3: The following questions try to assess some factors that are likely to influence your online shopping
intention. Please indicate the number best indicates the degree to which you agree or disagree with each of the
following statements. 1 means "Strongly Disagree" and 7 being "Strongly Agree".

1. Using Internet for online shopping is easy

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2. Shopping online is fun and I enjoy it

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3. I like to shop with my family members or friends

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4. When I make a purchase my friend's opinion is important to me

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5. I will have no problem in shopping online if I get to know that my friends and relatives are doing it without any
problems
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6. Sharing my experience through online product reviews will make me noticeable

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7. I don't shop online because Internet speed (Web page download time) is very slow
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8. I do not shop online as I do not have a computer at home

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9. I do not shop online as I do not have a computer with Internet connection

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10. I don't shop online as I don't have a credit-card

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11. I am confident of shopping online even if no one is there to show me how to do it

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12. I feel confident of using Internet for shopping after seeing someone else using it
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13. I am usually the first in my group to try out new technologies

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14. My friends approach me for consultation if they have to try something new
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Section-4:

Part-1: The following section will just ask you about your online behavior. Please mark one of the 14 below options in terms of
times spent or frequency of use. (Example: If you spend maximum out of total time spent on Internet in information search then
mark it).

1. I use the Internet mostly for:

Browsing _____ Information search _____ E-mails _____ Chatting _____

Work ____ Social networking ____ Blogging ____ Entertainment _____

Buying _____ Banking ____ Product Search ____ Don't use ____

Others (Pls. specify) ___________________.

Part-2: The following section will just ask you about some more questions on your online behavior. (Please select the one you
find most appropriate in your case).

2. I use the Internet mostly at:

Home - Work/School - Internet Café - Do not use

Others (Pls. specify) _______________________________________________________.

3. For how long you have been actively using the Internet:
Less than 3 Months - 3 - 6 Months - 6 - 12 Months - 1 - 2 Years - 2 - 3 Years - 3 - 5
Years - More than 5 Years - Don't use at all

4. If you have been, then for how long you have been using Internet for shopping:
Less than 3 Months - 3 - 6 Months - 6 - 12 Months - 1 - 2 Years - 2 - 3 Years - 3 - 5 Years
- More than 5 Years - Don't use at all

5. If you have, then how many times have you bought things on Internet (during the past six months)?
Never - 1-2 times - 3-5 times - 6-10 times - 11-20 times - 21 times or more - Not sure

6. What do you prefer buying from the Internet?

Apparels - Electronic goods - Books Cinema Tickets / Online Movies /Shows - Financial Services or Banking - Stuff available only online - Anything -
Do not buy online - Unique daily use items (eg: Torch with blinker light, car seat belt cutter and hammer (eg: Torch with blinker light, car seat belt cutter
and hammer (eg: Torch with blinker light, car seat belt cutter and hammer) all in one) or an artifact)

7. In the past 6 months what would be your estimate of online expenditure (in IRR)?

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0 1-10000 100001-50000 50001-150000 15001-30000 300001-500000 500001 +

9. Do you currently own your own computer? Yes No 10. Do

you have an Internet connection at home? Yes No 11. What
kind of Internet connection do you have? Dial-up - DSL - Through some cable network - Don't have

12. Do you have a credit-card? Yes No

13. How do you pay for online shopping?

Credit Card - Debit Card - Cash Card - Checks - Cash on delivery – Wiretransfer - Never bought online
Others (Pls. Specify___________________)
14. I will grade my Internet using skills as:
Skilled - Knowledgeable - Less Knowledgeable - Want to learn Internet search and browsing techniques.
Section-5:

Please tick the option that you find most suitable:

1. What is your age?
Under 21 - 21 - 29 - 30 - 39 - 40 - 49 - 50(+)
2. What is your gender? Male Female

3. Number of people living in your household (including yourself): __________________.

4. In what industry do you work (eg medicine, academia, student, etc.)? ____________.
5. Marital Status:

Single or Never Married - Married - Widowed - Divorced/Separated

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