WHAT MAKES ONLINE SHOPPERS DIFFERENT
FROM NON-ONLINE SHOPPERS IN THAILAND?
Theingi*,
Department of Marketing,
Martin de Tours School of Management and Economics,
Assumption University, Thailand
email: theingi@au.edu
Cho Mon Aung
Department of Marketing,
Martin de Tours School of Management and Economics,
Assumption University, Thailand
email: chomonaung@hotmail.com
* Corresponding author (Author has only one name).
Abstract
This study conducts 3 focus groups to understand the online shopping process of Thai
consumers and presents descriptive characteristics of online and non-online shoppers in
Thailand. This study also examines the factors that differentiate between online (N= 581) and
non-online shoppers (N= 485) in Thailand. The findings indicate that online shoppers are
significantly different from non-online shoppers in many aspects. Online shoppers in Thailand
are more likely to engage in both traditional and online word of mouth communication, have
lower perceived risks and higher level of positive attitude toward online shopping, are more
willing to have personal interaction with online vendors, have lower perceived price and are
more attractive to online promotion than non-online shoppers.
Keywords: online shopping, personal interaction, word of mouth communication, Thailand.
1. Introduction
Internet retailing or e-retailing is a growing phenomenon around the world due to the ever
increasing usage of the internet. Between 2005 and 2009, the global internet population
increased from 1 billion to 1.6 billion people (IMAP, 2010). According to the Nielson global
online survey, about 875 million people (one-eighth of the world’s population) have shopped
online and these people accounted for 85% of the world’s online population (Nielson, 2008).
However, internet penetration in Asia (23.5%) is lower than the world average (30.2%)
(Internet World Stats, 2011). In a similar trend, the number of internet users in Thailand is
relatively low compared to other Asian countries like China, Malaysia, South Korean, Japan,
Philippines even though trend had been growing dramatically to 18.3 million users (27.3% of
Thai population) in June, 2009, with a 13.7% growth from 2008 (NECTEC, 2011; Internet
World Stats, 2011).
According to the Nielsen (Thailand) report in 2008, 61% of Thai internet users had used the
internet to make a purchase with the growth of 27% compared to the past two years. Nearly
73% of e-commerce transactions in Thailand were targeted at final consumers and valued at
63.4 billion baht in 2008 (Pornwasin, 2008). Moreover, during the past four years, the number
of online shoppers in Thailand had increased 30-40% due to the growing number of online
retailers and relatively low investment required in starting online businesses (Chinmaneevong,
2009). For example, http://shopping.sanook.com/ has 27 categories and 186 sub-categories of
products and services, with around 200,000 new items added per month. It is the most popular
website in Thailand drawing 1 million visitors daily selling variety of products in association
with eBay.
Despite the fast growing e-retailing market, marketers have a limited understanding of internetrelated consumer behavior (Stewart, Wettstein and Bristow, 2004) especially in developing
countries like Thailand. In Thailand, the main reasons against online purchase were risk-related
factors such as lack of trust toward merchandisers, lack of physical touch regarding products,
and unwillingness to reveal credit card numbers, and Thai consumers’ perception towards
online purchase as a high risk process (Laosethakul and Boulton, 2007). Due to high
uncertainty avoidance and collectivistic nature (Pornpitakpon, 2000), Thai people tend to seek
information and the opinions of others in an effort to save search time about the product and to
lessen the risk in purchasing online. However, most studies to date have mainly investigated
the word of mouth communication as post purchase behavior of consumers (Brown, Barry,
Dacin, and Gunst, 2005) and ignored the important role of both traditional and online word of
mouth communication in determining online shopping behavior. Moreover, the importance of
personal interaction between online shoppers and sellers is largely ignored in online shopping
literature.
Thus, the main purposes of this study are to understand the online shopping process of Thai
consumers and to explore the differences between online and non-online shoppers such as in
terms of word of mouth communication and personal interaction between sellers and buyers. In
this study, an online shopper is defined as an internet user who bought or ordered goods on the
internet by making payment in the form of bank transfer or credit card or any other online
payment.
2. Literature Review and Hypotheses Development
Many previous studies on online purchase behavior have focused on the Technology
Acceptance Model (TAM) and Theory of Planned Behavior (TPB) in explaining the internet
adoption process and purchase behavior. According to TPB, attitude toward behavior,
subjective norms (perceived social pressure) and perceived behavioral control (people’s
perception of the ease or difficulty of performing the behavior of interest) influence intention
which in turn determines purchase behavior (Ajzen, 1991). In addition, Mitchell (1999) argued
that the perceived risk of using the internet was paid attention by many marketing practitioners
and researchers since it was powerful to explain consumer behavior. Due to the high
uncertainty avoidance nature of most Thai people (Pornpitakpon, 2000), it is worthwhile to
investigate the factors reducing perceived risk in online purchase. Based on these theories, this
study examines how attitude, perception and perceived risks differentiate between online and
non-online shoppers.
2.1 Perceived Risks
Sheth and Venkateson (1968), based on the risk-taking theory, reported that during the
purchase process, most consumers had some level of perceived risk. Perceived risk was defined
as a perception of consumers on uncertainty and negative consequences in purchasing a good
or service (Dowling and Staelin, 1994). Lwin and Williams (2006) indicated that shopping
online (non-store retailing) was riskier than shopping at traditional stores, due to the e-security
concern (Helander & Khalid, 2000). Moreover, Bramall et al., (2004) also argued that reduced
perceived risk associated with buying from a particular online retailer tends to increase
consumer’s willingness to purchase from that online retailer. Hence, it is imperative that
reduction of perceived risk could lead to consumers’ willingness to shop online. It is
hypothesized that
H1: Online shoppers tend to have lower level of perceived risk than non-online shoppers.
2.2 Word of Mouth Communication
Several previous researches discussed how consumers’ perceived risk can be reduced in
different ways. Based on the findings of a number of scholars such as Dowling and Staelin
(1994) and Chauduri (1997), consumers searched for information to avoid or reduce the
perceived risk. Moreover, according to Chaudhuri (2000) and Larson, Engelland and Taylor
(2004), consumers searched more for information when they perceived that there was a high
risk when purchasing products. Consumers tend to have a low level of confidence while
purchasing online due to lack of experience with products in terms of tastes, sounds, scents,
tactile impressions, and visual images (Hirschman and Holbrook, 1982). To overcome this
limitation, traditional as well as online WOM from other people provided indirect experience
of products to the consumers (Park, Lee and Han, 2007) and allowed consumers to save search
time about the product (Henning-Thurau and Walsh, 2003). According to the Neilson’s study
in 2007, 51% of Thai respondents consider web-based opinions as sources of trustworthy
information and 81% of them trust what they hear from other consumers (Neilson, 2007).The
above behavior is consistent with the Theory of Risk Taking (Taylor, 1974) which discussed
that the consumers tended to reduce the uncertainty of outcome by seeking word of mouth
information.
Westbrook (1987) defined word of mouth communication as an informal communication
between two or more customers about the products or the firms. There are two types of word of
mouth communication (WOM): traditional and online. Sun, Youn, Wu and Kuntaraporn (2006)
mentioned in their study that traditional WOM consisted of spoken words exchanged with a
friend or relative in a face to face situation while online WOM usually involved personal
experiences and opinions transmitted through the written word. The online WOM had widened
the circle of sources and was found in the form of consumer chats, guest books, discussion
forums and boycott sites. Moreover, with the growing popularity of social networks such as hi5
and Facebook, these networks could become an effective source to reach a larger number of
consumers (Maneerunsee, 2009) and to conduct online WOM. Previous literature showed the
effects of positive WOM for online retailers as being the most effective form to advertise
(Enos, 2001). Park, Lee and Han (2007) also found that at Amazon.com, online word of mouth
communication in terms of online consumer reviews has a positive effect on consumer
purchase intention. Hence, it is likely that online shoppers seek more word of mouth
information than non-online shoppers. Therefore, it is hypothesized that
H2: Online shoppers tend to engage more in word of mouth communication than nononline shoppers.
2.3 Attitude towards online shopping
Ryan (1982) found a strong impact of consumers’ attitude on consumers’ purchase intention.
For example, a positive attitude toward e-commerce (Helander and Khalid, 2000), a positive
attitude toward internet shopping (Shim et al., 2001), and attitude toward online stores (Kim
and Park, 2005) have significant influence on online purchase intention. Kim, Kim, and
Lennon (2009) also found consistent results that there was a positive relationship between the
consumer’s purchase intention and their attitude toward a website. Moreover, Shim and
Watchravesringkan (2003) also indicated a positive relationship between consumers’ attitude
toward online shopping and online purchase intention for apparel. Hence, it is hypothesized that
H3: Online shoppers tend to have a more favorable attitude towards online shopping than
non-online shoppers.
2.4 Marketing related factors
Consumers generally like to feel and touch the products, try them or test them out and compare
the prices before they purchase the item (Monsuwe’ et al., 2004). For example, consumers
consider purchasing standardized, staple or familiar goods online more than personal-care
products since those products do not require pre-trial or physical contact (Monsuwe’ et al.,
2004). According to a Nielson Global Consumer Report (2010), 46% of global consumers
surveyed in 2009 purchased books in the last three months. Similarly, Balabanis and Vassileiou
(1999) reported that one of the reasons for consumers not purchasing online was inability to
touch the product. Bramall et al., (2004) also argued that expensive products and unfamiliarity
with brand name enhance the perceived risk of consumers in making online purchase. Thus,
products such as DVDs/games were the most popular purchase items among Thai internet
consumers followed by books, computer hardware and air tickets (Nielson, 2008) because
physical feel and touch were not that important for buying these products and they were less
complicated in nature.
According to Lim and Dubinsky (2004), prices and product comparisons could be done at the
same time and reduced the search costs while purchasing online. Moreover, online retailers
could offer promotional tools such as lotteries, online games, appetizers, special offers, and
several links to other websites (Spiller and Lohse, 1998). These types of offers could lower
their perceived risk of shopping online and enhance online shopping. Thus, if the product’s
availability online had competitive price and promotion, it was likely to offset the risk of online
transactions in consumer markets (Laosethakul and Boulton, 2007) and could lead to online
shopping behavior. Hence, it is hypothesized that
H4: Online shoppers tend to perceive lower level of product complexity, lower price and
attractive online promotion than non-online shoppers.
2.5 Personal interaction with sales person
The interaction between the store salesperson and consumers is one of the influencing factors
on consumer purchase behavior in retailing. According to Reynolds and Arnold (2000), there
were some consumers seeking to get assistance from the salesperson for reasons of social
interaction and personal enjoyment. Moreover, consumer’s perceived risk was reduced by the
assistance of the store salesperson and advice (Conchar, Zinkhan, Peters and Olavarrieta ,
2004). In addition, Thai people perceived that online shopping was impersonal and lacked the
human-touch activities (Laosethakul and Boulton, 2007). However, there is a lack of studies
about the role of personal interaction with online retailers/sellers in online shopping. Sheth and
Parvatiyar (1995) reported that the seller’s interactions with the buyer such as providing
information and service during the purchase process could create buyer’s confidence in
shopping in consumer markets. Thus, it is logical to assume that online shoppers are more
likely to have personal interaction with online retailers/sellers, and non-online shoppers are
more comfortable buying at the traditional retail stores. Hence, it is hypothesized that
H5a: Online shoppers are more willing to have personal interaction with salesperson at
online retail stores than non-online shoppers.
H5b: Non-Online shoppers are more willing to have personal interaction with salesperson
at traditional retail stores than online shoppers.
3. Results of Focus Groups
This study employs three focus groups to have better understanding of Thai online shoppers;
one focus group for university students and two focus groups consisted of working people of
ages ranging from 18 – 35 years. Each group consisted of 6 participants and the duration of
each focus group was approximately one and a half hours. According to the focus group results
from 18 persons in total, the prominent reason for buying online is product uniqueness followed
by product quality and variety, and lower prices. In addition, 50 percent (9 persons) of the
participants mainly used the internet to search for the products of their interest and related
information. The products that the participants were mainly interested in and purchased were
digital cameras and spare parts, PDAs and mobile phones, fashion clothes and shoes, and MP3
players. Moreover, they intended to purchase some old products which are difficult to find in
the market, such as limited or special edition products, and new fashionable products which
were not yet available in Thailand.
3.1 Online shopping process
When discussing online shopping process, the findings from all participants are consistent. As
an initial step, like any other online shoppers in most countries, they searched for necessary
information about a particular product such as product information and price from the various
sources of websites. Most of the participants read the comments and reviews from the web
boards and chat rooms, and also checked the ratings or votes by other buyers to reduce the
risks. After price comparisons were made, participants called the sellers to check the seller’s
credibility when browsing Thai websites, and to ensure the merchandise delivery, payment
methods and security were acceptable.
Even though most participants searched for product information online, they avoid online
payment regardless of online purchase made in Thailand or overseas. For example, to make a
purchase overseas, they will ask their friends overseas to buy the products for them instead of
asking the sellers to ship directly to Thailand because shipping cost is expensive and postal
system is not as reliable as developed countries. Moreover, most of the participants buy from
Thai websites rather than foreign websites. When buying in Thailand, most participants prefer
to call sellers (website owners) in Thailand for additional information and to hear their tone of
voice to ensure the trustworthiness of sellers. Hence, purchase behavior of most participants is
different from conventional online shoppers. Once they decided to purchase the products, most
participants used bank transfer or cash (in case of face to face transaction with sellers) and only
a few participants used credit cards. Their buying process indicates that there is a high concern
for risks associated with buying online. Consequently, online shoppers tend to find alternative
ways such as word of mouth communication to reduce their perceived risks.
The results from the focus group also indicated the use of credit cards and security as the first
and most important issue considered by the buyers, followed by the leakage of personal
information, lack of trust on sellers, long waiting time for merchandise delivery, lack of
merchandise trial, and cheating problems such as delivery of the merchandises which are
different from what the sellers claimed or showed on the web.
The findings from the focus groups in Thailand indicated the importance of word of mouth
communications and marketing related factors (product, pricing) in the buying decision. More
importantly, the results also show how they reduced perceived risks through personal
interaction with online sellers/retailers. Hence, the following conceptual framework was
developed based on the results from focus groups and hypotheses development.
Figure 1. Conceptual framework
Perceived Risk
H1
Word of Mouth
communication
 Traditional
 Online
H2
H1
P Attitude towards online
H3
shopping


Online shoppers
Non-online
shoppers
H4
Marketing related factors
 Product
Complexity
 Price
 Promotion
H5
Personal interaction
with Salesperson
4. Questionnaire Design
The final data collection process mainly focused on quantitative technique due to the nature of
the research which examines the relationship between variables and wants to generalize the
findings based on an acceptable sample size of respondents. A structured survey was chosen as
it provides quick, relative inexpensive, efficient and accurate means of assessing information
about the respondents (Zikmund, 2003). Based on the literature review and results from three
focus groups, a four-page questionnaire, which consisted of three main parts covering 51
questions, was developed for this study. It took about 15-20 minutes for each respondent to
complete the questionnaire. The first part covered the general information regarding the
behavior of online and non-online shoppers. The second part addressed perceived risk, word of
mouth communications, attitude toward online shopping, marketing related variables and
personal interaction between sellers and buyers. The last part was about the personal
information about the respondents and their internet behaviors.
The survey questionnaire included a cover letter and a copy of the questionnaire. A 7 point
Likert scale was used in the questionnaire due to its popularity and being easy to administer
(Maxim, 1999; Zikmund, 2003). Most questions in the questionnaire were in the form of a
statement and the respondents were asked to indicate their attitudes by rating how strongly they
agree or disagree with the statements. The questionnaire was first developed in English and
translated into Thai language by a Thai graduate from Business English Faculty. In order to
ensure the accuracy of the questionnaire translation, a researcher at ABAC Poll and two
colleagues checked the translation of questionnaires in Thai. They not only gave comments on
the translation of the questionnaire but also the format and logical flow of the questions, and
the questionnaire was adjusted accordingly.
5. Pretest
The Pretest was conducted to detect ambiguity or bias in the questions and to ensure that
respondents understood the questionnaire, and were familiar with terms used in the
questionnaire. The questionnaire was pretested with a sample size of 100 respondents using
convenience sampling in February, 2010. The majority of the respondents (46%) were male
and 73% of the respondents held a bachelor’s degree. In addition, 46% of them were between
18 to 23 years old.
Reliability analysis was performed on the main variables such as word of mouth communication,
consumer attitude toward online shopping, marketing-related characteristics, perceived risk and
personal interaction between sellers and buyers. The results of the reliability analysis indicated
that Cronbach alpha value was higher than 0.7.
6. Research Methodology
Quota sampling was employed to have a good representative of population and grouped based on
both online shoppers and non-online shoppers, male and female of the internet users and age group
reflecting the proportion of Bangkok population. According to the Nielsen (Thailand) report of
2008, 61% of Thai internet users (13.4 million users) had used the internet to make a purchase.
Among the internet users, 72.5% of them were female. Field researchers were assigned to gather
the data from 1185 respondents consisting of 716 online shoppers (60%) and 469 non-online
shoppers (40%) to represent the population of internet users in Thailand. The online shoppers
sample was further divided based on the gender (70% female and 30% male) and age group to
ensure the representative of population. According to a survey conducted by National Electronic
and Computer Technology Center (NECTEC) in 2006, the highest percentage of internet users
(28%) in Thailand resides in Bangkok (Charnsiripinyo, 2008). Thus, the scope of study covered
only Thai consumers in Bangkok. The data was collected in March 2010 in 14 Districts in
Bangkok.
The sample consists of those who are over 18 years old and has used internet at least once during
the last three months. The self-administered questionnaire was distributed to the households, office
workers and students in 14 districts and most of the questionnaires were collected during the lunch
time. Based on the quota sampling, field researchers were assigned to collect the data from 1185
respondents but the total complete and usable responses were 1076.The data was collected with
the support of ABAC Poll’s research team which consists of one researcher, one assistant
researcher and 13 field researchers.
7. Data Analysis
Data analysis involved several stages; Descriptive analysis, discussion of demographic
characteristics of respondents, their internet usage behaviors and online shopping behavior,
followed by exploratory factor analysis (EFA) which presents the factors resulting from the
analysis together with Cronbach alpha values to indicate the reliability of each construct. Based
on the results of EFA from total sample (consisting of both online shoppers and non-online
shoppers), hypotheses were tested to investigate the differences between online shoppers and
non-online shoppers.
The sample size consists of 1076 usable responses (581 online shoppers and 495 non-online
shoppers) and the majority of the respondents (61.8%) are female which roughly represent the
population of women internet users in Thailand since 72.5% of internet users in Thailand were
female (Pornwasin, 2010). The majority of the respondents are aged between 30-35 (24.3%),
followed by the second largest group which is between 24-29 years old (23.4%), and 18-23
years old group (18.1%). Moreover, regarding the level of education, 68.5% of total
respondents hold Bachelor’s Degrees, while 19.2% of them have high school education or
lower than high school regulation and 9.6% of respondents are with Master’s or higher
degrees. With regards to monthly household income level, the majority of respondents (37.9%)
earn below 20,000 baht per month followed by 32.4% of respondents with monthly household
income level of 20,001-40,000 baht and 12% of them with 40,001-60,000 baht. In addition, the
sample represents the respondents from different walks of life as 42.2% of them are company
employees and twenty percent are state employees, followed by students (18.9%) and business
owners (17.7%). Hence, the demographic characteristics of the respondents represent people
from different age groups, education levels, occupations and income levels.
In addition, it is interesting that majority of the respondents (26.4%) have been using internet
for more than 10 years and 46.5% of respondents spent one to three hours a day using internet
and 25.7% of the respondents used internet 4 to 6 hours a day. Moreover 33.9% of online
shoppers spent 2,001 (~ 70 US$) to 4,000 baht (~ 130 US$) per each online purchase while
28.6% of them used 1000 baht (~ 35 US$) or less for average online purchase. These are
encouraging trends for online retailers in Thailand (see Table 1).
Table 1
Demographic Characteristics of respondents and internet usage
Demographic
characteristics of
respondents
Category
Respondents
Online shoppers
54%
Non-online shoppers
46%
Education
High school and lower
19.2%
Bachelor’s degree
68.5%
Master’s degree or higher
Income
Occupation
Duration of internet
usage
percentage
9.6%
Below Baht 20,000 (~ 670 US$)
37.9%
Baht 20,001 (~ 670 US$) – 40,000 (~1,300 US$)
32.4%
Baht 40,001(~1,300 US$) – 60,000 (~2,000 US$)
12%
Baht 60,001 (~2,000 US$) – 80,000 (~2,600 US$)
9%
Baht 80,001 (~2,600 US$) – 100,000 (~ 3,500 US$)
4.8%
Baht 100,000 (~ 3,500 US$) and over
3.1%
Student
18.9%
Company employee
42.2%
Government employee
20.4%
Own business
17.7%
Others
0.5%
Less than one year
5.2%
1-3 years
18.3%
4-6 years
24.9 %
7-9 years
24.5 %
10 years or more
26.4 %
Table 1 Continued
Demographic Characteristics of respondents and internet usage
Hours spent on internet per
day
Less than one hour
13.2 %
1-3 hours
46.5 %
4-6 hours
25.7 %
7-9 hours
9.7 %
10 hours or more
4.9%
Average spending per
transaction
Bt 1,000 (~30 US$) or less
28.6 %
Bt 1,001(~30 US$) – Bt 2,000 (~70US$)
33.9 %
(answered by non-online
shoppers only)
Bt 2,001(~70US$) – Bt 4,000 (~130 US$)
22.0% %
Bt 4,001(~130 US$) – Bt 7,000 (~200
US$)
6.2%%
Bt 7,001 (~200 US$) and above
9.4% %
Exchange rate: 1 US$ = 30.34 baht (September 8, 2011)
The second part of the descriptive analysis shows that most respondents use the internet for
entertainment purposes (77.1%) followed by communication (73.5%) and work purposes
(71.4%). The respondents could give more than one answer for the above question. Out of a
total of 1076 respondents, 581 of them are online shoppers and their main reason for buying
online is due to convenience to buy online (67.5%) followed by ease of searching information
(52%) and less time consuming (30.6%). The respondents were asked to give more than one
answer for their reasons. On the other hand, non-online shoppers’ reason for not purchasing
online is due to the security concern about payment system (46.7%) as well as their distrust on
product quality (38.8%) and inability to see the products before purchase (29.7%). The results
also show that 45.1% of respondents made their purchase one to 6 months ago, 20.4% less than
a month ago and 19.3% made their purchase 7 to 12 months ago (See Table 2). This may be an
encouraging trend not only for online retailers, but also for traditional retailers concerned about
expanding their channels online.
It is also interesting to note that the largest group of the respondents (21.4%) purchase clothes
and accessories online, the second largest group (15.3%) purchases air tickets, and the third
largest group purchases CD/DVD (11.7%), cosmetics (11.6%), and computers and accessories
(10.9%) respectively. These findings are quite similar to The National Statistical Office's
survey in 2009 which revealed that the popular products purchased via online trading in
Thailand were fashion apparel, gems and jewelry, computer and electronic goods
(Pratruangkrai, 2010) but the findings are quite different from Nielson Global Consumer
Report (2010) in which 46% of global consumers surveyed in 2009 purchased books.
According to the UNESCO, only 5 books were read per person per year in Thailand, while
neighboring countries, such as Vietnam excel with 60 books and Malaysia with 40 books per
person (UNESCO, 2011).
Furthermore, the finding shows that the majority of online shoppers uses ATM or bank
transfers (54.8%) in purchasing online while 27.7% of them use credit cards, and 6.2% used
Pay Pal service for their last purchase. Among online shoppers, the check (4%) is the least used
method for purchasing online. The results also indicate that 54.4% of Thai online shoppers
prefer to purchase online from home, 36.3% from work place and 5.7% from internet café.
These findings provide a better understanding of both non-online and online shoppers’
behavior in Thailand (see Table 2 for more detailed results).
Table 2
General Information on Online Shoppers and Non-online shoppers
Category
Purpose of using internet of
online shoppers and non-online
shoppers (can give more than
one answer)
percentage
Entertainment
77.1%
Communication
73.5%
Research
16.3%
Networking
21.7%
Work
71.4%
Reading News
58%
Playing games
44.1%
Others
2.5%
Reasons for not buying online
I don’t have credit card
13.7%
(answered by non-online
shoppers)
I don’t feel secure about the payment
system
46.7%
It takes some time to receive the
product.
6.9%
I don’t have a chance to see the product
before making purchase
29.7%
I’m not interested in online purchasing
31.3%
I don’t trust the product quality
38.8%
Others
1.4%
Table 2 Continued
General Information on Online Shoppers and Non-online shoppers
Reason for buying online
The price is cheaper to buy online
22.4%
(answered by online shoppers)
It is convenient to buy online
67.5%
It is easier to search for the information
52%
It is less time consuming
30.6%
The product is not available at the
traditional stores
23.1%
Others
1.2%
Product category
Clothes/accessories
21.4%
(answered by online shoppers)
Cosmetics
11.6%
Air tickets
15.3%
CD/DVD
11.7%
Mobile phone
5.7%
Online game
5.5%
Computer/hardware/accessories
10.9%
Books
5.3%
Car accessories/vehicle
5.7%
Buddha pendant/amulet
1.1%
Toys
2.1%
Others
3.7%
Type of payment
Pay Pal
6.2%
(answered by online shoppers)
Credit cards
Check
ATM/Bank Transfer
27.7%
4%
54.8%
M-pay (through mobile phone)
5.5%
Others
1.7%
Place of online purchase
Home
54.4%
(answered by online shoppers)
Work Place
36.3%
School/University
3.4%
Internet Cafe'
5.7%
Due to the large number of items on the survey instruments, the Exploratory Factor Analysis
(EFA) was employed using SPSS to condense 26 variables into a smaller number of factors:
word of mouth communication (9 items), personal interaction with sales person (7 items), and
marketing related factors (10 items). The results of the EFA are presented in Table 3 to Table
5. As expected, the items measured word of mouth communication have been reduced to two
factors, traditional and online word mouth communication explaining 69.1% of the total
variance. The factor loadings of traditional and online word of mouth fell between the good
range of 0.60 and 0.87 as loadings of 0.5 or greater were considered significant (Hair,
Anderson, Tatham and Black 1998). In addition, Cronbach’s alpha values of both word of
mouth communications indicate good reliability (see Table 3).
Table 3
Exploratory Factor Analysis for Word of Mouth Communication
Factor Loading
Word of Mouth Communication
Traditional
Online
WOM
WOM
I receive the recommendation to buy online
through friends or colleagues or family
0.77
I value the information I receive from
friends or colleagues or family.
0.87
The information I receive from friends or
colleagues or family is helpful for my
decision making.
0.79
I always read reviews that are presented on
the website.
0.77
The reviews presented on the website are
helpful for my decision making.
0.85
The reviews presented on the website make
me confident in purchasing the product.
0.86
I receive the recommendation to buy online
from friends or colleagues or family through
the internet.
0.60
I value the information I receive from
friends or colleagues or family through
internet.
0.66
The information I receive from friends or
colleagues or family through internet are
helpful for my decision making.
0.68
Reliability (standardized alpha coefficient)
0.83
0.89
Eigen value
5.1
1.1
Percentage of variance
56.7%
12.4%
Total variance explained= 69.1%
Even though the questionnaire items for consumers’ personal interaction with sales people
were adopted from previous studies, they did not separate into personal interaction with sales
person at traditional store or online store. The questions for personal interaction with sales
person at online store were developed based on the consistent results of all focus groups’ desire
to have personal interaction with online vendors in domestic market to reduce their perceived
risks or to hear the tone of voice of the online vendors to boost their confidence in buying
online. Here, EFA was conducted on seven items measuring personal interaction with the sales
person which have been reduced into two groups; personal interaction with sales person at the
traditional store and personal interaction with sales person at the online store, providing 73.9%
of the total variance explained with the Eigen value of 3.9 and 1.3 respectively. Each factor has
Cronbach’s alpha value of 0.86 each and the factor loading ranges from 0.73 to 0.89 indicating
good reliability of items measured and acceptable factor loadings (See Table 4 for more
details).
Table 4
Exploratory Factor Analysis for personal interaction with salesperson
Factor Loading
Personal action with salesperson
Traditional
Online
store
Store
I am satisfied with the level of service sales
people provided at traditional store.
0.87
I am willing to discuss my needs in buying
products with sales people at the traditional
store.
0.88
I feel safe in my transactions with sales
people at the traditional store.
0.81
If possible, I would like to send email to
online retailers to inquire about the product
before making purchase.
0.73
If possible, I would like to make a phone
call to online retailers to inquire about the
product before making purchase.
0.77
If possible, I would like to hear the voice of
online retailers to ensure whether she/he is
trustworthy.
0.82
If possible, I would like to have personal
interaction with online retailers before
making a purchase.
0.89
Reliability (standardized alpha coefficient)
0.86
0.86
Eigen value
3.9
1.3
Percentage of variance
55.3%
18.6%
Total variance explained= 73.9%
Another EFA was conducted on ten items representing marketing related factors. The results
indicated three factors. However, the last factor which is supposed to measure the complexity
of the products is not conceptually acceptable since they are loaded to other factors. Hence, the
items related to complexity of the products were deleted from the study and EFA was
conducted on 6 items that measured the perceived price and perceived online promotion. Table
5 indicates that two factors representing perceived online promotion and perceived price
explained 74. 7% of the variance with Eigen value of 3.1 and 1.4 respectively. The factor
loading ranges from 0.72 to 0.90 and these two factors have very good reliability value of 0.86
and 0.79 respectively as shown in Table 5. The rest of the constructs in the study are shown
below in Table 6. The reliability of consumer attitude towards online shopping and perceived
risks are 0.88 and 0.91 respectively.
Table 5
Exploratory Factor Analysis for perceived online promotion and perceived price
Factor Loading
Perceived
promotion
The online retailers usually offer variety of promotion
such as free sample, premium, bonus pack and
coupons.
0.85
The online retailers usually offer more attractive
promotion than traditional stores.
0.90
I purchase the products online because of the variety of
promotion such as free sample, premium, bonus pack
and coupons.
0.85
Perceived
price
The price for the online product is more expensive than
traditional store.
0.88
The price for the online product is much higher than I
expected.
0.88
What I would expect to pay for online product is high.
0.72
Reliability (standardized alpha coefficient)
0.86
0.79
Eigen value
3.1
1.4
Percentage of variance
Total variance explained= 74.7 %
51.7%
23%
Table 6
Exploratory Factor Analysis for consumer attitude and perceived risk
Constructs and Items
Consumer attitude towards online shopping
Reliability (α)
0.88
Using the internet for shopping is enjoyable
Using the internet for shopping is convenient
Using the internet for shopping is interesting
Using the internet for shopping is secure
Using the internet for shopping is necessary
Using the internet for shopping is a good idea.
Perceived risk
0.91
I am not sure of internet payment system and hesitate to use
them
I do not feel safe exposing my personal information when I
buy goods online.
To buy a product from online retailers will be a high potential
for loss.
Online retailers’ product information is not generally
trustworthy.
8. Research Findings and Managerial Implications
The Independent sample t-test was used to determine the difference between online and nononline shoppers. Table 7 indicates that the mean values of all the constructs are significantly
different between online shoppers and non-online shoppers except consumers’ preference for
personal interaction at traditional stores. It shows that both online shoppers and non-online
shoppers wish to have the same level of desire to have personal interaction with salesperson of
the traditional stores (H5b). Hence, Hypotheses 1, 2 and 3 are fully accepted, and H5 and H4
are partly accepted. H4 is partly accepted because as mentioned earlier in data analysis section,
consumer perception towards complexity of product was dropped out from the study after EFA
was performed. Table 7 indicates that online shoppers (mean = 4.5) have lower perceived risk
towards online shopping than non-online shoppers (mean= 4.9). This result is consistent with
previous studies of Chang and Chen (2008) and Kuhlmeier and Knight (2009) which found
that perceived risk was an important factor in making online purchases. This study also found
that online shoppers are more likely to engage in both traditional and online word of mouth
communication than non-online shoppers. This finding was also supported by Duan, Gu and
Whinston (2008) in which positive WOM has an impact on sales and repurchase intention of
customers.
In addition, online shoppers (mean = 4.7) have more positive attitude toward online shopping
than non-online shoppers (mean = 3.5). Moreover, online shoppers find online promotion more
attractive (mean = 4.3) than non-online shoppers (mean = 3.9) and tend to think that online
shopping is a bit less expensive (mean = 4.2) than traditional shopping (mean = 4.3). In
addition, online shoppers (mean = 4.5) are more likely to engage in personal interaction with
online sales person than non-online shoppers (mean =4.3).
Table 7
Independent sample t-test between online shoppers and non-online shoppers
Online shoppers
(N = 581)
Non-online shoppers
(N =485)
Mean
Mean
Significance
level
Results
H1: Perceived risk
4.5
4.9
0.00
Supported
H2a:Traditional Word of Mouth
4.2
3.6
0.00
Supported
H2b: Online Word of Mouth
4.4
3.7
0.00
Supported
H3: Attitude towards online shopping
4.7
3.5
0.00
Supported
H4a: Perceived online promotion
4.3
3.9
0.00
Supported
H4b: Perceived price
H5a: Personal interaction at traditional
stores
4.2
4.3
0.02
Supported
4.6
4.6
0.75
H5b: Personal interaction at online stores
4.8
4.3
0.00
Composites
Not supported
Supported
1= strongly disagree, 7= strongly agree
Thus, marketers should emphasize reaching the online and non-online shoppers and reducing their
risks through social networking sites, and enhance promotional activities and pricing strategies to
attract them to purchase more online. Moreover, online shoppers are also more likely to engage in
personal interaction with online sellers/retailers than non-online shoppers. Hence, in order to
encourage non-online shoppers to be online shoppers, both traditional and electronic word of
mouth communication regarding lower price and online promotion should be targeted at them
which is likely reduce their perceived risks and urge non-online shoppers to purchase online.
Even if word of mouth communication does not always lead to online shopping, it will create
brand awareness or awareness of online retailing. Moreover, online retailers should facilitate their
websites to be convenient, enjoyable and accommodates personal communication with potential
online shoppers if they wish to communicate to the retailers. Since now many online retailing
websites starting to provide personal communication online, this must be an important trend for
the future of online retailing.
Furthermore, in order to have better understanding of online and non-online shoppers, crosstabulation test using chi-square was conducted to see whether gender, level of education, income
level, years of internet usage and hours spent on internet could differentiate between online and
non-online shoppers. The results indicate that there is no significant relationship between online
and non-online shoppers in terms of gender and hours spent on internet. However, there are
significant relationships in terms of income level, education level and years of internet usage. The
results found that majority of online shoppers (34.8%) earns more than 40,000 baht a month (more
than 1,300 US$) and majority of non-online shoppers (44.8%) makes 20,000 baht (700 US$) or
less. In addition, 57.1% of online shoppers have been using internet for more than 6 years whereas
54.5% of non-online shoppers surfed internet for 6 years or less. Similarly, 23.4% of non-online
shoppers have lower than bachelor degree education while only 16.6% of online shoppers
accounts for the same category. Hence, the findings suggest that those who with higher level of
education and higher income, and those who have used internet for more than 6 years are more
likely to be online shoppers and online retailers should target their marketing activities at those
groups.
Furthermore, focus groups’ findings provide insightful information on online shopping behaviors
of Thai consumers and focus groups’ results such as importance of personal interaction and word
of mouth communications are strongly supported by survey results. Hence, this study also
contributes to the importance of personal interaction with online retailers in differentiating
between online shoppers and non-online shoppers in Thailand. This finding is different from
previous studies in online shopping as most studies do not consider the importance of personal
interaction between online shoppers and online retailers/sellers. This study also highlights the
important role of both traditional and online WOM in explaining the difference between online
and non-shoppers, extending the deeper understanding of online purchase behavior of consumers
in Thailand.
9. Limitations and future research
Like any other researches, the study has some limitations. Firstly, due to time and cost
constraints, a cross-sectional study was conducted for this research. The study only looks at
how word of mouth communication, personal interaction with salesperson, consumer attitude
toward online shopping, their perceived price and online promotion in determining online and
non-online shoppers at a particular period of time. However, market condition especially
online retailing is changing rapidly and this study fails to examine how their attitude and
behavior will change over time. Secondly, from a methodological point of view, although
probability sampling would have been the ideal, quota sampling, non-probability sampling,
was applied due to the difficult circumstances during the data collection process. However, the
acquisition of a large sample size might compensate for the above weakness and might have
better representation of the population. Finally, the data was collected only in Bangkok and
surrounding areas making it difficult to generalize the findings to the overall Thai population.
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Acknowledgement:
We would like to express our appreciation to Dr. Noppadon Kannika, director of the ABAC Poll,
and his research team, Ms, Natenapist La-iad, deputy director, Ms. Wanwisa Charoennan, Mr.
Subhachai Yodkhaek, Ms. Brenda Martin, Mr. Panupong Dintordan and 13 field researchers for
their advice and support in moderating focus groups and data collection. We would also like to
thank our chairperson, Dr. Suwanna Kowathanakul, and colleagues for their support and
comments on our questionnaire.