INTERNET SHOPPING IN RETAIL PARADISE:

A RESEARCH AGENDA FOR SMALL ECONOMIES

Clarence Leng Wee NG, DBA

University of South Australia c/o 881 N. Bridge Road, 22-12 Singapore 198784, Singapore

Email: ngylw003@mymail.unisa.edu.au

Kai-Uwe SEIDENFUSS, PhD

University of South Australia c/o Roppongi Hills Res. D-1107

6-12-4 Minato-ku 106-0032 Tokyo, Japan

Email: seidenfuss@yahoo.com

ABSTRACT

Significant advancement has been made to develop and promote small but major (island)

economies to be regional hubs for e-commerce, online shopping has yet to fully establish

itself. While most research in the field applied the Technology Acceptance Model (TAM)

to Western countries; this study presents recent findings on actual online shopping in

Singapore. Adding security concerns, Internet self-efficacy, product involvement, and

trust to the TAM variables, the results confirm prior findings – and also show increased

online shopping activity. Age, gender, occupation, and highest education level attained,

however, do not affect online shopping participation. A specific conceptual model

adjustment is suggested for small island economies – be it for online window shopping or

for actual online purchases.

INTRODUCTION

In the pursuit to be a regional hub for e-commerce, Singapore has been developing and

promoting its Info-Communication Technology (ICT) capabilities, such as in the area of

technological research and advancement, infrastructural development, and formulating

favourable policies, to attract both businesses and consumers to adopt this new form of

trade. It was even argued that the distinct nature of this city state gives it unique

characteristics that are favourable towards e-commerce. A highly educated and affluent

population of 4-plus million is concentrated in a compact island of only 640 square

kilometres, allows the country to enjoy significant economies of density in providing

communication infrastructure to the whole population, thus readying Singaporeans to

adopt an alternative channel in shopping (Wong, 2003).

Given the generally positive developments in Singapore, it is observed that “virtual

retailing has yet to establish itself in a prominent position in the country’s markets” (Liao

and Cheung, 2001, pp.299). Actual online sales are well behind previous forecasts. Not

only are business-to-consumer (B2C) online shopping transactions trailing behind

business-to-business (B2B) transactions on the Internet, Singapore also lags behind

developments in the area of online shopping in comparable countries (Liao and Cheung,

2001; Teo, 2002).

At the same time, literature in this field indicates that theory development is still

incomplete. A review shows four major themes: (i) most of the research is based in

Western countries, (ii) most research are based in large countries, mostly the US, in

particular in applying the Technology Acceptance Model (TAM) to online shopping (Davis,

1989; King and He, 2006; McCloskey, 2003), (iii) while TAM is a well-established model,

many, such as McCloskey (2003), have suggested the need for incorporating additional

factors or integrating other theories with TAM to improve “its specificity and explanatory

power” (Shang, Chen and Shen, 2004, pp.401; Hu, Chau, Sheng and Tam, 1999; Legris,

Ingham and Collerette, 2003), and (iv) the literature shows that so far TAM has not been

used to study the activity of product information gathering separately from the next step of

the actual purchase, thus leaving a “blind spot” in this process, such as McCloskey (2003).

It is against this background that more research can add to knowledge by defining a

specific research agenda along the following dimensions: (i) research based in a small

developing or newly developed economy, (ii) to inquire the specifics of a small island

environment, such as Singapore or Hong Kong, where logistics do not play a role from a

customer perspective, (iii) to add new variables to the TAM model reflecting these

environmental factors, and (iv) to study both the information gathering behaviour and the

act of actual purchase.

LITERATURE REVIEW

The review of literature focuses on the key Technology Acceptance Model and several

other studies on user attitudes, product involvement and the online environment as the

basis to explain the attitudes of online shoppers.

Information technology presents a potential to improve work performance (Curley, 1984;

Edelman, 1981; Sharda, Barr and McDonnell, 1988) but these performance gains are not

fulfilled given the users’ unwillingness to accept and use such IT systems (Bowen, 1986;

Young, 1984). User acceptance of information technology and its application has been

studied since the mid-1970s, with this problem remaining persistent and important (Davis,

1989).

Technology Acceptance Model

Davis (1986; 1989) relied on the general and well-established Theory of Reasoned Action

(Fishbein and Ajzen, 1975; Ajzen and Fishbein, 1980) as the foundation to build the

Technology Acceptance Model (TAM) to explain the causal chain linking the external

variables to the user acceptance and actual use of PC-based applications in the area of

multimedia, image processing, and pen-based computing in a workplace (Davis, Bagozzi

and Warshaw, 1989; Davis and Venkatesh, 1995). Davis (1986; 1989) proposed two

theoretical constructs, perceived usefulness and perceived ease of use, as

fundamental determinants of computer system use, likewise the acceptance of

information technology. Perceived usefulness refers to whether people use or not use a

computer application to the extent they believe it will help them with their work. Perceived

ease of use then refers to the belief that the computer system is too difficult to use and

the effort of using the computer application outweighs the benefits of usage.

TAM has often been used to explain and predict end-user behaviour and use of IT. Many

studies have concluded that TAM yields consistent results in predicting the acceptance of

IT, other studies have also suggested the need for incorporating additional factors or

integrating other theories with TAM to improve “its specificity and explanatory power”

(Shang, Chen and Shen, 2004, pp.401; Hu, Chau, Sheng and Tam, 1999; Legris, Ingham

and Collerette, 2003).

King and He (2006), with reference to 88 published studies, conducted a statistical meta-

analysis of the TAM as it was applied in various fields. The study found that TAM is a

valid and robust model that was widely used due to its understandability and simplicity,

and has wider applicability. The study explained that though TAM may be popular, it is

imperfect, and not all TAM relationships were borne out in all studies.

Despite the fact that the original model was intended for use in an organisational context,

the versatility of TAM is evident in the adaptation of the model to cover a myriad of

different areas that are related to the use of IT in a variety of different settings. More

importantly, Doherty and Ellis-Chadwick (2006) reviewed 265 journal articles on Internet

shopping and revealed that TAM has been widely used as the basis to uncover

consumers’ perceptions, beliefs and attitudes. The authors termed these as ‘character

variables’ as they were most likely to influence consumers’ intention to shop online,

thereby validating this study’s use of TAM as the base model.

McCloskey (2003) adapted TAM in the study of electronic commerce and included

security concerns as the additional variable. Additionally, the author proposed that the

variables directly influence usage needs, which are the actual purchases made online.

McCloskey’s (2003) treatment of TAM provides advantages to develop TAM further and

to adopt it as the basis for developing the conceptual framework. The author has shown

that by adding variables to the model further refines it to suit the relevance of the specific

study.

Selected Studies on Online Shopping

Liao and Cheung (2001) found that the life content of products, or the extent to which

products are essential to daily lives, transactions security, prices of the products sold,

perceived vendor quality, IT education and Internet usage affect the willingness of

Singapore shoppers to make purchases over the Internet.

Lian and Lin (2008) studied more than 200 undergraduate students in Taiwan on the

effects of consumer characteristics on the acceptance of online shopping and it was

compared among different types of product. The authors found that personal

innovativeness of information technology (PIIT), perceptions of Web security and high

product involvement positively affect user attitudes whereas increased privacy concerns

has a negative effect. Internet self-efficacy has registered no significant impact which the

authors attributed to the students’ high level of proficiency in using the Internet as well as

their regular contact with that technology.

Both Liao and Cheung (2001) and Lian and Lin (2008) appeared to be closely related in

terms of the variables adopted. The relationships of some of the variables between the

two studies are discussed:

PIIT (Lian and Lin, 2008) closely relates to IT education and Internet usage factors in

Liao and Cheung (2001) as both variables in the latter study are connected to and

contribute toward PIIT, as according to its definition of willingness to try a new

information technology (Agarwal and Prasad, 1998). However, PIIT was not

considered in the study as Singapore is ranked high in e-readiness and has a high

degree of Internet use. Also, given that TAM is widely used and it provides a more

direct measurement of IT acceptance than PIIT.

Internet self-efficacy (Lian and Lin, 2008) is associated with IT education and

Internet usage (Liao and Cheung, 2001) factors as the former have defined Internet

self-efficacy as “the belief of individuals in their capacity to organise and successfully

execute Internet use” (Lian and Lin, 2008, pp. 51), which invariably involves IT

education in furthering the use of IT and it is associated with the level of acceptance

(pp. 53) and use. Internet self-efficacy appears to capture the essences of both IT

education and Internet usage and thereby presenting itself as an all-encompassing

variable that this study would adopt.

Product involvement (Lian and Lin, 2008) corresponds closely to life content of

products variable in Liao and Cheung (2001) as it has been defined as the relevance

of a product to a consumer’s need and want (Lian and Lin, 2008). Similarly, the price

variable in Liao and Cheung (2001) would have an impact on consumer’s need and

want, following demand theory (pp. 303). Given that more extensive research was

done on the product involvement variable than Liao and Cheung’s (2001) life content

of products variable, and that existing scales for the variable are available and easily

adopted, this study will adopt product involvement for obvious reasons.

Liao and Cheung (2001) studied the effects and relationships between consumer

attitudes and online shopping, just as B2C e-commerce is being introduced into

Singapore. Given the passage of time, some of the factors would have become outdated

or irrelevant. For example, Internet usage in Singapore has risen to about 74 percent of

respondents who claimed to have accessed the Internet within the last one week, with

more than 70 percent using it on a daily basis in 2007 (Infocomm Development Authority,

2008). This is compared to less than 50 percent of the Singapore population that have

used the Internet in 2002 and less than 40 percent in 2001 (Infocomm Development

Authority, 2004). Similar usage patterns are also observed in Hong Kong and this

increased usage could also infer familiarity and confidence, perhaps reducing the impact

of the IT education factor has on online shopping (Census and Statistics Department,

2008). However, this study is still relevant to the proposed topic and will be considered in

the development of the proposed conceptual model.

Lian and Lin (2008) provide a good alternative reference to Liao and Cheung (2001) since

some variables in both studies appear to be related in measuring similar outcomes.

Though it is a good reference, Lian and Lin (2008) conducted the study on university

students in Taiwan. Thus the gap exists as Taiwan, Hong Kong and Singapore are

dissimilar countries in Asia, not only in terms of culture (Hofstede, 1997) but also per

capita GDP (Central Intelligence Agency, 2008).

Despite the arguments above, the study by Liao and Cheung (2001) specifically

examines the Singapore online shopping environment which corresponds to the aim of

researching attitudes and behaviour of online shoppers in small island states. However, it

is also argued that some of the variables in the conceptual model of Lian and Lin (2008)

closely resemble that of Liao and Cheung (2001) and that the former would be a better

reference given its recent publication. This gives rise to the possibility of adopting some

of the variables identified by both Liao and Cheung (2001) and Lian and Lin (2008) to the

modified TAM as presented by McCloskey (2003) so as to adapt and modify TAM further

for use.

Trust

Jarvenpaa, Tractinsky and Vitale (2000) studied consumers’ perceptions of trust in

commercial stores on the Internet, a relatively new distribution channel, explained that

trust would be critical when the consumer did not have control over the merchant or store,

when the decision to be made was important and where the environment was uncertain.

A number of other studies investigated the variable of trust in online stores, such as

Pavlou (2003) and Van der Heijden, Verhagen and Creemers (2003), generally found that

the element of trust had a significant impact on consumers’ willingness to shop online.

This study further argues that trust in online store is similar to the perceived online vendor

quality variable as discussed in the study by Liao and Cheung (2001). Liao and Cheung

(2001) found that perceptions of online vendor quality would positively influence

shoppers’ willingness to shop online such that they would feel ‘safe about purchasing

without worrying about lemons’ (pp. 304), thus implying that shoppers’ trust in the online

stores would positively impact online shopping. As such, trust would replace the

perceived e-vendor quality variable found in the study by Liao and Cheung (2001) as trust

has gained more prominence in research and has readily available scales to be deployed.

Gaps in Selected Studies on Online Shopping

McCloskey (2003) also noted the lack of research that distinguishes between information

gathering and actual purchase. Wong (2003) explained the unique geographical and

infrastructural characteristics Singapore has as compared to other localities, may actually

limit the growth of online shopping as shoppers in Singapore may prefer to visit shopping

malls and neighbourhood stores. The study of Singapore’s online shopping scene by

Liao and Cheung (2001), as well as other related studies (Lian and Lin, 2008; Wee and

Ramachandra, 2000), also did not take this into consideration, which presents an

opportunity for this study to investigate on. Extending this discussion, this study seeks to

investigate if shoppers in Singapore tend to use the Internet for information gathering

than making purchases online.

Liao and Cheung (2001) studied the effects and relationships between consumer

attitudes and online shopping, just as B2C e-commerce was introduced to Singapore.

Given the passage of time, some of the factors would have become outdated or irrelevant.

The recent study by Lian and Lin (2008) provided a good alternative reference to Liao

and Cheung (2001) since some variables in both studies appeared to be related in

measuring similar outcomes. However, Lian and Lin (2008) conducted the study on

university students in Taiwan which went against the argument of generalisability of the

findings.

Other related studies have either been purely exploratory without the use of any

theoretical models or statistical analyses, such as Teo (2002), or they have been built on

unrelated, or even ad-hoc frameworks, such as Wee and Ramachandra (2000), Liao and

Cheung (2001), and Lian and Lin (2008). In some cases, the studies did not explain the

selection of variables at all, such as Lian and Lin (2008). Moreover, the selection of the

various product or service types in Lian and Lin (2008) could be broadly classified into

specific product categories. Computer and computer-related products would appeal to

consumers with specific interest in computers, whereas books, online news and

magazines would appeal to those who have taken an interest in reading, which went

against the authors’ argument of generalisability of their findings.

Despite the arguments above, the study by Liao and Cheung (2001) specifically

examined Singapore’s online shopping environment which is in line with the aim of this

study. However, it has been argued that some of the variables in the conceptual model of

Lian and Lin (2008) closely resembled that of Liao and Cheung (2001) and that the

former would be a better reference given its recent publication. This gives rise to the

possibility of adopting some of the variables identified by both Liao and Cheung (2001)

and Lian and Lin (2008) to the modified TAM as presented by McCloskey (2003) so as to

adapt and modify TAM further for use in the study of online shopping in Singapore.

The original TAM states that perceived usefulness and perceived ease of use impact

attitude toward use, which impacts behavioural intentions, which in turns impacts actual

usage’ of online shopping (McCloskey, 2003, pp. 49). This study uses TAM in its adapted

form by McCloskey (2003), including security concerns and includes the variables of

Internet self-efficacy, product involvement and trust from various studies to develop a

new proposed conceptual framework for research.

Proposed Conceptual Framework

Source: authors

STUDY AND FINDINGS

The aims of this research had been to explore the issues relating to the use of online

shopping by shoppers in Singapore so as to better understand the factors that affect their

adoption of this new form of technology. It further sought to uncover the attitudes of

Internet users toward online shopping and the factors that influence them to buy online.

With the knowledge gained, businesses can be better informed whilst developing

strategies to establish or to improve their online distribution platform, at the same time,

how to better integrate their existing business model with online shopping.

This study surveyed members of the Singapore public that were visiting an IT trade show

and a consumer electronics shopping mall. This involved having the research assistants

randomly intercept visitors exiting the trade show premise or shopping mall, and inviting

them to participate in the pen-and-paper survey. Upon completion of the questionnaires,

the participants deposited the questionnaires into a sealed box.

Trade show visitors that were not aware of online shopping, as well as those who

qualified but declined to participate, were not given the paper questionnaires. A rejection

rate of about 30 – 50 percent was registered. Shoppers who were qualified and accepted

the offer to participate spent an average of 8 minutes to complete each questionnaire. A

total of 198 questionnaires were returned over four days of the show.

Similarly, research assistants were deployed at the public walkways leading to Funan

DigitalLife Mall shopping complex, a shopping mall dedicated to selling consumer

electronic products, to solicit for more respondents. A rejection rate of about 40 – 60

percent was registered and total of 62 questionnaires were returned for this exercise.

Of the 260 returned questionnaires, it was reported that eight respondents returned

incomplete questionnaires after informing the research assistants that they could not

continue with the survey for various reasons. Despite this, a rate of return of about 97

percent was registered.

In the actual study, 252 questionnaires were collected and 248 were found to be usable

which forms the final sample size. Thereafter, the 248 questionnaires were screened to

ascertain the number of participants and non-participants of online shopping, based on

the screening questions. Almost all the respondents (98.4%) have used the Internet to

search for products, with a majority (31.9%) spending a few times a month on this

technology to search for information about the products of their interest. Out of the 248

respondents, there were 199 participants of online shopping while 49 had not used the

technology to purchase products online.

Socio-Demographics

Among the socio-demographics data analysed, there is a statistically significant

relationship between income levels and the participation of online shopping (Pearson Chi-

Square = 15.008, df = 4, p = 0.005). Age, gender, highest education level attained and

occupation do not present any statistically significant relationships with online shopping

participation, which is in line with the findings of Sim and Koi (2002), but not Teo (2006).

Descriptive Statistics – Income

Participant of Online Shopping

(% within the income category)

Non-Participant of Online Shopping

(% within the income category)

Total

S$25,000 and below 80 (71.4) 32 (28.6) 112

S$25,001 - $50,000 69 (88.5) 9 (11.5) 78

S$50,001 – S$70,000 23 (85.2) 4 (14.8) 27

S$70,001 – S$100,000 8 (66.7) 4 (33.3) 12

S$100,001 and above 18 (100.0) 0 (0.0) 18

Total 198 49 247

Pearson Chi-Square = 15.008, df = 4, p = 0.005

Income levels do affect the intended outcome, that is, participation in online shopping.

However, Teo (2002) presents the argument that lower income earners, such as students,

might not have met the minimum requirements set by credit card companies for the

ownership of credit cards, which is one of the main modes of transaction for online

purchases. Sim and Koi (2002) also found that adopters of online shopping had higher

income and were more likely to own credit cards that would facilitate these transactions.

This is in line with the findings of Donthu and Garcia (1999) and Tan (1999) that

discovered that participants of online shopping were older and wealthier than non-

participants. According to an online shopping survey (MasterCard Worldwide, 2010), 26

percent of respondents cited absence of credit cards being one of the reasons for not

participating in online shopping. This point should be asked in future online shopping

studies.

From a broader perspective, income levels affect GDP at PPP, an indicator of the

standard of living in an economy. As previously discussed, although Singapore ranked

higher than South Korea, Japan, and Taiwan in GDP (PPP) (Central Intelligence Agency,

2008), these neighbouring Asian economies outperformed Singapore in terms of having

greater numbers of their residents participating in online shopping (MasterCard

Worldwide, 2010). This conundrum presented by this socio-demographic measure of

income clearly demonstrates that other factors, to be discussed in subsequent sections,

are at play in influencing shoppers in Singapore to shop online and income is just one

part of the whole equation.

This study finds that age, gender, highest education level attained and occupation do not

affect an individual’s participation in online shopping. Though this contradicts Teo’s

(2006) study, this study argues that perhaps online shopping is far more pervasive now

than before. Regardless of age, gender, education level and occupation, shoppers in

Singapore are more willing to participate in online shopping in the current times,

somewhat a reflection of changing lifestyles as people use more of the Internet for

various purposes in their daily lives.

Experience

The survey asked respondents to indicate the number of years in using the Internet and

computer as well as the number of hours in using the Internet, computer and email per

day. Of all these measures, number of years of Internet use (F-value = 4.786, df = 1, p =

0.030) and number of hours of email use per day (F-value = 8.355, df = 1, p = 0.004)

presented statistically significant relationships with participation in online shopping.

Descriptive Statistics – Number of Years of Internet Use

N Mean Std.

DeviationStd. Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

Participant of Online Shopping

199 10.87 3.871 0.274 10.33 11.41 3 30

Non-Participant of Online Shopping

49 9.55 3.373 0.482 8.58 10.52 3 20

Total 248 10.61 3.808 0.242 10.13 11.09 3 30

F-value = 4.786, df = 1, p = 0.030

Descriptive Statistics – Number of Hours of Email Use per Day

N Mean Std.

DeviationStd. Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

Participant of Online Shopping

199 3.30 3.379 0.240 2.82 3.77 0 16

Non-Participant of Online Shopping

49 1.84 2.059 0.294 1.25 2.43 0 8

Total 248 3.01 3.212 0.204 2.61 3.41 0 16

F-value = 8.355, df = 1, p = 0.004

On analysing the results for number of years of Internet use, an additional year of Internet

use was seen in participants of online shopping, compared to non-participants. It could

be assumed that with more experience in Internet use, it will likely increase the

participation in online shopping, due to the familiarity with the technology, greater

awareness of its different uses and applications, or simply due to an increased comfort

level.

This study finds that the average number of years of Internet use for participants of online

shopping is 10.9 years. Compared to Teo (2002), a majority of respondents of the online

shopping study in Singapore conducted in early 2000s indicated they had used the

Internet for about two to four years. In another later study, Teo and Liu (2007) reported

that 19.6 percent of Singapore respondents had four years or less of experience with the

Internet and about 18.2 percent had more than seven years. Evidently, this study finds

that the number of years of Internet use has increased as the technology matures over

time. The usage of the Internet appears to be a necessary experience for users to

embark on more sophisticated usage, such as online shopping.

On analysing the results for number of hours of email use per day, the results show that

there was almost twice the number of hours of email use per day for participants of online

shopping as compared to non-participants. However, the numbers of hours of computer

and Internet use per day were not statistically significant contributors to this purpose.

This is an interesting finding as email use is not always contingent upon the existence of

Internet connection, as in the case of inter-organisation email communications through

Intranet or Blackberry systems, yet email use was significant as a contributor to online

shopping participation.

According to McCloskey (2003), the number of hours of email use has no significant

impact on any of the measures. However, the number of hours of Internet use did

positively impact on the decision to buy online, the number of items to buy, the frequency

to buy online and the amount of money spent in online shopping. Partially contradicting

McCloskey (2003), Van Slyke, Comunale, and Belanger (2002), who in their respective

studies found that both computer and email use had significant impact on the participation

in online shopping. The authors explained that the lack of Internet experience might

contribute to perceptions of online shopping complexity. Regardless of contradictory

findings in the literature, shoppers in Singapore, as uncovered by this study, are more

inclined to shop online if they gained more usage of the Internet and email

communication, a situation that managers can capitalise on in developing their messages

to their target audience.

Online Shopping Experience by Participants of Online Shopping

Participants of online shopping are found to have shopped an average of 21 times in the

past year. Comparing to Teo (2006), about 50 percent of respondents who shopped

online made less than one purchase in a month from the Internet and about 40 percent

made about one purchase in a month and that approximated to about 12 purchases in a

year, which is also lower than the result presented by this study. This difference can be

attributed to the maturity of the technology which might encourage more people to

embark on online shopping now than before. Buying behaviour could have also changed,

leading to more online shopping participants. It is also worthy to note that Teo’s (2006)

study is based on data collected in 2002, which is more than seven years old.

Descriptive Statistics - Number of Times Used the Internet to Purchase Products Last Year

N Minimum Maximum Mean Std. Deviation

Number of times used the Internet to purchase products last year

199 1 1000 21.46 91.482

In comparing the number of items purchased, this study finds that about 27 percent of the

respondents bought one to three items and about a quarter of the respondents bought

more than ten items in the last year. In comparison to Teo’s (2006) Singapore study,

about 43 percent bought about one to three items, 32 percent bought between four to six

items, and less than five percent bought more than ten items. This shows that online

shoppers in Singapore are buying more items now than before. Confirming this trend, the

MasterCard Worldwide (2010) study showed that shoppers in Singapore purchased about

five items in a quarter, which approximated to about 20 items in a year

Descriptive Statistics – Number of Items Purchased Online Last Year

Frequency Percent Teo (2006) - Percent

None 0 0 8.1

1 – 3 68 27.4 43.6

4 – 6 42 16.9 32.1

7 – 10 28 11.3 11.7

More than 10 61 24.6 4.6

Subtotal 199 80.2 100.0

Non-Participants 49 19.8

Total 248 100.0

Of all these items purchased online, majority of respondents in this study spent between

$101 and $300 in the last year. The next largest group spent between $51 and $100.

About nine percent, or 24 respondents, spent more than $1000 in online shopping.

Seven years ago, Teo (2002) found that online shoppers were spending smaller amounts

in online shopping some years back with a majority (34.3%) spending between $21 and

$50 (refer to Table 4.19). This shows that online shoppers in Singapore are spending

more now than in 2002. In McCloskey’s study (2003) of shoppers in the United States,

the average amount spent on online shopping was about US$389 which approximated to

about $540 in Singapore currency using the prevailing exchange rate at the time this

study was conducted.

Descriptive Statistics – Average Amount of Money Spent on Online Shopping Last Year

Frequency Percent Teo (2002) - Percent

$1 to $20 8 3.2 8.5

$21 to $50 25 10.1 34.3

$51 to $100 47 19.0 30.5

$101 to $300 51 20.6 14.4

Frequency Percent Teo (2002) - Percent

$301 to $600 26 10.5 1.7

$601 to $1000 18 7.3 3.4

$1001 or more 24 9.7 7.2

Subtotal 199 80.2 100.0

Non-Participants 49 19.8

Total 248 100.0

The differences between Teo (2002; 2006), McCloskey (2003) and this study could be

attributed to the use of different sampling frames of respondents in Singapore, and

perhaps due to the difference in time frames. Both studies by Teo (2002; 2006) were

based on data collected in 2002 by way of an Internet-based survey that was publicised

through newsgroups, emails and certain websites, and the shopping environment in

Singapore is different now as compared to seven years ago. McCloskey (2003) had her

study based on students in the United States. Comparing the sampling choices, this study

uses simple random sampling which is superior to the others in achieving results that are

more generalisible, thus generating analyses that are more widely applicable to the

population in Singapore.

From the data, it appears that shoppers in Singapore are buying and spending more on

online shopping and this augurs well for retailers who have or are going to establish this

new distribution channel for their products. All these results point toward a healthy online

shopping environment and they also serve as indicators that online shopping is becoming

more of a common practice than initially thought.

Product Categories Searched For by Non-Participants and Purchased by

Participants of Online Shopping

The top three product categories searched for by non-participants are: travel, computer

hardware and software, and CDs and entertainment. The top three product categories

purchased by participants are: clothing and accessories, travel, and computer hardware

and software.

Descriptive Statistics – Top-, Second-, and Third-Most Purchase Product Category by Participants of Online Shopping

Top-Most

(% within top-most purchased category)

Second-Most (% within second-most purchased category)

Third-Most (% within third-most purchased category)

Books 17 (9%) 19 (10%) 23 (12%)

CDs and Entertainment

19 (10%) 25 (13%) 35 (18%)

Clothing / Accessories 48 (24%) 31 (16%) 19 (10%)

Computer Hardware and Software

36 (18%) 31 (16%) 20 (10%)

Flowers and Gifts 4 (2%) 11 (6%) 21 (11%)

Food and Drink 6 (3%) 11 (6%) 13 (7%)

Health and Beauty 8 (4%) 20 (10%) 12 (6%)

Home and Garden 0 (0%) 2 (1%) 1 (1%)

Insurance 2 (1%) 6 (3%) 11 (6%)

Travel 47 (24%) 36 (18%) 35 (18%)

Others 12 (6%) 7 (4%) 9 (5%)

Total 199 (100%) 199 (100%) 199 (100%)

Comparing to Teo (2006), the top three product categories bought were: books (26%),

CDs and entertainment (18%), and travel (18%). In comparison to another empirical

study in Singapore, Wee and Ramachandra (2000), the top three product categories were:

books and magazines (39%), theatre and movie tickets (37%) and computer software

(31%). The above comparisons show that travel (including airline tickets), computer

hardware and software, as well as entertainment and leisure products, such as movie /

concert tickets, CDs and entertainment, and books and magazines, are the recurring

product categories over the past decade that consumers shop for over the Internet.

Clothing and accessories is the new and popular product category uncovered by this

study which might indicate that online shopping participants are moving beyond the

recurring product categories. This might also indicate that new groups of consumers that

seek clothing and fashion accessories have joined the growing number of online

shopping participants and adopted this new shopping platform.

Retailers ought to be mindful in selecting and offering the right types of products for sale

over Internet as data have shown that not all product categories are equally popular. It

would also be relevant for existing businesses to consider if online shopping is something

they should or should not be considering. The data provide guidance for managers who

need to plan for the kind of products that they intend to offer in their virtual storefront so

as to capitalise on the right product categories that would appeal to the shoppers.

Usage of Internet for Information Gathering (Window Shopping)

The so-called “window shopping” hypothesis is for this research translated into the inquiry

if there were more shoppers in Singapore that used the Internet to gather information

about products than for the purpose of making purchases. The Binomial Test attempts to

show the minimum threshold population participation rate for statistical significance, given

the sample size obtained in this study. As a result, the “window shopper hypothesis” is

not supported as the present 80.2 percent sample participation rate for online shopping

participation is statistically higher (p < 0.001) than a postulated 75 percent population

participating in online shopping.

Binomial Test of Participants of Online Shopping

Population Proportion

p - value

50% <0.001

60% <0.001

70% <0.001

75% 0.031

76% 0.066

This study’s 80.2 percent participation level differs from earlier findings of Teo (2006)

which found that 42.5 percent of respondents were adopters of online shopping and 24.9

percent obtained by Wee and Ramachandra (2000). As such, there has been a two fold

increase in online shopping participation as compared to previous studies.

According to Teo (2006), the percentage of online shoppers had increased over the years,

which is not surprising given the advancement in the development of technological

infrastructure and the high Internet penetration rate. The Infocomm Development

Authority (2009) annual infocomm usage survey reported that 76 percent of households

in Singapore have Internet access and the percentage of Internet users registered a

record high of 69 percent. The survey also reported that the high percentage of Internet

users belonging to the age groups of 15 to 24 years old and 25 to 34 years old which,

incidentally, correspond to two of the highest age groups of respondents surveyed in this

study. Also, the same infocomm usage survey reported that the highest percentage of

online shoppers belonged to the 25 to 34 years age group.

Even though Wong (2003) argued that the unique geographical characteristics of

Singapore, the well-established infrastructure and transportation network, as well as the

presence of many neighbour shops and easy accessibility to big shopping malls might

negate the attractiveness of shopping online, there is still a growing number of online

shoppers in Singapore. This study established that many shoppers in Singapore used the

Internet to shop online, more than those who merely sought information about products or,

in other words, “window shop” via the Internet.

FUTURE CONCEPTUAL MODEL

McCloskey’s (2003) conceptual model is useful in this discussion as it has been primed to

address the subject matter and the scales have been modified to suit the application

being addressed, which is online shopping. As such, the independent variables from

McCloskey (2003) are: ease of use, usefulness and security concerns. Additionally, the

dependent variables that measure online shopping from that same study are: whether

there is participation in online shopping, the frequency of online shopping, the number of

times online shopping is attempted, and the amount spent on online shopping. Here,

McCloskey (2003) tried to measure the attitudes between users and non-users of online

shopping by distinguishing if actual purchases had taken place or the respondents were

merely browsing. This would be useful in discovering the attitudes and profile differences

between the two groups of people, which addresses one of the dimensions identified

earlier.

Upon review of Liao and Cheung (2001), Lian and Lin (2008) and the other studies

mentioned, other variables are proposed to be included in the modified TAM (McCloskey,

2003) such as, Internet self-efficacy, product involvement, and trust to build on the

existing knowledge, which addresses the dimension of adding variables to the original

TAM so as to better reflect the specific environments.

The resultant model, a modified TAM that is combined with variables culled from various

studies that are based in Asia, specifically small island states like Hong Kong and

Singapore, would adequately explore the nature of online shopping unique to countries

with the reputation of being the shopping paradise in Asia. All these would adequately

add to the findings presented in this study.

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