The University of Western Australia The Graduate School of Management

The Use of a Single Smart Card for Transit and Non-Transit Systems: A Singapore Case Study

Doctor of Business Administration Thesis Proposal

XXXXX Student Number: XXXX

Supervisor: XXXXX

7 May 2004

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Contents

Page No

Abstract 3

1. Introduction

1.1 Background 3 1.2 Purpose of the study 4 1.3 Research Questions (Hypothesis) 5

2. Literature review for Proposed Research

2.1 Research on Contactless Smart Card 5 2.2 Consumer Behaviour 7 2.3 Expectancy Value Model 7 2.4 Theory of Reasoned Action 9 2.5 Case Studies in Theory of Reasoned Action 11 2.6 Theory of Planned Behaviour 13 2.7 Operational Definition 15 2.8 Case Studies in Theory of Planned Behaviour 17

3. Research Design Population of Interest 19 Measures 19 Investigative Methods 21 Method of Analysis 22 Limitations 23

4. Schedule 24 Bibliography 25 Appendix 1 Scholars and Resource Required 28 Appendix 2 What is Smart Card? 29 Appendix 3 Transport Systems around the world 31 Appendix 4 Table 1: Study on TRA versus TPB 36 Table 2: TPB case study 37 Appendix 5 Sample Questionnaires 38

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Abstract The proposed research will look into the acceptance of the EZ-link card (a contactless

smart card) by the general public for use in non-transit services.

The EZ-Link card is owned by EZ-Link Pte Ltd which is a subsidiary of the Land

Authority (LTA) of Singapore. However, the daily operations of the EZ-Link card system

is managed by Transit Link Pte Ltd (TL). Presently, the EZ-Link card is solely used in the

transit system and on some trial systems for non-transit services. As of now, there is no

published research to show what would encourage people to use a single smart card for

transit and non-transit services.

The research will adopt the Theory of Planned Behaviour as a framework to address a

social psychology issue. Survey questionnaires will be designed to measure the 3

independent variables (Attitude, Subjective Norm and Perceived Behaviour Control) that

would affect the dependent variable (Intention). The research will adopt a two-phase

approach that is a group interview followed by a survey. The data collected from the

survey will be subjected to a quantitative analysis.

The findings of the research could facilitate in understanding the factors that could affect

the EZ-link card non-transit business.

1.0 Introduction

1.1 Background

In April 2002, the Land Transport Authority (LTA) of Singapore launched the

contactless smart card known as the EZ-link card through its subsidiary

company, EZ-Link Pte Ltd. The EZ-link card replaced the magnetic Farecard

that was commissioned in 1990. Unlike the conventional contact smart cards, the

EZ-link card does not require the customer to slot the card into a reader to be

processed in order to pay for a particular trip fare in the transit system.

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The EZ-link card is currently used only for transit purposes, such as, payment of

fares on buses and MRT/LRT trains. It is not being used for non-transit purposes.

There are many other non-transport related contact smart cards that are being

used for non-transit transactions such as payment of car park fees, making

phone calls, withdrawing of cash from ATM machines, buying food in fast-food

outlets, and so on. Unlike the EZ-link card, these contact smart cards require the

customers to slot them into readers to be processed. Although these cards are

approved by the Monetary Authority of Singapore and issued by the banks and

credit card companies, they take a longer time to validate and process at the

contact reader.

While the transport companies in Hong Kong and Korea have been able to use

the contactless smart card for both transit and non-transit transactions, LTA has

yet to do so. LTA has introduced pilot projects in 2002 where the EZ-link card

could be used for non-transit applications - in 12 school canteens for buying

food; in 1 hotel for guests to access their room and to buy pastry; and in 2 public

libraries to pay for book overdue fines. So far, there is no survey being carried

out to evaluate the acceptance of the EZ-link card by the customers.

However, EZ-Link Pte Ltd has already signed a memorandum of understanding

with QB Pte Ltd to extend the use of the EZ-Link card to the general market.

This cast a doubt on whether the acceptance of the EZ-Link card by the

customers, is for evaluating a new technology or for non-transit services.

1.2 Purpose of the Study

The purpose of my research study is therefore, to accurately collect data on the

acceptance of the general public in the use of the EZ-link card for non-transit

services. The findings of this study would then be used to determine the viability

of the EZ-link card in non-transit business in the face of the competition from

existing contact smart cards circulating in the general market.

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1.3 Research Questions (Hypothesis)

The proposed research question is “Would current transit customers use the

EZ-Link card for non-transit services?”

The following hypothesis will be tested to support the research question:

H1: Positive attitudes to outcomes of using EZ-Link card for non-transit services

will be associated with stronger intentions to use the card for non-transit

services.

H2: If transit customers expect to please others by using the EZ-Link card for

non-transit services then it will be associated with stronger intentions to use

the card for non-transit services.

H3: If transit customers could decide for themselves to use the EZ-Link card for

non-transit services then it will be associated with stronger intentions to use

the card for non-transit services.

2.0 Literature Review for Proposed Research

2.1 Research on Contactless Smart Card

The contactless smart card has been introduced in Hong Kong and Korea for

transit and non-transit applications. However, there is no published research on

acceptance for a single contactless smart card by customers for use in both

transit and non-transit services in both countries. A technical literature review on

what is smart card is listed in Appendix 2. In addition, a literature review on

transport systems in selected cities around the world is listed in Appendix 3.

A literature search however, has revealed that one study was done in Dublin,

Ireland. The “GAUDI” field trial was done for a period of 3 months in 4

organisations (Dublin Bus, Telecom Eireann, Irish Toll Roads and National Toll

Roads). A total of 1,540 smart cards known as “DASH” were issued but only two

thirds of the cards were used during the trial. The questionnaire was sent to 800

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cardholders to elicit general attitude and a response rate of 33% was achieved

via mail. The research showed that there was a general acceptance of using a

single card for multiple services, where payment was made via e-purse in the

card. Most expressed that the system was easy to understand (87%) and easy

to use (89%) to pay for goods and services. When asked of their willingness to

continue to use DASH if extended citywide, 91% expressed an opinion that they

would use it. It is also interesting to note that the cardholders preferred to use the

card for services at the bank ATMs, parking meters, cinema and supermarket

check-out (Blythe 1996).

In Hong Kong, it is estimated that the non-transit business is about 10% of the

transit business even though it was introduced about 4 years ago. The Hong

Kong Monetary Authority has given Creative Star, the Central Clearinghouse, a

limited banking license to operate the non-transit services. It is also noted that

Creative Star could only proliferate up to 25% of transit business. In the case of

Singapore, the Monetary Authority has not officially capped the non-transit

business.

The government is keen to license a single card that could be used for transit

and non-transit business. Hence, LTA has signed a memorandum of

understanding with QB Pte Ltd to proliferate the non-transit applications. Green

Dot Payment Service Pte Ltd, the parent company of QB Pte Ltd, carried out an

informal study involving a 100 people in Singapore to find out the following:

▪ What are the key considerations in using the smart card for non-transit

services?

▪ Who are people will likely to use the non-transit services?

The study revealed these people found the smart card convenient, easy to use

and more secured than the magnetic card. However, this study was not rigorous

and not backed by theories. Therefore there is a dire need to carry out a proper

research study to determine the potential of EZ-link card for non-transit business

in Singapore. The use of EZ-Link card could very much be driven by customer

behaviour in the following transactions - at restaurants, bookshops, fast food

outlets, taxis, cinemas, theatres, zoo, convenient stores and so on. Therefore,

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without understanding what the consumer wants or determining the customers’

intentions, it may be too risky to invest in the non-transit business.

From the above, it is evident that there is no survey conducted to measure the

level of acceptance of customers for transit as well as non-transit services using

the contactless smart card. Hence, it is necessary to

1) design a survey in the Singapore context to meet the purpose of this

proposed research.

2) to look outside the transport industry for research models, to study the

acceptance level of the general public on the use of the EZ-link card for

non-transit services.

2.2 Consumer Behaviour

Solomon (2004) states that there are many different perspectives that shape the

field of consumer behaviour, which is also interdisciplinary. He argues that a

given consumer phenomenon can be studied in different ways and at different

levels depending on the training and interests of the researchers studying it. The

consumer behaviour is the study of processes involved when individuals or group

select, purchase, use or dispose of products, services, ideas, or experiences to

satisfy needs and desires (Solomon, 2004). In the case of the proposed research

study, the researcher aims to collect data on the acceptance level of the general

public on the use of the EZ-link card for non-transit applications.

2.3 Expectancy Value Model

One of the well-known multiattribute Fishbein model (also known as expectancy-

value model) that was used until the mid-1990’s can be expressed as follows

(Ajzen, 1991, p191):

Ao =

n

i 1ib ie

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where Ao= attitude toward the object

ib = the strength of the belief that the object has attribute i1

ie = the evaluation of attribute i1

n = the number of salient attributes

Ajzen (1991) elaborates that in the expectancy-value model, the attitudes

develop reasonably from the beliefs people hold about the object of the attitude.

He states that we form beliefs about an object by associating it with certain

attributes that is with other objects, characteristics, or events.

Engel et al (1995), highlights that the model proposes that attributes towards a

given object (such as a product) is based on the summed set of beliefs about the

object’s attributes weighted by the evaluation of these attributes. In their model,

the term evaluation of an attribute is typically measured on a 7-point Likert scale

evaluating scale ranging from “very good” to “very bad”. They illustrate this term

in the following example.

Given three brands of consumers’ running shoes, the salient attributes are

identified by asking the consumers the brand they would use, within the product

category (for example: whether the shoes are shock absorbent; priced less than

$50; durable; comfortable to wear; etc). Consumers may not always reveal their

true feelings, if the item is cheap.

The most frequently mentioned attributes are considered as the most salient. For

each brand, it would be necessary to assess consumers’ beliefs and attribute. If

three brands and six attributes were considered, a total of 18 belief

measurements would be required. The evaluation attribute is typically measured

on a 7-point Likert scale (from “very good” to “very bad”), to evaluate buying a

running shoe priced at less than $50. Similarly, beliefs could be measured on a

7-point Likert scale (“very likely” to “very unlikely”), to look at how likely that

brand of a running shoes will be priced at less than $50?

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Solomon (2004) argues that though the Fishbein model had been used for many

years, it suffers from a major problem. He argues that in many cases, knowledge

of a person’s attitude is not a very good predictor of behaviour. Similarly, Wicker

(1996) also argued that some researchers have been discouraged and

questioned if attitudes are of any use in understanding behaviour (cited in

Solomon, 2004).

Ajzen and Fishbein (1977,1980) refined the expectancy-value model to give rise

to ‘Theory of Reasoned Action’. They argued that to predict a single behaviour

we have to assess the person’s attitude towards the behaviour and not his

attitude towards the target at which the behaviour is directed. This model now

measures attitude toward the act of buying, rather than only the attitude toward

the product itself. It focuses on perceived consequences of purchases (Solomon,

2004).

2.4 Theory of Reasoned Action

According to Ajzen and Fishbein (1980), the theory of reasoned action indicates

that a person’s intention is a function of 2 determinants, one personal in nature

and the other reflecting social behaviour. The personal factor is individual’s

positive and negative evaluation of performing the behaviour. This is termed as

“attitude toward the behaviour”. The second determinant of intention is the

person’s perception of the social pressures put on him to perform or not perform

the behaviour in question and this is termed as “subjective norm” (Ajzen and

Fishbein, 1980). According to the theory, attitudes are a function of beliefs.

Subjective norms are also function of beliefs, but beliefs of a different kind. It is

namely the person’s belief that specific individuals or groups think he should or

should not perform the behaviour and these beliefs are termed normative beliefs.

Ajzen and Fishbein (1980) recognize the importance of external variables shown

in Figure 1 that may influence the beliefs a person hold or the relative importance

he attaches to attitudinal and normative considerations. In such cases, it could

influence the behaviour. However, they argue that external variables have little

bearing on the validity of the theory. They stated that one of the major

disadvantages of relying on the external variables to explain behaviour is that

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different kinds of external variables have to be invoked for different behaviour

domains.

Source: Understanding Attitudes and Predicting Social Behaviour with external variables (Ajzen

and Fishbein, 1980, p84)

Ajzen and Kerbs (1994) states the attitude and subjective norm combine in a

weighted linear fashion to produce the intention. The complete model

representation of Theory for Reasoned Action can be written as follows:

B ≈ I α [w1 AB + w2 SN]

Where B is behaviour, I is intention, AB is the attitude towards the behaviour, SN

is the subjective norm, w1 and w2 are empirically determined weights. Over the

past 25 years hundreds of studies have evaluated this theory and the average

correlation between intention and behaviour was 0.62, and the average multiple

correlation for prediction of intention was 0.68.

Ajzen and Fishbein (1980) believe that the theory of reasoned action already

identifies a small set of concepts which are assumed to account for relations (or

Attitude toward

the behaviour

(AB)

Relative importance of

attitudinal and normative

considerations (w1 /w2)

Subjective norm

(SN)

Intention

( I )

Behaviour

( B)

Beliefs that the

behaviour leads to

certain outcome

Evaluation of the

outcomes

Motivation to

comply with the

specific referents

Beliefs that specific

referents think I should

or should not perform

the behaviour

External Variables

Demographic

▪ Age, sex

▪ Occupation

▪ Socioeconomic status

▪ Religion

▪ Education

Attitudes towards target

▪ Attitude toward people

▪ Attitude toward

institution

Personality traits

▪ Introversion –

Extraversion

▪ Neuroticism

▪ Authoritarianism

▪ Dominance

Figure 1: Theory of Reasoned Action

W1

W2

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lack of relations) between any external variable and any kind of behaviour that is

under an individual’s volitional control.

Engel et al (1995) also argues that as behaviour becomes more dependant on

factors outside a person’s control, the less the behaviour is under volitional

control (represents the degree to which a behaviour can be performed at will).

For instance, you can control over whether you will continue to read the rest of

the sentence. The presence of these uncontrollable factors can therefore

interfere with the person’s ability to do what he or she intended to do. When this

occurs, intentions will become less accurate predictors of behaviour.

2.5 Case Studies in Theories of Reasoned Action

Ajzen & Madden (1997) also supported the theory of reasoned action, which has

been successful in predicting a variety of behaviours, such a blood donations

and smoking marijuana, it would do poorly in predicting behaviours over which

the individual had only limited control (because they required skills, abilities,

opportunities or cooperations of others). To improve predictions of this kind of

behaviour, a model must assess not only intentions but also the extent to which

the individual is capable of exerting control over the behaviour in question. The

authors carried out 2 studies to test and to show the improvements achieved

between theory of reasoned action and theory of planned behaviour.

The 1st study was to test the hypothesis that a measure of perceived behaviour

control would improve the prediction of behaviour intentions over a prediction

that is based solely on attitudes towards the behaviour and subjective norms.

The study was conducted in the context of regular class meetings where 169

college students (45 males and 124 females) were involved. The study was

designed to discover why students attend, or fail to attend, class sessions.

Attendance data was collected at 16 regular class sessions. A total of 11

possible consequences of attending class regularly or of missing some sessions

with a 7-point Likert scales was introduced in the questionnaire. These

consequences were taken from Fredricks and Dosset (1983) that was cited in

Ajzen and Madden (1986). The 11 products served as belief-based measure of

attitude toward attending class. The Cronbach’s alpha coefficient of this scale

was 0.61. Also a direct measure of attitude was obtained using semantic

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differential scales. A total of 22 bipolar adjective scales were adopted from

Osgood et al (1975) cited in Ajzen and Maden (1986).

Furthermore, a confirmatory factor analysis using LISREL 5 resulted in a set of 8

evaluating scales: rewarding-punishing, useful-useless, good-bad, harmful-

beneficial, wise-foolish, happy-sad, sharp-dull, and attractive-unattractive. The

sum over these scales served as the measure of attitude; its Cronbach’s alpha

coefficient = 0.86 was achieved. Two types of measures showed reasonable

correlations. Intention was accessed by means of 3 questions on estimates of

likelihood of attending class. The behaviour to be predicted, class attendance,

was measured. Hierarchical regression statistical procedure was used to assess

the independent contributions of each of the various variables to predict

intentions in the 2 theories (Reasoned Action and Planned Behaviour). The result

of the study showed both attitude and subjective norm predicted intension with a

multiple correlation of 0.55. The introduction of perceived behaviour control into

the regression equation improved the multiple correlations from 0.55 to 0.68.

This is illustrated in Table 1, Appendix 4.

The 2nd study looked at American students (90 college students – 34 males and

56 females) having to sit for 3 exams in each semester, and getting a grade “A”

in a course a behavioural goal. The assumption made was that experience

gained in taking 2 exams would increase students’ accuracy of the control they

perceived over their performance in these tests (that is, it was valid assumption

by the fact that correlations between expected and received grades increased

substantially over the 2 exams).

A pilot study with 21 students was done to list advantages and disadvantages of

getting a grade “A” in a particular course, the people who might approve or

disapprove of getting a grade “A”, and factors that might help prevent them from

getting a grade “A”. A total of 10 salient consequences of receiving a grade “A” ,

and a 7-point Likert scale was used. Data were collected twice (1st – about 3

weeks into the spring semester, and 2nd – sometime at the end of the semester).

A belief-based measure of attitude toward receiving a grade “A” was summed

over 10 products. The Cronbach’s alpha coefficients for the 1st and 2nd waves

were 0.61 and 0.58 respectively. The subjective norms were also assessed and

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the Cronbach’s alpha coefficients for the 1st and 2nd waves were 0.79 and 0.81

respectively. The results of the 2nd study also supported the theory of planned

behaviour.

2.6 Theory of Planned Behaviour

Ajzen’s theory of planned behaviour is an extension of the theory of reasoned

action (Ajzen and Fishbein, 1980).

Ajzen (1991) states that the more favourable the attitude and subjective norm

with respect to a behaviour, and the greater the perceived behaviour control, the

stronger should be an individual’s intention to perform the behaviour under

consideration. The theory of planned behaviour is illustrated in Figure 2.

Source: The Theory of Planned Behaviour (Ajzen, 1991, pg182)

Figure 2: Theory of Planned Behaviour

Attitude

toward the

behaviour

(A)

Subjective

norm

(SN)

Perceived

behaviour

control

(PBC)

Intention

(I)

Behaviour

(B)

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Ajzen (1991) states that the theory of planned behaviour postulates 3

independent determinants of intentions and they are:

a. Attitude towards behaviour (A) – refers to degree to which the person

has a favourable on unfavourable evaluation of the behaviour in question.

This can be expressed mathematically as:

Ao α

n

i 1ib ie

Where, the strength of each salient belief (b) is multiplied by subjective

evaluation (e) of the belief’s attributes and the resulting products are

added up over the n salient beliefs. Attitude (A) is proportional to the

summative index.

b. Subjective norm (SN) - refers to perceived social pressure to perform or

not to perform the behaviour. This can be expressed mathematically as:

SN α

n

i 1in im

Where the strength of each normative belief (n) is multiplied by the

person’s motivation to comply (m) with the referent in question, and the

subjective norm (SN) is directly proportional to the sum of the resulting

products across the n salients referents.

c. Perceived behaviour control (PBC) – refers to perceived ease or

difficulty of performing the behaviour and it is assumed to reflect past

experience as well as anticipated impediments and obstacles. This can

be expressed mathematically as:

PBC α

n

i 1ic ip

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Where each control belief (c) is multiplied by the perceived power (p) of

the particular control factor to facilitate or inhibit performance of the

behaviour, and the resulting products are summed across the n salients

control beliefs to produce the perception of behaviour control (PBC).

2.7 Operational Definition

In reviewing the theory of planned behaviour for the proposed research, we need

to measure intention, which is related to the research question “Would current

transit customers use the EZ-Link card for non-transit services?” Since

non-transit services are not available to transit customers, it may not be

appropriate to measure behaviour as such the proposed research will look at

measuring the intention. The operational definition of variables for the proposed

research is listed below:

Intention ( I )

“To use the EZ-

Link card for Non-

Transit services”

Behaviour ( B )

Figure 3: Theory of Planned Behaviour

applied to Proposed Research

+

-

+

-

+

Attitude to outcome of the behaviour (A)

Subjective

Norm (SN)

Perceived behaviour

control (PBC)

Behavioural Beliefs (B1)

Normative

Beliefs (B2)

Control beliefs (B3)

or

or

or

I = Intention to use EZ-Link card for non-transit services

A = Attitude towards the use of EZ-Link card for non-transit services

SN = Subjective norms for use of EZ-Link card for non-transit services

PBC = Perceived behaviour control of use of EZ-Link card for non-transit services

H1

H2

H3

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Ajzen (2002) claims that all predictor variables in the theory of planned behaviour

can be accessed directly by asking the respondents to judge each set of scales.

He states that attitude towards the behaviour, subjective norm and perceived

behaviour control can also be measured indirectly based on their corresponding

beliefs.

In the direct measurement, only A, SN and PBC will be taken into consideration

whilst for indirect measurement it will only require B1, B2 and B2 to be

considered. Ajzen (2002) states that we could either use the direct or indirect

measurement to determine the intention.

Ajzen and Krebs (1994) argue that PBC is expected to interact with other

constructs in the theory: attitude and subjective norm should influence intentions

to the extent the PBC is high. Similarly, the effect of intention on behaviour also

depends on the degree of perceived behaviour control:

B ≈ PBC · I α PBC [w1 AB + w2 SN]

Ajzen and Kerbs (1994) states that most of the variations in intentions and

behaviour can be accounted for by linear combinations, and interactions terms

are typically not significant. Because of these findings, simpler linear models

have actually been evaluated in most applications of the theory, as follows:

B ≈ [w1 I + w2 PBC]

I α [w1 AB + w2 SN + w3 PBC ] , This is a direct measurement.

(where I is intention to perform the behaviour, A is attitude towards the

behaviour, SN is subjective norm and PBC is perceived behaviour control. The

w1, w2 and w3 are empirically determined weights.)

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The constructs in figure 3 can be mathematically expressed as follows

Studies found that inclusion of perceived behaviour control significantly improves

prediction of intentions, and in many instances also prediction of behaviour

(Ajzen and Krebs, 1994).

Ajzen (1991) states that the theory of planned behaviour is far superior in terms

of predicting intentions with the addition of the perceived behavioural construct.

The multiple correlations ranged from as low as 0.43 to a high of 0.94, with an

average correlation of 0.71.

2.8 Case Studies in Theory of Planned Behaviour

Bamberg, Ajzen & Schmidt (2003) carried out their research relying on theory of

planned behaviour to investigate the effect of an intervention in introducing a

prepaid bus ticket to see there is an increase use of the bus among college

students.

The 1st set of data (1st wave) was collected during the spring semester

registration period, about 2 months prior to introduction of the new bus ticket. A

total of 3,491 questionnaire forms were distributed and 1,874 (54%) were

returned. The 2nd set of data collection (2nd wave) was done via mail, 1 year after

the 1st wave. A total of 1,316 completed questionnaire forms were returned. The

participants were 42% males (aged 20 to 37 years). Most of the items in the

B α I (where B is Behaviour and I is Intention to perform behaviour)

I α + w1 A + w2 SN + w3 PBC

( Where A is attitude, SN is Subjective norm and PBC is Perceived behaviour control and w1, w2 and w3 are empirically determined weights. These are used for direct measurement. Alternatively, the beliefs could be used as indirect measurement. The different beliefs in the theory of planned behaviour are : B1 is behaviour beliefs about consequences of behaviour, B2 is normative beliefs about behaviour and B3 is control beliefs which provide the basics for perceptions of behaviour control ).

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questionnaire were designed to assess the constructs of theory of planned

behaviour. The alternative travel modes such as driving a car, riding the bus,

riding a bicycle, and walking were evaluated as shown in Table 2, Appendix 4.

The data for bus and car use were submitted to structural equation analyses,

using LISREL 8 computer program. The fit between the structural model and

data were evaluated by means of 3 indexes: goodness-of-fit (GFI), adjusted

goodness-of-fit (AGFI), and root means square error of approximation (RMSEA).

The GFI estimates the variance in the model, and the AGFI adjusts this estimate

by taking into account the degree of freedom. Both estimates can vary from 0 to

1 and the good fit is indicated by values above 0.95. A RMSEA value of 0.05 or

less is acceptable.

The result of the research found that intervention (of introducing a pre-paid bus

ticket that offered unlimited rides on local bus system) had influenced attitudes

towards bus use, subjective norms, and perceptions of behavioural control that

support the theory. The attitudes, subjective norms and perceptions of behaviour

control accounted for 49% of the variance in intentions in the 1st wave and 64%

in the 2nd wave. The model fit was found to be adequate, GFI = 0.98, AGFI =

0.96 and RMSEA = 0.02. The theory provided an accurate prediction of intention

and behaviour both before and after the intervention.

The above study provides some similarities in the way the proposed research is

to be carried out. The above case study looked at the intention to use a pre-paid

bus ticket. Furthermore, it looked at the effect of intervention of introducing a pre-

paid bus ticket, and to see if more students will switch to bus, as a preferred

mode of travel to the college. The proposed research will not focus on the

measurement of behaviour since currently the EZ-Link card is not widely used by

the general public. The proposed research will focus on the intention to use the

EZ-Link card for non-transit services and the direct measurement of attitude,

subjective norm and perceived behavioural control will be carried out.

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3.0. Research Design & Methodology

3.1 Populations of Interest

For the proposed research, the data will be collected from the general public in

Singapore who commute in the public transport system. Currently, we have

issued some 4.7 million smart cards, and about 1.3 million smart cards (28%) are

used daily in the public transport system. Vaus (2002) states that sample size

depends on 2 key factors which are as follows:

• The degree of accuracy we require for the sample; and

• The extent to which there is variation in the population in regard to the key

characteristics of the study.

Vaus (2002) states that the amount of error we are prepared to tolerate and how

this could be used to generalise the population is important. The rule is to halve

the sampling error we have to quadruple the sample size. Many survey

companies limit their samples to 2000 since beyond this point the extra cost has

insufficient payoff in terms of accuracy.

Hopkins et al (1996) states that a random sample of 400 estimates a parameter

for a 100 million almost as accurately as a sample of 400 drawn randomly for a

population of 10,000. Vaus (2002) suggests that a sample size of 200 for about

7% sampling error at 95% confidence level. Hair et al (1998) recommends a

sample size ranging from 100 to 200. He says that as the sample size becomes

large, exceeding 400 to 500, the maximum likelihood estimation method

increases in its sensitivity to detect the differences among data. Also, the

goodness-of-fit will result in a poor fit. He states that to test a model a sample

size of 200 is adequate and it is reckoned to be a “critical sample size”. For the

proposed research a sample size of 200 will be used though there are 1.3 million

EZ-Link customers using the transit system daily.

3.2 Measures

Sample questionnaires on how to measure the different variables as

recommended by Ajzen (2002) are illustrated in Appendix 5.

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Ajzen (2002) states that “according to the theory of planned behaviour, human

action is guided by three kinds of considerations:

a. beliefs about the likely outcomes of the behaviour and the evaluations of

these outcomes (Behaviour beliefs);

b. beliefs about the normative expectations of others and motivation to comply

with these exceptions (normative beliefs); and

c. beliefs about the about the presence of factors that may facilitate or impede

performance of the behaviour and the perceived power of these factors

(control beliefs) “.

Ajzen (2002) also states that the above “ respective aggregates, behaviour

beliefs produce a favourable or unfavourable attitude toward the behaviour;

normative beliefs result in perceived social pressure or subjective norm; and

control beliefs give rise to perceived behaviour control. In combination, attitude

towards behaviour, subjective norm and perception of behaviour control lead to

the formation of a behaviour intention”. The interlink between the constructs is

illustrated in figure 3.

Ajzen (2002) claims that all predictor variables in the theory of planned behaviour

can be accessed directly by asking the respondents to judge each set of scales.

He states that attitude towards the behaviour, subjective norm and perceived

behaviour control can also be measured indirectly based on their corresponding

beliefs.

Ajzen (2002) states that often investigators assume direct measures of the

theory’s construct could be based on selected questions or by adapting previous

study. This he says could produce low reliabilities and leads to underestimate of

the relations in the theory’s constructs and its predictive validity. In order to

secure reliable, internally consistent measures, it is necessary to select

appropriate items in the formative stages of the investigations. Different items

may have to be used for different behaviours and for different research

populations.

21

Ajzen (2002) claims that a pilot work is required to identify accessible

behavioural, normative and control beliefs. Respondents are given a description

of the behaviour and asked a series of questions. The responses can be used to

identify personal accessible beliefs, that is, the unique beliefs of each research

participants, or to construct a list of modal accessible beliefs, that is, a list of the

commonly held beliefs in the research population.

Ajzen (2002), states that are some differences between direct and indirect

measures. He says, by “measuring beliefs we could understand the underlying

cognitive foundation, that is, we can explore why people hold certain attitudes,

subjective norms and perceptions of behaviour control. This information could be

useful in designing effective program for behaviour intervention. The other

difference is in the way reliabilities of direct and indirect measures can be

estimated. According to Ajzen (2002), direct measure emphasized the need to

ensure high internal consistency in the measures of behaviour, measures of

intention, attitude, subjective norm and perceived behavioural control. However,

for theoretical reasons, the same requirement is not imposed on belief-based

measures of attitude, subjective norm and perceived behavioural control.

In the case of the proposed research, the direct measure approach will be

adopted as could provide the required high internal consistency in the measures

of behaviour, measures of intention, attitude, subjective norm and perceived

behavioural control.

3.3 Investigative Methods

The proposed research will be carried out in 2 phases. The 1st phase will be a

qualitative data gathering process. A total of 30 EZ-Link card customers will be

interviewed. These are people who are using the existing EZ-Link card in the

public transport system. The people selected will comprise child, adult and senior

citizen and possibly from different walks of life. This will facilitate a better

representation of the entire population of EZ-Link customers. Punch (1998),

highlights that group interview can stimulate people to provide their views,

perceptions, motives and reasons, when a researcher tries to probe certain

22

aspects of people’s behaviour. This interview is conducted to evaluate how

respondents interpret question’s meaning, and to check whether the range of

response alternative is sufficient and make necessary amendment to

questionnaire before a final survey is done (Vaus, 2003). The 1st phase will be

used to construct the questionnaire that will be used in the 2nd phase.

In the 2nd phase a survey form will be used to collect the data. This will be

subjected to quantitative analysis. Based on Dillman’s (1978, 2000) suggestion,

the following mode of data collection technique will be employed via the final set

of questionnaire (cited in Vaus 2002, pg 127):

• Face to face interview (intercept) technique of transit customers at bus

interchanges and MRT stations. The response rate for this technique could

be in the region of 70%. The reason being, it would reach out to the existing

transit customers that we are interested in the proposed research.

Furthermore, the data that will be collected will be unbiased.

• As an alternative method, the questionnaire forms could be deployed via my

company EZ-Link card sales outlets located in MRT stations and bus

interchanges. The completed forms could be either mail-in or handed over to

any of the sales outlets.

3.4 Methods of Analysis

Kline (2002) states that many psychologists believe that confirmatory factor

analysis is a superior method compared to exploratory factor analysis because it

test hypotheses, which is fundamental to the scientific method. This technique is

referred to as the measurement model that examines the relationships between

the latent variables and can test complex psychological hypotheses. Hair,

Anderson, Tatham & Black (1998) states that structural equation modelling

(SEM) encompasses confirmatory factor analysis that estimates a series of

separate, but independent variables, multiple regression equations

simultaneously by specifying the structural model used in LISREL (a popular

software package).

23

The 2 SEM characteristics are:

a. Estimation of multiple and interrelated dependence relationships and

b. The ability to represent unobserved concepts in these relationships and

account for measurement errors in the estimation process.

For the proposed research, the SEM will be employed and a 2-step approach will

be adopted. Firstly, measurement model for each of the variables (Intention,

Attitude, Subjective Norm and Perceived Behaviour Control) will be carried out.

Secondly, each of the models in the first analysis will be integrated to derive the

final structural model and goodness of fit tests will be carried out.

3.5 Limitations Consumer behaviour is dynamic and demands are ever changing on a daily

basis. As such, the viability of EZ-Link card for non-transit services depends on

how fast it is implemented in the market with the appropriate services.

24

4. Schedule

Completion Date

Activity

Duration

Oct – Dec 2003 Literature Review and submission 2 months

4 May 2004 Submit Thesis Proposal 4 months

14 May 2004 Defend Thesis Proposal 1 day

May 2004

Approval of Thesis Proposal

1 month

Aug 2004

Expand the Literature review – Chap 2

3 months

Nov 2004

• Methodology – Chap 3

• Ethics committee approval

• Design and Pilot Test questionnaires (Group interview)

3 months

Dec 2004

• Issue questionnaire

• Receive completed forms

1 month

Feb 2005

Data analysis

2 months

May 2005

Complete data analysis

3 months

Sep 2005

• Introduction – Chap 1

• Data analysis – Chap 4

4 months

Oct 2005

Conclusion – Chap 5

1 months

Dec 2005

Complete Thesis revision and submit

2 months

25

Bibliography

Ajzen, I. 1991, 'The Theory of Planned Behavior', Organizational Behavior and Human Decision Processes, no. 50, pp. 179-211. Ajzen, I., Constructing a TpB Questionnaire: Conceptual and Methodological Considerations [Online], Available: http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf [5/2/2004]. Ajzen, I. & Fishbein, M. 1977, 'Attitude-behaviour relations: A theoritical analysis and review of empirical research', Psychological Bulletin, no. 84, pp. 888-918. Ajzen, I. & Fishbein, M. 1980, Understanding Attitudes and Predicting Social Behaviour, Prentice Hall, New Jersey. Ajzen, I. & Fishbein, M. 2003, 'Questions raised by a Reasoned Action Approach: Reply to Ogden (2003)', Health Psychology, In press. Ajzen, I. & Krebs, D. 1994, 'Attitude Theory and Measurement: Implications for Survey Research', in Trends and Perspectives in Empirical Social Research, eds I. Borg & P. P. Mohler, Walter de Gruyter & Co, Berlin, pp. 251-265. Ajzen, I. & Madden, T. J. 1997, 'Prediction of goal-directed behaviour: Attitudes, intentions, and perceived behavioural control', in The Blackwell Reader in Social Psychology, eds M. Hewstone, A. S. R. Manstead & W. Stroebe, Blackwell Publisher Ltd, Oxford, pp. 245-267. Bamberg, S., Ajzen, I. & Schmidt, P. 2003, 'Choice of Travel Mode in the Theory of Planned Behaviour: The Roles of Past Behaviour, Habit, and Reasoned Action', Basic and Applied Social Psychology, vol. 25, no. 3, pp. 175-187. Bennett, S. 2001, 'Prestige on Target for August 2002 Launch', International Railway Journal. Blythe, P., Global View of Transport Applications [Online], Available: http://www.smartcard.co.uk/resources/articles/transport.html [6/12/03]. Clarke, G. 2002, 'Using Smart Cards to Gain Market Share', Journal of Consumer Behaviour, vol. 2, no. 2, pp. 203-204. Cubic, Test phase completed on Cubic developed smart card systems for Washington, D.C. buses [Online], Available:

26

http://www.cubic.com/corp/news/pressrelease/2003/WMATABusISQTcompleted.htm [21/12/03]. Dillman, D. A. 1978, Mail and Telephone Surveys: The total Design Method, Wiley, New York. Dillman, D. A. 2000, Mail and Internet Surveys: The Total Design Method, Wiley, New York. Engel, J. F., Blackwell, R. D. & Miniard, P. W. 1995, Consumer Behavior, The Dryden Press, Orlando. Fedricks, A. J. & Dossett, K. L. 1983, 'Attitude-behaviour relations: A comparison of the Fishbein-Ajen and the Bentler-Speckart models.' Journal of Personality and Social Psychology, vol. 45, pp. 12-512. Fox, H. 2000, World Bank Urban Transport Strategy Review – Mass Rapid Transit in Developing Countries. Guthery, S. B. & Jurgensen, T. M. 1998, Smart Card Developer's Kit, Macmillian Technical Publishing, Indiana. Hair, J. F., Anderson, R. E., Tatham, R. L. & Black, W. C. 1998, Multivariate Data Analysis, 5th edn, Prentice Hall, Inc, New Jersey. Hopkins, K. D., Hopkins, B. R. & Glass, G. V. 1996, Basic Statistics for the Behaviour Sciences, 3rd edn, A Simon & Schuster Company, Boston. Kline, P. 2002, An Easy Guide to Factor Analysis, Routledge, London. Montner, D., Public Transit Smart Card may be catalyst for cross payment opportunities, according to New Smart Card Alliance paper [Online], Smart Card Alliance, Available: http:/www.smartcardalliance.org/aboutalliance/press10132003.cfm [22/12/03]. Osgood, C. E., Suci, G. J. & Tannenbaum, P. H. 1975, The measurement of meaning, University of Illinois Press. Peterson, A. H. 1999, 'Waiting for Smart Cards', Credit Union Magazine, July, vol. 65, no. 7, pp. pp 54-57. Punch, K., Introduction to Social Research: Quantitative and Qualitative Approaches, Sage Pulications, London.

27

Solomon, M. R. 2004, Consumer Behavior: Buying, Having and Being, 6th edn, Pearson Education, Inc, New Jersey. System, A. C., Smart Card Resource [Online], Available: http://www.acs.com.hk/smartcardoverview.asp [6/12/03]. Tan, C. 2003, 'SingTech unit may take over EZ-Link eventually', The Straits Times, 25/11/03, p. A16. Townend, R. C., Finance: History, Development & Market Overview [Online], Available: http://www.smartcard.co.uk/resources/articles/finance.html [6/12/03]. Tse, L., (15 Nov 2003), Octopus Card - Multipurpose Smart Card [Online], Visionengineer.com, Available: http://www.visionengineer.com/tech/octopus.shtml [6/12/03]. Vaus, D. d. 2002, Surveys in Social Research, 5th edn, Allen and Unwin, New South Wales. Wicker, A. 1969, 'Attitudes versus Actions: The Relationship of Verbal and Overt Behavioural responses to Attitude objects', Journal of Social Issues, no. 25, p. 65.

28

Appendix 1

Scholars 1 Michael R Solomon:

Professor of Consumer Behaviour in Department of Consumer Affairs, College of Human Affairs at Auburn University

2. Icek Ajzen:

Professor of Psychology, University of Massachusetts (Amherst) 3. Martin Fishbein:

Professor, Annenberg School for Communication, University of Pennsylvania

4. Mary Sheehan

Professor and Director of CARRS-Q, Queens University of Technology, Brisbane

Required Resource The following resources are required : 1. Obtain approval from the UWA ethics committee for the proposed

research survey. 2. Conduct group Interview with selected Transit customers using EZ-Link

cards. 3. Conduct face-to-face interview with Transit customers using EZ-Link cards

at MRT stations and bus interchanges.

29

Appendix 2

What is a Smart Card?

Smart Card is not a new technology but French journalist Roland Moreno is

widely accredited for inventing the Smart Card in 1974. However, it was Jurgen

Dethloff in Germany and Kunitaka Arimura of the Arimura Technology Institute in

Japan who filed the first patents in February 1969 and March 1970, respectively.

Moreno's worldwide patents covered the concept of embedding a microcontroller

into a regular bank style plastic card (Townend 1996).

A smart card is similar in size in comparison with the well-known magnetic

striped credit card. However, a smart card is embedded with an integrated circuit

(IC). This allows us to store personal data and monetary value securely.

Furthermore, a smart card is superior to the magnetic striped card as it as a

larger storage capacity, more reliable, more difficult to duplicate. It also provides

the required security level that is required for an electronic purse (e-purse). The

e-purse in the smart card stores small amount of currency, usually less than

$1,000. This purse can be perform debit and credit functions (Guthery &

Jurgensen 1998). Smart cards are now being used in a wide range of

applications, such as access control (security door access), banking (bankcard),

GSM mobile phones, public transportation and pay TV (System 2003).

In a smart card there is an integrated circuit (IC), which can be a memory IC or

micro-controller unit (MCU). When a high level of security is required, e.g. when

the smart card is deployed to store money or to store confidential data securely,

an MCU-based smart card will be used. A card operating system (COS) is a

piece of firmware to be stored in the ROM of the micro-controller integrated

circuit embedded inside a smart card.

30

Microprocessor Chip Diagram, courtesy of Gemplus (cited from Smart Card Alliance-

Industry information- www.smartcardallianace.org/industry_info)

The COS has four main functions:

1. establish and control the communication link between the smart card and

the card accepting device (smart card reader),

2. manage the Electrically Erasable Programmable Read-only Memory

(EEPROM) allocation in the smart card,

3. control the access to the allocated memory areas according to defined

access conditions,

4. perform security algorithms (encryption/decryption, password verification)

for the authentication of, and the secure communication between the

smart card and the smart card reader. This is to ensure that the card

security is addressed when developing the COS.

Today, there are two basic kinds of smart cards, according to the Smart Card

Forum (Peterson 1999).

An "intelligent" smart card with a powerful central processing unit (CPU) that

actually stores and secures information. The decisions are made based on card

issuer's specific business needs. These cards offer read and write capability,

meaning new information can be added and processed.

31

Appendix 3 Transport Systems Around the World

The American Public Transport Association (APTA) reports that ridership in the public

transportation is increasing 22% in the last 6 years. They estimate that 14 million

Americans move through public systems daily and about 9.4 billion trips were recorded

in 2002. Transit systems in various cities in US are in the process of replacing their end-

of-life systems with smart card systems. This attributes to 17 million smart cards in use

as payment cards in transit systems, where various trials are in placed (Montner 2003).

The benefit of smart cards for transit fare payment is clearly evident easy of use by

customers, where they have to simply tap the card on the gate reader to enter and exit a

MRT station or board/alight the bus. Furthermore, the overall throughput at the MRT

gate and bus validators is improved. The overall operating cost saving in maintenance

as compared to electro-mechanical components in the magnetic card systems, benefits

transport operators. The Singapore case is no different, where the Land Transport

Authority had invested a total of $300 million to replace the magnetic Farecard system in

April 2002, after 12 years of operations.

The integrated payment or the sharing of a common payment mechanism between

different operators allows passengers to switch from one mode of travel to another

seamlessly (bus–train–LRT). Furthermore, only one central clearinghouse is required to

carry out the settlement between the operators for their daily operations. The single

smart card also provides an additional opportunity to integrate the central clearinghouse

with non-transit operators, like the case of Korea (Pusan) and Hong Kong. Based on

this, other transit operators around the world are looking to emulate this model, if

possible.

Use of Smart Card in Transport Sector

Hong Kong

Before smart card was introduced, Hong Kong MTR used magnetic card for the rail

system and just like Singapore. In 1997, smart card was introduced which allowed other

modes of travel such as trains, ferries, buses and light rail to use a single smart card

(called Octopus) for travel. Prior to smart card all other modes accepted cash payments

except the Hong Kong rail (MTR), which used the magnetic plastic card.

32

In replacing the “Ticketing Technology (magnetic to smart card)”, Hong Kong took note

of the following considerations (Fox 2000) :

a. Issue ticket automatically to improve customer service and reduce the operations

and maintenance costs.

b. Improve the security of the ticket to prevent fraud cases.

c. Increase the add-value amount into the card.

d. Ensure the card has a longer life span as compared to Magnetic stored valued

card.

e. Tickets can be used on more than one service provider systems. For example,

the Hong Kong cards can be used on the MTR, LRT, KCRC suburban rail, ferries

and some buses.

Octopus is an electronic ticketing system that uses a contactless smart card with a built-

in microchip to capture the current and previous rides and fares are computed and

deducted automatically by each systems (Tse 2003). This is definitely an efficient way to

operate different business. Also, the revenue apportionment between the operators is

settled centrally via a Central Clearing house at Creative Star.

Octopus card can also be used for non-Transit systems such as public payphones,

vending machines and photo booths within the transport premises. The card can be

used to purchase cakes in a cake shop, pay for meals in fast food restaurants and car

park fees. My conversation with Hong Kong counterparts reveals that the non-transit

operations generate about 20% of the revenue as compared with Transit operations.

Octopus has at least 9 million cards in circulation and over seven million transactions

are processed every day that amounts to a total of US$2.2 billion in one year.

Hong Kong is a small place with a high-density population and it is good example of how

transit and non-transit operations have been put into action.

London

London too started with a magnetic card for the rail public transport system. The buses

accepted cash, as a mode of payment until in 1996, trials of contactless Smart Card on

London Transport (LT) buses in the Harrow area began with a demonstration of the

technology on route 212 between Walthamstow and Chingford using some 1,000

volunteers who already use bus passes on a regular basis (Blythe 1996).

33

The Harrow trial involved 200 buses on 19 bus routes operated by five bus companies

and used off-the-shelf technology from Buscom, Finland, and their proximity card.

Initially the cards were issued as Smart Photocards to be checked against a contactless

card reader each time the passenger boards a trial bus. It is also used along with

existing magnetic travel passes for visual checking throughout the LT bus and

underground network. The magnetic travel pass was used at the underground stations.

A consortium responsible for delivering the Prestige project (Procurement of Revenue

Services) is Transys, comprising Electronic Data Systems (EDS), Cubic Transporation

Systems, ICL Enterprises, and WS Atkins Rail. Transys was awarded a 17-year £1

billion contract by London Transport (LT) under the British government's Private Finance

Initiative to design, install, and operate the new integrated fare collection system on

London Underground and buses (Bennett 2001).

Transys invested about £150 million to develop the system. EDS and Cubic, each of

them have a 37.5% holding, head the consortium. EDS will operate Prestige, while

Cubic supplies and maintains the equipment. ICL, which has a 20% stake, will take

charge of revenue collection technology and central clearinghouse function, while WS

Atkins, with the remaining 5% share, is provides traffic planning and consultancy

services.

The work includes installing 16,500 remote ticketing devices (RTDs), of which 7000 to

be installed at station gates, passenger-operated machines, ticket office machines, and

validators (smart card readers) used throughout London Underground. A total of 320

multi-lingual touch screen ticket-issuing machines are also being installed at stations on

the network, while gates are being installed at those stations which were previously

ungated.

Retail outlets which act as ticket sales agents will have automatic machines retrofitted

with RTDs. Currently, there are about 2200 agents in London. Transys Sales Service, a

newly created subsidiary of Transys, will handle this operation.

The new contactless smart cards (Oyster) will be recyclable and can be topped up with

additional value. Transys will look at further applications of the smart card technology

and has already had preliminary discussions with banks to consider how they can also

be used effectively as credit cards.

34

THE Prestige project is launched in August 2002 as part of a four-year implementation

programme. A total of 3 million smart cards will be used during the first year of operation

and this new technology is working alongside the existing magnetic stripe system to

ensure there is no disruption. Since the introduction of the project, a total of 12 million

Oyster transactions were logged as of July 2003.

Washington

The Washington bus project amounting to US$135million was awarded to Cubic in 1997.

Cubic conducted a pilot study for participating agencies Washingston Metropolitan Area

Transit Authority (WMATA), Maryland Transit Administration (MTA), Montogomery

County, Maryland Ride-on, and North Virgina Transportation Commission- creators of

the regional bus fare collection system. Once completed, the system will include 5,000

smart card enabled terminals throughout bus, subway and commuter rail lines (Cubic

2003).

In February 2003, a 90-day test period for 80 Metro buses on the new fareboxes and

WMATA’s SmarTrip system was completed (the nation’s first interstate “contactless”

smart card system).

The regional SmarTrip system involves 17 transit agencies that carries about ½ billion

passenger trips per year. The fare processing will be seamlessly linked via the SmarTrip

card, currently used by 300,000 patrons of Washington Metro subway and parking

services. In May 2003 onwards, 1,500 Metro buses will be installed with fareboxes. The

SmarTrip has been in operations since 1999 when Cubic was awarded the WMATA’s

rail and park-and-ride facilities. In 2002, Maryland MTA awarded Cubic a US$38.1

million contract to expand the SmarTrip system Maryland’s subway, light rail and

commuter rails systems.

The Washington project extends over few years and during which new initiatives may

evolve such as non-Transit application just like Hong Kong and Korea. I will be

monitoring the progress of this project.

Singapore

In Singapore, the Magnetic card called Farecard was implemented in 1990 and over the

last 12 years some 9 million Farecards were sold. The reason for such a large number

35

(3 times the size of Singapore population) of card distributed was due to customers

purchasing more than one Farecard. The Farecard was possible to be used on bus,

MRT and LRT. There were number of schemes available such as park and ride, tourist

pass, concession pass (for students and senior citizen), adult card. Transit Link Pte Ltd

(TL) did the central clearing function and financial settlement the system for all the public

transport operators.

In 1996, TL carried out a pilot smart card project to evaluate the feasibility of introducing

the smart card system. The trial was successful but TL board of directors differed in their

views to convert the Farecard to smart card system due to substantial investment and at

the time the smart cost was about $18 per card. In 2000, the government Land

Transport Authority (LTA) foresaw that with the expansion of rail network the Farecard

system will not meet the future business needs and awarded the smart card project to

ERG (an Australian company) to install and commission the smart card project. In April

2002, the smart card project was officially launched to the public after 2 trials. A total of 8

months were observed to operate the old Farecard system and smard card system to

ensure the transition period address all the technical issues of the new system. The

Farecard system was shutdown for public use in December 2002. This was also

accelerated to be ready before the new North East Line is commissioned for public

launch in first quarter of 2003. At the moment about 4.7 million smart card is already

issued to the public and most of the schemes that were available in the Farecard system

is implemented in the smart card system. The transit system generates about $1 billion

transactions in a year.

Besides the using the smart card for public transport system, there are number of non-

transit services trials that were introduced in 2002, which registers about $4 million

transactions in a year. Base on the successful trial, EZ-Link Pte Ltd (subsidiary of LTA)

has awarded a MOU to QB Pte Ltd to implement the non-transit services in a larger

scale. QB estimates that they’re a potential of $2.4 billion per year (ie. only 20 to 30 %

of the micro payment market) to be made through non-transit services (Tan 2003).

36

Appendix 4

Note:

Ajzen and Madden (1997), 1st study to test the hypothesis that a measure of perceived

behaviour control would improve the prediction of behaviour intentions over a prediction

that is based solely on attitudes towards the behaviour and subjective norms. The study

was conducted in the context of regular class meetings where 169 college students (45

males and 124 females) were involved. The study was designed to discover why

students attend, or fail to attend, class sessions.

The study showed an improvement in reliability in theory of planned behaviour

(.68) compared with theory of reasoned action (.55) for Intention.

1

37

Note:

Bamberg, Ajzen & Schmidt (2003) carried out their research relying on theory of planned behaviour to investigate the effect of an intervention in introducing a prepaid bus ticket to see there is an increase use of the bus among college students. Most of the items in the questionnaire were designed to assess the constructs of theory of planned behaviour. The alternative travel modes such as driving a car, riding the bus, riding a bicycle, and walking were evaluated In 1st wave a total of 3,491 questionnaire forms were distributed and 1,874 (54%) were returned. In the 2nd wave a total of 1,316 completed questionnaire forms were returned. Table 1 shows the means and standard deviations of different measures in the two waves of data collection. The use of bicycle declined slightly from 36% to 33% whilst student driving car to campus declined from 46% to 31% (p < .01). Pre-paid ticket has lured many students to give up cars for bus as a mode of travel to campus.

2

38

Appendix 5

Ajzen (2002) : Sample Questionnaires to measure the different variables As it can be seen from the theory of planned behaviour, human action is guided by 3 considerations, they are: Behaviour beliefs, Normative beliefs and Control beliefs. Asking the respondents on a set of scales can directly assess all predictor variables in the theory. To secure reliable, internally consistent measures, it is necessary to select appropriate items in the formative stages of the investigations. The group interview will assist in constructing the appropriate questionnaire for the final survey.

The measures below are based on direct measurement of the constructs. A total of 30 EZ-Link card customers will be interviewed. These are people who are using the existing EZ-Link card in the public transport system. The people selected will comprise child, adult and senior citizen and possibly from different walks of life. This will facilitate a better representation of the entire population of EZ-Link customers. A group interview will be conducted to stimulate people’s views, perceptions, motives and reasons that will assist in designing a set of questionnaire for the survey. The questions listed below only serves as a guide for the group interview. There are many other issues that need to be addressed in terms of customer intending to use the EZ-Link card for non-transit services. Some of the issues that need to be discussed in the group interview :

• Fees charged for a particular non-transit service.

• Will the non-transit service be available throughout Singapore?

• Will there be a loyalty scheme tied to frequent users of a particular non-transit service?

• How to entice customers to use a particular non-transit service?

• How many services would a customers opt for in one card?

Intentions

I1. I intend to use the EZ-Link card daily for non-transit services

Extremely unlikely …………………………………Extremely likely (7-point scale)

I2. I will try to use the EZ-Link card daily for non-transit services Definitely true……………………………………….Definitely false (7-point scale) I3 I plan to use the EZ-Link card daily for non-transit services Strongly disagree……………………………………Strongly agree (7-point scale)

39

Attitude Towards the Behaviour A1 For me to use the EZ-Link card daily for non-transit services (Semantic 7-point

differential scale – bipolar adjectives used) Risky…………………………………………………..Safe Pleasant………………………………………………Unpleasant Good………………………………………………….Bad Worthless……………………………………………..Valuables Enjoyable……………………………………………..Unenjoyable

Subjective Norm Several differential questions should be formulated to obtain a direct measure of subjective norm. S1 Most people who are important to me think that I should …………………………………………………….I should not (7-point scale) Use the EZ-Link card daily for non-transit services

S2 It is expected of me to use the EZ-Link card daily for non-transit services Extremely likely……………………………………………Extremely unlikely S3 The people in my life whose opinions I value would Approve……………………………………………………………………Disapprove Of me using the EZ-Link card daily for non-transit services

Perceived Behaviour Control A direct measure of perceived behaviour control should capture people’s confidence that they are capable of performing the behaviour under investigation. Item of this kind are often said to capture the respondent’s sense of self-efficacy with respect to performing the behaviour.

P1 From me to use the EZ-Link card daily for non-transit services would be Impossible……………………………………………….Possible (7-point scale) P2 If I wanted to I could use the EZ-Link card daily for non-transit services Definitely true……………………………………………Definitely false