the effects of low-cost-carriers on regional dispersal of domestic

The Effects of Low-Cost-Carriers on Regional

Dispersal of Domestic Visitors in Australia

Examinations of the effects on visitors’ dispersal sourced from intra-

modal and inter-modal differentials

by

Tay T.R. Koo

A Doctoral Thesis

Submitted in Fulfilment of the Requirements for the Award of Doctor

of Philosophy of The University of New South Wales

Revised July 2009

Supervisor: Dr. Richard C.L. Wu

Co-supervisor: Professor Larry Dwyer

Department of Aviation

i

ABSTRACT

This thesis was conceived in the context of post-2000 proliferation of Australian

low-cost carriers and regional dispersal policy of the Australian government. The

broad aim of this thesis is to examine the effects of low-cost carriers on regional

dispersal of domestic visitors. Based on existing theoretical frameworks of

tourists’ spatial behaviour and multi-destination travel itinerary, two theoretical

constructs - intra-modal and inter-modal effects – were developed to

conceptualise the regional dispersal effects of low-cost carriers. The former refers

to differences between low-cost carriers and other models of airline business, and

the latter refers to differences between low-cost carriers and other modes of

transport. Logit models and national-level revealed preference data were used to

examine the intra-modal effects, while stated choice method was used to examine

the inter-modal effects on two representative regional tourism destinations -

Ballina-Byron and Cairns - in Australia.

This thesis provides evidence that suggest low-cost carrier air arrivals tend to

disperse for reasons that are different from network carrier air arrivals, supporting

the significance of intra-modal effects on regional dispersal. It is claimed that the

intra-modal effect is one reason why some destinations observe high growth in

airport activity as a result of low-cost carrier entry, but the levels of tourism

activities do not match that extrapolated from the level of growth in the incoming

air traffic. Two case studies have shown that (1) ground transport policy can

completely offset the negative effects on tourists’ dispersal propensity stemming

from pre-determined trip characteristics, although the effectiveness of such policy

variables varies significantly across destinations; and (2) significant discounts in

airfares are sufficient to trigger a modal switch, even in situations when a car is

the most suitable mode for the trip, suggesting a real possibility of a bypass of

ground-mode-reliant regions. The findings should be of interest to regional

destination managers with low-cost carrier services as much as for managers in

peripheral destinations without low-cost carrier services.

ii

LIST OF PUBLICATIONS FROM THE THESIS

Peer-reviewed journal

Koo, T.R, Wu, C. L., and Dwyer, L. M., (2009) Transport and Regional Dispersal

of Tourists: Is modal substitution a source of conflict between low-fare air

services and regional dispersal? Journal of Travel Research (in press, accepted

20th January 2009).

Koo, T.R, Wu, R. and Dwyer, L. (2009) “Ground Travel Mode Choices Of Air

Arrivals At Regional Destinations: The Significance Of Tourism Attributes And

Destination Contexts” Research in Transportation Economics: a special issue on

tourism (in press, accepted 1st September 2009)

Full conference papers:

Koo, T.R., Wu, C.L., Dwyer, L.M. (2007) Low Cost Carriers, Mode Choice and

Regional Tourism Destinations in Australia, Air Transport Research Society

(ATRS) conference, Berkeley, California

iii

Working papers published in conference proceedings:

Koo, T.R., Wu, C.L., Dwyer, L. (2009) “The effects of affordable air transport on

regional dispersal propensity of tourists: a logit analysis of the National Visitor

Survey data” CAUTHE February 2009, Fremantle, working paper presentation

Koo, T.R. (2008) "Affordable air travel and regional dispersal in Australia"

conference proceedings CAUTHE February 2008, Gold Coast, working paper

presentation

Koo, T.R. (2007) “The impact of Low-cost-airlines on regional tourism

destinations: issues and challenges” conference proceedings CAUTHE February

2007, Sydney, working paper presentation

iv

ACKNOWLEDGEMENT

I am overwhelmed with gratitude for the help and support I received from my

principal supervisor, Richard Wu. This dissertation would not have been possible

without Richard’s guidance. I am sincerely thankful to my co-supervisor, Larry

Dwyer, for his consistent advice and support, as well as his sense of humour and

perspective in the midst of chaos. I would like to thank my father, Matt C.D., who

has been my informal third supervisor, and my mother, Vivian G.S., and my

sister, Su-jie. I would like to acknowledge the Cooperative Research Centre for

Sustainable Tourism, established by the Commonwealth Government of

Australia, and the Department of Aviation in the University of New South Wales,

for financial support and professional development opportunities. I would like to

extend my thanks to the staff of Australian Regional Tourism Research Centre,

Ballina airport, Cairns airport and the Research and Strategy team in Tourism

Australia, for their help with survey design and data collection.

v

TABLE OF CONTENTS

1. INTRODUCTION......................................................................1-1

1.1. LOW COST AIR TRANSPORT AND DISPERSAL .............................................1-1

1.1.1. Research significance ......................................................................1-1

1.1.2. Low Cost carriers' effect on dispersal: an issue of spatial scale........1-3

1.1.2. The link between Low Cost carriers and spatial behaviour of tourists

…………...……………………………………………………………….. 1-4

1.2. RESEARCH AIMS......................................................................................1-5

1.2.1. Statement of the general aim............................................................1-5

1.2.2. Statement of the specific aims .........................................................1-6

1.3. NOTES ON METHODS .............................................................................1-13

1.3.1. Discrete choice models..................................................................1-13

1.3.2. Stated choice data..........................................................................1-19

1.4. CONTRIBUTION TO KNOWLEDGE ............................................................1-21

1.4.1. Contributions and limitations.........................................................1-22

1.4.2. Key stakeholders ...........................................................................1-23

1.5. STRUCTURE OF THE THESIS ....................................................................1-25

2. DISPERSAL AND LOW COST CARRIERS ............................2-1

2.1 INTRODUCTION ........................................................................................2-1

2.2 DISPERSAL...............................................................................................2-2

2.2.1 Definition of ‘regions’, domestic dispersal and regional dispersal.....2-2

2.3 THE CHARACTERISTICS OF LCCS ..............................................................2-6

2.3.1 The LCC model................................................................................2-6

2.3.2 Point-to-point network (P2P)............................................................2-7

2.3.3 Use of secondary and regional airports .............................................2-9

2.3.4 Short-haul and Low-cost customer service......................................2-11

2.3.5 Ticket distribution, fare structure and passenger-handling...............2-12

2.4 BACKGROUND: PRECURSOR TO LCC GROWTHS IN AUSTRALIA................2-14

2.4.1 Deregulation of the airline industry in Australia .............................2-16

2.4.2 Privatisation of the domestic airports..............................................2-17

2.4.3 Foreign ownership cap ...................................................................2-18

2.5 AUSTRALIAN LCCS AND THEIR IMPACT ON DOMESTIC DISPERSAL............2-19

2.5.1 First wave of LCCs in Australia (1990 – 1993)...............................2-19

2.5.2 Duopoly period (1994 – 1999)........................................................2-19

2.5.3 Second wave of LCCs (2000 – 2006) .............................................2-21

2.5.4 The ‘third’ wave (post-2006) ..........................................................2-25

2.6 SUMMARY .............................................................................................2-27

vi

3. REGIONAL DISPERSAL PROPENSITY AND LOW-COST

CARRIERS .....................................................................................3-1

3.1 INTRODUCTION ........................................................................................3-1

3.2 THE SPATIAL PATTERNS OF TOURISTS’ REGIONAL DISPERSAL.....................3-2

3.3 THE EFFECTS OF LCCS ON REGIONAL DISPERSAL.......................................3-8

3.3.1 Spatial configuration of the destinations .........................................3-11

3.3.2 Length of stay.................................................................................3-12

3.3.3 Variety and multiple-benefit seeking behaviour..............................3-13

3.3.4 Risk and uncertainty reduction: distance travelled ..........................3-15

3.3.5 Heterogeneity in preferences (Travel party)....................................3-16

3.3.6 Trip arrangement (package tourism)...............................................3-18

3.3.7 First timers, repeaters, and destination familiarity...........................3-19

3.3.8 Travel mode choice to and within the destination ...........................3-21

3.3.9 Socio-economic variables...............................................................3-23

3.3.10 Other variables and issues.............................................................3-24

3.4 SUMMARY .............................................................................................3-25

4. THE ‘CHARACTERISTICS’ MODEL .......................................4-1

4.1 INTRODUCTION ........................................................................................4-1

4.2 METHOD..................................................................................................4-3

4.2.1 Data .................................................................................................4-3

4.2.2 The Model........................................................................................4-4

4.2.3 Dependent and independent variables...............................................4-6

4.3 RESULTS AND DISCUSSION .......................................................................4-9

4.3.1 Number of stopovers ......................................................................4-13

4.3.2 Length of stay.................................................................................4-14

4.3.3 Distance .........................................................................................4-15

4.3.4 Spatial configuration of the destinations .........................................4-16

4.3.5 Accommodation Type ....................................................................4-17

4.3.6 Accompanying travel party type.....................................................4-18

4.3.7 Other variables ...............................................................................4-18

4.4 LIMITATIONS .........................................................................................4-19

4.5 CONCLUSION .........................................................................................4-20

vii

5. THE CAIRNS EXPERIMENT...................................................5-1

5.1 INTRODUCTION ........................................................................................5-1

5.2 REGIONAL DISPERSAL AND TRANSPORT ....................................................5-2

5.3 THE MODEL.............................................................................................5-5

5.4 ALTERNATIVES AND ATTRIBUTES .............................................................5-6

5.4.1 Alternatives......................................................................................5-6

5.4.2 Attributes and attribute level labels.................................................5-12

5.5 EXPERIMENTAL DESIGN..........................................................................5-15

5.5.1 Orthogonal main effects design ......................................................5-15

5.5.2 Coding and design orthogonality ....................................................5-16

5.5.3 The survey......................................................................................5-17

5.6 RESULTS................................................................................................5-18

5.6.1 Descriptive statistics.......................................................................5-18

5.6.2 Model results..................................................................................5-21

5.7 DISPERSAL AND RENTAL CARS................................................................5-25

5.7.1 Transport attributes.........................................................................5-25

5.7.2 Trip characteristics .........................................................................5-26

5.8 DISPERSAL AND PUBLIC TRANSPORT .......................................................5-28

5.8.1 Transport attributes.........................................................................5-28

5.8.2 Trip characteristics .........................................................................5-29

5.9 LIMITATIONS AND FUTURE RESEARCH.....................................................5-30

5.10 CONCLUSION .......................................................................................5-32

APPENDIX 5.1 ..............................................................................................5-34

6. THE BALLINA-BYRON EXPERIMENT ..................................6-1

6.1 INTRODUCTION ........................................................................................6-1

6.2 TOURISTS’ DISPERSAL ..............................................................................6-2

6.3 THE MODEL .............................................................................................6-5

6.4 DATA ......................................................................................................6-6

6.4.1 Case study region............................................................................6-6

6.4.2 Stated choice data ...........................................................................6-9

6.4.3 Choice alternatives........................................................................6-10

6.5 ATTRIBUTES OF MODAL ALTERNATIVES ..................................................6-11

6.6 EXPERIMENTAL DESIGN AND SURVEY .....................................................6-17

6.7 RESULTS................................................................................................6-19

6.8 DISCUSSION AND IMPLICATIONS .............................................................6-23

6.9 LIMITATIONS AND FURTHER RESEARCH...................................................6-27

6.10 CONCLUSION .......................................................................................6-29

APPENDIX 6.1 ..............................................................................................6-31

APPENDIX 6.2 ..............................................................................................6-32

viii

7. CONCLUSION, LIMITATIONS & FUTURE RESEARCH .......7-1

7.1 REVIEW ...................................................................................................7-1

7.2 KEY FINDINGS..........................................................................................7-2

7.3 CONTRIBUTION TO KNOWLEDGE AND IMPLICATIONS FOR STAKEHOLDERS ..7-5

7.3.1 Contribution to theory.....................................................................7-5

7.3.2 Implications for policy....................................................................7-6

7.3.3 Implications for destinations ...........................................................7-7

7.4 LIMITATIONS AND FUTURE RESEARCH.......................................................7-8

7.4.1 Applicability of the results ..............................................................7-8

7.4.2 Limitations of the MNL: utility compensation perspective and taste

heterogeneity ............................................................................................7-9

7.4.3 Operationalising ‘dispersal’ ..........................................................7-11

7.4.4 Integrating destination and mode choice .......................................7-11

7.4.5 The time attribute in leisure and tourism .......................................7-12

7.5 TOWARDS AN INTEGRATED MODEL OF INDIVIDUAL TOURISTS' SPATIAL CHOICE

AND TOURISM YIELD ....................................................................................7-14

REFERENCES…………………………………………………… .R-1

ix

LIST OF TABLES

Table 1.1 Effects of LCCs on the regional dispersal propensity of visitors: intra-

modal propositions ...........................................................................................1-8

Table 2.1 Summary of definitions.....................................................................2-5

Table 2.2 Product features of LCCs and FSCs (NCs) ........................................2-7

Table 2.3 Top 30 Australian domestic airports in terms of incoming passenger

flows .....……………………………………………………………………….2-23

Table 3.1 Summary of the relationships discussed in section 3.3.....................3-10

Table 4.1 Summary of the relationships between LCC and dispersal.................4-2

Table 4.2 Origin-destination sample..................................................................4-4

Table 4.3 Independent variables........................................................................4-8

Table 4.4 Model summary ..............................................................................4-12

Table 4.5 Model results...................................................................................4-13

Table 5.1 Three choice dimensions ...................................................................5-7

Table 5.2 List of attribute level labels .............................................................5-13

Table 5.3 Model summary ..............................................................................5-21

Table 5.4 Model output: North........................................................................5-22

Table 5.5 Model output: South........................................................................5-23

Table 5.6 Inclusive value (IV) parameters.......................................................5-25

Table 6.1 Attributes ........................................................................................6-15

Table 6.2 IV parameter results ........................................................................6-20

Table 6.3 Summary Statistics..........................................................................6-20

Table 6.4 MNL estimation results ...................................................................6-21

x

LIST OF FIGURES

Figure 1.1 Spatial representation of tourists' travel patterns.............................1-10

Figure 1.2 Schematic diagram of the thesis .....................................................1-26

Figure 2.1 Tourism Regions: An example of New South Wales........ ….……...2-4

Figure 2.2 Revenue Passenger Demand........................................... ….……...2-20

Figure 2.3 Domestic airfare indices................................................. ….……...2-21

Figure 2.4 Domestic revenue passenger growth from 1992/1993.....................2-24

Figure 2.5 Overnight trips made by air by purpose..........................................2-25

Figure 3.1 Spatial representation of tourists' travel patterns...............................3-5

Figure 3.2 Travel party characteristics of air travellers....................................3-18

Figure 4.1 Regional dispersal: ground transport vs. air transport .....................4-10

Figure 4.2 Regional dispersal by airline ..........................................................4-11

Figure 4.3 Marginal effects of stopovers on dispersal propensity ....................4-14

Figure 5.1 Map of the Cairns region…………………………….………….….5-11

Figure 5.2 Sample choice shares across alternatives........................................5-20

Figure 6.1 Patterns of multi-destination travel...................................................6-3

Figure 6.2 Map of Northern New South Wales..................................................6-8

1-1

1. INTRODUCTION

This thesis aims to study the relationship between low-cost carriers and dispersal

with the aid of Australian data and case studies. Discrete choice analysis is the

approach adopted to examine the relationships. The aims, methodology, and

anticipated outcomes of this thesis are introduced in this Chapter. In addition, this

introductory chapter aims to provide sufficient information so that the reader can

obtain a good sense of the links among the five ensuing chapters.

1.1. Low-cost air transport and dispersal

1.1.1. Research significance

When the U.S. domestic market was officially deregulated in 1978, average

airfares came down, capacity increased, more airlines commenced services and

the aviation network proliferated over the U.S. (e.g. Meyer and Oster 1987,

Doganis 2002). One significant development subsequent to deregulation was the

proliferation of a new kind of jet carriers in the 1980s. Meyer and Oster (1987)

observed,

”the emergence of the new entrant jets was almost surely the least

anticipated major event of deregulation prior to the fact ... The niches served

by these carriers were largely markets left vacant because of previous

1-2

regulatory policies, and in keeping with the identity of these under-attended

market niches, most of the new entrant jet carriers attempted to do

something that predecessor established carriers did not. In most instances

they either entered a market that was not previously served or entered a

previously well served market while offering substantially lower fares”

(p.49-50)

It was evident from the observations made by Meyer and Oster that the new

entrant “jets” were loosely equivalent to what is today widely known as the Low

Cost Carriers (LCCs) or Value Based Airlines (VBA). There are numerous

variations to the LCC business model but research has shown that its low-margin,

high-volume and low-fare foci are distinguishing features of the LCCs from

network carriers (NC) (Lawton 2002). One significant source of variation within

the LCC model is in the way they reduce costs. The cost reduction strategies

manifest as characteristics of LCCs, which Gillen and Lall (2004) observed to be

uniform fleet, greater use of airports excess-in-capacity, and the specific focus on

maintaining a low-cost base in order to maintain the low-fare. Australia too has

been subject to the entry of LCCs since the deregulation of the aviation sector in

1990, and as discussed in Chapter 2, Australian LCCs are also broadly congruent

to the characteristics mentioned above.

Meanwhile, in recognition of the importance of tourism to regional economic

prosperity (as well as alleviating urban congestion by diverting tourist flows), the

Australian federal government prioritised the ‘greater regional dispersion of

domestic and international tourists’ as a key policy goal in the medium term

(former Department of Industry, Tourism and Resources (DITR 2003)). Although

there has been a newly elected government in 2008, the emphasis on tourism

policy to promote greater dispersal will remain an important policy agenda due to

the continuing reliance of the rural regions on tourism for income and

employment. For instance, the Jackson report (2009) prepared by the National

Tourism Steering Committee to inform the development of a new National Long-

Term Tourism Strategy outlined the significance of regional tourism, stating,

“tourism provides opportunities for regional and remote communities to grow

1-3

jobs, diversify their economic base, and generate higher standards of living.

Nearly half of total tourism expenditure (47 per cent) occurs in the regions”

(p.10). Greater dispersal of visitors is maintained in the charter of federal tourism

agencies.

Given Australia’s large and highly urbanised geographic characteristics, air

transport is a vital form of transport for many tourism destinations located beyond

the key metropolises. In some regional destinations, air services are the only real

option for the accessing tourists. New air services have the potential to introduce

tourism destinations to new markets. Such is the importance of air transport for

dispersal, as part of the initiatives outlined in the Tourism White Paper (2003/04),

the Australian government commits to ensure that “the airline services to regional

destinations are considered as part of a broad Government policy to assist regional

tourism” (DITR 2003). As for the definition of ‘regional dispersal’, in policy and

practice, the regional dispersal of domestic tourists are defined as ‘a trip that

involves at least one night stay outside the state capitals and the Gold Coast’1.

Chapter 2 provides a more detailed description of the origins of the definitions

adopted.

1.1.2. Low Cost Carriers’ effect on dispersal: an issue of spatial scale

The Australian LCCs exhibit the characteristics of low airfares, excess capacity

airports and uniform fleet mentioned previously. The combined effects of these

characteristics (the first two in particular) are positive for regional dispersal. Thus,

LCC proliferation in the recent years can be viewed as an important agent that

assists the government’s tourism policy of greater regional dispersal. More

specifically, it can be viewed that the ‘dispersal of tourists beyond capital cities’

helps alleviate urban congestions and contribute towards moderating the

imbalances in regional economic development across regions. Hence, in a way,

LCC can be viewed as a distributive agent.

1 Dispersal of International tourists involves a stay in Sydney, Melbourne, Brisbane and Perth

only, and excludes other state capitals such as Canberra, Adelaide, Darwin and Hobart.

1-4

However, the definition of ‘regions’ encompasses a very large geographic area. In

fact, according to the standard definition adopted in the tourism industry, regional

dispersal is always achieved as long as an overnight trip is undertaken beyond a

few nodes (capital cities). While such measure is sufficient at the level of the

federal governance, it is insufficient to bring more localised dispersal issues into

light at the State and Territory level. The measure of regional dispersal at the state

and local levels are more relevant for state and local governments, especially if

they have some sort of regional economic development mandates in their charter

(which may be the case for most states and territories). As illustrated later in this

chapter, a distinction is made between domestic dispersal and regional dispersal in

this thesis to reflect the differences in spatial scales.

1.1.3. The link between Low Cost Carriers and spatial behaviour of tourists

Bieger and Wittmer (2006) asserted that the 21st century proliferation of LCCs in

Europe is the third revolution in aviation from the viewpoint of tourism, preceded

by the charter sector in the 1970s and the aviation deregulation in the1990s. For

tourism, one major consequence of the LCCs has been the generation of new

tourist flows to existing and new destinations. This is not surprising given the fact

that transport cost is a significant determinant of tourism demand (see for

example, Crouch 1995, Sinclair 1998). The low airfares, however, were found to

be associated with not only greater demand for tourism, but also demand for a

certain type of tourism, as well as different travel characteristics.

An early evidence of the impact of affordable air transport (scheduled air

transport) on tourist behaviour can be found in Mings and McHugh (1992). They

studied the spatial configuration of the travel patterns of tourists travelling to

Yellowstone National Park. They distinguished four types of spatial patterns:

direct, partial orbit, full orbit and fly-drive. The pattern most profound at the time

was the fly-and-drive pattern, which was associated with a number of key trip and

traveller characteristics. Specifically, they observed that the fly-drive pattern was

positively associated with increasing trip distance, number of visits to other

1-5

national parks, and length of the trip. Also there were more incidences of first

time visits for this pattern compared to others. In regards to the socio-economic

characteristics of tourists, they found that the fly-drive travel pattern was

associated with tourists belonging to greater income and education levels. Mings

and McHugh concluded, “perhaps this reflects increasing affluence, constraints on

leisure time, and growing appreciation of … the American West” (p.46).

Mings and McHugh, however, did not explicate the link between the development

in the aviation sector and the pattern of tourism they observed. Based on the

inverse relationship between tourism demand and transport cost, we can deduce

that the emergence of the fly-drive travel in the U.S. and the advent of new entrant

jets, are causally related. Thus, we can propose a relationship between the new

entrant jets, or the LCCs, and the spatial configuration of tourists’ travel patterns.

Dispersal can be viewed as a special case of tourists’ spatial behaviour. Exploring

the relationships between these two concepts, i.e. the LCCs and spatial behaviour,

is the general premise of this thesis.

1.2. Research aims

1.2.1. Statement of the general aim

G1. Examine the effects of LCCs on the regional dispersal of domestic

visitors in Australia.

The over-arching aim of this thesis is to examine the effects of LCCs on the

regional dispersal of tourists in Australia. This necessarily involves explicating

the theoretical link between LCCs and dispersal, as well as to empirically testing

these relationships. It was previously hinted that the LCCs can be viewed as

1-6

agents of change in two facets: (1) in changing the volume of tourist flows to

regional destinations and (2) in affecting the trip and tourist characteristics of

these flows. To proceed, five specific aims are devised. Although each specific

aims address different research questions, these aims were devised in a way that,

collectively, contribute towards providing a thesis to the general aim (G1).

1.2.2. Statement of the specific aims

The first specific aim, A1 (see below), is addressed in Chapter 2. Related to this

aim are two specific purposes. The first purpose is to provide the necessary

background information on the Australian aviation environment to better

understand the issues that this thesis aims to address. This is done by outlining the

precursor to LCC growth in Australia, followed by a survey of international

literature on the LCC models and characteristics. The second purpose is to discuss

the Australian LCCs with a focus on their impact on dispersal. In order to do this,

there is a need to distinguish between domestic dispersal and regional dispersal.

As Chapter 2 will show in detail, domestic dispersal is appropriate for use at the

federal level, while regional dispersal is more relevant for State and Territory

governments. Furthermore, it is shown in Chapter 2 that while there is evidence of

LCCs’ contribution to domestic dispersal in Australia, issues remain as to what

the effects of LCCs are on the regional dispersal of tourists. Thus, A1 is to

A1. Provide an interpretative survey of the aviation and tourism research

literature, and the secondary data sources relevant in understanding the link

between LCCs and domestic dispersal (Chapter 2).

A1 addresses the issue of LCC and the volume of tourist flows. A2 (see below)

addresses the characteristics of these flows from the regional dispersal viewpoint.

In addressing A2, Chapter 3 aims to interweave the literatures of LCCs and

regional dispersal to ascribe a cause-effect structure. Research on multi-

destination travel and tourists’ spatial behaviour was found to be the most relevant

literature in providing a conceptual framework for the LCC and dispersal

1-7

problem. In theory, at least two underlying sources can be responsible for the

differences between the regional dispersal of tourists who used LCCs (called the

‘LCC tourists’ here after) and the regional dispersal of tourists who did not. One

is due to the differences between the LCCs and the NCs. This is called the intra-

modal source of difference. The second difference arises from the fact that LCC is

a type of air transport, thus, it is also subject to the same constraints as all air

transport services. This is called the inter-modal source of difference. These two

sources form the basis of the cause-effect structure imposed in Chapter 3. A2 is

to,

A2. Identify and explicate the relationships between regional dispersal and

LCCs based on aviation, tourism and spatial behaviour research (Chapter 3)

The causal relationships between LCC and regional dispersal are summarised

below (Table 1.1). Each hypothesis is explained in greater detail in Chapter 3.

1-8

Table 1-1. Effects of LCCs on the regional dispersal propensity of visitors: intra-

modal propositions

Factors Effects on regional dispersal

propensity

LCC demand characteristics from

dispersal viewpoint

1. Spatial

configuration of

destinations

Different tourism regions will be

associated with different levels of

dispersal



dispersal

2. Length of stay Length of stay is positively related to

dispersal

LCC demand will be less sensitive to length

of stay than NC demand

3. Variety and

multiple-benefit

seeking behaviour

Greater variety in the reasons for

travel, and larger share of VFR

related travels, are positively

related to dispersal

Variety in the travel purpose, and the

large share of VFR travels, are important

sources of dispersal for the LCC arrivals

4. Risk and

uncertainty

Greater risk and uncertainty about

the trip may affect dispersal

positively or negatively

LCC demand may be more sensitive to

risk and uncertainty, hence the effect of

distance on dispersal may be magnified

5. Heterogeneity in

preferences

Greater heterogeneity in a travel

group may affect dispersal


LCCs serve proportionately more couples

and group travels, but there is no clear

proposition on the differential effect of

heterogeneity on dispersal between LCC

and NC

6. First time or repeat

visitation

First visitation can have a positive or

negative effect on dispersal; repeat

visitation has a positive effect on

dispersal

LCC stimulates first-time visitors to the

destination, which may increase or

decrease dispersal. Second-home

travellers are expected to be an important

source of dispersal of the LCC arrivals

7. Package tourism Package tourism is negatively


Disproportionately large share of LCC

arrivals are FIT tourists, therefore, they

are less constrained spatially.

8. Transport 'to' and

'within' the

destination

Addressed in Chapter 5 and

Chapter 6

Addressed in Chapter 5 and Chapter 6

1-9

A2 explored and identified causal relationships between the LCCs and the

dispersal propensity arising from intra-modal differences. The natural extension

of A2 is to empirically test the proposed relationships. Thus, A3 is to

A3. Build and test a causal model of regional dispersal and the intra-modal

differences (Chapter 4);

As previously mentioned, in addressing the issue of differential characteristics of

tourists, it is useful to consider intra-modal and inter-modal effects. A2 identified

the causal relationships between LCCs and regional dispersal arising from the

intra-modal differences, while A3 empirically tests these relationships. The

remaining problem is to examine the differences in the dispersal propensity

sourced from inter-modal differences. As mentioned previously, the conceptual

framework for this problem is based on tourists’ spatial behaviour and multi-

destination travel research literature.

Previous research in the field identified a number of trip itinerary patterns, which

were empirically found to be robust across spatial scales (e.g. inter-continental

scale to local scale) and countries. The previously mentioned study by Mings and

McHugh (1992) discovered that the majority of the variation in U.S. domestic

trips to Yellowstone Park can be categorised into one of four trip structures: direct

route; partial orbit; full orbit, and fly-drive. Lue et al. (1993) introduced structure

to these itineraries in developing their conceptual framework for multi-destination

travels. The trip itineraries were structured into five basic patterns of single and

multi-destination trips, extending the Mings and McHugh’s four spatial patterns.

In this thesis, the trip patterns are grouped into three main types. This is shown in

Figure 1.1, which integrates the five patterns proposed by Lue et al. into three

patterns: single-destination (SDT), multi-destination type 1 (MD1) and multi-

destination type 2 (MD2). SDT refers to a ‘direct-route’ travel that involves

overnight stay in a single-destination. MD1 and MD2 represent two ways that

LCCs can induce a change in the patterns of regional dispersal in Australia.

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Moscardo et al. (2004) have shown that the transport modes chosen by travellers

to and within the destination constrain travellers’ travel patterns. Furthermore,

they show that the ‘access points’ for transport, e.g. the location of the airport in

relation to the wider destination region, affect the spatial pattern of the trip.

Consequently, a shift in the destination access mode from air towards a car will be

accompanied by a change in the trip type to the region. Since 2001, many regions

in Australia were subject to LCC entry, increasing the importance of air transport

for tourism in the regions. This also amplifies the importance of destination

transportation for regional dispersal because the air leisure arrivals, unlike self-

drive tourists, rely mostly on travel modes available in the destination.

A challenge for the government at the regional level may be to reconcile the

potential conflicts arising from policy objectives that do not necessarily promote

Figure 1.1 Spatial representation of tourists' travel patterns (modified from Lue et al.

1993)

D

a

b

c

Regional tour/partial orbit

b

D

a

c

Single destination/Base camp

d

Trip chaining

c

D

a

b

En route

HOME

MD1

MD2

SDT

1-11

the same travel mode best for regional dispersal. The examination of the

destination travel mode choice of the air arrivals forms the next specific aim. This

case study will be referred to as the ‘Cairns experiment’ hereafter, reflecting the

name of the case study region.

A4. Examine the trade-offs between destination transport factors and

tourists’ travel characteristics in the choice of the air arrivals’ regional

dispersal (Chapter 5, ‘The Cairns experiment’)

Specifically, the following research question has been devised:

‘Can (and how) destination transportation policy stimulate the dispersal of

the air arrivals, even in situations where the air arrivals exhibit trip

characteristics that are dispersal-averse?’

Thus, the following hypothesis will be tested in the ‘Cairns experiment’:

‘Ground travel mode attributes and destination attributes can completely

offset the negative effects on tourists’ dispersal propensity stemming from

pre-determined trip characteristics’

The first inter-modal issue addressed by A4 pertained to MD1, while the second

issue arise from MD2. MD2 includes the trip-chaining and en route patterns. One

way that these patterns distinguish themselves from MD1 is through the main

mode of travel used by the tourists. Air travel does not offer the spontaneity and

flexibility of that offered by cars (e.g. Stewart and Vogt 1997:458). Thus in the

regional tourism context, MD2 is difficult to achieve with air travel, but most

easily with cars.

In MD2, the peripheral destinations (e.g. ‘a’ and ‘b’ in Figure 1.1) impacted by

the LCCs are commonly en route. These destinations usually do not command

large enough demand to sustain their own LCC services from ‘Home’. This will

be a problem if there is substantial modal shift from the ground modes towards air

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travel on the travel corridor. A sizable substitution effect will adversely impact

regional dispersal because it will generate a bypass effect. This modal substitution

issue is related to the final aim of this thesis. This case study will be referred to as

the ‘Ballina-Byron experiment’ hereafter.

A5. Examine inter-regional travel mode substitution as a source of conflict

between low fare air services and regional dispersal by applying a stated

choice experiment (Chapter 6, ‘The Ballina-Byron experiment’)

Specifically, the following research question has been devised:

‘Can (and how) low airfares induce tourists to switch from car to air, even

in situations where a car may be the most suitable mode of dispersal for the

trip?’

The following hypothesis will be tested in the ‘Ballina-Byron experiment’:

Low airfares can induce tourists to switch from car to air by offsetting the

positive utility gained from choosing a car, even in situations where the car

may be the most suitable mode of dispersal for the trip.

The preceding discussion of MD1 and MD2 demonstrates that different regional

destinations will be subject to different channels of LCC impact, i.e. via modal

substitution (transport ‘to’ the destination) or modal complementarity (transport

‘within’ the destination and its relationship with the regional dispersal of the air

arrivals). The trip patterns, individually or in combination of one another, enables

a depiction of large variations of trips into a parsimonious set. When applied to a

destination, the trip patterns generate specific LCC and dispersal issues. The

applicability for a specific regional destination depends on which trip structure

(SDT, MD1 or MD2) characterises the destination’s main demand. For instance,

MD1 is most applicable to trips originating from Sydney or Melbourne, travelling

to destinations along the Queensland’s Eastern Coast, whereas MD2 is most

applicable to shorter trips (less than 800km).

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A4 and A5 help fill a gap in the transport-tourism research. A4 concerns intra-

regional mode choice, whereas A5 concerns inter-regional mode choice. Lumdson

and Page (2004) noted a need for more cross-fertilisation between tourism

research and the established field of transport economics, stating,

“modal competition has attracted highly quantitative and theoretical research

by modelling travel behaviour. Yet the explicit tourism and leisure

dimension remains a virgin area for research to understand the relationship

between the potential for modal switching for pleasure travel rather than the

prevailing focus of many transport studies on commuting”

While this thesis will not fully address the gap in the knowledge identified by

Lumsdon and Page, it aims to make some progress by cross-applying methods

established in the transport economics literature to the problems in tourism.

1.3. Notes on methods

1.3.1. Discrete choice models

The empirical work of this thesis applies several varieties of discrete choice

models, namely the multinomial logit (MNL) and nested logit, as well as the basic

logit model of binary choice. The aim here is to outline the common and the most

fundamental aspects of the choice models applied in Chapter 4, 5 and 6. More

information is provided in the methodology sections of each Chapter. The

following explanations on discrete choice models are sourced mainly from a

classic discrete choice analysis text by Ben-Akiva and Lerman (1985).

1-14

Random Utility Theory (RUT) forms the basis of discrete choice models. The

microeconomic consumer theory maps consumption bundles of goods on a

continuous space. By assumption, the quantity of goods consumed can be in

integers. Consequently, calculus can be used to derive and solve demand

functions for a utility maximised bundle of goods. The problem occurs with the

standard method when it is applied to situations where a consumer chooses only

one option from a number of mutually exclusive alternatives. This is because

consumer choice necessarily implicates a consumption of only one good and zero

consumption of other alternatives in the choice set, which results in “corner

solutions” that cannot be solved by calculus (Ben Akiva and Lerman 1985).

Random utility framework provides an alternative approach that overcomes this

problem.

RUT assumes that the utility function for a given good can be decomposed into a

non-random (or systematic) and a random (or stochastic) component. The

randomness is assumed to arise from four sources (Ben Akiva and Lerman

attribute this to Manski (1977)): the analyst does not observe all explanatory

variables of the alternatives (or often called the ‘attributes’ in the literature); there

is taste heterogeneity across individuals that analysts cannot observe; the analyst

cannot measure and quantify the variables perfectly; and the use of proxy and

instrumental variables results in a loss of information. Due to these reasons, at

least some aspect of the utility of a good is uncertain. Random utility function can

be expressed in the following way:

Ui =Vi + �i Eq. (1)

where Ui is the utility level of a good i (or an ‘alternative’ as often referred to in

the literature) and Vi is the systematic component of the utility (the part we can

measure and observe), whereas �i is the error term that represents the random part

of the utility. A key feature of discrete choice models is the probability

distribution ascribed to the error term. Different assumptions result in different

discrete choice models. The most common assumption is the Gumbel extreme

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value type I distribution. Daniel McFadden was the first to derive a multinomial

logit (MNL) model. The MNL takes the following form:

pni =eVni

eVnj

j

� Eq.(2)

where Pni denotes the probability of an individual n choosing alternative i, Vni

represents the systematic components of the utility described by the attributes,

socio-economic and trip characteristics of alternative i for an individual n.

Likewise, Vnj represents the observed variables for all alternatives in the choice

set. In the MNL, it is the relative utility of one alternative to another that matters.

When each of the random term (unobserved) is assumed to have Gumbel

distribution, the difference in the random component of each utility function is

logistically distributed. The linearly additive utility functions, Vni , are first

estimated from the data, and then it is transformed into probability estimates with

the logarithmic function. Hence, the term ‘logit’ comes from the phrase,

‘logarithmic transformation’ (Louviere et al. 2000).

In Chapter 5 and Chapter 6 of this thesis, we primarily make use of the MNL. In

particular, the results presented in these Chapters (Table 5.3 and Table 6.3)

pertain to the coefficients of the utility function of the following form:

Vni =� i + �iXni + � iTni + �iZni Eq. (3)

where Vni is the level of utility for individual n choosing alternative i . Vni is a

function of the levels of the attributes Xni where �i is a vector of coefficients to

be estimated for each attribute of each alternative i . Tni is the trip characteristics

where � i represents the vector of coefficients for each trip attribute. Zni is the

individual’s characteristics with coefficients vector�i.

1-16

One reason why discrete choice models are such powerful analytical tools is

because the comparison of coefficients provides trade-off information. Because all

explanatory variables are expressed in terms of their contribution to the common

unit called utility, when a coefficient of a ‘price’ variable is expressed as a ratio of

other variables, monetary values can be ascribed. This subsequently has important

interpretation as willingness-to-pay measures and the estimation of welfare and

consumer surplus. For instance, a ratio of the coefficient of price to travel time

provides a monetary value of travel time. Such interpretation is given when

appropriate in this thesis, although in most cases, the MNL in this thesis is used to

estimate the coefficients of the explanatory variables and to test for the statistical

significance of these variables.

It should be noted that the model applied in Chapter 4 is a simplified version of

the MNL. It is a logit model with only two available options. The model is of the

form:

pn (dispersal =1) =eVni

1+ eVni Eq. (4)

where all terms are as defined previously. There is another methodological

difference between Chapter 4 and the other two empirical Chapters. While

Chapter 4 used ‘revealed preference’ data, Chapters 5 and 6 used ‘stated choice’

data. This difference is very important and it is explained in 1.4.2.

The IIA axiom and the limitations of MNL

One important limitation in the MNL is the independence of the irrelevant

alternatives (IIA) axiom. This property stems from the ‘independent and

identically distributed error term’ assumption that gives the MNL the analytically

convenient closed-form solution (Ben Akiva and Lerman 1985). In the axiom of

IIA, “no provision is made for different degrees of substitutability or

complementarity among the choices” (Hausman and McFadden, 1984: 1220). The

IIA assumption is equivalent to constant cross effects in the MNL model (Ben

1-17

Akiva and Lerman 1985). Thus, the IIA assumption should be tested, and when

the assumption is violated, non-IIA models should be considered. In the following

section, the thesis presents commonly used IIA tests and other logit models that

relax the assumption of IIA.

Hausman and McFadden (1984) show two ways to test the assumption of IIA.

The first test shown below does not require an alternative model, whereas the

second test does. For the former, they have shown that a violation of IIA will

mean that the coefficients from a MNL with a subset of alternatives, i.e., the

restricted model, will be statistically different from the coefficients estimated with

all the alternatives, i.e., the unrestricted model. The Hausman-McFadden test

provides a way of testing the differences in the coefficients. The test is:

q = [br � bu � ] [Vr �Vu]�1[br � bu] Eq. (5)

br and bu indicate, respectively, vector of restricted (a subset of alternatives) and

unrestricted (all alternatives are included in the model) model coefficients. V is

the variance-covariance matrix of the estimated coefficients. q is the Hausman-

McFadden statistic and this has a chi-square distribution with degrees of freedom

equal to the number of coefficients in the restricted model.

Alternative discrete choice models relax the assumption of IIA. A natural

extension to the MNL model is the nested logit model (Hensher et al. 2005). The

nested logit partly relaxes the IIA assumption by partitioning similar or dissimilar

alternatives. If alternative 1 and 2 are considered more similar than they are to

alternative 3, then the central idea is that “the individual forms a weighted average

of the attributes of alternatives 1 and 2, sometimes called the inclusive value,

which is closely related to his consumer surplus” (Hausman and McFadden

1984:1227). Thus, a utility function can be specified to include a ‘composite’

utility (inclusive value),

Vn =�n + �in Xin + �( i+1),nIVn Eq. (6)

1-18

where n is a nest of similar alternatives (e.g, Jetstar and Virgin Blue may be in

one nest and Train and Bus in the other). The inclusive value (IV) is defined as,

IVn = ln �j�neV j�

� �

�

Eq. (7)

The IV in the nest, n, can be viewed as a weighted average, or an expected

maximum utility from a composite of alternatives (alternative j) (Hensher et al.

2005). The nested logit estimation procedure involves an estimation of an IV

parameter for each nest. The IV parameter is a function of the scale parameter,

which is assumed away in the MNL due to the independent and identically

distributed error term assumption (hence, the scale parameter is absent from

equation (2)). Scale parameter, � , is equal to

� =� 2

6� 2 Eq. (8)

It can be shown that the IV parameter is equal to the ratio of a scale parameter

from one nest and a scale parameter from a higher nest (Hensher et al. 2005). If

the IV parameter is statistically equal to ‘1’, then the nested logit model collapses

to a MNL. The IV test, which involves a Wald-test of significance on the IV

parameter, is inclusive in the nested logit model estimation process.

The MNL is the ‘work horse’ in many applications due to its analytically

convenient closed form solution (Hensher et al. 2005). By the same token, the

MNL is limited in its ability to account for the individual variation in preferences,

as well as in its ability to correctly predict market share in situations where the

IIA axiom is violated. Given the fact that (1) the thesis aims for understanding

than prediction (the former leads to the latter but not the other way around –

Louviere et al. 2000); (2) the thesis aims to better understand the general

relationships between the independent and dependent variables rather than the

taste heterogeneity across individuals, the impact of the MNL’s limitations are

1-19

minimized. The empirical chapters of this thesis apply primarily the MNL, and

when appropriate, the nested logit.

While there are extensions to the MNL and nested logit, such as random

parameter logit (mixed logit), the application of these models are beyond the

scope of this thesis. The random parameter logit (RPL) model enables the

estimation of a unique coefficient on the X variables for each individual.

However, this research does not need the RPL because the thesis focuses on the

estimation of the signs and weights of the coefficients of the independent

variables, not the individual variation in the coefficient estimates for each

independent variable. This thesis in the final Chapter (Chapter 7) dedicates a

section to discuss how the mixed logit models can improve and extend this

research.

1.3.2. Stated choice data

In many econometric applications, data are collected on the choices already made

in the market. That is, we observe the choices made by consumers in the market

and the attributes of the chosen goods and services such as price and product

characteristics (Louviere et al. 2000). This type of data is collectively called

‘revealed preference’ or RP data. The National Visitor Survey data used in

Chapter 4 is a data of this sort where survey respondents are asked to recall the

trip they made in the past and the various aspects of that trip. Although such data

are common for various reasons, in social sciences, it is particularly common

because data from experiments are not readily available, and often ethically and

instrumentally infeasible. Nonetheless, RP data are subject to limitations in the

context of choice. Louviere et al. (2000) provided a number of reasons.

Perhaps the most interesting reason concerns the limited variability and high

collinearity of the values of explanatory variables in the marketplace. This is

partly because competitors match prices, and product features remain constant

1-20

over time. For example, the time it takes to travel from Sydney to Ballina-Byron

by car or air; or the qualitative features of public transport services such as driver

‘quality’, which may remain rigid over time due to the time it takes to set up new

training programs and the lag time involved with publicly funded projects.

Consequently, choices observed on RP data tend to be poorly conditioned

(Hensher et al. 2005). Interestingly, Louviere et al. (2000) argued,

“as markets mature and more closely satisfy the assumptions of free

competition, the attributes of products should become more negatively

correlated, becoming perfectly correlated in the limit … technology drives

other correlations between product attributes, so as to place physical,

economic or other constraints on product design. For example, one cannot

design a car that is both fuel efficient and powerful because the laws of

physics intervene. Thus, reliance on RP data alone can (and often does)

impose very significant constraints on a researcher’s ability to model

behaviour reliably and validly (p.22, parenthesis in original text).”

There are two additional reasons why stated choice data were used in the second

and the third empirical studies. One is due to the lack of availability of secondary

data sources on the alternatives and the alternatives’ attributes (as well as the

alternatives’ attributes’ levels) considered by the decision maker in the choice

process. This is particularly the case with data on airfares. Louviere et al. (2000)

argued that by creating these data based on a rigorous scientific experimental

design procedure, we are able to formulate a causal model of choice, with the

added advantage of reducing the invalid inferences from ‘chance’ relationships.

The second reason is related to the fact that stated choice data is capable of

accounting for new product features or alternatives that currently do not exist.

Given the aim of Chapter 5, it was necessary to create a public transport

alternative with some hypothetical attributes. Eaton and Holding (1996)

concluded that ultimately, public projects need to be able to induce a change in

behaviour - in their use of transport mode from private cars to public vehicles - for

policy to be effective. Given the fact that such a policy can be expensive and

1-21

riddled with conflicting interests, as mentioned previously, Eaton and Holding

advocated an ‘experimental’ approach to first demonstrate the potential. This is

similar to the feasibility assessments akin to transport project appraisals for

airports and road infrastructure upgrades, which are often irreversible and have

very high fixed costs.

1.4. Contribution to knowledge

1.4.1. Contributions and limitations

Tourism research to this day has largely neglected an analytical approach to

assessing the trade-offs between travel mode choice and spatial behaviour. This

thesis contributes by providing a utility compensation perspective on the tourists’

choice of transport and the resulting spatial behaviour of tourists. The utility

compensation perspective highlights the importance of trip characteristics in a

way that can be directly compared to the importance of travel mode attributes.

Although not without limitations (discussed in Chapter 7), this approach enables a

comparison of the utility gained from paying low airfares with the utility

associated with trip context such as length of stay, trip structure (single or multi-

destination), or the level of destination familiarity.

Although the application of discrete choice models (micro-econometric choice

models) is advanced in transport mode choice research, it is mostly applied in a

journey-to-work and intra-urban trip context. In long-distance travel applications,

the theoretically important tourism variables are often not included in the analysis.

This thesis extends the analysis beyond the traditional economic variables of

mode choice by including theoretically significant tourism variables in the long-

distance leisure trip context. It is shown in this thesis that in long-distance leisure

travels, trip characteristics vary widely across individuals and travel parties, and

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these have significant influence on the choice of travel modes, sometimes to an

extent that trip characteristics offset the marginal benefit gained from the changes

in travel mode attributes. Therefore, while this research extends the boundaries to

which discrete choice models can be applied, the real theoretical contribution of

this thesis is in highlighting how our understanding of the relationship between

long-distance leisure mode choice and spatial distribution of tourists can improve

by accommodating tourism variables in the discrete choice framework. Thus, this

thesis’ contribution extends beyond the demonstrating of the applicability of

discrete choice models to new settings; rather, the application of choice analysis

to long-distance leisure trips raises interesting questions about the choice models.

This point is revisited in Chapter 7.

The results from this thesis should be relevant in a setting where the geographic

region is large and multi-modes of transport are real options for tourists.

However, the results are also sensitive to context; the relevance of the results

should be assessed with caution in settings where the transport market is regulated

by the government. In other words, the assumption made in this thesis is that all

travel modes are free to enter/exit the market, as well as to set their fares and

capacity without government intervention, i.e., a deregulated market. Nonetheless,

the results should be relevant to assessing the spatial impact of tourism in large

developing economies undergoing deregulation of the transport markets

(particularly the aviation market). Deregulation of the aviation market is likely to

increase discount fares, thereby decreasing average fare levels, which will

generate new demand for tourism. This thesis will provide insight into the

dispersal impact of such changes for secondary and regional tourism destinations.

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1.4.2. Key stakeholders

State and local governments

The findings from this thesis should be of relevance to government with mandates

emphasising the greater balance in the distribution of economic benefits across the

regions. Transport issues are often at the centre of public policy agenda where the

government may promote certain modes of travel over others to meet a wider

policy objective (e.g. reduce carbon emissions). Conflicts may arise between

policy objectives such as dispersal and environmental preservation; for instance,

while car is a pertinent mode of travel for regional dispersal, environmental policy

may advocate a shift away from car towards public transport. Furthermore, local

level tourism and transport planning issues have a strong political dimension

because the competition for public funds increases at this level of governance

(Gunn 1988). Thus, information on the trade-offs between travel modes and

regional dispersal contribute towards providing diagnostic information, which will

help in overcoming these conflicts.

Domestic airports and airlines

Australian domestic airports serving regional tourism destinations - regardless of

the ownership structure - usually have tourism development objectives in their

charter in recognition of the mutually beneficial relationship between the growth

of airports and growth of tourism. For smaller regional airports, significant

investment is necessary to be able to facilitate the entry of LCC services; for

example, on runway and terminal space upgrades and purchase of security

equipment. The information on modal substitution and the associated changes in

the tourists’ travel patterns will help assess the impact of such investments. While

airlines are concerned mostly with the demand for air services between two

points, increasingly, the ancillary revenue is becoming an important aspect of

LCC business (CAPA 2008). Better understanding of passenger travel behaviour

in the destination can increase airlines’ ancillary revenues; for instance, such

information can help airlines to exploit opportunities for partnerships and

financial innovations with tourism businesses.

1-24

Destination marketing organisations

Since the use of one travel mode over another will be associated with a particular

travel itinerary, i.e. different patterns of dispersal, mode choice studies can help

identify ‘linkage patterns’ of different destinations. Consequently, such

information contributes to recognising natural partners in regional or locational

cooperation (Opperman 1995; Lue et al. 1993). This is particularly relevant for

state tourism organizations whose roles are to facilitate liaison and provide

cooperative marketing for the diverse range of tourism regions within their

jurisdiction.

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1.5. Structure of the thesis

As mentioned earlier, the general aim of this thesis is to examine the effects of

LCCs on the regional dispersal of domestic visitors in Australia. This goal is

subdivided into five specific aims. There are five Chapters (Chapter 2 to Chapter

6) that sequentially address A1 to A5. The structure of this thesis is summarised in

the schematic diagram below (Figure 1.2). The aims of this thesis are reiterated

here, acknowledging that the aims are to understand why relationships occur as

well as how they occur.


literature relevant to understanding the link between LCCs and domestic

dispersal (Chapter 2);


LCCs based on aviation, tourism and spatial behaviour research (Chapter 3);


differences between LCCs and NCs (Chapter 4);


tourists’ travel characteristics in the choice of the air arrivals’ regional

dispersal (Chapter 5);


between low fare air services and regional dispersal (Chapter 6).

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Figure 1.2. Schematic diagram of the thesis

The effects of LCCs on dispersal

Effect on the volume

of tourist inflow (Chapter 2)

Effect on travel characteristics

and dispersal propensity (A2, Chapter 3)

Dispersal propensity

differential sourced from

intra-modal differences

(LCC vs. NC) (A3, Chapter 4)


differential sourced from

inter-modal differences

(Air travel vs. car travel) (A2, Chapter 3)


and inter-regional

mode choice (travel

mode choice to the

destination)

(A5, Chapter 6)


and intra-regional

mode choice (travel

mode choice within

the destination)

(A4, Chapter 5)

1-27

Chapter 2 introduces the necessary background information on LCCs and regional

dispersal. The Chapter discusses the defining features of LCCs and their strategies

in lowering costs. The Chapter also introduces the Australian aviation

environment and provides an outline of the Australian LCC history. In this

Chapter, ‘domestic dispersal’ is distinguished from ‘regional dispersal’. It is

argued that while LCCs have contributed to the domestic dispersal of tourists in

Australia, more research and data are required to examine the effects of LCC on

the regional dispersal of tourists.

Chapter 3 interweaves the literatures on LCCs and the literatures on regional

dispersal to impose a cause-effect structure between the two concepts. A

distinction is made between intra-modal differences and inter-modal differences.

Chapter 3 identifies and explains the relationships between LCCs and dispersal

sourced from intra-modal differences. Chapter 3 also provides a literature review

that forms the basis for the research issues examined in the Cairns case study

(Chapter 5) and the Ballina-Byron case study (Chapter 6).

The empirical work in this thesis is framed in three inter-related empirical

research issues. Chapter 4 empirically tests the propositions put forward in

Chapter 3, which addresses the intra-modal issue. This is named in this thesis as

‘the characteristics model’. The remaining two Chapters address the regional

dispersal issues stemming from the fact that LCC is a form of air transport.

Chapter 5 concerns the mode choices made by the air arrivals within destination

regions, and the travel modes’ links with regional dispersal. Chapter 6 focuses on

the travel mode choices in travelling to the regional destinations. Both Chapters

provide an introduction to the research problem before proceeding to the details of

the methods used, including the details on the experimental design for the stated

choice experiments. The studies are named the ‘Cairns experiment’ and the

‘Ballina-Byron experiment’ respectively. Chapter 7 is a concluding chapter.

Research limitations are also discussed, along with future research directions.

2. DISPERSAL AND LOW COST CARRIERS

2.1 Introduction

Low Cost Carriers (LCCs) are airline business models with the primary aim to

achieve lower cost structure. The various strategies they employ to achieve the

low cost manifest as a common set of characteristics that are in many ways

different from the network carriers (NCs). The core characteristics of LCCs are

the offering of affordable airfares and point-point services. These are desirable

features from the regional tourism destinations’ perspective because they help

stimulate tourism demand. This is also a policy priority for governments willing

to alleviate congestion in urban centres, and to capitalise on the economic benefits

that tourism is capable of generating for the regions.

The primary aim of this Chapter is to introduce the two central concepts of this

thesis - dispersal and LCCs. In doing so, we accomplish the first specific aim of

this thesis. Related to this aim are two specific purposes. The first purpose is to

provide the necessary background information on the Australian aviation

environment to better understand the issues this thesis aims to address. This is

done by outlining the precursor to LCC growth in Australia, which is followed by

a survey of international literature on LCC models and characteristics. The second

purpose is to overview Australian LCCs with a focus on their impact on dispersal.

In order to do this, this Chapter first distinguishes between domestic dispersal and

regional dispersal. It will be shown that while there is evidence of LCCs’

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contribution to domestic dispersal in Australia, issues remain as to what the effect

of LCCs are on the regional dispersal of tourists. Regional dispersal is the primary

focus of the subsequent Chapters.

2.2 Dispersal

2.2.1 Definition of ‘regions’, domestic dispersal and regional dispersal

A geographical unit, Tourism Region, is important for the definition of dispersal

in this thesis. Each state and territory tourism organisations in Australia delineate

its territory into a number of Tourism Regions. The delineating method differs for

each state, resulting in a variety of number and sizes of Tourism Regions.

Tourism Regions are also revised almost every year. In 2007, there were 89

Tourism Regions in Australia.

Australian government agency such as the former Bureau of Tourism Research

(Tourism Research Australia as of 2004) defines ‘rural’ as tourism regions outside

Adelaide, Brisbane, Canberra, Darwin, Hobart, the Gold Coast, Melbourne, Perth

and Sydney. To be precise, this is a de facto definition because the official

definition only excludes capital cities. Gold Coast is not a capital city but it is now

standard practice to exclude this destination from the regions. Although this

definition excludes other large populous centres in the rural regions, these cities

are only a small fraction of ‘rural’ Australia, and visitors to these cities “may still

pursue activities and experiences which are non-urban in character” (O’Halloran

et.al. 2000:60). Often in practice, the term ‘regional’ is used as a synonym for

‘rural’ (Kelly 2001:1 as cited by Centre for Regional Tourism Research). Thus,

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when we refer to ‘regional dispersal’, it is a trip that involves at least an overnight

stay in a tourism region other than the cities listed above.

While this thesis adopts the same definition to preserve consistency with

government agencies, another branch of classification is added to ‘rural’ to further

differentiate the main cities in each tourism region from the rest. This is necessary

because a rural visitor - a person staying at least one night in the ‘rural’ tourism

region - includes those who visited the main city. If a visitor spent all of his or her

overnight stay(s) in the city, then this visitor’s trip is not ‘rural’ in its character.

To be more geographically specific in the definition of ‘regional dispersal’,

distinction should be made between a visitor who stayed in the main city and a

visitor who stayed in the rural regions.

In this thesis, rural regions are divided into ‘cities’ and ‘all other’ regions. In the

tourism literature, these cities are often referred to as ‘gateway cities’. Gateway

cities provide most of the functional facilities for tourists, as a transport hub for

instance, acting as a main point of entry and exit for tourists visiting the wider

region (Lew and McKercher 2002). These points in the tourism regions are

referred to ‘gateways’ and the remainder ‘periphery’. In Figure 2.1, gateways are

Coffs Harbour and Ballina-Byron within the tourism regions of North Coast and

Northern Rivers respectively. All other areas of the tourism regions outside these

gateways are the periphery. As it will be shown next, a trip to the regions will be

referred to ‘domestic dispersal’ and a trip that involves at least one night stay in

the periphery, ‘regional dispersal’. This distinction is important; while dispersal as

currently defined by the tourism industry remains relevant for tourism and

transport policy at the federal level of governance, at the local level, dispersal is

important to an extent that travellers diffuse from gateways into peripheral

destinations. Definitions are summarised in Table 2.1.

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Trips of interest in this thesis are those originating from capital cities and Gold

Coast. Over 60% of the Australian population resides in these cities (ABS 2007).

Many rural trips, however, do originate from non-capital cities (40% of the

Australian population live outside these regions). Nonetheless, the focus is on the

cities mentioned previously because these are the main origins for domestic air

travel demand. In fact, at the time of writing, all domestic routes by Qantas

(excluding regional subsidiaries and Qantaslink), Jetstar, Virgin Blue and Tiger

airways were either from/to the ‘capital cities or Gold Coast’.

Therefore, domestic dispersal represents trips originating from capital cities (and

Gold Coast) destined for the regions. Regional dispersal involves trips that entail

at least one night stay in the regions beyond the gateway cities, i.e. peripheral

destinations. Regional dispersal includes multi-destination trips as long as the trip

Figure 2.1 Tourism Regions: An example of New South Wales (based on Australian

Bureau of Statistics Tourism Regions Map release 2007)

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involves at least one night stay in the periphery. As per domestic dispersal, a trip

must originate from capital cities or Gold Coast for it to constitute a regional

dispersal. Thus, regional dispersal is embedded in domestic dispersal.

Table 2.1 Summary of definitions

Regions refer to all geographic areas outside the capital cities and Gold Coast tourism

regions. Capital cities are Adelaide, Brisbane, Canberra, Darwin, Hobart, Melbourne,

Perth and Sydney.

Tourism regions are regional boundaries classified by state tourism organizations. Each

state has a different number of tourism regions varying widely in geographic size and the

extent to which tourism contributes to the regional economy.

Gateways are the main points of entry and exit for tourists in a given tourism region.

Usually, these are the largest cities in respective tourism regions, and each city has an

airport (often of the same name as the city) with regular ‘domestic’ air services.

Periphery or peripheral destinations are destinations within tourism regions located

beyond the geo-political bounds of the gateway city. These destinations vary in its

reliance on tourism ranging from towns to small rural communities.

Domestic dispersal occurs when a trip (1) originates from capital cities (and Gold Coast);

and (2) involves at least an overnight stay in tourism regions other than the capital cities

(and Gold Coast), i.e. overnight stays in gateways or peripheries.

Regional dispersal occurs only when a trip (1) originates from capital cities (and Gold

Coast); and (2) involves at least one overnight stay in a peripheral destination during the

trip.

Source: created by the author

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2.3 The characteristics of LCCs

2.3.1 The LCC model

Low-cost carrier (LCC) is a type of airline business model pioneered by

Southwest airlines in the U.S. (O’Connell and Williams 2005, Lumsdon and Page

2004, Gillen and Lall 2004, Lawton 2002). It is difficult to provide a definition

applying to all low-cost carriers (LCCs) due to the numerous variants of LCCs

(Francis et al. 2006). There has been an explosion in the air transport research

literature that addressed the definition and characteristics, especially following the

successes of European adaptation of the LCC model - Ryanair and Easyjet - since

the late 1990s (e.g. Dobruszkes 2007; O’Connell and Williams 2005; Page 2005;

Francis et al. 2004; Gillen and Lall 2004; Burghouwt et al. 2003; Lawton 2002;

Williams 2002 and 2001).

In general, LCC is an airline business model aiming to have a low cost base to

offer lower airfares (Lawton 2002). Lawton (2002) shows that the LCC model is a

low-margin and high-volume airline business that relies on the virtuous circle of

demand stimulation and economies of density (reduction in unit costs as a result

of greater demand density). O’Connell and Williams (2005) summarised the key

features of LCC models worldwide. Table 2.2 is based on O’Connell and

Williams (2005) detailing the differences in the product features of LCCs and

NCs. In many situations, an airline regarded as a LCC will have a combination of

these features. It is widely observed that LCC services are predominantly point-

to-point, short-haul, and to a less extent, have a uniform fleet, although this is as

far as the similarities between LCCs go (Gillen and Lall 2004). Some of the key

features are discussed in greater detail below.

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Table 2.2. Product features of LCCs and FSCs (NCs)

Product features Low cost carrier (LCC) Full service carrier (FSC or NCs)

Brand One brand: low fare Brand extensions: fare+service

Fares Simplified: fare structure Complex fare: structure+yield mgt

Distribution Online and direct booking Online, direct, travel agent

Check-in Ticketless Ticketless, IATA ticket contract

Airports Secondary (mostly) Primary

Connections Point-to-point Interlining, code share, global alliances

Class segmentation One class (high density) Two class (dilution of seating capacity)

Inflight Pay for amenities Complementary extras

Aircraft utilisation Very high Medium to high: union contracts

Turnaround time 25 min turnarounds Low turnaround: congestion/labour

Product One product: low fare Multiple integrated products

Ancillary revenue Advertising, on-board sales Focus on the primary product

Aircraft Single type: commonality Multiple types: scheduling complexities

Seating Small pitch, no assignment Generous pitch, offers seat assignment

Customer service Generally under performs Full service, offers reliability

Operational

activities Focus on core (flying) Extensions: e.g., maintenance, cargo

(adopted from: O’Connell and Williams 2005)

2.3.2 Point-to-point network (P2P)

A dominant pattern of airline network emerged following the deregulation in the

U.S. was the hub and spoke system (HSS) (Meyer and Oster 1987, Doganis 2002,

Franke 2004). Franke (2004) has shown that the HSS allows the maximisation of

coverage over origin-destination pairs and different customer segments by

concentrating the inbound flights into a single hub, while maximising the

connectivity for the outbound flights from that hub. This process, however, is

inherently complex and entails inefficiencies, which are paid by the passengers

through higher fares and inconveniences (stopovers). Franke summarised the

negative consequences, stating,

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“the negative aspects of this strategy are a loss of convenience for the

passengers (who would prefer direct flights), and a considerable cost penalty

for the airline on the operational side. Waved traffic means massive peaks in

hub operation, resulting temporary congestion (reduced airside productivity),

time-critical connections (special processes required), and strongly

fluctuating utilisation of ground handling facilities/workers (reduced

landside productivity). Furthermore, congestion plus a multitude of time-

critical connections typically lead to poor punctuality performance” (Franke

2004: 16, parentheses in original)

The point-to-point (P2P) strategy represents a case on the other side of the

extreme where each destination-origin pair is served directly. In a pure P2P,

passengers will use each airport as entry and exit points than as a connecting

point. Gillen and Lall (2004) argued that Southwest airlines primarily derives its

low cost from this strategy. As Gillen and Lall argued, P2P is the most important

feature that enables the fast turn-around of aircraft at airports. This enables the

airline to avoid the costly delays associated with connections in hubs. This quick

turnaround maximises the aircraft utilisation rate per day. Given that an aircraft is

one of the most expensive investments an airline makes, maximization of its use

is the most important source of cost saving; for example, a 25 minute turnaround

compared to an one hour turnaround will yield an extra two return services on a

given day, which results in greater fleet utilisation and staff productivity (Barrett

2004).

Reynolds-Feighan (2001) studied the traffic concentration patterns of LCCs and

NCs. He found that the domestic aviation network in the U.S. decentralised over

the period between 1969 and 1999. Reynolds-Feighan (2001) argued that this is

due to the LCCs’ P2P network. While LCCs, on average, have lower levels of

traffic concentration, Reynolds-Feighan also found considerable variations within

the LCCs: there were LCCs operating a single hub and spoke system (e.g.

America West, TransAir); as well as pure P2P (e.g. Southwest). Swan (2007) in

fact argued that the conjecture that LCCs are P2P is a misleading

oversimplification because even the ‘purists’ such as Ryanair provides significant

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level of connecting traffic – 15% (Southwest with 30%) - in comparison to

traditional carriers, which is often 50%. In fact, Franke (2004) argued that the

route network provided by the P2P strategy by some LCCs are so comprehensive

that there are opportunities for ‘random connections’ by the passenger themselves.

In Europe, Dobruszkes (2007) concluded that post-deregulation network patterns

were largely induced by the LCCs. He observed that the European airline

networks changed from a ‘radial’ pattern to a ‘star-shaped’ pattern following the

proliferation of Ryanair and Easyjet. While not as spatially comprehensive as that

of U.S. or Europe, the Australian LCC network broadly resembles the P2P

network structure of U.S. and Europe. Sinha (2001) has shown that the Australian

network is mostly P2P because it has a high level of demand concentration on a

few large nodes; namely, the demand is concentrated between the state and

territory capitals.

2.3.3 Use of secondary and regional airports

Secondary airport refers to an airport providing a ‘secondary’ access to a major

population centre. One well-known example of a secondary airport is London’s

Stanstead airport used by Ryanair, which is located relatively peripheral to

Heathrow and Gatwick. Regional airports, on the other hand, provide access to

those travelling to or/and from smaller regions and cities, rather than acting as a

substitute to a major gateway airport in large metropolitan centres.

Often, regional and secondary airports are excess-in-capacity; therefore, much

less conducive to congestion and delays (Gillen and Lall 2004). Warnock-Smith

and Potter (2005), based on a survey of managers across eight LCCs in UK and

Europe, found that ‘quick turnaround facilities’ and ‘convenient slot times’ were

the top considerations in the choice of airport for entry decisions. In many routes

where one end is characterised by a busy and congested airport, and the other, by

a secondary or regional airport, convenient slot time introduces greater flexibility

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in scheduling for airlines. Excess-in-capacity airports are much more likely to

provide this flexibility than the congested ones.

The Warnock-Smith and Potter (2005) study found that ‘discounts on aeronautical

charges’ ranked fourth in importance. Discounts on airport costs can be an

important source of cost saving for LCCs. This is because the share of

aeronautical charges of total costs will be higher for LCCs than it is for legacy

carriers (Lawton 2002). Moreover, during negotiation, LCCs will have greater

leverage with the smaller airports because fewer airlines serve them. A widely

documented case is Ryanair, which threatens to fly elsewhere if their terms are not

met (terms with respect to landing fees, bridge fees, passenger fees, etc). The fact

that 93% of Ryanair’s routes are exclusive to the airline (as at the end of 2005; see

Dobruszkes 2007) provides some indication as to how important the secondary or

regional airports are to Ryanair’s cost reduction strategy.

In summary, the study by Warnock-Smith and Potter found the following factors

important in airport choices (from most important to least important):

o high existing demand for LCC services;

o quick turnaround facilities;

o convenient slot times;

o good aviation fee discounts;

o positive economic forecasts for the region;

o efficient airport management;

o high level of airline competition;

o good experience of LCCs;

o good non-aviation revenues and ownership;

While slot times and turnaround facilities are important determinants of airport

choice, adequate demand is the most important factor. Consequently, LCCs tend

to favour entry on routes with dense demand and excess-in-capacity airports. As

shown later in this Chapter, these characteristics have the combined effect of

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stimulating leisure demand for regional tourism destinations in Australia, i.e.

contributes to greater domestic dispersal of tourists.

2.3.4 Short-haul and Low-cost customer service

Most LCC services are short-haul services. Only recently the long-haul adaptation

of the LCC model has emerged; for example, with AirAsia X and Jetstar

International. ‘Short-haul’ typically refers to up to 3 hours in flight duration.

Short-haul flights characterise many flights within large domestic markets such as

the U.S., Brazil and Australia, and highly liberalized international markets such as

intra-European routes and Trans-Tasman routes between New Zealand and

Australia. Thus, the LCC model is highly compatible with the patterns of air

transport demand within Australia, as well as between Australia and New

Zealand.

The short-haul focus provides a number of important cost advantages. First, it

enables the airline to have a uniform fleet, typically that of B737s and A320s,

which were proven popular among LCCs due to cost efficiencies for the short-

haul stage lengths. For instance, Southwest airlines commands more than 500

B737s and no other type of aircraft, and similarly, Ryanair has a fleet of B737s as

does Virgin Blue in Australia (up until the end of 2007). Having a uniform fleet

generates significant level of economies of scale in maintenance costs (Lawton

2002, Gillen and Lall 2004, Franke 2004, etc). For instance, Hansson et al. (2003)

estimated that 13% of the cost differences between European LCCs and NCs

came from lower maintenance costs (as cited by Franke 2004). Long-range

aircraft is needed if an airline is to provide long-haul services, and this will

require that the airline diversify not only in fleet composition but also in the

maintenance and infrastructure costs at the airport. Furthermore, since long-haul

services are more likely to need to draw traffic from a wider market catchment,

there will be a need for greater coordination with the feeder and spoke services at

the origin and destination. This adds to the overall complexity of the airline

operation, which is inconsistent with the LCC model.

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The short-haul focus is also closely linked to the customer service costs. LCC is a

low-cost customer service model with minimum level of perks (Lawton 2002).

The low-cost in-flight service has several advantages for the LCCs. A widely

observed feature of the LCC service is the absence of free meals or snacks, in-

flight entertainment, and lower staff per passenger ratio. This enables the airline to

reduce its cost base, as well as providing the airline with a source of ancillary

revenue by charging extra for these services. Second, Gillen and Lall (2004) noted

that the avoidance of catering for hot-meals, for example, is possible due to the

short-haul nature, and contributes toward reducing the turnaround time. Finally,

the short-haul flights enable the LCC to configure its aircraft into single class

service. Multi-class configurations add extra costs and detract the focus from the

low-margin and high-volume model.

The short-haul focus, therefore, enables the LCC to derive its lower cost structure

from simplicity. This strategy is linked to the low-cost customer service, and the

greater dependence on ancillary revenue. It is also the case that short-haul services

are closely linked to the core strategy of point-point services, which enables faster

turnaround time and avoids the complexity in operating a multi-aircraft fleet.

2.3.5 Ticket distribution, fare structure and passenger handling

Internet technology enabled LCCs to reduce distribution costs by bypassing

intermediaries. Although travel agents and call centres were replaced by internet

for many airlines (not only LCCs), LCCs have generally embraced the internet

technology to control costs (Duval 2008). In fact, Hansson et al. (2003) estimated

that 15% of the cost difference between European LCCs and NCs comes from

‘innovative direct sales’ and ‘lower Global Distribution System (GDS) charges’

(as cited by Franke 2004).

Simplified fare structure of the LCC not only reflects the differences in the cabin

class, such as business vs. economy, it also reflects the differences in the revenue

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management practices and inter-lining employed by the airline. For instance, ‘full

economy’ fares with flexible ticket-change conditions by the network carriers are

designed for business travellers, with high fares covering the costs of insurance

against over-booking, and to compensate for the potential schedule changes by the

traveller (Mason 2006). This contrasts with a pure LCC, which offers a single-leg

and non-refundable ticket that can be changed to a different flight with a fixed

administrative fee and the price differential (Mason 2006). The NCs tend to

implement many fare options. In contrast, LCCs often offer a more simplified

ticket structure to undercut the NCs on price (Marcus and Anderson 2008).

LCCs’ simplified fare structure has made fares more transparent and amenable for

interpretation by consumers. In a P2P network (cf. section 2.3.2), each ‘leg’ of the

trip is purchased as an independent trip. If the traveller is travelling two ‘legs’,

then the airline treats this as two separate trips. In many instances, even if a

traveller is travelling on the same airline on two legs, the traveller needs to check-

in and collect her baggage twice. For NCs with hub and spoke networks, the two

leg journeys are sold as a single product to the traveller. However, the

convenience of the single check-in and baggage transfer is often done at the

expense of increasing level of complexity of interlining and coordination with

other airlines. In such cases, the airlines usually have agreements on the number

of seats per aircraft that can be sold as a two-leg bundled product, or

independently sold as a separate journey. This complexity causes the fare itself to

be complex because a situation where a two-leg trip is much cheaper than a

single-leg (on the same route) can arise. As Clippinger and Strong (1987) noted

following the deregulation in the U.S., travel agents were the “official interpreters

of the mysteries of air travel” (p.125). LCCs were able to eliminate this problem

because their fare structure and network strategy were simple; eliminating one of

the important roles of travel agents.

With respect to passenger handling in airports, LCCs often lead the

implementation of cost reducing innovations (Swan 2007). Electronic check-ins

and electronic tickets reduce labour costs, and effectively transfer the onus of

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some traditional check-in tasks to the passengers. The benefit of unallocated

seating is that it gives incentives for passengers to arrive early to secure a good

seat, which contributes to reducing delay risks (CAPA 2007). Some LCCs (e.g.

Jetstar and Ryanair) offer reduced fares to hand-carry only passengers. Less

baggage not only contributes to lower fuel costs (because of lower weight), but

also speeds up the check-in process, which reduces the risk of delay and

congestions at airport check-in counters. Most of the characteristics and practices

discussed above fit quite well with the Australian LCCs. This is discussed in more

detail in the following section.

2.4 Background: Precursor to LCC growth in Australia

Aviation research has identified several factors underpinning the growth of LCCs.

Francis et al. (2005) noted demand related features such as increasing income and

population. Factors more specific to airlines were the entrepreneurial flair of the

business leaders (Tony Ryan and Richard Branson) and the brand of affordable air

travel (Francis et al. 2005). Furthermore, strong financial backup, which enables

the airline to sustain prolong period of losses, was identified as a key factor

especially in situations where the incumbents react with strong competitive

pressures by matching prices and increasing capacity (Forsyth 2003). Timing of

entry also played an important part for some airlines. For instance, the success of

Virgin Blue in the Australian domestic market was partly influenced by the

collapse of Ansett. Also, technological advances, such as the internet, helped

LCCs to reduce costs associated with distribution (Mason and Alamdari, 2007).

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Franke (2004) argued that the existing inefficiencies of the legacy carriers had

given opportunities for LCCs to thrive in the short-haul markets. Specifically,

Franke (2004) noted,

“[the] carriers had built their complex operational model around the needs of

their least valuable clients (low-yield connecting passengers), whom they

forced to connect at hubs in order to maximise the airlines’ overall

destination portfolio: a situation paid for by their own premium clients. A

crisis soon developed during the second half of 2000 when, faced with an

economic downturn, these high-value passengers, showed a growing

reluctance to pay premium prices” (p.16)

The LCC model is also more resilient in times of weakening demand than the

legacy counterparts (Gillen and Lall 2004). Specifically, the legacy carriers adapt

to business cycles by shifting the cost base to the premium markets during the

high seasons, while shifting to the lower cost module during the troughs (Gillen

and Lall 2004). As put forward by Gillen and Lall, the LCCs were permanently on

the ‘lower cost model’. Consequently, LCCs were able to continue growing, even

in times when the legacy carriers were suffering from heavy losses. Notable

examples are Ryanair and Southwest, which continued to make profits in periods

of high volatility and levels of bankruptcies. However, the current global financial

crisis will affect all air travel as a result of reductions in discretionary income.

Experiences in the U.S. and Europe show that deregulation (of the aviation

industry) was a necessary precursor to the entry, adaptation and evolution of the

LCC model (Dobruszkes 2007, Franke 2004, Meyer and Oster 1987). Following

the deregulation in the U.S., Meyer and Oster (1987) observed,

”the emergence of the new entrant jets was almost surely the least

anticipated major event of deregulation prior to the fact ... The niches served

by these carriers were largely markets left vacant because of previous

regulatory policies, and in keeping with the identity of these under-attended

market niches, most of the new entrant jet carriers attempted to do

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something that predecessor established carriers did not. In most instances

they either entered a market that was not previously served or well served or

entered a previously well served market while offering substantially lower

fares” (p.49-50)

Broadly, deregulation refers to a relaxation of set of rules and intervention

governing an industry. This is in order to “make markets more effective conduits

between consumers and producers”, which includes various measures to make

firms to be more productive and efficient in meeting consumers’ needs and wants

(Forsyth 1992:5). To achieve this, microeconomic reform involves a “thorough

dismantling of the comprehensive system of government regulation and control”

(Kahn unknown year). In addition, there are needs to improve the functioning of

closely associated markets such as airlines and airports to improve matters overall

(Forsyth 1992).

2.4.1 Deregulation of the airline industry in Australia

In 1946, with the view that the Australian airline industry was a natural

monopoly, the Australian government established a wholly stated owned airline,

Trans Australia Airlines (TAA) (BTCE 1991). Subsequently, this was

transformed into a ‘two-airline policy’ to allow for limited competition, which

implicitly had an effect of striking some sort of a balance between the benefits of

competition and cost savings from scale economies (Hooper and Findlay, 1998).

Australian National Airways (ANA) was the other domestic airline, which was

subsequently bought by Ansett Airlines (Hooper and Findlay, 1998). In this

period, Qantas was the only designated carrier for international operations; Qantas

was not allowed to provide domestic services. Thus, there were TAA and Ansett

for domestic services, while international services were exclusive to Qantas. Soon

after deregulation, TAA was re-branded as Australian Airlines, which was

eventually purchased by Qantas in 1991.

The two-airline policy became under increasing criticism because the public could

not see effective competitions in the market; for example, BTCE noted that "both

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airlines were operating the same equipment on the same routes with the same

schedules for the same fares" (BTCE, 1991:3). An Independent Review of

Economic Regulation of Domestic Aviation (the May Review) in the 1980s

reached the conclusion that Australian aviation was characterised by low labour

productivity, yet, high and stable profits, with its focus almost exclusively on the

business market. The consequences were the under-developed leisure air travel

market and the absence of charter alternatives (Dwyer and Forsyth 1992).

In October 1990, the two-airline policy was terminated, and this removed

constraints for domestic airlines in the following areas (BTCE 1991):

o Control over aircraft imports;

o Capacity allowed and supplied on trunk routes by each airline;

o Abolishment of the Independent Air Fares Committee in setting fare levels;

o Entry/exit barriers to domestic trunk routes.

The effect of deregulation was immediate with the entry of Australia’s first LCC -

Compass airline. Before we introduce the topic on LCC entry in Australia, we

briefly introduce two other regulatory reforms that accompanied the deregulation.

2.4.2 Privatisation of the domestic airports

An additional barrier to entry for new entrants was removed when the airport

sector was privatised. All major airports were privatised in 1997 and 1998. These

airports included all capital city airports (Sydney Kingsford Smith in 2002) and a

selection of other airportsi (Kain and Webb 2003). Price caps were removed on

aeronautical charges in all capital city airports in 2002 except for Hobart (Kain

and Webb 2003). Local council owned airports such as Coffs Harbour and Ballina

airports were corporatised, and these airports were expected to generate returns

through aeronautical charges, as well as non-aeronautical charges (e.g. parking).

Pricing reform also took place in the air traffic control and airspace management

services provided by Airservice Australia, which involved moves toward user-

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based and cost reflective pricing strategies (Kain and Webb 2003). Consequently,

the airports in the regions now had greater bargaining flexibility with the airlines,

for instance, on landing fees and passenger charges, which generated commercial

opportunities for LCCs to service regional airports at reduced costs.

2.4.3 Foreign ownership cap

Another policy change that led to the entry of LCCs in Australia was the

abolishment of the foreign ownership cap on domestic airlines. Full deregulation

of the domestic sector is not yet complete because the seventh freedom is

permitted only on a case-by-case basis, while cabotage (the eighth freedom) is

still forbidden in Australia. Rather, the government preferred lifting ownership

controls to promote competition in the domestic aviation sector. In 2000, the

Australian government in order to promote competition amended domestic airline

guidelines to allow full foreign ownership of Australian domestic airlines, under

the proviso it is not in conflict with national interests (Bureau of Infrastructure

2008). Lifting of the foreign ownership cap, for example, enabled Virgin Blue in

2000 and Tiger Airways in 2007 to obtain rights for Australian domestic services

despite the fact that their majority stakes were held by foreign investors.

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2.5 Australian LCCs and their impact on domestic dispersal

2.5.1 First wave of LCCs in Australia (1990 – 1993)

The Australian aviation sector was deregulated on the 30th

of October 1990. By

December 1990, Compass commenced operation with one-class configured

A300s (BTCE 1991). Compass, at one point, had 10% of the total aviation market

and up to 21% share of the routes it serviced (BTCE 1991). But the airline

experienced problems in gaining access to airport slots and suffered from delays

in aircraft delivery (Grimm and Miloy 1993). In addition, Compass’ entry was

met with strong capacity increases and fare discounting by incumbent carriers;

contributing to Compass’ amounting debt. Compass was subsequently grounded

within a year of commencing operation. Former regional carrier, Southern Cross

Airlines, adopted the Compass brand and launched Compass Mark II in 1992.

Compass II, however, lasted less than a year. In the 'first wave' of LCC entry,

Ansett and Qantas with their ‘deep pockets’ were able to sustain losses for a

longer period of time than the new entrants (Sinha 2001, Forsyth 2003). LCCs

failed to sustain their presence in the market; however, their effect on competition

perpetuated as competition intensified between the two incumbents in the period

following the first wave (BTCE 1991).

2.5.2 Duopoly period (1994 – 1999)

A duopoly comprising Qantas and Ansett emerged in the domestic aviation sector

during this period. Although there was no new LCC entry, two distinct post-

deregulatory effects were observed in the period between 1993 and 1996 in

Australia. First, revenue passenger numbers continued to increase as shown in

Figure 2.2. (although it flattened between 1996 and 1999). The second effect was

related to airfares. As expected, the average fare levels have decreased following

liberalisation (see Figure 2.3). This is a widely documented fact in the aviation

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research literature worldwide (for instance, Williams 2002, Button and Stough

2000, Borenstein and Rose 1994, Meyer and Oster 1987, Winston and Morrison

1986). A notable effect of deregulation on price is the widening of price

dispersion, which is also consistent with the post-deregulation effects observed in

the U.S. and Europe (Williams 2002ii, Borenstein and Rose 1994

iii). As shown in

Figure 2.3, disparity is evident in domestic prices throughout 1992 – 2008.

However, the period between 1996 and 1999 was characterized by a flat demand,

although there were strong fluctuations in the levels of fare discounting. This

began to change from the late 1990s when two LCCs made their way into the

domestic market.

Figure 2.2. Revenue Passenger Demand (source: Bureau of Transport and Regional

Economics, Aviation Statistics 2007)

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Figure 2.3 Domestic Airfare Indices (source: Bureau of Transport and Regional

Economics, Aviation Statistics 2008)

2.5.3 Second wave of LCCs (2000 – 2006)

Impulse and Virgin Blue’s entry marked the 'second wave' of LCC entry. Impulse

was originally a regional carrier, which expanded its operation to domestic trunk

routes, entering into direct competition with Ansett and Qantas. Impulse, similar

to the predecessors, did not succeed against the incumbents, and was absorbed by

the Qantas group in 2001.

Two important features in the second wave contributed towards Virgin Blue’s

success. First, Ansett collapsed in September 2001 leaving a very large capacity

gap in Australia. Second, Virgin Blue was in a much better financial position than

its predecessors (Compass I and II and Impulse) as part of the international

conglomerate, the Virgin Group (Forsyth 2003). Virgin Blue grew to gain over

35% of the domestic market share by 2007 (CAPA, 2007).

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Figure 2.2 shows that the demand for air travel continued to increase beyond the

pre-Ansett collapse level following the launch of Jetstar in 2004. By 2007, Jetstar,

which was a fully owned subsidiary of Qantas, had 12% of the domestic market

(CAPA 2007). Both Virgin Blue and Jetstar commenced services as LCCs,

resembling the Southwest airlines, with uniform fleet and direct shuttle flights.

Both airlines also adopted the features of Ryanair (at the time of the start-up) by

not offering ancillary features such as frequent flyer programs. But their models

began to evolve - this is discussed in Section 2.5.4.

From a regional tourism point of view, Virgin Blue and Jetstar sought to link

excess capacity airports in the regions with the major cities. Table 2.3 lists the

airports with LCC services showing that many regional airports have gained air

traffic, in some cases, by multiple-fold in a period of only a few years. Ballina-

Byron and Launceston are some of the examples. The effect of LCCs on trip

generation is illustrated in Figure 2.4. The generative effect is shown by the

‘wedge’, beginning in 2001/2002, between ‘total domestic passenger demand’ and

‘total domestic passenger demand to capital cities (incl. Gold Coast)’. Figure 2.4.

shows a strong growth in domestic air travel demand to regional destinations

following the entry of Virgin Blue and that the upward trend continued following

the entry of Jetstar in 2004.

2-23

Table 2.3 Top 40 Australian domestic airports in terms of incoming passenger flows

(Source: compiled from Bureau of Transport and Regional Economics, Aviation

Statistics 2007. Note: [*] shows airports with LCC services as at March 2007)

Airport Pax. 2000/01 Pax. 2005/06 Pax. Growth % change

Sydney 7,609,862 8,795,031 1,185,169 16 Capital city

Melbourne 6,146,495 8,077,308 1,930,813 31 Capital city

Brisbane 4,524,200 5,833,024 1,308,824 29 Capital city

Adelaide 1,900,557 2,488,121 587,564 31 Capital city

Perth 1,629,751 2,327,417 697,666 43 Capital city

Gold Coast 898,896 1,653,793 754,897 84 LCC service

Cairns 962,124 1,281,078 318,954 33 LCC service

Canberra 640,915 1,008,934 368,019 57 Capital city

Hobart 276,937 799,558 522,621 Capital city

Darwin 418,401 506,208 87,807 21 Capital city

Townsville 283,065 471,483 188,418 67 LCC service

Launceston 1,280 451,927 450,647 >300 LCC service

Maroochydore 69,466 391,690 322,224 >300 LCC service

Williamtown 25,666 341,602 315,936 >300 LCC service

Alice Springs 350,293 294,439 -55,854 -16

Mackay 94,235 286,314 192,079 204 LCC service

Hamilton Island 140,608 199,591 58,983 42 LCC service

Uluru 218,415 189,648 -28,767 -13

Rockhampton 78,736 177,552 98,816 126 LCC service

Karratha 83,838 120,394 36,556 44

Broome 108,530 118,613 10,083 9 LCC service

Prosperpine 21,110 110,573 89,463 >300 LCC service

Ballina 76 103,566 103,490 >300 LCC service

Coffs Harbour 84 92,845 92,761 >300 LCC service

Kalgoorlie 98,068 86,433 -11,635 -12

Hervey Bay 0 55,493 55,493 LCC service

Port Hedland 40,827 54,970 14,143 35

Newman 18,868 50,510 31,642 168

Mount Isa 33,664 45,649 11,985 36 LCC service

Gove 82,285 45,127 -37,158 -45

2-24

Figure 2.4. Domestic Revenue Passenger Growths from 1992/1993 (source: compiled

from Bureau of Transport and Regional Economics, Aviation Statistics 2007. note:

1992/93 - 2006/2007 (1992/93 = 0): Capital cities (incl. Gold Coast) vs. All other)

The second wave resulted in the greater domestic dispersal of national visitors.

The National Visitor Survey data provides insights into the travel characteristics

of domestic dispersal. As shown by Figure 2.5, ‘holiday and leisure’ and ‘visiting

friends and relatives (VFR)’ have increased in shares at the expense of ‘business’.

The ‘business’ share decreased nine percentage points between 1999 and 2008.

2-25

Figure 2.5 Overnight trips made by air by purpose (source: National Visitor Surey,

Tourism Research Australia 2008)

2.5.4 The ‘third’ wave (post-2006)

In late 2007, a Singapore airline backed subsidiary, Tiger airways, made entry in

the domestic market. The airline established its base in Melbourne, with low-cost

services to Perth and Darwin. The airline’s direct impact on total domestic

capacity was marginal because they had only five A320s to deploy in Australia.

However, the airline had an impact on the incumbent low-cost carrier, Jetstar, by

forcing it to pursue the same routes as Tiger, as well as commencing services

to/from Melbourne airport (which Jetstar previously avoided, preferring

Melbourne’s secondary airport, Avalon).

Another important change in the Australian airline sector that signified a third

wave was the evolution of two incumbent LCCs. Virgin Blue increasingly

focussed on becoming a network carrier similar to Qantas. When examined

2-26

against the characteristics of LCCs discussed previously, Virgin Blue’s move

towards the NC model is evident because of its expansion into (1) smaller

regional markets with lower demand density (with medium size Embraer aircraft);

(2) increasing use of hub-spoke strategy (e.g. Cairns – Sydney – Ballina as oppose

to Cairns – Ballina direct); (3) introduction of business lounges and premium

seating class; (4) code-sharing or/and interlining arrangement with domestic and

international airlines (e.g. Delta Airlines, Regional Express, Malaysia Airlines),

and (5) multi-fleet, including long-haul aircraft (e.g. B777-ERs for Sydney – U.S.,

Embraer 117 and 119s for Sydney - Tamworth). This period also marks a new

phase in that the focus of the incumbent LCCs became increasingly towards long-

haul international markets. Jetstar International was established in 2007 and

commenced services to Thailand, Hawaii and Japan as a long-haul low-cost

service provider. However, domestic services remain as the most important source

of business for all incumbent carriers because they represent the majority of total

passenger demand for Australian carriers.

At this point it is worthwhile to note that whether or not Virgin Blue is a LCC is

of little consequence to this thesis. This is because most key changes in Virgin

Blue’s strategy did not take effect until the final quarter of 2007 – and we will be

using secondary data sources from the years 2006 and 2007. Furthermore, given

that it is the LCCs’ effect on regional destinations that is of primary interest, the

exact location in which an airline is placed along an airline business spectrum is

of less importance. Rather, it could be said that the wider focus of this thesis is

affordable air travel, which has been instigated by the LCCs and the competition

that the LCC entry has introduced to the domestic aviation market since 2000.

2-27

2.6 Summary

This Chapter introduced two central concepts of this thesis - dispersal and LCCs.

Background information was provided on LCCs’ main characteristics, as well as

information on the precursor of LCC growth in Australia. Against this

background, the Chapter introduced the three Australian LCCs – Virgin Blue,

Jetstar and Tiger – followed by an outline of the changing nature of these LCCs.

The Chapter concluded that the LCCs’ characteristics have had the combined

effect of generating air leisure travel demand for regional tourism destinations.

Thus, the first specific aim of this thesis has been addressed, which was to

‘provide an interpretative survey of relevant literature and secondary data sources

to understand the link between LCCs and domestic dispersal of tourists’. What

remains is the question on the effects of LCCs on regional dispersal, which will

be the primary focus of the remainder of this thesis. The following Chapter

explicates the relationships between LCCs and regional dispersal.

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i The full list of airports leased in this period were: Melbourne, Brisbane, Perth, Canberra,

Adelaide, Darwin, Alice Springs, Coolangatta, Hobart, Launceston, Townsville, Mount

Isa, Tennant Creek, Archerfield, Jandakot, Moorabbin and Parafield.

ii Transportation Research Board (1999) shows that in the U.S., the highest 5% of fare

payers’ contribution to airline revenue has increased from 8% to 18%, while the lowest

25% of fare payers’ contribution decreased from 14% to 10% (as cited in Williams 2002).

iii Borenstein and Rose (1994) used a Gini Index to analyse the price dispersion and

obtained an ‘expected absolute fare difference’ of 36% for a given air service.

Importantly, they concluded that although the absolute values vary extensively across

routes, the differences in fares paid were prominent across passengers than across

carriers.

3-1

3. REGIONAL DISPERSAL PROPENSITY

AND LOW-COST CARRIERS

3.1 Introduction

The aim of this chapter is to introduce the relevant literature on the effect of LCCs

on regional dispersal. In doing so, this chapter accomplishes the second specific

aim of this thesis (A2), which is to ‘identify and explicate the relationships

between regional dispersal and LCCs’. This chapter has two sub-aims. The first is

to provide an overview of the determinants of dispersal. The second is to identify

and explain the relationships between these determinants and LCCs. This research

draws from the literatures on tourists’ spatial behaviour, particularly multi-

destination travels, because ‘dispersal’ is a special case of tourists’ spatial

behaviour. The scope of the literature review extends as far as spatial behaviour

research is relevant for analysing the relationships between LCCs and regional

dispersal. Section 3.2 outlines the framework applied, while Section 3.3 explains

the key determinants in the context of LCCs and regional dispersal.

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3.2 Spatial patterns of tourists’ regional dispersal

This section explains the general patterns of tourists’ spatial behaviour identified

in the tourism literature. Due to the spatial nature of the topic, spatial behaviour

received much attention from geographers. Pearce (1979), on the subject of

tourism geography wrote,

“tourism has been variously defined but may be thought of as the

relationships and phenomena arising out of the journeys and temporary stays

of people travelling primarily for leisure or recreational purpose … the

geography of tourism is concerned essentially, though not exclusively, with

the spatial expression of these relationships and phenomena” (p.248)

Dispersal is one such spatial manifestation arising from leisure travels. Inherent in

dispersal, therefore, is the spatial expression of tourists’ behaviour. A distinction

can be made between ‘behavour in space’ and ‘spatial behaviour’ (Walmsley and

Lewis 1994). On this distinction, Walmsley (2004) has shown that the analysis of

the former,

“involves description of the context in which the behaviour in question

occurs and the relating of behaviour to that specific context … the study of

‘spatial behaviour’ focuses on trying to find the general in the particular in

the sense of distilling the rules, principles, and laws that describe behaviour

independently of the context in which it occurs. In other words, with “spatial

behaviour”, the search is for general principles of people-environment

interaction and for understanding of how humans as a whole behave in

certain types of settings (e.g. shopping centres, theme parks) rather than with

particular contexts (e.g. Oxford Street, London, Disneyland)” (p.50)

This thesis aims to find the general relationships between LCCs (or equivalent air

transport services) and regional dispersal. Specifically, dispersal is achieved when

many destinations are visited within the same trip, or when a unique trip is

3-3

undertaken on many parts of the destination (Wu and Carson 2008). Although the

previous chapter introduced a more simplified definition of regional dispersal, as

‘an overnight trip in the periphery’, it is apparent from Wu and Carson (2008) that

dispersal can be achieved by a multi-destination or a single-destination trip to the

periphery.

In general, dispersal reflects tourists’ motivation to visit the periphery. Cooper

(1981), who was one of the first to have studied the linkages between ‘spatial and

temporal patterns of tourists’ and tourist characteristics, noted that a general

spatial pattern involves a movement outward from a touring centre, and towards

locations with declining tourism facilities. He concluded that the “wave-like pulse

of visits outward from a touring centre and down the hierarchy” (p.369) is

probably a general phenomenon that can be observed in a variety of places and

locations.

Fennell (1996) argued that recognising what is ‘core’ and what is ‘periphery’

depends on tourists’ “inherent activity-based motivations” (p.816). Thus, the

‘core’ for a traveller will depend on the subjective interests and activities sought

by the traveller. In Fennell’s study, the ‘special interest’ groups were more

specific in their activities; consequently, the special interest groups had a diffused

pattern of travel and stayed in the outskirts of the UK’s Shetland region. The

majority of the ‘general interest’ group had a high representation in Lerwick,

which is the main township of the region. Thus, dispersal reflects tourists’

motivation that tends be more ‘special interests’ than ‘general interests’.

Tourism researchers have identified a number of specific trip itinerary patterns.

These trip structures were found to be robust across spatial scales (e.g. inter-

continental scale to local scale) and countries. The Mings and McHugh (1992)

study was one of the early studies that has identified such patterns. They

discovered that the majority of the variation in U.S. domestic trips to Yellowstone

Park can be categorised into one of four trip structures: direct route; partial orbit;

full orbit, and fly-drive. Lue, Crompton and Fesenmaier (1993) imposed a

3-4

structure to these itineraries. The trip itineraries were structured into five basic

patterns of single and multi-destination trips, which extended the four identified

by Mings and McHugh (1992). Opperman (1995) further extended this into two

single-destination and five multi-destination trips to account for trip patterns

common in international travels.

The framework developed by Lue et al. (1993) has been applied to a variety of

situations and contexts, and has formed the basis for further studies on multi-

destination travel itinerary; for instance, on domestic travel in the U.S. (Stewart

and Vogt 1997); domestic travel by international tourists in Queensland, Australia

(Tideswell and Faulkner 1999); trip patterns in New South Wales, Australia

(Parolin 2001); and South Australia (Wu and Carson 2008); international tourists

to Malaysia (Opperman 1995), and the role of Hong Kong in tourists’

international travel itinerary (Lew and McKercher 2002). In Figure 3.1, the five

patterns proposed by Lue et al. (1993) were integrated into three patterns of

single-destination (SDT), multi-destination type 1 (MD1) and multi-destination

type 2 (MD2). Each pattern is discussed below.

3-5

The single-destination trip (SDT) is equivalent to the direct-route pattern in Mings

and McHugh (1992). While Lue et al. (1993) defined the second pattern – ‘base

camp’ or ‘BC’ - as a multi-destination trip, Opperman (1995) suggested that this

is an extension of the single-destination trip because it involves an overnight stay

in a single destination with radiant day-trips to the periphery. Here, BC too will be

defined as a SDT. Revisiting the definition introduced in Chapter 2, this type of

trip can be both a domestic and regional dispersal. A SDT will constitute a

domestic dispersal if the destination of stay is only in the ‘gateway’, while

regional dispersal if the chosen destination is in the ‘periphery’.

The second group of travel patterns includes partial orbit and fly-and-drive trips,

or in Lue et al. (1993) terms, the ‘regional tour’ pattern. This pattern is of

particular relevance to ‘LCC and dispersal’ because it is a representative form of

regional dispersal that can be achieved with air travel. Many regional dispersal

D

a

b

c

Regional tour/partial

orbit

b

D

a

c

Single destination/Base camp

d

Trip chaining

c

D

a

b

En route

HOME

MD1

MD2

SDT

Figure 3.1 Spatial representation of tourists' travel patterns (modified from Lue et.al.1993)

3-6

trips are this type in Australia; originating from the large metropolises destined

for the ‘sun, sand and sea’ destinations along the Eastern Coast. These patterns are

collectively denoted ‘MD1’ (multi-destination 1). Note that when an extra trip

links up ‘c’ with ‘D’, this becomes a ‘full-orbit’ pattern.

MD1 highlights the fact that regional dispersal of air arrivals depends on the

determinants of travel from ‘D’ to {a, b, c}. These determinants are discussed in

greater detail in section 3.3. Moscardo et al. (2004) have shown that transport

mode chosen by travellers ‘to’ and ‘within’ destinations constrains the travellers’

travel pattern. Furthermore, they have shown that the ‘access points’ for transport,

e.g. location of the airport in relation to the wider destination region, affects the

spatial pattern of the trip. Consequently, as illustrated by Moscardo et al. (2004)

in their Great Barrier Reef (GBR) case study, a shift in the destination access

mode from air towards a car is accompanied by a change in the trip and traveller

characteristics to the region.

More recently, the GBR region was subject to several LCC entries on a number of

locations (e.g. Hamilton Island, Mackay, Townsville, Cairns and Rockhampton –

see Table 2.3 for traffic growth in these airports), rendering air transport as an

increasingly significant source of leisure arrivals. A related problem is the

question over the type of ground transport mode a destination should promote to

achieve the greater regional dispersal of the air arrivals. This issue is a significant

one for government policy because governments may have other policy objectives

that do not necessarily promote the travel mode best for regional dispersal. This

issue will be revisited in Chapter 5.

The final type, MD2 (multi-destination 2), includes ‘trip-chaining’ and ‘en route’

patterns. ‘En route’ occurs when a trip stopovers in ‘a’ or/and ‘b’ on the way to

‘D’ (a trip that takes the route stopping-over at ‘c’ or/and ‘d’ is also en route).

‘Trip-chaining’ occurs when a trip includes destinations {a … d} with different

access and return route. A way these patterns distinguish themselves from MD1 is

through the main mode of travel used by tourists. Air travel does not offer the

3-7

spontaneity and flexibility of that offered by cars (e.g. Stewart and Vogt

1997:458). Hence, in the regional tourism context, MD2 is difficult to achieve

with air travel but easily achieved with cars.

In MD2, the peripheral destinations (e.g. ‘a’ and ‘b’) impacted by the LCCs are en

route. This is because these peripheral destinations usually do not command large

enough demand to sustain their own LCC services from “Home”. Consequently,

these peripheral destinations are bypassed by the LCCs. This will be a problem if

there is substantial substitution effect from ground travel modes toward air on the

travel corridor. A significant substitution effect will adversely impact regional

dispersal because the substitution effect will generate a bypass effect. The modal

substitution issue is addressed in greater detail in Chapter 6.

Finally, the preceding discussions on MD1 and MD2 demonstrate that different

regional destinations will be subject to different channels of LCC impact, i.e. via

modal substitution (transport ‘to’ the destination) or modal complementarity

(transport ‘within’ the destination). The trip patterns, individually or in

combination of one another, enable a depiction of a large number of trip variations

into a parsimonious set of trips. The trip patterns are capable of highlighting

specific LCC and dispersal issues. Thus, whether or not a particular issue applies

to a regional destination depends on which trip structure (SDT, MD1 or MD2)

characterises the travellers, and the destination’s position with respect to the

travellers’ overall trip structure. For instance, MD1 is most applicable to trips that

originate from Sydney or Melbourne destined along the Queensland’s Eastern

Coast, whereas MD2 is most applicable to intra-state trips often shorter in

distance (less than 800km).

The review of multi-destination trip factors is a useful starting point for building

cause-effect structures of regional dispersal. This is because the force that

increases the incidences of multi-destination travels also increases the travellers’

propensity to visit the periphery. The remainder of Chapter 3 focuses on (1)

identifying the determinants of regional dispersal and (2) generating propositions

3-8

on how these determinants are affected by the LCCs. As outlined in Chapter 1,

this chapter discusses the intra-modal source of difference. Chapter 4 empirically

examines these propositions.

3.3 The effects of LCCs on regional dispersal

It was argued that the multi-destination travel literature is a useful starting-point

for identifying the dispersal determinants. The Lue et al. (1993) study was one of

the earliest to provide a framework on the topic of multi-destination travel. They

suggest five main factors. First, heterogeneity of preferences in a travel group can

be more easily satisfied with visitations to a greater number of destinations. This

is related to another reason given, which is the need for variety by tourists,

triggering the need to visit more than one destination, especially if the marginal

cost of doing so is relatively low.

There are two other factors. One is related to reduction of risk and uncertainty,

and the other, travel monetary costs. Diversification reduces the risk associated

with relying on a single destination to provide all the expected utility during the

trip. As for travel costs, costs such as long-distance transportation are fixed costs

incurred regardless of other attributes of the trip (e.g. length of stay); thus, visiting

multi-destinations by combining several individual trips into one, is a way to

realise cost savings. Finally, Lue et al. (1993) argued that visiting friends and

relatives (VFR) travel purpose is likely to increase the number of stopovers and

destinations visited.

Tideswell and Faulkner (1999: 365) added another five factors that can act to

stimulate or constrain multi-destination travels. Specifically, Tideswell and

Faulkner, based on a review of earlier work by Opperman (1994), Debbage (1991)

3-9

and Lue et al. (1993), added the following factors: package tour or free-

independent travel; primary mode of transport used; travel time constraint; repeat

visit or not, and the ‘spatial configuration of destinations’. These factors are

summarised in Table 3.1. The remainder of this chapter addresses each factor in

turn. Emphasis is placed on the relationship of these factors with the LCCs, and

where appropriate, the relationship with affordable air travel generally.

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propensity

LCC demand characteristics from

dispersal viewpoint

1. Spatial

configuration of

destinations



dispersal



dispersal

2. Length of stay Length of stay is positively related to

dispersal

LCC demand will be less sensitive to length

of stay than NC demand

3. Variety and

multiple-benefit

seeking behaviour

Greater variety in the reasons for

travel, and larger share of VFR

related travels, are positively


Variety in the travel purpose, and the

large share of VFR travels, are important

sources of dispersal for the LCC arrivals

4. Risk and

uncertainty

Greater risk and uncertainty about

the trip may affect dispersal


LCC demand may be more sensitive to

risk and uncertainty, hence the effect of

distance on dispersal may be magnified

5. Heterogeneity in

preferences

Greater heterogeneity in a travel

group may affect dispersal


LCCs serve proportionately more couples

and group travels, but there is no clear

proposition on the differential effect of

heterogeneity (on dispersal) between

LCC and NC

6. First time or repeat

visitation

First visitation can have a positive or

negative effect on dispersal; repeat

visitation has a positive effect on

dispersal

LCC stimulates first-time visitors to

destinations, which may increase or

decrease dispersal. Second-home

travellers are expected to be an important

source of dispersal of the LCC arrivals

7. Package tourism Package tourism is negatively


Disproportionately large share of LCC

arrivals are FIT tourists; therefore, they

are less constrained spatially.

8. Transport 'to' and

'within' the

destination

Addressed in Chapter 5 and

Chapter 6

Addressed in Chapter 5 and Chapter 6

Table 3.1 Summary of the relationships discussed in section 3.3

3-11

3.3.1 Spatial configuration of the destinations

In the context of tourism destinations, “Wall (1997) is credited with emphasizing

the importance of spatial configuration as an attraction attribute” (Weaver, 2006:

93). The spatial distribution patterns of destinations will result in similar patterns

of tourist activities. For instance, a ‘node’ will draw a concentration of activities,

whereas a linear pattern of attractions will yield linear movement of tourists

(Weaver 2006). Tideswell and Faulkner (1999) summarised the influence of

spatial configuration on multi-destination travel. The proposition was that “the

existence of a range of complementary tourist attractions/destinations within

“reasonable proximity” of a region increases the number of stopovers made by

tourists” (p.369). Tideswell and Faulkner, however, did not empirically examine

the influence of this effect. Hwang and Fesenmaier (2003), in their study of the

domestic trips in the U.S., found that the spatial patterns of travel differed widely

between and within the Midwest states, concluding that geographic characteristics

do influence the spatial behaviour of tourists. Generally, the presence of a variety

of activities at a destination causes a spatially concentrated pattern of travel; for

example, trips concentrate towards urban areas and gateways for this reason. On

the other hand, scattered attractions and destinations cause a spatially expansive

behaviour.

The influence of LCC on the relationship between spatial configuration and

dispersal is illustrated in the research literature. For instance, Papatheodorou

(2002) has documented that the proliferation of affordable air services through

charter and low-cost carriers had resulted in the ‘anarchic urbanisation and

congestions’ in some tourism centres in the Mediterranean region. Contrasting

scenarios also prevail. Francis et al. (2004) illustrated a case where the entry of

LCC has resulted in a greater use of the regional airport, but tourists passing

through the airport did not travel to the destination that the airport was originally

purported to serve. Rather, tourists used the airport as a point of entry and exit to

travel elsewhere. The differences in the LCC arrivals’ trip patterns in the two

examples given above can be partly attributed to the differences in the spatial

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configuration of destinations and attractions, and the proximity of the airport to

these destinations.

3.3.2 Length of stay

Length of stay and spatial behaviour are related. A positive relationship may be

expected from the fact that leisure trips are time-constrained, and this renders

tourists’ activity patterns highly time-sensitive (Landau, Prashker, and Hirsh 1981

as cited by Debbage 1991). Fennell (1996), in his account of tourists’ behaviour

over space and time, added that “when time is short, space is conserved” (p.814).

Mansfeld (1991), on the other hand, noted how the effect of length of stay on

spatial behaviour may not always be the same, because time constraint may

induce a tourist to see as much as possible.

Evidence in the research literature has shown a relationship between airline

business models and travellers’ length of stay. Early evidence comes from the

study by Pearce (1987) on the spatial pattern of package tours in Spanish

destinations. Since the 1970s, charter carriers, as part of their inclusive tour

packages, required a fixed duration trip typically for a week or two. However, this

changed quickly with the emergence of LCCs on the traditional charter routes in

Europe (Williams 2002). This is substantiated by the Alegre and Pou (2006) study

of the microeconomic determinants of length of stay; the study shows that the

length of stay in the Balaeric Islands between 1989 and 2003 declined by 25%.

Although Alegre and Pou (2006) did not explicitly address the emergence of low

cost carriers and its potential association with the changes in the length of stay,

they did note the shift towards greater flexibility in the length of stay from the

traditional ‘bimodal’ distribution (either 1 week or 2 weeks stay) to the ‘four to

five day stays’. It is probable, as observed by Pearce (1987), that the previous

pattern of one or two weeks stay was an outcome of the package/ticket conditions

of the charter carriers.

3-13

In recent years, researchers identified LCCs with short and frequent breaks; for

instance, Mason (2005) cited a study by Mintel that found UK residents were 70%

more likely to take a short break in 2004 compared to 1999, and argued that the

LCCs, to an extent, fuelled this trend. Graham (2006), in exploring the various

sources of LCC demand, noted that the short but more frequent travelling to the

second homes by the affluent population in UK was an emerging trend associated

with the LCCs. She argued that the ‘cash-rich’ and ‘time-poor’ society is

conducive to short and frequent trips. This trend is not endemic to UK. The trend

towards short and more frequent break is also recognised in Australia (TTF 2003).

However, the Australian evidence on LCCs and ‘trips to second homes’ is weak

with NVS showing less than 1% of VFR travellers staying in their ‘own property’

during the trip. Generally, the literature concurs with the view that the additional

choice brought by LCCs in the selection of trip duration and times, “brought out

travel behaviour patterns that were suppressed by the inflexible travel packages

that were previously available” (Mason 2005:24).

LCC travellers may be constrained in their time-budget for two other reasons.

First, air transport is chosen by those with high opportunity cost of time,

compared to other modes of travel. Second, LCCs stimulate disproportionately

more of the time-poor travellers, as well as the affluent travellers - particularly

with second homes - travelling in greater frequency. Njegovan (2006)’s

econometric study of UK residents found that low airfares trigger a substitution

from domestic leisure/durable goods (in UK) towards short overseas travel,

providing some evidence on the relationship between low airfares and short-

breaks. While a trade-off between lower airfares and travel frequency is likely, the

lower airfare is unlikely to induce an increase in the length of stay if travellers are

time-constrained.

3.3.3 Variety and multiple-benefit seeking behaviour

Lue et al. (1993) argued that a tourist might seek a variety of activities from a

single place (such as a gateway) or obtain multiple benefits from multiple places.

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In both cases, the effect on dispersal will be similar because seeking greater

variety of activities will generally increase the need to explore the destination

region, including the periphery. Given the fact that land-uses become more

homogenous down the urban-to-rural hierarchy (Johnston et al. 2000), seeking

multiple benefits and activities will increase the need to be spatially expansive in

the rural-regions.

Tideswell and Faulkner (1999) argued that the ‘number of different travel purpose

stated’ is a good proxy to test for the variety effect. They also accounted for the

proposition put forward by Lue et al. (1993) that ‘visiting friends and relatives’

(VFR), as a travel purpose, increases the likelihood of a multi-destination trip.

The effect of VFR trips may increase the dispersal propensity because residential

areas tend to be located in suburbs, which is often located beyond the functional

and recreational centres. It is briefly noted here that VFR as a source of dispersal

is less desirable from the expenditure viewpoint because it injects less expenditure

into the region’s economy. For instance, NVS (2007) shows that holiday travellers

contribute $637 per visitor, or $144 per visitor night, whereas the figures for VFR

were $283 and $81 respectively. Thus, even if this type of trip constitutes

dispersal, the economic contribution is much less than what the visitor dispersal

volume may suggests.

As a result of LCC proliferation, in the short-haul travel market, leisure travellers

are increasing in share of total passenger mix relative to business (Dresner, 2006).

The significance of the LCC clientele depends on the level of the variety of

travellers using LCC services. For instance, Figure 2.5 has shown that LCCs were

instrumental in the stimulation of both VFR and holiday flows. As for business

travel, Mason (2001) found that business travellers, in particular the small and

medium businesses, are important patrons of LCC services. Similar findings

appeared in the study by Fourie and Lubbe (2006), who found that LCCs and NCs

compete for the business markets in South Africa.

3-15

Overall, it may be argued that a greater range of travel purpose (business, VFR

and holiday) characterises the LCC demand than other airline business models,

although some variation is expected across routes; for instance, the Sydney-

Hamilton Island route which was serviced by Qantas was recently replaced by

Jetstar entirely. In such a case, the traveller can only fly with LCCs, hence, the

proposed differences between LCCs and NCs cannot be applied. In the aggregate,

the greater variety in the reasons for travel and the larger share of VFR related

travels, have positive effects on dispersal propensity. Hence, it is proposed that

variety-seeking behaviour is a significant source of dispersal for LCC passengers.

3.3.4 Risk and uncertainty reduction: distance travelled

Time constraints, and the desire to enjoy the trip, will lead tourists to visit a large

site as a way to reduce uncertainty (Cooper 1981). Thus, one can propose a

negative relationship between regional dispersal and levels of uncertainty. In

contrast, when tourists diversify their ‘destination portfolio’ in order to diversify

risk, multi-destination travels can be positively related to the level of uncertainty.

Tideswell and Faulkner (1999), assuming that greater distance is associated with

greater uncertainty about the destination, found a positive relationship between

distance and the number of multi-destination stopovers. Debbage (1991) argued

that greater distance implies greater time and monetary costs; thus, travel to

farther destinations increases the tourists’ propensity to ‘see more and do more’.

Hwang and Fesenmaier (2003) have shown that 80% of single-destination U.S.

domestic travellers travelled round-trip distance of 340 miles or less, while the

equivalent figure for multi-destination travellers was 760 miles. In summary,

while multi-destination trips are positively affected by uncertainty, it is difficult to

ascertain the direction of the relationship between uncertainty and regional

dispersal.

Graham A. (2006) argued that increases in the flying propensity of the population

is an important source of demand for the LCCs. As discussed previously, Mason

(2005) further noted how the ’new’ demand for air travel shows signs of

3-16

destination neutrality. A combined effect of these developments can be

summarised as having created a demand that may be more sensitive to risk and

uncertainty, because they are likely to be first time visitors (although not

necessarily first time flyers) to a particular destination. Thus, LCC proliferation

may magnify the effect of uncertainty on dispersal because LCC tourists may be

more sensitive to uncertainty. It is possible for LCCs to diminish the effect of

uncertainty on dispersal because LCCs lower travel costs, reducing the need to

‘see and do more’; decrease in the travel costs reduces the dispersal propensity.

Distance must be used with caution when it is used as a proxy to travel cost

because travel distance does not affect the travel cost as much as other factors -

such as the level of competition, the market structure and the regulatory regime on

a given route. All Australian domestic routes are deregulated; thus, airlines are

free to charge whatever they wish, as long as it does not violate the general

competition rules. While this was not a problem for the Tideswell and Faulkner

(1999) study because they examined the behaviour of international tourists, the

same cannot be said for domestic visitors.

3.3.5 Heterogeneity in preferences (Travel party)

Heterogeneity in the preferences of a travel party increases in the number of

people in a travel group, which increases the multi-destination travel propensity

(Tideswell and Faulkner 1999). The extent of the heterogeneity also depends on

the nature of the travel group; for instance, travelling with ‘family and relatives

with children’ may differ from ‘adult couple without children’ for reasons other

than the travel party size. Similar to the discussion on the dispersal impact of

‘uncertainty and risk reduction’, the heterogeneity could increase or decrease the

dispersal propensity. Figure 3.2 shows that travel party size on air travel increased

between 1999 and 2007, and by assumption, the party heterogeneity also

increased. Lower airfares tend to promote travels of greater party size for the

following but not exhaustive reasons. First, leisure travellers are more likely to be

travelling with ‘others’ than business travellers. Even when business associates

3-17

travel together, their heterogeneity in preferences will have little effect because of

their restricted freedom over destination choices and length of stay. Second, lower

airfares enable larger travel groups such as family and ‘relatives and friends’ to

choose air travel. Thus, the share of ‘couples’, ‘family and relatives’ and ‘friends’

increased since the second wave in Australia.

The assumption that preference heterogeneity can be proxied by travel party size

requires further empirical investigation. Travel party heterogeneity can be a

positive or negative influence on multi-destination and dispersal propensity. The

former is possible if the preference heterogeneity requires the party to diffuse in

search of greater variety of activities, while the latter is likely if the party, for

reasons such as logistics and organisational limitations, is constrained by the large

travel party size. By the same token, visitors may concentrate in main tourism

centres where large variety of activities can be found to satisfy the variety in the

preferences of the travelling group. However, no clear proposition on the

differential effect of travel party size (on dispersal) across airlines can be

ascertained.

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Figure 3.2 Travel party characteristics of air travellers (source: compiled from

National Visitor Survey 1999 and 2007)

3.3.6 Trip arrangement (package tourism)

Package traveller’s behaviour can be spatially confined because of the

predetermined routes and places of visits; or it can stimulate a touring type into

regions that otherwise will not be exposed to tourists (Tideswell and Faulkner

1999). An important example of a relationship between package tourism and air

transport is the inclusive tour charter (ITC) packages developed in Europe 40

years ago. The extent of the ITC was such that the charter revenue passenger

kilometres (RPK) surpassed that of scheduled RPK in the 1970s (McDonnell

Douglas 1977 as cited by Pearce 1987). Similar to the LCCs, charter carriers and

the ITC packages contributed to the domestic dispersal of tourists. Pearce (1987)

concluded that the charter package tourism in Europe was characterised by an

insular and ‘spatially selective’, ‘pleasure periphery’ in Southern Europe. While

Pearce provided an interpretative survey of the spatial patterns of package and

charter tourism, the level of analysis did not extend to that of the spatial behaviour

in the destination and the surrounds, i.e. regional dispersal.

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As shown in Chapter 2, the LCC model shares similar features with the charter

carriers in its various strategies to reduce unit costs; for instance, LCCs target

leisure destinations where low-margins but high-volume markets can be

sustained, and by directly accessing the smaller regional airports, LCCs can avoid

the delays and congestions prevalent in large airports. A major difference between

LCCs and charter carriers (other than that LCC is a scheduled service) is that

while charter carriers were often owned and operated by vertically integrated

tourism firms (Williams 2002), the LCC model derived its cost reduction from

simplicity in fares and ‘unbundling’ of air services (CAPA 2006). Thus, the LCC

model is an accomplice to ‘free and independent’ (FIT) travellers. With the direct

booking and independent pricing of each leg, LCCs effectively passed on the

responsibility of ‘packaging’ a holiday to the tourist.

The proposition put forward by Tideswell and Faulkner (1999) suggests that FIT

demand may or may not contribute to dispersal propensity. However, this

proposition was for international tourists visiting Australia, for whom the role of

package tours in introducing destinations and experiences otherwise difficult to

gain, is substantial. The same reasoning does not apply to domestic travellers

because domestic tourists do not face the same barriers in regards to language, the

level of uncertainty, etc. Thus, it is proposed that package tourism is negatively

related to dispersal.

3.3.7 First timers, repeaters, and destination familiarity

Cooper (1981) argued that the centralising force of tourists in large sites is

prevalent in the early stages of their visits. From a tourist’ motivation perspective,

however, the literature suggests an opposite effect to that proposed by Cooper.

Opperman (1997) summarised Gitelson and Crompton’s (1984) study of the

differences in the first and repeat visitors, noting that first time visitors are

younger, have greater motivations and purpose for variety and new experiences,

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and they are relatively distant from travel motivations such as VFR or ‘seeking

relaxations’.

Opperman (1997) provides some evidence that first time visitors contribute more

to dispersal than repeat visitors. Based on the analysis of international tourists in

New Zealand (NZ), Opperman found that first time visitors were more active and

explorative, indicative by the fact that they visited more sites during their stay

than repeaters. For instance, first time visitors to NZ listed an average of 6.4

activities or attractions compared with 3.6 destinations by repeat visitors. The

results also implied that first time visitors, while representing a greater share in

the primary destinations, also visited an average of 5.9 destinations compared to

3.6 by repeat visitors. The results have shown that first time visitors had greater

relative share in 95 of the 110 destinations surveyed in NZ, which suggests that

first time visitors are also important contributors to dispersal.

Recently, Li et al. (2008) provided an overview of the research on first and repeat

visitors, concluding that first time visitors may be driven by novelty more than by

familiarity (Li et al. 2008: 278). They noted that relaxation and familiarity are the

most important reasons for repeat visitors, while gaining new experiences is the

motivation for first time visitors. They found that first time visitors were more

travel and tourism oriented in comparison to repeat visitors who were more

interested in the pursuit of specific activities. Building upon Fennell’s (1996)

argument introduced earlier, repeat visitors’ tendency to pursue specific activities

implies their greater dispersal propensity. Although Li et al. (2008) did not allude

to spatial behaviour directly, they noted that first time visitors are found to be

more extensive in their destination exploration, while repeat visitors were more

intensive in their use of time across smaller range of destinations.

Going back to the risk and uncertainty reduction perspective, Hwang et al. (2006)

wrote, “the more familiar the tourist is with the location, the more knowledge one

has of different kinds of local activities and attractions to fill an entire trip

schedule” (p.1060). Thus, tourists who are familiar with the destination are able to

3-21

engage in time-consuming activities with less need to diversify their risks across

several destinations. In summary, a consensus is yet to be achieved in the

literature on the effect of first-time visitation on the dispersal propensity. Repeat-

visitors, with their specific activity focus, are an important source of regional

dispersal, but it is just as likely that first-time visitors will exhibit high dispersal

propensity due to their ‘exploration and new experiences driven’ nature.

As discussed above, a widely observed pattern of travel behaviour commonly

associated with the proliferation of LCCs is the emergence of short and more

frequent breaks. This increases the number of destination alternatives in a tourist’s

holiday choice set. From the viewpoint of a single destination, greater destination

alternatives for the tourists will increase the level of first time visitors. However,

as previously discussed, it is uncertain whether or not the greater incidences of

first time visits will increase dispersal.

3.3.8 Travel mode choice to and within the destination

As discussed in 3.2, there are two regional dispersal issues closely related to

ground travel modes. The first issue is related to the transport mode choice within

the destination. Thus, the dispersal of the air arrivals is influenced by the ground

travel attributes and ground travel mode availability at the destination. The second

issue is related to the regional dispersal impacted by the substitution of modes

from the car to air in getting to a destination. We briefly discuss these

relationships in this chapter. These issues will be revisited in much greater detail

as case studies in Chapter 5 and Chapter 6 respectively.

Tourists arriving on air transport need to rely on the access and availability of

local travel modes to realise their desired spatial behaviour. Visitors not restricted

in travel mobility are “more spatially adventurous” (Debbage 1991: 368). Travel

modes at the destination are important determinants of dispersal because the

different modes are related to the different levels of ‘mobility’ (Lew and

McKercher 2006).

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There is little evidence available on the type of travel modes used by the air

arrivals in the regions. Nor is there information on the preferences of LCC tourists

toward a certain ground mode, and whether or not there is a significant difference

between those who were enticed by a lower fare to travel as oppose to those who

were not. However, we can assert that a greater proportion of LCC passengers are

likely to be enticed by low fares than NCs. One European example suggested that

LCC tourists have the greater propensity to hire rental cars (Barrett 2004), which

supports the industry observations on the emergence of fly-drive travel in

Australia during the second-wave (Tourism Australia, 2005). Data suggests that

the rental car industry has been one of the winners from LCC proliferation; for

instance, the Australian tourism satellite account (TSA) shows that the rental car

industry has grown proportionately more (34% - although from a lower base)

between 2000/01 and 2006/07 than the total industry gross value-added, which

has been 17% (ABS 2009). A counter argument is that LCC tourists, as air

travellers, face an additional burden of organising transport at the destination,

which negatively affects their dispersal propensity.

The second issue is related to the bypass of destinations as a result of modal

substitution. These two issues are linked in that a travel mode choice to get to a

destination influences the travel mode used in the destination; for instance, if a

tourist drove from the origin to the destination then that tourist will presumably

also use the same vehicle in the destination. Thus, it is reasonable to think that

tourists factor-in their need and desire for mobility at the destination in their

choice of the main travel mode. Limtanakool et al. (2006) argued that the choice

of private car in long-distance journey partly arises from the fact that car offers

the flexibility to visit the attractions that have poor accessibility, e.g. residential

neighbourhoods and out-of-town recreational areas. They argued that leisure trips

are more likely to use private vehicles because leisure trips often involve travel

with other people, which makes private car cheaper and more convenient, etc.

Chapter 6 aims to address the question, ‘do cheap fares induce substitution

3-23

towards air travel even in situations where the private vehicle is more convenient

and appropriate?’

3.3.9 Socio-economic variables

Most of the variables identified previously are readily measured for empirical

analysis. However, other variables such as travel motivations and traveller

personality are much more difficult to measure. Mansfeld (1990) noted how it is

the variety in travel motivations and decision processes that underpins the variety

in spatial behaviour. Motivations and attitudes are often measured by

psychographic approaches (Mansfeld 1990); e.g., the study by Moscardo and

Pearce (2004) on the interaction between travel mode choice and travel

motivations. However, one limitation of such an approach is that data is often not

available through secondary sources. This is perhaps in reflection of the fact that

data obtained on psychographic studies tend to be highly destination, place or

product specific (Mansfeld 1990). An alternative approach is to use proxies to

capture the differences in tourists’ motivations from one individual to another.

Mansfeld (1991) argued that the use of socio-economic variables is a feasible

approach in discriminating between tourists’ motivations (and the implied spatial

behaviour) because tourists’ motivations are formed in the context of the socio-

economic ‘environment’. Thus, there is a good reason to believe that tourists who

exhibit similar background will show similar spatial behaviour. Socio-economic

variables are not “direct travel determinant(s), but as a personal situation that

might result in or impinge upon certain subjective travel motivations” (Mansfeld

1991:383). It is assumed that these factors can “effectively discriminate between

different patterns of tourist spatial behaviour” (Debbage 1991, p.254).

Similar rationale underpins the use of socio-economic variables in the spatial

applications of micro-econometric choice models. In disaggregate behavioural

analyses, it is common to include socio-economic variables as ‘conditioning

variables’ for variation in tastes and preferences of individuals. Income and age

3-24

are frequently used variables to approximate the effect of taste heterogeneity

(Hensher et.al. 2005). Income, for example, is included in the model based on the

assumption that individuals with high income display considerably different tastes

compared to lower income individuals; income is not included as a measure of the

purchasing power (Jara-Diaz, 1991). In the context of multi-destination travels,

Tideswell and Faulkner (1999) used income as a proxy for the level of ‘economic

rationalism’, arguing that higher income individuals are also more economically

rational, which increases their tendency to visit multiple destinations. Thus, in the

studies of spatial behaviour, if data permit, socio-economic variables should be

included in the analyses.

3.3.10 Other variables and issues

Type of Accommodation

Information on the type of accommodation chosen is relevant to dispersal for two

reasons. First, accommodation type such as resorts provides facilities within the

complex that may reduce the need to venture out. Second, the chosen

accommodation is a useful indicator of the intention and motivation of the

traveller. For instance, choosing to use ‘camping grounds and caravan parks’

indicate the travellers’ trip motivation and special interests, which is associated

with a greater tendency to disperse to the periphery. Another example is the

choice of ‘friends and relatives’ property’. This is related to dispersal because

such choice of accommodation contains some information about the location of

travellers’ main overnight stay. Given the fact that 80% of VFR trips stay in

‘friends and relatives property’ (NVS 2007), it is plausible to suggest that VFR

trips will have the greater tendency to visit the residential areas of the destination

region.

Trip expenditure

In the microeconomic theory of choice, there is an important difference between

income and expenditure. The former, as previously discussed, is a determinant of

taste heterogeneity (Jara-Diaz 1991). Mansfeld (1991) observed that higher

3-25

expenditure is associated with a more expansive spatial behaviour (greater

incidences of ‘moving from places to places’). While greater trip budget may

allow for greater spatial behaviour, it is also the case that the desire or the need to

realise an expansive spatial coverage usually requires larger trip spend. In

addition, greater expenditure may arise from heavy shopping activity and city-

centre activities that are not necessarily spatially expansive. This issue of

‘association not causality’ applies to other variables outlined in this Chapter. For

instance, with respect to length of stay, a traveller may choose a short duration

trip because that is all that’s needed for the trip, not because the traveller has a

strict time constraint. Having said this, in a time-poor and cash-rich society, it is

likely that the traveller is time constrained.

3.4 Summary

This Chapter focussed on the relationships between the LCCs and regional

dispersal. The outcomes from this Chapter are twofold. First, the spatial patterns

identified (SDT, MD1 and MD2) provided a framework in which specific LCC

and regional dispersal related issues could be clarified and addressed. Two such

issues emerged; (a) the extent to which destination policy control variables, in

particular ground transportation, can influence the regional dispersal of the air

arrivals; and (b) the extent to which LCCs can trigger a ‘bypass’ of the regional

destinations without domestic air services, by promoting a modal substitution of

tourists from ground travel modes towards air travel. These issues are further

examined in Chapter 5 and Chapter 6 respectively. Second, this chapter generated

propositions on how the LCC demand may be different from the NC demand with

respect to their effect on dispersal. Propositions were explicated and summarised

in Table 3.1. The following chapter empirically tests these propositions.

4-1

4. THE ‘CHARACTERISTICS’ MODEL

4.1 Introduction

The proliferation of Low Cost Carriers (LCC) marked a new era in air travel,

generating much interest in the industry and academic literature on the LCC

model and its impact on various aspects of aviation and tourism. This research

concerns the LCCs’ impact on regional dispersal of tourists. If we have an

empirical model that specifies the relationships between dispersal propensity and

trip characteristics, we can then ask the question, ‘how are the empirical models

of dispersal and trip characteristics differ between LCC and NC travellers?’ Put

differently, ‘are there sufficient differences between the LCC and NC travellers to

imply a divergent behaviour at the destination?’ The propositions were introduced

in Chapter 3. The primary aim of this Chapter is to test these propositions. Many

studies have outlined the various factors influencing spatial behaviour. But the

relevance of these studies in understanding the impact of LCC, and more

generally, the impact of the increases in the air arrivals, is left unexplored. The

contribution of this Chapter is in partly filling this void in the research literature.

This Chapter briefly re-introduces the relationships between LCCs and dispersal,

then we outline the steps involved in building the dispersal model with the

National Visitor Survey data (section 4.3). Logit model results are presented and

discussed (section 4.4). As for definitions, the LCCs in Australia are Virgin Blue

and Jetstar, and a trip is classified as regional dispersal if it had at least one night

stay in the periphery of a given Tourism Region. Note that Tiger airways has been

excluded from the analysis due to very low sample size (NVS 2007 has one

4-2

month of Tiger airways data because the airline entered the market in December

2007).

In this empirical study, six of the propositions derived from Chapter 3 are tested.

These are summarised in Table 4.1 along with the test results. Details of the test

results will be discussed in later sections.

Note: asterisk (*) indicates variables empirically examined. Other variables were omitted

from the analysis due to data limitations.


propensity

Propositions on the characteristics of LCC

demand from a dispersal viewpoint

Test results

Number of stopovers* Number of stopovers is positively

related to dispersal propensity

Number of stopovers is positively related to

dispersal propensity

supported

Preference

heterogeneity*

Greater heterogeneity can have a

positive or negative effect on

dispersal

There is no clear proposition, but LCCs serve

proportionately more couples and family

travels than NCs.

-

Risk and uncertainty* Greater risk and uncertainty can

have a positive or negative effect

on dispersal

LCC demand may be more sensitive to risk

and uncertainty, hence the effect of distance on

dispersal may be magnified

supported

Length of stay* Length of stay is positively related

to dispersal

LCC demand will be less sensitive to length of

stay than NC demand

supported

Spatial configuration

of destinations*



dispersal

Different tourism regions will be associated

with different levels of dispersal

supported

Variety and multiple-

benefit seeking

behaviour

Greater variety in the reasons of

travel increases dispersal

propensity.

LCC demand has higher dispersal propensity

because of the greater variety in the reasons of

travel

not tested

(data

limitation)

VFR travel purpose* VFR related travels are positively


VFR is an important source of dispersal of the

LCC arrivals

supported

Package tourism Package tourism is negatively


Disproportionately large share of LCC arrivals

are FIT tourists, therefore they are less

constrained spatially.

not tested

(data

limitation)

Table 4.1 Summary of the relationships between LCC and dispersal

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4.2 Method

4.2.1 Data

National Visitor Survey 2006 and 2007

National Visitor Survey (NVS) is the largest (in terms of sample sizes) travel

survey available in Australia with approximately 40,000 trip samples each year.

The survey collects a large amount of information on a range of variables. All

variables are collected at an individual trip level. It is the most comprehensive

disaggregate data source on Australian domestic travel, which began collecting

information on the domestic airline used from 2006. This survey is managed

quarterly by the federal tourism research agency, Tourism Research Australia

(TRA).

Study sample

This study examines ‘all leisure trips (Holiday or VFR) made by Australians

originating from state capitals and destined (with at least one night stay) to

Tourism Regions directly serviced by LCCs’. Tourism Regions are administrative

boundaries set-up by state and territory tourism organisations (as outlined in

Chapter 2, Figure 2.2). The origin-destination pairs are shown in Table 4.2. Six

pairs1 were of distance too short (less than three hours drive) for air travel to be of

any significance. Consequently they were removed from the sample. The study

sample also excluded trips with greater than four stopovers (overnight). To

maximise sample size, 2006 and 2007 samples were combined to obtain a total

sample of 3,761 trips, of which 3,042 trips were leisure.

1 Brisbane – Maroochydore; Brisbane – Ballina; Gold Coast – Ballina; Gold Coast;

Maroochydore; Sydney – Newcastle; Hobart – Launceston.

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The National Visitor Survey (NVS) shows that between the year-ending 1998 and

2007, the proportion of gateway only visitors (non-dispersal) varied between 67%

and 71% (or the regional dispersal varied between 29% and 33%). However, even

the NVS is limited at this level of disaggregation due to high confidence intervals.

Thus, these figures should be used as a guide only.

4.2.2 The Model

We apply a binary logit model to the regional dispersal problem. Dispersal is

defined as having a discrete binary outcome, i.e. to disperse or not. The model

applied in this study is advantageous over alterative methods considered, such as

analysis of variance, in that the model provides a ceteris paribus effect of the

independent variable on the discrete dependent variable (disperse or not).

In general, the following utility function is estimated for each option in the

conditional logit model (introduced in Chapter 1),

Vni =� i + �iXni + � iTni + �iZni Eq. (1)

Origin Destination State (destination)

Cairns Queensland

Sydney Launceston Tasmania

Melbourne Townsville Queensland

Brisbane Maroochydore Queensland

Adelaide Williamtown New South Wales

Perth Mackay Queensland

Hobart Rockhampton Queensland

Darwin Broome Western Australia

Canberra Proserpine (Whitsundays) Queensland

Gold Coast Hervey bay Queensland

Ballina New South Wales

Coffs Harbour New South Wales

Table 4.2 Origin-Destination sample

4-5






There is one important difference between the conditional logit above and the

multinomial logit model applied in this study. In the conditional logit, the effects

of both attributes and individual characteristics can be estimated. The distinction

is that the former varies across choice alternatives (e.g. airfares on Jetstar vs.

airfares on Qantas), while the latter varies across individuals (e.g. airline used,

length of stay of a trip, or income level of an individual). The multinomial logit

model (which often refers to conditional logit today – and referred to as such in

the subsequent Chapters) is technically a ‘characteristics model’ (Maddala 1986).

We apply this model because the alternatives, to disperse or not, are functions of

the individual trip characteristics, which varies across individuals not across

choice alternatives.

The characteristics model is algebraically equivalent to the conditional logit

model (Maddala 1986). Simply put, for a binary outcome, the probability of

dispersal reduces to the following form:

pn (dispersal =1) =eVni

1+ eVni Eq. (2)

where

Vni =� i + � iTni + �iZni Eq. (3)

4-6

All terms are defined the same way as Eq.(1). Trip characteristics, Tni , refers to

trip factors outlined in Table 4.1.

4.2.3 Dependent and independent variables

The dependent variable: operationalising ‘dispersal’

Regional dispersal is defined as a trip with at least one night stay in a region

outside the gateway city. Thus, an issue arise as to how the boundary of the

gateway is defined. In this study, we chose to use a politically salient boundary,

the Local Government Area (LGA), to distinguish the gateway from the

periphery. This is the smallest political spatial unit in Australia. The most basic

unit is the Statistical Local Area (SLA) established by the Australian Bureau of

Statistics (ABS) for the Census. In most cases, several SLAs form one LGA. Most

gateways in the regions are made-up of either one or two LGAs. Several, or

sometimes numerous LGAs form a Tourism Region (there are approximately

1,000 LGAs in Australia but only 80 Tourism regions). Now that we have defined

the basic spatial unit of a gateway and the periphery, we can move onto the task of

defining whether or not an individual trip constitutes dispersal.

An overnight trip can belong to one of the following mutually exclusive category

of trips:

o Trips that involve stay only in the gateway (denote this SDG);

o Trips that involve stay only in the periphery (denote this SDP);

o Trips that involve multiple stays only in the gateways (denote this MDG);

o Trips that involve multiple stays only in the periphery (denote this MDP);

o Trips that involve mixture of nights in both the gateway and the periphery

(denote this MDX)

Based on the regional dispersal definition adopted, we can reduce the five

categories above into a binary outcome:

4-7

(1) No dispersal option: a gateway(s) only trip (SDG or MDG); or

(2) Dispersal option: a trip that involves at least one night stay in ‘periphery’

(SDP or MDP or MDX)

Thus in Eq[2], P(Y=1) is P[(SDP or MDP or MDX) = 1].

4-8

The independent variables

Independent variables consist trip characteristics, Tni , and individual

characteristics, Zni (summarised in Table 3).

Note: asterisk (*) denotes the reference level

Three variables require further explanation: number of stopovers; type of

accommodation; and spatial configuration of destinations. The previous Chapter

has shown that dispersal is positively related to multi-destination travel patterns;

Factors (variable codes) Coded values

Number of stopovers (up to 4 stopovers only)

One overnight stopover* Dummy (0 or 1)

Two overnight stopovers (stops2) Dummy (0 or 1)

Three overnight stopovers (stops3) Dummy (0 or 1)

Four overnight stopovers (stops4) Dummy (0 or 1)

Preference Heterogeneity (travel party)

Alone* Dummy (0 or 1)

Couples (coup) Dummy (0 or 1)

Family (fam) Dummy (0 or 1)

Friends and Relatives with Children (vfrch) Dummy (0 or 1)

Friends and Relatives without Children (vfrnoch) Dummy (0 or 1)

Risk and Uncertainty (short - 800km or less) Dummy (0 or 1)

Length of stay (nights) Number of nights

Spatial configuration of destinations

Major International* Dummy (0 or 1)

Peri-Capital regions (pcap) Dummy (0 or 1)

Coastal with significant international visitors (coint) Dummy (0 or 1)

Coastal with mostly domestic visitors (codem) Dummy (0 or 1)

VFR travel purpose (proxy: type of accommodation)

Friends or relatives' property (frp) Dummy (0 or 1)

Repeat visitation (proxy: type of accommodation) Dummy (0 or 1)

Own property (own)

Type of accommodation (remaining types)

Four star or greater hotels or resorts (lux) Dummy (0 or 1)

All other* Dummy (0 or 1)

Table 4.3 Independent variables

4-9

thus, ‘number of stopovers’ variable is included in the model. Accommodation

types are used as proxies. There are two such proxies: friends or relatives’

property (FRP), and ‘own’ property. The former is used as a proxy for VFR.

Given the fact that 80% of VFR trips are FRP (NVS 2007), the inclusion of both

VFR and FRP would result in collinearity problems. The FRP variable was

chosen over the VFR variable because the former provided a better model fit.

‘Own property’ was the best available data to account for the effect of ‘repeat’

visitation, which is not available in the NVS.

As for the spatial configuration of destinations, there is no clear guideline as to

how this variable should be operationalised in the literature. What we know is that

a desirable feature of the variable which operationalises the spatial configuration,

should capture the ‘principal components’ of destinations from a tourism

viewpoint. Tourism and Transport Forum (TTF 2002) groups the 80 Tourism

Regions in Australia into eleven geographical categories based on their essential

tourism and physical geographic characteristics. This was used to differentiate

between tourism regions in the sample.

4.3 Results and Discussion

The model was estimated with maximum likelihood. The modelling process

involved the evaluation of likelihood ratio tests, asymptotic t-tests and

comparisons of Akaike information criterion (AIC). The asymptotic t-test results

are shown with the coefficient estimates. In the estimation, a weighting variable

was used for each individual trip. Tourism Research Australia (who manages the

NVS) calculates the weights for each sample to account for the fact that

respondents are asked for the last two trips and the fact that single-person

households are over-represented in the sample. The sample is also adjusted to the

4-10

known age-sex distribution of the population. The weighting variable was

obtained with the unit record data from Tourism Research Australia.

Figure 4.1 shows that ground transport modes (car, train and coach) are much

more widely used for regional dispersal than air transport. In fact, 83% of leisure

tourists who used ground travel modes as their ‘main’ mode of travel have

undertaken ‘dispersal’ trips, whereas the equivalent figure for air transport is only

42%. This is not surprising as ground modes, especially the car, offers the most

spatial flexibility – a feature of this travel mode widely recognised in research

(e.g. Page 1994). Interestingly, LCCs are associated with lower dispersal than

NCs. Figure 4.2 shows that dispersals for Virgin Blue and Jetstar were 38% and

33% respectively, while Qantas’ dispersal was 51%. The confidence interval is

large at this level of detail; therefore, the percentage figures should be viewed

only as a subject of interest and not as evidence of conclusive findings.

Figure 4.1 Regional Dispersal: ground transport vs. air transport (source: NVS 2006

and 2007)

4-11

Figure 4.2 Regional Dispersal by Airline (source: NVS 2006 and 2007 (1,190

observations))

Table 4.4 shows the summary results of the NC model and the LCC model. Both

models were identically specified. The ratio of the two log-likelihood values is an

indicator of ‘goodness of fit’, i.e. the pseudo R^2. There is no standard to which

pseudo R^2 can be compared against, except that higher pseudo R^2 indicates a

better fit (e.g. Borooah 1996). Hensher et al. (2005), based on simulations, have

shown that a value of 0.3 is a good benchmark. The model falls short of this mark.

However, this was expected given that information such as airfares, which is an

important determinant of airline choice, was missing from the model. For the

purpose of this Chapter, the current models are sufficient for our primary aim to

hypothesis-test the factors in Table 4.1. An extension of this research will be to

build on this model to incorporate travel modal attributes using stated choice data.

The stated choice experiment approach is adopted in Chapters 5 and 6.

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In Table 4.5, NC and LCC columns show the coefficients and the statistical

significance of the X variables. The final column, ‘NC-LCC’, shows the results

from a statistical test to see whether or not the difference between the coefficients

of NC and LCC on a common factor is statistically significant. We used the

following asymptotic t-test suggested by Ben Akiva and Lerman (1985: 202):

�kNC��k

LCC

(var(�kNC ) + var(�k

LCC ))

The results reveal that dispersal factors differentially affect NC and LCC.

FSC LCC

LL of no coefficient model -260.23 -551.75

LL of Constant only model -258.55 -501.88

LL of the full model -213.78 -420.47

Observations 383 796

DF 19 18

P(Y=1) 0.51 0.34

Pseudo R^2 (LL constant only) 0.17 0.16

Pseudo R^2 (LL no coefficient) 0.18 0.23

Table 4.4 Model summary

4-13

Note: [*] next to results of NC and LCC indicate 10% level of significance, [**] 5% and

[***] 1%. [*] in the variable column indicates the reference group (cf. Table 4.2).

4.3.1 Number of stopovers

As expected, the greater the number of overnight stopovers, the greater the

dispersal propensity. This variable is one of the most significant independent

variable in terms of coefficient size. Furthermore, the relationship between the

Factors (variable codes) NC LCC NC - LCC

Constant -0.38 -1.39 *** -

Number of stopovers (up to 4 stopovers only)

One overnight stopover* reference level

Two overnight stopovers (stops2) 2.43 *** 2.32 *** -

Three overnight stopovers (stops3) 3.24 *** 3.34 *** -

Four overnight stopovers (stops4) 1.97 * - - -

Travel Party

Alone* reference level

Couples (coup) -0.03 - 0.41 - -

Family (fam) -0.64 * 0.63 ** ***

Friends and Relatives with Children (vfrch) -0.67 - 0.78 ** **

Friends and Relatives without Children (vfrnoch) 0.38 - 0.08 - -

Distance (short - 800km or less) 1.68 *** -1.11 *** ***

Length of stay (nights) 0.05 ** -0.04 * ***

Spatial configuration of destinations

Major International* reference level

Peri-Capital regions (pcap) -0.44 - 0.73 *** **

Coastal with significant international visitors

(coint) -0.45 - 0.90 *** ***

Coastal with mostly domestic visitors (codem) -0.89 * 0.02 - -

Type of accommodation

Own (own) 0.14 - 3.83 *** **

Friends or Relatives' Property (frp) -0.02 - 0.62 ** *

Four star or greater hotels or resorts (lux) -0.61 * -0.04 - -

All other* reference level

Age -0.02 - 0.01 - -

Table 4.5 Model results

4-14

number of overnight stopovers and dispersal is non-linear: the marginal utility of

moving from one to two stopovers is greater than that of moving from two to

three stopovers (see Figure 4.3). The final column of Table 4.5 shows that the

coefficients of NC and LCC models on the number of stopover are not statistically

different between the two models. The 4th

stopover variable in the LCC model

was omitted due to absence of valid observations.

Results indicate that the marginal effect on the dispersal propensity will be the

greatest if single stopover tourists were targeted. This way the limited marketing

and investment resources can be used to their most effect. For greater dispersal,

research and management should focus on trips up to three stopovers, not only

because they take up the majority of visitations in Australia, but also because the

marginal improvement in the dispersal propensity is the greatest in this range.

Figure 4.3. Marginal effects of stopovers on dispersal propensity (source: modelled

results of NVS 2006 and 2007. Note: based on the ‘NC’ model results)

4.3.2 Length of stay

Length of stay has a positive and statistically significant coefficient in the NC

model. However, length of stay is statistically significant but negative in the LCC

model. Two points are worth noting. First, the average length of stay of LCC

tourists is one night less (6.9 nights) than NC nights (8 nights), and the standard

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deviation is also lower among the LCC tourists. Second, the LCC model shows

that even if length of stay increases, it will be of little effect towards dispersal.

Both points support the hypothesis devised earlier in Chapter 3, although the level

of support is not strong (shown by the weak coefficient). A case could be made

that LCC tourists are constrained by time, and this time-constraint provides little

room for tourists’ dispersal propensity to be swayed by the length of stay.

However, an average of 6.9 nights against an average of 8 nights is not a wide

difference. Therefore, the evidence on length of stay as an intra-modal source of

difference is weak. It may be the case that for the LCC demand, when length of

stay increases, the desire for expansive spatial behaviour is met with other forms

of travel such as day-trips, rather than a change in the overnight destination

(because of the time constraint). However, the day-trip hypothesis was not

testable from the current study.

The result on the length of stay may be affected by the fact that 30% of the leisure

travel samples were VFR. VFR travellers’ spatial behaviour will be determined

largely by the residential locations of friends and relatives. Thus, at least from a

dispersal point of view, length of stay may not be so relevant for VFR trips. This

does not mean that VFR travellers do not disperse; rather, the length of stay of

VFR travellers, independent from other variables, has no bearing on the

propensity to disperse.

4.3.3 Distance

It was proposed that the LCC demand is more responsive to distance, i.e. the

coefficient of the LCC model will be larger than the NC model. The results show

that the two models differ in their signs (+/-). The NC model has a positive

coefficient on ‘short’ (less than 800km), whereas the LCC model has a negative

coefficient. In the light of the hypotheses summarised in Table 4.1, this means

that the LCC model supports the view that greater risk and uncertainty stimulates

dispersal. In contrast, NC tourists focus on gateways as a consequence of greater

4-16

risk and uncertainty. The differences in the coefficients are statistically significant

at the 1% level.

This evidence suggests a link between passenger characteristics, airlines and

tourists’ destination spatial behaviour. In other words, LCC demand is associated

with tourists who respond in a spatially expansive manner in order to reduce the

uncertainty and risk, whereas NC demand is characterised by tourists who focus

on the gateway in response to the uncertainty. One potential explanation for this

difference may be traced to the operational differences between the airline

business models. LCCs commonly adopt Point-Point (and direct) routes, whereas

NCs commonly use hub-spoke (hence the name ‘network’ carrier). As distance

increases, NC services are more likely to be hub-spoke to regional destinations.

The network strategy may have a limiting effect on the spatial behaviour of

tourists because greater time is spent on connections and in-flight; potentially at

some expense of time and energy at the destination.

4.3.4 Spatial configuration of the destinations

The characteristics model supports the proposition that different spatial

configuration of destinations (reflecting different physical factor endowments, as

well as the patterns of human landscape) will produce different dispersal

propensity. In the LCC model, coastal international destinations have the highest

influence on dispersal (0.9), followed closely by peri-capital regions (0.73). The

model shows statistically insignificant differences between major international

destinations and coastal domestic tourism regions.

In the NC model, major international destination tourism region is the only region

with a statistically significant effect on dispersal (positive effect). One potential

explanation for the differences in the results of the NC and the LCC models is that

major international destination such as Cairns (based on TTF classification

described earlier) is by far the largest market for Qantas (the NC) in the study

sample. It is also the case that Cairns and the surrounding destinations (between

4-17

30% and 44%) have much greater incidences of multi-destination travel by

domestic visitors than the national average (11%). The ‘major international

destination’ variable consequently stands out from all other variables in the

model. Given the fact that spatial configuration is often fixed, or supply inelastic,

understanding the response of the new air arrivals to this configuration is

important for destination managers and planners.

4.3.5 Accommodation Type

In the LCC model, ‘own property’ has a large positive effect on dispersal.

Nonetheless, this variable is a proxy, and not fully reflective of the broad range of

repeat visitors. Similarly, ‘FRP’ (friends and relatives’ property) has a positive

coefficient. This is partly due to homes located in the residential areas of a

destination, which is in the outskirts of the main centres (same reason applies to

interpreting the coefficient of ‘friends and relatives property’). As for the NC

model, luxury resorts and hotels have a positive effect on dispersal. This is

expected because they are often located in the CBD and offer superior facilities,

reducing the need for tourists to venture beyond.

It is briefly noted here that ‘FRP and ‘own’ as sources of dispersal are less

desirable from an expenditure viewpoint because they inject less expenditure into

the region’s economy. For instance, NVS (2007) shows that holiday contributed

an average of $637 per visitor, or $144 per visitor night, whereas the figures for

VFR (who make up the majority of FRP) were $283 and $81 respectively

(Tourism Australia 2008). Thus, even if this type of trip contributes to greater

dispersal, its corresponding economic contribution to the region is much less than

what the visitor dispersal volume may indicate. Dispersal arising from VFR may

add to dispersal visits and nights, but comparatively little to expenditure and

financial yield. Further to financial yield, given the traditionally labour-intensive

nature of the accommodation industry (Dwyer, Forsyth and Spurr 2003), the

marginal effect of a dollar spent by dispersing tourists may contribute little to the

economic yield in the regions. Moreover, the level of leakages will be significant

4-18

in peripheral regions because small regional economies tend to have a more

homogenous industry base; consequently, significant share of tourists’

expenditure will leak-out as import payments to other regions and abroad.

4.3.6 Accompanying travel party type

In the LCC model, compared to ‘travelling alone’, ‘travelling with family’ and

‘travelling with friends and relatives with children’ have a greater effect on

dispersal. In the NC model, ‘travelling with family’ has a positive effect on

dispersal. This travel party type, however, was the only party to have a

statistically significant effect in the NC model. The result supports the view that

greater preference heterogeneity will cause greater dispersal among the LCC

tourists. However, in the NC model, the negative coefficient indicates a tendency

for NC tourists to spatially ‘agglomerate’ than to disperse, when the heterogeneity

increases. The LCC model supports the view that greater heterogeneity is a

positive source of dispersal, whereas the NC model supports that greater

heterogeneity constrains tourists’ spatial behaviour.

4.3.7 Other variables

In discrete choice theory, income is often used as a taste parameter (e.g. Ben

Akiva and Lerman 1985). The behavioural implication of income differentials is

that the spatial behaviour of tourists with low income will be different from that

of high income (e.g. Mansfeld 1992). Unfortunately, the income variable had

large incidences of missing data (approximately 20% of the sample). Although

various strategies to overcome the missing data problem were considered, it was

judged most appropriate to exclude this variable from the analysis. The ‘package-

trip’ and ‘rental vehicle’ variables were also omitted from the analyses for similar

reasons. Age variables were included in the model but they were found

statistically insignificant.

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4.4 Limitations

Some of the sample route pairs shown in Table 4.2 have small sample sizes, and

not served by all three airlines. For instance, Jetstar completely replaced Qantas

services to Hamilton Island in 2006. Consequently, on this route, the route

specific effect on dispersal is confounded with the airline specific effects. Future

studies should be more case specific, although this may be a problem due to low

sample sizes. Furthermore, alternative methods of operationalising dispersal

should be considered in the future. The use of LGA boundaries, while politically

salient, is arbitrary for the tourists (since they have little or no knowledge of these

boundaries). One way this can be done is to specify a multinomial model with

levels of dispersal as dependent variables.

Two methodological limitations are noted: the use of characteristics model, and

the use of revealed preference data. The characteristics model applied in this study

specifies an outcome (dispersal or not) as a function of trip characteristics. With

such an approach, there is a problem of not knowing the direction of cause and

effect, i.e. the length of stay may be negatively related to dispersal because

tourists are constrained by the time-budget, or it may be that tourists choose

short-trips (a short getaway in the gateway). One solution to this problem is to

apply an experimental approach. This way, the researcher controls the

environment in which tourists make choices. This method (stated choice method)

has been applied to tourism related issues for some time (e.g. Louviere and

Hensher 1983). Stated choice method will be applied in the following two case

studies.

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4.5 Conclusion

This Chapter aimed to empirically test the relationships between regional

dispersal and affordable air services. The study found some clear differences

between the LCC and the NC model; preference heterogeneity (larger travelling

party size); travelling to second homes; staying at friends and relatives’ property,

and the risk and uncertainty factors were major sources of dispersal in the LCC

model. The evidence from this study supports the view that intra-mode

differences can be a differentiating factor of the behaviour of tourists at the

destination. It was shown that some of this information is contained in the

tourists’ airline choice.

5-1

5. THE CAIRNS EXPERIMENT

5.1 Introduction

Low Cost Carriers (LCCs) stimulate domestic dispersal of tourists in Australia.

Regional destinations experienced increased number of air arrivals as a

consequence. The greater pool of visitors creates opportunities to increase

regional dispersal. The corollary is the increasing reliance of a destination on air

transport. What are the factors that influence the air arrivals to disperse? Is it

possible to induce tourists to disperse by implementing appropriate destination

transport policy? Specifically, can destination transportation policy stimulate the

dispersal of the air arrivals, even in situations where the air arrivals exhibit trip

characteristics that are dispersal-adverse? These are the questions that this Chapter

aims to answer.

In this Chapter, we address the fourth specific aim of this thesis, which is to

‘examine the effects of destination transport factors and tourists’ travel

characteristics on air arrivals’ regional dispersal by applying a stated choice

experiment’. A research design that accommodates trip characteristics and

destination transport attributes so that their influences on regional dispersal can be

compared, will provide us with the capacity to draw conclusions on the likelihood

of destination transportation policy to stimulate dispersal (of the air arrivals), even

in situations when the air arrivals exhibit dispersal-averse propensity.

Transport issues are often at the centre of public policy agenda where

governments promote certain modes of travel over others to meet a wider policy

objective (e.g. reduce carbon emissions). Thus, public policy can be subject to

5-2

conflicting interests (Eaton and Holding 1996). For instance, a conflict may arise

between the objective to maximise dispersal and the objective aiming to maximise

the use of public transport; this is because while a car is a pertinent mode of travel

for regional dispersal, environment-led policy may advocate a shift away from a

car towards public transport. Research on the connection between local travel

modes and regional dispersal can provide diagnostic information to help make

more informed choices on the allocation of public funds.

Eaton and Holding (1996) concluded that public projects need to be able to induce

a change in behaviour; thus, given the fact that public projects can be expensive

and riddled with conflicting interests, an ‘experiment’ may be desirable to first

demonstrate the potential of the project. In addition to the reasons discussed in

Chapter 1 (p1-19) and Chapter 4 (p4-19), stated choice method was chosen for

this case study because the method enables the researcher to control the levels and

type of travel attributes whether or not the attributes are actual or hypothetical. As

highlighted in 1.4 (Chapter 1), this provides an opportunity to empirically test the

effects of travel mode variables under ‘what if’ scenarios. An example may be to

examine the effectiveness of public bus attributes designed to facilitate greater

regional dispersal of tourists in the regions. This Chapter adopts such a method by

applying a stated choice experiment.

5.2 Regional dispersal and transport

A case study approach is adopted in order to progress through the research aims.

Cairns and the Tropical North Queensland tourism region (TNQ) is the largest

regional destination in Australia in terms of domestic air arrival volume (see

Table 2.3). Prideaux (2000) has illustrated the relationship between the growth in

domestic and international tourism with the development of air transport services

and transport infrastructure in Cairns. He noted that beginning in the early 1980s,

5-3

air transport (and its declining relative costs compared to other modes of

transport) became an important part of tourism development in the region. In the

mid to late 1990s, a different trend emerged in the region. Moscardo et al. (2004)

noted that between 1996 and 2001, the trend from air towards the use of long-

distance road was related to the greater use of Whitsundays as the point of access

to the Great Barrier Reef (GBR) than Cairns. Moscardo et al. (2004) argued that

access modes are significant constraints for the tourists because a given mode

fixates the arrival point to a particular access node, in which the subsequent travel

patterns are influenced. The modal shift, they argued, was associated with more

than just a shift in the access points, rather it was related to certain characteristics

and constraints of tourists. They argued that the modal switch was associated with

a shift from mass tourism towards smaller and specialised tourism. They noted

that the patterns of tourism also became more peripheralised and diffused across

the wider regions. The characteristics of tourists, they argued, also changed

towards that of more repeat visit oriented, variety and activity-seeking tourists.

Overall, Moscardo et al. (2004) stressed the importance of the arrival transport

mode as an agent of change in the patterns of regional tourism.

Since the Moscardo et al. (2004) analysis of change in regional tourism, the

‘second-wave’ of Low Cost Carriers (LCCs) proliferated in Australia. This

accelerated the tourist flows to Cairns. Cairns airport is today the second largest

non-capital airport (after Gold Coast) in both international and domestic arrivals,

although LCCs’ impact was mostly on domestic air services (but this does not

mean that the impact was mostly on the domestic tourists because most

international tourists travelling to Cairns have to use the domestic network as

well).

Despite the reversal of trends towards air travels in the regions, it is very unlikely

that the patterns of regional tourism will revert to the pre-1996 scenario for at

least two reasons. First, LCCs tend to seek excess-in-capacity airports located in

destinations with dense demand for leisure travels. During the second-wave, five

airports (Cairns, Townsville, Mackay, Hamilton Island and Rockhampton) in the

5-4

GBR region gained direct access from the key metropolises through LCCs. These

airports were previously not attended by direct domestic services, or they did, but

did not have the exposure to markets that LCCs create. These airports are roughly

equidistant from one another by approximately 4 hours drive along the 2,000km

stretch of the GBR region (with the exception of Hamilton Island, which is nearby

Mackay), which opens up possibilities for a numerous variation in the travel

patterns compared to the pre-1996 era. A consequence of the modal shift has been

the bypass of ground-mode-reliant smaller and peripheral destinations in the

North Queensland region (Whyte and Prideaux 2007). Second, as discussed in

Chapter 3, LCC demand characteristics tend towards free and independent

travellers (FIT), which lends support towards the continuation of the post-1996

trends observed by Moscardo et al.

The increasing emphasis on air travel for tourism is representative of the

experiences of many other Australian regional destinations. An obvious example

is the airports outlined above, all of which have experienced large increases in the

volume of air traffic inflows since 2001 (please refer to Table 2.3). The peripheral

destinations surrounding the gateway cities, however, do not command sufficient

demand for a separate LCC service; rather, they must rely on the air arrivals to

disperse from the gateway. The issue this research aims to highlight is the

effectiveness of ground transportation in increasing the dispersal propensity of

tourists from the gateway.

The air leisure arrivals in Cairns have available to them the following trip

alternatives (please refer to Figure 3.1): (1) at least one night stay in the periphery

(single-destination trip destined for the periphery or a full/partial orbit pattern);

(2) gateway only trip; and (3) stay overnight only in the gateway but take day-

trips (base-camp). In the definition we adopted, only the first of these patterns

constitutes regional dispersal. We build a choice model to test the effectiveness of

ground transport factors in tourists’ choice between the three trip alternatives. The

following sections introduce the methodology. The methodology sections describe

5-5

the discrete choice model applied, the factors influencing dispersal and transport

mode choice, the experimental design, and the data collection procedure.

5.3 The Model

The basic discrete choice model for multi-alternatives is the multinomial logit

model (MNL). In this Chapter, the following utility function is estimated for each

mode of transport and in each destination context.

Vni =� i + �iXni + � iTni + �iZni Eq. (5.2)






One limitation of the MNL is the independence of irrelevant alternative property

(IIA), which results in the constant cross elasticity of the attributes. This occurs as

a consequence of the assumption of ‘independent and identically distributed (IID)

error term’, which enables the derivation of the simple MNL form. Violation of

this assumption generates unrealistic market share prediction of the choice

alternatives (see Ben Akiva and Lerman (1985) illustration of the ‘blue-bus and

red-bus’ problem). Nested logit is a natural extension of this model by partly

relaxing the assumption of constant and equal variances of the error terms (i.e.,

IID). Both models were estimated in this study. As shown later, MNL was found

sufficient for our purpose.

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5.4 Alternatives and attributes

5.4.1 Alternatives

Three pieces of information is required for the model to yield the results necessary

to achieve the aim of this research: information on the trip structure chosen by the

tourists; information on the travel mode used by the tourists; and information on

the context in which these decisions were made. The third is related to the

destination context in which the tourists make their choice. Destination contexts

are used here to represent a collection of destination attributes. Thus, the contexts

are expected to have a significant influence on the choice behaviour of tourists.

The three choice dimensions are illustrated in Table 5.1. Each dimension is

discussed below.

5-7

Note: Rental (RC); Public Bus (PB); Small organised tour (Tour); Take a

overnight trip and travel by rental car (RCD); Take a day-trip and travel by public

bus (PBB) etc. Destination expenditure attribute is generic for all ‘overnight’ and

‘day-trip’ alternatives.

Trip structure (dimension 1)

As previously mentioned, regional dispersal is a trip that involves at least one

night stay in the ‘periphery’. This is defined as a region outside the politically

salient boundary of Cairns city. An alternative to dispersal is a trip that involves

overnight stays only in the gateway, i.e. Cairns city. Finally, a day-trip option

from Cairns to the periphery is added to the choice experiment as an alternative to

an overnight trip. Thus, the three trip alternatives are: ‘gateway only’ vs. ‘base-

camp (day-trip beyond the gateway)’ vs. ‘at least one overnight stay beyond the

gateway’.

Travel mode (dimension 2)

There are several alternative modes of travel available for the type of trips

mentioned above; including, rental vehicles, taxi, tour shuttles or four wheeled-

drive operators, rail services, as well as non-motorised travel modes. According to

Choice dimension and attributes

North South

Trip structure (dimension1)Cairns and

GBR only

Travel mode (dimension 2) RC PB RC PB TOUR-

Destination (dimension 3)

North/

South

North/

South

North/

South

North/

South

North/

South-

Abbreviation for the choice

alternatives RCD PBD RCB PBB TOUR Gateway

Attributes [Destination expenditure] - 2 2

Attributes [Price] Yes Yes Yes Yes Yes - 5 5

Attributes [Travel time] Yes Yes Yes Yes Yes - 5 5

Attributes [car type] Yes - Yes - - - 2 2

Attributes [sightseeing stopovers] - Yes - Yes - - 2 2

Attributes [Driver characteristics] - Yes - Yes - - 2 2

Attributes [Frequency] - Yes - Yes - - 2 2

Total 20 20

AlternativesNumber of

attributes

[ Generic for d/t ][Generic for o/n ]

[Overnight trip] [ Day-trip ]

Table 5.1 Three choice dimensions

5-8

information available from the regions’ travel internet sites, the most popular

form of travel is a day-trip by a car or through a tour operator. Currently, public

transport (e.g. Sunbus) is available within town centres and the suburbia, as well

as on selected inter-regional routes. Other services such as Skyrail and boats also

provide transport for tourists, although they are more limited to specific locations

and tour activities, such as rainforest tours and the tour of certain islands in the

Great Barrier Reef.

Three salient travel mode alternatives were identified from the viewpoint of

transport and tourism policy on regional dispersal. In addition to rental cars,

public transport was identified as a potential alternative. The third alternative is

located ‘in-between’ on what may be called the ‘characteristics space’ with public

bus on one end of the spectrum and the car on the other. ‘Small-group tours’ often

offer a level of flexibility and privacy that may be perceived to be a mixture of the

car and the public bus; for instance, while the car is wholly flexible for the trip

desired and the public bus more restrictive because of its scheduled and ‘public’

nature of its characteristics, small-group tour belongs to neither of the categories.

Rather, it shares some aspects of both. The three alternatives are different from

each other to an extent that it helps to preserve the IID assumption, which renders

the MNL model more appropriate.

Thus, there are six alternatives each with the Vni in Equation 5.2. The alternatives

are products of the ‘trip structure’ dimension and the ‘travel mode’ dimension

shown in Table 5.1. These are:

o Overnight trip beyond Cairns using a rental car (denoted by RCD);

o Day-trip beyond Cairns using a rental car (denoted by RCB);

o Overnight trip beyond Cairns via public bus (denoted by PBD);

o Day-trip beyond Cairns via public bus (denoted by PBB);

o Day-trip beyond Cairns with a small group tour operator (denoted by

Tour);

o Stay in Cairns only

5-9

Destination (dimension 3)

Finally, two destination contexts are added to the experiment to account for the

effect of destination characteristics. Tropical North Queensland region (TNQ) is

characterised by its diverse range of attractions. This engenders a major challenge

for delineating an appropriate ‘destination’ boundary, as well as a challenge in the

identification of a parsimonious set of destination contexts so that the size of the

experimental design remains practically feasible. For instance, it is commonly

cited in traveller information brochures that TNQ offers experiences ranging from

the City (Cairns) and beaches, to rainforests and tablelands, and the GBR. Current

travel patterns were used as the basis for reduction in the number of destination

contexts. Tourism accommodation establishments, bed-spaces and room number

statistics released by the Australian Bureau of Statistics (ABS) were consulted to

identify the key destinations of overnight stays. Based on these figures, it was

identified that most (over 90%) of accommodation establishments were in the

Coastal regions (including Cairns, which has a 67% share).

Within the coastal regions, Local Government Area (LGA) profiles published by

Tourism Research Australia (TRA) were used to delineate two contrasting

geographic regions: the North and the South (Figure 5.1 shows the map of the

region used in the actual survey). Douglas and Johnstone are the representative

LGA in the North and the South respectively. The LGA profiles show that the

Johnstone LGA (south of Cairns) and the Douglas LGA (north of Cairns) are

similar in that:

• a high proportion of travellers to those regions are for leisure (holiday or

VFR) purpose (87% and 91% respectively);

• ‘beach’ is the main activity that overnight tourists engage in these

destinations (53% and 61% respectively);

• a high proportion of visitations is likely to be part of a multi-destination

travel itinerary, probably involving overnight stay(s) in Cairns (44% and

41% of Johnstone and Douglas overnight visitors also stayed overnight

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elsewhere in their trip, compared to 30% of visitors to Cairns and 11%

national average).

However, these are as far as the strong similarities are observed. Key differences

are:

• the average spend differs significantly, with per night expenditure of $92

in Johnstone against $223 in Douglas, indicating that Johnstone is a more

affordable alternative;

• Johnstone has a much higher share of ‘caravan parks’ accommodation than

Douglas (according to ABS, Johnstone shares 2% of ‘hotels, motels and

apartments’ bed spaces, but 13% of the region’s caravan parks. Equivalent

figures for Douglas are 20% and 12% respectively);

• the length of stay in Douglas is higher (5.2 nights) than Johnstone (3.8

nights), thus, the South is relatively less popular in both volume of traffic

and in number of nights;

• only 24% of Johnstone visitors are of interstate origin whereas the

equivalent figure for Douglas was 65%. This reflects the fact that many of

the air arrivals (from Sydney, Melbourne) are also less familiar with the

South, and the visitation to this region is of lower priority than the North

for these visitors.

Thus, it was judged that these two regions – the North and the South - were

sufficiently different in characteristics to interact differently with the mode

choices of the air arrivals.

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Figure 5-1 Map of the Cairns region shown to the survey respondents

(drawn by the author)

5-12

5.4.2 Attributes and attribute level labels

Some of the most common mode choice attributes in the journey-to-work trip

contexts are price and time. In addition, there is a wide range of qualitative

variables (although in practice, some attributes are used more often than others)

such as frequency, expected delays, etc (see Hensher and Prioni 2002). These

variables are also sometimes collectively referred to as ‘instrumental’ variables,

including ‘flexibility’ and ‘convenience’ as well as costs (Anable and Gatersleben

2005).

Eaton and Holding (1996) suggested that the following factors are important in

the choice of public transport for recreational travel to National Parks (in order of

importance): punctuality; convenient park; lower fares, and the use of ‘novelty

vehicles’. More recently, Lumsdon (2006) provided a qualitative study on the

issues surrounding the promotion of public transport for tourism in the UK. Based

on in-depth interviews of key stakeholders, Lumsdon (2006) found that

‘sightseeing’ is an important market segment for leisure and recreational use of

public bus services. This implied that certain public transport attributes were more

desirable than others. Two of the main attributes noted by Lumsdon were ‘en

route stopover opportunities’ and ‘driver knowledge about the destination and

friendliness’. These are also examined in this Chapter. Interestingly, Eaton and

Holding (1996) and Lumsdon (2006) did not stress travel time as a significant

factor in the patronage of public transport over private car. The discussion section

revisits this topic on travel time.

Two groups of travel mode attributes were identified above: economic variables

and ‘tourism’ variables. Economic variables are widely used in urban mode

choice research, but tourism variables are rarely considered. Destination

expenditure attribute was added as a quantitative measure of destination

characteristics. As previously mentioned, the use of ‘destination contexts’ design

accounted for the destination attributes. The attribute level labels are summarised

in Table 5.2 below.

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(i) Price

For the rental car alternative, the attribute level labels were based on daily and

five-day rates of the major rental firms. Public bus fares were based on current

inter-regional bus fares (e.g. SunExpress). A ‘free’ ride attribute level label was

added to the experiment to maximise the level of conditioning for this alternative.

The ability to analyse an effect of a hypothetical alternative such as a ‘free public

bus’ is one important advantage of the stated choice method. Finally, the labelling

of price attribute for the tour alternative was based on day-tour information from

brochures and websites. All websites were accessed in the first week of August

for prices in the period between 21st and 27

th of August, which was the actual

survey period in Cairns.

Attributes Attribute level labels

Rentcal car alternative

Daily rate (incl. fuel) $50, $100, $150

One-way 'in-vehicle' travel time 1 hour, 2 hours, 3 hours

Car type economy, luxury, 4WD

Public bus alternative

Price per person Free, $40, $80

One-way 'in-vehicle' travel time 1 hour, 2hours, 3 hours

Driver attribute below expectation, average, above expectation

Sightseeing Non, 1 or 2 stopovers, more than 2 stopovers

Frequency every 1 hour, every 2 hours, every 3 hours

Small group all-inclusive tour

Price per adult (child) $100($50), $150($75), $200($100)

One-way 'in-vehicle' travel time 1 hour, 2hours, 3 hours

Destination expenditure (per night per person)

Northern destinations $120, $170, $220

Southern destinations $70, $120, $170

Table 5.2 List of attribute level labels

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(ii) In-vehicle travelling Time (one-way) and frequency

The attribute level labels for the travel time attribute was based on ‘google map’

information on distance and imputed travel time. The attribute labels for

frequency were based on current frequency of regional bus services.

(iii) Rental vehicle type

For the rental car alternative, rental vehicle type was also added as an attribute.

This was considered important because consumers often associate quality and

price in their choice (Hensher et.al. 2005); thus, to not include information on the

quality of rental vehicle may induce tourists to choose a high price alternative

because they associate this with higher quality. Consequently, this has the danger

of measuring the combined effects of price and quality, not only price.

(iv) ‘Tourism variables’ for public transport

As mentioned previously, two tourism attributes are added to the public transport

alternative: ‘one or two stopover in special places’ and ‘driver knowledge and

friendliness’.

(v) ‘Comfort’ factors

Anable and Gatersleben (2005) have shown that ‘freedom’ and ‘control’ are the

affective qualities of a car that travellers emphasise over public transport. In

addition, ‘flexibility’ and ‘convenience’ of a car are also important (e.g. Anable

and Gatersleben 2005). While each of these factors could not be included in the

experimental design for practical reasons (to contain the size of the experimental

design), the survey asked the respondents to rate the ‘comfort’ of travel modes on

a Likert scale. Although this is an imperfect measure of the affective factors, it

captures some aspects of the qualitative attributes that may not be so easy to

explicate in the experimental design. Similar methods have been used, for

instance by Koppelman and Sethi (2005), in inter-regional mode choice

experiments.

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(vi) Expenditure at the destination

It was shown previously that destinations in the South are much more affordable

than those in the North. Different levels of expenditure per night per person were

specified in reflection of this difference. It was shown that average daily

expenditure in Cairns is approximately an average of the expenditures in the

North and the South.

5.5 Experimental design

5.5.1 Orthogonal main effects design

A key issue in the experimental design for choice modelling is whether or not a

design should allow for testing of the violation of the identical and independently

distributed error terms (IID) assumption. The outcome from such a test

subsequently provides the basis for extending the analysis with more sophisticated

models (Louviere et.al. 2000). Given the choice dimensions of this study, it was

appropriate for the experimental design to be non-IID, so that non-IID models

could be estimated from the data collected (e.g. nested logit). A sufficient

condition for a non-IID design is when all attributes are orthogonal with one

another within and between alternatives (Louviere et.al.2000). Thus, for this

study, a design that can accommodate at least 20 orthogonal attribute columns

was required (the number of attributes shown in Table 5-1).

A fractional factorial of 320

was selected. The fractional factorial allows up to 20

orthogonal columns, each with three levels. In 54 treatment combinations (choice

sets), this is an orthogonal main effects only plan. Thus, the effects of two-way

and higher order interaction are not protected from confounding with the main

effects; for instance, the effect of price of an attribute is independently estimated

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from effects of all other attributes, but there is no guarantee that this effect will be

independent from the interaction effect of, say, price and time. This design was

replicated to produce choice scenarios in the context of trips to the Northern

region and another complete set of scenarios in the context of trips to the Southern

region. Thus, there are 104 treatment combinations in total (after removing the

treatment combinations without any designed trade-offs), and this was blocked

into 26 versions to produce four choice scenarios for each respondent. All

alternatives are present in the choice scenarios, and each respondent received two

scenarios each from the North and South destination contexts.

5.5.2 Coding and design orthogonality

Effects coding enables the model to estimate the effect of a particular variable as a

deviation from the grand mean (the mean of the unobserved utility). This coding

scheme is necessary in order to estimate non-linear effects without the non-linear

effects confounding with the alternative specific constant (e.g. Hensher et.al.

2005). However, the effects coding scheme generates correlation among the levels

of the same variable. As previously discussed, one advantage of using a stated

choice experiment is that the values of the explanatory variables are not

correlated. But orthogonality can be lost in many ways (see Louviere et.al. 2000

for details). In this study, the effects-coding structure of the variables from ‘high’

to ‘medium’ to ‘low’ is one source of correlation within a given attribute of an

alternative. Pairwise correlation matrix is a common strategy to test for design

correlations. As expected, the effects coding structure gives rise to a correlation of

approximately 0.5 within the levels of a given independent variable.

The extent to which 0.5 is a problem is difficult to know, although a rule of thumb

value, for instance, 0.8 can be used as a benchmark (Hensher et.al. 2005). As a

consequence of the correlation, the coefficient estimates may become unstable

and standard errors may become very large, affecting the asymptotic t-tests of

statistical significance (Greene 2002). Thus, we interpret the coefficients with

caution in discussing the results. It is noted that correlation is only a problem

between the levels of an attribute of an alternative; for example, the correlation is

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introduced between a ‘high’ price and a ‘low’ price of the rental car alternative,

and not between the price and time attributes of rental cars. All designed

attributes, by the virtue of the design, are orthogonal with respect to all other

attributes within and across alternatives.

5.5.3 They survey

The survey was conducted at the Cairns domestic airport terminal in the period

between 22nd

and 27th of August in 2008. The peak period in Cairns tourism is

between April and October, as other months are part of the wet season. There was

a continuous flow of visitors throughout the day, to Sydney, Melbourne, Brisbane,

Perth and Adelaide. All visitors who regarded themselves as residents of these

cities were eligible for an interview; provided the purpose of their trip was

‘visiting friends and relatives’ or/and holiday, and they had taken one of Jetstar,

Qantas or Virgin Blue flights.

While the primary component of the survey was the hypothetical choice scenarios,

other trip information was gathered. The questionnaires on trip information were

designed to mimic that of National Visitor Survey conducted by Tourism

Research Australia. While most of the questions on trip details and personal

information were not found intrusive, a question on ‘income’ was ignored by

more than 20% of the respondents. Consequently, this variable was dropped from

the models. Pilot surveys were distributed to the students and staff of University

of New South Wales, Research and Strategy division in Tourism Australia, and

Cairns airport for feedback. A sample of the survey is provided in the Appendix.

Collection method was ‘simple random’ in that, for instance, interviewers

approached travellers taking seating on every second row in the departure lounge

area. The turnover of travellers was high. The final two days focussed on

obtaining a more representative sample across demographic groups (age and

gender), representing a stratified random sampling technique. The data collection

exercise aimed for eight replications of the entire design, or 208 respondents.

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After discarding unreliable responses, 196 surveys were judged usable, providing

at least seven replications with a total of 784 choice observations. The face-to-

face survey helped to assure reliable and informed responses.

5.6 Results

5.6.1 Descriptive statistics

The sample collected was slightly skewed towards male (59%). As a benchmark,

the National Visitor Survey statistics on Cairns show that the share of 25-44 and

45-64 should be approximately the same (TRA 2008). Age groups of 18-25, 36-

45 and 46-55 were approximately equally represented with shares between 16 –

20% of the total sample. The age group 26-35 represented 31% of the sample,

while the 56-65 age group accounted for 11%, and over 65 with 4%.

Trip characteristics information is presented below. 100% of the sample departed

Cairns via air transport. However, there was a small percentage of sampled

individuals who arrived on modes other than air travel such as train or rental

vehicles. These respondents were subsequently removed from the analysis. The

following trip characteristics are highlighted:

o Nearly half of the visitors sampled used ‘rental cars’ as a main mode of

ground transport in the destination (43%). This is followed by walking

(20%), private vehicle (11%), tour company (7%) and public bus (5%). The

cases of private vehicles apply to friends’ and relatives’ vehicles.

o Half of the sample stated ‘hotels, motels and apartments’ as their main

accommodation (51%). This type of accommodation, together with

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‘resorts’, accounted for 80% of the sample, while 10% indicated friends’

and relatives’ property.

o In regards to destination activities, 80% of the sample stated ‘eating out’ as

their main form of travel activity, followed by ‘walk or drive around’

(75%) and ‘visiting the rainforest’ (56%). This pattern is consistent with the

LGA profiles mentioned previously. Surprisingly, only 47% of the sample

stated ‘Great Barrier Reef’ as one of their travel activity, indicating the

diverse range of activities tourists seek from Cairns and the TNQ region.

Further, the high proportion of ‘walk or drive around’ (75%) and ‘go to the

beach’ (58%) against relatively low incidences of ‘day-trips with a tour

company’ (31%) indicate that tourists prefer to do things themselves than to

rely on the services provided by the local tour operators.

o Couples represented 46% of the sample, travelling alone represented 30%

and group of three represented 14%. The goal was to obtain one survey per

travel group, however, on several occasions ‘couples’ participated in the

survey separately. This most likely contributed to the inflated sampling of

couples. Nonetheless, their stated choice data remains valid.

o As for length of stay, the sample median was 4.5 nights, while the average

was 5.4 nights. This is consistent with the 4.8 average nights found in the

published sources (e.g. TRA 2008). Over 92% of the sample recorded trip

durations less than 11 nights. Finally, 59% of the sample was repeat

travellers, and 41% was first time visitors to Cairns. Unfortunately, the data

on repeat visitation for domestic travellers are not available to compare.

While the author is not claiming the data to be statistically representative of

Cairns’ entire visitor population, the data collected are demonstrated to be

consistent with the best available secondary data on the region.

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Figure 5.2 shows that the option to ‘hire a rental car and take a overnight trip’

(41.5%) is the most popular choice, followed by ‘staying in Cairns’ (25.6%).

There appears to be a small market for public transport with a choice share of

13.3% for both overnight and day-trips. The surprising result was the little choice

preferences for ‘organised day-tours’, reflecting the potential cannibalisation of

this market when leisure-purpose-built public transport alternatives are

introduced. By the same token, the small sample choice shares of the ‘Base-camp

PB’ and ‘Base-camp Tour’ alternatives suggest that the interpretation of the

results on these alternatives should be undertaken with caution. Consequently, the

discussion in this Chapter focuses mostly on the alternatives with more

statistically reliable results such as ‘Dispersal RC’ and ‘Gateway/GBR’. In

aggregate, the distributional patterns across choice alternatives in the North and

South are similar. However, Public Bus and day-trips are more popular for travel

to the South.

Figure 5-2 Sample choice shares across alternatives

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5.6.2 Model results

Various model specifications and nested logit structures were applied to the

choice alternatives above. However, the evidence from these models provided

support for the use of a multinomial logit model with each of the ‘trip-structure’ –

‘travel mode’ combination as an independent alternative. This is discussed later in

this section. Table 3 shows the MNL model performance indicators. The model

fits slightly better for the ‘trip to the North’ scenarios than the South.

The model results are shown below (Table 5.4 and Table 5.5). ‘Organised all-

inclusive tour’ (Tour) was estimated without the alternative-specific-constant (the

Tour option was the base alternative). There are two models – one for the North

and one for the South – shown in Table 5.4 and Table 5.5 respectively. Some

variables were excluded during the modelling process because they were not

statistically significant across all alternatives. The coefficients represent the

marginal effect of a variable on an alternative’s level of utility (see Equation 1).

For example, the coefficient value of -0.88 on ‘PBD $80’ variable in Table 5.4

shows that the utility from choosing ‘overnight trip beyond Cairns on public bus’

decreases by 0.88 unit of utility (‘utils’) when public bus fare to travel to the

Northern destinations is $80. The actual trip characteristics, which were also

collected from the survey, are dummy coded. Thus, the base case is shown in

brackets, e.g. ‘Repeat visit’ (first-time). Interpretation of coefficients is similar to

the travel mode attributes. Thus, -0.629 on ‘RCD repeat’ variable shows that

repeat visitors obtain less utility from choosing ‘overnight trip beyond Cairns on a

rental vehicle’ by 0.629 than first-time visitors. The primary interest here is in

finding the significant factors, and the extent to which these factors may affect

North South

Adjusted pseudo R^2 0.267 0.239

Log likelihood (model) -505.8844 -521.2965

No coefficient LL -702.3697 -702.3697

Table 5.3 Model summary

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relative utility levels. Thus, the discussion on actual probability values and

predicted choice shares is omitted.

Note: Asterisk [*] indicates asymptotic t-test significance at 10%, [**] 5%, [***] 1%. [^]

indicates generic parameter within trip structure (e.g. for overnight trip regardless of

travel mode). Please refer to Table 5.3 for more details on generic parameters. [#]

indicates generic parameter within travel modes (e.g. the coefficient on PB means that the

coefficient is equal for both PBD and PBB). Abbreviations used: hotels, motels and

serviced apartments (HMA); friends and relatives’ property (FRP); camping and caravan

parks (CNC).

Trip to the Northern region

Variables Coefficient P-value Variables Coefficient P-value

Constants Repeat visit (base: first time)

RCD 1.263 ** RCD repeat -0.629 ***

PBD -1.795

RCB -1.156 Accommodation type (base: 'all other')

PBB -1.329 * RCD resort 0.247

Gateway 2.090 *** RCD HMA -0.078

RCD FRP -1.406 ***

Price PBD resort 2.166 **

PBD $80 -0.880 *** PBD CNC 3.841 ***

PBD $40 0.154 PBD HMA 2.463 **

Tour $200 -0.939 *

Tour $150 0.070 Travel party # (base: travelling alone)

PB two adults 1.067 **

Time PB three or four adults

RCD 3 hours 0.075 0.348

RCD 2 hours -0.288 *

Age group # (base: 18-25)

Destination Expenditure ^ PB 26-35 -1.168 **

Overnight trip in the North $220 PB 36-45 -0.275

-0.474 *** PB 46-55 -0.449

Overnight trip in the North $170 PB 56-65 0.101

0.220 PB over 65 -0.409

Comfort

RCD comfort 0.201 ***

RCB comfort 0.306 ***

Table 5.4 Model output: North

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Note: Asterisk [*] indicates asymptotic t-test significance at 10%, [**] 5%, [***] 1%. [^]

indicates generic parameter within trip structure (e.g. for overnight trip regardless of

travel mode). Please refer to Table 5.3 for more details on generic parameters. [#]

indicates generic parameter within travel modes (e.g. the coefficient on PB means that the

coefficient is equal for both PBD and PBB). Abbreviations used: hotels, motels and

serviced apartments (HMA); friends and relatives’ property (FRP); camping and caravan

parks (CNC).

Trip to the Southern region Trip to the Southern region (cont…)

Variables Coefficient Variables Coefficient

Constants Repeat visit (base: first time)

RCD 1.13 * RCD repeat -0.56 **

PBD -0.32 RCB repeat -0.83 ***

RCB 0.50

PBB -1.02 Accommodation type (base: 'all other')

Gateway 2.19 *** PBD CNC 2.90 ***

PBD HMA 0.79

Price PBD FRP 1.48

PBD $80 -0.50 * PBD resort -0.11

PBD $40 -0.26

PBB $80 -1.39 ** Travel party # (base: travelling alone)

PBB $40 -0.14 RCD two adults 0.46 *

RCD three or four adults

Driver knowledge and friendliness 0.08

Above expectation (PBD) RCD more than four adults

0.48 ** 3.40 ***

As expected (PBD) PBB two adults 1.42 **

-0.47 * PBB three or four adults

1.09 **

Sightseeing (number of stopovers)

More than two stopovers

-0.60 ** Length of stay (base: 1-3 nights)

One or two stopovers RCD 4-6 nights 0.32 **

0.61 ** RCD 7-10 nights 0.07

RCD 11 or over 0.42 **

Comfort PBD 4-6 nights 1.02 ***

RCD comfort 0.14 * PBD 7-10 nights 0.40 **

RCB comfort 0.14 PBD 11 or over 0.17

RCB 4-6 nights 0.50 ***

RCB 7-10 nights 0.19

Destination Expenditure ^ RCB 11 or over 0.14

Overnight trip in the South $170

-0.26 * Age group # (base: 18-25)

Overnight trip in the South $120 PB 26-35 -0.19

-0.22 PB 36-45 -0.32

Day-trip in the South $170 PB 46-55 -0.91 *

-0.55 *** PB 56-65 -2.69 **

Day-trip in the South $120 PB over 65 0.71

-0.07

Table 5.5 Model output: South

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Two tests described in Chapter 1 were applied to the Cairns data. The Hausman-

McFadden test was applied in the following way. For models ‘North’ and ‘South’,

two most prominent alternatives (in terms of sample choice shares) were removed

from unrestricted models. Thus, four tests were conducted in total: a restricted

model without the ‘rental car overnight alternative’ (one for north and one for

south); a restricted model without the ‘public bus overnight alternative’ (one for

north and one for south). The tests revealed that when the rental car ‘overnight’

alternative was removed from the North and the South model, evidence to reject

the IIA assumption was insufficient (Hausmand and McFadden statistics of -18.4

and -12.53 respectively). As for the public bus alternative, the North model

violated the IIA assumption at the level of 1% significance, whereas the South

model did not. The reliability of the Hausman-McFadden test has been called into

question for relatively small sample sizes (Fry and Harris, 1996). Given the small

choice shares of public bus alternatives in the sample, it is appropriate that further

tests are applied.

The second IIA test applied was the IV test. Table 6 shows the inclusive value

parameters (IV parameters) of the model nested in travel mode and that nested in

trip structure. The nested logit models were specified as per the models that

generated the results in Table 5.4 and Table 5.5. The IV parameter estimation

results are either statistically insignificant from ‘0’ or ‘1’, or they exceed the value

of ‘1’. The latter case violates the utility maximisation assumption that underpins

discrete choice analysis, whereas values of 0 or 1 indicate that the specified nest is

not significant statistically (Hensher et.al. 2005). In particular, given the

incidences of the statistically equivalent value of ‘1’ in these models (IVRC and

IVPB in the travel mode nest, and IV day-trips for both North and South in trip

structure nest), there is evidence that the nested model collapses to a simple

multinominal logit model. Thus, this simplifies our modelling task to a situation

where each of the ‘trip structure’ – ‘travel mode’ combinations is an independent

alternative uncorrelated in their stochastic utilities of one another.

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Note: [*] indicates not statistically different from ‘1’ at 10% level, [**] 5% and

[***] 1%.

The following discussion concentrates mostly on the overnight trips of tourists

and the significant travel mode attributes associated with the overnight trips.

Overnight trips typically inject greater expenditures into peripheral destinations;

thus, this type of trip may be of most interest to them. Overnight trips were also

the most popular choices (see Figure 5.2) and consequently less subjected to the

problems associated with low choice samples.

5.7 Dispersal and rental cars

5.7.1 Transport attributes

Destination expenditures and perceived comfort exert significant influence on the

choice of RCD in both destination contexts. The perceived comfort of the rental

vehicle is a strong source of utility (the coefficient indicates a change in one unit

in the Likert scale). In fact, it can be concluded that perceived comfort is one of

the most important reason why a car is a popular choice, supporting the Anable

and Gatersleben (2005) study that has shown the importance of affective factors

(such as ‘freedom’ and ‘control’) of a car over public transport. Furthermore, the

flexibility the rental vehicles offer (much in the same vein as the private vehicle),

North South

Travel mode nest

IV RC 1.1 ** 0.17

IV PB 11.6 1.2 ***

Gateway 1 1

Trip structure nest

IV overnight 7.5 2.6 **

IV day-trip 1.6 ** 0.997 ***

Gateway 1 1

Table 5.6 Inclusive value (IV) parameters

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is a significant factor that is difficult to be replaced by other modes. The

significance of ‘comfort’ reflects these qualities. This supports Eaton and Holding

(1996) who argued that the popularity of the car cannot be replaced by other

modes, especially when the travel modes are compared against the same

attributes. Rather, they argued, other modes must capitalise on what the private

vehicle cannot offer.

Destination expenditures influence tourists’ choice of modes and trip structure.

Tourists were unresponsive to price and travel time attributes of rental cars, at

least when compared with the utility gained from qualitative (and affective)

features such as comfort. One key feature of rental vehicles is that the cost per

head decreases up to the vehicle’s capacity limit, diminishing the importance of

price as travel party size increases. This helps to explain why the price of travel

mode is not significant but the price of destination is significant (destination

expenditure variables). Destination expenditures are typically greater than

expenditures on transport, and this renders the responsiveness to destination

expenditures greater. In addition, the rental vehicle rate was indicated in the

survey as ‘per day’ cost, whereas the destination expenditure was ‘per person per

day’ cost.

5.7.2 Trip characteristics

Repeat visitors to the region are less likely to choose RCD and RCB. In both

destination contexts, the magnitude of the negative effect of repeat visitation (-

0.56 in the South model) is strong enough to offset the utility gained from savings

in destination expenditure (+0.48 utility earned by saving $100 in expenditures

(going from $170 to $70 per day)). Thus, it is more difficult to entice repeat

travellers to choose RCD or RCB option with control variables such as price, than

it is for first time visitors. The result suggests that first time visitors are more

likely to use rental cars for dispersal, while repeat visitors are less likely to do so,

possibly because the repeat visitors’ greater destination familiarity enables them

to exploit other alternatives. As discussed in Chapter 3, Li et al. (2008) noted that

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first time visitors are more extensive in their destination exploration, while repeat

visitors are more intensive in their use of time across a smaller range of

destinations and activities. Further, it has been suggested that “the more familiar

the tourist is with the location, the more knowledge one has of different kinds of

local activities and attractions to fill an entire trip schedule” (Hwang et.al. 2006:

1060), which renders repeat visitors more specific in the activities pursued, but

also less explorative, diminishing the need for a travel mode that provides this

capacity for the visitor.

Individual trip characteristics are significant constraints for the choice of RCDS

alternative (superscript denoting ‘South’). The utility functions differ for the two

destination contexts in two ways. First is that the attribute coefficients, such as

‘destination expenditure’ has less influence on the trip to the South than to the

North. Second, trip characteristics such as length of stay and travel party size

exert significant influence on the choice of RCDS

but not for RCDN. This is an

interesting finding given that these two trip characteristics are important

determinants of multi-destination travels and dispersal. The utility from choosing

RCDS increases as travel party size increases; for instance, compared to solo

traveller, couples yield statistically significant 0.46 utils, three or four adults yield

0.08 (but not statistically significant), and more than four adults yield statistically

significant 3.4 utils. The utility from choosing PBD increases as length of the trip

increases; for instance, compared to a trip between 1-3 nights, a trip between 4-6

nights yields statistically significant 1.02 utils, while a trip with 7-10 nights yields

a statistically significant 0.4 utils.

Length of stay is positively related to greater dispersal and multi-destination travel

(“when time is short, space is conserved” - Fennell 1996). Greater travel party

size indicates heterogeneity in preferences, which results in greater need to visit

multiple places (Tideswell and Faulkner 1999), and by implication, greater need

to be more spatially expansive and disperse. In other words, a trip to the South

becomes more likely only when there is sufficient time and preference

heterogeneity in the travelling group, reflecting the fact that the South is less

5-28

popular and known to the tourists. Importantly, both variables are in many

instances determined prior to the arrival, thus this result shows the relative

ineffectiveness of destination control variables, e.g. price, for dispersal to the

South.

5.8 Dispersal and public transport

5.8.1 Transport attributes

There are significant differences in the factors that determine PBDN

with PBDS. A

key finding is that a choice of PBDN

and PBDS is associated with a different

responsiveness to different public transport attributes. The PBDS alternative is

determined by the qualitative attributes of public bus, as well as price, whereas

only price matters for the choice of PBDN. A high level of ‘driver knowledge and

friendliness’ and ‘1 or 2 stopover for sightseeing’ are qualitative features of public

bus design that may contribute to greater rider-ship, but only for trips to the

South. For this alternative, the disutility of price (PBD $80 coefficient of -0.5) can

be almost completely offset by offering good driver service (‘above expectation’

yields 0.48 unit of utility) or more than offset by a stopover opportunity en route

for sightseeing (‘one or two stopovers’ yields 0.61). The combined offering of

two attributes will increase the utility of PBD to go to the South by 1.09 units

(0.48 + 0.61) (or even more if interaction effects are present).

The differences in the utility functions of alternatives between destination

contexts can be attributed to two factors. First is the relatively unknown status of

the South compared with the North. Thus, qualitative attributes of public transport

services are important for tourists with little familiarity and knowledge of the

destination. The second explanation refers to market segments. Lumsdon (2006)

described two market segments for public bus services: ‘sightseeing’, and

‘activity seekers’. The study argued that the latter will be much less concerned

5-29

with the ‘transport as tourism’ aspect of the trip; rather, this group will use the bus

purely as a vehicle to travel between origin and destination in pursuit of their

sought activities, or in Lumsdon and Page’s (2004) terms, ‘transport for tourism’.

The significant utility gained from the qualitative attributes indicates that

sightseeing tourists may be the primary source of demand for the South. Southern

destinations may generate demand from the sightseeing tourists because it is an

unfamiliar destination.

5.8.2 Trip characteristics

Trip characteristics have statistically significant influences on the choice of the

PBDS

alternative but not on the choice of the PBDN alternative. Greater travel

party heterogeneity and length of stay positively influence the choice of PBDS.

The coefficients are large relative to transport modal attributes, implying that

attractive travel mode attributes themselves may not be sufficient to compensate

for pre-determined trip characteristics such as short length of stay. Generally,

‘camping and caravan’ (CNC) is associated with the greater use of public

transport service. Furthermore, tourists with ‘friends and relatives property’ (FRP)

as their main accommodation, were observed to be public transport averse,

presumably because family and friends are able to provide the necessary mobility

in the destination.

Travel decisions to the South depend on the length of stay. Greater length of stay

tends to promote overnight trips as well as day-trips, which is expected given the

positive relationship between length of stay and dispersal. However, this is not the

case for the North, where length of stay was found statistically insignificant in all

alternatives (subsequently dropped from the model). This reflects the fact that the

northern region is a prime attractor of tourists to Cairns and TNQ. The effect of

length of stay (less than 4 nights in this study) may not be an important variable

for many of the well-known regions because these destinations are often the main

reason for the trip to Cairns. However, for a relatively unknown periphery, length

of stay is an important determinant. This is not surprising because the southern

5-30

destinations will be ranked lower in the tourists’ priority list, which will be

considered for a visit when the utility from visiting the primary destinations has

been fulfilled. One implication is that the trend of short-frequent break will not

contribute dispersal to the peripheral destinations in the South and alike. This has

important ramifications for the dispersal of LCC-induced tourists because the

LCCs have been observed to be associated with short-frequent breaks.

5.9 Limitations and future research

The time and frequency variables were statistically insignificant in this study. The

determining power of travel time in travel mode choice is significant in the

context of journey-to-work (JTW) trips (e.g. Redmond and Mokhtarian 2001) and

in long-distance inter-regional trips (e.g. Hensher 1997, Koppelman and Sethi

2005). This insignificant result may be a reflection of the relatively time-

insensitive nature of leisure travellers, in particular when the range of travel time

examined was between one to three hours. The result implies that peripheral

destinations are not significantly disadvantaged by the fact that their destinations

are an hour further from another destination. In fact, the evidence supports Page’s

(1994) argument that in tourism, transport is not only a cost to be minimised, but

also an integral part of tourists’ overall travel experience. An extension of this

argument is a possibility of positive utility attached to travel time, in which case

we should not observe a significant negative relationship between utility and

travel time. The positive utility in travel time is illustrated in the intra-mode JTW

trips; for example, Redmond and Mokhtarian (2001) show that commuters prefer

a short commuting time than none. Perhaps future studies can apply a similar

approach to a finer market segment in order to isolate the positive and the

negative effect of time on utility.

5-31

Overall, the finding on the time variable is in-line with the qualitative work of

Lumdson (2006) and Eaton and Holding (1996) reviewed earlier. In both studies,

in-vehicle travel time is not mentioned as a key determinant for the demand of

public transport in the context of recreational trips. Nonetheless, the importance of

the frequency attribute is noted in their studies. Surprisingly, this research found

no significant effect of frequency on mode choice. This potentially illustrates one

important issue with stated choice experiments. The choice scenarios are

formulated with pre-determined set of attributes that describe a choice alternative,

which cannot be exhaustive for practical reasons. Thus, attribute specification

must be parsimonious. While frequency is an important attribute, from

respondents’ viewpoint, this may be a proxy for a more salient and ambiguous

feature such as ‘convenience’. Specifying a ‘convenience’ attribute that

summarises frequency, as well as features such as schedules and reliability, may

yield a different outcome. Such specification should be explored in tourism

problems in the future.

Finally, while this study provides insights into dispersal and travel mode choice

behaviour of the air arrivals, the results and conclusion cannot be extended to the

behaviour of some market segments in the Cairns region. For instance,

campervans and backpacker segments were not explicitly considered in this study.

The backpacker segment is related to the high level of international visitors in the

Cairns region, which highlights another limitation of this study - that only

domestic visitors’ dispersal and mode choice behaviour were considered.

Furthermore, due to resource constraints, the survey could be carried out over a

limited period. While the survey period has been carefully selected (e.g., avoiding

special events, etc.), the author acknowledges that the short survey period imposes

some limitations on the findings. In general, the samples collected are more

representative of the behaviour of tourists during peak-holiday season than off-

peak. Thus, in situations of excess capacity, the behaviour of tourists is likely to

be different from that observed in this Chapter.

5-32

5.10 Conclusion

The aim of this Chapter was to provide insight into the likelihood of destination

transportation policy to stimulate dispersal of the air arrivals, even in situations

where the air arrivals exhibit trip characteristics that may be dispersal averse. The

use of stated choice data and the application of choice modelling provide the

ceteris paribus effects of attributes (both actual and hypothetical attributes) and

trip characteristics on choice. This allows a direct comparison of transport

attributes and trip characteristics from a utility compensation perspective. This

study has shown that appropriate ground travel mode attributes can offset some

or all of the negative effects of trip characteristics on tourists’ dispersal

propensity. However, the extent to which this is feasible depends on the

destination contexts. Dispersal to the North is easy to entice because northern

destinations are one of the primary reasons why travellers fly to Cairns in the first

place. However, this is not the case for the southern destinations.

One significant outcome from this study was the importance of trip characteristics

on dispersal to the southern destinations. The relative importance of trip

characteristics compared with the coefficients of modal attributes was very strong,

indicating that individual trip characteristics are binding constraints to dispersal to

the South. The length of stay and travel party size variables were constraints that

tended to reduce the propensity of air arrivals in Cairns from dispersing to the

southern destinations. Hence, ground transport is of little effect in promoting

dispersal of the air arrivals to the South because trip characteristics are in many

instances pre-determined.

For those choosing rental cars, perceived ‘comfort’ is the primary source of utility

for using this mode for dispersal. Thus, the quantitative attributes such as price

and time are relatively ineffective in contrast to the subjective and more

5-33

qualitative elements. As expected, there was a strong relationship between a car

and dispersal. This relationship was evident in both destination contexts.

However, destination context changed the relationship between dispersal and

public transport markedly. The clear difference was that the travel to the northern

region was related to the functional elements of the public bus alternative such as

price, whereas the South emphasised the qualitative attributes such as adequate

‘stopovers for sightseeing’ and good ‘driver knowledge and friendliness’. This is

in part a reflection of the ‘sightseeing’ market characteristics to the South, which

is related to the fact that tourists are generally less familiar with the South.

The findings are relevant for destination managers and policy makers. Firstly,

destination transport policy aimed at assisting dispersal must be devised upon

adequate assessments of the factors that constrain tourists’ travel. Specifically,

this study provided some evidence supporting the attractiveness of qualitative

attributes of public bus services, and importantly, demonstrated how the

effectiveness of such design differs across destinations. Public transport is often

an important component in the pursuit of environmental objective by government.

This research has generated empirical evidence highlighting the importance of

weighing up tourism and regional dispersal implications of public transport

policy. Although the data examined in this Chapter were collected in the Tropical

Northern part of Australia, this research should be of relevance to many regions

interested in understanding the relationship between destination transport and

spatial behaviour of the air arrivals, which experienced vast growth in the recent

years due to the advent of low-cost carriers.

5-34

Appendix 5.1

6-1

6. THE BALLINA-BYRON EXPERIMENT

6.1 Introduction

The emergence of LCCs has improved air travel access to regions outside the

capital cities in Australia by offering discounted tickets and non-stop services

from key domestic origin markets. By the same token, it has also increased the

competitiveness of air travel against other modes of travel in regions traditionally

reliant on ground modes. Recent research by Whyte and Prideaux (2007) in North

Queensland (Australia) has shown the relative decline of car and long-distance

coach travel between 2001 and 2005, while air travel increased in the same period

largely marked by the proliferation of two Australian LCCs (Virgin Blue and

Jetstar). As a result, tourism businesses located between tourism generating

regions and regional destinations experienced declines in visitation (Whyte and

Prideaux 2007).

In Australia, car is the dominant travel mode used for visiting rural regions (TTF

2002). The car allows travellers the flexibility to establish their own travel

itinerary (Taplin and McGinley 2000), whilst air travel often does not offer the

same flexibility and spontaneity in the choice of travel routes (Stewart and Vogt

1997). Consequently, travel mode is an important means by which the different

levels of spatial ‘degrees of freedom’ for tourists are achieved (Lew and

McKercher 2006). In fact, recent research has shown that the spatial pattern of

travel and travel mode used are related to the travel experience sought. Moscardo

and Pearce (2004) studied the moderating role of lifecycle factors in the choice of

long-distance mode of travel, and found that self-drive tourists are considerably

6-2

different from non-self drive tourists in the travel experience sought in the North

Queensland Region. In particular, the study found that self-drive tourists tend to

place more importance on visiting rural communities than other travellers.

There is a potential conflict between the increasing use of air travel and dispersal.

This is because dispersal typically requires a high degree of mobility, which can

be most easily met by using the car, but is most difficult to meet by air transport.

Conversely, according to the law of demand in microeconomic theory, the

improved affordability of airfares is a potent force in increasing the demand for

air travel. Specifically, the objective of this Chapter is to examine the proposition

that LCC proliferation adversely affects regional dispersal. This shall be

approached via the analysis of the trade-offs involved in leisure travellers’ travel

mode choice decisions. This Chapter accomplishes the final specific aim of this

thesis (A5), which is to examine inter-regional travel mode substitution as a

source of conflict between low fare air services and regional dispersal by applying

a stated choice experiment.

6.2 Tourists’ dispersal

Australia’s national tourism organization, Tourism Australia, uses the definition

of ‘regional dispersal’ as trips originating in State and Territory capital cities into

destinations other than these cities and the Gold Coast. In this chapter the regions

are dichotomised into ‘gateways’ or ‘periphery’. Lew and McKercher (2002)

defined gateways as the first destination of overnight stay in the trip, which can be

either a point of entry or the main destination itself. In Australia, the gateways are

almost always the largest townships of the tourism-regions. For the purpose of

this research, a single destination trip is defined as a trip that involves a stay only

in one gateway, whereas a multi-destination trip involves at least one overnight

6-3

stay in the gateway and one in the periphery. The cases in which a trip involves

stopovers on more than one gateway are not considered in this research.

Dispersal is achieved when many destinations are visited within the same trip, or

when a unique trip is undertaken in many parts of the destination (Wu and Carson

2008). From the viewpoint of individual preferences, it is possible for there to be

as many variations in spatial behaviour of tourists at the destination and in the

region surrounding the destination, as there are individuals travelling. Lue,

Crompton and Fesenmaier (1993) conceptualised the variation in the patterns of

trip itinerary into five basic patterns of multi-destination trips. Oppermann (1995)

developed this further into two single-destination and five multi-destination trips.

The multi-destination trip patterns identified have been applied to differing

contexts by researchers; on a domestic-regional level (Stewart and Vogt 1997), to

travel by international tourists (Tideswell and Faulkner 1999), as well as inter-

continental travel (Lew and McKercher 2002). Some of the common trips

featured in these studies that are relevant to this research are the patterns of

‘regional tour’ and ‘en route’ travels (Figure 6.1). In this research, a single-

destination trip refers to a trip that only involves an overnight stay in the

‘gateway’ (denoted ‘D’), while multi-destination trips involve overnight stops in

at least two different destinations, one of which is the gateway.

D

a

b c t

d

e

f

HOME

D

c

a

b

En route

Regional tour/partial

orbit

HOME

D

d

e

f

Combined en route and

regional tour

Figure 6.1 Patterns of multi-destination travel (modified from Lue et.al. 1993 and

Oppermann 1995)

6-4

The consequences of modal substitution towards air travel can be detrimental to

the peripheral destinations. Substitution away from ground modes implies

bypassing smaller destinations located between major origin markets and popular

domestic leisure destinations. A destination such as Port Macquarie, a seaside

town located between Sydney and Byron-Ballina in New South Wales, is an

examplei. This relationship can be seen in Figure 1. In the ‘combined en route and

regional tour’ diagram, ti represents the transport linkage between home and

destination, and the subscript (i) represents the available travel mode on this link,

such as car or air. If substitution occurs toward air travel due to low fares, then the

smaller destinations ‘a’, ‘b’ and ‘c’ will be bypassed, with the only possibility of

visitation conceivable when the traveller travels back from ‘D’.

Modal substitution is not the only channel of influence of affordable air travel on

dispersal. If the cheap and direct flights stimulate a greater number of tourists to

‘D’ then this increases the pool of tourists that may travel further to the peripheral

destinations of ‘d’, ‘e’ and ‘f’. In some circumstances, even the destinations ‘a’,

‘b’ and ‘c’ may experience an increase in visitations from the travellers flying into

‘D’. This may occur when the return route or mode is different from that used for

access, such as when the tourist uses a car to travel back ‘home’, or when the air

arrivals take day-trips from ‘D’ to the surrounding periphery using local transport.

It is acknowledged that these sources of change in spatial patterns have important

implications for the evaluation of the net effect of affordable air travel on

dispersal. The two sources outlined above, however, were not considered in this

study because it was assumed that the majority of travellers on the corridor use

the same mode to travel both ways. Second, day-trips from ‘D’ represent a ‘base-

camp’ pattern, which does not constitute the ‘dispersal’ defined in this study. The

primary focus of this research is on the effect of modal substitution on regional

destinations, e.g. ‘a’, ‘b’ and ‘c’. As explained below, the decision by tourists to

disperse to ‘d’, ‘e’ and ‘f’ is viewed as an exogenous factor that this research

controls using a stated choice experiment.

6-5

Tourists’ travel mode choice on each leg of the journey does not occur in

isolation; rather, it is influenced by the entire trip and the context in which travel

decisions are made (Page 2005). Thus, in light of the ‘combined en route and

regional tour’ diagram in Figure 1, while leisure tourists’ long-distance travel

mode choice applies only to the ti segment of the journey, the decision of whether

or not the tourists’ trips involve dispersal to ‘d’, ‘e’, ‘f’ will affect the mode

choice on ti. For instance, on distances where ground modes compete with air

travel, a possible scenario is that if the tourist’s itinerary includes a visit to ‘d’

then driving the entire trip may become more attractive than when the tourist only

requires a trip to ‘D’. Subsequently, a tourist may decide to make this switch in

travel mode. This implies a linkage between the destinations ‘d’, ‘e’, ‘f’ and ‘a’,

‘b’, ‘c’, because driving the entire distance inadvertently provides opportunities

for en route visitations along ti. In contrast, flying will preclude this possibility,

resulting in a complete bypass (corridor effect) unless some form of vehicle is

used to travel back down to ‘c’ from the gateway (D). In this chapter, we examine

the effect of multi-destination trips on mode choice, i.e. the effect of trips with

and without visits to ‘d’, ‘e’, or ‘f’, on mode choices along ti.

6.3 The model

Similar to Chapter 5, the MNL model is applied in this study. Please see Chapter

1 for details on discrete choice models. This Chapter examines the factors

affecting mode choice in differing trip contexts, e.g. single-destination vs. multi-

destination. The experimental design used in this chapter enables the estimation of

the mode choice model for each trip context separately (i.e., two separate

equations), as well as in a single equation that includes both contexts. For the

6-6

former, the following utility function is estimated for each mode of transport in

each trip context.

Vni =� i + �iXni + �iZni Eq. (2)

Vni is the level of utility for individual n choosing alternative i . Vni is a function

of the levels of the attributes Xni where �i is a vector of coefficients to be

estimated for each attribute of each alternative i . Zni is the individual’s

characteristics with coefficients vector�i. As for the single equation approach,

Oppewal and Timmermans (1991) have shown that the following utility function

can be estimated given an appropriate experimental design:

Vni =� i + �d� i + � iXni + �d� iXni + �iZni Eq. (3)

The additional term in Eq (3) is �d , which is a dummy term that takes the value of

‘0’ when the choice is made under a ‘single destination trip context’ and ‘1’ when

the trip is ‘multi-destination’. �d interacts with the alternative specific constants

(� i) and the alternative specific attributes of travel modes (�iXni). The latter

enables, in a single model, the estimation of separate coefficient for each trip

context of the same attribute. Both models were applied in this study.

6.4 Data

6.4.1 Case study region

The data collection regions were Ballina and Byron in the Northern Rivers

tourism region of New South Wales, Australia (Figure 2). Byron is a popular

6-7

seaside leisure destination, where 22% of total trips originate from Sydney and

26% from Brisbane (TRA 2008)ii. The Ballina-Byron airport is located in Ballina,

which is a 25 minute drive from Byron. The leisure travelers (holiday and

‘visiting friends and relatives’ travel purpose) on the corridor from Sydney to

Byron were chosen as study subjects for two main reasons. First, two LCCs,

Virgin Blue and Jetstar, commenced services to the Ballina-Byron airport

introducing low fares and greater ticket discounting practices. Thus, it was

expected that travelers on this route are familiar with the air travel alternatives and

the low fares frequently advertised. Second, the corridor is approximately 800km,

a distance sufficient for competition to prevail between private car, coach, rail and

air travel.

6-8

Figure 6.2. Northern New South Wales Coast (Source: drawn by the author based

on ‘Tourism Regions classification’ of New South Wales State Tourism

Organisation)

N

Byron

Port Macquarie

(380km OR 4.5 hours

drive from Sydney)

Ballina (800km OR

8.5 hours drive

from Sydney)

Gold Coast (850km

OR 10 hours drive

from Sydney)

Coffs Harbour (550

km OR 6 hours

drive from Sydney)

QUEENSLAND

NEW SOUTH

WALES

NORTHERN

RIVERS

TOURISM

REGION

NEW ENGLAND

TOURISM REGION

HUNTER

TOURISM REGION Main Highway

(Train runs

roughly parallel)

Sydney

NORTH COAST

TOURISM REGION

6-9

6.4.2 Stated choice data

Econometric models often use data collected on choices already made in the

market, commonly referred to as ‘revealed preference’ data. Revealed preference

data suffer from a lack of variation in the levels of explanatory variables and

difficulties in observing the alternatives actually considered by the decision maker

(Hensher et.al. 2005). ‘Stated choice’ data on the other hand, involve presenting

to a decision maker a combination of alternatives (e.g. flying or driving) and

attributes (e.g. price) as hypothetical scenarios (see Figure 5.2). An example of

stated choice application on long-distance travel mode choice is the study by

Hensher (1997), which used this method to estimate the demand for a then

planned high-speed-rail between Sydney and Canberra. More recently in tourism,

Crouch et.al. (2007) applied the stated choice method to examine preferences in

the allocation of discretionary expenditure on domestic tourism against

alternatives such as reducing household debts and overseas holiday, while

Huybers (2002) and Huybers (2003) applied this method to the short-break

destination choice of Sydney and Melbourne residents.

The stated choice method was used in this research for several reasons. First,

stated choice method is an experiment that manipulates the control variables. For

example, airfares are systematically varied across the choice alternatives so that

their influence on respondent’s choice of travel mode can be estimated in a

controlled environment. This approach overcomes the pitfalls in the revealed

preference data such as lack of variation in the levels of variables (Louviere et.al.

2000). Additionally, alternatives considered and the prices paid by tourists are

information often not readily available in secondary data sources or in the form of

revealed preference data. Finally, this method allows the analyst to vary other

aspects of the trip so as to answer a question central to this chapter: “How would

you change your current choice had your trip involved a stay at least two hours

drive away from the main town centre?” This allowed the researchers to estimate

the effect of change in trip context on travel mode choice in a controlled

environment.

6-10

By applying the stated choice framework, we are able to estimate the extent to

which each factor influences travel mode choice. The controlled factors are travel

mode attributes (e.g. airfare) and trip characteristics (or trip context) (multi-

destination vs. single-destination trip). The stated choice method is particularly

appropriate when the study is interested in the willingness-to-pay and trade-offs

among choice alternatives, rather than market share predictions (Hensher et.al.

2005). Since the objective of this chapter is to extract the trade-offs between

modal specific attributes (e.g. price) and trip context (single destination vs. multi-

destination), stated choice data were chosen. The following sections on research

methodology outline the discrete choice model, the data collection region, choice

alternatives, attributes considered, and experimental design for the stated choice

survey.

6.4.3 Choice alternatives

The feasible set of alternatives for this study included Car, Rental Car,

Bus/Coach, Train, Virgin Blue (DJ), Jetstar (JQ), Regional Express (REX), and

flights to Gold Coast airport. Technically, transport to Gold Coast airport is not an

independent mode; rather, it represents an alternative route. The decision to

include flights to the Gold Coast was made in consultation with local industry

practitioners and researchers. Gold Coast airport is only one hour driving distance

from Byron and there are high levels of air service frequencies to the Gold Coast

compared to only daily services on the route between Ballina-Byron and Sydney.

Thus, withdrawing this alternative would exclude a prominent form of competing

air transport to Ballina-Byron. Whilst trains no longer operate directly to Byron,

the inclusion in this study does not pose a problem. In fact, the ability to account

for an unavailable mode is an important advantage of stated choice experiments,

applied previously in studies examining the viability of currently unavailable

alternatives (e.g. Hensher 1997).

6-11

6.5 Attributes of modal alternatives

Modal attributes in the model were based on the literature review of inter-regional

mode choice studies. This research aimed to provide a comprehensive

specification of modal attributes recognising that under-specified models will

increase the likelihood of violating the identical and independent distribution

(IID) assumption of the error terms in MNL models (Louviere et.al. 2000;

Hensher et.al. 2005). Consequently, attribute specification was based on a

literature review of modal attributes not only on inter-regional mode choice, but a

wider survey of the literature including those studies that examined the

importance of ‘qualitative’ variables such as road conditions, safety, schedules

and delay risks for public transport alternatives.

Service qualities are generally more difficult to account for in models because of

their subjective nature (Hensher et.al. 2005). Service ‘convenience’ is often

associated with service schedule and frequency in the travel mode choice

literature. Frequency of the transport service, as with price and time, frequently

appears in the attribute specification and is easily quantified. For example,

Koppelman and Sethi (2005) used a schedule convenience attribute that included

arrival and departure time of the day as dummy variables, as well as a measure of

the reliability of the transport service by incorporating an ‘unreasonable delay’

dummy variable.

The nature of the qualitative variables is likely to differ for each mode. On non-

urban driving it was found that, in Australia, the top three issues for the regional

motorists were: behaviour of other drivers, condition of roads, and safety and

6-12

accidents (ANOP Research Services, 2005). Hence, the model specification for a

car alternative should include road quality and safety variables. Previous studies

such as Greene and Hensher (2003), in specifying the stated choice experiment

attributes for road types in long-distance travel, used attributes such as number of

lanes, the existence of median strips and percentage of free flow time etc. On road

safety and risk, Rizzi and Ortuzar (2003) investigated the impact of perceived

road risk on route choice for inter-urban trips using the yearly fatal accident rate

on the given route.

In regard to attribute levels, most of the attribute level labels were based on real

market information so that the designed choice scenarios were as realistic as

possible. The attributes and attribute level labels are explained in detail below,

and summarised in Table 6.1.

Price

The prices for air transport mode were obtained from Jetstar, VirginBlue and

Regional Express websites on the 17th

of November 2006 for the period between

18th

of November and the 25th

of January; and again on the 27th of December 2006

for the period between 28th of December 2006 and 29

th of January 2007. Based on

the published fares in the period above, this experiment controlled for three levels

of air ticket price: $80; $150, and $220. $80 was one of the lowest fares available

in that period, and $220 was the highest. Similarly, the train and coach prices

were based on the published fares on company websites (Countrylink, Greyhound

and McCafferty). The price attribute level labels for train and coach were $60,

$120 and $180. Finally, rental rate per day was specified in the model for the

rental car alternative. As per the other alternatives, the labels were based on real

market price of several rental car companies in Ballina-Byron. These were $30,

$60 and $90 per day.

In Australia, more than three-quarter of the motorists have a good idea of the

petrol price at a given point in time (ANOP, 2005). Therefore, it was viewed that

the fuel price per litre was an appropriate measure of the motorists’ perception of

6-13

the price of travel on car. Fuel price ranges were obtained from the Australian

Automobile Association monthly average fuel prices for Sydney Metropolitan

Area between December 1998 and December 2006. The prices fluctuated around

$1.10/litre. Based on the 1998-2006 fuel price time series, three fuel price level

labels were $0.70, $1.10 and $1.50.

Time

The time attribute is in two parts: ‘in-vehicle time’ (IVT) and ‘out-of-vehicle

time’ (OVT). The attribute level labels used for all modes are based on published

information from airport transfer operators, flight schedules and travel guides. For

Jetstar, Virgin Blue, Regional Express and the flight to Gold Coast, the IVT was

controlled at the levels of 1 hour, 1.5 hours and 2 hours. For OVT, this varied

among 2 hours, 3 hours and 4 hours. For other scheduled transport services such

as Coach and Train alternatives, the IVT was varied among 11 hours, 13 hours

and 15 hours, whereas the OVT ranged from 1 hour, 3hours and 5 hours. For

private and rental car alternatives, combined IVT and OVT were specified. The

‘door-to-door time’ variable had three levels, e.g. 7 hours, 9 hours and 11 hours.

Road risk

The Pacific Highway is the major artery that runs for most of the Sydney-

Ballina/Byron route and it rates as one of the worst roads in regards to safety and

risk (AAA 2005)iii

. Road safety and risk was measured by the level of fatal

accident rate with the following labels: ‘50% reduction in fatal accidents’, ‘no

change’, and ‘50% increase in fatal accidents’. Such approach to road safety and

risk in stated choice experiments has been demonstrated in previous studies such

as Rizzi and Ortuzar (2003).

Road condition

At the time of the survey, 243km of the 618km (40%) of Pacific Highway was in

the form of dual divided lanes with a median (RTA 2006)iv

. The reminder of the

highway was in the form of undivided two or four lanes. However, it was

expected that additional sections of the undivided lanes were to be upgraded in the

6-14

following years. The road condition attribute labels were ‘30% upgrade’, ‘60%

upgrade’ and ‘90% upgrade’ of the highway.

Reliability

Airline on-time performance data are available from the Bureau of Transport and

Regional Economics (BTRE) aviation statistics. The figure shows the percentage

of airline arriving and/or departing within 15 minutes of scheduled time. REX and

Jetstar had a 90% on time performance, while Virgin Blue’s performance was

83% in 2006. In the experiment, the labels were ‘75%’, ‘85%’ and ‘95%’ on-time

performance. The reliability attribute was omitted for the coach and train

alternatives due to limited data on the actual levels.

Schedules

Schedule attributes were labelled according to arrival and departure times. For

‘air’ alternatives, these were departures and arrivals in the ‘morning’, ‘afternoon’,

and in the ‘evening’. For train and coach alternatives, the equivalent labels were

‘morning departure and night arrival’, ‘night departure and morning arrival’, and

‘arrival between 12am and 6am’.

Frequency

Virgin Blue and Jetstar operate daily services, whereas Regional Express (a

regional carrier) alternative and Sydney-Gold Coast alternative operate more

frequently. Thus, the attribute level label has been adjusted accordingly. Virgin

Blue and Jetstar attribute labels were ‘4 per week’, ‘daily’ and ‘4 per day’, while

for Regional Express and Sydney-Gold Coast, the labels were ‘daily’, ‘4 per day’

and ‘10 per day’. At the time of the survey there were between three and four

daily coach services on the travel corridor. To reflect this, the experiment

controlled coach schedule frequency for ‘daily’, ‘4 per day’ and ‘10 per day’. For

the train alternative, the labels were ‘4 per week’, ‘daily’ and ‘4 per day’ to reflect

the fact that train services are less frequent than coach services in the current

market.

6-15

Table 6.1a Attributes (abbreviation used for model estimation in brackets e.g. (price1))

Ticket price Fuel price In-vehicle time Out-vehicle time Door-Door time Frequency

Jetstar$80$150 (price1)$220 (price) -

1 hour

1.5 hour (it1)

2 hour (it)

2hr

3hr (ot1)

4hr (ot)-

4/week

Daily (freq1)

4/day (freq)

Virgin Blue $80

$150 (price1)

$220 (price)-

1 hour

1.5 hour (it1)

2 hour (it)

2hr

3hr (ot1)

4hr (ot)-

4/week

Daily (freq1)

4/day (freq)

Regional Express $80

$150 (price1)

$220 (price)-

1 hour

1.5 hour (it1)

2 hour (it)

2hr

3hr (ot1)

4hr (ot)-

Daily

4/day (freq1)

10/day (freq)

Fly to Gold Coast $80

$150 (price1)

$220 (price)-

1 hour

1.5 hour (it1)

2 hour (it)

2hr

3hr (ot1)

4hr (ot)-

Daily

4/day (freq1)

10/day (freq)

Rental car

-

$0.70/litre

$1.10/litre (price1)

$1.50/litre (price)- -

7 hours

9 hours (it1)

11 hours (it)-

Private car

-

$0.70/litre

$1.10/litre (price1)

$1.50/litre (price)- -

7 hours

9 hours (it1)

11 hours (it)-

Train $60

$120 (price1)

$180 (price)-

11 hours

13 hours (it1)

15 hours (it)

1 hr

3 hrs (ot1)

5 hrs (ot)-

4/week

Daily (freq1)

4/day (freq)

Coach $60

$120 (price1)

$180 (price)-

11 hours

13 hours (it1)

15 hours (it)

1 hr

3 hrs (ot1)

5 hrs (ot)-

Daily

4/day (freq1)

10/day (freq)

6-16

Table 6.1b Attributes cont. (abbreviation used for model estimation in brackets e.g.

(price1))

6-17

6.6 Experimental design and survey

A fractional factorial of the 344

full factorial design was selected for this

experiment. This fractional factorial only allowed for the independent estimation

of the main effects of each attribute. This orthogonal array provided up to 44

control variables in three levels so that non-linear effects could be estimated.

After removing two treatment combinations without designed trade-offs, 106

choice sets were generated with a total of 44 attributes across eight alternatives,

and three attribute levels for each attribute (please see Table 1 for each

alternative’s attributes). Thus, each attribute of an alternative is orthogonal to all

other attributes of that alternative as well as the attributes of all other alternatives.

This constituted an orthogonal main effect only design, where the main effects are

not protected from potential confoundment with two-way and higher order

interaction effects (Louviere et.al. 2000). All alternatives were available in all

choice scenarios.

In addition, to test for the effect of trip context on mode choices (single

destination trip vs. multi-destination trip), the design was duplicated so the

context of a single destination trip and a multi-destination trip could be presented

with the exact same design. That is, respondents were asked to make mode choice

decisions under scenarios when the trip involves only a single destination and

scenarios of multi-destinations. This duplication procedure is in-line with that

suggested by Oppewal and Timmermans (1991) for a single equation model with

context effects. Thus, a complete design had 212 choice sets (106 multiplied by

two) blocked by 53 so that four choice sets (212 divided by 53) were shown to

each respondent during the survey.

The survey was undertaken in the main beach area of Byron Bay and at the

departure lounge of Ballina-Byron airport, which is the main gateway airport of

the region. Simple random sampling strategy was employed. Departing travelers

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were approached in the departure lounge, or while they were in the long queue to

the security screening point. For tourists surveyed at the main beach area, each

survey distributor approached newly arriving visitors in their allocated area of the

beach. The respondents were screened to ensure their trips involved ‘a stay of at

least one night in Byron, on a trip purpose other than business or work’. In

addition, the visitors had to be permanent residents of Greater Metropolitan

Sydney to ensure that all travelers meet the basic choice context, thus excluding

those who used Sydney as a transit point. Upon consultation with local tourism

research office, the survey was undertaken over the course of eight days between

the 20th

– 27th

of January 2007 with five survey distributors. This period is

traditionally the final week of the summer peak in Ballina-Byron. The survey was

face-to-face where possible (except in the departure lounge) to assure response

quality.

In total, 340 respondents attempted the survey of which 302 were usable for

empirical analysis. The survey distributors were asked to keep records of the

number of people they approached, and from this we were able to impute that the

response rate was approximately 20% for the beach visitors and 10% for the

departing visitors at the departure lounge. 80 of the 302 valid samples came from

the surveys conducted at the airport. This gave a total sample of 1,202

observations (excluding six missing observations) across 302 individuals. The

samples were:

• 49% between the age 18 and 35, which is consistent with the fact that

Byron is favored by young travelers as a beach and surfing destination;

• age group 36-45 and 46-55 represented 19% and 20% respectively, while

only 3.5% was over the age of 65;

• gender distribution was slightly skewed towards female (62%);

• over 94% of the sample was traveling in a party size of four or less and

29% of the total was traveling alone.

6-19

6.7 Results

The results discussed herein pertain to the utility function only. That is, we

present the outputs for the utility functions, i.e. theVni in Eq.(2) and Eq.(3), and

do not produce probability estimates. Thus, the emphasis in this chapter is on the

effects of the attributes and trip context (trip characteristics) on the utility levels

relative to the base alternative, train. The results between the two approaches, i.e.

single equation and separate equation approach, are nearly equivalent to one

another. To preserve flow, the single equation model outputs are shown in the

main text, whilst the separate model outputs are shown in Appendix 6.1. All

control variables were effects-coded.

Hausman-Mcfadden test was applied to single and multi-destination models. Due

to a large number of alternative specific parameters in these models, a procedure

outlined in Hensher et al. (2005) was followed. For both models, the effect of an

absence of the car, Jetstar and Virgin Blue was tested (these three alternatives

were most popular in the choice sample). In all six cases, the Hausman-Mcfadden

test was negative; this indicates that there is insufficient evidence to reject the IIA

axiom and that the MNL model is adequate. To be sure, Inclusive Value (IV) test

was conducted. The IV test can be more powerful in revealing IIA violation

(Hausman-Mcfadden 1984). Nested multinomial logit specification between ‘air’

and ‘ground’ alternatives revealed a violation of the IID assumption for the

single-destination model. This is shown by the significant IV parameter on the

‘air’ nest at the 5% level (Table 6.2). Nonetheless, the nested logit did not

contradict the results of the MNL (see Appendix 6.2). We persevered with the

MNL model results because they illustrate our points in a simpler manner. Table

6.3 shows the summary statistics of the MNL models (both single equation and

separate models).

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The final result of the single equation model is presented in Table 6.4. Each of the

coefficients is interpreted as a ceteris paribus effect on the total utility of a given

travel mode (relative to the train alternative). Attributes found to be insignificant

for all the alternatives in the model were dropped during the model estimation

process. The key purpose of Table 6.4 is to show the results we wished to

highlight the most in the context of the research question of this chapter.

Consequently, some context-interaction variables were omitted from the model.

The train mode was the base alternative for all alternative specific constants and

variables. In Table 6.4, the variables with a single asterisk (*) are significant at

10%, two (**) and three (***) represent significance at 5% and 1% respectively.

Single equation

model

Single-destination

model

Multi-destination

model

Log Likelihood (no coefficient) -1831.582 -1249.7444 -1249.7444

Adjusted pseudo R^2 0.262 0.282 0.246

No. of observations 1,208 604 604

Table 6.3 Summary Statistics

Table 6.2 IV parameter results

Single-destination model (for

the 'Air' nest; 'Ground' IV = 1)

Multi-destination model (for

the 'Air' nest; 'Ground' IV = 1)

IV parameters 0.64577 0.15311

P-value 0.0564 0.4212

No. of observations 604 604

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Note: DJ = Virgin Blue; JQ = Jetstar; REX = Regional Express; GC = Gold Coast; RC = Rental car; MD = multi-destination; freq = frequency

Coefficients P-value Coefficients P-value Coefficients P-value

Constants MD constants Inertia (drove before)

CAR 0.28 CAR -0.27 Car 0.42 **

COACH -1.70 * COACH -1.03

DJ 0.43 DJ -1.03 ** Inertia (flew before)

GC -0.85 GC -0.71 DJ 1.08 ***

JQ 0.37 JQ -0.85 * GC 0.68 **

REX -0.07 REX -0.91 ** JQ 1.22 ***

RC -1.89 ** RC 0.17 REX 0.90 ***

Price ('High' price) MD-on-price ('High' price) Travel party size

CAR -0.02 - - CAR 0.40 ***

COACH -1.24 * - - COACH 0.14

DJ -0.58 *** DJ 0.16 DJ 0.29

GC -0.29 GC -0.55 GC 0.38 **

JQ -0.72 *** JQ 0.23 JQ 0.34 **

REX -0.71 *** REX 0.09 REX 0.32 *

RC 0.11 - - RC 0.40 **

Price ('Medium' price) MD-on-price ('Medium' price) Age

CAR -0.02 - - CAR 0.84 ***

COACH 0.41 - - COACH 0.54

DJ -0.06 DJ 0.08 DJ 0.92 ***

GC -0.57 ** GC 0.37 GC 0.79 ***

JQ 0.03 JQ -0.26 JQ 0.82 ***

REX -0.22 REX 0.15 REX 0.85 ***

RC -0.11 - - RC 0.73 **

Freq ('High' frequency) MD-on-freq ('High' frequency)

COACH 0.96 ** - -

DJ -0.15 DJ 0.37 *

GC -0.19 GC 0.06

JQ 0.47 *** JQ -0.37 *

REX 0.29 ** REX -0.08

Freq ('Medium' frequency) MD-on-freq ('Medium' frequency)

COACH -1.24 * - -

DJ 0.11 DJ -0.18

GC 0.11 GC -0.04

JQ -0.30 ** JQ 0.11

REX -0.15 REX 0.31

Schedule (arrival in the afternoon)

COACH 0.00

DJ -0.23 **

GC 0.00

JQ -0.11

REX 0.00

Variables Variables Variables

Table 6.4 MNL estimation results

6-22

Price (Price ($220) and Price1 ($150))

Price variables were highly significant for all air alternatives. For instance, when

the price was ‘high’ ($220 one-way) Virgin Blue yields a loss of 0.58 in utility,

but gained 0.58 when the price was very low ($80)v (given that the coefficient on

‘price $150’ is effectively zero). Thus, there is a 1.16 utility difference (0.58 – (-

0.58)) between a Virgin Blue flight when the price is $80 compared with a Virgin

Blue flight when the price is $220. The same applies to all other alternatives.

Time variables

All time variables were not significant at 5% level. They were subsequently

removed from the model and the table. This is surprising because time is often an

important explanatory variable in urban mode choice studies, although it is

usually the case that leisure tourists are less responsive to time than business

travellers. Potential reasons for this result are discussed in the next section.

The surrogates for convenience (schedules and frequency)

Virgin Blue’s morning arrival was preferred to an afternoon arrival. For a given

frequency, it appears that tourists will derive some additional utility if the arrival

time is earlier than the current 12pm arrival service. Frequency is statistically

significant for Jetstar and REX. For instance, thrice-daily frequency is a positive

source of utility for tourists choosing Jetstar.

Other variables

The significant age coefficients for each mode show that, as age increases, the

attractiveness of alternatives other than train increases relative to the train

alternative. Risk, road condition, fuel price and reliability variables were either

insignificant, or statistically significant but too small relative to other statistically

significant variables such as price and trip context.

Inertia effect

A person’s choice in the experiment is explained, to an extent, by the mode

actually chosen in the current trip. If the person drove to Byron, then the utility

6-23

from choosing the car mode increases by 0.42 units of utility relative to choosing

the train mode in the choice scenario. Similarly, if the person actually flew to the

destination for the trip on which the survey was undertaken, choosing to fly again

generally yields much greater utility than choosing the car mode or any other

alternatives.

Trip context effect (multi-destination (MD) constants, multi-destination effect on

price (MD-on-price) and multi-destination effect on frequency (MD-on-freq))

The ‘context’ effect has a similar level of influence on JQ, DJ and REX. If a

visitor, ‘in addition to a stay in Byron, is to stay at least one night in regions at

least two hours drive away from Byron’, then the utility derived from air transport

diminishes. For example, the utility earned from flying with Virgin, Jetstar and

REX decrease by a constant of 1.03, 0.85 and 0.91 respectively. The context can

also moderate the influence that modal attributes have on choice. The variables

under ‘MD-on-price’ and ‘MD-on-freq’ show the effect of context on price and

frequency. With the exception of frequency and price, the context-and-attribute

‘interaction’ effects were mostly insignificant. These variables were subsequently

omitted from the model and the table.

6.8 Discussion and implications

The results show that multi-destination context has an effect of shifting the utility

functions of air transport alternatives by a negative constant relative to single-

destination trips. This is shown by the significant MD-constant variables but

mostly insignificant MD-on-price and MD-on-freq variables. Whilst the overall

utility function shifts, the ‘slopes’ of the utility functions remain equal. This has

an interesting interpretation in random utility theory. The alternative specific

6-24

constants can be viewed as the average impact of the unobserved utility on the

alternatives (Hensher et.al. 2005). This suggests that the important determinants

of travel mode choice from single vs. multi-destination point of view were not

captured by our model’s attributes; rather their effects were captured by the MD-

constants. Variables that should be included in the future are ‘affective’ factors

such as ‘a sense of freedom’ or other functional factors such as ‘a degree of

flexibility’ (Anable and Gatersleben 2005). The differences in trip context are

more likely to manifest through these attributes of travel modes.

Results show that modal substitution is a source of conflict between LCCs and

regional dispersal. There is evidence that a modal switch would occur from car to

air even in situations when car may be the most suitable mode for the trip. The

findings show tourists experience disutility from flying when the trip involves

travel beyond the gateway regions, i.e. dispersal. This is shown by the negative

MD-constant variables on air travel alternatives. In fact, the ‘increase in utility

sourced from a decrease in airfare from $220 to $150’ is insufficient to offset the

‘loss in utility of air travel due to the need to disperse’ (or simply put, the

influence of ‘context’). However, in situations when the price decreases from

$220 to $80, the gain in utility is sufficient to offset the disutility of context,

ceteris paribus. For instance, Virgin Blue’s utility increases by 1.16 when the

price drops from $220 to $80 (see Results section), which is larger than the

disutility of 1.03 caused by the shift in the choice scenarios from single-

destination to multi-destination travel. This suggests that, in the presence of ‘low’

airfares, multi-destination trip arrivals by air will increase, because even if air

travel may ‘inconvenience’ tourists’ travel upon arrival, tourists are willing to

trade-off the ‘inconvenience’ for the low price, regardless of the trip context.

Thus, from this we can learn how LCC can introduce a greater mixture of tourists

arriving by air. This consequently has the effect of reducing the bias that air

modes have in bringing greater single-destination than multi-destination

travellers.

6-25

The results have a number of implications regarding the nature of the relationship

between regional destinations and airlines, as well as for the subsequent

challenges for destination managers. First, the level of airfare is an important

factor that determines whether or not mode choices cause conflicts between

affordable air-services and regional dispersal. When airfare levels are medium to

high, the trip context effect dominates the utility gained from a decrease in fares.

However, when airfares become low, tourists are much more likely to switch to

air even in situations when car may be the most suitable form of transport for the

trip. This implies a bypass of destinations (e.g. Port Macquarie in Figure 2) en

route by those travellers making the switch from ground modes toward air. It is

noted, however, that more research is needed in order to determine whether the

accessing tourists who paid low airfares may use rental cars to visit the peripheral

destinations, or limit their travels to the gateway only. The extent to which this

occurs will determine the ‘net’ effects of affordable airfares on tourist dispersal,

as well as on the region’s tourism economy.

Second, the results from this study have shown that in the presence of low

airfares, multi-destination trip arrivals on air will increase and that these travellers

should be identified and targeted to encourage dispersal from the gateway. The

consequences of direct and cheap air travel on rapid urbanisation and congestions

in tourism destinations have been documented in the tourism research literature

(e.g. Papatheodorou 2002). In Australia, the spatial pattern of air travel demand is

such that individual LCC services to peripheral regions within close proximity is

not economically viable for the LCCs. Therefore, for those regions in the vicinity

of the gateways, it is important to provide sufficient means of ground transport by

which the demand for dispersal to the periphery is facilitated and enticed.

Otherwise, increased congestion may appear in the gateway cities, causing the

very problem that the Australian government aims to relieve (as outlined earlier in

introduction).

Third, the results of this study have implications for cooperative marketing and

the developments of niche markets. Through travel mode choice, marketing

6-26

promotion for multi-destination trips in one area may induce an unintended yet

favourable impact for the destinations en-route. In our example, greater dispersal

to the regions peripheral to Byron will induce more car travel along the corridor

because driving the entire trip becomes a more attractive option. This increases

the likelihood of planned or spontaneous stopovers en-route in regional centres

such as Port Macquarie or Coffs Harbour, which belong to a different

administrative boundary (for tourism) to Ballina and Byron (see Figure 2).

Knowledge of the ‘natural partners’ among regional destinations can help regional

tourism organizations to mobilise marketing resources more effectively. This

research has shown that the greater understanding of mode choice can help to

identify the linkage patterns between two regions belonging to different geo-

political boundaries.

Fourth, the results from this study have demonstrated that the linkage patterns

among regional destinations may change as a result of changes in airline services.

It was shown previously that car travel benefits both the destinations en route and

those peripheral to the gateway. However, when airfares are low, flying becomes

a more attractive option, inducing tourists to bypass en route destinations while

maintaining their visits to the periphery of the gateway. As a consequence of

changes in airfares, what may have previously been a natural partnership between

two regions may no longer be so, tilting towards that of competition than

complementarity through modal substitution.

Fifth, the significance of the inertia effect indicates that there is a degree of

rigidity in the willingness of tourists to switch modes. That is, tourists have the

tendency to drive if they have driven to the destination before. Given that this

study was undertaken in a static setting, the inertia effect can be interpreted as a

short-run rigidity that draws parallel to the inelastic nature of demand for many

goods and services in the short-run, but elastic in the long-run. LCC proliferation

is seen as a crucial step towards the development of air travel in tourism much in

the same way as the development of the charter sector and aviation deregulation

(Bieger and Wittmer 2006). Among the many effects of the significant changes in

6-27

the air travel market, Quiggin (1997) argued that one effect of aviation

deregulation in Australia was the ‘demonstration effect’ to travellers, that air

travel was no longer a luxury reserved only for the affluent travellers. Hence, in

the long-run, greater flexibility in substitutions between those two modes (car and

air) can perhaps be expected.

6.9 Limitations and further research

One surprising result from this study was the lack of significance of the time

attributes. The author proposes the following explanation. The utility function

specified for each mode in the MNL model was made of each mode’s attributes

i.e. cross effects were not estimated with a MNL model (Eq(2)). Thus, in

specifying the time attribute in the experiment, the attribute levels varied in the

time specific to that mode e.g. car’s time varied from 7 to 11 hours (a variation of

up to four hours), whereas air modes varied from 1.5 hour to 3.5 hours (two hours

variation). It is plausible that the study subjects were not responsive to differences

in time because air is still the fastest mode by more than three hours when the

upper and lower bounds for air and private car times are compared. This absolute

time advantage of air travel holds even when out-of-vehicle time is added. As a

matter of fact, inter-regional mode choice studies such as Hensher (1997), have

shown that leisure tourists, compared to business travellers, are less responsive to

time but much more in price. Thus, our result is not so surprising in this respect.

Furthermore, this result may be a reflection of the differences in tourists’

behaviour compared to other choice contexts, as noted by Debbage (1991:266) in

the study of tourists’ spatial behaviour in the Bahamas, “research in other fields

(intra-urban commuting patterns, consumer shopping behaviour, and residential

location decisions) may not be directly transferable to tourist behaviour”. Thus,

6-28

more empirical investigation into the sources that generate these differences is an

important research issue for the future.

As for other attributes, the schedule-frequency nested attribute specification may

be appropriate for future tests. This specifies a relationship between the two

attributes, which will yield an output that is more amenable to interpretation

relative to the case when they are independently specified. For instance, rather

than an independent specification of ‘morning arrival’ and ‘three flights a day’, a

nested schedule-frequency attribute have an interpretation that ‘a morning arrival

flight of the three flights available’.

The lack of observed choices for alternatives such as coach, train and rental car

are likely to have contributed to some inaccuracies in the respective alternatives’

parameter estimates. While a choice based sampling strategy was considered, this

necessarily is a strategy for revealed preference data collection. Moreover, some

modes on Sydney-Byron segment were favored by a particular group of tourists,

e.g. the popularity of coach services by international backpackers, who were not

the subjects of this study. Although tourist data at the level of Sydney-Byron is

not readily available, recent statistics released by the Australian Federal

government agency, Tourism Research Australia, shows that only 7% of domestic

overnight visitors to Byron arrive on modes other than car (74%) or air (19%)vi

(TRA 2008). Thus, small sample sizes for the other modes are consistent with the

true market share of the population.

For future work, this research can be extended in a number of ways. For instance,

the number of alternatives can be reduced to air and car, and specify a tree

(nested) structure that examines the choice of transport mode at the destination,

given the mode used to access the destination. Such specification will allow a

comparison of a choice between ‘drive only’ and ‘fly and then drive’. Capitalising

on fly-drive market is an important challenge for the destinations located

peripheral to the gateway, and may also offer opportunities for the destinations en

route as travellers may travel back home in the hired vehicle.

6-29

Our results show, in regards to dispersal, low airfares can increase the mix of

tourists arriving by air. If the LCCs remain low-cost primarily to offer low-fares

(e.g. abstain from providing ‘business’ class), and if Ballina-Byron is served by at

least two competing airlines, presumably then ticket-discounting practices will

continue on this corridor. Since the availability of ground travel modes at the

destination is critical for tourists’ spatial behaviour at the destination, the

provision of transport at the destination/gateway will become an important

challenge for destination managers. Regional tourism organizations and

government agencies responsible for the management and distribution of benefits

from tourism for their respective tourism regions would require more information

on the level of influence a better local transport system might have on the

dispersal of tourists and the associated economic benefits. For these problems, the

nested structure mentioned above can include other alternative travel modes such

as public bus services, shuttle buses and rental cars. When the stated choice

experiment is applied in such a context, we can generate information on the effect

of ‘ground travel mode availability’ on the propensity of tourists arriving by

LCCs to ‘venture beyond the gateways’, so as to evaluate the impact of regional

transport infrastructure on tourist dispersal. Such line of research extension is

discussed further in Chapter 7.

6.10 Conclusion

This chapter has analysed the relative importance of travel mode attributes and

trip characteristics on mode choices of leisure tourists on the Sydney to Byron-

Ballina travel corridor in Australia. The results empirically demonstrated that

travel mode choice can be an avenue of conflict between LCC service

6-30

proliferation and tourists’ regional dispersal. The study found that when airfares

become low, tourists are much more likely to switch to air even in situations

where car may be the most suitable mode of dispersal for the trip. Thus, when

airfares are low, the complementary relationship between two regional

destinations that stem from the use of cars along the travel itinerary, may reverse

to that of conflict, as a result of modal substitution from car towards air travel.

The results have shown that trip context triggers a shift, but does not induce a

change in the slope of the utility functions. It was argued that this supports the

inclusion of qualitative and affective factors of travel mode choice in future

studies.

Although Australian data was employed in this study, the results should be of

interest to regional destinations worldwide. This is particularly the case for those

destinations that are geographically large and where choice between a domestic

flight and alternative ground transportation is a real option for potential travellers.

The issues surrounding the implications of the growth of LCC for towns, which

have traditionally relied on ground transportation for accessing tourists, have been

under-researched despite their substantial importance to regional destination

managers. The issues addressed in this chapter go at least some way to filling this

research gap.

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APPENDIX 6.1

Single Destination Model Multi-Destination Model t statistic


Constant

CAR 0.739 CAR 1.141 ** -0.437

COACH 0.125 COACH -1.536 1.093

DJ 1.133 * DJ 0.421 0.760

GC 0.333 GC -0.961 1.196

JQ 1.288 ** JQ 0.252 1.104

REX 0.721 REX -0.057 0.793

RC 0.181 RC -0.312 0.586

Price ('High' price)

CAR 0.062 CAR -0.068 0.612

COACH -0.804 COACH -0.620 -0.378

DJ -0.580 *** DJ -0.421 *** -0.746

GC -0.223 GC -0.781 ** 1.260

JQ -0.723 *** JQ -0.502 *** -0.979

REX -0.729 *** REX -0.617 *** -0.373

RC -0.007 RC -0.201 0.446

Price ('Medium' price)

CAR 0.136 CAR -0.118 1.236

COACH -0.497 COACH 0.474 -1.090

DJ -0.052 DJ 0.034 -0.431

GC -0.586 ** GC -0.206 -0.903

JQ 0.020 JQ -0.220 1.164

REX -0.199 REX -0.068 -0.501

RC 0.056 RC 0.069 -0.032

6-32

APPENDIX 6.2

[*] Wald-test 10% level of significance; [**] 5%; [***] 1%


Base alternative = Rental car

Constant

CAR 0.920 CAR 0.922 **

COACH 0.235 COACH 0.221

DJ 1.318 * DJ 2.272

GC 0.521 GC 1.884

JQ 1.473 ** JQ 2.876

REX 0.907 REX 2.318

TRAIN 0.181 TRAIN 0.671

CAR 0.062 CAR -0.068

COACH -0.804 COACH -0.620

DJ -0.578 *** DJ -0.621 ***

GC -0.223 GC -0.239

JQ -0.723 *** JQ -0.734 ***

REX -0.729 *** REX -0.750 ***

TRAIN 0.068 TRAIN 0.063

CAR 0.136 CAR -0.118

COACH -0.497 COACH 0.474

DJ -0.054 DJ -0.064

GC -0.586 * GC -0.579 *

JQ 0.020 JQ 0.031

REX -0.199 REX -0.221

TRAIN -0.637 TRAIN -0.643

Single Destination Model (MNL) Single Destination Model (Nested)

Price ('High' price)

Price ('Medium' price)

6-33

i Virgin Blue commenced Sydney – Port Macquarie services in early 2008.

ii Tourism Research Australia; based on three-year average to June 2007

iii Australian Automobile Association Road Assessment Program 2005

iv Road Traffic Authority 2006

v The control variables are effects coded, e.g. 'Price' represents 'high' price ($220)

and 'Price1' represents 'medium' level price ($150). The coefficient for the 'low'

level price ($80) is obtained by {-(coefficient ‘price’ + coefficient ‘price1’)}.

Thus, if ‘price’ coefficient is (-0.58) and ‘price1’ coefficient is ‘zero’ then the

coefficient of $80 is {-(-0.58 + 0)}, which is 0.58.

vi Based on three-year average to June 2007

7-1

7. CONCLUSION, LIMITATIONS & FUTURE

RESEARCH

7.1 Review

LCCs have stimulated air travel demand to the regions. It was shown in Chapter 2

that the LCCs have stimulated domestic dispersal, and increased the share of air

travel over other modes of travel, which also had an effect of increasing the

reliance of regional destinations on air transport. It was then proposed that the

natural path to follow was to examine the effect of LCCs on regional dispersal of

tourists. This was the general aim of this thesis (denoted G1).

G1. Examine the effects of LCCs on the regional dispersal of domestic

visitors in Australia.

Altogether, there were five specific research aims. These are revisited below:


literature and the secondary data sources relevant to understanding the link

between LCCs and domestic dispersal (Chapter 2);


LCCs based on aviation, tourism and spatial behaviour research (Chapter 3);

7-2


differences between LCCs and NCs (Chapter 4);


tourists’ travel characteristics in the choice of air arrivals’ regional dispersal

(Chapter 5, ‘The Cairns experiment’);


between low fare air services and regional dispersal (Chapter 6, ‘The

Ballina-Byron experiment’);

A1 and A2 were interpretative surveys of the relevant literature and secondary

data sources. The completion of A1 and A2 equipped us with the necessary

contextual information and conceptual framework to derive the propositions for

the empirical studies. The general research problem was framed in three inter-

related research issues, which were individually examined in Chapters 4, 5 and 6.

This concluding Chapter is organised as follows. First, key findings from the

empirical studies are briefly revisited. The subsequent section illuminates

implications for the field’s theoretical development and government policy. Then,

research limitations and some critical junctions for future research are outlined.

7.2 Key findings

Air arrivals increased to the regions as a result of the LCCs. NVS data indicated

that the periphery’s share of the total air arrivals were between 29% and 33% over

the last ten years. In other words, while it was clear that the LCCs contributed, in

relative terms, more to the regions than the state capitals (incl. Gold Coast), there

7-3

is insufficient evidence to suggest a differential effect on the gateways and the

periphery.

Traffic volume figures hide much of what is interesting about the LCCs, such as

the differential characteristics of tourists, and the impact of the characteristics on

dispersal propensity. The differences in the type of demand associated with the

LCCs are documented in the aviation and tourism research literature. The results

from the characteristics model have shown that some of these differences are

empirically supported (please refer to Table 4.1). In particular, the following

results and implications were highlighted. First, staying in one’s own property and

friend and relatives’ property were important sources of dispersal for the LCC

arrivals, implying that their economic impact may be lower due to the lower levels

of expenditure injected. Second, risk and uncertainty reduction, and preference

heterogeneity of the travel group, were particularly important motivating factors

of dispersal for the air arrivals.

The results have provided evidence that, given the assumption of significant

airfare differential between the NCs and LCCs, there will be discernible

differences between the characteristics of LCC arrivals and NC arrivals. This is a

significant finding because it provides a link between airline service types and

dispersal impact. In particular, the evidence suggests that dispersal sourced from

the LCC arrivals may inject much less expenditure than the NC arrivals. This

explains why some destinations observe high growth in airport activity but the

levels of tourism activity do not reflect the levels suggested by the airport activity.

The analyses in this thesis has produced evidence that suggests affordable air

arrivals tend to disperse for reasons that are different from the traditional air

arrivals.

The trip factors examined in Chapter 4 were mostly exogenous to the destination,

i.e. determined before arrival at the destination. In Australia, the geography of air

travel demand is such that point-to-point LCC services to every peripheral regions

7-4

located within ‘close proximity’ to one another is not economically viable for the

LCCs. Whether or not destination transport can influence the dispersal of air

arrivals is relevant for the peripheral destinations looking to entice dispersal of the

air arrivals. Obtaining an answer to this question, ‘can destination transportation

policy stimulate the dispersal of the air arrivals, even in situations where the air

arrivals exhibit trip characteristics that are dispersal-adverse?’ was the aim of the

Cairns experiment.

The stated choice experiment in Chapter 5 has shown that appropriate ground

travel mode attributes can offset some or all of the negative effects of trip

characteristics on the choice of tourists to disperse. However, the extent to which

this is feasible depends on destination context. In Chapter 5 it was shown that the

dispersal to the North is easy to entice because northern destinations, which

include Douglas and Daintree, are much more popular than southern destinations.

The northern destinations are in fact the key attractions for the travellers flying to

Cairns in the first place. To the less-popular destination region - the South - the

importance of trip characteristics compared to modal attributes was strong,

indicating that individual trip characteristics are binding constraints on dispersal

to the South. Length of stay and travel party size were constraints that tended to

reduce air arrivals in Cairns from reaching the southern destinations during their

travel. Further, it was found that there are prospects for cheap public transport

equipped with appropriate qualitative attributes to stimulate some demand to

relatively unknown destinations. But this may be politically difficult to implement

due to its potential conflict with regional tour operators who are likely to lose

market share if the scheme is introduced.

For regional tourism destinations reliant mostly on ground travel modes, the

extent of the low-airfare-induced-modal-substitution will determine the extent of

the bypass effect. The final research question was ‘can low airfares induce

tourists to switch from car to air, even in situations where the car may be the most

suitable mode of dispersal for the trip?’ The Ballina-Byron experiment has shown

that when airfares become low, tourists are more likely to switch to air even in

7-5

situations where the car may be the most suitable form of transport for the trip.

In many cases, peripheral destinations will be subject to a mixture of the two

issues presented in Chapter 5 and 6. Consequently, better understanding of trip

itineraries becomes an important task for destinations. While regional tourism

destinations are clearly affected by the airlines’ conducts and performance, they

have little influence over the airlines and airfares. Thus, continuous monitoring

and understanding of the effects of airline strategies on destinations (such as

airfare changes and flight frequency changes) are essential market intelligence

that can benefit regional tourism.

7.3 Contribution to knowledge and implications for stakeholders

7.3.1 Contribution to theory

As shown by the literature review, the spatial behaviour of tourists in destinations

and the proliferation of affordable air services are linked; for instance, the linkage

between fly-and-drive patterns in the Mings and McHugh (1992) study and the

proliferation of new-entrant-jet carriers in the U.S. Another example is the

reduction in the length of stay of Western European travellers to the

Mediterranean, which is associated with the LCCs’ growing share of traditional

charter routes at the expense of charter carriers. In both instances, the link

between spatial behaviour and airline business models has not been explicitly

recognised. This thesis makes an original contribution to developing a theoretical

link between the intra-modal transport choice (e.g., airline choice) and the spatial

behaviour of tourists. This thesis shows that the effects of LCC (and more

generally the effects of affordable air travel) on regional dispersal are trip

characteristics oriented as much as traffic volume.

7-6

Research in tourism has largely neglected an analytical approach to assessing the

trade-offs between travel mode choice and spatial behaviour. This thesis

contributes by providing a utility compensation perspective on tourists’ choice of

transport and the resulting spatial behaviour of tourists. The thesis also examined

the trade-offs between ‘economic’ factors and ‘tourism’ factors of mode choice. It

was also shown in this thesis that in long-distance leisure travels, trip

characteristics vary widely across individuals and travel parties, and these have

significant influence on the choice of travel modes. In some situations, trip

characteristics offset the marginal utility gained from the changes in travel mode

attributes. Therefore, the theoretical contribution of this thesis is in highlighting

how our understanding of the relationship between long-distance leisure mode

choice and spatial distribution of tourists can be improved by accommodating

tourism variables and a wider range of trip characteristics in the discrete choice

framework.

7.3.2 Implications for policy

The findings from this thesis should be of relevance to governments whose

mandates may emphasise greater balance in the distribution of economic benefits

from tourism. Cheap air transport can trigger a bypass effect of ground-mode-

reliant destinations through inter-modal substitution. Cheap air transport can also

stimulate tourists that are dispersal-averse. Thus, cheap air transport can

contribute to the disparity in levels of growth between airports and tourism

destinations. This illustrates some of the challenges in policy implementation

because the dispersal of primary interest at the federal level - domestic dispersal -

may conflict with the objective of greater (regional) dispersal at the state and local

level.

7-7

Local transport issues are often at the centre of public policy agenda in state and

local governments. As Gunn (1988) noted, local level tourism-transport planning

and policy have a strong political dimension because the competition for funding

tends to be greater at this level. This renders prioritisation an important task in the

allocation of resources and policy making. One issue is that tourism is often at the

lower end of the priority list behind social, environmental and other economic

objectives (Ashworth 2009). This thesis highlights the growing importance of

demand for local transportation by air leisure arrivals. In particular, the results

have shown that public transportation can be an important mode of travel that

meets the interests of a number of policy objectives, including environmental

policies aimed at reducing car-usage and tourism policies aimed at greater

regional dispersal. The findings from this thesis help bring the tourism and

dispersal concerns to the forefront of regional and local transport policy

appraisals.

7.3.3 Implications for destinations

To illustrate the relevance and value of these results, we discuss the results in the

context of cooperative marketing of regional tourism destinations. Trip

characteristics of the air arrivals are often determined prior to arrival, preventing

tourists from dispersing to the peripheries. Thus, engineering greater dispersal is a

formidable task because it requires the understanding of tourists’ trip planning

stages. Being able to exert some sort of influence at this level by a single

destination region is a difficult task because it is very expensive to do so (research

and marketing costs), and also due to the free-rider problem of destination

marketing. If air travel is the only real option for many accessing tourists, then it

is probably appropriate that cooperative marketing arrangements take place

because peripheral destinations will only collectively command an adequate

demand for regular LCC services.

7-8

However in situations where both ground and air travel modes are real options for

accessing tourists, the same conclusion no longer holds. The Ballina-Byron

experiment presented in Chapter 6 has demonstrated that the linkage patterns

among regional destinations may change as a result of LCCs and affordable air

travel. The second experiment has shown that when airfares are low, flying

becomes an attractive option, which induces tourists to bypass en-route

destinations. As a consequence, what may have been a natural partnership

between two regions, i.e. peripheral destinations located en route and those

surrounding the gateway, may no longer be so, tilting the relationship between

two regional destinations towards that of competition through modal substitution.

This relationship should be considered for a more efficient allocation of regional

tourism organisation’s funds.

7.4 Limitations and future research

7.4.1 Applicability of the results

The research issues are not restricted to any particular location in Australia, rather

they stem from trip itinerary literature based on several international empirical

work. The results from two choice experiments are most relevant for

geographically large tourism regions; perhaps a rule of thumb indicator of a large

tourism region may be a tourism region spanning at least 2-3 hours drive from one

end to the other end of the tourism region boundary. For the Cairns experiment,

the results are applicable to destinations with the mixture of following

characteristics: (1) only one regional airport option for LCC services or alike in

the tourism region; (2) the tourism region’s reliance on air services for incoming

7-9

tourists is significant; (3) there is a disparity in the popularity of peripheral

destinations (e.g. hinterland vs. coastal, rural vs. cities). As for the Ballina-Byron

experiment, the results should be of relevance for those destinations that are

geographically large and where a choice between domestic flights and alternative

ground transportations is a real option for potential travellers.

There are some limitations on the validity and generalisability of results on the

alternatives with low sample choices. There was an under-representation of some

choices in the experiments; for instance, the share of the train alternative in the

Ballina-Byron study, or the share of the day-trip by public bus alternative to the

northern destinations of Cairns. This has rendered model estimation for these

alternatives and attributes difficult. While this reflects a common problem in

many primary data based research, it is nonetheless a factor that limits our

interpretation of the models for those alternatives. This also highlights a potential

issue with stated choice methods. For this research, we brought closure to this

issue by noting that we cannot control the number of ‘observed’ samples for all

alternatives because they are the very choices that we aim to collect from the

field. Instead, we controlled explanatory variables - and this is the key advantage

of the stated choice method because we control the conditions under which

choices are made. Choice-based sampling strategy, which solves the problem

outlined here, could not be used because it is a revealed preference, not stated

choice, sampling method.

7.4.2 Limitations of the MNL: utility compensation perspective and taste

heterogeneity

This thesis contributes to tourism research by providing a utility compensation

perspective on tourists’ choice of transport and spatial behaviour; the utility

compensation perspective highlights the importance of trip characteristics and

‘contextual utility’ in a way that can be directly compared to the effects of travel

7-10

mode attributes. However, the utility compensation perspective has major

drawbacks; for instance, there are interpretation issues of ‘utility’. Further, since

the interpretation of utility can be meaningful only in a relative sense (relative

utility), confusion can easily arise. In particular, there is a need for more research

on the theoretically appropriate interpretation of trip characteristics and contextual

utility in long-distance leisure travel context. Given the fact that discrete choice

models include socio-economic characteristics information to account for taste

and preference heterogeneity of individuals (Ben Akiva and Lerman 1985), the

following questions may require further attention: how should we interpret the

coefficients of trip characteristics? Should they be considered as having direct

utility? Or are they ‘moderators’ akin to socio-economic characteristics? Is this

approach consistent with utility maximisation?

The discrete choice models applied in this thesis estimated a coefficient vector

assumed to be equal for all tourists in the sample. For instance, all sampled

individuals were treated as having the same responsiveness to a unit change in

airfares, or the same responsiveness to a change in the number of stopovers on

public transport. However, these coefficients are likely to vary across market

segments; for example, ‘general sightseeing’ tourists are likely to gain more

utility than ‘activity-specific’ tourists when there is a stopover for sightseeing

opportunities on a public transport service. Another example, in the context of air

travel and dispersal, may be that ‘psycho-centric’ tourists exhibit different

responsiveness to airfares to ‘allocentric’ tourists. As a result, the former may

have a higher willingness-to-pay for a mode that can provide the required

flexibility of a private vehicle, and consequently lower responsiveness to airfare

discounting practices in choosing air travel. A similar line of reasoning was used

in Chapter 6 to argue that airfares play a role in increasing the mixture of tourists

to a destination (in terms of bringing a greater variety of spatial behaviour).

Such an issue – the heterogeneity in tastes and preferences - cannot be fully

explored with the multinomial logit model because dichotomy of tourists such as

7-11

that described above (allocentric and psychocentric) are not easily observed by the

analyst. The effect of such latent variables cannot be explicated in the standard

multinomial logit models. The problem outlined above is essentially a problem of

analysis and manipulation of the error structures of discrete choice models to

capture the effects of latent variables such as tourists’ allocentricity. By extending

the methodological boundary towards random probit or mixed logit models (or

random coefficient models), such an issue can be explored in much greater detail.

Future studies can test the effects of these latent variables on the relationships

between tourists’ mode choice and spatial choice behaviour.

7.4.3 Operationalising ‘dispersal’

Alternative methods in operationalising dispersal should be considered in the

future. The LGA boundaries as used in the first empirical study, while based on

geo-politically salient boundaries, are arbitrary for tourists since they have little or

no knowledge of the boundaries. An alternative may be to specify a measure of

dispersal that is continuous, such as an index, or dependent variables that

categorise dispersal in levels such as ‘high’, ‘medium’ or ‘low’ (ordered).

Econometric investigation into the appropriate level of geographic delineation

was done using multinomial logit models by Eymann and Ronning (1997).

Similar application to delineating ‘dispersal’ boundaries will advance this field of

research by providing a theoretical and empirical basis to measuring dispersal.

7.4.4 Integrating destination and mode choice

A number of extensions on the current research are desirable. The central premise

of this research was the choice behaviour of tourists in intra-regional and inter-

regional transport mode choice contexts. While both stated choice experiments

accounted for some contextual information, e.g. trip contexts, this was largely

7-12

exogenous to the model of mode choice. For instance, the Ballina-Byron case

study examined the moderating influence of single and multi-destination trips on

long-distance travel mode choice, while the Cairns case study examined the

moderating influence of northern and southern destinations on intra-regional

travel mode choice, as well as the choice to disperse or not. While such designs

enable us to extract the moderating influence of destination on mode choice, these

designs cannot illuminate situations where tourists choose a distant leisure

destination (1 hour flight) over closer destination (say, less than 3 hours drive) as

a result of cheap airfares.

Models that endogenise destination choice within travel mode choice (or vice

versa) can be used to examine the influence of transport modal attributes, namely

cheap airfares, on the choice of destinations. Such research design will be capable

of estimating an econometric model that may be able to explain ‘destination

neutrality’ phenomenon observed by Mason (2005), where tourists tend to

substitute destinations based on cheap airfares. Australian examples will be the

effect of cheap air travel on the choice between the series of regional destinations

on the Eastern Coast, which can be viewed broadly similar in their ‘sun, sand and

sea’ attributes, or the choice between destinations in the outback and the coastal

beaches. Such research design can also embed the model of ‘regional dispersal’

within ‘domestic dispersal’. Moreover, such an analytical approach has the

potential to add to existing research on destination price competitiveness (e.g.

Dwyer et.al. 2000) to help answer a question such as ‘how would a long-haul

LCC or bilateral capacity relaxations impact on the competitiveness of Australia

as a destination compared to other long-haul alternatives such as U.S. or Europe

by international visitors?’

7.4.5 The time attribute in leisure and tourism

An interesting finding from this research was that the ‘time’ attribute was

insignificant. The determining power of travel time in travel mode choice is

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significant in the context of journey-to-work trips (e.g. Redmond and Mokhtarian

2001) and in long-distance inter-regional trips (e.g. Hensher 1997, Koppelman

and Sethi 2005). The insignificant result may be a reflection of the relatively time-

insensitive nature of leisure travellers, in particular when the range of travel time

examined is between one to three hours. One implication of this result is that

peripheral destinations are not significantly disadvantaged by the fact that they are

an hour further from the gateway relative to another destination, at least within the

travel time range examined here. In fact, the evidence supports Page’s (1994)

argument that in tourism, transport is not only a cost to be minimised, but also an

integral part of tourists’ overall travel experience. A positive utility could be

attached to travel time, in which case we will not observe a significant negative

relationship between utility and time. The positive utility in travel time is also

illustrated, albeit to a small extent, in the intra-mode journey-to-work trips; for

example, Redmond and Mokhtarian (2001) have shown that travellers to work

prefer a short commuting time than none. By the same token, if this was the case

then the choice experiments should have observed a positive significant value on

time. Perhaps future studies can apply a similar approach with the aim of isolating

the positive and the negative effect of time on utility.

Overall, the finding on time is in-line with the qualitative work of Lumdson

(2006) and Eaton and Holding (1996) outlined in Chapter 5. In both studies, travel

time was not mentioned as a key determinant for the demand of public transport

for leisure travel to UK’s National Parks. The results from our experiments

support the relative unimportance of travel time for leisure travellers. This is in

line with Debbage (1991) who noted in the context of tourists’ spatial behaviour

in the Bahamas, “research in other fields (intra-urban commuting patterns,

consumer shopping behaviour, and residential location decisions) may not be

directly transferable to tourist behaviour” (p.266). Thus, empirical work into the

sources that generate these differences is an important research agenda for the

future.

7-14

7.5 Towards an integrated model of tourists’ spatial choice and

tourism yield

In this thesis, it is assumed that greater dispersal is equivalent to the greater

visitations in the regions beyond capital and gateway cities. While this is a

common measure used for decision-making in the industry, number of visitations

has several shortcomings. A more comprehensive measure is tourism yield. Yield

in tourism is variously defined; Dwyer et al. (2007) classified four types of yield:

expenditure (tourists’ spend), financial (impact on firms’ profits and sales),

economic (income and employment generated) and sustainable yield

(environmental and social impact). Causal relationships between LCCs and

dispersal should be developed with respect to various types of yield.

As discussed in Chapter 3 and Chapter 4, VFR travel purpose has increased in the

share of air travel as a result of increased air travel affordability. Significant

proportion of VFR saves on accommodation expenses by staying in ‘friends and

relatives’ property’, which reduces the level of tourist expenditure. Dispersal

arising from VFR may add to dispersal visits and nights, but comparatively little

to expenditure and financial yield. Further to financial yield, given the

traditionally labour-intensive nature of the accommodation industry (Dwyer,

Forsyth and Spurr 2003), the marginal effect of a dollar spent by dispersing

tourists may contribute little to the economic yield in the regions. Moreover, the

level of leakages will be significant in peripheral regions because small regional

economies tend to have a more homogenous industry base; consequently,

significant share of tourists’ expenditure will leak-out as import payments to other

regions and abroad.

Finally, sustainable yield is increasingly becoming of great import for the industry

and governments. Sustainable yield includes the environmental and social impact

(Dwyer et al. 2007) although not exclusively; Becken and Simmons (2008) added

‘regional dispersion’ in sustainable yield to account for the significant

7-15

contribution of tourism in ‘sustaining’ the well being of regional economies. In

fact, one emerging theme from research on tourism yield is that there are trade-

offs among yield types (Dwyer et al. 2007, Becken and Simmons 2008), and

transport and aviation is an important part of the trade-offs. A recent study by

Becken and Simmons (2008) on the yield of international visitors in New Zealand

found that there are trade-offs between financial yield and sustainable yield; for

instance, the ‘coach traveller’ (a tourist segment that uses air transport as a

primary mode to travel within NZ) had high financial yield but performed poorly

on dispersal, although the segment’s absence from road-based tourism meant that

its carbon footprint was low as well.

Here we can appreciate the complex trade-offs in the context of LCCs - cheap air

travel - and dispersal. For instance, air transport might be positively related to

financial yield and sustainable yield (compared to car-based tourism) but it is

negatively related to dispersal. The implication is that policy aimed at greater

dispersal will be achieved, to an extent, at the expense of environmental and

economic yields. The role of LCCs, or affordable air travel, is paramount in

moderating these links and trade-offs.

R-1

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