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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
Different tourism regions will be
associated with different levels of
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
positively or negatively
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
related to dispersal
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
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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).
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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)
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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
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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
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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
1-22
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.
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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.
A1. Provide an interpretative survey of the aviation and tourism research
literature relevant to understanding the link between LCCs and domestic
dispersal (Chapter 2);
A2. Identify and explicate the relationships between regional dispersal and
LCCs based on aviation, tourism and spatial behaviour research (Chapter 3);
A3. Build and test a causal model of regional dispersal and the intra-modal
differences between LCCs and NCs (Chapter 4);
A4. Examine the trade-offs between destination transport factors and
tourists’ travel characteristics in the choice of the air arrivals’ regional
dispersal (Chapter 5);
A5. Examine inter-regional travel mode substitution as a source of conflict
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)
Dispersal propensity
differential sourced from
inter-modal differences
(Air travel vs. car travel) (A2, Chapter 3)
Dispersal propensity
and inter-regional
mode choice (travel
mode choice to the
destination)
(A5, Chapter 6)
Dispersal propensity
and intra-regional
mode choice (travel
mode choice within
the destination)
(A4, Chapter 5)
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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).
2-22
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.
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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.
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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
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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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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
Different tourism regions will be
associated with different levels of
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
positively or negatively
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
related to dispersal
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.
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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.
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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
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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.
Factors Effects on regional dispersal
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*
Different tourism regions will be
associated with different levels of
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
related to dispersal
VFR is an important source of dispersal of the
LCC arrivals
supported
Package tourism Package tourism is negatively
related to dispersal
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
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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.
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
4-15
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
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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)
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.
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.
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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
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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
5-27
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
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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.
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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))
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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.
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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
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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
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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.
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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
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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
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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,
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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
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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.
6-31
APPENDIX 6.1
Single Destination Model Multi-Destination Model t statistic
Variables Coefficient P-value Variables Coefficient P-value
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%
Variables Coefficient P-value Variables Coefficient P-value
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:
A1. Provide an interpretative survey of the aviation and tourism research
literature and the secondary data sources relevant to understanding the link
between LCCs and domestic dispersal (Chapter 2);
A2. Identify and explicate the relationships between regional dispersal and
LCCs based on aviation, tourism and spatial behaviour research (Chapter 3);
7-2
A3. Build and test a causal model of regional dispersal and the intra-modal
differences between LCCs and NCs (Chapter 4);
A4. Examine the trade-offs between destination transport factors and
tourists’ travel characteristics in the choice of air arrivals’ regional dispersal
(Chapter 5, ‘The Cairns experiment’);
A5. Examine inter-regional travel mode substitution as a source of conflict
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
7-13
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
References
Alegre, J. and Pou, L. (2006) “The length of stay in the demand for tourism”
Tourism Management, 27(6), 1343-1355
Anable, J and Birgitta G. (2005) “All work and no play? The role of instrumental
and affective factors in work and leisure journeys by different travel modes.”
Transportation Research Part A: Policy and Practice, 39(2-3): 163-181
ANOP Research Services Pty Ltd (2005) "Motorists' Attitudes 2005 ANOP
National Survey Prepared for the Australian Automobile Association."
http://www.aaa.asn.au/publications/polls.php
Ashworth, G. (2009) “Heritage Management and Urban Tourism”. Keynote in
CAUTHE, Proceedings of the 18th Annual Conference, Carlsen, J., Hughes, M,
Holmes, K. and Jones, R. (ed), Curtin University of Technology
Australian Automobile Association, "Australian Road Assessment Program Risk
Maps". http://www.ausrap.org/ausrap/riskmaps.htm
Australian Bureau of Statistics, “statistical local areas” http://abs.gov.au
Australian Bureau of Statistics, “Tourist Accommodation Statistics 2007”
Australian Bureau of Statistics, Tourism Regions Map release 2007
R-2
Barrett, S. D. (2004). "How do the demands for airport services differ between
full-service carriers and low-cost carriers?" Journal of Air Transport Management
10(1): 33.
Becken, S. and Simmons, D. (2008). “Using the concept of yield to assess the
sustainability of different tourist types” Ecological Economics, v.67 (3): 420-429
Ben-Akiva, M. and Lerman, S. R., 1985. Discrete Choice Analysis: Theory and
Application to Travel Demand. MIT Press, Cambridge Masachusetts.
Bieger, T. and Wittmer, A. (2006) Air transport and tourism – Perspectives and
challenges for destinations, airlines and governments, Journal of Air Transport
Management, 12(1), p.40-46
Borenstein, S. and Rose, N. (1994). “Competition and Price Dispersion in the U.S.
Airline Industry” Journal of Political Economy 102(4): 653-683
Borooah, V.K. (2002). Logit and Probit: Ordered and Multinomial Models.
Thousand Oaks, CA, Sage University Papers Series on Quantitative Applications
in the Social Sciences.
Bureau of Infrastructure (2008), Aviation Policy Green Paper – Flight Path to the
Future, Department of Infrastructure, Transport, Regional Development and Local
Government
Bureau of Transport and Communication Economics (1991) Domestic Aviation of
Australia: First year after deregulation, BTCE
Bureau of Transport and Communication Economics (1993) Domestic Aviation of
Australia: Three years after deregulation, BTCE
R-3
Bureau of Transport and Regional Economics (2007) "Transport Statistics."
http://www.btre.gov.au
Bureau of Transport and Regional Economics (BTRE), Aviation Statistics,
available from http://btre.gov.au
Burghouwt, G., Hakfoort, J. and Eck, J. (2003), The spatial configuration of
airline networks in Europe, Journal of Air Transport Management 9: 309-323
Button, K. and Stough, R. (2000) “Air Transport Networks: Theory and Policy
Implications” Edward Elgar Cheltenham
Centre for Asia Pacific Aviation (2006) Peanuts: the low cost airline weekly
no.138 October 3rd
Centre for Asia Pacific Aviation (2007) Peanuts: the low cost airline weekly
no.120 July 3rd
Chou, Y. (1993). “Airline deregulation and nodal accessibility” Journal of
Transport Geography 1(1), p.36-46
Clippinger, M. and Strong, J. (1987) “Changes in Distribution Channels and the
Travel Agency Business” in Meyer, J. and Oster, C. (ed) Deregulation and the
future of intercity passenger travel, MIT Press
Cooper, C.P. (1981) Spatial and temporal patterns of tourist behaviour. Regional
Studies, 15(5), 359-371
Crouch, G.I. (1995) A meta-analysis of tourism demand, Annals of Tourism
Research, 22(1), p.103-118
R-4
Crouch, G.I. (1996) “Demand elasticities in international marketing: A meta-
analytical application to tourism” Journal of Business Research, 36(2): 117-136
Crouch, G.I., Oppewal, H., Huybers,T., Dolnicar,S. Louviere, J.J. and Devinney.
T. (2007) "Discretionary Expenditure and Tourism Consumption: Insights from a
Choice Experiment." Journal of Travel Research, 45(3):247-258
Debbage, K. (1991) "Spatial Behavior in a Bahamian Resort." Annals of Tourism
Research vol.18:251-268
Department of Industry, Tourism and Resources (2003) "Tourism White Paper - a
Medium to Long Term Strategy for Tourism." Department of Industry, Tourism
and Resources.
Dobruszkes, F. (2007). "An analysis of European low-cost airlines and their
networks." Journal of Transport Geography In Press, Corrected Proof.
Doganis, R. (2006) The Airline Business, Routledge
Dresner, M. (2006) Leisure versus business passengers: Similarities, differences,
and implications. Journal of Air Transport Management 12 (1), 28-32
Duval, T. (2008) Tourism and Transport: Modes, Networks and Flows, Channel
View Publications
Dwyer, L. and Forsyth, P. (1992) The reform of air transport and its impact on
tourism in Forsyth, P.(ed) Microeconomic Reform in Australia, Allen and Unwin
Dwyer, L. and Forsyth, P. (1993) “Assessing the Benefits and Costs of Inbound
Tourism” Annals of Tourism Research, 20 (4) pp.751-768
R-5
Dwyer, L., Forsyth, P. and Rao, P.(2000) "The Price Competitiveness of Travel
and Tourism: a comparison of nineteen destinations" Tourism Management,
Special issue: the Competitive Destination, 21(1) pp 9-22.
Dwyer, L., Forsyth, P. and Spurr, R.(2003), ‘Inter-industry effects of tourism
growth: implications for destination managers’, Tourism Economics, 9(2): 117-
132.
Dwyer, L., P. Forsyth, L. Fredline, L. Jago, M. Deery and S. Lundie (2007)
“Yield Measures for Australia’s Special Interest Inbound Tourism Markets”
Tourism Economics 13 (3), 421–440
Eaton, B. and D. Holding. (1996) “The evaluation of public transport alternatives
to the car in British National Parks.” Journal of Transport Geography 4(1): 55-65
Eymann, A. Ronning, G. (1997) Microeconometric models of tourists’ destination
choice, Regional Science and Urban Economics, 27(6), pp.735-761
Fennell, D.A. (1996) “A tourist space-time budget in the Shetland islands.”
Annals of Tourism Research 23(4): 811-829
Forsyth, P. (1992) Microeconomic Reform in Australia, Allen and Unwin
Forsyth, P. (2003). "Low-cost carriers in Australia: experiences and impacts."
Journal of Air Transport Management 9(5): 277.
Forsyth, P. (2006) Martin Kunz Memorial Lecture. Tourism benefits and aviation
policy, Journal of Air Transport Management, 12(1): 3-13
Fourie, C & Lubbe, B 2006, “Determinants of selection of full-service airlines and
low-cost carriers: A note on business travellers in South Africa”, Journal of Air
Transport Management, vol. 12, issue 2, 98-102
R-6
Francis, G., A. Fidato, I. Humphreys (2003). "Airport-airline interaction: the
impact of low-cost carriers on two European airports." Journal of Air Transport
Management 9(4): 267.
Francis, G., I. Humphreys, S. Ison, M. Aicken (2006) "Where next for low cost
airlines? A spatial and temporal comparative study." Journal of Transport
Geography In Press, Corrected Proof.
Francis, G., Dennis, N., Ison, S., Humphreys, I. (2007) The transferability of the
low-cost model to long-haul airline operations, Journal of Air Transport
Management 28(2) p.391-398
Franke, M. (2004). "Competition between network carriers and low-cost carriers--
retreat battle or breakthrough to a new level of efficiency?" Journal of Air
Transport Management 10(1): 15.
Gillen, D. and A. Lall (2004). "Competitive advantage of low-cost carriers: some
implications for airports." Journal of Air Transport Management 10(1): 41.
Graham, A. (2000) “Demand for leisure air travel and limits to growth” Journal of
Air Transport Management, 6(2), 109-118
Graham, A. (2006) “Have the major forces driving leisure airline traffic
changed?” Journal of Air Transport Management, 12(1), 14-20
Graham, B. and Guyer, C. (2000) “The role of regional airports and air services in
the United Kingdom” Journal of Transport Geography 8, 249-262
Greene, W. and D.A. Hensher. (2003) "A latent clas model for discrete choice
analysis: constrasts with mixed logit." Transportation Research Part B:
Methodological 37: 681-698.
R-7
Greene, W.H. (2002) LIMDEP Version 8.0 Reference Guide, Econometrics
Software, Inc.
Grimm, C.M. and Miloy, H.B. (1993) Australian domestic aviation deregulation:
impacts and implications, Logistics and Transportation Review 29
Gunn, C. A. (1988) Tourism Planning, Taylor & Francis
Hanlon, J.P. (1992) ‘Regional air services and airline competition’, Tourism
Management, 13(2), p181-195
Hensher, D.A. (1997) "A practical approach to identifying the market potential for
high speed rail: A case study in the Sydney-Canberra corridor." Transportation
Research Part A: Policy and Practice 31(6): 431-446.
Hensher, D.A., Prioni, P. (2002) “A Service Quality Index for Area-wide Contract
Performance Assessment.” Journal of Transport Economics and Policy 36(1): 93-
113
Hensher, D.A., John M. Rose and William Greene. (2005) Applied Choice
Analysis: A Primer, Cambridge University Press.
Hooper, P. Findlay, C. (1998) Developments in Australia’s aviation policies and
current concerns, Journal of Air Transport Management, 4(3) p.169-176
http://abs.gov.au
Huybers, T. (2003) ‘Modelling short-break holiday destination choices’, Tourism
Economics 9(4): 389-405
R-8
Hwang, Y. and Fesenmaier, D. R. (2003), “Multidestination Pleasure Travel
Patterns: Empirical Evidence from the American Travel Survey”. Journal of
Travel Research 42(2): 166
Hwang, Y., Gretzel, U. and Fesenmaier, D. (2006) “MULTICITY TRIP
PATTERNS Tourists to the United States.” Annals of Tourism Research 33(4):
1057-1078
Ito, H. and Lee, D. ‘Low Cost Carrier Growth in the U.S. Airline Industry: Past,
Present, and Future’ (April 9, 2003). Brown University Department of Economics
Paper No. 2003-12. Available at SSRN: http://ssrn.com/abstract=719741
Jara-Diaz, S.R. (1991) “Income and taste in mode choice models: Are they
surrogates?” Transportation Research Part B: Methodological 25(5): 341-350
Johnston, R.J., Gregory, D., Pratt, G., Watts M. (2000) “The Dictionary of Human
Geography”. Blackwell publishing.
Kain, J., Webb, R. (2003). Turbulent times: Australian airline industry issues.
Report available online at http://www.aph.gov.au/library/pubs/rp/2002-
03/03RP10.pdf. Website last accessed 7th December 2005.
Kelly, Ian (ed.) (2001) Australian Regional Tourism Handbook: Industry
Solutions 2001 Centre for Regional Tourism Research. CRC for Sustainable
Tourism Pty. Ltd.
Koppelman, F.S. and Vaneet S. (2005) "Incorporating variance and covariance
heterogeneity in the Generalised Nested Logit model: an application to modelling
long distance travel choice behaviour." Transportation Research Part B:
Methodological 39(9): 825-853.
R-9
Lawton, T.C. (2002) Cleared for take-off: Structure and strategy in the low fare
airline business, Ashgate.
Lew, A. and McKercher, B. (2002) "Trip destinations, gateways and itineraries:
the example of Hong Kong." Tourism Management 23(6): 609-621.
Lew, A. and McKercher, B. (2006) "Modelling Tourist Movements: A Local
Destnation Analysis." Annals of Tourism Research 33(2): 403-423.
Li, X., Cheng, C., Kim, H., Petrick, J. (2008) “A systematic comparison of first-
time and repeat visitors via a two-phase online survey” Tourism Management
29(3): 429-438
Limtanakool, N., Dijst, M. and Schwanen, T. (2006) “The influence of socio-
economic characteristics, land use and travel time considerations on mode choice
for medium –and longer-distance trips” Journal of Transport Geography, 14(5),
327-341
Louviere, J.J. and Hensher, D.A. (1983) "Using discrete choice models with
experimental design data to forecast consumer demand for a unique cultural
event." Journal of Consumer Research, 10(3):348-61.
Louviere, J.J. and Hensher, D.A., and Swait, J.D. (2000) Stated Choice Methods:
Analysis and Application, Cambridege University Press.
Lue, C.C., J.L. Crompton and D.R. Fesenmaier. (1993) "Conceptualization of
Multi-destination Pleasure Trips." Annals of Tourism Research, 20: 289-301
Lumsdon, L. (2006). “Factors affecting the design of tourism bus services.”
Annals of Tourism Research 33(3): 748-766
R-10
Lumsdon, L. and Page, S. (2004). "Progress in Transport and Tourism Research:
Reformulating the Transport-Tourism Interface and Future Research Agendas" in
Tourism and Transport: Issues and Agenda for the New Millennium. edited by
Lumsdon, Les and Stephen, J. Page, 1-28. Elsevier.
Maddala, G.S. (1986) Limited dependent and qualitative variables in
econometrics, Econometric Society Monographs (No. 3)
Mansfeld, Y. (1990) "Spatial Patterns of International Tourist Flows: Towards a
Theoretical Framework". Progress in Human Geography. 14 (3), 372-390
Mansfeld, Y.(1992) Tourism: Towards a behavioural approach, Progress in
Planning, 38(1), 1-92
Marcus, B. and Anderson, C.K. (2008) Revenue management for low-cost
providers, European Journal of Operational Research 188: 258-272
Mason, K. (2001) “Marketing low-cost airline services to business travellers”
Journal of Air Transport Management, 7(2), 103-109
Mason, K. (2005) Observations of fundamental changes in the demand for
aviation services, Journal of Air Transport Management, 11(1), 19-25
Mason, K (2006) The value and usage of ticket flexibility for short haul business
travellers, , Journal of Air Transport Management 12: 92-97
Mason, K., Alamdari, F.(2007) EU network carriers, low cost carriers and
consumer behaviour: A Delphi study of future trends, Journal of Air Transport
Management, 13(5), p.299-310
Meyer, J.R. and Clinton V. O. (1987) Deregulation and the future of intercity
passenger travel, MIT Press.
R-11
Mings, R.C. and McHugh, K.E. (1992) The Spatial Configuration of Travel to
Yellowstone National Park, Journal of Travel Research, 30(4), p.38-46
Morley, C. (1994) "Discrete Choice Analysis of the Impact of Tourism Prices."
Journal of Travel Research, 33(2): 8-14
Morrison, S. and Winston, C. (1986) The Economics Effects of Airline
Deregulation, Brookings Institution Press
Moscardo, G., Saltzer, R., Norris, A., McCoy, A. (2004) Changing Patterns of
Regional Tourism: Implications for tourism on the Great Barrier Reef, The
Journal of Tourism Studies, Vol. 15, No. 1, p.34 – 50
Moscardo, G. and Phillip P. (2004) "Life Cycle, Tourist Motivation and
Transport: Some Consequences for the Tourist Experience." in Tourism and
Transport: Issues and Agenda for the New Millennium. edited by Lumsdon, Les
and Stephen, J. Page, 1-28. Elsevier.
Mules, T. and Huybers, T. (2002) Substitution between tourism destinations: An
application of discrete choice modelling, Sustainable Tourism Cooperative
Research Centre Project 43001
Nelson, R. and G. Wall (1986) Transportation and accommodation: Changing
interrelationships on Vancouver Island, Annals of Tourism Research, Vol. 13,
239-260
Nicolau, J.L. and Francisco J. M. (2006) "The influence of distance and prices on
the choice of tourist destinations: The moderating role of motivations." Tourism
Management, 27(5):982-996
R-12
Njegovan, N. (2006) “Elasticities of demand for leisure air travel: A system
modelling approach” Journal of Air Transport Management, 12(1), 33-39
O’Halloran M., Cook S., Sbragi A. and Buchanan, I. (2000) BTR Occasional
Paper Number 30, Rural Tourism in Australia: The visitor’s perspective, Bureau
of Tourism Research, Canberra.
O'Connell, J. F. and G. Williams (2005). "Passengers' perceptions of low cost
airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air
Asia and Malaysia Airlines." Journal of Air Transport Management 11(4): 259.
Oppermann, M. (1994) “Regional aspects of tourism in New-Zealand” Regional
Studies, 28(2), 155-167
Oppermann, M. (1995) "A Model of Travel Itineraries." Journal of Travel
Research, 33(4): 57-61
Oppermann, M. (1997) “First-time and repeat visitors to New Zealand” Tourism
Management, 18(3), 177-181
Oppewal, H. and Timmermans. H. (1991). "Context effects and decompositional
choice modelling " Papers in Regional Science 70(2).
Page, Stephen. (1994). Transport and Tourism: Global Perspectives. Pearson
Education
Page, Stephen. (2005). Transport and Tourism: Global Perspectives. Pearson
Education, 2nd Edition
Pantazis, N. and I. Liefner "The impact of low-cost carriers on catchment areas of
established international airports: The case of Hanover Airport, Germany."
Journal of Transport Geography In Press, Corrected Proof.
R-13
Papatheorodou, A. (2002) "Civil Aviation Regime and Leisure Tourism in
Europe." Journal of Air Transport Management 8(6): 381-388
Parolin, B. (2001) “Structure of Day Trips in the Illawarra Tourism Region of
New South Wales” The Journal of Tourism Studies, 2(1), 11-27
Pearce, D.G. (1979) “Towards a geography of tourism” Annals of Tourism
Research, 6(3), 443-454
Pearce, D.G. (1987) “Spatial patterns of package tourism in Europe” Annals of
Tourism Research, 8(4), 183-201
Pina, A.I. and Diaz Delfa M.T. (2005) "Rural tourism demand by type of
accommodation." Tourism Management 26(6): 951-959
Prideaux, B. (2000) “The role of the transport system in destination development”
Tourism Management 21: 53-63
Quiggin, John. (1997) "Evaluating Airline Deregulation in Australia." The
Australian Economic Review, 39(1): 45-56
Redmond, L., S. and Mokhtarian, P. (2001), “The positive utility of the commute:
modelling ideal commute time and relative desired commute amount.”
Transportation, 28: 179-205
Reynolds-Feighan, A. (2001) Traffic distribution in low-cost carriers and full-
service carrier networks in the US air transportation market, Journal of Air
Transport Management 7: 265-275
Rizzi, L.I. and Ortuzar, J. (2003) "Stated preference in the valuation of interurban
road safety." Accident Analysis and Prevention 35: 9-22.
R-14
Road Traffic Authority. "Pacific Highway Upgrade." Road Traffic Authority.
http://www.rta.nsw.gov.au/constructionmaintenance/majorconstructionprojectsreg
ional/pacifichighwayupgrade/index.html
Sinclair, T.(1998) Tourism and economic development: A survey, Journal of
Development Studies, 34(5), p.1-51
Sinha, D. (2001) Deregulation and liberalisation of the airline industry: Asia,
Europe, North America and Oceania, Ashgate
Stewart, S. and Vogt, C. (1997), "Multi-destination Trip Patterns." Journal of
Travel Research, 33:57-61
Swan, W. (2007) Misunderstandings about airline growth, Journal of Air
Transport Management 13: 3-8
Taplin, J. and McGinley, Carmel (2000) "A linear program to model daily car
touring choices." Annals of Tourism Research 27(2): 451-467.
Tideswell, C. and Faulkner, B. (1999), "Multidestination Travel Patterns of
International Visitors to Queensland." Journal of Travel Research, 37:364-74
Timmermans, H.J.P. and R.G. Golledge. (1990) "Applications of behavioural
research on spatial problems II: preference and choice." Progress in Human
Geography. 14.(3): 311-354.
Tourism Australia. (2005). “Emerging Travel Patterns in Fly-Drive Interstate
Leisure Tourism”, prepared by Tourism and Aviation Economics for Tourism
Australia
Tourism Research Australia (2008) “Travel by Australians” Quarterly Results of
the National Visitor Survey.
R-15
Tourism Research Australia. (2007) "Local Government Area Profiles."
http://tra.australia.com/regional.asp
Tourism Transport Forum. (2002) “Keeping the bush in the game - New
approaches to making regional tourism more competitive”, prepared by Hocking
Research and Consulting for Tourism Transport Forum
Urry, J. (1991) The Tourist Gaze Sage
Wall, G. (1997) Tourist Attractions: Points, Lines and Areas, Annals of Tourism
Research, 24/1:240-3
Walmsley, D.J. (2004), “Behavioural approaches in Tourism Research”, in Lew,
A, Hall, C.M., Williams, A.M. (ed) A Companion to Tourism, Blackwell
Warnock-Smith, D. and Potter, A. (2005) ‘An exploratory study into airport
choice factors for European low-cost airlines’ Journal of Air Transport
Management, Volume 11, Issue 6, November 2005, Pages 388-392
Weaver, D. (2006) Sustainable Tourism: Theory and Practice, Butterworth-
Heinemann
Whyte, R. and Prideaux, B. (2007) "Impact of Low Cost Carriers on Regional
Tourism," refereed paper in CAUTHE, Proceedings of the 17th Annual
Conference, McDonnell, I., Grabowski, S., March, R., (Eds) CD-ROM,
University of Technology, Sydney.
Williams, G. (2001). "Will Europe's charter carriers be replaced by "no-frills"
scheduled airlines?" Journal of Air Transport Management 7(5): 277
Williams, G. (2002) Airline Competition: Deregulation's Mixed Legacy, Ashgate.
R-16
Wu, C.L. and Carson, D. (2008) "Spatial and Temporal Tourist Dispersal
Analysis in Multi Destination Travel." Journal of Travel Research, 46(3): 311-317
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