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December 1990

Modelling long distance pleasure travel mode usingperceived modal attributesHume Winzar

Phil Pidcock

Lester Johnson

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Recommended CitationWinzar, Hume; Pidcock, Phil; and Johnson, Lester, "Modelling long distance pleasure travel mode using perceived modal attributes"(1990). School of Business Discussion Papers. Paper 20.http://epublications.bond.edu.au/discussion_papers/20

"Modelling Long Distance Pleasure Travel ModeUsing Perceived Modal Attributes"

December 1991

DISCUSSION PAPER NO 20

Gold Coast,QLD,4229

HumeWinzarPhil Pidcock

Lester Johnson

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B o N D u N I v E R s I T y

Modelling Long Distance Pleasure Travel Mode UsingPerceived Modal Attributes

HumeWinzar

Phil Pidcock

Lester Johnson

School of Business

Bond University

Gold Coast, Queensland, 4229

Australia

1

Modelling Long Distance Pleasure Travel Mode UsingPerceived Modal Attributes

Abstract

Modelling transport choice behaviour traditionally has been addressed using

objective measures of attribute data: price, time, etc. The authors argue that

actual brand attributes may not be a good causal influence on consumer

choice of long distance tourism travel. Choice is more likely to be a function

of brand perceptions or brand image, which in turn is a function of past

experience, expectations, promotional influences, family life stage and other

personal factors.

The authors re-examine data gathered in 1985 on tourist travel between Perth

and Sydney or Melbourne, Australia; about 2400 miles. The authors model

actual behaviour as a function of respondents' perceptions of travel mode

attributes using a customized Multinomial Logit programme which develops

separate explanatory models for identified market segments. Results

demonstrate the improvement in predictability over non-segmented models.

Implications for tourism operators in promotions, product design and market

targeting are discussed.

2

Modelling Long Distance Pleasure Travel Mode UsingPerceived Modal Attributes

Introduction

One aspect of long distance pleasure travel (e.g., coast to coast) within a large

contiguous geographic area, such as the U.S., Australia or Europe, that is of

potential interest to tourism authorities and marketers is the mode choice

decision of individuals engaged in such travel. Understanding this decision

process can provide tourism analysts with choice of pleasure travel mode

and, perhaps, point to ways that certain modes (e.g., train or bus) can be

effectively improved, thereby potentially increasing total demand for pleasure

travel as well as facilitating mode switching behaviour.

There exists a great deal of literature in transportation concerned with

modelling the choice of mode in various contexts (See Ortuzar and Willumsen

1990 for an extensive review). Most of this modelling has used actual modal

attributes and!or the characteristics of individuals as explanatory variables.

This is particularly the case when the mode choice decision has been

modelled using the popular multinomiallogit (MNL) tnodel or one of its

variants (see Hensher and Johnson 1981 for a comprehensive review of logit

modelling and Gensch and Recker 1979 for its use in marketing). However,

one might argue that it would be more relevant to use traveller perceptions of

the modal attributes as explanatory variables since it has been suggested that

choice can be as much a function of perceptions and image of alternatives

(Mowen 1990). Of course it is reasonable to say that attributes will drive

perceptions which will, in turn, drive choices, but it is just as true that prior

experience, imagery and the influences of word-of-mouth and reference

group behaviour will affect which alternatives and attributes are considered

in the first place (McConnell 1986). This point is particularly important in the

choice of tourism destinations and the mode of travel (Woodside and

Lysonski 1989). For a comprehensive discussion of these issues see Moutinho

(1987.)

One of the goals of this paper is to demonstrate the use of perceptions data in

modelling the choice of mode for long distance pleasure travel. This is done

using data from a study of long distance passenger rail service in Australia

known as the Indian Pacific (IP) which runs from Sydney on the East coast to

3

Perth on the West coast, a distance of about 3900 kilometres, or 2400 miles

(ARRDO 1986). One part of the ARRDO study revolved around the use of

stated preference (i.e., conjoint analysis) techniques (Louviere and

Woodworth 1983, Louviere 1988) to examine the effects on mode share of

changing attributes of the IP (Johnson and Nelson 1991). In the current paper,

we use the actual choice made by respondents for a recent east-west trip

(known as revealed preference data - see Wardman 1988) and model these as

a function of traveller perceptions of various modal attributes.

In the next section, we provide a description of the data used in the study.

This is followed by a brief outline of logit modelling and the segmentation

scheme adopted to improve the predictive capability of the final models.

Section 4 contains a discussion of our empirical results and we finish with

some marketing implications of the modelling effort.

Data

Data used in this study came from a survey conducted in Perth, Melbourne

and Sydney over the period 22 August to 8 September 1985. A total of 660

questionnaires were mailed out to qualifying responl:J.ents (see below) and 487

completed interviews were obtained (169 in Perth, 155 in Melbourne, and 163

in Sydney) of which 413 were for non-business travellers.

The procedure used to ensure that a reasonably random sample of travellers

was obtained consisted of drawing random samples of households from

telephone directories in the three cities. If a person in the household had

travelled east-west over the past two years, or had a firm intention to travel

over the next two years, then they were invited to participate in the survey. A

total of 5646 telephone contacts were required to achieve the final sample.

Since we were interested in modelling the choice of travel mode for already

completed trips, we were restricted to using only 197 observations out of the

total of 413 pleasure travellers.

For each respondent, we used the chosen mode (plane, train, bus or car) as the

dependent variable in the modelling exercise described in the next section.

Preliminary qualitative research suggested about seventeen service

characteristics affected choice of travel mode. Perceptions of these

4

characteristics for each mode were used in the modelling exercise as

explanatory variables. They were:

1 Speed (getting to destination quickly)

2 Interest (boring Vs interesting/entertaining)

3 Stopovers (ability to stop at places of interest)

4 Comfort (cramped Vs comfortable)

5 Food Quality

6 Facilities (modern facilities and appearance)

7 Social environment, (opportunity to meet people)

8 Relaxing

9 Planning (need to plan ahead)

10 Safety

11 Scenery (ability to watch scenery)

12 Price (expensive Vs cheap)

13 Punctuality (usually on time Vs late)

14 Booking convenience (easy to arrange tickets)

15 Friendly service

16 Reliability (frequency of cancellations) and

17 Convenience (of departure and arrival times)

Clearly attributes 13 through 17 are not relevant to the car mode so

perceptions for these attributes were only collected for plane, train and bus.

Attribute scores were derived from self-explicated evaluations of attribute

level for each travel alternative, moderated by the relative importance of each

attribute to each respondent. That is:

Where:

Vikj = attribute score for attribute i on alternative k by individual j,

Eikj = evaluation of level of attribute i on alternative k by individual j,

~j = importance of attribute i to individual j.

Thus where Eikj is measured on a 7-point rating scale (-3 to +3) and ~j is

measured on a 10 point importance scale (O='not important at all',

9='extremely important') the evaluation score Vikj has a potential range of -27

to +27.

5

Model

Market Share was modelled using the basic multinomiallogit model (Gensch

and Recker, 1979) in which:

Where:

Pi =Market share of alternative i, or probability of purchase of alternative i,

Vi = Value, or estimated utility of alternative i,

Aij =level of attribute j in alternative i, and

~j =attribute parameters to be estimated.

The term eXP(Vi) means that we take the value Vi to its exponent, i.e., eVi•

The consumer market for long distance pleasure travel is not homogeneous.

The ARRDO research identified different segments based on: age of traveller;

length of stay at destination; purpose of trip (visit friends & relatives, family

holiday, etc); travelling companions (alone, family, partner); and city of

origin. As a result, model estimates for the total market are unlikely to be as

accurate and interpretable as models derived separately for different

segments.

It is possible to infer total-industry market shares for each mode from the

share estimates of separate models with:

Where:

Sk =Share of alternative k for total market

ski =Share of alternative k for market segment i,

TCi =Relative size of segment i.

Our aim is to refine the accuracy of the market share estimation by first

breaking up the market share of east-west travel into segments, then

modelling the market share for each segment using a basic multinomiallogit

model, followed by aggregation of the segments to estimate total share of

east-west travel. The innovation here is the disaggregation and modelling of

the travel market into segments for estimation purposes, prior to re­

aggregation for total market share.

6

Market segments were based on measures of demographics that we

considered likely to mediate the relationship between perceptions and travel

mode choice: Age, Income, Marital status, Employment status, and the

presence of Children in the travelling party.

We considered that: (a) higher income travellers were less likely to take the

cheaper, least comfortable, mode (bus); conversely, unemployed and other

low income earners will be more inclined to take this mode, (b) Older and

retired travellers would be less concerned with travel time, (c) Parents would

be concerned with travel time and cramped conditions.

Measures of each of these variables, including dummy variables for marital

status, employment and children, were standardized to mean zero and

standard deviation one and submitted to K-means cluster analysis. A three­

cluster solution provided the most interpretable results, and the best recovery

in a confirmatory discriminant analysis.

Descriptive statistics for each of the three groups are presented in Table 1

below.

Table 1.

Identified Market Segments

Silver Families YoungHairs with Kids UncommItted

n 69 73 55

Mean Age 63 39 30

Relative Income Low High Medium

Employed 4% 66% 85%

Married 70% 99% 0%

Children 7% 63% 9%

We can broadly describe the three segments in the following way:

Group 1 is made up largely of retired folk, in couples or widowed, with no

dependants travelling with them. Income is low, but this measure does not

take into account access to capital. Travel marketers would call this group

"Silver-hairs".

Group 2 is made up of middle aged families. Almost all are married, and the

vast majority have children accompanying them on their holiday journey.

7

. Average incomes are the highest of the three groups, but the variation in

income also is the greatest. Family life stage for many is "Full-nest." We call

the group "Families with Kids."

Group 3 is dominated by younger, single travellers with no children. While

average incomes are midway between groups one and two, the distribution is

actually bimodal, with almost equal proportions of very high income earners,

and relatively low income earners. A larger sample size and more well

defined discriminators may have shown separate representatives of "Back­

packers" and "Yuppies". Here, this group can be collectively called "Young

Uncommitted."

Parameter estimates for the multinomiallogit choice models were derived

using maximum likelihood techniques contained in the adaptable computer

algorithm: BLOGIT (Crittle and Johnson 1981).

Empirical Results

Table 2 shows the estimated coefficients for the whole sample and for the

three market segments. The MNL models for different segments show

markedly different coefficients from the group model, especially in the

attribute-specific constants (intercepts) and in the attributes Comfort, Social,

and Friendly. Generally goodness of fit, measured by the Rho-squared

statistic (see footnote), is better for the separate segments model, despite the

substantial reduction in degrees of freedom1. (It is not valid to calculate a

combined Log-Likelihood Function by simply summing individual segment

LLF's because these involve the summation of logs.)

Tables 3 and 4 show the whole sample and combined segments prediction

success tables (Hensher & Johnson 1981) respectively. We can see that, as

1 The goodness of fit statistic Rho-squared (p2) is given by the complement of the ratio ofthe log-likelihood function of the model under consideration to the log-likelihood of the nullmodel, Le., where all betas are set to zero. That is,

p2; 1- (LLF(~)/LLF(O»)

Oearly p2 can range from 0 to 1, with the larger the value the better the fit of the model or, inother words, the greater the explanatory power of the predictor variables. It should be notedthat values of p2 of between 0.2 and 0.4 are considered extremely good fits, so the analystshould not be looking for values in excess of 0.9 as we may be used to with R2 in ordinaryregression.Where models with different numbers of parameters are compared then we use Rho-barsquared (pbar2) which is an adjustment for degrees of freedom. See Henscher & Johnson(1981) p.51 and McFadden (1979) for details.

8

expected, the combined segments models provide a better prediction success

with the combined model providing nearly 60% success compared with 50%

success for the whole sample model. We would expect that segments which

are more well defined than the available demographic groups used here

would provide even better prediction accuracy. Two commonly used

measures in travel research include "length of visit'~ and "purpose of visit,"

which includes visiting friends and relatives, family holiday, and so on. If

these measures were included in our segmentation schema, for example, then

we could expect significantly better prediction results to emerge.

In Table 2, Alternative-Specific Constants CASC) are presented for Air, Train

and Bus to give intercept terms with respect to Car mode, which is implicitly

set to zero. Thus the ASC's are dummy variables which can be interpreted as

overall attractiveness scores for the specific alternatives if all the attribute

evaluations were zero. The following seventeen sets of beta estimates indicate

the contribution of each attribute perception of travel mode choice. Note

these beta estimates are based on the raw evaluation scores; they are not

standardized betas and cannot be' compared for relative contribution, except

by the strength of their significance levels.

While results for the separate segments show a clear improvement in

goodness-of-fit and a marginal improvement in prediction accuracy, the most

striking result is the difference in the beta estimates among the segments and

between each segment and the whole sample. Importantly, where the whole

model suggests no significant impact of a perceptual variable, that variable is

actually very important to the travel decision for at least one segment. We

find this in estimates for INTEREST, COMFORT, FACILITIES, and

PLANning. Conversely, we discovered that some variables are determined

significant in the undifferentialted model but which subsequently only appear

significant in certain segments, not in all three. For example, DURATION of

trip and CONVENIENCE of terminal location and scheduling appear

significant for the whole sample but are only really important to the Families

with Kids segment. Similarly, FRIENDLY service appears significant in the

whole sample model, but when segmented was found to be of major concern

only to the Silver Hairs segment.

9

Marketing Implications

This choice modelling exercise revealed that choice of travel mode in long

distance vacation-oriented trips in Australia is influenced by travellers'

perceptions about the trip, as well as exhibiting demographic segmentation

characteristics.

This paper demonstrates the viability of modelling travel choice behaviour as

afunction of perceptions of the attributes of available alternatives. The results

show that although the whole sample model (representing the general

vacation-travelling public) presents some non-significant perceptual

variables, the segment models reveal perception variables which are both

significant in the choice process, and, moreover, segment-specific. This is

important because it allows the marketer to 'get closer' to the decision process

of the target consumer. If we know which attribute perceptions figured

prominently in the choice process, we can be confident (more confident than

with a choice model based on attribute measures alone) that manipulating the

marketing mix in a way designed to maximise these desired [target]

perceptions will result in a better probability of the target travel mode (or

brand, to generalise beyond travel) being chosen by its target segment.

Results are doubly interesting because the data set was quite severely limited,

(in terms of the richness of perception and other data gathered) yet still

revealed clear superiority for segment-based choice models. As noted in the

results section, with more well-defined segments we would expect the

segment-based choice predictions to be improved even more, relative to the

results obtained for an unsegmented model.

Thus we found that for three simple demographic segments, certain

perceptions could tentatively be viewed as positioning variables. For the

"silver hairs" segment, for example, perceptions of FRIENDLY SERVICE, and

an INTERESTING and COMFORTABLE trip are significant determinants of

mode of long distance pleasure travel. With research into the attractiveness

(e.g., the size) of this segment, management may decide to 'turn up the dials'

on these key variables, to improve the probability of choice among this

segment. Emphasis on these dimensions in promotions and product design

will have greatest effect on this segment.

Similarly "families with kids", which as Table 1 indicates have the highest

income of the three identified segments, are influenced by perception of TRIP

10

DURATION, CONVENIENCE, the NEED TO PLAN, and, incongruously,

SCENERY. Scenery appears incongruous because the other perceptions seem

to indicate a preference for air travel, for which scenery could be hard to

provide.

The "young uncommitted" segment shows predictable positioning

perceptions, given their demographic characteristics: COST and the NEED

TO PLAN, FACILITIES and SCENERY. Emphasis on budget, flexibility and

the enjoyment of the travelling experience should be most featured in

promotions and packaging for this segment.

The potential usefulness of these models is that they enable us to measure the

importance of the perceptions about travel mode (or any product's attribute

set) held by individual consumers. We are thus able to create segments based onsomething much closer to the choice process - perceptions of product - and relate

these perceptions to things we can measure (and therefore use to assess the size

and other important aspects to do with a segment's attractiveness, and

lifestyle characteristics of each segment for guidance in setting marketing mix

objectives). This gives us a capability to create market segments at the most

sensitive level - consumer choice/decision process - from an empirical basis.

This may confer an advantage on this approach compared with other possible

approaches that might be used for the same purpose - such as conjoint

analysis - which rely on an experimental basis.

It is relatively easy to profile these 'perception segments' in demographic

(easy to quantify) terms. These results then enable us to assess the size of

segments which are more meaningful than mere demographic segments, and

we are able to design more sharply-focussed marketing strategies targeted on

the positioning perceptions which the choice modelling analysis reveals.

To observe that "Families with kids" are concerned with duration and

convenience, and that "Young uncommitted" are concerned with budget and

experience, and so on, seems both obvious and redundant. This research goes

much further than that however. We have shown that we can make sound

estimates of how much these factors affect responsiveness of each segment to

different transport offerings. Marketers for specific transport alternatives can

estimate the degree of change required in consumer attitudes that is required

to bring about significant change in behaviour. Clearly these are important

for establishing promotions objectives, and estimating market share.

11

Future research in this area then should address the nature of attitude and

perception measures that are most closely associated with the purchase

decision measures.

12

Table 2Estimated Coefficients for Whole Sample

and Selected Segments

Whole Silver Families Youn~Sample Hairs with kids UncommItted

ASC-AIR .722* -.367 2.424*** 1.324

ASC-TRAIN -1.063*** -.830 -1.208 -1.982**

ASC-BUS -1.310**** -1.346** -1.638* -2.206***

DURATION .110**** .025 .155**** .125*

INTEREST .034 .153** -.012 -.035

STOPOVER .011 -.059 -.024 .062

COMFORT .029 .099** .058 -.022

FOOD .025 -.019 .022 .015

FACILITIES -.016 -.082 .005 .206**

SOCIAL .067** .065 .072 .075

RELAX .020 .057 -.022 .018

PLAN .012 .012 -.084** .159***

SAFETY -.040** -.052 -.028 -.056

SCENERY .117**** .017 .354**** .163**

COST .051*** .054* .048 .134***

PUNCTUAL .044 .028 .023 .082

BOOKING -.038 -.087* -.151* .026

FRIENDLY .084** .222*** .080 .011

RELIABLE .069*** .096* .115* .114

CONVENIENT -.091**** -.076* -.183*** .001

Summary Statistics

Samrcle size 197 69 73 55LLF 0) -237.60 -93.56 -72.37 -58.51LtF(~) -185.82 -73.57 -42.17 -37.42P 0.218 0.214 0.417 0.360Pbar2 0.191 0.130 0.359 0.272

Significance level: '" :::: 0.<.20, ** :;;; 0.<.10, *** = 0.<.05, ****:;;; a.<.01

13

Table 3.Whole Sample Prediction Success Table

AIR TRAIN BUS CAR

AIR 101 65.238 9.487 8.378 17.898

TRAIN 25 8.400 7.272 4.248 5.081

BUS 25 9.963 2.782 7.500 4.755

CAR 46 17.400 5.460 4.874 18.266

101 25 25 46

ProportionSuccessfullyPredicted .... 64.5% 29.1% 30.0% 39.7%

Overall Prediction Success =49.89%

Table 4.Combined Segments Prediction Success Table

AIR TRAIN 'BUS CAR

AIR 101 73.389 6.961 6.068 14.582

TRAIN 25 6.633 9.762 4.438 4.167

BUS 25 7.474 3.422 9.541 4.463

CAR 46 13.504 4.855 4.953 22.688

101 25 25 46

ProportionSuccessfullyPredicted ..... 72.7% 39.1% 38.2% 49.2%

Overall Prediction Success =58.57%

14

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132

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15

Wardman, M. (1988) "A Comparison of Revealed Preference and Stated

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16